Biodivers Data J. 2021; 9: e66509. 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. Keywords: biodiversity, climate change, climate models, conservation, Iberian Peninsula, species distribution models. 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 (Whittaker et al. 2005). Species distributions are primarily shaped by historical and contemporary events, in which environmental and landscape factors play a decisive role in determining spatial and temporal distribution status and trends
(Nogués-Bravo et al. 2018). In this regard, climate change has been widely acknowledged as one of the major current and future threats for global biodiversity (Sippel et al. 2020,
Raven and Wagner 2021), causing geographical distribution shifts of a large number of species and, consequently, leading to species extinction events, the disruption of entire ecosystems and also deprivation of human well-being (Pecl et al.
2017, Turner et al. 2020). As such, providing detailed and informative climatic data at both spatial and temporal scales is paramount for better predicting potential environmental impacts on biodiversity and associated ecosystems, which ultimately support optimised conservation planning under global change
(Newbold 2018). One of the most important tools for assisting efficient management and biodiversity conservation planning is species distribution modelling (SDMs; Araújo et al. 2019). These methods derive statistical
relationships between geographical species occurrences and environmental predictors (such as climatic factors), which can be consequently used to spatially and temporally predict species distributions under different environmental scenarios (Guisan et al. 2017). In order to efficiently support biodiversity conservation under future environmental conditions, the combined effect of
landscape, concrete land cover information and climate factors must be taken into account to improve the model predictive accuracy of potential future changes of species distributions (Triviño et al. 2018, Pausas and Millán 2019). 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 (Navarro and Pereira 2012). For instance, the Transboundary Biosphere Reserve
of Meseta Ibérica (BRMI), one of the largest reserves and important areas for wildlife in Europe, with around 1,132,000 hectares (www.unesco.org), is currently subjected to processes of rural land abandonment and climatic instabilities that have contributed to the disruption of ecosystem
processes (e.g. escalation of extreme wildfires; Sil et al. 2019). The Reserve encompasses five natural parks and several Natura 2000 sites, comprising high landscape heterogeneity and biodiversity. As an example, the Reserve supports a large number of vertebrate species (around 250 species;
www.unesco.org), including several emblematic taxa of conservation concern, such as the black stork [Ciconia nigra (Linnaeus, 1758)], the Egyptian vulture [Neophron pernocterus (Linnaeus, 1766)], the Iberian frog
[Rana iberica (Boulenger, 1879)] and the Seoane’s viper [Viper seoanei (Lataste, 1879)]. However, the current climatic and landscapes changes constitute major threats for the local biodiversity and compiling framework data about how these impacts might influence species distribution patterns in the future could contribute to regional and local conservation efforts. 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. Additional informationThe 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. 1). The climate model simulations are provided for one historical period (daily data from 1989 to 2005) in the Iberian Peninsula (at 9 × 9 km) and two periods (daily data from 1989 to 2005 and from 2021 to 2050) in the Meseta Ibérica (at 1 × 1 km). Future climate data are available from four Global-Regional Climate Model chains and two Representative Concentration Pathways (RCP 4.5 and 8.5). The SDMs are provided for both areas (10 × 10 km in the Iberian Peninsula and 1 × 1 km in the Meseta Ibérica) and for one historical period in the Iberian Peninsula (mean between 1989-2005) and two periods in the Meseta Ibérica (mean between 1989-2005 and mean between 2021 and 2050). The data are provided in compressed folders, containing the following information:
Sampling methodsStep descriptionPresence/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 (Martí and Del Moral 2003, Equipa Atlas 2008). Presence/absence data for reptile and amphibian species were extracted from the Atlas of Amphibians and Reptiles of Portugal and Spain, at 10 km resolution (Pleguezuelos et al. 2002, Loureiro et al. 2008). Only native species with at least one presence in the BRMI were selected. In addition, species with less than 30 presences in the Iberian Peninsula were excluded to avoid model overfitting (see Araújo et al. 2019). In the end, data were obtained for 207 species: 168 birds, 24 reptiles and 15 amphibians (see Table 1). Taking into account the taxonomic uncertainties of some species (see Table 1), the species list was determined according to the most recently updated versions of the Altases to avoid any taxonomic conflicts (Sillero et al. 2014). Table 1.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.
The daily climatic data of temperature and precipitation were retrieved from the E-OBS database v.20.0e (Cornes et al. 2018), from 1989 to 2005. Future climatic data were developed from the following model chains in order to account for potential stochasticity of climate model projections: CNRM-CERFACS-CNRM-CM5 (CNRM), ICHEC-EC-EARTH (ICHEC), IPSL-IPSL-CM5A-MR (IPSL) and MPI-M-MPI-ESM-LR (MPI) models, generated within the EURO-CORDEX project (Jacob et al. 2020) and is available for two Representative Concentration Pathways, one intermediate scenario where emissions start to decline after 2040 (RCP 4.5) and one extreme scenario where emissions experience a continuous increase (RCP 8.5). Climate model data were bias-corrected using quantile mapping and E-OBS as a baseline for the overlapping period between EURO-CORDEX and E-OBS (1989-2005). Both historical and future climate datasets contain three variables: daily total precipitation, maximum and minimum temperatures. For the data collected, temporal and spatial (Biosphere Reserve of Meseta Ibérica and the Iberian Peninsula) domains were extracted and data were bilinearly interpolated to common 9 km grids. Subsequently, a spatial downscaling of temperatures was performed, using the digital elevation model from the Shuttle Radar Topography Mission (SRTM) databases, at 1 km grid resolution and the vertical temperature gradient (altitudinal correction). Precipitation totals were bilinearly interpolated to the same 1 km grid. 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 (https://www.r-project.org). A Variance Inflation Factor (VIF) analysis between the bioclimatic variables and Spearman correlation tests were conducted using the “usdm” package of R software v.4.0.5 (Suppl. material 1). Highly correlated variables (VIF > 3 and Spearman correlation > 0.7 or < -0.7) were excluded to avoid multicollinearity issues (Guisan et al. 2017). Eight bioclimatic predictors were ultimately selected and implemented in the species distribution models (SDMs; Table 2). Table 2.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.
Single-species ensemble models were built for each species at the Iberian Peninsula scale using the “biomod2” R package (Thuiller et al. 2009; http://r-forge.r-project.org/R/?group_id=302) at 10 km resolution. Although the original climate data were obtained at 9 x 9 km, the SDMs were performed at 10 x 10 km to match the spatial resolution of the Atlases' data. Then, the modelling of the climate suitability (hereafter “climate species models”) for each species using the aforementioned bioclimatic variables for 2005 (derived from the mean between 1989 and 2005) was conducted. The ensemble models were built using six modelling techniques (specifically, Generalised Linear Models, Generalised Addictive Models, Random Forests, Artificial Neural Networks, Gradient Boosting Models and Multiple Adaptive Regression Splines), in order to deal with inter-model variabilities (Thuiller et al. 2009). A repeated (10 times) split-sample approach was used to allow independency between model calibration and model evaluation. Each model was trained using 80% of the data, while the remaining 20% were used for model validation using the area under the curve (AUC) of a Receiver-Operating Characteristic (ROC) curve and the True Skill Statistics (TSS). An ensemble-forecasting framework was then applied by stacking the single-species models using a weighted average approach available in “biomod2”, using AUC values as model weights. The ensemble models were then projected to the Meseta Ibérica at 1 km resolution for the historical (1989-2005; Fig. 2) and future (2021-2050) periods for the four climate models and two RCP scenarios (Fig. 3). Finally, ensemble model predictions were reclassified into binary presence/absence maps through ROC optimised thresholds available in the “biomod2” package (see Thuiller et al. 2009). 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. 3). According to the SDMs, the majority of species are expected to be negatively affected by climate change scenarios (see Fig. 3). In fact, climatic suitable areas for 52% and 57% of the species are predicted to decrease under the intermediate (RCP 4.5) and extreme (RCP 8.5) climate change scenarios, respectively (see example in Fig. 3). Future climatic instabilities might contribute to distribution contractions and shifts, which might increase species vulnerability to extinction due to stochastic effects. Nonetheless, future studies should focus on combining the effects of land-use change and climate factors, in order to improve model predictive accuracy of future impacts on species distributions and, thus, to better support conservation planning and actions in the study area. Geographic coverageDescriptionThe 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). Coordinates40.588 and 42.384 Latitude; -7.692 and -5.613 Longitude. Temporal coverageNotesClimate 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). Usage licenceUsage licenceCreative Commons Public Domain Waiver (CC-Zero) Data resourcesData package titleClimate models and species distribution models of amphibians, birds and reptiles of the Iberian peninsula and the Biosphere Reserve of Meseta Ibérica) Number of data sets2 Data set 1.Data set nameClimate models DescriptionDaily 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. Data set 1.
Data set 2.Data set nameSpecies distribution models DescriptionSpecies 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. Data set 2.
Supplementary MaterialSupplementary material 1Pearson correlation analysis between bioclimatic variables Data type Statistical analyses File: oo_524471.pdf João C. Campos; Sara Rodrigues; Teresa Freitas; João A. Santos; João P. Honrado, Adrián Regos
AcknowledgementsThis 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. Author contributionsDraft 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. References
Articles from Biodiversity Data Journal are provided here courtesy of Pensoft Publishers Wem gehört die Firma Ariel?Ariel ist eine Marke für Waschmittel, die seit den 1960er Jahren auf dem deutschen Markt vertreten ist, und eines der meistverkauften Waschmittel Europas. Hersteller ist Procter & Gamble.
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Wer hat Ariel Waschmittel erfunden?Hersteller ist Procter & Gamble. Im Jahr 1966 erschien Ariel mit der Ariel-Trommel unter dem Slogan „Ariel zum Reinweichen“ erstmals auf dem deutschen Markt. Das erste Vollwaschmittel folgte 1968.
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