Abstract
Information on ecological systems often comes from diverse sources with varied levels of complexity, bias, and uncertainty. Accordingly, analytical techniques continue to evolve that address these challenges to reveal the characteristics of ecological systems and inform conservation actions. We applied multiple statistical learning algorithms (i.e., machine learning) with a range of information sources including fish tracking data, environmental data, and visual surveys to identify potential spawning aggregation sites for a marine fish species, permit (Trachinotus falcatus), in the Florida Keys. Recognizing the potential complementarity and some level of uncertainty in each information source, we applied supervised (classic and conditional random forests; RF) and unsupervised (fuzzy k-means; FKM) algorithms. The two RF models had similar predictive performance, but generated different predictor variable importance structures and spawning site predictions. Unsupervised clustering using FKM identified unique site groupings that were similar to the likely spawning sites identified with RF. The conservation of aggregate spawning fish species depends heavily on the protection of key spawning sites; many of these potential sites were identified here for permit in the Florida Keys, which consisted of relatively deep-water natural and artificial reefs with high mean permit residency periods. The application of multiple machine learning algorithms enabled the integration of diverse information sources to develop models of an ecological system. Faced with increasingly complex and diverse data sources, ecologists, and conservation practitioners should find increasing value in machine learning algorithms, which we discuss here and provide resources to increase accessibility.
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Acknowledgements
This project was funded by Bonefish and Tarpon Trust with support from Costa Del Mar, The March Merkin Fishing Tournament, Hell’s Bay Boatworks, and private donations. Additional support was provided by a NASEM Gulf Research Program through a hurricane recovery grant, and the acoustic receiver array was partially supported by a loan from the Ocean Tracking Network. We thank the fishing guides and anglers who assisted with the telemetry array design and Permit tagging for this project including Captains Travis and Bear Holeman, Will Benson, Rob Kramarz, Zack Stells, Brandon, and Jared Cyr, Chris Trosset, Nathaniel Linville, Ian Slater, Augustine Moss, Sandy Horn, Ted Margo, Richard Berlin, and Jeff Rella. We also thank the researchers that shared permit acoustic telemetry detection data with us through integrated Tracking of Animals in the Gulf (iTAG) and Florida Acoustic Telemetry Network (FACT), in particular Harold "Wes" Pratt of Mote Marine Laboratory, funded by The Shark Foundation/Hai Stiftung, and Mike McAllister. Brownscombe is supported by a Banting Postdoctoral Fellowship, Dalhousie University, Carleton University, and Bonefish and Tarpon Trust. This research was conducted with permission of the Florida Keys National Marine Sanctuary under permit # FKNMS-2013-040-A2, and the Florida Fish and Wildlife Conservation Commission under permit # SAL-16-1205.
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JWB designed and conducted data collection, analysis, and manuscript writing. LPG, DM, AA, JH, SKL-B, AJA, AJD, SJC contributed to data collection and manuscript preparation.
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Communicated by Yannis Papastamatiou.
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Brownscombe, J.W., Griffin, L.P., Morley, D. et al. Application of machine learning algorithms to identify cryptic reproductive habitats using diverse information sources. Oecologia 194, 283–298 (2020). https://doi.org/10.1007/s00442-020-04753-2
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DOI: https://doi.org/10.1007/s00442-020-04753-2