Man’s best friend may be nature’s worst enemy, study on pet dogs finds
Curtin University
New Curtin University research into the overlooked environmental impact of pet dogs has found far-reaching negative effects on wildlife, ecosystems and climate.
While ecological damage caused by cats has been extensively studied, the new research found dogs, as the world’s most common large carnivores, present a significant and multifaceted environmental threat.
Lead researcher Associate Professor Bill Bateman, from Curtin’s School of Molecular and Life Sciences, said the research found that human-owned, pet dogs disturb and directly harm wildlife, particularly shorebirds, even when leashed.
“As well as predatory behaviour like chasing wildlife, dogs leave scents, urine and faeces, which can disrupt animal behaviour long after the dogs have left,” Associate Professor Bateman said.
“Studies have found that animals like deer, foxes and bobcats in the US are less active or completely avoid areas where dogs are regularly walked, even in the absence of the dogs
“Dog waste also contributes to pollution in waterways and inhibits plant growth, while wash-off from chemical treatments used to clean and guard dogs from parasites can add toxic compounds to aquatic environments.
“In addition, the pet food industry, driven by a vast global dog population, has a substantial carbon, land and water footprint.”
Associate Professor Bateman said addressing these challenges required a careful balance between reducing environmental harm and maintaining the positive role of dogs as companions and working animals.
“Dogs are incredibly important to people’s lives and their roles range from providing companionship to contributing to conservation efforts as detection dogs,” Associate Professor Bateman said.
“However, the sheer number of pet dogs globally, combined with uninformed or lax behaviours by some owners, is driving environmental issues that we can no longer ignore.”
The study also sheds light on barriers to sustainable pet ownership, finding that while the dog food industry is a key factor in national sustainability action plans, only 12 to 16 per cent of dog owners are willing to pay more for eco-friendly pet food, largely due to rising costs. Additionally, a lack of awareness among owners about the impact of dogs on the environment compounds the issue.
“Many owners simply don’t realise the environmental damage dogs can cause, from disturbing wildlife to polluting ecosystems,” Associate Professor Bateman said.
“Others may feel their individual actions won’t make a difference, leading to a ‘tragedy of the commons’ where shared spaces like beaches and woodlands suffer cumulative degradation
“Restrictive measures such as banning dogs from sensitive areas are necessary for protecting vulnerable species but they are not a complete solution. We are calling for a collaborative effort between dog owners, conservation groups and policymakers to develop strategies that balance pet ownership with environmental care.”
The paper, ‘Bad Dog? The environmental effects of owned dogs,’ has been published in Pacific Conservation Biology and can be found online here: https://doi.org/10.1071/PC24071
Method of Research
Literature review
Subject of Research
Animals
Article Title
Bad Dog? The environmental effects of owned dogs
Article Publication Date
9-Apr-2025
Canine EEG helps human: cross-species and cross-modality epileptic seizure detection via multi-space alignment
Science China Press
image:
For temporal similarity, intracranial EEG from both canines and humans exhibits large fluctuations during epileptic seizures, indicating the transferability of time-domain features across species. For entropy similarity, the approximate entropy of intracranial EEG from both species increases significantly during seizures, indicating the transferability of entropy features across species. For spectral similarity, power spectral density spectrograms derived from consecutive Fourier transforms for both species show increased power across all channels during seizures, suggesting the transferability of frequency-domain features.
view moreCredit: ©Science China Press
The study, led by Professor Dongrui Wu from the Huazhong University of Science and Technology, first analyzed the feature similarities across species (canine/human) and modalities (scalp/intracranial EEG), from the perspective of temporal, spectral, and entropy features (Figure 1). For temporal features, EEG signals from both canines and humans exhibit large fluctuations during epileptic seizures, indicating the transferability in the time domain. For entropy features, the approximate entropy of intracranial EEG from both species increases significantly during seizures, indicating their transferability across species. For spectral features, power spectral density spectrograms derived from consecutive Fourier transforms for both species show an increase in the power across all channels during seizures, suggesting the transferability in the frequency domain.
However, discrepancies across species and modalities are also evident (Figure 2). Input space disparity across species is highlighted by the discrepancy in electrode configurations between species. In terms of data acquisition devices, canine intracranial EEG signals were captured using implanted intracranial electrodes, whereas human scalp EEG signals were collected via non-invasive scalp electrodes. Even for the same signal modality, the number and configuration of electrodes can be significantly different, e.g., 16 intracranial electrodes were used for canines’ intracranial EEG data, whereas only 6 were used for humans’ intracranial EEG data. Feature distribution gaps between canines and humans are also significant.
This work considers the setting that the target species itself has little or no labeled data, and some labeled data from an auxiliary species/modality are used to train a seizure classifier. It addresses the following challenges in cross-species and cross-modality transfer:
1. Differences in electrode configurations, sampling rates, and signal characteristics present significant obstacles to aligning the input space of distinct species and modalities.
2. In addition to the input heterogeneity, distribution discrepancies across species, datasets, and subjects also introduce large heterogeneities in the feature and output spaces.
3. Limited labeled data for the target species, a common yet critical limitation in automatic seizure detection.
The team found that utilizing cross-species auxiliary labeled data is beneficial. Euclidean alignment reduces the input space discrepancy, domain adaptation helps the feature space distribution alignment, and knowledge distillation benefits the output space alignment. The proposed joint alignment mechanism in the input-feature-output space enables epilepsy pattern transfer across biological barriers (Figure 3).
This is a pilot study that provides insights into the challenges and promise of multi-species and multi-modality data integration, offering an effective solution to collecting huge EEG data to train large brain models.
See the article:
Canine EEG helps human: cross-species and cross-modality epileptic seizure detection via multi-space alignment
https://doi.org/10.1093/nsr/nwaf086
Journal
National Science Review
Input space disparity across species is highlighted by the discrepancy in electrode configurations between species. Sixteen intracranial electrodes were used for canines’ intracranial EEG data collection in the Kaggle dataset, whereas only six were used for humans’ intracranial EEG data collection in the Freiburg dataset. Canine intracranial EEG signals were captured using intracranial electrodes linked to implanted devices, whereas human scalp EEG signals in the NICU and CHSZ datasets were collected via scalp electrodes, demonstrating the configuration differences. Feature distribution gaps between canines and humans are also distinct.
The framework of cross-species and cross-modality transfer network utilizes intracranial/scalp EEG data from canines and humans (left). ResizeNet, which projects EEG signals of the species with higher dimensionality to a lower dimensionality to match their feature spaces (right).
Credit
©Science China Press
Credit
©Science China Press
Do “optimistic” versus “pessimistic” medical detection dogs perform differently?
Study uncovers links between detection success and dogs’ affective and behavioral tendencies
PLOS
image:
Overhead view of the experimental room. Shows the judgement bias test (JBT) arrangement with room measurements, the items utilized in JBT and their location, and the dog’s initial position. Cam=Camera. Bowl locations on JB screen: P = Positive; NP = Near positive; M = Middle; NN=Near negative; N = Negative.
view moreCredit: Bistre Dabbah et al., 2025, PLOS One, CC-BY 4.0 (https://creativecommons.org/licenses/by/4.0/)
A new, exploratory study has revealed statistical links between the performance of medical detection dogs and their scores on behavioral and affective tests, finding that more “optimistic” dogs tended to perform better overall on detection tasks, but “pessimistic” dogs had higher scent detection specificity. Sharyn Bistre Dabbah of the University of Bristol, U.K., and colleagues present these findings in the open-access journal PLOS One on April 9, 2025.
Animal researchers commonly use a method called judgment bias testing to help assess animals’ emotional states. For example, dogs may first be trained to associate a specific location in a room with treats, and a different location with no treats. When presented with intermediate locations where a treat may or may not be present, the dogs’ responses are assessed as a proxy for their emotional states. Dogs who tend to run quickly to intermediate locations in hopes of a treat may be considered to have a more “optimistic” emotional state.
However, prior research involving judgment bias testing has neglected medical detection dogs—dogs that can use scent to detect disease or assist people with chronic health conditions, such as warning a diabetic patient of low blood sugar. Now, Bistre Dabbah and colleagues have analyzed statistical associations between judgment bias scores, behavioral assessment scores, and scores on medical detection tasks for 27 medical detection dogs and 39 detection dogs in training.
The analysis showed that more “optimistic” dogs, older dogs, and dogs who scored higher on tests assessing confidence, food orientation, and playfulness tended to score higher on detection tasks. However, among the fully trained medical detection dogs, those who were more “pessimistic” achieved a higher degree of specificity in scent detection tasks.
This study does not establish any cause-effect relationships, and more research will be needed to further evaluate the findings. However, the researchers suggest that these relationships might indicate that differences in dogs’ searching styles and performance in detection tasks could be influenced by underlying differences in affective or cognitive processes. They note that, pending further research, judgment bias testing could hold promise as a screening tool for potential detection dogs.
The authors add:
“Our findings suggest that dogs displaying more ‘optimistic’ responses in a judgement bias test tend to demonstrate higher overall ability, as rated by their trainers. Understanding this link could help us to better train, select, and support successful medical detection dogs.”
“Dogs that approached ambiguous cues more quickly—indicative of ‘optimism’—were associated with higher levels of traits such as confidence and playfulness. These insights could have important implications for training and selection methods in medical detection dogs.”
“Working through the judgment bias test was particularly interesting, as it is a relatively simple and inexpensive methodology with the potential to provide valuable insights into dogs’ personalities and decision-making processes. These findings could be relevant not only to the performance of working dogs but also to the welfare of companion dogs.”
In your coverage, please use this URL to provide access to the freely available article in PLOS One: https://plos.io/42cgr1u
Citation: Bistre Dabbah S, Mendl M, Guest C, Rooney NJ (2025) An exploratory study of associations between judgement bias, demographic and behavioural characteristics, and detection task performance in medical detection dogs. PLoS ONE 20(4): e0320158. https://doi.org/10.1371/journal.pone.0320158
Author countries: U.K.
Funding: The current study was supported by a doctoral scholarship awarded to SBD from the Council of Science and Technology. MX (Consejo Nacional de Ciencia y TecnologÃa, CONACyT) (reference number: 472257; https://conahcyt.mx/) In partnership with the University of Bristol, UK (https://www.bristol.ac.uk/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Caption
Front view of the judgement bias test (JBT) apparatus containing five possible bowl locations. The positive ‘P’ (marked in green) and negative N’ (marked in red) locations on either side of the wooden panel were the conditioned locations. The other three positions in between those locations (near positive, middle and near negative) presented ambiguous test locations.
Judgement bias test (JBT) dog training. Dog being trained to associate the Positive’ P’ location with a food reward (A) and the Negative location ‘N’ with the absence of reward (B). Locations were on opposite sides for half of the dogs.
Credit
Bistre Dabbah et al., 2025, PLOS One, CC-BY 4.0 (https://creativecommons.org/licenses/by/4.0/)
Journal
PLOS One
Method of Research
Observational study
Subject of Research
Animals
Article Title
An exploratory study of associations between judgement bias, demographic and behavioural characteristics, and detection task performance in medical detection dogs
Article Publication Date
9-Apr-2025
COI Statement
I have read the journal's policy and the authors of this manuscript have the following competing interests: CG and NR are employed by Medical Detection Dogs, the charity where the dogs from this study are trained and data collection was conducted. However, they were not involved in data collection or analysis for this study. This does not alter our adherence to PLOS ONE policies on sharing data and materials.
