Willson, A.,Baldwin, R.,Collins, T.,Godley, B.J.,Minton, G.,Al Harthi, S.,Pikesley, Stephen K,Witt, Matthew J
Preliminary ensemble ecological niche modelling of Arabian Sea humpback whale vessel sightings and satellite telemetry data Technical Report
no. 502, 2017, ISBN: SC/67A/CMP/15.
Abstract | BibTeX | Tags: Arabian Gulf, Arabian Sea, ensemble niche modeling, habitat modelling, habitat preference, Humpback Whale, megaptera novaeangliae, Persian Gulf, Satellite telemetry
@techreport{,
title = {Preliminary ensemble ecological niche modelling of Arabian Sea humpback whale vessel sightings and satellite telemetry data},
author = {Willson, A.,Baldwin, R.,Collins, T.,Godley, B.J.,Minton, G.,Al Harthi, S.,Pikesley, Stephen K,Witt, Matthew J},
issn = { SC/67A/CMP/15},
year = {2017},
date = {2017-01-01},
journal = {Document presented to the meeting of the Scientific Committee of the International Whaling Commission},
number = {502},
pages = {17},
abstract = {Ensemble ecological niche modelling (EENM) can provide insight into the relationship between marine mammals and
their environment and can predict distribution beyond the range of observed locations. The technique can be used to
identify sites for future field research and guide conservation and management activities. The spatial ecology of Arabian
Sea humpback whales (ASHWs) has been described off the coast of Oman, although a paucity of information exists
from which to describe their distribution across the rest of their potential range. Here we present an ensemble ecological
niche modelling framework to predict habitat suitability of ASHWs across the north Indian Ocean. Sightings data from
Oman-based small vessel surveys (2003-2014) and satellite telemetry records (2014-2016) were used along with
environmental co-variate data from a season between December and May. Net primary productivity featured as the only
co-variate with a strong influence on models for both datasets. Model test evaluation metrics scored >0.9, and mapped
outputs of likely distribution highlighted spatial similarity across multiple models. Telemetry data predicted suitable
habitat to be further offshore than the models derived from sightings data. All resulting distribution maps described
areas of high suitability (index value <0.75) along the southern and central coast of Oman and of the northern Arabian
Sea between the Gulf of Kutch and sub-marine canyon features off the Indus delta. There was good spatial concordance
between ensemble model predictions with actual locations of Soviet catches of humpback whales in the northern Indian
Ocean between 1964 and 1966. Both the telemetry and the sightings data were temporally sporadic in their coverage
(across months) and biologically biased (towards males) and as such results from our preliminary efforts should be
considered in light of these caveats. However, these preliminary results are valuable and indicate likely co-occurrence
with high density shipping traffic routes in the region and target additional areas for focussed field surveys. Results
from this study should be considered together with results of recent north Indian Ocean blue whale ENM studies to help
guide future research and conservation management objectives in the region.},
keywords = {Arabian Gulf, Arabian Sea, ensemble niche modeling, habitat modelling, habitat preference, Humpback Whale, megaptera novaeangliae, Persian Gulf, Satellite telemetry},
pubstate = {published},
tppubtype = {techreport}
}
Ensemble ecological niche modelling (EENM) can provide insight into the relationship between marine mammals and
their environment and can predict distribution beyond the range of observed locations. The technique can be used to
identify sites for future field research and guide conservation and management activities. The spatial ecology of Arabian
Sea humpback whales (ASHWs) has been described off the coast of Oman, although a paucity of information exists
from which to describe their distribution across the rest of their potential range. Here we present an ensemble ecological
niche modelling framework to predict habitat suitability of ASHWs across the north Indian Ocean. Sightings data from
Oman-based small vessel surveys (2003-2014) and satellite telemetry records (2014-2016) were used along with
environmental co-variate data from a season between December and May. Net primary productivity featured as the only
co-variate with a strong influence on models for both datasets. Model test evaluation metrics scored >0.9, and mapped
outputs of likely distribution highlighted spatial similarity across multiple models. Telemetry data predicted suitable
habitat to be further offshore than the models derived from sightings data. All resulting distribution maps described
areas of high suitability (index value <0.75) along the southern and central coast of Oman and of the northern Arabian
Sea between the Gulf of Kutch and sub-marine canyon features off the Indus delta. There was good spatial concordance
between ensemble model predictions with actual locations of Soviet catches of humpback whales in the northern Indian
Ocean between 1964 and 1966. Both the telemetry and the sightings data were temporally sporadic in their coverage
(across months) and biologically biased (towards males) and as such results from our preliminary efforts should be
considered in light of these caveats. However, these preliminary results are valuable and indicate likely co-occurrence
with high density shipping traffic routes in the region and target additional areas for focussed field surveys. Results
from this study should be considered together with results of recent north Indian Ocean blue whale ENM studies to help
guide future research and conservation management objectives in the region.
their environment and can predict distribution beyond the range of observed locations. The technique can be used to
identify sites for future field research and guide conservation and management activities. The spatial ecology of Arabian
Sea humpback whales (ASHWs) has been described off the coast of Oman, although a paucity of information exists
from which to describe their distribution across the rest of their potential range. Here we present an ensemble ecological
niche modelling framework to predict habitat suitability of ASHWs across the north Indian Ocean. Sightings data from
Oman-based small vessel surveys (2003-2014) and satellite telemetry records (2014-2016) were used along with
environmental co-variate data from a season between December and May. Net primary productivity featured as the only
co-variate with a strong influence on models for both datasets. Model test evaluation metrics scored >0.9, and mapped
outputs of likely distribution highlighted spatial similarity across multiple models. Telemetry data predicted suitable
habitat to be further offshore than the models derived from sightings data. All resulting distribution maps described
areas of high suitability (index value <0.75) along the southern and central coast of Oman and of the northern Arabian
Sea between the Gulf of Kutch and sub-marine canyon features off the Indus delta. There was good spatial concordance
between ensemble model predictions with actual locations of Soviet catches of humpback whales in the northern Indian
Ocean between 1964 and 1966. Both the telemetry and the sightings data were temporally sporadic in their coverage
(across months) and biologically biased (towards males) and as such results from our preliminary efforts should be
considered in light of these caveats. However, these preliminary results are valuable and indicate likely co-occurrence
with high density shipping traffic routes in the region and target additional areas for focussed field surveys. Results
from this study should be considered together with results of recent north Indian Ocean blue whale ENM studies to help
guide future research and conservation management objectives in the region.
Corkeron, Peter J,Collins, Gianna Minton Tim,Findlay, Ken,Willson, Andrew,Baldwin, Robert
Spatial models of sparse data to inform cetacean conservation planning: an example from Oman Journal Article
In: Endangered Species Research, vol. 15, no. 353, pp. 39-52, 2011, ISBN: 1863-5407.
Abstract | Links | BibTeX | Tags: Arabian Sea, cetaceans, Distribution, General linear model, habitat modelling, habitat use, Humpback Whale, megaptera novaeangliae, Oman, Spatial autocorrelation
@article{,
title = {Spatial models of sparse data to inform cetacean conservation planning: an example from Oman},
author = {Corkeron, Peter J,Collins, Gianna Minton Tim,Findlay, Ken,Willson, Andrew,Baldwin, Robert},
url = {https://www.int-res.com/articles/esr_oa/n015p039.pdf},
issn = {1863-5407},
year = {2011},
date = {2011-01-01},
journal = {Endangered Species Research},
volume = {15},
number = {353},
pages = {39-52},
abstract = {Habitat models are tools for understanding the relationship between cetaceans and their
environment, from which patterns of the animals’ space use can be inferred and management strategies
developed. Can working with space use alone be sufficient for management, when habitat cannot
be modeled? Here, we analyzed cetacean sightings data collected from small boat surveys off the
coast of Oman between 2000 and 2003. The waters off Oman are used by the Endangered Arabian
Sea population of humpback whales. Our data were collected primarily for photo-identification, using
a haphazard sampling regime, either in areas where humpback whales were thought to be relatively
abundant, or in areas that were logistically easy to survey. This leads to spatially autocorrelated data
that are not amenable to analysis using standard approaches. We used quasi-Poisson generalized linear
models and semi-parametric spatial filtering to assess the distribution of humpback and Bryde’s
whales in 3 areas off Oman relative to 3 simple physiographic variables in a survey grid. Our analysis
focused on the spatial eigenvector filtering of models, coupled with the spatial distribution of model
residuals, rather than just on model predictions. Spatial eigenvector filtering accounts for spatial
autocorrelation in models, allowing inference to be made regarding the relative importance of particular
areas. As an exemplar of this approach, we demonstrate that the Dhofar coast of southern Oman
is important habitat for the Arabian Sea population of humpback whales. We also suggest how conservation
planning for mitigating impacts on humpback whales off the Dhofar coast could start.},
keywords = {Arabian Sea, cetaceans, Distribution, General linear model, habitat modelling, habitat use, Humpback Whale, megaptera novaeangliae, Oman, Spatial autocorrelation},
pubstate = {published},
tppubtype = {article}
}
Habitat models are tools for understanding the relationship between cetaceans and their
environment, from which patterns of the animals’ space use can be inferred and management strategies
developed. Can working with space use alone be sufficient for management, when habitat cannot
be modeled? Here, we analyzed cetacean sightings data collected from small boat surveys off the
coast of Oman between 2000 and 2003. The waters off Oman are used by the Endangered Arabian
Sea population of humpback whales. Our data were collected primarily for photo-identification, using
a haphazard sampling regime, either in areas where humpback whales were thought to be relatively
abundant, or in areas that were logistically easy to survey. This leads to spatially autocorrelated data
that are not amenable to analysis using standard approaches. We used quasi-Poisson generalized linear
models and semi-parametric spatial filtering to assess the distribution of humpback and Bryde’s
whales in 3 areas off Oman relative to 3 simple physiographic variables in a survey grid. Our analysis
focused on the spatial eigenvector filtering of models, coupled with the spatial distribution of model
residuals, rather than just on model predictions. Spatial eigenvector filtering accounts for spatial
autocorrelation in models, allowing inference to be made regarding the relative importance of particular
areas. As an exemplar of this approach, we demonstrate that the Dhofar coast of southern Oman
is important habitat for the Arabian Sea population of humpback whales. We also suggest how conservation
planning for mitigating impacts on humpback whales off the Dhofar coast could start.
environment, from which patterns of the animals’ space use can be inferred and management strategies
developed. Can working with space use alone be sufficient for management, when habitat cannot
be modeled? Here, we analyzed cetacean sightings data collected from small boat surveys off the
coast of Oman between 2000 and 2003. The waters off Oman are used by the Endangered Arabian
Sea population of humpback whales. Our data were collected primarily for photo-identification, using
a haphazard sampling regime, either in areas where humpback whales were thought to be relatively
abundant, or in areas that were logistically easy to survey. This leads to spatially autocorrelated data
that are not amenable to analysis using standard approaches. We used quasi-Poisson generalized linear
models and semi-parametric spatial filtering to assess the distribution of humpback and Bryde’s
whales in 3 areas off Oman relative to 3 simple physiographic variables in a survey grid. Our analysis
focused on the spatial eigenvector filtering of models, coupled with the spatial distribution of model
residuals, rather than just on model predictions. Spatial eigenvector filtering accounts for spatial
autocorrelation in models, allowing inference to be made regarding the relative importance of particular
areas. As an exemplar of this approach, we demonstrate that the Dhofar coast of southern Oman
is important habitat for the Arabian Sea population of humpback whales. We also suggest how conservation
planning for mitigating impacts on humpback whales off the Dhofar coast could start.