Big data help to define climate change challenges for the typical Mediterranean species Cistus ladanifer L

article

Almeida, Alice Maria
Ribeiro, M.M.
Ferreira, Miguel R.
Roque, N.
Quintela-Sabarís, Celestino
Fernandez, P.

ALMEIDA, A.M.[et al.] (2023) - Big data help to define climate change challenges for the typical Mediterranean species Cistus ladanifer L. Frontiers in Ecology and Evolution. DOI 10.3389/fevo.2023.1136224

10.3389/fevo.2023.1136224

Big data help to define climate change challenges for the typical Mediterranean species Cistus ladanifer L

Rock rose
Species distribution modeling
Biomod2
Ensemble modeling
Climate change

CENTRO-01-0145-FEDER-000020
info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00239%2F2020/PT
info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00681%2F2020/PT
info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F05183%2F2020/PT

2023-03-22T12:53:01Z
2023-03-22T12:53:01Z
2023

Climate change’s huge impact on Mediterranean species’ habitat suitability and spatial and temporal distribution in the coming decades is expected. The present work aimed to reconstruct rockrose (Cistus ladanifer L.) historical and future spatial distribution, a typically Mediterranean species with abundant occurrence in North Africa, Iberian Peninsula, and Southern France. The R ensemble modeling approach was made using the biomod2 package to assess changes in the spatial distribution of the species in the Last Interglacial (LIG), the Last Glacial Maximum (LGM), and the Middle Holocene (MH), in the present, and in the future (for the years 2050 and 2070), considering two Representative Concentration Pathways (RCP 4.5 and RCP 8.5). The current species potential distribution was modeled using 2,833 occurrences, six bioclimatic variables, and four algorithms, Generalized Linear Model (GLM), MaxEnt, Multivariate Adaptive Regression Splines (MARS), and Artificial Neural Networks (ANN). Two global climate models (GCMs), CCSM4 and MRI-CGCM3, were used to forecast past and future suitability. The potential area of occurrence of the species is equal to 15.8 and 14.1% of the study area for current and LIG conditions, while it decreased to 3.8% in the LGM. The species’ presence diaminished more than half in the RCP 4.5 (to 6.8% in 2050 and 7% in 2070), and a too low figure (2.2%) in the worst-case scenario (RCP 8.5) for 2070. The results suggested that the current climatic conditions are the most suitable for the species’ occurrence and that future changes in environmental conditions may lead to the loss of suitable habitats, especially in the worst-case scenario. The information unfolded by this study will help to understand future predictable desertification in the Mediterranean region and to help policymakers to implement possible measures for biodiversity maintenance and desertification avoidance.
info:eu-repo/semantics/publishedVersion

openAccess
http://creativecommons.org/licenses/by/4.0/

eng