Modelled mid‐trophic pelagic prey fields improve understanding of marine predator foraging behaviour
Biophysical interactions are influential in determining the scale of key ecological processes within marine ecosystems. For oceanic predators, this means foraging behaviour is influenced by processes shaping the distribution of prey. However, oceanic prey is difficult to observe and its abundance an...
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Veröffentlicht in: | Ecography (Copenhagen) 2020-07, Vol.43 (7), p.1014-1026 |
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creator | Green, D. B. Bestley, S. Trebilco, R. Corney, S. P. Lehodey, P. McMahon, C. R. Guinet, C. Hindell, Mark A. |
description | Biophysical interactions are influential in determining the scale of key ecological processes within marine ecosystems. For oceanic predators, this means foraging behaviour is influenced by processes shaping the distribution of prey. However, oceanic prey is difficult to observe and its abundance and distribution is regionally generalised. We use a spatiotemporally resolved simulation model to describe mid‐trophic prey distribution within the Southern Ocean and demonstrate insights that this modelled prey field provides into the foraging behaviour of a widely distributed marine predator, the southern elephant seal.
From a five‐year simulation of prey biomass, we computed climatologies of mean prey biomass (average prey conditions) and prey biomass variability (meso‐scale variability). We also compiled spatially gridded metrics of seal density and diving behaviour from 13 yr of tracking data. We statistically modelled these metrics as non‐linear functions of prey biomass (both mean and variability) and used these to predict seal distribution and behaviour. Our predictions were consistent with observations (R2adj = 0.23), indicating that seals aggregate in regions of high mesoscale activity where eddies concentrate prey. Here, seals dived deeper (R2marg = 0.12, R2cond = 0.51) and spent less time hunting (R2marg = 0.05, R2cond = 0.56), likely targeting deep but profitable prey patches. Seals generally avoided areas of low eddy activity where prey was likely dispersed. Most seals foraged south of the Subantarctic Front, despite north of the front exhibiting consistently high simulated prey biomasses. This likely reflects seal prey or habitat preferences, but also emphasises the importance of mesoscale prey biomass variability relative to regionally high mean biomass. This work demonstrates the value of coupling mechanistic representations of prey biomass with predator observations to provide insight into how biophysical processes combine to shape species distributions. This will be increasingly important for the robust prediction of species’ responses to rapid system change. |
doi_str_mv | 10.1111/ecog.04939 |
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From a five‐year simulation of prey biomass, we computed climatologies of mean prey biomass (average prey conditions) and prey biomass variability (meso‐scale variability). We also compiled spatially gridded metrics of seal density and diving behaviour from 13 yr of tracking data. We statistically modelled these metrics as non‐linear functions of prey biomass (both mean and variability) and used these to predict seal distribution and behaviour. Our predictions were consistent with observations (R2adj = 0.23), indicating that seals aggregate in regions of high mesoscale activity where eddies concentrate prey. Here, seals dived deeper (R2marg = 0.12, R2cond = 0.51) and spent less time hunting (R2marg = 0.05, R2cond = 0.56), likely targeting deep but profitable prey patches. Seals generally avoided areas of low eddy activity where prey was likely dispersed. Most seals foraged south of the Subantarctic Front, despite north of the front exhibiting consistently high simulated prey biomasses. This likely reflects seal prey or habitat preferences, but also emphasises the importance of mesoscale prey biomass variability relative to regionally high mean biomass. This work demonstrates the value of coupling mechanistic representations of prey biomass with predator observations to provide insight into how biophysical processes combine to shape species distributions. This will be increasingly important for the robust prediction of species’ responses to rapid system change.</description><identifier>ISSN: 0906-7590</identifier><identifier>EISSN: 1600-0587</identifier><identifier>DOI: 10.1111/ecog.04939</identifier><language>eng</language><publisher>Oxford, UK: Blackwell Publishing Ltd</publisher><subject>Biomass ; Computer simulation ; Diving behavior ; Ecological effects ; ecosystem modelling ; Eddies ; Environmental changes ; Environmental Sciences ; Foraging behavior ; Foraging habitats ; Habitat preferences ; Hunting ; kerguelen plateau ; Linear functions ; Marine ecology ; Marine ecosystems ; micronekton ; Mirounga leonina ; Predator-prey simulation ; Predators ; predators prey interaction ; Prey ; Seals ; Seals (animals) ; Small mammals ; southern elephant seal ; Southern Indian Ocean ; Variability</subject><ispartof>Ecography (Copenhagen), 2020-07, Vol.43 (7), p.1014-1026</ispartof><rights>2020 The Authors. Ecography published by John Wiley & Sons Ltd on behalf of Nordic Society Oikos</rights><rights>COPYRIGHT 2020 John Wiley & Sons, Inc.</rights><rights>Copyright Wiley Subscription Services, Inc. Jul 2020</rights><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4102-b6c523989d0f7822bd95aab16a2705bbce87d9ebb2fbf072809527f3dee7358c3</citedby><cites>FETCH-LOGICAL-c4102-b6c523989d0f7822bd95aab16a2705bbce87d9ebb2fbf072809527f3dee7358c3</cites><orcidid>0000-0001-5241-8917 ; 0000-0001-9712-8016 ; 0000-0002-8293-0863 ; 0000-0002-2753-4796 ; 0000-0003-2481-6947 ; 0000-0002-0346-3129 ; 0000-0002-7823-7185 ; 0000-0001-9342-669X ; 0000-0003-0768-2203</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1111%2Fecog.04939$$EPDF$$P50$$Gwiley$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1111%2Fecog.04939$$EHTML$$P50$$Gwiley$$Hfree_for_read</linktohtml><link.rule.ids>230,314,777,781,861,882,1412,11543,27905,27906,45555,45556,46033,46457</link.rule.ids><backlink>$$Uhttps://hal.science/hal-02543064$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Green, D. B.</creatorcontrib><creatorcontrib>Bestley, S.</creatorcontrib><creatorcontrib>Trebilco, R.</creatorcontrib><creatorcontrib>Corney, S. P.</creatorcontrib><creatorcontrib>Lehodey, P.</creatorcontrib><creatorcontrib>McMahon, C. R.</creatorcontrib><creatorcontrib>Guinet, C.</creatorcontrib><creatorcontrib>Hindell, Mark A.</creatorcontrib><title>Modelled mid‐trophic pelagic prey fields improve understanding of marine predator foraging behaviour</title><title>Ecography (Copenhagen)</title><description>Biophysical interactions are influential in determining the scale of key ecological processes within marine ecosystems. For oceanic predators, this means foraging behaviour is influenced by processes shaping the distribution of prey. However, oceanic prey is difficult to observe and its abundance and distribution is regionally generalised. We use a spatiotemporally resolved simulation model to describe mid‐trophic prey distribution within the Southern Ocean and demonstrate insights that this modelled prey field provides into the foraging behaviour of a widely distributed marine predator, the southern elephant seal.
From a five‐year simulation of prey biomass, we computed climatologies of mean prey biomass (average prey conditions) and prey biomass variability (meso‐scale variability). We also compiled spatially gridded metrics of seal density and diving behaviour from 13 yr of tracking data. We statistically modelled these metrics as non‐linear functions of prey biomass (both mean and variability) and used these to predict seal distribution and behaviour. Our predictions were consistent with observations (R2adj = 0.23), indicating that seals aggregate in regions of high mesoscale activity where eddies concentrate prey. Here, seals dived deeper (R2marg = 0.12, R2cond = 0.51) and spent less time hunting (R2marg = 0.05, R2cond = 0.56), likely targeting deep but profitable prey patches. Seals generally avoided areas of low eddy activity where prey was likely dispersed. Most seals foraged south of the Subantarctic Front, despite north of the front exhibiting consistently high simulated prey biomasses. This likely reflects seal prey or habitat preferences, but also emphasises the importance of mesoscale prey biomass variability relative to regionally high mean biomass. This work demonstrates the value of coupling mechanistic representations of prey biomass with predator observations to provide insight into how biophysical processes combine to shape species distributions. This will be increasingly important for the robust prediction of species’ responses to rapid system change.</description><subject>Biomass</subject><subject>Computer simulation</subject><subject>Diving behavior</subject><subject>Ecological effects</subject><subject>ecosystem modelling</subject><subject>Eddies</subject><subject>Environmental changes</subject><subject>Environmental Sciences</subject><subject>Foraging behavior</subject><subject>Foraging habitats</subject><subject>Habitat preferences</subject><subject>Hunting</subject><subject>kerguelen plateau</subject><subject>Linear functions</subject><subject>Marine ecology</subject><subject>Marine ecosystems</subject><subject>micronekton</subject><subject>Mirounga leonina</subject><subject>Predator-prey simulation</subject><subject>Predators</subject><subject>predators prey interaction</subject><subject>Prey</subject><subject>Seals</subject><subject>Seals (animals)</subject><subject>Small mammals</subject><subject>southern elephant seal</subject><subject>Southern Indian Ocean</subject><subject>Variability</subject><issn>0906-7590</issn><issn>1600-0587</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><sourceid>WIN</sourceid><recordid>eNp9kc2u0zAQhS0EEuXChieIxAqklLET2_Gyqu4PUtHdwNryz7j1VRoXJy3qjkfgGXkSHIJYMrMYafyd0bEOIW8prGmpj-jSfg2tatQzsqICoAbeyedkBQpELbmCl-TVOD4BUKZEtyLhc_LY9-irY_S_fvyccjodoqtO2Jv9PDNeqxCx92MVj6ecLlidB495nMzg47CvUqiOJscBZ9abKeUqpFzE5c3iwVxiOufX5EUw_Yhv_s4b8vXu9sv2od493n_abna1aymw2grHWaM65SHIjjHrFTfGUmGYBG6tw056hdayYANI1oHiTIbGI8qGd665Ie-XuwfT61OOxdlVJxP1w2an5x0w3jYg2gst7LuFLb_6dsZx0k_F6VDsadZSRUVDZVuo9ULtTY86DiFN2bjSHo_RpQFDLPuNpCC5AC6K4MMicDmNY8bwzwcFPYek55D0n5AKTBf4e7ly_Q-pb7eP95TxjjW_Aa2flZo</recordid><startdate>202007</startdate><enddate>202007</enddate><creator>Green, D. 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B. ; Bestley, S. ; Trebilco, R. ; Corney, S. P. ; Lehodey, P. ; McMahon, C. R. ; Guinet, C. ; Hindell, Mark A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4102-b6c523989d0f7822bd95aab16a2705bbce87d9ebb2fbf072809527f3dee7358c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Biomass</topic><topic>Computer simulation</topic><topic>Diving behavior</topic><topic>Ecological effects</topic><topic>ecosystem modelling</topic><topic>Eddies</topic><topic>Environmental changes</topic><topic>Environmental Sciences</topic><topic>Foraging behavior</topic><topic>Foraging habitats</topic><topic>Habitat preferences</topic><topic>Hunting</topic><topic>kerguelen plateau</topic><topic>Linear functions</topic><topic>Marine ecology</topic><topic>Marine ecosystems</topic><topic>micronekton</topic><topic>Mirounga leonina</topic><topic>Predator-prey simulation</topic><topic>Predators</topic><topic>predators prey interaction</topic><topic>Prey</topic><topic>Seals</topic><topic>Seals (animals)</topic><topic>Small mammals</topic><topic>southern elephant seal</topic><topic>Southern Indian Ocean</topic><topic>Variability</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Green, D. B.</creatorcontrib><creatorcontrib>Bestley, S.</creatorcontrib><creatorcontrib>Trebilco, R.</creatorcontrib><creatorcontrib>Corney, S. P.</creatorcontrib><creatorcontrib>Lehodey, P.</creatorcontrib><creatorcontrib>McMahon, C. R.</creatorcontrib><creatorcontrib>Guinet, C.</creatorcontrib><creatorcontrib>Hindell, Mark A.</creatorcontrib><collection>Wiley Online Library Open Access</collection><collection>Wiley Free Content</collection><collection>CrossRef</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Hyper Article en Ligne (HAL)</collection><jtitle>Ecography (Copenhagen)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Green, D. B.</au><au>Bestley, S.</au><au>Trebilco, R.</au><au>Corney, S. P.</au><au>Lehodey, P.</au><au>McMahon, C. R.</au><au>Guinet, C.</au><au>Hindell, Mark A.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Modelled mid‐trophic pelagic prey fields improve understanding of marine predator foraging behaviour</atitle><jtitle>Ecography (Copenhagen)</jtitle><date>2020-07</date><risdate>2020</risdate><volume>43</volume><issue>7</issue><spage>1014</spage><epage>1026</epage><pages>1014-1026</pages><issn>0906-7590</issn><eissn>1600-0587</eissn><abstract>Biophysical interactions are influential in determining the scale of key ecological processes within marine ecosystems. For oceanic predators, this means foraging behaviour is influenced by processes shaping the distribution of prey. However, oceanic prey is difficult to observe and its abundance and distribution is regionally generalised. We use a spatiotemporally resolved simulation model to describe mid‐trophic prey distribution within the Southern Ocean and demonstrate insights that this modelled prey field provides into the foraging behaviour of a widely distributed marine predator, the southern elephant seal.
From a five‐year simulation of prey biomass, we computed climatologies of mean prey biomass (average prey conditions) and prey biomass variability (meso‐scale variability). We also compiled spatially gridded metrics of seal density and diving behaviour from 13 yr of tracking data. We statistically modelled these metrics as non‐linear functions of prey biomass (both mean and variability) and used these to predict seal distribution and behaviour. Our predictions were consistent with observations (R2adj = 0.23), indicating that seals aggregate in regions of high mesoscale activity where eddies concentrate prey. Here, seals dived deeper (R2marg = 0.12, R2cond = 0.51) and spent less time hunting (R2marg = 0.05, R2cond = 0.56), likely targeting deep but profitable prey patches. Seals generally avoided areas of low eddy activity where prey was likely dispersed. Most seals foraged south of the Subantarctic Front, despite north of the front exhibiting consistently high simulated prey biomasses. This likely reflects seal prey or habitat preferences, but also emphasises the importance of mesoscale prey biomass variability relative to regionally high mean biomass. This work demonstrates the value of coupling mechanistic representations of prey biomass with predator observations to provide insight into how biophysical processes combine to shape species distributions. This will be increasingly important for the robust prediction of species’ responses to rapid system change.</abstract><cop>Oxford, UK</cop><pub>Blackwell Publishing Ltd</pub><doi>10.1111/ecog.04939</doi><tpages>13</tpages><orcidid>https://orcid.org/0000-0001-5241-8917</orcidid><orcidid>https://orcid.org/0000-0001-9712-8016</orcidid><orcidid>https://orcid.org/0000-0002-8293-0863</orcidid><orcidid>https://orcid.org/0000-0002-2753-4796</orcidid><orcidid>https://orcid.org/0000-0003-2481-6947</orcidid><orcidid>https://orcid.org/0000-0002-0346-3129</orcidid><orcidid>https://orcid.org/0000-0002-7823-7185</orcidid><orcidid>https://orcid.org/0000-0001-9342-669X</orcidid><orcidid>https://orcid.org/0000-0003-0768-2203</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Biomass Computer simulation Diving behavior Ecological effects ecosystem modelling Eddies Environmental changes Environmental Sciences Foraging behavior Foraging habitats Habitat preferences Hunting kerguelen plateau Linear functions Marine ecology Marine ecosystems micronekton Mirounga leonina Predator-prey simulation Predators predators prey interaction Prey Seals Seals (animals) Small mammals southern elephant seal Southern Indian Ocean Variability |
title | Modelled mid‐trophic pelagic prey fields improve understanding of marine predator foraging behaviour |
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