Nocturnal activity by the primarily diurnal Central American agouti (Dasyprocta punctata) in relation to environmental conditions, resource abundance and predation risk

An animal's fitness is in part based on its ability to manage the inherent risks (foraging costs, predation, exposure to disease) with the benefits (resource gain, access to mates, social interactions) of activity (Abrams 1991, Altizer et al. 2003, Lima & Bednekoff 1999, Rubenstein & Hohmann 1989, Wikelski et al. 2001). Thus, understanding an animal's pattern of activity is key to understanding behavioural and ecological processes. However, while numerous laboratory methodologies are available to continuously quantify activity over long periods of time, logistical difficulties have greatly hindered activity studies of animals in the field (DeCoursey 1990)

Camera Traps as Sensor Networks for Monitoring Animal Communities

Studying animal movement and distribution is of critical importance to addressing environmental challenges including invasive species, infectious diseases, climate and land-use change. Motion sensitive camera traps offer a visual sensor to record the presence of a broad range of species providing location – specific information on movement and behavior. Modern digital camera traps that record video present not only new analytical opportunities, but also new data management challenges. This paper describes our experience with a terrestrial animal monitoring system at Barro Colorado Island, Panama. Our camera network captured the spatio-temporal dynamics of terrestrial bird and mammal activity at the site - data relevant to immediate science questions, and long-term conservation issues. We believe that the experience gained and lessons learned during our year-long deployment and testing of the camera traps as well as the developed solutions are applicable to broader sensor network applications and are valuable for the advancement of the sensor network research. We suggest that the continued development of these hardware, software, and analytical tools, in concert, offer an exciting sensor-network solution to monitoring of animal populations which could realistically scale over larger areas and time span

Scatter hoarding by the Central American agouti: a test of optimal cache spacing theory

Optimal cache spacing theory predicts that scatter-hoarding animals store food at a density that balances the gains of reducing cache robbery against the costs of spacing out caches further. We tested the key prediction that cache robbery and cache spacing increase with the economic value of food: the ratio of food to consumer abundance. We quantified cache pilferage and cache spacing by the Central American agouti, Dasyprocta punctata, in the tropical forest of Barro Colorado Island, Panama, across 10 1 ha plots that encompassed a more than100-fold range in the availability of Astrocaryum palm seeds, the agouti's principal food. We found that caches were pilfered at higher rates in plots with lower seed availability, and that agoutis cached seeds further away and into lower densities where seed availability was lower. Food scarcity apparently increased the pressure of food competitors on caches, stimulating agoutis to put more effort into caching seeds to create lower cache densities, fully consistent with theory. We conclude that the optimal cache density depends not only on the nutritional value of food but also on the economic value, which may vary in space as well as tim

Quantifying the sensitivity of camera traps:an adapted distance sampling approach

1. Abundance estimation is a pervasive goal in ecology. The rate of detection by motion-sensitive camera traps can, in principle, provide information on the abundance of many species of terrestrial vertebrates that are otherwise difficult to survey. The random encounter model (REM, Rowcliffe et al. 2008) provides a means estimating abundance from camera trap rate but requires camera sensitivity to be quantified. 2. Here, we develop a method to estimate the area effectively monitored by cameras, which is one of the most important codeterminants of detection rate. Our method borrows from distance sampling theory, applying detection function models to data on the position (distance and angle relative to the camera) where the animals are first detected. Testing the reliability of this approach through simulation, we find that bias depends on the effective detection angle assumed but was generally low at less than 5% for realistic angles typical of camera traps. 3. We adapted standard detection functions to allow for the possibility of smaller animals passing beneath the field of view close to the camera, resulting in reduced detection probability within that zone. Using a further simulation to test this approach, we find that detection distance can be estimated with little or no bias if detection probability is certain for at least some distance from the camera. 4. Applying this method to a 1-year camera trapping data set from Barro Colorado Island, Panama, we show that effective detection distance is related strongly positively to species body mass and weakly negatively to species average speed of movement. There was also a strong seasonal effect, with shorter detection distance during the wet season. Effective detection angle is related more weakly to species body mass, and again strongly to season, with a wider angle in the wet season. 5. This method represents an important step towards practical application of the REM, including abundance estimation for relatively small

The effect of feeding time on dispersal of Virola seeds by toucans determined from GPS tracking and accelerometers

Seed dispersal is critical to understanding forest dynamics but is hard to study because tracking seeds is difficult. Even for the best-studied dispersal system of the Neotropics, Virola nobilis, the dispersal kernel remains unknown. We combined high-resolution GPS/3D-acceleration bird tracking, seed-retention experiments, and field observations to quantify dispersal of V. nobilis by their principal dispersers, Ramphastos toucans. We inferred feeding events from movement data, and then estimated spatio-temporally explicit seed-dispersal kernels. Wild toucans moved an average of 1.8 km d-1 with two distinct activity peaks. Seed retention time in captive toucans averaged 25.5 min (range 4–98 min). Estimated seed dispersal distance averaged 144 ± 147 m, with a 56% likelihood of dispersal >100 m, two times further than the behaviour-naive estimate from the same data. Dispersal was furthest for seeds ingested in the morning, and increased with seed retention time, but only up to 60 min after feeding. Our study supports the long-standing hypothesis that toucans are excellent dispersers of Virola seeds. To maximize seed dispersal distances trees should ripen fruit in the morning when birds move the most, and produce fruits with gut-processing times around 60 min. Our study demonstrates how new tracking technology can yield nuanced seed dispersal kernels for animals that cannot be directly observed

Camera traps as sensor networks for monitoring animal communities

Studying animal movement and distribution is of critical importance to addressing environmental challenges including invasive species, infectious diseases, climate and land-use change. Motion sensitive camera traps offer a visual sensor to record the presence of a species at a location, recording their movement in the Eulerian sense. Modern digital camera traps that record video present new analytical opportunities, but also new data management challenges. This paper describes our experience with a year-long terrestrial animal monitoring system at Barro Colorado Island, Panama. The data gathered from our camera network shows the spatio-temporal dynamics of terrestrial bird and mammal activity at the site-data relevant to immediate science questions, and long-term conservation issues. We believe that the experience gained and lessons learned during our year long deployment and testing of the camera traps are applicable to broader sensor network applications and are valuable for the advancement of the sensor network research. We suggest that the continued development of these hardware, software, and analytical tools, in concert, offer an exciting sensor-network solution to monitoring of animal populations which could realistically scale over larger areas and time span