Climatic variables and ecological modelling data for birds, amphibians and reptiles in the Transboundary Biosphere Reserve of Meseta Ibérica (Portugal-Spain)

Biodiversity Data Journal 9: e66509 doi: 10.3897/BDJ.9.e66509 Data Paper Climatic variables and ecological modelling data for birds, amphibians and reptiles in the Transboundary Biosphere Reserve of Meseta Ibérica (Portugal-Spain) João C. Campos , Sara Rodrigues , Teresa Freitas , João A. Santos , João P. Honrado , Adrián Regos ‡ InBIO/CIBIO - Centro de Investigação em Biodiversidade e Recursos Genéticos, Campus Agrário de Vairão, Rua Padre Armando Quintas, n° 7, 4485-661 Vairão, Porto, Portugal § CITAB - Centro de Investigação e de Tecnologias Agro-Ambientais e Biológicas, Universidade de Trás-os-Montes e Alto Douro, Apartado 1013, 5001-801, Vila Real, Portugal | Departamento de Zooloxía, Xenética e Antropoloxía Física, Universidade de Santiago de Compostela, 15782, Santiago de Compostela, Spain Corresponding author: João C. Campos ([email protected]) Academic editor: Etielle Andrade Received: 25 Mar 2021 | Accepted: 12 May 2021 | Published: 24 Jun 2021 Citation: Campos JC, Rodrigues S, Freitas T, Santos JA, Honrado JP, Regos A (2021) Climatic variables and ecological modelling data for birds, amphibians and reptiles in the Transboundary Biosphere Reserve of Meseta Ibérica (Portugal-Spain). Biodiversity Data Journal 9: e66509. https://doi.org/10.3897/BDJ.9.e66509 Abstract Background 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 ‡ ‡ § § ‡ | © Campos J et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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. New information 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. Introduction 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 2Campos J et al 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 socioeconomical 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. Climatic variables and ecological modelling data for birds, amphibians ... 3 General description Purpose: These datasets were developed to provide framework data for biodiversity conservation in one of the most diverse Biosphere Reserves in Europe. Additional information: 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. 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: 1. 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; 2. 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). Figure 1. 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). 4Campos J et al Sampling methods Step description: 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 (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). Group Scientific name Code N AUC threshold Climate models AUC TSS Amphibia Alytes cisternasii ACI 1253 0.8 0.96 0.795 Amphibia Alytes obstetricans AOB 2336 0.8 0.927 0.681 Amphibia Bufo spinosus BSP 4471 0.7 0.915 0.654 Amphibia Discoglossus galganoi DGA 1930 0.7 0.993 0.924 Amphibia Epidalea calamita ECA 3973 0.7 0.949 0.757 Amphibia Hyla molleri HMO 1502 0.8 0.957 0.759 Amphibia Lissotriton boscai LBO 1695 0.8 0.948 0.76 Amphibia Lissotriton helveticus LHE 701 0.8 0.971 0.833 Amphibia Pelobates cultripes PCU 2221 0.8 0.968 0.786 Amphibia Pelophylax perezi PPE 5587 0.8 0.989 0.932 Amphibia Pelodytes punctatus PPU 1765 0.7 0.95 0.776 Amphibia Pleurodeles waltl PWA 1897 0.8 0.918 0.659 Amphibia Rana iberica RIB 953 0.8 0.984 0.871 Amphibia Salamandra salamandra spp. SSA 2422 0.8 0.928 0.706 Amphibia Triturus marmoratus spp. TMA 2485 0.7 0.924 0.673 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. Climatic variables and ecological modelling data for birds, amphibians ... 5 Group Scientific name Code N AUC threshold Climate models AUC TSS Birds Accipiter gentilis ACCGENT 2266 0.7 0.991 0.895 Birds Accipiter nisus ACCNISU 2565 0.7 0.984 0.88 Birds Acrocephalus arundinaceus ACRARUN 1348 0.8 0.99 0.908 Birds Acrocephalus scirpaceus ACRSCIR 1581 0.7 0.991 0.912 Birds Aegithalos caudatus AEGCAUD 4157 0.7 0.888 0.599 Birds Alauda arvensis ALAARVE 2999 0.8 0.896 0.62 Birds Alcedo atthis ALCATTH 2285 0.7 0.861 0.542 Birds Alectoris rufa ALERUFA 5050 0.7 0.946 0.803 Birds Anas clypeata ANACLYP 141 0.8 0.987 0.945 Birds Anas platyrhynchos ANAPLAT 3354 0.7 0.871 0.56 Birds Anas strepera ANASTRE 305 0.8 0.981 0.913 Birds Anthus campestris ANTCAMP 2248 0.8 0.896 0.614 Birds Anthus spinoletta ANTSPIN 439 0.8 0.987 0.908 Birds Anthus trivialis ANTTRIV 1163 0.8 0.97 0.846 Birds Apus melba APUMELB 1047 0.7 0.975 0.849 Birds Apus pallidus APUPALL 847 0.8 0.945 0.75 Birds Aquila chrysaetos AQUCHRY 700 0.7 0.968 0.835 Birds Ardea cinerea ARDCINE 543 0.7 0.994 0.944 Birds Ardea purpurea ARDPURP 259 0.8 0.977 0.872 Birds Asio flammeus ASIFLAM 77 0.8 0.991 0.973 Birds Asio otus ASIOTUS 1362 0.7 0.893 0.597 Birds Athene noctua ATHNOCT 4424 0.7 0.962 0.793 Birds Aythya ferina AYTFERI 195 0.8 0.987 0.94 Birds Bubo bubo BUBBUBO 2141 0.7 0.88 0.601 Birds Bubulcus ibis BUBIBIS 287 0.8 0.964 0.827 Birds Burhinus oedicnemus BUROEDI 2264 0.8 0.975 0.836 Birds Buteo buteo BUTBUTE 4504 0.7 0.867 0.546 Birds Calandrella brachydactyla CALBRAC 2245 0.8 0.992 0.909 Birds Alauda rufescens CALRUFE 246 0.8 0.985 0.903 Birds Caprimulgus europaeus CAPEURO 1979 0.8 0.899 0.618 Birds Caprimulgus ruficollis CAPRUFI 1781 0.8 0.916 0.656 Birds Carduelis spinus CARSPIN 84 0.8 0.99 0.963 6Campos J et al Group Scientific name Code N AUC threshold Climate models AUC TSS Birds Hirundo daurica CECDAUR 1253 0.8 0.992 0.952 Birds Certhia brachydactyla CERBRAC 2336 0.7 0.868 0.56 Birds Cettia cetti CETCETT 4471 0.7 0.927 0.674 Birds Charadrius dubius CHADUBI 1930 0.7 0.989 0.896 Birds Chersophilus duponti CHEDUPO 3973 0.8 0.98 0.907 Birds Chlidonias hybrida CHLHYBR 1502 0.8 0.991 0.959 Birds Ciconia ciconia CICCICO 1695 0.8 0.927 0.705 Birds Ciconia nigra CICNIGR 701 0.8 0.964 0.838 Birds Cinclus cinclus CINCINC 2221 0.8 0.937 0.728 Birds Circus aeruginosus CIRAERU 5587 0.8 0.979 0.891 Birds Circus cyaneus CIRCYAN 1765 0.8 0.963 0.832 Birds Circaetus gallicus CIRGALL 1897 0.7 0.944 0.728 Birds Circus pygargus CIRPYGA 953 0.7 0.992 0.913 Birds Cisticola juncidis CISJUNC 2422 0.8 0.97 0.814 Birds Clamator glandarius CLAGLAN 2485 0.7 0.994 0.925 Birds Coccothraustes coccothraustes COCCOCC 2266 0.8 0.965 0.818 Birds Columba livia COLLIVI 2565 0.7 0.945 0.787 Birds Columba oenas COLOENA 1348 0.8 0.917 0.68 Birds Columba palumbus COLPALU 1581 0.7 0.947 0.793 Birds Corvus corone CORCORO 4157 0.8 0.936 0.701 Birds Coracias garrulus CORGARR 2999 0.8 0.927 0.705 Birds Corvus monedula CORMONE 2285 0.7 0.992 0.902 Birds Coturnix coturnix COTCOTU 5050 0.7 0.934 0.717 Birds Cuculus canorus CUCCANO 141 0.7 0.98 0.856 Birds Cyanopica cyana CYACYAN 3354 0.8 0.954 0.765 Birds Dendrocopos major DENMAJO 305 0.8 0.974 0.814 Birds Dendrocopos minor DENMINO 2248 0.8 0.95 0.751 Birds Egretta garzetta EGRGARZ 439 0.8 0.976 0.878 Birds Elanus caeruleus ELACAER 1163 0.8 0.943 0.734 Birds Emberiza calandra EMBCALA 1047 0.7 0.908 0.695 Birds Emberiza cia EMBCIA 847 0.8 0.94 0.681 Birds Emberiza cirlus EMBCIRL 700 0.7 0.991 0.901 Climatic variables and ecological modelling data for birds, amphibians ... 7 Group Scientific name Code N AUC threshold Climate models AUC TSS Birds Emberiza citrinella EMBCITR 543 0.8 0.983 0.898 Birds Emberiza hortulana EMBHORT 259 0.8 0.947 0.755 Birds Erithacus rubecula ERIRUBE 77 0.8 0.905 0.619 Birds Falco naumanni FALNAUM 1362 0.8 0.93 0.723 Birds Falco peregrinus FALPERE 4424 0.8 0.99 0.892 Birds Falco subbuteo FALSUBB 195 0.7 0.975 0.819 Birds Ficedula hypoleuca FICHYPO 2141 0.8 0.975 0.899 Birds Fringilla coelebs FRICOEL 287 0.7 0.901 0.644 Birds Fulica atra FULATRA 2264 0.8 0.927 0.688 Birds Gallinula chloropus GALCHLO 4504 0.7 0.874 0.593 Birds Galerida cristata GALCRIS 2245 0.8 0.934 0.701 Birds Galerida theklae GALTHEK 246 0.8 0.943 0.710 Birds Garrulus glandarius GARGLAN 1979 0.8 0.945 0.717 Birds Gyps fulvus GYPFULV 1781 0.7 0.999 0.98 Birds Hieraaetus fasciatus HIEFASC 84 0.8 0.997 0.956 Birds Hieraaetus pennatus HIEPENN 1253 0.7 0.99 0.889 Birds Himantopus himantopus HIMHIMA 2336 0.8 0.921 0.668 Birds Ixobrychus minutus IXOMINU 4471 0.8 0.991 0.944 Birds Jynx torquilla JYNTORQ 1930 0.7 0.989 0.891 Birds Lanius collurio LANCOLL 3973 0.8 0.971 0.855 Birds Lanius excubitor LANEXCU 1502 0.7 0.885 0.611 Birds Lanius senator LANSENA 1695 0.8 0.947 0.761 Birds Larus ridibundus LARRIDI 701 0.8 0.994 0.968 Birds Loxia curvirostra LOXCURV 2221 0.8 0.931 0.733 Birds Lullula arborea LULARBO 5587 0.7 0.99 0.897 Birds Luscinia megarhynchos LUSMEGA 1765 0.7 0.992 0.923 Birds Cyanecula svecica LUSSVEC 1897 0.8 0.995 0.969 Birds Melanocorypha calandra MELCALA 953 0.8 0.918 0.681 Birds Merops apiaster MERAPIA 2422 0.8 0.938 0.717 Birds Milvus migrans MILMIGR 2485 0.7 0.976 0.835 Birds Milvus milvus MILMILV 2266 0.8 0.938 0.727 Birds Monticola saxatilis MONSAXA 2565 0.8 0.941 0.751 8Campos J et al Group Scientific name Code N AUC threshold Climate models AUC TSS Birds Monticola solitarius MONSOLI 1348 0.8 0.992 0.908 Birds Motacilla alba MOTALBA 1581 0.7 0.971 0.864 Birds Motacilla cinerea MOTCINE 4157 0.8 0.94 0.7 Birds Motacilla flava MOTFLAV 2999 0.8 0.97 0.836 Birds Muscicapa striata MUSSTRI 2285 0.7 0.977 0.835 Birds Neophron percnopterus NEOPERC 5050 0.7 0.97 0.876 Birds Nycticorax nycticorax NYCNYCT 141 0.8 0.995 0.974 Birds Oenanthe hispanica OENHISP 3354 0.8 0.909 0.686 Birds Oenanthe leucura OENLEUC 305 0.8 0.945 0.754 Birds Oenanthe oenanthe OENOENA 2248 0.8 0.923 0.674 Birds Oriolus oriolus ORIORIO 439 0.7 0.91 0.666 Birds Otis tarda OTITARD 1163 0.8 0.961 0.797 Birds Otus scops OTUSCOP 1047 0.7 0.925 0.695 Birds Periparus ater PARATER 847 0.8 0.92 0.669 Birds Parus caeruleus PARCAER 700 0.7 0.884 0.599 Birds Parus cristatus PARCRIS 543 0.8 0.985 0.863 Birds Parus major PARMAJO 259 0.7 0.935 0.745 Birds Passer hispaniolensis PASHISP 77 0.8 0.942 0.736 Birds Passer montanus PASMONT 1362 0.7 0.869 0.541 Birds Pernis apivorus PERAPIV 4424 0.8 0.937 0.736 Birds Perdix perdix PERPERD 195 0.8 0.993 0.954 Birds Petronia petronia PETPETR 2141 0.8 0.905 0.63 Birds Phasianus colchicus PHACOLC 287 0.8 0.997 0.985 Birds Phoenicurus ochruros PHOOCHR 2264 0.8 0.91 0.632 Birds Phoenicurus phoenicurus PHOPHOE 4504 0.8 0.949 0.77 Birds Phylloscopus bonelli PHYBONE 2245 0.8 0.906 0.626 Birds Phylloscopus collybita PHYCOLL 246 0.8 0.922 0.678 Birds Phylloscopus ibericus PHYIBER 1979 0.8 0.935 0.729 Birds Pica pica PICPICA 1781 0.7 0.86 0.536 Birds Picus viridis PICVIRI 84 0.7 0.868 0.551 Birds Podiceps cristatus PODCRIS 1253 0.8 0.978 0.889 Birds Podiceps nigricollis PODNIGR 2336 0.8 0.993 0.962 Climatic variables and ecological modelling data for birds, amphibians ... 9 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). Data set name: Species distribution models Download URL: Part 1: https://zenodo.org/record/4598254#.YFTkjdxUnIU Part 2: https://zenodo.org/record/4599822#.YFTlv9xUnIU Description: 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 Acknowledgements 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. Author contributions Draft preparation: JCC. Analyses and preparation of climate data: TF, JAS, JCC. Species distribution modelling and data preparation: SR, JCC. Visualisation: JCC. 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Diversity and Distributions 11 (1): 3‑23. https://doi.org/10.1111/j.1366-9516.2005.00143.x 18 Campos J et al Supplementary material Suppl. material 1: Pearson correlation analysis between bioclimatic variables Authors: João C. Campos; Sara Rodrigues; Teresa Freitas; João A. Santos; João P. Honrado, Adrián Regos Data type: Statistical analyses Download file (543.99 kb) Climatic variables and ecological modelling data for birds, amphibians ... 19