Data from: Understanding spatial distributions: negative density-dependence in prey causes predators to trade-off prey quantity with quality
Negative density-dependence is generally studied within a single trophic level, thereby neglecting its effect on higher trophic levels. The ‘functional response’ couples a predator's intake rate to prey density. Most widespread is a type II functional response, where intake rate increases asymp...
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Dryad Digital Repository
2019
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Online Access: | https://doi.org/10.5061/dryad.d75hq |
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fttriple:oai:gotriple.eu:50|dedup_wf_001::5c86805ffa6cd90df2fa06ac9b7f2c31 2023-05-15T15:48:24+02:00 Data from: Understanding spatial distributions: negative density-dependence in prey causes predators to trade-off prey quantity with quality Bijleveld, Allert I MacCurdy, Robert B Chan, Ying-Chi Penning, Emma Gabrielson, Richard M Cluderay, John Spaulding, Eric L Dekinga, Anne Holthuijsen, Sander Ten Horn, Job Brugge, Maarten Van Gils, Jan A Winkler, David W Piersma, Theunis Gabrielson, Rich M. MacCurdy, Robert B. Spaulding, Eric L. Winkler, David W. Bijleveld, Allert I. Van Gils, Jan A. 2019-07-15 https://doi.org/10.5061/dryad.d75hq undefined unknown Dryad Digital Repository https://dx.doi.org/10.5061/dryad.d75hq http://dx.doi.org/10.5061/dryad.d75hq lic_creative-commons 10.5061/dryad.d75hq oai:easy.dans.knaw.nl:easy-dataset:92716 oai:services.nod.dans.knaw.nl:Products/dans:oai:easy.dans.knaw.nl:easy-dataset:92716 10|opendoar____::8b6dd7db9af49e67306feb59a8bdc52c re3data_____::r3d100000044 10|openaire____::9e3be59865b2c1c335d32dae2fe7b254 10|re3data_____::94816e6421eeb072e7742ce6a9decc5f 10|re3data_____::84e123776089ce3c7a33db98d9cd15a8 10|eurocrisdris::fe4903425d9040f680d8610d9079ea14 10|openaire____::081b82f96300b6a6e3d282bad31cb6e2 negative density-dependence optimal foraging phenotype-limited spatial distribution predator-prey dynamics resource-selection modelling type IV functional response western Europe Wadden Sea The Netherlands Holocene Calidris canutus Cerastoderma edule Life sciences medicine and health care envir geo Dataset https://vocabularies.coar-repositories.org/resource_types/c_ddb1/ 2019 fttriple https://doi.org/10.5061/dryad.d75hq 2023-01-22T17:42:06Z Negative density-dependence is generally studied within a single trophic level, thereby neglecting its effect on higher trophic levels. The ‘functional response’ couples a predator's intake rate to prey density. Most widespread is a type II functional response, where intake rate increases asymptotically with prey density; this predicts the highest predator densities at the highest prey densities. In one of the most stringent tests of this generality to date, we measured density and quality of bivalve prey (edible cockles Cerastoderma edule) across 50 km² of mudflat, and simultaneously, with a novel time-of-arrival methodology, tracked their avian predators (red knots Calidris canutus). Because of negative density-dependence in the individual quality of cockles, the predicted energy intake rates of red knots declined at high prey densities (a type IV, rather than a type II functional response). Resource-selection modelling revealed that red knots indeed selected areas of intermediate cockle densities where energy intake rates were maximized given their phenotype-specific digestive constraints (as indicated by gizzard mass). Because negative density-dependence is common, we question the current consensus and suggest that predators commonly maximize their energy intake rates at intermediate prey densities. Prey density alone may thus poorly predict intake rates, carrying capacity and spatial distributions of predators. spatial raster with cockle densities corresponding to Fig3AThe coordinate reference system is EPSG:32631 - WGS 84 / UTM zone 31N, and cockle density is presented in numbers per square meter.resource_landscape-Fig3A_cockle_density.tifSpatial raster with relative AFDMflesh corresponding to Fig3BThe coordinate reference system is EPSG:32631 - WGS 84 / UTM zone 31N. Relative AFDMflesh is presented as the ratio of AFDMflesh to average AFDMflesh for cockles of identical length (see main article).resource_landscape-Fig3B_relative_AFDMflesh.tifspatial raster with predicted intake rates corresponding to ... Dataset Calidris canutus Unknown |
institution |
Open Polar |
collection |
Unknown |
op_collection_id |
fttriple |
language |
unknown |
topic |
negative density-dependence optimal foraging phenotype-limited spatial distribution predator-prey dynamics resource-selection modelling type IV functional response western Europe Wadden Sea The Netherlands Holocene Calidris canutus Cerastoderma edule Life sciences medicine and health care envir geo |
spellingShingle |
negative density-dependence optimal foraging phenotype-limited spatial distribution predator-prey dynamics resource-selection modelling type IV functional response western Europe Wadden Sea The Netherlands Holocene Calidris canutus Cerastoderma edule Life sciences medicine and health care envir geo Bijleveld, Allert I MacCurdy, Robert B Chan, Ying-Chi Penning, Emma Gabrielson, Richard M Cluderay, John Spaulding, Eric L Dekinga, Anne Holthuijsen, Sander Ten Horn, Job Brugge, Maarten Van Gils, Jan A Winkler, David W Piersma, Theunis Gabrielson, Rich M. MacCurdy, Robert B. Spaulding, Eric L. Winkler, David W. Bijleveld, Allert I. Van Gils, Jan A. Data from: Understanding spatial distributions: negative density-dependence in prey causes predators to trade-off prey quantity with quality |
topic_facet |
negative density-dependence optimal foraging phenotype-limited spatial distribution predator-prey dynamics resource-selection modelling type IV functional response western Europe Wadden Sea The Netherlands Holocene Calidris canutus Cerastoderma edule Life sciences medicine and health care envir geo |
description |
Negative density-dependence is generally studied within a single trophic level, thereby neglecting its effect on higher trophic levels. The ‘functional response’ couples a predator's intake rate to prey density. Most widespread is a type II functional response, where intake rate increases asymptotically with prey density; this predicts the highest predator densities at the highest prey densities. In one of the most stringent tests of this generality to date, we measured density and quality of bivalve prey (edible cockles Cerastoderma edule) across 50 km² of mudflat, and simultaneously, with a novel time-of-arrival methodology, tracked their avian predators (red knots Calidris canutus). Because of negative density-dependence in the individual quality of cockles, the predicted energy intake rates of red knots declined at high prey densities (a type IV, rather than a type II functional response). Resource-selection modelling revealed that red knots indeed selected areas of intermediate cockle densities where energy intake rates were maximized given their phenotype-specific digestive constraints (as indicated by gizzard mass). Because negative density-dependence is common, we question the current consensus and suggest that predators commonly maximize their energy intake rates at intermediate prey densities. Prey density alone may thus poorly predict intake rates, carrying capacity and spatial distributions of predators. spatial raster with cockle densities corresponding to Fig3AThe coordinate reference system is EPSG:32631 - WGS 84 / UTM zone 31N, and cockle density is presented in numbers per square meter.resource_landscape-Fig3A_cockle_density.tifSpatial raster with relative AFDMflesh corresponding to Fig3BThe coordinate reference system is EPSG:32631 - WGS 84 / UTM zone 31N. Relative AFDMflesh is presented as the ratio of AFDMflesh to average AFDMflesh for cockles of identical length (see main article).resource_landscape-Fig3B_relative_AFDMflesh.tifspatial raster with predicted intake rates corresponding to ... |
format |
Dataset |
author |
Bijleveld, Allert I MacCurdy, Robert B Chan, Ying-Chi Penning, Emma Gabrielson, Richard M Cluderay, John Spaulding, Eric L Dekinga, Anne Holthuijsen, Sander Ten Horn, Job Brugge, Maarten Van Gils, Jan A Winkler, David W Piersma, Theunis Gabrielson, Rich M. MacCurdy, Robert B. Spaulding, Eric L. Winkler, David W. Bijleveld, Allert I. Van Gils, Jan A. |
author_facet |
Bijleveld, Allert I MacCurdy, Robert B Chan, Ying-Chi Penning, Emma Gabrielson, Richard M Cluderay, John Spaulding, Eric L Dekinga, Anne Holthuijsen, Sander Ten Horn, Job Brugge, Maarten Van Gils, Jan A Winkler, David W Piersma, Theunis Gabrielson, Rich M. MacCurdy, Robert B. Spaulding, Eric L. Winkler, David W. Bijleveld, Allert I. Van Gils, Jan A. |
author_sort |
Bijleveld, Allert I |
title |
Data from: Understanding spatial distributions: negative density-dependence in prey causes predators to trade-off prey quantity with quality |
title_short |
Data from: Understanding spatial distributions: negative density-dependence in prey causes predators to trade-off prey quantity with quality |
title_full |
Data from: Understanding spatial distributions: negative density-dependence in prey causes predators to trade-off prey quantity with quality |
title_fullStr |
Data from: Understanding spatial distributions: negative density-dependence in prey causes predators to trade-off prey quantity with quality |
title_full_unstemmed |
Data from: Understanding spatial distributions: negative density-dependence in prey causes predators to trade-off prey quantity with quality |
title_sort |
data from: understanding spatial distributions: negative density-dependence in prey causes predators to trade-off prey quantity with quality |
publisher |
Dryad Digital Repository |
publishDate |
2019 |
url |
https://doi.org/10.5061/dryad.d75hq |
genre |
Calidris canutus |
genre_facet |
Calidris canutus |
op_source |
10.5061/dryad.d75hq oai:easy.dans.knaw.nl:easy-dataset:92716 oai:services.nod.dans.knaw.nl:Products/dans:oai:easy.dans.knaw.nl:easy-dataset:92716 10|opendoar____::8b6dd7db9af49e67306feb59a8bdc52c re3data_____::r3d100000044 10|openaire____::9e3be59865b2c1c335d32dae2fe7b254 10|re3data_____::94816e6421eeb072e7742ce6a9decc5f 10|re3data_____::84e123776089ce3c7a33db98d9cd15a8 10|eurocrisdris::fe4903425d9040f680d8610d9079ea14 10|openaire____::081b82f96300b6a6e3d282bad31cb6e2 |
op_relation |
https://dx.doi.org/10.5061/dryad.d75hq http://dx.doi.org/10.5061/dryad.d75hq |
op_rights |
lic_creative-commons |
op_doi |
https://doi.org/10.5061/dryad.d75hq |
_version_ |
1766383385910968320 |