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There is a growing acceptance of the use of drones (also known as unmanned aerial vehicles, UAVs and remotely piloted aircraft systems, RPAS) to detect wildlife, largely because of the capacity for drones to rapidly cover large areas compared to ground survey methods. While drones can aid the capture of large amounts of imagery, detection requires either manual evaluation of the imagery or automated detection using machine learning algorithms. While manual evaluation of drone-acquired imagery is possible and sometimes necessary, the powerful combination of drones with automated detection of wildlife in this imagery is much faster and, in some cases, more accurate than using human observers. Despite the great potential of this emerging approach, most attention to date has been paid to the development of algorithms, and little is known about the constraints around successful detection (P. W. J. Baxter, and G. Hamilton, 2018, Ecosphere, 9, e02194). We reviewed studies that were conducted over the last 5 years in which wildlife species were detected automatically in drone-acquired imagery to understand how technological constraints, environmental conditions and ecological traits of target species impact detection with automated methods. From this review, we found that automated detection could be achieved for a wider range of species and under a greater variety of environmental conditions than reported in previous reviews of automated and manual detection in drone-acquired imagery. A high probability of automated detection could be achieved efficiently using fixed-wing platforms and RGB sensors for species that were large and occurred in open and homogeneous environments with little vegetation or variation in topography while infrared sensors and multirotor platforms were necessary to successfully detect small, elusive species in complex habitats. The insight gained in this review could allow conservation managers to use drones and machine learning algorithms more accurately and efficiently to conduct abundance data on vulnerable populations that is critical to their conservation.", "publicationTitle": "Methods in Ecology and Evolution", "publisher": "", "place": "", "date": "2021", "volume": "12", "issue": "6", "section": "", "partNumber": "", "partTitle": "", "pages": "1103-1114", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1111/2041-210X.13581", "citationKey": "", "url": "https://onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.13581", "accessDate": "2025-12-23T23:21:35Z", "PMID": "", "PMCID": "", "ISSN": "2041-210X", "archive": "", "archiveLocation": "", "shortTitle": "Automated detection of wildlife using drones", "language": "en", "libraryCatalog": "Wiley Online Library", "callNumber": "", "rights": "© 2021 British Ecological Society", "extra": "_eprint: 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"note": "", "contentType": "application/pdf", "charset": "", "filename": "main.pdf", "md5": "07ee03a4345a3afcb6e08fc3a19bfa2f", "mtime": 1766531886238, "tags": [], "relations": {}, "dateAdded": "2025-12-23T23:18:06Z", "dateModified": "2025-12-23T23:18:08Z" } }, { "key": "HQM9LUUQ", "version": 6030, "library": { "type": "group", "id": 22818, "name": "Aerial Wildlife Survey", "links": { "alternate": { "href": "https://www.zotero.org/groups/aerial_wildlife_survey", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/22818/items/HQM9LUUQ", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/aerial_wildlife_survey/items/HQM9LUUQ", "type": "text/html" } }, "meta": { "createdByUser": { "id": 6138136, "username": "kirkl", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/kirkl", "type": "text/html" } } }, "creatorSummary": "Xu et al.", "parsedDate": "2025", "numChildren": 1 }, "data": { "key": "HQM9LUUQ", "version": 6030, "itemType": "journalArticle", "title": "Bounding box versus point annotation: The impact on deep learning performance for animal detection in aerial images", "creators": [ { "creatorType": "author", "firstName": "Zeyu", "lastName": "Xu" }, { "creatorType": "author", "firstName": "Tiejun", "lastName": "Wang" }, { "creatorType": "author", "firstName": "Andrew K.", "lastName": "Skidmore" }, { "creatorType": "author", "firstName": "Richard", "lastName": "Lamprey" }, { "creatorType": "author", "firstName": "Shadrack", "lastName": "Ngene" } ], "abstractNote": "Bounding box and point annotations are widely used in deep learning-based animal detection from remote sensing imagery, yet their impact on model performance and training efficiency remains insufficiently explored. This study systematically evaluates the influence of these two annotation methods using aerial survey datasets of African elephants and antelopes across three commonly employed deep learning networks: YOLO, CenterNet, and U-Net. In addition, we assess the effect of image spatial resolution and the training efficiency associated with each annotation method. Our findings indicate that when using YOLO, there is no statistically significant difference in model accuracy between bounding box and point annotations. However, for CenterNet and U-Net, bounding box annotations consistently yield significantly higher accuracy compared to point-based annotations, with these trends remaining consistent across different spatial resolution ranges. Furthermore, training efficiency varies depending on the network and annotation method. While YOLO exhibits similar convergence speeds for both annotation types, U-Net models trained with bounding box annotations converge significantly faster, followed by CenterNet, where bounding box-based models also show improved convergence. These findings demonstrate that the choice of annotation method should be guided by the specific deep learning architecture employed. While point-based annotations are more cost-effective, their lower training efficiency in U-Net and CenterNet suggests that bounding box annotations are preferable when maximizing both accuracy and computational efficiency. Therefore, when selecting annotation strategies for animal detection in remote sensing applications, researchers should carefully balance detection accuracy, annotation cost, and training efficiency to optimize performance for specific task requirements.", "publicationTitle": "ISPRS Journal of Photogrammetry and Remote Sensing", "publisher": "", "place": "", "date": "04/2025", "volume": "222", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "99-111", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "ISPRS Journal of Photogrammetry and Remote Sensing", "DOI": "10.1016/j.isprsjprs.2025.02.017", "citationKey": "", "url": "https://linkinghub.elsevier.com/retrieve/pii/S092427162500067X", "accessDate": "2025-12-23T23:18:08Z", "PMID": "", "PMCID": "", "ISSN": "09242716", "archive": "", "archiveLocation": "", "shortTitle": "Bounding box versus point annotation", "language": "en", "libraryCatalog": "DOI.org (Crossref)", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [ "L3N9ZD5A" ], "relations": {}, "dateAdded": "2025-12-23T23:18:08Z", "dateModified": "2025-12-23T23:18:08Z" } }, { "key": "BH294363", "version": 6028, "library": { "type": "group", "id": 22818, "name": "Aerial Wildlife Survey", "links": { "alternate": { "href": "https://www.zotero.org/groups/aerial_wildlife_survey", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/22818/items/BH294363", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/aerial_wildlife_survey/items/BH294363", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/22818/items/U5RV59ZP", "type": "application/json" } }, "meta": { "createdByUser": { "id": 185613, "username": "simbamangu", "name": "Howard L Frederick", "links": { "alternate": { "href": "https://www.zotero.org/simbamangu", "type": "text/html" } } } }, "data": { "key": "BH294363", "version": 6028, "parentItem": "U5RV59ZP", "itemType": "attachment", "linkMode": "imported_url", "title": "Snapshot", "accessDate": "2025-10-01T08:29:56Z", "url": "https://onlinelibrary.wiley.com/doi/abs/10.1002/ece3.72197", "note": "", "contentType": "text/html", "charset": "utf-8", "filename": "ece3.html", "md5": "20c85573d1f16a1462bbd427a27899db", "mtime": 1759307396845, "tags": [], "relations": {}, "dateAdded": "2025-10-01T08:29:56Z", "dateModified": "2025-10-01T08:29:56Z" } }, { "key": "6NHFE5XS", "version": 6025, "library": { "type": "group", "id": 22818, "name": "Aerial Wildlife Survey", "links": { "alternate": { "href": "https://www.zotero.org/groups/aerial_wildlife_survey", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/22818/items/6NHFE5XS", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/aerial_wildlife_survey/items/6NHFE5XS", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/22818/items/U5RV59ZP", "type": "application/json" } }, "meta": { "createdByUser": { "id": 185613, "username": "simbamangu", "name": "Howard L Frederick", "links": { "alternate": { "href": "https://www.zotero.org/simbamangu", "type": "text/html" } } }, "numChildren": 0 }, "data": { "key": "6NHFE5XS", "version": 6025, "parentItem": "U5RV59ZP", "itemType": "attachment", "linkMode": "imported_url", "title": "Full Text PDF", "accessDate": "2025-10-01T08:29:45Z", "url": "https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/ece3.72197", "note": "", "contentType": "application/pdf", "charset": "", "filename": "Juma et al. - 2025 - Elephant Movement Data Provides Insights Into Conservation Challenges and Successes in the Ruaha–Run.pdf", "md5": "522bc464211108d4b7ad040e4e60b21f", "mtime": 1759307385069, "tags": [], "relations": {}, "dateAdded": "2025-10-01T08:29:45Z", "dateModified": "2025-10-01T08:29:45Z" } }, { "key": "KGMG3C2V", "version": 6024, "library": { "type": "group", "id": 22818, "name": "Aerial Wildlife Survey", "links": { "alternate": { "href": "https://www.zotero.org/groups/aerial_wildlife_survey", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/22818/items/KGMG3C2V", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/aerial_wildlife_survey/items/KGMG3C2V", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/22818/items/U5RV59ZP", "type": "application/json" } }, "meta": { "createdByUser": { "id": 185613, "username": "simbamangu", "name": "Howard L Frederick", "links": { "alternate": { "href": "https://www.zotero.org/simbamangu", "type": "text/html" } } }, "numChildren": 0 }, "data": { "key": "KGMG3C2V", "version": 6024, "parentItem": "U5RV59ZP", "itemType": "note", "note": "

e72197 ECE-2025-02-00432.R2

", "tags": [], "relations": {}, "dateAdded": "2025-10-01T08:29:43Z", "dateModified": "2025-10-01T08:29:43Z" } }, { "key": "U5RV59ZP", "version": 6024, "library": { "type": "group", "id": 22818, "name": "Aerial Wildlife Survey", "links": { "alternate": { "href": "https://www.zotero.org/groups/aerial_wildlife_survey", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/22818/items/U5RV59ZP", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/aerial_wildlife_survey/items/U5RV59ZP", "type": "text/html" } }, "meta": { "createdByUser": { "id": 185613, "username": "simbamangu", "name": "Howard L Frederick", "links": { "alternate": { "href": "https://www.zotero.org/simbamangu", "type": "text/html" } } }, "creatorSummary": "Juma et al.", "parsedDate": "2025", "numChildren": 3 }, "data": { "key": "U5RV59ZP", "version": 6024, "itemType": "journalArticle", "title": "Elephant Movement Data Provides Insights Into Conservation Challenges and Successes in the Ruaha–Rungwa Landscape", "creators": [ { "creatorType": "author", "firstName": "Ramadhan", "lastName": "Juma" }, { "creatorType": "author", "firstName": "Alfred", "lastName": "Kikoti" }, { "creatorType": "author", "firstName": "Kristen D.", "lastName": "Snyder" }, { "creatorType": "author", "firstName": "Eivin", "lastName": "Røskaft" }, { "creatorType": "author", "firstName": "Peter S.", "lastName": "Ranke" }, { "creatorType": "author", "firstName": "Han", "lastName": "Olff" }, { "creatorType": "author", "firstName": "Alex", "lastName": "Lobora" }, { "creatorType": "author", "firstName": "Godwell Ole", "lastName": "Meing'ataki" }, { "creatorType": "author", "firstName": "Michael H.", "lastName": "Kimaro" } ], "abstractNote": "The African savannah elephant (Loxodonta africana) is listed as endangered on the IUCN Red List. The Ruaha–Rungwa ecosystem supports Tanzania's largest elephant population and remains a critical yet understudied stronghold for elephant conservation in East Africa. Effective conservation in this ecosystem requires understanding elephant movements across large landscapes and varying levels of disturbance. This study addressed the gap by utilizing GPS-satellite tracking of 28 elephants over a 4-year period in Tanzania's Ruaha–Rungwa ecosystem. It examined their home ranges and movement patterns inside and outside protected areas and identified key elephant hotspots. The findings aim to improve conservation strategies and mitigate human–elephant conflicts through better habitat management and protection efforts. The study found no significant difference between home range estimates from Minimum Convex Polygon and Adaptive Kernel Density Estimation, with 28 collared elephants averaging 2536 km2 at the 99% isopleth. Home range size varied by age and season but not by sex. Elephants moved faster at night in less protected areas during both wet and dry seasons. Population-level habitat use was similar across protection levels. Village lands in western Rungwa Game Reserve and its surrounding areas, as well as the eastern regions outside Ruaha National Park, served as key refuge sites during the wet season. Findings suggest elephants in the Ruaha–Rungwa ecosystem require vast areas, with adjacent lands playing a crucial role in their survival. High elephant activity in village lands within the western Rungwa Game Reserve and Lunda-Mkwambi Game Controlled Area highlights the potential for human–elephant conflict, emphasizing the need to reopen the wildlife corridor for linking Ruaha–Rungwa and Rukwa–Katavi ecosystems. This study offered crucial insights into elephant movement and behavior in a landscape increasingly shaped by human–elephant conflict and habitat fragmentation, informing strategies for connectivity, land-use planning, and coexistence.", "publicationTitle": "Ecology and Evolution", "publisher": "", "place": "", "date": "2025", "volume": "15", "issue": "10", "section": "", "partNumber": "", "partTitle": "", "pages": "e72197", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1002/ece3.72197", "citationKey": "", "url": "https://onlinelibrary.wiley.com/doi/abs/10.1002/ece3.72197", "accessDate": "2025-10-01T08:29:42Z", "PMID": "", "PMCID": "", "ISSN": "2045-7758", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "en", "libraryCatalog": "Wiley Online Library", "callNumber": "", "rights": "© 2025 The Author(s). Ecology and Evolution published by British Ecological Society and John Wiley & Sons Ltd.", "extra": "_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/ece3.72197", "tags": [ { "tag": "GPS-satellite technology", "type": 1 }, { "tag": "Ruaha–Rungwa ecosystem", "type": 1 }, { "tag": "elephant speed", "type": 1 }, { "tag": "elephants", "type": 1 }, { "tag": "protection levels", "type": 1 } ], "collections": [], "relations": {}, "dateAdded": "2025-10-01T08:29:43Z", "dateModified": "2025-10-01T08:29:43Z" } }, { "key": "6HYZDFL6", "version": 6040, "library": { "type": "group", "id": 22818, "name": "Aerial Wildlife Survey", "links": { "alternate": { "href": "https://www.zotero.org/groups/aerial_wildlife_survey", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/22818/items/6HYZDFL6", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/aerial_wildlife_survey/items/6HYZDFL6", "type": "text/html" } }, "meta": { "createdByUser": { "id": 185613, "username": "simbamangu", "name": "Howard L Frederick", "links": { "alternate": { "href": "https://www.zotero.org/simbamangu", "type": "text/html" } } }, "creatorSummary": "Ke et al.", "parsedDate": "2024-04-03", "numChildren": 1 }, "data": { "key": "6HYZDFL6", "version": 6040, "itemType": "journalArticle", "title": "Deep learning workflow to support in-flight processing of digital aerial imagery for wildlife population surveys", "creators": [ { "creatorType": "author", "firstName": "Tsung-Wei", "lastName": "Ke" }, { "creatorType": "author", "firstName": "Stella X.", "lastName": "Yu" }, { "creatorType": "author", "firstName": "Mark D.", "lastName": "Koneff" }, { "creatorType": "author", "firstName": "David L.", "lastName": "Fronczak" }, { "creatorType": "author", "firstName": "Luke J.", "lastName": "Fara" }, { "creatorType": "author", "firstName": "Travis J.", "lastName": "Harrison" }, { "creatorType": "author", "firstName": "Kyle L.", "lastName": "Landolt" }, { "creatorType": "author", "firstName": "Enrika J.", "lastName": "Hlavacek" }, { "creatorType": "author", "firstName": "Brian R.", "lastName": "Lubinski" }, { "creatorType": "author", "firstName": "Timothy P.", "lastName": "White" } ], "abstractNote": "Deep learning shows promise for automating detection and classification of wildlife from digital aerial imagery to support cost-efficient remote sensing solutions for wildlife population monitoring. To support in-flight orthorectification and machine learning processing to detect and classify wildlife from imagery in near real-time, we evaluated deep learning methods that address hardware limitations and the need for processing efficiencies to support the envisioned in-flight workflow. We developed an annotated dataset for a suite of marine birds from high-resolution digital aerial imagery collected over open water environments to train the models. The proposed 3-stage workflow for automated, in-flight data processing includes: 1) image filtering based on the probability of any bird occurrence, 2) bird instance detection, and 3) bird instance classification. For image filtering, we compared the performance of a binary classifier with Mask Region-based Convolutional Neural Network (Mask R-CNN) as a means of sub-setting large volumes of imagery based on the probability of at least one bird occurrence in an image. On both the validation and test datasets, the binary classifier achieved higher performance than Mask R-CNN for predicting bird occurrence at the image-level. We recommend the binary classifier over Mask R-CNN for workflow first-stage filtering. For bird instance detection, we leveraged Mask R-CNN as our detection framework and proposed an iterative refinement method to bootstrap our predicted detections from loose ground-truth annotations. We also discuss future work to address the taxonomic classification phase of the envisioned workflow.", "publicationTitle": "PLOS ONE", "publisher": "Public Library of Science", "place": "", "date": "3 Apr 2024", "volume": "19", "issue": "4", "section": "", "partNumber": "", "partTitle": "", "pages": "e0288121", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "PLOS ONE", "DOI": "10.1371/journal.pone.0288121", "citationKey": "", "url": "https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0288121", "accessDate": "2025-09-09T13:10:54Z", "PMID": "", "PMCID": "", "ISSN": "1932-6203", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "en", "libraryCatalog": "PLoS Journals", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Algorithms", "type": 1 }, { "tag": "Birds", "type": 1 }, { "tag": "Deep learning", "type": 1 }, { "tag": "Glycogen storage diseases", "type": 1 }, { "tag": "Michigan", "type": 1 }, { "tag": "Remote sensing imagery", "type": 1 }, { "tag": "Surveys", "type": 1 }, { "tag": "Wildlife", "type": 1 } ], "collections": [], "relations": {}, "dateAdded": "2025-09-09T13:13:04Z", "dateModified": "2025-09-09T13:13:04Z" } }, { "key": "VN2JTCZW", "version": 6023, "library": { "type": "group", "id": 22818, "name": "Aerial Wildlife Survey", "links": { "alternate": { "href": "https://www.zotero.org/groups/aerial_wildlife_survey", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/22818/items/VN2JTCZW", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/aerial_wildlife_survey/items/VN2JTCZW", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/22818/items/8L2VPNAE", "type": "application/json" } }, "meta": { "createdByUser": { "id": 185613, "username": "simbamangu", "name": "Howard L Frederick", "links": { "alternate": { "href": "https://www.zotero.org/simbamangu", "type": "text/html" } } }, "numChildren": 0 }, "data": { "key": "VN2JTCZW", "version": 6023, "parentItem": "8L2VPNAE", "itemType": "attachment", "linkMode": "imported_url", "title": "Full Text", "accessDate": "2025-09-09T11:46:31Z", "url": "https://www.mdpi.com/2504-446X/6/12/409/pdf?version=1670852588", "note": "", "contentType": "application/pdf", "charset": "", "filename": "Inman and Leggett - 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When traditional survey methods have been evaluated, a range of factors have been found to influence detection probabilities, including individual differences among conspecific animals, which can thus introduce biases into survey counts. There has been no such evaluation of drone‐based surveys using automated detection in a natural setting. This is important to establish since any biases in counts made using these methods will need to be accounted for, to provide accurate data and improve decision‐making for threatened species. In this study, a rare opportunity to survey a ground‐truthed, individually marked population of 48 koalas in their natural habitat allowed for direct comparison of the factors impacting detection probability in both ground observation and drone surveys with manual and automated detection. We found that sex and host tree preferences impacted detection in ground surveys and in manual analysis of drone imagery with female koalas likely to be under‐represented, and koalas higher in taller trees detected less frequently when present. Tree species composition of a forest stand also impacted on detections. In contrast, none of these factors impacted on automated detection. This suggests that the combination of drone‐captured imagery and machine learning does not suffer from the same biases that affect conventional ground surveys. This provides further evidence that drones and machine learning are promising tools for gathering reliable detection data to better inform the management of threatened populations.", "publicationTitle": "Ecology and Evolution", "publisher": "", "place": "", "date": "06/2021", "volume": "11", "issue": "11", "section": "", "partNumber": "", "partTitle": "", "pages": "6649-6656", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Ecology and Evolution", "DOI": "10.1002/ece3.7518", "citationKey": "", "url": "https://onlinelibrary.wiley.com/doi/10.1002/ece3.7518", "accessDate": "2025-09-09T11:39:13Z", "PMID": "", "PMCID": "", "ISSN": "2045-7758, 2045-7758", "archive": "", "archiveLocation": "", "shortTitle": "Evaluating new technology for biodiversity monitoring", "language": "en", "libraryCatalog": "Semantic Scholar", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "detection probability" }, { "tag": "drone" }, { "tag": "koala" } ], "collections": [], "relations": {}, "dateAdded": "2025-09-09T13:13:04Z", "dateModified": "2025-09-09T13:13:04Z" } }, { "key": "7TG6TFER", "version": 6013, "library": { "type": "group", "id": 22818, "name": "Aerial Wildlife Survey", "links": { "alternate": { "href": "https://www.zotero.org/groups/aerial_wildlife_survey", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/22818/items/7TG6TFER", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/aerial_wildlife_survey/items/7TG6TFER", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/22818/items/8L2VPNAE", "type": "application/json" } }, "meta": { "createdByUser": { "id": 185613, "username": "simbamangu", "name": "Howard L Frederick", "links": { "alternate": { "href": "https://www.zotero.org/simbamangu", "type": "text/html" } } }, "numChildren": 0 }, "data": { "key": "7TG6TFER", "version": 6013, "parentItem": "8L2VPNAE", "itemType": "note", "note": "[TLDR] A new technique is provided and the utility of drones to determine age and sex structures of hippo populations is demonstrated, providing one of the first age structure breakdowns of a hippo population not from culled hippos.", "tags": [], "relations": {}, "dateAdded": "2025-09-09T13:13:04Z", "dateModified": "2025-09-09T13:13:04Z" } }, { "key": "8L2VPNAE", "version": 6013, "library": { "type": "group", "id": 22818, "name": "Aerial Wildlife Survey", "links": { "alternate": { "href": "https://www.zotero.org/groups/aerial_wildlife_survey", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/22818/items/8L2VPNAE", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/aerial_wildlife_survey/items/8L2VPNAE", "type": "text/html" } }, "meta": { "createdByUser": { "id": 185613, "username": "simbamangu", "name": "Howard L Frederick", "links": { "alternate": { "href": "https://www.zotero.org/simbamangu", "type": "text/html" } } }, "creatorSummary": "Inman and Leggett", "parsedDate": "2022-12-12", "numChildren": 3 }, "data": { "key": "8L2VPNAE", "version": 6013, "itemType": "journalArticle", "title": "Hidden Hippos: Using Photogrammetry and Multiple Imputation to Determine the Age, Sex, and Body Condition of an Animal Often Partially Submerged", "creators": [ { "creatorType": "author", "firstName": "Victoria L.", "lastName": "Inman" }, { "creatorType": "author", "firstName": "Keith E. A.", "lastName": "Leggett" } ], "abstractNote": "Demographic Information on threatened species is important to plan conservation actions. Due to their aquatic lifestyle, the subtle nature of hippo sexual dimorphism, and their occurrence in inaccessible areas, it is difficult to visually determine hippo ages and sexes. Previously, hippo body lengths have been measured from drone images and used to estimate age. However, due to hippos’ propensity to be partially submerged, it is often difficult to obtain the required measurements. We used the novel technique of multiple imputation to estimate missing body measurements. Further, we explored if male and female hippos could be differentiated in drone images based on body proportions, also examining body condition indices and how these varied seasonally. Multiple imputation increased the number of hippos that we aged threefold, and the body lengths we obtained fell within the range provided in literature, supporting their validity. We provide one of the first age structure breakdowns of a hippo population not from culled hippos. Accounting for overall size, males had wider necks and snouts than females. Hippo body condition varied seasonally, indicating responses to resources and reproduction. We provide a new technique and demonstrate the utility of drones to determine age and sex structures of hippo populations.", "publicationTitle": "Drones", "publisher": "", "place": "", "date": "2022-12-12", "volume": "6", "issue": "12", "section": "", "partNumber": "", "partTitle": "", "pages": "409", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Drones", "DOI": "10.3390/drones6120409", "citationKey": "", "url": "https://www.mdpi.com/2504-446X/6/12/409", "accessDate": "2025-09-09T11:46:22Z", "PMID": "", "PMCID": "", "ISSN": "2504-446X", "archive": "", "archiveLocation": "", "shortTitle": "Hidden Hippos", "language": "en", "libraryCatalog": "Semantic Scholar", "callNumber": "", "rights": "https://creativecommons.org/licenses/by/4.0/", "extra": "", "tags": [ { "tag": "Hippopotamus" }, { "tag": "aerial survey" }, { "tag": "demography" }, { "tag": "drone" } ], "collections": [], "relations": {}, "dateAdded": "2025-09-09T13:13:04Z", "dateModified": "2025-09-09T13:13:04Z" } } ]