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Poor transferability of species distribution models for a pelagic predator, the grey petrel, indicates contrasting habitat preferences across ocean basins.

Torres LG, Sutton PJ, Thompson DR, Delord K, Weimerskirch H, Sagar PM, Sommer E, Dilley BJ, Ryan PG, Phillips RA - PLoS ONE (2015)

Bottom Line: These results indicate that habitat use reflects both its availability and bird preferences, such that the realized distribution patterns differ for each population.The spatial predictions by the three SDMs were compared with tracking data and fishing effort to demonstrate the conservation pitfalls of extrapolating SDMs outside calibration regions.Although SDMs can elucidate potential distribution patterns relative to large-scale climatic and oceanographic conditions, knowledge of local habitat availability and preferences is necessary to understand and successfully predict region-specific realized distribution patterns.

View Article: PubMed Central - PubMed

Affiliation: Marine Mammal Institute, Department of Fisheries and Wildlife, Oregon State University, Newport, Oregon, United States of America.

ABSTRACT
Species distribution models (SDMs) are increasingly applied in conservation management to predict suitable habitat for poorly known populations. High predictive performance of SDMs is evident in validations performed within the model calibration area (interpolation), but few studies have assessed SDM transferability to novel areas (extrapolation), particularly across large spatial scales or pelagic ecosystems. We performed rigorous SDM validation tests on distribution data from three populations of a long-ranging marine predator, the grey petrel Procellaria cinerea, to assess model transferability across the Southern Hemisphere (25-65°S). Oceanographic data were combined with tracks of grey petrels from two remote sub-Antarctic islands (Antipodes and Kerguelen) using boosted regression trees to generate three SDMs: one for each island population, and a combined model. The predictive performance of these models was assessed using withheld tracking data from within the model calibration areas (interpolation), and from a third population, Marion Island (extrapolation). Predictive performance was assessed using k-fold cross validation and point biserial correlation. The two population-specific SDMs included the same predictor variables and suggested birds responded to the same broad-scale oceanographic influences. However, all model validation tests, including of the combined model, determined strong interpolation but weak extrapolation capabilities. These results indicate that habitat use reflects both its availability and bird preferences, such that the realized distribution patterns differ for each population. The spatial predictions by the three SDMs were compared with tracking data and fishing effort to demonstrate the conservation pitfalls of extrapolating SDMs outside calibration regions. This exercise revealed that SDM predictions would have led to an underestimate of overlap with fishing effort and potentially misinformed bycatch mitigation efforts. Although SDMs can elucidate potential distribution patterns relative to large-scale climatic and oceanographic conditions, knowledge of local habitat availability and preferences is necessary to understand and successfully predict region-specific realized distribution patterns.

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Related in: MedlinePlus

Predicted habitat suitability for grey petrels in January across the Southern Hemisphere.Predictions derived from boosted regression tree models for the (a) Antipodes, (b) Kerguelen and (c) combined populations. Location of grey petrel colonies at Antipodes Island (black star), Kerguelen Island (white star), and Marion Island (red star) shown. The January 50% and 90% density contours for tracked grey petrels from Antipodes (black lines), Kerguelen (white lines), and Marion (red lines) islands are displayed. Predicted habitat suitability ranges from low (0) to high (1) on a constant colour scheme between plots. Maps in Molleweide, datum wgs84.
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pone.0120014.g002: Predicted habitat suitability for grey petrels in January across the Southern Hemisphere.Predictions derived from boosted regression tree models for the (a) Antipodes, (b) Kerguelen and (c) combined populations. Location of grey petrel colonies at Antipodes Island (black star), Kerguelen Island (white star), and Marion Island (red star) shown. The January 50% and 90% density contours for tracked grey petrels from Antipodes (black lines), Kerguelen (white lines), and Marion (red lines) islands are displayed. Predicted habitat suitability ranges from low (0) to high (1) on a constant colour scheme between plots. Maps in Molleweide, datum wgs84.

Mentions: Results of the model validations are reflected in the spatial predictions of habitat suitability for grey petrels across the Southern Hemisphere by each model for each month (January displayed in Fig. 2; other months in S3 A-E Fig.). All three models demonstrated high interpolative capability. However, when the models were transferred to novel regions to test their ability to extrapolate spatial predictions, very low habitat suitability was incorrectly predicted for areas of known occurrence of this species (e.g., within the 50% density contours for tracked birds).


Poor transferability of species distribution models for a pelagic predator, the grey petrel, indicates contrasting habitat preferences across ocean basins.

Torres LG, Sutton PJ, Thompson DR, Delord K, Weimerskirch H, Sagar PM, Sommer E, Dilley BJ, Ryan PG, Phillips RA - PLoS ONE (2015)

Predicted habitat suitability for grey petrels in January across the Southern Hemisphere.Predictions derived from boosted regression tree models for the (a) Antipodes, (b) Kerguelen and (c) combined populations. Location of grey petrel colonies at Antipodes Island (black star), Kerguelen Island (white star), and Marion Island (red star) shown. The January 50% and 90% density contours for tracked grey petrels from Antipodes (black lines), Kerguelen (white lines), and Marion (red lines) islands are displayed. Predicted habitat suitability ranges from low (0) to high (1) on a constant colour scheme between plots. Maps in Molleweide, datum wgs84.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4352036&req=5

pone.0120014.g002: Predicted habitat suitability for grey petrels in January across the Southern Hemisphere.Predictions derived from boosted regression tree models for the (a) Antipodes, (b) Kerguelen and (c) combined populations. Location of grey petrel colonies at Antipodes Island (black star), Kerguelen Island (white star), and Marion Island (red star) shown. The January 50% and 90% density contours for tracked grey petrels from Antipodes (black lines), Kerguelen (white lines), and Marion (red lines) islands are displayed. Predicted habitat suitability ranges from low (0) to high (1) on a constant colour scheme between plots. Maps in Molleweide, datum wgs84.
Mentions: Results of the model validations are reflected in the spatial predictions of habitat suitability for grey petrels across the Southern Hemisphere by each model for each month (January displayed in Fig. 2; other months in S3 A-E Fig.). All three models demonstrated high interpolative capability. However, when the models were transferred to novel regions to test their ability to extrapolate spatial predictions, very low habitat suitability was incorrectly predicted for areas of known occurrence of this species (e.g., within the 50% density contours for tracked birds).

Bottom Line: These results indicate that habitat use reflects both its availability and bird preferences, such that the realized distribution patterns differ for each population.The spatial predictions by the three SDMs were compared with tracking data and fishing effort to demonstrate the conservation pitfalls of extrapolating SDMs outside calibration regions.Although SDMs can elucidate potential distribution patterns relative to large-scale climatic and oceanographic conditions, knowledge of local habitat availability and preferences is necessary to understand and successfully predict region-specific realized distribution patterns.

View Article: PubMed Central - PubMed

Affiliation: Marine Mammal Institute, Department of Fisheries and Wildlife, Oregon State University, Newport, Oregon, United States of America.

ABSTRACT
Species distribution models (SDMs) are increasingly applied in conservation management to predict suitable habitat for poorly known populations. High predictive performance of SDMs is evident in validations performed within the model calibration area (interpolation), but few studies have assessed SDM transferability to novel areas (extrapolation), particularly across large spatial scales or pelagic ecosystems. We performed rigorous SDM validation tests on distribution data from three populations of a long-ranging marine predator, the grey petrel Procellaria cinerea, to assess model transferability across the Southern Hemisphere (25-65°S). Oceanographic data were combined with tracks of grey petrels from two remote sub-Antarctic islands (Antipodes and Kerguelen) using boosted regression trees to generate three SDMs: one for each island population, and a combined model. The predictive performance of these models was assessed using withheld tracking data from within the model calibration areas (interpolation), and from a third population, Marion Island (extrapolation). Predictive performance was assessed using k-fold cross validation and point biserial correlation. The two population-specific SDMs included the same predictor variables and suggested birds responded to the same broad-scale oceanographic influences. However, all model validation tests, including of the combined model, determined strong interpolation but weak extrapolation capabilities. These results indicate that habitat use reflects both its availability and bird preferences, such that the realized distribution patterns differ for each population. The spatial predictions by the three SDMs were compared with tracking data and fishing effort to demonstrate the conservation pitfalls of extrapolating SDMs outside calibration regions. This exercise revealed that SDM predictions would have led to an underestimate of overlap with fishing effort and potentially misinformed bycatch mitigation efforts. Although SDMs can elucidate potential distribution patterns relative to large-scale climatic and oceanographic conditions, knowledge of local habitat availability and preferences is necessary to understand and successfully predict region-specific realized distribution patterns.

Show MeSH
Related in: MedlinePlus