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

Effects of Vegetation and Background Noise on the Detection Process in Auditory Avian Point-count Surveys

We used a bird-song simulation system to experimentally assess the effects of habitat, vegetation structure, and background noise on detection probability in aural avian point counts. We simulated bird songs of seven species in two habitats (mixed pine–hardwood forest and deciduous forest) and two leaf conditions (leaves on and leaves off) with two levels of background noise (~40 dB and ~50 dB). Estimated detection probabilities varied greatly among species, and complex interactions among all the factors existed. Background noise and the presence of leaves on trees decreased detection probabilities, and estimated detection probabilities were higher in mixed pine–hardwood forest than in deciduous forest. At 100 m, average estimated detection probabilities ranged from 0 to 1 and were lowest for the Black-and-white Warbler (Mniotilta varia) and highest for the Brown Thrasher (Toxostoma rufum). Simulations of expected counts, based on the best logistic model, indicated that observers detect between 3% (for the worst observer, least detectable species, with leaves on the trees and added background noise in the deciduous forest) and 99% (for the best observer, most detectable species, with no leaves on the trees and no added background noise in the mixed forest) of the total count. The large variation in expected counts illustrates the importance of estimating detection probabilities directly. The large differences in detection probabilities among species suggest that tailoring monitoring protocols to specific species of interest may produce better estimates than a single protocol applied to a wide range of species

Efficient use of information in adaptive management with an application to managing recreation near golden eagle nesting sites.

It is generally the case that a significant degree of uncertainty exists concerning the behavior of ecological systems. Adaptive management has been developed to address such structural uncertainty, while recognizing that decisions must be made without full knowledge of how a system behaves. This paradigm attempts to use new information that develops during the course of management to learn how the system works. To date, however, adaptive management has used a very limited information set to characterize the learning that is possible. This paper uses an extension of the Partial Observable Markov Decision Process (POMDP) framework to expand the information set used to update belief in competing models. This feature can potentially increase the speed of learning through adaptive management, and lead to better management in the future. We apply this framework to a case study wherein interest lies in managing recreational restrictions around golden eagle (Aquila chrysaetos) nesting sites. The ultimate management objective is to maintain an abundant eagle population in Denali National Park while minimizing the regulatory burden on park visitors. In order to capture this objective, we developed a utility function that trades off expected breeding success with hiker access. Our work is relevant to the management of human activities in protected areas, but more generally demonstrates some of the benefits of POMDP in the context of adaptive management

Bird community shifts associated with saltwater exposure in coastal forests at the leading edge of rising sea level.

Rising sea levels dramatically alter the vegetation composition and structure of coastal ecosystems. However, the implications of these changes for coastal wildlife are poorly understood. We aimed to quantify responses of avian communities to forest change (i.e., ghost forests) in a low-lying coastal region highly vulnerable to rising sea level. We conducted point counts to sample avian communities at 156 forested points in eastern North Carolina, USA in 2013-2015. We modelled avian community composition using a multi-species hierarchical occupancy model and used metrics of vegetation structure derived from Light Detection and Ranging (LiDAR) data as covariates related to variation in bird responses. We used this model to predict occupancy for each bird species in 2001 (using an analogous 2001 LiDAR dataset) and 2014 and used the change in occupancy probability to estimate habitat losses and gains at 3 spatial extents: 1) the entire study area, 2) burned forests only, and 3) unburned, low-lying coastal forests only. Of the 56 bird species we investigated, we observed parameter estimates corresponding to a higher likelihood of occurring in ghost forest for 34 species, but only 9 of those had 95% posterior intervals that did not overlap 0, thus having strong support. Despite the high vulnerability of forests in the region to sea level rise, habitat losses and gains associated with rising sea level were small relative to those resulting from wildfire. Though the extent of habitat changes associated with the development of ghost forest was limited, these changes likely are more permanent and may compound over time as sea level rises at an increasing rate. As such, the proliferation of ghost forests from rising sea level has potential to become an important driver of forest bird habitat change in coastal regions

A Field Evaluation of the Time-of-Detection Method to Estimate Population Size and Density for Aural Avian Point Counts

The time-of-detection method for aural avian point counts is a new method of estimating abundance, allowing for uncertain probability of detection. The method has been specifically designed to allow for variation in singing rates of birds. It involves dividing the time interval of the point count into several subintervals and recording the detection history of the subintervals when each bird sings. The method can be viewed as generating data equivalent to closed capture-recapture information. The method is different from the distance and multiple-observer methods in that it is not required that all the birds sing during the point count. As this method is new and there is some concern as to how well individual birds can be followed, we carried out a field test of the method using simulated known populations of singing birds, using a laptop computer to send signals to audio stations distributed around a point. The system mimics actual aural avian point counts, but also allows us to know the size and spatial distribution of the populations we are sampling. Fifty 8-min point counts (broken into four 2-min intervals) using eight species of birds were simulated. Singing rate of an individual bird of a species was simulated following a Markovian process (singing bouts followed by periods of silence), which we felt was more realistic than a truly random process. The main emphasis of our paper is to compare results from species singing at (high and low) homogenous rates per interval with those singing at (high and low) heterogeneous rates. Population size was estimated accurately for the species simulated, with a high homogeneous probability of singing. Populations of simulated species with lower but homogeneous singing probabilities were somewhat underestimated. Populations of species simulated with heterogeneous singing probabilities were substantially underestimated. Underestimation was caused by both the very low detection probabilities of all distant individuals and by individuals with low singing rates also having very low detection probabilities

Linking demographic rates to local environmental conditions: Empirical data to support climate adaptation strategies for Eleutherodactylus frogs

Conducting managed species translocations and establishing climate change refugia are adaptation strategies to cope with projected consequences of global warming, but successful implementation requires on-the-ground validation of demographic responses to transient climate conditions. Here we estimated the effect of nine abiotic and biotic factors on local occupancy and an index of abundance (few or chorus) for four amphibian species (Eleutherodactylus wightmanae, E. brittoni, E. antillensis, and E. coqui) in Puerto Rico, USA. We also assessed how the same factors influenced reproductive activity of E. coqui and how species responded to hurricane María (20 September 2017). As predicted, occupancy and abundance of E. wightmanae, E. brittoni and E. coqui were positively and strongly influenced by abiotic covariates (e.g., relative humidity) that characterize high elevation, mesic habitats. E. antillensis exhibited the opposite pattern, with highest probabilities (≥0.6) recorded at ≤300 m and with average relative humidity80% and temperature of ≤26 °C. Moderate to high probabilities of detecting a chorus (0.4–0.7) were recorded at sites with average temperatures>26 °C, but no reproductive activity was detected, implying that monitoring abundance alone could misrepresent the capacity of a local population to sustain itself. The possibility underscores the importance of understanding the interplay between local demographic and environmental parameters in the advent of global warming to help guide monitoring and management decisions, especially for high elevation specialists. Hurricanes can inflict marked reductions in population numbers, but impacts vary by location and species. We found that the abundance (chorus) of E. antillensis and E. brittoni increased after the hurricane, but the abundance of the other two species did not differ between years. Lack of impacts was probably mediated by low structural damage to forest tracts (e.g., 9% canopy loss). Our findings help assess habitat suitability in terms of parameters that foster local population growth, which provides a basis for testing spatio-temporal predictions about demographic rates in potential climate refugia and for designing criteria to help guide managed translocations