Optimizing survey conditions for Burmese python detection and removal using community science data
Abstract Burmese pythons (Python bivittatus) have demonstrated prolific spread and low detectability within their invasive range in Florida, USA. Consequently, programs exist which incentivize contractors to remove pythons. While surveying, contractors collect data on search effort and python captur...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-84641-4 |
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| author | Kelly R. McCaffrey Melissa A. Miller Sergio A. Balaguera-Reina Alexander S. Romer Michael Kirkland Amy Peters Edward F. Metzger LeRoy Rodgers Frank J. Mazzotti |
| author_facet | Kelly R. McCaffrey Melissa A. Miller Sergio A. Balaguera-Reina Alexander S. Romer Michael Kirkland Amy Peters Edward F. Metzger LeRoy Rodgers Frank J. Mazzotti |
| author_sort | Kelly R. McCaffrey |
| collection | DOAJ |
| description | Abstract Burmese pythons (Python bivittatus) have demonstrated prolific spread and low detectability within their invasive range in Florida, USA. Consequently, programs exist which incentivize contractors to remove pythons. While surveying, contractors collect data on search effort and python captures. We examined data from South Florida Water Management District’s Python Elimination Program to determine the effect of operational and environmental covariates on two measures of survey outcome: success (i.e., probability of removing at least one python) and efficiency (i.e., the number of pythons removed per survey hour). Additionally, we assessed the spatial distribution of contractor search effort and removals. Warm temperatures (> 25 °C) improve survey outcomes, especially when surveys occur late at night and during the wet season (May–Oct). The most efficient interval for conducting surveys occurs from 20:00 to 02:00. The spatial distribution of python removals is concentrated in four regions and coincides with contractor search effort. Our results provide insights into optimizing removal efforts for invasive Burmese pythons in Florida, which may allow for increases in removal efficiency. Moreover, this study demonstrates that community science data can be used to synthesize recommendations for invasive species removal efforts. |
| format | Article |
| id | doaj-art-64f73acf301a4ba09acb5da6690777bb |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-64f73acf301a4ba09acb5da6690777bb2025-01-19T12:24:26ZengNature PortfolioScientific Reports2045-23222025-01-0115111410.1038/s41598-024-84641-4Optimizing survey conditions for Burmese python detection and removal using community science dataKelly R. McCaffrey0Melissa A. Miller1Sergio A. Balaguera-Reina2Alexander S. Romer3Michael Kirkland4Amy Peters5Edward F. Metzger6LeRoy Rodgers7Frank J. Mazzotti8Department of Wildlife Ecology and Conservation, Fort Lauderdale Research and Education Center, University of FloridaDepartment of Wildlife Ecology and Conservation, Fort Lauderdale Research and Education Center, University of FloridaDepartment of Wildlife Ecology and Conservation, Fort Lauderdale Research and Education Center, University of FloridaDepartment of Wildlife Ecology and Conservation, Fort Lauderdale Research and Education Center, University of FloridaSouth Florida Water Management DistrictSouth Florida Water Management DistrictSouth Florida Water Management DistrictSouth Florida Water Management DistrictDepartment of Wildlife Ecology and Conservation, Fort Lauderdale Research and Education Center, University of FloridaAbstract Burmese pythons (Python bivittatus) have demonstrated prolific spread and low detectability within their invasive range in Florida, USA. Consequently, programs exist which incentivize contractors to remove pythons. While surveying, contractors collect data on search effort and python captures. We examined data from South Florida Water Management District’s Python Elimination Program to determine the effect of operational and environmental covariates on two measures of survey outcome: success (i.e., probability of removing at least one python) and efficiency (i.e., the number of pythons removed per survey hour). Additionally, we assessed the spatial distribution of contractor search effort and removals. Warm temperatures (> 25 °C) improve survey outcomes, especially when surveys occur late at night and during the wet season (May–Oct). The most efficient interval for conducting surveys occurs from 20:00 to 02:00. The spatial distribution of python removals is concentrated in four regions and coincides with contractor search effort. Our results provide insights into optimizing removal efforts for invasive Burmese pythons in Florida, which may allow for increases in removal efficiency. Moreover, this study demonstrates that community science data can be used to synthesize recommendations for invasive species removal efforts.https://doi.org/10.1038/s41598-024-84641-4Machine learningInvasive speciesCommunity scienceBurmese pythonEvergladesApplied ecology |
| spellingShingle | Kelly R. McCaffrey Melissa A. Miller Sergio A. Balaguera-Reina Alexander S. Romer Michael Kirkland Amy Peters Edward F. Metzger LeRoy Rodgers Frank J. Mazzotti Optimizing survey conditions for Burmese python detection and removal using community science data Scientific Reports Machine learning Invasive species Community science Burmese python Everglades Applied ecology |
| title | Optimizing survey conditions for Burmese python detection and removal using community science data |
| title_full | Optimizing survey conditions for Burmese python detection and removal using community science data |
| title_fullStr | Optimizing survey conditions for Burmese python detection and removal using community science data |
| title_full_unstemmed | Optimizing survey conditions for Burmese python detection and removal using community science data |
| title_short | Optimizing survey conditions for Burmese python detection and removal using community science data |
| title_sort | optimizing survey conditions for burmese python detection and removal using community science data |
| topic | Machine learning Invasive species Community science Burmese python Everglades Applied ecology |
| url | https://doi.org/10.1038/s41598-024-84641-4 |
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