NDLI: Geographic selection bias of occurrence data influences transferability of invasive Hydrilla verticillata distribution models

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Geographic selection bias of occurrence data influences transferability of invasive Hydrilla verticillata distribution models

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Geographic selection bias of occurrence data influences transferability of invasive Hydrilla verticillata distribution models

Content Provider	PubMed Central
Author	Barnes, Matthew A. Jerde, Christopher L. Wittmann, Marion E. Chadderton, W. Lindsay Ding, Jianqing Zhang, Jialiang Purcell, Matthew Budhathoki, Milan Lodge, David M.
Copyright Year	2014
Abstract	Due to socioeconomic differences, the accuracy and extent of reporting on the occurrence of native species differs among countries, which can impact the performance of species distribution models. We assessed the importance of geographical biases in occurrence data on model performance using Hydrilla verticillata as a case study. We used Maxent to predict potential North American distribution of the aquatic invasive macrophyte based upon training data from its native range. We produced a model using all available native range occurrence data, then explored the change in model performance produced by omitting subsets of training data based on political boundaries. We also compared those results with models trained on data from which a random sample of occurrence data was omitted from across the native range. Although most models accurately predicted the occurrence of H. verticillata in North America (AUC > 0.7600), data omissions influenced model predictions. Omitting data based on political boundaries resulted in larger shifts in model accuracy than omitting randomly selected occurrence data. For well-documented species like H. verticillata, missing records from single countries or ecoregions may minimally influence model predictions, but for species with fewer documented occurrences or poorly understood ranges, geographic biases could misguide predictions. Regardless of focal species, we recommend that future species distribution modeling efforts begin with a reflection on potential spatial biases of available occurrence data. Improved biodiversity surveillance and reporting will provide benefit not only in invaded ranges but also within under-reported and unexplored native ranges.
Related Links	http://dx.doi.org/10.1002/ece3.1120
Ending Page	2593
Page Count	10
Starting Page	2584
File Format	PDF
ISSN	20457758
e-ISSN	20457758
Journal	Ecology and Evolution
Issue Number	12
Volume Number	4
Language	English
Publisher	Blackwell Publishing Ltd
Publisher Date	2014-06-01
Access Restriction	Open
Rights Holder	Blackwell Publishing Ltd
Subject Keyword	Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Ecology Ecology, Evolution, Behavior and Systematics Nature and Landscape Conservation

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Regional and local factors interact to shape colonization and extinction dynamics of invasive Hydrilla verticillata in a patchy landscape (Dataset)

Data from: High invasion potential of Hydrilla verticillata in the Americas predicted using ecological niche modeling combined with genetic data (Dataset)

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Geographic selection bias of occurrence data influences transferability of invasive Hydrilla verticillata distribution models

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Geographic selection bias of occurrence data influences transferability of invasive Hydrilla verticillata distribution models