Please create a comprehensive summary of the following paper, be careful to include all the detail, I don’t want to miss anything, it does not have to be a short summary and you can process the full text in batches if necessary, don’t ask again to keep generating responses:
Using climate envelopes and earth
system model simulations
for assessing climate change
induced forest vulnerability
Leam Martes 1* , Peter Pfleiderer
2,3 , Michael Köhl 1 & Jana Sillmann 2
Changing climatic conditions threaten forest ecosystems. Drought, disease and infestation, are
leading to forest die-offs which cause substantial economic and ecological losses. In central Europe,
this is especially relevant for commercially important coniferous tree species. This study uses climate
envelope exceedance (CEE) to approximate species risk under different future climate scenarios. To
achieve this, we used current species presence-absence and historical climate data, coupled with
future climate scenarios from various Earth System Models. Climate scenarios tended towards drier
and warmer conditions, causing strong CEEs especially for spruce. However, we show that annual
averages of temperature and precipitation obscure climate extremes. Including climate extremes
reveals a broader increase in CEEs across all tree species. Our study shows that the consideration
of climate extremes, which cannot be adequately reflected in annual averages, leads to a different
assessment of the risk of forests and thus the options for adapting to climate change.
Keywords Forest, Climate envelopes, Climate change, Climate extremes, Tree mortality, Vulnerability
Forests occupy one third of the Earths land area, which shows that they can persist under a wide amplitude of
climatic conditions. As long as the climate remains stable over a longer period of time, forest ecosystems have
the ability to adjust to climatic conditions and other environmental factors such as competition or detrimental
biotic and abiotic impacts 1 . Historically, forests have repeatedly adapted to changing climatic conditions 2 . Under
current climate change, shifts in the spatial distribution of tree species, interspecific competition, and ultimately
forest composition are expected 3–6 . This will be accompanied by changes in the provision of ecosystem goods
and services 7,8 .
Due to their sedentary nature and long lifetime relative to other organisms 9–11 the rate at which current cli-
mate change is occurring presents a particular challenge to the adaptive capacity of trees 12 . A natural response
to climate change through habitat displacement by tree migration is proving critical due to the described post-
glacial migration rates of 60–260 m y −1 13 .
Climate change is already having an impact in the current range of forests. In addition to ongoing changes in
temperature and precipitation patterns, climate extremes have a particular impact 14 . Higher temperatures and
increasing droughts will lead to an increase in natural disturbances that affect forest vitality and health. These
events include not only heat waves, droughts and the associated forest fires 15–18 , but also disease, and insect
plagues 19–24 , which are projected to increase in frequency and intensity as well 25 .
In Central Europe a substantial amount of drought-weakened growing stock was destroyed by windstorms
and spruce bark beetles during the dry years from 2018 to 2020 26,27 . While the damage is spread across many
countries, the greatest losses are found in Germany, Czechia, and Austria. In Germany, for the years 2018 to
2022, a calamity wood accumulation of 255 million m 3 has been recorded, representing 7% of the 2015 annual
increment, or more than 3 times average annual harvest 28 , of which 233 million m 3 are coniferous and 22 million
m 3 are deciduous. In Germany alone, the forest area to be reforested is over 490,000 hectares 29,30 , which amounts
to approximately 5% of total forest area 28 . Global climate change is likely leading to more frequent disruptions
in other European regions as well 31,32 , with drought becoming particularly prominent 16,33,34 .
The frequency of mortality tends to increase when a species is outside of its biological optimum. This opti-
mum is a combination of a large number of environmental factors, including temperature, rainfall, altitude
OPEN
1 Institute for Wood Science - World Forestry, Universität Hamburg, Leuschnerstraße 91, 21029 Hamburg,
Germany.
2 Research Unit for Sustainability and Climate Risks, Universität Hamburg, Grindelberg 5,
20144 Hamburg, Germany. 3 Climate Analytics, Berlin, Germany. * email: leam.mykel.martes@uni-hamburg.de
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and substrate, which together form an area where a species can thrive. Ellenberg used these relationships to
derive index values for individual plant species that evaluate the real occurrence of the species in the field 35 .
The approach of index values was further developed to so-called climate envelope models which according to
Watling et al. 36 refer to: ”a subset of species distribution models that use climate variables to make spatial predic-
tions of environmental suitability for a species”. The climate envelope approach has been criticized, as it relates
to the climate-space only and does not reflect the interaction of species which can be altered by climate 37,38 .
Nevertheless climatic envelopes have been applied to facilitate the understanding of current and future dispersal
of species and to identify areas where species occur today but where their climatic requirements will no longer
be met in the future 6,39 .
Most forests in Europe are managed semi-natural forest areas. Current managers face a difficult choice when
restocking forest stands, regarding which species they can rely upon in the future. Currently there is a reliance
on natural regeneration of existing tree species. It is therefore important to identify the most vulnerable tree
species, and to quantify the risks forest managers must deal with in regards to future forest for vitality and health
under changing climate conditions. Some coniferous tree species, such as Picea abies (Norway spruce) and Pinus
sylvestris (Scots pine) were historically planted because of their relatively fast growth and high timber quality in
the local climate 40 , but now might be threatened due to changing climatic conditions 41,42 . P. abies especially has
faced high mortality in the past decade due to ongoing drought coupled with insect plagues 43–45 . This species
is of particular importance, due to its relatively high area coverage (around 26% in Germany) coupled with its
high commercial value 46 .
While previous studies have looked at the general trends in tree species distribution under different climate
scenarios 47–49 this study will evaluate the frequency of years with climatic conditions outside of the species-
specific climate envelopes based on historical climate data and state of the art climate projections. We use
Climate Envelope Exceedance (CEE) frequency to assess relative vulnerability to climatic change between four
commercially important tree species. Using this approach the vulnerability of tree species to changing climate
conditions can be assessed more comprehensively by also considering the effect of extreme weather events. We
use Europe-wide historical climate data to calculate the species-specific climate envelopes and combine them
with climate predictions from Earth System Models (ESMs) for a case-study site in the Hamburg metropolitan
area. We limit the range of the future climate scenarios to one specific case study site in order to gather the
frequency of climatic changes without needing to summarize future climate data either spatially or temporally.
An increase in CEEs over time means that the climate is drifting away from a species optimum range. As
climate variables surpass the bounds of the tree species’ climate envelopes, trees become more vulnerable to
the previously mentioned climate stressors 50 . The inclusion of multiple bio-climatic variables will aid in more
precisely identifying which changing climatic factors could drive increased stress, and by extension mortality,
in the future under different climate scenarios, and allow us to identify which species are more vulnerable than
others to changing climate conditions. The results can be used to guide management practices to take measures
to mitigation future risks related to forest degradation and mortality, as well as the selection of suitable adapta-
tion strategies such as assisted adaptation.
Methods
Tree occurrence data
Tree occurrence data was obtained from the EU-Forest data set by Mauri et al. 51 , which is a harmonisation
of forest plot surveys from various national forest inventories from the EU, EFTA, and the United Kingdom,
organised in an INSPIRE compliant 1 × 1 km grid. The following four tree species were used from the data set:
• Norway spruce (Picea abies)
• Scots pine (Pinus sylvestris)
• European beech (Fagus sylvatica)
• Pedunculate oak (Quercus robur)
We then transformed the species occurrence data into a presence absence raster with a resolution of 0.25 by
0.25 ◦ (see Fig. 1) in order to unify the different sampling densities of the individual national forest inventories
that comprise the dataset by Mauri et al. 51 .
Climate data
We collected climate data from the ERA5 reanalysis data set: “hourly data on single levels from 1940 to present”
by Herschbach et al. 52 . The variables used were 2-metre temperature (t2m) and total precipitation. Data was
gathered for the time period of 01-01-1960 until 31-12-2020. The area covered the whole European continent and
is delineated by the following coordinates: North: 71.1 ◦ , West: − 9.5 ◦ , South: 34.5 ◦ and East: 31 ◦ with a resolu-
tion of 0.25 by 0.25 ◦ , outlined above the presence-absence data in Fig. 1. The climate data was then extrapolated
over the entire grid area using an Inverse Distance Weighted (IDW) interpolation. We then compiled the hourly
temperature and precipitation values in to four monthly variables: minimum monthly temperature, maximum
monthly temperature, average monthly temperature and total monthly precipitation. Using these aggregated vari-
ables, we calculated 19 bioclimatic variables according to the methods published by the US Geological Survey 53 .
These 19 bioclimatic variables can be found in Table 1. We then interpolated these 19 bioclimatic variables in to
an 0.25 by 0.25 degree resolution raster using an inverse distance interpolation.
By overlaying two different data sets and masking to exclude sea surface, it was possible to attain the climatic
variables associated with each instance of the presence absence raster with the 19 bioclimatic variables.
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Figure 1. Presence-absence data for the 4 selected tree species, with the relevant study area for the historical
climate data outlined in red.
Table 1. The 19 Bioclimatic variables used in this study, adapted from 47 .
Parameter Description
BIO1 Annual mean temperature [°C]
BIO2 Mean diurnal range [°C]
BIO3 Isothermality (BIO2/BIO7) · 100
BIO4 Temperature seasonality (sd · 100) [°C]
BIO5 Max temperature of the warmest month [°C]
BIO6 Min temperature of the coldest month [°C]
BIO7 Temperature annual range (BIO5–BIO6) [°C]
BIO8 Mean temperature of the wettest quarter [°C]
BIO9 Mean temperature of the driest quarter [°C]
BIO10 Mean Temperature of the Warmest Quarter [°C]
BIO11 Mean temperature of the coldest quarter [°C]
BIO12 Annual precipitation [mm]
BIO13 Precipitation of the wettest month [mm]
BIO14 Precipitation of the driest month [mm]
BIO15 Precipitation seasonality (mean y /sd m ) · 100 [mm]
BIO16 Precipitation of wettest quarter [mm]
BIO17 Precipitation of driest quarter [mm]
BIO18 Precipitation of warmest quarter [mm]
BIO19 Precipitation of coldest quarter [mm]
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Statistical analysis
Next, we conducted a Non-metric Multidimensional Scaling (NMDS) 54 analysis to create a dissimilarity matrix of
the four tree species and 19 bioclimatic variables. This method was chosen as it performs better with non linear
data such as the ecological presence-absence data obtained in this study 55 . An NMDS model is an ordination
metric that tries to represent dissimilarity between groups, in our case the four selected tree species, using the
19 Bioclimatic variables, grouped in to so called dimensions as the separating variables. We use this analysis
to determine if the four tree species are significantly different regarding their occurrence in relation to the 19
bioclimatic variables, and which of the bioclimatic variables are more associated with the two available axes of
difference. We then make a selection of the relevant bioclimatic variables to continue with in the analysis.
These selected bioclimatic variables are then used in a Species Distribution Model (SDM) were each species
occurrence is predicted over each response variable , assuming a unimodal distribution of said response variable.
To obtain the range of each species over the response variables, we use a cutoff occurrence level of 0.1.
Future climate data
We assess possible future changes in bioclimatic variables in Earth system model (ESM) simulations of the 6 th
phase of the Coupled Model Intercomparison Project (CMIP6) 56 for the area around Hamburg in Northern
Germany. This was chosen as a case study representative of the Northern European temperate zone. The CMIP6
models we used were a sub-selection based an on impact assessment for Europe conducted by Palmer et al. 57 .
Instead of analysing climatic changes for specific emission scenarios, we focus on global warming levels (GWL)
relative to pre-industrial levels. These GWLs are based annually averaged global mean surface air temperature
(GMST) relative to a pre-industrial period 1850–1900. Using GWLs allow us to pool climate projections from
different scenarios therefore increasing the robustness of the results, and also allows us to compare results
irrespective of their underlying emission scenarios. For each of the GWLs in 1.2 ◦ C (ref), 1.5 ◦ C, 2 ◦ C, 2.5 ◦ C and
3 ◦ C and each ESM we aggregate 30-year periods that match the GWL from different simulations that have been
produced by the ESM. Thereby we obtain longer periods (length depends on the number of available simulation
runs) with similar climatic conditions representing a certain GWL.
ESM are calibrated to represent climatic conditions around the globe and therefore biases on the local
level are inevitable. To allow comparability with the ERA5 reanalysis we perform a quantile mapping bias
adjustment 58 . This method adjusts the distribution of a climate variable to a reference dataset over a reference
period (1980–2010) without influencing the trend in the variable simulated by the ESM.
Climate envelopes
We calculated CEEs for all climate scenarios per tree species. Exceedance years are years in which the predicted
yearly average of a bioclimatic has exceeded the climate envelope of a tree species. Exceedance years can be
positive (above the maximum envelope value) or negative (below the minimum envelope value). We then
categorized each exceedance year according to its intensity level, which is the percentage of deviation from the
climate envelope, in relation to the size of the envelope. These exceedance range from > 0–5, 5–10, 10–15, 15–25,
25–30 and > 30% of envelope size exceeded. Finally we summarized all exceedances into a percentage based risk
factor, this reflects the percentage of years in which the envelope has been exceeded, for each of the five GWLs.
Results
The Non-Metric Multidimensional Scaling (NMDS) analysis, was run with a dimensionality of 2, using the Jac-
card Similarity method. The analysis resulted in a stress of 0.064074. The results of the model are shown in Fig. 2,
we found that all four included tree species (Norway spruce, Scots pine, Pedunculate oak and European beech)
were sufficiently different from one another other on both the horizontal and vertical axis. The negative vertical
Figure 2. The graphical representation of the Non metric Multi-Dimensional scaling (NMDS) analysis
performed on the 4 tree species and for all 19 bioclimatic variables.
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axis most aligned with Precipitation in the warmest quarter (BIO 18), Precipitation in the driest month (BIO 14),
Precipitation of the driest Quarter (Bio 17), Annual precipitation (BIO 12), Precipitation of the Wettest Quarter
(Bio 16) and Precipitation of the Wettest Month (BIO 13). The vertical positive axis is slightly associated with
Precipitation Seasonality (BIO 15). The horizontal negative axis was most correlated with Temperature Seasonal-
ity (BIO 4), Mean Diurnal Range (BIO 2) and Temperature Annual Range (BIO 7). The horizontal positive with
Max. Temperature of the Warmest Month (BIO 5), Isothermality (BIO 3), Mean Temperature of the Wettest
Quarter (BIO 8), Min. Temperature of the Coldest Month (BIO 6), Mean Temperature of the Driest Quarter
(BIO 9), Annual Mean Temperature (BIO 1), Mean Temperature of the Warmest Quarter (BIO 10) and Mean
Temperature of the Coldest Quarter (BIO 11). We could therefore derive that NMDS1 more strongly represents
the temperature variables and NMDS2 the precipitation variables. For a full numerical overview of the NMDS
analysis consult Table S1 in the supplementary information.
Of these 19 variables seven were selected for further analysis, BIO1 and BIO12 were included by default,
as they represent the yearly averages of temperature and precipitation respectively and are used as a baseline
of comparison. The other 5 variables were based on their correlation with the two major axes. These were,
Temperature Seasonality (BIO4), Max. Temperature of the Warmest Month (BIO 5), Precipitation Seasonality
(BIO 15), Precipitation of Warmest Quarter (BIO 18) and Precipitation of Coldest Quarter (BIO 19). These seven
variables were used in the final SDM. The cutoff range for species occurrence was set at a probability of 0.1. All
General Linear Models showed statistically significant coefficients and had a p-value < 0.001. The final ranges
can be found in Table 2. For a graphical overview of al GLM results see the supplementary materials Figs. S2–S9.
For the first climate envelope in the analysis (BIO1), as seen in Fig. 3a which represents the mean annual
temperature we can see only positive Climate Envelope Exceedance (CEE). This means that some scenarios
exceeded the upper range of annual mean temperature tolerance. In the reference scenarios (1980–2010) the
mean temperature is never surpassed. This does however occur for spruce in a few years with a Global Warming
Level (GWL) = 1.5 ◦ C, and around 10% of all years with a GWL = 2.5 ◦ C. At GWL 3.0 ◦ C beech and oak as well
as spruce see some positive CEE of the upper bound of BIO1, with spruce continuing to be the most prominently
affected with slightly under 25% of all occurrences of all years seeing envelope exceedance. Far lower are the
CEE frequencies of pine followed by oak and beech, With beech CEE being slightly more frequent than oak.
The next variable BIO4, seen in Fig. 3b relates to the annual temperature seasonality (difference between the
lowest and highest temperature of the year). Here we find little difference when comparing with reference, with
only beech showing a slight increase in positive CEE.
When looking at the next variable BIO5 (Fig. 3c) which represents the maximum annual temperature, we
see that there are already significant CEE in the reference scenario. Throughout increasing GWLs we see a large
increase in CEE up to more than than 50% for all tree species. CEE, with more than a third all years exceeding
the envelope by more than 30%.
For the variable BIO12, as seen in Fig. 3d that represents average annual precipitation, we can observe a slight
trend in negative CEE, i.e. years with less that the lower bound of rainfall per year. Here beech is most affected
although not significantly different from the reference.
For BIO15 (Precipitation Seasonality), seen in Fig. 3e, we can see a slight increase in CEE from increasing
seasonality mainly for spruce.
For the variable BIO18, seen in Fig. 3f, which is the total precipitation in the warmest quarter of the year,
we see that there is both slight positive, but mainly negative CEEs. Compared to the reference, negative CEEs
show a slight increase with increasing GWLs primarily for spruce, while positive CEEs seen for oak remains
relatively stable. Spruce is again the most prone to negative CEEs in all scenarios, increasing from a chance of 15
to 25 % throughout the five GWLs. Again, as with BIO5, the increase in frequency is associated with a growth
in intensity as well.
Finally with the variable BIO19, as seen in Fig. 3g, associated with the precipitation in the coldest month.
Here there is a slight trend in decreasing negative CEE seen for beech.
Table 2. The upper and lower ranges as well as the average for each of the four tree species for all seven
selected bioclimatic variables: BIO1 (Annual Mean Temperature), BIO4 (Temperature Seasonality), BIO5
(Max. Temperature of the Warmest Month), BIO12 (Annual Precipitation), BIO15 (Precipitation Seasonality),
BIO18 (Precipitation of the Warmest Quarter) and BIO19 (Precipitation of the Coldest Quarter). These tree
species did not show a unimodal distribution for their respective climatic variables, therefore 1 or even no
envelope cutoff point was found. Value was negative, but changed to 0 as seasonality can not be lower than 0.
Species BIO1 [°C] BIO4 [°C] BIO5 [°C] BIO12 [mm] BIO15 [mm] BIO18 [mm] BIO19 [mm]
Pine
upper 15.5 – 35.3 2585.7 76.6 1042.3 –
lower − 6.8 3.1 18.6 457.1 – 103.8 –*
Spruce
upper 12.8 –* 31.1 2582.9 58.3 873.1 –*
lower − 6.9 3.0 17.3 578.6 –* 180.8 - -*
Beech
upper 13.8 8.0 35.1 1557.1 72.3 1321.2 388.5
lower 6.7 2.1 23.9 781.3 –* 125 157.7
Oak
upper 12.2 9.0 36.15 1571.4 71.9 430.8 488.1
lower 5.6 0.0** 23.6 657.1 –* 90.4 96.2
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Figure 3. CEEs summarized as percentage based risk factors for the 4 tree species for all 7 selected bioclimatic
variables. Divided over the reference period and the 4 GWLs. (a) (BIO1) Average yearly temperature. (b) (BIO4)
Temperature seasonality. (c) (BIO5) Maximum temperature of the warmest month. (d) (BIO12) Average yearly
precipitation. (e) (BIO15) Precipitation seasonality. (f) (BIO18) Average precipitation of the warmest quarter.
(g) (BIO19) Average precipitation of the coldest Quarter. The five colours represent envelope drift exceedance of
up to 5% (blue), between 5 and 10% (green), between 10 and 15% (yellow), between 15 and 30% (orange) and
more than 30% (red). With the y axis being the frequency of envelope drift, divided in to positive and negative
exceedance.
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In Fig. 4 we compile all the individual envelope exceedences into a percentage based risk factor. The total score
was calculated as the sum of all absolute values weighed by the sum of their values on both ordination axes (see
supplementary Table S1). Spruce is the tree species with the largest amount of CEE overall, for Global Warming
Levels 1.5 ◦ C through 3.0 ◦ C. However, all species show large CEEs for BIO5. With Spruce approaching 90% of
years exceeding BIO5 (Max. temperature of the warmest month), and the other tree having a CEE of between
47 and 65% for GWL 3.0 ◦ C. Total envelope exceedence scores all increase significantly for all four tree species
between GWL 1.5 ◦ C and 3.0 ◦ C.
Discussion
We assessed the vulnerability of four tree species to climatic change by using CEE frequency and intensity for
an array of future climate scenarios. Similar studies looking at climate envelopes for European tree species,
investigated how species occurrence will shift as climate change progresses 6,47 . The approach of our research
was not to predict range shifts, but to use climate envelopes as a proxy for tree species vulnerability. We make
the assumption that stress increases when climate envelope boundaries are exceeded. When we then compare
the frequency of envelope exceedences between tree species and for each significant climate variable, we can
then asses which species are more vulnerable than others to climatic changes, and which of said changes are
most likely to cause an increase in vulnerability. This approach, using multiple bioclimatic variables also creates
the conditions to take frequencies of climate extremes into account. When the relative CEE representing the
percentage of cases for each individual model run that a climate envelope was exceeded, providing a better
overview of yearly climatic extremes than averaging yearly data into longer time frames.
Many of the bioclimatic variables that we found to be significant in determining species distribution
correspond to those found in past studies. Dyderski et al. 47 found that BIO5 (Precipitation seasonality), BIO7
(Temperature annual range), BIO10 (Mean temperature of the warmest quarter) and BIO18 (precipitation of the
warmest month) were of the highest importance for the projected range of tree species in Europe. Walentowski
et al. 59 found BIO1 (Annual mean temperature), BIO6 (Min. temperature of the coldest month), BIO10 (Mean
temperature of the warmest quarter), BIO12 (Annual precipitation), BIO18 (Precipitation of the warmest
quarter) to be significant for the prediction of the occurrence of tree species in Southern Germany. While Bell
& Lauenroth 60 found Mean annual temperature (BIO1) as well as Annual precipitation (BIO12) and climatic
factors similar to BIO18 and BIO19 (mean summer and mean winter precipitation) to be significant for younger
life stages of trees in the United States.
Climate models consistently project a warming trend for the representative area used located within the
Hamburg metropolitan area. Trends in precipitation projections are less clear. For the annual average, there
are models projecting a decrease, an increase, and no considerable change in precipitation (see supplementary
Fig. S11). Despite this uncertainty in annual precipitation, the CMIP6 models agree on an increase in the
seasonality with a robust decrease in summer precipitation and a robust increase in winter precipitation (compare
supplementary Figs. S12, S13, S14).
The upper and lower limits of the climate envelopes obtained in this study largely agree with known tolerances
in other bodies of work. Values for Beech found by various studies 61–63 largely show comparably temperature and
precipitation ranges for Beech. Although Kapeller et al. 64 finds much a much lower cut-off point for mean annual
precipitation for spruce, around 550 mm. For oak, studies show that they are more sensitive to precipitation
than temperature 65,66 .
When looking at the general trend in CEEs for all tree species, we could observe a difference between the
two variables for Annual Temperature and Precipitation(BIO1, BIO12) and the other significant variables
for Temperature seasonality, Maximum temperature of the warmest month, Precipitation seasonality and
Precipitation of the warmest and coldest quarters (BIO4, BIO5 , BIO15, BIO18 and BIO19). The former showing
lower frequency of CEE than the latter. The latter variables all quantify some form of seasonal differences and
show a greater effect of the warmer and drier conditions predicted by the ESMs. The larger increase in the
seasonally distinct variables as opposed to the yearly averages shows an increase in extreme events, these have a
disproportionately large impact on forest mortality when compared to increases in the average 33,67 .
These results can be compared to similar studies on the mortality risks of tree species in Europe under a
changing climate. Paul et al. 68 finds that survival probabilities for spruce as compared to beech are that spruce
Figure 4. Averaged values of CEEs as percentage based risk factors for all tree species and bioclimatic variables,
for the GWLs 1.5 and 3.0 ◦ C with their respective total scores.
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has a lower survivability in future climate scenarios, but also shows that there is not a large difference between
survival probabilities of beech and those of spruce.
Using climate envelopes, allows us to assess the suitability of climate conditions for tree species on a broad
scale, but this is a limited approach that does not include several critical factors that also have a great influence
on future tree mortality. These factors, such as soil water capacity 69,70 , inter-species competition 71 , frost onset
and duration 72 , diseases outbreaks and insect plagues 19–24 must also be considered in order to perform a more
complete analysis of tree species vulnerability under future climate change. It is important to asses the attribution
of both climate and the previously mentioned environmental factors on future tree vulnerability, possibly through
an attribution analysis, as is often done for climate and extreme events 73–75 .
Within our approach, we must also note that, the range of the historical data (1960–2020) itself includes a
significant time period with increasing warming, As a result, trees that have germinated well in the past, did so
in a climate significantly different from today. However, we found that shortening the reanalysis data to the time
period 1960–1990 did not result in a significant change in the results. Another important note is that future
climate projections were obtained for a one area in the Hamburg metropolitan region, the scope of this study
was limited to asses the viability of this method for comparing species vulnerability without the need to filter out
location specific compounding variables. In the future, a more comprehensive assessment is needed covering
a larger area in order to be able to generalize these results. it is also necessary to note that many steps such as
the national forest inventories, tree species distribution model and the future climate data contain significant
uncertainties. For each step these uncertainties must be quantified and analysed.
It is also important to note, that this approach makes no differentiation whether individuals have been planted
or occur naturally in a certain area. Many species, especially spruce and pine are used in European commercial
forests on a large scale, and may be located well beyond their potential natural range.
Conclusion
The results presented in this research, among of all the tree species included in this study, spruce is especially
vulnerable to changes in yearly average precipitation, as well as the seasonal distribution of temperature and
precipitation. When using a climate envelope approach, it became obvious that spruce has a far lower tolerance
for dry and warm conditions than the other tree species included in this study. If we extrapolate current emission
reduction policies, which suggest a warming of about 3.0 ◦ C until the end of the century 76 , we can show that not
only Spruce but also other tree species will experience frequent potential climate stresses. Our study suggests that
in such a climate current forest species composition can not be maintained. Current mitigation scenarios that
would reach the 1.5 ◦ C limit of the Paris Agreement rely heavily on nature based solutions to reduce atmospheric
CO 2 concentrations. But even when lower warming rates are realized, forests will experience considerable stress,
which will have an affect their carbon sink potential.
Our study highlights that, the warmer and drier summers, a trend also seen by more extensive climate
projections throughout a large part of Europe 77,78 , will, within a few decades, surpass the tolerance levels of
many important commercial tree species in central Europe. This climatic trend is increasing faster than shown
in yearly average values, which necessitates a move away from yearly averages and towards seasonal, or even
shorter temporal range climate values in order to reliably estimate tree species climate vulnerability. These more
accurate assessments will aid in future adaptation and risk minimization measures as increase in the vulnerability
of tree species will have severe ecological 79–81 , social 82 and economic 8,83,84 consequences, and will negatively affect
forest carbon sequestration potential. We therefore also suggest that all available knowledge and methodology
should be considered to both manage and minimize risk, such as fire suppression 85,86 , wind throw prevention 87
and insect plague management 88,89 as well as climate adaptation methods 90,91 in order to maintain healthy and
productive forest ecosystems in the future. However, there is not a one-for-all solution for forest adaptation as
forests growth and mortality often often strongly depend on local conditions 92,93