Testing projected wild bee distributions in agricultural habitats: predictive power depends on species traits and habitat type
| Autor: | Stuart P. M. Roberts, Merijn Bos, Jacobus C. Biesmeijer, Menno Reemer, Simon G. Potts, G. Arjen de Groot, Leon Marshall, Luísa G. Carvalheiro, Jeroen Scheper, Jesús Aguirre-Gutiérrez, David Kleijn |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2015 |
| Předmět: |
Arable fields
Species distribution Biodiversity Plant Ecology and Nature Conservation Biology Generalist and specialist species Transect Ecology Evolution Behavior and Systematics Original Research Nature and Landscape Conservation Ecology Land use Model validation MAXENT Sciences bio-médicales et agricoles 15. Life on land PE&RC Traits Habitat Wildlife Ecology and Conservation Predictive power Dierecologie Plantenecologie en Natuurbeheer Wild bees Animal Ecology Orchards Arable land |
| Zdroj: | Ecology and Evolution 5 (2015) 19 Ecology and Evolution, 5(19), 4426-4436 Ecology and Evolution Ecology and evolution, 5 (19 |
| ISSN: | 2045-7758 |
| Popis: | Species distribution models (SDM) are increasingly used to understand the factors that regulate variation in biodiversity patterns and to help plan conservation strategies. However, these models are rarely validated with independently collected data and it is unclear whether SDM performance is maintained across distinct habitats and for species with different functional traits. Highly mobile species, such as bees, can be particularly challenging to model. Here, we use independent sets of occurrence data collected systematically in several agricultural habitats to test how the predictive performance of SDMs for wild bee species depends on species traits, habitat type, and sampling technique. We used a species distribution modeling approach parametrized for the Netherlands, with presence records from 1990 to 2010 for 193 Dutch wild bees. For each species, we built a Maxent model based on 13 climate and landscape variables. We tested the predictive performance of the SDMs with independent datasets collected from orchards and arable fields across the Netherlands from 2010 to 2013, using transect surveys or pan traps. Model predictive performance depended on species traits and habitat type. Occurrence of bee species specialized in habitat and diet was better predicted than generalist bees. Predictions of habitat suitability were also more precise for habitats that are temporally more stable (orchards) than for habitats that suffer regular alterations (arable), particularly for small, solitary bees. As a conservation tool, SDMs are best suited to modeling rarer, specialist species than more generalist and will work best in long-term stable habitats. The variability of complex, short-term habitats is difficult to capture in such models and historical land use generally has low thematic resolution. To improve SDMs' usefulness, models require explanatory variables and collection data that include detailed landscape characteristics, for example, variability of crops and flower availability. Additionally, testing SDMs with field surveys should involve multiple collection techniques. info:eu-repo/semantics/published |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Plný text