Predicting Distributions of Invasive Species

Abstract

Note to reader: This chapter was primarily written in 2011 and will be published in a book,(due to be published in 2016). This can be cited according to its arXiv reference. This is the second version I have submitted to arXiv, with relatively minor changes mostly to wording. Predicting distributions of invasive speciesJane Elith School of BioSciences, The University of Melbourne 3010. Australia j.elith@unimelb.edu.au AbstractThis chapter aims to inform a practitioner about current methods for predicting potential distributions of invasive species. It mostly addresses single species models, covering the conceptual bases, touching on mechanistic models, and then focusing on methods using species distribution records and environmental data to predict distributions. The commentary in this last section is oriented towards key issues that arise in fitting, and predicting with, these models (which include CLIMEX, MaxEnt and other regression methods). In other words, it is more about the process of thinking about the data and the modelling problem (which is a challenging one) than it is about one technique versus another. The discussion helps clarify the necessary steps and expertise for predicting distributions. Some researchers are optimistic that correlative models will predict with high precision; while that may be true for some species at some scales of evaluation, I believe that the issues discussed in this chapter show that substantial errors are reasonably likely. I am hopeful that ongoing developments will produce models better suited to the task and tools to help practitioners to better understand predictions and their uncertainties.

Zonation of Vegetation Related to Penguin Rookeries on King George Island, Maritime Antarctic

“…The random effect term b i is used to account for unstructured over-dispersion (OD) and is typically taken to be independently identically distributed with variance 2 , and follows a normal distribution. Explanatory variables have been centered by subtracting the mean values and standardized by dividing the standard deviation to improve the efficiency of the Markov Chain Monte Carlo (MCMC) algorithm (Elith, 2002).…”

Explicitly integrating a third dimension in marine species distribution modelling

“…Using ocean depth as a predictor variable may, however, prove problematic because depth is a distal predictor, which correlates inconsistently with causative predictors (e.g. temperature, salinity) across large geographic ranges (Elith 2015, see also McArthur et al 2010. In contrast, other variables derived from bathymetry, such as seabed rugosity and slope (see Lecours et al 2016 for a comprehensive review of marine geomorphometry), can improve models of habitat suitability for sessile benthic organisms as submarine topography is expected to Studies are classified as using only surface variables (e.g.…”

Invasion stages and potential distributions of seven exotic terrestrial isopods in Japan

“…These results implied that these species have not yet filled all suitable environments in Japan and that there is a risk that their distribution areas will spread further in the future. The results also indicated that combined data from native and introduced regions were useful for estimating the potential distributions of these exotic species in the invaded region (Le Maitre et al 2008, Elith 2015, Ray et al 2016). The present study evaluated the invasive stages of these species in Japan by applying the theoretical framework of Gallien et al (2012), which has been successfully used to evaluate the invasive stages of some exotic species (Kumar et al 2015, Zhu et al 2017.…”