Models of presence-only data produce spatially explicit suitability surfaces that represent habitat suitability ( Elith et al. from atlas records) and biophysical variables can be used to fit predictive ‘niche-based models’ of distribution using numerous methods ( Elith et al. The first step is to distinguish ‘suitable’ from ‘unsuitable’ habitat, and two general approaches have been used to do this. for recent and/or slow invaders Ward 2007 Phillips, Chipperfield & Kearney 2008), methods are required for discriminating suitable habitat that is occupied from that which is unoccupied. Since the distributions of many established invasive plants and animals may be much smaller than their maximum distributions (e.g. However, methods for differentiating the current and potential distributions of established invasive species have received little attention. Predicting and quantifying the current and potential distributions of established invasive species is a critical step in evaluating management options: for example, control and eradication efforts should focus on the current distribution, containment should focus on the interface between the current and potential distributions, and incursion monitoring should focus on the potential distribution ( Myers et al. 2006) and there is much interest in managing these populations ( Myers et al.
Invasive species can have important detrimental environmental, economic and social impacts ( Mack et al. Managers can then focus control and/or containment actions within the current distribution and establish surveillance monitoring to detect incursions within the potential distribution. Synthesis and applications.Our framework enables managers to robustly estimate the current and potential distributions of established invasive species using either presence-only and/or presence–absence data.
Several discrete areas of potential distribution were identified as priorities for surveillance monitoring with the aim of detecting and managing incursions of sambar deer.ĥ. The current distribution was the suitable habitat within a kernel that had a 99♵% chance of including the presence locations pooled from incidental sightings and field surveys: the potential distribution was suitable habitat outside that kernel. The congruence of the two models suggests that any spatial and detection biases in the presence-only data were relatively unimportant in our study.Ĥ.We predicted the extent of suitable habitat from the occupancy model using a threshold that gave a false negative error rate of 0♰5. A Bayesian state-space occupancy model was used to predict probability of suitable habitat from the field data.ģ.The Maxent and occupancy models predicted similar spatial distributions of habitat suitability for sambar deer in Victoria and there was a strong positive correlation between the rankings of cells by the two approaches. Field sampling, consisting of faecal pellet surveys, sign surveys and camera trapping, was conducted in 80 4-km 2 grid cells. We then used that model to stratify field sampling, with proportionately greater sampling of cells with high predicted habitat suitability. This study compares the two approaches, and develops a unifying framework, using the case of invasive sambar deer Cervus unicolor in Victoria, Australia.Ģ.We first used 391 incidental sightings of sambar deer and 12 biophysical variables to construct a presence-only habitat suitability model using Maxent. In particular, there is uncertainty among invasive species managers about the value of information from incidental sightings compared to data from designed field surveys. 1.Predicting the current and potential distributions of established invasive species is critical for evaluating management options, but methods for differentiating these distributions have received little attention.
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