Dataset Information

A generalised random encounter model for estimating animal density with remote sensor data.

ABSTRACT: Wildlife monitoring technology is advancing rapidly and the use of remote sensors such as camera traps and acoustic detectors is becoming common in both the terrestrial and marine environments. Current methods to estimate abundance or density require individual recognition of animals or knowing the distance of the animal from the sensor, which is often difficult. A method without these requirements, the random encounter model (REM), has been successfully applied to estimate animal densities from count data generated from camera traps. However, count data from acoustic detectors do not fit the assumptions of the REM due to the directionality of animal signals.We developed a generalised REM (gREM), to estimate absolute animal density from count data from both camera traps and acoustic detectors. We derived the gREM for different combinations of sensor detection widths and animal signal widths (a measure of directionality). We tested the accuracy and precision of this model using simulations of different combinations of sensor detection widths and animal signal widths, number of captures and models of animal movement.We find that the gREM produces accurate estimates of absolute animal density for all combinations of sensor detection widths and animal signal widths. However, larger sensor detection and animal signal widths were found to be more precise. While the model is accurate for all capture efforts tested, the precision of the estimate increases with the number of captures. We found no effect of different animal movement models on the accuracy and precision of the gREM.We conclude that the gREM provides an effective method to estimate absolute animal densities from remote sensor count data over a range of sensor and animal signal widths. The gREM is applicable for count data obtained in both marine and terrestrial environments, visually or acoustically (e.g. big cats, sharks, birds, echolocating bats and cetaceans). As sensors such as camera traps and acoustic detectors become more ubiquitous, the gREM will be increasingly useful for monitoring unmarked animal populations across broad spatial, temporal and taxonomic scales.

SUBMITTER: Lucas TC

PROVIDER: S-EPMC4974944 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description: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 implicationsWe 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.

Dataset Information

A generalised random encounter model for estimating animal density with remote sensor data.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets