Species-Habitat Associations: Spatial data, predictive models, and ecological insights
Matthiopoulos, J.; Fieberg, J.R.; Aarts, G.M (2020). Species-Habitat Associations: Spatial data, predictive models, and ecological insights. University of Minnesota Libraries Publishing: Minnesota. https://doi.org/10.24926/2020.081320
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| Author keywords |
Animal Movement; Migration Niche; Theory Point Process; Models Habitat; Selection Species; Distributions; Statistical models; Utilization Distributions |
| Authors | | Top |
- Matthiopoulos, J.
- Fieberg, J.R.
- Aarts, G.M, more
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| Abstract |
Ecologists develop species-habitat association (SHA) models to understand where species occur, why they are there and where else they might be. This knowledge can be used to designate protected areas, estimate anthropogenic impacts on living organisms and assess risks from invasive species or disease spill-over from wildlife to humans. Here, we describe the state of the art in SHA models, looking beyond the apparent correlations between the positions of organisms and their local environment. We highlight the importance of ecological mechanisms, synthesize diverse modelling frameworks and motivate the development of new analytical methods. Above all, we aim to be synthetic, bringing together several of the apparently disconnected pieces of ecological theory, taxonomy, spatiotemporal scales, and mathematical and statistical technique in our field. The first edition of this ebook reviews the ecology of species-habitat associations, the mechanistic interpretation of existing empirical models and their shared statistical foundations that can help us draw scientific insights from field data. It will be of interest to graduate students and professionals looking for an introduction to the ecological and statistical literature of SHAs, practitioners seeking to analyse their data on animal movements or species distributions and quantitative ecologists looking to contribute new methods addressing the limitations of the current incarnations of SHA models. |
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