Extending density surface models to include multiple and double-observer survey data

Spatial models of density and abundance are widely used in both ecological research (e.g., to study habitat use) and wildlife management (e.g., for population monitoring and environmental impact assessment). Increasingly, modellers are tasked with integrating data from multiple sources, collected via different observation processes. Distance sampling is an efficient and widely used survey and analysis technique. Within this framework, observation processes are modelled via detection functions. We seek to take multiple data sources and fit them in a single spatial model. Density surface models (DSMs) are a two-stage approach: first accounting for detectability via distance sampling methods, then modelling distribution via a generalized additive model. However, current software and theory does not address the issue of multiple data sources. We extend the DSM approach to accommodate data from multiple surveys, collected via conventional distance sampling, double-observer distance sampling (used to account for incomplete detection at zero distance) and strip transects. Variance propagation ensures that uncertainty is correctly accounted for in final estimates of abundance. Methods described here are implemented in the dsm R package. We briefly analyse two datasets to illustrate these new developments. Our new methodology enables data from multiple distance sampling surveys of different types to be treated in a single spatial model, enabling more robust abundance estimation, potentially over wider geographical or temporal domains.

Double-observer approach with camera traps: Towards an unbiased density estimation of unmarked animal populations

Camera traps are a powerful research tool with a wide range of applications in animal ecology, conservation, and management. However, camera traps may not always detect animals passing in front, and the probability of successfully detecting animals (i.e. camera sensitivity) may vary spatially and temporarily. This constraint may create a substantial bias in estimating critical parameters, such as the density of unmarked populations or animal activity levels.We applied the ‘double-observer approach’ to estimate detection probability and correct potentially imperfect detection. This involved two camera traps being set up at a camera station to monitor the same focal area. The detection probability and the number of animal passes were concurrently estimated with a hierarchal capture-recapture model for stratified populations using a Bayesian framework. Monte Carlo simulations were performed to test the reliability. We then estimated the detection probabilities of a camera model (Browning Strike Force Pro) within an equilateral-triangle focal area (1.56 m2) for 12 ground-dwelling mammals in Japan and Cameroon. We also evaluated the possible difference in detection probability between daytime and nighttime by incorporating it as a covariate.We found that the double-observer approach reliably quantifies camera sensitivity and provides unbiased estimates of the number of animal passes, even when the detection probability varies among animal passes or camera stations. The camera sensitivity did not change between daytime and nighttime either in Japan or Cameroon, providing the first evidence that the number of animal passes per unit time may be a viable index of animal activity levels. Nonetheless, the camera traps missed animals within the focal area by 4 %–36%. Current density estimation models relying on perfect detection may underestimate animal density by the same order of magnitude.Our results showed that the double-observer approach might be effective in correcting imperfect camera sensitivity. The hierarchical capture-recapture model used here can estimate the distribution of detection probability and the number of animals passing concurrently, and thus, it is easily incorporated in the current density estimation models. We believe that this approach could make a wide range of camera-trapping studies more accurate.

Initial Tracking Parameter Estimation of Magnetic Ship Based on PSO

We consider the problem of tracking a surface magnetic ship as it travels in a straight line path with the exertion of a magnetometer located at the seabed. Note that the initial filter parameters are prior information and the tracking performance depends on the initial filter parameters, and traditional estimation of initial filter parameters is to apply the filter bank algorithm, but there are several obvious defects in this method. In this paper, a novel algorithm based on the particle swarm optimization (PSO) algorithm is proposed to estimate initial parameters of the filter, and the model of uniformly magnetized ellipsoid is adopted to fit the magnetic field of the ship. The simulation results show that, under the condition of no prior information, the estimated ship parameters based on the observation of the single-observer are invalid, whereas the estimated ship parameters based on the observation of the double-observer are valid. Further, the estimated results of real-world recorded magnetic signals show that the ship parameters estimated by PSO based on the double-observer are also valid, as the estimated parameters are used as the initial parameters of the unscented Kalman filter (UKF), and a ship can be tracked effectively by the UKF filter. Moreover, the estimated half focal length can be used as a feature to distinguish noise environment, ships with different sizes, and mine sweepers.

Population assessment of the Endangered Nilgiri tahr Nilgiritragus hylocrius in the Anamalai Tiger Reserve, using the double-observer survey method

The Nilgiri tahr Nilgiritragus hylocrius is an Endangered species of mountain ungulate endemic to the Western Ghats of India, a biodiversity hotspot. Habitat fragmentation, hunting and a restricted range are the major threats to this species. Although several surveys have assessed the species’ status, a population estimate based on a scientifically robust method is needed. We used the double-observer method to estimate the population of the Nilgiri tahr in the Anamalai Tiger Reserve, a protected area in the Western Ghats. We walked 257 km of transects across the Reserve, covering 36 grassland blocks (i.e. clusters of montane grasslands that were relatively separate from each other). We counted a minimum of 422 individuals in 28 groups, and estimated the tahr population in the study area to be 510 individuals (95% CI 300–858) in 35 groups. The male:female ratio was 0.71 and the young:female ratio was 0.56. Comparing our estimate with previous surveys suggests that the Nilgiri tahr population in Anamalai Tiger Reserve is stable. We found the double-observer survey method to be appropriate for population estimation and long-term monitoring of this species, and make recommendations for improved field protocols to facilitate the implementation of the method in the tropical mountains of the Western Ghats. Our findings suggest that the Reserve harbours 20–25% of the global population of the Nilgiri tahr, highlighting the area's importance for the conservation of this species.