Monitoring Wildlife Populations Research Articles

Investigating hunting in a protected area in Southeast Asia using passive acoustic monitoring with mobile smartphones and deep learning

Hunting is one of the most serious threats to wildlife populations. Guns can be used to hunt both terrestrial and arboreal species. While many methods and techniques have been developed to monitor wildlife populations, few techniques have been developed that can meet the demand needed to monitor threats to wildlife populations and support intervention activities; this is particularly true in Southeast Asia. Here, we used smartphones to record gunshots at 64 sites that were systematically spaced in Chu Mom Ray National Park, Vietnam from July to November 2019. Our specific goals were to: 1) quantify temporal and spatial patterns in gunshots in the national park; 2) investigate the correlation between gunshots and seven environmental variables including forest quality, elevation, and distance to the nearest village or ranger post; and 3) compare the performance of three convolutional neural network (CNN) architectures for automatically detecting gunshots. We manually annotated spectrograms of gunshots using Raven Pro 1.6. Using the manual detection approach, we identified 115 gunshots at 30 sites. The number of gunshots recorded at each recording site over two days varied from 0 to 11. On average, there were about 0.93 gunshots recorded per day per recording site. Hunting activity showed a strong temporal trend. The number of gunshots detected increased gradually from early morning, reaching the peak at noon, with the most gunshots recorded from 10:00 to 14:00 local time, equivalent to 0.18 gunshots per hour per site. No gunshots were detected from 22:00 to 04:00 in the morning. There were no strong relationships between the number of gunshots and all environmental variables, with all the correlation coefficients smaller than 0.3. Comparing three CNN architectures — AlexNet, VGG16, and ResNet18— implemented in the ‘torch for R’ ecosystem, we found that both AlexNet and VGG16 architectures led to acceptable performance for automated detection (F1 score > 0.80), but the ResNet18 architecture did not perform well for this task. We found low generalizability, as the models had relatively low performance on an open dataset from Belize (F1 score ∼ 0.52). To implement effective patrols, protected-area managers should make a hunting-risk map. The effort required for this task can be substantially reduced by using a combination of PAM and automated gunshot detection, however these approaches still require creation of a site-specific training and test dataset, along with manual verification of the detections.

Using animal–vehicle collision data for wildlife population monitoring

Using animal–vehicle collision data for wildlife population monitoring

Correcting for measurement errors in a long‐term aerial survey with auxiliary photographic data

AbstractLong‐term, large‐scale monitoring of wildlife populations is an integral part of conservation research and management. However, some traditional monitoring protocols lack the information needed to account for sources of measurement error in data analyses. Ignoring measurement error, such as partial availability, imperfect detection, and species misidentification, can lead to mischaracterizations of population states and processes. Accounting for measurement error is key to robust monitoring of populations, which can inform a wide variety of decisions, including harvest, habitat restoration, and determination of the legal status of species. We undertook an effort to retroactively minimize bias in a large‐scale, long‐term monitoring program for marine birds in the Salish Sea, Washington, USA, by conducting an auxiliary study to jointly estimate components of measurement error. We built a novel model in a Bayesian framework that simultaneously harnessed human observer and photographic data types to produce estimates necessary to correct for the effects of partial availability, imperfect detection, and species misidentification. Across all 31 species identified in photographs, both observers had instances of undercounting and overcounting birds but tended to undercount (observers undercounted totals across all species on 69.3%–78.9% of transects). We estimated species‐specific correction factors that can be used to correct both historical and future counts from the Salish Sea survey, which has been running since 1992. Our novel modeling framework can be applied in other multispecies monitoring contexts where minimal photographic data can be collected for the purposes of correcting for measurement error in large‐scale, long‐term datasets.

Wildlife Tracker using Deep Learning

Wildlife tracker using deep learning is a project aimed at developing a system that can automatically detect wild animals using deep learning techniques. Conservationists and researchers face challenges in accurately and efficiently detecting and monitoring wildlife populations in vast and often remote habitats. Traditional methods of wildlife monitoring, such as manual surveys or camera traps, are labour-intensive, time-consuming, and may not provide real-time insights into population dynamics or threats facing wildlife species. The proposed system aims to address these challenges by developing an automated animal detection system using the YOLOv8 object detection model. The system will be trained on a diverse dataset of wildlife images, encompassing various species and environmental conditions. Upon deployment, the system will analyse input images and accurately identify and localize animals within the scene in real-time. Through this approach, the system will provide conservationists and researchers with timely and actionable information for monitoring wildlife populations, assessing habitat health, and implementing targeted conservation interventions. Evaluation of the system's performance will involve metrics such as detection accuracy, precision, recall, and processing speed, ensuring reliable and efficient wildlife detection capabilities. Overall, the proposed system seeks to enhance wildlife conservation efforts by leveraging advanced technology to improve wildlife monitoring and management practices and provide real-time alert.

Towards automated animal density estimation with acoustic spatial capture-recapture.

Passive acoustic monitoring can be an effective way of monitoring wildlife populations that are acoustically active but difficult to survey visually, but identifying target species calls in recordings is non-trivial. Machine learning (ML) techniques can do detection quickly but may miss calls and produce false positives, i.e., misidentify calls from other sources as being from the target species. While abundance estimation methods can address the former issue effectively, methods to deal with false positives are under-investigated. We propose an acoustic spatial capture-recapture (ASCR) method that deals with false positives by treating species identity as a latent variable. Individual-level outputs from ML techniques are treated as random variables whose distributions depend on the latent identity. This gives rise to a mixture model likelihood that we maximize to estimate call density. We compare our method to existing methods by applying it to an ASCR survey of frogs and simulated acoustic surveys of gibbons based on real gibbon acoustic data. Estimates from our method are closer to ASCR applied to the dataset without false positives than those from a widely used false positive "correction factor" method. Simulations show our method to have bias close to zero and accurate coverage probabilities and to perform substantially better than ASCR without accounting for false positives.

Roles of Wildlife Reservoirs in the Maintenance and Transmission of Zoonotic Diseases in Nigeria

Purpose: The aim of the study was to examine roles of wildlife reservoirs in the maintenance and transmission of zoonotic diseases in Nigeria. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The study revealed that wildlife populations serve as reservoirs for a diverse array of pathogens, including viruses, bacteria, and parasites, many of which have the potential to spill over into human populations and cause disease outbreaks. Factors such as habitat destruction, encroachment into wildlife habitats, wildlife trade, and climate change contribute to increased interactions between humans and wildlife, facilitating the transmission of zoonotic pathogens. Unique Contribution to Theory, Practice and Policy: Ecological niche theory & Host-Pathogen Coevolution Theory may be used to anchor future studies on roles of wildlife reservoirs in the maintenance and transmission of zoonotic diseases in Nigeria. Strengthen surveillance systems to monitor wildlife populations, identify emerging zoonotic pathogens, and assess the risk of spillover events. This includes implementing targeted surveillance programs in high-risk regions and species known to harbor zoonotic pathogens. Develop and implement wildlife conservation policies that prioritize the protection of biodiversity and ecosystem health while considering the potential risks associated with zoonotic disease transmission.

Deep learning workflow to support in-flight processing of digital aerial imagery for wildlife population surveys.

Deep learning shows promise for automating detection and classification of wildlife from digital aerial imagery to support cost-efficient remote sensing solutions for wildlife population monitoring. To support in-flight orthorectification and machine learning processing to detect and classify wildlife from imagery in near real-time, we evaluated deep learning methods that address hardware limitations and the need for processing efficiencies to support the envisioned in-flight workflow. We developed an annotated dataset for a suite of marine birds from high-resolution digital aerial imagery collected over open water environments to train the models. The proposed 3-stage workflow for automated, in-flight data processing includes: 1) image filtering based on the probability of any bird occurrence, 2) bird instance detection, and 3) bird instance classification. For image filtering, we compared the performance of a binary classifier with Mask Region-based Convolutional Neural Network (Mask R-CNN) as a means of sub-setting large volumes of imagery based on the probability of at least one bird occurrence in an image. On both the validation and test datasets, the binary classifier achieved higher performance than Mask R-CNN for predicting bird occurrence at the image-level. We recommend the binary classifier over Mask R-CNN for workflow first-stage filtering. For bird instance detection, we leveraged Mask R-CNN as our detection framework and proposed an iterative refinement method to bootstrap our predicted detections from loose ground-truth annotations. We also discuss future work to address the taxonomic classification phase of the envisioned workflow.

An adaptation of dual‐frame sampling for estimating bat population trends in multi‐hibernacula systems

AbstractReliable estimates of population trends are important for prioritizing and implementing wildlife management actions. Abundance estimates are the most informative metric of population status but are difficult and costly to achieve for rare or elusive species. For bat species that are highly mobile and nocturnal, abundance estimates are usually only possible at their roosts. Although they are relatively easy to census in individual roosts, it is often unclear whether observed trends are representative of broader populations trends. Dual‐frame sampling is useful for monitoring wildlife populations that exhibit site fidelity and are highly visible but where the proportion of the population in known sites is unknown. We adapted a dual‐frame sampling approach to estimate abundance of Townsend's big‐eared bats (Corynorhinus townsendii) hibernating in a system of >800 caves at Lava Beds National Monument (LBNM) in northern California, USA, in 2013–2022. Beginning with a list of opportunistically discovered hibernacula, we expanded inference to the entire system by randomly selecting sites and stratifying survey effort according to number of bats observed during previous counts. Following 4 years of surveys, we began substituting survey‐informed estimates for some site types to improve efficiency of the monitoring effort. We estimated that a mean of 2,216 ± 112 (SE) bats hibernated at the study site annually and observed fluctuations of up to 22% around the mean population estimate and as much as 21% from the previous year. Despite annual fluctuations, we did not detect a trend in the number of hibernating bats at LBNM over the study period, underlining the challenges of detecting population trends in long‐lived species not exposed to pronounced stressors. Because our analytical approach generates reliable estimates of trend while accommodating annual variability in survey effort among years, it should be attractive to wildlife managers faced with shifting priorities and resources.

WildARe-YOLO: A lightweight and efficient wild animal recognition model

For the protection of endangered species and successful wildlife population monitoring, wild animal recognition is essential. While deep learning models like YOLOv5 have shown promise in real-time object recognition, their practical applicability may be constrained by their high processing requirements. In this paper, we suggest a faster and lighter version of YOLOv5s for wild animal recognition. To lower computational costs for model parameters and floating-point operations (FLOPs) for the backbone, our suggested model includes Mobile Bottleneck Block modules and an improved StemBlock. We also use Focal-EIoU as a loss function to gauge the accuracy of the predicted bounding boxes during inference and employ a BiFPN-based neck. We tested our technique on three datasets, including Wild Animal Facing Extinction, Fishmarket, and MS COCO 2017. Additionally, our technique is compared with state-of-the-art deep learning models, and from the baseline model we recorded a 17.65% increase in FPS, 28.55% model parameters reduction, and 50.92% in FLOPs reduction. Furthermore, our model has a faster model loading time, which is critical for deployment in remote areas. This enables real-time species recognition on basic hardware, aiding conservation efforts through rapid analysis. The model advances deep learning in ecology by balancing efficiency with performance.

Fair concordance between Google Trends and Danish ornithologists in the assessment of temporal trends in Danish bird populations highlights the informational value of big data

The ongoing depletion of natural systems and associated biodiversity decline is of growing international concern. Climate change is expected to exacerbate anthropogenic impacts on wild populations. The scale of impact on ecosystems and ecosystem services will be determined by the impact on a multitude of species and functional groups, which due to their biology and numbers are difficult to monitor. The IPCC has argued that surveillance or monitoring is critical and proposed that monitoring systems should be developed, which not only track developments but also function as “early warning systems.” Human populations are already generating large continuous datasets on multiple taxonomic groups through internet searches. These time series could in principle add substantially to current monitoring if they reflect true changes in the natural world. We here examined whether information on internet search frequencies delivered by the Danish population and captured by Google Trends (GT) appropriately informs on population trends in 106 common Danish bird species. We compared the internet search activity with independent equivalent population trend assessments from the Danish Ornithological Society (BirdLife Denmark/DOF). We find a fair concordance between the GT trends and the assessments by DOF. A substantial agreement can be obtained by omitting species without clear temporal trends. Our findings suggest that population trend proxies from internet search frequencies can be used to supplement existing wildlife population monitoring and to ask questions about an array of ecological phenomena, which potentially can be integrated into an early warning system for biodiversity under climate change.

Animal Density Estimation for Large Unmarked Populations Using a Spatially Explicit Model

AbstractObtaining abundance and density estimates is a particularly important aspect within wildlife conservation and management. To monitor wildlife populations, the use of motion-sensor camera traps is becoming increasing popular due to its non-invasive nature. However, animal identification is not always feasible in practice due to poor quality images and/or individuals not having uniquely identifiable physical characteristics. Spatially explicit models for unmarked individuals permit the estimation of animal density when individuals cannot be uniquely identified. Due to the structure of these models, a Bayesian super-population (data augmentation) approach is often used to fit the models to data, which involves specifying some reasonably large upper limit for the population. However, this approach presents substantial computational challenges for larger populations, as demonstrated by the motivating dataset relating to barking deer (Muntiacus muntjak) collected in Ujung Kulon National Park, Indonesia (with a population size in the low thousands). We develop a new and computationally efficient Bayesian algorithm for fitting the models to data that does not require specifying an upper population limit a priori. We apply the new algorithm to the large barking deer dataset, where the standard super-population approach is computationally expensive, and demonstrate a substantial improvement in computational efficiency.Supplementary material to this paper is provided online.

Towards standardising the collection of game statistics in Europe: a case study

In order to monitor wildlife populations in a manner that supports policy makers and natural resource managers, data must be collected using frameworks and methodologies that allow for comparisons between projects and across time. Though hunting statistics may represent a reliable data source for monitoring population trends in game species, a standardised framework for collecting and analysing this data has never been established in Europe, even within countries. Here we describe a case study on the use of hunting statistics in Spain in order to (i) describe the variability in big game statistics collection frameworks across mainland regions of Spain and (ii) propose a minimum common denominator for a standardised approach at the country level. The main differences in methodologies identified are that each region collects different variables, uses different spatial and temporal resolution, and follows different methodologies. We described spatial patterns by grouping regions based on similarities in the hunting data collection system and identified socio-economic factors as a potential driver of differences in methodologies among regions. Hunting effort-related variables and improved temporal resolution (to the event level) must be incorporated in order to achieve country-level standardisation of methodologies. The use of application software to collect information from the field in a standardised way is recommended, which necessitates engaging stakeholders as part of the monitoring process. Applications software should be designed intentionally, and only after clear objectives for the monitoring program have been defined. Making hunting data open access will improve collaboration and information transfer to scientific and professional sectors. Our recommendations can be adapted to other European countries, which would make hunting data more useful for population monitoring and wildlife policy-making at large spatial scales. Initiatives such as the “European Wildlife Observatory” (www.wildlifeobservatory.org), a network of wildlife observation and monitoring points in Europe, may improve data exchange and standardise protocols, leading to better utilisation of hunting statistics for European wildlife population monitoring.

Changes in the Eurasian Otter (Lutra lutra) Population Including Assessment of Damage in the Last Decade: Is Their Protection Still Necessary?

Monitoring wildlife populations requires trustworthy assessment methods to get reliable data to be able to make reasonable management decisions. The Eurasian Otter (Lutra lutra) experienced a dramatic population decline throughout Central Europe during the twentieth century but is currently recovering in both distribution range and population size. Their numbers are regularly monitored by the relevant authorities, who issue an annual report on the occurrence of Eurasian Otters and the financial damage caused by them. These data for the last decade show that the abundance of Eurasian Otters in 2020 was 72% higher than in 2011 and that financial losses on fish farms has increased by 148% over the same period. In view of these facts, it is appropriate to discuss whether the Eurasian Otter is still an endangered species in Central Europe or whether the time has come for government regulation of their numbers, as has already happened with other initially protected animal, great cormorant (Phalacrocorax carbo).

Role of Wildlife in The Transmission of Zoonotic Diseases in Ghana

Purpose: The aim of the study was to investigate the role of wildlife in the transmission of zoonotic diseases.
 Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low-cost advantage as compared to field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries.
 Findings: This transmission often occurs at the interface between humans, domestic animals, and wildlife. Factors like habitat destruction, increased human-wildlife interactions, and the wildlife trade have heightened the risk of zoonotic disease emergence. Understanding these dynamics is crucial for early detection and prevention of zoonotic outbreaks, as wildlife plays a pivotal role in the maintenance and amplification of these pathogens, posing ongoing challenges to global public health.
 Unique Contribution to Theory, Practice and Policy: One Health Theory, Ecological Reservoir Theory and Behavioral Ecology Theory Change may be used to anchor future studies on the role of wildlife in the transmission of zoonotic diseases. Strategies enhanced surveillance and monitoring of wildlife populations, especially those in close proximity to human settlements, are crucial. Governments and international organizations should prioritize habitat conservation and the protection of biodiversity.

Age-specific reproduction in female Steller sea lions in Southeast Alaska.

Age-, region-, and year-specific estimates of reproduction are needed for monitoring wildlife populations during periods of ecosystem change. Population dynamics of Steller sea lions (Eumetopias jubatus) in Southeast Alaska varied regionally (with high population growth and survival in the north vs. the south) and annually (with reduced adult female survival observed following a severe marine heatwave event), but reproductive performance is currently unknown. We used mark-resighting data from 1006 Steller sea lion females marked as pups at ~3 weeks of age from 1994 to 1995 and from 2001 to 2005 and resighted from 2002 to 2019 (to a maximum age of 25) to examine age-, region-, and year-specific reproduction. In the north versus the south, age of first reproduction was earlier (beginning at age 4 vs. age 5, respectively) but annual birth probabilities of parous females were reduced by 0.05. In an average year pre-heatwave, the proportion of females with pup at the end of the pupping season peaked at ages 12-13 with ~0.60/0.65 (north/south) with pup, ~0.30/0.25 with juvenile, and ~0.10 (both regions) without a dependent. In both regions, reproductive senescence was gradual after age 12: ~0.40, 0.40, and 0.20 of females were in these reproductive states, respectively, by age 20. Correcting for neonatal mortality, true birth probabilities at peak ages were 0.66/0.72 (north/south). No cost of reproduction on female survival was detected, but pup production remained lower (-0.06) after the heatwave event, which if sustained could result in population decline in the south. Reduced pup production and greater retention of juveniles during periods of poor prey conditions may be an important strategy for Steller sea lions in Southeast Alaska, where fine-tuning reproduction based on nutritional status may improve the lifetime probability of producing pups under good conditions in a variable and less productive environment.

A guidance on how to start up a national wildlife population monitoring program harmonizable at European level

A guidance on how to start up a national wildlife population monitoring program harmonizable at European level

To boldly go: methods to quantify personality in mustelids

Animal personality has wide-reaching impacts on the individual fitness and life history traits of many wild vertebrates. Recent work has highlighted personality not only as a driver of sample bias during wildlife population monitoring, but also as a potential underlying mechanism influencing the success of population translocations and invasive species control. Mustelids are the largest family in the order Carnivora and are of particular interest to conservation, with members on the IUCN red list of threatened species and the list of the world's most disruptive invasive species. Despite the widespread implications of personality for wildlife research, conservation and management, there exists little to no personality work in the mustelid literature. To address this gap, we used the stoat, Mustela erminea, as a model species to evaluate three assays to quantify personality in a wild-caught mustelid tested under captive conditions. Our intent is to highlight the importance of personality for wildlife conservation and management, and to provide robust assays for mustelid researchers and managers to modify and utilize.

Passive monitoring of avian habitat on working lands.

Intensive agricultural landscapes are a challenge for wildlife managers, policy makers, and landowners hoping to increase the diversity of desired wildlife species, such as grassland birds, which require urgent conservation action. In intensive agricultural landscapes, like those of the midwestern USA, most land area is privately owned and operated and managed primarily for production. Conducting ecological research in intensive agricultural landscapes thus requires collaborative approaches aimed at farm owners and operators. Recent advances in acoustic data collection and high-resolution habitat mapping, including low-cost acoustic recorders and satellite remote sensing, may be well positioned to address this challenge by enabling expanded assessments and monitoring of wildlife populations and habitats across regions. This study examined fine grain habitat characteristics and their relationship with avian biodiversity in intensive agricultural landscapes at 44 agricultural sites across Iowa, USA. Passive acoustic monitoring and manual identification of bird species allowed for measurement of vocalizing bird richness. High resolution mapping of noncrop vegetation provided detailed information on small noncrop vegetation habitat complexes within row-crop agriculture. Measures of image texture provided characterizations of compositional heterogeneity within noncrop vegetation. General linear Poisson modeling demonstrated robust associations between noncrop vegetation and vocalizing bird richness, yet variation in grassland bird richness was not well predicted by noncrop vegetation. Noncrop vegetation texture demonstrated potential as a predictor of vocalizing bird richness, though not better than or when combined with noncrop vegetated area, indicating it may not be an independent measure of habitat quality. Passive acoustic monitoring resulted in useful data at 44 out of 60 originally selected sites, with some lost to failed recorders and/or collaboration issues. Challenges remain in detecting habitat characteristics that promote grassland birds in row crop landscapes. Working toward probabilistic research design across privately owned working landscapes and incorporating more detailed management practice information would improve the transferability of this approach to farmland management and policy.

Population dynamics of the last leopard population of eastern Indochina in the context of improved law enforcement

Poaching, a major threat to wildlife worldwide, is pushing species toward extinction. To reduce poaching pressure and combat biodiversity loss, improved law-enforcement efforts reportedly are required; the effectiveness of which can be determined through rigorous monitoring of wildlife populations, particularly of endangered large carnivores. In the Eastern Plains Landscape of Cambodia, law-enforcement efforts increased to counter the severe threats from illegal activities; however, it is unknown if these strategies are benefiting the population of the Critically Endangered Indochinese leopard (Panthera pardus delacouri), the last large felid population of eastern Indochina. We used open population spatial capture-recapture models to estimate density, survival, per-capita recruitment, and population growth rates of leopard using data from 7 camera-trap surveys conducted over 11 years (2009–2019). We found that the population (a) declined by over 82 % (from 1.5 to 0.3 leopard/100 km2), (b) had low survival probability (0.58) and low recruitment rates (males: 0.04, females: 0.24), and (c) is expected to continue declining. An additional survey in 2021 failed to detect leopard, suggesting the species now is functionally extinct, if not fully extirpated, from the landscape. Over the study period, there was a drastic increase in human activity, with a 20-fold increase in detection frequencies of humans and a 1000-fold increase in lethal-trap encounter rate. The rise in anthropogenic pressures, particularly snaring, appeared to be the primary reason for the leopard decline, indicating the last decade of management interventions was insufficient to conserve the species, which now appears to be extirpated in all of Cambodia, Laos and Vietnam. This has implications for leopard conservation in the wider region, notably that efforts should now focus on populations within the two remaining strongholds. Our results suggest that increases in law-enforcement efforts alone are unlikely to protect wildlife in eastern Indochina; thus, additional strategies are needed to address the region's snaring crisis, including legislative reforms, community engagement, and programs that reduce demand for wildlife meat and products. Long-term studies of remaining Indochinese leopard populations coupled with timely and effective conservation actions are needed to avoid the complete demise of this subspecies.

Cooke’s index: A simple, cost-effective method for multiple practitioners to estimate European rabbit abundance

The development of evidence-based tools that help to monitor wildlife populations is essential to assess the success of management interventions. Here, we evaluated the reliability of a simple method to estimate the abundance of the European rabbit (Oryctolagus cuniculus), which is likely one of the most managed vertebrate species worldwide. Cooke’s method involves noting the distribution and frequency of sightings of rabbit fecal pellets and accordingly scoring the abundance of rabbits on a scale 0–5. To assess its reliability, we used information collected during 101 rabbit surveys conducted across mainland Spain during the summer of 2022. In each 4-km walked transect, we estimated rabbit abundance using Cooke’s method every 400 m (i.e., 10 indexes), and pellet counts (2 plots per 100 m walked, i.e., 80 counts), which were used as a reference index. Our results revealed a strong correlation between the estimates of rabbit abundance obtained using Cooke’s method and the number of pellets counted per m2 regardless of the statistical approach used. This suggests that Cooke’s method provides a simple and inexpensive way of estimating the abundance of European rabbits, which we believe that can be very useful for non-expert land managers after a very short training period. Importantly, our findings demonstrate that it is reliable to estimate rabbit abundance in different land uses. In addition, Cooke’s method can be used in different seasons, is not affected by circadian activity of rabbits, seems to work well across the whole range of rabbit abundance, and may be useful for estimating rabbit abundance at different spatial scales. As everyday people rather than scientists are generally involved in managing European rabbits, we strongly recommend this simple method that allows rabbit abundance to be quickly estimated so that management decisions can be made. More broadly, we encourage researchers to investigate the usefulness of similar monitoring tools for other mammal species that are often surveyed through counting their pellets.