TY - JOUR
T1 - Prediction of large whale distributions
T2 - A comparison of presence-absence and presence-only modeling techniques
AU - Fiedler, Paul C.
AU - Redfern, Jessica V.
AU - Forney, Karin A.
AU - Palacios, Daniel M.
AU - Sheredy, Corey
AU - Rasmussen, Kristin
AU - García-Godos, Ignacio
AU - Santillán, Luis
AU - Tetley, Michael J.
AU - Félix, Fernando
AU - Ballance, Lisa T.
N1 - Funding Information:
We thank the many sea-going scientists who tirelessly collected eastern tropical Pacific cetacean ecosystem survey data, the officers and crew of the NOAA research vessels on which these data were collected, and others who have submitted sightings to SIBIMAP. Elizabeth Becker provided edited SWFSC sightings and effort data. Tim Gerrodette was chief scientist for these surveys through 1998. Tomo Eguchi and the reviewers provided valuable comments. This project was funded by the National Oceanic and Atmospheric Administration, National Marine Fisheries Service, Southwest Fisheries Science Center
Publisher Copyright:
© 2018 Fiedler, Redfern, Forney, Palacios, Sheredy, Rasmussen, García-Godos, Santillán, Tetley, Félix and Ballance.
PY - 2018/11/12
Y1 - 2018/11/12
N2 - 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.
AB - 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.
KW - Eastern tropical Pacific
KW - Generalized additive model
KW - Maximum entropy
KW - Species distribution model
KW - Whale
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U2 - 10.3389/fmars.2018.00419
DO - 10.3389/fmars.2018.00419
M3 - Article
SN - 2296-7745
VL - 5
JO - Frontiers in Marine Science
JF - Frontiers in Marine Science
IS - NOV
M1 - 419
ER -