Understanding species distribution in dynamic populations: a new approach using spatio‐temporal point process models

Abstract

Understanding and predicting a species’ distribution across a landscape is of central importance in ecology, biogeography and conservation biology. However, it presents daunting challenges when populations are highly dynamic (i.e. increasing or decreasing their ranges), particularly for small populations where information about ecology and life history traits is lacking. Currently, many modelling approaches fail to distinguish whether a site is unoccupied because the available habitat is unsuitable or because a species expanding its range has not arrived at the site yet. As a result, habitat that is indeed suitable may appear unsuitable. To overcome some of these limitations, we use a statistical modelling approach based on spatio‐temporal log‐Gaussian Cox processes. These model the spatial distribution of the species across available habitat and how this distribution changes over time, relative to covariates. In addition, the model explicitly accounts for spatio‐temporal dynamics that are unaccounted for by covariates through a spatio‐temporal stochastic process. We illustrate the approach by predicting the distribution of a recently established population of Eurasian cranes Grus grus in England, UK, and estimate the effect of a reintroduction in the range expansion of the population. Our models show that wetland extent and perimeter‐to‐area ratio have a positive and negative effect, respectively, in crane colonisation probability. Moreover, we find that cranes are more likely to colonise areas near already occupied wetlands and that the colonisation process is progressing at a low rate. Finally, the reintroduction of cranes in SW England can be considered a human‐assisted long‐distance dispersal event that has increased the dispersal potential of the species along a longitudinal axis in S England. Spatio‐temporal log‐Gaussian Cox process models offer an excellent opportunity for the study of species where information on life history traits is lacking, since these are represented through the spatio‐temporal dynamics reflected in the model.

Disentangling drivers of spatial autocorrelation in species distribution models

“…count data). Furthermore, the key concept to disentangle drivers of spatial autocorrelation by using longitudinal data of multiple sampling events in the same environment can be transferred to many methods, including more advanced forms of Bayesian modelling (Kéry and Royle 2016) and dynamic range models (Soriano‐Redondo et al 2019). Finally, our concept is not limited to a single species, but could also be used in joint species distribution models (Pollock et al 2014, Lany et al 2019).…”

Incorporating time into the traditional correlational distributional modelling framework: A proof‐of‐concept using the Wood Thrush Hylocichla mustelina

“…Most studies applied a ‘seasonal’ modelling approach—modelling of a single facet of a species' life history (e.g. breeding or wintering) using time‐averaged approaches (Laube et al., 2015; Skov et al., 2016; Soriano‐Redondo et al., 2019). Fink et al.…”

Spatio-temporal point processes as meta-models for population dynamics in heterogeneous landscapes

“…The adaptation of our approach of defining a marked STPP meta-model may be relevant and insightful in various contexts. Examples are occurrence locations and times of earthquake epicentres (Lombardo et al, 2019), wildfires (Opitz et al, 2020), epidemiological outbreaks (White et al, 2018a), biodiversity hotspots and species distribution (Soriano-Redondo et al, 2019), pollutant concentrations (Lindström et al, 2014) or local maxima or minima in meteorological events (Heaton et al, 2011). In most ecological process space and time are closely intertwined and not separable as in our case, where pest introductions and subsequent peaks depend on local temporal dynamics driven by local spatial structure.…”