Dynamic species distribution models from categorical survey data

Abstract

Summary1. 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. 2. 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. 3. 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. 4. 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. 5. 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.

Establishing the link between habitat selection and animal population dynamics

“…Our synthesis can lead to joint inference on spatiotemporal data, yielding parameterized population models that can be used for forecasts outside the range of observed scenarios. While sharing with other recent work (Mieszkowska et al 2013) the motivation of confronting spatiotemporal dynamics with data empirically, our model's mechanistic component is greater and more expandable. Further, rather than requiring geo-referenced data on growth (in the form of spatial layers for survival and fecundity, e.g., Aldridge and Boyce [2008], DeCesare et al [2014]), we fit to nonspatial population time series, such as those available from long-term monitoring studies (e.g., Saether 1997, Gaillard et al 1998, Brook et al 2000, Parmesan and Yohe 2003, Stuart et al 2004, Strayer et al 2006.…”

“…While many ecological questions on species distribution are motivated by population dynamics (e.g., viability of fragmented populations, spatial management of pests, species range shifts), an explicit connection between observed distributions and dynamics is rarely pursued (Railsback et al 2003, Guisan and Thuiller 2005, Zurell et al 2009, Gaillard et al 2010, McLoughlin et al 2010, Mieszkowska et al 2013. This, and other broadly recognized issues with SDMs have thus far been investigated with literature reviews, or comparative studies between existing frameworks that, in their majority, make the assumption that populations are at a state of equilibrium (Guisan and Zimmermann 2000, Arau´jo and Guisan 2006, Randin et al 2006, Elith and Graham 2009, Elith and Leathwick 2009, Zurell et al 2009, Hoffman et al 2010, Matthiopoulos and Aarts 2010.…”

Historical comparisons reveal multiple drivers of decadal change of an ecosystem engineer at the range edge

“…It is difficult to say whether this increase and new colonization is a direct result of warming temperatures, but the pattern is certainly in line with observations for a range of other intertidal and fish species in the region (e.g., Mieszkowska et al. 2006, 2013; Hiddink and ter Hofstede 2008; Firth et al. 2009; Hawkins et al.…”

“…2009, 2013a; Mieszkowska et al. 2013). For example, the extremely cold winter of 1962/1963 had a dramatic effect on marine organisms in Britain with widespread population decreases and localized extinctions (Crisp 1964).…”

Large brown seaweeds of the British Isles: Evidence of changes in abundance over four decades

“…Although this study examines average climatic conditions, extreme events may be driving some are temporally biased and the median difference between observations is 11 years, which means we cannot replicate analyses such as those relying on year on-year transitional models (Mieszkowska et al 2013). Methodological advances are required in order to extract the full potential from these data.…”