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EXPLORING THE RELATIONSHIP BETWEEN FOREST
MICROCLIMATE AND CANOPY CHARACTERISTICS IN TEMPERATE
FORESTS ACROSS EUROPE
Gauthier Buyse Student number: 01304221
Promotor: Prof. dr. ir. Pieter De Frenne
Prof. dr. ir. Kris Verheyen
Tutor: Sanne Govaert
Master’s Dissertation submitted to Ghent University in partial fulfilment of the requirements for the
degree of Master of Science in Bioscience Engineering: Forest and Nature Management
Academic year: 2017 - 2018
Copyrights The author and promotors authorize this thesis to be available for consultation and copying parts for
individual use. Any other use, such as quoting results without acknowledgement, is subject to copyright
restrictions.
08/06/2018
Gauthier Buyse
Promotors: Prof. dr. ir. Pieter De Frenne and Prof. dr. ir. Kris Verheyen
Tutor: Sanne Govaert
Signatures
i
Acknowledgement
This master's dissertation was a very interesting and educational research where I trained several skills
e.g. writing, understanding and interpretation of papers, working with R, work with a vertex,
organisation skills… and learned a lot of new knowledge. The master's dissertation was the perfect
combination of field work, literature study and statistical analysis of the data. The journey for the data
collection through France, The Netherlands, Germany, Sweden and Czech Republic was an unique and
very instructive experience.
If there occurred a problem, I could always appeal to several people. First, I want to acknowledge my
promotors and tutor Prof. dr. ir Pieter De Frenne, Prof. dr. ir Kris Verheyen and Sanne Govaert for the
support. Thereafter I want to thank the local collaborators of the ten regions for providing information,
the localisation of the temperature data loggers and providing the macroclimate data: ir Fabien Spicher
(University of Picardie Jules Verne), dr. ir Jonathan Lenoir (University of Picardie Jules Verne), Prof.
ir dr. Wolfgang Schmidt (University of Göttingen), apl. Prof. dr. rer. nat. Monika Wulf (University of
Potsdam), Prof. Jörg Brunet (Swedish University of Agricultural Sciences), M.Sc. Martin Kopecký
(Department of GIS and Remote Sensing, Institute of Botany of the CAS), dr. hab. Bogdan Jaroszewicz
(Faculty of Biology, University of Warsaw), dr.ir. Jan den Ouden (Wageningen University), Dr Denise
Pallett (Centre for Ecology & Hydrology) and Fero Malis.
I also want to thank people who helped and accompanied me with the fieldwork: Prof. dr. ir Pieter De
Frenne, dr. Florian Zellweger, ir. Leen Depauw, ir. Sybryn Maes, Sébastien Buyse, Aaron Goethals,
Matthias Janssens, Laurens Saelens and Wouter Van Speybroeck. I want to thank Bram Sercu for the
clarifying explanation about hemispherical pictures. I want to acknowledge Florian Zellweger for
deriving the landscape data. The landscape data is produced using Copernicus data and information
funded by the European Union - EU-DEM layers. In particular, I want to thank Prof. dr. ir Pieter De
Frenne, Sanne Govaert and dr. Florian Zellweger for the follow up, feedback and good advice. I
definitely learned skills and knowledge that I will need in my future career.
ii
iii
Table of contents
List of Abbreviations v
Abstract vii
Abstract (NL) ix
1. Literature study 1
1.1. Climate change ………………………………………………………………...……………...…1
1.1.1. Causes ……………...…………...…………………………………………………………...2
1.1.2. Consequences……………………...………………………………...……………………....3
1.1.2.1. The past 150 years………………………………………………………………………3
1.1.2.2. The future: The next 100 years………………………………………………………….7
1.2. Effect on species…………………………………………………………………..…………….10
1.2.1. The past 150 years…………………………………………...………………………..……10
1.2.1.1. Individual level: phenology, physiology, morphology…………………………...……10
1.2.1.2. Population & Community level: distribution range changes……………………...……12
1.2.1.3. Ecosystem level…………………………………………………...…………..………14
1.2.2. The future: The next 100 years……….…………………………………………………..…15
1.2.2.1. Individual level: phenology, physiology, morphology……………………………..…15
1.2.2.2. Population & Community level: distribution range changes…………………………..16
1.2.2.3. Ecosystem level……………………………………………………………..………...17
1.3. Microclimate versus macroclimate……………………………………………………..……….18
1.3.1. Difference between macro- and microclimate………………………………………...……18
1.3.1.1. Human microclimates……………………………………………………………...….20
1.3.1.2. Natural microclimates………………………………………………...…...………….21
1.4. Influence of the forest structure on the microclimate……………………………………...…….22
1.4.1. Influence of the tree height……………………………………………………………...….22
1.4.2. Influence of the forest density………………………………………………………………22
1.4.3. Influence of the tree species…………………………………………………...……………23
1.5. Research questions………………………………………………………………………...……24
1.5.1. How much is the temperature buffered in forests?.................................................................24
1.5.2. Is there an effect of forest and landscape characteristics on the amount of buffering?..........24
iv
2. Materials and methods 25
2.1. Study sites………………………………………………………..…………………..…………25
2.2. Microclimate temperature data………………………………………………………………….26
2.3. Macroclimate temperature outside forests…………………………………………………...….28
2.4. Forest characteristics…………………………………………………………………...……….29
2.5. Landscape characteristics…………………………………………………………………...…..30
2.6. Data analysis………………………………………………………………………………….....31
3. Results 33
3.1. Similarity between the forest and outside temperature……………………………..…...………33
3.2. Quantification of the buffering……………………………………………………………...…..36
3.3. Relation of forest characteristics with the buffering………………………………………….....39
3.4. Effect of landscape characteristics…………………………………………………………..…..51
3.4.1. Relation with the distance to the coast…………………………………………………...…51
3.4.2. Relation with the latitude……………………………………………………………..…….53
3.4.3. Relation with the elevation above sea level………………………………………..……..…54
3.4.4. Relation with the slope of the plots……………………………………………………...….56
3.4.5. Relation with the amount of forest edge in a radius of 500 m………………………………58
3.4.6. Relation with the relative elevation of the plots in a radius of 250 m………………………60
3.4.7. Relation with the north orientation (northness) of the plots………………………………...61
3.4.8. Relation with the east orientation (eastness) of the plots……………………………………63
4. Discussion 64
4.1. Buffering in the four seasons……...……………………………………………………...……..64
4.2. Buffering of the temperature as a function of forest and landscape characteristics……………...66
4.3. Buffering of the temperature as a function of landscape characteristics…………………………68
5. Conclusions and management implications 72
6. Bibliography 74
7. Appendix 89
v
List of Abbreviations
BI Bialowieza
CO Compiègne
DBH Diameter at breast height
DEM Digital Elevation Model
GO Göttingen
IPCC The Intergovernmental Panel on Climate Change
KO Koda Woods
LAI Leaf Area Index
NCI Neighbourhood Competition Index
PR Prignitz
R2c Conditional R squared
R2m Marginal R squared
SKA Skåne
SP Speulderbos
TB Tournibus
Tmax Maximum temperature
Tmean Mean temperature
Tmin Minimum temperature
UHI Urban Heat Island
WW Wytham Woods
ZV Zvolen
vi
vii
Abstract
In the last decades, temperatures have been rising around the globe due to anthropogenic activities. The
climate gets warmer and extreme weather events occur more often. It is expected that global warming
and the frequency of extreme events will continue to increase. Climate change is having a great impact
on several levels of biological organisation such as species, populations, communities and terrestrial
ecosystems. Plants are flowering earlier, animals reproduce earlier, wake up earlier from hibernation,
appear earlier and return earlier from hibernation area. Species are also migrating to more elevated areas
or towards the poles. The impact of climate change on organisms will keep raising in the future.
However, organisms experience temperatures at small spatial scales (so called microclimates), where
climatic conditions can significantly deviate from the macroclimate. One of the reasons microclimates
are ecologically important is that they can potentially protect species against climate variability and
longer-term changes. Thus, microclimates provide microrefugia which allow species and populations to
survive in locations which may be deemed unsuitable using low resolution observations and models.
In the context of global warming and the pressure on biodiversity, the relation between forest
microclimate and canopy- and landscape characteristics in temperate deciduous forests across Europe
has been explored. This study focusses on the quantification of the buffering and the relationship
between the buffering and forest canopy- and landscape characteristics. Indeed, forest canopy
characteristics (such as tree height, distances among neighbouring trees, density, the tree cover and the
shrub cover) and landscape characteristics (such as the terrain topography, elevation above sea level and
amount of forest surrounding the plot) can potentially all affect the buffering. A distinction is also made
between the four seasons and between the daily mean, minimum and maximum temperature (Tmean, Tmin
and Tmax). Microclimate temperature data were collected during one entire year between February 2017
and February 2018 in 100 plots in deciduous forests in 10 regions across the European continent.
Macroclimate data were obtained from the closest weather station in the open field. The buffering is
always calculated as the forest temperature minus the outside temperature such that negative values
reflect cooler forest temperatures. Across all regions, the forest Tmin was 0.41 to 1.35 °C warmer
compared to the open field. The forest Tmax was 0.24 to 2.05 °C colder than in the open field (except in
spring when the forest Tmax was 0.32 °C warmer compared to the outside Tmax). A colder Tmax and a
warmer Tmin compensated each other which resulted in a similar Tmean between the micro- and
macroclimate seen over the entire measuring period, as well as in autumn and in winter. In spring and
summer, the forest Tmean was respectively 0.27 °C warmer and 0.49 °C colder compared to the outside
temperature.
The forest Tmax increased (in each season except winter) and forest Tmin decreased (in spring and autumn)
with increasing openness of the forest. The forest Tmax increased with increasing tree and shrub cover (in
each season except winter). The forest Tmax increased with increasing tree height in each season. The
forest Tmax decreased (in spring and summer) and the forest Tmin increased (in summer, autumn and
winter) with increasing distance to the coast. The forest Tmin became warmer (in each season) with
increasing elevation above sea level of the plots after a correction of the temperature for the elevation
above sea level of the plots. An increase in the north orientation of the plots resulted in an increase of
the forest Tmax in spring, autumn and winter. The relative elevation of the plots in a radius of 250 m
viii
relative to the lowest point in that radius was important for the buffering of Tmin. The forest Tmin increased
with increasing relative elevation of the plot in a radius of 250 m in each season.
Forest managers and planners can actively mitigate the effects of climate change by considering the
relationships between the buffering of the forest temperature and the forest and landscape characteristics.
Efforts can be made to reduce the openness of the forest by increasing the tree and/or shrub cover which
will result in a higher density and total cover of trees and shrubs of the forest. The increased density will
mitigate extreme Tmax and the forest will serve as a refugium for species that cannot cope with the
elevated temperatures caused by global warming. The forest manager could also adapt the management
system of the forest. Instead of harvesting by means of a clear cut or shelterwood system, the forester
could opt to harvest via a group-selection or selection forest system. The group-selection and selection
forest system retain a more closed forest during rejuvenation and thus no large open spaces are created
in which more extreme Tmax and Tmin can be reached. The adjustment of the orientation (for example
more north oriented) and relative elevation of forest stands are more expensive, radical and risky
measures. With these types of measures, it is first necessary to think carefully if the advantages will
exceed the disadvantages. Therefore, forest managers have possibilities to actively mitigate the effects
of climate change inside forests in function of the conservation of biodiversity and maintenance of
ecosystem functions.
ix
Samenvatting
In de laatste decennia zijn de temperaturen wereldwijd gestegen als gevolg van menselijk activiteiten.
Het klimaat wordt warmer en extreme weersomstandigheden komen vaker voor. Er wordt verwacht dat
de opwarming van het klimaat en de frequentie van extreem weer zal blijven toenemen.
Klimaatverandering heeft een grote invloed op verschillende biologische niveaus zoals deze van soorten,
populaties, gemeenschappen en terrestrische ecosystemen. Als gevolg van het veranderende klimaat
komen planten vroeger in bloei, planten dieren zich vroeger voort, worden deze vroeger wakker uit de
winterslaap, verschijnen deze vroeger en keren deze vroeger terug uit overwinteringsgebieden…
Soorten migreren ook naar hogere gebieden of richting de polen. De impact van klimaatverandering op
organismen zal in de toekomst blijven toenemen. Echter, organismen ervaren temperaturen op kleine
ruimtelijke schalen (zogenaamde microklimaten), waar klimatologische omstandigheden aanzienlijk
kunnen afwijken van het macroklimaat. Een van de redenen waarom microklimaten van ecologisch
belang zijn, is dat ze mogelijk soorten kunnen behoeden voor klimaatvariabiliteit en veranderingen op
de langere termijn. Microklimaten bieden dus microrefugia waarin soorten en populaties kunnen
overleven op locaties die mogelijk ongeschikt worden geacht door waarnemingen en modellen met een
lage resolutie.
In het kader van de opwarming van de aarde en de druk op de biodiversiteit werd de relatie tussen het
microklimaat in Europese gematigde loofbossen en bos- en landschapseigenschappen onderzocht. Deze
studie focust op de kwantificering van de buffering, en de relatie tussen de buffering en de bos- en
landschapseigenschappen. Inderdaad, boseigenschappen (zoals boomhoogte, afstand tussen naburige
bomen, dichtheid van de kroon, boombedekking en struikbedekking) en landschapseigenschappen
(zoals de terreintopografie, hoogte boven zeeniveau en de hoeveelheid bos rondom de plot) hebben een
mogelijke invloed op de buffering van de temperatuur. Er wordt ook een onderscheid gemaakt tussen
de vier seizoenen en tussen de dagelijkse gemiddelde, minimum- en maximumtemperatuur (Tmean, Tmin
en Tmax). Gegevens van het microklimaat werden verzameld gedurende één jaar tussen februari 2017 en
februari 2018 in 100 plots in loofbossen in 10 regio’s doorheen Europa. Macroklimaatgegevens werden
verkregen van het dichtstbijzijnde weerstation in open veld. De buffering werd steeds berekend als de
microklimaat temperatuur min de macroklimaattemperatuur zodat negatieve waarden overeenkomen
met koudere temperaturen in het bos. De microklimaat Tmin was 0,41 tot 1,35 °C warmer vergeleken met
de macroklimaat Tmin. De Tmax in het bos was 0,24 tot 2,05 °C kouder dan in open veld (behalve in de
lente, dan was de microklimaat Tmax 0,32 °C warmer vergeleken met het open veld). Een kouder
microklimaat Tmax en een warmer microklimaat Tmin compenseerden elkaar wat resulteerde in een
vergelijkbare Tmean tussen het micro- en het macroklimaat gedurende de gehele meetperiode en zowel in
de herfst als in de winter. In het voorjaar en de zomer was de Tmean in het bos respectievelijk 0,27 °C
warmer en 0,49 °C kouder in vergelijking met de macroklimaat temperatuur.
De Tmax in het bos werd warmer (in elk seizoen behalve de winter) en de microklimaat Tmin werd kouder
(in de lente en de herfst) met toenemende openheid van het bos. De Tmax in het bos steeg met boom- en
struikbedekking (in elk seizoen behalve de winter). De Tmax in het bos steeg met toenemende
boomhoogte in elk seizoen. De Tmax in het bos daalde (in de lente en de zomer) en de Tmin van het bos
nam toe (in zomer, herfst en winter) met toenemende afstand tot de kust. De Tmax in het bos werd kouder
(in de zomer) en de Tmin in het bos werd warmer (in elk seizoen) met toenemende hoogte boven de
x
zeespiegel van de plots na een correctie van de temperatuur voor de hoogte van de plots. Een toename
van de oriëntatie naar het noorden van de plots resulteerde in een toename van de Tmax in lente, de herfst
en de winter. De relatieve hoogte van de plots in een straal van 250 m ten opzichte van het laagste punt
in die straal was belangrijk voor de buffering van Tmin. De Tmin in het bos nam toe met toenemende
relatieve hoogte van het perceel in een straal van 250 m in elk seizoen.
Bosbeheerders en -planners kunnen de effecten van klimaatverandering actief reduceren door rekening
te houden met de relatie tussen de buffering van de microklimaattemperatuur en de bos- en
landschapskarakteristieken. Er kunnen inspanningen worden gedaan om de densiteit van het bos te
verhogen door de boom- en/of struikbedekking te verhogen, wat resulteert in een hogere dichtheid en
totale bedekking van bomen en struiken in het bos. De verhoogde dichtheid zal extreme Tmax bufferen
en het bos zal dienen als een refugium voor soorten die niet bestand zijn tegen de hogere temperaturen
veroorzaakt door de klimaatverandering. De bosbeheerder kan ook het beheersysteem van het bos
veranderen. In plaats van te oogsten via een kaalkap of schermslag, kan de boswachter kiezen om te
oogsten via een plenter- of femelslag. De plenter- en femelslag behouden een meer gesloten bedekking
tijdens de verjonging en dus worden er geen grote open ruimten gecreëerd waarin extremere Tmax en Tmax
bereikt kunnen worden. De aanpassing van de oriëntatie (bijvoorbeeld meer naar het noorden
georiënteerd) en relatieve hoogte van de bestanden zijn duurdere, meer ingrijpende en meer risicovolle
maatregelen. Bij dit soort maatregelen moet eerst goed worden nagedacht als de voordelen de nadelen
zullen overstijgen. Bosbeheerders hebben dus mogelijkheden om actief de effecten van
klimaatverandering in bossen te verminderen in functie van het behoud van de biodiversiteit en het
onderhouden van ecosysteem diensten.
1
1. Literature study
1.1. Climate change Pearson and Palmer (2000) report that the CO2 concentrations from 60 until 52 million years ago were
above 2000 ppmv. Between 55 and 40 million years ago a long-term cooling trend was initiated, and the
carbon dioxide concentration dropped fast in this period (Pearson and Palmer, 2000). The last 24 million
years the CO2 concentration was more stable than before and did not reach 500 ppmv (Figure 1) (Pearson
and Palmer, 2000).
Petit et al. (1999) have studied the climate and atmospheric history of the past 420 000 years from the
Vostok ice core, Antarctica. The late Quaternary period (the last one million years) is characterized by
a series of glacial and interglacial periods with cycles that last about 100 000 years (Imbrie et al., 1992).
Barnola et al. (1987) and Chappellaz et al. (1990) report there is close correlation between the Antarctic
temperature and atmospheric concentrations of CO2 and CH4. The temperature fluctuated around 8 °C
between the glacials and interglacials (Petit et al., 1999). The last 420 000 year the CO2 concentration
during the glacials was around 180 ppmv and rose to 280 - 300 ppmv during the interglacials (Petit et
al., 1999). The methane concentrations varied from 320 - 350 to 650 - 770 ppbv between glacials and
interglacials respectively (Petit et al., 1999). Preindustrial Holocene levels for CO2 and CH4 are 280
ppmv and 650 ppbv and are found during all interglacials. In 1999 the carbon dioxide concentration was
360 ppmv and the methane concentration was 1700 ppbv (Petit et al., 1999). According the National
and Oceanic and Atmospheric Administration (NOAA, 2017) the carbon dioxide concentration was
403.5 ppmv in October 2017. The levels of CO2 and CH4 are unprecedented during the past 420 000
years (Petit et al., 1999). The temperature estimates for 2100 exceed the most comprehensive estimates
of global temperature change during the last interglacial, the warmest interval in the past 400 000 years
(Petit et al., 1999). Not climate change is the biggest problem but the speed of which it happens is the
problem (Diffenbaugh et al., 2013). The predicted potential global warming until 2100 is comparable to
the biggest global change in 65 million years but ten to hundred times faster (Diffenbaugh et al., 2013).
Figure 1: Evolution of the atmospheric carbon dioxide concentration in ppm in function of the time. (a) the last 60
million years, (b) the last 25 million years (Pearson and Palmer, 2000).
2
1.1.1. Causes
The impact of anthropogenic activities is rising and has already passed the influence of the natural
activities (The Intergovernmental Panel on Climate Change (IPCC), 2014). Imbrie et al. (1992) and
Berger (1978) documented that much of the climate variability during the glacial and interglacial period
occurs with periodicities corresponding to that of the precession, obliquity and eccentricity of the Earth’s
orbit. Barnola et al. (1987) and Chappellaz et al. (1990) have found a remarkable relation between the
Antarctic temperature and the carbon dioxide and methane concentration. This high correlation indicates
that CO2 and CH4 may have contributed to the glacial-interglacial changes over this entire period by
amplifying the orbital forcing (Genthon et al., 1987; Lorius et al., 1990; Raynaud et al., 1993). In the
past 1000 years 22 to 23 % of the decadal-scale temperature variations before 1850 was due to changes
in volcanism (Crowley, 2000). The effect of variation of irradiation on the temperature for the same
period before 1850 varies between 9 and 45 % (Crowley, 2000). Crowley (2000) describes that solar
variability and volcanism are only responsible for a quarter of the total 20th-century warming. Natural
variability plays only a subsidiary role in the 20th century warming and most of the warming is due to
the anthropogenic increase in greenhouse gases (Crowley, 2000). The IPCC (2014) defines natural and
anthropogenic substances and processes that alter the Earth’s energy budget as physical drivers of
climate change. Atmospheric concentrations of greenhouse gases are at levels that are unprecedented in
at least 800 000 years (IPCC, 2014). Concentrations of carbon dioxide, methane and nitrous oxide have
shown increases since 1750 with respectively 40 %, 150 % and 20 % (Figure 2) (IPCC, 2014). Half of
the cumulative anthropogenic CO2 emissions between 1750 and 2011 have occurred in the last 40 years
and the total annual anthropogenic greenhouse gases emissions have continued to increase over 1970 to
2010 with larger absolute increases between 2000 and 2010 (IPCC, 2014). An important parameter for
climate change is radiative forcing (IPCC, 2014). The IPCC (2014) defines radiative forcing as the
quantification of the perturbation of energy into the Earth system caused by natural and anthropogenic
substances and processes that alter the Earth’s energy budget. Radiative forcing larger than zero lead to
a near-surface warming, and radiative forcing smaller than zero lead to a cooling (IPCC,2014). The total
anthropogenic radiative forcing over 1750–2011is calculated to be a warming effect of 2.3 [1.1 to 3.3]
Wm-2 (The values in brackets indicate a 90% uncertainty interval) (Figure 3), and it has increased more
rapidly since 1970 than during prior decades (IPCC, 2014). Carbon dioxide is the largest single
contributor to radiative forcing over the period 1750–2011 (IPCC, 2014).
Figure 2: Evolution of the concentrations of CO2,
CH4 and N2O in the atmosphere since 1750 (IPCC,
2014).
Figure 3: Radiative forcing of climate change during the
industrial era (1750–2011). Bars show radiative forcing from
well-mixed greenhouse gases (WMGHG), other anthropogenic
forcings, total anthropogenic forcings and natural forcings. The
error bars indicate the 5 to 95% uncertainty. Other
anthropogenic forcings include aerosol, land use surface
reflectance and ozone changes. Natural forcings include solar
and volcanic effects. The total anthropogenic radiative forcing
for 2011 relative to 1750 is 2.3 Wm-2 (IPCC, 2014).
3
1.1.2. Consequences
1.1.2.1. The past 150 years
The ocean warming is largest near the surface and the upper 75 m warmed by 0.11 [0.09 to 0.13] °C
(The values in brackets indicate a 90% uncertainty interval) per decade over the period 1971 to 2010
(Figure 4) (IPCC, 2014). The globally averaged combined land and ocean surface temperature data as
calculated by a linear trend show a warming of 0.85 [0.65 to 1.06] °C (The values in brackets indicate a
90% uncertainty interval) over the period 1880 to 2012 (Figure 4) (IPCC, 2014). Easterling et al. (1997)
had similar results and report that the trend for Tmax excluding the effects of large urban areas is +0.82
°C per 100 years and for Tmin is +1.79 °C per 100 years. The differential changes in daily Tmax and Tmin,
result in a narrowing of the diurnal temperature range (Easterling et al., 1997). The diurnal temperature
range trend is -0.79 °C per 100 years (Easterling et al., 1997). Jones & Moberg, 2003 found that the
average annual temperatures are getting warmer across nearly all temperate land areas in the northern
hemisphere. Most stations in Europe, East Asia, and Alaska have significant trends of + 0.3 °C per
decade or greater (Schwartz et al., 2006). The rest of North America exhibits a more complex pattern
with in most regions warming but also some regions with cooling. Central Asia is the only area that does
not show strong warming (Schwartz et al., 2006). Seasonal average temperatures show that annual
warming is clearly being caused primarily by the winter and spring seasons (Robeson, 2004; Klein Tank
et al., 2002). Winter temperatures are broadly similar to the annual, except that warming is less intense
in Western Europe and western North America, but more intense in eastern North America (Schwartz
et al., 2006). In spring, warming is considerably reduced but still present in Europe and East Asia.
Summer warming is weaker everywhere, nevertheless, most areas still show significant warming (Jones
& Moberg, 2003). Only East Asia and far Western Europe show significant warming in autumn and
much of Eastern Europe, Central Asia, and North America show weak warming or cooling (Jones &
Moberg, 2003; Robeson, 2004).
IPCC (2014) reports that it is very likely that the number of cold days and nights has decreased and the
number of warm days and nights has increased on the global scale. For every country where the number
of frost days has been examined by Easterling et al. (1997), they have become fewer in number. Walther
et al. (2002) found that the freeze-free periods in most mid- and high latitude regions are lengthening
and satellite data reveal a 10% decrease in snow cover and ice extent since the late 1960s. The incidence
of summer heat waves has increased during the 20th century (IPCC, 2001; Schär et al., 2004; McGregor
et al., 2005; Beniston & Stephenson, 2004). Klein Tank et al. (2002) did research about the European
climate in the 20th century. An asymmetric behaviour of warm- and cold-spell days is found for summer
and winter warming (Klein Tank et al., 2002). Klein Tank et al. (2002) defined cold/warm spells at a
given site as periods of at least six consecutive days with daily mean temperature below/above the
lower/upper tenth percentile of the temperature distribution for each calendar day in the 1961–90
standard normal period. The number of warm-spell days shows a five times larger trend than number of
cold-spell days (table 1). Alexander et al. (2006) also studied cold- and warm spells and concluded that
the annual occurrence of cold spells significantly decreased while the annual occurrence of warm spells
significantly increased. Kunkel et al. (2004) report about the length of the - 2.2 °C freeze period (number
of days between the first autumn freeze and last spring freeze). Kunkel et al. (2004) concluded that the
duration of this period is decreasing, with the most dramatic change in East Asia, and weaker change in
Europe and central North America. Heino et al., (1999); Robeson, (2002); Menzel et al., (2003); Meehl
et al., (2004); Feng & Hu, (2004) describe that while first freeze dates in autumn are getting somewhat
later, the decrease in the freeze period is being driven primarily by earlier spring last freeze dates. Their
4
results show these getting earlier on average at a rate of - 1.5 days per decade (Heino et al., 1999;
Robeson, 2002; Menzel et al., 2003; Meehl et al., 2004; Feng & Hu, 2004). Alexander et al., (2006)
report that the annual occurrence of frost days has decreased by approximately 16 days on average. The
length of the period spent with no average daily temperatures below 5 °C, is increasing in most regions
at an average rate of 1.6 days per decade with the permanent crossing date in spring contributing most,
by getting earlier at a rate of -1.4 days per decade (Menzel et al., 2003).
Figure 4: (a): Observed globally averaged combined land and ocean surface temperature (°C) anomaly relative to 1986
– 2005 between 1850 and 2012. (b) Observed change in surface temperature (°C) between 1901 and 2012. (c) Sea ice
extent (million km2) in function of the time. (d) Global average sea level (m) relative to 1986 – 2005. (e) Observed change
in annual precipitation over land between 1951 and 2010. (IPCC, 2014)
Table 1: Average change in summer (April–September) and winter (October–March) mean temperature, number of
warm-spell days and number of cold-spell days between 1976 and 1999. The 95% confidence intervals are shown in
parentheses. The climatological means (1961–90) for the number of warm/cold-spell days are shown in square brackets
(Klein Tank et al., 2002).
5
Alexander et al. (2006) observed global changes in daily climate extremes of temperature and
precipitation over the 1951-2003 period. They found out that 74% (73%) of the land area sampled shows
a significant decrease (increase) in the annual occurrence of cold nights (warm nights) (Figure 5).
Globally the annual number of warm nights (cold nights) increased (decreased) by about 25 (20) days
since 1951 (Alexander et al., 2006). Trends in maximum temperature extremes showed similar patterns
of change, although of smaller magnitude (Alexander et al., 2006). Alexander et al. (2006) found a
reduction in the occurrence of cold nighttime temperatures over the period 1901–2003, particularly for
the most recent 25 years. There is also a marked increase in the occurrence of warm nighttime
temperatures during the last century, again with strongest change in the last few decades (Alexander et
al., 2006). The coldest minimum temperature, the warmest minimum temperature, the coldest maximum
temperature and the hottest maximum temperature have also increased in the latter half of the 20th
century (Alexander et al., 2006).
There are likely more land regions where the number of heavy precipitation events has increased than
where it has decreased (IPCC, 2014). Alexander et al. (2006) found that, when averaged across the
globe, the number of extreme precipitation events in a year has been increasing. There have been
significant increases of up to two days per decade in the number of days in a year with heavy
precipitation in south-central United States and parts of South America (Alexander et al., 2006). In the
mid- and high latitudes of the Northern Hemisphere the precipitation increases with 0.5 ± 1 % per decade
(Climate Change, Third Assessment Report of the Intergovernmental Panel on Climate Change IPCC,
2001). The increase occurs mostly in autumn and winter whereas, in the sub-tropics, precipitation
generally decreases by about 0.3 % per decade (Climate Change, Third Assessment Report of the
Intergovernmental Panel on Climate Change IPCC, 2001). Studies of the one day and multiday heavy
precipitation events in the United States and other countries show a tendency toward more days with
heavy precipitation totals over the 20th century (Karl & Knight, 1998; Zhai et al., 1999; Kunkel et al.,
1999). The annual number of days exceeding 50.8 mm and 101.6 mm of precipitation has increased in
the United States since 1910 (Karl et al., 1996). Most countries that experienced a significant increase
or decrease in monthly or seasonal precipitation also experienced a disproportionate change in the
amount of precipitation falling during the heavy and extreme precipitation events (Easterling et al.,
2000b and Groisman et al., 1999). Dai et al. (1998) conclude that the overall areas of the world affected
either by drought or excessive wetness have increased. Knutson et al. (2010) report that the sea surface
temperatures in most tropical cyclone formation regions have increased by several tenths of a degree
Celsius during the past several decades. Klein Tank et al. (2002) also describes the precipitation changes
(Figure 6). The average rain intensity per wet day (≥1 mm) increases over Europe, both at stations with
positive trends and at stations with negative trends in total winter precipitation amount. Figure 6 shows
that most stations in Europe got wetter in the winter between 1946 and 1999 or have a trend that is not
significant at the 25 % level. The more wet stations are located mostly in the north and the west of
Europe while the more drier stations are more located in the south of Europe. No information was given
about the precipitation changes in summer by Klein Tank et al. (2002).
6
Figure 5: Trends (in days per decade, maps) and annual time series anomalies relative to 1961–1990 mean values (plots)
for annual series of percentile temperature indices for 1951–2003 for (a) cold nights (b) warm nights (c) cold days (d)
warm days. Trends were calculated only for the grid boxes with sufficient data (at least 40 years of data during the
period and the last year of the series is no earlier than 1999). Black lines enclose regions where trends are significant at
the 5% level. The red curves on the plots are nonlinear trend estimates obtained by smoothing using a 21-term binomial
filter (Alexander et al., 2006).
7
Figure 6: Trends in winter (October–March) precipitation amount between 1946 and 1999. Precipitation amount was
calculated as percentage anomalies with respect to the 1961-1990 means. Yellow corresponds to drier conditions, violet
to wetter conditions. Green is used for trends that are not significant at the 25% level (Klein Tank et al., 2002).
1.1.2.2. The future: The next 100 years
Warming
Zwiers & Kharin (1998) describe the results from climate models. These results include increases in
mean temperatures that lead to more extreme high temperatures and fewer extreme low temperatures,
along with reduced diurnal temperature range. Beniston et al. (2007) suggest that the number of
heatwaves will continue to increase in Europe in the 21st century. Regions such as France and Hungary
may experience as many days per year above 30 °C in the future as there are currently experienced in
Spain and Sicily (Figure 7) (Beniston et al., 2007). The IPCC report (2014) suggests that the global
mean surface temperature change for the period 2016-2035 relative to 1986-2005 will likely be in the
range 0.3 °C to 0.7 °C. Relative to 1850-1900, global surface temperature change for the end of the 21st
century (2081-2100) is projected to likely exceed 1.5 °C for three climate models (IPCC, 2014).
Warming is likely to exceed 2 °C according to two climate models (IPCC, 2014). The Arctic region will
continue to warm more rapidly than the global mean (IPCC, 2014). The mean warming over land will
be larger than over the ocean and larger than global average warming (Figure 7) (IPCC, 2014). It is
virtually certain that there will be more frequent hot and fewer cold temperature extremes over most
land areas on daily and seasonal timescales (IPCC, 2014). The IPCC report suggests that global warming
will continue beyond 2100 under all scenarios except one. Surface temperatures will remain
approximately constant at elevated levels for many centuries after a complete cessation of net
anthropogenic CO2 emissions (IPCC, 2014).
8
Humidity and precipitation
The intensity of precipitation events will increase (Kothavala, 1997) and a general drying of
midcontinental areas during summer will occur (Wetherald & Manabe, 1999). This will increase the
chance of drought (Kothavala, 1999), the frequency of low summer precipitation, the probability of dry
soil and the occurrence of long dry spells (Gregory et al., 1997). Beniston et al. (2007) report that the
drought over southern Iberia will last 20 to 30 days longer in the future. The IPCC (2014) suggests that
changes in precipitation in a warming world will not be uniform (Figure 8).
Figure 7: Mean annual number of days above 30 °C simulated by the HIRHAM4 regional climate model for the
1961–1990 (upper) and 2071–2100 (lower) periods (Beniston et al., 2007).
9
Figure 8: Multi-model mean projections (i.e. the average of the model projections available) for the 2081–2100 period.
(a) Change in annual mean surface temperature (°C) (b) change in annual mean precipitation (%). The number of
models used to calculate the multi-model mean is indicated in the upper right corner of each panel. Dots on (a) and (b)
indicates regions where the projected change is large compared to natural internal variability (i.e. greater than two
standard deviations of internal variability in 20-year means) and where 90% of the models agree on the sign of change.
Diagonal lines on (a) and (b) shows regions where the projected change is less than one standard deviation of natural
internal variability in 20-year means (IPCC, 2014).
Wind and storms
Future projections based on theory and high-resolution dynamical models suggest that climate change
will result in an intensity increase of 2 to 11 % of the globally averaged intensity of tropical cyclones
by 2100 (Knutson et al., 2010). The globally averaged frequency of tropical cyclones is projected to
decrease with 6 to 34 % by 2100 (Knutson et al., 2010). But higher resolution modelling studies project
an increase in the frequency of the most intense cyclones and an increase of 20 % of the precipitation
rate within 100 km of the storm centre (Knutson et al., 2010). Beniston et al. (2007) suggest that the
wind speeds will increase with 2.5 % to more than 10 % in a European latitude band extending roughly
from 45–55 °N. The changes generally decrease to small or even negative values on either side of this
band (Beniston et al., 2007).
10
1.2. Effect on species
1.2.1. The past 150 years
Penuelas et al. (2013) describe that climate change is having a great impact on several biological levels
such as organisms, populations, communities and terrestrial ecosystems by changing phenotypes,
genotypes, growth, phenology, the distribution of organisms, species competitive ability, ecological
relationships and the risk of extinction in communities. Ecosystems are therefore changing in structure
and function and have significant feedbacks on climate change itself (Penuelas et al., 2013).
Temperature and precipitation levels have direct effects on species but for many species, climate has
indirect effects through the sensitivity of habitat or food supply to temperature and precipitation.
(McCarty, 2001).
1.2.1.1. Individual level: phenology, physiology, morphology
A shift in phenology is one of the most conspicuous responses of plants and animals to current climate
change (Körner, 1995; Peñuelas & Filella, 2001; Fitter & Fitter, 2002; Peñuelas et al., 2002; Peñuelas
et al., 2009; Chuine et al., 2012). Climate warming has changed the lifecycles of plants and animals,
advancing the biological spring and delaying the arrival of biological autumn and winter (Peñuelas et
al., 2002; Peñuelas et al., 2009; Badeck et al., 2004; Menzel et al., 2006; Steltzer & Post, 2009; Fridley,
2012). Menzel et al. (2006) observed that leaf unfolding had advanced 2.5 days per 1 °C of temperature
increase, and leaf fall was delayed 1 day per 1 °C of temperature increase. Parmesan & Yohe, (2003)
observed, in a review of available global data, an advance in leaf unfolding of 2.3 days per decade.
Schwartz et al. (2006) suggest that the date of the first leaves are getting earlier in nearly all parts of the
northern hemisphere. This change is strongest in Central Europe and North America, and weaker in East
Asia, Eastern Europe, and part of Western Europe. Central Asia is the only sizeable area without a trend.
The average rate of change over the 1955-2002 period is approximately -1.2 days per decade (Schwartz
et al., 2006). Climate change has also an effect on blooming of species. In six of the fifteen species with
available data, blooming had advanced at a rate of 20 days per 50 years and no species flowered
significantly later (Oglesby & Smith 1995).
Figure 9: Phenological changes (point in time of leaf unfolding, point in time of leaf fall, duration of the growth period
and point in time of the flowering) of the different species in the Montseny mountains (Catalonia, NE Spain) in the last
50 years of the 20th century (Penuelas et al., 2013).
Leaf colour changes show a progressive delay of 0.3 ± 1.6 days per decade in Europe whereas the length
of the growing season has increased in some areas by up to 3.6 days per decade over the past 50 years
(Menzel & Fabrian, 1999; Walther et al., 2001). Meteorological satellites over the northern hemisphere
show an increase in the growing season of approximately 12 days since the early 1980s, primarily due
to an advance in the onset of spring by about 8 days (Myneni et al. 1997). Observations of plant
11
phenology in Europe suggest a 10.8 day lengthening of the growing season, including an advance in
spring of 6 days and a delay in autumn of 4.8 days (Menzel & Fabian 1999).
In British birds, 31 % of species since 1971, and 53 % of species since 1939, show long-term, significant
trends toward earlier breeding (Crick et al., 1997; Crick & Sparks, 1999). Only one species is nesting
later (Crick et al., 1997; Crick & Sparks, 1999). From 1971 to 1995, 78% of 65 bird species examined
started breeding earlier (Crick et al. 1997). Within individual species, there were significantly earlier
breeding dates, averaging 9 days earlier in spring (Crick et al. 1997). Temperature and precipitation
explain most of the variation in the timing of breeding (Crick & Sparks 1999). Among six species of
British amphibians, five are breeding significantly earlier since 1978 (Beebee, 2009). In New York,
records of spring arrival for 76 species of migrating land birds date back to 1903 (Oglesby & Smith
1995). Over a 90-year period, 39 species arrived significantly earlier, 35 species showed no significant
changes, and only 2 species arrived later in the spring (Oglesby & Smith 1995).
Northward expansion of bird species in North America and Europe has been widely observed (Kalela,
1949; Williamson, 1975; Brewer, 1991; Johnson, 1994; Burton, 1995; Root & Weckstein, 1995).
Thomas and Lennon (1999) present evidence linking northward movements of British birds to climate
change. The authors compared the breeding ranges of birds in 1968–1972 to ranges in 1988–1991. Of
59 species occupying southern Great Britain, the northern boundary of their ranges shifted an average
of 19 km to the north (including those species showing no changes or southward retractions). Birds
confined to the north (42 species) showed little change in the southern boundary of their ranges (Thomas
& Lennon 1999). This comparison shows that the northern and southern range boundaries of species are
not equally sensitive to climate change (Thomas & Lennon 1999).
The shifts in plant phenology produce a mismatch in species involved in the same biotic relationships,
leading to disequilibrium in the sizes of populations (Both et al., 2006). Mismatches have been singularly
observed in mutualistic plant-pollinator relationships (Memmott et al., 2007; Hoover et al., 2012) and
in plant–herbivore relationships (Post et al., 2008; Green, 2010). McCarty (2001) has the same concern
and reports that the timing of life-history events depends on factors besides temperature, and a shift in
phenology may disrupt important correlations with other ecological factors. The shift in timing can
disturb plant-animal interactions such as pollination and seed dispersal which depend on the synchrony
between species (McCarty, 2001). Warming has significant direct effects on animal phenology by
lengthening the period of summer activity and by increasing the number of reproductive cycles and
larval size in insects (Stefanescu et al., 2003; Harada et al., 2005; Altermatt, 2010) or by changing the
sex ratios in populations of turtles (Tucker et al., 2008). The species-specific phenological responses of
animals of the same community can be very different, with further consequences for biotic relationships
(Stefanescu et al., 2003). Guo et al. (2009) observed that the mid- and late-season species of
grasshoppers in Inner Mongolia tended to advance the reproductive period, overlapping it with the early-
season species, thus increasing the competition among several species of grasshoppers.
Penuelas et al. (2013) conclude on individual level that the plasticity and degree of each individual to
present intense responses at molecular, physiological, phenological and morphological levels are the
first ‘resources’ to cope with the new climatic situation. The responses of organisms are unable to
prevent defoliation, decreases in growth, mortality, migration and shifts in the distributions of species
(Peñuelas & Boada, 2003; Peñuelas et al., 2007,2007b, 2008; Allen et al., 2010; Carnicer et al., 2011).
Moreover, these responses at the level of individual organisms differ among individuals and species of
12
the same community (Ogaya & Peñuelas, 2006; Volder et al., 2010; Kardol et al., 2010; Ogaya et al.,
2011), implying further changes in community composition and feedback effects on climate change.
1.2.1.2. Population & Community level: distribution range changes
At a population level plants can tolerate environmental changes ‘in situ’ by a combination of phenotypic
plasticity and genotypic adaptation (Jump & Peñuelas, 2005). The existence and magnitude of
phenotypic plasticity, however, is under genetic control and is not unlimited (Jump & Peñuelas, 2005).
Phenotypic plasticity is submitted to strong selection pressure in the range limits of species distribution
by the need of species communities to adapt to extreme conditions for the species (Fallour-Rubio et al.,
2009; Mátyás et al., 2009). Phenotypic plasticity is therefore likely to be under strong directional
selection under climate change (Jump & Peñuelas, 2005). The microevolution of a population in
response to climate change is frequently related mainly to adaptation to altered seasonal events, such as
drought or changes in seasonal length, rather than to the direct effect of a change in temperature
(Bradshaw & Holzapfel, 2006).
There is accumulating evidence of changes in the distribution of organisms in response to climatic
changes. In plants, the shifts currently most widely observed are those due mainly to drought interacting
with hot summers that increase the limitation of water and erode the trailing range edge populations of
a species, resulting in a contraction of its distribution toward wetter and cooler higher latitudes and
altitudes (Pigott & Pigott, 1993; Allen & Breshears, 1998; Colwell et al., 2008; Kullman, 2008; Jump
et al., 2009, 2009b; Harrison et al., 2010) or due to elevated temperatures that allow population expansion
at the leading range edge (Walther, 2003; Peñuelas et al., 2007a,b; Kullman, 2008; Crimmins et al.,
2009; Jump et al., 2009,2009b). Range shifts of plants occur due to the combination of population
expansion at the leading edges of distributions, through increased reproduction and establishment, and
retraction at the trailing edges driven by elevated mortality and declines in growth and reproduction
(Allen & Breshears, 1998; Peñuelas & Boada, 2003; Jump et al., 2006, 2006b, 2007, 2009; Peñuelas et
al., 2007, 2007b; Colwell et al., 2008; Worrall et al., 2008). Poleward and upward shifts of species ranges
have occurred across a wide range of taxonomic groups and geographical locations during the twentieth
century (Hughes, 2000; McCarty, 2001; Walther et al., 2001; Easterling et al., 2000). In western North
America, Edith's Checkerspot butterfly (Euphydryas editha) has shifted its range northward (by 92 km)
and upward (by 124 m) during the 20th century (Parmesan, 1996). This closely matches the temperature
increase over the same region and time period where mean temperature isotherms shifted 105 km
northward and 105 m upward (Karl et al., 1996). For animals, an increasing number of studies have
shown changes in species distributions related to warming and drought (Guo et al., 2009; Lenoir et al.,
2010; Hufnagel & Kocsis, 2011). Because of their higher mobility, animals have a greater capacity than
plants to escape unfavourable climatic conditions (Penuelas et al., 2013). The number of limitations and
constraints of latitudinal shifts are large, from geographic natural barriers and lack of adequate food
sources to human-driven constraints such as urbanization and habitat conversion (Jump et al., 2009,
2009b).
Apart from drought and warming themselves, Hobbie & Chapin (1996); Shaver et al. (2000); Schmidt
et al. (2002); Beier et al. (2008); Li et al. (2011); Sardans et al. (2012) observed a shift in availability of
soil nutrients, an abiotic effect of climate change. Because organisms regularly respond to climate
change by shifting their chemical composition and use of resources, they can exert an effect on
ecosystemic C, N and P cycles that thereafter can produce feedback effects on the community species
that must respond to these cycles (Finzi et al., 2011). The direct effects of climate change on the different
13
species of a community also change the biotic relationships among the species. Species must therefore
adapt to new scenarios of competitive and trophic relationships (Penuelas et al., 2013).
De Frenne et al. (2013) studied thermophilization of temperate forests in Europe and North-America
and report that significant community turnover took place over time in temperate forests. On average,
one-third of the species present in the old surveys has been replaced by other species (De Frenne et al.,
2013). This floristic turnover partly arose from the non-random replacement of species in terms of their
temperature preferences, illustrated by significant thermophilization both in European and eastern North
American forests. On average, the estimated thermophilization rate was 0.041 °C⋅decade-1 (range across
ten different modelling methods was 0.027–0.056 °C⋅decade-1) (De Frenne et al., 2013).
Thermophilization was significantly positive in 20 of 29 regions, significantly negative in eight study
regions, and unchanged in one region (Figure 10) (De Frenne et al., 2013).
De Frenne et al. (2013) report that the overall thermophilization of understory plant communities has
been driven by concurrent gains of relatively warm-adapted species and loss of cold-adapted taxa. In
the eastern North American forest plots, both warm-adapted and cold-tolerant species have increased
due to continuous immigration of new species (i.e., overall increase in species richness), which does not
occur in the European plots (De Frenne et al., 2013). The mean thermophilization of understory plant
communities that was observed across temperate deciduous forests in two continents expands on earlier
findings (Gottfried et al., 2012; Bertrand et al., 2011; Lenoir et al., 2008) that mountain vegetation
Figure 10: Thermophilization of temperate forest understories across Europe and North America. (A and B) mean
thermophilization (positive values denote increases over time) for all data and in European and American forests (A)
and for the individual regions (B). (C) Mean shifts in relatively cold-adapted (blue) and warm-adapted species (red) for
all plots, and in Europe and North America. Positive values reflect positive shifts of the left and right tail, i.e. decreases
of cold-adapted and increases of warm-adapted taxa, respectively. Error bars denote the 95% confidence intervals
based on 500 resampled species’ temperature preferences (De Frenne et al., 2013).
14
communities are showing increases of lower-altitude species at higher altitudes, leading to novel species
assemblages (De Frenne et al., 2013). The thermophilization of vegetation is consistent with the
warming climate observed across the regions: the mean rise in April-to-September temperatures between
the old and recent survey was 0.28 °C⋅decade-1 (De Frenne et al., 2013). De Frenne et al. (2013) found
a positive relationship between the thermophilization and the region-specific April-to-September
temperature change, indicating higher thermophilization in areas with higher rates of warming.
European and North American temperate deciduous forest vegetation is therefore changing as expected
by macroclimate warming, but thermophilization lags rising temperatures (De Frenne et al., 2013).
1.2.1.3. Ecosystem level
Ciais et al. (2005) report a 30 % reduction in gross primary productivity of forests over Europe, due to
the heatwave in 2003. In European forests, there was a mean reduction in net primary production of 16
g C m2 per month in the summer of 2003 compared to 1998-2002, corresponding to a gross primary
production reduction of 28 g C m2 per month (Ciais et al., 2005). Ciais et al. (2005) suggest that
productivity reduction in eastern and western Europe can be explained by rainfall deficit and extreme
summer heat which will occur more often in the future. The ecosystem respiration decreased together
with gross primary productivity, rather than accelerating with the temperature rise (Ciais et al., 2005).
Ciais et al. (2005) describe that such a reduction in Europe's primary productivity is unprecedented
during the last century. An increase in future drought events could turn temperate ecosystems into carbon
sources, contributing to positive carbon-climate feedbacks which is already observed in the tropics and
at high latitudes (Cox et al., 2000; Friedlingstein et al., 2001). When changes in phenology and plant
communities are large, at regional and continental scales, they can exert significant feedback effects on
climate (Peñuelas et al., 2009). Lengthening the period of plant activity can increase the uptake of
atmospheric CO2 (Peñuelas & Filella, 2001) thereby buffering the increased levels of CO2. Despite the
lengthening of plant activity, the increase in frequency and severity of drought seems to have precluded
the expected increase in tree growth (Peñuelas et al., 2011,2011b) and in the fixation of CO2 (Angert et
al., 2005; Ciais et al., 2005; Buermann et al., 2007; Zhao & Running, 2010).
The emissions of plant biogenic volatile organic emissions (BVOCs) increase with temperature and
longer periods of plant activity (Peñuelas & Llusia, 2003; Peñuelas et al., 2005; Blanch et al., 2007,
2011). Although their atmospheric lifetime is short, BVOCs have an important influence on climate
through the formation of aerosols that can cool the Earth’s surface during the day by intercepting solar
radiation (Claeys et al., 2004; Kullman, 2008). In some areas of North America, spring temperatures are
different after leaf emergence due to increases in latent heat (Schwartz, 1996; Fitzjarrald et al., 2001).
Increasing the duration of green cover can therefore generate a cooling by sequestering more CO2 and
by increasing evapotranspiration (Penuelas et al., 2013). On the other hand, higher plant production and
increased evapotranspiration decrease soil moisture and may generate abrupt rises of temperature when
drought precludes evapotranspiration (Penuelas et al., 2013). A prolonged green period with increased
evapotranspiration may have enhanced recent summer heatwaves in Europe by lowering soil moisture
(Zaitchik et al., 2006; Fisher et al., 2007b). Decreases of soil moisture have a negative effect on late
cooling and consequently increase surface temperature (Fisher et al., 2007).
15
1.2.2. The future: The next 100 years
1.2.2.1. Individual level: phenology, physiology, morphology
Disturbance of species interactions, together with the low probability that phenotypic, genotypic and
migrational responses will allow most species to tolerate rapid climate change, suggest a range-wide
increase in individual mortality (Peñuelas et al., 2001b) and therefore in the risk of local extinction (Jump
& Peñuelas, 2005). Furthermore, extreme temperatures in summer, which further exacerbate drought,
increase dieback and reproductive failure in large areas on a continental scale (Peñuelas et al., 2001b;
Saxe et al., 2001; Breshears et al., 2005; Körner, 2007; Fensham et al., 2009; Peng et al., 2011).
Defoliation and dieback thus increase when the phenotypic and genotypic capacity and the capacity of
population movement are insufficient to cope with climate change (Ogaya & Peñuelas, 2007; Carnicer
et al., 2011). The consequences of exceeding such tolerance thresholds are evident from historical data
in the Mediterranean area showing substitution of forest by shrublands and deserts in relatively short
periods of time (Estiarte et al., 2008).
Figure 11: Summary of several the predicted aspects of climate change and some examples of their likely effects on
different levels of biodiversity (Bellard et al., 2012).
16
1.2.2.2. Population & Community level: distribution range changes
IPCC (2014) reports that continued high emissions would lead to mostly negative impacts for
biodiversity and ecosystem services. The IPCC (2014) report describes that the impact on Earth’s
biodiversity is moderate under an additional warming between 1 °C and 2 °C and that there will be
extensive biodiversity loss, with associated loss of ecosystem goods and services under an additional
warming of 3 °C. Climate change will be the second most important driver of biodiversity change in
2100, mostly because of the expected warming at high latitudes (Sala et al., 2000) (Figure 12). Thomas
et al. (2004) did simulations of the biodiversity in 2050 for different climate change scenario’s. Thomas
et al. (2004) concluded that, for scenarios of maximum expected climate change, 33 % of the species
which disperse well and 58 % of the species which disperse slow are expected to become extinct in
2050. For mid-range climate change scenarios, is this respectively 19 % and 45 % and for minimum
expected climate change 11% and 34 % (Thomas et al., 2004). The projected extinction varies between
parts of the world and between taxonomic groups (Thomas et al., 2004). Thuiller et al. (2005) projected
late 21st century distributions for 1350 European plants species under seven climate change scenarios
and report that many European species could be threatened by future climate change. More than half of
the species Thuiller et al. (2005) studied could be vulnerable or threatened by 2080. Modelled species
loss and turnover were found to depend strongly on the degree of change of temperature and moisture
conditions (Thuiller et al., 2005). Despite the coarse scale of the analysis, species from mountains could
be seen to be disproportionably sensitive to climate change (±60% species loss) (Thuiller et al., 2005).
The boreal region was projected to lose few species, although gaining many others from immigration
(Thuiller et al., 2005). Elevated temperatures can directly threaten the survival of populations by
restricting migration to higher altitudes (Shoo et al., 2005). Populations of tropical animals, particularly
of ectotherms such as insects and reptiles, are especially threatened under warming because they
currently live very close to their optimal temperatures. Those species that live in sites with limited
possibilities for migration, such as mountainous areas or islands, have a high risk of local extinction
(Chiu et al., 2012).
Figure 12: Relative effect of major drivers of changes on biodiversity. Expected biodiversity change for each biome for
the year 2100 was calculated as the product of the expected change in drivers times the impact of each driver on
biodiversity for each biome. Values are averages of the estimates for each biome and they are made relative to the
maximum change, which resulted from change in land use. Thin bars are standard errors and represent variability
among biomes. (Sala et al., 2000).
17
Highly diverse ecosystems are sensitive to losses of biodiversity in response to warming and drought
(van Peer et al., 2004). Because of their high biodiversity, tropical forests particularly suffer from the
impacts of the current rapid climate change (Penuelas et al., 2013). Moreover, a reduction in the
availability of water has a large impact on tropical forests because of the long-term adaptations of their
organisms to high temperatures and availability of water (Penuelas et al., 2013). Current models project
a high risk of losses of biodiversity in tropical forests by warming (Malcolm et al., 2005). In the dry
tropical forests of Central America, a rapid increase in drought by the lengthening of the drought season
by four weeks can cause the extinction of 25–40 % of forest species (Condit, 1998). Sensitivity may
also be high in temperate or boreal systems of low diversity (Penuelas et al., 2013). When dieback occurs
in the two main species, which form the canopy, it can generate strong transformations at the ecosystem
scale, from forest to shrubland for example (Penuelas et al., 2013).
1.2.2.3. Ecosystem level
Climate change will also have an influence on ecosystem level, but this effect is very difficult to predict.
An example of the complicity is given: Increasing atmospheric CO2 concentrations, may stimulate
growth and productivity (Neufeld & Young, 2003) and therefore increase canopy density. However, this
only applies to regions where there is adequate moisture or growth is currently limited by cold (Chmura
et al., 2011; Wertin et al., 2012). Moreover, higher growth would also increase competition for nutrients
between established trees and understorey vegetation (Neufeld & Young, 2003). Increasing
temperatures also affect phenology, specifically, a significant advancement of spring has been observed
(Menzel et al., 2006). Prolonged spring growth activity may deplete soil moisture and therefore decrease
moderating capacity in summer of the forest, besides other known risks of earlier phenology such as
damage by late frost (Neufeld & Young, 2003) and inappropriate root-to-shoot ratio to sustain dry
summer periods (Richter et al., 2012). Demey et al. (2015) report that the reaction of ecosystems on
climate change is delayed causing that the consequences become visible very late. Climate change will
also include higher risks for the delivery of ecosystem services for example the wood production
(Demey et al., 2015). It is expected that the economic value of the European forests will decrease with
28 % by 2100 (Hanewinkel et al., 2013).
18
1.3. Microclimate versus macroclimate The climate is a general term for the measurement of the mean and variability of relevant quantities of
certain variables (such as temperature, precipitation or wind) over a period, ranging from months to
thousands or millions of years (WMO, 2017). The classical period is 30 years (WMO, 2017). The WMO
wrote in 2010 a Guide to Meteorological Instruments and Methods of Observation with the standards
and good practices for representative measurements of macroclimate variables. According to good
WMO-practices outdoor instruments should be installed on a level piece of ground and the ground
should be covered with short grass or a surface representative of the locality and surrounded by open
fencing or palings to exclude unauthorized persons (WMO, 2010). The site should be well away from
trees, buildings, walls or other obstructions (WMO, 2010). The distance of any such obstacle (including
fencing) from the rain gauge should not be less than twice the height of the object above the rim of the
gauge, and preferably four times the height (WMO, 2010).
1.3.1. Difference between macro- and microclimate
Geiger et al. (2009) defines a microclimate as the suite of climatic conditions measured in localized
areas near the earth's surface. A macroclimate is a climate that extends over a larger area than a
microclimate. According Geiger et al. (2009) a macroclimate has a horizontal size which is bigger than
200 km and a vertical size from 1 to 10 km (table 3). A microclimate on the other hand has a horizontal
size from 1 mm to 100 m and a vertical size from -10 to 10 m (Geiger et al., 2009) (Figure 13 + table
2). Microclimates have influence on the circumstances of both microclimates and macroclimates (Geiger
et al., 2009). A macroclimate can contain multiple microclimates such as microclimates in cities and
forests
Bramer et al. (2018) defines a microclimate as fine scale climate variations which are, at least
temporarily, decoupled from the background atmosphere. A wide range of variables, or combinations
of variables, can be used to characterise microclimate, including temperature, precipitation, solar
radiation, cloud cover, wind speed and direction, humidity, evaporation, and water availability (Bramer
et al., 2018). These are influenced by fine resolution biotic and abiotic variations, including topography,
soil type, land cover (especially vegetation), and proximity to the coast (Bramer et al., 2018). Bramer et
al. (2018) consider microclimates to typically have a spatial resolution of <100 m, and to be within a
few metres of the vegetation canopy. The temporal resolution may vary depending on the process or
Figure 13: Difference between a macro- and microclimate (Ball, 2014).
19
application being studied, but generally timescales of hours (or higher frequency) are appropriate (table
2) (Bramer et al., 2018).
Table 2: Several definitions for microclimate by a number of authors (Bramer et al., 2018).
Geiger et al.,
2009
Barry and Blanken, 2016 Littmann, 2008 Orlanski, 1975; WMO,
2010b,2014
Horizontal scale 0.001 to 100 m <~50 m (defined by
vegetation canopy height)
10-100 m2 <100 m
Vertical scale -10 to 10 m < A few 100 m
Time scale < 10 sec < Minute
Table 3: Several definitions for macroclimate by a number of authors (Bramer et al., 2018).
Geiger et al., 2009 Barry and Blanken, 2016 Orlanski, 1975; WMO, 2010b, 2014
Horizontal scale > 200 km >50 km 100– 3000 km
Vertical scale 1-10 km
Time scale Days to weeks > hours
Macroclimates are measured in open field according to the standards of the WMO, but microclimates
can be measured under canopy for example in forests. The classic meteorological stations have guidance
criteria published by the WMO, which are designed to limit local climate influences (WMO, 2014).
Utilising observations on a macroclimate scale to assess ecological processes that have a strong
microclimate influence will decrease the accuracy of predictions of species’ responses to climate change
(Slavich et al., 2014). Bramer et al., (2018) describe that climate is the key to the physiology and
development of organisms, their ecological interactions and resulting geographical distributions.
Different parts of the same organism may be at different temperatures: tree leaf temperature is different
to that of the trunk and different again to root temperature (Kollas et al., 2014). It has been established
that the seasonal mean temperatures that species experience can deviate by as much as 5 °C from the
macroclimate (Scherrer and Körner, 2010; Suggitt et al., 2011). One of the reasons microclimates are
ecologically important is that they can potentially buffer species against climate variability and longer-
term changes, hence providing microrefugia which allow species and populations to survive in locations
which may be deemed unsuitable using low resolution observations and models (De Frenne et al., 2013;
Lenoir et al., 2017; Maclean et al., 2015; Slavich et al., 2014; Suggitt et al., 2015).
20
1.3.1.1. Human microclimates
People are concerned with both wanted and unwanted microclimates. Those microclimates act on
different spatial scales e.g. urban heat islands (UHI) act on scales of 1 km² or more, while microclimates
in forests or mountains act on smaller spatial scales such as 1 m² or less. An example of an unwanted
microclimate is an UHI (Figure 14). Macintyre et al. (2017) did research about urban heat islands in the
United Kingdom and concluded that The UHI intensity across the region is on average 2.1 °C, + 1.4 °C
in daytime and + 2.9 °C at nighttime. UHI have serious consequences. Heaviside et al., (2016) suggests
that the UHI may contribute around half of the heat related mortality experienced during heatwaves.
Increasing urbanisation and climate change will increase heat related health risks in urban areas
(Macintyre et al., 2017). Yao et al. (2017) found that on average, temperature is 4.09 °C warmer in
summer days across 31 Chinese cities. Examples of wanted microclimates can be found in the
agriculture. The climate and thus the microclimate are of great importance for the wine production and
quality. Grapes need sufficient light and warmth to produce sugar. Grape cultivation takes place on
southern slopes to provide the grapes with sufficient light and warmth. The study of Webb et al. (2008)
reveals the sensitivity of wine grape quality to climate. By 2030, Webb et al., 2008 estimate a 5 to 7%
decrease in the quality of Chardonnay (allowing for model uncertainty), a 6 to 7 % decrease in the
quality of Cabernet Sauvignon, and a 9 to 11 % decrease for Traminer. By 2050, the decreases are 12 to
16, 11 to 19 and 19 to 26 %, respectively. Another example is the production of crops in greenhouses to
achieve a warmer temperature than outside. The warmer temperature is needed for the growth of the
crops. A third example of microclimates in the agriculture are the measures taken to protect the blossoms
of fruit trees by frost in spring. The blossoms are lost if they get frozen. Measures to protect the blossoms
are sprinkling with water to form a layer of ice as long as the temperature remains below zero degrees.
During the formation of the ice layer coagulation heat is set free and the blossoms don’t get frozen.
Other measures are hot air cannons or paraffin candles to heat up the air. Fruit growers can also use
wind machines to mix the colder and hotter layers of air.
Figure 14: Potential transect of the temperature in an urban area (Epa,2014).
21
1.3.1.2. Natural microclimates
An example of natural microclimates are bird nests. The climate in the nests is very important for the
survival of the young birds. A second example is the microclimate in turtle nests. Cagle et al. (1993)
report that temperature and water availability influence the phenotypes of hatchling reptiles.
Microclimate is also very important in nests of bees. Simpson (1961) reports that the temperature in the
centre of a bee nest is around 35 °C and that the temperature in the centre of the nest varies in a range
of 5 °C while the outside temperature can range from – 40 °C to 40 °C or more. Badano et al. (2015)
report that microclimate is an important driver of tree recruitment in human-disturbed forests. Many
species require microclimates (Peterman et al., 2013; Peterman and Semlitsch, 2013). Organisms can
also change the microclimate of the location they occupy, for example the metabolic heat production of
bats can measurably influence cave microclimates (Baudunette et al., 1994). Microclimates could also
result in microrefugia for species, locations in the landscape where conditions remain suitable for some
time under climate change (Bramer et al., 2018).
Morecroft et al. (1998) monitored forest microclimate continuously for more than 3 years at two sites in
deciduous woodland at Wytham Woods, Oxford, UK. These data were compared with values from an
open site at the same location. During the winter, the mean values of air temperatures under the canopy
were close to the air temperature at the grassland site: air temperatures either did not differ or were up
to 0.2 °C cooler for the whole period of study (Morecroft et al., 1998). In summer, the differences were
larger: mean air temperature was 0.9 °C cooler in the forest site (Morecroft et al., 1998). Tmax in the
woodland followed a similar pattern to mean values but with more pronounced differences, being 2–3
°C colder than those for grassland in summer and autumn (Morecroft et al., 1998). In winter and spring,
the maxima were similar under the canopy and in the open (Morecroft et al., 1998). Wind speed was
substantially lower under the canopy than in the grassland site (Morecroft et al., 1998). Renaud &
Rebetez (2009) found clear differences between below-canopy and open-site temperatures. Maximum
temperatures were on average 2.37 °C cooler and minimum temperatures 0.77 °C warmer under the
canopy between April and October 2003 (Renaud & Rebetez, 2009). The study took place on 14
locations in Switzerland between April and October 2003 in deciduous, mixed and coniferous forests.
The temperature under canopy is buffered (Renaud & Rebetez, 2009; Morecroft et al., 1998; Ferrez et
al., 2011; Von Arx et al., 2012) but the absolute value of the buffering depends on several factors.
Scheffers et al. (2014) found that microhabitats reduced the mean temperature by 1–2 °C and reduced
the duration of extreme temperature exposure by 14–31 times. Scheffers et al. (2014) concluded that
microhabitats have extraordinary potential to buffer climate and likely reduce mortality during extreme
climate events.
22
1.4. Influence of the forest structure on the microclimate Bramer et al. (2018) report that microclimates are affected by the shape of the landscape, including the
steepness and aspect of slopes, height above sea level, proximity to the sea or inland water, and whether
a site is in a valley or at the top of a hill. Plants also modify the conditions found within or below their
canopies, with the structure of vegetation playing an important role (Bramer et al., 2018). De Frenne et
al. (2013) report that recent forest canopy closure in northern-hemispheric temperate forests has buffered
the impacts of macroclimate warming on ground-layer plant communities, thus, slowing changes in
community composition.
1.4.1. Influence of the tree height
Extreme temperatures in forests with smaller trees were less buffered (Baker et al., 2014; Ferrez et al.,
2011). Baker et al. (2014) did research about the microclimate in different forest types. Baker et al.
(2014) distinguished between mature and regeneration forests. The regeneration forests have ages of 7,
27 and 45 years. A forest of 7 years will have smaller trees compared to forests of 27 or 45 years or
mature forests. Regeneration forests had higher afternoon temperatures and lower levels of relative
humidity in all three age classes of regeneration forest compared to the associated mature forests (Baker
et al., 2014). The 7 year old regeneration forest generally had the greatest differences in microclimate
with mature forest (Baker et al., 2014). In general, the scale of the differences between mature and
regeneration forests diminished with the age class of the regeneration forests, although the 27 and the
45 year old regeneration forests were not significantly different in any of the metrics (p > 0.05) (Baker
et al., 2014). Compared to the 27 and 45 year old regeneration forests, the 7 year old regeneration forest
had higher average afternoon temperature (p = 0.053) (Baker et al., 2014). These results suggest that the
tree height has an influence on the forest microclimate, but the effect of forest age may not be neglected.
Ferrez et al. (2011) found that the forest cover of former coppices has a weaker impact on the extreme
maximum temperatures compared to high forests.
1.4.2. Influence of the forest density
Von Arx et al. (2013) studied the difference in daily maximum temperature (Tmax) between below-
canopy versus open-area in forests with varying Leaf Are Index (LAI) (Figure 15). LAI gives an
indication of the density of the forest stand because LAI is defined as the one-sided green leaf area per
unit ground surface area. The influence of LAI on below-canopy microclimate depended on soil
moisture (Von Arx et al., 2013). The moderating capacity of dense canopy (LAI > 4) on Tmax in summer
was significantly larger when soils were moist than when they were dry (-3.3 vs. -2.8 °C) (Von Arx et
al., 2013). Below sparse canopy (LAI < 4), the overall largest dependence of moderating capacity on
soil moisture was observed in summer, when Tmax was reduced by 1.3 °C with moist soils and only by
0.1 °C with dry soils (Von Arx et al., 2013). Below dense canopy, the largest dependence of moderating
capacity on soil moisture was observed in spring, with a reduction of Tmax by 2.7 °C when soils were dry
and 1.7 °C when soils were moist. Anderson et al. (2007) report that mean air Tmax were 1-4 °C warmer
in thinned than unthinned stands. Overstory thinning results in a greater daytime influx of solar radiation
with higher near-surface Tmax and a greater nighttime loss of longwave radiation with lower near-surface
temperatures (Rambo & North, 2009). This in turn results in more extreme diurnal swings of the
temperature (Rambo & North, 2009). Dense tree canopies cause not only colder ground-layer
temperatures but also increase relative air humidity and shade in the understory (Geiger et al, 2009;
Chen et al., 1999; Norris et al., 2012; Von Arx et al., 2013). Higher relative humidity in dense forests
can also protect forest herbs and tree seedlings from summer drought, decreasing mortality and thus
buffering the impacts of large-scale climate change (Von Arx et al., 2013; Lendzion & Leuschner, 2009).
23
1.4.3. Influence of the tree species
Different species can influence the buffering of the temperature in various ways. Some tree species have
a denser crown (i.e. beech (Fagus sylvatica), Hornbeam (Carpinus betulus)) while others have a more
open canopy (i.e. Betula spp.). As seen in section 1.4.2, canopy density, often expressed as LAI, affects
the buffering of the temperature. Evergreen and deciduous species will have a different influence on the
buffering. The LAI of deciduous species in winter and parts of spring and autumn is strongly reduced
mean while the LAI of evergreen species remains approximately constant throughout the year. Kovacs
et al. (2017) conclude that the importance of tree species in the upper canopy layer on the microclimate
in closed mature forests is lower than expected. Hornbeam was the most significant driver in the
maintenance of humid microclimates in mature forests with continuous canopy cover because Carpinus
betulus creates a secondary canopy layer (with an average height of 10-15 m) (Kovacs et al., 2017). Due
to the denser foliar layer and well-developed canopy structure, midstory species could slow down
evaporation, resulting in a more even temperature gradient and higher humidity below the canopy
(Unterseher & Tal, 2006). Shrubs and young trees, situated below the main canopy, increase humidity
by stronger shading and by reducing wind speed by filling the trunk space with variously dense foliage,
therefore creating a more moderate microclimate (Bigelow and North, 2012; Campanello et al., 2007;
Geiger et al., 2009).
Renaud & Rebetez (2009) report that the difference between Tmax below-canopy and in the open field is
higher in deciduous and mixed forests, especially those with beech as the dominant tree species,
compared to conifer forests. For minimum temperature (Tmin), in contrast, the discrepancy was higher in
conifer forests but, as for maximum temperature, it was also higher during warmer episodes (Renaud &
Rebetez, 2009). In summer, the greatest differences were measured under beech and beech-silver fir
forests with values 6 to 8 °C colder below-canopy compared to open-field (Renaud et al., 2011). In
winter, the difference was highest in the conifer sites at the subalpine level, where maximum temperature
values were frequently up to 9 °C colder below-canopy compared to open-field (Renaud et al., 2011).
Ferrez et al. (2011) have similar results and conclude that the cooling effect of vegetation is generally
larger with beech (3.27 °C on average) than with oaks (Quercus spp.) or conifers (2.29 °C on average).
Figure 15: Difference in maximum daily temperature (ΔTmax) between below-canopy (bc) and open-area (oa) plots
with varying leaf area index (LAI) based on long-term (1998–2011) data from 11 contrasting forest ecosystems in
Switzerland. Significant regression curve (P ≤ 0.05) and corresponding 95% confidence envelopes are given (Von Arx
et al., 2013).
24
The cooling shelter of conifers is more regular through the year (conifer sites have a mean amplitude of
1.70 °C) than the shelter of deciduous trees, the variation of which is larger (2.17 °C on average) (Ferrez
et al., 2011). Moreover, the impact of conifer forests is larger in winter, whereas deciduous forests have
a larger impact in summer (Ferrez et al., 2011). This relationship was reversed at nighttime and early in
the morning with 1.0 °C (Tmean) and 1.1 °C (Tmin) warmer conditions below pine canopy, thus pine forests
cooled down less during the night than the other forest types
1.5. Research questions
1.5.1. How much is temperature buffered in forests?
The buffering of the minimum, maximum and mean temperature (Tmin, Tmax and Tmean) of the 100 plots
in the 10 regions across Europe will be studied and quantified. Buffering is calculated as the forest
temperature minus the outside temperature such that negative values reflect cooler forest temperatures.
Temperatures are expected to be buffered, which means that the forest Tmax will be colder compared to
the outside Tmax and the forest Tmin will be warmer than the outside Tmin. The effect on the buffering of
Tmean is less certain and will depend on the buffering of Tmin and Tmax because those two effects will likely
compensate each other. The buffering will depend on the season. Most buffering of Tmin and Tmax is
expected in summer because the cover of the trees and shrubs of deciduous forests is then highest. The
least buffering is expected in winter because there are no leaves on deciduous trees which can enhance
the buffering. The buffering in spring and autumn is expected to be intermediate to the amount of
buffering in winter and summer.
1.5.2. Is there an effect of forest and landscape characteristics on the
amount of buffering?
The effects of the forest canopy characteristics (such as tree height, distances among neighbouring trees,
density, the tree cover and the shrub cover) and landscape characteristics (such as the terrain topography,
elevation above sea level and amount of forest surrounding the plot) will depend on the season and the
type of buffering. Relations between the buffering of Tmean and the forests characteristics are uncertain
because the buffering of Tmin and Tmax will likely compensate each other. Most significant relations
between the buffering of the temperature and the forest characteristics are expected in summer and the
least significant relations in winter due to the presence and absence of canopy. Temperature is expected
to be more buffered in more dense forests stands, in stands with higher trees and shrub cover and stands
with thicker and more neighbouring trees. Temperature is also expected to be more buffered with
increasing distance to the coast due to a more continental climate. The relative elevation of the plots is
expected to have an effect on Tmin. Lower relative elevation is expected to result in lower Tmin. More
south oriented plots are expected to have warmer Tmax.
25
2. Materials and methods
2.1. Study sites In this study, a plot is defined as a circular area with a radius of 9 m around the central tree with the
temperature data logger. Temperature data were collected in 100 plots divided over ten regions in ancient
temperate deciduous forests across Europe. Hence, each region had ten plots where the microclimate
temperature has been measured and additional measurements of forest characteristics have been done.
The ten regions are shown in Figure 16 and more information about the ten regions is given in table 5.
Based on the world map of the Köppen-Geiger climate classification, Wytham, Compiègne, Tournibus
and Speulderbos are situated in the Cfb climate zone and the other regions are situated in the Dfb climate
zone (Peel et al., 2007). A Cfb climate is a temperate climate without dry season and a moderate summer
and a Dfb climate is a continental climate without dry season and a warm summer (Peel et al., 2007).
The most common trees species and their abundance are shown in table 4. Between 1990 and 2015, the
proportion of land area covered with forests in the European Union increased from 35 to 38 % (The
World Bank, 2018). Thus, a significant and increasing amount of the surface in Europe is covered by
forest. Forests are important for biodiversity conservation and a great number of ecosystem services
such as carbon sequestration, nutrient cycling, recreation, water and air purification, flood buffering,
climate regulation, tree regeneration. Forests play an important role in the conservation of biodiversity.
Many species depend on forests for their survival such as tree, shrub, herb and grass species, spring
flowers, a large number of animals (i.e. birds, amphibians, insects). Thus, it is important to conduct
research on the possible role of forests in mitigating the effects of climate change and the implications
for biodiversity. It is also important for forest managers on how they can play a role in mitigating the
effects of climate change with management actions.
Table 4: The presence of several tree and shrub species in the plots.
Tree/shrub species Present in percentage of plots (%)
Quercus robur 30
Quercus petraea 27
Fagus sylvatica 36
Carpinus betulus 35
Fraxinus excelsior 30
Alnus glutinosa 11
Corylus avellana 24
26
2.2. Microclimate temperature data The temperature data were collected with EasyLog EL-USB-1 data loggers produced by Lascar
electronics. Data was collected from the 22th of February 2017 until the 22th of February 2018. In each
region temperature measurements were taken every hour in ten plots with an accuracy of 0.5 °C. The
temperature data loggers were installed on a tree at one meter height and facing north (Figure 17). A
radiation shield protected the temperature data loggers against precipitation and direct solar radiation
(Figure 17). Ten sensors were installed at each region (100 sensors in total) but at the end of the
measuring period there were fourteen sensors (14 %) of which the data is partly or completely missing
due to dead batteries, damage or theft by animals, fallen on the ground or manufacturing errors.
Figure 16: Map with the ten regions where microclimate measurements and measurements of forest characteristics took
place. WW = Wytham woods, SP = Speulderbos, TB = Tournibus, CO = Compiègne, GO = Göttingen, PR = Prignitz,
SKA = Skåne, KO = Koda woods, ZV = Zvolen, BI = Bialowieza.
Figure 17: (a) A temperature data logger in a radiation shield. (b) A temperature data logger attached to a tree.
27
Table 5: Description of the study sites. Mean annual temperature (°C) and mean annual precipitation were obtained via https://www.climatedata.eu/ (Climate data, 2018). Distance to
the sea (km) and elevation above sea level (m) for each plot were obtained via a Pan-European digital elevation model (DEM) provided by the EU through the Copernicus program (EU,
2018). The EU-DEM is a 3D raster dataset with elevation captured at 1 arc seconds or about every 30 metres. Distance to the sea (km) and elevation above sea level (m) were calculated
as the mean value of the 10 plots in that specific region.
Country Region Latitude &
longitude (°)
Distance to
the sea (km)
Elevation
above sea
level (m)
Mean annual
temperature (°C)
Mean annual precipitation
(mm)
Belgium Tournibus (TB) 50°19' N 4°34' E 107 249 10.3 818
Czech Republic Koda Woods (KO) 49°56' N 14°06' E 409 379 8.8 493
France Compiègne (CO) 49°23' N 2°52' E 127 100 11.5 585
Germany Göttingen (GO) 51°32' N 10°01' E 221 405 8.6 698
Germany Prignitz (PR) 53°15' N 12°03' E 82 74 9 591
Netherlands Speulderbos (SP) 52°15’ N 5°41' E 79 59 9.7 776
Poland Bialowieza (BI) 52°44' N 23°53' E 300 172 6.8 592
Slovakia Zvolen (ZV) 48°32' N 19°08' E 518 559 10.7 583
Sweden Skåne (SKA) 55°40' N 13°31' E 24 72 8.2 552
United
Kingdom Wytham Woods (WW) 51°46' N 1°20' W 72 113 9.6 754
28
2.3. Macroclimate temperature outside forests Open field temperature data were collected from the closest open field weather stations. Due to
elevational differences between the forest and the weather station, the macroclimate data were corrected
for elevation above sea level with a factor of 0.6 °C per 100 m (table 6). Table 6 shows also the sources
of the macroclimate data.
Table 6: Data sources of the macroclimate data, the mean elevation above sea level (m) of the ten plots in each region
and the elevation above sea level (m) of the open field weather station in each region.
Region Source of macroclimate data Mean elevation
above sea level of
the 10 plots (m)
Elevation above sea
level of the open
field weather station
(m)
Wytham The UK Environmental Change
Network (ECN) from the Centre for
Ecology & hydrology (CEH) (2018)
113 160
Skåne Swedish Meteorological and
Hydrological Institute (SMHI) (2018)
72 20, 25, 55, 72, 103,
114*
Speulderbos Koninklijk Nederlands Meteorologisch
Instituut (KNMI) (2018)
59 45
Prignitz Deutschen Wetterdienst (DWD) (2018) 74 30
Göttingen Deutschen Wetterdienst (DWD) (2018) 405 167
Tournibus Koninklijk Meteorologisch Instituut
(KMI) (2018)
249 280
Bialowieza the Institute of Meteorology and Water
Management (IMGW-PIB) (2018)
172 164
Koda
woods
Czech Hydrometeorological Institute
(CHMI) (2018)
379 322
Compiègne Metéo France (2018) 100 92
Zvolen National Forest Centre and from the
university of Zvolen (NLC) (2018)
559 353
*: Since several macroclimate weather stations are located in Skåne, the open field data that has been used (and
therefore the elevation above sea level of the weather station) depends on the exact location of each plot in Skåne.
29
2.4. Forest characteristics A set of measurements was taken in each plot (Figure 18):
• The diameter of all trees, in a plot with radius of 9 m around the central tree with the temperature
data logger, with minimal diameter at breast height (DBH) of 7.5 cm was measured with
callipers or measuring tape. The distance between the trees and the sensor tree was measured
with a Vertex hypsometer and the tree species was written down.
• The height of the central tree with the temperature data logger was measured with a Vertex
hypsometer (Vertex IV). Two measurements took place per tree from two different directions.
• The percentage cover per tree and shrub species in each plot with a radius of 9 m was estimated.
Trees were defined as plants taller than 7 meters and shrubs were defined as plants with a height
between 1 and 7 meters.
• A spherical densiometer was used in four measurement points in each plot. The four
measurement points were at 4.5 m from the central tree with the temperature data logger. One
measurement point was delineated in each wind direction. The measurements with the
densiometer always pointed north.
• An additional GPS measurement next to the central tree was done with the SXBlue II + GNSS
to have more accurate coordinates for the calculation of the landscape characteristics.
The measurements took place in summer and autumn between July and October 2017.
Several variables were derived from these field measurements. The openness of each plot was calculated
as the mean value of the four densiometer measurements per plot. The tree height was calculated as the
mean value across the two height measurements per plot. The neighbourhood competition index (NCI)
was also calculated. The first step in the calculation of the NCI is dividing the DBH (m) by the distance
from the central tree (m). This is done for each tree with a DBH > 7.5 cm in the plot with a radius of 9
m around the central tree with the temperature data logger. The second step is the sum of the previous
value for all the trees within the plot. Thus, with n trees with a DBH > 7.5 cm in the plot with a radius
of 9 m, the NCI = ∑ 𝐷𝐵𝐻 (𝑚)/𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒 𝑡𝑜 𝑡ℎ𝑒 𝑐𝑒𝑛𝑡𝑟𝑎𝑙 𝑡𝑟𝑒𝑒 (𝑚)𝑛𝑖=1
Figure 18: Visualisation of the measurements in each plot. The middle represents the central tree with the temperature
data logger. The height of the central tree was measured from two directions. The outer circle represents the plot with
a radius of 9 m in which the diameter of all trees with minimal DBH of 7.5 cm was measured and from which the NCI
was calculated. In the four circles at 4.5 m north, east, south and west from the central tree spherical densiometer
measurements were taken. The percentage cover per tree and shrub species were estimated in the circle with a radius
of 9 m.
30
2.5. Landscape characteristics In addition to the measurements in the forest, several landscape characteristics were derived, namely the
amount of forest cover and forest edge in a radius of 500 m of the plots, the relative elevation of the plot
relative to the lowest elevation in a radius of 250 m of the plot, the slope, the north (northness) and east
(eastness) orientation, the elevation above sea level, the latitude and the distance to the coast of the plots.
Forest cover data was taken from a global dataset with a spatial resolution of 25 m (Hansen et al., 2013).
Within a given circular buffer area the percentage of area covered by forest in the year 2016, i.e. the
latest year for which data was consistently available, was calculated. Similarly, forest edge was
calculated by adding up all contour lines of the forest map within a given buffer area. Forest edge thus
describes the total length (in kilometres) of forest pixel sides neighbouring non-forest pixels. For the
calculations the rasterToContour and SpatialLinesLengths functions in respectively the “raster”
(Hijmans, 2017) and “sp” (Pebesma & Bivand, 2005; Bivand et al., 2013) packages, were used. For each
plot, forest cover and forest edge lengths were calculated for a circular buffer area of 500 m. A higher
forest cover in the vicinity may result in more buffering because less radiation will reach the soil and
more evapotranspiration will occur which causes that the environment will heat up less. At night,
minimum temperatures are warmer with higher forest cover because plants re-emit absorbed energy
which raises minimum temperatures (Geiger et al., 2009). The same reasoning can be followed for the
amount of forest edge because a forest edge indicates the presence of an more open vegetation.
A Pan-European digital elevation model (DEM) provided by the EU through the Copernicus program
(EU, 2018) was used to calculate four topographic variables that affect microclimatic gradients: relative
elevation, slope, northness and eastness. The EU-DEM is a 3D raster dataset with elevation above sea
level captured at 1 arc seconds or about every 30 metres. Relative elevation describes the elevation of
each plot relative to the elevation of the surrounding terrain. Positive values indicate a higher relative
elevation, e.g. a ridge. Negative values indicate a lower relative elevation, e.g. a valley bottom. Relative
elevation is representative for cold air drainage and pooling, which are important processes affecting
minimum temperatures at night and during winter (Daly et al., 2010; Pepin et al., 2011). Therefore, plots
with a low relative elevation are expected to have a colder Tmin. The relative elevation was calculated by
subtracting minimum elevation above sea level within a given circular buffer area from the elevation
above sea level of each plot (Meineri & Hylander, 2017). The relative elevation for a circular buffer area
of 250 m was calculated. The calculation of the slope was based on Jones (1998).
Aspect was first derived from the EU-DEM to calculate the northness and eastness of the plots. Aspect
computes the azimuthal direction of steepest slope through the points. Aspect is typically measured in
degrees from north but presents a difficulty that values numerically distant may be oriented in the same
general direction (e.g. 1° and 359°). Thus, it is better to split aspect into two components following
conversion from degrees to radians: eastness = sin(aspect) and northness = cos(aspect). These indices
of northness and eastness provide continuous measures (-1 to +1) describing orientation of the plots.
More south oriented plots are expected to have warmer Tmax because more solar radiation will reach the
soil of the plot. The slope of a plot can influence the buffering of the temperature caused by the flow of
warmer and colder (= heavier and denser) air. Cold air will drain downwards. A slope of the plot in
southern direction is similar to a south orientation and will have the same effect.
31
For each plot, the distance (in meters) to the nearest coastline based on a global vector data set
(https://www.naturalearthdata.com/downloads/10m-physical-vectors/10m-coastline/, version 4.1.0,
downloaded on 12 Jan 2018) was calculated. For this purpose, the gDistance function in the “rgeos”
package (Bivand & Rundel, 2017) was used in R. The effect of elevation above sea level and distance
to the coast are studied because increasing elevation above sea level and increasing distance to the coast
result in (slightly) different climates. For example continental climates have more extreme Tmax and Tmin
compared to temperate climates. The same reasoning applies to the latitude because an increasing or
decreasing latitude will result in (slightly) different climates.
2.6. Data analyses Data analysis were performed in R (version R-3.4.3) (R Core Team, 2018). Buffering was always
calculated as the daily mean forest temperature minus the daily mean outside temperature such that
negative values reflect cooler forest temperatures. The mean buffering of Tmean of a plot was calculated
as the mean buffering value of the daily Tmean of that plot over the entire period. The mean buffering of
Tmin and Tmax were calculated in an analogous way. The same calculation was also done for each season
for Tmean, Tmin and Tmax following the meteorological definitions: winter defined as December, January
and February, spring as March, April and May, summer as June, July and august and autumn as
September, October and November.
Thereafter, linear mixed effect models (lme-function) via the package “nlme” (Pinheiro et al., 2017)
were applied to examine the relations between the forest and landscape parameters and the mean
buffering of Tmean, Tmin and Tmax over the entire measuring period and in the four seasons. The lme-
function was used because linear relations are expected between the buffering of the temperature and
the different parameters. This does not exclude the occurrence of non-linear relationships. A mixed-
effect model is used because the model contains both fixed and random effects factors. Fixed effects
were the forest and landscape characteristics and the used random effect was the region of the plot. Due
to correlation between characteristics, linear mixed effect models were made with only one fixed effect,
along with the random effect (region). To make sure that the lme-function can be used, the assumptions
of the linear mixed effects models were assessed. The following assumptions must be assessed: Are the
residuals homoscedastic? And are the errors normally distributed? A wide range of model-checking
plots is shown in Figures 37, 38 and 39 in appendix. In Figures 37, 38 and 39 in appendix in the plots
on the right side can be seen that the errors are approximately normal distributed because no major
deviations can be seen. Little deviations can only be seen at the ends of the QQ-plots which is normal.
On the left side of Figures 37, 38 and 39 in appendix can be seen that the residuals behave normal
because no trends can be seen in the distribution of the residuals.
The buffering of the daily Tmean, Tmin and Tmax was quantified. The marginal R squared (R2m) and the
conditional R squared (R2c) are calculated with the function r.squaredGLMM() via the “MuMin”
package (Barton, 2018). The marginal R2 represents the variance explained by fixed factors and the
conditional R2 is interpreted as variance explained by both fixed and random factors (i.e. the entire
model).
Several forest variables such as the openness, tree cover, shrub cover and total cover of trees and shrubs
are related to each other. Various landscape variables are also related to each other i.e. distance to the
coast, latitude, elevation above sea level. A correlation test was done with function cor.test in R.
Correlation heatmaps were made with the “ggplot2” package (Wickham, 2009) and the “reshape2”
32
package (Wickham, 2007). The correlation heatmaps are shown in Figures 40 and 41 in appendix.
Several plots were created with the ggplot function from the “ggplot2” package (Wickham., 2009).
Multiplots are obtained with the plot_grid() function from the “cowplot” package (Claus O. Wilke,
2017). To read in the excel files in R the packages “xlsx” (Adrian & Dragulescu, 2014) and “rJava”
(Urbanek, 2017) were used.
33
3. Results
3.1. Similarity between the forest and outside temperature The similarity between the forest temperature and the outside temperature has been assessed. The
outside Tmean and the forest Tmean of each plot in the ten regions are shown in Figure 19. Each region
shows clearly a very similar trend between the forest and outside temperature.
Figure 19 (a): Course of the daily outside Tmean (black lines, 1 per region) and daily forest Tmean (red lines, 10 per region
except Skåne: 4 lines due to the use of different open field weather stations) for Skåne, Göttingen and Speulderbos.
34
Figure 19 (b): Course of the daily outside Tmean (black lines, 1 per region) and daily forest Tmean (red lines, 10 per region,
except Zvolen: 9 lines due to the loss of one temperature data logger) for Zvolen, Tournibus, Prignitz and Wytham
woods.
35
Figure 19 (c): Course of the daily outside Tmean (black lines, 1 per region) and daily forest Tmean (red lines, 10 per region)
for Bialowieza, Koda woods and Compiègne.
36
3.2. Quantification of the buffering Table 7 shows the quantification of the buffering of the daily Tmean, Tmin and Tmax in the four seasons and
over the whole measuring period. A negative buffering means colder forest temperatures. Figure 20
gives a graphical representation of the quantification of the buffering in the four seasons and shows the
positive buffering values of Tmin, the negative buffering values of Tmax (except in spring) and little
buffering of Tmean (except in summer (negative buffering) and in spring (positive buffering)). The red
line represents a buffering of 0 °C thus no difference between outside and forest temperature.
Table 7: Quantification of the buffering. A negative value means colder temperatures inside the forest.
Type of buffering Period Value (°C)
Buffering of Tmean
Entire year -0.057
Spring 0.27
Summer -0.49
Autumn -0.0030
Winter -0.0011
Buffering of Tmin
Entire year 0.89
Spring 0.99
Summer 1.35
Autumn 0.84
Winter 0.41
Buffering of Tmax
Entire year -0.70
Spring 0.32
Summer -2.05
Autumn -0.89
Winter -0.24
37
From table 7 and Figure 20 it can be noted that the deciduous forests across Europe buffered the
temperature. Over an entire year, Tmin was 0.89 °C warmer and Tmax was 0.70 °C colder in the forests
compared to open field. Little difference was found between the Tmean in the forests and the Tmean in the
open field over the entire measuring period (-0.0.57 °C). The temperature was most buffered in summer.
Tmax and Tmean were respectively 2.05 and 0.49 °C colder and Tmin was 1.35 °C warmer in summer in
forests compared to the open field. Buffering occurred less in autumn and even less in winter (table 7).
In spring the Tmean, Tmin and Tmax were warmer in forests compared to the open field. The forest Tmin was
buffered in each season. The forest Tmax was buffered in each season except spring. The forest Tmax in
spring was 0.32 °C warmer compared to the open field.
Figure 20: Buffering of (a) Tmean, (b) Tmin and (c) Tmax in the four seasons. A negative value means colder temperatures
inside the forest. The red line represents a buffering of 0 °C.
38
Figure 21 (a) shows the buffering of Tmax in summer in the ten regions. There are only a few plots where
the forest Tmax was warmer than the outside Tmax seen over the entire period. The mean value of each
region shows a colder forest Tmax. Forests within many regions were buffered between one and three
degrees Celsius. Thus, the variation between regions was rather limited. But the variation between the
buffering values of Tmax within one region was variable. The variation between the plots in Wytham was
approximately 3.5 °C while in Tournibus the variation was approximately 1 °C.
Figure 21 (b) shows the buffering of Tmin in summer over the ten regions. The buffering of Tmin in summer
shows more variation between the plots compared to the buffering of Tmax in summer. In some regions
(Skåne, Tournibus) the forest Tmin was very similar to the outside Tmin, whereas in Göttingen, the Tmin
was buffered with approximately 3.5 °C. The variation of the buffering values of the Tmin within one
region was variable. The region of Zvolen had the most (± 2.5 °C) and the region of Speulderbos the
least (± 0.75 °C) variation between the buffering of Tmin in the ten plots.
Figure 21: (a) The buffering of Tmax in summer and (b) The buffering of Tmin in summer in the ten regions. A negative
value means colder temperatures inside the forest. The red line represents a buffering of 0 °C. WW = Wytham woods,
SP = Speulderbos, TB = Tournibus, CO = Compiègne, GO = Göttingen, PR = Prignitz, SKA = Skåne, KO = Koda woods,
ZV = Zvolen, BI = Bialowieza.
39
3.3. Relation of forest characteristics with the buffering Table 8, 9 and 10 show the relations between the buffering of respectively the daily Tmean, Tmin and Tmax
and the forest characteristics openness, tree height, NCI, cover of the tree layer, cover of the shrub layer
and total cover of trees and shrubs. Significant p-values (p < 0.05) are shown in bold. In addition to the
p-values, the t-values and the slopes are given. The slope indicates how much the buffering of the
temperature increases or decreases with an increase of one unit of the forest characteristic. Finally, R2m
and R2c are given. R2m is the marginal R2 and represents the variance explained by fixed factors. R2c is
the conditional R2 and is interpreted as variance explained by both fixed and random factors (i.e. the
entire model).
Table 8: Relation between the buffering of Tmean over the entire measuring period and in the four seasons and the forest
characteristics. Significant values (p<0.05) are shown in bold. R2m is the marginal R2 and represents the variance
explained by fixed factors (forest characteristics). R2c is the conditional R2 and is interpreted as variance explained by
both fixed and random (region) factors (i.e. the entire model). (*) for p < 0.05, (**) for p < 0.01 and (***) for p < 0.001.
NCI = Neighbourhood Competition Index
Buffering Statistic Openness Tree
height
NCI Cover of
the tree
layer
Cover of
the
shrub
layer
Total cover of
trees and
shrubs
Tmean over
the entire
year
Slope 0.0054 0.011 -0.0045 -0.0012 -0.0014 -0.0014
t 2.05 2.08 -0.060 -1.27 -1.52 -2.06
p 0.043* 0.04* 0.95 0.21 0.13 0.043*
R2m 0.027 0.030 2.70*10-5 0.010 0.017 0.032
R2c 0.51 0.48 0.47 0.49 0.48 0.50
Tmean in
spring
Slope 0.0068 0.011 -0.025 -0.0016 -0.0013 -0.0016
t 2.30 1.94 -0.30 -1.51 -1.18 -2.01
p 0.024* 0.056 0.77 0.13 0.24 0.047*
R2m 0.030 0.025 0.00067 0.014 0.0098 0.029
R2c 0.56 0.53 0.53 0.54 0.53 0.55
Tmean in
summer
Slope 0.016 0.016 -0.099 -0.0040 -0.0034 -0.0040
t 4.54 2.17 -0.95 -3.03 -2.59 -4.34
p p<0.0001*** 0.033* 0.34 0.0033** 0.011* p<0.0001***
R2m 0.12 0.034 0.0069 0.057 0.048 0.12
R2c 0.53 0.48 0.47 0.50 0.49 0.57
Tmean in
autumn
Slope 0.00098 0.0091 -0.0022 0.000059 -0.00055 -0.00028
t 0.43 2.06 -0.035 0.072 -0.69 -0.46
p 0.66 0.043* 0.97 0.94 0.49 0.64
R2m 0.00082 0.019 5.70*10-6 2.18*10-5 0.0023 0.0011
R2c 0.68 0.69 0.68 0.68 0.68 0.68
Tmean in
winter
Slope -0.00079 0.0081 0.10 0.00067 -0.00014 0.00027
t -0.33 1.71 1.58 0.77 -0.17 0.43
p 0.74 0.091 0.12 0.44 0.87 0.67
R2m 0.00055 0.015 0.015 0.0029 0.00015 0.0011
R2c 0.61 0.62 0.59 0.61 0.62 0.62
The buffering of Tmean had five significant relations in the summer, three over the entire year, two in
spring, one in autumn and none in winter. In summer, only the Neighbourhood Competition Index (NCI)
had no significant relationship with the buffering of Tmean. The relation with the openness and the total
cover of trees and shrubs of the forest were the most significant (p<0.0001). In summer, there were also
40
significant relations with the tree and shrub cover with p-values respectively 0.0033 and 0.011. The
buffering of Tmean decreased with respectively 0.0040 and 0.0034 °C per percent tree and shrub cover
which means colder forest Tmean with increasing tree and shrub cover. In spring there was a significant
relation with the openness (p = 0.024) and the total cover of trees and shrubs of the forest (p = 0.047).
Similar to summer the buffering of Tmean increased with increasing openness in spring thus the forest
Tmean became warmer with increasing openness (Figure 22). The increase varied from 0.0054 °C over
the entire year until 0.016 °C in summer per percent openness. The total cover of trees and shrubs had a
significant relation with the buffering of Tmean over the entire year (p = 0.043), in spring (p = 0.047) and
in summer (p<0.0001). The buffering of Tmean decreased with increasing total cover of trees and shrubs
(Figure 23). This indicates that the forest Tmean became colder with increasing total cover of trees and
shrubs in the forest. The temperature decreased from 0.0014 (over the entire year) until 0.0040 °C (in
summer) per percent total cover of trees and shrubs. The tree height had a significant relation with the
buffering of Tmean over the entire year (p = 0.04), in summer (p = 0.033) and in autumn (p = 0.043). The
buffering of Tmean increased with increasing tree height (= warmer forest Tmean) (Figure 24). In summer,
Tmean in the forest increased with 0.016 °C per meter tree height. R2c varied mostly between 45 and 65
% which means that the entire model explained 45 to 65 % of the total variance in significant relations.
R2m never exceeded 12 % thus the individual characteristics never explained more than 12 % of the
total variance in significant relations.
Figure 22 shows the relation between the openness of the forest and the buffering of Tmean. Each point
represents one of the hundred plots divided over the ten regions. Several plots showed positive buffering
of Tmean which indicates that the forest Tmean in that season was warmer, especially in spring. In spring
and summer, the buffering values increased with increasing openness which means that Tmean increased
with increasing openness. In autumn (p = 0.66) and winter (p = 0.74), no significant relation was found
between the buffering of Tmean and the openness which is indicated by a flat regression line (blue line)
that lies around the 0 °C buffering line (red line). The strongest relation was found in summer which
can be seen by the steep regression line. Furthermore, it can be noted that the regression line in summer
starts around -0.7 °C and becomes positive around 40 % openness while the regression line in spring
starts around 0.2 °C and gets more and more positive. This indicates the warmer forest Tmean in spring
and the colder forest Tmean in summer.
Figure 23 is analogous to Figure 22 but shows the relation with the total cover of trees and shrubs. There
was again most buffering in summer (steepest regression line) with decreasing buffering with increasing
total cover of trees and shrubs. Again, especially in spring, many plots showed warmer microclimate
temperatures. The regression line in summer starts with negative values and gets more negative whereas
the regression line in spring becomes never negative. The flat regression lines in winter (p = 0.67) and
autumn (p = 0.64) indicate that there is no significant relation with the total cover of trees and shrubs.
Figure 24 shows the relation between the buffering of Tmean and the tree height. Significant relations
were found in summer and autumn. But the relation in spring and winter were nearly significant with p-
values respectively 0.056 and 0.091. The buffering increased with increasing tree height thus the
microclimate Tmean increased with increasing tree height. The regression lines in autumn and winter are
very similar, with 0 °C buffering if trees are approximately 27 m high. Smaller trees resulted in slightly
colder forest Tmean and higher trees in slightly warmer forest Tmean. In summer the regression line is
always negative and in spring almost always positive.
41
Figure 22: Relation between the openness (%) and the buffering of Tmean (°C) in the four seasons over all the plots. (a)
Relation in spring. (b) Relation in summer. (c) Relation in autumn. (d) Relation in winter. A negative value means colder
temperatures inside the forest. The red line represents a buffering of 0 °C. The blue lines are regression lines of the
linear mixed effect models with the confidence interval of the regression lines shown in grey. p-values of the linear mixed
effect models are given.
42
Figure 23: Relation between the total cover of trees and shrubs (%) and the buffering of Tmean (°C) in the four seasons
over all the plots. (a) Relation in spring. (b) Relation in summer. (c) Relation in autumn. (d) Relation in winter. A
negative value means colder temperatures inside the forest. The red line represents a buffering of 0 °C. The blue lines
are regression lines of the linear mixed effect models with the confidence interval of the regression lines shown in grey.
p-values of the linear mixed effect models are given.
43
Figure 24: Relation between the tree height (m) and the buffering of Tmean (°C) in the four seasons over all the plots. (a)
Relation in spring. (b) Relation in summer. (c) Relation in autumn. (d) Relation in winter. A negative value means colder
temperatures inside the forest. The red line represents a buffering of 0 °C. The blue lines are regression lines of the
linear mixed effect models with the confidence interval of the regression lines shown in grey. p-values of the linear mixed
effect models are given.
44
Table 9: Relation between the buffering of Tmin over the entire measuring period and in the four seasons and the forest
characteristics. Significant values (p<0.05) are shown in bold. R2m is the marginal R2 and represents the variance
explained by fixed factors (forest characteristics). R2c is the conditional R2 and is interpreted as variance explained by
both fixed and random (region) factors (i.e. the entire model). (*) for p < 0.05, (**) for p < 0.01 and (***) for p < 0.001.
NCI = Neighbourhood Competition Index
Buffering Statistic Openness Tree
height
NCI Cover of
the tree
layer
Cover of the
shrub layer
Total cover of
trees and
shrubs
Tmin over the
entire year
Slope -0.011 -0.0019 0.17 0.0022 0.0014 0.0019
t -2.69 -0.21 1.43 1.38 0.91 1.69
p 0.0086** 0.83 0.16 0.17 0.36 0.094
R2m 0.013 9.23*10-5 0.0046 0.0036 0.0017 0.0063
R2c 0.86 8.50*10-1 0.85 0.85 0.86 0.86
Tmin in spring
Slope -0.016 -0.0082 0.266 0.0027 0.0022 0.0028
t -2.72 -0.69 1.60 1.27 1.07 1.76
p 0.0081** 0.49 0.11 0.21 0.29 0.082
R2m 0.015 0.0011 0.0068 0.0035 0.0027 0.0077
R2c 0.85 0.84 0.84 0.84 0.84 0.85
Tmin in
summer
Slope -0.0086 0.0044 0.18 0.0012 0.00042 0.00090
t -1.83 0.47 1.37 0.72 0.25 0.72
p 0.071 0.64 0.17 0.47 0.80 0.47
R2m 0.0050 0.00037 0.0033 0.00080 0.00011 0.00096
R2c 0.89 0.88 0.88 0.88 0.88 0.88
Tmin in
autumn
Slope -0.010 -0.00047 0.13 0.0017 0.0018 0.0019
t -2.57 -0.054 1.05 1.05 1.17 1.65
p 0.011* 0.96 0.30 0.30 0.24 0.10
R2m 0.015 7.36*10-6 0.0029 0.0025 0.0034 0.0072
R2c 0.83 0.83 0.82 0.83 0.83 0.84
Tmin in winter
Slope -0.0059 0.0025 0.12 0.0022 -0.00025 0.0010
t -1.87 0.39 1.32 1.90 -0.21 1.22
p 0.065 0.70 0.19 0.061 0.83 0.23
R2m 0.0067 0.00031 0.0039 0.0067 9.85*10-5 0.0033
R2c 0.85 0.85 0.85 0.86 0.85 0.86
The buffering of Tmin had only three significant relations with forest characteristics namely the relation
with the openness of the forest in autumn (p = 0.011), spring (p = 0.0081) and over the entire measuring
period (p = 0.0086). The buffering of Tmin decreased with respectively 0.010, 0.016 and 0.011 °C per
percent openness. Therefore, Tmin is colder in more open forests (Figure 25). R2c varies mostly between
83 and 86 % which is a lot higher compared to the buffering of Tmean. The random factor (region) explains
20 to 40 % more variance in comparison with the buffering of Tmean in significant relations. R2m is 1.5
% at the most thus the openness did not explain more than 1.5 % of the variance of the buffering of Tmin
in significant relations.
Figure 25 shows the relation between the buffering of Tmin and the openness of the forest. Highest
buffering values were found in spring and summer (up to 4°C) which indicates that the forest Tmin warmer
was. In several plots, forest Tmin was colder which can be seen at the negative buffering values. The
relations in spring and autumn are significant and they also have the steepest regression lines but the p-
values in summer and winter are respectively 0.071 and 0.065 which is nearly significant.
45
Figure 26 shows the buffering of Tmin over the entire measuring period per region in function of the
openness. Regions as Göttingen and Zvolen had a more positive buffering of Tmin compared to other
regions such as Wytham and Tournibus. In Wytham and Tournibus there were even several plots with
colder forest Tmin compared to the open field. Within one region the buffering decreased with increasing
openness. Thus, Tmin became colder with increasing openness.
Figure 25: Relation between the openness (%) and the buffering of Tmin (°C) in the four seasons over all the plots. (a)
Relation in spring. (b) Relation in summer. (c) Relation in autumn. (d) Relation in winter. A negative value means colder
temperatures inside the forest. The red line represents a buffering of 0 °C. The blue lines are regression lines of the
linear mixed effect models with the confidence interval of the regression lines shown in grey. p-values of the linear mixed
effect models are given.
.
46
Figure 26: Buffering of Tmin (°C) over the entire measuring period per region in function of the openness (%). A negative
value means colder temperatures inside the forest. The red line represents a buffering of 0 °C. The blue line is a
regression line of the linear mixed effect model. WW = Wytham woods, SP = Speulderbos, TB = Tournibus, CO =
Compiègne, GO = Göttingen, PR = Prignitz, SKA = Skåne, KO = Koda woods, ZV = Zvolen, BI = Bialowieza.
47
Table 10: Relation between the buffering of Tmax over the entire measuring period and in the four seasons and the forest
characteristics. Significant values (p<0.05) are shown in bold. R2m is the marginal R2 and represents the variance
explained by fixed factors (forest characteristics). R2c is the conditional R2 and is interpreted as variance explained by
both fixed and random (region) factors (i.e. the entire model). (*) for p < 0.05, (**) for p < 0.01 and (***) for p < 0.001.
NCI = Neighbourhood Competition Index
Buffering Statistic Openness Tree
height
NCI Cover of the
tree layer
Cover of
the shrub
layer
Total cover
of trees and
shrubs
Tmax over
the entire
year
Slope 0.038 0.037 -0.39 -0.0079 -0.0066 -0.0078
t 7.47 3.02 -2.28 -3.66 -3.089 -5.43
p p<0.0001*** 0.033* 0.025* 0.0004*** 0.0027** p<0.0001***
R2m 0.27 0.064 0.042 0.086 0.061 0.16
R2c 0.62 0.47 0.43 0.49 0.54 0.65
Tmax in
spring
Slope 0.047 0.050 -0.56 -0.0091 -0.0065 -0.0086
t 6.41 3.02 -2.32 -2.95 -2.14 -4.05
p p<0.0001*** 0.0034** 0.023* 0.0041** 0.035* 0.0001***
R2m 0.20 0.066 0.047 0.057 0.032 0.10
R2c 0.62 0.49 0.42 0.50 0.52 0.61
Tmax in
summer
Slope 0.069 0.045 -0.83 -0.016 -0.012 -0.016
t 9.59 2.34 -3.19 -5.06 -3.79 -7.70
p p<0.0001*** 0.022* 0.002** p<0.0001*** 0.0003*** p<0.0001***
R2m 0.37 0.039 0.076 0.14 0.087 0.24
R2c 0.68 0.48 0.48 0.55 0.58 0.74
Tmax in
autumn
Slope 0.027 0.031 -0.24 -0.0040 -0.0050 -0.0048
t 6.35 3.21 -1.73 -2.25 -2.93 -3.99
p p<0.0001*** 0.0019** 0.087 0.027* 0.0043** 0.0001***
R2m 0.21 0.067 0.022 0.034 0.052 0.097
R2c 0.60 0.54 0.49 0.48 0.58 0.61
Tmax in
winter
Slope 0.00709 0.017 0.078 0.00022 -0.00078 -0.00054
t 1.98 2.38 0.77 -0.17 -0.60 -0.56
p 0.050 0.020* 0.44 0.87 0.55 0.58
R2m 0.020 0.030 0.0035 0.00015 0.0020 0.0019
R2c 0.61 0.61 0.60 0.59 0.61 0.61
The buffering of Tmax had a significant relation with all the forest characteristics over the entire
measuring period, in spring and in summer. In autumn, only the NCI (p = 0.087) had no significant
relation with the buffering of Tmax. In winter only the tree height (p = 0.02) had a significant relation
with Tmax. Hence, the tree height had a significant relation with the buffering of Tmax over the entire year
and in each season (Figure 27). The buffering of Tmax increased with 0.017 to 0.050 °C per meter tree
height. Thus, the forest Tmax got warmer with increasing tree height. The forest Tmax increased with 0.027
to 0.069 °C per percent openness and decreased with 0.0045 to 0.016 °C per percent total cover of trees
and shrubs. In significant relations, R2c varied between 42 and 74 % which is similar to R2c of the
buffering of Tmean. In significant relations, R2m varied between 3 and 37 % which is nearly 35 % higher
than the highest values of R2m by the buffering of Tmean and Tmin. Thus, in some cases a lot more variation
of the buffering of Tmax was explained by the forest parameters. Especially the openness and the total
cover of trees and shrubs had high R2m values (except in winter). The buffering of Tmax over the entire
year (p = 0.025), in spring (p = 0.023) and in summer (p = 0.002) had a significant relation with the NCI
(Figure 28). The buffering of Tmax became more negative with increasing NCI, which means a colder
forest Tmax with increasing NCI. The buffering of Tmax decreased between 0.39 and 0.83 °C per one unit
NCI.
48
Figure 27: Relation between tree height (m) and the buffering of Tmax (°C) in the four seasons over all the plots. (a)
Relation in spring. (b) Relation in summer. (c) Relation in autumn. (d) Relation in winter. A negative value means colder
temperatures inside the forest. The red line represents a buffering of 0 °C. The blue lines are regression lines of the
linear mixed effect models with the confidence interval of the regression lines shown in grey. p-values of the linear mixed
effect models are given.
Figure 27 shows a positive relation between the tree height and the buffering in each season. Most plots
had a negative buffering value of Tmax in summer and autumn. Therefore, the forest Tmax was colder in
summer and autumn. In winter and especially in spring, more plots had a positive buffering value of
Tmax thus a warmer forest Tmax. Figure 28 is analogous to Figure 27 but the relation with the NCI is
shown. No significant relation was found between the buffering of Tmax in autumn and winter which can
be seen on the relative flat regression lines in those seasons. The regression lines in summer and spring
are a lot more steeper and show a negative relation.
49
Figure 28: Relation between Neighbourhood Competition Index (NCI) and the buffering of Tmax (°C) in the four seasons
over all the plots. (a) Relation in spring. (b) Relation in summer. (c) Relation in autumn. (d) Relation in winter. A
negative value means colder temperatures inside the forest. The red line represents a buffering of 0 °C. The blue lines
are regression lines of the linear mixed effect models with the confidence interval of the regression lines shown in grey.
p-values of the linear mixed effect models are given.
Figure 29 indicates the buffering of Tmax over the entire measuring period per region in function of the
tree cover. Within one region the buffering decreased with increasing tree cover. Thus, the forest Tmax
became colder with increasing tree cover.
50
Figure 29: Relation between the tree cover (%) and the buffering of Tmax (°C) in summer for the ten regions. A negative
value means colder temperatures inside the forest. The red line represents a buffering of 0 °C. The blue line is the
regression line of the linear mixed effect model. WW = Wytham woods, SP = Speulderbos, TB = Tournibus, CO =
Compiègne, GO = Göttingen, PR = Prignitz, SKA = Skåne, KO = Koda woods, ZV = Zvolen, BI = Bialowieza.
51
3.4. Effect of landscape characteristics The relationships between landscape characteristics obtained from the digital elevation model (DEM)
and the forest map in Hansen et al. (2013) and the buffering of the daily Tmean, Tmin and Tmax are shown
below. The percentage forest cover in a radius of 500 m around the plot had no significant relations with
the buffering.
3.4.1. Relation with the distance to the coast
Relations between the buffering of the temperature and the distance to the coast of the plots are shown
in table 11.
Table 11: Relations between the buffering of the temperature and the distance to the coast. Significant values (p<0.05)
are shown in bold. (*) for p < 0.05, (**) for p < 0.01 and (***) for p < 0.001. R2m is the marginal R2 and represents the
variance explained by the distance to the coast of the plots. R2c is the conditional R2 and is interpreted as variance
explained by both fixed and random (region) factors (i.e. the entire model)
Buffering variable Period Slope t p R2m R2c
Tmin
Entire year
Spring
0.0033
0.0034
2.11
1.59
0.037*
0.12
0.26
0.17
0.85
0.84
Summer 0.0039 2.09 0.040* 0.25 0.88
Autumn 0.0031 2.21 0.030* 0.25 0.82
Winter 0.0031 2.99 0.0036** 0.40 0.85
Tmax
Entire year
Spring
-0.0016
-0.0026
-1.70
-2.04
0.094
0.045*
0.10
0.15
0.42
0.44
Summer
Autumn
Winter
-0.0035
-0.00072
-0.00020
-2.73
-0.79
-0.23
0.0077**
0.43
0.82
0.20
0.029
0.0033
0.42
0.49
0.62
Tmean
Entire year
Spring
Summer
0.0011
0.00091
0.00075
3.30
1.67
1.13
0.0014**
0.10
0.26
0.27
0.13
0.058
0.48
0.55
0.48
Autumn 0.0017 8.17 p<0.0001*** 0.59 0.65
Winter 0.0013 3.07 0.0028** 0.32 0.63
Table 11 shows that R2m of the significant relations varies between 15 to 59 % thus 15 to 59 % of the
variation in the buffering is explained by the distance to the coast. Tmean and Tmin show a positive relation
with the distance to the coast (Figure 30). The buffering of Tmean and Tmin increased with increasing
distance to the coast namely 0.0011 until 0.0039 °C per km from the coast. The Tmax showed a negative
relation with the distance to the coast namely a decrease of 0.0026 until 0.0035 °C per km from the coast
(Figure 30). This means that the forest Tmax became colder and the forest Tmean and Tmin became warmer
with increasing distance to the coast.
Figure 30 shows a clear trend of the buffering of Tmin in summer to more positive values with increasing
distance from the coast and an inverse trend for the buffering of Tmax in summer. Thus, the extreme
temperatures were more buffered with increasing distance from the coast because the buffering of Tmin
increased (warmer forest Tmin) and the buffering of Tmax decreased (colder forest Tmax).
52
Figure 30: Relation between the buffering of Tmax and Tmin in summer (°C) and the distance to the coast (km) of the
plots. A negative value means colder temperatures inside the forest. The red line represents a buffering of 0 °C. The
green (Tmin) and orange (Tmax) lines are regression lines of the linear mixed effect models. p-values of the linear mixed
effect models are given.
53
3.4.2. Relation with the latitude
Relations between the buffering of the temperature and the latitude of the plots are shown in table 12.
Table 12: Relations between the buffering of the temperature and the latitude of the plots. Significant values (p<0.05)
are shown in bold. (*) for p < 0.05, (**) for p < 0.01 and (***) for p < 0.001. R2m is the marginal R2 and represents the
variance explained by the latitude of the plots. R2c is the conditional R2 and is interpreted as variance explained by
both fixed and random (region) factors (i.e. the entire model)
Buffering variable Period Slope t p R2m R2c
Tmin
Entire year
Spring
Summer
-0.20
-0.21
-0.29
-1.46
-1.19
-1.76
0.15
0.24
0.08
0.15
0.10
0.20
0.87
0.85
0.90
Autumn -0.21 -1.68 0.10 0.18 0.85
Winter -0.12 -1.18 0.24 0.10 0.86
Tmax
Entire year
Spring
Summer
Autumn
-0.0011
-0.022
0.096
-0.0013
-0.013
-0.18
0.72
-0.018
0.99
0.85
0.47
0.99
8.52*10-6
0.0018
0.025
1.66*10-5
0.44
0.47
0.45
0.52
Winter -0.051 -0.80 0.43 0.038 0.61
Tmean
Entire year
Spring
Summer
-0.070
-0.073
-0.058
-2.22
-1.79
-1.14
0.029*
0.08
0.26
0.17
0.14
0.061
0.48
0.54
0.48
Autumn -0.090 -2.43 0.017* 0.26 0.67
Winter -0.063 -1.60 0.11 0.13 0.62
The latitude had only significant relations with Tmean namely over the entire measuring period (p = 0.029)
and in autumn (p = 0.017). Both relations are negative thus the buffering of Tmean decreased with
increasing latitude. Figure 31 indicates that in the more northern regions, the forest Tmean tends to be
colder than the outside Tmean. The reverse can be observed in the more southern regions.
Figure 31: Relation between the buffering of Tmean over the entire year (°C) and the latitude (°) of the plots. A negative
value means colder temperatures inside the forest. The red line represents a buffering of 0 °C. The blue line is the
regression line of the linear mixed effect model with the confidence interval of the regression line shown in grey. p-value
of the linear mixed effect model is given.
54
3.4.3. Relation with the elevation above sea level
Relations between the buffering of the temperature and the elevation above sea level of the plots are
shown in table 13.
Table 13: Relations between the buffering of the temperature and the elevation above sea level of the plots. Significant
values (p<0.05) are shown in bold. (*) for p < 0.05, (**) for p < 0.01 and (***) for p < 0.001. R2m is the marginal R2 and
represents the variance explained by the elevation above sea level of the plots. R2c is the conditional R2 and is interpreted
as variance explained by both fixed and random (region) factors (i.e. the entire model)
Buffering variable Period Slope t p R2m R2c
Tmin
Entire year 0.0065 5.61 p<0.0001*** 0.56 0.94
Spring 0.0073 4.77 p<0.0001*** 0.51 0.92
Summer 0.0076 6.08 p<0.0001*** 0.58 0.95
Autumn 0.0054 4.93 p<0.0001*** 0.52 0.92
Winter 0.0039 4.82 p<0.0001*** 0.52 0.91
Tmax
Entire year
Spring
Summer
Autumn
Winter
-0.0017
-0.0022
-0.0035
-0.0011
-0.000087
-1.83
-1.72
-2.79
-1.29
-0.12
0.07
0.09
0.0065**
0.20
0.91
0.12
0.12
0.22
0.067
0.00072
0.48
0.50
0.48
0.53
0.62
Tmean
Entire year 0.0013 4.77 p<0.0001*** 0.39 0.52
Spring 0.0014 3.48 0.0008*** 0.33 0.58
Summer 0.0012 2.21 0.030* 0.16 0.49
Autumn 0.0017 6.76 p<0.0001*** 0.59 0.71
Winter 0.0014 3.32 0.0013** 0.35 0.69
The p-values in table 13 of the relations between Tmin and the elevation above sea level are always
p<0.0001 which indicates strong relations. R2c of significant relations varied between 48 % and 95 %
therefore several models explained more than 90 % of the total variation of the buffering. R2m of
significant relations was also very high namely up to 59 %. Thus, the elevation above sea level explained
59 % of the buffering of Tmean in autumn. The buffering of Tmean and Tmin had a significant positive relation
with the elevation above sea level in each season (Figure 32). The buffering increased with 0.0012 to
0.0076 °C per meter elevation above sea level. The buffering of Tmax had only one significant relation,
namely in summer (p = 0.0065) which was a negative relation. The buffering of Tmax decreased with
0.0035 °C per meter elevation above sea level (Figure 32). Figure 32 shows a trend towards a more
positive buffering of Tmin with increasing elevation above sea level. The buffering of Tmean had an
analogue trend but less pronounced. The buffering of Tmax had an inverse trend. The higher the plot above
sea level the more the extreme temperatures were buffered (warmer forest Tmin and colder forest Tmax).
55
Figure 32: Relation between the buffering of Tmean, Tmin and Tmax in summer (°C) and the elevation above sea level (m)
of the plots. A negative value means colder temperatures inside the forest. The red line represents a buffering of 0 °C.
The blue (Tmin), orange (Tmax) and green (Tmean) lines are regression lines of the linear mixed effect models. p-values of
the linear mixed effect models are given.
56
3.4.4. Relation with the slope of the plots
Relations between the buffering of the temperature and the slope of the plots are shown in table 14.
Table 14: Relations between the buffering of the temperature and the slope of the plots. Significant values (p<0.05) are
shown in bold. (*) for p < 0.05, (**) for p < 0.01 and (***) for p < 0.001. R2m is the marginal R2 and represents the
variance explained by the slope of the plots. R2c is the conditional R2 and is interpreted as variance explained by both
fixed and random (region) factors (i.e. the entire model)
Buffering variable Period Slope t p R2m R2c
Tmin
Entire year
Spring
Summer
0.0061
0.0057
-0.0021
0.43
0.30
-0.14
0.67
0.77
0.89
0.00063
0.00034
5.24*10-5
0.85
0.83
0.88
Autumn 0.00087 0.062 0.95 1.49*10-5 0.82
Winter 0.0068 0.65 0.51 0.0014 0.85
Tmax
Entire year
Spring
Summer
Autumn
0.034
0.059
0.046
0.017
1.78
2.25
1.52
1.09
0.08
0.027*
0.13
0.28
0.035
0.056
0.025
0.012
0.43
0.47
0.46
0.48
Winter 0.016 1.42 0.16 0.018 0.58
Tmean
Entire year
Spring
Summer
0.014
0.019
0.016
1.76
2.08
1.37
0.08
0.041*
0.18
0.034
0.045
0.021
0.44
0.52
0.43
Autumn 0.0037 0.52 0.61 0.0019 0.67
Winter 0.0081 1.07 0.29 0.0098 0.59
The significant relations with the slope of the plots explained only 4.5 to 5.6 % of the variation of the
buffering. Two significant relations between the buffering of the temperature and the slope of the plots
were found namely with Tmax (p = 0.027) and Tmean (p = 0.041) in spring. The relation was positive namely
an increase of the buffering with 0.019 to 0.059 °C per degree of the slope (Figure 33). Thus, a higher
slope increased the chance of a positive buffering which indicates warmer forest Tmax and Tmean in spring.
A higher slope of the plots increased the chance of more extreme microclimate temperatures.
57
Figure 33: Relation between the buffering of Tmean and Tmax in spring (°C) and the slope (°) of the plots. A negative value
means colder temperatures inside the forest. The red line represents a buffering of 0 °C. The orange (Tmax) and green
(Tmean) lines are regression lines of the linear mixed effect models. p-values of the linear mixed effect models are given.
58
3.4.5. Relation with the length of forest edge in a radius of 500 m
Relations between the buffering of the temperature and the length of forest edge (km) in a radius of 500
m of the plots are shown in table 15. The length of forest edge was calculated by adding up all contour
lines of the forest map (Hansen et al., 2013) within a radius of 500 m. Forest edge thus describes the
total length (in kilometres) of forest pixel sides neighbouring non-forest pixels.
Table 15: Relations between the buffering of the temperature and the length of forest edge in a radius of 500 m (km).
Significant values (p<0.05) are shown in bold. (*) for p < 0.05, (**) for p < 0.01 and (***) for p < 0.001. R2m is the
marginal R2 and represents the variance explained by length of forest edge in a radius of 500 m of the plots. R2c is the
conditional R2 and is interpreted as variance explained by both fixed and random (region) factors (i.e. the entire model)
Buffering variable Period Slope t p R2m R2c
Tmin
Entire year
Spring
Summer
-0.072
-0.073
-0.093
-1.99
-1.49
-2.39
0.049*
0.14
0.019*
0.015
0.010
0.018
0.85
0.84
0.89
Autumn -0.070 -1.94 0.056 0.017 0.83
Winter -0.025 -0.93 0.35 0.0034 0.85
Tmax
Entire year
Spring
Summer
Autumn
0.071
0.099
0.093
0.096
1.44
1.44
1.18
2.44
0.15
0.15
0.24
0.017*
0.027
0.027
0.018
0.067
0.43
0.47
0.44
0.54
Winter 0.061 2.11 0.038* 0.041 0.63
Tmean
Entire year
Spring
Summer
-0.000037
0.0080
0.0097
-0.0017
0.33
0.32
0.998
0.74
0.75
3.63*10-8
0.0013
0.0013
0.48
0.54
0.46
Autumn 0.0078 0.42 0.68 0.0014 0.69
Winter 0.014 0.72 0.47 0.0048 0.63
Significant relations with the length of forest edge in a radius of 500 m of the plot (km) explained only
1.5 to 6.7 % of the total variance of the buffering of Tmin or Tmax in that period. There are significant
relations with the buffering of Tmin (over the entire measuring period (p = 0.049) and in summer (p =
0.019)) and Tmax (in autumn (p = 0.017) and in winter (p = 0.038)). The buffering of Tmin decreased with
0.072 to 0.093 °C per km of forest edge in a radius of 500 m of the plot while the buffering of Tmax
increased with 0.017 to 0.038 °C per km forest edge. Hence, forest Tmin and Tmax became more extreme
with increasing length of forest edge in a radius of 500 m of the plot (warmer forest Tmax and colder
forest Tmin) (Figure 34).
59
Figure 34: Relation between the buffering of Tmin in summer (°C) (a), buffering of Tmax in autumn (b) and the length of
forest edge in a radius of 500 m (km) around the plots. A negative value means colder temperatures inside the forest.
The blue line is a regression line of the linear mixed effect model with the confidence interval of the regression line
shown in grey. p-values of the linear mixed effect models are given.
60
3.4.6. Relation with the relative elevation of the plots in a radius of 250 m
The relative elevation should not be confused with the elevation above sea level. The relative elevation
is the result of subtracting the minimum elevation above sea level within a radius of 250 m of the plot
from the elevation above sea level of each plot. Therefore, the relative elevation indicates the elevation
of the plots relative compared to the minimum elevation in a circular area of 250 m around the plots.
Relations between the buffering of the temperature and the relative elevation of the plots in a radius of
250 m are shown in table 16.
Table 16: Relations between the buffering of the temperature and the relative elevation of the plots in a radius of 250
m. Significant values (p<0.05) are shown in bold. (*) for p < 0.05, (**) for p < 0.01 and (***) for p < 0.001. R2m is the
marginal R2 and represents the variance explained by the relative elevation of the plots in a radius of 250 m. R2c is the
conditional R2 and is interpreted as variance explained by both fixed and random (region) factors (i.e. the entire model)
Buffering variable Period Slope t p R2m R2c
Tmin
Entire year 0.011 4.41 p<0.0001*** 0.054 0.85
Spring 0.013 4.08 0.0001*** 0.054 0.83
Summer 0.011 4.12 0.0001*** 0.039 0.88
Autumn 0.0079 3.11 0.0025** 0.036 0.81
Winter 0.0055 4.19 0.0001*** 0.11 0.67
Tmax
Entire year
Spring
0.00023 0.62 0.95 3.90*10-5 0.41
-0.00074
0.00095
-0.15 0.88 0.00022 0.43
Summer 0.16 0.87 0.00027 0.43
Autumn -0.0011 -0.35 0.72 0.0012 0.46
Winter 0.0037 1.75 0.084 0.021 0.62
Tmean
Entire year 0.0073 5.17 p<0.0001*** 0.23 0.48
Spring 0.0081 5.27 p<0.0001*** 0.24 0.50
Summer 0.0085 4.16 0.0001*** 0.17 0.43
Autumn 0.0043 3.33 0.0013** 0.074 0.65
Winter 0.0076 4.28 p<0.0001*** 0.049 0.86
The buffering of Tmin and Tmean had a positive significant relation in each season with the relative
elevation of the plot in a radius of 250 m. No significant relation with the buffering of Tmax was found.
The buffering of Tmin and Tmean increased with 0.0043 to 0.013 °C per meter relative elevation of the plot
in a radius of 250 m (Figure 35). Figure 35 shows a trend towards a more positive buffering with
increasing relative elevation. Plots with a low relative elevation had a more extreme (=colder) Tmin
compared to plots with a higher relative elevation.
61
3.4.7. Relation with the north orientation (northness) of the plots
Relations between the buffering of the temperature and the north orientation (northness) of the plots are
shown in table 17.
Table 17: Relations between the buffering of the temperature and the north orientation (northness) of the plots.
Significant values (p<0.05) are shown in bold. (*) for p < 0.05, (**) for p < 0.01 and (***) for p < 0.001. R2m is the
marginal R2 and represents the variance explained by the north orientation of the plots. R2c is the conditional R2 and is
interpreted as variance explained by both fixed and random (region) factors (i.e. the entire model)
Buffering variable Period Slope t p R2m R2c
Tmin
Entire year
Spring
-0.023
-0.023
-0.42
-0.31
0.68
0.76
0.00027
0.00018
0.85
0.83
Summer -0.0032 -0.055 0.96 3.83*10-6 0.88
Autumn 0.018 0.32 0.75 0.00020 0.83
Winter -0.027 -0.67 0.51 0.00070 0.85
Tmax
Entire year -0.25 -3.29 0.0014** 0.061 0.46
Spring
Summer
-0.38
-0.21
-3.66
-1.65
0.0004***
0.10
0.074
0.017
0.50
0.43
Autumn -0.23 -3.87 0.0002*** 0.074 0.55
Winter -0.197 -4.76 p<0.0001*** 0.078 0.68
Tmean
Entire year -0.10 -3.08 0.0028** 0.048 0.52
Spring
Summer
-0.14
-0.072
-3.92
-1.51
0.0002***
0.13
0.069
0.013
0.59
0.46
Autumn -0.065 -2.30 0.024* 0.018 0.70
Winter -0.088 -3.04 0.0031** 0.034 0.65
Figure 35: Relation between the buffering of Tmin and Tmean in summer (°C) and the relative elevation of the plots in a
radius of 250 m (m). A negative value means colder temperatures inside the forest. The green (Tmin) and orange (Tmean)
lines are regression lines of the linear mixed effect models. p-values of the linear mixed effect models are given.
62
The north orientation (northness) of the plots had a significant relation with the buffering of Tmean and
Tmax in each season except summer, but no significant relation with the buffering of Tmin (table 17). The
variation of the buffering of significant relations explained by the marginal R squared varies between
1.8 and 7.4 %. All significant relations were negative relations. The buffering of Tmean and Tmax decreased
with respectively 0.065 and 0.25 °C per unit northness (=cos(aspect)) (Figure 36). In Figure 36, a trend
is shown towards decreasing buffering of Tmax and Tmean which means colder forest Tmax and Tmean with
increasing northness of the plots compared to the open field.
Figure 36: Relation between the buffering of Tmax and Tmean in spring (°c) and the northness of the plots. A negative
value of the buffering means colder temperatures inside the forest. The orange (Tmax) and green (Tmean) lines are
regression lines of the linear mixed effect models. p-values of the linear mixed effect models are given.
63
3.4.8. Relation with the east orientation (eastness) of the plots
Relations between the buffering of the temperature and the east orientation (eastness) of the plots are
shown in table 18.
Table 18: Relations between the buffering of the temperature and the eastness of the plots. Significant values (p<0.05)
are shown in bold. (*) for p < 0.05, (**) for p < 0.01 and (***) for p < 0.001. R2m is the marginal R2 and represents the
variance explained by the east orientation of the plots. R2c is the conditional R2 and is interpreted as variance explained
by both fixed and random (region) factors (i.e. the entire model)
Buffering variable Period Slope t p R2m R2c
Tmin
Entire year
Spring
-0.042
0.010
-0.66
0.12
0.51
0.91
0.00076
0.000027
0.85
0.83
Summer -0.053 -0.77 0.44 0.00082 0.88
Autumn -0.077 -1.22 0.23 0.0031 0.85
Winter -0.070 -1.52 0.13 0.0039 0.85
Tmax
Entire year -0.16 -1.77 0.08 0.020 0.43
Spring
Summer
-0.27
-0.21
-2.09
-1.47
0.039*
0.15
0.029
0.014
0.46
0.44
Autumn -0.085 -1.17 0.25 0.0083 0.48
Winter -0.040 -0.76 0.45 0.0027 0.60
Tmean
Entire year -0.061 -1.55 0.12 0.015 0.47
Spring
Summer
-0.053
-0.081
-1.18
-1.45
0.24
0.15
0.0082
0.013
0.52
0.44
Autumn -0.055 -1.70 0.09 0.011 0.69
Winter -0.021 -0.61 0.54 0.0017 0.62
The eastness of the plot had only a significant relation with the buffering of Tmax in spring (p = 0.039).
The buffering decreased with increasing eastness thus Tmax in the plots is less extreme (=colder) with
increasing eastness of the plots compared to the open field.
64
4. Discussion
4.1. Buffering in the four seasons
The analysis of the data shows that the temperature was often buffered inside temperate forests across
Euro²pe. Differences in buffering occurred between the daily Tmean, Tmin and Tmax and between each of
the four seasons. The daily forest Tmin was 0.89, 0.99, 1.35, 0.84 and 0.41 °C warmer compared to the
outside temperature in respectively the whole measuring period, spring, summer, autumn and winter.
Holbo and Childs (1987) and Moore et al. (2005) explain the warmer forest nighttime temperatures with
the fact that overstory canopy insulates the understory environment from longwave radiative losses. At
the same time, solar radiation intercepted and absorbed by the canopy during the day is re-radiated as
longwave heat energy by canopy biomass and tree boles (Geiger et al., 2009). Those clarifications also
immediately explain why most buffering occurred in summer and the least buffering occurred in winter.
Since the forests were deciduous forests at each of the ten regions, no leaves were present in winter and
the densest canopy was present in summer. In summer there was also more solar radiation and therefore
more energy to absorb. Renaud & Rebetez (2009) found that Tmin was 0.75 °C warmer under canopy
compared to open field. The study took place on fourteen locations in Switzerland between April and
October 2003 in deciduous, mixed and coniferous forests. In summer (June, July and August) Tmin was
0.84 °C warmer under canopy (Renaud & Rebetez, 2009). In this study, Tmin in summer was on average
buffered with 1.35 °C which is a similar value compared to the study of Renaud & Rebetez (2009).
The daily forest Tmax was 0.70, 2.05, 0.89 and 0.24 °C colder in forests compared to the outside
temperature in respectively the whole measuring period, summer, autumn and winter. In spring the forest
Tmax was 0.32 °C warmer compared to the open field. Raynor (1971); Aston (1985); Aussenac (2000)
explain the colder forest Tmax in closed forests, the canopy absorbs much of the incoming energy leading
to decreases in sub-canopy temperatures with increasing leaf area. In comparison, more energy
penetrates the canopy in sparse vegetation types, thus energy exchange is more affected by sub-canopy
heat sinks such as tree trunks and the soil surface in sparse vegetation types (Baldocchi et al., 2000).
The solar radiation will not only be absorbed by the vegetation, but a part will also be reflected. The
combination of reflection and absorption of solar radiation by vegetation in deciduous forests explains
the seasonal pattern of the buffering of Tmax. Hutchison & Matt (1977) found that the greatest amounts
of radiation are received within the forest in the spring before leaf expansion begins which explains the
warmer forest Tmax in spring. Hence, solar radiation could heat up sub-canopy heat sinks such as tree
trunks and the soil surface. The least radiation is received with the lower solar elevations and shorter
day lengths of early autumn while the forest is still fully leafed (Hutchison & Matt, 1977). With leaf fall
later in the autumn, radiation in the forest increases slightly but then decreases again with the winter
decline of insolation (Hutchison & Matt, 1977). Thus, most buffering occurred when most canopy was
present thus when most energy can be absorbed (summer). In autumn, leaves started to fall therefore
less energy could be absorbed what resulted in less buffering. Even less buffering occurred in winter
when there were no leaves in the canopy.
The increasing heat in spring could mix or spread less easy compared to open field as there are lower
wind speeds inside forests compared to open fields (Morecroft et al., 1998; Grimmond et al., 2000),
65
what resulted in warmer forest Tmax in spring. Davies-Colley et al. (2000) found that at a point 80 m from
the edge of mature native broadleaf rainforest adjoining grazed pasture in New Zealand, wind speed was
only 20 % of that in the open field. Chen et al. (1993) discovered that the wind velocity as a percentage
of that in a clear cut was about 15-20 % in the forest. In winter, the sun had not enough power to heat
up the sub-canopy heat sinks.
Another cooling effect in forests is evapotranspiration. Allen et al. (1998) defines evapotranspiration
(ET) as the combination of two separate processes whereby water is converted to water vapor on the
one hand from the soil surface by evaporation and on the other hand from plan tissues by transpiration
in to the atmosphere. Apart from the water availability in the topsoil, the evaporation from a forest is
mainly determined by the fraction of the solar radiation reaching the soil surface (Allen et al., 1998).
This fraction decreases with increasing density of the forest and the canopy shades more and more of
the ground area (Allen et al., 1998). When little or no vegetation is present, water is predominately lost
by soil evaporation, but with increasing canopy cover, transpiration becomes the main process (Allen et
al., 1998). Energy is needed to convert liquid water into water vapor (= latent heat). Thus, a part of the
available energy will appear as latent heat and not as sensible heat giving vegetation a cooling effect.
Again, most cooling effect will occur when most vegetation/canopy is present in deciduous forests
(summer) and less cooling effect in spring, autumn and winter.
Renaud & Rebetez (2009) observed that Tmax was on average 2.37 °C cooler under the canopy between
April and October 2003. In summer (June, July and August) Tmax was 2.62 °C cooler under canopy
compared to open field (Renaud & Rebetez, 2009). The buffering value of 2.62 °C from Renaud &
Rebetez (2009) is similar with the found value of 2.05 °C. There was also more buffering of Tmax in
summer in the study of Renaud & Rebetez (2009) compared to the period of April - October which also
corresponds with this research. Morecroft et al. (1998) measured maximum temperatures in the
woodland being 2 - 3°C colder than those for grassland in summer and autumn (Morecroft et al., 1998).
In winter and spring, the maxima were similar under the canopy and in the open (Morecroft et al., 1998).
This corresponds with the obtained values in this research with most buffering of Tmax in summer and
autumn (-2.05 and -0.89 °C) and similar temperatures between forest and open in winter and spring (-
0.24 and 0.32 °C).
The daily forest Tmean was 0.057, 049, 0.0030 and 0.0011 °C colder compared to the open field in
respectively the entire measuring period, summer, autumn and winter. In spring, the forest Tmean was
0.27 °C warmer compared to the open field. Renaud et al. (2011) discovered that Tmean values were colder
below-canopy in deciduous and mixed forests, 1 to 2 °C in summer, up to 1 °C in winter and on a yearly
average. Morecroft et al. (1998) monitored forest microclimate for more than three years at two sites in
deciduous woodland at Wytham Woods (which is one of the ten regions in this study). These data were
compared with values from an open site at the same location. During the winter, the mean values of air
temperatures under the canopy were close to the air temperature at the grassland site: air temperatures
either did not differ or were up to 0.2 °C cooler for the whole period of study (Morecroft et al., 1998).
The results of the study of Morecroft et al. (1998) are very similar to the results of this study with
none/little buffering of Tmean during winter. In summer, the differences were larger: mean air temperature
was 0.9 °C cooler in the high forest site (Morecroft et al., 1998) which is also very similar to this study
(0.49 °C cooler in forests in summer).
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4.2. Buffering of the temperature as a function of forest
characteristics Several forest characteristics were correlated (Figure 40 in appendix) which indicates that similar
relations can be seen with different characteristics. A higher openness of the forest indicates a lower
tree, shrub and total cover of trees and shrubs. More tree and/or shrub cover results in a higher total
cover of trees and shrubs. A higher neighbourhood competition index (NCI) suggests more tree cover
(thus more total cover of trees and shrubs) and less openness because a higher NCI is obtained by both
more trees and/or bigger trees. A plot with n trees with a DBH > 7.5 cm in the plot with a radius of 9 m
around the central tree with the temperature logger will result in NCI = ∑ 𝐷𝐵𝐻 (𝑚)/𝑛𝑖=1
𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒 𝑡𝑜 𝑡ℎ𝑒 𝑐𝑒𝑛𝑡𝑟𝑎𝑙 𝑡𝑟𝑒𝑒 (𝑚)
As indicated in section 4.1, buffering strongly depends on the size and density of the canopies of the
trees and therefore on the seasons (in deciduous forests) (explanations by Raynor, 1971; Aston, 1985;
Geiger et al., 2009) which can be seen in the number of significant relations. No leaves were present in
winter thus the density of the deciduous forests were low and there was little tree, shrub and total cover
of trees and shrubs. Only one significant relation with a forest characteristic (tree height) was found in
winter. In spring and autumn there were already nine and seven significant relations. In summer most
significant relations were found namely eleven. The clarifications of Raynor (1971); Aston (1985);
Geiger et al. (2009) explain the significant relations of Tmean, Tmin and Tmax with the openness, NCI, tree,
shrub and total cover of trees and shrubs of the plots because these characteristics were correlated
(Figure 40 in appendix).
A remarkable fact is the number of significant relations between the buffering of Tmin and the forest
characteristics namely three significant relations and each of them with the openness of the plots, despite
the correlations between the density of the forest and the tree, shrub and total cover of trees and shrubs
with a correlation of respectively -0.51, -0.39, -0.61. The significant relations between the openness and
Tmin occurred over the entire measuring period, in spring and in autumn. Therefore, in terms of forest
characteristics, the buffering of Tmin was mainly dependent on the openness of the plots. The p-values
and the marginal R squared are relatively constant across the three significant relations namely with p
between 0.0081 and 0.011 and the marginal R2 between 1.3 and 1.5 %. Hence, the significant relations
between the openness and Tmin explains only 1.3 to 1.5 % of the variation of the buffering of Tmin. The
buffering of Tmin seems to depend mainly on landscape characteristics such as relative elevation of the
plots and less on forest characteristics.
The buffering of Tmax and Tmean had respectively 24 and 11 significant relations with the forest
characteristics. Tmean was dependent on several forest characteristics and not only on the openness of the
plots. The highest marginal R2 (12 %) were found with the most significant (p<0.0001) relations between
Tmean and the forest characteristics, namely with the openness and the total cover of trees and shrubs in
summer. Therefore, the openness and the total cover of trees and shrubs each explain 12 % of the
variation of the buffering of Tmean in summer. Less significant relations between Tmean and the forest
characteristics explain less variation of the buffering of Tmean. Most variation is therefore explained by
the forest characteristics in summer since the densest canopy is present then in deciduous forests. Not
only the season determined the size of the marginal R2 but also the forest characteristics themselves.
The openness and the total cover of trees and shrub explain more of the variation of the buffering of
Tmean compared to the tree height, tree and shrub cover. The random (=region) and fixed factor of linear
67
mixed effect models with significant relations with Tmean explain 48 to 57 % of the variation of the
buffering of Tmean except the model with tree height in in autumn which explains 69 %. Hence, the
significant linear mixed effect models between Tmean and the forest characteristics explain 20 to 30 %
less variation compared to the significant models between Tmin and the forest characteristics. Tmax had
significant relations with each forest characteristic over the whole measuring period, in spring and
summer. In autumn Tmax had significant relations with each forest characteristic except the NCI and one
significant relation was found between Tmax and a forest characteristic in winter namely with the tree
height. Thus, the presence or absence of leaves on the trees certainly had an effect on the number and
significance of the relations. Therefore, Tmax depended on all the measured forest characteristics in spring
and summer and all but one in autumn. The most significant relations (p<0.0001) with Tmax are found
with the openness in spring, summer and autumn and in summer with the cover of the tree layer and
with the total cover of trees and shrubs. Large differences occur in the explanation of the marginal and
conditional variation of the buffering depending on the season and the forest characteristic. In general,
more significant relations explain more conditional and marginal variation. Further research may
possibly provide an explanation for the large differences in number of significant relations and variation
explained between Tmax, Tmin, Tmean and the forest characteristics.
Von Arx et al. (2013) concluded that the moderating capacity of dense canopies (with a LAI > 4) on
Tmax in summer was between -2.8 and -3.3 °C (colder in forests). Below sparse canopy (LAI < 4) Tmax in
summer was reduced by 0.1 to 1.3 °C. Von Arx et al. (2013) observed that the buffering capacity of
canopy increased with the density of the canopy. Forest with less canopy cover tend to experience greater
variability with higher maximum temperatures and lower minimum temperatures (Chen et al., 1999;
Clinton, 2003; Chen et al., 1993). An increased buffering of Tmax in summer was also found in the ten
regions across Europe. An increase of the forest Tmax in summer of 0.069 °C per percent openness was
found. This reasoning can be extended for tree, shrub and total cover of trees and shrubs because a higher
LAI implies a lower openness and higher tree and/or shrub and total cover of trees and shrubs (Figure
40 in appendix). In summer significant relations were found between the buffering of Tmax and the tree,
shrub and total cover of trees and shrubs. The buffering of Tmax in summer decreased with respectively
0.016, 0.012 and 0.016 °C per percent tree, shrub and total cover of trees and shrubs. The mean buffering
of Tmax in summer across the ten regions was -2.05 °C which is very similar to the results of Von Arx et
al. (2013). Not matching results were found in spring. Von Arx et al. (2013) discovered a buffering of
Tmax by -1.7 to -2.7 °C below dense canopies while the buffering across the ten regions in Europe in this
study in spring was + 0.32 °C. Thus, Von Arx et al. (2013) observed colder forest Tmax temperatures in
spring while warmer forest Tmax were found in this report. A possible explanation is fact that the study
sites in Von Arx et al. (2013) were not exclusively deciduous forests. Von Arx et al. (2013) had eleven
study sites and five study sites had 0 to 15 % deciduous tree species. The other six study sites had 50 to
100 % deciduous tree species. Thus, five sites can be categorized as coniferous forests. Coniferous
forests are evergreen what means that solar radiation can not heat up the soil surface in spring and the
temperature will also be buffered in spring.
The reason for warmer Tmin with increasing density of the forest is decreasing cover from shrubs and/or
trees and therefore increasing openness which results in increasing loss of longwave heat radiation from
the ground and vegetation, leading to increasingly cooler nighttime air temperatures (Mahrt, 1985).
Moreover, open sky is cold relative to forest canopy and consequently emits less longwave radiation
downwards towards the surface (Groot and Carlson, 1996). Hence, plots with little tree and/or shrub
cover had no insulating blanket to protect the understory environment from radiative heat loss to the
cold, open atmosphere. Consequently, more open plots had significantly lower morning minimum
68
temperatures compared to more closed plots where nighttime temperatures were kept more buffered
from the open atmosphere by intact canopy. The colder Tmax with increasing density of the forest can be
explained by the fact that a higher openness of forests increases air circulation, leading to greater
advective mixing of air and reducing variation in temperature (Chen et al., 1993; Heithecker and
Halpern, 2007). Anderson et al. (2007) consider penetration of low-angle solar radiation into and
beneath tree canopies as an important factor for surface warming in sparse stands, while being relatively
unimportant in closed canopy forest.
Positive significant relations were found between the buffering of Tmean, Tmax and the tree height up to an
increase of the temperature with 0.050 °C per meter tree height. The forest Tmax and Tmean increased with
increasing tree height which is remarkable. Tmax was expected to decrease with increasing tree height. In
forests with taller trees, air is less mixed compared to forests with smaller trees (if all other factors
remain constant). Ferrez et al. (2011) observed that the cooling effect of the vegetation in a high forest
stronger was compared to former coppices namely respectively -0.42 °C and -0.21 °C. A possible
explanation is the fact that several plots with the highest trees were also plots with a high openness since
the tree height shows some correlation (0.12) with the openness, tree and total cover of the trees and
shrubs. A higher openness resulted in a warmer Tmax. For example the plots in Compiègne have tall trees
but most plots had few trees due to thinning and/or seed cutting. Since the temperature data logger was
always attached to a tree, the openness could still be relatively low (due to the canopy of the central tree)
while the openness in the wider environment of the temperature data logger was higher.
4.3. Buffering of the temperature as a function of landscape
characteristics
Correlations also occur between the landscape characteristics obtained from the EU-DEM or from the
forest map in Hansen et al. (2013) (Figure 41 in appendix). Examples of correlated characteristics are:
forest cover and the length of forest edge (-0.49); distance to coast and elevation above sea level (0.86);
slope and relative elevation (0.50). In a number of regions (i.e. Göttingen and Zvolen) the forest
temperature was buffered more compared to other regions (i.e. Wytham and Compiègne) and this could
have several causes such as distance to the coast, elevation above sea level, relative elevation,
complexity of the forests. In several regions (i.e. Zvolen and Wytham) there was more variation in the
buffering of the forest temperature between the ten plots in that region compared to other regions (i.e.
Tournibus and Speulderbos) which is mainly caused by the homogeneity of the plots. A homogeneous
forest in terms of forest and landscape characteristics such as openness, tree species, microrelief, tree
height and relative elevation will have less variation in the buffering of the temperature compared to
more heterogeneous forests.
Significant relations were found between Tmean, Tmax and Tmin with the distance to the coast. The relations
between Tmin and Tmean with the distance to the coast were positive whereas the relations with Tmax were
negative. Forest Tmin and Tmean became warmer and Tmax got colder compared to the open field with
increasing distance to the coast which indicates that less extreme forest temperatures occurred with
increasing distance to the coast. There are two possible explanations for this effect. The first possible
clarification is the fact that more extreme open field temperatures occurred with increasing distance
from the coast. Regions closer to the sea are more likely to be situated in the Cfb climate zone and
regions further from the coast more likely to be situated in the Dfb climate zone (Peel et al., 2007).
69
Regions with a continental climate experience more extreme temperatures, since there is less buffering
effect of the ocean. Maritime climates experience buffering effects of the ocean. In winter, the
temperature of the ocean is warmer compared to the temperature of the inland and the inverse
phenomenon is seen in summer. Regions closer to the ocean are more affected by the buffering of the
ocean compared to regions further from the ocean. Renaud et al. (2011); Renaud & Rebetez (2009)
discovered that the strength of the cooling effect in deciduous and mixed forests depended on the
absolute value of Tmax: the warmer the temperature, the stronger the influence of the forest. I.e., during
the exceptionally hot summer 2003 in Europe, more cooling effect occurred than in any of the other
summers of the study (1998 to 2007) (Renaud et al., 2011; Renaud & Rebetez, 2009). The second
possible explanation is the fact that the wind speeds are lower in continental climates thus less mixing
of the air and more possibilities of microclimate buffering.
Significant relations were found between the distance to the coast and Tmax in spring and summer and in
each season except spring with Tmin. The strongest relation (lowest p-value) between the distance to the
coast and Tmax/Tmin were respectively in summer (p = 0.0077) and in winter (p = 0.0036). This can be
explained by the fact that in summer the highest Tmax were reached seen over the entire period and the
differences between the temperate and continental climate Tmax were on a maximum in summer. Warmer
Tmax were reached in continental climates compared to temperate climates thus more buffering of Tmax
occurred in continental climates following the research of Renaud et al. (2011) and Renaud & Rebetez
(2009). In winter, the differences between the temperate and continental climate Tmin were on a
maximum and Tmin was lower in continental climates compared to temperate climates. The impact of
vegetation cover on extreme temperature values can be expected to be different from its impact on
average values (Renaud et al., 2011; Renaud & Rebetez, 2009). More insight into the specific impact of
different vegetation cover types on temperature extremes is required to understand the potential impact
of climate change on forests.
The slope of the plots had two significant relations namely with Tmax and with Tmean in spring. The slope
of the plots seems relatively important for the buffering of the temperature in spring. The buffering of
Tmax increased with 0.027 °C per degree slope of the plot thus forest Tmax became warmer with increasing
slope. An possible explanation for the increasing Tmax with the slope is the fact that there were no leaves
on the trees in the deciduous forests in at least a part of spring thus solar radiation could reach and heat
up sub-canopy heat sinks. Plots that had more south or west orientated slopes could obtain warmer Tmax
due to more perpendicular incoming solar radiation at the point of time when Tmax was reached. Plots
which had more east or north oriented slopes could obtain colder Tmax because solar radiation had more
difficulties reaching those plots. It is possible that more perpendicular solar radiation in the west and
south oriented plots had a stronger effect compared to the colder Tmax in the more east and north oriented
plots. Further research is needed to clarify the effect of slope on the buffering of the temperature. Ferrez
et al. (2011) observed that the cooling effect of vegetation increased with the slope, namely 0.13 °C each
10 %, based on a study of the air temperature data from fourteen sites in Switzerland. Therefore, Ferrez
discovered an increasing cooling effect of vegetation while in this study a significant warming of Tmax
and Tmean was found in spring with increasing slope of the plots.
The length of forest edge in radius of 500 m of the plot had four significant relations namely two with
Tmin (over the entire year and in summer) and two with Tmax (in autumn and winter). All four relations
indicate more extreme forest temperatures (colder Tmin and warmer Tmax) with increasing length of forest
edge in a radius of 500 m of the plot in the respective seasons. The more extreme forest temperatures
70
with increasing length of forest edge in a radius of 500 m of the plot can be explained by the fact that a
forest edge indicates the presence of more open spaces in the vicinity. As seen earlier, open spaces reach
more extreme temperatures. A decreased buffering due to an increased length of forest edge in a radius
of 500 m will probably have little effect on fauna and flora because warmer forest Tmax (relatively,
compared to plots with less forest edge in the vicinity) occurred in winter and autumn whereas the
warmest forest and open field Tmax are reached in summer. Warmer forest Tmax compared to the open
field in summer would have been a threat to the fauna and flora because summer is when temperature is
most likely to be biologically important as limiting environmental factor for organisms that require
cooler environments. The colder Tmin in summer compared to plots with less length of the forest edge in
a radius of 500 m will also pose no threat to the fauna and flora because the coldest Tmin are reached in
winter.
Forest Tmin and Tmean decreased and forest Tmax increased with increasing elevation above sea level of the
plots. Less extreme forest temperatures occurred with increasing elevation above sea level which is very
similar to the effects seen with the distance the coast due to the correlation between those two
characteristics (Figure 41 in appendix). For example Göttingen, Zvolen and Koda woods are regions far
from the coast but are also regions with most elevation above sea level and the regions such as Skåne,
Speulderbos, Prignitz which are close to the coast have the lowest elevations above sea level (table 5).
Elevation above sea level was especially important for Tmin because Tmin had a significant relation in each
season and over the entire measuring period. The p-values of those relations were <0.0001 while Tmax
had only one significant relation (in summer). The marginal R2 of the linear mixed effect models with
Tmin and the elevation above sea level varied between 51 and 58 %. Therefore, 51 to 58 % of the variation
in the buffering of Tmin was explained by the elevation above sea level. Ferrez et al., 2011 observed that
elevation above sea level was not linked to the cooling effect of vegetation which is remarkable because
very strong relations were found between the buffering of the temperature and the elevation above sea
level in this study. Hence, further research is needed to clarify the relation between the buffering of the
temperature and the elevation above sea level.
The relative elevation of the plots in a radius of 250 m had significant relations with Tmean and Tmin in
each season but not with Tmax. It seems that the relative elevation of the plots is important for Tmin. Tmin
and Tmean increased with increasing relative elevation of the plots in a radius of 250 m. Hence, minimum
temperatures got less extreme with increasing relative elevations. Pepin et al. (2011) also discovered
that the local topography had a stronger influence on nocturnal temperatures (= mostly Tmin) compared
to Tmax. Colder Tmin in plots with lower relative elevation are caused by cold air drainage and pooling,
which are important processes affecting minimum temperatures at night and during winter (Daly et al.,
2010; Pepin et al., 2011). Many cold adapted flora and fauna inhabit cold pool locations because of the
distinct microclimates (Millar and Westfall, 2007; Tenow and Nilssen, 1990; Virtanen et al., 1998).
The north orientation of the plots seems important for the buffering of Tmax since Tmax had a significant
relation with the north orientation of the plots in each season except summer. The buffering of Tmax
became more negative (colder forest Tmax) with increasing north orientation of the plots. This can easily
be explained by the fact that solar radiation enters from the east, south or west. North orientated plots
received less solar radiation thus the plots heated up less, resulting in a colder Tmax. No significant
relations (p between 0.51 and 0.96) were found between the buffering of Tmin and the northness because
there was no solar radiation at night to cause temperature differences. No significant relationship (p =
0.10) between the buffering of Tmax in summer and the northness was found because in summer a dense
71
canopy is present and none/little solar radiation could heat up sub-canopy heat sinks and cause higher
Tmax. Ferrez et al. (2011) had similar results and found that a northerly orientation of the forest is
important for the cooling effect of the vegetation namely -0.66 °C for the northerly oriented sites against
-0.06 °C for the other sites compared to open field.
The east orientation of the plots is less important compared to the north orientation because east and
west oriented plots both receive solar radiation respectively in the forenoon and late afternoon/evening.
Only one significant relation was found between the buffering of the temperature and the east orientation
of the plots, namely with the buffering of Tmax in spring. The relation shows that the buffering of Tmax
decreased with -0.27 °C per unit eastness (=sin(aspect)) of the plot. Therefore, more east orientated plots
had lower Tmax in spring compared to more west orientated plots. A possible explanation is on one hand
the higher solar irradiation in spring compared to the other seasons in deciduous forests due to absence
of the leaves in at least a part of the spring and the increasing solar power. On the other hand, the
maximum temperature is mostly reached in the afternoon when the solar radiation reaches the west-
oriented plots. The absence of leaves on the trees in combination with solar radiation could result in
higher forest Tmax in spring compared to the open field.
Several landscape characteristics such as elevation above sea level and distance to coast explained more
variation of the buffering compared to the forest characteristics. Landscape characteristics were
especially important for Tmin and less important for Tmax but most variation of the buffering of Tmax was
still explained by the landscape characteristics. The greater importance of the landscape characteristics
for Tmin and the forest characteristics for Tmax can be seen in the number of significant relations between
Tmin and Tmax with the forest and landscape characteristics. The forest characteristics had most significant
relations with Tmax namely 24 whereas Tmin only three significant relations had with the forest
characteristics. With the landscape characteristics the opposite is seen, Tmin had most significant relations
namely sixteen and Tmax had only eleven significant relations with the landscape characteristics.
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5. Conclusions and management implications
Due to the comprehensive dataset with data from 100 plots at 10 regions across the European continent,
buffering could be quantified, and several significant relations could be found between the buffering of
Tmean, Tmin and Tmax and various forest and landscape parameters. The forest Tmin was 0.41 to 1.35 °C
warmer in comparison with the macroclimate temperature. The forest Tmax was 0.24 to 2.05 °C colder
compared to open field, except in spring. It is remarkable that in spring, the forest Tmax was 0.32 °C
warmer compared to the open field. In spring, averaged over all 100 plots, more extreme (warmer) Tmax
were reached in deciduous forests compared to the open field. A colder forest Tmax and a warmer forest
Tmin compensated each other which resulted in a similar Tmean between the micro- and macroclimate seen
over the entire measuring period, in autumn and in winter. In spring and summer, the forest Tmean was
respectively 0.27 °C warmer and 0.49 °C colder compared to the outside temperature. Thus, seen over
the entire measuring period the forest Tmean and the open field Tmean were similar (-0.057 °C colder in
deciduous forests compared to the open field). Meanwhile, less extreme Tmax (except in spring) and Tmin
are reached in forests compared to the open field.
Forest Tmax increased (in each season except winter) and forest Tmin decreased (in spring and autumn)
with increasing openness of the forest. The density/openness of the forest is the most determining forest
characteristic for the buffering of the forest temperature in deciduous forests across Europe. It is
remarkable that the forest Tmax increased with increasing tree height in each season. Thus, higher forests
experienced warmer Tmax compared to forests with smaller trees in each season. The forest Tmax decreased
(in spring and summer) and the forest Tmin increased (in summer, autumn and winter) with increasing
distance to the coast. Thus, more buffering of the temperature can be obtained in forests that are further
from the coast. The forest Tmin increased (in each season) with increasing elevation above sea level of
the plots after a correction of the temperature for the elevation above sea level of the plots. An increase
in the north orientation of the plots resulted in a decrease of the forest Tmax in spring, autumn and winter.
The relative elevation of the plots in a radius of 250 m relative to the lowest point in that radius was
important for the buffering of Tmin. The forest Tmin increased with increasing relative elevation of the plot
in a radius of 250 m in each season.
Due to climate change, extreme Tmax will pose a problem for various organisms. Although spring frost
can also cause damage to organisms that are already in bloom or awakened from their hibernation (which
happens earlier due to the changing climate). Tmin is mainly dependent on the landscape characteristics.
Fortunately, Tmax is less dependent on the landscape characteristics compared to Tmin and more dependent
on the forest characteristics. This mean that forest managers and planners can actively mitigate the
effects of climate change by taking into account the relationships between the buffering of the forest
temperature and the forest and landscape characteristics to conserve ecosystem services and
biodiversity. Efforts can be made to reduce the openness of the forest by increasing the tree and/or shrub
cover which will result in a higher density and total cover of trees and shrubs of the forest. The increased
density will mitigate extreme Tmax and the forest will serve as a refugium for species that can’t cope with
the elevated temperatures caused by global warming. Several ways to increase the density of forests
exist. For example, a second tree layer and/or shrub layer can be introduced with species such as hedge
maple (Acer campestre), hawthorn (Crataegus spp.), hazel (Corylus avellana), black elder (Sambucus
73
nigra) … Forest managers can choose to plant trees with a canopy with high density such as beech
(Fagus sylvatica). Ferrez et al., 2011 found that he cooling effect is generally larger with beech (3.27
°C on average) than with oaks or conifers (2.29 °C on average). But forest managers and -planners must
also consider the light requirements of plant species. Denser canopies result in less sub-canopy light
which can be detrimental to certain species. The forest manager could also adapt the management system
of the forest. Instead of harvesting by means of a clear cut or shelterwood system, the forester could opt
to harvest via a group-selection or selection forest system. The group-selection and selection forest
system retain a more closed forest during rejuvenation and thus no large open spaces are created in
which more extreme Tmax and Tmin can be reached. The adjustment of the orientation (for example more
north oriented) and relative elevation of forest stands are more expensive, radical and risky measures.
With these types of measures, it is first necessary to think carefully if the advantages will exceed the
disadvantages. Therefore, Forest managers have possibilities to actively mitigate the effects of climate
change inside forests in function of the conservation of biodiversity and maintenance of ecosystem
functions.
Further research is needed to provide information about the relation between the buffering of the
temperature and tree height. The relationships found in this study are the inverse of what was expected.
Research is also needed to clarify why the forest characteristics are more important for the buffering of
Tmax compared to Tmin and the inverse for the landscape characteristics. Further research is needed to
create certainty about the reason why elevation above sea level and distance to coast are important for
the buffering of the temperature. Finally, research can be done to study the effect of different forest
management systems on the buffering of the temperature (i.e. the effect of a clear cut, selection forest
system, group selection forest) or to study the effect of different tree species on the buffering of the
temperature (i.e. tree species with denser and more sparse canopies). It has been shown that deciduous
forests buffer the temperature thus research can be done to study if species and biodiversity in general
makes use of this temperature buffering to cope with global warming.
74
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7. Appendix
Figure 37: Model-checking plots. Left side: plot of the residuals. Right side: QQ plot. Upper plots: Model with the
buffering of Tmean in function of the openness. Lower plots: Model with buffering of Tmean in summer in function of the
tree height.
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Figure 38: Model-checking plots. Left side: plot of the residuals. Right side: QQ plot. Upper plots: Model with the
buffering of Tmin in function of the neighbourhood competition index (NCI). Lower plots: Model with buffering of Tmin
in spring in function of the tree cover.
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Figure 39: Model-checking plots. Left side: plot of the residuals. Right side: QQ plot. Upper plots: Model with the
buffering of Tmax in function of the shrub cover. Lower plots: Model with buffering of Tmax in autumn in function of the
total cover of trees and shrubs.
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Figure 40: Correlation heatmap of the forest characteristics. Blue and brown colours indicate correlations between the
various factors (i.e. density, tree, shrub and total cover of trees and shrubs). White tones indicate no or little correlation.
MeanDens = openness, SensTreeHeight = tree height.
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Figure 41: Correlation heatmap of the landscape characteristics. Blue and brown colours indicate correlations between
the various factors (i.e. distance to the coast, latitude, elevation above sea level). White tones indicate no or little
correlation. Forcov_500 = forest cover in a radius of 500 m, foredg_500 = amount of forest edge in a radius of 500 m,
relE250min = relative elevation of the plot seen from the lowest point in a radius of 250 m, northness and eastness =
north and east orientation of the plots, dist2coast = distance to the coast.