Landscape Ecology

, Volume 33, Issue 2, pp 257–274 | Cite as

Modeling relative habitat suitability of southern Florida for invasive Burmese pythons (Python molurus bivittatus)

  • Holly E. Mutascio
  • Shannon E. Pittman
  • Patrick A. Zollner
  • Laura E. D’Acunto
Research Article



Invasive Burmese pythons are altering the ecology of southern Florida and their distribution is expanding northward. Understanding their habitat use is an important step in understanding the pathways of the invasion.


This study identifies key landscape variables in predicting relative habitat suitability for pythons at the present stage of invasion through presence-only ecological niche modeling using geographical sampling bias correction.


We used 2014 presence-only observations from the EDDMapS database and three landscape variables to model habitat suitability: fine-scale land cover, home range-level land cover, and distance to open freshwater or wetland. Ten geographical sampling bias correction scenarios based on road presence and sampling effort were evaluated to improve the efficacy of modeling.


The best performing models treated road presence as a binary factor rather than a continuous decrease in sampling effort with distance from roads. Home range-level cover contributed the most to the final prediction, followed by proximity to water and fine-scale land cover. Estuarine habitat and freshwater wetlands were the most important variables to contribute to python habitat suitability at both the home range-level and fine-scale. Suitability was highest within 30 m of open freshwater and wetlands.


This study provides quantifiable, predictive relationships between habitat types and python presence at the current stage of invasion. This knowledge can elucidate future targeted studies of python habitat use and behavior and help inform management efforts. Furthermore, it illustrates how estimates of relative habitat suitability derived from MaxEnt can be improved by both multi-scale perspectives on habitat and consideration of a variety of bias correction scenarios for selecting background points.


Burmese pythons Ecological niche modeling Invasive species Landscape-level habitat MaxEnt Python molurus bivittatus Southern Florida 



We thank R. Snow, C. Bargeron, P. Andreadis, I. Bartoszek, and C. Ervin for providing data and insight into the development of the model. We thank J.D. Willson and S. Fei for comments that greatly improved this manuscript. This work was partially funded by a Purdue University Knox Fellowship grant to H. Mutascio and the National Science Foundation Postdoctoral Fellowship in Biology Program Grant No. 1309144. We also thank The McIntire-Stennis Cooperative Forestry Research Program for their financial support.

Supplementary material

10980_2017_597_MOESM1_ESM.docx (450 kb)
Supplementary material 1 (DOCX 450 kb)


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Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2017

Authors and Affiliations

  • Holly E. Mutascio
    • 1
  • Shannon E. Pittman
    • 2
  • Patrick A. Zollner
    • 1
  • Laura E. D’Acunto
    • 1
  1. 1.Department of Forestry and Natural ResourcesPurdue UniversityWest LafayetteUSA
  2. 2.Department of BiologyDavidson CollegeDavidsonUSA

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