Habitat suitability predictions for 15 species of cephalopods in the Southern Ocean
Progress Code: completed Statement: Although we have attempted to account for the spatial distribution of survey efforts in the modelling procedure, these results should still be treated with caution, particularly for species with small sample sizes or where one particular area dominates the occurre...
Format: | Dataset |
---|---|
Language: | unknown |
Published: |
Australian Ocean Data Network
|
Subjects: | |
Online Access: | https://researchdata.edu.au/habitat-suitability-predictions-southern-ocean/2817132 |
id |
ftands:oai:ands.org.au::2817132 |
---|---|
record_format |
openpolar |
spelling |
ftands:oai:ands.org.au::2817132 2023-12-03T10:30:28+01:00 Habitat suitability predictions for 15 species of cephalopods in the Southern Ocean Spatial: westlimit=-180; southlimit=-90; eastlimit=-180; northlimit=-40 Temporal: From 2012-07-01 to 2016-06-30 https://researchdata.edu.au/habitat-suitability-predictions-southern-ocean/2817132 unknown Australian Ocean Data Network https://researchdata.edu.au/habitat-suitability-predictions-southern-ocean/2817132 AAS_4124_cephalopod_habitat_suitability Dataset DOI AU/AADC > Australian Antarctic Data Centre, Australia biota oceans EARTH SCIENCE > BIOSPHERE > ECOSYSTEMS > MARINE ECOSYSTEMS BIOLOGICAL CLASSIFICATION > ANIMALS/INVERTEBRATES > MOLLUSKS > CEPHALOPODS > SQUIDS Computer > Computer MODELS AMD/AU AMD CEOS GEOGRAPHIC REGION > POLAR OCEAN > SOUTHERN OCEAN dataset ftands 2023-11-06T23:50:06Z Progress Code: completed Statement: Although we have attempted to account for the spatial distribution of survey efforts in the modelling procedure, these results should still be treated with caution, particularly for species with small sample sizes or where one particular area dominates the occurrence record. The predictor variables used were drawn from satellite and similar sources. The information from such variables rarely provides direct characterization of the primary processes affecting the species distribution. For example, there are no direct estimates of squid prey distributions. Instead, these variables typically provide proxy information such as water mass properties or primary productivity. The spatial and temporal scales of this information often do not match the scales experienced by the animals. Furthermore, predictor variables in the Southern Ocean are typically highly correlated because of the strong latitudinal and seasonal gradient that affects oceanic and atmospheric conditions. Because of these factors, it is rarely obvious which particular predictor variable is the most appropriate proxy to use in a given model. The dates provided in temporal coverage correspond to the start and stop dates of the project. Our understanding of how environmental change in the Southern Ocean will affect marine diversity,habitats and distribution remain limited. The habitats and distributions of Southern Ocean cephalopods are generally poorly understood, and yet such knowledge is necessary for research and conservation management purposes, as well as for assessing the potential impacts of environmental change. We used net-catch data to develop habitat suitability models for 15 of the most common cephalopods in the Southern Ocean. Full details of the methodology are provided in the paper (Xavier et al. (2015)). Briefly, occurrence data were taken from the SCAR Biogeographic Atlas of the Southern Ocean. This compilation was based upon Xavier et al. (1999), with additional data drawn from the Ocean ... Dataset Southern Ocean Research Data Australia (Australian National Data Service - ANDS) Southern Ocean |
institution |
Open Polar |
collection |
Research Data Australia (Australian National Data Service - ANDS) |
op_collection_id |
ftands |
language |
unknown |
topic |
biota oceans EARTH SCIENCE > BIOSPHERE > ECOSYSTEMS > MARINE ECOSYSTEMS BIOLOGICAL CLASSIFICATION > ANIMALS/INVERTEBRATES > MOLLUSKS > CEPHALOPODS > SQUIDS Computer > Computer MODELS AMD/AU AMD CEOS GEOGRAPHIC REGION > POLAR OCEAN > SOUTHERN OCEAN |
spellingShingle |
biota oceans EARTH SCIENCE > BIOSPHERE > ECOSYSTEMS > MARINE ECOSYSTEMS BIOLOGICAL CLASSIFICATION > ANIMALS/INVERTEBRATES > MOLLUSKS > CEPHALOPODS > SQUIDS Computer > Computer MODELS AMD/AU AMD CEOS GEOGRAPHIC REGION > POLAR OCEAN > SOUTHERN OCEAN Habitat suitability predictions for 15 species of cephalopods in the Southern Ocean |
topic_facet |
biota oceans EARTH SCIENCE > BIOSPHERE > ECOSYSTEMS > MARINE ECOSYSTEMS BIOLOGICAL CLASSIFICATION > ANIMALS/INVERTEBRATES > MOLLUSKS > CEPHALOPODS > SQUIDS Computer > Computer MODELS AMD/AU AMD CEOS GEOGRAPHIC REGION > POLAR OCEAN > SOUTHERN OCEAN |
description |
Progress Code: completed Statement: Although we have attempted to account for the spatial distribution of survey efforts in the modelling procedure, these results should still be treated with caution, particularly for species with small sample sizes or where one particular area dominates the occurrence record. The predictor variables used were drawn from satellite and similar sources. The information from such variables rarely provides direct characterization of the primary processes affecting the species distribution. For example, there are no direct estimates of squid prey distributions. Instead, these variables typically provide proxy information such as water mass properties or primary productivity. The spatial and temporal scales of this information often do not match the scales experienced by the animals. Furthermore, predictor variables in the Southern Ocean are typically highly correlated because of the strong latitudinal and seasonal gradient that affects oceanic and atmospheric conditions. Because of these factors, it is rarely obvious which particular predictor variable is the most appropriate proxy to use in a given model. The dates provided in temporal coverage correspond to the start and stop dates of the project. Our understanding of how environmental change in the Southern Ocean will affect marine diversity,habitats and distribution remain limited. The habitats and distributions of Southern Ocean cephalopods are generally poorly understood, and yet such knowledge is necessary for research and conservation management purposes, as well as for assessing the potential impacts of environmental change. We used net-catch data to develop habitat suitability models for 15 of the most common cephalopods in the Southern Ocean. Full details of the methodology are provided in the paper (Xavier et al. (2015)). Briefly, occurrence data were taken from the SCAR Biogeographic Atlas of the Southern Ocean. This compilation was based upon Xavier et al. (1999), with additional data drawn from the Ocean ... |
format |
Dataset |
title |
Habitat suitability predictions for 15 species of cephalopods in the Southern Ocean |
title_short |
Habitat suitability predictions for 15 species of cephalopods in the Southern Ocean |
title_full |
Habitat suitability predictions for 15 species of cephalopods in the Southern Ocean |
title_fullStr |
Habitat suitability predictions for 15 species of cephalopods in the Southern Ocean |
title_full_unstemmed |
Habitat suitability predictions for 15 species of cephalopods in the Southern Ocean |
title_sort |
habitat suitability predictions for 15 species of cephalopods in the southern ocean |
publisher |
Australian Ocean Data Network |
url |
https://researchdata.edu.au/habitat-suitability-predictions-southern-ocean/2817132 |
op_coverage |
Spatial: westlimit=-180; southlimit=-90; eastlimit=-180; northlimit=-40 Temporal: From 2012-07-01 to 2016-06-30 |
geographic |
Southern Ocean |
geographic_facet |
Southern Ocean |
genre |
Southern Ocean |
genre_facet |
Southern Ocean |
op_source |
AU/AADC > Australian Antarctic Data Centre, Australia |
op_relation |
https://researchdata.edu.au/habitat-suitability-predictions-southern-ocean/2817132 AAS_4124_cephalopod_habitat_suitability Dataset DOI |
_version_ |
1784256316933406720 |