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1 October 2011 Examining Local Transferability of Predictive Species Distribution Models for Invasive Plants: An Example with Cogongrass (Imperata cylindrica)
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Abstract

Species distribution modeling is a tool that is gaining widespread use in the projection of future distributions of invasive species and has important potential as a tool for monitoring invasive species spread. However, the transferability of models from one area to another has been inadequately investigated. This study aimed to determine the degree to which species distribution models (SDMs) for cogongrass, developed with distribution data from Mississippi (USA), could be applied to a similar area in neighboring Alabama. Cogongrass distribution data collected in Mississippi were used to train an SDM that was then tested for accuracy and transferability with cogongrass distribution data collected by a forest management company in Alabama. Analyses indicated the SDM had a relatively high predictive ability within the region of the training data but had poor transferability to the Alabama data. Analysis of the Alabama data, via independent SDM development, indicated that predicted cogongrass distribution in Alabama was more strongly correlated with soil variables than was the case in Mississippi, where the SDM was most strongly correlated with tree canopy cover. Results suggest that model transferability is influenced strongly by (1) data collection methods, (2) landscape context of the survey data, and (3) variations in qualitative aspects of environmental data used in model development.

Nomenclature: Cogongrass, Imperata cylindrica (L.) Beauv. IMPCY.

Interpretive Summary: Early detection of, and rapid response to, new populations of invasive plants optimizes economic and environmental considerations for weed management, but efficient means of appropriately targeting monitoring sites is a critical missing component to such an approach. Species distribution modeling is a tool that could enhance detection efforts for invasive weeds during the earliest stages of spread into new geographic areas. This approach was tested within a geographic information system (GIS) framework, using data on locations of cogongrass in southeastern Mississippi and southwestern Alabama to determine the reliability of models and their transferability among study areas. Initial model development agreed with prior work on cogongrass habitat within the southeastern United States, in terms of the environmental variables found to be important, but there was weak transferability of models between the two study areas. Two potential explanations for this low transferability were that the data were collected in different portions of the landscape in the two areas (along road corridors in Mississippi but throughout commercially managed forests in Alabama) and that there seemed to be some incongruence in soil data within and between study areas. This suggests that, although relatively robust SDMs can be developed for a given area, those models should not be extrapolated to other areas without careful examination of the underlying environmental predictors. Nevertheless, results do suggest that within an individual area of interest and with proper care, modeling approaches such as that used in this study have potential to contribute to efforts at developing scientifically and statistically based weed management programs for invasive species.

Weed Science Society of America
Gary N Ervin and D. Christopher Holly "Examining Local Transferability of Predictive Species Distribution Models for Invasive Plants: An Example with Cogongrass (Imperata cylindrica)," Invasive Plant Science and Management 4(4), 390-401, (1 October 2011). https://doi.org/10.1614/IPSM-D-10-00077.1
Received: 7 November 2010; Accepted: 1 July 2011; Published: 1 October 2011
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