Elena Vanguelova, Nathan Brown, Samantha Broadmeadow, …€¦ · Elena Vanguelova, Nathan Brown, Samantha Broadmeadow, Sue Benham, Frank Ashwood, Diogo Pinho and Sandra Denman Forest

Predisposition factors to Oak decline

Links between soil biochemistry with Oak nutrient

and health status

Elena Vanguelova, Nathan Brown, Samantha Broadmeadow, Sue Benham, Frank Ashwood, Diogo Pinho and Sandra Denman

Forest Research, UK

2

Acute Oak Decline (AOD) and Chronic Oak Decline

Chronic Oak decline – poor crown development – Almillaria – since the 1900

Dried fluid crusted in bark splits

Agrilus Biguttatus beetle

exit holes associated with

stem bleeding

Acute Oak decline syndrome – profuse bleeding

extending up to the canopy of the tree since the 1980-1990s

Oaks – the iconic trees of Britain

Pedunculate oak (Quercus robur)

Sessile oak (Quercus petraea)

7th ICP scientific meeting, Riga, May, 2018

Manion Decline Spiral

Decline syndromes -Manion, 1981

• Causes of Decline diseases are

multifaceted and multi-

staged, developing over

various time periods.

• Predisposition is fundamental

to the onset of Decline, but

lacking qualitative and

quantitative evidence.

• The linkages between Decline

factors and host effects are

poorly understood


4

Acute Oak Decline (AOD) survey sites

• Locations of woodlands used in this study from systematic and citizens surveys.

• AOD positive locations are shown as dark red dots and negative locations are shown as

small white squares.

• Datasets used in the spatial modelling.

Full data n=544;

Survey only includes only sightings from survey selected hectads (n=371);

2014 only includes only data from the second focused survey (n=253).


5

Spatial datasets used in the study

Dataset Resolution Parameters/Description Source

Climatic parameters 5 km x 5 km grid mean air temperature, rainfall, sunshine duration, wind speed,

growing season length, growing season degree days.

UK Met Office Parry and

Hollis (2006)

Day degrees

above 11.5 oC

5 km x 5 km grid Calculated in CLIMEX. 11.5 oC corresponds to estimates of the

development thresholds for A. biguttatus (Reed et al., 2017), using

average monthly temperatures (1971-2000).

UK Met Office

Atmospheric deposition 5 km x 5 km grid wet SO4 (non marine sources), dry SO2/SO4 (non marine sources),

wet NH4, wet NO3 dry NO2/NO3,/HNO3, dry NH3/NH4, total N,

Ca+Mg+K (non marine sources) deposition

(CEH, 2006)

National Soil Map

1: 25,000

Polygon shapefile The soils of England and Wales are classified according to the

English and Welsh Soil Classification system (Avery, 1980).

(Cranfield University,

2004)

National Forest

Inventory

woodland map

Polygon shapefile The 2013 woodland area map was used to calculate the area of

woodland in each National Soil Map sub-type.

(Forestry Commission

2011)

Hydrology of Soil Types

(HOST)

Polygon shapefile Using the HOST class soils were reclassed as: well drained,

seasonally water logged or permanently wet.

(Boorman et. al., 1995)

FC Grand Database Woodland habitat

and management

map

Forestry Commission spatial data of woodland habitat and

management supported through grants

(Pyatt, Ray and

Fletcher, 2001).


6

-GAM model with logistic regression,

-positive = AOD, negative = not,

-with confidence intervals for the estimate 80% and 95%

Mean annual rainfall (mm) 1991-

2010

0

100

200

300

400

500

600

700

800

900

1000

AOD NO AOD

>200mm

rainfall

difference in

average annual

rainfall

(1991-2010)

Rainfall


7

Elevation (m)

0

20

40

60

80

100

120

AOD NO AOD

Elevation

> 30 m

difference in

elevation

between AOD

and NO AOD

plots





8

Mean growing degree days

1000

1100

1200

1300

1400

1500

1600

1700

1800

AOD NO




>100 days

difference

Temperature

effect (mean

growing degree

days >11.5 C

(sum 1961/90)

Temperature


9

No significant spatial trends found for

Growing season length (1971-2000)

Mean Growing Season

Length (days)

300

301

302

303

304

305

306

307

308

309

310

311

AOD NO AOD

About 5 days

difference

(1971-2000) in

mean growing

season length

between AOD

and NO AOD

plots

Growing Season

Links with Temperature effect and

likely links with beetles distribution

temperature threshold


10

significant higher

(~7kg/ha/a) dry input of

NO2, NO3 and H to AOD

than NO AOD plots

Effect on tree canopy

Nitrogen deposition

significant lower

(~3kg/ha/a) wet input of

NO3 and NH4 to AOD

than NO AOD plots

Effect on tree roots and N uptake


11

Sulphur deposition

Significantly

lower input of

dry SO2 and SO4

to AOD than NO

AOD plots

Significantly

lower input of

total wet SO4

deposition at

AOD sites


12

~83% sites on drained and seasonally waterlogged soils, both drought sensitive soils

44

17

39

0

5

10

15

20

25

30

35

40

45

50

Drained soils Semi-drained soils Hydropmorphic soils

AO

D s

ites d

istr

ibuti

on (

%)

~83

HOST - proportion of rainfall lost as a runoff

30

31

32

33

34

35

36

37

AOD NO AOD

AOD presense/absence

Ra

infa

ll l

os

s s

ru

no

ff (

%)

P<0.05*

AOD site with higher proportion of rainfall lost as a runoff, suggesting the soils are drought sensitiveBut no spatial relationship found

Soils and hydrology

Significant difference between AOD and non AOD sites only found on seasonally waterlogged clay soil


13

Final spatial model for AOD risk

Final model predictions generated using the full AOD dataset at 1 km square scale

Predisposition (probability of

AOD occurrence) is shown on a

maximum to minimum scale

Useful tools (spatial and statistical modelling) for development of future predictive spatial mapping and modeling

on tree susceptibility to any other tree pests/diseases - Brown et al., 2017, FEM, 407, 145-154


Tree Health monitoring sites

14

Cronic Oak decline sites

Acute Oak decline sites

Monitoring

Tree crown condition, decline

symptoms, beetle exit holes, bark

lesions, swaps for bacteria.

Remission of trees observed, e.g.

callusing bark lesion, improved

crown condition


15

Soil and root sampling – 5 points per tree

Site/tree scale study

Foliar sampling – 4 cardinal direction

1) In each site 10 healthy and 10 AOD or COD per

3 decline stages trees have been chosen = 20 trees

10 monitoring sites in England(6 Acute Oak Decline and 3 Chronic Oak Decline)

2) For each tree, 5 soils and root samples were

taken with soil cylindrical core (8 cm diameter,

15 cm depth)

3) Each soil/root sample is split to Litter,

Humus and mineral soil 0-15-15-30 cm depth

4) From the same 20 trees (10 AOD and

10 healthy) foliar sampled from the 4

cardinal direction (August/September)

before leave fall.

5) Chemical/physical and biological

analysis

2m radius

S

N EW


16

Results from COD site on clay soils

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Healthy Symptomatic

Speculation

Soil NO2+NO3-N (ppm)

-0.1

0.1

0.3

0.5

0.7

0.9

1.1

1.3

1.5

Healthy Symptomatic

Speculation

Soil NH4-N (ppm)


17

Results so far at COD sites

No difference in fine root biomass at Chronic Oak declined compared to healthy trees

But significant difference in root N content and morphological traits


Percentage of root in cores of 3 AOD/COD sites

18

0

10

20

30

40

50

60

70

80

90

100

% of cores with root

0-15 cm

15-30cm

Attingham ParkGreat Monks wood Winding Wood

Significantly less fine roots at Acute and Chronic Oak declined compared

to healthy trees at sites where bot COD and AOD present


19

1

1.2

1.4

1.6

1.8

2

2.2

2.4

2.6

2.8

3

Big Wood Chestnut Speculation

Foliar N (%)

Healthy

Symptomatic

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0.2


Foliar P (%)

Healthy

Symptomatic

Foliar N concentration significantly lower in

symptomatic trees than healthy trees in all three

sites but only in Big Wood site levels are nearly

critical in symptomatic trees

Foliar P concentration significantly lower in

symptomatic trees than healthy trees in Big Wood and

Chestnut but not at Speculation, but all under critical

P levels

Foliar nutrients content at 3 COD sites


20

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9


Foliar Ca (%)

Healthy

Symptomatic

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18


Foliar Mg (%)

Healthy

Symptomatic

Foliar Ca concentration significantly lower in

symptomatic trees than healthy trees in all three

sites

Foliar Mg concentration significantly lower and under

critical levels in symptomatic trees than healthy trees

in Big Wood and Chestnut but not at Speculation

Foliar K concentration significantly lower in

symptomatic trees than healthy trees

in Big Wood and Chestnut but not at Speculation

0

0.2

0.4

0.6

0.8

1

1.2

1.4


Foliar K (%)

Healthy

Symptomatic

Foliar nutrients content at 3 COD sites


Methods

Rhizosphere soil

sampling

Healhy and AOD trees

DNA Extraction

and PCR

16S rDNA (bacteria)

ITS2 (fungi)

High-throughput

sequencing

MiSeq Illumina 2x300bp

Data processing

QIIME

Statistical analysis

R and PRIMER

Richmond HatchlandsEastnor

Linking the rhizosphere microbiome and acute oak decline – Diogo Pinho

Sites – Oak - AOD parklands


Results

Bacteria Fungi

PERMANOVA: Soil type p<0,0001; Site p<0,001; SoxSi p<0,01

BEST Analysis: Soil pH; Soil TOC

PERMANOVA: Soil type p<0,0001; Site p<0,0001; SoxSi p<0,0001

BEST Analysis: Root nitrogen; Root P/K; Soil moisture; Soil pH

Bacterial and fungal communities are significantly impacted by forest location and soil type

RhizosphereRhizosphere

Bulk soil

Bulk soil

Soil and root chemical parameters are the main drivers of microbial composition.


Eastnor Richmond Hatchlands

Bac

teri

aF

un

gi

Pseudo-F = 1,6282; p = 0,021

Pseudo-F = 2,8493; p = 0,025

Pseudo-F = 1,3442; p = 0,035

Preliminary results suggest a link between belowground microbial composition and tree health

Results


24

• Elevation, Rainfall and Temperature are significant predisposition factors for Oak health (pests and diseases).

• Dry and Wet nitrogen and sulphur and base cations deposition are also

significant predisposition factors for Oak health (pests and diseases).

• Soil type/nutrient /moisture status are important factors at spatial scale but even more significant in relation to tree health status at stand/tree level due

to small scale variability.

• Preliminary results suggest a link between belowground microbial

composition and tree health.

• Soil and root chemical parameters are the main drivers of microbial

composition.

• Pleminary results suggest strong links between belowground traits and tree health, but proof of cause and effect is required.

Conclusions so far

24 7th ICP scientific meeting, Riga, May, 2018

25

Thank you!


Documents

Elena Vanguelova, Nathan Brown, Samantha Broadmeadow, …€¦ · Elena Vanguelova, Nathan Brown, Samantha Broadmeadow, Sue Benham, Frank Ashwood, Diogo Pinho and Sandra Denman Forest