Prediction of Large Whale Distributions: A Comparison of Presence–Absence and Presence-Only Modeling Techniques Article Swipe
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· 2018
· Open Access
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· DOI: https://doi.org/10.3389/fmars.2018.00419
Species distribution models that predict species occurrence or density by quantifying relationships with environmental variables are used for a variety of scientific investigations and management applications. For endangered species, such as large whales, models help to understand the ecological factors influencing variability in distributions and to assess potential risk from shipping, fishing, and other human activities. Systematic surveys record species presence and absence, as well as the associated search effort, but are very expensive. Presence-only data consisting only of sightings can increase sample size, but may be biased in both geographical and niche space. We built generalized additive models (GAMs) using presence-absence sightings data and maximum entropy models (Maxent) using the same presence-absence sightings data, and also using presence-only sightings data, for four large whale species in the eastern tropical Pacific Ocean: humpback (Megaptera novaeangliae), blue (Balaenoptera musculus), Bryde's (Balaenoptera edeni), and sperm whales (Physeter macrocephalus). Environmental variables were surface temperature, surface salinity, thermocline depth, stratification index, and seafloor depth. We compared predicted distributions from each of the two model types. Maxent and GAM model predictions based on systematic survey data are very similar, when Maxent absences are selected from the survey trackline data. However, we show that spatial bias in presence-only Maxent predictions can be caused by using pseudo-absences instead of observed absences and by the sampling biases of both opportunistic data and stratified systematic survey data with uneven coverage between strata. Predictions of uncommon large whale distributions from Maxent or other presence-only techniques may be useful for science or management, but only if spatial bias in the observations is addressed in the derivation and interpretation of model predictions. © 2018 Fiedler, Redfern, Forney, Palacios, Sheredy, Rasmussen, García-Godos, Santillán, Tetley, Félix and Ballance.
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fmars.2018.00419
- https://www.frontiersin.org/articles/10.3389/fmars.2018.00419/pdf
- OA Status
- gold
- Cited By
- 53
- References
- 60
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2901785862
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2901785862Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3389/fmars.2018.00419Digital Object Identifier
- Title
-
Prediction of Large Whale Distributions: A Comparison of Presence–Absence and Presence-Only Modeling TechniquesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2018Year of publication
- Publication date
-
2018-11-12Full publication date if available
- Authors
-
Paul C. Fiedler, Jessica V. Redfern, Karin A. Forney, Daniel M. Palacios, Corey Sheredy, Kristin Rasmussen, Ignacio García-Godos, Luis Santillán, Michael J. Tetley, Fernando Félix, Lisa T. BallanceList of authors in order
- Landing page
-
https://doi.org/10.3389/fmars.2018.00419Publisher landing page
- PDF URL
-
https://www.frontiersin.org/articles/10.3389/fmars.2018.00419/pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.frontiersin.org/articles/10.3389/fmars.2018.00419/pdfDirect OA link when available
- Concepts
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Balaenoptera, Whale, Generalized additive model, Environmental niche modelling, Geography, Minke whale, Species distribution, Fishing, Humpback whale, Environmental science, Fishery, Ecological niche, Habitat, Ecology, Statistics, Biology, MathematicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
53Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 7, 2024: 6, 2023: 10, 2022: 6, 2021: 9Per-year citation counts (last 5 years)
- References (count)
-
60Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| corresponding_institution_ids | https://openalex.org/I106745318, https://openalex.org/I1308126019, https://openalex.org/I4210109599 |
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