The effects of sampling bias and model complexity on the predictive performance of MaxEnt species distribution models.

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

SUBMITTER: Syfert MM 

PROVIDER: S-EPMC3573023 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature