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Cited by 505Wildlife Ecology and ConservationAnimal Behavior and ReproductionData Mining and Machine Learning Applications

The DOI matches its Crossref record; 97 of the 104 references it cites were found in a registry or an index, and 2 are not checked yet; none of the 49 sources its claims cite is checked for support yet.

Applications of machine learning in animal behaviour studies

Open access
Valletta, John Joseph, Torney, Colin, Kings, Michael, Thornton, Alex, Madden, Joah·2017·Animal Behaviour
DOI 10.1016/j.anbehav.2016.12.005
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Abstract

In many areas of animal behaviour research, improvements in our ability to collect large and detailed data sets are outstripping our ability to analyse them. These diverse, complex and often high-dimensional data sets exhibit nonlinear dependencies and unknown interactions across multiple variables, and may fail to conform to the assumptions of many classical statistical methods. The field of machine learning provides methodologies that are ideally suited to the task of extracting knowledge from these data. In this review, we aim to introduce animal behaviourists unfamiliar with machine learning (ML) to the promise of these techniques for the analysis of complex behavioural data. We start by describing the rationale behind ML and review a number of animal behaviour studies where ML has been successfully deployed. The ML framework is then introduced by presenting several unsupervised and supervised learning methods. Following this overview, we illustrate key ML approaches by developing data analytical pipelines for three different case studies that exemplify the types of behavioural and ecological questions ML can address. The first uses a large number of spectral and morphological characteristics that describe the appearance of pheasant, Phasianus colchicus, eggs to assign them to putative clutches. The second takes a continuous data stream of feeder visits from PIT (passive integrated transponder)-tagged jackdaws, Corvus monedula, and extracts foraging events from it, which permits the construction of social networks. Our final example uses aerial images to train a classifier that detects the presence of wildebeest, Connochaetes taurinus, to count individuals in a population. With the advent of cheaper sensing and tracking technologies an unprecedented amount of data on animal behaviour is becoming available. We believe that ML will play a central role in translating these data into scientific knowledge and become a useful addition to the animal behaviourist's analytical toolkit.

Checked 2026-10-09 · 2 references not checked yet

Overview

AI snapshot

Written from the paper’s full text.

TL;DR

This review introduces machine learning to animal behaviourists and illustrates unsupervised and supervised workflows through three case studies: assigning pheasant eggs to clutches, extracting jackdaw foraging events to build social networks, and detecting wildebeest in aerial images. It argues ML is well suited to complex, high-dimensional behavioural data but is not a silver bullet and requires careful validation.

Key findings

  • Pheasant eggs: 549 eggs from 30 females in 10 pens were described by 21 morphological and spectral measures; PCA retained four components explaining 96% of variance, and k-means/hierarchical clustering with silhouette width indicated k = 3 clutches.
  • Jackdaws: around 1000 PIT-tagged individuals across three colonies were studied; an illustrative 4-week data set from 141 individuals at one feeder was modelled with a Gaussian mixture model and BIC, and the resulting network confirmed associations between mated adults, parents and offspring, and unrelated individuals.
  • Wildebeest: 250 aerial images of 64 × 64 pixels (50 each for wildebeest, zebra, trees, rocks and grass) were classified using HOG features and random forests; 175 images were used for training and 75 for testing, and from over 1000 features only a couple of hundred were needed, though two wildebeest in the test set were misclassified as trees.
  • The review states ML is hypothesis-free and prediction-focused, can handle nonlinearities and interactions, but can learn patterns different from human cues and is vulnerable to biased training data and mistaking correlation for causation.

Methods

  • Narrative review plus three worked case studies, with documented code at https://github.com/jjvalletta/ML_Animal_Behaviour; methods covered include PCA, k-means, hierarchical clustering, Gaussian mixture models, decision trees, random forests, cross-validation and feature selection.
  • Case-study data: pheasant eggs (30 females, 10 pens, 549 eggs, 21 morphological/spectral measures, reflectance spectra 300–700 nm); jackdaw PIT-tag feeder visits (around 1000 birds across three colonies; illustrative 4 weeks from 141 individuals at one feeder); wildebeest aerial photos (250 images, 64 × 64 pixels, five classes).
  • Main analyses: PCA and clustering for pheasant eggs; GMM with BIC and adjacency-matrix construction for jackdaw social networks; HOG feature extraction, t-SNE visualization, random forests with variable importance and a confusion matrix for wildebeest detection.

Limitations

  • The paper states ML is “no silver bullet”: models may use cues unlike human cues (e.g. wildebeest misclassified as trees), biased training data can cause erratic behaviour (the tank/no-tank anecdote), observational data can mistake correlation for causation, and reproducibility over independent data sets is the most rigorous validation.
  • The paper states that determining the number of clusters k is a fundamental yet unsolved problem, is mathematically subjective, and the final decision rests with the practitioner.
  • Evident from the text: the case studies are illustrative rather than fully validated—pheasant maternity validation by protein fingerprinting is proposed but not performed; the jackdaw analysis uses only 4 weeks, one feeder and 141 individuals; and the wildebeest classifier uses five classes for illustration although automated counting would require only two.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1016/j.anbehav.2016.12.005

Cites

97 of 99 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Verification

Claims the paper makes

0 of 49 cited sources checked · 0 supported · 0 flagged

Show all 27 claims

  • It may state two separate claims as one. 29
  • Probably not a substantial claim. 39
56 of 56

  • The paper relies on this without support.

    In many areas of animal behaviour research, improvements in the ability to collect large and detailed data sets are outstripping the ability to analyse them.

    “In many areas of animal behaviour research, improvements in our ability to collect large and detailed data sets are outstripping our ability to analyse them.”

  • The paper relies on this without support.

    These diverse, complex and often high-dimensional data sets exhibit nonlinear dependencies and unknown interactions across multiple variables, and may fail to conform to the assumptions of many classical statistical methods.

    “These diverse, complex and often highdimensional data sets exhibit nonlinear dependencies and unknown interactions across multiple variables, and may fail to conform to the assumptions of many classical statistical methods.”

  • The paper relies on this without support.

    Animal behaviour researchers are in a position where automatically collecting detailed data sets is becoming commonplace, but extracting knowledge from them is a daunting task, mainly due to the lack of accessible analytical tools.

    “Animal behaviour researchers are thus in a position where automatically collecting detailed data sets is becoming commonplace, but extracting knowledge from them is a daunting task, mainly due to the lack of accessible analytical tools.”

  • The paper relies on this without support.

    Without direct observation of laying or destructive genetic sampling from eggs, it is not possible to assign eggs to females.

    “Without direct observation of laying or destructive genetic sampling from eggs, it is not possible to assign eggs to females.”

  • The paper relies on this without support.

    Uncovering social structures in jackdaws allows us to infer individualized social relationships and determine how novel information spreads through social groups.

    “Uncovering social structures in jackdaws allows us to infer individualized social relationships and determine how novel information spreads through social groups.”

  • The paper relies on this without support.

    It is unclear how one specifies the length of foraging events without fixing an arbitrary time window.

    “It is unclear, however, how one specifies the length of these foraging events without fixing an arbitrary time window.”

  • The paper relies on this without support.

    Currently counting wildebeest from aerial photos is time consuming, as thousands of aerial photos need to be counted manually.

    “Currently this process is time consuming, as thousands of aerial photos need to be counted manually.”

  • The paper relies on this without support.

    Machine learning can relieve this burden by detecting wildebeest in an image and therefore automating this aspect of the count.

    “Machine learning can relieve this burden by detecting wildebeest in an image and therefore automating this aspect of the count.”

  • The paper relies on this without support.

    Research on jackdaw social structure has largely been confined to captive studies and there is limited information on patterns of association in natural populations.

    “However, research has largely been confined to captive studies and there is limited information on patterns of association in natural populations.”

  • The paper relies on this without support.

    HOGs features are hard to interpret and are likely to exhibit nonlinear interactions among them.

    “HOGs features are hard to interpret and are likely to exhibit nonlinear interactions among them (a stark contrast to the pheasant eggs data where PCA was justified by the linear relationships between variables of the same theme).”

  • The paper relies on this without support.

    Biased training data sets cause erratic behaviour of the predictive model.

    “For example, biased training data sets cause erratic behaviour of the predictive model.”

  • The paper relies on this without support.

    Features that are directly relevant to the outcome being predicted tend to make the predictive performance insensitive to the choice of algorithm.

    “Features that are directly relevant to the outcome being predicted tend to make the predictive performance insensitive to the choice of algorithm.”

  • The paper relies on this without support.

    ML offers a hypothesis-free approach to model complex data sets where the type of relationship between measured variables is unknown.

    “ML offers a hypothesis-free approach to model complex data sets where the type of relationship between measured variables is unknown.”

  • The paper relies on this without support.

    ML algorithms mine for patterns in the data that are best at predicting an outcome.

    “ML algorithms mine for patterns in the data that are best at predicting an outcome.”

  • The paper relies on this without support.

    Typically, ML algorithms mine for patterns in observational data, rather than experimental data, where correlation can be mistaken for causation.

    “Typically, ML algorithms mine for patterns in observational data, rather than experimental data, where correlation can be mistaken for causation.”
15 of 15

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

  • Opposite objectives stated for separation between clusters

    Minor

    The clustering section says to maximize similarity between clusters, while Figure A1 says to maximize the distance between clusters. Those are opposite objectives.

  • Opposite objectives stated for similarity within clusters

    Minor

    The clustering section says observations within a group should be similar, but Figure A1 says to minimize within-group similarity.

2 of 2

Citations

Cites

Flagged first, then by citations

Show all 104 references

5 of the 104 references couldn’t be matched to a known paper.

Research works printed but not linked: 3

  1. Bishop, 2006, Vol. 4
  2. Broom, 1993, Vol. 30
  3. Hastie, 2009, Vol. 1

Cited by

Sorted by citations

See all 505

Related

Metadata

Authors
Valletta, J. J., Torney, C., Kings, M., Thornton, A., & Madden, J.
Affiliations
University of Exeter (GB)
Corresponding author
Valletta, John Joseph
ORCID
Venue
Animal Behaviour
Publisher
Elsevier BV
Published
2017-02
Volume
124
Pages
203-220
ISSN
0003-3472 (ISSN-L)
DOI
10.1016/j.anbehav.2016.12.005
OpenAlex
W2581974582
Cited by
505
References
104
Citations by year
2026: 29 · 2025: 57 · 2024: 68 · 2023: 70 · 2022: 84 · 2021: 69 · 2020: 47 · 2019: 48 · 2018: 27 · 2017: 5
FWCI
26.26
Type
journal article
Language
en
Open access
Licence
Locations
  • Animal Behaviour (publishedVersion, cc-by)
  • ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) (acceptedVersion)
  • Open Research Exeter (University of Exeter) (acceptedVersion, cc-by)
  • Figshare (submittedVersion)
Funders
  • MRC (MR/M003906/1)
  • BBSRC David Phillips Fellowship (BB/H021817/1)
  • BBSRC SWDTP studentship (630051486)
  • ERC consolidator award (616474)
Topics
  • Physical Sciences › Environmental Science › Ecology › Wildlife Ecology and Conservation
  • Life Sciences › Agricultural and Biological Sciences › Ecology, Evolution, Behavior and Systematics › Animal Behavior and Reproduction
  • Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
Keywords
machine learning, behavioural data analysis, unsupervised learning, supervised learning, jackdaw, social networks, aerial imagery, population counting, passive integrated transponders

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