Aim: Understanding the drivers of species distribution ranges and population genetic structure can help predict species' responses to global change, while mitigating threats to biodiversity through effective conservation measures. Here, we combined species habitat suitability through time with process-based models and genomic data to investigate the role of landscape features and functional connectivity in shaping the population genetic structure of Northern chamois. Location: European Alps. Taxon: Northern chamois (Rupicapra rupicapra). Methods: Using a model that simulates dispersal and tracks the functional connectiv- ity of populations over dynamic landscapes, we modelled the response of the cham- ois to climate change from the last glaciation (20,000 years ago) to the present. We reconstructed species habitat suitability and landscape connectivity over time and simulated cumulative divergence of populations as a proxy for genetic differentiation. We then compared simulated divergence with the actual population structure of 449 chamois (with >20 k SNPs) sampled across the Alps. Results: We found that Alpine populations of chamois are structured into two main clades, located in the south-western and the eastern Alps. The contact zone between the two lineages is located near the Rhone valley in Switzerland. Simulations repro- duced the geographic differentiation of populations observed in the genomic data, and limited dispersal ability and landscape connectivity co-determined the fit of the simulations to data. Main conclusions: The contemporary genetic structure of the chamois across the Alps is explained by limited functional connectivity in combination with large rivers or val- leys acting as dispersal barriers. The results of our analysis combining simulations with population genomics highlight how biological characteristics, habitat preference and landscapes shape population genetic structure over time and in responses to climate change. We conclude that spatial simulations could be used to improve our under- standing of how landscape dynamics, shaped by geological or climatic forces, impact intra- and interspecific diversity.

Leugger, F.; Broquet, T.; Nikolaus Karger, D.; Rioux, D.; Buzan, E.; Corlatti, L.; Crestanello, B.; Curt-Grand-Gaudin, N.; Hauffe, H.C.; Roleckova, B.; Sprem, N.; Tissot, N.; Tissot, S.; Valterova, R.; Yannic, G.; Pellissier, L. (2022). Dispersal and habitat dynamics shape the genetic structure of the Northern chamois in the Alps. JOURNAL OF BIOGEOGRAPHY, 49 (10): 1848-1861. doi: 10.1111/jbi.14363 handle: http://hdl.handle.net/10449/76415

Dispersal and habitat dynamics shape the genetic structure of the Northern chamois in the Alps

Barbara Crestanello;Heidi Christine Hauffe;
2022-01-01

Abstract

Aim: Understanding the drivers of species distribution ranges and population genetic structure can help predict species' responses to global change, while mitigating threats to biodiversity through effective conservation measures. Here, we combined species habitat suitability through time with process-based models and genomic data to investigate the role of landscape features and functional connectivity in shaping the population genetic structure of Northern chamois. Location: European Alps. Taxon: Northern chamois (Rupicapra rupicapra). Methods: Using a model that simulates dispersal and tracks the functional connectiv- ity of populations over dynamic landscapes, we modelled the response of the cham- ois to climate change from the last glaciation (20,000 years ago) to the present. We reconstructed species habitat suitability and landscape connectivity over time and simulated cumulative divergence of populations as a proxy for genetic differentiation. We then compared simulated divergence with the actual population structure of 449 chamois (with >20 k SNPs) sampled across the Alps. Results: We found that Alpine populations of chamois are structured into two main clades, located in the south-western and the eastern Alps. The contact zone between the two lineages is located near the Rhone valley in Switzerland. Simulations repro- duced the geographic differentiation of populations observed in the genomic data, and limited dispersal ability and landscape connectivity co-determined the fit of the simulations to data. Main conclusions: The contemporary genetic structure of the chamois across the Alps is explained by limited functional connectivity in combination with large rivers or val- leys acting as dispersal barriers. The results of our analysis combining simulations with population genomics highlight how biological characteristics, habitat preference and landscapes shape population genetic structure over time and in responses to climate change. We conclude that spatial simulations could be used to improve our under- standing of how landscape dynamics, shaped by geological or climatic forces, impact intra- and interspecific diversity.
Geogenomics
Landscape genetics
Palaeo-environmental modelling
Population genetics
Process-based modelling
Range dynamics
Species distribution modelling
Settore BIO/05 - ZOOLOGIA
2022
Leugger, F.; Broquet, T.; Nikolaus Karger, D.; Rioux, D.; Buzan, E.; Corlatti, L.; Crestanello, B.; Curt-Grand-Gaudin, N.; Hauffe, H.C.; Roleckova, B.; Sprem, N.; Tissot, N.; Tissot, S.; Valterova, R.; Yannic, G.; Pellissier, L. (2022). Dispersal and habitat dynamics shape the genetic structure of the Northern chamois in the Alps. JOURNAL OF BIOGEOGRAPHY, 49 (10): 1848-1861. doi: 10.1111/jbi.14363 handle: http://hdl.handle.net/10449/76415
File in questo prodotto:
File Dimensione Formato  
2022 JoB Hauffe.pdf

accesso aperto

Tipologia: Versione editoriale (Publisher’s layout)
Licenza: Creative commons
Dimensione 12.65 MB
Formato Adobe PDF
12.65 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10449/76415
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 3
  • ???jsp.display-item.citation.isi??? 3
social impact