ReviewNature communications2022
Perspectives in machine learning for wildlife conservation.
Review in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 129 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
129 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Monitoring birds interacting with power lines: a systematic review of detection technologies and the persistent gap in transmission line applications.Environmental monitoring and assessment · 2026Pooled it
- Analyzing the enhancement of CNN-YOLO and transformer based architectures for real-time animal detection in complex ecological environments.Scientific reports · 2025Pooled it
- The study of social animal migrations: a synthesis of the past and guidelines for future research.Proceedings. Biological sciences · 2025Pooled it
- From pixels to conservation: Deep learning for automated monitoring of rare and endangered wildlife.iScience · 2026Article
- Beyond unit-level certification: An operational agenda for biodiversity-positive tropical production landscapes.iScience · 2026Review
- AI-driven technological breakthroughs and practical pathways for biodiversity conservation and ecological management.iScience · 2026Review
- Underwater Computer Vision for Ecologists: A Framework for Curating Image Training Datasets for Object Detection.Sensors (Basel, Switzerland) · 2026Article
- Deep Learning-Based Monitoring of European Hamster Activity.Ecology and evolution · 2026Article
- From data to decisions: The role of machine learning in precision ecological restoration.Ecological applications : a publication of the Ecological Society of America · 2026Review
- Confidence Thresholds: Towards an Automated Workflow for Unmarked Wildlife Population Modelling With Camera Trap Data.Ecology and evolution · 2026Article
- Deep blueprint: A literature review and guide to automated image classification for ecologists.The Journal of animal ecology · 2026Review
- DSCF-DET: An RT-DETR-Based Framework for Fine-Grained Detection of Musk Deer and Visually Similar Artiodactyls.Biology · 2026Article
- An Automated Population Monitoring Framework forSensors (Basel, Switzerland) · 2026Article
- Deep Audio Detection Networks From Passive Acoustic Monitoring of a Critically Endangered Primate.Integrative zoology · 2026Article
- Classifying behaviors from animal-borne cameras using machine learning: automated identification of breathing events in sea turtles.The Journal of experimental biology · 2026Article
- Combining Artificial Intelligence and Local Ecological Knowledge to Document the Largest Ever-Counted Cape Buffalo Mega-Herd.Ecology and evolution · 2026Article
- An accurate, efficient, and accessible AI-powered solution for wildlife re-identification in conservation.Scientific reports · 2026Article
- An annotated aerial imagery dataset for automated detection of harbour seals in Svalbard, Norway.Scientific data · 2026Article
- RCAF-Net: Wildlife Target Detection in Complex Forest Scenarios.Animals : an open access journal from MDPI · 2026Article
- What fieldwork makes possible: perspectives from Japanese primatology in a technological era.Primates; journal of primatology · 2026Article
69 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
18 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Inexpensive and accessible sensors are accelerating data acquisition in animal ecology. These technologies hold great potential for large-scale ecological understanding, but are limited by current processing approaches which inefficiently distill data into relevant information. We argue that animal ecologists can capitalize on large datasets generated by modern sensors by combining machine learning approaches with domain knowledge. Incorporating machine learning into ecological workflows could improve inputs for ecological models and lead to integrated hybrid modeling tools. This approach will require close interdisciplinary collaboration to ensure the quality of novel approaches and train a new generation of data scientists in ecology and conservation.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.