Zoeken
Zoeken kan via de modus 'eenvoudig zoeken' (één veld) of uitgebreid via 'geavanceerd zoeken' (meerdere velden). Zo kan je bv. zoeken op een combinatie van een auteursnaam (auteur), een jaartal (jaar) en een documenttype.
Boekenmand
Nuttige resultaten kan je aanvinken en toevoegen aan een mandje. De inhoud hiervan kan je exporteren of afdrukken (naar bv. PDF).
RSS
Op de hoogte blijven van nieuw toegevoegde publicaties binnen uw interessegebied? Dit kan door een RSS-feed (?) te maken van jouw zoekopdracht.
nieuwe zoekopdracht
Generalized functional responses for species distributions
Matthiopoulos, J.; Hebblewhite, M.; Aarts, G.M; Fieberg, J. (2011). Generalized functional responses for species distributions. Ecology 92(3): 583-589. dx.doi.org/10.1890/10-0751.1
In: Ecology. Ecological Society of America: Brooklyn, NY. ISSN 0012-9658; e-ISSN 1939-9170
|   |
| Trefwoord |
|
| Author keywords |
Canis lupis; climate change; generalized linear mixed model; habitatpreference; home range; predictive models; simulation study; space-use;spatial ecology; species distributions; utilization distribution; wolf |
| Auteurs | | Top |
- Matthiopoulos, J.
- Hebblewhite, M.
- Aarts, G.M
- Fieberg, J.
|
|
|
| Abstract |
Researchers employing resource selection functions (RSFs) and other related methods aim to detect correlates of space-use and mitigate against detrimental environmental change. However, an empirical model fit to data from one place or time is unlikely to capture species responses under different conditions because organisms respond nonlinearly to changes in habitat availability. This phenomenon, known as a functional response in resource selection, has been debated extensively in the RSF literature but continues to be ignored by practitioners for lack of a practical treatment. We therefore extend the RSF approach to enable it to estimate generalized functional responses (GFRs) from spatial data. GFRs employ data from several sampling instances characterized by diverse profiles of habitat availability. By modeling the regression coefficients of the underlying RSF as functions of availability, GFRs can account for environmental change and thus predict population distributions in new environments. We formulate the approach as a mixed-effects model so that it is estimable by readily available statistical software. We illustrate its application using (1) simulation and (2) wolf home-range telemetry. Our results indicate that GFRs can offer considerable improvements in estimation speed and predictive ability over existing mixed-effects approaches. |
IMIS is ontwikkeld en wordt gehost door het VLIZ.
