Abstract
Species distribution models (SDMs) trained on presence-only data are frequently used in ecological research and conservation planning. However, users of SDM software are faced with a variety of options, and it is not always obvious how selecting one option over another will affect model performance. Working with MaxEnt software and with tree fern presence data from New Zealand, we assessed whether (a) choosing to correct for geographical sampling bias and (b) using complex environmental response curves have strong effects on goodness of fit. SDMs were trained on tree fern data, obtained from an online biodiversity data portal, with two sources that differed in size and geographical sampling bias: a small, widely-distributed set of herbarium specimens and a large, spatially clustered set of ecological survey records. We attempted to correct for geographical sampling bias by incorporating sampling bias grids in the SDMs, created from all georeferenced vascular plants in the datasets, and explored model complexity issues by fitting a wide variety of environmental response curves (known as “feature types” in MaxEnt). In each case, goodness of fit was assessed by comparing predicted range maps with tree fern presences and absences using an independent national dataset to validate the SDMs. We found that correcting for geographical sampling bias led to major improvements in goodness of fit, but did not entirely resolve the problem: predictions made with clustered ecological data were inferior to those made with the herbarium dataset, even after sampling bias correction. We also found that the choice of feature type had negligible effects on predictive performance, indicating that simple feature types may be sufficient once sampling bias is accounted for. Our study emphasizes the importance of reducing geographical sampling bias, where possible, in datasets used to train SDMs, and the effectiveness and essentialness of sampling bias correction within MaxEnt.
Highlights
Species distribution models (SDMs), which predict a species’ probability of occurrence across a landscape by relating documented locations of that species to environmental information, are frequently used in ecological, environmental and climate change research [1,2,3,4,5]
Correcting bias in the herbarium and National Vegetation Survey databank (NVS) datasets led to dramatic increases in Area Under the Curve (AUC) and COR values when model predictions were compared with observed tree fern presences and absences in the independent Land Use and Carbon Analysis System (LUCAS) dataset (Table 1)
Correcting for geographical sampling bias approximately halved the false-absence and false-presence error rates of distribution maps predicted with the NVS dataset (Table 2; Figure S1), and approximately halved the false absence rate of distribution maps predicted with the herbarium dataset, paradoxically the false presence rate increased following the correction (Table 2)
Summary
Species distribution models (SDMs), which predict a species’ probability of occurrence across a landscape by relating documented locations of that species to environmental information, are frequently used in ecological, environmental and climate change research [1,2,3,4,5]. There is a ready supply of environmental information, including global databases of climate and digital elevation models [9] and user-friendly software packages. These technological advances mean that, as never before, SDMs are being used in ecological research and conservation planning. This paper explores the consequences of correcting for geographical sampling bias and non-automatically selecting model functional forms on the predictive ability of MaxEnt, one of the best performing species distribution modelling techniques for analysis of presence-only data [10,11,12,13]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
