Model-based control of observer bias for the analysis of presence-only data in ecology

Presence-only data, where information is available concerning species presence but not species absence, are subject to bias due to observers being more likely to visit and record sightings at some locations than others (hereafter "observer bias"). In this paper, we describe and evaluate a model-based approach to accounting for observer bias directly - by modelling presence locations as a function of known observer bias variables (such as accessibility variables) in addition to environmental variables, then conditioning on a common level of bias to make predictions of species occurrence free of such observer bias. We implement this idea using point process models with a LASSO penalty, a new presence-only method related to maximum entropy modelling, that implicitly addresses the "pseudo-absence problem" of where to locate pseudo-absences (and how many). The proposed method of bias-correction is evaluated using systematically collected presence/absence data for 62 plant species endemic to the Blue Mountains near Sydney, Australia. It is shown that modelling and controlling for observer bias significantly improves the accuracy of predictions made using presence-only data, and usually improves predictions as compared to pseudo-absence or "inventory" methods of bias correction based on absences from non-target species. Future research will consider the potential for improving the proposed bias-correction approach by estimating the observer bias simultaneously across multiple species. © 2013 Warton et al

Using Ecological Modelling Tools to Inform Policy Makers of Potential Changes in Crop Distribution: An Example with Cacao Crops in Latin America

International audienceSpecies distribution models (SDM) is a powerful simulation tool that has become widely used in the ecological and agronomical sciences. The use of easily available presence data, global downscaled climate layers and software that can run on desktop computer has contributed to their popularity. The most used application is based on maximum entropy models that fit presence data to a series of environmental descriptors. SDM can be used to predict crop distribution under future conditions but the level of uncertainty of those models can be very high. The best use of these models is to be used as generators of hypothesis to be combined with other type of analysis