Using centroids of spatial units in ecological niche modelling: Effects on model performance in the context of environmental data grain size

Aim Ecological niche models (ENMs) typically require point locations of species' occurrence as input data. Where exact locations are not available, geographical centroids of the respective administrational spatial units (ASUs) are often used as a substitute. We investigated how the use of ASU centroids in ENMs affects model performance, what role the size of ASUs plays, and what effects different grain sizes of explanatory variables have. Location Europe. Major taxa studied Virtual species. Methods We set up a two-factorial study design with artificial ASUs of three different sizes and environmental data of four commonly used grain sizes, repeated over three study regions. To control other factors that may affect ENM performance, we created a virtual species with a known response to environmental variables, precise and even sampling and a known spatial distribution. We ran a series of Maxent models for the virtual species based on centroids and precise occurrence locations under varying ASU and grain sizes. Results The use of ASU centroids introduces a value frequency mismatch of the explanatory variables between centroids and true occurrence locations, and it has a negative effect on ENM performance. Value frequency mismatch, negative effect on ENM performance and over-prediction of the species' range all increase with ASU size. The effect of grain size of environmental data, on the contrary, was small in comparison. Main conclusions ENMs built upon ASU centroids can suffer considerably from the introduced error. For ASUs that are sufficiently small or show low spatial heterogeneity of explanatory variables, ASU centroids can still be a viable and convenient surrogate for precise occurrence locations. When possible, however, central tendency values (median, mean) that represent the whole ASU rather than just a single point location need to be considered.

Automated summary

The use of ASU centroids in ENMs can lead to a mismatch in the explanatory variables and have a negative impact on model performance. This negative effect and over-prediction of species' range increase with ASU size, while the effect of grain size of environmental data is relatively small.