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Illuminating patterns of firefly abundance using citizen science data and machine learning models.
McNeil, Darin J; Goslee, Sarah C; Kammerer, Melanie; Lower, Sarah E; Tooker, John F; Grozinger, Christina M.
Afiliación
  • McNeil DJ; Department of Forestry and Natural Resources, University of Kentucky, Lexington, KY 40506, USA. Electronic address: darin.j.mcneil@uky.edu.
  • Goslee SC; United States Department of Agriculture - Agricultural Research Service, Pasture Systems and Watershed Management Research Unit, University Park, PA 16802, USA.
  • Kammerer M; United States Department of Agriculture - Agricultural Research Service, Pasture Systems and Watershed Management Research Unit, University Park, PA 16802, USA.
  • Lower SE; Department of Biology, Bucknell University, Lewisburg, PA 17837, USA.
  • Tooker JF; Department of Entomology, Insect Biodiversity Center, Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, PA 16802, USA.
  • Grozinger CM; Department of Entomology, Insect Biodiversity Center, Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, PA 16802, USA.
Sci Total Environ ; 929: 172329, 2024 Jun 15.
Article en En | MEDLINE | ID: mdl-38608892
ABSTRACT
As insect populations decline in many regions, conservation biologists are increasingly tasked with identifying factors that threaten insect species and developing effective strategies for their conservation. One insect group of global conservation concern are fireflies (Coleoptera Lampyridae). Although quantitative data on firefly populations are lacking for most species, anecdotal reports suggest that some firefly populations have declined in recent decades. Researchers have hypothesized that North American firefly populations are most threatened by habitat loss, pesticide use, and light pollution, but the importance of these factors in shaping firefly populations has not been rigorously examined at broad spatial scales. Using data from >24,000 surveys (spanning 2008-16) from the citizen science program Firefly Watch, we trained machine learning models to evaluate the relative importance of a variety of factors on bioluminescent firefly populations pesticides, artificial lights at night, land cover, soil/topography, short-term weather, and long-term climate. Our analyses revealed that firefly abundance was driven by complex interactions among soil conditions (e.g., percent sand composition), climate/weather (e.g., growing degree days), and land cover characteristics (e.g., percent agriculture and impervious cover). Given the significant impact that climactic and weather conditions have on firefly abundance, there is a strong likelihood that firefly populations will be influenced by climate change, with some regions becoming higher quality and supporting larger firefly populations, and others potentially losing populations altogether. Collectively, our results support hypotheses related to factors threatening firefly populations, especially habitat loss, and suggest that climate change may pose a greater threat than appreciated in previous assessments. Thus, future conservation of North American firefly populations will depend upon 1) consistent and continued monitoring of populations via programs like Firefly Watch, 2) efforts to mitigate the impacts of climate change, and 3) insect-friendly conservation practices.
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Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Cambio Climático / Luciérnagas / Aprendizaje Automático / Ciencia Ciudadana Límite: Animals Idioma: En Revista: Sci Total Environ Año: 2024 Tipo del documento: Article

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Cambio Climático / Luciérnagas / Aprendizaje Automático / Ciencia Ciudadana Límite: Animals Idioma: En Revista: Sci Total Environ Año: 2024 Tipo del documento: Article