The use of machine learning to detect foraging behaviour in whale sharks: a new tool in conservation

Whitehead, Darren A.; Magaña, Felipe Galván; Hoyos-Padilla, Mauricio; Armas, Rogelio González; Pancaldi, Francesca; Olivier, Damien

doi:10.1111/jfb.14589

Cited by 6 publications

(1 citation statement)

References 29 publications

(39 reference statements)

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“…In addition, ML models are well suited to the iterative modeling framework due to their automated approach, fast development process ( Tarca et al, 2007 ) and highly scalable nature ( Farley et al, 2018 ). This enables them to take advantage of other big data attributes, including its widespread proliferation, global coverage, and rapid updating ( Whitehead et al, 2020 ). As new data become available, ML frameworks can be updated to reflect new understanding.…”

Section: Introductionmentioning

confidence: 99%

Testing a Generalizable Machine Learning Workflow for Aquatic Invasive Species on Rainbow Trout (Oncorhynchus mykiss) in Northwest Montana

et al. 2021

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Biological invasions are accelerating worldwide, causing major ecological and economic impacts in aquatic ecosystems. The urgent decision-making needs of invasive species managers can be better met by the integration of biodiversity big data with large-domain models and data-driven products. Remotely sensed data products can be combined with existing invasive species occurrence data via machine learning models to provide the proactive spatial risk analysis necessary for implementing coordinated and agile management paradigms across large scales. We present a workflow that generates rapid spatial risk assessments on aquatic invasive species using occurrence data, spatially explicit environmental data, and an ensemble approach to species distribution modeling using five machine learning algorithms. For proof of concept and validation, we tested this workflow using extensive spatial and temporal hybridization and occurrence data from a well-studied, ongoing, and climate-driven species invasion in the upper Flathead River system in northwestern Montana, USA. Rainbow Trout (RBT; Oncorhynchus mykiss), an introduced species in the Flathead River basin, compete and readily hybridize with native Westslope Cutthroat Trout (WCT; O. clarkii lewisii), and the spread of RBT individuals and their alleles has been tracked for decades. We used remotely sensed and other geospatial data as key environmental predictors for projecting resultant habitat suitability to geographic space. The ensemble modeling technique yielded high accuracy predictions relative to 30-fold cross-validated datasets (87% 30-fold cross-validated accuracy score). Both top predictors and model performance relative to these predictors matched current understanding of the drivers of RBT invasion and habitat suitability, indicating that temperature is a major factor influencing the spread of invasive RBT and hybridization with native WCT. The congruence between more time-consuming modeling approaches and our rapid machine-learning approach suggest that this workflow could be applied more broadly to provide data-driven management information for early detection of potential invaders.

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Section: Introductionmentioning

confidence: 99%