Modeling spatially varying landscape change points in species occurrence thresholds
by T. Wagner and S. Miday, Abstract. Predicting species distributions at scales of regions to continents is often necessary, as largescale
phenomena influence the distributions of spatially structured populations. Land use and land cover
are important large-scale drivers of species distributions, and landscapes are known to create species
occurrence thresholds, where small changes in a landscape characteristic results in abrupt changes in
occurrence. The value of the landscape characteristic at which this change occurs is referred to as a change
point. We present a hierarchical Bayesian threshold model (HBTM) that allows for estimating spatially
varying parameters, including change points. Our model also allows for modeling estimated parameters in
an effort to understand large-scale drivers of variability in land use and land cover on species occurrence
thresholds. We use range-wide detection/nondetection data for the eastern brook trout (Salvelinus
fontinalis), a stream-dwelling salmonid, to illustrate our HBTM for estimating and modeling spatially
varying threshold parameters in species occurrence. We parameterized the model for investigating
thresholds in landscape predictor variables that are measured as proportions, and which are therefore
restricted to values between 0 and 1. Our HBTM estimated spatially varying thresholds in brook trout
occurrence for both the proportion agricultural and urban land uses. There was relatively little spatial
variation in change point estimates, although there was spatial variability in the overall shape of the
threshold response and associated uncertainty. In addition, regional mean stream water temperature was
correlated to the change point parameters for the proportion of urban land use, with the change point value
increasing with increasing mean stream water temperature. We present a framework for quantify
macrosystem variability in spatially varying threshold model parameters in relation to important largescale
drivers such as land use and land cover. Although the model presented is a logistic HBTM, it can
easily be extended to accommodate other statistical distributions for modeling species richness or
abundance.
Publication Date: 2014
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