Activity patterns of tayra (Eira barbara) across their distribution

Species' activity patterns are driven by the need to meet basic requirements of food, social interactions, movement, and rest, but often are influenced by a variety of biotic and abiotic factors. We used camera-trap data to describe and compare the activity patterns of the relatively poorly studied tayra (Eira barbara) across 10 populations distributed from the south of Mexico to the north of Argentina, and attempted to identify biotic or abiotic factors that may be associated with variation in level of diurnality. In a subset of sites we also aimed to document potential seasonal variation in activity. We used a kernel density estimator based on the time of independent photographic events to calculate the proportion of diurnal, crepuscular, and nocturnal activity of each population. Tayras were mostly active during diurnal periods (79.31%, 759 records), with a lower proportion of crepuscular activity (18.07%, 173 records) yet we documented some variation in patterns across the 10 study areas (activity overlap coefficient varied from Δ4 = 0.64 to Δ1 = 0.95). In northern localities, activity peaked twice during the day (bimodal) with most activity ocurring in the morning, whereas closer to the geographical equator, activity was constant (unimodal) throughout the day, peaking at midday: activity either was unimodal or bimodal in southern localities. Despite investigating multiple potential abiotic and biotic predictors, only latitude was associated with variation in the proportion of diurnal activity by tayras across its range, with increased diurnal activity closer to the equator. Seasonal comparisons in activity showed a tendency to reduce diurnality in dry versus rainy seasons, but the pattern was not consistently significant. This is the most comprehensive description of tayra activity patterns to date, and lends novel insight into the potential flexibility of the species to adapt to local conditions.Fil: Villafañe Trujillo, Álvaro José. Universidad Autonoma de Queretaro.; MéxicoFil: Kolowski, Joseph M.. Instituto de Pesquisas Ecológicas; BrasilFil: Cove, Michael V.. University of Belize; BeliceFil: Medici, Emilia Patricia. Instituto de Pesquisas Ecológicas; BrasilFil: Harmsen, Bart J.. University of Belize; BeliceFil: Foster, Rebbeca J.. University of Belize; BeliceFil: Hidalgo Mihart, Mircea G.. Universidad Juárez Autónoma de Tabasco,; MéxicoFil: Espinosa, Santiago. Universidad Autónoma de San Luis Potosí; MéxicoFil: Ríos Alvear, Gorky. Universidad de Porto; PortugalFil: Reyes Puig, Carolina. Universidad de Porto; PortugalFil: Reyes Puig, Juan Pablo. Universidad de Porto; PortugalFil: Da Silva, Marina Xavier. Universidad Central del Ecuador; EcuadorFil: Paviolo, Agustin Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Biología Subtropical. Universidad Nacional de Misiones. Instituto de Biología Subtropical; ArgentinaFil: Cruz, Paula Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Biología Subtropical. Universidad Nacional de Misiones. Instituto de Biología Subtropical; ArgentinaFil: López González, Carlos Alberto. Universidad Autonoma de Queretaro.; Méxic

An empirical evaluation of camera trap study design: How many, how long and when?

Abstract Camera traps deployed in grids or stratified random designs are a well‐established survey tool for wildlife but there has been little evaluation of study design parameters. We used an empirical subsampling approach involving 2,225 camera deployments run at 41 study areas around the world to evaluate three aspects of camera trap study design (number of sites, duration and season of sampling) and their influence on the estimation of three ecological metrics (species richness, occupancy and detection rate) for mammals. We found that 25–35 camera sites were needed for precise estimates of species richness, depending on scale of the study. The precision of species‐level estimates of occupancy (ψ) was highly sensitive to occupancy level, with 0.75) species, but more than 150 camera sites likely needed for rare (ψ < 0.25) species. Species detection rates were more difficult to estimate precisely at the grid level due to spatial heterogeneity, presumably driven by unaccounted habitat variability factors within the study area. Running a camera at a site for 2 weeks was most efficient for detecting new species, but 3–4 weeks were needed for precise estimates of local detection rate, with no gains in precision observed after 1 month. Metrics for all mammal communities were sensitive to seasonality, with 37%–50% of the species at the sites we examined fluctuating significantly in their occupancy or detection rates over the year. This effect was more pronounced in temperate sites, where seasonally sensitive species varied in relative abundance by an average factor of 4–5, and some species were completely absent in one season due to hibernation or migration. We recommend the following guidelines to efficiently obtain precise estimates of species richness, occupancy and detection rates with camera trap arrays: run each camera for 3–5 weeks across 40–60 sites per array. We recommend comparisons of detection rates be model based and include local covariates to help account for small‐scale variation. Furthermore, comparisons across study areas or times must account for seasonality, which could have strong impacts on mammal communities in both tropical and temperate sites

CameraStationVegetationData

This file contains all the vegetation measurements taken at each camera station pair. Cameras were set up in the Summer and Fall seasons of 2013 and 2014 on the grounds of the Smithsonian Conservation Biology Institute in Front Royal, VA USA. Cameras were only established in forest habitat. Camera stations were made up of pairs of cameras, one at a log feature or game trail, the other at a random nearby location. Vegetation measurements were taken at the midpoint between the two cameras. In addition to the PairID, Grid location, and vegetation sampling date, 4 vegetation variables are included. "CovAll" is the overall level vegetative cover percentage recorded as the average percent of a 2-m high cover pole obscured from 10 m away in each cardinal direction. Cover pole was divided in 20 1-dm sections. Data were recorded as the percentage of 1-dm sections obscured ( >50%) by vegetative or structural cover. "CovLow" is the average percent of low portion (1.5 m in height and 7.5 cm dbh counted within 3 arm's-length transects, divided by 33 square meters. Transects used were same as for "UnderStem_D"

Data from: Camera trap placement and the potential for bias due to trails and other features

Camera trapping has become an increasingly widespread tool for wildlife ecologists with large numbers of studies relying on photo capture rates or presence/absence information. It is increasingly clear that camera placement can directly impact this kind of data, yet these biases are poorly understood. We used a paired camera design to investigate the effect of small-scale habitat features on species richness estimates, and capture rate and detection probability of mammal species in the Shenandoah Valley of Virginia, USA. Cameras were deployed at either log features or on game trails with a paired camera at a nearby random location. Overall capture rates were significantly higher at trail and log cameras compared to their paired random cameras, and some species showed capture rate increases as high as 9.7 times greater at feature-based cameras. We recorded more species at both log (17) and trail features (15) than at their paired control sites (13 and 12 species, respectively) yet richness estimates were indistinguishable after 659 and 385 camera nights of survey effort, respectively, for log and trail features. We detected significant increases (ranging from 11-33%) in detection probability for 5 species resulting from the presence of game trails. Detection probability was also influenced by the presence of a log feature for six species. Bias was most pronounced for the three rodents investigated, where in all cases detection probability was substantially higher (24.9-38.2%) at log cameras. Our results indicate that small-scale factors, including the presence of game trails as well as other features, can have significant impacts on the frequency and probability of species detection when camera traps are employed. Significant biases may result if the presence and quality of these features are not documented and either incorporated into analytical procedures, or controlled for in study design

AllMammalPhotos_archive

This file contains a row for each photo image recorded on camera traps during this study. The study was conducted during Summer and Fall months of 2013 and 2014 on the grounds of the Smithsonian Conservation Biology Institute in Front Royal, Virginia USA. All records of birds and humans have been removed. Cameras were established in pairs with a treatment camera (set up with a log in view, or on a game trail) and a nearby random location. End Date refers to the date after which at last one camera in the pair stopped functioning. All photo records from BOTH cameras in the pair taken after this date were removed. The grounds of the study area were divided into grids (500m by 500m) and grids not containing forest were not used. Grid codes are included with each image as is the UTM coordinate of the camera station (UTM Zone 17). "UnderCat" is a three level descriptor for level of understory vegetation at the site. "LogD" refers to log diameter in centimeters, and "TrailQ" is a scale of trail quality, with 1 being the highest quality. "PairDist_m" is the distance between the two cameras in the pair, in meters. All other details are explained in the manuscript. NOTE: Photograph capture times for deployment SCBI2013.4T are 7 hrs ahead of the actual photo time. The Date and Time listed for photos from Deployment SCBI2013.37C are incorrect and could be rectified. Photos all occurred between the deployment and pull dates, but times and actual dates are incorrect for each photo

Percent increase or reduction in detection probability due to camera placement (trail or log).

<p>Percent increase or reduction in detection probability due to camera placement (trail or log).</p

Sample-based species accumulation curve from 24 paired cameras, run for an average of 25.6 camera nights, with one camera in each pair oriented toward a trail feature, and the other at a nearby (mean = 23.5 m) random location.

<p>Shaded areas represent the 95% confidence intervals, with darker shaded areas representing confidence interval overlap between the two scenarios. Black dots indicate the actual sampling effort of this study. The vertical line with label indicates the point at which the confidence intervals of the feature and control groups overlap.</p