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Applications of machine learning in animal behaviour studies
Open accessAbstract
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.
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Claims the paper makes
29 Not checked yet · 23 No basis given · 18 Own interpretation · 13 Own result
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Thus, PC1 is a measure of eggshell brightness, PC2 is a measure of eggshell greenness, PC3 is a measure of egg size and PC4 is a measure of egg shape.”
§ Applications of machine learning in animal behaviour studies > Pheasant Eggs
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The jackdaw social network we generated (see Fig. 4e) confirms the existence of foraging associations between mated adults and between parents and offspring.”
§ Applications of machine learning in animal behaviour studies > Cluster validity
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As depicted in Fig. 4b, the data are dominated by short intervisit periods corresponding to foraging events, interleaved with long periods of inactivity.”
§ Applications of machine learning in animal behaviour studies > Jackdaw Associations
plainn = 141- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Fig. 3c depicts the PCA outcome, where the first four components explained 96% of the variance in the data and were kept as features for subsequent analysis.”
- Also stated in: § CONCLUDING REMARKS · plain
“The first four components explained 96% of the variance in the data and were kept as features for subsequent analysis.”
§ Applications of machine learning in animal behaviour studies > Pheasant Eggs
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Also stated in: § CONCLUDING REMARKS · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“From an initial pool of over 1000 features only a couple of hundred predictors were needed to achieve good predictive performance on the training data set.”
§ Applications of machine learning in animal behaviour studies > Wildebeest Identification
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We could use the 'raw' data, that is, the individual pixel intensities; however, when we projected them onto the first two principal components for visualization (see Appendix Fig. A5a) no obvious groupings were present.”
§ Applications of machine learning in animal behaviour studies > Wildebeest Identification
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Thus, PC1 is a measure of eggshell brightness, PC2 is a measure of eggshell greenness, PC3 is a measure of egg size and PC4 is a measure of egg shape.”
§ Applications of machine learning in animal behaviour studies > Pheasant Eggs
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Thus, PC1 is a measure of eggshell brightness, PC2 is a measure of eggshell greenness, PC3 is a measure of egg size and PC4 is a measure of egg shape.”
§ Applications of machine learning in animal behaviour studies > Pheasant Eggs
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The outcome was similar to that from k-means, but using dendrograms, the results can be visualized irrespective of dimensionality (as opposed to k-means where results had to be projected onto the first two PCs), and also allows us to identify subgroups within each cluster.”
§ Applications of machine learning in animal behaviour studies > Pheasant Eggs
plainSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A correlogram of all 21 morphological and spectral measures showed strong linear associations between some of the variables (see Fig. 3b).”
§ Applications of machine learning in animal behaviour studies > Pheasant Eggs
boostedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- It may state two separate claims as one. 29
- Probably not a substantial claim. 39
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“When projected onto the first two PCs, the HOGs features showed promising grouping by class, although it was still somewhat concealed (see Appendix Fig. A5b).”
§ Applications of machine learning in animal behaviour studies > Wildebeest Identification
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It also reveals strong associations between unrelated individuals and shows how these associations relate to the spatial proximity between nestboxes.”
§ Applications of machine learning in animal behaviour studies > Cluster validity
boostedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For example, the confusion matrix in Fig. 5e shows how two wildebeests in the test data were mistakenly labelled as trees.”
§ Applications of machine learning in animal behaviour studies > CONCLUDING REMARKS
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Visual inspection of eggs suggested that withinindividual variation in egg morphology may be low while interindividual variation is high.”
§ Applications of machine learning in animal behaviour studies > Pheasant Eggs
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“The focus is on inference; estimating the parameters of the statistical model that most likely gave rise to the observed data, and providing uncertainty bounds for these estimates.”
§ Applications of machine learning in animal behaviour studies > Abstract
What the sources say
- Not checked yetcites Breiman, 2001b
- Not checked yetcites Friedman, 2001 source
- Not checked yetcites Zoubin Ghahramani, 2015
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Statistical models start with an assumption about the underlying data distribution (e.g.Gaussian, Poisson).”
§ Applications of machine learning in animal behaviour studies > Abstract
What the sources say
- Not checked yetcites Breiman, 2001b
- Not checked yetcites Friedman, 2001 source
- Not checked yetcites Zoubin Ghahramani, 2015
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“This allows us to answer important questions across a range of topics, including movement ecology, social structure, collective behaviour, communication and welfare.”
§ Applications of machine learning in animal behaviour studies > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
- Rests on an assumption: 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.
- Rests on an assumption: 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.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The field of machine learning provides methodologies that are ideally suited to the task of extracting knowledge from these data.”
§ Applications of machine learning in animal behaviour studies > Abstract
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
- Rests on an assumption: 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.
- Rests on an assumption: 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.
- Rests on an assumption: 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.
None of the sources cited for this claim have been checked yet.
In the paper
“Both statistical modelling and ML seek to build a mathematical description, a model, of the data and the underlying mechanism it represents; thus inevitably there is substantial overlap between the two”
§ Applications of machine learning in animal behaviour studies > Abstract
What the sources say
- Not checked yetcites Breiman, 2001b
- Not checked yetcites Friedman, 2001 source
- Not checked yetcites Zoubin Ghahramani, 2015
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In animal behaviour, ML approaches can address otherwise intractable tasks, such as classifying species, individuals, vocalizations or behaviours within complex data sets.”
§ Applications of machine learning in animal behaviour studies > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
- Rests on an assumption: 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.
- Rests on an assumption: 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.
None of the sources cited for this claim have been checked yet.
In the paper
“For ML, the focus is typically on prediction; without necessarily assuming a functional distribution for the data, a model that achieves optimal predictive performance is identified.”
§ Applications of machine learning in animal behaviour studies > Abstract
What the sources say
- Not checked yetcites Breiman, 2001b
- Not checked yetcites Friedman, 2001 source
- Not checked yetcites Zoubin Ghahramani, 2015
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
In the paper
“ML was used in a comparative assessment of welfare across multiple laboratory populations of mice (Chesler et al., 2002) permitting a wide range of potential explanatory factors, each with diverse distribution, to be considered simultaneously as well as the interactions between them.”
§ Applications of machine learning in animal behaviour studies > Abstract
What the sources say
- Not checked yetcites Chesler et al., 2002 source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In this case, the individual pixel intensities are not explicitly related to the image's class, suggesting the need for hand-crafted features that describe the object in each image.”
§ Applications of machine learning in animal behaviour studies > Wildebeest Identification
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The OOB data are also used to compute an estimate of the importance of every predictor, which can be subsequently used for feature selection.”
§ Applications of machine learning in animal behaviour studies > Random Forests
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Using such a classifier may soon enable the fully automated detection and identification of animals from aerial count images.”
§ Applications of machine learning in animal behaviour studies > Wildebeest Identification
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
- Rests on an assumption: Machine learning can relieve this burden by detecting wildebeest in an image and therefore automating this aspect of the count.
None of the sources cited for this claim have been checked yet.
In the paper
“Random forests (Breiman, 2001a;Cutler et al., 2007) is an ensemble method developed to mitigate the problem of overfitting in decision trees.”
§ Applications of machine learning in animal behaviour studies > Random Forests
What the sources say
- Not checked yetcites Breiman, 2001a
- Not checked yetcites Cutler et al., 2007 source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“While in traditional statistical modelling a hypothesis (model) is put forward and is then accepted/rejected depending on how consistent it is with the measured observations, ML methods learn this hypothesis directly from the training data set.”
§ Applications of machine learning in animal behaviour studies > Abstract
What the sources say
- Not checked yetcites Breiman, 2001b
- Not checked yetcites Friedman, 2001 source
- Not checked yetcites Zoubin Ghahramani, 2015
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“ML algorithms can deal with nonlinearities and interactions among variables because the models are flexible enough to fit the data (as opposed to rigid linear regression models, for example).”
§ Applications of machine learning in animal behaviour studies > SUPERVISED LEARNING
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Random forests can handle thousands of mixed categorical and continuous predictors and are robust to outliers; however, the interpretability of plain decision trees is lost.”
§ Applications of machine learning in animal behaviour studies > Random Forests
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, this flexibility needs to be constrained to avoid fitting noise (overfitting).”
§ Applications of machine learning in animal behaviour studies > SUPERVISED LEARNING
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“It is clear that ML can address different objectives in numerous distinct fields of animal behaviour and is thus becoming a staple approach for novel methods (Dankert, Wang, Hoopfer, Anderson, & Perona, 2009;Kabra et al., 2013).”
§ Applications of machine learning in animal behaviour studies > Abstract
What the sources say
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Finer scale discriminations are possible at both the individual level (Cheng, Xie, Lin, & Ji, 2012) and the bird song elements level (Ranjard & Ross, 2008).”
§ Applications of machine learning in animal behaviour studies > Abstract
What the sources say
- Not checked yetcites Cheng et al., 2012 source
- Not checked yetcites Ranjard & Ross, 2008
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Co-occurrences in time could be determined by ML clustering methods with individuals in the same foraging event (cluster) considered to have a social affiliation (Psorakis et al., 2015).”
§ Applications of machine learning in animal behaviour studies > Abstract
What the sources say
- Not checked yetcites Psorakis et al., 2015 source
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Female pheasants may nest communally or lay eggs in multiple nests (Yom-Tov, 1980).”
§ Applications of machine learning in animal behaviour studies > Pheasant Eggs
What the sources say
- Not checked yetcites Yom-Tov, 1980 source
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“k-means clustering is popular because it is intuitive and computationally inexpensive.”
§ Applications of machine learning in animal behaviour studies > k-means
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“ML is used to automate the classification of behaviours/activities (Kabra et al., 2013) and tracking movement trajectories (Dell et al., 2014).”
§ Applications of machine learning in animal behaviour studies > Abstract
What the sources say
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It is this hypothesis-free approach that makes ML an attractive choice for dealing with complex data sets.”
§ Applications of machine learning in animal behaviour studies > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, they are prone to overfitting (small changes in the training data set may lead to significantly different trees) and their predictive performance can be poor compared to other algorithms.”
§ Applications of machine learning in animal behaviour studies > Decision Trees
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“If achieved, this will have major implications for conservation as currently the process of detecting and identifying animals within images is the main bottleneck for estimating population sizes from aerial surveys (Torney et al., 2016).”
§ Applications of machine learning in animal behaviour studies > Wildebeest Identification
What the sources say
- Not checked yetcites Torney et al., 2016 source
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
- Rests on an assumption: Currently counting wildebeest from aerial photos is time consuming, as thousands of aerial photos need to be counted manually.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“GMMs, however, assume that data are generated from a mixture of multivariate Gaussians and lack a global optimum solution.”
§ Applications of machine learning in animal behaviour studies > Gaussian mixture model (GMM)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Determining the number of distinct clusters k in a data set is a fundamental yet unsolved problem.”
§ Applications of machine learning in animal behaviour studies > How many clusters?
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“Such methods appear to be robust and capture real-life pair bonds well (Psorakis, Roberts, Rezek, & Sheldon, 2012).”
§ Applications of machine learning in animal behaviour studies > Abstract
What the sources say
- Not checked yetcites Psorakis et al., 2012 source
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Decision trees are readily interpretable, directly used to generate rules, and computationally inexpensive to train, evaluate and store.”
§ Applications of machine learning in animal behaviour studies > Decision Trees
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The rationale behind dimensionality reduction is simple; although the collected data may seem high dimensional, the structure of the data can be represented by fewer variables.”
§ Applications of machine learning in animal behaviour studies > Dimensionality Reduction
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Using unsupervised learning methods, we were able to reduce the dimensionality of the problem to a few biologically meaningful features, and subsequently assign eggs to females.”
§ Applications of machine learning in animal behaviour studies > Pheasant Eggs
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: Without direct observation of laying or destructive genetic sampling from eggs, it is not possible to assign eggs to females.
In the paper
“Based on the work of Psorakis et al. (2012), Fig. 4a summarizes the workflow used to convert a long series of time stamps into a social network.”
§ Applications of machine learning in animal behaviour studies > Jackdaw Associations
What the sources say
- Not checked yetcites Psorakis et al. (2012) source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“They can handle both categorical and continuous data, and are fairly robust to outliers.”
§ Applications of machine learning in animal behaviour studies > Decision Trees
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, it is only applicable to continuous data where a mean is defined.”
§ Applications of machine learning in animal behaviour studies > k-means
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Feature extraction is a key step in ML; finding representations that are directly relevant to the task at hand (e.g.discriminating between two classes) will almost always result in better predictive accuracy than employing more complex models.”
§ Applications of machine learning in animal behaviour studies > Feature extraction
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“There is also no guarantee of a global optimum solution; thus, the user is advised to start the algorithm at multiple distinct centroids.”
§ Applications of machine learning in animal behaviour studies > k-means
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“ML can tackle a wide range of tasks, including classifying observations into predefined sets (Kabra, Robie, Rivera-Alba, Branson, & Branson, 2013), clustering data into groups that share an underlying process (Zhang, O'Reilly, Perry, Taylor, & Dennis, 2015) and regressing an outcome of interest against multiple factors and elucidating their contributory effect (Chesler, Wilson, Lariviere, Rodrig”
§ Applications of machine learning in animal behaviour studies > Abstract
What the sources say
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“This knowledge can then be used to infer individual decision rules in collective motion (Katz, Tunstrom, Ioannou, Huepe, & Couzin, 2011;Nagy, Kos, Biro, & Vicsek, 2010) and to compute activity budgets for individuals without the need for continuous human observation or time-consuming video analysis.”
§ Applications of machine learning in animal behaviour studies > Abstract
What the sources say
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“There are also cases where a mix of labelled and unlabelled data are available, which would require the use of a different type of ML methods: semisupervised learning (Chapelle, Sch€ olkopf, & Zien, 2006).”
§ Applications of machine learning in animal behaviour studies > CONCLUDING REMARKS
What the sources say
- Not checked yetcites Chapelle, Schölkopf, & Zien, 2006
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“PC1 is a measure of eggshell brightness, PC2 is a measure of eggshell greenness, PC3 is a measure of egg size and PC4 is a measure of egg shape.”
§ Applications of machine learning in animal behaviour studies > Additional information
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, as we have seen earlier, unsupervised learning methods are routinely used with labelled data during the data exploration stage.”
§ Applications of machine learning in animal behaviour studies > CONCLUDING REMARKS
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“PC1 is a measure of eggshell brightness, PC2 is a measure of eggshell greenness, PC3 is a measure of egg size and PC4 is a measure of egg shape.”
§ Applications of machine learning in animal behaviour studies > Additional information
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“To this end, automatically extracting predictive features from 'raw' data is the central theme of a new set of methods, in an active area of research, called Deep Learning (LeCun, Bengio, & Hinton, 2015).”
§ Applications of machine learning in animal behaviour studies > CONCLUDING REMARKS
What the sources say
- Not checked yetcites LeCun, Bengio, & Hinton, 2015 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“High-profile failures highlight how even experts in the field of ML can fall victim of common pitfalls in data-driven analytics.”
§ Applications of machine learning in animal behaviour studies > CONCLUDING REMARKS
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The type of data (labelled or unlabelled) and the particular task at hand ultimately dictate the category of algorithms to choose from (see Table 1).”
§ Applications of machine learning in animal behaviour studies > CONCLUDING REMARKS
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Instead of using hand-crafted features (as we did in the wildebeest identification problem), Deep Learning automatically discovers features amenable for prediction by recursively applying simple but nonlinear transformations to the data.”
§ Applications of machine learning in animal behaviour studies > CONCLUDING REMARKS
What the sources say
- Not checked yetcites LeCun, Bengio, & Hinton, 2015 source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Associations between an outcome and a set of inputs could have occurred by chance, owing to a confounding factor or a biased data set.”
§ Applications of machine learning in animal behaviour studies > CONCLUDING REMARKS
What the sources say
- Not checked yetcites MacKinnon, Krull, & Lockwood, 2000 source
- Not checked yetcites Zuur, Ieno, & Elphick, 2010
- Not checked yetcites A. Liu & Ziebart, 2014
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Reproducibility of a predictive model over multiple independent data sets over time is ultimately the most rigorous form of validation.”
§ Applications of machine learning in animal behaviour studies > CONCLUDING REMARKS
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“These methods make use of unlabelled data to improve the performance of predictive models identified from labelled observations.”
§ Applications of machine learning in animal behaviour studies > CONCLUDING REMARKS
What the sources say
- Not checked yetcites Chapelle, Schölkopf, & Zien, 2006
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“However, there are no hard and fast rules to this process and it has been shown that the performance of several state-of-the-art supervised learning algorithms are similar (Fern andez-Delgado, Cernadas, Barro, & Amorim, 2014).”
§ Applications of machine learning in animal behaviour studies > CONCLUDING REMARKS
What the sources say
- Not checked yetcites Fernandez-Delgado, Cernadas, Barro, & Amorim, 2014
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“PC1 is a measure of eggshell brightness, PC2 is a measure of eggshell greenness, PC3 is a measure of egg size and PC4 is a measure of egg shape.”
§ Applications of machine learning in animal behaviour studies > Additional information
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“PC1 is a measure of eggshell brightness, PC2 is a measure of eggshell greenness, PC3 is a measure of egg size and PC4 is a measure of egg shape.”
§ Applications of machine learning in animal behaviour studies > Additional information
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
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.”
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.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Applications of machine learning in animal behaviour studies > Clustering
“each cluster should be sufficiently different (maximize intercluster similarity; see Appendix Fig. A1).”
§ Applications of machine learning in animal behaviour studies > Additional information
“maximize the distance between distinct clusters (intercluster distance).”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Applications of machine learning in animal behaviour studies > Clustering
“The data in each group should be similar (minimize intracluster distance)”
§ Applications of machine learning in animal behaviour studies > Additional information
“to minimize the within-group similarity (intracluster distance)”
Citations
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538 · cited 2×
“Both statistical modelling and ML seek to build a mathematical description, a model, of the data and the underlying mechanism it represents; thus inevitably there is substantial overlap between the two (Breiman, 2001b; Friedman, 2001; Zoubin Ghahramani, 2015).”
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016 · cited 2×
“The caret package in R (Kuhn, 2008) and scikit-learn module in Python (Pedregosa et al., 2011) provide a common interface to a wide range of ML algorithms, together with numerous auxiliary functions to perform visualization, cross-validation, model evaluation and more.”
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482 · cited 1×
“Examples of these techniques include LASSO (Tibshirani, 1996), where within a linear regression framework coefficients of poor predictors are ‘shrunk’ to zero.”
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584 · cited 1×
“Such higher-order structures were extracted using rotation-invariant histograms of oriented gradients (HOGs; Dalal & Triggs, 2005; Liu et al., 2014; Torney et al., 2016).”
(2008) · Journal of Machine Learning Research · Cited by 33,051
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
Data Mining: Practical Machine Learning Tools and Techniques
Witten, I. H., Frank, E., & Hall, M. A. (2011) · Elsevier eBooks · doi:10.1016/c2009-0-19715-5 · Cited by 25,543
Book Review: Pattern Recognition and Machine Learning
Nasrabadi, N. M. (2007) · Journal of Electronic Imaging · doi:10.1117/1.2819119 · Cited by 22,066
Silhouettes: A graphical aid to the interpretation and validation of cluster analysis
Rousseeuw, P. J. (1987) · Journal of Computational and Applied Mathematics · doi:10.1016/0377-0427(87)90125-7 · Cited by 21,392 · cited 2×
“In the section Case Studies, we present two approaches to estimating k, using silhouette width (Rousseeuw, 1987) and the Bayesian information criterion (BIC).”
Research works printed but not linked: 3
- Bishop, 2006, Vol. 4
- Broom, 1993, Vol. 30
- Hastie, 2009, Vol. 1
Cited by
Sorted by citations
Leakage and the reproducibility crisis in machine-learning-based science
Kapoor, S., & Narayanan, A. (2023) · Patterns · doi:10.1016/j.patter.2023.100804 · Cited by 1,387
Perspectives in machine learning for wildlife conservation
Tuia, D., Kellenberger, B., Beery, S., Costelloe, B. R., Zuffi, S., Risse, B., Mathis, A., Mathis, M. W., van Langevelde, F., Burghardt, T., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-27980-y · Cited by 740
“A growing body of literature promotes the use of ML in various ecological subfields by educating domain experts about ML approaches (35, 49, 50), their utility in capitalizing on big data (32, 51), and, more recently, their potential for ecolog-”
BackgroundApplications for deep learning in ecology
Christin, S., Hervet, É., & Lecomte, N. (2019) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13256 · Cited by 724
“Machine learning has already been successfully applied to ecology to perform tasks such as acoustic classification 12 , ecological modelling 13 or studying animal behaviour 14 .”
MethodBenti, N. E., Chaka, M. D., & Semie, A. G. (2023) · Sustainability · doi:10.3390/su15097087 · Cited by 470
“The applications of ML span diverse industries such as healthcare, finance, e-commerce, and others [39–44].”
BackgroundMorota, G., Ventura, R. V., Silva, F. F. E., Koyama, M., & Fernando, S. C. (2018) · Journal of Animal Science · doi:10.1093/jas/sky014 · Cited by 245
“The prediction of imputation accuracy, based uniquely on the relatedness of low-density genotypes to those in a reference dataset using a high-density panel, was investigated by Ventura et al. (2016).”
MethodDitria, E. M., Lopez‐Marcano, S., Sievers, M., Jinks, E. L., Brown, C. J., & Connolly, R. M. (2020) · Frontiers in Marine Science · doi:10.3389/fmars.2020.00429 · Cited by 204
“This apparently never-ending stream of data brings a new challenge for ecologists, to find or develop the analytical tools needed to extract information from the immense volumes of incoming images and video content (Valletta et al., 2017).”
BackgroundLi, N., Ren, Z., Li, D., et al. (2019) · animal · doi:10.1017/s1751731119002155 · Cited by 191
“Furthermore, advanced data processing techniques make it possible to analyse animal behaviour continuously, automatically, accurately and in real time (Valletta et al., 2017; Van Hertem et al., 2017).”
BackgroundDetection of sick broilers by digital image processing and deep learning
Zhuang, X., & Zhang, T. (2019) · Biosystems Engineering · doi:10.1016/j.biosystemseng.2019.01.003 · Cited by 189
Development of an early warning algorithm to detect sick broilers
Zhuang, X., Minna, B., Guo, J., Wu, S., & Zhang, T. (2017) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2017.11.032 · Cited by 171
Automatic recognition of lactating sow postures from depth images by deep learning detector
Zheng, C., Zhu, X., Yang, X., Wang, L., Tu, S., & Xue, Y. (2018) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2018.01.023 · Cited by 150
Mansbridge, N., Mitsch, J., Bollard, N. J., Ellis, K. A., Miguel-Pacheco, G. G., Dottorini, T., & Kaler, J. (2018) · Sensors · doi:10.3390/s18103532 · Cited by 143
Smith, J. E., & Pinter‐Wollman, N. (2020) · Journal of Animal Ecology · doi:10.1111/1365-2656.13362 · Cited by 143
Escape responses of fish: a review of the diversity in motor control, kinematics and behaviour
Domenici, P., & Hale, M. E. (2019) · Journal of Experimental Biology · doi:10.1242/jeb.166009 · Cited by 131
Deep learning for environmental conservation
Lamba, A., Cassey, P., Segaran, R. R., & Koh, L. P. (2019) · Current Biology · doi:10.1016/j.cub.2019.08.016 · Cited by 144
REFORMS: Consensus-based Recommendations for Machine-learning-based Science
Kapoor, S., Cantrell, E. M., Peng, K., Pham, T. H., Bail, C. A., Gundersen, O. E., Hofman, J. M., Hullman, J. R., Lones, M. A., Malik, M. M., et al. (2024) · Science Advances · doi:10.1126/sciadv.adk3452 · Cited by 255
Behaviour classification of extensively grazed sheep using machine learning
Fogarty, E. S., Swain, D. L., Cronin, G. M., Moraes, L. E., & Trotter, M. G. (2019) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2019.105175 · Cited by 124
A hierarchical 3D-motion learning framework for animal spontaneous behavior mapping
Huang, K., Han, Y., Chen, K., Pan, H., Zhao, G., Yi, W., Li, X., Liu, S., Wei, P., & Wang, L. (2021) · Nature Communications · doi:10.1038/s41467-021-22970-y · Cited by 128
Walton, E., Casey, C., Mitsch, J., Vázquez-Diosdado, J. A., Yan, J., Dottorini, T., Ellis, K. A., Winterlich, A., & Kaler, J. (2018) · Royal Society Open Science · doi:10.1098/rsos.171442 · Cited by 119
“Random forest algorithms can improve the overfitting problem of decision trees algorithms while maintaining a relatively low computational cost [2].”
BackgroundInvited review: Cattle lameness detection with accelerometers
O’Leary, N., Byrne, D. T., O’Connor, A. H., & Shalloo, L. (2020) · Journal of Dairy Science · doi:10.3168/jds.2019-17123 · Cited by 117
Machine learning in landscape ecological analysis: a review of recent approaches
Stupariu, M., Cushman, S. A., Pleșoianu, A., Pătru-Stupariu, I., & Fürst, C. (2021) · Landscape Ecology · doi:10.1007/s10980-021-01366-9 · Cited by 124
“…enable analysis of datasets that until recently have been intractably large and complex, and could offer solutions for extracting relevant information (Ma et al. 2014) and further translating data into scientific knowledge (Valletta et al. 2017; Shirk et al. in review; Jones et al. in review).”
BackgroundLewis, M. A., Fagan, W. F., Auger‐Méthé, M., Frair, J. L., Fryxell, J. M., Gros, C., Gurarie, E. D., Healy, S. D., & Merkle, J. A. (2021) · Frontiers in Ecology and Evolution · doi:10.3389/fevo.2021.681704 · Cited by 115
Torney, C. J., Lloyd‐Jones, D. J., Chevallier, M., Moyer, D. C., Maliti, H. T., Mwita, M., Kohi, E. M., & Hopcraft, G. C. (2019) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13165 · Cited by 110
“…essential components of the ecologist’s tool-102 box (Bruijning et al., 2018; Christin et al., 2018; Dell et al., 2014; Mac Aodha et al., 2018; Valletta et al., 2017; Weinstein, 2018) and have been applied previously to the task of counting aerial images of animals (Bajzak and Piatt, 1990;…”
MethodSelf-citationAstill, J., Dara, R. A., Fraser, E., et al. (2018) · Frontiers in Veterinary Science · doi:10.3389/fvets.2018.00263 · Cited by 109
“The use of machine learning has already been employed to study a variety of wild and farmed animal behaviors (59).”
BackgroundBrowning, E., Bolton, M., Owen, E., Shoji, A., Guilford, T., & Freeman, R. (2018) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.12926 · Cited by 107
“The computational power requirements and perceived complexity of constructing deep learning networks may have hindered their use in previous studies; Valletta et al. (2017) in their review highlight the potential for using deep learning methods in predictive animal behaviour studies, but conclude…”
BackgroundBrewster, L. R., Dale, J. J., Guttridge, T. L., Gruber, S. H., Hansell, A. C., Elliott, M., Cowx, I. G., Whitney, N. M., & Gleiss, A. C. (2018) · Marine Biology · doi:10.1007/s00227-018-3318-y · Cited by 108
“The optimum number of clusters can often be ambiguous with too few clusters resulting in similar behaviours being grouped together, whilst too many may artificially separate behaviours (Sakamoto et al. 2009; Whitney et al. 2010; Gleiss et al. 2017; Valletta et al. 2017).”
BackgroundMethodInfluentialLivestock Management With Unmanned Aerial Vehicles: A Review
Alanezi, M. A., Shahriar, M. S., Hasan, M. B., Ahmed, S., Sha’aban, Y. A., & Bouchekara, H. R. E. H. (2022) · IEEE Access · doi:10.1109/access.2022.3168295 · Cited by 124
“ML has also been used in animal behavior studies [128], and improvements are expected with the advent of data.”
BackgroundMethodDamage assessment of smart composite structures via machine learning: a review
Khan, A., Kim, N., Shin, J. K., Kim, H. S., & Youn, B. D. (2019) · JMST Advances · doi:10.1007/s42791-019-0012-2 · Cited by 98
Machine learning-enabled optimization of extrusion-based 3D printing
Rahmani Dabbagh, S., Ozcan, O., & Tasoglu, S. (2022) · Methods · doi:10.1016/j.ymeth.2022.08.002 · Cited by 94
Costs and benefits of social relationships in the collective motion of bird flocks
Ling, H., McIvor, G. E., van der Vaart, K., Vaughan, R., Thornton, A., & Ouellette, N. T. (2019) · Nature Ecology & Evolution · doi:10.1038/s41559-019-0891-5 · Cited by 92
Self-citationPairing camera traps and acoustic recorders to monitor the ecological impact of human disturbance
Buxton, R. T., Lendrum, P. E., Crooks, K. R., & Wittemyer, G. (2018) · Global Ecology and Conservation · doi:10.1016/j.gecco.2018.e00493 · Cited by 91
“Technological advances in both hardware and software have streamlined the use of each method, and machine learning approaches have revolutionized the types of information extracted from such data and the speed with which processing occurs (Valletta et al., 2017).”
MethodEthoflow: Computer Vision and Artificial Intelligence-Based Software for Automatic Behavior Analysis
Bernardes, R. C., Lima, M. A. P., Guedes, R. N. C., da Silva, C. B., & Martins, G. F. (2021) · Sensors · doi:10.3390/s21093237 · Cited by 81
“The use of computational tools in behavioral assessments allows accurate and long-term evaluations of animals [2,4].”
BackgroundHigh-throughput mouse phenomics for characterizing mammalian gene function
Brown, S. D. M., Holmes, C. C., Mallon, A., Meehan, T. F., Smedley, D. P., & Wells, S. (2018) · Nature Reviews Genetics · doi:10.1038/s41576-018-0005-2 · Cited by 84
Animal tag technology keeps coming of age: an engineering perspective
Holton, M. D., Wilson, R. P., Teilmann, J., & Siebert, U. (2021) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2020.0229 · Cited by 77
“The vast array of machine-learning programmes [76] makes it easy to collect data and, with little more than cursory inspection, analyse it within a programming ‘black box’.”
BackgroundArndt, S. S., Goerlich, V. C., & van der Staay, F. J. (2022) · Frontiers in Animal Science · doi:10.3389/fanim.2022.908513 · Cited by 89
Machine learning to detect behavioural anomalies in dairy cows under subacute ruminal acidosis
Wagner, N., Antoine, V., Mialon, M., Lardy, R., Silberberg, M., Koko, J., & Veissier, I. (2020) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2020.105233 · Cited by 81
“Machine Learning (ML) can greatly help the study of behaviour (Valletta et al., 2017).”
BackgroundMethodAffective State Recognition in Livestock—Artificial Intelligence Approaches
Neethirajan, S. R. (2022) · Animals · doi:10.3390/ani12060759 · Cited by 78
“Following the supervised training stage, unsupervised ML models applied aids in the determination of clusters of similar behavioural and affective state descriptors from unannotated sensor data obtained from farm animals [36,114,115].”
MethodReview on methods used for wildlife species and individual identification
Petso, T., Jamisola, R. S., & Mpoeleng, D. (2021) · European Journal of Wildlife Research · doi:10.1007/s10344-021-01549-4 · Cited by 82
“A case study of using aerial images to train a classifier that detects the presence of wildebeests for population census is shown in Valletta et al. (2017).”
BackgroundSakai, K., Oishi, K., Masafumi, M. I. W. A., Kumagai, H., & Hirooka, H. (2019) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2019.105027 · Cited by 72
Early Life Experience Shapes Male Behavior and Social Networks in Drosophila
Bentzur, A., Ben-Shaanan, S., Benichou, J. I. C., Costi, E., Levi, M., Ilany, A., & Shohat-Ophir, G. (2020) · Current Biology · doi:10.1016/j.cub.2020.10.060 · Cited by 69
van Beest, F. M., Mews, S., Elkenkamp, S., Schuhmann, P., Tsolak, D., Wobbe, T., Bartolino, V., Bastardie, F., Dietz, R., von Dorrien, C., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-42109-w · Cited by 70
Weise, F. J., Hauptmeier, H., Stratford, K. J., Hayward, M. W., Aal, K., Heuer, M., Tomeletso, M., Wulf, V., Somers, M. J., & Stein, A. B. (2019) · Frontiers in Ecology and Evolution · doi:10.3389/fevo.2018.00242 · Cited by 75
“It also requires intensive monitoring of different ecological variables (Supplementary Data 10) and can benefit from machine learning to infer behavioral patterns from animal GPS data (Valletta et al., 2017).”
BackgroundAn automatic recognition framework for sow daily behaviours based on motion and image analyses
Yang, A., Huang, H., Zheng, B., et al. (2020) · Biosystems Engineering · doi:10.1016/j.biosystemseng.2020.01.016 · Cited by 66
Automated Video Behavior Recognition of Pigs Using Two-Stream Convolutional Networks
Zhang, K., Li, D., Huang, J., & Chen, Y. (2020) · Sensors · doi:10.3390/s20041085 · Cited by 69
“Deep learning, which is an excellent research method in computer vision techniques, would be widely applied in the study of the animal behavior community [21].”
MethodJellytoring: Real-Time Jellyfish Monitoring Based on Deep Learning Object Detection
Martín-Abadal, M., Ruiz‐Frau, A., Hinz, H., & González-Cid, Y. (2020) · Sensors · doi:10.3390/s20061708 · Cited by 66
Tamura, T., Okubo, Y., Deguchi, Y., Koshikawa, S., Takahashi, M., Chida, Y., & Okada, K. (2019) · Animal Science Journal · doi:10.1111/asj.13184 · Cited by 64
A guide to sampling design for GPS ‐based studies of animal societies
He, P. J., Klarevas‐Irby, J. A., Papageorgiou, D., Christensen, C., Strauss, E. D., & Farine, D. R. (2022) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13999 · Cited by 76
Analysis of feeding and drinking behaviors of group-reared broilers via image processing
Li, G., Zhao, Y., PURSWELL, J. L., Du, Q., Chesser, G. D., & Lowe, J. W. (2020) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2020.105596 · Cited by 61
Bobbo, T., Biffani, S., Taccioli, C., Penasa, M., & Cassandro, M. (2021) · Scientific Reports · doi:10.1038/s41598-021-93056-4 · Cited by 68
A survey of machine learning approaches in animal behaviour
Kleanthous, N., Hussain, A. J., Khan, W., Sneddon, J. C., Al-Shamma'a, A. I., & Liatsis, P. (2022) · Neurocomputing · doi:10.1016/j.neucom.2021.10.126 · Cited by 68
Machine Learning Approaches for the Prioritization of Genomic Variants Impacting Pre-mRNA Splicing
Rowlands, C. F., Baralle, D., & Ellingford, J. M. (2019) · Cells · doi:10.3390/cells8121513 · Cited by 59
“Machine learning is already being applied to great effect in diverse biological fields, such as the modelling of social networks in animal behavior studies [19,20] and protein secondary structure prediction [21].”
BackgroundUse of vocalisation to identify sex, age, and distress in pig production
Cordeiro, A. F. D. S., Nääs, I. D. A., Leitão, F. D. S., de Almeida, A. C. M., & de Moura, D. J. (2018) · Biosystems Engineering · doi:10.1016/j.biosystemseng.2018.03.007 · Cited by 61
Assessing the effects of sampling frequency on behavioural classification of accelerometer data
Hounslow, J. L., Brewster, L. R., Lear, K. O., Guttridge, T. L., Daly, R., Whitney, N. M., & Gleiss, A. C. (2019) · Journal of Experimental Marine Biology and Ecology · doi:10.1016/j.jembe.2018.12.003 · Cited by 61
Vázquez-Diosdado, J. A., Paul, V., Ellis, K. A., Coates, D., Loomba, R., & Kaler, J. (2019) · Sensors · doi:10.3390/s19143201 · Cited by 60
The Future of Artificial Intelligence in Monitoring Animal Identification, Health, and Behaviour
Congdon, J. V., Hosseini, M., Gading, E. F., Masousi, M., Franke, M., & MacDonald, S. E. (2022) · Animals · doi:10.3390/ani12131711 · Cited by 62
“The payoff of this technology is that it allows for longer durations of monitoring, without the need for human observers, resulting in mass data collection [30].”
BackgroundAdvances in image acquisition and processing technologies transforming animal ecological studies
Nazir, S., & Kaleem, M. (2021) · Ecological Informatics · doi:10.1016/j.ecoinf.2021.101212 · Cited by 58
Artificial intelligence for fish behavior recognition may unlock fishing gear selectivity
Abangan, A. S., Kopp, D., & Faillettaz, R. (2023) · Frontiers in Marine Science · doi:10.3389/fmars.2023.1010761 · Cited by 61
“Tools for automatic behavior recognition are being developed mainly in aquaculture (Valletta et al., 2017; Niu et al., 2018) and in coastal fish communities (e.”
BackgroundAnimal behavior analysis methods using deep learning: A survey
Fazzari, E., Romano, D., Falchi, F., & Stefanini, C. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128330 · Cited by 77
Epilepsy and its neurobehavioral comorbidities: Insights gained from animal models
Löscher, W., & Stafstrom, C. E. (2022) · Epilepsia · doi:10.1111/epi.17433 · Cited by 61
Electronic traps for detection and population monitoring of adult fruit flies (Diptera: Tephritidae)
Shaked, B., Amore, A., Ioannou, C. S., et al. (2017) · Journal of Applied Entomology · doi:10.1111/jen.12422 · Cited by 54
Noninvasive Technologies for Primate Conservation in the 21st Century
Piel, A., Crunchant, A., Knot, I. E., Chalmers, C., Fergus, P., Mulero-Pázmány, M., & Wich, S. (2021) · International journal of primatology · doi:10.1007/s10764-021-00245-z · Cited by 5
Clare, M., Sonnewald, M., Lguensat, R., Deshayes, J., & Balaji, V. (2022) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2022ms003162 · Cited by 63
“In further work, we could consider using a Gaussian Mixture Model (GMM) (Valletta et al., 2017) instead of k-means clustering.”
MethodSex inequality driven by dispersal
Chen, Y., Ge, E., Zhou, L., Du, J., & Mace, R. H. (2023) · Current Biology · doi:10.1016/j.cub.2022.12.027 · Cited by 93
The quest to develop automated systems for monitoring animal behavior
Siegford, J. M., Steibel, J. P., Han, J., Benjamin, M., Brown-Brandl, T. M., Dórea, J. R. R., Morris, D., Norton, T., Psota, E. T., & Rosa, G. J. M. (2023) · Applied Animal Behaviour Science · doi:10.1016/j.applanim.2023.106000 · Cited by 55
Gan, H., Ou, M., Huang, E., Xu, C., Li, S., Li, J., Liu, K., & Xue, Y. (2021) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2021.106357 · Cited by 52
Moulin, T. C., Covill, L., Itskov, P. M., Williams, M. J., & Schiöth, H. B. (2020) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2020.11.014 · Cited by 52
“technologies is necessary for handling the large multidimensional datasets generated (Hillar et al., 2018; Valletta et al., 2017).”
BackgroundAdvances in bio-logging techniques and their application to study navigation in wild seabirds
Yoda, K. (2018) · Advanced Robotics · doi:10.1080/01691864.2018.1553686 · Cited by 51
Deep Learning Applications in Solid Waste Management: A Deep Literature Review
Shahab, S., Anjum, M., & Umar, M. S. (2022) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2022.0130347 · Cited by 53
“However, conventional AI methods have limited capacity to analyze this huge amount of data which is a bottleneck for researchers [68].”
BackgroundCampbell, M., Miller, P. C., Díaz-Chito, K., et al. (2024) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2023.108591 · Cited by 49
“Once trajectory-based datasets are established, clustering methodologies can be applied to elucidate underlying associated patterns in an object ’ s movement by grouping trajectories which share similar characteristics (Rabaud and Belongie, 2006; Valletta et al., 2017).”
BackgroundMethodDual attention-guided feature pyramid network for instance segmentation of group pigs
Hu, Z., Yang, H. M., & Lou, T. (2021) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2021.106140 · Cited by 50
Drones for Sheep Livestock Monitoring
Al-Thani, N., Albuainain, A., Al-Naimi, F., et al. (2020) · IEEE Mediterranean Electrotechnical Conference (MELECON) · doi:10.1109/melecon48756.2020.9140588 · Cited by 48
A machine learning approach for classifying and quantifying acoustic diversity
Keen, S. C., Odom, K. J., Webster, M. S., Kohn, G. M., Wright, T. F., & Araya‐Salas, M. (2021) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13599 · Cited by 48
“All statistical analyses were conducted using the r packages cluster , tsne , mass and adehabitat hr (Calenge, 2006; Donaldson, 2016; Maechler et al., 2018; Venables & Ripley, 2002).”
MethodArtificial intelligence-driven phenotyping of zebrafish psychoactive drug responses
Bozhko, D. V., Myrov, V. O., Kolchanova, S. M., Polovian, A. I., Galumov, G. K., Demin, K. A., Zabegalov, K. N., Strekalova, T., de Abreu, M. S., Petersen, E. V., et al. (2021) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2021.110405 · Cited by 50
Using tri-axial accelerometer loggers to identify spawning behaviours of large pelagic fish
Clarke, T. M., Whitmarsh, S. K., Hounslow, J. L., Gleiss, A. C., Payne, N. L., & Huveneers, C. P. M. (2021) · Movement Ecology · doi:10.1186/s40462-021-00248-8 · Cited by 47
“used machine learning (ML) algorithms to train models based on patterns in the collected data allowing predictions from unseen data [3, 18, 19, 61].”
BackgroundMethodInfluentialDeep reinforcement learning for conservation decisions
Lapeyrolerie, M., Chapman, M., Norman, K., & Boettiger, C. (2022) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13954 · Cited by 53
“Applications of ML in ecology have to-date illustrated the promise of two methods: supervised learning (M. B. Joseph 2020) and unsupervised learning (Valletta et al. 2017).”
MethodMcCarthy, E. D., Martin, J. M., Boer, M. M., & Welbergen, J. A. (2021) · Remote Sensing in Ecology and Conservation · doi:10.1002/rse2.202 · Cited by 47
“Machine learning-enabled classification techniques are increasingly being applied in ecology, including species population monitoring (Dell et al., 2014; Tabak et al., 2019; Valletta et al., 2017; Yang et al., 2015).”
BackgroundBhuiyan, M. R., & Wree, P. (2023) · IEEE Access · doi:10.1109/access.2023.3331092 · Cited by 45
“In [48] this work is an attempt to acquaint animal behaviorists unfamiliar with machine learning (ML) to the potential of these approaches for assessing intricate behavioral data.”
BackgroundA scoping review of the use of bioacoustics to assess various components of farm animal welfare
Coutant, M. P., Villain, A. S., & Briefer, E. F. (2024) · Applied Animal Behaviour Science · doi:10.1016/j.applanim.2024.106286 · Cited by 45
“…and a potential lack of conformity to the assumptions of many classical statistical models, machine learning algorithms offer a versatile alternative to more classical statistical analyses, and have become more common than ever in bioacoustic analyses (Rutz et al., 2023; Valletta et al., 2017).”
BackgroundIntroduction to Machine Learning
Kumar, M., & Bhawna (2024) · Studies in computational intelligence · doi:10.1007/978-981-97-5624-7_2 · Cited by 43
Individual signatures outweigh social group identity in contact calls of a communally nesting parrot
Smith‐Vidaurre, G., Araya‐Salas, M., & Wright, T. F. (2019) · Behavioral Ecology · doi:10.1093/beheco/arz202 · Cited by 42
Fraiwan, M., & Abutarbush, S. M. (2020) · Journal of Equine Veterinary Science · doi:10.1016/j.jevs.2020.102973 · Cited by 42
Yan, H., Ran, Q., Hu, R., et al. (2022) · Ecological Indicators · doi:10.1016/j.ecolind.2022.108738 · Cited by 41
Deep Learning Empowered Wearable-Based Behavior Recognition for Search and Rescue Dogs
Kasnesis, P., Doulgerakis, V., Uzunidis, D., et al. (2022) · Sensors · doi:10.3390/s22030993 · Cited by 41
Kittichotsatsawat, Y., Tippayawong, N., & Tippayawong, K. Y. (2022) · Scientific Reports · doi:10.1038/s41598-022-18635-5 · Cited by 47
A Review of Automated Bioacoustics and General Acoustics Classification Research
Mutanu, L. M., Gohil, J., Gupta, K., Wagio, P., & Kotonya, G. (2022) · Sensors · doi:10.3390/s22218361 · Cited by 42
Elephants and algorithms: a review of the current and future role of AI in elephant monitoring
Brickson, L. L., Zhang, L., Vollrath, F. W., Douglas‐Hamilton, I., & Titus, A. (2023) · Journal of The Royal Society Interface · doi:10.1098/rsif.2023.0367 · Cited by 55
“This is an active area of research in the fields of neuroscience and behavioural biology, and we refer readers to Anderson et al. [96], Valletta et al. [97], Datta et al. [98] and Couzin et al. [99] for more pedagogical introductions, comprehensive overviews, and nuanced reviews of the field.”
BackgroundMattern, T., McPherson, M. D., Ellenberg, U., et al. (2018) · PeerJ · doi:10.7717/peerj.5459 · Cited by 39
Sex inequality driven by dispersalPreprint
Chen, Y., Ge, E., Zhou, L., et al. (2022) · doi:10.31219/osf.io/k8ew2 · Cited by 39
Data leakage jeopardizes ecological applications of machine learning
Stock, A., Gregr, E. J., & Chan, K. M. A. (2023) · Nature Ecology & Evolution · doi:10.1038/s41559-023-02162-1 · Cited by 46
Animal-borne technologies in wildlife research and conservation
Rafiq, K., Pitcher, B. J., Cornelsen, K. A., Hansen, K. W., King, A. J., Appleby, R. G., Abrahms, B. L., & Jordan, N. R. (2021) · doi:10.1093/oso/9780198850243.003.0006 · Cited by 40
Qiao, B., Li, C., Allen, V. W., Shirasu‐Hiza, M., & Syed, S. (2018) · eLife · doi:10.7554/elife.34497 · Cited by 40
“Machine-learning is increasingly gaining popularity due to its applicability to virtually any problem involving pattern classification, including in studies aimed at deconstructing stereotyped behavior in the fruit fly (Branson et al., 2009; Kabra et al., 2013; Kain et al., 2013; Mendes et al., 2013; Valletta et al.,…”
BackgroundHirakawa, T., Yamashita, T., Tamaki, T., Fujiyoshi, H., Umezu, Y., Takeuchi, I., Matsumoto, S., & Yoda, K. (2018) · Ecosphere · doi:10.1002/ecs2.2447 · Cited by 38
Zebrafish behavior feature recognition using three-dimensional tracking and machine learning
Yang, P., Takahashi, H., Murase, M., & Itoh, M. (2021) · Scientific Reports · doi:10.1038/s41598-021-92854-0 · Cited by 37
“Machine learning has attracted considerable attention from behavior researchers 24–26 .”
BackgroundMachine Learning and Its Applications in Studying the Geographical Distribution of Ants
Chen, S., & Ding, Y. (2022) · Diversity · doi:10.3390/d14090706 · Cited by 38
Machine learning for ecological analysis
Yu, Z., Bu, C., & Li, Y. (2025) · Chemical Engineering Journal · doi:10.1016/j.cej.2025.160780 · Cited by 37
Hossein‐Zadeh, N. G. (2025) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2025.110395 · Cited by 43
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“Feature selection also elucidates the optimal subset of predictors and causal drivers under the operating system of interest (Valletta et al., 2017).”
BackgroundMethodGagaoua, M., Monteils, V., & Picard, B. (2018) · Journal of the Science of Food and Agriculture · doi:10.1002/jsfa.9301 · Cited by 35
Weber‐Stadlbauer, U., & Meyer, U. (2019) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2019.02.006 · Cited by 32
Animal Movement Prediction Based on Predictive Recurrent Neural Network
Rew, J., Park, S., Cho, Y., Jung, S., & Hwang, E. (2019) · Sensors · doi:10.3390/s19204411 · Cited by 39
Csoltova, E., & Mehinagic, E. (2020) · Frontiers in Psychology · doi:10.3389/fpsyg.2020.02131 · Cited by 41
Metadata
- Authors
- Valletta, J. J., Torney, C., Kings, M., Thornton, A., & Madden, J.
- Affiliations
- University of Exeter (GB)
- Corresponding author
- Valletta, John Joseph
- Venue
- Animal Behaviour
- Publisher
- Elsevier BV
- Published
- 2017-02
- Volume
- 124
- Pages
- 203-220
- ISSN
- 0003-3472 (ISSN-L)
- 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
- hybrid
- doi.org
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-09, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
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