Modelling invasion for a habitat generalist and a specialist plant species

Abstract

Predicting suitable habitat and the potential distribution of invasive species is a high priority for resource managers and systems ecologists. Most models are designed to identify habitat characteristics that define the ecological niche of a species with little consideration to individual species’ traits. We tested five commonly used modelling methods on two invasive plant species, the habitat generalist Bromus tectorum and habitat specialist Tamarix chinensis, to compare model performances, evaluate predictability, and relate results to distribution traits associated with each species. Most of the tested models performed similarly for each species; however, the generalist species proved to be more difficult to predict than the specialist species. The highest area under the receiver‐operating characteristic curve values with independent validation data sets of B. tectorum and T. chinensis was 0.503 and 0.885, respectively. Similarly, a confusion matrix for B. tectorum had the highest overall accuracy of 55%, while the overall accuracy for T. chinensis was 85%. Models for the generalist species had varying performances, poor evaluations, and inconsistent results. This may be a result of a generalist's capability to persist in a wide range of environmental conditions that are not easily defined by the data, independent variables or model design. Models for the specialist species had consistently strong performances, high evaluations, and similar results among different model applications. This is likely a consequence of the specialist's requirement for explicit environmental resources and ecological barriers that are easily defined by predictive models. Although defining new invaders as generalist or specialist species can be challenging, model performances and evaluations may provide valuable information on a species’ potential invasiveness.

Knowing the past to predict the future: land‐use change and the distribution of invasive bullfrogs

“…Habitat suitability models are often used for this task, with applications in risk assessment or in control strategies (e.g., Ward, 2007;Evangelista et al, 2008;Nielsen et al, 2008;Ficetola et al, 2009). However, long term historical data are seldom available (Loo et al, 2007;Vallecio et al, 2009), and it is therefore difficult to evaluate whether model predictions correspond to the actual invasion dynamics.…”

Accounting for residential propagule pressure improves prediction of urban plant invasion

“…As observed in Fig. 2a, invasive species with wide environmental tolerances will likely have a vast potential distribution predicted by models that consider only environmental factors and such large risk areas can overwhelm regional scale monitoring and control efforts (Evangelista et al 2008). This work has shown the potential for accounting for rFOI and/or nFOI to derisk areas of suitable habitat that have low propagule pressure, thereby enabling land managers to focus on a feasible number of forest locations for monitoring and control of invaders.…”

“…SDMs statistically relate species occurrences with environmental and/or climatic predictors in a geographic information system to predict the probability of species presence on the basis of habitat suitability (Elith and Leathwick 2009). This approach has been met with mixed success, as SDMs predict invader distribution much more accurately for species with narrow habitat requirements than those that possess wider environmental tolerances (Evangelista et al 2008). Furthermore, deriving a generalist invader's true ecological niche based solely on habitat suitability data can lead to SDMs with poor predictive performance due to variations in propagule pressure, which as we show below, can be conflated with suitability (Chytrý et al 2008).…”

Bringing Modeling to the Masses: A Web Based System to Predict Potential Species Distributions

“…Over the past few decades, statistical and technical capabilities have progressed to a level where we now have a variety of techniques for predicting the potential distribution of a given species [10][11][12][13]. These techniques use field observations of a species (i.e., presence-absence data or presence-only data) as a dependent variable, while independent or predictive variables represent environmental conditions (e.g., elevation, minimum temperature, mean monthly precipitation).…”