how much does climate change threaten european forest ......project* or predict*]) (20 march 2017)....

P R IMA R Y R E S E A R CH A R T I C L E

How much does climate change threaten European foresttree species distributions?

Marcin K. Dyderski1,2 | Sonia Pa�z3 | Lee E. Frelich4 | Andrzej M. Jagodzi�nski1,2

1Institute of Dendrology, Polish Academy of

Sciences, K�ornik, Poland

2Department of Game Management and

Forest Protection, Faculty of Forestry,

Pozna�n University of Life Sciences, Pozna�n,

Poland

3Faculty of Forestry, Pozna�n University of

Life Sciences, Pozna�n, Poland

4Department of Forest Resources, Center

for Forest Ecology, University of

Minnesota, St. Paul, MN, USA

Correspondence

Andrzej M. Jagodzi�nski, Institute of

Dendrology, Polish Academy of Sciences,

K�ornik, Poland.

Email: [email protected]

Funding information

Institute of Dendrology, Polish Academy of

Sciences, K�ornik, Poland

Abstract

Although numerous species distribution models have been developed, most were

based on insufficient distribution data or used older climate change scenarios. We

aimed to quantify changes in projected ranges and threat level by the years 2061–

2080, for 12 European forest tree species under three climate change scenarios.

We combined tree distribution data from the Global Biodiversity Information Facil-

ity, EUFORGEN, and forest inventories, and we developed species distribution mod-

els using MaxEnt and 19 bioclimatic variables. Models were developed for three

climate change scenarios—optimistic (RCP2.6), moderate (RCP4.5), and pessimistic

(RPC8.5)—using three General Circulation Models, for the period 2061–2080. Our

study revealed different responses of tree species to projected climate change. The

species may be divided into three groups: “winners”—mostly late-successional

species: Abies alba, Fagus sylvatica, Fraxinus excelsior, Quercus robur, and Quercus

petraea; “losers”—mostly pioneer species: Betula pendula, Larix decidua, Picea abies,

and Pinus sylvestris; and alien species—Pseudotsuga menziesii, Quercus rubra, and

Robinia pseudoacacia, which may be also considered as “winners.” Assuming limited

migration, most of the species studied would face a significant decrease in suitable

habitat area. The threat level was highest for species that currently have the north-

ernmost distribution centers. Ecological consequences of the projected range con-

tractions would be serious for both forest management and nature conservation.

K E YWORD S

climate change, disturbance, extinction, forest management, forest policy, habitat suitability,

species distribution model

1 | INTRODUCTION

Climate change significantly influences geographical distributions of

plant species worldwide (Scheffers et al., 2016). Most of these

changes are connected with warming temperatures and decreasing

precipitation during the growing season (IPCC, 2013). Warming cli-

mates also increase frequencies of catastrophic winds, insect out-

breaks, and forest fires (Seidl, Schelhaas, Rammer, & Verkerk, 2014).

Tree survival rates usually decrease (S�aenz-Romero et al., 2017).

These unsuitable conditions elicit four types of reactions for trees:

(i) persistence, due to acclimatization and phenotypic plasticity, (ii)

evolution (local adaptation), (iii) migration, or (iv) death (Bussotti, Pol-

lastrini, Holland, & Br€uggemann, 2015). Climate change may also

increase risk of invasions by alien tree species (Brundu & Richardson,

2016; Kleinbauer, Dullinger, Peterseil, & Essl, 2010) as well as out-

breaks of invasive pests, for example, Bursaphelenchus xylophilus

(Mota et al., 1999). However, due to increased CO2 concentration,

relative net primary production and wood production may increase

(Lindner et al., 2014; Sohngen & Tian, 2016). Drought resistance of

trees may increase; however, it may be a short-term effect, due to

increasing evapotranspiration which can reduce this feedback (Lind-

ner et al., 2014). Mechanisms of climate change vary regionally,

Received: 20 July 2017 | Accepted: 30 August 2017DOI: 10.1111/gcb.13925

1150 | © 2017 John Wiley & Sons Ltd wileyonlinelibrary.com/journal/gcb Glob Change Biol. 2018;24:1150–1163.

http://orcid.org/0000-0001-6899-0985http://orcid.org/0000-0001-6899-0985http://orcid.org/0000-0001-6899-0985http://wileyonlinelibrary.com/journal/GCB

leading to different impacts; for example, climate change would be

less severe for plants in northern than in southern Europe, due to

increases in precipitation that may exceed increases in evapotranspi-

ration in the north (Lindner et al., 2014). An example is Quercus pet-

raea, a species in which growth increment is expected to decrease in

the southern part of its range, but to increase in the northern part

(S�aenz-Romero et al., 2017).

Trees are foundational elements of forest ecosystems (Ellison

et al., 2005), having important influences on the resource environ-

ment and ecological function. Dominant tree species determine

nutrient cycling (e.g., Hobbie et al., 2006; Mueller et al., 2012; Reich

et al., 2005), light availability (e.g., Canham, Finzi, Pacala, & Burbank,

1994; Knight, Oleksyn, Jagodzinski, Reich, & Kasprowicz, 2008; Nii-

nemets, 2010), and microclimate (von Arx, Dobbertin, & Rebetez,

2012). Therefore, transitions in dominant tree species due to climate

change will also cause changes to properties of whole ecosystems

and dependent organisms, such as epiphytes (e.g., Kir�aly & �Odor,

2010; Me�zaka, Br�umelis, & Piter�ans, 2012; Woziwoda, Staniaszek-

Kik, & Stefa�nska-Krzaczek, 2016), understory vegetation (e.g.,

Augusto, Dupouey, & Ranger, 2003; Knight et al., 2008; Wulf &

Naaf, 2009), mycorrhizal fungi (e.g., Dickie et al., 2006; Kałucka &

Jagodzi�nski, 2016; Trocha et al., 2012), and soil biota (e.g., Mueller

et al., 2015, 2016). Thus, changes in dominant tree species may alter

many dependent species. Particular types of ecosystems, which are

connected to certain dominant tree species, may relocate or even

disappear (Hickler et al., 2012). Many species are claimed to be

extinct, both regionally or globally, due to climate change (e.g., Sven-

ning & Skov, 2006), decreasing biodiversity at local, regional, and

global scales (Scheffers et al., 2016; Thuiller et al., 2011). For that

reason, nature conservation management plans should accommodate

changing climates (Oliver, Smithers, Beale, & Watts, 2016).

Climate is an important driver of the physiological processes con-

nected with tree performance and survival (Kozlowski & Pallardy,

1997). Limits to species occurrences may result from lack of growth

ability or phenological disruptions such as flower freezing, failure of

fruit ripening (Morin, Augspurger, & Chuine, 2007; Scheffers et al.,

2016), or drought (Gazol et al., 2015; Nogu�es-Bravo et al., 2014).

Also, climate change-mediated disturbances affect growth of trees,

especially due to catastrophic winds and herbivorous insect out-

breaks (Seidl et al., 2014). Moreover, unsuitable climate may increase

problems with tree regeneration, especially as young trees may be

more sensitive to limiting factors (especially drought) than older ones

(Niinemets & Valladares, 2006). Species distributions have the most

profound impacts on ecological processes that can be attributed to

climate change (Scheffers et al., 2016); therefore, the best possible

predictions for future changes in tree species distributions are

needed.

Because climate is known as the main driver of species distribu-

tions (Ellenberg, 1988; Elton, 1958), there are numerous papers pro-

viding species distribution models, based on climatic variables. We

analyzed 3,073 papers indexed in the Web of ScienceTM database,

revealed by searching the phrase ([climate change] AND tree* AND

[distribution* OR range OR niche OR dispersal] AND [model* OR

project* OR predict*]) (20 March 2017). From these papers, only

124 met the following criteria: (i) considered tree species, (ii) pro-

vided distribution model(s) for at least one species, and (iii) future

projection(s) for at least one climate change scenario (Appendix S1).

These papers covered 524 taxa. Only 25% of them had projections

for more than 10 species and 50% of them for only a single species.

Most of the papers are focused on threatened species (e.g., Akhter

et al., 2017), invasive species (e.g., Camenen, Port�e, & Benito

Garz�on, 2016), or plantations (e.g., Li, Xu, Guo, & Du, 2016). Some

broader studies concerned European (e.g., Sykes, Prentice, & Cramer,

1996) or American (e.g., Iverson, Prasad, Matthews, & Peters, 2008)

forest tree species. The most frequently studied species were Fagus

sylvatica (in 16 papers), Pinus sylvestris and Quercus robur (both 12),

Acer saccharum and Populus tremuloides (both 10), and Q. petraea (9).

Most of the species were covered by only one paper (54%), and

only 12% were covered by five or more studies. Spatial extents of

the studies analyzed were usually limited to one country or region

within a country. Only 18 of the studies were conducted at conti-

nental or broader spatial extents. Forty-one percent of the studies

covered only one climate change scenario, which is insufficient to

make conclusions about possible threats (Harris et al., 2014). Most

of the studies were based on older versions of climate change pro-

jections, provided by the IPCC Fourth Assessment Report (AR4;

71%). Only 20 studies refer to the scenarios from the most recent

IPCC Fifth Assessment Report (AR5), of which six concerned Eur-

ope. Nevertheless, these studies cover from one to four species and

only three of them cover the whole continent. This rapid literature

review allowed us to conclude that new updated projections of cli-

mate change-driven range shifts for the main European tree species

are needed.

Most of the authors based their models on distribution data

coming from forest inventories (41 papers), biodiversity databases

(41), herbaria records (21), or their own investigations (28). However,

although forest inventories and literature data provide valuable infor-

mation about species distributions, their sampling intensities may be

insufficient and inconsistent (Duputi�e, Zimmermann, & Chuine,

2014). Only eight studies used data from the Global Biodiversity

Information Facility (GBIF), providing both archived information from

professional databases as well as those obtained using citizen

science tools. The benefit from using GBIF data is derived from addi-

tional sampling of areas usually not covered by forest inventories

and too large for reliable sampling by researchers. This information

may supplement the existing knowledge about species distributions

(Ashcroft, Gollan, & Batley, 2012; Huettmann, Artukhin, Gilg, &

Humphries, 2011). Furthermore, conclusions from recently published

papers containing predictions of tree responses to climate change

are equivocal. For example, just for one species—Abies alba—there

are two opposite conclusions about future persistence along the

southern edge of its distribution (Maiorano et al., 2013; Tinner et al.,

2013). For that reason, we believe that supplementing existing data

on tree species distributions by GBIF observations may improve spe-

cies distribution models and predicted changes in future climate

change scenarios.

DYDERSKI ET AL. | 1151

Current state of the art climate models, combined with extended

datasets on tree species distributions, leads to new possibilities for

analyzing future distributions and answering these questions con-

cerning impacts of climate change: How much does climate change

threaten European forest tree species distributions? Which species

win and which lose under three climate change scenarios (described

below)? What proportion of the current range would be lost for each

species and climate scenario? What are the prospects for range

expansion for each species and scenario? Thus, we aimed to quantify

changes in habitat suitability of 12 European forest tree species and

assess the threat level for each species under three climate change

scenarios for the period 2061–2080 using three General Circulation

Models (GCMs).

2 | MATERIALS AND METHODS

2.1 | Species studied and species distribution data

We chose 12 tree species considered important for forest manage-

ment or as foundational elements (Ellison et al., 2005) of temperate

and boreal forests in Europe: A. alba, Betula pendula, F. sylvatica,

Fraxinus excelsior, Larix decidua, Picea abies, P. sylvestris, Pseudotsuga

menziesii, Q. petraea, Q. robur, Q. rubra, and Robinia pseudoacacia.

Three of them—P. menziesii, Q. rubra, and R. pseudoacacia—are the

three most important alien tree species in Europe used in forest

management due to their high commercial value. For purposes of

this study, Europe was defined as the area within longitudes from

10°W to 45°E and latitudes from 33°N to 72°N. A grid system with

2.50 resolution and a total of 1,235,520 raster cells covering Europe

was established to analyze the tree distribution data described

below. For each species studied, we compiled a database of occur-

rences, using four main sources: Global Biodiversity Information

Facility—GBIF (2017), the EUFORGEN database (http://www.eufor

gen.org/distribution_maps.html), the Joint Research Centre distribu-

tion maps (JRC; http://forest.jrc.ec.europa.eu/), and Polish forest

inventory data (Bank Danych o Lasach, 2015). For the GBIF data, we

excluded all the observations from urban green areas, botanical gar-

dens, and other collections and before 1950. We decided to include

additional data from Poland, as it contains many additional points

not covered by the other datasets. Because the distribution data

sources differed in form (spatial points and polygons), we trans-

formed all of them into spatial points at 2.50 resolution in a WGS-84

spatial coordinates system. As most of these records have no acqui-

sition dates, there is concern that observations made outside the

period 1960–1990 (current climatic data) would bias the results.

However, due to longevity of the tree species studied, we may

assume that this problem is marginal. We joined all the points, over-

layed them onto a European land map, and visually inspected the

overlay, to delete all the points with coordinates located in the sea.

As sampling effort differed geographically and among datasets, for

each species, we resampled the data within a 0.5° grid—within each

grid cell we randomly selected only one data point from the distribu-

tion data in the 2.50 grid. Thus, we created a new raster layer for

each species with resolution of 2.50 , but with reduced number of

data points and uniform density distribution. By resampling, we over-

came the problem of uneven sampling intensity (Rocchini & Garzon-

Lopez, 2017) and we ensured that observations were evenly dis-

tributed within the geographical range. After resampling, we

obtained at least 788 points at 2.50 resolution for each species

(Table 1), a sufficient number to develop MaxEnt models of potential

TABLE 1 Overview of data extent and species distribution models for each species studied

Species Number of recordsa Number of raster cellsbNumber of pointsafter resampling AUCtest

c Probability thresholdd Groupe

Abies alba 119,134 54,459 1,178 0.8535 .2396 Winner

Betula pendula 824,219 384,913 3,370 0.7258 .4250 Loser

Fagus sylvatica 419,502 134,611 1,764 0.8327 .4105 Winner

Fraxinus excelsior 763,612 408,052 3,483 0.6799 .3992 Winner

Larix decidua 99,315 56,299 1,121 0.8761 .3400 Loser

Picea abies 568,894 329,956 3,147 0.7679 .4289 Loser

Pinus sylvestris 769,575 380,998 3,748 0.6959 .4175 Loser

Pseudotsuga menziesii 129,051 19,073 788 0.8998 .3019 Alien

Quercus petraea 337,106 195,239 2,091 0.8037 .3076 Winner

Quercus robur 711,186 272,376 2,850 0.7280 .4238 Winner

Quercus rubra 51,089 26,710 813 0.9122 .3581 Alien

Robinia pseudoacacia 100,789 41,465 1,254 0.8524 .3886 Alien

aNumber of species occurrence records (data points with coordinates) used in this study.bNumber of raster cells with at least one occurrence for the considered species.cArea under receiver operator curve calculated using the independent testing dataset (20% of observations not used for model building).dProbability used as the threshold for discrimination of model output, the value where the sum of sensitivity and specificity is the highest.eClassification according to proportion of threatened area: “winners” are species with less than 50% of current distribution threatened under pessimisticscenarios (RCP8.5), “‘losers” are species with more than 50%, and “alien” are species geographically alien to Europe.

1152 | DYDERSKI ET AL.

http://www.euforgen.org/distribution_maps.htmlhttp://www.euforgen.org/distribution_maps.htmlhttp://forest.jrc.ec.europa.eu/

distribution of each species based on bioclimatic variables as

described below. Using primary (nonresampled) distribution data, we

also prepared a raster layer with current distribution mapped at

2.50 resolution for each species, so that the models derived from

the resampled data could be applied to the full 2.50 grid for a

more detailed quantification of climate change effects on each

species. We also removed outliers, defined as points situated fur-

ther than 1° from the nearest points, to limit spatial extent and

make our conclusions more conservative. Although these points

might reflect suitable microhabitats outside the main range of a

given species, we also considered that single outlier points are

more likely to result from random noise in the data than consis-

tent patterns, so that removing them could improve the models

described below.

2.2 | Predictors—current and future climate data

We used 19 bioclimatic variables (Hijmans, Cameron, Parra, Jones, &

Jarvis, 2005; O’Donnel & Ignizio, 2012; Table 2; Appendix S2)

derived from monthly temperature and precipitation records and

available in the WorldClim database, which describe the actual state

of climate by specifying the yearly and seasonal variations of tem-

perature and precipitation. As at the continental and global scales,

we assumed that climate is the main factor shaping species distribu-

tions (Pearson & Dawson, 2003). We did not take into account other

factors, such as soil type, elevation, and land use. We also used the

projected values of 19 bioclimatic variables for the period 2061–

2080, using the three GCMs, as there is large variability among

GCMs (Goberville, Beaugrand, Hautek�eete, Piquot, & Luczak, 2015).

We chose three GCMs: HadGEM2-ES (Jones et al., 2011), IPSL-

CM5A-LR (Dufresne et al., 2012), and MPI-SM-LR models (Giorgetta

et al., 2013). These GCMs were chosen from seven selected by

Goberville et al. (2015), reflecting low, moderate, and high levels of

occurrence changes for two sample species. Nevertheless, these

three GCMs are only a sample of the wide range of possible climate

change trajectories described by 19 GCMs available in WorldClim,

and therefore, results presented in this study do not cover all of the

possible projections of climate change effects.

For each GCM, we analyzed three climate change scenarios:

optimistic, moderate, and pessimistic. These scenarios are expressed

by the representative concentration pathways (RCPs), using values

comparing the level of radiative forcing between the preindustrial

era and 2100 (Harris et al., 2014; IPCC, 2013; van Vuuren et al.,

2011). The optimistic scenario—RCP2.6—assumes that in 2100, CO2

concentration will reach 450 ppm, global mean temperatures will

increase by 0.2–1.8°C, and has no strict reference in previous (AR4)

guidelines. The moderate scenario—RCP4.5—assumes 650 ppm CO2

and 1.0–2.6°C increase by 2100, and refers to AR4 guideline sce-

nario B1. The pessimistic scenario—RCP8.5—assumes 1,350 ppm

CO2 and 2.6–4.8°C increase by 2100, and refers to A1F1 scenario of

IPCC AR4 guidelines (Harris et al., 2014; van Vuuren et al., 2011).

Raster maps of current (1960–1990) and projected (2061–2080) bio-

climatic variables at 2.50 resolution were obtained from the World-

Clim 1.4 dataset (http://www.worldclim.org/; Hijmans et al., 2005;

Appendix S2).

2.3 | Model development and evaluation

We modeled potential distribution of the species studied using

MaxEnt models with default settings (Elith et al., 2011; Phillips,

Anderson, & Schapire, 2006). This method was chosen as MaxEnt

has been developed to process presence-only data and, in contrast

to generalized linear models and other classification tools, does not

need absence data in the theoretical assumptions, but uses back-

ground data (i.e., pseudoabsences). For each species, we randomly

selected 10,000 background points as pseudoabsences. The MaxEnt

model searches for patterns of presences distinct from the back-

ground data. For that reason, the prevalence of background points

makes the model more conservative because the model requires a

stronger signal of presences than would be the case for equal pro-

portions of presences and pseudoabsences (Elith et al., 2011). The

prevalence of background points was maintained by using the

resampled occurrences dataset. Models were built using 80% of the

2.50 raster cells and the remaining 20% was used as a test set for

model evaluation. As a criterion of model performance, we used area

under receiver operator curve (AUC) because it depends on true

positive and true negative rates (i.e., rates of positive and negative

overlapping of the current and projected ranges). MaxEnt model out-

put is the probability of species occurrence in each raster cell.

Therefore, to obtain the presence/absence output, we used as a

TABLE 2 Overview of bioclimatic variables used in this study

Abbreviation Parameter

BIO1 Annual Mean Temperature [°C]

BIO2 Mean Diurnal Range [Mean of monthly (max temp–min

temp)] [°C]

BIO3 Isothermality (BIO2/BIO7) (* 100) [°C]

BIO4 Temperature Seasonality (standard deviation *100) [°C]

BIO5 Max Temperature of Warmest Month [°C]

BIO6 Min Temperature of Coldest Month [°C]

BIO7 Temperature Annual Range (BIO5–BIO6) [°C]

BIO8 Mean Temperature of Wettest Quarter [°C]

BIO9 Mean Temperature of Driest Quarter [°C]

BIO10 Mean Temperature of Warmest Quarter [°C]

BIO11 Mean Temperature of Coldest Quarter [°C]

BIO12 Annual Precipitation [mm]

BIO13 Precipitation of Wettest Month [mm]

BIO14 Precipitation of Driest Month [mm]

BIO15 Precipitation Seasonality (Coefficient of Variation:

mean/SD*100) [%]

BIO16 Precipitation of Wettest Quarter [mm]

BIO17 Precipitation of Driest Quarter [mm]

BIO18 Precipitation of Warmest Quarter [mm]

BIO19 Precipitation of Coldest Quarter [mm]


http://www.worldclim.org/

threshold the probability value at which the sum of the sensitivity

(true positive rate, see below for more detailed explanation of true

positive and true negative) and specificity (true negative rate) was

the highest, to balance false positives and false negatives (Fielding &

Bell, 1997). As only climatic predictors were used for modeling, we

assumed that model output as a geographic range projected for each

species represented the climatic optimum, that is, climatic niche

(Hutchinson, 1957).

2.4 | Data analyses

Within the study, we used averaged output from the three GCMs

while the supplementary materials show results of model predictions

from single GCMs and RCPs. When the species distribution models

that we developed were applied to the projected climate change

scenarios and GCMs and confronted with the observed distribution

for each species, there were four possible scenarios for each 2.50

raster cell: (i) positive; (ii) negative, overlap of current and future pro-

jected ranges; (iii) potential range expansion; and (iv) potential range

contraction. Positive overlap of current and future projected ranges

(true positive) indicates that under changing climate, the species in

the raster cell under consideration still will be located in its climatic

niche in the period 2061–2080. Negative overlap of current and

future projected ranges indicates lack of a given species currently

and in the period 2061–2080. Potential range expansion, that is,

when a species does not occur currently and it has been predicted

that it will occur in the period 2061–2080, indicates potentially suit-

able future habitat. However, due to dispersal limitation, its occur-

rence is uncertain and cannot be considered without detailed data

about dispersal capacity (Meier, Lischke, Schmatz, & Zimmermann,

2012). Potential range contraction indicates that under the consid-

ered climate change scenario, the species currently in the particular

raster cell will be outside its climatic optimum in the period

2061–2080. As trees are long-lived organisms and climate change

may increase the occurrence of disturbances and biotic damaging

agents, for example, fungi and insects (Seidl et al., 2014), we

assumed this part of the observed distribution was an area where

occurrence of the species is threatened. Potential range contraction

in current habitat suitability may include both habitat unsuitability

and spatial bias of the model. Therefore, we assumed that the pro-

portion of potential range contraction within the observed range

(i.e., false negatives obtained by comparing model predicted range

under current climatic conditions with the current occurrence data)

was a measure of bias in prediction of threatened area, which refers

to the type II threat risk measure of Ohlem€uller, Gritti, Sykes, and

Thomas (2006). In this meaning, we assumed prediction bias for each

species as the proportion of the number of false negatives compared

with the number of all the observed presences among raster cells.

To address the uncertainty of threat levels, we used standard error

(SE) to supplement mean threat levels, calculated using projections

for each species from the three GCMs included in the study. After

threat analysis, we divided the species studied into three groups:

“winners”—species with less than 50% of current distribution

threatened under the pessimistic scenario (RCP8.5), “losers”—species

with more than 50%, and “alien”—species geographically alien to

Europe.

Principal components analysis (PCA) based on the presence/ab-

sence occurrences from averaged outputs of each species for all ras-

ter cells (n = 1,235,520) was used to describe differences in

distributions among the 12 species for current conditions and the

three climate change scenarios (n = 12 9 4 = 48). PCA was chosen,

as it gave biologically meaningful output and the algorithm was able

to do computations on an extensive matrix with empty rows, in con-

trast to correspondence analysis and detrended correspondence

analysis. Further inspection of PCA results showed that there were

no problems with artifacts and horseshoe effects. We also confirmed

the applicability of both PCA axes using a screeplot—we took into

account only those components with eigenvalues higher than that

predicted by a random broken stick model (Jackson, 1993). We used

species-scenario scores, that is, values of principal components for

each species in each scenario, for analyses of changes in potential

distribution. All analyses were conducted using R software (R Core

Team, 2015).

3 | RESULTS

The species showed individualistic differences in contribution of par-

ticular bioclimatic variables to the models predicting their current

ranges (Figure 1; Appendix S3). The most frequent four parameters

with the highest importance were annual temperature range (bio7),

mean temperature of the warmest quarter (bio10), maximum temper-

ature of the warmest month (bio 5), and precipitation of the warm-

est quarter (bio18). However, the importance of these variables

differed among species, and they varied among groups of species

which, as described below, differed in response to climate change

—“winners” with a net gain in range as the climate warms,

“losers” with a net loss of range, and alien species (Figure 1). There

was a particularly large difference in the importance of variables con-

nected to warming and drying (bio5 and bio10) between the “winner’

and “loser” groups of species, while annual temperature range (bio7)

was particularly important for models of the alien species. Quality of

models, expressed by AUC, ranged from 0.680 to 0.912 (Table 1,

mean 0.802 � 0.023) and was positively correlated with the numberof occurrences (r2 = .91, p < .001). Almost all AUC values were above

0.7, indicating good model performance (Elith et al., 2011).

All species differed in projected range changes among the three

climate change scenarios and GCMs (Appendixes S4–S6). Particular

GCMs differed in the magnitude of distribution shifts, but directions

of shifts were consistent among GCMs. For averaged GCM outputs

within native coniferous species (Figure 2), we project northward

shifts in potential distributions and increasing threatened area in the

southern and eastern parts of distributions. However, the highest

changes were predicted for P. abies and P. sylvestris (Figure 2 i–p)

and the lowest—for A. alba (Figure 2 a–d). The occurrence of L. de-

cidua (Figure 2 e–h) would decrease and in the pessimistic scenario


(Figure 2h), it would be nearly limited to mountain ranges. F. sylvat-

ica (Figure 3 a–d) revealed a pattern similar to A. alba, and it was

similar to other deciduous species with more extensive current dis-

tributions—F. excelsior, Q. petraea, and Q. robur (Figure 3 e–p). In the

cases of Q. petraea and Q. robur, although both species occupy simi-

lar areas, Q. robur tends to decrease more extensively in the south-

ern part of its current distribution; however, its potential range shifts

more northward. B. pendula (Figure 4 a–d) exhibits a pattern similar

to P. abies and P. sylvestris (Figure 2 i–p). For the alien species (Fig-

ure 4 e–o), especially P. menziesii, potential distributions were two-

to sixfold larger than currently observed.

Principal components analysis (Figure 5) describes differences

among species distributions in all climate change scenarios. PC1 axis

explains 41.4% of variance and is positively correlated with relative

change in extents of distributions (r2 = .45, p < .001). PC2 axis

explains 21.5% of variance and is positively correlated with the latitu-

dinal (r2 = .62, p < .001) and longitudinal centers of distributions

(r2 = .32, p < .001). PCA results show that in general ranges of species

occurring at lower latitudes will expand and those occurring at higher

latitudes will lose portions of their current ranges while gaining little or

no new potential distribution areas. An exception is L. decidua, which

although it has a relatively low mean latitude, exhibited changes similar

to P. abies, P. sylvestris, and B. pendula. These species’ predicted reac-

tions are consistent and increase with climate change severity. In the

cases of A. alba, P. menziesii, and Q. rubra, the direction of potential

distribution changes depends on climate change severity—in the opti-

mistic and moderate scenarios, these species will gain new potential

distribution areas—but will lose distribution areas for the pessimistic

scenario, compared to the current situation.

Analyzing only changes in observed distributions, the proportion

of threatened cells increases with climate change scenario severity

for almost all of the species (Figure 6) and differed among GCMs

(Appendix S7), up to sixfold in the case of RCP8.5 for R. pseudoaca-

cia. For the results averaged among three GCMs, magnitudes of the

effects were similar for the optimistic and moderate scenarios but

doubled for the pessimistic scenario. For the optimistic scenario, rel-

ative threatened area was higher than 20% for six species and ran-

ged from 29.5% (P. sylvestris) to 53.3% (Q. rubra), with an average of

39.5 � 3.7%. In the moderate scenario, proportion of threatenedarea higher than 20% occurred for eight species and ranged from

12.8% (A. alba) to 65.6% (Q. rubra), with an average of 46.5 � 5.2%.In the pessimistic scenario, the proportion of threatened area higher

than 20% occurred for 11 species and ranged from 25.5% (Q. pe-

traea) to 86.8% (P. menziesii), with an average of 58.7 � 6.7%. Therelationship between threatened proportion of current distribution

and latitudinal center of distribution for each scenario was not statis-

tically significant. However, after removing three species outlying

from the trend—L. decidua, P. menziesii, and Q. rubra—there was a

F IGURE 1 Relative importance of bioclimatic variables (Table 2) to species distribution models within three groups of species, distinguishedby geographical origin and response to climate change. Boxes represent the interquartile range, bars—range without outliers, dots—outlierswithin each group


strong, statistically significant relationship for each scenario (p < .01,

r2 = .84, .72, and .59 for RCP2.6, RCP4.5, and RCP8.5, respectively;

Figure 7).

4 | DISCUSSION

Our study found different responses of tree species to climate change.

These species may be divided into three groups: “winners”—mostly

late-successional species (A. alba, F. sylvatica, F. excelsior, Q. robur,

and Q. petraea); “losers”—mostly pioneer and coniferous species

(B. pendula, L. decidua, P. abies, and P. sylvestris); and alien species

(P. menziesii, Q. rubra, and R. pseudoacacia). The last species may also

be considered a “winner” because the other two alien species had pro-

jected increases in their range in the optimistic and moderate scenar-

ios and strongly decreased range in the pessimistic scenario, while

R. pseudoacacia was projected to increase in all scenarios. Neverthe-

less, “winners” seem to lose less of their currently occupied range (ex-

cept for Q. robur in RCP8.5) as well as gain new areas.

4.1 | Model and prediction limitations

We found a different type of relationship between model AUC and

number of grid cells occupied by species than Hanspach, K€uhn,

Pompe, and Klotz (2010), who found decreasing trends for interme-

diate proportions of occupied area. Our study used data with finer

resolution, only one functional group (trees), and assessed only 12

species; thus, our results cannot be directly compared. Hanspach

et al. (2010) found decreasing model quality for species related to

human activity; however, this limitation is not apparent in our mod-

els as indicated by high AUC for the three alien species (Table 1).

Regarding future predicted ranges, as our models do not take into

account migration rate, our conclusions refer to the 2nd (“Change in

climatically suitable area”) and 5th (“Change in average climatic suit-

ability in already occupied cells”) measures of risk, according to

Ohlem€uller et al. (2006). Examining the variability among GCMs, we

can conclude that threat level strongly depends on which GCM is

used, similar to Goberville et al. (2015). In our study, HadGEM2-ES

predicted the highest threat level for 9 of 12 species studied in

(a) (b) (c) (d)

(e) (f) (g) (h)

(i) (j) (k) (l)

(m) (n) (o) (p)

F IGURE 2 Current and projected ranges of Abies alba (a–d), Larix decidua (e–h), Picea abies (i–l), and Pinus sylvestris (m–p) predicted forcurrent climate (a, e, i, m) and for three climate change scenarios: optimistic (RCP2.6; panels b, f, j, n), moderate (RCP4.5; panels c, g, k, o), andpessimistic (RCP8.5; panels d, h, l, p). Green area represents overlap of current and future projected ranges, blue—potential range expansionand red—potential range contraction (see Materials and Methods for details). Spatial resolution: 2.50 [Colour figure can be viewed atwileyonlinelibrary.com]


RCP2.6 and 11 of 12 for RCP4.5 and 8.5 (Appendix S7), which

shows the need for analyzing more than one GCM and high uncer-

tainty in accuracy of GCM predictions.

4.2 | Why are pioneer and coniferous speciespredicted to be more threatened?

According to Rapoport’s rule, we expected that species with more

northern distribution centers should have wider ranges of climatic

requirements (Gaston, Blackburn, & Spicer, 1998). In our study, pio-

neer and coniferous species had northern distributions and were

more threatened than others due to a lack of potentially colonizable

area northward. Despite the better dispersal abilities of pioneer spe-

cies and their ca. 40 times higher migration rate (Meier et al., 2012),

lack of colonizable areas would be a more serious threat than disper-

sal limitations. Our assessment of threat levels takes into considera-

tion only observed distributions and therefore makes our predictions

more serious for these species. Three species exhibit patterns outly-

ing from the trend—L. decidua, P. menziesii, and Q. rubra. For

L. decidua, this likely results from its alpine distribution—this species

occurs mostly in subalpine areas in lower latitudes and its require-

ments are similar to the boreal species (Ellenberg, 1988). The highest

threat level for L. decidua confirms findings of Ohlem€uller et al.

(2006). Our conclusion that species with more northward distribu-

tions are more threatened is supported by decreasing drought toler-

ance for species with distributions at higher latitudes (Nogu�es-Bravo

et al., 2014).

Our model for P. abies is more optimistic than those of other

studies such as Sykes et al. (1996), who predicted greater decline of

this species in the mountains and highlands of Central Europe. Both

P. abies and P. sylvestris showed similar patterns, despite having dif-

ferent current distributions, in contrast to Sykes et al. (1996), who

claimed that these species should show different patterns, due to

different physiological limitations. However, our models for these

two species are consistent with predictions of Hanewinkel, Cull-

mann, Schelhaas, Nabuurs, and Zimmermann (2013) and Schueler

et al. (2014), suggesting decline of coniferous species in the temper-

ate zone of Europe. This shift of dominant tree species may

(a) (b) (c) (d)

(e) (f) (g) (h)

(i) (j) (k) (l)

(m) (n) (o) (p)

F IGURE 3 Current and projected ranges of Fagus sylvatica (a–d), Fraxinus excelsior (e–h), Quercus petraea (i–l), and Quercus robur (m–p)predicted for current climate (a, e, i, m) and for three climate change scenarios: optimistic (RCP2.6; panels b, f, j, n), moderate (RCP4.5; panelsc, g, k, o), and pessimistic (RCP8.5; panels d, h, l, p). Green area represents overlap of current of future projected ranges, blue—potential rangeexpansion and red—potential range contraction (see Materials and Methods for details). Spatial resolution: 2.50 [Colour figure can be viewed atwileyonlinelibrary.com]


influence the potential of European forests to mitigate global warm-

ing connected with the differing albedos of coniferous and decidu-

ous species and their rotation ages (Naudts et al., 2016).

4.3 | Why do mid- and late-successional speciesseem to win?

Better performance of mid- and late-successional species than pio-

neer species under changing climate may be an effect of better

drought tolerance (Niinemets & Valladares, 2006; Nogu�es-Bravo

et al., 2014). Our model for Q. robur showed a more optimistic sce-

nario for the western part of the current distribution and less opti-

mistic for the Balkanian Peninsula and Italy than Sykes et al. (1996).

For F. sylvatica, our model showed a lower decrease in the western

part of the current distribution, but higher in the southern Carpathi-

ans, than Sykes et al. (1996). Our model is also similar to other stud-

ies, which predict a higher threat in the eastern part of the

distribution (e.g., Meier et al., 2012; Ohlem€uller et al., 2006). For

Q. petraea, our model predicted threats to areas which are claimed

by S�aenz-Romero et al. (2017) to face the highest reduction in

growth increment and survival. On the other hand, we did not take

into account migration rates, which for mid- and late-successional

species are usually low (Meier et al., 2012). Therefore, potentially

colonizable areas in the northern and eastern parts of the distribu-

tion could remain uncolonized. Nevertheless, it does not influence

our risk assessment, but may suggest that, in cases where coloniz-

able area is available, boreal forests can be less threatened than

southern temperate and mountain forests. Although Hanewinkel

et al. (2013) predicted that the dominant forest type in Central Eur-

ope will change from P. sylvestris to Q. robur and Q. petraea, in the

most severe scenario even these species seem to be threatened.

4.4 | Why are alien species predicted to be morethreatened?

The high proportion of threatened areas for alien species does not

reflect real potential threats, as these species currently occupy only

small parts of their potential distributions. This results from

(a) (b) (c) (d)

(e) (f) (g) (h)

(i) (j) (k) (l)

(m) (n) (o) (p)

F IGURE 4 Current and projected ranges of Betula pendula (a–d), Pseudotsuga menziesii (e–h), Quercus rubra (i–l), and Robinia pseudoacacia(m–p) predicted for current climate (a, e, i, m) and for three climate change scenarios: optimistic (RCP2.6; panels b, f, j, n), moderate (RCP4.5;panels c, g, k, o), and pessimistic (RCP8.5; panels d, h, l, p). Green area represents overlap of current of future projected ranges, blue—potentialrange expansion and red—potential range contraction (see Materials and Methods for details). Spatial resolution: 2.50 [Colour figure can beviewed at wileyonlinelibrary.com]


constraints other than climate (Bradley, Early, & Sorte, 2015) and the

small amount of time since introduction (Kowarik, 1995). The latter

point may be supported by better performance of R. pseudoacacia,

introduced in 1635 as an ornamental, and used for the first large-

scale afforestation in 1750 (290 ha; V�ıtkov�a, M€ullerov�a, S�adlo, Pergl,

& Py�sek, 2017), than P. menziesii (1827; Schmid, Pautasso, & Hold-

enrieder, 2014) and Q. rubra, which was also introduced in 17th cen-

tury as an ornamental plant, but was not further spread by foresters

until the late 19th century (Woziwoda et al., 2014). Therefore, their

true dispersal capacity has not yet been manifested; thus, these

results are biased by the highest uncertainty among the species

studied. Moreover, the lower performance of P. menziesii is con-

nected with mismatch in climate conditions between source and

target localities, and therefore, lower level of adaptation (Chakra-

borty et al., 2016).

4.5 | How will climate change affect natureconservation?

The most important threat connected with climate change is trans-

formation of major vegetation types (Hickler et al., 2012). Hanewin-

kel et al. (2013) predicted changes in structure of dominant tree

species in Europe that are consistent with our results. Retreat of pio-

neer species, which dominate in European forests, would cause

retreat of rare types of ecosystems, in which these species are foun-

dational elements, but occur in specific conditions, for example,

F IGURE 5 Results of principalcomponents analysis conducted onpresence/absence data of each speciesunder three projected climate changescenarios (optimistic—RCP2.6, moderate—RCP4.5, pessimistic—RCP8.5) for averagedGCM’s outputs and current conditions(n = 12 9 4 = 48) for all raster cells(n = 1,235,520). PC1 axis explained 41.4%of the variability and PC2–21.5%. Pointsizes (relative change) refer to the extentof change in number of occupied rastercells compared to the current conditions

F IGURE 6 Proportion (�SE) of currentobserved distribution in which presencewas not confirmed by the prediction withinthree climate change scenarios: optimistic(RCP2.6), moderate (RCP4.5), andpessimistic (RCP8.5)


dunes and peatlands. This phenomena may be especially clear at

their southern and eastern margins of distribution. In Europe, climate

change is claimed to be one of the main threats to 24 of the 42

threatened forest plant community types (Janssen et al., 2016).

Decreased survival of certain species (e.g., S�aenz-Romero et al.,

2017) may also decrease availability of old trees, which are an

important microhabitat for other organisms, for example, epiphytes

(Kir�aly, Nascimbene, Tinya, & �Odor, 2013; Me�zaka et al., 2012). On

the other hand, predicted losses of suitable habitats for the species

studied are lower than those predicted for North America by

McKenney, Pedlar, Lawrence, Campbell, and Hutchinson (2007),

which may suggest lower threat levels for European species. Never-

theless, this difference may also be caused by the uncertainty con-

nected with the differences among GCMs.

4.6 | How will climate change affect forestmanagement?

Hanewinkel et al. (2013) claim that climate change will result in net

replacement of highly productive forests (P. abies and P. sylvestris) by

species with lower productivity (Quercus spp.) by 2100. Due to the

longevity of trees and their rotation age (Pretzsch, 2009), it is likely

that most of the trees living now will persist to 2070. Most of the

foresters surveyed would adapt their management to changing cli-

mate if growth declines of trees were over 20% and if regeneration

failures were over 25% (Seidl, Aggestam, Rammer, Blennow, & Wolf-

slehner, 2016). Thus, changes in forest management probably would

be slower than the changes of tree species composition. This pro-

cess may also be masked by adaptation (Bussotti et al., 2015). On

the other hand, due to predicted increases in disturbances and insect

outbreaks (Seidl et al., 2014), it is likely that most P. abies wood in

the future may come from salvage cuttings, the economic value of

which would decrease due to lower quality and higher supply of

wood in short time windows. Rotation age of oaks and beech is usu-

ally longer than for pines and spruce, and due to elongated time of

management, amount of money spent on silviculture would be

higher. Therefore, economical losses may be higher than those pre-

dicted by Hanewinkel et al. (2013). On the other hand, the highest

relative basal area increment of trees is not connected with occur-

rence probability, and trees at range margins may exhibit better

F IGURE 7 The relationship between threatened proportion of current distribution and latitudinal center of distribution for each climatechange scenario: optimistic (RCP2.6; panel a), moderate (RCP4.5; panel b), and pessimistic (RCP8.5; panel c) calculated for averaged GCMsoutputs. Three species outlying from the trend—L. decidua, P. menziesii, and Q. rubra were removed from the regression analysis and arerepresented by gray dots. Gray areas around the regression lines represent standard errors (SE) of each model


growth (Dolos, Bauer, & Albrecht, 2015). However, even for one of

the least threatened species—Q. petraea—trees at its western and

southern range margin would suffer from tree height increment

reduction (S�aenz-Romero et al., 2017). For that reason, future stud-

ies should rather focus on the impact of climate change on tree

growth.

ACKNOWLEDGMENTS

The study was financially supported by the Institute of Dendrology,

Polish Academy of Sciences, K�ornik, Poland. We thank an anony-

mous reviewer for valuable and helpful comments which greatly

improved the manuscript.

ORCID

Andrzej M. Jagodzi�nski http://orcid.org/0000-0001-6899-0985

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How to cite this article: Dyderski MK, Pa�z S, Frelich LE,

Jagodzi�nski AM. How much does climate change threaten

European forest tree species distributions?. Glob Change Biol.

2018;24:1150–1163. https://doi.org/10.1111/gcb.13925

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