Abstract

Summary To generate realistic predictions, species distribution models require the accurate coregistration of occurrence data with environmental variables. There is a common assumption that species occurrence data are accurately georeferenced; however, this is often not the case. This study investigates whether locational uncertainty and sample size affect the performance and interpretation of fine‐scale species distribution models. This study evaluated the effects of locational uncertainty across multiple sample sizes by subsampling and spatially degrading occurrence data. Distribution models were constructed for kelp (Ecklonia radiata), across a large study site (680 km2) off the coast of southeastern Australia. Generalized additive models were used to predict distributions based on fine‐resolution (2·5 m cell size) seafloor variables, generated from multibeam echosounder data sets, and occurrence data from underwater towed video. The effects of different levels of locational uncertainty in combination with sample size were evaluated by comparing model performance and predicted distributions. While locational uncertainty was observed to influence some measures of model performance, in general this was small and varied based on the accuracy metric used. However, simulated locational uncertainty caused changes in variable importance and predicted distributions at fine scales, potentially influencing model interpretation. This was most evident with small sample sizes. Results suggested that seemingly high‐performing, fine‐scale models can be generated from data containing locational uncertainty, although interpreting their predictions can be misleading if the predictions are interpreted at scales similar to the spatial errors. This study demonstrated the need to consider predictions across geographic space rather than performance alone. The findings are important for conservation managers as they highlight the inherent variation in predictions between equally performing distribution models, and the subsequent restrictions on ecological interpretations.

Highlights

  • Species distribution models (SDMs) have been used widely in biogeography to characterize the ecological niche of species and to predict the geographic distribution of their habitat (Elith et al 2006; Araujo & Peterson 2012)

  • This study evaluated the effects of locational uncertainty across multiple sample sizes by subsampling and spatially degrading occurrence data

  • Generalized additive models were used to predict distributions based on fine-resolution (2Á5 m cell size) seafloor variables, generated from multibeam echosounder data sets, and occurrence data from underwater towed video

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Summary

Introduction

Species distribution models (SDMs) have been used widely in biogeography to characterize the ecological niche of species and to predict the geographic distribution of their habitat (Elith et al 2006; Araujo & Peterson 2012) Despite their increasing use, SDMs pose many conceptual problems (Jimenez-Valverde, Lobo & Hortal 2008; Soberon & Nakamura 2009) and encompass many methodological uncertainties (Barry & Elith 2006; Heikkinen et al 2006; Rocchini et al 2011). In a recent study, Rattray et al (2014) quantified the propagated error associated with each component of underwater camera positioning (a technique commonly used to collect observation data in marine ecosystems) They found a linear increase in location error with camera depth, equating to a 1Á5 m horizontal error near the surface and 5Á7 m error at a depth of 100 m. This suggests that the maximum error in location of a species observation may often exceed the resolution of the predictor data sets, and, locational uncertainty remains an issue with data sets collected using modern positioning systems

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