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...

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Format: Dataset
Language:unknown
Published: Australian Ocean Data Network
Subjects:
AMD
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 &gt BIOSPHERE &gt ECOSYSTEMS &gt MARINE ECOSYSTEMS BIOLOGICAL CLASSIFICATION &gt ANIMALS/INVERTEBRATES &gt MOLLUSKS &gt CEPHALOPODS &gt SQUIDS Computer &gt Computer MODELS AMD/AU AMD CEOS GEOGRAPHIC REGION &gt POLAR OCEAN &gt 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 &gt
BIOSPHERE &gt
ECOSYSTEMS &gt
MARINE ECOSYSTEMS
BIOLOGICAL CLASSIFICATION &gt
ANIMALS/INVERTEBRATES &gt
MOLLUSKS &gt
CEPHALOPODS &gt
SQUIDS
Computer &gt
Computer
MODELS
AMD/AU
AMD
CEOS
GEOGRAPHIC REGION &gt
POLAR
OCEAN &gt
SOUTHERN OCEAN
spellingShingle biota
oceans
EARTH SCIENCE &gt
BIOSPHERE &gt
ECOSYSTEMS &gt
MARINE ECOSYSTEMS
BIOLOGICAL CLASSIFICATION &gt
ANIMALS/INVERTEBRATES &gt
MOLLUSKS &gt
CEPHALOPODS &gt
SQUIDS
Computer &gt
Computer
MODELS
AMD/AU
AMD
CEOS
GEOGRAPHIC REGION &gt
POLAR
OCEAN &gt
SOUTHERN OCEAN
Habitat suitability predictions for 15 species of cephalopods in the Southern Ocean
topic_facet biota
oceans
EARTH SCIENCE &gt
BIOSPHERE &gt
ECOSYSTEMS &gt
MARINE ECOSYSTEMS
BIOLOGICAL CLASSIFICATION &gt
ANIMALS/INVERTEBRATES &gt
MOLLUSKS &gt
CEPHALOPODS &gt
SQUIDS
Computer &gt
Computer
MODELS
AMD/AU
AMD
CEOS
GEOGRAPHIC REGION &gt
POLAR
OCEAN &gt
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