Open AccessCombining global climate and regional landscape models to improve prediction of invasion risk
Author(s) -
Kelly Ruth,
Leach Katie,
Cameron Alison,
Maggs Christine A.,
Reid Neil
Publication year - 2014
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.12194
Subject(s) - environmental niche modelling , range (aeronautics) , niche , abiotic component , ecological niche , ecology , environmental science , macroecology , climate change , geography , biotic component , scale (ratio) , species distribution , physical geography , environment variable , land use , biogeography , habitat , biology , cartography , materials science , composite material
Aim It is widely acknowledged that species distributions result from a variety of biotic and abiotic factors operating at different spatial scales. Here, we aimed to (1) determine the extent to which global climate niche models (CNMs) can be improved by the addition of fine‐scale regional data; (2) examine climatic and environmental factors influencing the range of 15 invasive aquatic plant species; and (3) provide a case study for the use of such models in invasion management on an island. Location Global, with a case study of species invasions in Ireland. Methods Climate niche models of global extent (including climate only) and regional environmental niche models (with additional factors such as human influence, land use and soil characteristics) were generated using maxent for 15 invasive aquatic plants. The performance of these models within the invaded range of the study species in Ireland was assessed, and potential hotspots of invasion suitability were determined. Models were projected forward up to 2080 based on two climate scenarios. Results While climate variables are important in defining the global range of species, factors related to land use and nutrient level were of greater importance in regional projections. Global climatic models were significantly improved at the island scale by the addition of fine‐scale environmental variables (area under the curve values increased by 0.18 and true skill statistic values by 0.36), and projected ranges decreased from an average of 86% to 36% of the island. Main conclusions Refining CNMs with regional data on land use, human influence and landscape may have a substantial impact on predictive capacity, providing greater value for prioritization of conservation management at subregional or local scales.