A comparison of landscape fragmentation analysis programs for identifying possible invasive plant species locations in forest edge
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When predicting locations of invasive plant species, mapping habitat fragmentation can be an important part of the prediction process. There are many different fragmentation mapping programs, each computing a unique set of fragmentation metrics that can be used in modeling probabilities of invasive species presence.
In this study, we compare the results from five freely available fragmentation programs: FRAGSTATS; the Landscape Fragmentation Tool; Shape Metrics; Patch Analyst; and PolyFrag. We compare these programs quantitatively on their ability to predict invasive plant presence and qualitatively for ease of use.
The programs were compared using invasive plant inventories completed by The Nature Conservancy on parcels within the Coastal Watershed in New Hampshire, USA. Known locations of invasive plants, pseudo-absence locations, and metrics derived from each of the fragmentation programs were used to create maps of predicted presence for the parcels. The maps were compared and assessed for accuracy.
FRAGSTATS and PolyFrag created prediction maps with the highest accuracies and were relatively easy to use. The other programs had lower accuracies or were more difficult to implement. Both FRAGSTATS and PolyFrag compute similar fragmentation metrics and the models found similar metrics significant in predicting presence. Both programs predicted that invasive plants were less likely to be found in deciduous forests than in either mixed or coniferous forests.
At the parcel level, some fragmentation programs result in metrics with more predictive power. Based on this analysis, we recommend FRAGSTATS for use with raster datasets and PolyFrag for vector datasets.
KeywordsAccuracy assessment Edge Fragmentation FRAGSTATS Invasive plants Landscape metrics New England PolyFrag Raster Vector
The authors would like to thank the anonymous reviewers for their helpful suggestions and insightful comments that greatly improved our manuscript. Partial funding was provided by the New Hampshire Agricultural Experiment Station. This is Scientific Contribution Number 2592. This work was also supported by the USDA National Institute of Food and Agriculture McIntire-Stennis Project 0225003, as well as in part by Grant/Cooperative Agreement Number 08HQGR0157 from the United States Geological Survey via a sub-award from AmericaView. The contents of this manuscript are solely the responsibility of the authors and do not necessarily represent the official views of the USGS.
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