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Optimizing survey conditions for Burmese python detection and removal using community science data
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DOI：10.1038/s41598-024-84641-4.png)
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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 degrees 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.
Keyword:
Machine learning
Invasive species
Community science
Burmese python
Everglades
Applied ecology
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28.3W
被引数:
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