Implementing and interpreting local‐scale invasive species distribution models

Abstract

Aim Use of local‐scale non‐native plant species (NNS) distribution models has the potential to decrease survey effort and improve population prioritization for management. We developed and evaluated data collection methods and minimum sampling requirements to inform local‐scale models of NNS distribution. We also evaluated overall model predictive performance for 16 species at two sites and determined how classes of variables contributed to model performance and suggest invasion drivers. Location Wyoming and Idaho, USA Methods A simulation study was used to test the efficiency of different sampling methods to predict imposed species distributions. Empirical distribution models of species occurrence data from two environmentally disparate sites were cross‐validated at increasing sample sizes, and the asymptotic maximum predictive performance and relative contribution of classes of variables were determined for 16 NNS. Results Transect sampling was the most efficient method for maximizing model performance after accounting for logistics. Minimum sample sizes to reach model maximum predictive performance were similar for the simulation (< 0.5% of study area) and empirical studies (mean of 0.13% using transects). Maximum predictive performance tended to be greater at the site with steeper environmental gradients, and topo‐climatic/biotic variables were most important to model improvement. Main conclusions Local‐scale SDMs can be useful to NNS managers. Using transect methodology, enough data can be collected (ca. 0.13% of the management area) to fit models within logistical/budgetary constraints. These models are most predictive for well‐established species as opposed to new invaders and in areas with steeper environmental gradients. Finally, topo‐climatic/biotic predictors are the most important variables for predicting more established species, but disturbance and dispersal limitation should be considered and quantified to ensure variables associated with dominant processes are included in the SDM.

A standardized set of metrics to assess and monitor tree invasions

“…Species distribution models (SDMs) provide a good first estimate of potential distribution (Thuiller et al 2005) and provide significant value for management, though the temp-tation to overstate the meaning of the quantitative results needs to be tempered by the various method-ological and theoretical limitations to the approach Nunez and Medley 2011). Consequently, we recommend using a climate-based SDM as a first approximation of whether naturaliza-tion might be limited by physiology, but fine-scaled distribution predictions linked to probability of occurrence models are likely to be more useful for on-ground management (Brummer et al 2013;Kaplan et al 2014;Rew et al 2006). Conservation assessments, however, usually base abundance on the numbers of individuals and how this number changes with time.…”

Adaptive invasive species distribution models: a framework for modeling incipient invasions

“…For SDMs applied specifically to invasive species, the term invasive species distribution model(s) (iSDM) has emerged (Table 1; Vaclavik and Meentemeyer 2009;Gallien et al 2012;Brummer et al 2013). Using iSDMs to estimate sets of environmental conditions under which invasive species may establish and spread will increase management efficacy and efficiency by helping prevent invasions and/or minimize their spread and detrimental effects in vulnerable locations (Peterson 2003).…”

“…Other species with assumed stable geographic ranges may be capable of future expansions as local conditions change (Hill et al 2012) or niche infilling unfolds (Webber et al 2012;Bradley et al 2015). In general, the closer an invasive species is to environmental equilibrium, the more accurate and robust models developed for it are likely to be Brummer et al 2013), although effects may differ between spatial scales (Bradley et al 2015). Mechanistic, process-based approaches to iSDM development, which emphasize physiological limitations and other fundamental constraints on organism distribution and abundance, can be useful under disequilibria (Pearson and Dawson 2003); however, care must be taken to ensure other model assumptions are satisfied.…”

“…Obvious requirements associated with the development of realistic simulations are extensive ecological, environmental, and technical knowledge (Austin 2002). Examples of the application of simulations to invasive species include: testing alternative sampling methods (Brummer et al 2013); predicting persistence and spread following disturbance (Smolik et al 2010;Brown et al 2013;Shackelford et al 2013); identifying source populations (Stevenson et al 2012); testing for Allee effects (Leung et al 2004); evaluating the roles of propagule and colonization pressure (Lockwood et al 2009); and evaluating the efficacy of ecosystem-scale management (Marvier et al 2004). …”

“…Numerous biotic, abiotic, resource, and non-resource variables may be important for predicting the distributions of well-established invasive species, whereas geographic location and disturbance frequency and intensity may be most useful for making predictions about recently-introduced invasives (Marvier et al 2004;Guisan and Thuiller 2005;Brummer et al 2013). Similarly, environmental influences on sessile species or mobile species with small home ranges may be easier to quantify than those on mobile species with large home ranges (Guisan et al 2007;Elith and Leathwick 2009), just as predictions for narrowly-distributed specialists may be more accurate than those for wide-ranging generalists (Hernandez et al 2006;McPherson and Jetz 2007;Tsoar et al 2007)-among which are some of the most successful invaders (Marvier et al 2004).…”