Prediction of Large Whale Distributions: A Comparison of Presence–Absence and Presence-Only Modeling Techniques
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 fac...
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2018
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Online Access: | https://doi.org/10.3389/fmars.2018.00419 https://doaj.org/article/ab58e61d3d4c46d098d004e9bd378d1b |
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ftdoajarticles:oai:doaj.org/article:ab58e61d3d4c46d098d004e9bd378d1b 2023-05-15T15:36:25+02:00 Prediction of Large Whale Distributions: A Comparison of Presence–Absence and Presence-Only Modeling Techniques 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. Ballance 2018-11-01T00:00:00Z https://doi.org/10.3389/fmars.2018.00419 https://doaj.org/article/ab58e61d3d4c46d098d004e9bd378d1b EN eng Frontiers Media S.A. https://www.frontiersin.org/article/10.3389/fmars.2018.00419/full https://doaj.org/toc/2296-7745 2296-7745 doi:10.3389/fmars.2018.00419 https://doaj.org/article/ab58e61d3d4c46d098d004e9bd378d1b Frontiers in Marine Science, Vol 5 (2018) species distribution model maximum entropy generalized additive model whale eastern tropical Pacific Science Q General. Including nature conservation geographical distribution QH1-199.5 article 2018 ftdoajarticles https://doi.org/10.3389/fmars.2018.00419 2022-12-31T15:43:09Z 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. Article in Journal/Newspaper Balaenoptera musculus Megaptera novaeangliae Physeter macrocephalus Directory of Open Access Journals: DOAJ Articles Pacific Gam ENVELOPE(-57.955,-57.955,-61.923,-61.923) Frontiers in Marine Science 5 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
species distribution model maximum entropy generalized additive model whale eastern tropical Pacific Science Q General. Including nature conservation geographical distribution QH1-199.5 |
spellingShingle |
species distribution model maximum entropy generalized additive model whale eastern tropical Pacific Science Q General. Including nature conservation geographical distribution QH1-199.5 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. Ballance Prediction of Large Whale Distributions: A Comparison of Presence–Absence and Presence-Only Modeling Techniques |
topic_facet |
species distribution model maximum entropy generalized additive model whale eastern tropical Pacific Science Q General. Including nature conservation geographical distribution QH1-199.5 |
description |
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. |
format |
Article in Journal/Newspaper |
author |
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. Ballance |
author_facet |
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. Ballance |
author_sort |
Paul C. Fiedler |
title |
Prediction of Large Whale Distributions: A Comparison of Presence–Absence and Presence-Only Modeling Techniques |
title_short |
Prediction of Large Whale Distributions: A Comparison of Presence–Absence and Presence-Only Modeling Techniques |
title_full |
Prediction of Large Whale Distributions: A Comparison of Presence–Absence and Presence-Only Modeling Techniques |
title_fullStr |
Prediction of Large Whale Distributions: A Comparison of Presence–Absence and Presence-Only Modeling Techniques |
title_full_unstemmed |
Prediction of Large Whale Distributions: A Comparison of Presence–Absence and Presence-Only Modeling Techniques |
title_sort |
prediction of large whale distributions: a comparison of presence–absence and presence-only modeling techniques |
publisher |
Frontiers Media S.A. |
publishDate |
2018 |
url |
https://doi.org/10.3389/fmars.2018.00419 https://doaj.org/article/ab58e61d3d4c46d098d004e9bd378d1b |
long_lat |
ENVELOPE(-57.955,-57.955,-61.923,-61.923) |
geographic |
Pacific Gam |
geographic_facet |
Pacific Gam |
genre |
Balaenoptera musculus Megaptera novaeangliae Physeter macrocephalus |
genre_facet |
Balaenoptera musculus Megaptera novaeangliae Physeter macrocephalus |
op_source |
Frontiers in Marine Science, Vol 5 (2018) |
op_relation |
https://www.frontiersin.org/article/10.3389/fmars.2018.00419/full https://doaj.org/toc/2296-7745 2296-7745 doi:10.3389/fmars.2018.00419 https://doaj.org/article/ab58e61d3d4c46d098d004e9bd378d1b |
op_doi |
https://doi.org/10.3389/fmars.2018.00419 |
container_title |
Frontiers in Marine Science |
container_volume |
5 |
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1766366766847492096 |