Biodiversity Data Journal : Data Paper (Biosciences)
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Data Paper (Biosciences)
Climatic variables and ecological modelling data for birds, amphibians and reptiles in the Transboundary Biosphere Reserve of Meseta Ibérica (Portugal-Spain)
expand article infoJoão 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
Open Access

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 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 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.

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:

  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).

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 (Anthus trivialis; 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 (Anthus trivialis; 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 name: 
Climate models
Data format: 
netCDF (.nc)
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.

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 name: 
Species distribution models
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. Review and editing: all authors.

References

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