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Predicting the occurrence of nonindigenous species using environmental and remotely sensed data

Published online by Cambridge University Press:  20 January 2017

Lisa J. Rew ,
Bruce D. Maxwell  and
Richard Aspinall
Bruce D. Maxwell
Affiliation:
Department of Land Resources and Environmental Sciences, Montana State University, Bozeman, MT 59717
Richard Aspinall
Affiliation:
Geographic Information and Analysis Center, Montana State University, Bozeman, MT 59717
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Abstract

To manage or control nonindigenous species (NIS), we need to know where they are located in the landscape. However, many natural areas are large, making it unfeasible to inventory the entire area and necessitating surveys to be performed on smaller areas. Provided appropriate survey methods are used, probability of occurrence predictions and maps can be generated for the species and area of interest. The probability maps can then be used to direct further sampling for new populations or patches and to select populations to monitor for the degree of invasiveness and effect of management. NIS occurrence (presence or absence) data were collected during 2001 to 2003 using transects stratified by proximity to rights-of-way in the northern range of Yellowstone National Park. In this study, we evaluate the use of environmental and remotely sensed (LANDSAT Enhanced Thematic Mapper +) data, separately and combined, for developing probability maps of three target NIS occurrence. Canada thistle, dalmation toadflax, and timothy were chosen for this study because of their different dispersal mechanisms and frequencies, 5, 3, and 23%, respectively, in the surveyed area. Data were analyzed using generalized linear regression with logit link, and the best models were selected using Akaike's Information Criterion. Probability of occurrence maps were generated for each target species, and the accuracies of the predictions were assessed with validation data excluded from the model fitting. Frequencies of occurrence of the validation data were calculated and compared with predicted probabilities. Agreement between the observed and predicted probabilities was reasonably accurate and consistent for timothy and dalmation toadflax but less so for Canada thistle.

Keywords

Canada thistle, Cirsium arvense L. CIRARdalmation toadflax, Linaria dalmatica (L.) P. Mill. LINDAtimothy, Phleum pratense L. PHLPRGeneralized linear modelinvasive specieslogistic regressionnonnative speciespredictive mappingsurveystratified sampling
Type
Symposium
Information
Weed Science , Volume 53 , Issue 2 , April 2005 , pp. 236 - 241
Copyright
Copyright © Weed Science Society of America 

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