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On October 22, 2024, 5:19:52 PM UTC,
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Added resource Movie760_20140608_11594800-20140608_13244700.mp4 to Data from: Prey ingestion rates revealed by back-mounted accelerometers in Eurasian spoonbills
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| 2 | "author": null, | 2 | "author": null, | ||
| 3 | "author_email": null, | 3 | "author_email": null, | ||
| 4 | "code": "7b.b.yd", | 4 | "code": "7b.b.yd", | ||
| 5 | "contributor": "NIOZ Royal Netherlands Institute for Sea Research", | 5 | "contributor": "NIOZ Royal Netherlands Institute for Sea Research", | ||
| 6 | "creator_user_id": "20ff4b59-2dcf-4b58-8a9a-8805da8c9f5c", | 6 | "creator_user_id": "20ff4b59-2dcf-4b58-8a9a-8805da8c9f5c", | ||
| 7 | "creators": | 7 | "creators": | ||
| 8 | me\":\"Lok\",\"orcid\":\"0000-0003-3753-8501\",\"affiliation\":\"Royal | 8 | me\":\"Lok\",\"orcid\":\"0000-0003-3753-8501\",\"affiliation\":\"Royal | ||
| 9 | Netherlands Institute for Sea | 9 | Netherlands Institute for Sea | ||
| 10 | pondingauthor\":true,\"contactemailaddress\":\"tamar.lok@nioz.nl\"}]", | 10 | pondingauthor\":true,\"contactemailaddress\":\"tamar.lok@nioz.nl\"}]", | ||
| 11 | "dataset_persistent_id": "DOI:10.25850/nioz/7b.b.yd", | 11 | "dataset_persistent_id": "DOI:10.25850/nioz/7b.b.yd", | ||
| 12 | "deposit_date": "2023-01-12", | 12 | "deposit_date": "2023-01-12", | ||
| 13 | "depositor": "Tamar Lok", | 13 | "depositor": "Tamar Lok", | ||
| 14 | "distribution_date": "2023-01-12", | 14 | "distribution_date": "2023-01-12", | ||
| 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", | ||
| 18 | "funding_references": | 18 | "funding_references": | ||
| 19 | \":\"WF209925\",\"awardtitle\":\"Metawad\",\"formatted\":\"Waddenfonds | 19 | \":\"WF209925\",\"awardtitle\":\"Metawad\",\"formatted\":\"Waddenfonds | ||
| 20 | - WF209925 - Metawad\"},{\"name\":\"Netherlands Organisation for | 20 | - WF209925 - Metawad\"},{\"name\":\"Netherlands Organisation for | ||
| 21 | Scientific Research | 21 | Scientific Research | ||
| 22 | (NWO)\",\"awardnumber\":\"016.Veni.192.245\",\"awardtitle\":\"Veni | 22 | (NWO)\",\"awardnumber\":\"016.Veni.192.245\",\"awardtitle\":\"Veni | ||
| 23 | grant Tamar Lok\",\"formatted\":\"Netherlands Organisation for | 23 | grant Tamar Lok\",\"formatted\":\"Netherlands Organisation for | ||
| 24 | Scientific Research (NWO) - 016.Veni.192.245 - Veni grant Tamar | 24 | Scientific Research (NWO) - 016.Veni.192.245 - Veni grant Tamar | ||
| 25 | Lok\"},{\"name\":\"Netherlands Organisation for Scientific Research | 25 | Lok\"},{\"name\":\"Netherlands Organisation for Scientific Research | ||
| 26 | (NWO)\",\"awardnumber\":\"Spinoza Premium | 26 | (NWO)\",\"awardnumber\":\"Spinoza Premium | ||
| 27 | 2014\",\"awardtitle\":\"Spinoza Premium Theunis Piersma\"}]", | 27 | 2014\",\"awardtitle\":\"Spinoza Premium Theunis Piersma\"}]", | ||
| 28 | "groups": [], | 28 | "groups": [], | ||
| 29 | "id": "2e941b9b-8483-4ee3-ba10-0cdc987c0f61", | 29 | "id": "2e941b9b-8483-4ee3-ba10-0cdc987c0f61", | ||
| 30 | "isopen": false, | 30 | "isopen": false, | ||
| 31 | "license_title": null, | 31 | "license_title": null, | ||
| 32 | "maintainer": null, | 32 | "maintainer": null, | ||
| 33 | "maintainer_email": null, | 33 | "maintainer_email": null, | ||
| 34 | "metadata_created": "2024-10-22T11:50:05.438553", | 34 | "metadata_created": "2024-10-22T11:50:05.438553", | ||
| n | 35 | "metadata_modified": "2024-10-22T17:18:35.552327", | n | 35 | "metadata_modified": "2024-10-22T17:19:52.185979", |
| 36 | "name": "7bbyd", | 36 | "name": "7bbyd", | ||
| 37 | "notes": "Background\nQuantifying foraging success in space and time | 37 | "notes": "Background\nQuantifying foraging success in space and time | ||
| 38 | and among individuals is essential for answering many\necological | 38 | and among individuals is essential for answering many\necological | ||
| 39 | questions and may guide conservation efforts. However, collecting this | 39 | questions and may guide conservation efforts. However, collecting this | ||
| 40 | information is challenging for\nspecies that forage on mobile prey and | 40 | information is challenging for\nspecies that forage on mobile prey and | ||
| 41 | are difficult to observe visually, for example, because they forage | 41 | are difficult to observe visually, for example, because they forage | ||
| 42 | in\ninaccessible areas or at night. In such cases, the use of tracking | 42 | in\ninaccessible areas or at night. In such cases, the use of tracking | ||
| 43 | devices that simultaneously collect location and\nacceleration data | 43 | devices that simultaneously collect location and\nacceleration data | ||
| 44 | may provide a solution if foraging success can be extracted | 44 | may provide a solution if foraging success can be extracted | ||
| 45 | successfully. The aim of this study\nwas to assess how well searching | 45 | successfully. The aim of this study\nwas to assess how well searching | ||
| 46 | for and ingesting prey, among other behaviours, could be distinguished | 46 | for and ingesting prey, among other behaviours, could be distinguished | ||
| 47 | from\n20 Hz acceleration data collected by GPS/ACC-trackers mounted on | 47 | from\n20 Hz acceleration data collected by GPS/ACC-trackers mounted on | ||
| 48 | the back of Eurasian spoonbills Platalea\nleucorodia. Upon capturing a | 48 | the back of Eurasian spoonbills Platalea\nleucorodia. Upon capturing a | ||
| 49 | prey, spoonbills make a distinct movement with their head and back to | 49 | prey, spoonbills make a distinct movement with their head and back to | ||
| 50 | throw the\nprey from the tip of the bill into the | 50 | throw the\nprey from the tip of the bill into the | ||
| 51 | throat.\n\nMethods\nWe compared the behavioural classification | 51 | throat.\n\nMethods\nWe compared the behavioural classification | ||
| 52 | performance of random forest models that were trained and tested\non | 52 | performance of random forest models that were trained and tested\non | ||
| 53 | video-annotated acceleration data segments of different (fixed or | 53 | video-annotated acceleration data segments of different (fixed or | ||
| 54 | flexible) lengths. The best-performing\nmodel was then applied to 4 | 54 | flexible) lengths. The best-performing\nmodel was then applied to 4 | ||
| 55 | years of data of spoonbills foraging in the Wadden Sea during the | 55 | years of data of spoonbills foraging in the Wadden Sea during the | ||
| 56 | breeding\nseason, to explore seasonal and annual variation in prey | 56 | breeding\nseason, to explore seasonal and annual variation in prey | ||
| 57 | ingestion rates.\n\nResults\nHighest classification accuracies (as | 57 | ingestion rates.\n\nResults\nHighest classification accuracies (as | ||
| 58 | indicated by the F-measure, a balanced measure of precision | 58 | indicated by the F-measure, a balanced measure of precision | ||
| 59 | and\nsensitivity) of foraging behaviours were achieved by analysing | 59 | and\nsensitivity) of foraging behaviours were achieved by analysing | ||
| 60 | short fixed-length segments (0.4\u20130.8 s) | 60 | short fixed-length segments (0.4\u20130.8 s) | ||
| 61 | or\n\u201cflexibly-cut\u201d segments. The F-measure was very high (> | 61 | or\n\u201cflexibly-cut\u201d segments. The F-measure was very high (> | ||
| 62 | 0.90) for searching, standing, sitting and flying\n(distinguishing | 62 | 0.90) for searching, standing, sitting and flying\n(distinguishing | ||
| 63 | active and passive flight), 0.73 for ingesting prey and 0.65 for | 63 | active and passive flight), 0.73 for ingesting prey and 0.65 for | ||
| 64 | walking. False positive and\nnegative prey ingestions were equally | 64 | walking. False positive and\nnegative prey ingestions were equally | ||
| 65 | likely and most often confused with searching, resulting in a close | 65 | likely and most often confused with searching, resulting in a close | ||
| 66 | match\nbetween the predicted and observed prey ingestion rates. | 66 | match\nbetween the predicted and observed prey ingestion rates. | ||
| 67 | Application of the best-performing model revealed\nstrong seasonal | 67 | Application of the best-performing model revealed\nstrong seasonal | ||
| 68 | patterns in prey ingestion rates in the Wadden Sea that varied between | 68 | patterns in prey ingestion rates in the Wadden Sea that varied between | ||
| 69 | years.\n\nConclusions\nWe show that prey ingestion rates of Eurasian | 69 | years.\n\nConclusions\nWe show that prey ingestion rates of Eurasian | ||
| 70 | spoonbills can be fairly accurately estimated from acceleration\ndata. | 70 | spoonbills can be fairly accurately estimated from acceleration\ndata. | ||
| 71 | These results are promising for the use of spoonbills equipped with | 71 | These results are promising for the use of spoonbills equipped with | ||
| 72 | GPS/ACC-trackers as monitors of\nspatial and temporal variation in the | 72 | GPS/ACC-trackers as monitors of\nspatial and temporal variation in the | ||
| 73 | availability of small fish and shrimp, which is key to understand | 73 | availability of small fish and shrimp, which is key to understand | ||
| 74 | their\nforaging and migratory movements of spoonbills and provides | 74 | their\nforaging and migratory movements of spoonbills and provides | ||
| 75 | information on the quality of (coastal) wetlands.", | 75 | information on the quality of (coastal) wetlands.", | ||
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