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Evaluating WorldClim Version 1 (1961-1990) as the Baseline for Sustainable Use of Forest and Environmental Resources in a Changing Climate

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  • Italian National Research Council - Institute of Biosciences and BioResources (IBBR) - Florence division

Abstract and Figures

WorldClim version 1 is a high-resolution, global climate gridded dataset covering 1961-1990; a "normal" climate. It has been widely used for ecological studies thanks to its free availability and global coverage. This study aims to evaluate the quality of WorldClim data by quantifying any discrepancies by comparison with an independent dataset of measured temperature and precipitation records across Europe. BIO1 (mean annual temperature, MAT) and BIO12 (mean total annual precipitation, MAP) were used as proxies to evaluate the spatial accuracy of the WorldClim grids. While good representativeness was detected for MAT, the study demonstrated a bias with respect to MAP. The average difference between WorldClim predictions and climate observations was around +0.2 • C for MAT and −48.7 mm for MAP, with large variability. The regression analysis revealed a good correlation and adequate proportion of explained variance for MAT (adjusted R 2 = 0.856) but results for MAP were poor, with just 64% of the variance explained (adjusted R 2 = 0.642). Moreover no spatial structure was found across Europe, nor any statistical relationship with elevation, latitude, or longitude, the environmental predictors used to generate climate surfaces. A detectable spatial autocorrelation was only detectable for the two most thoroughly sampled countries (Germany and Sweden). Although further adjustments might be evaluated by means of geostatistical methods (i.e., kriging), the huge environmental variability of the European environment deeply stressed the WorldClim database. Overall, these results show the importance of an adequate spatial structure of meteorological stations as fundamental to improve the reliability of climate surfaces and derived products of the research (i.e., statistical models, future projections).
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sustainability
Article
Evaluating WorldClim Version 1 (1961–1990)
as the Baseline for Sustainable Use of Forest and
Environmental Resources in a Changing Climate
Maurizio Marchi 1, * , Iztok Sinjur 2, Michele Bozzano 3and Marjana Westergren 2
1CREA—Research Centre for Forestry and Wood, I-52100 Arezzo, Italy
2Slovenian Forestry Institute, Vecna pot 2, 1000 Ljubljana, Slovenia; iztok.sinjur@gozdis.si (I.S.);
marjana.westergren@gozdis.si (M.W.)
3European Forest Institute, 53113 Bonn, Germany; michele.bozzano@efi.int
*Correspondence: maurizio.marchi@crea.gov.it; Tel.: +39-0575-353021; Fax: +39-0575-353490
Received: 29 April 2019; Accepted: 27 May 2019; Published: 29 May 2019


Abstract:
WorldClim version 1 is a high-resolution, global climate gridded dataset covering 1961–1990;
a “normal” climate. It has been widely used for ecological studies thanks to its free availability and
global coverage. This study aims to evaluate the quality of WorldClim data by quantifying any
discrepancies by comparison with an independent dataset of measured temperature and precipitation
records across Europe. BIO1 (mean annual temperature, MAT) and BIO12 (mean total annual
precipitation, MAP) were used as proxies to evaluate the spatial accuracy of the WorldClim grids.
While good representativeness was detected for MAT, the study demonstrated a bias with respect to
MAP. The average dierence between WorldClim predictions and climate observations was around
+0.2
C for MAT and
48.7 mm for MAP, with large variability. The regression analysis revealed a
good correlation and adequate proportion of explained variance for MAT (adjusted R
2
=0.856) but
results for MAP were poor, with just 64% of the variance explained (adjusted R
2
=0.642). Moreover
no spatial structure was found across Europe, nor any statistical relationship with elevation, latitude,
or longitude, the environmental predictors used to generate climate surfaces. A detectable spatial
autocorrelation was only detectable for the two most thoroughly sampled countries (Germany and
Sweden). Although further adjustments might be evaluated by means of geostatistical methods
(i.e., kriging), the huge environmental variability of the European environment deeply stressed the
WorldClim database. Overall, these results show the importance of an adequate spatial structure of
meteorological stations as fundamental to improve the reliability of climate surfaces and derived
products of the research (i.e., statistical models, future projections).
Keywords: spatial analysis; 1961–1990 normal period; spatial interpolation; geostatistics; ecological
mathematics
1. Introduction
Easy access to standardized climate data with global coverage is paramount for the advancement of
many ecological studies and to understand future ecosystem services provided by forest systems [
1
3
]
and productive lands in agriculture [
4
,
5
]. One of the main aims for researchers dealing with
environmental resources has become to forecast possible impacts of climate change on organisms and
to evaluate possible mitigation [
6
9
]. In the past few decades, many conservation strategies have been
suggested in order to maintain human well-being and ensure an adequate level of welfare [
10
] from
(relatively) simple management strategies [
4
,
11
], including “assisted migration” [
12
14
], a controversial
protocol that includes translocating more adapted or resilient genotypes for conservation or to improve
the resilience of ecosystems. Such eorts are often driven by statistical models [
14
17
] and management
Sustainability 2019,11, 3043; doi:10.3390/su11113043 www.mdpi.com/journal/sustainability
Sustainability 2019,11, 3043 2 of 14
simulators [
18
,
19
], with both genetic variation and phenotypic plasticity included in the statistical
models as covariates [
20
22
]. However, despite modelling eorts, such studies always require
absolutely reliable climate data to be used as both baseline (e.g., 30-year average climate data) and
for future predictions. Furthermore, while the uncertainty around GCMs and future trajectories
is well known [
23
25
], information on current ecological limits of forest tree species has also been
questioned [
26
]. In this context, the interest of researchers in gridded climate datasets has grown strongly.
The interpolation method, the spatial resolution and the coverage are the three main features that
researchers use to select the most suitable datasets for their research [
27
30
]. The first release of the
WorldClim dataset [
31
] is probably the most famous gridded climate dataset, widely used for ecological
studies and freely available from (www.worldclim.org). Thanks to its high resolution (30 arc-second in
the WGS84 reference system and approximately 1 km at the equator), global coverage, and availability,
it has been used and cited more than 5200 times since publication [
31
]. The dataset is suitable for
basic and applied studies in ecology, including forestry and ecological modeling [
32
34
], as well
as to construct related datasets such as bio-geographical zones or environmental stratifications [
35
].
One of the main products of this database is “version 1”, representative of the 1961–1990 climate
normal period for the whole globe, including Antarctica. This version 1 dataset was generated by
interpolating weather station data with the ANUSPLIN software (version 4.3) using latitude, longitude,
and elevation as independent variables. The software implements a thin-plate smoothing spline
procedure, using every station as a data point. A second-order spline function was fitted by the
Authors using the above three variables, which produced the lowest overall cross-validation errors [
31
].
Considering the ANUSPLIN program creates a continuous surface projection, the LAPGRD program
was used to create a global grid of climate surfaces with 30 arc-seconds horizontal and vertical resolution
commonly referred to as 1 km
2
resolution. Raster maps for monthly precipitation amount and mean,
maximum, and minimum air temperature were then provided. Raw data came from weather stations
retrieved from various databases including GHCN, WMO climatological normals, FAOCLIM 2.0,
CIAT, and regional databases and, where possible, restricted to the period 1950–2000. Quality control
measures were taken to remove duplicate records, giving precedence to the GHCN database. After the
quality control check and cleaning, the database consisted of precipitation records from 47,554 locations
and mean air temperature from 24,542 locations [
31
]. Then elevation bias in weather stations was
related to latitude and presence of mountain ranges. However, local records from many European
countries were not easily accessible and WorldClim climate surfaces for Europe were constructed using
1263 records for air temperature and 2116 for precipitation.
WorldClim version 1 has recently been acknowledged to be representative of the 1961–1990 climate
normal period. This time-slice has been widely used as the pre-industrial climate in many papers
about the potential impact of climate change on ecosystems [
1
,
3
,
26
,
28
,
36
,
37
] and other ecological fields.
Nevertheless, given the detailed description provided by the Authors in their paper, the question
remains whether the quality of the WorldClim climate surfaces as a proxy of the climate baseline is
adequate in complex environments such as, for instance, the European environment.
The present study aims to assess and quantify the reliability of WorldClim climate raster maps
for Europe. We compared WorldClim with observed average values for mean annual temperature
and total annual precipitation for the period 1961–1990. Data were retrieved for the whole of Europe
building an independent dataset with data from many meteorological services. Then statistical analysis
was run in order to evaluate the reliability of this dataset across the study area.
2. Materials and Methods
2.1. Construction and Description of the Database Used for Comparison
To investigate WorldClim’s reliability in predicting baseline climate conditions we compiled
an independent climate dataset by collecting data from weather services across Europe which were
already freely available or delivered upon request (Table 1). All data were specifically requested or
Sustainability 2019,11, 3043 3 of 14
downloaded as monthly averaged values over the 30-year normal period (1961–1990). Local monthly
air temperature averages (MAT) and precipitation sums (MAP) were aggregated to calculate annual
values. In total we retrieved data from 6659 meteostations across Europe, with 1759 records for
temperature and 6526 records for precipitation (Figure 1). Most of the records were retrieved for
Germany and Sweden with 4825 and 1391 meteostations, respectively, while for some countries, records
were much fewer (e.g., Spain, France, Italy) or totally absent (e.g., Serbia, Poland, Romania).
Nevertheless, even if not equally distributed geographically, neither balanced concerning the
ecological regions of Europe, we considered the distribution of the collected data as adequate for the
purpose. Despite the lack of uniform coverage of both geography and ecological regions, we considered
the data collected to be adequate for subsequent analysis.
Moreover we tested the random distribution of MAT and MAP with the randtest package of
the R statistical language [
38
]. The database was carefully checked and cleaned to remove entries
with missing data and to geo-reference each record. Very few points (112), corresponding to less than
1% of all the records, lay outside country borders or land masses due to coordinate uncertainties,
which reflects the high-quality of the new database. Such records were removed completely from the
database in order to avoid any influences on the calculations.
Page 4 of 14
Switzerlan
d 12 11 12
[39]
United
Kingdom 38 38 37
TOTAL 6659 1759 6526
MEAN 266 70 261
ST. DEV 988.64 179.54 969.08
Figure 1. Spatial distribution of the compiled dataset for: (a) temperature records and (b) precipitation
records. Each dot represents a meteorological station. The darker the area, the more data were
retrieved.
2.2. Comparisons and Statistical Procedures
The BIO1 (mean annual air temperature) and BIO12 (mean total annual precipitation) variables
of WorldClim were used as proxies to evaluate the spatial accuracy of raster surfaces. The strata were
first downloaded from the official WorldClim web portal. Then, using an overlay function, the
corresponding values of the two climate variables were extracted for each meteorological station in
our database. A linear regression analysis was then applied to analyze the relationships between the
predicted WorldClim value and the observed value in our dataset. The adjusted R2 was used to
measure the amount of environmental variability expressed by WorldClim. Then the difference
between the WorldClim value and the observed value (30-years normal value from our database)
was calculated for each location of our database. To avoid confusion and mathematical balancing
between positive and negative values, which might seriously affect the analysis, both the raw
discrepancy (BIAS) and its absolute value (ABIAS) were calculated. To study possible trends across
the data, we looked at the relationships between BIAS and the predictors used by the authors of
WorldClim during the spatial interpolation process (i.e., latitude, longitude, elevation). Then, we
retrieved the complete database of meteorological stations used by the WorldClim authors from
Figure 1.
Spatial distribution of the compiled dataset for: (
a
) temperature records and (
b
) precipitation
records. Each dot represents a meteorological station. The darker the area, the more data were retrieved.
Sustainability 2019,11, 3043 4 of 14
Table 1. Structure of the compiled database.
Country Total Meteostations MAT Records MAP Records Data Source
Albania 3 3 3
[39]
Austria 23 21 23
Belgium 9 9 9
Bulgaria 18 18 18
Croatia 1 1 1
Czech 20 19 20
Denmark 4 4 4
Finland 18 17 18
France 76 75 76
Germany 4825 719 4733 [40]
Greece 26 25 26 [41]
Hungary 9 9 9
[39]
Ireland 6 6 6
Italy 30 30 30
North Macedonia
1 1 1
Montenegro 1 1 1
Netherlands 5 5 5
Norway 18 18 18
Portugal 18 18 18
Slovakia 14 14 14
Slovenia 42 42 42 [42]
Spain 51 51 51 [39]
Sweden 1391 604 1351 [43]
Switzerland 12 11 12 [39]
United Kingdom 38 38 37
TOTAL 6659 1759 6526
MEAN 266 70 261
ST. DEV 988.64 179.54 969.08
2.2. Comparisons and Statistical Procedures
The BIO1 (mean annual air temperature) and BIO12 (mean total annual precipitation) variables
of WorldClim were used as proxies to evaluate the spatial accuracy of raster surfaces. The strata
were first downloaded from the ocial WorldClim web portal. Then, using an overlay function,
the corresponding values of the two climate variables were extracted for each meteorological station
in our database. A linear regression analysis was then applied to analyze the relationships between
the predicted WorldClim value and the observed value in our dataset. The adjusted R
2
was used
to measure the amount of environmental variability expressed by WorldClim. Then the dierence
between the WorldClim value and the observed value (30-years normal value from our database)
was calculated for each location of our database. To avoid confusion and mathematical balancing
between positive and negative values, which might seriously aect the analysis, both the raw
discrepancy (BIAS) and its absolute value (ABIAS) were calculated. To study possible trends across
the data, we looked at the relationships between BIAS and the predictors used by the authors
Sustainability 2019,11, 3043 5 of 14
of WorldClim during the spatial interpolation process (i.e., latitude, longitude, elevation). Then,
we retrieved the complete database of meteorological stations used by the WorldClim authors
from www.arcgis.com/home/item.html?id=7644c6e78c1644b4bde2edfc44787520) and clipped to the
European environment (Table 2).
We calculated the average distance of each meteorological station in our database from the
geographically closest five stations in the WorldClim dataset. We expected a smaller dierence where
WorldClim stations were denser. Finally, the spatial autocorrelation of BIAS was evaluated using
geostatistical analysis implemented in R using the gstat package [
44
] and modelling the semivariance
of BIAS as a function of the spatial distance between records.
The whole structure of the data collection and analysis procedure is graphically reported on
Figure 2.
Table 2. Number of meteorological stations per country used by Hijmans et al. [31] in Europe.
Country Temperature Precipitation Country Temperature Precipitation
Albania 0 7 Latvia 3 9
Andorra 0 0 Liechtenstein 0 0
Armenia 2 2 Lithuania 16 19
Austria 3 25 Luxembourg 1 6
Belarus 8 22 North Macedonia 7 7
Belgium 3 18 Malta 1 3
Bosnia and Herz. 7 10 Moldova 2 3
Bulgaria 4 15 Monaco 0 0
Croatia 13 13 Montenegro 5 2
Czech Republic 7 16 Netherlands 7 10
Denmark 19 41 Norway 8 54
Estonia 3 12 Poland 18 63
Faeroe Islands 1 1 Portugal 16 18
Finland 19 32 Romania 11 28
France 82 107 Russia 44 124
Georgia 1 20 San Marino 0 0
Germany 89 116 Serbia 23 12
Gibraltar 0 1 Slovakia 3 10
Greece 26 48 Slovenia 6 2
Guernsey 0 0 Spain 60 117
Hungary 8 20 Sweden 16 60
Ireland 16 51 Switzerland 8 20
Isle of Man 0 1 Turkey 513 548
Italy 133 151 Ukraine 22 81
Jersey 0 3 UK 29 188
Summary statistics Temperature records Precipitation records
TOTAL 1263 2116
MEAN 25 42
SD 74.86 84.89
Sustainability 2019,11, 3043 6 of 14
Page 6 of 14
SD 74.86 84.89
Figure 2. Flowchart of the data collection and statistical analysis we made to test the reliability of
WorlClim version 1 data.
3. Results
The compiled database included 25 European countries, albeit with an unbalanced distribution.
Overall, an average of 266 records per country (both MAT and MAP) was included in the database.
However, the difference among countries was huge, with a standard deviation of ± 988.64 records
per country. This large standard deviation was caused by the disproportionate number of records for
Sweden and Germany. Temperature (MAT) values ranged from 5.8 °C to 21.2 °C, while precipitation
(MAP) was between 104.8 mm and 3318 mm. The mean difference between the interpolated
WorldClim values and the observed values was 0.22 °C for temperature and 48.7mm for
precipitation (Table 3), with a high coefficient of variation (6.82 for MAT and 3.40 for MAP). BIAS
ranged between 10.6 °C and 13.2 °C for MAT and between 1578.1 mm and 950.8 mm for MAP.
Mean ABIAS was 0.76 for MAT and 98.56 for MAP.
Results of the regression analysis for MAT and MAP are shown in Figure 3. Residuals of linear
models were randomly distributed for both of the analyzed variables and were highly significant (p
< 2.2 × 10
–16
). Concerning MAT, the good correlation and adequate proportion of explained variance
point to a low discrepancy between the two datasets; WorldClim explained 86% of the variance
(adjusted R
2
= 0.856) with a residual random standard error of 1.50°C, intercept of 0.202 °C and slope
almost equal to 1 (0.996). The regression line and the expected regression line for a perfect match
between the two datasets almost overlapped. For MAP, 64% (adjusted R
2
= 0.642) of the variance of
the precipitation dataset was explained by a linear regression model, with a residual standard error
of 159.6 mm. The match between the two regression lines was considerably low (Figure 3, right) with
the slope of the regression coefficient higher than 1. WorldClim was characterized by higher values
than observed under 500 mm precipitation and lower values above this threshold. As overall, a
general overestimation of MAP values was detected in dry areas (<500 mm) with an underestimation
in the remaining zones.
Table 3. Difference between local data and WorldClim’s surfaces.
Figure 2.
Flowchart of the data collection and statistical analysis we made to test the reliability of
WorlClim version 1 data.
3. Results
The compiled database included 25 European countries, albeit with an unbalanced distribution.
Overall, an average of 266 records per country (both MAT and MAP) was included in the database.
However, the dierence among countries was huge, with a standard deviation of
±
988.64 records
per country. This large standard deviation was caused by the disproportionate number of records for
Sweden and Germany. Temperature (MAT) values ranged from
5.8
C to 21.2
C, while precipitation
(MAP) was between 104.8 mm and 3318 mm. The mean dierence between the interpolated WorldClim
values and the observed values was 0.22
C for temperature and
48.7mm for precipitation (Table 3),
with a high coecient of variation (6.82 for MAT and 3.40 for MAP). BIAS ranged between
10.6
C
and 13.2
C for MAT and between
1578.1 mm and 950.8 mm for MAP. Mean ABIAS was 0.76 for MAT
and 98.56 for MAP.
Results of the regression analysis for MAT and MAP are shown in Figure 3. Residuals of linear
models were randomly distributed for both of the analyzed variables and were highly significant
(
p<2.2 ×10–16)
. Concerning MAT, the good correlation and adequate proportion of explained variance
point to a low discrepancy between the two datasets; WorldClim explained 86% of the variance
(adjusted R
2
=0.856) with a residual random standard error of 1.50
C, intercept of
0.202
C and slope
almost equal to 1 (0.996). The regression line and the expected regression line for a perfect match
between the two datasets almost overlapped. For MAP, 64% (adjusted R
2
=0.642) of the variance of
the precipitation dataset was explained by a linear regression model, with a residual standard error of
159.6 mm. The match between the two regression lines was considerably low (Figure 3, right) with the
slope of the regression coecient higher than 1. WorldClim was characterized by higher values than
observed under 500 mm precipitation and lower values above this threshold. As overall, a general
overestimation of MAP values was detected in dry areas (<500 mm) with an underestimation in the
remaining zones.
Table 3. Dierence between local data and WorldClim’s surfaces.
Variable AVR SD CV MAX MIN ABSAVR
MAT [C] 0.22 1.50 6.82 10.62 13.21 0.76
MAP [mm]
48.70 165.35 3.40 1578.10 950.80 98.56
AVR =average value; SD =standard deviation; CV =coecient of variation; MAX =maximum dierence;
MIN =minimum dierence; ABSAVR =average of absolute values.
Sustainability 2019,11, 3043 7 of 14
Figure 3.
Results of the regression analysis for: (
a
) temperature represented by Bio1 variable of
WorldClim database on x-axis and (
b
) precipitation represented by Bio12 variable of WorldClim
database versus observed values on the y-axis. Regression coecients at top-left of each figure. The 1:1
line of a perfect match shown dashed.
No relationship was found between the modelling error (ER) detected for MAT and MAP and the
environmental predictors used for spatial interpolation across Europe. Modelled linear regressions
explained less than 5% of variation, with one exception (Table 4). This lack of correlation can also be
observed in Figure 4where ABIAS is plotted against the average spatial distance of the “observed”
meteorological station from the five WorldClim stations.
Table 4.
Linear regression parameters when modelling error (ER) and the environmental predictors.
Each predictor was tested separately (ADF5NM=Average distance from the five nearest meteorological
stations).
Variable Predictor Intercept Slope Explained Variance p-Value
MAT
Latitude 0.29 0.000000 0.56% 0.00092
Longitude 0.34 0.000000 1.20% 0.00000
Elevation 0.54 0.001025 4.95% 0.00000
ADF5NM 0.09 0.000002 0.18% 0.04138
MAP
Latitude 45.16 0.000014 0.05% 0.04492
Longitude 202.28 0.000061 4.33% 0.00000
Elevation 8.15 0.206690 10.26% 0.00000
ADF5NM 107.02 0.001059 1.61% 0.00000
The spatial distribution of BIAS in the two most represented countries is shown in Figure 5for the
two investigated variables. Spatial aggregation is especially evident in Sweden, where most of the
“large dots” are clustered in the south of the country. For Sweden and Germany, variograms of the
MAP variable were fitted by means of an exponential variogram model and revealed a clear spatial
autocorrelation, especially for Germany (Figure 6).
Sustainability 2019,11, 3043 8 of 14
Page 8 of 14
Figure 4. Relationship between the detected differences (WorldClim value, observed) and the average
spatial distance of the observed record (new database) from the five nearest WorldClim reference
stations for (a) temperature and (b) precipitation.
The spatial distribution of BIAS in the two most represented countries is shown in Figure 5 for
the two investigated variables. Spatial aggregation is especially evident in Sweden, where most of
the “large dots” are clustered in the south of the country. For Sweden and Germany, variograms of
the MAP variable were fitted by means of an exponential variogram model and revealed a clear
spatial autocorrelation, especially for Germany (Figure 6).
Figure 4.
Relationship between the detected dierences (WorldClim value, observed) and the average
spatial distance of the observed record (new database) from the five nearest WorldClim reference
stations for (a) temperature and (b) precipitation.
Page 8 of 14
Figure 4. Relationship between the detected differences (WorldClim value, observed) and the average
spatial distance of the observed record (new database) from the five nearest WorldClim reference
stations for (a) temperature and (b) precipitation.
The spatial distribution of BIAS in the two most represented countries is shown in Figure 5 for
the two investigated variables. Spatial aggregation is especially evident in Sweden, where most of
the “large dots” are clustered in the south of the country. For Sweden and Germany, variograms of
the MAP variable were fitted by means of an exponential variogram model and revealed a clear
spatial autocorrelation, especially for Germany (Figure 6).
Figure 5.
Spatial distribution of ABIAS for temperature (
a
and
b
) and precipitation (
c
and
d
) across
Sweden and Germany, the two most sampled countries in the database. The larger the gray dot,
the greater the dierence between WorldClim and the independent dataset.
Sustainability 2019,11, 3043 9 of 14
Page 9 of 14
Figure 5. Spatial distribution of ABIAS for temperature (a and b) and precipitation (c and d) across
Sweden and Germany, the two most sampled countries in the database. The larger the gray dot, the
greater the difference between WorldClim and the independent dataset.
Figure 6. (Semi)variograms for precipitation calculated by the gstat package in R for Sweden (a) and
Germany (b). There is a rather clear spatial structure that might be used as input for further
geostatistical procedures (i.e., kriging) in order to adjust WorldClim raster maps.
4. Discussion
The quality of the reference baseline climate has a fundamental role in predictions of the
potential impact of climate change on organisms and natural ecosystems. The stability and reliability
of the estimated projections calculated from species distribution models [14,45,46], management
simulators [18,47], or the estimation of the geographical shift of climate zones [48] rely on the
differences between current and future climate. While the representativeness of WorldClim is
adequate concerning air temperatures, large differences were found in the precipitation surfaces. Our
results demonstrate a systematic difference of 0.76 °C between observed and interpolated values.
According to the most recent IPCC report [49], the observed increase in temperature has been around
0.2 °C per decade. As a consequence such difference might affect future projections of WorldClim
dataset adding uncertainties on the further modelling efforts [22,50]. In this case a likelihood analysis
should be more adequate than deterministic ones and in order to include a sensitivity analysis and
evaluate the probability of success of empirical models based on phenotypic plasticity and applied
future projections [51]. Precipitation, by contrast, proved to be the main weakness of WorldClim
surfaces in Europe. Despite its relatively small spatial extent, the European environment is
characterized by many different forest systems that reflect broad climate variability, spanning from
the Mediterranean to the Arctic.
The 1961–1990 baseline period is a fundamental dataset for ecological modelling because records
from earlier periods were often affected by different instrumentation or changes in observational
practice [30,37]. Therefore, numerous studies from climatology to biology, ecology and forestry
[36,48,49] have used this baseline period, and WorldClim has been used extensively. We can expect
a further warming trend in the next two decades at a rate of about 0.1 °C per decade, due mainly to
the slow response of the oceans. As a consequence, even though the linear regression analysis showed
a good match between observed and interpolated data (adjusted R2 = 0.856), the difference is higher
than the expected rate of change, which could heavily affect model predictions, adding uncertainties
on future projections and smoothing results (i.e., land suitability projections) in an uncontrolled way
[14,53–55]. This issue is then amplified when analysing MAP, where higher differences were found
in combination with a poor regression analysis result. As a consequence, important biases may be
introduced when using WorldClim’s precipitation dataset. This is particularly true when WorldClim
is used as the reference line and climate projections are locally downscaled and added to the
WorldClim surfaces, as in the “Delta method” [56]. As a result, the calculation of climate indices
might be difficult. For example, many studies used reference evapotranspiration [57–59] as the main
predictor in statistical models [3,60,61]. In this case, the mathematical combination of differences in
Figure 6.
(Semi)variograms for precipitation calculated by the gstat package in R for Sweden (
a
) and
Germany (
b
). There is a rather clear spatial structure that might be used as input for further geostatistical
procedures (i.e., kriging) in order to adjust WorldClim raster maps.
4. Discussion
The quality of the reference baseline climate has a fundamental role in predictions of the
potential impact of climate change on organisms and natural ecosystems. The stability and reliability
of the estimated projections calculated from species distribution models [
14
,
45
,
46
], management
simulators [
18
,
47
], or the estimation of the geographical shift of climate zones [
48
] rely on the
dierences between current and future climate. While the representativeness of WorldClim is adequate
concerning air temperatures, large dierences were found in the precipitation surfaces. Our results
demonstrate a systematic dierence of 0.76
C between observed and interpolated values. According
to the most recent IPCC report [
49
], the observed increase in temperature has been around 0.2
C
per decade. As a consequence such dierence might aect future projections of WorldClim dataset
adding uncertainties on the further modelling eorts [
22
,
50
]. In this case a likelihood analysis should
be more adequate than deterministic ones and in order to include a sensitivity analysis and evaluate
the probability of success of empirical models based on phenotypic plasticity and applied future
projections [
51
]. Precipitation, by contrast, proved to be the main weakness of WorldClim surfaces
in Europe. Despite its relatively small spatial extent, the European environment is characterized by
many dierent forest systems that reflect broad climate variability, spanning from the Mediterranean
to the Arctic.
The 1961–1990 baseline period is a fundamental dataset for ecological modelling because
records from earlier periods were often aected by dierent instrumentation or changes in
observational practice [
30
,
37
]. Therefore, numerous studies from climatology to biology, ecology and
forestry
[36,48,52]
have used this baseline period, and WorldClim has been used extensively. We can
expect a further warming trend in the next two decades at a rate of about 0.1
C per decade, due mainly
to the slow response of the oceans. As a consequence, even though the linear regression analysis showed
a good match between observed and interpolated data (adjusted R
2
=0.856), the dierence is higher
than the expected rate of change, which could heavily aect model predictions, adding uncertainties
on future projections and smoothing results (i.e., land suitability projections) in an uncontrolled
way [
14
,
53
55
]. This issue is then amplified when analysing MAP, where higher dierences were
found in combination with a poor regression analysis result. As a consequence, important biases
may be introduced when using WorldClim’s precipitation dataset. This is particularly true when
WorldClim is used as the reference line and climate projections are locally downscaled and added to
the WorldClim surfaces, as in the “Delta method” [
56
]. As a result, the calculation of climate indices
might be dicult. For example, many studies used reference evapotranspiration [
57
59
] as the main
predictor in statistical models [
3
,
60
,
61
]. In this case, the mathematical combination of dierences
in MAT and MAP might introduce uncontrolled biases through the study area. These biases could
Sustainability 2019,11, 3043 10 of 14
represent a critical issue, especially in the Mediterranean and anywhere else that moisture deficit is
identified as the most relevant climate driver.
The main advantage of our compiled dataset might be its representativeness at small scale.
The authors of WorldClim themselves warn that the high resolution of the climate surfaces does not
imply high data quality in all places as this depends on local climate variability, quality and density of
observations and the degree of the fitted spline [
31
]. In a similar study, when compared with PRISM and
Daymet datasets for the continental United States, many concerns were expressed, especially regarding
the quality of WorldClim’s precipitation grids in mountainous areas [
34
,
35
,
62
,
63
]. For this reason,
several studies at regional or national scale at higher resolution (e.g., 100–250 m) preferred the use of
meteorological variables obtained at nearby observational sites [
28
,
64
66
]. Regardless of the distances
of the investigation sites from the locations where meteorological datasets were gathered, orography
and land use, and the surrounding area and variable characteristics, must be considered. At small scale
their variability may be a strong driver of frequently overlooked heterogeneities, leading to significant
discrepancies in transferred datasets used for otherwise appropriate processing methods [67,68].
The lack of any relationship between BIAS and the main physiographic parameters (i.e., latitude,
longitude, and elevation) does not allow for any statistical adjustment (e.g., downscaling, locally
calibrated lapse rate, etc.) for either temperature or precipitation. However precipitation regimes are
very dicult for meteorological stations to record properly and this issue has often been found in other
databases [
59
,
69
]. Many more data are required, especially in the case of forest monitoring, as a result
of the lack of temporal autocorrelation during the timeframe [70].
The need for a freely available and representative global climate dataset is large and growing,
as evidenced by WorldClim’s citation statistics. These goals can be achieved with local up-to-date
monitoring networks, which could play a key role in evaluating global grids at small scale [
71
] as well as
providing data for the construction of additional global climate datasets. Harmonization eorts, as well
as increased representativeness of the established networks, are paramount for construction of more
accurate climate surfaces. Enhanced data recovery with regular spatial coverage may overcome the lack
of dense environmental or climatological sampling [
28
,
70
,
72
,
73
]. Derived surfaces are fundamental in
order to plan future management strategies. For instance, and concerning forestry, additional strata,
such as homogeneous climate zones, are needed as a fundamental tool to plan the transfer of genetic
resources and reproductive materials across specific geographic areas [
74
,
75
]. WorldClim grids were
interpolated with spline functions, a fast method known to yield results similar to polynomial functions
but without mathematical instability. Such methods do not consider the spatial autocorrelation between
observations, only partially achieved by more complex models where latitude and longitude are
included as predictive variables [
28
,
76
]. Therefore, the exhibited spatial aggregation of the BIAS in the
case of denser observations of our dataset (i.e., Sweden and Germany) may be relevant for research
activities and improvements of the climate surfaces.
5. Conclusions
A new updated beta version of WorldClim has recently been released for the 1971–2000 time
period. This “Version 2” (http://worldclim.org/version2), along with the need for carefully evaluating
the quality of records used for modelling and keeping climate databases up-to-date, is an essential
requirement for the adequate development of tools and informative systems. The lack of reliability on
MAP values can be seen as the main shortcoming of the WorldClim database in Europe and elsewhere.
However, precipitation is much more dicult to interpolate, given its low spatial and temporal
autocorrelation as well as the lack of statistical relationships with some of the main physiographic
parameters, such as elevation. Further research should focus on this parameter, seeking more significant
determinants of MAP, given its importance in climate change scenarios where drought stresses are
predicted to be the most relevant issue.
Author Contributions:
Conceptualization, M.W., M.B. and M.M.; methodology, M.M. and M.W.; software, M.M.;
validation, M.M., I.S. and M.W.; formal analysis, M.M.; investigation, M.M., M.B. and M.W.; resources, M.B.; data
Sustainability 2019,11, 3043 11 of 14
curation, M.M., I.S. and M.W.; writing—original draft preparation, M.M. and M.W.; writing—review and editing,
M.M., I.S., M.B. and M.W.; visualization, M.M.; supervision, M.B. and M.W.; project administration, M.W. and
M.B.; funding acquisition, M.M., M.W. and M.B.
Funding:
Maurizio Marchi was funded by EU, in the framework of the Horizon 2020 B4EST project “Adaptive
BREEDING for productive, sustainable and resilient FORESTs under climate change”, UE Grant Agreement 773383
(http://b4est.eu/). Marjana Westergren and Iztok Sinjur were funded by the Slovenian Research Agency, Research
programme Forest Biology, Ecology and Technology P4-0107.
Acknowledgments:
We thank Gregor Vertaˇcnik from National Meteorological Service of Slovenia and Athanasios
D. Sarantopoulos from the Hellenic National Meteorological Service Division of Climatology for contributing data.
Conflicts of Interest: The authors declare no conflict of interest.
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2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (http://creativecommons.org/licenses/by/4.0/).
... There are two versions of WorldClim bioclimatic variables. WorldClim version 1.4 is a global climate gridded data set for the years 1961-1990 (excluding Antarctica) at 3 resolutions (2.5 min, 5 min, 10 min) (Hijmans et al., 2005;Marchi et al., 2019). WorldClim version 2.0 is a new data set containing grids with interpolated data from between 9000 and 60000 weather stations for 4 different spatial resolutions from 30 s (~1 km) to 10 min (~340 km) for the years 1971-2000 (Fick and Hijmans, 2017). ...
... In parallel, various large scale gridded interpolated temperature and precipitation data sets at different spatiotemporal resolutions have been developed from in situ measurements to estimate bioclimatic variables (Hijmans et al., 2005;Fick and Hijmans, 2017;Vega et al., 2018;Marchi et al., 2019). Unfortunately, in situ measured temperature and precipitation data with long temporal coverage are only available from a limited number of meteorological stations with inadequate spatial coverage (Otgonbayar et al., 2019). ...
... In recent years, at global level, bioclimatic variables mostly have been estimated from two commonly used types of data sets, namely WorldClim data sets (Fick and Hijmans, 2017;Marchi et al., 2019), and MERRAclim data sets (Vega et al., 2018). WorldClim version 1 and version 2 are global gridded data sets at a spatial resolution of~1 km 2 . ...
Article
Global maps of bioclimatic variables currently exist only at very coarse spatial resolution (e.g. World-Clim). For ecological studies requiring higher resolved information, this spatial resolution is often insufficient. The aim of this study is to estimate important bioclimatic variables of Mongolia from Earth Observation (EO) data at a higher spatial resolution of 1 km. The analysis used two different satellite time series data sets: land surface temperature (LST) from Moderate Resolution Imaging Spectroradiometer (MODIS), and precipitation (P) from Climate Hazards Group Infrared Precipitation with Stations (CHIRPS). Monthly maximum, mean, and minimum air temperature were estimated from Terra MODIS satellite (collection 6) LST time series product using the random forest (RF) regression model. Monthly total precipitation data were obtained from CHIRPS version 2.0. Based on this primary data, spatial maps of 19 bioclimatic variables at a spatial resolution of 1 km were generated, representing the period 2002–2017. We tested the relationship between estimated bioclimatic variables (SatClim) and WorldClim bioclimatic variables version 2.0 (WorldClim) using determination coefficient (R2), root mean square error (RMSE), and normalized root mean square error (nRMSE) and found overall good agreement. Among the set of 19 WorldClim bioclimatic variables, 17 were estimated with a coefficient of determination (R2) higher than 0.7 and normalized RMSE (nRMSE) lower than 8%, confirming that the spatial pattern and value ranges can be retrieved from satellite data with much higher spatial resolution compared to WorldClim. Only the two bioclimatic variables related to temperature extremes (i.e., annual mean diurnal range and isothermality) were modeled with only moderate accuracy (R2 of about 0.4 with nRMSE of about 11%). Generally, precipitation-related bioclimatic variables were closer correlated with WorldClim compared to temperature-related bioclimatic variables. The overall success of the modeling was attributed to the fact that satellite-derived data are well suited to generated spatial fields of precipitation and temperature variables, especially at high altitudes and high latitudes. As a consequence of the successful retrieval of the bioclimatic variables at 1 km spatial resolution, we are confident that the estimated 19 bioclimatic variables will be very useful for a range of applications, including species distribution modeling.
... The WorldClim dataset in 30 arc-second resolution is widely used for SDMs because of its availability and global coverage (Marchi et al., 2019), and it is better at capturing the environmental variables than coarser resolutions where there is a limited number of observation stations, particularly in mountainous and remote areas (Fick & Hijmans, 2017). Although the resolution of 30 arc-second is useful for modeling the distribution of widespread species, it seems to cause SDMs to omit or underestimate the distribution of rare and restricted range species. ...
... Although the resolution of 30 arc-second is useful for modeling the distribution of widespread species, it seems to cause SDMs to omit or underestimate the distribution of rare and restricted range species. A study on habitat loss of species in the Swiss Alps under climate change impacts at a local-scale (25 m × 25 m grid cells) predicted that 100% of species persisted in their habitats under the local-scale models while all their suitable habitats were lost under the European-scale model (10' × 10') (Marchi et al., 2019). Organisms, especially restricted-range and locally endemic species, experience or highly adapt to the local environment in a way that is quite different from a large-scale environment (Collen et al., 2014;Peterson et al., 1998). ...
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In this study, 19 surface bioclimatic variables of high spatial resolution 0.00226o (~ 250 m) are generated in a Geographic Information System by the combination of (1) the raster dataset of monthly temperature and precipitation obtained from the global WorldClim database at 0.00833o spatial resolution for the period of 1960–2000; and (2) the climate data (temperature and precipitation) of the Central Highlands and Southern Central Coast collected from the 31 temperature and 97 precipitation recording sites for the period of 1991–2015. The statistical downscaling method is applied, using multiple linear regression analysis, in which elevation, geographic coordinates, and distance from the coast are treated as independent variables, to estimate the distribution of temperature; and the B-Spline interpolation method combined with multiple linear regression analysis is employed on precipitation over the study area. The outcomes of the two main analyses are computed to create 19 high spatial resolution bioclimatic variables. While using only local climate data on analyzing the regression models results in high fluctuation of estimated temperature, the combination of the two datasets is more informative. The spatial distribution of our interpolated precipitation is similar to the WorldClim data but has a smaller difference in the mean annual precipitation. The results, which shows higher spatial resolution and are closer to the observed data than those from the WorldClim, could be better applied for predicting species distribution in the region.
... For Europe, Marchi et al. [65] assessed good representativeness for mean annual temperature, but high discrepancies for mean annual precipitation between WORLDCLIM 1.4 and climate observations. Generally, they stress that the main shortcoming of WORLD-CLIM 1.4 is the lack of reliable precipitation values due to its low spatial and temporal autocorrelation and missing statistical relationships with physiographic parameters like elevation. ...
... Generally, they stress that the main shortcoming of WORLD-CLIM 1.4 is the lack of reliable precipitation values due to its low spatial and temporal autocorrelation and missing statistical relationships with physiographic parameters like elevation. The lacking capability of WORLDCLIM 1.4 to capture precipitation in mountainous regions has been debated (e.g., in References [61,[65][66][67][68]). To overcome this challenge, the CHELSA climate dataset includes orographic wind effects and boundary layers in the validation of the main correction step to increase validity of precipitation at stations before downscaling to 1 km resolution [34]. ...
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Comparing and evaluating global climate datasets and their effect on model performance in regions with limited data availability has received little attention in ecological modeling studies so far. In this study, we aim at comparing the interpolated climate dataset Worldclim 1.4, which is the most widely used in ecological modeling studies, and the quasi-mechanistical downscaled climate dataset Chelsa, as well as their latest versions Worldclim 2.1 and Chelsa 1.2, with regard to their suitability for modeling studies. To evaluate the effect of these global climate datasets at the meso-scale, the ecological niche of Betula utilis in Nepal is modeled under current and future climate conditions. We underline differences regarding methodology and bias correction between Chelsa and Worldclim versions and highlight potential drawbacks for ecological models in remote high mountain regions. Regarding model performance and prediction plausibility under current climatic conditions, Chelsa-based models significantly outperformed Worldclim-based models, however, the latest version of Chelsa contains partially inherent distorted precipitation amounts. This study emphasizes that unmindful usage of climate data may have severe consequences for modeling treeline species in high-altitude regions as well as for future projections, if based on flawed current model predictions. The results illustrate the inevitable need for interdisciplinary investigations and collaboration between climate scientists and ecologists to enhance climate-based ecological model quality at meso- to local-scales by accounting for local-scale physical features at high temporal and spatial resolution.
... html). These are simulated dataset widely used in climatic and ecological studies and species distribution modeling (Panagos et al. 2017;Marchi et al. 2019). The current dataset was created by interpolation of mean monthly climate data from weather stations on different resolution grid while Future dataset is based on nine models i.e., BCC-CSM2-MR, CNRM-CM6-1, CNRM-ESM2-1, CanESM5, GFDL-ESM4, IPSL-CM6A-LR, MIROC-ES2L, MIROC6, MRI-ESM2-0. ...
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All sort of vegetation is highly responsive to climatic factors and therefore distribution and redistribution of vegetation is bound to be affected by the change in the climatic conditions. The present episode of climate change is rapid in nature with it fastest temperature rise in Himalayas after poles on the earth, rendering vegetation of this region vulnerable to redistribution in space and time. Therefore, accurate modeling of the potential distribution of plants native to the Himalayan area is essential. Machine learning has improved the accuracy of species distribution models to a greater extent. The effects of climate change on the spread of Banj oak, a prominent tree species of the mid-Himalayas in Uttarakhand's Kumaun area, were simulated in this study. The generalized linear model (GLM), boosted regression tree (BRT), and maximum entropy (MaxEnt) were used to achieve this. The models' accuracies were calculated and compared. The accuracy was determined using the area under the curve (AUC) and receiver operating characteristics (ROC) curves. The MaxEnt model outperformed the rest two models and therefore it was utilized for modeling and prediction of potential distribution of Banj oak for the present and future. The results with higher accuracy (i.e., AUC > 0.95) model suggested that the areal expansion of potential distribution of Banj oak is going to crunch down by more than 1000 sq. km. as compared to today by the year of 2070, highlighting the gravity of climate change. This areal reduction of broadleaf tree is limited in the lower latitude. Higher altitudes were predicted to enjoy expansion of the aforesaid species. This study is a stand-alone contribution to the species distribution modeling of Quercus leucotrichophora in the mid-elevations of the Central Himalayas in India.
... Los datos de las variables ambientales se compilaron a partir de la base de datos WorldClim v. 2.1 (Fick & Hijmans, 2017), ya que es ampliamente utilizada para estudios ecológicos gracias a su libre disponibilidad, cobertura global y buena calidad (Marchi et al., 2019). Extrajimos un conjunto de 19 variables bioclimáticas, datos sobre radiación solar y datos de elevación, todos a una resolución de 2,5 arc-min (equivalente a 4,5 kilómetros cuadrados (km2) en el ecuador). ...
... Thus, regardless of the database analyzed, it is noted that the generation capacity of the surface runoff in HHR 1 is more evident than in HHR 2 and 3, in agreement with the results obtained by Louzada (2018). Also, the variation of β 0 between HHR 2 and 3 is a strong indicator that the overestimation of precipitation in HHR 3 was more significant than in HHR 2. The overestimation of WorldClim's precipitation data in drier regions has been reported by other authors (Karger et al. 2017;Marchi et al. 2019) when evaluating the data from version 1 (Hijmans et al. 2005). In the present study, this was also evident with the estimate of β 0 in the adjustment of regionalization equations for HHR 2 and 3. ...
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In streamflow regionalization studies, the uncertainties associated with the precariousness or the lack of precipitation data is a problem faced by researchers and water resources managers, especially in emerging countries like Brazil. Therefore, the use of satellite-based precipitation products has an advantage, to allow accurate estimates of precipitation in regions where data are precarious. Thus, the objective of this study was to compare different precipitation databases as predictors of regionalized flows. The data used were from rain gauge stations that have been interpolated in the study area using simple kriging, TRMM 3B43-V7 Satellite and Worldclim 2 data. The results of the statistical indexes show that the best results were obtained when the streamflow regionalization equations were obtained using precipitation data from the rain gauge stations and interpolated with simple kriging. It was also possible to verify that the WorldClim 2 data also presented satisfactory results for all the applied statistical indices, which was not found when the TRMM 3B43-v7 data were used. Thus, it can be considered that the data from WorldClim 2 are a good alternative to replace the data observed in the rain gauge stations, which would result in time saving by researchers and managing bodies in the streamflow regionalization studies, since such data are already readily available to be put to use.
... Se aplicó un modelo de interpolación para cubrir la superficie completa del globo, incluyendo como covariables la altitud, la distancia a la costa y variables derivadas de información satelital. Recientemente se ha publicado una evaluación del uso de la versión 2 de la base en la Argentina (Bustos et al. 2017) y existen numerosos trabajos sobre la evaluación de la base de datos WorldClim versión 1 en distintas regiones (e.g., Europa) (Marchi et al. 2019). Sin embargo, es importante considerar que las estaciones meteorológicas no se distribuyen de manera homogénea y que la precisión de la base disminuye con la altitud y con la distancia a las estaciones meteorológicas (Fick and Hijmans 2017). ...
... Climate services including C3S may not guarantee the same quality of data in all locations as this quality depends on the climate variability in each location, on the quality and the density of the observations, and on the interpolation results (Hijmans et al., 2005). Marchi et al. (2019) suggest establishing local up-to-date climate services networks that would provide data for constructing European level climate datasets and accurate interpolated climate data. Derived climate surfaces are fundamental in the forestry sector as they allow to plan the transfer of genetic resources across specific geographic areas. ...
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The aim of this study is to assess the potential of Earth Observation and climate data for the forestry sector focusing on the Copernicus Climate Change Service (C3S). Although forestry researchers recognize the importance of Earth Observation and climate data, forestry practitioners currently work mainly with land cover information, largely neglecting climate data. Understanding its potential for the forestry sector becomes thus important, as to align the vast offer of climate services in Europe to different forestry users and stakeholders’ necessities. Interviews, surveys, and dedicated workshops were used to collect a series of forestry end-users’ needs and requirements regarding climate data. End-user’s requirements were categorized through a SWOT analysis, which allowed to identify perceived internal strengths and weaknesses, external opportunities and threats to the increased use of the C3S. Results indicate that improved climate services for the forestry sector based on C3S data would benefit from enhanced training on the use of climate data, improved provision of services integrating climate with non-climate data, the provision of new variables and indicators, and the integration of machine learning techniques for developing data and information in support of the deployment of climate services. These findings are relevant to close the gap between demand and supply of climate services for the forestry sector and provide a basis for further exploring the value of climate data in serving a wide array of forestry stakeholders. Going forward, increased knowledge on user requirements from both forest practitioners and policy-makers can be beneficial to develop accessible tailored services.
... migrating individuals; Barton and Hewitt 1982), artefacts of the bioclimatic layers used (e.g. Marchi et al. 2019;Poggio et al. 2018), or even just a high tolerance regarding abiotic conditions (e.g. Preston and Johnson 2020). ...
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Human-induced ecological and climatic changes have led to the decline and even local extinction of many formerly widely distributed temperate and cold-adapted species. Determining the exact causes of this decline remains difficult. Bryodemella tuberculata was a widely distributed orthopteran species before the mid-19th century. Since then, many European populations have suffered drastic declines and are now considered extinct or critically endangered. We used ecological niche modelling based on a large dataset of extant and extinct occurrence data to investigate whether poor climatic suitability in the periphery of its global range was a possible cause of the local extinction of the European populations of B. tuberculata. We also used population genetics based on the COI marker to estimate and compare the genetic diversity of extant populations. We found that Europe still provides highly suitable habitats close to the climatic optimum, contradicting the assumption of climate change as major driver of this decline. Instead, changes in land-cover and other anthropogenic modifications of the habitats at the local scale seem to be the major reasons for local extinctions. Genetic analysis suggests Central Asia as center of diversity with a stable population size, whereas the effective sizes of the remaining European populations are decreasing. We found European genetic lineages nested within Central Asian lineages, suggesting a Central Asian source distribution area. Our results suggest that the declining European populations represent relics of a formerly wider distribution, which was fragmented by changes in land-use. These relics are now threatened by limited connectivity and small effective population sizes. Specific conservation actions, such as the restoration of former or potential new habitats, and translocation of individuals from extant populations to these restored sites may help slow, stall, or even revert the extinction process.
Technical Report
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Il lavoro presenta il quadro di unione nazionale delle regioni di provenienza per i materiali forestali forestali di riproduzione. Vengono individuate 19 regioni di significato ecologico, di cui si riporta una descrizione sintetica in termini fisiografici, paesaggistici e forestali. La cartografia vuole essere un contributo preliminare per l'individuazione delle regioni di provenienza per le varie specie forestali (o gruppi di specie), che potrà essere realizzato integrando le conoscenze sulla variabilità genetica delle specie forestali. La base comune di dati territoriali (unità fisio-grafiche) consente il dialogo con Carta della Natura, nella prospettiva di ottimizzare la gestione e la conservazione della biodiversità forestale. 1 Premessa La Direttiva 1999/105/CE (art. 2, punto g) definisce le regioni di provenienza (RdP) per una specie o sottospecie, il territorio o l'insieme dei territori soggetti a condizioni ecologiche sufficientemente uniformi e sui quali si trovano soprassuoli o fonti di semi con caratteristiche fenotipiche o genetiche analoghe, tenendo conto dei limiti altimetrici ove appropriato. * Luisa Cagelli e Fulvio Ducci sono membri della Commissione Tecnica Dlgs 386/2003. Il lavoro è stato realizzato in parti uguali dagli autori negli aspetti concettuali e di stesura finale del testo. Prima stesura del testo a cura di Fulvio Ducci, Giuseppe Pignatti e Paolo Camerano. Elaborazione cartografica e GIS a cura di Giuseppe Pignatti. Gli autori ringraziano Immacolata Librandi (MIPAAF) per la revisione finale del testo.
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Aim To test whether intraspecific trait responses to climate among populations across species distribution ranges can be untangled using field observations, under the rationale that, in natural forest tree populations, long‐term climate shapes population responses while recent climate change drives phenotypic plasticity. Location Europe. Time period 1901–2014. Taxa Silver fir (Abies alba Mill.) and sessile oak [Quercus petraea (Matt.) Liebl.]. Methods We estimated the variation of individual tree height as a function of long‐term and short‐term climates to tease apart provenance effects (variation among populations of different geographical origin), plasticity and their interaction, using mixed‐effect models calibrated with national forest inventory data (in‐situ models). To validate our approach, we tested the ability of in‐situ models to predict independently tree height observations in common gardens experiments where provenance and plastic effects can be measured and separated. In‐situ model predictions of tree height variation among provenances and among planting sites were compared to observations in common gardens and to predictions from a similar model calibrated using common garden data (ex‐situ model). Results In Q. petraea, we found high correlations between in‐situ and ex‐situ model predictions of provenance and plasticity effects and their interaction for tree height (r > .80). We showed that the in‐situ models significantly predicted tree height variation among provenances and sites for A. alba and Q. petraea. Spatial predictions of phenotypic plasticity across species distribution ranges indicate decreasing tree height in populations of warmer climates in response to recent anthropogenic climate warming. Main conclusions Our modelling approach using national forest inventory observations provides a new perspective for understanding patterns of intraspecific trait variation across species ranges. Its application is particularly interesting for species for which common garden experiments do not exist or do not cover the entire climatic range of the species.
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The future dynamics of forest species and ecosystems depend on the effects of climate change and are related to forest management strategies. The expected impacts of climate change are linked to forest growth and productivity. An increase in the length of the growing season and greater productivity are likely as well as shifts in average climatic values and more variable frequencies, intensities , durations and timings of extreme events. The main aim of this work is to assess and describe the climatic requirements for Italian forest tree species. We used 7,272 field observations from Italian National Forest Inventory plots and average annual temperatures and precipitation as interpolated from raster maps with 1 km spatial resolution. On this basis we evaluated the current observed distributions of the 19 most important tree species in Italy with respect to potential climatic limits based on expert knowledge and the available literature. We found that only 46% of the observations fall within the potential joint temperature and precipitation limits as defined by expert knowledge. For precipitation alone, 70% of observations were within the potential limits, and for temperature alone, 80% of observations were within the potential limits. Similarity between current observed and potential limits differ from species-to-species with broadleaves in general more frequently distributed within the potential climatic limits than conifers. We found that ecological requirements and potential information should be revised for some species, particularly for the Pinus genus and more frequently for precipitation. The results of the study are particularly relevant given the threat of climate change effects for Italian forests which are broadly acknowledged to be a biodiversity hotspot. Further investigations should be aimed at modelling the effects of climate changes on Italian forests as a basis for development of mitigation and adaptation forest management strategies.
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Aim To test whether adaptive and plastic trait responses to climate across species distribution ranges can be untangled using field observations, under the rationale that, in natural forest tree populations, long-term climate shapes local adaptation while recent climate change drives phenotypic plasticity. Location Europe. Time period 1901-2014. Taxa Silver fir ( Abies alba Mill.) and sessile oak ( Quercus petraea (Matt.) Liebl.). Methods We estimated the variation of individual tree height as a function of long-term and short-term climates to tease apart local adaptation, plasticity and their interaction, using mixed-effect models calibrated with National Forest Inventory data ( in-situ models). To validate our approach, we tested the ability of in-situ models to predict independently tree height observations in common gardens where local adaptation to climate of populations and their plasticity can be measured and separated. In-situ model predictions of tree height variation among provenances (populations of different geographical origin) and among planting sites were compared to observations in common gardens and to predictions from a similar model calibrated using common garden data ( ex-situ model). Results In Q. petraea , we found high correlations between in-situ and ex-situ model predictions of provenance and plasticity effects and their interaction on tree height ( r > 0.80). We showed that the in-situ models significantly predicted tree height variation among provenances and sites for Abies alba and Quercus petraea . Spatial predictions of phenotypic plasticity across species distribution ranges indicate decreasing tree height in populations of warmer climates in response to recent anthropogenic climate warming. Main conclusions Our modelling approach using National Forest Inventory observations provides a new perspective for understanding patterns of intraspecific trait variation across species ranges. Its application is particularly interesting for species for which common garden experiments do not exist or do not cover the entire climatic range of the species.
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The assessment of seed zones or regions of provenance (RoP) to preserve local adaptation of tree species is an effective tool for the correct management of forest reproductive materials. The RoP for a species or sub-species is the area or group of areas subject to sufficiently uniform ecological conditions in which stands or seed sources show similar phenotypic or genetic characters, taking into account altitudinal boundaries where appropriate. However, the delin-eation of RoPs is commonly based on estimates of intrinsic environmental ho-mogeneity, mainly climate and/or soil characteristics. The integration of genetic data into RoP maps is an important strategy to obtain a sound tool for managing forest reproductive materials. A study on Quercus suber (cork oak) in Sardinia (Italy) was carried out with the aim of determining ecological regions of provenance, investigating the genetic diversity among populations at the regional scale and identifying possible areas of interest for valorising the available germplasm. Identification of these areas was performed by Reserve Selection Analysis, which allows to identify priority areas by assessing the minimum number of sites required to include all the genetic diversity estimated by genetic analysis. Four spatial clusters were obtained based on environmental data: the northern and northern-eastern parts of the island were included in the Northern RoP; the second RoP covered the western part; and the third RoP enclosed the southeastern region. The last group was distributed on the central part of the island (Central RoP) and includes the higher elevations. The sampled populations showed a low differentiation among populations and low diversity. According to the Reserve Selection Analysis, four conservation priority areas were identified. These indications can be useful at the local level because these sites can be proposed as stands for seed collection for future plantations .
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Over the years, with the efforts of many researchers, the field of species distribution model (SDM) has been well explored. The model of fuzzy matter elements (FME), which, combined with GIS to predict species distribution, has received extensive attention since its emergence. Based on previous studies, this paper improved FME, extended the scope of the membership degree and habitat suitability index, and explored the unsuitable areas of species. We have enhanced the limitation effect of key variables on species habitats, making the operation of FME more consistent with biological laws. By optimizing the FME, it could avoid the accumulation of predicted errors with multi-variables, and make the predicted results more reasonable. In this study, Gynostemma pentaphyllum (Thunb.) Makino was used as an example. The experimental process used several major environmental variables (climate, soil, and terrain variables) to predict the habitat suitability distribution of G. pentaphyllum in China for its current and future period, which includes the period of 2050s (average for 2041–2060) and 2070s (average for 2061–2080) under representative concentration pathways 4.5 (RCP4.5). The results of the analysis showed that the model performed well with a high accuracy by reducing the redundancy of the environmental data. The study could relieve the reliance on a large database of environmental information and propose a new approach for protecting the G. pentaphyllum in unsuitable areas under climate change.
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Landscape fragmentation typical of the Mediterranean region is the result of long-term settlement history and continuous socioeconomic interactions among countries. In complex agro-ecosystems of the Mediterranean basin, formulation of practical guidelines aimed at counteract soil and land degradation, water depletion, rural area depopulation, and the loss of agricultural knowledge is imperative. Based on a multidisciplinary, integrated approach, the present contribution discusses the role of traditional agricultural systems in ecosystem services provision, considering together economic sustainability and the medium-term ecological benefits. A permanent monitoring of rural areas specialized in traditional crop production such as olive and vine may support optimal selection of cultivars finely adapted to a warm climate. A competitive agricultural system may consider human well-being, social equity, and conservation of natural resources, to ensure a high level of services for current and future generations. Recovery and conservation of agricultural resources provide positive externalities and social benefits at both local and regional levels. Understanding the multiple use and functions of tree crop landscapes will contribute to improve food security, land quality, and the provision of related ecosystem services.
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Spatial modelling is a fundamental tool to support forest management strate- gies. National Forest Inventories (NFIs) provide extensive and detailed data for spatial analysis. In this study, the most recent Italian NFI (INFC2005) was used to evaluate possible refinements on species distribution model (SDM) tech- niques and to derive the future scenarios for two target species (Fagus sylva- tica L. and Abies alba Mill.) sharing a similar ecological environment and geo- graphic range. A weighted SDM and a provenance distribution model (PDM) were tested, based on tree-level selection of NFI plots using species basal area as a filter. Two climate projections were analysed for 2050s according to the IPCC 5th Assessment Report (AR5). The results were evaluated as possible guidelines for management of the Italian region of the EUFGIS network, where many marginal forest populations (MaPs) are currently included as genetic con- servation units (GCUs). The uncertainty of coordinates of inventory points did not affect the results of SDM. No statistical differences were found when com- paring the niche realization for the two model species (ANOVA ptextgreater0.05) mainly due to spatial autocorrelation between the environmental predictors. Based on the classic SDM evaluation method (True Skill Statistic - TSS) little improve- ments in predictions were observed when weighting each presence/absence records, possibly due to the lack of adequate ancillary data but also to the evaluation method. A higher accuracy of predictions (TSStextgreater0.85) was obtained when different “provenances” were modelled separately, due to the reduction in the “background noise”. We showed that for classical SDM, the prevalence of certain ecological features of some locations may drive algorithms to pro- duce coarse averaged predictions. Provenance distribution modelling may rep- resent a valuable step forward in spatial analysis, particularly for the detec- tion of marginal peripheral populations. The exact spatial co-ordinates of plots and additional information on site quality (e.g., stand age, site index, etc.) in NFI data could greatly help in better weighting presence/absence data and properly test the new evaluation methods
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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 Facility, EUFORGEN and forest inventories, and we developed species distribution models 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 Q. petraea; “losers” – mostly pioneer species: Betula pendula, Larix decidua, Picea abies and Pinus sylvestris and alien species – Pseudotsuga menziesii, Q. rubra and Robinia pseudoacacia, which may be also considered as “winners”. Assuming limited migration, most of the species studied would face significant decrease of suitable habitat area. The threat level was highest for species that currently have the northernmost distribution centers. Ecological consequences of the projected range contractions would be serious for both forest management and nature conservation. This article is protected by copyright. All rights reserved.