Limits...
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.

Show MeSH

Related in: MedlinePlus

Grey petrel distribution in January from three colonies overlaid on January oceanographic climatologies.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. (a) Mixed layer depth (m), (b) mean temperature in upper 50m (C°), (c) surface currents (m/s) over depth (m), (d) eddy kinetic energy ((cm s-1)2). Maps in native projection of environmental layers: geographic, datum wgs84.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4352036&req=5

pone.0120014.g004: Grey petrel distribution in January from three colonies overlaid on January oceanographic climatologies.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. (a) Mixed layer depth (m), (b) mean temperature in upper 50m (C°), (c) surface currents (m/s) over depth (m), (d) eddy kinetic energy ((cm s-1)2). Maps in native projection of environmental layers: geographic, datum wgs84.

Mentions: Although the population models indicated similar ecological relationships between grey petrel distribution data and the common predictor variables, the shape of these functions varied relative to the range of environmental variation (Fig. 3; note that portions of functional relationships with little data as indicated by rug plots are disregarded during interpretation). Mixed layer depth (MLD) had a large contribution in all three models (21–18%; Table 2), with the results indicating that grey petrels used habitat with a similar range of MLDs (Antipodes: 50–80 m; Kerguelen: 50–130 m; Combined: 50–100 m). Yet, the distribution of presences (50% contour) and available habitat (90% contour) relative to composite images of MLD in January (Fig. 4a) indicate that tracked birds had variable selection patterns in relation to available habitat: the Kerguelen birds used habitat with the deepest available MLD, whereas birds from Antipodes used habitat over a ridge of shallow MLD between two deeper areas, and those from Marion used an area with moderate MLD adjacent to an area with shallow MLD. These results illustrate how inference of species habitat preference via use-availability models is dependent on the available habitat assessed [47].


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)

Grey petrel distribution in January from three colonies overlaid on January oceanographic climatologies.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. (a) Mixed layer depth (m), (b) mean temperature in upper 50m (C°), (c) surface currents (m/s) over depth (m), (d) eddy kinetic energy ((cm s-1)2). Maps in native projection of environmental layers: geographic, datum wgs84.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0120014.g004: Grey petrel distribution in January from three colonies overlaid on January oceanographic climatologies.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. (a) Mixed layer depth (m), (b) mean temperature in upper 50m (C°), (c) surface currents (m/s) over depth (m), (d) eddy kinetic energy ((cm s-1)2). Maps in native projection of environmental layers: geographic, datum wgs84.
Mentions: Although the population models indicated similar ecological relationships between grey petrel distribution data and the common predictor variables, the shape of these functions varied relative to the range of environmental variation (Fig. 3; note that portions of functional relationships with little data as indicated by rug plots are disregarded during interpretation). Mixed layer depth (MLD) had a large contribution in all three models (21–18%; Table 2), with the results indicating that grey petrels used habitat with a similar range of MLDs (Antipodes: 50–80 m; Kerguelen: 50–130 m; Combined: 50–100 m). Yet, the distribution of presences (50% contour) and available habitat (90% contour) relative to composite images of MLD in January (Fig. 4a) indicate that tracked birds had variable selection patterns in relation to available habitat: the Kerguelen birds used habitat with the deepest available MLD, whereas birds from Antipodes used habitat over a ridge of shallow MLD between two deeper areas, and those from Marion used an area with moderate MLD adjacent to an area with shallow MLD. These results illustrate how inference of species habitat preference via use-availability models is dependent on the available habitat assessed [47].

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