ReviewEcology letters2022
Towards the fully automated monitoring of ecological communities.
Review in Ecology letters, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 1 of them a synthesis 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
45 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Analyzing the enhancement of CNN-YOLO and transformer based architectures for real-time animal detection in complex ecological environments.Scientific reports · 2025Pooled it
- Advances in causal discovery methods for ecological time series.Biological reviews of the Cambridge Philosophical Society · 2026Review
- Confidence Thresholds: Towards an Automated Workflow for Unmarked Wildlife Population Modelling With Camera Trap Data.Ecology and evolution · 2026Article
- Predictors of extinction risk in large tropical forest mammals: From global to local.Science advances · 2026Article
- Autonomous cameras reveal larval reef fish responses to acoustic enrichment and lunar phase.Scientific reports · 2026Article
- National-scale acoustic monitoring of avian biodiversity and migration.Communications biology · 2026Article
- Article
- From one tree to the canopy: an evolutionary design trajectory for tethered robotics in forest environments.Frontiers in robotics and AI · 2026Article
- Narrowing farmland biodiversity knowledge gaps with Digital Agriculture.NPJ sustainable agriculture · 2026Review
- Article
- Improved Prediction of Eurasian Beaver Gnawing Preferences in Riparian Habitats: A Machine Learning Approach.Ecology and evolution · 2025Article
- Extending mammal specimens with their essential phenotypic traits.Journal of mammalogy · 2025Article
- Multivariate signals of population collapse in a high-throughput ecological experiment.Ecology · 2025Article
- Machine learning training data: over 500,000 images of butterflies and moths (Lepidoptera) with species labels.Scientific data · 2025Article
- Area of Habitat maps for national key protected wildlife in China.Scientific data · 2025Article
- Comparing point counts, passive acoustic monitoring, citizen science and machine learning for bird species monitoring in the Mount Kenya ecosystem.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2025Article
- Opportunities and challenges for monitoring terrestrial biodiversity in the robotics age.Nature ecology & evolution · 2025Article
- Article
- Advancing the Spatiotemporal Dimension of Wildlife-Pollution Interactions.Environmental science & technology letters · 2025Review
- Cas9-based enrichment for targeted long-read metabarcoding.Royal Society open science · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
High-resolution monitoring is fundamental to understand ecosystems dynamics in an era of global change and biodiversity declines. While real-time and automated monitoring of abiotic components has been possible for some time, monitoring biotic components-for example, individual behaviours and traits, and species abundance and distribution-is far more challenging. Recent technological advancements offer potential solutions to achieve this through: (i) increasingly affordable high-throughput recording hardware, which can collect rich multidimensional data, and (ii) increasingly accessible artificial intelligence approaches, which can extract ecological knowledge from large datasets. However, automating the monitoring of facets of ecological communities via such technologies has primarily been achieved at low spatiotemporal resolutions within limited steps of the monitoring workflow. Here, we review existing technologies for data recording and processing that enable automated monitoring of ecological communities. We then present novel frameworks that combine such technologies, forming fully automated pipelines to detect, track, classify and count multiple species, and record behavioural and morphological traits, at resolutions which have previously been impossible to achieve. Based on these rapidly developing technologies, we illustrate a solution to one of the greatest challenges in ecology: the ability to rapidly generate high-resolution, multidimensional and standardised data across complex ecologies.
Indexed as
Identifiers
What Socratic 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 Socratic graph.