Open AccessCombining point‐process and landscape vegetation models to predict large herbivore distributions in space and time—A case study of Rupicapra rupicapra
Author(s) -
Thuiller Wilfried,
Guéguen Maya,
Bison Marjorie,
Duparc Antoine,
Garel Mathieu,
Loison Anne,
Renaud Julien,
Poggiato Giovanni
Publication year - 2018
Publication title -
diversity and distributions
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.918
H-Index - 118
eISSN - 1472-4642
pISSN - 1366-9516
DOI - 10.1111/ddi.12684
Subject(s) - vegetation (pathology) , biological dispersal , climate change , ecology , occupancy , environmental science , herbivore , distribution (mathematics) , physical geography , species distribution , geography , habitat , biology , mathematics , medicine , population , demography , pathology , sociology , mathematical analysis
Aim When modelling the distribution of animals under current and future conditions, both their response to environmental constraints and their resources’ response to these environmental constraints need to be taken into account. Here, we develop a framework to predict the distribution of large herbivores under global change, while accounting for changes in their main resources. We applied it to Rupicapra rupicapra , the chamois of the European Alps. Location The Bauges Regional Park (French Alps). Methods We built sixteen plant functional groups ( PFG s) that account for the chamois’ diet (estimated from sequenced environmental DNA found in the faeces), climatic requirements, dispersal limitations, successional stage and interaction for light. These PFG s were then simulated using a dynamic vegetation model, under current and future climatic conditions up to 2100. Finally, we modelled the spatial distribution of the chamois under both current and future conditions using a point‐process model applied to either climate‐only variables or climate and simulated vegetation structure variables. Results Both the climate‐only and the climate and vegetation models successfully predicted the current distribution of the chamois species. However, when applied into the future, the predictions differed widely. While the climate‐only models predicted an 80% decrease in total species occupancy, including vegetation structure and plant resources for chamois in the model provided more optimistic predictions because they account for the transient dynamics of the vegetation (−20% in species occupancy). Main conclusions Applying our framework to the chamois shows that the inclusion of ecological mechanisms (i.e., plant resources) produces more realistic predictions under current conditions and should prove useful for anticipating future impacts. We have shown that discounting the pure effects of vegetation on chamois might lead to overpessimistic predictions under climate change. Our approach paves the way for improved synergies between different fields to produce biodiversity scenarios.