Corresponding author: João C. Campos (
Academic editor: Etielle Andrade
Climate change has been widely accepted as one of the major threats for global biodiversity and understanding its potential effects on species distribution is crucial to optimise conservation planning in future scenarios under global change. Integrating detailed climatic data across spatial and temporal scales into species distribution modelling can help to predict potential changes in biodiversity. Consequently, this type of data can be useful for developing efficient biodiversity management and conservation planning. The provision of such data becomes even more important in highly biodiverse regions, currently suffering from climatic and landscape changes. The Transboundary Biosphere Reserve of Meseta Ibérica (BRMI; Portugal-Spain) is one of the most relevant reserves for wildlife in Europe. This highly diverse region is of great ecological and socio-economical interest, suffering from synergistic processes of rural land abandonment and climatic instabilities that currently threaten local biodiversity.
Aiming to optimise conservation planning in the Reserve, we provide a complete dataset of historical and future climate models (1 x 1 km) for the BRMI, used to build a series of distribution models for 207 vertebrate species. These models are projected for 2050 under two climate change scenarios. The climatic suitability of 52% and 57% of the species are predicted to decrease under the intermediate and extreme climatic scenarios, respectively. These models constitute framework data for improving local conservation planning in the Reserve, which should be further supported by implementing climate and land-use change factors to increase the accuracy of future predictions of species distributions in the study area.
Herein, we provide a complete dataset of state-of-the-art historical and future climate model simulations, generated by global-regional climate model chains, with climatic variables resolved at a high spatial resolution (1 × 1 km) over the Transboundary Biosphere Reserve of Meseta Ibérica. Additionally, a complete series of distribution models for 207 species (168 birds, 24 reptiles and 15 amphibians) under future (2050) climate change scenarios is delivered, which constitute framework data for improving local conservation planning in the reserve.
Understanding how species are globally distributed and identifying the key factors that influence their spatial and temporal distribution patterns are essential first steps for solid biodiversity conservation planning (
One of the most important tools for assisting efficient management and biodiversity conservation planning is species distribution modelling (SDMs;
Improving the predictive power of SDMs becomes paramount in highly biodiverse regions currently under severe climatic and landscape changes. In Europe, Mediterranean rural areas are perfect examples of highly diverse regions from an ecological and socio-economical point of view, suffering from increased effects of landscape and climatic changes (
Here, we present a complete dataset of historical (serving as temporal baseline data) and future climate models with a high spatial resolution (1 × 1 km) for the Transboundary Biosphere Reserve of Meseta Ibérica (Portugal-Spain), as well as a complete series of distribution models for 207 vertebrate species (168 birds, 24 reptiles and 15 amphibians), projected for a historical period (1989-2005) and for future climate change scenarios (2021-2050) in the Reserve.
These datasets were developed to provide framework data for biodiversity conservation in one of the most diverse Biosphere Reserves in Europe.
The climate model datasets (comprising three main variables – daily total precipitation, maximum and minimum temperatures) are provided for two main areas: the Iberian Peninsula and the Transboundary Biosphere Reserve of Meseta Ibérica (Fig.
The data are provided in compressed folders, containing the following information:
Climate model files encompassing three climatic variables in netCDF format (files organised according to each area and temporal period) and the corresponding bioclimatic variables available in .tiff format; Species models for 207 vertebrate species, including the corresponding spatial projections for the historic and future scenarios (files organised according to each species, area and temporal period).
Presence/absence data for bird species present in the Iberian Peninsula were obtained from the Spanish and Portuguese Atlas of Breeding Birds, at 10 km resolution (
The daily climatic data of temperature and precipitation were retrieved from the E-OBS database v.20.0e (
The main climate variables (i.e. daily precipitation, maximum temperature and minimum temperature) were used to calculate 19 bioclimatic variables through the “dismo” package from the R software v.4.0.5 (
Single-species ensemble models were built for each species at the Iberian Peninsula scale using the “biomod2” R package (
The ensemble models were then projected to the Meseta Ibérica at 1 km resolution for the historical (1989-2005; Fig.
This dataset contributes towards updating the current knowledge on the potential effects of climate change on the distribution of three main taxonomic groups in one of the largest Biosphere Reserves in Europe. In general, a wide range of species responses to climate change were observed, which might be explained by species-specific ecological preferences. The extent of species responses varied according to the four climate models due to the potential stochasticity of climate projections, but the predicted positive or negative climatic effects were congruent amongst all models for each species (see Fig.
The geographic range of the data covers the entire continental area of the Iberian Peninsula at 10 km of spatial resolution (45.158ºN and 35.347ºN Latitude; 9.560ºW and 3.889ºE Longitude) and the Transboundary Biosphere Reserve of Meseta Ibérica at 1 km of spatial resolution (42.384ºN and 40.588ºN Latitude; 7.692ºW and 5.613ºW Longitude).
40.588 and 42.384 Latitude; -7.692 and -5.613 Longitude.
Climate data cover the historical period between 1989 and 2005 (daily data) and a future period between 2020 and 2050 (daily data of four climate models under the RCP 4.5 and RCP 8.5 scenarios).
Species distribution models (climate species models) for the 207 vertebrate species cover the historical period of 2005 (average of the bioclimatic variables between 1989 and 2005) and a future period of 2050 (average between 2020 and 2050, for each of the four climate models and RCP scenarios).
Creative Commons Public Domain Waiver (CC-Zero)
Climate models and species distribution models of amphibians, birds and reptiles of the Iberian peninsula and the Biosphere Reserve of Meseta Ibérica)
2
Climate models
netCDF (.nc)
Part1:
Daily climate variables (daily precipitation, maximum temperature and minimum temperature) for a historical (1989-2005) and future period (2021-2050), for four climate models (CNRM, ICHEC, IPSL and MPI) and two Representative Concentration Pathways (RCP 4.5 and 8.5). Climatic variables are provided at 9 × 9 km resolution for the Iberian Peninsula (only for the historical period) and at 1 × 1 km and for the Transboundary Biosphere Reserve of Meseta Ibérica (both periods). Data divided into two parts.
Column label | Column description |
---|---|
Files of the historic period - AREA_EOBS_H_ALT_VAR_1 | Code description - AREA refers to the Iberian Peninsula (PI) or Meseta Ibérica (MI), EOBS to the historic climatic dataset of reference (E-OBS), H to the historical period (H), ALT to the altitudinal-based correction of climate variables, VAR to the three provided variables (RR - daily preciptation; TMAX - Maximum temperature; TMIN - Minimum temperature) and 1 to the spatial resolution (1 km). |
Files of the future period - MI_MODEL_RCP_MR_ALT_VAR_1 | Code description - MI refers to the Meseta Ibérica, MODEL to the climate model used (CNRM-CERFACS-CNRM-CM5 - CNRM; ICHEC-EC-EARTH - ICHEC; IPSL-IPSL-CM5A-MR - IPSL; MPI-M-MPI-ESM-LR - MPI), RCP to the Representative Concentration Pathway (RCP 4.5 - 45; RCP 8.5 - 85), MR to the future period, ALT to the altitudinal-based correction of climate variables, VAR to the three provided variables (RR - daily preciptation; TMAX - Maximum temperature; TMIN - Minimum temperature) and 1 to the spatial resolution (1 km). |
Species distribution models
Part 1:
Species distribution models of 207 vertebrates distributed in the Iberian Peninsula and the Transboundary Biosphere Reserve of Meseta Ibérica. The models are available at 10 × 10 km resolution for the Iberian Peninsula (climate models for 2005). Model projections are available for 2005 and 2050 (for the CNRM, ICHEC, IPSL and MPI climate models and the RCP 4.5 and RCP 8.5 scenarios) for the Biosphere Reserve at 1 × 1 km resolution. Data divided into two parts.
Column label | Column description |
---|---|
Climate models | Species distribution models of 207 vertebrates for 2005 and 2050 |
This research was supported by Portuguese national funds through FCT - Foundation for Science and Technology, I.P., under the FirESmart project (PCIF/MOG/0083/2017) and by project INMODES (CGL2017-89999-C2-2-R), funded by the Spanish Ministry of Science and Innovation. AR was supported by the Xunta de Galicia (ED481B2016/084-0) and the IACOBUS programme (INTERREG V-A España–Portugal, POCTEP 2014-2020). This work was also supported by National Funds by FCT - Portuguese Foundation for Science and Technology, under the project UIDB/04033/2020.
Draft preparation: JCC. Analyses and preparation of climate data: TF, JAS, JCC. Species distribution modelling and data preparation: SR, JCC. Visualisation: JCC. Review and editing: all authors.
Geographic location of the study areas: the Iberian Peninsula (climate variables and biodiversity data provided at 10 × 10 km resolution) and the Transboundary Biosphere Reserve of Meseta Ibérica (data provided at 1 × 1 km resolution).
Example of the historical climate (1989-2005) model projections obtained for the Iberian Peninsula (I.P.; 10 × 10 km) and the Transboundary Biosphere Reserve of Meseta Ibérica (M.I.; 1 × 1 km). The models present the ensemble suitability values for the Tree pipit (
Example of future climate model projections for 2050 obtained for the Transboundary Biosphere Reserve of Meseta Ibérica (M.I.; 1 × 1 km). The models present the ensemble suitability values for the Tree pipit (
Species information: taxonomic group, scientific name, species code and number of presences used for modelling (N). The quality threshold (area under the curve - AUC) used for model selection (to be included on ensemble modelling) are indicated. The accuracy metrics of ensemble species distribution models (SDMs), measured by the AUC and True Skill Statistics (TSS), are also mentioned. Ten model replicates were conducted for each species.
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ACI | 1253 | 0.8 | 0.96 | 0.795 |
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AOB | 2336 | 0.8 | 0.927 | 0.681 |
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BSP | 4471 | 0.7 | 0.915 | 0.654 |
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DGA | 1930 | 0.7 | 0.993 | 0.924 |
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ECA | 3973 | 0.7 | 0.949 | 0.757 |
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HMO | 1502 | 0.8 | 0.957 | 0.759 |
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LBO | 1695 | 0.8 | 0.948 | 0.76 |
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LHE | 701 | 0.8 | 0.971 | 0.833 |
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PCU | 2221 | 0.8 | 0.968 | 0.786 |
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PPE | 5587 | 0.8 | 0.989 | 0.932 |
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PPU | 1765 | 0.7 | 0.95 | 0.776 |
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PWA | 1897 | 0.8 | 0.918 | 0.659 |
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RIB | 953 | 0.8 | 0.984 | 0.871 |
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SSA | 2422 | 0.8 | 0.928 | 0.706 | |
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TMA | 2485 | 0.7 | 0.924 | 0.673 | |
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ACCGENT | 2266 | 0.7 | 0.991 | 0.895 |
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ACCNISU | 2565 | 0.7 | 0.984 | 0.88 |
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ACRARUN | 1348 | 0.8 | 0.99 | 0.908 |
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ACRSCIR | 1581 | 0.7 | 0.991 | 0.912 |
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AEGCAUD | 4157 | 0.7 | 0.888 | 0.599 |
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ALAARVE | 2999 | 0.8 | 0.896 | 0.62 |
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ALCATTH | 2285 | 0.7 | 0.861 | 0.542 |
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ALERUFA | 5050 | 0.7 | 0.946 | 0.803 |
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ANACLYP | 141 | 0.8 | 0.987 | 0.945 |
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ANAPLAT | 3354 | 0.7 | 0.871 | 0.56 |
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ANASTRE | 305 | 0.8 | 0.981 | 0.913 |
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ANTCAMP | 2248 | 0.8 | 0.896 | 0.614 |
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ANTSPIN | 439 | 0.8 | 0.987 | 0.908 |
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ANTTRIV | 1163 | 0.8 | 0.97 | 0.846 |
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APUMELB | 1047 | 0.7 | 0.975 | 0.849 |
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APUPALL | 847 | 0.8 | 0.945 | 0.75 |
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AQUCHRY | 700 | 0.7 | 0.968 | 0.835 |
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ARDCINE | 543 | 0.7 | 0.994 | 0.944 |
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ARDPURP | 259 | 0.8 | 0.977 | 0.872 |
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ASIFLAM | 77 | 0.8 | 0.991 | 0.973 |
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ASIOTUS | 1362 | 0.7 | 0.893 | 0.597 |
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ATHNOCT | 4424 | 0.7 | 0.962 | 0.793 |
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AYTFERI | 195 | 0.8 | 0.987 | 0.94 |
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BUBBUBO | 2141 | 0.7 | 0.88 | 0.601 |
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BUBIBIS | 287 | 0.8 | 0.964 | 0.827 |
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BUROEDI | 2264 | 0.8 | 0.975 | 0.836 |
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BUTBUTE | 4504 | 0.7 | 0.867 | 0.546 |
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CALBRAC | 2245 | 0.8 | 0.992 | 0.909 |
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CALRUFE | 246 | 0.8 | 0.985 | 0.903 |
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CAPEURO | 1979 | 0.8 | 0.899 | 0.618 |
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CAPRUFI | 1781 | 0.8 | 0.916 | 0.656 |
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CARSPIN | 84 | 0.8 | 0.99 | 0.963 |
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CECDAUR | 1253 | 0.8 | 0.992 | 0.952 |
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CERBRAC | 2336 | 0.7 | 0.868 | 0.56 |
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CETCETT | 4471 | 0.7 | 0.927 | 0.674 |
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CHADUBI | 1930 | 0.7 | 0.989 | 0.896 |
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CHEDUPO | 3973 | 0.8 | 0.98 | 0.907 |
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CHLHYBR | 1502 | 0.8 | 0.991 | 0.959 |
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CICCICO | 1695 | 0.8 | 0.927 | 0.705 |
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CICNIGR | 701 | 0.8 | 0.964 | 0.838 |
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CINCINC | 2221 | 0.8 | 0.937 | 0.728 |
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CIRAERU | 5587 | 0.8 | 0.979 | 0.891 |
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CIRCYAN | 1765 | 0.8 | 0.963 | 0.832 |
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CIRGALL | 1897 | 0.7 | 0.944 | 0.728 |
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CIRPYGA | 953 | 0.7 | 0.992 | 0.913 |
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CISJUNC | 2422 | 0.8 | 0.97 | 0.814 |
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CLAGLAN | 2485 | 0.7 | 0.994 | 0.925 |
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COCCOCC | 2266 | 0.8 | 0.965 | 0.818 |
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COLLIVI | 2565 | 0.7 | 0.945 | 0.787 |
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COLOENA | 1348 | 0.8 | 0.917 | 0.68 |
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COLPALU | 1581 | 0.7 | 0.947 | 0.793 |
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CORCORO | 4157 | 0.8 | 0.936 | 0.701 |
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CORGARR | 2999 | 0.8 | 0.927 | 0.705 |
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CORMONE | 2285 | 0.7 | 0.992 | 0.902 |
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COTCOTU | 5050 | 0.7 | 0.934 | 0.717 |
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CUCCANO | 141 | 0.7 | 0.98 | 0.856 |
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CYACYAN | 3354 | 0.8 | 0.954 | 0.765 |
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DENMAJO | 305 | 0.8 | 0.974 | 0.814 |
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DENMINO | 2248 | 0.8 | 0.95 | 0.751 |
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EGRGARZ | 439 | 0.8 | 0.976 | 0.878 |
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ELACAER | 1163 | 0.8 | 0.943 | 0.734 |
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EMBCALA | 1047 | 0.7 | 0.908 | 0.695 |
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EMBCIA | 847 | 0.8 | 0.94 | 0.681 |
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EMBCIRL | 700 | 0.7 | 0.991 | 0.901 |
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EMBCITR | 543 | 0.8 | 0.983 | 0.898 |
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EMBHORT | 259 | 0.8 | 0.947 | 0.755 |
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ERIRUBE | 77 | 0.8 | 0.905 | 0.619 |
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FALNAUM | 1362 | 0.8 | 0.93 | 0.723 |
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FALPERE | 4424 | 0.8 | 0.99 | 0.892 |
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FALSUBB | 195 | 0.7 | 0.975 | 0.819 |
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FICHYPO | 2141 | 0.8 | 0.975 | 0.899 |
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FRICOEL | 287 | 0.7 | 0.901 | 0.644 |
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FULATRA | 2264 | 0.8 | 0.927 | 0.688 |
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GALCHLO | 4504 | 0.7 | 0.874 | 0.593 |
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GALCRIS | 2245 | 0.8 | 0.934 | 0.701 |
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GALTHEK | 246 | 0.8 | 0.943 | 0.710 |
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GARGLAN | 1979 | 0.8 | 0.945 | 0.717 |
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GYPFULV | 1781 | 0.7 | 0.999 | 0.98 |
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HIEFASC | 84 | 0.8 | 0.997 | 0.956 |
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HIEPENN | 1253 | 0.7 | 0.99 | 0.889 |
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HIMHIMA | 2336 | 0.8 | 0.921 | 0.668 |
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IXOMINU | 4471 | 0.8 | 0.991 | 0.944 |
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JYNTORQ | 1930 | 0.7 | 0.989 | 0.891 |
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LANCOLL | 3973 | 0.8 | 0.971 | 0.855 |
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LANEXCU | 1502 | 0.7 | 0.885 | 0.611 |
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LANSENA | 1695 | 0.8 | 0.947 | 0.761 |
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LARRIDI | 701 | 0.8 | 0.994 | 0.968 |
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LOXCURV | 2221 | 0.8 | 0.931 | 0.733 |
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LULARBO | 5587 | 0.7 | 0.99 | 0.897 |
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LUSMEGA | 1765 | 0.7 | 0.992 | 0.923 |
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LUSSVEC | 1897 | 0.8 | 0.995 | 0.969 |
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MELCALA | 953 | 0.8 | 0.918 | 0.681 |
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MERAPIA | 2422 | 0.8 | 0.938 | 0.717 |
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MILMIGR | 2485 | 0.7 | 0.976 | 0.835 |
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MILMILV | 2266 | 0.8 | 0.938 | 0.727 |
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MONSAXA | 2565 | 0.8 | 0.941 | 0.751 |
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MONSOLI | 1348 | 0.8 | 0.992 | 0.908 |
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MOTALBA | 1581 | 0.7 | 0.971 | 0.864 |
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MOTCINE | 4157 | 0.8 | 0.94 | 0.7 |
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MOTFLAV | 2999 | 0.8 | 0.97 | 0.836 |
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MUSSTRI | 2285 | 0.7 | 0.977 | 0.835 |
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NEOPERC | 5050 | 0.7 | 0.97 | 0.876 |
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NYCNYCT | 141 | 0.8 | 0.995 | 0.974 |
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OENHISP | 3354 | 0.8 | 0.909 | 0.686 |
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OENLEUC | 305 | 0.8 | 0.945 | 0.754 |
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OENOENA | 2248 | 0.8 | 0.923 | 0.674 |
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ORIORIO | 439 | 0.7 | 0.91 | 0.666 |
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OTITARD | 1163 | 0.8 | 0.961 | 0.797 |
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OTUSCOP | 1047 | 0.7 | 0.925 | 0.695 |
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PARATER | 847 | 0.8 | 0.92 | 0.669 |
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PARCAER | 700 | 0.7 | 0.884 | 0.599 |
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PARCRIS | 543 | 0.8 | 0.985 | 0.863 |
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PARMAJO | 259 | 0.7 | 0.935 | 0.745 |
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PASHISP | 77 | 0.8 | 0.942 | 0.736 |
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PASMONT | 1362 | 0.7 | 0.869 | 0.541 |
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PERAPIV | 4424 | 0.8 | 0.937 | 0.736 |
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PERPERD | 195 | 0.8 | 0.993 | 0.954 |
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PETPETR | 2141 | 0.8 | 0.905 | 0.63 |
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PHACOLC | 287 | 0.8 | 0.997 | 0.985 |
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PHOOCHR | 2264 | 0.8 | 0.91 | 0.632 |
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PHOPHOE | 4504 | 0.8 | 0.949 | 0.77 |
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PHYBONE | 2245 | 0.8 | 0.906 | 0.626 |
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PHYCOLL | 246 | 0.8 | 0.922 | 0.678 |
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PHYIBER | 1979 | 0.8 | 0.935 | 0.729 |
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PICPICA | 1781 | 0.7 | 0.86 | 0.536 |
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PICVIRI | 84 | 0.7 | 0.868 | 0.551 |
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PODCRIS | 1253 | 0.8 | 0.978 | 0.889 |
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PODNIGR | 2336 | 0.8 | 0.993 | 0.962 |
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PRUCOLL | 4471 | 0.8 | 0.994 | 0.957 |
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PRUMODU | 1930 | 0.8 | 0.976 | 0.844 |
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PTEALCH | 3973 | 0.8 | 0.974 | 0.877 |
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PTEORIE | 1502 | 0.8 | 0.968 | 0.84 |
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PTYRUPE | 1695 | 0.8 | 0.992 | 0.902 |
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PYRGRAC | 701 | 0.8 | 0.992 | 0.947 |
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PYRPYRR | 2221 | 0.8 | 0.917 | 0.681 |
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RALAQUA | 5587 | 0.7 | 0.995 | 0.948 |
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RECAVOS | 1765 | 0.8 | 0.99 | 0.945 |
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REGIGNI | 1897 | 0.8 | 0.928 | 0.693 |
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REGREGU | 953 | 0.8 | 0.928 | 0.899 |
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REMPEND | 2422 | 0.8 | 0.966 | 0.824 |
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RIPRIPA | 2485 | 0.7 | 0.993 | 0.932 |
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SAXRUBE | 2266 | 0.8 | 0.978 | 0.888 |
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SAXTORQ | 2565 | 0.7 | 0.898 | 0.622 |
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SERCITR | 1348 | 0.8 | 0.984 | 0.904 |
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SITEURO | 1581 | 0.8 | 0.949 | 0.736 |
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STENILO | 4157 | 0.8 | 0.996 | 0.981 |
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STRALUC | 2999 | 0.7 | 0.991 | 0.896 |
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STRDECA | 2285 | 0.7 | 0.898 | 0.651 |
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STRTURT | 5050 | 0.7 | 0.927 | 0.697 |
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STUUNIC | 141 | 0.7 | 0.923 | 0.71 |
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SYLATRI | 3354 | 0.7 | 0.991 | 0.902 |
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SYLBORI | 305 | 0.8 | 0.931 | 0.712 |
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SYLCANT | 2248 | 0.8 | 0.896 | 0.602 |
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SYLCOMM | 439 | 0.7 | 0.899 | 0.606 |
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SYLCONS | 1163 | 0.8 | 0.947 | 0.747 |
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SYLHORT | 1047 | 0.7 | 0.983 | 0.881 |
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SYLMELA | 847 | 0.8 | 0.926 | 0.663 |
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SYLUNDA | 700 | 0.7 | 0.906 | 0.643 |
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TACRUFI | 543 | 0.7 | 0.967 | 0.817 |
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TETTETR | 259 | 0.8 | 0.988 | 0.913 |
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TICMURA | 77 | 0.8 | 0.997 | 0.975 |
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TRITOTA | 1362 | 0.8 | 0.994 | 0.98 |
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TROTROG | 4424 | 0.8 | 0.931 | 0.667 |
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TURPHIL | 195 | 0.8 | 0.936 | 0.704 |
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TURVISC | 2141 | 0.7 | 0.896 | 0.637 |
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TYTALBA | 287 | 0.7 | 0.947 | 0.749 |
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UPUEPOP | 2264 | 0.7 | 0.904 | 0.66 |
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VANVANE | 4504 | 0.8 | 0.979 | 0.927 |
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AER | 2245 | 0.7 | 0.932 | 0.73 |
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AFR | 246 | 0.8 | 0.957 | 0.781 |
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BCI | 1979 | 0.8 | 0.914 | 0.655 |
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CAU | 1781 | 0.8 | 0.954 | 0.787 |
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CBE | 84 | 0.7 | 0.993 | 0.943 |
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CGI | 1253 | 0.7 | 0.932 | 0.715 |
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CST | 2336 | 0.7 | 0.993 | 0.924 |
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EOR | 4471 | 0.8 | 0.996 | 0.954 | |
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HHI | 1930 | 0.8 | 0.918 | 0.692 |
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IMO | 3973 | 0.8 | 0.995 | 0.965 | |
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LSC | 1502 | 0.8 | 0.971 | 0.831 |
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MBR | 1695 | 0.8 | 0.943 | 0.732 | |
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MLE | 701 | 0.8 | 0.918 | 0.661 |
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MMO | 2221 | 0.7 | 0.973 | 0.868 |
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NAS | 5587 | 0.7 | 0.866 | 0.543 |
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NMA | 1765 | 0.7 | 0.966 | 0.809 |
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PAL | 1897 | 0.8 | 0.916 | 0.677 |
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PBO | 953 | 0.8 | 0.994 | 0.95 |
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PGU | 2422 | 0.7 | 0.984 | 0.885 |
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TLE | 2485 | 0.7 | 0.944 | 0.746 | |
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TMR | 2266 | 0.8 | 0.914 | 0.674 |
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VLA | 2565 | 0.7 | 0.994 | 0.931 |
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VSE | 1348 | 0.8 | 0.986 | 0.93 |
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ZSC | 1581 | 0.7 | 0.866 | 0.574 |
Description of the bioclimatic variables used in species distribution models. The code, name, units and the regional (Iberian Peninsula) and local (Biosphere Reserve of Meseta Ibérica) ranges are indicated for each variable.
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BIO3 | Isothermality | Coefficient | 25 – 43 | 33 - 40 |
BIO4 | Temperature Seasonality | Coefficient | 387 - 870 | 666 - 813 |
BIO10 | Mean Temperature of Warmest Quarter | ºC | 11.2 – 28.4 | 15.2 – 26.8 |
BIO11 | Mean Temperature of Coldest Quarter | ºC | -7.8 – 12.9 | -3.1 – 6.7 |
BIO15 | Precipitation Seasonality | Coefficient | 23 – 94 | 47 - 76 |
BIO16 | Precipitation of Wettest Quarter | mm | 200 - 2200 | 510 - 1110 |
BIO17 | Precipitation of Driest Quarter | mm | 0 - 470 | 0 - 130 |
BIO19 | Precipitation of Coldest Quarter | mm | 30 - 1130 | 120 - 470 |
Pearson correlation analysis between bioclimatic variables
Statistical analyses
File: oo_524471.pdf