Combining Regional Habitat Selection Models for Large-Scale Prediction: Circumpolar Habitat Selection of Southern Ocean Humpback Whales
نویسندگان
چکیده
Machine learning algorithms are often used to model and predict animal habitat selection—the relationships between occurrences characteristics. For broadly distributed species, selection varies among populations regions; thus, it would seem preferable fit region- or population-specific models of for more accurate inference prediction, rather than fitting large-scale using pooled data. However, where the aim is make range-wide predictions, including areas which there no existing data selection, how can regional best be combined? We propose that ensemble approaches commonly combine different a single region reframed, treating as candidate models. By doing so, we incorporate variation when predictive across large ranges. test this approach satellite telemetry from 168 humpback whales five geographic regions in Southern Ocean. Using random forests, fitted relating whale locations, versus background 10 environmental covariates, made circumpolar prediction selection. also models, predictions input features four approaches: an unweighted ensemble, weighted by similarity each cell, stacked generalization, hybrid wherein covariates were new model. tested performance these on independent validation dataset sightings whaling catches. These multiregional resulted with higher naive machine algorithms. This yield animals may show
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ژورنال
عنوان ژورنال: Remote Sensing
سال: 2021
ISSN: ['2315-4632', '2315-4675']
DOI: https://doi.org/10.3390/rs13112074