Correction: Publication reform to safeguard wildlife from researcher harm

Field, Kate A.; Paquet, Paul C.; Artelle, Kyle A.; Proulx, Gilbert; Brook, Ryan K.; Darimont, Chris T.

doi:10.1371/journal.pbio.3000752

Cited by 3 publications

(2 citation statements)

References 1 publication

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“…Many traditional wild animal monitoring methods have been widely used [16,17], including surveys [5][6][7], tracking [8][9][10][11], sampling [12][13][14][15], and so on. All of these methods are labor intensive, especially for investigating rare wild animals such as giant pandas [45].…”

Section: Intelligent Wildlife Monitoringmentioning

confidence: 99%

“…Researchers and wildlife management agencies in many countries are monitoring animals in the wild for conservation purposes as animal diversity is declining at an unprecedented rate [3,4]. Conventionally, animal monitoring is studied by human experts who gather data through surveys [5][6][7], tracking [8][9][10][11], sampling [12][13][14][15], and other observational ways [16,17]. However, there are significant labor and time costs associated with these traditional methods [18].…”

Section: Introductionmentioning

confidence: 99%

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Monitoring Endangered and Rare Wildlife in the Field: A Foundation Deep Learning Model Integrating Human Knowledge for Incremental Recognition with Few Data and Low Cost

Mou,

Liang,

et al. 2023

Animals

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Intelligent monitoring of endangered and rare wildlife is important for biodiversity conservation. In practical monitoring, few animal data are available to train recognition algorithms. The system must, therefore, achieve high accuracy with limited resources. Simultaneously, zoologists expect the system to be able to discover unknown species to make significant discoveries. To date, none of the current algorithms have these abilities. Therefore, this paper proposed a KI-CLIP method. Firstly, by first introducing CLIP, a foundation deep learning model that has not yet been applied in animal fields, the powerful recognition capability with few training resources is exploited with an additional shallow network. Secondly, inspired by the single-image recognition abilities of zoologists, we incorporate easily accessible expert description texts to improve performance with few samples. Finally, a simple incremental learning module is designed to detect unknown species. We conducted extensive comparative experiments, ablation experiments, and case studies on 12 datasets containing real data. The results validate the effectiveness of KI-CLIP, which can be trained on multiple real scenarios in seconds, achieving in our study over 90% recognition accuracy with only 8 training samples, and over 97% with 16 training samples. In conclusion, KI-CLIP is suitable for practical animal monitoring.

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Section: Intelligent Wildlife Monitoringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Monitoring Endangered and Rare Wildlife in the Field: A Foundation Deep Learning Model Integrating Human Knowledge for Incremental Recognition with Few Data and Low Cost

Mou,

Liang,

et al. 2023

Animals

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The ethical assessment of Assisted Reproductive Technologies (ART) in wildlife conservation

de Mori,

Mercugliano,

Biasetti

et al. 2024

Biological Conservation

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Methods for Monitoring Large Terrestrial Animals in the Wild

Prosekov

Kuznetsov

Rada

et al. 2020

Forests

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Reliable information about wildlife is absolutely important for making informed management decisions. The issues with the effectiveness of the control and monitoring of both large and small wild animals are relevant to assess and protect the world’s biodiversity. Monitoring becomes part of the methods in wildlife ecology for observation, assessment, and forecasting of the human environment. World practice reveals the potential of the joint application of both proven traditional and modern technologies using specialized equipment to organize environmental control and management processes. Monitoring large terrestrial animals require an individual approach due to their low density and larger habitat. Elk/moose are such animals. This work aims to evaluate the methods for monitoring large wild animals, suitable for controlling the number of elk/moose in the framework of nature conservation activities. Using different models allows determining the population size without affecting the animals and without significant financial costs. Although, the accuracy of each model is determined by its postulates implementation and initial conditions that need statistical data. Depending on the geographical, climatic, and economic conditions in each territory, it is possible to use different tools and equipment (e.g., cameras, GPS sensors, and unmanned aerial vehicles), a flexible variation of which will allow reaching the golden mean between the desires and capabilities of researchers.

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Correction: Publication reform to safeguard wildlife from researcher harm

Cited by 3 publications

References 1 publication

Monitoring Endangered and Rare Wildlife in the Field: A Foundation Deep Learning Model Integrating Human Knowledge for Incremental Recognition with Few Data and Low Cost

Monitoring Endangered and Rare Wildlife in the Field: A Foundation Deep Learning Model Integrating Human Knowledge for Incremental Recognition with Few Data and Low Cost

The ethical assessment of Assisted Reproductive Technologies (ART) in wildlife conservation

Methods for Monitoring Large Terrestrial Animals in the Wild

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