Abstract
Research has shown that varying spatial scale through the selection of the total extent of investigation and the grain size of environmental predictor variables has effects on species distribution model (SDM) results and accuracy, but there has been minimal investigation into the interactive effects of extent and grain. To do this, we used a consistently sampled range-wide dataset of giant panda occurrence across southwest China and modeled their habitat and distribution at 4 extents and 7 grain sizes. We found that increasing grain size reduced model accuracy at the smallest extent, but that increasing extent negated this effect. Increasing extent also generally increased model accuracy, but the models built at the second-largest (mountain range) extent were more accurate than those built at the largest, geographic range-wide extent. When predicting habitat suitability in the smallest nested extents (50 km2), we found that the models built at the next-largest extent (500 km2) were more accurate than the smallest-extent models but that further increases in extent resulted in large decreases in accuracy. Overall, this study highlights the impacts of the selection of spatial scale when evaluating species’ habitat and distributions, and we suggest more explicit investigations of scale effects in future modeling efforts.
Highlights
Research has shown that varying spatial scale through the selection of the total extent of investigation and the grain size of environmental predictor variables has effects on species distribution model (SDM) results and accuracy, but there has been minimal investigation into the interactive effects of extent and grain
Empirical investigations of the effects of spatial scale are rare, and mainly involve varying the “local extent” surrounding a species or individual location within which environmental data are averaged[4,6]. While this may constitute an effective way to study the effects of spatial scale on species distributions and habitat selection, the effect of varying the local extent itself is constrained by both the grain size of the included environmental variables and the total extent of the study area
A deeper analysis of the interactive effects of the total extent and grain size of environmental variables on habitat and species distribution modeling is crucial, since the range of scales used to model habitat or species distributions can vary considerably, with potentially important effects on model accuracy and sensitivity. To address these knowledge gaps, here we evaluate the effects of simultaneously changing the grain size and total extent, as well as the use of different environmental predictor variables, on habitat suitability and species distribution modeling of a demonstration species, the giant panda (Ailuropoda melanoleuca)
Summary
Research has shown that varying spatial scale through the selection of the total extent of investigation and the grain size of environmental predictor variables has effects on species distribution model (SDM) results and accuracy, but there has been minimal investigation into the interactive effects of extent and grain. Empirical investigations of the effects of spatial scale are rare, and mainly involve varying the “local extent” surrounding a species or individual location within which environmental data are averaged[4,6] While this may constitute an effective way to study the effects of spatial scale on species distributions and habitat selection, the effect of varying the local extent itself is constrained by both the grain size of the included environmental variables and the total extent of the study area. One of the main reasons behind the usage of varying spatial scales between studies is that the goals of habitat modeling for a species can vary widely, ranging from investigations on the within-home range habitat selection of individual animals[9] to the delineation of a species’ entire distribution[10] These goals directly relate with Johnson’s (1980) four orders of habitat selection, which in descending order include geographic range, home range, differential space use within the home range, and the final procurement of resources[11]. The potential tradeoffs in model performance that occur with the aggregation of some variables to allow for the addition of others, and how this interacts with changes in the extent of analysis, is another scale-related topic that has been understudied
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