An automated device for provoking and capturing wildlife calls

Identifying unknown Indian wolves by their distinctive howls: its potential as a non-invasive survey method

Previous studies have posited the use of acoustics-based surveys to monitor population size and estimate their density. However, decreasing the bias in population estimations, such as by using Capture-Mark-Recapture, requires the identification of individuals using supervised classification methods, especially for sparsely populated species like the wolf which may otherwise be counted repeatedly. The cryptic behaviour of Indian wolf (Canis lupus pallipes) poses serious challenges to survey efforts, and thus, there is no reliable estimate of their population despite a prominent role in the ecosystem. Like other wolves, Indian wolves produce howls that can be detected over distances of more than 6 km, making them ideal candidates for acoustic surveys. Here, we explore the use of a supervised classifier to identify unknown individuals. We trained a supervised Agglomerative Nesting hierarchical clustering (AGNES) model using 49 howls from five Indian wolves and achieved 98% individual identification accuracy. We tested our model's predictive power using 20 novel howls from a further four individuals (test dataset) and resulted in 75% accuracy in classifying howls to individuals. The model can reduce bias in population estimations using Capture-Mark-Recapture and track individual wolves non-invasively by their howls. This has potential for studies of wolves' territory use, pack composition, and reproductive behaviour. Our method can potentially be adapted for other species with individually distinctive vocalisations, representing an advanced tool for individual-level monitoring.

Acoustic localization at large scales: a promising method for grey wolf monitoring

Background

The grey wolf (Canis lupus) is naturally recolonizing its former habitats in Europe where it was extirpated during the previous two centuries. The management of this protected species is often controversial and its monitoring is a challenge for conservation purposes. However, this elusive carnivore can disperse over long distances in various natural contexts, making its monitoring difficult. Moreover, methods used for collecting signs of presence are usually time-consuming and/or costly. Currently, new acoustic recording tools are contributing to the development of passive acoustic methods as alternative approaches for detecting, monitoring, or identifying species that produce sounds in nature, such as the grey wolf. In the present study, we conducted field experiments to investigate the possibility of using a low-density microphone array to localize wolves at a large scale in two contrasting natural environments in north-eastern France. For scientific and social reasons, the experiments were based on a synthetic sound with similar acoustic properties to howls. This sound was broadcast at several sites. Then, localization estimates and the accuracy were calculated. Finally, linear mixed-effects models were used to identify the factors that influenced the localization accuracy.

Results

Among 354 nocturnal broadcasts in total, 269 were recorded by at least one autonomous recorder, thereby demonstrating the potential of this tool. Besides, 59 broadcasts were recorded by at least four microphones and used for acoustic localization. The broadcast sites were localized with an overall mean accuracy of 315 ± 617 (standard deviation) m. After setting a threshold for the temporal error value associated with the estimated coordinates, some unreliable values were excluded and the mean accuracy decreased to 167 ± 308 m. The number of broadcasts recorded was higher in the lowland environment, but the localization accuracy was similar in both environments, although it varied significantly among different nights in each study area.

Conclusions

Our results confirm the potential of using acoustic methods to localize wolves with high accuracy, in different natural environments and at large spatial scales. Passive acoustic methods are suitable for monitoring the dynamics of grey wolf recolonization and so, will contribute to enhance conservation and management plans.

No trespassing: using a biofence to manipulate wolf movements

Context Conserving large carnivores can be challenging because of conflicts with human land use and competition with humans for resources. Predation on domestic stock can have negative economic impacts particularly for owners of small herds, and tools for minimising carnivore depredation of livestock are needed. Canids use scent marking to establish territories and avoid intraspecific conflict. Exploiting scent-marking behaviour may provide a means for manipulating canid movements. Aims We hypothesised that human-deployed scent marks (i.e. ‘biofence’) could be used to manipulate the movements of grey wolves (Canis lupus) in Idaho, USA. Methods We deployed 65 km of biofence within three wolf-pack territories during summer 2010 and 2011 and used location data from satellite-collared wolves and sign surveys to assess the effectiveness of biofencing. Key results Location data provided by satellite-collared wolves and sign surveys in 2010 showed little to no trespass of the biofence, even though the excluded areas were used by the packs in previous summers. We also opportunistically deployed a biofence in between a rendezvous site of a resident pack and a nearby sheep grazing allotment; the pack was not implicated in any depredations in summer 2010, even though they had killed sheep every year since 2006. Location data provided by satellite-collared wolves in summer 2011 showed that wolves did trespass biofences. Conclusions Biofencing effectively manipulated the movements of wolves in the first year of our study, but not the second. Implications Our work suggests that biofencing may be most limited by the apparent necessity to maintain a continuous presence once the biofence is established. The inherent labour and costs associated with such efforts may limit the usefulness of biofencing. Our work can be improved on through further testing that maintains biofencing over a longer timeframe (>3 months), samples several animals per treatment pack, and uses a treatment and control design.