Pairing camera traps and acoustic recorders to monitor the ecological impact of human disturbance

Implications of the scale of detection for inferring co-occurrence patterns from paired camera traps and acoustic recorders

Multifunctional landscapes that support economic activities and conservation of biological diversity (e.g., cattle ranches with native forest) are becoming increasingly important because small remnants of native forest may comprise the only habitat left for some wildlife species. Understanding the co-occurrence between wildlife and disturbance factors, such as poaching activity and domesticated ungulates, is key to successful management of multifunctional landscapes. Tools to measure co-occurrence between wildlife and disturbance factors include camera traps and autonomous acoustic recording units. We paired 52 camera-trap stations with acoustic recorders to investigate the association between 2 measures of disturbance (poaching and cattle) and wild ungulates present in multifunctional landscapes of the Colombian Orinoquía. We used joint species distribution models to investigate species-habitat associations and species-disturbance correlations. One model was fitted using camera-trap data to detect wild ungulates and disturbance factors, and a second model was fitted after replacing camera-trap detections of disturbance factors with their corresponding acoustic detections. The direction, significance, and precision of the effect of covariates depended on the sampling method used for disturbance factors. Acoustic monitoring typically resulted in more precise estimates of the effects of covariates and of species-disturbance correlations. Association patterns between wildlife and disturbance factors were found only when disturbance was detected by acoustic recorders. Camera traps allowed us to detect nonvocalizing species, whereas audio recording devices increased detection of disturbance factors leading to more precise estimates of co-occurrence patterns. The collared peccary (Pecari tajacu), lowland tapir (Tapirus terrestris), and white-tailed deer (Odocoileus virginianus) co-occurred with disturbance factors and are conservation priorities due to the greater risk of poaching or disease transmission from cattle.

A comparison of the sampling effectiveness of acoustic recorder, camera trap and point count methods in sampling nocturnal birds in Afrotropical landscapes

Conservation decisions for bird diversity in the Afrotropics are often based on ecological studies utilizing diurnal bird species likely owing to difficulties associated with sampling nocturnal birds. It is therefore important to compare the sampling effectiveness of some of the available techniques that can be used in nocturnal bird surveys to guide future long-term survey efforts. Thus, we compared the sampling effectiveness of point count, acoustic recorder and camera trap for estimating nocturnal bird species richness and also across habitat types. We surveyed 20 points that were spaced at least 500 m apart in November and December 2021 in the Amurum Forest Reserve and its surroundings in Jos-Nigeria. At each point, we used two camera traps, one at the ground and the other at 2.0 m. We also used one acoustic recorder as well as a 15-min point count during each survey at each point. We encountered 11 nocturnal bird species, primarily nightjars but also owls. While we did not encounter any species with the camera traps, all 11 species were recorded using the acoustic recorder. All species except for Ketupa lacteaus were recorded in point count. Eight species were recorded in the gallery, seven in rocky and nine in savannah. Species richness and estimation using the acoustic recorder and point count were similar across habitat types. We conclude that either point count or acoustic recorders are useful for nocturnal bird surveys in Afrotropical environments. However, the choice of methods should be based on the research questions as some questions may be better answered by a specific method or even a combination of both.

Capturing wild animal welfare: a physiological perspective

Affective states, such as emotions, are presumably widespread across the animal kingdom because of the adaptive advantages they are supposed to confer. However, the study of the affective states of animals has thus far been largely restricted to enhancing the welfare of animals managed by humans in non-natural contexts. Given the diversity of wild animals and the variable conditions they can experience, extending studies on animal affective states to the natural conditions that most animals experience will allow us to broaden and deepen our general understanding of animal welfare. Yet, this same diversity makes examining animal welfare in the wild highly challenging. There is therefore a need for unifying theoretical frameworks and methodological approaches that can guide researchers keen to engage in this promising research area. The aim of this article is to help advance this important research area by highlighting the central relationship between physiology and animal welfare and rectify its apparent oversight, as revealed by the current scientific literature on wild animals. Moreover, this article emphasises the advantages of including physiological markers to assess animal welfare in the wild (e.g. objectivity, comparability, condition range, temporality), as well as their concomitant limitations (e.g. only access to peripheral physiological markers with complex relationships with affective states). Best-practice recommendations (e.g. replication and multifactorial approaches) are also provided to allow physiological markers to be used most effectively and appropriately when assessing the welfare of animals in their natural habitat. This review seeks to provide the foundation for a new and distinct research area with a vast theoretical and applied potential: wild animal welfare physiology.

Systematic review of machine learning methods applied to ecoacoustics and soundscape monitoring

Soundscape ecology is a promising area that studies landscape patterns based on their acoustic composition. It focuses on the distribution of biotic and abiotic sounds at different frequencies of the landscape acoustic attribute and the relationship of said sounds with ecosystem health metrics and indicators (e.g., species richness, acoustic biodiversity, vectors of structural change, gradients of vegetation cover, landscape connectivity, and temporal and spatial characteristics). To conduct such studies, researchers analyze recordings from Acoustic Recording Units (ARUs). The increasing use of ARUs and their capacity to record hours of audio for months at a time have created a need for automatic processing methods to reduce time consumption, correlate variables implicit in the recordings, extract features, and characterize sound patterns related to landscape attributes. Consequently, traditional machine learning methods have been commonly used to process data on different characteristics of soundscapes, mainly the presence-absence of species. In addition, it has been employed for call segmentation, species identification, and sound source clustering. However, some authors highlight the importance of the new approaches that use unsupervised deep learning methods to improve the results and diversify the assessed attributes. In this paper, we present a systematic review of machine learning methods used in the field of ecoacoustics for data processing. It includes recent trends, such as semi-supervised and unsupervised deep learning methods. Moreover, it maintains the format found in the reviewed papers. First, we describe the ARUs employed in the papers analyzed, their configuration, and the study sites where the datasets were collected. Then, we provide an ecological justification that relates acoustic monitoring to landscape features. Subsequently, we explain the machine learning methods followed to assess various landscape attributes. The results show a trend towards label-free methods that can process the large volumes of data gathered in recent years. Finally, we discuss the need to adopt methods with a machine learning approach in other biological dimensions of landscapes.

Camera trapping expands the view into global biodiversity and its change

Growing threats to biodiversity demand timely, detailed information on species occurrence, diversity and abundance at large scales. Camera traps (CTs), combined with computer vision models, provide an efficient method to survey species of certain taxa with high spatio-temporal resolution. We test the potential of CTs to close biodiversity knowledge gaps by comparing CT records of terrestrial mammals and birds from the recently released Wildlife Insights platform to publicly available occurrences from many observation types in the Global Biodiversity Information Facility. In locations with CTs, we found they sampled a greater number of days (mean = 133 versus 57 days) and documented additional species (mean increase of 1% of expected mammals). For species with CT data, we found CTs provided novel documentation of their ranges (93% of mammals and 48% of birds). Countries with the largest boost in data coverage were in the historically underrepresented southern hemisphere. Although embargoes increase data providers' willingness to share data, they cause a lag in data availability. Our work shows that the continued collection and mobilization of CT data, especially when combined with data sharing that supports attribution and privacy, has the potential to offer a critical lens into biodiversity. This article is part of the theme issue 'Detecting and attributing the causes of biodiversity change: needs, gaps and solutions'.

Estimating encounter probabilities among recreational trail user groups

The global rise in nature-based recreation increases the need for research on visitor activity use and interaction especially for multi-use trail systems. Conflict often arises during negatively perceived physical encounters (i.e., direct observation) of different user groups. Our study addresses these encounters on a winter multi-use refuge in Fairbanks, Alaska. Our goal was to develop a method that generates spatially and temporally explicit estimates of trail occupancy and encounter probabilities among different user groups. We used trail cameras with optic alteration to protect individual identity. We monitored winter recreational activity from November 2019 to April 2020 (n = 133 days) and sorted users into three user groups: 1) motor-powered, 2) dog-powered, and 3) human-powered. We calculated the total number of occurrences and proportion of activity across all user groups at each camera location. We identified hotspots of activity overlap (e.g., near trail access points), and peak times (14:01-15:00), days (Saturdays and Sundays), and months (December, February, and March) that may have had higher potential for physical encounters and conflict. We used multiplication and addition probability rules to estimate two probabilities: 1) the probability of user groups occupying individual trail segments, and 2) the probability of encounter between different user groups. We scaled up these probability estimates both temporally (hourly and daily) and spatially (refuge quadrant and refuge-wide). Researchers can adapt our novel method to any recreational trail system to identify locations with potential for congestion and conflict. This method can help inform management that improves visitor experience and overall trail user satisfaction.

Management implications

We provide managers of recreational trail systems with a quantitative, objective, and noninvasive method to monitor activity among trail user groups. This method can be altered both spatially and temporally to fit any recreational trail system's research questions. These questions may involve congestion, trail carrying capacity, or user group and wildlife encounters. Our method advances current knowledge of trail use dynamics by quantifying the extent of activity overlap between different user groups that may be prone to conflict. Managers can use this information to incorporate relevant management strategies to mitigate congestion and conflict for their own recreational trail system.

An overview of remote monitoring methods in biodiversity conservation

Conservation of biodiversity is critical for the coexistence of humans and the sustenance of other living organisms within the ecosystem. Identification and prioritization of specific regions to be conserved are impossible without proper information about the sites. Advanced monitoring agencies like the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) had accredited that the sum total of species that are now threatened with extinction is higher than ever before in the past and are progressing toward extinct at an alarming rate. Besides this, the conceptualized global responses to these crises are still inadequate and entail drastic changes. Therefore, more sophisticated monitoring and conservation techniques are required which can simultaneously cover a larger surface area within a stipulated time frame and gather a large pool of data. Hence, this study is an overview of remote monitoring methods in biodiversity conservation via a survey of evidence-based reviews and related studies, wherein the description of the application of some technology for biodiversity conservation and monitoring is highlighted. Finally, the paper also describes various transformative smart technologies like artificial intelligence (AI) and/or machine learning algorithms for enhanced working efficiency of currently available techniques that will aid remote monitoring methods in biodiversity conservation.

Disturbance Ecology Meets Bovine Tuberculosis (bTB) Epidemiology: A Before-and-After Study on the Association between Forest Clearfelling and bTB Herd Risk in Cattle Herds

Disturbance ecology refers to the study of discrete processes that disrupt the structure or dynamics of an ecosystem. Such processes can, therefore, affect wildlife species ecology, including those that are important pathogen hosts. We report on an observational before-and-after study on the association between forest clearfelling and bovine tuberculosis (bTB) herd risk in cattle herds, an episystem where badgers (Meles meles) are the primary wildlife spillover host. The study design compared herd bTB breakdown risk for a period of 1 year prior to and after exposure to clearfelling across Ireland at sites cut in 2015–2017. The percent of herds positive rose from 3.47% prior to clearfelling to 4.08% after exposure. After controlling for confounders (e.g., herd size, herd type), we found that cattle herds significantly increased their odds of experiencing a bTB breakdown by 1.2-times (95%CIs: 1.07–1.36) up to 1 year after a clearfell risk period. Disturbance ecology of wildlife reservoirs is an understudied area with regards to shared endemic pathogens. Epidemiological observational studies are the first step in building an evidence base to assess the impact of such disturbance events; however, such studies are limited in inferring the mechanism for any changes in risk observed. The current cohort study suggested an association between clearfelling and bTB risk, which we speculate could relate to wildlife disturbance affecting pathogen spillback to cattle, though the study design precludes causal inference. Further studies are required. However, ultimately, integration of epidemiology with wildlife ecology will be important for understanding the underlying mechanisms involved, and to derive suitable effective management proposals, if required.

Bioacoustic Detection of Wolves: Identifying Subspecies and Individuals by Howls

Simple Summary

This study evaluates the use of acoustic devices as a method to monitor wolves by analyzing different variables extracted from wolf howls. By analyzing the wolf howls, we focused on identifying individual wolves, subspecies. We analyzed 170 howls from 16 individuals from the three subspecies: Arctic wolves (Canis lupus arctos), Eurasian wolves (C.l. lupus), and Northwestern wolves (C.l. occidentalis). We assessed the potential for individual recognition and recognition of three subspecies: Arctic, Eurasian, and Northwestern wolves.

Abstract

Wolves (Canis lupus) are generally monitored by visual observations, camera traps, and DNA traces. In this study, we evaluated acoustic monitoring of wolf howls as a method for monitoring wolves, which may permit detection of wolves across longer distances than that permitted by camera traps. We analyzed acoustic data of wolves’ howls collected from both wild and captive ones. The analysis focused on individual and subspecies recognition. Furthermore, we aimed to determine the usefulness of acoustic monitoring in the field given the limited data for Eurasian wolves. We analyzed 170 howls from 16 individual wolves from 3 subspecies: Arctic (Canis lupus arctos), Eurasian (C. l. lupus), and Northwestern wolves (C. l. occidentalis). Variables from the fundamental frequency (f0) (lowest frequency band of a sound signal) were extracted and used in discriminant analysis, classification matrix, and pairwise post-hoc Hotelling test. The results indicated that Arctic and Eurasian wolves had subspecies identifiable calls, while Northwestern wolves did not, though this sample size was small. Identification on an individual level was successful for all subspecies. Individuals were correctly classified with 80%–100% accuracy, using discriminant function analysis. Our findings suggest acoustic monitoring could be a valuable and cost-effective tool that complements camera traps, by improving long-distance detection of wolves.

Noninvasive Technologies for Primate Conservation in the 21st Century

Observing and quantifying primate behavior in the wild is challenging. Human presence affects primate behavior and habituation of new, especially terrestrial, individuals is a time-intensive process that carries with it ethical and health concerns, especially during the recent pandemic when primates are at even greater risk than usual. As a result, wildlife researchers, including primatologists, have increasingly turned to new technologies to answer questions and provide important data related to primate conservation. Tools and methods should be chosen carefully to maximize and improve the data that will be used to answer the research questions. We review here the role of four indirect methods—camera traps, acoustic monitoring, drones, and portable field labs—and improvements in machine learning that offer rapid, reliable means of combing through large datasets that these methods generate. We describe key applications and limitations of each tool in primate conservation, and where we anticipate primate conservation technology moving forward in the coming years.

Noise and landscape features influence habitat use of mammalian herbivores in a natural gas field

Anthropogenic noise is a complex disturbance known to elicit a variety of responses in wild animals. Most studies examining the effects of noise on wildlife focus on vocal species, although theory suggests that the acoustic environment influences non-vocal species as well. Common mammalian prey species, like mule deer and hares and rabbits (members of the family Leporidae), rely on acoustic cues for information regarding predation, but the impacts of noise on their behaviour has received little attention. We paired acoustic recorders with camera traps to explore how average daily levels of anthropogenic noise from natural gas activity impacted occupancy and detection of mammalian herbivores in an energy field in the production phase of development. We consider the effects of noise in the context of several physical landscape variables associated with natural gas infrastructure that are known to influence habitat use patterns in mule deer. Our results suggest that mule deer detection probability was influenced by the interaction between physical landscape features and anthropogenic noise, with noise strongly reducing habitat use. In contrast, leporid habitat use was not related to noise but was influenced by landscape features. Notably, mule deer showed a stronger predicted negative response to roads with high noise exposure. This study highlights the complex interactions of anthropogenic disturbance and wildlife distribution and presents important evidence that the effects of anthropogenic noise should be considered in research focused on non-vocal specialist species and management plans for mule deer and other large ungulates.

Methods for Monitoring Large Terrestrial Animals in the Wild

Reliable information about wildlife is absolutely important for making informed management decisions. The issues with the effectiveness of the control and monitoring of both large and small wild animals are relevant to assess and protect the world’s biodiversity. Monitoring becomes part of the methods in wildlife ecology for observation, assessment, and forecasting of the human environment. World practice reveals the potential of the joint application of both proven traditional and modern technologies using specialized equipment to organize environmental control and management processes. Monitoring large terrestrial animals require an individual approach due to their low density and larger habitat. Elk/moose are such animals. This work aims to evaluate the methods for monitoring large wild animals, suitable for controlling the number of elk/moose in the framework of nature conservation activities. Using different models allows determining the population size without affecting the animals and without significant financial costs. Although, the accuracy of each model is determined by its postulates implementation and initial conditions that need statistical data. Depending on the geographical, climatic, and economic conditions in each territory, it is possible to use different tools and equipment (e.g., cameras, GPS sensors, and unmanned aerial vehicles), a flexible variation of which will allow reaching the golden mean between the desires and capabilities of researchers.

Acoustic vs. photographic monitoring of gray wolves (Canis lupus): a methodological comparison of two passive monitoring techniques

Remote camera traps are often used in large-mammal research and monitoring programs because they are cost-effective, allow for repeat surveys, and can be deployed for long time periods. Statistical advancements in calculating population densities from camera-trap data have increased the popularity of camera usage in mammal studies. However, drawbacks to camera traps include their limited sampling area and tendency for animals to notice the devices. In contrast, autonomous recording units (ARUs) record the sounds of animals with a much larger sampling area but are dependent on animals producing detectable vocalizations. In this study, we compared estimates of occupancy and detectability between ARUs and remote cameras for gray wolves (Canis lupus Linnaeus, 1758) in northern Alberta, Canada. We found ARUs to be comparable with cameras in their detectability and occupancy of wolves, despite only operating for 3% of the time that cameras were active. However, combining cameras and ARUs resulted in the highest detection probabilities for wolves. These advances in survey technology and statistical methods provide innovative avenues for large-mammal monitoring that, when combined, can be applied to a broad spectrum of conservation and management questions, provided assumptions for these methods are rigorously tested and met.

Characterising the harmonic vocal repertoire of the Indian wolf (Canis lupus pallipes)

Vocal communication in social animals plays a crucial role in mate choice, maintaining social structure, and foraging strategy. The Indian grey wolf, among the least studied subspecies, is a social carnivore that lives in groups called packs and has many types of vocal communication. In this study, we characterise harmonic vocalisation types of the Indian wolf using howl survey responses and opportunistic recordings from captive and nine packs (each pack contains 2–9 individuals) of free-ranging Indian wolves. Using principal component analysis, hierarchical clustering, and discriminant function analysis, we found four distinct vocalisations using 270 recorded vocalisations (Average Silhouette width Si = 0.598) which include howls and howl-barks (N = 238), whimper (N = 2), social squeak (N = 28), and whine (N = 2). Although having a smaller body size compared to other wolf subspecies, Indian wolf howls have an average mean fundamental frequency of 422 Hz (±126), which is similar to other wolf subspecies. The whimper showed the highest frequency modulation (37.296±4.601) and the highest mean fundamental frequency (1708±524 Hz) compared to other call types. Less information is available on the third vocalisation type, i.e. ‘Social squeak’ or ‘talking’ (Mean fundamental frequency = 461±83 Hz), which is highly variable (coefficient of frequency variation = 18.778±3.587). Lastly, we identified the whine, which had a mean fundamental frequency of 906Hz (±242) and is similar to the Italian wolf (979±109 Hz). Our study’s characterisation of the Indian wolf’s harmonic vocal repertoire provides a first step in understanding the function and contextual use of vocalisations in this social mammal.

Autonomous sound recording outperforms human observation for sampling birds: a systematic map and user guide

Autonomous sound recording techniques have gained considerable traction in the last decade, but the question remains whether they can replace human observation surveys to sample sonant animals. For birds in particular, survey methods have been tested extensively using point counts and sound recording surveys. Here, we review the latest evidence for this taxon within the frame of a systematic map. We compare sampling effectiveness of these two survey methods, the output variables they produce, and their practicality. When assessed against the standard of point counts, autonomous sound recording proves to be a powerful tool that samples at least as many species. This technology can monitor birds in an exhaustive, standardized, and verifiable way. Moreover, sound recorders give access to entire soundscapes from which new data types can be derived (vocal activity, acoustic indices). Variables such as abundance, density, occupancy, or species richness can be obtained to yield data sets that are comparable to and compatible with point counts. Finally, autonomous sound recorders allow investigations at high temporal and spatial resolution and coverage, which are more cost effective and cannot be achieved by human observations alone, even though small-scale studies might be more cost effective when carried out with point counts. Sound recorders can be deployed in many places, they are more scalable and reliable, making them the better choice for bird surveys in an increasingly data-driven time. We provide an overview of currently available recorders and discuss their specifications to guide future study designs.