ArticleEcology and evolution2020
New technologies in the mix: Assessing N-mixture models for abundance estimation using automated detection data from drone surveys.
Article in Ecology and evolution, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- The first use of a photogrammetry drone to estimate population abundance and predict age structure of threatened Sumatran elephants.Scientific reports · 2023Article
- Artificial intelligence for automated detection of large mammals creates path to upscale drone surveys.Scientific reports · 2023Article
- Two Methods of Monitoring Cats at a Landscape-Scale.Animals : an open access journal from MDPI · 2021Article
- Evaluating new technology for biodiversity monitoring: Are drone surveys biased?Ecology and evolution · 2021Article
- New technologies in the mix: Assessing N-mixture models for abundance estimation using automated detection data from drone surveys.Ecology and evolution · 2020Article
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Authors and funding
3 authors.
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Abstract
Reliable estimates of abundance are critical in effectively managing threatened species, but the feasibility of integrating data from wildlife surveys completed using advanced technologies such as remotely piloted aircraft systems (RPAS) and machine learning into abundance estimation methods such as N-mixture modeling is largely unknown due to the unique sources of detection errors associated with these technologies.We evaluated two modeling approaches for estimating the abundance of koalas detected automatically in RPAS imagery: (a) a generalized N-mixture model and (b) a modified Horvitz-Thompson (H-T) estimator method combining generalized linear models and generalized additive models for overall probability of detection, false detection, and duplicate detection. The final estimates from each model were compared to the true number of koalas present as determined by telemetry-assisted ground surveys.The modified H-T estimator approach performed best, with the true count of koalas captured within the 95% confidence intervals around the abundance estimates in all 4 surveys in the testing dataset (
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