Calcium in the Muskoka River Watershed Patterns, trends, the …digitalcollections.trentu.ca/islandora/object/etd:514... · 2016-12-20 · with long-term data. All lakes below the

Calcium in the Muskoka River Watershed –

Patterns, trends, the potential impact of forest harvesting on

lake Ca levels and steps toward an ecosystem approach to mitigation

A Thesis Submitted to the Committee on Graduate Studies in Partial

Fulfilment of the Requirements for the Degree of Master of Science

in the Faculty of Arts and Science

TRENT UNIVERSITY

Peterborough, Ontario, Canada

© Copyright by Carolyn Roberta Reid, 2015

Environmental and Life Sciences M.Sc. Graduate Program

May 2015

ii

ABSTRACT

Calcium in the Muskoka River Watershed –

Patterns, trends, the potential impact of forest harvesting on

lake Ca levels and steps toward an ecosystem approach to mitigation

Carolyn R. Reid

Decreasing lake calcium (Ca) concentration, in lakes located in base poor

catchments of the Muskoka River Watershed (MRW) in south-central Ontario, is a well-

established acid-rain driven legacy effect threatening the health and integrity of aquatic

ecosystems that can be compounded by additional Ca removals through forest harvesting.

The objectives of this thesis were to assess patterns and temporal trends in key water

chemistry parameters for a set of lakes in forested catchments in the MRW in south-

central Ontario, to predict the pre-industrial steady state lake Ca concentration and the

potential impact of harvesting on lake Ca levels in lakes located in managed MRW Crown

forests, and to assess potential effects of various mitigation strategies in Ca depleted

managed forests. Mean lake Ca (mg L–1) in 104 lakes across the MRW have decreased

by 30% since the 1980’s with the rate of decrease slowing over time. Mean Lake SO4

(mg L–1), and Mg (mg L–1) concentration also decreased significantly with time (37% and

29%, respectively) again with a declining rate of decrease, while mean lake pH and DOC

increased significantly between the 1980’s and the 1990’s (16% and 12%, respectively)

but exhibited no significant pattern after that. Principal components and GIS spatial

analyses of 75 lakes with data from 2011 or 2012 water seasons suggested that smaller

lakes, at higher elevation in smaller catchments with higher runoff and minimally

impacted by the influence of roads and agriculture are associated with lower Ca

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concentrations and thus are the lakes at risk of amplified Ca depletion from forest

harvesting. Spatial analyses of harvested catchments indicated that, under the proposed

10 year land forest management cut volumes, 38% of 364 lakes in the MRW will fall

below the critical 1 mg L–1 Ca threshold compared with 8% in the absence of future

harvesting. With respect to potential mitigation measures, soil pH and foliar Ca were

indicated by meta-analysis to be more responsive to lime addition studies while soil base

saturation and tree growth appeared more responsive to wood-ash addition. Future

research should address the spatial extent of lakes at risk and identify when critical levels

will be reached under harvesting regimes. Further investigation into the use of Ca-

addition as a tool for managing the cumulative effects of past, present and future stressors

is recommended.

Keywords: calcium, lakes, harvesting, lime, wood-ash, Muskoka River Watershed

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Acknowledgements

I have always believed that in order to “up your game” in anything that you have to play

with someone with much better skills. That axiom can be extended to the enrichment of

all types of abilities and particularly to the development of critical thinking skills.

Accordingly, I thank my supervisor Shaun Watmough for providing me with this

incredible opportunity to enhance my intellectual abilities and for his unwavering

support, guidance and infinite patience during my time at Trent University. Shaun

possesses a rare, unique and enviable intellect and yet remains wholly unaffected by

hubris. His good-humoured guidance has allowed me to steer away from a

preoccupation with minutia (well, for the most part) and on to a logical, well-defined

path. It has been an honour and a privilege to have him as my supervisor.

In addition, I thank Julian Aherne and Colin Whitfield for serving on my advisory

committee. Their questions, constructive comments and helpful insights enabled me to

look at the problems and challenges that arose in the development of my thesis from

new perspectives and with renewed energies. I sincerely appreciate all their input,

support and consideration and for being so patient and so generous with their very

valuable time.

I also thank Steve Munroe, Operations Manager and Barry Davidson, Forest Manager,

both of Westwind Forest Stewardship Inc. and Gord Cumming, Chief Forester for the

Algonquin Forest Authority, for their time and contributions, without which, this thesis

would not have been possible. I admire, and am grateful for, their dedication and

commitment to furthering the knowledge of sustainable forest management, especially

in an environment where multiple stressors are taking their toll on the forest

ecosystem. I was also exceedingly fortunate to have had Samantha Luke’s assistance.

Her GIS “mad skills” were invaluable to the development of this thesis.

Finally, I want to thank my amazingly generous family and friends for their continued

support, understanding and tolerance, and for not judging me when the house was in

complete disarray, Willow needed walking, dinner was late, and although present in

body, I was absent in thought. To my children; Jessica, Nick and Casey and my

grandchildren; Monty and Sully, I love you and miss spending time with you and think

of you every day, life is so much better with you in it. I hope you understand my need

to make a difference.

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Co-Authorship Statement

This thesis is based on two manuscripts which have either been accepted for publication

(Chapter 3) or will be submitted for publication (Chapter 2). Carolyn Reid, who led the

development of the research problem, formulation of the study design, completion of the

data analysis, interpretation of results and writing of manuscripts, will be lead author on

each manuscript. Shaun Watmough, who suggested the initial research problem,

contributed to the formulation of the study design and the interpretation of results and

provided invaluable editorial guidance on the writing, will be co-author on all

manuscripts. For Chapter 2, Samantha Luke will be acknowledged as ministering the

compilation of the spatial analysis datasets.

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Table of Contents

ABSTRACT ...................................................................................................................... ii

Acknowledgements ........................................................................................................... iv

Co-Authorship Statement ................................................................................................... v

List of Figures .................................................................................................................... x

List of Tables ................................................................................................................... xii

1. General Introduction ................................................................................................. 1

1.1 The calcium problem ................................................................................................ 1

1.2 Why is calcium important? ........................................................................................ 3

1.3 Study area – The Muskoka River Watershed ............................................................ 3

Crown land forest management in the MRW ..................................................................... 6

1.4 Impacts of forest harvesting – biomass export driven Ca decline ............................. 7

1.5 Mitigation ................................................................................................................ 11

1.6 Thesis objectives ..................................................................................................... 13

1.7 References ............................................................................................................... 14

2. Calcium levels in lakes of the Muskoka River Watershed - patterns, trends,

predictions and the potential impacts of tree harvesting on critical levels ...................... 21

2.1 Abstract ................................................................................................................... 21

2.2 Introduction and rationale ....................................................................................... 21

2.3 Methods ................................................................................................................... 25

Crown land forest management in the MRW .................................................................. 27

Lake datasets .................................................................................................................... 28

Temporal trends in lake chemistry ................................................................................... 29

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Long-term trends analyses ................................................................................................ 30

Physical and chemical parameter pattern analysis ............................................................ 31

Projected impact of timber harvesting on lake Ca levels – GIS spatial evaluation .......... 32

Steady-State Water Chemistry (SSWC) model (Henriksen et al. 1992; Henriksen and

Posch 2001) ...................................................................................................................... 33

Biomass export driven Ca decline ................................................................................... 35

2.4 Results ..................................................................................................................... 37

Lake chemistry ................................................................................................................. 37


Mann-Kendall long-term trend analysis ........................................................................... 41

Non-parametric Correlations and Principal Components Analysis .................................. 43

Spatial evaluation used for projected impact of timber harvesting on lake Ca levels ...... 45

Steady-State Water Chemistry (SSWC) model (Henriksen et al. 1992; Henriksen and

Posch 2001) and quantifying the potential impact of harvesting on lake Ca levels ......... 46

2.5 Discussion ............................................................................................................... 52

Lake chemistry ................................................................................................................. 52


Long-term trend analysis .................................................................................................. 57

Spatial patterns in lake Ca ................................................................................................ 57

Steady-State Water Chemistry (SSWC) model (Henriksen and Posch 2001) ................. 58

Biomass export driven Ca decline .................................................................................... 59

Uncertainty ....................................................................................................................... 63

2.6 Conclusions ............................................................................................................. 65

2.7 References ............................................................................................................... 67

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APPENDIX 2A ................................................................................................................ 80

3. Evaluating the effects of liming and wood-ash treatment on forest ecosystems

through systematic meta-analysis..................................................................................... 85

3.1 Abstract ................................................................................................................... 85

3.2 Introduction ............................................................................................................. 86

3.3 Methods ................................................................................................................... 89

Study Selection ................................................................................................................. 89

Statistical Analysis ........................................................................................................... 91

Quantitative synthesis – Meta-analysis ............................................................................ 91

Meta-analysis and Effect Size Metrics ............................................................................. 91

Data Mining – Recursive Partitioning Decision Tree Analysis ........................................ 91

3.4 Results ..................................................................................................................... 97

Study Selection ................................................................................................................. 97

Average Effect Size .......................................................................................................... 98

Zero Effect Frequency .................................................................................................... 102

Recursive Partitioning of Effect Sizes - Decision Tree Analysis ................................... 102

pH Effect Size Regressive Analysis ............................................................................... 102

Base Saturation ............................................................................................................... 103

Ca Foliar Concentration Effect Size Regressive Analysis .............................................. 103

Tree Growth Effects Size Regressive Analysis .............................................................. 104

Uncertainty ..................................................................................................................... 104

3.5 Discussion ............................................................................................................. 109

Soil pH ........................................................................................................................... 109

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Base Saturation ............................................................................................................... 111

Foliar Calcium Concentration ........................................................................................ 112

Tree Growth ................................................................................................................... 113

C:N Ratio, ECM Root Colonization and Microbial Indices ........................................... 115

3.6 Other issues ........................................................................................................... 118

3.7 Conclusions ........................................................................................................... 119

3.8 References ............................................................................................................. 121

APPENDIX 3A .............................................................................................................. 131

Table 3A.1 References ................................................................................................... 137

4.1 Implications for recovery and an ecosystem approach to mitigation .................... 148

Brief Background ........................................................................................................... 148

4.3 Ecosystem mitigation - benefits to lakes, soils and trees - win/win/win ............... 150

4.4 Knowledge Gaps ................................................................................................... 151

4.5 Directions of future research ................................................................................. 152

Four key objectives that could define the structure of future Ca study. ......................... 152

4.6 Conclusions ........................................................................................................... 154

4.7 References ............................................................................................................. 155

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List of Figures

Photo 1.1: Lake St. Nora (top) and Deer Lake (bottom), southern boreal lakes on the

Precambrian Shield in the Muskoka River Watershed…………………………………...4

Photo 1.2: Selection harvesting area in the French-Severn forest near Dorset, Ontario in

the Muskoka River Watershed. Areas were harvested two years prior to photos.

Harvesting residues (tree tops and branches) are left onsite……………………………...8

Photo 1.3: Seedtree shelterwood harvesting area in the French-Severn forest near Dorset,

Ontario in the Muskoka River Watershed………………………………………………...9

Figure 2.1: The Muskoka River Watershed in south-central Ontario (inset). Crown land,

the study area, comprises approximately 50% of the land surface area….…………...…27

Figure 2.2: Key data inputs in estimating MRW lake Ca (mg L–1) on a catchment basis

under predicted tree harvesting and no harvesting 2009–2020……….………………....38

Figure 2.3: Lake patterns for mean Ca concentrations (mg L–1) for N = 104 MRW lakes

with long-term data. All lakes below the 1:1 line decreased in lake Ca (mg L-1) between

the time periods 1981–1990 and 2003–2012. The mean decrease for Ca was 29.4% ±

12% and the mean decrease for SO4 depicted in the inset graph was 36.7% ± 19%........40

Figure 2.5: Principal components analysis of physical and chemical variables for 75

MRW lakes with data from 2011-2012. Principal component 1 described 24% of the

variation in the included parameters. Note: c-dsg is the mean down slope distance

gradiant, cuca is the catchment area, zmean is the mean lake depth, c-con and c-

deciduous are the catchment % of coniferous and deciduous trees respectively,

CLIDUR is lake chloride, CAU is lake calcium concentration, c-urd and c-prd are

catchment unpaved and pave roads respectively, l_elev is lake elevation. In every case,

Dep is deposition and c_ is catchment…..……………………………………………..44

Figure 2.6: 590 (of more than 2000) lakes with Ca concentration data in Muskoka

River Watershed catchments. Lakes most at risk ≥2 mg L–1 (n=177) of additional Ca

removals are represented by red (0.00 – 1.04 mg L–1) and orange (1.05 – 2.04 mg L–1)

dots. The concentration value of 0 represents levels undetected by the equipment used.

Predicted harvesting cuts (2009–2020) are denoted by dark green…………………....49

Figure 2.4: Boxplot distributions over three time periods based on mean lake values of Ca, SO4, Mg, pH, NO3 and DOC for 104 lakes. Outliers are represented by separate dots. Note: all units are in mg L–1 except for pH ...………............................................40

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Figure 2.7: Enlarged area of catchments for n=364 lakes located in catchments with

harvesting cuts with the blown up area indicating an area of high concentration of small

lakes in small catchments and many with Ca ≤2 mg L–1. This area is impacted by

harvesting cuts from two Forest Management Plans (dark and light yellow)…………..50

Figure 2.8: Current and predicted lake concentration as a function of no harvest (steady

state) and harvest volume removals of 100%, 59% (average reported annual volume

removal level) and 40% (verbally reported removal level) of the FMP for 364 (371-7 as

7 lakes with “0” concentration were not included on the graph) lakes in MRW

catchments with predicted harvesting cuts from 2009–020. The red vertical lines denote

literature suggested critical Ca thresholds of 1 mg L–1 (50 µeq L–1) (Hessen et al. 2000)

and as used in Watmough et al. 2003 and of 1.5 mg L–1 (75 µeq L–1) as indicated by

recent work in MRW lakes (Ashforth and Yan 2008)………………………………….51

Figure 2A.1: MRW lake data sets and methods of analysis…………………………….81

Figure 2A.2: Percent of lakes (N=590) sampled by sampling year. Approximately 15%

of the lakes were sampled in 2011-2012………………………………………………..84

Fig. 3.1: Soil pH mean effect size (pH treatment – pH control) optimal decision tree for

all trials………………………………………………………………………………...105

Fig. 3.2: Base Saturation mean effect size (BS treatment – BS control) optimal decision

tree for all trials………………………………………………………………………..105

Fig. 3.3: Foliar calcium concentration mean effect size ((Treatment/Control) −1) %)

optimal decision tree for all trials……………………………………………………..106

Fig. 3.4: Tree growth (of combined measures) mean effect size ((Treatment/Control) −1)

%) optimal decision tree for all trial………………………………………………….106

Fig. 3.5: Driver column contributions for pH, foliar Ca concentration and tree growth for

decision tree and bootstrap forest models. Arrows indicate the strongest drivers of

positive effect response to liming or wood-ash treatment………………………….…107

Fig. 3.6: Mean tree growth (of combined measures) effect size ((Treatment/Control) −1)

%) of softwood treated with wood-ash by trial duration time. Database trials were not

evenly spread over the duration span of years, therefore they were separated by effect

size responses. The largest mean increase in growth effect size over that of the mean

control effect size occurred 10+ years post-treatment…….………………….............108

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xii

List of Tables

Table 2.1: Mean lake parameter and mean difference in lake parameter and associated

standard error (SE) between three sampling time periods for 104 MRW lakes. Lake Ca

is in bold type…………………………………………………………………………..39

Table 2.2: Mann-Kendall long-term trend analysis for 24 MRW lakes with Ca ≤2 mg

L–1 located in catchments with Forest Management Plan predicted cuts to the year

2020……………………………………………………………………………………42

Table 2.3: Summary of results of a multi-group Mann-Kendall trend test to determine

consistent regional trends across lakes in Ca, Mg, K and SO4 concentrations (mg L–1)

in 24 Precambrian Shield lakes with Ca≤2 (mg L–1). Consistent strong declining

trends across lakes were indicated in Ca and SO4 and a less strong but still significant

declining trend in Mg. No trend across lakes was indicated for K…………………...42

Table 2.4: Non-parametric Spearman’s p correlation analysis between lake Ca (mg L–1)

and chemical and physical parameters for 75 lakes sampled in 2011-2012…………..43

Table 2.5: Lakes ≤2 mg L–1 in catchments with harvesting cuts (N=177) means,

standard deviations and ranges for chemical and physical parameters………………..46

Table 2.6: Most current, SSWC modelled and predicted % of lakes under varying

harvesting scenarios for two critical thresholds of lake Ca for Ca-rich Daphnia species,

50 µeq L–1 (Watmough et al. 2003), equivalent to 1.0 mg L–1 and 75 µeq L–1

(Ashforth and Yan 2008), equivalent to 1.5 mg L–1 for 364 MRW lakes. For simplicity

the percentages have been rounded to whole numbers……………………………….48

Table 2A.1: Equations derived from the SSWC model (Henriksen and Posch, 2001)

and the modified derivation equations used in calculations…………………………...80

Table 2A.2: Summary statistics (mean, max-min) for lake area, catchment area, lake

elevation, runoff and selected deposition and lake chemical variables for all lakes with

Ca data within crown land (590), lakes in catchments with harvesting cuts on crown

land (371) and two subsets of the former and latter lakes used in analysis. Values of 0

were assumed to be undetectable by equipment at the time…………………………...82

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xiii

Table 3.1: Summary of average, unweighted effect size, zero effect and sample size per

parameter. In addition to the pooled (combined) results, soil pH, Ca foliar nutrition and

tree growth (of the combined growth measures) were assessed by treatment type…..100

Table 3.2: Mean effect sizes of Ca-addition using the weighted effect size Hedges’ d

and the back-transformed natural log of the response ratio, lnR in comparison to the

unweighted relative values…………………………………………………………...101

Table 3A.1: List of studies included in the respective meta-analysis. Figure and table

columns list all figures and tables containing data from each study (note that many

studies had several distinct trials with variations in trial length, dose, addition type etc.)

………………………………………………………………………………………...131

1

1. General Introduction

1.1 The calcium problem

For thousands of years, water quality of lakes on the northern boreal shield was a

function of catchment physical conditions and chemical processes that occurred along

the streams that lead to, and within those lakes (Bormann and Likens 1967). It is

hypothesized that over time, a surplus of calcium (Ca) accumulated in the exchangeable

pools within catchment soils (Watmough and Dillon 2001). If not for anthropogenically

caused aberrant sulphur (S) and nitrogen (N) emissions, this pool of surplus Ca would

have remained relatively constant. Instead, the industrial revolution produced emissions

that caused unnaturally high acidic deposition for decades, leaving a legacy of unwanted

consequences.

Initially, enhanced acid deposition increased Ca concentrations in lakes as Ca

was leached from soil and into surface waters in association with strong (S) acids

(Christ et al. 1999). Leaching occurs when protons (H+) exchange with other cations

(Ca2+, magnesium (Mg2+), potassium (K+) and sodium (Na+) bound to soil exchange

sites, subsequently, these replaced cations then go into solution in soil water and become

mobilized in runoff (Foster et al. 1989). Calcium was depleted this way, from soils in

boreal shield areas with low base cation weathering rates and low atmospheric Ca

deposition, with a documented loss of 40% of exchangeable Ca at some sites over a

sixteen year period (Watmough and Dillon 2001) with a concurrent decrease in lake Ca

of 10-25% (Watmough and Dillon 2002). Preferential depletion of Ca occurred as it was

the most abundant soil exchangeable cation and because magnesium (Mg) and

potassium (K) are more tightly biologically cycled than Ca (Brady and Weil 2008). It

2

has been shown that with increasing soil acidification, increasing amounts of cations are

leached; with the level of leaching loss consistent with the size of the exchangeable base

cation pools (Ca2+>Mg2+>K+>Na+) (Haynes and Swift 1986).

Mineral weathering is the major source of Ca in temperate forest soils (Likens

and Bormann 1995) and although some areas in the Muskoka River Watershed (MRW)

are expected to have localised areas of calcite (Jeffries and Snyder 1983), the soils are

mainly podzols and brunisols (Soil Classification Working Group 1998) and are poorly

developed and dominated by poorly weatherable silicate minerals (plagioclase and

hornblende) (Kirkwood and Nesbitt 1991).

With Ca leaching rates exceeding the replenishment rate from weathering and

atmospheric input Ca soil pools have declined (Watmough et al. 2005). Calcium

weathering would take a long time to replenish boreal shield soil pools, even in the

absence of elevated acidic deposition and additional Ca removals through forest

harvesting. Mychorrhizal fungi have been shown to increase the rate of weathering by

secreting organic acids to access nutrients previously unavailable to plant roots (Van

Breemen et al. 2000). However, quantitative measures and contributions of biological

weathering to nutrient budgets have not been established (van Scholl et al. 2008) and

thus the rate of Ca weathering is assumed to be relatively constant (although, over

longer periods of time than considered here, changes in weathering rates could be

expected with climate changes and loss of weatherable materials).

As emission controls drastically reduced acidic deposition, Ca concentration

levels in most boreal shield lakes concurrently declined (Stoddard et al. 1999;

Watmough et al. 2003) and except in areas of other anthropogenic input (Yao et al.

3

2011), they have continued to do so (Watmough and Aherne 2008; Jeziorski 2008).

Contrary to expectations, chemical and biological recovery from acidification in these

boreal shield lakes remains lower than expected (Stoddard et al. 1999; Driscoll et al.

2001; Jeffries et al. 2003) with climate driven biogeochemistry dynamics indicated as

the primary cause (Schindler et al. 1996; Keller et al. 2007; Dillon et al 2007).

1.2 Why is calcium important?

Not only does Ca, as a base cation, act to buffer acidic deposition (Starr et al.

1998), it is also an essential nutrient for biota. Consequences of lowering lake Ca

concentrations below critical levels have included biota near extirpations (Jeziorski et

al. 2008) and taxa shifts (Jeziorski et al. 2014), that have induced food web changes

resulting in the increasing frequency of algal blooms, which in turn can affect water

quality (Korosi et al. 2012). While earlier studies indicated a critical level of 1.0 mg L–1

for Daphnia (Hessen at al. 2000) more recent research has indicated that growth,

reproduction and survival of some daphniids are negatively impacted below a Ca

concentration of 1.5 mg L–1 (Ashforth and Yan 2008; Cairns and Yan 2009).

Additionally, it should be emphasized that Ca is also an essential nutrient for

terrestrial biota. Calcium is required by plants for cell structure, cell division, and other

cellular processes including responses to stress (Cronan and Grigal 1995; McLaughlin

and Wimmer 1999; Horsley et al. 2000) and soil base cation losses from acidified soils

could potentially have negative effects on tree growth, health and vigor in harvested

forests (Horsley et al. 2000; Driscoll et al. 2001; Walmsley et al. 2009).

1.3 Study area – The Muskoka River Watershed

The MRW in south-central Ontario is a watershed that extends along the

4

Photo 1.1: Lake St. Nora (top) and Deer Lake (bottom), southern boreal lakes on the

Precambrian Shield.

5

Precambrian Shield from the Algonquin Highlands in Algonquin Provincial Park to

Georgian Bay, encompassing 763,800 hectares (Muskoka Watershed Council 2014). There

are over 2000 lakes (Photo 1.1 as examples) and innumerable streams, comprising over 15

percent of the surface area (Tran et al. 2009). Close to 80 percent of these southern boreal

lakes are dilute, oligotrophic lakes primarily located on base-poor silicate bedrock overlain

by thin glacial tills often less than 1 metre deep (Dillon et al. 1991; Watmough and Dillon

2004), and are poorly buffered (≤1.5 mg L–1 Ca) while only 6 percent of the lakes are

hardwater and strongly buffered (>3 mg L–1 Ca) (O’Connor et al. 2009).

The effects of acid rain and the impact on lake recovery have been well studied in

the MRW; however, in this region Ca decline can be amplified by forest harvesting,

which results in an additional export of Ca with biomass removal and forest regrowth

(Huntington et al. 2000; Watmough et al. 2003; Phillips and Watmough 2012). In view

of research indicating that some stands in Ca depleted, acid-sensitive ecosystems on the

Shield have more Ca in above-ground biomass than in the exchangeable soil pool

(Watmough and Dillon 2004), a detailed scientific assessment of the potential impact of

predicted harvesting on critical calcium levels in lakes of the MRW is essential.

Crown land forest management in the MRW

Approximately 50 percent of the land in the MRW is Crown land which is the focus

of this research as forest harvesting records are limited or unavailable for private land.

Forest harvesting operations on Crown land in the MRW are under the purvue of two

forest authorities. The Algonquin Forest Authority (AFA) was established in 1974 as a

commercially oriented, self-financing crown corporation responsible for forest

management (with timber rights) in Algonquin Park after a groundswell of concern over

6

the logging practices in the 70’s (Algonquin Forest Authority 2010). Westwind Forest

Stewardship Inc. (WFSI) took over from the MNR in 1998 as a not-for-profit,

community-based forest management company to manage the French/Severn Forest by

providing planning services, support, information, and compliance assistance to about

twenty-five logging contractors (Government of Ontario Environmental Registry 2007).

Both WFSI and AFA have Ontario Ministry of Natural Resources (MNR) Sustainable

Forest Licenses (SFL) specifying how allocation of annual allowable volume cuts

included in the current French/Severn 2009-2019 and Algonquin 2010-2020 Forest

Management Plans (FMPs), is to be divided among dozens of logging contractors. There

is currently no consideration of Ca sustainability in the calculation of allowable annual

harvesting cuts in the FMPs.

Past harvesting practices have shaped the MRW forests of today. Extensive and

successive, clearcut logging of MRW forests began in the mid-1800’s and, as market

needs dictated, harvesting was focussed on the high-grading of a series of single species

starting with white pine (Pinus strobus), then red pine (Pinus resinosa) in the 1800’s

followed by eastern hemlock (Tsuga canadensis), and yellow birch (Betula

alleghaniensis) in the 1930’s –1950’s with increased logging of sugar maple (Acer

saccharum) from the 1950’s to the present (French Severn Forest Authority 2009). As a

consequence, and in combination with no regeneration, the current French/Severn forests

have reduced numbers of naturally occurring species (white pine, hemlock, yellow birch)

and a high percentage of low grade trees (French Severn Forest Authority 2009).

Consequently, tolerant hardwood stands now dominate the French/Severn Forest with

sugar maple as the predominant species (French Severn Forest Authority 2009). The

Algonquin Park forests within the MRW are similarily characterised by deciduous stands

7

dominated by sugar maple and coniferous forests dominated by a mix of spruce, cedar,

hemlock and fir (Strickland 1993).

1.4 Impacts of forest harvesting – biomass export driven Ca decline

There are short-term and long-term effects on forest ecosystems from harvesting

but this research focuses on the long-term effects on Ca budgets. In a natural state, forests

are efficient in recycling, conserving and storing nutrients (Brady and Weil 2008) but

forest harvesting can disrupt the Ca cycle (Palviainen 2005). Without harvesting, Ca in

trees is internally cycled and returned to the soil mainly through litterfall (Likens and

Bormann 1995, Piirainen 2002) litter and fine root decomposition, and to a lesser extent

through canopy exchange (Kimmins et al. 1973). Biomass and associated Ca removal in

forest harvesting can contribute to Ca losses from soil (Olsson et al. 1995) and

consequently lower Ca export to lakes. Whole tree harvesting over several rotations for

example, has been found (or predicted) to deplete Ca levels in soil (Federer et al. 1989;

Vance 1996; Adams et al. 2000; Keller et al. 2001; Watmough et al. 2003) which can

have a negative effect on future rotations (Proe et al. 1996; Walmsley et al. 2009).

Additionally, escalating demand for bio-energy fuel by harvesting slash has led to

concerns over forest sustainability since the impact on Ca removals is analogous to that

of whole tree harvesting, with the consequential negative effect on tree growth

(Rosenberg and Jacobson, 2004; Ågren et al. 2010).

The three silvicultural systems used in the MRW FMPs are: selection (uneven-

aged with about 30% of stand cut every 20-25 year cycle with harvesting residues, tree

tops and branches, left onsite) (Photo 1.2), shelterwood (even-aged stands with mature

trees removed in two or more cuts every 15 years with 70 years after the last cut to

8

allow for natural regeneration with harvesting residues left on site) (Photo 1.3) and clearcut

or whole tree (even-aged stands with mature trees removed in one cut on a 90 year cycle).

There are small variations between the two FMPs in the percentages of methods used

during the 10-year plans, with the French/Severn harvesting percentages at 52/41/7 and

Algonquin at 54/41/5 (selection/shelterwood/clearcut respectively). In clearcut (whole

tree) harvesting, more biomass is removed in one cut causing Ca removals to at least

double, compared with the other methods of harvesting (Kimmins 1973, Walmsley et al.

2009). Although clearcutting accounts for only 5-7% of MRW harvest removals, the high

cost of fossil fuels and concerns over climate change may encourage biofuel harvesting of

residues currently left on the forest floor (from selection and shelterwood harvestin) with

resulting Ca removals similar to whole tree removals.

Preliminary estimations of the impacts of harvesting in the MRW using rudimentary

estimates of biomass removals indicated that lake Ca levels will continue to fall

(Watmough et al. 2003; Watmough and Aherne 2008). Any harvesting volume removal

for the MRW will result in lower Ca concentration in surface water because of the large

amount of Ca held in tree biomass that is permanently removed from the catchment and as

a result lakes in harvested catchments will be more sensitive to acid deposition (Warfvinge

and Sverdrup 1992), potentially causing MRW lake acidification and/or inhibiting lakes

from recovering, with those lakes at already low Ca levels at greatest risk of going below

critical thresholds.

The Steady-State Water Chemistry Model (using current lake chemistry and the F

factor) (Henriksen and Posch 2002) has been used previously to determine the impact of

harvesting on MRW soils and lakes with the benefit that it is the net result of integrated

9

Photo 1.2: Selection harvesting area in the French-Severn forest near Dorset, Ontario in the

Muskoka River Watershed. Areas were harvested two years prior to photos. Harvesting residues

(tree tops and branches) are left onsite.

10

Photo 1.3: Photo collage of a seed tree shelterwood harvesting area in the French-Severn

forest near Dorset, Ontario in the Muskoka River Watershed.

11

soil and water nutrient cycling (Watmough et al. 2003). However, earlier work used

estimates of biomass removals to estimate the long-term steady-state lake Ca under

various harvesting scenarios (Watmough et al. 2003). Incorporating prescribed allowable

annual harvesting cut information as a refinement of this approach should yield more

accurate results. By understanding the predicted changes in soil Ca that can be attributed

to forest harvesting, we can further the understanding of how MRW lake Ca levels will

change, at what volume of harvesting lake Ca levels could be sustained above critical

levels, and if remediation measures are necessary.

1.5 Mitigation

Given already depleted soil Ca pools, low Ca deposition levels and low rates of

weathering of silicate based minerals in the MRW, natural recovery of Ca levels in lakes

is unpromising for those lakes approaching and below critical levels. Ca-addition,

through lime and wood-ash treatment has been used in many jurisdictions for forest soil

amelioration with the goals of reducing soil acidity, increasing Ca concentrations in trees,

and improving tree growth with the most commonly used measures for gauging the

success of these goals being pH, Ca foliar concentration and various tree growth metrics.

Previous reviews of the effects of liming (Derome 1990; Huettl and Zoettl 1993;

Nohrstedt 2001; Formanek and Vranova 2003) and wood-ash addition (Vance 1996;

Demeyer et al. 2001; Aronsson and Ekelund 2004; Pitman 2006) have primarily been

narrative evaluations with one wood-ash meta-analysis (Augusto et al. 2008).

Comprehensive liming experiments aimed at improving forest growth in Finland

and Sweden began during the 1950s and 1960s (Tamm 1974; Derome et al. 1986) and in

the 1980s, lime was used to mitigate soil acidification caused by the effects of acid rain

(Derome et al. 1986; Huettl and Zoettl 1993; Nilsson et al. 2001). Wood-ash has also

12

often been used to address forest nutritional deficiencies caused by acid deposition and

whole-tree harvesting (Cronan and Grigal 1995; Olsson et al. 1996; Demeyer 2001).

With the removal of harvest residues for biofuels garnering much attention in a renewable

energy context, recycling of the ash produced has potential for use as a nutrient input in

forest systems (Pitman 2006).

Interest in the effects of Ca-addition in the remediation of acidified soils has

recently increased in Ontario with as yet unpublished but ongoing wood-ash addition

trials in two locations on the boreal shield. This research has been undertaken by a

collaborative group from the University of Toronto, Laurentian University, Ontario

Ministry of Natural Resources (OMNR) and the Canadian Forest Service. Preliminary

results have indicated increased soil pH and increased availability of Ca with no adverse

effects on microbial biomass, function or community structure at wood-ash addition

levels of 5000–7000 kg ha–1.

As conclusions from Ca-addition studies vary widely and recommendations are

often ambiguous, a more current and more extensive meta-analysis was deemed to be

necessary in a preliminary evaluation of the impacts of Ca-addition to acid-sensitive soils

(which could in turn impact surface water) and on the subsequent effects on tree

productivity.

13

1.6 Thesis objectives

The objectives of this thesis were: to assess patterns (relationships between lake Ca

concentration and water chemistry and landscape variables), and temporal trends in key

water chemical parameters for lake data sets in forested catchments in the Muskoka

River Watershed (MRW) in south-central Ontario, to predict the pre-industrial, pre-

forest harvesting steady-state of lake Ca and the potential impact of harvesting on lake

Ca levels in 364 lakes located in managed MRW Crown forests with harvest volume

removals between 2009–2020, and to assess effects of Ca-addition mitigation strategies

in Ca depleted managed forests.

Lake Ca patterns and trends were investigated through analysis of available long-

term chemical records. For 364 lakes, pre-industrial lake Ca concentrations, representing

no-harvest scenarios, were modeled based on lake chemical data and catchment physical

attributes. Future change in lake Ca for these 364 lakes was calculated based on

prescribed 2009–2020 tree biomass exports on a catchment basis as provided by forest

management authorities. To consider ecosystem mitigation strategies, the relationships

between Ca-addition (wood-ash and lime) and soil and tree indicators were investigated

through extensive meta-analyses and decision tree analyses of over 350 trials in acid-

sensitive ecosystems in eastern North America and parts of Europe.

Timber harvesting is, by its nature, a disturbance to the forest ecosystem and

sustainable forest harvesting strives to minimize the disturbance in both the short-term

and long-term. The overriding rational for this thesis was to provide the most up to date,

accurate information on the impact of forest harvesting on MRW lake Ca concentrations

as biomass removal driven lake Ca decline, in acid-sensitive areas on the boreal shield,

should be a consideration in sustainable forest harvesting.

14

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calcium decline in fresh waters. Science 322: 1374–1377.

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2014. The jellification of north temperate lakes. Proc. R. Soc. B 282: 20142449.

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acidified watershed: Plastic Lake, Ontario, Canada. Geochim. Cosmochim. Act.

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production linked to lakewater calcium decline through food web interactions.

Proceed. Roy. Soc. B. 279: 1210–1217.

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edition. Springer, New York. pp. 159.

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and terrestrial ecosystem. New Phytol. 142(3): 373–417.

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2. Calcium levels in lakes of the Muskoka River Watershed - patterns, trends,

predictions and the potential impacts of tree harvesting on critical levels

2.1 Abstract

Decreasing calcium (Ca) concentration in lakes located in base-poor catchments

of the Muskoka River Watershed in south-central Ontario is an acid-rain driven legacy

effect threatening the health and integrity of aquatic ecosystems, and is a problem that

can be compounded by Ca removals through forest harvesting. Mean lake Ca

concentration (mg L–1) in 104 lakes across the Muskoka River Watershed (MRW)

have decreased by ~ 30% since the 1980's with the rate of decrease slowing over time.

Mean lake SO4 (mg L–1), and Mg (mg L–1) also decreased significantly over time

(37% and 29%), again with a declining rate of decrease pattern, while lake mean pH

and DOC increased significantly between the 1980’s and the 1990’s (16% and 12%),

but exhibited no significant pattern after that. Data gathered in 2011 or 2012 suggested

that smaller lakes, at higher elevation, in smaller catchments with higher runoff that are

minimally impacted by the influence of roads (and associated sodium and/or calcium

chloride road dust suppressants and de-icers) and agriculture (and possible Ca input as

fertilizers), are associated with low Ca concentrations and thus are the lakes most at

risk of amplified Ca depletion below critical levels due to forest harvesting. Using

proposed 10 year land forest management cut volumes on crown land, and assuming

the same levels of continuous removal, 38% of 364 lakes in the MRW will fall below

the critical 1 mg L–1 Ca threshold compared with 8% in the absence of future

harvesting.

2.2 Introduction and rationale

Legacy effects of acid rain are testing the resilience of sensitive ecosystems on

22

the Precambrian Shield and some unprecedented, unforeseen problems have emerged

as a result. Anthropogenically induced increases in sulphur (S) deposition has led to

declines in soil Ca in thin, acid-sensitive, mineral poor soils in eastern North America

(Huntington et al. 2000; Watmough and Dillon 2003; Yanai et al. 2005) and Europe

(Sverdrup and Rosén 1998; Thimonier et al. 2000; Jonard et al. 2012). For decades

sulphate (SO4) has been the primary agent of acid precipitation but, more recently,

there has been increasing concern with possible nitrogen (N) saturation that could

cause an increase in nitrate (NO3-N) leaching, resulting in (among other negative

ecological effects), amplified Ca declines (Aber et al. 1998; Gradowski and Thomas

2008; Pardo 2011). Additionally, rising dissolved organic carbon (DOC) levels may

also offset some of the benefits of declining SO4 deposition (Monteith et al. 2007).

Despite a 60% mean reduction in S deposition and an 18% decrease in N

nationally in Canada from 1990 to 2010 (Environment Canada 2012) existing Ca

depletion in acid-sensitive soils and lakes can be exacerbated by tree harvesting and

harvesting biomass for biofuels (Johnson et al. 1994; Watmough and Dillon 2003,

Watmough et al. 2003, 2005; Agren and Lofgren 2012). Over 60 percent of Ca in

trees can be found in stemwood and bark (Paré et al. 2013), the bulk of which is

exported from a site during timber harvesting. Over time, this can further deplete soil

Ca beyond acidification induced losses (Federer et al.1989; Keller et al. 2001;

Watmough and Dillon 2003). Repeating harvesting cycles on shallow soils (Keller et

al. 2001; Watmough et al. 2003), can intensify losses and in turn can have negative

effects on tree and seedling health and growth and reduce stand productivity (Proe et al.

1996; McLaughlin and Wimmer 1999; Horsley et al. 2000; Walmsley et al. 2009).

23

Studies on the impacts of harvesting on surface water have taken place on

two spatial levels, catchment and stand; however, impacts of harvesting regimes on

surface water at the stand scale should not necessarily be extrapolated as they may be

significantly reduced when assessed at the scale of the watershed (Buttle and Metcalfe

2000). Watershed level results have varied, but many small watershed studies have

indicated changes in hydrology with harvesting (MacDonald et al. 2003; Neary et al.

2009; Zhang and Wei 2014) with the impacts decreasing with increasing watershed size

(Guillemette et al. 2005; Stednick 1996). Other short-term impacts include: increases in

surface water temperature (Prevost et al.1999, Bourque and Pomeroy 2001; Curry et al.

2002) and increases in surface water concentrations of SO4, phosphorus, (P), potassium

(K), magnesium (Mg) and Ca (Nicholson et al. 1982; Jewett et al. 1995; Carignan et al.

2000) and NO3 (Nicholson et al. 1982; Prevost et al.1999; Jerabkova et al. 2011). Over

time, base cation losses from the soil can decrease pH and acid-neutralizing capacity

(ANC) and eventually increase acidity and aluminum (Al) concentrations in surface

water (Hornbeck et al. 1990; Dahlgren and Driscoll 1994; Baldigo 2005).

With more than 2,000 lakes comprising over 15% of its total surface area, the

Muskoka River Watershed (MRW), located east of Georgian Bay on the Shield, has

been the focus of much lake Ca research in Ontario. Although acidic deposition has

decreased drastically since its peak in the 1970’s, low weathering rates and low Ca

deposition are typical of the MRW and, as a result, Ca leaching losses cannot be

readily replenished (Watmough and Aherne 2008). Many lakes in the MRW are

currently below a critical Ca level range of 1.5 – 2 mg L–1 (75 – 100) µ eq L –1),

necessary to sustain keystone aquatic biota (Ashforth and Yan 2008; Tan and Wang

24

2010).

Recent work has indicated that Ca declines in some MRW lakes have driven the

elimination of Ca-rich keystone Daphnia species, especially of the large algae grazer

Daphnia pulex. This species elimination has been linked to recent algal blooms (Korosi

et al. 2012). Decreases in lake Ca concentration in south-central Ontario are also

driving the replacement of Daphnia species by a jelly-clad species (Holopedium) with

much lower Ca requirements an lower lake food web nutrient value (Jeziorski et al.

2014). As lake Ca continues to decline the increasing abundance of Holopedium is

predicted to cause cascading effects in the food chain and threaten water quality in the

affected lakes (Jeziorski et al. 2014).

Recent field assessment in northeastern Ontario have demonstrated greater biomass

and nutrient removals during tree length selection harvesting (the method used in over 60

% of operations in the MRW) than modeled estimates had previously indicated (Hazlett

et al. 2014). Although site conditions and tree species differ from the MRW, the latter

study highlights how potential long-term impacts on soil nutrients may be

underestimated in modeled harvesting scenarios (Hazlett et al. 2014), underlining the

value of an analysis of the impact of applied forest management on Ca depletion. While

the collection of harvest forest residues (branches and foliage) for biofuels is not an

existing practice in Forest Management Plans (FMPs) in the MRW, it has the potential to

more than double the loss of forest stand Ca depending on the method of harvesting

(Paré et al. 2013; Lucas et al. 2014). In view of research indicating that some stands

25

in Ca depleted, acid-sensitive ecosystems on the Shield have more Ca in above

ground biomass than in the soil (Watmough and Dillon 2004), a detailed assessment

of the potential impact of FMP predicted, tree harvesting biomass Ca export on Ca

levels in lakes of the MRW is essential.

The overall aim of this study was to evaluate the long-term impacts of timber

harvesting on lake Ca levels. The objectives of this study were: (i) to identify spatial

patterns between lake Ca and landscape and water chemistry variables, to identify

temporal lake chemistry trends between time periods, and to identify chemical and

physical parameter significant correlations with lake Ca concentration, (ii) to

estimate pre-industrial, unharvested catchment lake Ca using the Steady-State Water

Chemistry (SSWC) model (Henriksen et al. 1992) and, (iii) based on 10 year Forest

Management Plans, to quantify the impact of harvesting on lake Ca levels on a

catchment-scale. The latter took into account: maximum allowable cut volumes,

historical annual cut volumes, rotation time and method of harvesting (((Algonquin

Forest Authority Forest Management Plan 2010–2020 (2010); French Severn Forest

Authority Forest Management Plan 2009–2019 (2009)), tree species specific

biomass equations (Lambert et al. 2005; Ung et al. 2008) and biomass based Ca

concentrations for different tree species components removed (stemwood, bark)

(Paré et al. 2013).

2.3 Methods

Study Area

The MRW, in south-central Ontario, is a watershed that extends along the

26

Precambrian Shield from the Algonquin Highlands in Algonquin Provincial Park to

Georgian Bay, encompassing 763,800 hectares (Muskoka Watershed Report Card

2014) (Fig. 1). The climate is north temperate/south boreal with long-term average

annual precipitation of 990 mm (half of which is evapotranspired with the other half

producing runoff) and a long-term average annual temperature of 5.1 °C (1980–2009)

(Environment Canada 2011).

There are over 2000 lakes and innumerable streams, comprising over 15

percent of the surface area (Tran et al. 2009). Seventy-seven percent of the lakes are

poorly buffered (<1.5 mg L–1 CaCO3) (O’Connor et al. 2009) and are dilute,

oligotrophic lakes primarily located on, base-poor silicate bedrock overlain by thin

glacial tills, often less than 1 metre deep (Dillon et al. 1991; Watmough et al. 2004),

while only 6 percent of the lakes are hardwater and strongly buffered (>3 mg L–1

CaCO3) (O’Connor et al. 2009).

Although some regions are expected to have localised areas of calcite (Jeffries

and Snyder 1983), the soils are mainly podzols and brunisols and are poorly

developed and dominated by poorly weatherable silicate minerals (plagioclase and

hornblende) (Kirkwood and Nesbitt 1991). Small wetlands are pervasive throughout

the MRW and cover 12 % of the total area, while forests comprise 66% of the land

cover with mixed hardwood forests dominating on deeper glacial tills and conifers

prevailing on thin soils (Tran et al. 2009).

Crown land forest management in the MRW

Approximately 50 percent of the land in the MRW is Crown land (Fig. 2.1) and

this study considered only areas of publicly owned Crown land due to the availability of

27

existing harvesting data. The Algonquin Forest Authority (AFA) and Westwind Forest

Stewardship Inc. (WFSI) oversee Crown land harvesting. They have Ontario Ministry

of Natural Resources (MNR) Sustainable Forest Licenses specifying how allocation of

their cutcut volume is to be divided among dozens of logging contractors (Government

of Ontario Environmental Registry 2007).

The three silvicultural systems (a set of harvest, renewal and maintenance

treatments) used in the FMPs are: selection (uneven-aged with about 30% of stand

every 20–25 year cycle, shelterwood (even-aged with mature trees removed in two or

more cuts on a 15 year cycle with a 70 year cycle between the last cut of one cycle and

the first of the next to allow for natural regeneration) and clearcut (even-aged, shade

intolerant stands with mature trees removed in one cut on a 90 year cycle). The average

harvesting yield in Algonquin has increased from 47 m3 ha–1 during 1990–95 to ~65 m3

ha–1 during 2000–2010 and is projected to be 58 m3 ha–1 during 2010–2020 (Algonquin

Forest Authority 2010). For the French/Severn, historical yields have been much lower

due to the condition of the forest such that during the first four years of the current

2009–2019 plan, the yield has averaged approximately 37 m3 ha–1 (WFSI Strategic Plan

2012). From 1999–2009 harvest area and volume managed by WFSI were

approximately 50% of planned due to poor quality, accessibility issues and market

demand (KBM Forestry Consultants 2012). Both forest authorities harvest in large

areas outside the MRW and thus silvicultural analysis data specific to the MRW were

generated with the provided FMPs in order to assess the impact of harvesting on lakes

in the study area.

28

Figure 2.1: The Muskoka River Watershed in south-central Ontario (inset).

Crown land, the study area, comprises approximately 50% of the land surface

area.

Lake datasets

There was no single data set that could be used for all the analyses in this study.

Chemical data for MRW lakes were collected during 1981–2012 through a number

of different research projects and lake surveys including ones from: the Canadian Wildlife

Service (CWS), the Ontario Ministry of Environment and Climate Change (OMOE)

Dorset Environmental Science Centre (DESC), the Algonquin Fisheries Assessment Unit,

the District Municipality of Muskoka and the Canadian Water Network (CWN).

29

The collated datasets varied in sampling points, times, methods and analysis, but provided

at least one Ca observation for: 590 lakes located within the study area, of these, 364

lakes were found in catchments with tree harvesting removals and 177 of the 364 lakes

had their most recent sample of Ca ≤ 2 mg L–1, 104 of the 590 lakes had the data required

that was evaluated for temporal trend analysis and 24 of those had data required for long-

term temporal trend analysis (Figure 2A.2). Finally, 75 lakes had data from 2011–2012

that was evaluated to identify patterns in chemical and physical parameters that were

strongly associated with lake Ca concentration (Figure 2A.2). Integrated lake samples

were given preference over unintegrated samples and average values were calculated if

more than one sample per year was available.

Temporal trends in lake chemistry

Changes in average lake Ca concentrations (mg L–1) were evaluated for 104

MRW lakes with time series data across three separate ten year time periods (1981–1990,

1991–2000 and 2003–2012 with matched pair t-tests (with repetition) in order to establish

any significant patterns in mean Ca over time. All data for each lake and time period were

averaged before analysis. Changes in mean lake concentrations (or values for pH) were

also similarly assessed for SO4, NO3, Mg, Na, pH and dissolved organic carbon (DOC)

trends. Although matched pair t-tests are not sensitive to deviations from normality,

non-parametric analyses of means were also evaluated using the Wilcoxon Signed Rank

test and the Sign test. While comparisons were not temporally consistent across lakes

over the three time periods, Ca depletion occurs relatively slowly and dramatic changes

don't normally occur over one or two years.

30

Long-term trends analyses

Trends analysis of lake data has been recommended as a fundamental way of

evaluating large, complex watersheds with widespread changes (Hirsch 1988; Hirsch et

al. 1991) and identification of similar long-term trends across multiple catchments of the

study area may indicate a regional response to direct or indirect influences (Paterson et

al. 2008). Long-term trends in annual and average concentrations for Ca, Mg, K, Na,

SO4 and Ca/Mg, Ca/Na lake ratios were evaluated for 24 lakes with five or more years

of annual means of the chemical parameters assessed (most of these lakes had 8 plus

years of data) with the trends test Kendall’s Tau. A non-parametric trend test, that

provides higher statistical power when data are not normally distributed, Kendall’s Tau

is robust against data gaps, outliers and serial correlation (Conover 1999). Aggregation

of the data set by using mean lake data or by computing means (where it was

possible) was used to reduce autocorrelation (Helsel and Hirsch 1992).

The Mann-Kendall procedure for difference in averages over time is

mathematically equal to the aforementioned estimator of correlation called Kendall’s

Tau (Conover 1999). The statistical methodology for Mann-Kendall trend analysis

has been previously detailed in Helsel and Hirsch (1992) and Lento et al. (2012) and

the analysis method has been used frequently in assessing long-term trends in water

quality (Clair et al. 1995; Jeffries et al. 1995). As the long-term trends were analysed

with annual mean values no trends in seasonality could be evaluated. To account for

the possibility of Type I errors, Benjamin and Hochberg’s (1995) false discovery rate

(FDR) correction procedure on the p value was used (Yan et al. 2008; Lento et al.

2012).

31

The van Belle and Hughes test (1984) for homogeneity has been used in

evaluation of aquatic trends (Yue et al. 2002; Lento et al. 2012) and was utilized in this

research to indicate if the trends across the 24 lakes with long-term data could be

denoted as regional. Sens slope or rate of decline was not included in this evaluation as

abrupt decreases in parameters following S emission controls were included within the

time-frame. For pattern and trend analysis, all statistical evaluation was performed

with JMP 11 (SAS 2013).

Physical and chemical parameter pattern analysis

Summary statistics were calculated for lake area, catchment area, lake elevation,

runoff and selected deposition and lake chemical variables for all lakes with Ca data

within crown land (590), lakes in catchments with harvesting cuts on crown land (364),

and three subsets of the former and latter lakes used in analysis (Table 2A.2). As many

factors and influences contribute to lake Ca concentration levels, it was determined if

there were any relationships that potentially could reflect major drivers of the lake Ca

concentrations for 75 MRW lakes with the most recent physical and chemical sampling

data from 2011-2012 (Table 2A.2). If there were several sampling points in these lakes,

average values were used since all analyses were catchment (not sub-catchment) based.

Non-parametric correlation analysis was performed with Spearman’s rho between lake

Ca and physical and chemical parameters available from 2011–2012 sampling. Physical

parameters in this data set (n=75) that were not measured were either modelled by

another researcher or highly correlated to the selected parameters (for example % of

catchment paved roads and % of urbanized catchment reflect each other, and %

catchment development and % sparse forest reflected % urbanized catchment).

32

A Principal Components Analysis (PCA) was conducted on this data-set as a

means of multivariate exploration in order to visually group various constituents across

sites. The PCA was performed to derive relationships between average lake Ca

concentration and a number of physical and chemical co-distributed variables. The

significance was tested with Bartletts test. JMP 10 software (SAS 2011) was used for

analysis.

Projected impact of timber harvesting on lake Ca levels – GIS spatial evaluation

Since catchments are delimited spatially by the geomorphological property of

drainage, MRW watershed boundaries were delineated (for lakes of all sizes) from a

hydrographic summary layer from the Natural Resources and Values Information

System (NRVIS 2009), and from a digital elevation model (DEM) file with a 1:50,000

spatial scale and 30 m by 30 m grid size (Geobase, 2013), resulting in the delineation of

993 catchments. Subsequently, raster maps of: land ownership from Ontario structured

data layers (Ontario Ministry of Natural Resources various years), lake chemistry (Ca,

Mg, K, Na, SO4, NO3 and Cl), MRW runoff coefficients interpolated from annual

average runoff measured over 30 years at long-term hydrologic gauging stations

throughout Ontario (Environment Canada HYDAT database 2012), interpolated wet plus

dry atmospheric deposition from eastern Canada averages for 1999–2002 (Environment

Canada), Forest Resource Inventory (FRI) (1:10,000) (Ontario Ministry of Natural

Resources 2004), and the French Severn FMP 2009–2019 (1:10,000) and Algonquin

FMP 2010-2020 (1:10,000), were obtained or developed on a catchment basis. The

amalgamated, 10 year FMPs, containing tree species volume removals by catchment and

associated catchment harvesting regimes raster maps, were used to derive predicted lake

33

chemistry post-harvesting. All GIS analysis was done with ArcGIS 10 and 10.1 (ESRI

2012), and data analysis was made using attribute tables within the ArcMap component.

Steady-State Water Chemistry (SSWC) model (Henriksen et al. 1992; Henriksen

and Posch 2001)

Ecosystem sensitivity to acidification is strongly dependent on mineral soil

weathering (chemical and physical breakdown of exposed minerals) rates, with

low weathering rates associated with sensitivity to acidic deposition (Warfvinge and

Sverdrup 1992). The rate of release of base cations (BC), Ca, Mg, K and Na, from

minerals largely determines a soils ability to buffer acidic deposition and is the only

self-repair method for anthropogenically acidified ecosystems (Holmquist 2001).

Atmospheric deposition and weathering of soil minerals represent the primary sources

of nutrient base cations to soils, however BC weathering rates are difficult to estimate

due to soil heterogeneity (Jönsson et al. 1995; Hodson et al. 1997) and some estimates

have been associated with at least 40% uncertainty (Hodson et al. 1997; Holmqvist

2001; Koseva et al. 2010). Consequently, mean annual surface water chemistry data

and mean annual runoff data were used to predict pre-industrial, unharvested

catchment lake Ca concentrations for 364 MRW lakes using the SSWC model

(Henriksen et al. 1992).

The SSWC model has been frequently used in North America (Hindar and

Henriksen 1998; Watmough et al. 2003; Watmough and Aherne 2008) and across

Europe (Posch et al. 2001) to estimate BC weathering rates and pre-industrial lake Ca

concentrations. The model assumes a steady state condition where mass balance

nutrient inputs balance outputs, allowing the use of mean chemistry values. The SSWC

model was used to estimate the base cation (BC) weathering rate from the measured

34

base cation flux and used the F-factor, the fraction of current base cations present in

lakes due to soil acidification, to account for part of the base cation leaching due to ion

exchange soil processes (Henriksen and Posch 2001):

(1) [BC]0 = [BC]t – F([SO4 ]t + [NO3 ]t – [SO4]0 – [NO3 ]0)

where the subscripts 0 and t represent pre-acidification and present concentrations,

respectively, and where [BC]0 is the pre-acidification base cation (BC = Ca + Mg + K +

Na) lake concentration. The Ca concentration existing in lakes at steady state under the

three harvesting scenarios was calculated using eq. 1 though the pre-industrial

concentration was calculated for Ca instead of [BC]0 and the removals exported from

harvesting were subtracted:

(2) [Ca]SS = [Ca]0 – (Ca up /Q)

where Q is the mean annual runoff (m year–1), [Ca]SS is the lake Ca concentration at

steady state under a harvesting scenario and [Ca]0 is the pre-industrial Ca concentration

calculated using eq. 1. Due to lack of a full suite of MRW lake data and the addition of

other anthropogenic base cation inputs to MRW catchments, the assessment was made

with a modified SSWC model (Table 2A.1) as has been done in the past (Watmough et

al. 2003). The unmodified SSWC model inputs for equations are: lake concentrations of

base cations (Ca, Mg, Na, and K), chloride (Cl), sulphate (SO4) and nitrate (NO3).

Chloride is used to make a sea salt correction to base cations and SO4 as the model

assumes that all Cl in runoff comes from sea salts (Henriksen and Posch 2001).

However, this is not true of the MRW lakes as many are subject to road salt. As Na and

Cl concentrations are believed to balance each other out, these inputs were excluded

35

from the equations. As well, K concentrations are very low in these lakes and are

thought to contribute little to the base cations and were excluded along with lake NO3

concentrations which are negligible in the MRW (Watmough and Dillon 2002;

Watmough et al. 2003). The SSWC model additional equations (Table 2A.1) and

assumptions used in this study are extensively detailed in Watmough et al. (2003) and

Watmough and Aherne (2008).

Biomass export driven Ca decline

The impact of harvesting on lake Ca was evaluated by subtracting the Ca

removed from the catchment in harvesting biomass from the SSWC calculated lake Ca

concentration to give the Ca level the lake would have, at steady state, under a specific

harvesting scenario (Watmough et al. 2003). The online calculator provided by Natural

Resources Canada 2014 was used to determine the average biomass in kg of the species-

specific tree stem and bark, as well as the quantity of Ca (g) contained in the biomass

using either diameter at breast height or a combination of diameter at breast height and

height (Lambert et al. 2005; Ung et al. 2008). Branch and foliage biomass and their

associated Ca were not included in the calculations as they were left on site in close to

100% of all harvesting operations (based on silvicultural method used). The quantity of

Ca (g) removed by kg of tree component was converted to Ca (g) removed by volume

(m3) using Canadian tree species live wood density estimates (Gonzalez 1990). The

equations used in these calculations, the methodology, and the corresponding error terms

in estimating species specific mean biomass and the associated mean Ca (g) are detailed

in the associated papers (Lambert et al. 2005; Ung et al. 2008).

The overall calculation was performed on a catchment-by-catchment basis for

36

three harvesting scenarios with differing cut volumes based on the annual allowable cut

(AAC). The annual allowable cut (AAC in m3) is the annual level of harvest permitted

on an area of Crown land over a specified number of years. In addition to the hind-cast

of a steady state lake Ca concentration calculation to determine background levels of pre-

industrial, unharvested catchment lake Ca, three biomass export predictions of lake Ca

were made at 100 %, 59% and 40% of the predicted 10 year annual allowable cut

volumes. The 40% removal level was based on email correspondence with the head

foresters who estimated approximately 40% of AAC in the 2009-2020 FMPs will be

removed mainly due to market constraints, but also to cutovers and unforeseen tree poor

quality issues. The 59% level of harvesting was the weighted average volume removal

level by catchment based on annual cut volumes that have been recently harvested as

indicated by 73% of total planned volume harvested in 2005–2010 by the Algonquin

Forest Authority (Cumming 2010) and 52% of total planned volume harvested in 1999–

2009 under Westwind (KBM Forestry Consultants Inc. 2012). The 40% and 59% levels

assumed the same proportion of species-specific cuts as in the 100% FMP by volume

scenario. For each of these harvesting scenarios, the amount in kg of Ca removed in

biomass was divided by the area of the catchment and then by the tree harvest rotation

length to obtain kg Ca ha–1 yr–1. To obtain the change in Ca in mg L–1, the predicted mg

Ca m–2 yr–1 was then divided by the annual runoff (L m–2 yr–1). This value was then

subtracted from the SSWC steady-state lake Ca concentration to obtain the predicted

impact of harvesting biomass export on lake Ca concentrations.

Using 100% of AAC volume prediction may seem high; however, although

demand for wood is currently dictated by a slow market, future demand could increase

37

as electricity prices continue to soar, making fuelwood more desirable for the consumer

that uses electricity to generate heat, and the collection of slash for biofuels more

economically feasible. The impact of increasing volume removals of trees (and

theoretically even beyond AAC with the ACE) was thus assessed using the 100% AAC

prediction. For this analysis seven lakes with “0” Ca concentration (levels assumed to be

low and undetectable with the equipment used at the time) were not included. Although

theses lakes were not arbitrarily assessed values it was thought reasonable to assume all

seven had their most current levels (as determined by their last sampling year) below 1.0

mg L–1 (50 µeq L–1).

2.4 Results

Lake Chemistry

Summary statistics, calculated for 75 MRW lakes with the most recent physical and

chemical data (sampled 2011–2012), indicated that both mean lake Ca (mg L–1) and Mg

(mg L–1) were slightly higher at 2.81 and 0.83, respectively, compared with 2.43 and 0.66

for the total number of lakes in the study area (n=590) and 2.14 and 0.61 for lakes in

catchments with harvesting cuts (n=364) (Table 2A.2). This lake data set (n=75) also had

both mean lake Na (mg L–1) and Cl (mg L–1) values that were markedly higher at 3.21 and

4.47 respectively compared to 1.04 and 0.80 for lakes in catchments in cuts (n=364)

(Table 2A.2). Mean lake elevation of 324 (m) was lowest for this data set (n=75)

compared to 346 (m) (n=590), 357 (m) (n=364), (and the highest mean elevation of 412

(m) (n=177)), as was mean runoff of 0.028 (L m–2 yr–1) compared to 0.029 (L m–2 yr–1)

(n=364) (Table 2A.2).

38

Figure 2.2 Key data inputs in estimating MRW lake Ca (mg L–1) on a catchment basis under

predicted tree harvesting and no harvesting 2009–2020. The equations that were used in

estimating steady state are detailed in Appendix 2A (Table 2A.1).


Both the mean lake SO4 (mg L–1) and Ca (mg L–1) decreased significantly over time

(n=104) (Table 2.1) with the mean lake SO4 concentration decreasing between the first

and last time frame by 2.72 mg L–1, a mean 36.7% decrease and the mean Ca (mg L–1)

decreasing overall by 0.65 mg L–1, a 29.4% mean decrease (Fig. 2.3). The relative

difference in mean lake Ca concentration between the 1980s and 1990s was

approximately four times greater than between the 1990s and the 2000s indicating that

the rate of Ca decline has decreased, as has the rate of decline of SO4 (Fig. 2.4). From

the earliest to the latest time period, lake Mg declined significantly by a mean of

Interpolated Annual Runoff

(L m-2)

39

0.18 mg L–1, a 28.5% mean decrease and also indicated the pattern of a declining rate of

decrease over time (Table 2.1, Fig. 2.4). Lake pH and DOC concentration increased

significantly, between the first and last time span with the former increasing by a mean

of 0.2 pH units, corresponding to a mean increase of 15.7%, and the latter increasing by

0.51 mg L–1, a mean increase of 11.6% (Table 2.1, Fig. 2.4). Both pH and DOC did not

increase significantly between the 1991–2000 and the 2003–2012 time periods. Lake

NO3 data was only available for the two later time periods, decreasing significantly by a

mean of 0.0012 mg L–1, a mean decrease of 9.8% (Table 2.1, Fig. 2.4). There were no

significant differences between any time periods indicated for Na.

Table 2.1: Mean lake parameter and matched pairs t test mean difference in lake parameter

and associated standard error (SE) between three sampling time periods for 104 MRW

lakes. Lake Ca is in bold type.

Time period

Ca (mg L-1) SO4 (mg L-1)Mg (mg L-1)

pH DOC (mg L-1) NO3 (mg L-1)

Matched pairs t test

Mean difference (mg L-1)

SE Upper 95% Lower 95%

P

1981-1990

2.21 6.93 0.63 5.73 4.91 ND

1981-1990 1991-2000

-0.53 -1.88 -0.15 0.18 0.38

0.04 0.16 0.01 0.03 0.11

-0.47 -1.57 -0.13 0.24 0.58

-0.61 -2.20 -0.17 0.13 0.17

0.0001 0.0001 0.0001 0.0001 0.0006

1991-2000

1.68 5.05 0.49 5.91 5.29

0.0122

1991-2000 2003-2012

-0.12 -0.83 -0.04 0.02 0.19

-0.001

0.03 0.11 0.01 0.02 0.11

0.002

-0.09 -0.61 -0.04 0.07 0.40

-0.007

-0.18 -1.06 -0.05 -0.02 -0.02

-0.013

0.0001 0.0001 0.0001

NS NS

0.0001

2003-2012

1.56 4.22 0.45 5.93 5.48

0.011

1981-1990 2003-2012

-0.65 -2.72 -0.19 0.21 0.57

0.03 0.18 0.01 0.03 0.12

-0.60 -2.35 -0.16 0.28 0.80

-0.73 -3.08 -0.21 0.14 0.34

0.0001 0.0001 0.0001 0.0001 0.0001

Note: Non-parametric evaluations of means with the Wilcoxon Signed Rank test yielded the identical significant differences

at the 0.0001 probability (P) level and the Sign test yielded the same decreasing patterns at P of 0.0001. ND means no data

and NS means not significant.

40

Mean lake Ca (mg L–1) 1981-1990

Mean lake SO4 (mg L–1) 1981-1990

Figure 2.3: Lake patterns for mean Ca concentrations (mg L–1) for N= 104 MRW lakes

with long-term data. All lakes below the 1:1 line decreased in lake Ca (mg L–1) between

the time periods 1981–1990 and 2003–2012. The mean decrease for Ca was 29.4% ± 12%

and the mean decrease for SO4 depicted in the inset graph was 36.7% ± 19%.

Figure 2.4: Boxplot distributions over three time periods based on mean lake values of Ca,

SO4, Mg, pH, NO3 and DOC for 104 lakes. Outliers are indicated by separate dots. Note: all

units are in mg L–1 except for pH.

Mea

n l

ake

Ca

(mg L

–1)

20

03

-20

12

Mea

n lak

e S

O4 (

mg L

–1)

2003

-2012

0 1 2 3 4 5 6 7 8 9 10 11

11

10

9

8

7

6

5

4

3

2

1

0

–

NO3

41

Mann-Kendall long-term trend analysis

Consistent with the large data set, Mann-Kendall long-term trend analysis for 24

MRW lakes with low Ca indicated significant decreasing trends in 13 lakes, with a

further four showing non-significant declining trends (Table 2.2). Significant declining

trends in lake Mg (mg L–1) were indicated in 10 lakes, non-significant declining trends in

5 lakes and no trends in 9 lakes (Table 2.2). There were no trends indicated for Na in any

of the lakes and thus Na was omitted from the results table. Significant declining trends

in lake K (mg L–1) were indicated in 6 lakes, non-significant declining trends in 2 lakes

and no trends in 16 lakes (Table 2.2). Significant declining trends in lake SO4 (mg L–1)

were indicated in 9 lakes, non-significant declining trends in 4 lakes and no trends in 11

lakes (Table 2.2). There were no long-term positive trends in any assessed parameters.

Strong significant homogeneity of long-term trends across groups of lakes

(subsets) found in multiple catchments was indicated by the van Belle and Hughes test

(1984) for lake Ca (n=17), SO4 (n=13) and a less strong but still significant trend for lake

Mg (n=15) but not for K (n=8) (Table 2.3). The homogeneity of trends was calculated

for all of the lakes indicating a decrease over time separately and the lakes showing no

trend were excluded from this analysis. The strongly significant long-term patterns over

time across groups of lakes for Ca, Mg and SO4 indicate a common lake response to direct

or indirect influences but do not indicate what those influences are. Conversely, the

heterogeneity across lake trends for K may indicate variability in lake response in these

parameters to direct or indirect influences. Lake Na concentration was not included in

this across lake analysis as there were no long-term lake Na (mg L–1) trends indicated.

42

Table 2.2: Mann-Kendall long-term trend analysis for 24 MRW lakes with Ca ≤2 mg L–1 located in

catchments across the MRW with Forest Management Plan predicted cuts to the year 2020.

MRW Lake

Bertie

Bright

Chub

Corbe

tt

Dace

Dale

Kagh

Little Drummer

Little East End

Lumber

MacKinaw

Martin

Namakootchie

Niger

North

Oak

Pincher

Quiver

Slim

Sunset

Thumb

Tonakela

Weed

Wee

West Otterpaw

Note: Declining trends that were significant at P < 0.05 after false discovery rate corrections utilizing Benjamin and Hochberg’s

(1995) method are printed in bold and with an asterisk (*), respectively. There was no difference between the uncorrected

and corrected significant trends. Unbolded trends had less than 7–9 years of data (yes) and blanks indicate no trends. As there

were no trends in Na (mg L–1) in any lake it was omitted from the table, in addition, there were no increasing trends for any

parameter. Percent change using Sens slope was not included as this trend time-frame included abrupt changes in parameter

concentrations (brought about by emission controls) which may skew slope.

Table 2.3: Summary of results of a multi-group Mann-Kendall trend test to determine consistent

regional trends across lakes in Ca, Mg, K and SO4 concentrations (mg L-1) in 24 Precambrian

Shield lakes with Ca≤2 (mg L-1). Consistent strong declining trends across lakes were indicated in

Ca and SO4 and a less strong but still significant declining trend in Mg. No trend across lakes was

indicated for K.

Number of lakes

Effect χ 2 trend df P

17 Ca Trend 16.49 16 <0.0001 15 Mg Trend 5.97 14 0.0113 8 K Trend 2.68 7 0.0886 13 SO4 Trend 36.23 12 <0.0001

Note: Lakes showing no trend were excluded from this analysis.

Time

span

Ca

declining

Mg

declining

K declining

SO4

declining

1990–2012

1982–2002 yes yes

1988–2012 yes* yes* yes* 1990–2004

1984–2004

1990–2004 yes* yes* 1988–2004 yes* yes* yes* yes* 1988–2004 yes* yes* 1989–2004 yes* yes* yes* yes* 1988–2004 yes* 1988–2002 yes yes

1980–2002 yes* 1984–2004 yes yes yes

1982–2003

1990–2006 yes* yes* yes* 1989–2012 yes* yes* yes* yes* 1990–2004 yes yes

1990–2004 yes

1989–2012 yes* yes* yes* 1982–2002 yes yes yes

1988–2004 yes* yes* yes* 1988–2012 yes* yes* yes* yes*

1984–2004 yes yes

1988–2002 yes* yes* yes* yes*

43

Non-parametric Correlations and Principal Components Analysis

For the 75 lakes with data from 2011-2012, chemical parameters significantly

positively correlated with lake Ca (mg L–1) were lake Cl (mg L–1), lake SO4 (mg L–1)

and Ca deposition (kg ha–1

yr–1

). There were no other chemical deposition parameters

that were significantly correlated with lake Ca. Lake pH was strongly significantly

positively correlated with lake Ca concentration. The physical parameters significantly

positively related with lake Ca (mg L–1) were paved road density, percent of

agriculture in the catchment area, unpaved road density, percent of rock and rubble and

lake area and catchment area and significant negative correlations with lake elevation,

runoff and % of deciduous trees in the catchment area were indicated (Table 2.4).

Table 2.4 Non-parametric Spearman’s p correlation analysis between lake Ca (mg L-1) and

chemical and physical parameters for 75 lakes sampled in 2011–2012.

5.3

Note: The scale of measurement was on a catchment basis for the majority of parameters. Significant correlations at p<0.05 are bolded as are the variables significantly correlated to lake Ca (mg L–1).

Variable by Variable Spearman’s ρ Prob>|ρ|Lake CA (mg L–1) Ca deposition (kg ha–1 yr–1) 0.4698 <0.0001

Lake CA (mg L–1) S deposition wet plus dry (kg ha–1 yr–1) -0.1843 0.1063

Lake CA (mg L–1) N deposition wet plus dry (kg ha–1 yr–1) -0.1158 0.3225

Lake CA (mg L–1) Lake SO4 (mg L–1) 0.4767 <0.0001

Lake CA (mg L–1) Lake Chloride (mg L–1) 0.8775 <0.0001

Lake CA (mg L–1) Runoff (mm yr–1) -0.2777 0.0139

Lake CA (mg L–1) Mean downslope distance gradient -0.1280 0.2640

Lake CA (mg L–1) Catchment area (ha) 0.3653 0.0011

Lake CA (mg L–1) Lake area (ha) 0.3286 0.0033

Lake CA (mg L–1) Lake mean depth (m) 0.1512 0.1863

Lake CA (mg L–1) Lake elevation (m) -0.4781 <0.0001

Lake CA (mg L–1) Catchment unpaved road (m–1) 0.6468 <0.0001

Lake CA (mg L–1) Catchment paved road density (m–1) 0.7332 <0.0001

Lake CA (mg L–1) Catchment percent rock/rubble 0.3460 0.0019

Lake CA (mg L–1) Catchment % agriculture 0.7019 <0.0001

Lake CA (mg L–1) Catchment % dense deciduous forest -0.3151 0.0050

Lake CA (mg L–1) Catchment % dense coniferous forest -0.2167 0.0567

Lake CA (mg L–1) Catchment % bog 0.0082 0.9444

Lake CA (mg L–1) Lake DOC (mg L–1) 0.1287 0.2779

Lake CA (mg L–1) Lake pH 0.7809 <0.0001

44

The corresponding PCA graph allowed for pattern visualization of the parameters

parameters positively clustered with lake Ca concentration, which were lake Cl (mg L–

1), paved roads density, percent of agriculture in the catchment area, unpaved roads

density, lake area and catchment area and pH while closely negatively associated

parameters included lake elevation and lake runoff and % deciduous trees (Fig 2.5).

Principal component 1 accounted for 24% of the variation in the data set and

principal component 2 accounted for 17%.

Figure 2.5: Principal components analysis of physical and chemical variables for 75 MRW lakes

with data from 2011–2012. Principal component 1 described 24% of the variation in the included

parameters while principal component 2 described 17% of the variation. Note: c_dsg is the

mean down slope distance gradient, cuca is the catchment area, zmean is the mean lake depth,

c_con and c_deciduous are the catchment % of coniferous and deciduous trees respectively,

CLIDUR is lake chloride, CAU is lake calcium concentration, c_urd and c_prd are catchment

unpaved and pave roads respectively, l_elev is lake elevation. In every case Dep is deposition

and c_ is catchment.

45

Spatial evaluation used for projected impact of timber harvesting on lake Ca levels

The 590 lakes with data located in crown catchments are widely dispersed across

the MRW however they are most highly concentrated in the far eastern portion. This

distribution most likely reflects that lakes with lower Ca were the ones chosen to be

monitored for effects of acid rain throughout the combined data sets (Fig 2.6). The

time of their sample ranged from 1980-2012 with only ~15% sampled in 2011–2012

(Fig 2A.2).

For the 590 lakes found in crown catchments, the lakes with low Ca

concentrations of ≤2.00 mg L–1 can be visually assessed to be highly concentrated in

the most intensively harvested eastern section (Figs 2.6, 2.7). They are comparatively

lightly interspersed throughout the rest of the MRW (Fig 2.6). There were no lakes

≤1.00 mg L–1 in the far western or central portion of the MRW. A visual assessment of

the very high concentration of small lakes in small catchments in the eastern portion of

the MRW is well represented in the magnified image with many of the points

representing these lake Ca concentrations lying on top of each other (Fig 2.7).

The 364 lakes found in catchments with harvesting cuts are representative of

the 590 sampled with regard to mean distributions of available parameters (Table

A2.2) and hence, are thought to be reasonably representative of unsampled lakes

across the MRW surrounded by crown or private harvested catchments. Attributes of

the 177 lakes with Ca ≤2.00 mg L–1 found in catchments with FMP harvesting cuts

were highly variable. But on average they were smaller lakes ((mean value of 47 ha

(S.D. = 244 ha)), located in smaller catchments ((mean value of 412 ha (S.D.= 650

ha)), found at higher elevations ((mean value of 412 m (S.D.= 97 m)), with lower Ca

total deposition ((mean value of 6.09 kg ha–1 yr–1 (S.D.= 0.30)) and lower S total

46

deposition ((mean value of 9.61 kg ha–1 yr–1 (S.D.= 0.21)), with higher runoff

((mean value of 0.030 L m–2 yr–1 (S.D.= 0.003 L m–2 yr–1)) and with a slightly

higher percentage of the catchment in deciduous trees ((mean value of 64 %

(S.D. = 11 %)) (Table 2.5, Fig 2.7).

Table 2.5: MRW Lakes ≤2.00 mg L–1 in catchments with harvesting cuts (N=177) mean values,

standard deviations and ranges for chemical and physical parameters.

Sampling period 1980-2012 Mean SD Range

Deposition Ca (kg ha–1 yr–1) (1999-2002) 6.09 0.30 6.89 - 5.69

Deposition S (kg ha–1 yr–1) (1999-2002) 9.61 0.21 9.05 - 10.0

Lake Ca (mg L–1) 1.41 0.35 0.50 - 2.04

Lake Mg (mg L–1) 0.44 0.10 0.13 - 0.73

Lake Na (mg L–1) 0.61 0.31 2.35 - 4.61

Lake K (mg L–1) 0.32 0.32 0.05 - 2.42

Lake SO4 (mg L–1) 3.11 1.04 1.38 - 5.87

Lake elevation (m) 412 97.3 290 - 518

Lake area (ha) 46.6 243.8 0.23 - 3149

Catchment area (ha) 412 649.9 29 - 34695

Catchment % coniferous 16.9 5.97 0.00 - 37.8

Catchment % deciduous 64.2 10.9 0.00 - 82.4

Catchment runoff (L m–2) 0.03 0.003 0.023 - 0.034


and Posch 2001) and quantifying the potential impact of harvesting on lake Ca

levels

A cumulative distribution graph of current and predicted lake concentration as a

function of no harvest (steady state) and three harvest volume removals of 100%, 59%

(average reported annual volume removal level) and 40% (verbally reported removal

level) of the FMP for MRW catchments with predicted harvesting cuts from 2009–2020

was used to present the impact of harvesting on 364 lakes.

Natural variability in the SSWC modeled pre-disturbance lake Ca condition for 364

47

MRW lakes ranged from 0.70 to 4.34 (mg L–1) with a similar wide range for the most

current Ca levels which went from 0.72 to 4.43 (mg L–1). Catchment Ca removal levels at

100% FMP ranged widely with annual removals from 0.06 to 7.55 (kg ha–1) (reflecting the %

of catchment harvested) affecting lake Ca changes with declines ranging from 0.02 to 2.93

(mg L–1). The three harvesting volume Ca removal scenarios denoted as the worst (100%

FMP), the most recent historical weighted mean cut scenario (59%) and the best (40% FMP)

with regard to the future impacts of harvesting on lake Ca, gave lake concentration ranges

from 0.11 to 3.81 mg L–1 (5.50 to 190 µeq L–1), 0.44 to 3.87 mg L–1 (22 to 193 µeq L–1) and

0.54 to 3.91 mg L–1 (27 to 195 µeq L–1) respectively. Based on the most recent measurement,

28% and 5% of 364 lakes were already below critical Ca levels of 1.5 mg L–1 (Ashforth and

Yan 2008; Tan and Wang 2010), and 1.0 mg L–1 (Hessen at al. 2000) respectively at their

time of sampling (Table 2.6, Fig. 2.8). The estimated SSWC lake Ca level represents the no

harvest, pre-industrial lake scenario and a predicted 37% of lakes will fall below the critical

level of 1.5 mg L–1 with 8% falling below 1.0 mg L–1 in the absence of forest harvesting

(Table 2.6, Fig. 2.9). Under a 100% FMP volume removal scenario 69% of lakes are

predicted to fall below this critical level with 38% falling below 1.0 mg L–1 while under a

59% FMP volume removal level, 56% of the lakes would be below 1.5 mg L–1 with 26%

falling below 1.0 mg L–1 (Table 2.6, Fig. 2.9). Under a 40% FMP harvest volume removal

the impact to the lakes was most similar to the no harvest steady state scenario but still much

higher with 50% of the lakes declining below 1.5 mg L–1 and 20% falling below 1 mg L–1.

48

Table 2.6: Most current, SSWC modelled and predicted % of lakes under varying

harvesting scenarios for two critical thresholds of lake Ca for Ca-rich Daphnia species,

50 µeq L–1 (Watmough et al. 2003), equivalent to 1.0 mg L–1 and 75 µeq L–1 (Ashforth

and Yan 2008), equivalent to 1.5 mg L–1 for N=364 MRW lakes. The percentages

have been rounded to whole numbers.

N

Critical

Lake Ca

Level

(µeq L–1)

Most

Current

Lake

Ca

SSWC

No

Harvest

Lake Ca

100% FMP

Harvest

Volume

Lake Ca

59% FMP

Harvest

Volume

Lake Ca

40% FMP

Harvest

Volume

Lake Ca

364 <50 5% 8% 38% 26% 20%

364 <75 28% 37% 69% 56% 50%

49

Lake Ca (mg L–1) for 590 lakes located in crown land catchments in the Muskoka River Watershed

Figure 2.6: 590 (of more than 2000) lakes with Ca concentration data in Muskoka River Watershed catchments. Lakes most at risk ≥2 mg L–1

(n=177) of additional Ca removals are represented by red (0.00 – 1.04 mg L–1) and orange (1.05 – 2.04 mg L–1) dots. The concentration value

of 0 represents levels undetectable by the equipment used. Predicted harvesting cuts (2009–2020) are denoted in the catchments by dark

green while the runoff gradient is based on a distinctly different moss green.

50

Area of heavily concentrated small, shallow lakes in small harvested MRW catchments.

Figure 2.7: Enlarged area of catchments for n=364 lakes located in catchments with harvesting cuts with the blown up area

indicating an area of high concentration of small lakes in small catchments and many with Ca ≤2 mg L–1 as indicated by the larger

red X symbol. This area is impacted by intensive harvesting cuts from two Forest Management Plans (dark and light yellow).

51

Figure 2.8: Current and predicted lake Ca concentration as a function of no harvest (steady state)

and harvest volume removals of 100%, 59% (average reported annual volume removal level) and

40% (verbally reported removal level) of the FMP for 364 lakes in MRW catchments with

predicted harvesting cuts from 2009-2020. The red vertical lines denote literature suggested

critical Ca thresholds of 1.0 mg L–1 (50 µeq L–1) (Hessen et al. 2000) and as used in Watmough et

al. 2003 and of 1.5 mg L–1 (75 µeq L–1) as indicated by recent work in MRW lakes (Ashforth and

Yan 2008).

I.0 mg L–1 I.5 mg L–1

52

2.5 Discussion

Lake chemistry

Wide ranges were indicated for Ca (0.78 – 15.7 mg L–1) over all the collated data

sets however, 96% of the 590 lakes had Ca concentrations <4.5, with 100% of the 364

lakes impacted by harvesting with Ca concentrations <4.5. The similar Ca

concentrations measured in the 590 lakes and the subset of 364 lakes impacted by

harvesting indicates the lakes in catchments with cuts are reasonably representative of

the larger data set. The lowest mean lake Ca (1.41 mg L–1) for those lakes in harvested

catchments most at risk to Ca depletion below critical levels (n=177) was half of that of

the data set of the 75 lakes most recently sampled. These 177 lakes were much smaller

lakes, located at the highest mean elevation in catchments with the highest mean runoff,

the lowest mean SO4 deposition and the lowest mean Ca deposition.

Notably, both mean lake Na and lake Cl mean values were much higher in the

most recently sampled lakes than in all other data sets, with mean lake Na being five

times higher than mean Na for all lakes with data in catchments in cuts and over eleven

times higher than those lakes in catchments with cuts with Ca ≤2 mg L–1. These

elevated Na and Cl concentrations may reflect contamination by drainage from road de-

icing chemicals (Ralston 1971; Kaushal 2005; Langen et al. 2006). Other sources of

high lake mean Cl concentrations could include farm sources of potassium chloride

fertilizer (potash) and animal waste and septic system leakage (Shaw et al. 2004). Calcium

can also be a major component of de-icing chemicals which could also contribute to

higher lake mean Ca concentration (Langen et al. 2006) in the data set from 2011– 2012

if used in the MRW.

53


Lake chemistry on the boreal shield has not improved as expected from reduced S

emissions and this has been attributed to some or a combination of possible factors

including: declining base cation concentrations (soil acidification), drought induced

mobilization of SO4, increasing NO3 concentration and increasing DOC levels (Jeffries

et al. 2003). The contribution of these factors is the focus of the evaluation of the

significant patterns and trends indicated.

The 104 MRW lakes with historical data had variable initial (1980s) chemical

characteristics, with lake SO4 ranging from 2.65–10 (mg L–1) and pH from 4.58 – 6.70

and with lake Ca ranging from 1.29 – 5.50 (mg L–1) and lake Mg ranging 0.39 – 1.60

(mg L–1). Over the study period (1980s–2012) SO4 deposition in southern Ontario

markedly decreased from the early 1980’s to the late 1990’s, and nationwide from 1990

to 2010 emissions of SO2 declined by 57 percent while emissions of NO3, decreased by

18 percent (Canada-U.S. Air Quality Agreement: Progress Report 2012). Mean lake SO4

(mg L–1),Ca (mg L–1) and Mg (mg L–1) decreased significantly over time across the

three time periods with a declining rate of decrease, while lake mean pH and DOC

increased significantly overall time but indicated no increase between the two most

recent time frames. Using lake SO4 as the measure of the state of lake recovery from

acidification, the lake mean recovery has slowed substantially, while using lake pH as

that metric, significant lake mean recovery is indicated between the 1980’s and 1990’s

but appears to have stalled after that. Lake mean NO3 data were only available for the

last two time frames, but indicated a significantly declining concentration (10%) which

could be expected with a decline in NO3 emissions from the same time frame (1990-

2010). Declining NO3 trends in streamwater have been previously demonstrated in

54

streams in south central Ontario and were arttributed to declining emissions

(Kothawala et al. 2011).

A declining rate of decrease of lake SO4 has also been reported in similar time

frames for lakes in eastern Canada however there was no trend indicated in lake Ca and

Mg with the explanation that these cations had “bottomed out” (Clair et al. 2011). A

declining rate of decrease of MRW lake SO4 was expected but it has been well

demonstrated that despite the large reduction in S deposition, declines in surface water

SO4 concentrations have been much less than what was anticipated in the central

Ontario region (Dillon et al. 1997; Dillon and Evans 2001; Dillon et al. 2003) indicating

a less than expected recovery response from anthropogenically caused lake acidification.

This less than expected recovery could be due to influences of climate causing export of

S from catchments (Dillon et al. 2003; Eimers et al. 2004).

In this study the mean pH of the 104 MRW lakes increased significantly by

15.7% overall but no significant increases were indicated between the period ending in

2000 and the one ending in 2012. MRW limited lake recovery as indicated by an

increased pH (toward the critical value of 6) in this research has been corroborated

within the earlier time frame in some northeastern Canadian lakes (Clair et al. 1995;

Bouchard et al. 1997; Stoddard et al. 1999), but in other research there has been little to

no change in pH in response to declining S emissions (Houle et al. 1997; Dillon et al.

1997) while Clair (2011) found a similar pattern to this investigation, where pH

significantly increased between 1990–2007, but not after that. The same influence of

climate patterns on S re-oxidation could have negatively impacted increases in pH in the

2000’s as the frequency of droughts and rewetting increased over time. Additionally,

55

increased NO3 leaching or DOC export may limit the increase in lake pH (Aber et al.

1989; Stoddard et al. 1999).

There have been increased DOC concentrations reported in surface waters in

North America coinciding with the large decreases in S emissions but the process

explanations have been varied (Driscoll et al. 2003; Evans et al. 2005). Declining

atmospheric S deposition has been identified as the major driver of DOC increases in

some work (Evans et al. 2006; Monteith et al. 2007) but other drivers have also been

suggested as being more influential. In this evaluation DOC concentration increased

significantly by 12% from the 1980’s to the 2000’s however, no significant changes

occurred between the period ending in 2000 and the one ending in 2012.

DOC trends have been related to changes in rainfall and temperature and the

drought/rewetting process in wetlands (Eimers et al. 2008). Changes in DOC

concentration in lakes also depend in part on changes in precipitation patterns and if

increases in precipitation occur in the future, DOC soil leaching and lake-water DOC

concentrations are expected to increase (Keller 2007). Recent climate evaluation in the

MRW indicated that annual precipitation decreased significantly during 1978–1998 and

then weakly increased during 1999–2013 (Yao et al. 2013). This weak increase and

decreasing declines of atmospheric S could partially explain the no change in DOC in

this research between the later time frames. A warming trend has also been indicated

from yearly mean maximum temperature from 1990–2010 from the Muskoka

Automated Weather Observation System climate station (Hadley et al. 2012).

56

Catchments with differing initial chemical conditions and physical attributes are

not expected to have identical responses to broad-scale regional drivers like declining S

deposition and it is not the purvue of this research to determine which drivers will have

more influence in DOC patterns. However, if predicted increases in precipitation and

temperature do continue, without major changes in acidic deposition, DOC is expected to

increase in the future (Clark et al. 2010).

Total N emissions have significantly increased since the 1950s and remain at

unnaturally high levels (Vitousek et al. 1997; Galloway et al. 2003). With S emission

regulation, the rate of N deposition relative to S deposition has increased since the 1980s

and this has brought attention to whether N deposition is now restraining lake recovery

(Galloway 1998; Galloway et al. 2003; Jeffries et al. 2003). Nitrogen can contribute to

acidification directly as NO3 and as ammonium as both nitrification and uptake of

ammonium produce hydrogen ions (Schindler et al. 1985).

Increased NO3 leaching contributes to soil acidification and depletion of soil Ca

(and other cations) with associated declines in lake Ca over the long-term (Aber et al.

1989; 1998; Gradowski and Thomas 2008; Pardo 2011). In recent years, NO3

deposition, although often assumed to have stayed the same, has significantly decreased

by 18% between 1990 and 2011 (Canada-U.S. Air Quality Agreement: Progress Report

2012). Although there was insufficient data to assess MRW lake NO3 in the 1980’s the

mean lake NO3 decreased significantly by almost 10% between the 1990’s and 2000’s

which may indicate that N soil saturation and associated NO3-N leaching are not major

concerns in these catchments. However, any future increases in N deposition could

become problematic for lakes with NO3 levels that are on the high end of the range.

57

Long-term trend analysis

Although there was variation in trends within the 24 lakes, there were consistent

regional decreasing patterns across 13 lakes in SO4, 17 in Ca and 15 in Mg

concentrations with no significant trends across lakes in K or Na. Similar regional trends

for Ca (Watmough and Aherne 2008; Palmer et al. 2011) and Na (Watmough and

Aherne 2008) have previously been identified in the MRW (Watmough and Aherne

2008). Differences in time-scale analysis, lake set analysis and heterogeneity of

catchment physical and chemical characteristics can account for trend variation but the

long-term decreasing patterns across groups of lakes gives credence to the cautious

extrapolation of these trends to other similar lakes in the MRW.

Spatial patterns in lake Ca

Principle components analysis of the lake data set with the most recent sampling

demonstrated strong relationships between lake Ca concentrations and catchment

chemical and physical properties. In a region where lake Ca concentration is, in general,

declining due primarily to decreasing SO4, lakes with higher Ca were the larger lakes

associated with larger catchments with higher densities of roads, higher percentages of

the catchment utilized for agriculture (with deeper, more fertile soils) and were located at

lower elevation with lower runoff and high lake Cl. Not surprisingly lakes with higher

Ca had higher associated pH. It is conceivable that larger lakes with higher densities of

paved roads have highly developed shorelines which has been shown contribute to

higher lake Ca levels (Yao et al. 2011) and/or received runoff from CaCl de-icers

(Ralsten 1971; Kaushal et al. 2005; Langen et al. 2006). Palmer et al. (2011) also found

increases in chloride were greater in developed lakes that were close to winter-

58

maintained roads. Addition of CaCl or KCl to farm soils could also contribute to higher

lake Ca and/or Cl concentrations (Shaw et al. 2004).

Conversely, smaller lakes in smaller catchments at higher elevation with higher

runoff and with lower densities of roads and a lower percentage of the catchment

utilized for agriculture and a higher percentage of deciduous trees would be associated

with lower Ca concentrations and are the lakes most at risk to amplified Ca depletion

from forest harvesting. MRW forest stands are dominated by deciduous tree species

which have higher Ca demands than coniferous and hence the negative correlation. Lake

elevation has been previously negatively correlated with lake Ca (D’Arcy and Carignan

1997; Ito et al. 2005; Wolniewicz et al. 2011). It has been suggested that lakes at higher

elevation are often headwater lakes with shallower soils and steeper slopes

(Wolniewicz et al. 2011) and as a consequence, have higher and more acidic runoff.

These characteristics can combine to minimize the contact of acid precipitation with

soils and consequently reduce neutralisation (Sullivan et al. 2007). It is reasonable to

consider that these lakes with lower Ca are located in relatively undeveloped areas with

less developed shorelines and not subject to the same anthropogenic additions of Ca

that the lakes with higher Ca are experiencing, as confirmed with a visual assessment

of the spatial analyses map.


and Posch 2001)

Knowledge of the historical pre-disturbance MRW lake conditions is crucial in

understanding how current lake levels relate to the historical condition however, long-

term monitoring of the lakes in this region only began in the early 1980’s and therefore

59

the historical condition has not been recorded and needed to be modeled. Steady state

modelling was used to estimate the steady state lake Ca level (SSWC pre-industrial Ca)

which represents what lake Ca levels were pre-industrially and where they will fall

in the absence of harvesting. Under this scenario Ca will decline as the soils reach

equilibrium with deposition and then the soils will no longer be acidifying. Future

changes in Ca deposition or Ca weathering rates could influence the SSWC estimates but

Ca deposition has been relatively low and thus small changes are thought to have little

influence while weathering rates are assumed to be stable over long periods of time

(Watmough and Dillon 2003). Weathering rates have been hypothesized to be

biologically accelerated by ectomychorrizal fungi in some nutrient deficient ecosystems

but the extent to which this could mitigate existing nutrient depletion and any resulting

declines in forest productivity has not been determined (Vadeboncoeur et al. 2014).

Biomass export driven Ca decline

Recent studies (Watmough and Phillips 2011; Malcolm et al. 2014; Monteith et al.

2014) have demonstrated that forest practices may contribute to the acidification of

surface waters while others have predicted there would be no impact depending on the

forest initial site conditions (Helliwell et al. 2014). According to the most recent sample

date, the 364 lakes have not yet reached steady-state with respect to the deposition that

they currently receiving. Calcium removal levels are equivalent to decreases in lake Ca

between 0.02 to 2.93 mg L–1 (with an estimated average value of 0.64 mg L-1

) under the

predicted 100% FMP harvest biomass export scenario, reflecting the range of

catchment volume removal levels and the % of catchment harvested.

60

Taking into consideration the forest conditions on a catchment by catchment

basis, under the proposed 10 year (2009–2020) forest management cut volumes, and

assuming these levels of harvesting are continuous, 38% of 364 lakes in the MRW are

predicted to fall below the 50 µeq L–1 (1.0 mg L–1) Ca critical threshold at steady-state

compared with 8% in the absence of future harvesting. This represents a large (~30%)

increase in lakes going under that critical level at steady state under 100% of the

predicted harvesting volume removal level. The identification of Ca critical levels is

uncertain because of non-linear impacts and confounding factors such as climate

change (Desellas et al. 2011).

It has been indicated that in the context of the lakes at most risk, the results for

the cumulative distribution graph indicate lakes with already low levels of Ca would

be most heavily impacted by additional Ca removals through harvesting. This is

intuitive, but the statistics are worth noting. For those lakes, with Ca already ≤2 mg L–

1, the percentage of lakes going below 1.0 mg L–1 at 100% FMP harvesting would be

approximately 75% compared to an unharvested steady state where less than 20% of

these low Ca lakes would go below 1.0 mg L–1.

An earlier SSWC analysis of 1300 acid-sensitive lakes in south-central Ontario,

including lakes in the MRW, predicted less than 1% of lakes would go below 1.0 mg

L–1 in a no harvest scenario with Ca removal levels from stem harvesting putting

23.3% of lakes below 1.0 mg L–1 with whole tree harvesting losses increasing that

percentage of lakes up to 52% (Watmough et al. 2003). A similar assessment of 300

acid-sensitive lakes in north-western Ontario indicated that under a no harvesting

scenario 1% of lakes would go below 1.0 mg L–1 with 17% going below the critical

61

level under stem only harvesting and 30% going below that level after whole tree

harvesting (Watmough and Phillips 2011).

This assessment predicted that 8%, of lakes compared with 1% in Watmough

et al. (2003) would go below 1.0 mg L–1 in the steady state, no harvest scenario. The

large difference can, in part, be explained by the continued decline in Ca

concentrations in catchment soils which is not adequately taken into account in the

SSWC model. The data set in this research extended to 2012, representing over a

decade of information since the data set from Watmough et al. (2003) was analysed.

Watmough et al. (2005) has previously shown that SSWC modeling of data for lakes

that was taken 13 years apart resulted in a mean decrease in pre-industrial

concentration of 15%, accounted for by continued soil Ca depletion. This indicates

that historical Ca concentration has been overestimated in past research.

The current evaluations represent more refined estimates than earlier work in

south-central Ontario for several other reasons. The MRW lakes were acid-sensitive

lakes with Ca<225 μeq L–1 (<4.5 mg L–1) while all three of the earlier assessments

evaluated acid-sensitivity in lakes as being Ca <300 μeqL–1 (<6 mg L–1). The

inclusion of more Ca-rich lakes in the earlier assessments could also account for the

differences in results compared to this study. If there were a large proportion of lakes

between 225–300 μeq L–1 in the earlier studies, this would skew results towards lower

estimates of the percentage of lakes going below critical levels compared to the MRW

database of lakes used here. In addition, the current assessment was based on species-

specific volume and Ca removals by catchment and the actual catchment-specific

combinations of harvest removal methods, and on interpolated regional runoff

assessed by catchment, none of which were incorporated into the earlier SSWC

62

estimations. It has been recommended that site specific hydrology, rather than

interpolated regional runoff values would improve SSWC estimations due to the

complexity and heterogeneity of runoff on a catchment scale (Gibson et al. 2010) but

that refinement in MRW estimates could be limited by practicality and cost.

On the ground, whole tree harvesting accounted for less than 10% of the

predicted removals in MRW catchments. However, the large increase in lakes falling

below the critical level when whole tree harvesting Ca losses were considered in

earlier work in the region (Watmough et al. 2003) and the effects on soil Ca in other

acid sensitive managed forests (Akselsson 2007; Kaarakka 2012) gives an indication

of similar heavy increases that could occur if harvesting for biofuels (with additional

tree biomass in branches and coarse woody debris and associated Ca removals) did

become a major forestry management practice in the MRW in the future.

A forestry management practice of a tree harvest removal level at 40% of the

AAC would be closest to the modeled no harvest, pre-industrial lake Ca concentration

level. This reduced volume removal level takes into account the current market

demand, bypass and unexpected quality issues. On an average catchment basis, for

those lakes most at risk to going below critical thresholds, harvesting above 40% of

allowable volume is indicated to be unsustainable. It should be noted that forty

percent of the 10 year AAC volume is an average value as the predicted area to be cut

varied tremendously, from 4% to 87%, by catchment.

Consequently, it is recommended that when determining sustainable

harvesting volumes the removal levels should included in the FMPs on a catchment

by catchment basis taking into account individual lake Ca concentrations, and the

potential of them declining below critical levels in the long-term.

63

Mitigation efforts on Crown land could investigate the possibility of the

addition of Ca to harvested catchments with lakes at risk of Ca depletion below critical

levels, to ensure current and continued volume removals do not exacerbate an already

tenuous lake health and integrity situation. Recent research has suggested that addition

of nutrients may be crucial to sustaining productivity and future rotations in Ca

depleted forests subject to short harvest, heavy cut rotations in the north-eastern United

States (Vadeboncoeur et al. 2014) and slow-release wood-ash has been suggested as an

economical Ca-addition that could close nutrient cycles (Pitman 2006). Future research

could involve both catchment trials and an examination of the effects of global

experimental lime and wood-ash treatment trials on ecosystem nutrient cycling (as

explored in Chapter 3 of this thesis), on Ca concentrations in lakes and on forest

productivity. Using Ca-addition as a tool for managing the cumulative effects of past,

present and future stressors, could not only benefit the integrity of the soils and lakes

and the productivity of managed forests, but also help ensure long-term sustainability

of both ecosystem and economic health in areas such as the MRW where the economy

is so intrinsically tied to the integrity of the environment and especially to lake water

quality.

Uncertainty

There are many unquantifiable uncertainties associated with the results

(although most have been associated with previous work as well) and thus the

predictions for these lakes should be interpreted with caution. To begin with, there is

inherent uncertainty and unknown propagation of error when combining several lake

64

data sets with differing sampling times, frequencies, procedures, equipment and analyses.

In addition, the likelihood that some of the lake sets were not sampled randomly could be

associated with spatial correlation and associated error. Modeling error also needs to be

accounted for. There is uncertainty in using the SSWC method to determine the impact

of harvesting since, although a steady state is assumed, S deposition can be dynamic

which could result in the F factor changing (Henriksen and Posch 2001), and soil Ca

concentration could fall (Watmough et al. 2005). With regard to the species-specific

biomass estimation and associated nutrient content there is large uncertainty associated

with the latter as high estimates of variability associated with nutrient content have been

observed frequently (Futter et al. 2012; Yanai et al 2012; Paré et al. 2013). However, it

has been suggested that combined species-specific estimates over many cut sites in a

catchment should tend towards a population mean (Paré et al. 2013). The interpolation of

deposition and runoff data introduces another level of unquantifiable uncertainty and the

FMPs are based on FRI data which can be notoriously inaccurate.

As well, data gaps in MRW lakes are widespread with less than one-third of all lakes

having any lake Ca concentration data, and of those that have data, less than 15% of the

lakes included in this research were recently sampled (2011-2012). Consequently, more

widespread sampling of lakes in harvested catchments is necessary to determine which

lakes are at most risk to Ca depletion below critical levels.

Despite these limitations, these predictions have been made with catchment based

forest management information and resources in order to give approximations of the

impact of harvesting Ca removal levels on MRW lake Ca concentrations. As such they

65

such they represent a refinement of previous regional estimations.

2.6 Conclusions

Lake Ca (mg L–1), SO4 (mg L–1), and Mg (mg L–1) in lakes across the MRW are

low and have decreased significantly over time with a declining rate of decrease pattern

while lake pH and DOC increased significantly between the 1980’s and the 1990’s but

exhibited no significant pattern after that. There were consistent long-term regional

decreasing patterns in SO4, Ca and Mg concentrations with no significant long-term

trends indicated in K or Na across 24 lakes. Using lake SO4 as a measure of the state of

lake recovery from acidification and lake Ca and Mg as indications of buffering ability,

significant lake recovery has now slowed substantially, while using lake pH as that

metric, significant lake recovery is indicated between the 1980’s and 1990’s but appears

to have stalled after that. Lake mean NO3 data was limited to the 1990’s time period

and later but indicated a significantly declining concentration which could be expected

with declining NO3 emissions from 1990-2010.

Ca removal levels in catchments that are based on annual planned volume

removals would result in a 30% increase in sampled lakes going below a critical level of

1 mg L–1 over that of modeled steady-state no harvest scenarios. The reported harvested

average level of 40% of planned volume removals would be the best case scenario in

having the least impact on lake Ca concentrations with 20% falling below 1 mg L–1. In

some MRW Crown land catchments, at lower elevations and in lower runoff areas that

are impacted by relatively high densities of roads and/or agricultural practices, the

maintenance of lake Ca levels above critical values may not be an issue. However, for

many lakes that are subject to multiple regional stressors, the decline of Ca, that is

66

essential to both forest and lake health and integrity, is beyond the bounds of natural

recovery. These are the lakes most at risk to supplementary Ca removals through tree

harvesting. For the MRW, this research indicates that these lakes are typically small

lakes with lake Ca concentrations approaching or below known critical Ca

concentrations that are located in relatively small intensively harvested catchments,

located at higher elevations with higher runoff and lower road densities.

This research highlights the need for further investigation into this Ca depletion

issue and possible ecosystem remediation efforts, in order to provide forest

management authorities the best available information. On Crown land, future,

sustainable forest management practices could investigate the possibility of the addition

of Ca to harvested catchments with lakes at risk of Ca depletion below critical levels, to

ensure current and continued volume removals do not exacerbate an already tenuous

lake health and integrity situation. Addressing the harvesting/Ca problem on private

land is an even more complex economic, social and environmental problem that may

well prove to be a daunting challenge for lake associations or other non-profit

associations to tackle and follow the lead of forest authority nutrient management

practices.

67

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80

APPENDIX 2A Table 2A.1: Equations derived from the SSWC model (Henriksen and Posch, 2001) and

the modified derivation equations used in calculations.

Note: Where UCA, UMG, UK, UNA and USO4 are the current concentrations of calcium, magnesium,

potassium and in sodium in μeq m-2 yr -1, BC stands for base cations, * means multiplied by. The F-factor

equation represents the fraction of current base cations present in lakes due to soil acidification (Brakke et al.

1990). Conversion factors (ie. 49.9) were used in the modified equations (Edwards 1986).

81

Figure 2A.1: Lake data sets and methods of analyses. 7 lakes had “0” Ca concentration and were excluded from the SSWC and impact of

harvesting on lake Ca calculations (the latter analyses then having n= 364).

MRW Lake Chemical Data Sets

82

Table 2A.2: Summary statistics (mean, max-min) for lake area, catchment area, lake elevation, runoff and selected deposition and lake chemical variables for all lakes with Ca data within crown land (590), lakes in catchments with harvesting cuts on crown land (371) and two subsets of the former and latter lakes used in analysis. Values of 0 were assumed to be undetectable by equipment at the time.

MRW Lakes

(N)

Quantiles

(%)

Lake Area

(ha)

Lake

Elevation

(m)

Catchment

Area

(ha)

Runoff

(L ha-1)

Lake Ca (mg L–1)

Lake Mg

(mg L–1)

Lake K (mg L–1)

Lake Na (mg L–1)

Lake SO4

(mg L–1)

Lake Cl (mg L–1)

Dep Ca (mg L–1)

Dep Mg (mg L–1)

Dep K (mg L–1)

Dep Na (mg L–1)

Dep S (mg L–1)

590

100.0

99.5

97.5

90.0

75.0

50.0

25.0

10.0

2.5

0.5

0.0

Mean

120638

6401.9

659.91

137.69

48.333

17.980

6.8805

2.3867

0.7219

0.3194

0.1399

114.79

543

536

528

480

422

322

245

196

201

193

179

346

68135

68135

34274

12115

3242.1

737.03

232.72

114.09

57.190

32.239

21.590

4289.6

0.035

0.035

0.034

0.032

0.031

0.028

0.026

0.024

0.023

0.022

0.022

0.028

15.7

12.5

5.70

3.67

2.90

2.20

1.57

1.24

0.78

0.00

0.00

2.43

5.27

2.85

1.39

0.99

0.81

0.60

0.46

0.34

0.24

0.00

0.00

0.66

NA NA

12.0

11.6

10.0

8.00

7.20

6.00

4.66

3.50

2.34

0.00

0.00

6.00

NA

7.17

7.16

7.14

6.88

6.70

6.23

5.90

5.77

5.65

5.58

5.45

6.29

0.88

0.88

0.87

0.86

0.85

0.84

0.82

0.81

0.79

0.78

0.78

0.84

0.44

0.44

0.44

0.44

0.43

0.43

0.42

0.41

0.41

0.41

0.41

0.43

1.07

1.06

1.06

1.06

1.05

1.04

1.03

1.02

1.02

1.01

1.01

1.04

10.0

10.0

9.97

9.89

9.80

9.60

9.39

9.10

9.05

8.97

8.93

9.57

371

100.0 120638 543 68135 0.035 4.43 3.53 1.12 14.9 12.0 26.9 7.01 0.88 0.44 1.07 10.0

99.5 7516.4 536 68135 0.035 4.40 3.53 1.95 13.6 11.9 21.0 7.00 0.88 0.44 1.06 10.0

97.5 930.72 528 34644 0.034 3.96 1.31 0.78 4.51 10.0 6.15 6.88 0.87 0.44 1.06 9.98

90.0 164.99 480 14226 0.033 3.40 1.28 0.55 1.65 8.00 1.64 6.81 0.85 0.44 1.06 9.90

75.0 48.674 422 4759.2 0.032 2.72 0.91 0.45 0.95 7.00 0.52 6.46 0.84 0.43 1.05 9.81

subset of

590 located in

50.0

25.0

15.563

5.3589

326

254

1563.8

375.54

0.029

0.027

2.10

1.48

0.75

0.56

0.38

0.29

0.71

0.56

4.86

3.80

0.24

0.00

6.14

5.90

0.83

0.82

0.43

0.43

1.04

1.04

9.68

9.44

catchments with cuts

10.0

2.5

1.6543

0.5892

201

201

175.64

72.751

0.025

0.024

1.1

0.78

0.43

0.00

0.20

0.00

0.45

0.00

2.95

0.00

0.00

0.00

5.80

5.73

0.81

0.79

0.42

0.41

1.03

1.02

9.14

9.05

0.5 0.2270 193 34.321 0.023 0.00 0.00 0.00 0.00 0.00 0.00 5.70 0.79 0.41 1.02 8.97

0.0 0.2258 179 28.589 0.023 0.00 0.00 0.00 0.00 0.00 0.00 5.69 0.78 0.41 1.01 8.97

Mean 157.97 357 5834.2 0.029 2.14 0.61 0.37 1.04 5.26 0.80 6.21 0.83 0.43 1.04 9.61

83

MRW Lakes

(N)

Quantiles

(%)

Lake Area

(ha)

Lake

Elevation

(m)

Catchment

Area (ha)

Runoff

(L ha-1)

Lake Ca (mg L–1)

Lake Mg (mg L–1)

Lake K (mg L–1)

Lake Na (mg L–1)

Lake SO4

(mg L–1)

Lake Cl (mg L–1)

Dep Ca (mg L–1)

Dep Mg (mg L–1)

Dep K (mg L–1)

Dep Na (mg L–1)

Dep S (mg L–1)

177 Ca ≤2

mg L–1

subset of 371

100.0

99.5

97.5

90.0

75.0

50.0

25.0

10.0

2.5

0.5

0.0

Mean

3149.1

3149.1

270.01

65.765

21.378

8.7954

2.8950

0.8494

0.3495

0.2258

0.2258

46.607

518

518

518

495

473

412

374

335

290

290

290

411.6

34695

34695

31131

12116

3351.9

1089.2

284.73

146.61

55.200

28.539

28.539

3665.8

0.034

0.034

0.034

0.033

0.032

0.030

0.028

0.026

0.024

0.023

0.023

0.030

2.00

2.00

2.00

1.86

1.72

1.45

1.22

0.78

0.00

0.00

0.00

1.41

3.53

3.53

0.68

0.57

0.52

0.44

0.35

0.27

0.00

0.00

0.00

0.44

1.12

1212

0.55

0.43

0.38

0.32

0.26

0.20

0.00

0.00

0.00

0.32

2.99

2.99

1.27

0.82

0.69

0.59

0.51

0.40

0.00

0.00

0.00

0.61

8.19

8.19

7.00

5.93

4.99

4.26

3.48

2.61

0.00

0.00

0.00

4.18

3.53

3.53

1.46

0.95

0.40

0.29

0.18

0.00

0.00

0.00

0.00

0.39

6.89

6.89

6.85

6.65

6.22

6.01

5.85

5.79

5.72

5.69

5.69

6.09

0.88

0.88

0.86

0.85

0.84

0.83

0.82

0.81

0.80

0.79

0.79

0.83

0.44

0.44

0.44

0.44

0.44

0.44

0.44

0.42

0.41

0.41

0.41

0.43

1.06

1.06

1.06

1.01

1.05

1.04

1.04

1.03

1.02

1.02

1.02

1.04

10.0

10.0

9.92

9.84

9.80

9.63

9.48

9.38

9.05

9.05

9.05

9.61

75 with

100.0 8598.9 528 24541 0.035 12.2 2.91 1.32 37.5 10.1 51.6 6.91 0.86 0.44 1.06 10.0

99.5 8598.9 528 24541 0.035 12.2 2.91 1.32 37.5 10.1 51.6 6.91 0.86 0.44 1.06 10.0

97.5 3239.8 496 22593 0.034 9.29 2.68 1.24 25.3 7.61 36.3 6.91 0.86 0.44 1.06 9.97

90.0 980.59 454 4213.0 0.033 5.62 1.78 0.75 6.41 5.23 10.8 6.74 0.86 0.44 1.06 9.87

75.0 396.34 365 1689.9 0.030 3.36 0.96 0.62 3.27 4.35 5.94 6.65 0.85 0.43 1.05 9.74

50.0 77.751 320 471.09 0.028 2.28 0.59 0.47 1.22 3.91 1.07 6.41 0.84 0.42 1.04 9.46

2011- 2012 25.0 25.659 248 182.96 0.025 1.46 0.43 0.35 0.76 3.20 0.24 6.30 0.84 0.42 1.03 9.25

data 10.0 13.430 227 66.898 0.024 1.16 0.33 0.25 0.58 2.55 0.16 5.77 0.82 0.41 1.02 9.05

2.5 10.362 200 37.594 0.020 0.88 0.29 0.12 0.46 1.67 0.06 5.71 0.81 0.41 1.02 9.01

0.5 8.9304 194 28.539 0.019 0.78 0.24 0.05 0.21 0.65 0.01 5.70 0.80 0.41 1.02 8.97

0.0 8.9304 194 28.539 0.019 0.78 0.24 0.05 0.21 0.65 0.01 5.70 0.80 0.41 1.02 8.97

Mean 394.37 324 1977.4 0.0277 2.81 0.83 0.50 3.21 3.95 4.47 6.38 0.84 0.42 1.04 9.49

84

Figure 2A.2: Percent of lakes (N=590) sampled by sampling year. Approximately 15% of the lakes were sampled in 2011 or 2012.

85

3. Evaluating the effects of liming and wood-ash treatment on forest ecosystems

through systematic meta-analysis

As published in The Canadian Journal of Forest Research 44: 867–

885 (2014) dx.doi.org/10.1139/cjfr-2013-0488

3.1 Abstract

Liming and wood-ash addition have long been used to attenuate the effects of

acidic deposition on forest soils with the goal of promoting tree growth. Quantitative

meta-analyses of treatment studies from managed forest ecosystems was performed in

order to assess general tendencies of effects of treatment on seven selected measures of

performance thought to reasonably reflect the effects of Ca-addition treatment. Over

350 independent trials from 110 peer-reviewed liming and wood-ash addition studies

were retrieved that were integrated to determine: soil pH, base saturation (BS), tree

foliar Ca concentration, tree growth, ectomychorrhizae root colonization, soil C/N ratio

and microbial indices. The results were quantified through three separate meta-analysis

effect size metrics: unweighted relative values and two weighted metrics, Hedges’d and

lnR. A surprising number of treatment trials (22-85%) reported no significant effect

and soil pH, and foliar Ca appeared more responsive to lime studies compared with

wood-ash, while BS and tree growth appeared more responsive to wood-ash addition.

For six of the seven parameters, estimated mean effect sizes were similar in magnitude

and positive in direction for all three meta-analysis metrics. Regression tree optimal

models explained: 38% of the variation in pH, 47% of the variation in BS, 51% of the

variation in foliar Ca concentration and 26% of the variation in tree growth. The largest

predictors of effect size, within our selected group, were as follows; for pH - soil type;

for base saturation – soil type, trial duration in years and species (hardwood

86

or softwood): for foliar Ca concentration - treatment dose and type and for tree growth -

trial duration, initial soil pH and tree species. This analysis indicates that Ca-additions

are not universally beneficial and provides insight into when Ca-additions to forest soils

are likely to be most effective.

3.2 Introduction

One of the long-term legacies of acid rain has been identified as calcium (Ca)

depletion in soils in Europe and eastern North America (Huntington et al. 1998;

Watmough and Dillon 2003; Jandyl et al. 2004; Yanai et al. 2005). Forest harvesting

can also contribute to Ca losses from soil (Lipas 1985; Olsson et al. 1996); whole tree

harvesting over several rotations for example, has been found (or predicted) to deplete

Ca levels in soil (Nykvist and Rosén 1985; Federer et al. 1989; Vance 1996;

Thimonier 2000; Adams et al. 2000; Keller et al. 2001; Watmough et al. 2003), which

can have a negative effect on tree growth within the second rotation (Proe et al. 1996;

Walmsley et al. 2009). Additionally, an escalating demand for bio-energy fuel by

harvesting slash (woody biomass) has led to concerns over forest sustainability since

the impact on Ca removals is analogous to that of whole tree harvesting, with the

consequential negative effect on tree growth (Rosenberg and Jacobson, 2004; Ågren et

al. 2010). Ca-addition, through lime and wood-ash treatment has been used for forest

soil amelioration with the goals of reducing soil acidity, increasing Ca concentrations

in trees, and improving tree growth with the most commonly used measures for

gauging the success of these goals being pH, Ca foliar concentration and various tree

growth metrics.

The use of lime to reduce the natural acidity of forest soils was first

87

recommended in Germany over 100 years ago (Messmer 1959). Forest liming

experimentation escalated with research revealing that Ca concentration in soil was

strongly connected with forest fertility and productivity (Viro 1951; Lipas 1985).

Comprehensive liming experiments aimed at improving forest growth began in Finland

and Sweden during the 1950s and 1960s (Tamm 1974; Derome et al. 1986) and in the

1980s, lime was used to mitigate soil acidification caused by the effects of acid rain

(Derome et al. 1986; Huettl and Zoettl 1993; Nilsson et al. 2001). Liming treatments

differ in types, mixtures, grain sizes, solubility and doses (Lundström et al. 2003). The

two most commonly used buffering compounds utilized in liming research have been

calcite (calcium carbonate, limestone, CaCO3) with a pH ~12.5 and dolomite

(CaMg(CO3)2), which has a slightly higher buffering capacity (Sverdrup 1985).

However, Ca can also be added as wollastonite (CaSiO3), gypsum (CaSO4), calcium

nitrate (Ca(NO3)2), and calcium chloride (CaCl2).

Wood-ash has also been used to address forest nutritional deficiencies caused by

acid deposition and whole-tree harvesting (Cronan and Grigal 1995; Olsson et al. 1996;

Demeyer 2001). While the removal of harvest residues for biofuels is garnering much

attention in a renewable energy context, recycling of the ash produced has growing

potential for use as a nutrient input in forest systems (Pitman 2006). In wood-ash, Ca is

usually in a CaCO3 form, which explains the liming effect of wood-ash (Steenari and

Lindqvist 1997). Although the neutralizing capacity of wood-ash is classified as high

with pH values ranging from 8.9 to 13.5 (Demeyer et al. 2001), the Ca concentration of

wood-ash used in soil amelioration is highly variable. It ranges from 13.2 to 92.4%

(Vance 1996), is often unknown or unrecorded and is dependent on the source tree used

88

(Someshwar 1996; Pitman 2006), the amount of bark it contains and the soil properties

from where it originated (Campbell 1990). Calcium is the most plentiful element in

wood-ash, followed by potassium (K), magnesium (Mg), aluminum (Al), iron (Fe), and

phosphorus (P), but there may also be potentially toxic elements such as cadmium (Cd)

and lead (Pb) (Park 2004). The percentage of nitrogen (N) is low due to its

volatilization during wood combustion.

Previous reviews of the effects of liming (Derome 1990; Huettl and Zoettl

1993; Nohrstedt 2001; Formanek and Vranova 2003) and wood-ash addition (Vance

1996; Demeyer et al. 2001; Aronsson and Ekelund 2004; Pitman 2006) have primarily

been narrative evaluations with one wood-ash meta-analysis (Augusto et al. 2008). Of

the liming reviews, three were specifically single-country analysis, and only one

included studies in Europe and North America of both lime and wood-ash (Lundström

et al. 2003). A more recent meta-analysis of fertilization studies in northeastern

deciduous forests evaluated limitation of primary production by N, P and Ca however,

the majority of treatments with Ca included fertilization with N and P (Vadeboncouer

2010). Existing review conclusions varied widely and recommendations were often

ambiguous. Potential complications include: different trial durations (Vance 1996;

Aronsson and Ekelund 2004), divergent site soil conditions with regard to acidity

(Derome et al. 1986; Nihlgård et al. 1988), variability in the initial nutritional status of

the treated stands (Huettl and Zoettl 1993), variations in nutrient demands as a result of

stand age (Hamburg et al. 2003; Vadeboncoeur 2010), variations in site including

rainfall and temperature regimes (Arvidsson and Lundkvist 2002; van der Perre et al.

2012), variations in the solubility of the treatment applied (Fyles et al. 1994; Fransman

89

and Nihlgård 1995), high variability in wood-ash with regard to both the Ca content

(Vance 1996; Pitman 2006) its pH (Demeyer et al. 2001), the role of mycorrhizal fungi

in weathering and nutrient uptake (Lundström et al. 2003) and tree species specific

responses to treatment (Dijkstra and Smits, 2002; Long et al. 2011).

The aim of our research was to quantify the results of Ca-addition, via an

integrated analysis of liming and wood-ash studies in managed forests in Europe and

North America. Systematic meta-analysis was used to determine the effects of Ca-

addition through lime or wood-ash treatment on seven selected measures of

performance; soil pH, base saturation (BS), tree foliar Ca concentration, tree growth,

ectomychorrhizae (ECM) root colonization, soil carbon to nitrogen (C/N) ratio and

microbial indices. Unweighted meta-analyses was used in order to maximise the

number of observations in our study and then compared this result with the calculated

effect sizes for two weighted meta-analyses metrics. Classification and regression trees

can help to explain variation or heterogeneity in response (in our case of a treatment

effect) by splitting variables into increasingly uniform groups (Breiman et al. 1984;

Breiman 1996). Thus, after meta-analysis, regression tree analysis was performed, with

the focus on four main parameters, pH, BS, foliar Ca concentration and tree growth, in

an attempt to identify drivers of response heterogeneity and under what conditions Ca-

amendments were most effective.

3.3 Methods

Study Selection

A search of primary research e-resources in the Web of Science produced 1154

articles from the keyword search “liming” and “forest” and 580 articles with the

90

combination of “wood-ash” and “forest” all published between 1986 and 2012

(approximately the last 25 years to August 2012). Experimental field trials were

selected for inclusion in the analysis if they presented at least one relevant

measurement and if the data from both the control and treatment were reported. Where

only graph results were presented the treatment and control values were ascertained by

hand measuring. In this study “lime” or “liming” refers to all such types of Ca-

addition and “wood-ash” refers to all varieties used and “treatment” encompasses both

types of Ca-addition. As our focus was on Ca, trials that included lime or wood-ash

treatment combined with fertilizer (N or P) addition were excluded, as it was thought

to be difficult to ascertain whether an effect (in particular a growth effect) was due to

Ca or instead to N or P addition. The exception to this occurred when the control

received identical N or P fertilization. Whole catchment liming treatments (aerial

spraying that focused on water chemistry, but reported at least one of our soil

parameters) were also included in the database. The trial results for both lime and

wood-ash treatment types were integrated despite being fully aware of their differences

but with the belief that during decision tree analyses it was highly likely that the

treatments would be partitioned out by their Ca dose (which for wood-ash was often

unknown and due to the aforementioned noted high Ca content variation, could not be

reasonably attributed an arbitrary percentage) irrespective of the treatment type. As

many factors and nutrients besides Ca contribute to tree growth, I was also interested

in determining if there were any partitions by treatment type (separations into lime and

wood-ash groups) which could potentially reflect the growth contribution of any

additional nutrients found in wood-ash.

91

Statistical Analysis

Quantitative synthesis - Meta-analysis

Meta-analysis depends upon estimating an effect size (i.e., the magnitude and

direction of the experimental effect) for each independent experiment (Gurevitch et al.

1992, Rosenberg et al. 1997). Meta-analysis have recently been commonly being used

to estimate the effects resulting from similar experimental trials in managed forests and

several different effect size metrics have been used (Augusto et al. 2008;

Vadeboncoeur 2010; Jerabkova et al. 2011; Lucas et al. 2011).

In the face of extensive parameter complexity, a simple approach was chosen.

Seven parameters were selected to quantify via treatment effect size: soil pH, BS, tree

foliar Ca concentration, tree growth (of some considered measure and these various

growth metrics included: mean annual basal area increment, mean annual height

increment, mean annual istem growth increment, mean annual relative growth rate,

mean annual radial increment, above ground net primary productivity and mean

annual dry leaf mass), ectomychorrhizae (ECM) root colonization, soil C/N ratio and

microbial (diversity, richness and abundance) indices. These parameters were chosen

as measures that might reasonably reflect the effect of Ca-addition treatment or have

treatment driven ecological impacts, especially with regard to tree growth.

A meta-analysis approach assumes there is independence among studies

(Hedges et al.1999). To meet this assumption, only measurements from each study

that could be assessed as independent were included in the datasets. Studies with more

than one experimental trial were incorporated into the databases however, when at

least two of the trials were obviously in very similar environmental conditions and

92

used the same treatment, then only the mean values of these trials were included. If

the trials differed by any measured parameter, then the results for each trial presented

within one paper received equal weight with the following noted exceptions. When

several sampling dates were presented in a paper, only the latest date was used unless

different sampling methodologies were followed and when research trials were

presented as a combined average by the author then only that average was included in

analysis. Dunnett’s test was used to test whether any average treatment values

calculated differed significantly from their controls at α = 0.05. Data were analysed

with JMP 10 PRO software (SAS 2011).

Meta-analyses and Effect Size Metrics

Initially an unweighted meta-analyses was performed in order to maximise the

number of observations include in our study and then compared this result with the

calculated effect sizes for two weighted meta-analyses metrics. The majority of

treatment trials included in our database did not present the variance of their results

but it has been demonstrated that a comprehensive meta-analysis can be carried out

even without variance weighting (Gurevitch and Hedges 1999; Augusto et al. 2008)

and therefore the analysis was begun with this inclusive but unweighted approach.

This methodology gives all independent observations equal weight in calculating

overall effect size means and their variance, however, when the main goal of analyses

is to document the direction and magnitude of effects and the heterogeneity between

original studies, a major elimination of studies could reduce the power of the meta-

analysis (Hillebrand and Cardinale 2010). To comprehensively identify causes for

variation in response, outlier trials were not downweighted or excluded from any

93

analyses. Since any level of heterogeneity is acceptable within a meta-analysis,

doing so could exclude important studies that could potentially contribute to the

understanding the processes behind the reported high variation in response to Ca-

addition (Higgins 2008). Heterogeneity, the presence of variation in true effect sizes

underlying the different studies not due to chance (Higgins 2008) was assumed but

later verified in the weighted meta-analyses.

For the unweighted analyses relative values were calculated to normalise a

treatment effect and to attenuate any site effect (Gurevitch and Hedges, 1999).

Relative values were calculated as follows: ((Treatment/Control) −1) % (Gurevitch

and Hedges, 1999; Augusto et al. 2008). For discontinuous, transformed or already

relative variables an absolute relative value, the mean difference, (Treatment −

Control) was used (Augusto et al. 2008). In all cases, no significant treatment effect

(no difference between the effect of the control and the effect of the wood ash or lime

treatment at alpha equal to 0.05) was attributed an effect size value of zero.

To assess the sensitivity of the results of our unweighted meta-analyses, two

other effect- size metrics commonly used in ecological evaluations were calculated;

Hedges’ d, which informs a weighted meta-analysis and lnR, the natural log of the

response ratio (R), both following the methodology of MetaWin 2.0 software

(Rosenberg et al. 2000). Means and confidence intervals for the average effects of our

seven parameters were estimated using a random model and standard, weighted meta-

analytic parametric methods (Gurevitch and Hedges 1999). Subsequently, the calculated

weighted results were compared to those of the initial unweighted meta-analyses to

assess the sensitivity of effect size magnitude and direction and then proceeded to

94

identify causes for heterogeneity in the effect size results. The summary statistics

required for both Hedges’ d and lnR, are the sample size, mean, and standard deviation.

The calculation of the commonly used, weighted meta-analysis effect metrics, Hedges’

d, has been comprehensively described in recent research (Jerabkova et al. 2010) as has

that of lnR (Vadeboncoeur 2010).

In the calculation of both Hedges’ d and lnR, the trials that did not report

standard deviation in our database (or a measure of variance that could be used to

derive standard deviation using the MetaWin statistical conversion calculator), were

assessed an estimated standard deviation using a median coefficient of variation

(Jerabkova et al. 2011). It is acknowledged that the use of such estimated variance,

carries uncertainty along with it. For the meta-analyses using the natural logarithm of

the response ratio (R) as the effect size, the ratio of percentage change in a parameter in

the treatment group to that of the control was calculated (Hedges et al. 1999). All

database values were expressed as factors with control values set at one, which allowed

for the inclusion of zero effect trials (as both the control and treatment would equal

one). Trials with means differing in sign could not be ln transformed and thus those

trials were omitted from the analysis (Rosenberg et al. 2000). To express the grand

mean response ratio and its confidence limits, the results were back transformed and

considered significant at α = 0.05 if the 95% confidence interval (CI) did not overlap a

value of one (Rosenberg et al. 2000). In addition to the uncertainty associated with

assessing estimated variance, it should be acknowledged that eliminating trials with

differing signs could result in increased overall effect sizes in meta-analysis results

using the ln response ratio metric (Rosenberg et al. 2000).

95

However, one advantage to using a log value is that it can reduce the statistical

weight of outliers or aberrant values of effect size (Hedges et al. 1999). Heterogeneity

was pre-supposed in all meta-analyses and accordingly, for both MetaWin effect

metrics, the the random effects meta-analysis model was chosen, which assumes study

results vary because there are real differences (heterogeneity) between studies

(Rosenberg et al. 2000). Heterogeneity was determined by the measure Q total where

the probability (chi square) <0.05 established heterogeneity with the implication that

there was more variation between studies than could be attributed to sampling error

(Rosenberg et al. 2000). To estimate the range of uncertainty for mean effect sizes,

resampling was performed in MetaWin, using 2,999, iterations to calculate bias-

corrected bootstrap 95% confidence limits as recommended. Rosenthal's Method was

used to calculate fail-safe numbers, which are values that indicate the number of non-

significant unpublished studies that would have had to be added to a meta-analysis to

change the summary mean effect results from significant to non-significant (Rosenthal

1979). If this fail-safe number is large in comparison to the number of trials in the

meta-analysis then the effect size magnitude can be regarded as a reliable

approximation of the true effect, even with the some publication bias (Rosenberg et al.

2000). A more intensive discussion of the statistics behind the three meta- analyses

models utilized is beyond the scope of this paper, but has been provided in Hedges and

Olkin (1985), Gueritch et al. (2001) and Rosenberg et al. (2000).

Data Mining - Recursive Partitioning Decision Tree Analysis

Recursive partitioning of datasets has three main attributes that were beneficial

to this analysis. Data mining can handle large datasets, is good for exploring

96

relationships without having a prior model and the results are very interpretable. The

four parameters with sample sizes greater than 50 were partitioned and with significant

positive treatment effects (pH, BS, foliar Ca concentration and tree growth), as it was

felt that the analyses would be plausible at this sample size level (SAS 2011). A

decision tree analysis of the soil C/N ratio was not included as the treatment effect size

was found to be either negligible or not significant. Seven potential key drivers of the

four selected parameters of performance were selected and recorded for each treatment

trial and included both categorical and continuous variables. These predictors

included: time since treatment (years), treatment (wood-ash or lime), dose (≤5,000 kg

ha–1or >5000 kg ha–1of treatment type), initial soil pH (<4.5, 4.5-6, >6), soil type

(organic or mineral), species (hardwood or softwood) and stand age at treatment (0-10,

>10-50, >50 years).

All results were initially entered and analysed as absolute values however during

the decision tree analysis each result was automatically attributed to a specified cluster

(i.e. <4.5, 4.5-6, >6) that was solely determined through recursive partitioning.

Therefore, after regression analysis, I assigned these automatically separated categories

as the relevant driver ranges. “Time since treatment” was the trial length in years and

“treatment” included all forms and methods of dispersion while the “dose” was based

on the treatment in kg per ha and not the Ca content of the treatment, which was highly

variable and often not provided (very few studies involving wood ash actually

determined the Ca concentration). To further clarify, soil type for the pH and BS effect

was determined to be either “organic” or “mineral” by horizon and the few samples

that were classified in an article by the researcher as mixed (i.e. 0-20 cm) were

97

included in the “mineral” category after initial decision tree analysis partitioned them

into the mineral soils group. In contrast, for Ca foliar concentration and tree growth

effects, the distinction between organic and mineral soils was based on the parent soil

characterisation (peaty, organic soil versus mineral soil). When pH-CaCl 2 was used in

a trial instead of pH-H2O, an estimated conversion value was calculated by using an

appropriate regression equation (Van Lierop 1981) which is a more accurate way of

converting data than by adding or subtracting a constant. JMP 10 PRO software (SAS

2011) was used to recursively partition data via the Decision Tree method, which can

incorporate both qualitative and quantitative data. Both the minimum split size and

the K-fold cross validation were set at five (SAS 2011).

Subsequent partitioning evaluations of the datasets by Bootstrap Forest were

made to address any uncertainty associated with the optimal model produced via

Decision Tree analysis. Although both predictive models identify dominant, effect

driving variables, the Decision Tree produces the optimal combination while Bootstrap

Forest utilizes coverage optimization with many decision trees. The minimum splits

per tree and the minimum split size reflected those of the four associated Decision

Trees. For both Decision Tree and Bootstrap Forest, an analysis of the column

contributions would divulge which factors were the major predictors of the effect size

results.

3.4 Results

Study Selection

The study included data from over 350 independent treatment studies found

within 110 peer-reviewed published articles. The studies were largely from temperate

98

climates within Europe and to a much smaller degree, eastern North America and were

published between 1986 and 2012 (Appendix 3A, Table 1). Only three articles had

initial soil pH > 6 and subsequently, that category was excluded from analysis due to

the possibility of large bias occurring as a result of small sample size (Gurevitch and

Hedges 1999). Summary statistics were calculated for soil pH, BS, tree foliar Ca, tree

growth and soil C/N ratio effect sizes with the treatments synthesized (lime and wood-

ash trials combined) and separated for comparison purposes (Table 3.1). Two

parameters had insufficient sample sizes to reasonably split results by treatment and

therefore all results for ECM root colonization and microbial indices were based on

synthesizing the lime and wood-ash treatments within their datasets. Effect sizes were

cautiously calculated for these two latter parameters, as the bias could potentially be

large with small sample sizes.

Average Effect Size

With regard to magnitude and direction, the three effect size metrics utilized

generated the same qualitative results for six of the seven parameters evaluated with

significant, positive, large effects for pH, BS, tree foliar Ca, tree growth and ECM root

colonization and no significant difference in effect between the control and treatment for

the soil C/N ratio (Table 3.1, 3.2). Thus it was demonstrated that the weighted Hedges’d

and lnR effect sizes supported the initial unweighted meta-analysis results with the back

transformed R being the metric most comparable to the unweighted analyses effect size

(Table 3.2). Soil pH and foliar Ca appear more responsive to lime studies compared

with wood-ash while BS and tree growth appear more responsive to wood-ash addition.

Sample sizes for ECM root colonization and microbial indices were too small to

meaningfully evaluate by treatment. In the evaluation of microbial indices effect sizes,

99

R, although very large due to the exclusion of five studies with differing control and

treatment effect signs, was not significant (Table 3.2). The exclusion of 20% of the

microbial indices studies suggests lnR is an inappropriate effect size for this parameter.

An evaluation of the robustness of the effect size results was calculated by Rosenthal’s

method in the Hedges’d and lnR meta-analysis and resulted in large fail-safe numbers

for all parameters (Table 3.2). Hedges’d and lnR, also tested for heterogeneity and

significant heterogeneity was demonstrated in the responses in all seven of our datasets

(Table 3.2).

Zero Effect Frequency

The percentage of treatments (lime and wood-ash integrated) that reported no

significant treatment effect when compared with their control ranged from 22 to 85 %

(Table 3.1, Fig. 3.1). Approximately three-quarters of treatment trials exhibited

positive effects on BS, while two-thirds of trials indicated positive effects on soil pH,

foliar Ca concentration and microbial indices. About one third of trials showed

positive tree growth (of the combined growth measures) or ECM root colonization

effects and only fifteen percent of trials demonstrated any differing effect from control

on the soil C/N ratio. The results by treatment suggest that lime treatment resulted in

more positive effects for pH and foliar Ca concentration.

100

Table 3.1: Summary of mean, unweighted effect size, zero effect and sample size per parameter. In

addition to the pooled (combined) results, soil pH, Ca foliar nutrition and tree growth (of the

combined growth measures) were assessed by treatment type (lime or wood-ash).

*Note: Zero Effect is the percentage of treatments in which effect size was equal to 0 (treatment effect did not

significantly differ from control effect at α = 0.05).

101

Table 3.2: Mean effect sizes of Ca addition using the weighted effect size Hedges' d and the back-

transformed natural log of the response ratio (R) in comparison with the unweighted relative

values (listed in this order for each parameter)

Effect Size

Parameter

Effect

Direction

Hedges’ d

R

(Unweighted

relative value)

Mean

Effect Size

Hedges’ d

R

(Unweighted

relative value)

Bootstrap

Confidence

Interval

Hedges’ d

R

(Unweighted

relative value)

Heterogeneity

chi –square

(p)

Hedges’ d

R

(Unweighted

relative value)

Rosenthal’s

Number

Hedges’ d

R

(Unweighted

relative value)

N

Hedges’ d

R

(Unweighted

relative

value)

Soil

pH-H2O

+

+

(+)

1.28

1.16

(0.68)

1.07 – 1.43

1.14 – 1.19

0.0005

<0.0001

39916

267752

250

250

250

Base

Saturation

+

+

(+)

1.46

1.28

(23.6)

1.06 – 2.34

1.22 – 1.34

<0.0001

<0.0001

15979

149924

79

79

79

Ca Foliar

Nutrition

+

+

(+)

2.19

1.38

(36.5)

1.56 – 2.90

1.26 – 1.51

<0.0001

<0.0001

5652

190369

55

55

55

Tree

Growth

(All

Measures

Combined)

+

+

(+)

0.59

1.20

(30.8)

0.35 – 0.90

1.07 – 1.40

<0.0001

<0.0001

2405

16193

121

121

121

ECM Root

Colonization +

+

(+)

1.28

1.18

(20.5)

0.29 – 2.94

1.03 – 1.43

0.04

0.01

403

371

10

10

10

Soil C/N 0

0

(-)

0.03

0.99

(-0.88)

-0.73 – 0.87

-0.01 – 1.00

0.0004

0.0066

0.00

0.00

67

67

67

Microbial

Indices

+

0

(+)

1.26

1.73

(9.96)

0.35 – 3.42

0.98 – 2.46

0.03

0.04

102982

189752

26

21

26

Note: Significant effects (p < 0.05) are presented in bold type; mean effect size gives the magnitude and direction of the

parameter effect; Bootstrap confidence intervals were bias-corrected; the unweighted relative value is from the initial meta-

analysis; for the effect metric Hedges' d, if the confidence intervals include 0, d is not significant at = 0.05; for the response ratio R, if confidence intervals overlap 1, R is not significant at = 0.05; heterogeneity 2 with p < 0.05 means that effects within

a category were significantly heterogeneous; Rosenthal's method gives the “fail-safe results”, the results of the file-drawer

analysis; N is the number of treatment trials.

102

Recursive Partitioning of Effect Sizes – Decision Tree Analysis

pH Effect Size Regressive Analysis

The optimal decision tree model for soil pH effect size explained 38% of the

variation in results with a RMSE of 0.63 pH units (Fig. 3.2). Soil type (by horizon) was

the largest predictor of effect size with organic soil horizons exhibiting a mean increase

of 1.04 pH units (SD = 0.83) over the control, compared with a 0.36 pH units increase

(SD = 0.60) for mineral soil horizons. Within organic soils, lime had a greater effect than

wood-ash and young stands (<50 years) treated with lime exhibited the greatest mean

effect (1.68 pH units, SD = 0.58). Wood-ash had a greater effect on organic soil pH

when soil conditions were more acidic (pH<4.5). In mineral soils the greatest effect

(0.64 pH units, SD = 0.70) was found in acidic soils (pH<4.5) that received the larger

treatment application (>5000 kg ha-1). At lower application rates (≤ 5000 kg ha-1) the

effect on pH was greatest when measured during the first 4 years post treatment addition.

Base Saturation

The optimal regression tree model for BS effect size explained 47% of the

variation in results with a RMSE of 14.8 (Fig 3.2) with the major predictors of effects

indicated as soil type, time since treatment and tree species (Fig. 3.5). The largest

increase in BS (42.4%, SD = 14.1) was found in organic soils of softwood stands where

the sample period was ≥10 years after treatment while hardwood stands had their largest

increase (33.8%, SD=20.2) at higher doses of treatment. The largest increase in BS in

mineral horizons (33.4%, SD = 19.9) occurred in wood-ash treatments ≥10 years after

application.

103

Ca Foliar Concentration Effect Size Regressive Analysis

The optimal decision tree model for foliar Ca concentration effect size was the

only model split initially by treatment (lime or wood-ash) and explained 51% of the

variation in results (Fig. 3.3) with the major predictors of effects indicated as treatment

dose and treatment type (Fig. 3.5). Limed stands exhibited a mean increase in foliar

Ca concentration effect size of 48.5% (SD = 39.6) over control, compared with just a

13.8% increase (SD = 17.5) for stands treated with wood-ash. Within limed stands,

those with the higher treatment (>5000 kg ha-1) had a greater mean foliar Ca

concentration increase (74.3%, SD = 47.4) than those with lower application rates (≤

5000 kg ha-1) (32.1%, SD = 22.3), and hardwood stands treated with lime at doses

>5000 kg ha-1 exhibited the greatest mean increase in foliar Ca concentration, (92.6 %,

SD = 35.6).

Tree Growth Effect Size Regressive Analysis

The optimal decision tree model for tree growth (of the combined growth

measures) explained only 26% of the variation in results with a RMSE of 69.3 % (Fig.

3.4) with the major predictors of effects indicated as the number of years since treatment,

the initial soil pH and tree species (Fig. 3.5). Tree growth showed the highest variation in

effect for the trials included in this research with 8% of treatments having negative

growth effects, 61% having no effect and the remaining 31% demonstrating positive

growth effects. Both softwood and hardwood stands demonstrated their largest mean

104

increases in growth on soils where the initial soil pH was 4.5-6. However, softwoods on

soils with pH >4.5, where tree growth was measured more than 10 years after treatment

application, had the highest overall mean growth increase over control (116%).

When softwood stands treated with wood-ash over differing trial durations were

analysed the results dramatically illustrated the importance of study length in detecting

and assessing mean growth effects (Fig. 3.6). For sites with initial low soil pH (<4.5),

hardwoods showed a greater growth increase than softwoods, especially at higher

doses (>5000 kg ha–1).

Uncertainty

Two bootstrap forest analyses were run using coverage optimization to account

for any uncertainty in the optimal recursive partitioning results. The major predictors

of effects for both 100 and 10,000 trees were found to be in good agreement with the

decision tree model for each parameter (Fig. 3.5).

1.1 Discussion

The effect of wood-ash and lime addition on the seven selected forest ecosystem

response variables was found to be highly variable with a surprising number of studies

that showed no significant effect compared with the control. If a mean treatment effect

had been obtained that was consistent across a series of studies from the populations

sampled the major focus could be put on its significance (Borenstein et al. 2009).

However, with the high dispersion around the summary effects, our quantitative results

highlight the tendencies of magnitude and direction and the significant variability in

105

105

Fig. 3.1: Soil pH mean effect size (pH treatment – pH control) optimal decision tree for all trials.

Fig. 3.2: Base Saturation mean effect size (BS treatment – BS control) optimal decision tree for all trials.

106

Fig. 3.3: Foliar calcium concentration mean effect size ((Treatment/Control) −1) %) optimal decision tree

for all trials.

Fig. 3.4: Tree growth (of combined measures) mean effect size ((Treatment/Control) −1) %) optimal

decision tree for all trials.

107

Fig. 3.5: Driver column contributions for pH, foliar Ca concentration and tree growth for

decision tree and bootstrap forest models. Arrows indicate the strongest drivers of positive

effect response to liming or wood-ash treatment.

108

Fig. 3.6: Mean tree growth (of combined measures) effect size ((Treatment/Control) −1) %) of

softwood treated with wood-ash by trial duration time. Database trials were not evenly spread

over the duration span of years, therefore they were separated by effect size responses. The

largest mean increase in growth effect size over that of the mean control effect size occurred

10+ years post-treatment.

109

treatment response that has been noted in previous narrative reviews (Vance 1996;

Formanek and Vranova 2003; Aronsson and Ekelund 2004; Pitman 2005). For the

most part, the sensitivity of the direction and magnitude of the results of our unweighted

meta-analyses were supported by the two weighted metrics calculated. This result is

consistent with the expectation that the more heterogeneous the effect sizes of

independent trials in a dataset are, the closer one approaches a meta-analysis statistical

weighting methodology where every study would have equal weight (Borenstein et al.

2009). Despite the high percentages of our sample studies demonstrating no

significantly differing effects from their controls and the large variability in study

significant effects, the comparable magnitude and direction of all four mean effect size

metrics calculated for pH, BS, foliar Ca and tree growth are robust and substantial

indicators of positive treatment outcomes.

Soil pH

The greater pH increase in organic horizons compared with mineral horizons is

supported by the results of a much more limited narrative review of lime and wood-ash

trials in Europe and North America (Lundström et al. 2003). Overall, organic horizons

of forests less than 50 years old treated with lime demonstrated the largest mean pH

increase. The greater positive response of organic horizons compared with mineral

horizons to lime and wood-ash treatments is in part due to the fact Ca is applied to the

upper organic horizons and takes time to reach the mineral soil and 50% of our studie

were five years or less. Recycling of Ca by forests and low leaching rates are thought to

contibute to much of the added Ca being held in organic horizons rather than leaching

110

into lower mineral horizons (Cho et al. 2011; Morrison and Foster 2001) as

demonstrated in an in situ isotopic tracing experiment where initially tracers were

strongly held in the litter layer but over time were very slowly vertically transferred

down through soil horizons (van der Heijden et al. 2013). This recent research

explains the much greater increase in pH in the mineral soil fifteen years post

application, than when sampled five years post (Moore et al. 2012). In the bootstrap

analyses time since treatment was an important factor in the soil pH response which

has been supported by previous narrative reviews (Vance 1996; Aronsson and

Ekelund 2004; Pitman 2006).

Lime treatment resulted in a larger pH response than wood-ash, which is

expected as the Ca concentration of the lime treatment used would be higher than the

same mass of wood-ash. Although it could not be assumed so, treatment type may have

been indicative simply of treatment dose, therefore an optimal decision tree model

combining those two potentially key drivers (Lime: L≤ 5,000 kg ha–1 and L>5,000 kg

ha–1, Wood-ash: WA ≤ 5,000 kg ha–1 and WA>5,000 kg ha–1) was evaluated to

determine the effect. The largest effect size was obtained in the organic horizon of

limed stands (L>5,000 kg ha–1) 50 years old or younger. Even though the models were

quite similar, I decided to keep treatment and treatment dose as separate parameters as

it wasn’t possible to separate out other influences within treatment type such as

solubility, form, composition or method of dispersal from the influence of dose or even

within the treatment dose category itself. In fact, the effects of wood-ash have been

previously shown to be highly influenced by the solubility and form of the treatment

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and not just the dose (Demeyer 2001; Nohrstedt 1992; Vance 1996; Eriksson 1998:

Eriksson et al. 1998) however this information was scarce in the research articles

within our study and thus could not be reasonably evaluated. The greater increase in

pH in organic horizons of young stands may possibly be due to thicker organic

horizons or to greater Ca demands of older forests resulting in fewer of the H+ ions in

soil being replaced by Ca. For mineral horizons, the largest increase in pH was for trials

with extremely acidic intial site conditons treated with high doses.

Base Saturation

Even though pH and BS are generally correlated (Beery and Wilding 1971) there

was a higher percentage of positive significant effect responses for soil BS compared

with pH. In this dataset, BS% at a given pH varied widely between soils, a result

which can be supported by a similar pH-base saturation anomaly demonstrated in

acidified forest soils of calcareous and noncalcareous parent material (Blaser et al.

2008). It can be inferred from Blaser’s research that the relationship between pH and

BS in treated acidified soils could be heavily influenced, not only by the chemistry of

soils derived from parent materials rich in base cations, but also by the chemistry of

Ca-addition. This result is also consistent with previous research demonstrating

pH(H2O) accounting for 46-70% of the total variability in BS% with a stronger

relationship found in organic soil horizons than mineral ones (Beery and Wilding

1971). This may be due in part to a potentially larger variability in pH measurements

(than BS) and/or a concurrent increase in protons, as a consequence of increased soil

processes such as nitrification. Similar to the bootstrap analyses of pH, the major

influence on BS response was soil type and the second strongest predictor was time

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since treatment (trial duration). With 27% of trials in the dataset being short-term (four

or less years in duration) it can be assumed that the time required for Ca and Mg to

vertically progress to the mineral horizons was insufficient (van der Heijden et al.

2013) and that the short-term lack of effects on base saturation in the mineral horizon

would not be indicative of long-term effects. In contrast to pH, wood-ash trials

produced a larger BS effect response than lime, which may be due to the solubility of

the varying treatments. The most soluble nutrient in wood-ash is K, followed by Ca and

Mg and it has been established that after the application of wood ash to a stand, K

concentrations increase as a result of both the high content and the high solubility of K.

In contrast to wood-ash, after the application of lime the decrease of acidity in soil

layers may take longer because of lime’s lower solubility (Meiwes 1995). Soil BS

response was also influenced by tree species (hardwood or softwood) in agreement

with well-established differing influences of tree species on soil organic matter,

acidification and cation and anion availability on the soil exchange complex

(Hallbäcken and Tamm 1986; Finzi et al. 1998).

Foliar Calcium Concentration

There was a strong increase in foliar Ca concentration in response to treatment,

which is consistent with narrative reviews (Vance, 1996; Demeyer et al. 2001;

Arvidsson and Lundkvist 2002) and with a previous meta-analysis (Augusto et al.

2008). Treatment dose and treatment type (wood-ash or lime) were the strongest

drivers of increased Ca foliar concentration effects. Although the Ca rate of transport

in the xylem upward to the foliage has been demonstrated to be slow compared to other

nutrients (Augusto et al. 2011), regressive partitioning did not identify trial duration

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time as one of the major drivers of increased foliar Ca concentration over the control.

This was a consequence of the relatively few studies in the Ca foliar database with trial

duration times of less than a year. Foliar Ca was much more responsive to lime

treatments compared with wood-ash and consequently the first partition was by

treatment type. Hardwood trees, treated with lime at the higher dose, demonstrated the

largest mean foliar Ca increase and this result may be due in part to the higher Ca

demands of hardwood tree species compared to softwood (Long et al. 2011). As with

the analysis of pH, it cannot be assumed that treatment was solely a reflection of dose

as we cannot separate out the effects of other treatment attributes such as form, method

of application and solubility. For wood-ash and lower lime applications, young stands

showed a very strong increase in foliar Ca over the controls. Young forests have been

shown to be able to effectively mobilize Ca from soil (Hamburg et al 2003). As well,

young stands are able to mobilize more Ca than they can accumulate in biomass, which

may result in increased concentrations of Ca in the soil exchangeable pool (Johnson et al.

1994), which could augment Ca cycling for decades (Martin et al. 1984) which in turn

could lead to both sustained positive Ca foliar and growth effects from treatment (as

compared to control effects).

Tree Growth

Initial soil pH, tree species (hardwood or softwood) and trial duration time, were

established as the strongest influences (in increasing order of importance) on mean tree

growth. Wood-ash amendments resulted in a greater response in tree growth than lime

for softwoods at the more acidic soil initial pH range. It is highly likely that the increased

growth response of wood-ash trials could be the result of the addition of other nutrients

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lacking in initial soil conditions such as Mg, P or K (Augusto et al. 2008). However, in

our decision tree and bootstrap analysis, regressive partitioning did not identify treatment

type as one of the major drivers of increased growth over the control.

Both softwood and hardwood stands demonstrated their largest mean increases

in growth on soils where the initial soil pH was 4.5-6. It can be postulated that these

less acidic sites had a greater growth response for the reason that they may have not

been as limited in other nutrients affecting growth as the more stressed acidic sites

(pH<4.5). Soil Ca limitation, as the result of chronic acidification originating from

atmospheric deposition, may limit tree growth and for sites affected in this way, liming

and wood-ash Ca-addition can induce pronounced soil changes but a growth effect

response of trees will not occur if Mg, K, N or P are also limiting (Formanek and

Vranova, 2003; Aronsson and Ekelund, 2004; Pitman 2006). Thus, one limitation of

our assessment was the lack of a holistic assessment of initial soil conditions, a direct

result of the lack of detailed information provided in the majority of included studies

concerning the initial soil conditions for all nutrients. Previous studies have shown that

in the long-term there are species-specific responses to Ca-additions. Long et al. (2011)

reported that sugar maple (Acer saccharum Marsh.), growth increased significantly in

response to Ca-addition, whereas American beech (Fagus grandifolia Ehrh.), had no

growth response while black cherry (Prunus serotina Ehrh.) experienced a negative

growth response.

Such species-specific variability in response underscores the complexity behind

tree growth requirements and could potentially explain some of the variability in

effects of hardwoods observed in this dataset. Nontheless, it is probable that the lower

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mean response in growth, compared with foliar Ca or soil pH, is largely a consequence of

the limited number of longer- term studies available in the literature. The lag time for

considerable tree growth response to wood-ash application highlighted in our quantitative

analysis has been noted as well in narrative evaluations (Vance, 1996: Pitman, 2006).

With our wood-ash dataset it was demonstrated that the short-term effects from treatment

varied widely from the longer time period effects. With 25% of the samples taken less

than 3.5 years after treatment and 50% less than 6 years after treatment, the temporal

variation in the dataset may explain much of the significant heterogeneity in effect size

and a substantial portion of the zero effect results. Specific treatments and site

conditions in our analyses appear to induce optimum growth effects but additional long-

term studies would be advantageous in order to estimate more comprehensive mean

growth effects and to explain more of the variability in the optimal regression and

bootstrap models.

C: N Ratio, ECM Root Colonization and Microbial Indices

Effect sizes were calculated for the C/N ratio, ECM root colonization, and

microbial indices. However, due to either non significant effects, small sample sizes or

a combination of both, a lot of the focus for these three parameters was placed on the

large heterogeneity in effect size results within the datasets.

Carbon to Nitrogen Ratio

The response of the soil C/N ratio to treatment addition was examined as some

studies have suggested the ratio would change (Geissen and Brümmer 1999; Nilsson et

al. 2001; Houle 2002). The soil C/N ratio exhibited little response to lime or wood-ash

addition, however this result does not indicate that independently C and N had no

116

response to treatment, merely that the net effect did not change the response ratio. In

fact, very little is known about the long-term effects of liming and wood-ash treatment

on C and N dynamics, as increases in N may be accompanied by similar increases in C,

with unknown causes (Bauhus et al. 2004). Parn (2004) concluded that even if a

treatment-induced increase in litter decomposition rate existed on site, it would be too

small to tease out from the effects of other factors that influence litter formation and

decomposition.

Ectomycorrhizal Fungi (ECM) Root Colonization

The strong, positive effect results from all three meta-analysis metrics calculated

for ECM root colonization should be viewed cautiously due to the very limited number

of studies found in the literature. However, as ECM fungi has been shown to enhance

plant access to base cations derived from weathering of minerals in soils (Landeweert

et al. 2001) it’s importance in tree growth is crucial. Treatment had a positive effect on

ECM root colonization as indicated by abundance, however, knowledge of the

weathering capacity of mycorrhizae and their responses to lime or wood-ash treatment

is very limited and it has been demonstrated that different species of ECM show large

variation in their uptake capacity of nutrients (Burgess et al. 1993; Wallander, 2000).

Shifts in community composition then, may be more indicative of relevant effect

responses to treatment than the abundance parameter used (Bakker et al. 2000). There

are very few studies on ECM community composition response to treatment but it has

been suggested that shifts in species composition caused by treatments, and, or

harvesting of tree residues for biofuels, could potentially mean a loss of weathering

capacity with impacts on both Ca soil cycling and subsequently on tree nutrition and

117

growth (Mahmood et al. 1999; Kjøller and Clemmensen 2009).

Microbial Diversity, Richness and Abundance Indices

An increase in the pH of lime or wood-ash treated soils generally increases

both microbial biomass and respiration rate (Bååth and Arnebrant, 1994; Fritze et al.

1994). Microbial indices in this synthesis were combined as few treatments were

found for each measure but this may have blurred the results as found in one study

included in this evaluation where abundance decreased with treatment while diversity

increased (Chagnon et al. 2001). The average effect size for microbial indices was

significantly positive for the dataset as calculated by the weighted and unweighted

meta-analyses, with 35% of treatments having no differing effect from the control

but treatment effects ranging widely from -74% to 156%. Of the 26 studies analysed,

only three demonstrated a significantly positive effect of increased microbial biomass

(Kreutzer 1995: Anderson 1998; Zimmerman and Frey 2002). A factor that

constrained our ability to detect significant effects on the back transformed response

ratio for microbial indices was the lower number of observations (N=21 instead of

26), a consequence of the constraints of the lnR analysis.

The majority of studies in our synthesized dataset were short-term, which may

also present a limitation in estimating effect size magnitude as lime applied to the

surface takes some time to reach the mineral soil horizon where microflora biomass

was often sampled. Knowledge of long-term effects of Ca-addition treatment on soil

micoflora was extremely limited in the literature with only one long-term study in our

dataset. This study indicated that microorganism response to treatment was highly

118

dependent on initial site conditions and recommended both benchmarking of existing

conditions before treatment and post treatment monitoring to avoid a loss of microbial

diversity that might ultimately result in a loss of functionality with regard to organic

matter turnover and nutrient cycling (Lorenz et al. 2000).

3.5 Other issues

There are many response parameters that were beyond the focus of this

investigation. In particular, large increases in soil pH have been shown to have

detrimental ecological effects to acidophilic ecosystems, particularly to the soil

bacteria and ectomycorrhizal community structure (Pitman 2006). The effects of Ca-

addition on understory flora have not been extensively studied however, biomass

increases in forbs, changes in plant tissue element concentrations (Pabian et al. 2012)

and shifts in understory species composition have been observed (Falkengren-Grerup

1995) and analyses of the effects on soil fauna have indicated marked shifts both in

species abundance and distribution (Bååth et al. 1980; Abrahamsen 1983) to the point

where liming is considered a disturbance with striking, soil macro-invertebrate,

species-specific responses induced by chemical soil changes (Auclerc et al. 2012;

Moore et al. 2013). An increase in pH, following liming, has been noted to promote

fine root development in the organic soil layers, increasing the danger of frost and

drought damage (Huettl and Zoettl1993; Kreutzer 1995; Formánek and Vranová 2002)

and increasing pH due to liming has increased nitrification, with a resulting risk of

nitrate leaching (Vadeboncoeur 2010 Kreutzer 1995; Huber et al. 2006) and cadmium

biotoxicity may result from the addition of wood-ash made from non- pure wood

residues (Aronsson and Ekelund 2004; Demeyer et al. 2001). It is unknown how the

119

effect sizes and drivers of effects that were demonstrated compare with the effects of

environmental change such as climate warming. Although I attempted to evaluate the

effects of climate through the Koppen-Geiger climate classification system (Kottek et

al. 2006) only two distinct classifications could be identified (Dfb and Dfc) and over

94% of the studies fell within one classification (Dfc) rendering the number of studies

in the second classification too small to meaningfully assess.

3.6 Conclusions

Wood-ash or lime addition is not universally beneficial and there was a large

variation in response of pH, BS, Ca foliar concentration, tree growth,

ectomychorrhizae root colonization, soil C/N ratio and microbial indices, with a

surprising number of Ca-addition treatment trials having no effect measurably different

from that of the control. Approximately three-quarters of treatment trials exhibited

positive effects on BS while two-thirds of trials indicated positive effects on soil pH,

foliar Ca concentration and microbial indices. One third of trials indicated positive tree

growth effects, ECM root colonization effects and only fifteen percent of trials

demonstrated any differing effect from control on the soil C/N ratio. Regression tree

models explained; 38% of the variation in pH, 47% of the variation in BS, 51% of the

variation in foliar Ca concentration and 26% of the variation in tree growth effect

sizes, with the corresponding bootstrap analyses in close agreement. According to

bootstrap analysis of 10,000 trees, the largest predictors of effect size were as follows;

for pH and BS - soil type, for foliar Ca concentration – treatment type and dose, and

for tree growth – trial duration in years, initial soil pH and tree species as denoted by

hardwood or softwood. The key role of temporal variation was highlighted in the

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decision tree and bootstrap regression analyses for growth, where a long lag time was

demonstrated between when wood-ash was applied and when the largest significant

growth effect response occurred. This research stands as a robust analysis of the

synthesis of treatment effects considered, highlights the critical role of long-term studies

in comprehensively assessing effects and offers suggestions for forest management best

practices that seek to offset Ca losses from continued or enhanced harvesting and acid-

induced leaching.

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131

APPENDIX 3A

Table 3A.1: List of studies included in the respective meta-analysis. Figure and table columns list all

figures and tables containing data from each study (note that many studies had several distinct trials).

Meta-analysis Treatment Figure Table

Soil pH Effect

Reference

lime 1, 5a 1, 2 Anderson & Joergensen 1997

lime 1, 5a 1, 2 Anderson 1998

lime 1, 5a 1, 2 Andersson & Persson 1988

lime 1, 5a 1, 2 Arnold & Van Diest 1993a

wood-ash 1, 5a 1, 2 Arvidsson & Lundkvist 2003

lime 1, 5a 1, 2 Backman et al. 2003

lime 1, 5a 1, 2 Blette & Newton 1996

lime 1, 5a 1, 2 Bona et al. 2008

wood-ash 1, 5a 1, 2 Bramryd and Fransman 1995

wood-ash 1, 5a 1, 2 Brunner et al. 2004

lime 1, 5a 1, 2 Butterbach-Bahl & Pappin 2002

lime 1, 5a 1, 2 Carnol et al. 2002

lime 1, 5a 1, 2 Chagnon et al. 2001

lime 1, 5a 1, 2 Cho et al. 2010

lime 1, 5a 1, 2 Cronan et al. 1992

lime 1, 5a 1, 2 Derome et al. 1986

wood-ash 1, 5a 1, 2 Ernfors et al. 2010

wood-ash 1, 5a 1, 2 Feldkirchner et al. 2003

lime 1, 5a 1, 2 Frank & Stuanes 2003

wood-ash 1, 5a 1, 2 Fritze et al. 1994

lime 1, 5a 1, 2 Fyles et al. 1994

lime 1, 5a 1, 2 Geary & Driscoll 1996

wood-ash 1, 5a 1, 2 Geibe et al. 2003

wood-ash 1, 5a 1, 2 Genenger et al. 2003

lime 1, 5a 1, 2 Giessen & Brummer 1999

lime 1, 5a 1, 2 Gradowski & Thomas 2008

lime 1, 5a 1, 2 Hahn & Marshner 1998

wood-ash 1, 5a 1, 2 Haimi et al. 2000

lime 1, 5a 1, 2 Hallbäcken & Zhang 1998

lime 1, 5a 1, 2 Houle et al. 2002

lime 1, 5a 1, 2 Ingerslev 1997

wood-ash 1, 5a 1, 2 Jacobson et al.2004

lime 1, 5a 1, 2 Juice et al. 2006

lime 1, 5a 1, 2 Kakei & Clifford 2000

lime 1, 5a 1, 2 Kreutzer 1995

lime 1, 5a 1, 2 Lehto & Malkonen 1994

lime 1, 5a 1, 2 Lofgren et al. 2009

132

lime 1, 5a 1, 2 Long et al. 1997

lime 1, 5a 1, 2 Long et al. 2011

lime 1, 5a 1, 2 Lorenz et al. 2000

wood-ash 1, 5a 1, 2 Lugwig et al. 2002

wood-ash 1, 5a 1, 2 Mahmood et al. 2003

wood-ash 1, 5a 1, 2 Maljanaen et al. 2006a

wood-ash 1, 5a 1, 2 Mandre 2005

lime 1, 5a 1, 2 Marschner & Wilcynzki 1991

wood-ash 1, 5a 1, 2 Moilanen et al. 2002

lime 1, 5a 1, 2 Moore et al. 2008

lime 1, 5a 1, 2 Musil & Pavlecik 2002

lime 1, 5a 1, 2 Nilsson et al. 2001

lime 1, 5a 1, 2 Nohrstedt 2002.

Wood-ash 1, 5a 1, 2 Norström et al. 2012

lime 1, 5a 1, 2 Nowotony et al. 1998

wood-ash 1, 5a 1, 2 Österås et al. 2005

wood-ash 1, 5a 1, 2 Ozolincius & Varnagirytė 2005

wood-ash 1, 5a 1, 2 Ozolincius et al. 2006

lime 1, 5a 1, 2 Pabian et al. 2012

wood-ash 1, 5a 1, 2 Parn 2004

lime 1, 5a 1, 2 Persson et al. 1995

wood-ash 1, 5a 1, 2 Pothoff et al. 2008

wood-ash 1, 5a 1, 2 Puttsepp et al. 2006

lime 1, 5a 1, 2 Rineau & Garbaye 2009

wood-ash 1, 5a 1, 2 Ring et al. 2006

lime 1, 5a 1, 2 Rosenberg et al. 2003

wood-ash 1, 5a 1, 2 Rosenberg et al. 2010

wood-ash 1, 5a 1, 2 Saarsalmi et al. 2001


lime 1, 5a 1, 2 Saarsalmi et al. 2011


lime 1, 5a 1, 2 Sikström et al. 2001

lime 1, 5a 1, 2 Simmons et al. 1996

lime 1, 5a 1, 2 Smallidge & Leopold 1997

wood-ash 1, 5a 1, 2 Solla-Gullon et al. 2006

lime 1, 5a 1, 2 Sramek et al. 2006

wood-ash 1, 5a 1, 2 Staples & Rees 2001

lime 1, 5a 1, 2 Veerkamp et al. 1997

lime 1, 5a 1, 2 Wanner et al. 1993

wood-ash 1, 5a 1, 2 Zimmerman & Frey 2002

Base Saturation Effect

wood-ash 2, 5b 1, 2 Arvidsson & Lundkvist 2003

lime 2, 5b 1, 2 Bakker 1999

133

lime 2, 5b 1, 2 Blette & Newton 1996

wood-ash 2, 5b 1, 2 Bramryd & Fransman 1995

wood-ash 2, 5b 1, 2 Brunner et al. 2004

lime 2, 5b 1, 2 Cho et al. 2010

lime 2, 5b 1, 2 Côté et al. 1995

lime 2, 5b 1, 2 Derome et al. 1986

lime 2, 5b 1, 2 Frank & Stuanes 2003

wood-ash 2, 5b 1, 2 Fritze et al. 1994

wood-ash 2, 5b 1, 2 Haimi et al. 2000

lime 2, 5b 1, 2 Hallbäcken & Zhang 1998

lime 2, 5b 1, 2 Houle et al. 2002

lime 2, 5b 1, 2 Lofgren et al. 2009

lime 2, 5b 1, 2 Moore et al. 2008

lime 2, 5b 1, 2 Nilsson et al. 2001

wood-ash 2, 5b 1, 2 Norström et al. 2012

wood-ash 2, 5b 1, 2 Pothoff et al. 2008

wood-ash 2, 5b 1, 2 Saarsalmi et al. 2001

wood-ash 2, 5b 1, 2 Saarsalmi et al. 2004

lime 2, 5b 1, 2 Sikström et al. 2001

lime 2, 5b 1, 2 Sramek et al. 2006

Foliar Ca Concentration

lime 3, 5c 1, 2 Arnold & Van Diest 1993b

wood-ash 3, 5c 1, 2 Arvidsson & Lundkvist 2002

lime 3, 5c 1, 2 Bakker 1999

lime 3, 5c 1, 2 Berger et al. 2001

lime 3, 5c 1, 2 Bona et al. 2008

lime 3, 5c 1, 2 Børja & Nilsen 2009

lime 3, 5c 1, 2 Côté et al. 1995

wood-ash 3, 5c 1, 2 Ernfors et al. 2010

lime 3, 5c 1, 2 Fyles et al. 1994

wood-ash 3, 5c 1, 2 Hallenbarter et al. 2002

lime 3, 5c 1, 2 Huber et al. 2006

lime 3, 5c 1, 2 Hugget et al. 2006

lime 3, 5c 1, 2 Kobe et al. 2002

lime 3, 5c 1, 2 Long et al. 1997

lime 3, 5c 1, 2 Long et al. 2011

lime 3, 5c 1, 2 Lugwig et al. 2002

wood-ash 3, 5c 1, 2 Lugwig et al. 2002

wood-ash 3, 5c 1, 2 Moilanen et al. 2002

wood-ash 3, 5c 1, 2 Moilanen et al. 2005

lime 3, 5c 1, 2 Moore & Ouimet 2006

wood-ash 3, 5c 1, 2 Ozolincius et al. 2007

lime 3, 5c 1, 2 Rosberg et al. 2006

134

wood-ash 3, 5c 1, 2 Saarsalmi et al. 2004

lime 3, 5c 1, 2 Saarsalmi et al. 2011

lime 3, 5c 1, 2 Sikström et al. 1997

lime 3, 5c 1, 2 Sikström et al. 2001

lime 3, 5c 1, 2 Smallidge & Leopold 1997

wood-ash 3, 5c 1, 2 Solla-Gullon et al. 2006

lime 3, 5c 1, 2 Sramek et al. 2006

wood-ash 3, 5c 1, 2 Varnagirytė-Kabašinskienė 2008

wood-ash 3, 5c 1, 2 Wang et al. 2007

Tree Growth Effect

lime 4, 5d 1, 2 Andersson & Persson 1988

lime 4, 5d 1, 2 Arnold & Van Diest 1993b

lime 4, 5d 1, 2 Bakker 1999

lime 4, 5d 1, 2 Bona et al. 2008

lime 4, 5d 1, 2 Børja & Nilsen 2009

wood-ash 4, 5d 1, 2 Clemenson-Lindell & Persson 1993

lime 4, 5d 1, 2 Côté et al. 1995

lime 4, 5d 1, 2 Derome et al. 1986

wood-ash 4, 5d 1, 2 Ernfors et al. 2010

wood-ash 4, 5d 1, 2 Feldkirchner et al. 2003

wood-ash 4, 5d 1, 2 Ferm 1992

lime 4, 5d 1, 2 Gebauer et al. 1998

wood-ash 4, 5d 1, 2 Genenger et al. 2003

lime 4, 5d 1, 2 Hahn & Marshner 1998

wood-ash 4, 5d 1, 2 Hallenbarter 2002

lime 4, 5d 1, 2 Hindar et al. 2003

lime 4, 5d 1, 2 Huber et al. 2004

lime 4, 5d 1, 2 Hugget et al. 2006

lime 4, 5d 1, 2 Ingerslev 1997

wood-ash 4, 5d 1, 2 Jacobson 2003

lime 4, 5d 1, 2 Juice et al. 2006

lime 4, 5d 1, 2 Kobe et al. 2002

lime 4, 5d 1, 2 Ljungstrom & Nihlgård 1995

lime 4, 5d 1, 2 Long et al. 1997

lime 4, 5d 1, 2 Long et al. 2011

lime 4, 5d 1, 2 Mandre et al. 2006

wood-ash 4, 5d 1, 2 Moilanen et al. 2002

wood-ash 4, 5d 1, 2 Moilanen et al. 2005

lime 4, 5d 1, 2 Moore & Oiumet 2006

lime 4, 5d 1, 2 Nowotony et al. 1998

wood-ash 4, 5d 1, 2 Ozolincius et al. 2006

wood-ash 4, 5d 1, 2 Parn 2005

lime 4, 5d 1, 2 Persson et al. 1995

135

wood-ash 4, 5d 1, 2 Puttsepp et al. 2006

lime 4, 5d 1, 2 Rosberg et al. 2006

wood-ash 4, 5d 1, 2 Saarsalmi et al. 2004

lime 4, 5d 1, 2 Saarsalmi et al. 2011

lime 4, 5d 1, 2 Safford & Czpowskyj 1986

lime 4, 5d 1, 2 Sikström et al. 1997

lime 4, 5d 1, 2 Sikström et al. 2001

lime 4, 5d 1, 2 Simmons et al. 1996

lime 4, 5d 1, 2 Smallidge & Leopold 1997

wood-ash 4, 5d 1, 2 Solla-Gullon et al. 2006

wood-ash 4, 5d 1, 2 Staples 2001

wood-ash 4, 5d 1, 2 Wang et al. 2007

lime 4, 5d 1, 2 Zaccherio & Finzi 2007

Microbial Indices Effect lime

1, 2

Anderson 1998

lime 1, 2 Carnol et al. 2002 Belgium

lime 1, 2 Chagnon et al. 2001

wood-ash 1, 2 Fritz et al. 1994

lime 1, 2 Geissen and Kampichler 2004

wood-ash 1, 2 Haimi et al. 2000

lime 1, 2 Hermannson et al. 2004

lime 1, 2 Kreutzer et al. 1995

lime 1, 2 Lorenz et al. 2000

wood-ash 1, 2 Zimmerman & Frey 2002

Soil C/N Ratio Effect

wood-ash 1, 2 Arvidsson & Lundkvist 2003

lime 1, 2 Backman 2003

lime 1, 2 Bauhaus 2004

lime 1, 2 Chagnon 2001

wood-ash 1, 2 Ernfors et al. 2010

wood-ash 1, 2 Fritze et al. 1994

lime 1, 2 Gradowski & Thomas 2008

lime 1, 2 Geissen & Brümmer 1999

wood-ash 1, 2 Haimi et al. 2000

lime 1, 2 Houle et al. 2002

wood-ash 1, 2 Jacobson et al. 2004

lime 1, 2 Lorenz 2000

lime 1, 2 Marschner & Wilcynzki 1991

lime 1, 2 Moore et al. 2008

lime 1, 2 Nilsson et al. 2001

wood-ash 1, 2 Parn 2004

lime 1, 2 Persson et al. 1995

wood-ash 1, 2 Rosenberg et al. 2010

136

ECM Root Colonization

wood-ash 1, 2 Saarsalmi et al. 2001

lime 1, 2 Saarsalmi et al. 2011

lime 1, 2 Sramek et al. 2006

lime 1, 2 Børja & Nilsen 2009

wood-ash 1, 2 Hagerberg & Wallander 2002

lime 1, 2 Juice et al. 2006

wood-ash 1, 2 Mahmood et al. 2002

lime 1, 2 Qian & Oberwinkler 1998

wood-ash 1, 2 Wallander et al. 2005

137

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4.1 Implications for recovery and an ecosystem approach to mitigation

4.2 Brief Background

Legacy effects have slowed the recovery of acidified waters in areas of eastern

Canada that feature both low base cation weathering rates and low base cation

deposition. Although, S deposition has decreased by approximately 50% since its peak

in 1970s (Jeffries et al. 2003) acid rain has left lingering effects in both soil

(Huntington et al. 2000; Watmough and Dillon 2003) and surface water of acid-

sensitive environments on the boreal shield (Dillon et al. 1992; Watmough et al. 2003;

Houle et al. 2006; Clair et al. 2007; MacDougal et al. 2009). Recovery of acidified

lakes and streams has been influenced by the size of base cation reservoirs present in

acid sensitive soils (Likens et al. 1996; Driscoll et al. 2001; Houle et al. 2006) and it

has been well established that these pools have been preferentially calcium (Ca)

depleted in poorly buffered areas (Johnson et al. 1994; Watmough and Dillon 2003).

In a large number of boreal shield lakes and streams, Ca is now low, is

continuing to decline and in some cases is approaching estimated pre-industrial, steady

state concentration levels. Small, shallow lakes at higher elevation in small catchments

with higher runoff, low base cation weathering rates and that are minimally impacted

by the influence of roads (and associated development, salt and/or road dust

suppressants) and agriculture (and associated development and Ca input with fertilizers)

are the lakes most at risk to amplified Ca depletion from forest harvesting. This risk

increases as the forest percentage and volume harvested in the catchment increases.

The only self-repair method for anthropogenically acidified ecosystems is the soils

ability to buffer acidic deposition which is mainly determined by the rate of release

149

of base cations from minerals (Holmquist 2001). Weathering rates are low in most areas of

the boreal shield and as a result, Ca has been preferentially depleted from base cation

reservoirs in acid sensitive soils mainly as an unexpected consequence of the impact of

acid rain (Watmough and Dillon 2003). This depletion of exchangeable soil base cations

and the resulting decline in lake Ca cannot be self-repaired by the ecosystem when sites

are harvested.

Many lakes in the MRW have been identified as lakes at risk of Ca limitation

below critical levels required by biota of 1.5–2 mg L–1 (Ashforth and Yan 2008; Tan and

Wang 2010) and recent work has indicated the elimination of some Ca-rich keystone biota

can contribute to lake algal blooms (Korosi et al. 2012) and jellification (Jeziorski et al.

2014). It is possible that some lakes may have had pre-industrial levels less than these

critical levels. However, what were once sufficient lake Ca levels in a natural, pristine,

steady state are likely no longer adequate to protect against the loss of Ca-sensitive species

when anthropogenic multiple stressors drive Ca lower. To depend on ecosystem self-repair

is to ignore the scientific evidence and seriousness of the Ca problem, as amelioration is

required to protect surface water quality and sustain critical Ca levels of impacted lakes.

Under proposed Crown land forest management cut volumes, ~38% of 364 lakes

in the MRW will fall below 1 mg L–1 which is considered a Ca threshold. Under

harvesting yields of 40% of the total allowable volume cut, ~ 20% will fall below Ca

thresholds compared with just ~8% in the absence of harvesting. Natural recovery of Ca

levels in those lakes approaching critical levels is unpromising given the depleted soil Ca

pools and low rates of weathering of silicate based minerals in the MRW. Anthropogenic

influence has increased Ca levels in some lakes, however, these lakes are the ones most

urbanized and the least likely to be impacted by tree harvesting.

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4.3 Mitigation on an ecosystem basis – benefits to lakes, soils and trees –

win/win/win

In the meta-analysis outlined in Chapter 3, it was demonstrated that over 75%

of Ca-addition to forests resulted in increases in soil base saturation (Reid and

Watmough 2014). Research studies focusing on the in-stream liming of lakes,

streams, and rivers have been limited but ongoing in eastern North America for over

30 years with varying results dependent upon lake and catchment physical and

chemical characteristics, however, most have been able to increase the Ca

concentration of surface waters (Clair and Hindar 2005). However, the majority of

the treatments have been in-stream liming trials of short duration that would require

expensive ongoing applications to maintain the aquatic benefits and that do not

address the underlying issue of soil base cation depletion (Clair and Hindar 2005).

With regard to ecosystem remediation, for most sites, one catchment-wide,

wood-ash application per harvest rotation has been shown to be able to replace any

soil Ca, Mg and K lost after harvesting (Vance 1996). As well, in recent research

and reviews of wood-ash addition, long-lasting positive effects of treatment on tree

growth have been observed after a lag time (Pitman et al. 2006; Saarsalmi et al.

2012; Maljanen et al. 2014; Reid and Watmough 2014).

Although there have been fewer investigations on surface water chemistry

from forest soils treated with Ca-addition, there have been increased

concentrations of Ca in leachate surface waters dependent on the form and dose of

wood-ash applied to the catchment (Williams et al. 1996; Piirainen et al. 2001;

2013; Núñez-Delgado et al. 2011).

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4.4 Knowledge Gaps

There are significant gaps in existing Boreal Shield lake chemical data,

which will require an expansion of fieldwork to address questions about the spatial

and temporal aspects of Ca decline patterns. Additionally, future work is required

to determine the impact of forest harvesting on Ca-poor soils and the subsequent

influence on surface water quality on both spatial and temporal scales. More

research will help improve understanding about the extent of Ca depletion and

could predict how soon critical Ca levels in lakes will be reached; moreover, it will

advance knowledge of the effects of Ca addition treatment on both soil and lake Ca

levels. As Mg is also declining, similar spatial and temporal research could enhance

the knowledge of the status of this nutrient on the Boreal shield. Additionally, the

close monitoring of lake pH should be a priority. Even though there has been some

recovery of lake pH, the continued decline of Ca could potentially drive pH lower.

DOC should also be evaluated regarding the potential to increase lake organic acids.

Ultimately, future work is expected to provide beneficial management practice

recommendations that seek to improve Ca pools in soil and concentrations in surface

water while increasing forest productivity (in effect a win/win/win goal).

152

4.5 Directions of future research

Existing soil and water data can be analysed and updated with field

measurements for as many MRW lakes as possible in order to achieve the most accurate

results. Research would involve a combination of fieldwork, data analysis, and dynamic

modeling with the overall goal of aiding forest authorities in achieving the highest

standards of sustainable forest management practices possible with regard to nutrients

essential to both forest productivity and water quality.

Four key objectives that could define the structure of future Ca study.

1. Objective one addresses the question of the spatial component of the problem. To

what extent does the footprint of acid rain reach with regard to decreasing surface water

Ca patterns? The uncertainties and unknowns associated with Ca depletion in soils and

surface water needs to be examined and their impact will be assessed. Uncertainties are

associated with: estimating Ca weathering rates through modeling (have weathering rates

changed since steady state preindustrial times?), the integrity and accuracy of Ca

deposition values (what is the confidence level in existing deposition data?), the impact

of changes in Ca deposition, the effects of climate change on changes in runoff volume,

lake volume and the subsequent impacts on Ca lake concentration, changes in demand

for wood products linked to forecasted harvesting volumes, and the effects on Ca

depletion of harvesting practices taking place on private land (for which little to no data

exist). Monte-Carlo based modeling could be used to determine the probabilities of

different outcomes occurring and to predict the bounds of Ca depletion in lakes. Monte

Carlo methods have proven to be extremely useful for simulating environmental

scenarios involving non-linear processes with significant uncertainty in inputs (Phillips

153

and Watmough 2013).

2. Objective two addresses the question of timing. We know surface water Ca is

declining but when will critical levels be reached? In order to estimate how soon lakes

spatially identified by objective one will reach identified critical Ca thresholds,

dynamic modeling with MAGIC will be utilized for those areas with sufficient data.

MAGIC, the model of acidification of groundwater in catchments (Cosby et al. 1985,

2001), has been used extensively in Europe and eastern North America and has proven

to be a useful model for simulating water acidification responses at the catchment scale

(Cosby et al. 1985; Aherne et al. 2003; Clair et al. 2003; Whitfield et al. 2007). The

model uses site-specific information to estimate “patterns, time scales and magnitudes”

of long-term responses in surface water chemistry to variations in the levels acidic

deposition (Cosby et al. 2001). One of the largest uncertainties in MAGIC modeling is

the estimated weathering rate. However, site-specific, soil surface area measurements

should reduce uncertainty associated with the weathering rate model predictions and is

preferred (Warfvinge and Sverdrup 1992).

3. Objective three addresses the question of mitigation of Ca depletion. Can the

addition of wood-ash to forest soils stop or slow the depletion of Ca levels? Further

work should examine the effects of experimental wood-ash treatment trials in eastern

Canada on ecosystem nutrient cycling and subsequently on Ca concentrations in lakes.

It should also investigate whether the future harvest volume increases could potentially

offset costs of such mitigation.

4. Based on the results of the first three objectives, it would be possible to recommend

beneficial forestry management guidelines to delay reaching critical Ca levels in

154

surface water. These nutrient management practices could, in effect, moderate Ca

depletion and slow the approach of critical levels. These recommendations could help

ensure the long-term sustainability of both ecosystem and economic health in areas

like the Muskoka River Watershed where the economy is intrinsically tied to the

integrity of the environment and especially to the lake water quality.

4.6 Conclusions

Decreasing Ca concentrations in lakes located in base poor catchments is an acid-

rain driven legacy effect threatening the health and integrity of surface water and is a

problem that can be compounded by additional Ca removals through forest harvesting.

For many lakes that are already subject to multiple stressors, the decline of this nutrient

that is essential to both forest and lake health and integrity, is beyond the bounds of

natural recovery. For some larger surface-fed lakes, in catchments impacted by

relatively high densities of roads and/or agriculture, maintenance of Ca levels above

critical values may not be an issue. However, for those small, shallow lakes located in

relatively small harvested catchments with lake Ca concentrations approaching or

below known critical Ca concentrations, future forest management practices that

include the addition of Ca to the harvested forests may be the best choice to maintain

lake quality. The use of Ca-addition in the MRW as an ecosystem tool for managing

the cumulative effects of past, present and future stress from harvesting could not only

benefit the integrity of the lakes and productivity of managed forests but could also

further the protection of an economy so dependent on the quality of its environment.

Further research into the potential application of this mitigation tool as a sustainable

forest management best practice should be pursued without delay.

155

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