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On October 22, 2024, 8:53:34 PM UTC, ckan_admin:
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Updated description of Data from: Prey ingestion rates revealed by back-mounted accelerometers in Eurasian spoonbills from
Background Quantifying foraging success in space and time and among individuals is essential for answering many ecological questions and may guide conservation efforts. However, collecting this information is challenging for species that forage on mobile prey and are difficult to observe visually, for example, because they forage in inaccessible areas or at night. In such cases, the use of tracking devices that simultaneously collect location and acceleration data may provide a solution if foraging success can be extracted successfully. The aim of this study was to assess how well searching for and ingesting prey, among other behaviours, could be distinguished from 20 Hz acceleration data collected by GPS/ACC-trackers mounted on the back of Eurasian spoonbills Platalea leucorodia. Upon capturing a prey, spoonbills make a distinct movement with their head and back to throw the prey from the tip of the bill into the throat. Methods We compared the behavioural classification performance of random forest models that were trained and tested on video-annotated acceleration data segments of different (fixed or flexible) lengths. The best-performing model was then applied to 4 years of data of spoonbills foraging in the Wadden Sea during the breeding season, to explore seasonal and annual variation in prey ingestion rates. Results Highest classification accuracies (as indicated by the F-measure, a balanced measure of precision and sensitivity) of foraging behaviours were achieved by analysing short fixed-length segments (0.4-0.8 s) or 'flexibly-cut' segments. The F-measure was very high (> 0.90) for searching, standing, sitting and flying (distinguishing active and passive flight), 0.73 for ingesting prey and 0.65 for walking. False positive and negative prey ingestions were equally likely and most often confused with searching, resulting in a close match between the predicted and observed prey ingestion rates. Application of the best-performing model revealed strong seasonal patterns in prey ingestion rates in the Wadden Sea that varied between years. Conclusions We show that prey ingestion rates of Eurasian spoonbills can be fairly accurately estimated from acceleration data. These results are promising for the use of spoonbills equipped with GPS/ACC-trackers as monitors of spatial and temporal variation in the availability of small fish and shrimp, which is key to understand their foraging and migratory movements of spoonbills and provides information on the quality of (coastal) wetlands.
toBackground Quantifying foraging success in space and time and among individuals is essential for answering many ecological questions and may guide conservation efforts. However, collecting this information is challenging for species that forage on mobile prey and are difficult to observe visually, for example, because they forage in inaccessible areas or at night. In such cases, the use of tracking devices that simultaneously collect location and acceleration data may provide a solution if foraging success can be extracted successfully. The aim of this study was to assess how well searching for and ingesting prey, among other behaviours, could be distinguished from 20 Hz acceleration data collected by GPS/ACC-trackers mounted on the back of Eurasian spoonbills Platalea leucorodia. Upon capturing a prey, spoonbills make a distinct movement with their head and back to throw the prey from the tip of the bill into the throat. Methods We compared the behavioural classification performance of random forest models that were trained and tested on video-annotated acceleration data segments of different (fixed or flexible) lengths. The best-performing model was then applied to 4 years of data of spoonbills foraging in the Wadden Sea during the breeding season, to explore seasonal and annual variation in prey ingestion rates. Results Highest classification accuracies (as indicated by the F-measure, a balanced measure of precision and sensitivity) of foraging behaviours were achieved by analysing short fixed-length segments (0.4–0.8 s) or “flexibly-cut” segments. The F-measure was very high (> 0.90) for searching, standing, sitting and flying (distinguishing active and passive flight), 0.73 for ingesting prey and 0.65 for walking. False positive and negative prey ingestions were equally likely and most often confused with searching, resulting in a close match between the predicted and observed prey ingestion rates. Application of the best-performing model revealed strong seasonal patterns in prey ingestion rates in the Wadden Sea that varied between years. Conclusions We show that prey ingestion rates of Eurasian spoonbills can be fairly accurately estimated from acceleration data. These results are promising for the use of spoonbills equipped with GPS/ACC-trackers as monitors of spatial and temporal variation in the availability of small fish and shrimp, which is key to understand their foraging and migratory movements of spoonbills and provides information on the quality of (coastal) wetlands.
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Changed the license of Data from: Prey ingestion rates revealed by back-mounted accelerometers in Eurasian spoonbills to Creative Commons Attribution 4.0 (previously None).
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Changed the version of Data from: Prey ingestion rates revealed by back-mounted accelerometers in Eurasian spoonbills to 1 (previously 2)
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| 9 | Netherlands Institute for Sea | 9 | Netherlands Institute for Sea | ||
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| 15 | "distributor": "Research Data Management(NIOZ Royal Netherlands | 15 | "distributor": "Research Data Management(NIOZ Royal Netherlands | ||
| 16 | Institute for Sea Research)", | 16 | Institute for Sea Research)", | ||
| 17 | "doi_date_published": "2023-01-30", | 17 | "doi_date_published": "2023-01-30", | ||
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| 37 | "notes": "Background\nQuantifying foraging success in space and time | 39 | "notes": "Background\nQuantifying foraging success in space and time | ||
| 38 | and among individuals is essential for answering many\necological | 40 | and among individuals is essential for answering many\necological | ||
| 39 | questions and may guide conservation efforts. However, collecting this | 41 | questions and may guide conservation efforts. However, collecting this | ||
| 40 | information is challenging for\nspecies that forage on mobile prey and | 42 | information is challenging for\nspecies that forage on mobile prey and | ||
| 41 | are difficult to observe visually, for example, because they forage | 43 | are difficult to observe visually, for example, because they forage | ||
| 42 | in\ninaccessible areas or at night. In such cases, the use of tracking | 44 | in\ninaccessible areas or at night. In such cases, the use of tracking | ||
| 43 | devices that simultaneously collect location and\nacceleration data | 45 | devices that simultaneously collect location and\nacceleration data | ||
| 44 | may provide a solution if foraging success can be extracted | 46 | may provide a solution if foraging success can be extracted | ||
| 45 | successfully. The aim of this study\nwas to assess how well searching | 47 | successfully. The aim of this study\nwas to assess how well searching | ||
| 46 | for and ingesting prey, among other behaviours, could be distinguished | 48 | for and ingesting prey, among other behaviours, could be distinguished | ||
| 47 | from\n20 Hz acceleration data collected by GPS/ACC-trackers mounted on | 49 | from\n20 Hz acceleration data collected by GPS/ACC-trackers mounted on | ||
| 48 | the back of Eurasian spoonbills Platalea\nleucorodia. Upon capturing a | 50 | the back of Eurasian spoonbills Platalea\nleucorodia. Upon capturing a | ||
| 49 | prey, spoonbills make a distinct movement with their head and back to | 51 | prey, spoonbills make a distinct movement with their head and back to | ||
| 50 | throw the\nprey from the tip of the bill into the | 52 | throw the\nprey from the tip of the bill into the | ||
| 51 | throat.\n\nMethods\nWe compared the behavioural classification | 53 | throat.\n\nMethods\nWe compared the behavioural classification | ||
| 52 | performance of random forest models that were trained and tested\non | 54 | performance of random forest models that were trained and tested\non | ||
| 53 | video-annotated acceleration data segments of different (fixed or | 55 | video-annotated acceleration data segments of different (fixed or | ||
| 54 | flexible) lengths. The best-performing\nmodel was then applied to 4 | 56 | flexible) lengths. The best-performing\nmodel was then applied to 4 | ||
| 55 | years of data of spoonbills foraging in the Wadden Sea during the | 57 | years of data of spoonbills foraging in the Wadden Sea during the | ||
| 56 | breeding\nseason, to explore seasonal and annual variation in prey | 58 | breeding\nseason, to explore seasonal and annual variation in prey | ||
| 57 | ingestion rates.\n\nResults\nHighest classification accuracies (as | 59 | ingestion rates.\n\nResults\nHighest classification accuracies (as | ||
| 58 | indicated by the F-measure, a balanced measure of precision | 60 | indicated by the F-measure, a balanced measure of precision | ||
| 59 | and\nsensitivity) of foraging behaviours were achieved by analysing | 61 | and\nsensitivity) of foraging behaviours were achieved by analysing | ||
| n | 60 | short fixed-length segments (0.4-0.8 s) or\n'flexibly-cut' segments. | n | 62 | short fixed-length segments (0.4\u20130.8 s) |
| 61 | The F-measure was very high (> 0.90) for searching, standing, sitting | 63 | or\n\u201cflexibly-cut\u201d segments. The F-measure was very high (> | ||
| 62 | and flying\n(distinguishing active and passive flight), 0.73 for | 64 | 0.90) for searching, standing, sitting and flying\n(distinguishing | ||
| 63 | ingesting prey and 0.65 for walking. False positive and\nnegative prey | 65 | active and passive flight), 0.73 for ingesting prey and 0.65 for | ||
| 64 | ingestions were equally likely and most often confused with searching, | 66 | walking. False positive and\nnegative prey ingestions were equally | ||
| 65 | resulting in a close match\nbetween the predicted and observed prey | 67 | likely and most often confused with searching, resulting in a close | ||
| 66 | ingestion rates. Application of the best-performing model | 68 | match\nbetween the predicted and observed prey ingestion rates. | ||
| 67 | revealed\nstrong seasonal patterns in prey ingestion rates in the | 69 | Application of the best-performing model revealed\nstrong seasonal | ||
| 68 | Wadden Sea that varied between years.\n\nConclusions\nWe show that | 70 | patterns in prey ingestion rates in the Wadden Sea that varied between | ||
| 69 | prey ingestion rates of Eurasian spoonbills can be fairly accurately | 71 | years.\n\nConclusions\nWe show that prey ingestion rates of Eurasian | ||
| 70 | estimated from acceleration\ndata. These results are promising for the | 72 | spoonbills can be fairly accurately estimated from acceleration\ndata. | ||
| 71 | use of spoonbills equipped with GPS/ACC-trackers as monitors | 73 | These results are promising for the use of spoonbills equipped with | ||
| 72 | of\nspatial and temporal variation in the availability of small fish | 74 | GPS/ACC-trackers as monitors of\nspatial and temporal variation in the | ||
| 73 | and shrimp, which is key to understand their\nforaging and migratory | 75 | availability of small fish and shrimp, which is key to understand | ||
| 74 | movements of spoonbills and provides information on the quality of | 76 | their\nforaging and migratory movements of spoonbills and provides | ||
| 75 | (coastal) wetlands.", | 77 | information on the quality of (coastal) wetlands.", | ||
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