Dynamic species distribution models from categorical survey data. (25th July 2013) - Mieszkowska, Nova; Milligan, Gregg; Burrows, Michael T.; Freckleton, Rob; Spencer, Matthew
Summary: Species distribution models are static models for the distribution of a species, based on Hutchinson's niche concept. They make probabilistic predictions about the distribution of a species, but do not have a temporal interpretation. In contrast, density‐structured models based on categorical abundance data make it possible to incorporate population dynamics into species distribution modelling. Using dynamic species distribution models, temporal aspects of a species' distribution can be investigated, including the predictability of future abundance categories and the expected persistence times of local populations, and how these may respond to environmental or anthropogenic drivers. We built density‐structured models for two intertidal marine invertebrates, the Lusitanian trochid gastropods Phorcus lineatus and Gibbula umbilicalis, based on 9 years of field data from around the United Kingdom. Abundances were recorded on a categorical scale, and stochastic models for year‐to‐year changes in abundance category were constructed with winter mean sea surface temperature (SST) and wave fetch (a measure of the exposure of a shore) as explanatory variables. Both species were more likely to be present at sites with high SST, but differed in their responses to wave fetch. Phorcus lineatus had more predictable future abundance and longer expected persistence times than G. umbilicalis . This is consistent with the longer lifespan of P. lineatus . Where data from multiple time points are available, dynamic species distribution models of the kind described here have many applications in population and conservation biology. These include allowing for changes over time when combining historical and contemporary data, and predicting how climate change might alter future abundance conditional on current distributions. Abstract : In order to understand population dynamics, data from many locations at multiple time points are needed. It can be very expensive and time‐consuming to get precise estimates of abundance at such large scales. Instead, categorical abundance estimates (with categories such as "not seen", "rare", "occasional", ...) are often used. Here, the authors describe ways of extracting approximate information on long‐term population dynamics from data of this kind.