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

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 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. João C. Campos, Sara Rodrigues, Teresa Freitas, João A. Santos, João P. Honrado, Adrián Regos.

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 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 [ (Linnaeus, 1758)], the Egyptian vulture [ (Linnaeus, 1766)], the Iberian frog [ (Boulenger, 1879)] and the Seoane’s viper [ (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.

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).

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).

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).

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).

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.

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
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
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
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
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
Birds	Prunella collaris	PRUCOLL	4471	0.8	0.994	0.957
Birds	Prunella modularis	PRUMODU	1930	0.8	0.976	0.844
Birds	Pterocles alchata	PTEALCH	3973	0.8	0.974	0.877
Birds	Pterocles orientalis	PTEORIE	1502	0.8	0.968	0.84
Birds	Ptyonoprogne rupestris	PTYRUPE	1695	0.8	0.992	0.902
Birds	Pyrrhocorax graculus	PYRGRAC	701	0.8	0.992	0.947
Birds	Pyrrhula pyrrhula	PYRPYRR	2221	0.8	0.917	0.681
Birds	Rallus aquaticus	RALAQUA	5587	0.7	0.995	0.948
Birds	Recurvirostra avosetta	RECAVOS	1765	0.8	0.99	0.945
Birds	Regulus ignicapillus	REGIGNI	1897	0.8	0.928	0.693
Birds	Regulus regulus	REGREGU	953	0.8	0.928	0.899
Birds	Remiz pendulinus	REMPEND	2422	0.8	0.966	0.824
Birds	Riparia riparia	RIPRIPA	2485	0.7	0.993	0.932
Birds	Saxicola rubetra	SAXRUBE	2266	0.8	0.978	0.888
Birds	Saxicola torquatus	SAXTORQ	2565	0.7	0.898	0.622
Birds	Serinus citrinella	SERCITR	1348	0.8	0.984	0.904
Birds	Sitta europaea	SITEURO	1581	0.8	0.949	0.736
Birds	Sterna nilotica	STENILO	4157	0.8	0.996	0.981
Birds	Strix aluco	STRALUC	2999	0.7	0.991	0.896
Birds	Streptopelia decaocto	STRDECA	2285	0.7	0.898	0.651
Birds	Streptopelia turtur	STRTURT	5050	0.7	0.927	0.697
Birds	Sturnus unicolor	STUUNIC	141	0.7	0.923	0.71
Birds	Sylvia atricapilla	SYLATRI	3354	0.7	0.991	0.902
Birds	Sylvia borin	SYLBORI	305	0.8	0.931	0.712
Birds	Sylvia cantillans	SYLCANT	2248	0.8	0.896	0.602
Birds	Sylvia communis	SYLCOMM	439	0.7	0.899	0.606
Birds	Sylvia conspicillata	SYLCONS	1163	0.8	0.947	0.747
Birds	Sylvia hortensis	SYLHORT	1047	0.7	0.983	0.881
Birds	Sylvia melanocephala	SYLMELA	847	0.8	0.926	0.663
Birds	Sylvia undata	SYLUNDA	700	0.7	0.906	0.643
Birds	Tachybaptus ruficollis	TACRUFI	543	0.7	0.967	0.817
Birds	Tetrax tetrax	TETTETR	259	0.8	0.988	0.913
Birds	Tichodroma muraria	TICMURA	77	0.8	0.997	0.975
Birds	Tringa totanus	TRITOTA	1362	0.8	0.994	0.98
Birds	Troglodytes troglodytes	TROTROG	4424	0.8	0.931	0.667
Birds	Turdus philomelos	TURPHIL	195	0.8	0.936	0.704
Birds	Turdus viscivorus	TURVISC	2141	0.7	0.896	0.637
Birds	Tyto alba	TYTALBA	287	0.7	0.947	0.749
Birds	Upupa epops	UPUEPOP	2264	0.7	0.904	0.66
Birds	Vanellus vanellus	VANVANE	4504	0.8	0.979	0.927
Reptilia	Acanthodactylus erythrurus	AER	2245	0.7	0.932	0.73
Reptilia	Anguis fragilis	AFR	246	0.8	0.957	0.781
Reptilia	Blanus cinereus	BCI	1979	0.8	0.914	0.655
Reptilia	Coronella austriaca	CAU	1781	0.8	0.954	0.787
Reptilia	Chalcides bedriagai	CBE	84	0.7	0.993	0.943
Reptilia	Coronella girondica	CGI	1253	0.7	0.932	0.715
Reptilia	Chalcides striatus	CST	2336	0.7	0.993	0.924
Reptilia	Emys orbicularis spp.	EOR	4471	0.8	0.996	0.954
Reptilia	Hemorrhois hippocrepis	HHI	1930	0.8	0.918	0.692
Reptilia	Iberolacerta monticola spp.	IMO	3973	0.8	0.995	0.965
Reptilia	Lacerta schreiberi	LSC	1502	0.8	0.971	0.831
Reptilia	Macroprotodon brevis spp.	MBR	1695	0.8	0.943	0.732
Reptilia	Mauremys leprosa	MLE	701	0.8	0.918	0.661
Reptilia	Malpolon monspessulanus	MMO	2221	0.7	0.973	0.868
Reptilia	Natrix astreptophora	NAS	5587	0.7	0.866	0.543
Reptilia	Natrix maura	NMA	1765	0.7	0.966	0.809
Reptilia	Psammodromus algirus	PAL	1897	0.8	0.916	0.677
Reptilia	Podarcis bocagei	PBO	953	0.8	0.994	0.95
Reptilia	Podarcis guadarramae	PGU	2422	0.7	0.984	0.885
Reptilia	Timon lepidus spp.	TLE	2485	0.7	0.944	0.746
Reptilia	Tarentola mauritanica	TMR	2266	0.8	0.914	0.674
Reptilia	Vipera latastei	VLA	2565	0.7	0.994	0.931
Reptilia	Vipera seoanei	VSE	1348	0.8	0.986	0.93
Reptilia	Zamenis scalaris	ZSC	1581	0.7	0.866	0.574

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.

Code	Variable name	Units	Iberian Peninsula	Meseta Ibérica
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

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).

Figure 2.

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 (; code: ANTRRIV).

Figure 3.

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 (; code: ANTRRIV), according to each climate model (CNRM, IPSL, ICHEC and MPI; Jacob et al. 2020) and each Representative Concentration Pathways scenarios (RCP 4.5; RCP 8.5).

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 coverage

Description

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).

Coordinates

40.588 and 42.384 Latitude; -7.692 and -5.613 Longitude.

Temporal coverage

Notes

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).

Usage licence

Usage licence

Creative Commons Public Domain Waiver (CC-Zero)

Data resources

Data package title

Climate models and species distribution models of amphibians, birds and reptiles of the Iberian peninsula and the Biosphere Reserve of Meseta Ibérica)

Number of data sets

2

Data set 1.

Data set name

Climate models

Data format

netCDF (.nc)

Number of columns

2

Download URL

Part1: https://zenodo.org/record/4589376#.YFTl3dxUnIU Part 2: https://zenodo.org/record/4590027#.YFTmBdxUnIU

Description

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.

Data set 2.

Species distribution models

1

Part 1: https://zenodo.org/record/4598254#.YFTkjdxUnIU Part 2: https://zenodo.org/record/4599822#.YFTlv9xUnIU

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.

Pearson correlation analysis between bioclimatic variables

Statistical analyses

File: oo_524471.pdf

Data set 1.

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).

Data set 2.

Column label	Column description
Climate models	Species distribution models of 207 vertebrates for 2005 and 2050