Implementing and interpreting local-scale invasive species distribution models

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 distri...

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Bibliographic Details
Published in:	Diversity & distributions 2013-08, Vol.19 (8), p.919-932
Main Authors:	Brummer, Tyler J., Maxwell, Bruce D., Higgs, Megan D., Rew, Lisa J.
Format:	Article
Language:	eng
Subjects:	Animal, plant and microbial ecology Applied ecology BIODIVERSITY RESEARCH Biological and medical sciences Biological invasions Conservation, protection and management of environment and wildlife drivers of invasion Ecological invasion Ecological modeling Flowers & plants Fundamental and applied biological sciences. Psychology General aspects General aspects. Techniques generalized linear model landscape-scale predictions Methods and techniques (sampling, tagging, trapping, modelling...) Modeling non-native plants Nonnative species Parks, reserves, wildlife conservation. Endangered species: population survey and restocking Plant ecology Plant populations Plants Predictive modeling Random sampling Renewable energy Sample size sampling design Simulations Species
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Summary:	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.
ISSN:	1366-9516 1472-4642