Performance evaluation of cetacean species distribution models developed using generalized additive models and boosted regression trees Article Swipe
Elizabeth A. Becker
,
James V. Carretta
,
Karin A. Forney
,
Jay Barlow
,
Stephanie Brodie
,
Ryan Hoopes
,
Michael G. Jacox
,
Sara M. Maxwell
,
Jessica V. Redfern
,
Nicholas B. Sisson
,
Heather Welch
,
Elliott L. Hazen
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1002/ece3.6316
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1002/ece3.6316
Species distribution models (SDMs) are important management tools for highly mobile marine species because they provide spatially and temporally explicit information on animal distribution. Two prevalent modeling frameworks used to develop SDMs for marine species are generalized additive models (GAMs) and boosted regression trees (BRTs), but comparative studies have rarely been conducted; most rely on presence‐only data; and few have explored how features such as species distribution characteristics affect model performance. Since the majority of marine species BRTs have been used to predict habitat suitability, we first compared BRTs to GAMs that used presence/absence as the response variable. We then compared results from these habitat suitability models to GAMs that predict species density (animals per km 2 ) because density models built with a subset of the data used here have previously received extensive validation. We compared both the explanatory power (i.e., model goodness of fit) and predictive power (i.e., performance on a novel dataset) of the GAMs and BRTs for a taxonomically diverse suite of cetacean species using a robust set of systematic survey data (1991–2014) within the California Current Ecosystem. Both BRTs and GAMs were successful at describing overall distribution patterns throughout the study area for the majority of species considered, but when predicting on novel data, the density GAMs exhibited substantially greater predictive power than both the presence/absence GAMs and BRTs, likely due to both the different response variables and fitting algorithms. Our results provide an improved understanding of some of the strengths and limitations of models developed using these two methods. These results can be used by modelers developing SDMs and resource managers tasked with the spatial management of marine species to determine the best modeling technique for their question of interest.
Related Topics
Concepts
Generalized additive model
Predictive power
Species distribution
Generalized linear model
Regression
Environmental niche modelling
Computer science
Distribution (mathematics)
Habitat
Ecology
Biology
Statistics
Machine learning
Mathematics
Ecological niche
Epistemology
Mathematical analysis
Philosophy
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1002/ece3.6316
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/ece3.6316
- OA Status
- gold
- Cited By
- 74
- References
- 120
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3024628712
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3024628712Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1002/ece3.6316Digital Object Identifier
- Title
-
Performance evaluation of cetacean species distribution models developed using generalized additive models and boosted regression treesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-05-11Full publication date if available
- Authors
-
Elizabeth A. Becker, James V. Carretta, Karin A. Forney, Jay Barlow, Stephanie Brodie, Ryan Hoopes, Michael G. Jacox, Sara M. Maxwell, Jessica V. Redfern, Nicholas B. Sisson, Heather Welch, Elliott L. HazenList of authors in order
- Landing page
-
https://doi.org/10.1002/ece3.6316Publisher landing page
- PDF URL
-
https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/ece3.6316Direct 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://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/ece3.6316Direct OA link when available
- Concepts
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Generalized additive model, Predictive power, Species distribution, Generalized linear model, Regression, Environmental niche modelling, Computer science, Distribution (mathematics), Habitat, Ecology, Biology, Statistics, Machine learning, Mathematics, Ecological niche, Epistemology, Mathematical analysis, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
74Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 20, 2024: 13, 2023: 12, 2022: 15, 2021: 11Per-year citation counts (last 5 years)
- References (count)
-
120Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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