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

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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

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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.