Precise Density Estimates Research Articles

How many sequences should I track when applying the random encounter model to camera trap data?

AbstractThe random encounter model (REM) is a camera trapping method to estimate population density (i.e. number of individuals per unit area) without the need for individual recognition. The REM can be applied considering camera trap data only by tracking the passages of animals in front of the camera (i.e. sequences). However, it has not been assessed how the number of sequences tracked (i.e. trajectory of the animal reconstructed) influences the REM estimates. In this context, we aimed to gain further insights into the relationship between the number of sequences tracked and reliability in REM estimates to optimize its applicability. We monitored multiple species using camera traps, and we applied REM to estimate population density. We considered red fox Vulpes vulpes, roe deer Capreolus capreolus, fallow deer Dama dama, red deer Cervus elaphus and wild boar Sus scrofa as model species. We tracked from a minimum of 154 (red fox) to a maximum of 527 (red deer) sequences per species, and we then sampled the dataset to simulate different scenarios in which a lower number of sequences were tracked (20, 40, 80 and 160). We also assessed the effect of adjusting the survey period to the minimum necessary to record the desired number of sequences. Our results suggest that tracking around 100 sequences returns a precision level equivalent to the one obtained by tracking a considerably higher number of sequences and reduced and optimized the human effort necessary to apply REM. Tracking less than 40 sequences could result in low precise density estimates. Our results also highlighted the relevance of considering study periods of ca. 2 months to increase the number of sequences recorded and tracking a random sample of them. Our results contribute to the optimization and harmonization of REM as a reference method to estimate wildlife population density without the need for individual identification. We make clear recommendations on the cost‐effective sample size for estimating REM parameters, optimizing the human effort when applying REM, and discouraging REM applications based on low sample sizes.

How to find a wolverine: Factors affecting detection at wolverine (Gulo gulo) bait stations in western Canada

AbstractHigh individual detection success enables precise estimates of density and the ability to monitor trends in abundance for wolverine and other low‐density species, information that is critical for the implementation and assessment of conservation measures. We evaluated a dataset that included six different wolverine capture–recapture studies over a large gradient in wolverine (Gulo gulo) density to provide recommendations for increasing detection. We examined factors related to bait station components, habitat, and seasonal timing. Accounting for variation in wolverine density and trap duration, our results suggest that bait stations setups having a run pole, frame, and camera to photograph unique ventral color patterns, in addition to hair snag devices, identify more individual wolverine than those without. The presence of snow is a habitat feature that also increases individual detection. Female detection rates were lower than male detection rates at the onset of the reproductive denning season in late February and early March compared with January and early February. We found latency to detection was independent of wolverine density, but greater in areas with human influence. Relatively high rates of genotyping success (55%) were predicted by even a single guard hair left at bait stations, while underfur required ~15 hairs for similar success. Longer sampling intervals reduced genotyping success in spring, more so for underfur than guard hair. Hair samples acquired from barbwire were of higher quality than those from either alligator clips or gun brushes. To improve individual detection for wolverine inventory and monitoring, we recommend deploying run pole setups in areas with low human disturbance that will retain snow into late spring. Extending the winter trapping effort into April and May could increase the chances of detecting denning females. Latency to detection suggests that traps should be active for more than a month, especially in human‐influenced areas, but genotyping success suggests that traps should also be cleared of hair samples at smaller intervals of a month or less, during late winter/spring.

Faecal Standing Crop with real time correction using scat detection dogs to estimate population density

Abstract Population density is fundamental information for assessing the conservation status of species and support management and conservation actions for in situ populations, but is unknown for many forest species due to their difficulty in detection. The Faecal Standing Crop (FSC) method using detection dogs is an alternative for cryptic or elusive species. An intrinsic difficulty of FSC is the ability to find faecal samples in the field and to estimate the probability of which faeces detection is influenced by degradation due to climatic conditions. Our goal was to propose a concurrent FSC parameter estimation using a scat detection dog under different climatic conditions and apply those parameters in a wild deer population. Ten faecal samples of grey brocket deer (Subulo gouazoubira) were placed weekly in a transect (24 × 1440 m) in both dry and wet seasons (12 weeks each). A scat detection dog was then employed to find experimental faecal samples to determine the FSC parameters that were subsequently used with naturally occurring faecal samples (also dog‐detected) to estimate population density. The oldest dog found samples were 21 (Dry) and seven (Wet) days after placement, resulting in dog efficiency of 22.5% (Dry) and 30% (Wet). Adjusting the model to account for efficiency and scat durability, we estimated similar, seasonal, densities of 4.51 individuals km−2 (SD = 2.21, Dry) and 5.37 individuals km−2 (SD = 3.71, Wet). Synthesis and applications: Our results demonstrate that our concurrent methodology corrected the effects of weather and habitat on FSC parameters thereby allowing for accurate population density estimation. Additionally, this method can provide reasonably precise density estimates with a logistically feasible sample size, as demonstrated by simulation. Following our recommendations, this method allows a reliable estimate of population density because it incorporates any influence of study area, dog ability and climate in faecal sample detection, providing fundamental information for the conservation of many cryptic and elusive species.

Using fusion effects to decrease uncertainty in distance sampling models when collating data from different surveys

AbstractEstimates of population abundance are required to study the impacts of human activities on populations and assess their conservation status. Despite considerable effort to improve data collection, uncertainty around estimates of cetacean densities can remain large. A fundamental concept underlying distance sampling is the detection function. Here we focus on reducing the uncertainty in the estimation of detection function parameters in analyses combining data sets from multiple surveys, with known effects on the precision of density estimates. We developed detection functions using infinite mixture models that can be applied on data collating multiple species and/or surveys. These models enable automatic clustering by fusing the species and surveys with similar detection functions. We present a simulation analysis of a multisurvey data set in a Bayesian framework where we demonstrated that distance sampling models including fusion effects showed lower uncertainty than classical distance sampling models. We illustrated the benefits of this new model using data of line transect surveys from the Bay of Biscay and Iberian Coast. Future estimates of abundance using conventional distance sampling models on large multispecies surveys or on data sets combining multiple surveys could benefit from this new model to provide more precise density estimates.

Precision and bias of spatial capture-recapture estimates: A multi-site, multi-year Utah black bear case study.

Spatial capture–recapture (SCR) models are powerful analytical tools that have become the standard for estimating abundance and density of wild animal populations. When sampling populations to implement SCR, the number of unique individuals detected, total recaptures, and unique spatial relocations can be highly variable. These sample sizes influence the precision and accuracy of model parameter estimates. Testing the performance of SCR models with sparse empirical data sets typical of low‐density, wide‐ranging species can inform the threshold at which a more integrated modeling approach with additional data sources or additional years of monitoring may be required to achieve reliable, precise parameter estimates. Using a multi‐site, multi‐year Utah black bear (Ursus americanus) capture–recapture data set, we evaluated factors influencing the uncertainty of SCR structural parameter estimates, specifically density, detection, and the spatial scale parameter, sigma. We also provided some of the first SCR density estimates for Utah black bear populations, which ranged from 3.85 to 74.33 bears/100 km2. Increasing total detections decreased the uncertainty of density estimates, whereas an increasing number of total recaptures and individuals with recaptures decreased the uncertainty of detection and sigma estimates, respectively. In most cases, multiple years of data were required for precise density estimates (<0.2 coefficient of variation [CV]). Across study areas there was an average decline in CV of 0.07 with the addition of another year of data. One sampled population with very high estimated bear density had an atypically low number of spatial recaptures relative to total recaptures, apparently inflating density estimates. A complementary simulation study used to assess estimate bias suggested that when <30% of recaptured individuals were spatially recaptured, density estimates were unreliable and ranged widely, in some cases to >3 times the simulated density. Additional research could evaluate these requirements for other density scenarios. Large numbers of individuals detected, numbers of spatial recaptures, and precision alone may not be sufficient indicators of parameter estimate reliability. We provide an evaluation of simple summary statistics of capture–recapture data sets that can provide an early signal of the need to alter sampling design or collect auxiliary data before model implementation to improve estimate precision and accuracy.

Camera trap distance sampling for terrestrial mammal population monitoring: lessons learnt from aUKcase study

AbstractAccurate and precise density estimates are crucial for effective species management and conservation. However, efficient monitoring of mammal densities over large spatial and temporal scales is challenging. In the United Kingdom, published density estimates for many mammals, including species considered to be common, are imprecise. Camera trap distance sampling (CTDS) can estimate densities of multiple species at a time and has been used successfully in a small number of studies. However, CTDS has typically been used over relatively homogeneous landscapes, often over large time scales, making monitoring changes (by repeating surveys) difficult. In this study, we deployed camera traps at 109 sites across an area of 2725 km2of varied habitat in North‐East England, United Kingdom. The 4‐month survey generated 51 447 photos of wild mammal species. Data were sufficient for us to use CTDS to estimate the densities of eight mammal species across the whole‐survey area and within four specific habitats. Both survey‐wide and habitat‐specific density estimates largely fell within previously published density ranges and our estimates were amongst the most precise produced for these species to date. Lower precision for some species was typically due to animals being missed by the camera at certain distances, highlighting the need for careful consideration of practical and methodological decisions, such as how high to set cameras and where to left‐truncate data. Although CTDS is a promising methodology for determining densities of multiple species from one survey, species‐specific decisions are still required and these cannot always be generalized across species types and locations. Taking the United Kingdom as a case study, our study highlights the potential for CTDS to be used on a national scale, although the scale of the task suggests that it would need to be integrated with a citizen science approach.

Population assessment without individual identification using camera-traps: A comparison of four methods

The use of camera traps to estimate population size when animals are not individually recognizable is gaining traction in the ecological literature, because of its applicability in population conservation and management.We estimated population size of synthetic animals with four camera trap sampling-based statistical models that do not rely on individual recognition. Using a realistic model of animal movement to generate synthetic data, we compared the random encounter model, the random encounter and staying time model, the association model and the time-to-event-model and we investigated the impact of violation of assumptions on the population size estimates.While under ideal conditions these models provide reliable population estimates, when synthetic animal movements were characterised by differences in speed (due to diverse behaviours such as locomotion, grazing and resting) none of the model provided both unbiased and precise density estimates. The random encounter model and the time-to-event-model provided precise results but tended to overestimate population size, while the random encounter and staying time model was less precise and tended to underestimate population size. Lastly, the association model was unable to provide precise results. We found that each tested model was very sensitive to the method used to estimate the range of the field-of-view of camera traps. Density estimates from both random encounter model and time-to-event-model were also very sensitive to biases in the estimate of animals’ speed. We provide guidelines on how to use these statistical models to get population size estimates that could be useful to wildlife managers and practitioners.

Estimating population density of insectivorous bats based on stationary acoustic detectors: A case study.

Automated recording units are commonly used by consultants to assess environmental impacts and to monitor animal populations. Although estimating population density of bats using stationary acoustic detectors is key for evaluating environmental impacts, estimating densities from call activity data is only possible through recently developed numerical methods, as the recognition of calling individuals is impossible.We tested the applicability of generalized random encounter models (gREMs) for determining population densities of three bat species (Common pipistrelle Pipistrellus pipistrellus, Northern bat Eptesicus nilssonii, and Natterer's bat Myotis nattereri) based on passively collected acoustical data. To validate the results, we compared them to (a) density estimates from the literature and to (b) Royle–Nichols (RN) models of detection/nondetection data.Our estimates for M. nattereri matched both the published data and RN‐model results. For E. nilssonii, the gREM yielded similar estimates to the RN‐models, but the published estimates were more than twice as high. This discrepancy might be because the high‐altitude flight of E. nilssonii is not accounted for in gREMs. Results of gREMs for P. pipistrellus were supported by published data but were ~10 times higher than those of RN‐models. RN‐models use detection/nondetection data, and this loss of information probably affected population estimates of very active species like P. pipistrellus.gREM models provided realistic estimates of bat population densities based on automatically recorded call activity data. However, the average flight altitude of species should be accounted for in future analyses. We suggest including flight altitude in the calculation of the detection range to assess the detection sphere more accurately and to obtain more precise density estimates.

Estimating the density of resident coastal fish using underwater cameras: accounting for individual detectability

Technological advances in underwater video recording are opening novel opportunities for monitoring wild fish. However, extracting data from videos is often challenging. Nevertheless, it has been recently demonstrated that accurate and precise estimates of density for animals (whose normal activities are restricted to a bounded area or home range) can be obtained from counts averaged across a relatively low number of video frames. The method, however, requires that individual detectability (PID, the probability of detecting a given animal provided that it is actually within the area surveyed by a camera) has to be known. Here we propose a Bayesian implementation for estimating PID after combining counts from cameras with counts from any reference method. The proposed framework was demonstrated using Serranus scriba as a case-study, a widely distributed and resident coastal fish. Density and PID were calculated after combining fish counts from unbaited remote underwater video (RUV) and underwater visual censuses (UVC) as reference method. The relevance of the proposed framework is that after estimating PID, fish density can be estimated accurately and precisely at the UVC scale (or at the scale of the preferred reference method) using RUV only. This key statement has been extensively demonstrated using computer simulations yielded by real empirical data. Finally, we provide a simulation tool-kit for comparing the expected precision attainable for different sampling effort and for species with different levels of PID. Overall, the proposed method may contribute to substantially enlarge the spatio-temporal scope of density monitoring programs for many resident fish.

Conventional distance sampling versus strip transects and abundance indices for estimating abundance of greater gliders (Petauroides volans) and eastern ringtail possums (Pseudocheirus peregrinus)

Abstract ContextAccurate estimates of abundance are extremely useful for wildlife management and conservation. Estimates generated from distance sampling are typically considered superior to strip transects and abundance indices, as the latter do not account for probability of detection, thereby risking significant error. AimTo compare density estimates generated from conventional distance sampling (CDS) of arboreal marsupials with strip transect density estimates and abundance indices. MethodsOff-track CDS and strip transects were used to estimate densities of P. volans and P. peregrinus across ~2.6km2 of remnant eucalypt forest at Mt Duval in north-eastern New South Wales. Key resultsCDS density estimates for P. volans (1.36ha−1, 95% confidence interval (CI) of 1.07–1.72ha−1) and P. peregrinus (0.28ha−1, 95% CI 0.22–0.35ha−1) were consistent with densities reported in other studies conducted in open eucalypt forests. A strip transect width of 40m for P. volans resulted in a collective set of values for density (1.35ha−1), error (s.e.±0.14), precision (cv 0.10) and 95% CI (1.07–1.62ha−1) closest to those associated with the CDS-generated density estimate (1.36ha−1, s.e.±0.15, cv 0.10, 95% CI 1.07–1.72ha−1). Strip widths of 10 to 40m resulted in density estimates for P. peregrinus closest to those generated through CDS, but much less precise. ConclusionsAlthough a 40-m wide strip transect provided a robust density estimate for P. volans at Mt Duval, this is unlikely to be consistent across different study areas. Strip transects provided less precise density estimates, or underestimated P. peregrinus density at Mt Duval, when compared with CDS density estimates. CDS should be favoured over strip transects or abundance indices for estimating P. volans and P. peregrinus abundance, because it is capable of providing more meaningful and robust abundance estimates by accounting for the probability of detection from the transect line across different habitats. ImplicationsResearchers, conservation managers and decision makers should be aware that common methods for assessing arboreal marsupial abundance have serious potential weaknesses. Thus, it would be prudent to invest in studies that address imperfect detection to improve the quality of monitoring data.

Evaluating spatially explicit density estimates of unmarked wildlife detected by remote cameras

Abstract Remote cameras have become a promising, cost‐effective tool for monitoring wildlife populations. Yet, for species where individuals are indistinguishable, remote cameras’ ability to provide robust and precise density estimates has been limited without the use of invasive marking. Using the American black bear as a model species, we evaluated methods for estimating wildlife densities using remote camera detections of unmarked individuals against estimates from spatial capture–recapture (SCR) models using individual detections. We also tested the effect of incorporating varying proportions of marked individuals on model accuracy and precision. Spatial count (SC) models using unmarked individuals produced estimates of bear density within 0.6% of those from SCR. We extended SC models to incorporate variation in density as a function of land use/land cover, and identified identical relationships between variation in bear densities and housing density as obtained using SCR. Incorporating individual detection data from simultaneous non‐invasive genetic sampling lead to more precise, but biased estimates. Synthesis and applications. Our results identify contexts in which camera count data can be used as an alternative to spatial capture–recapture (SCR) when individual identification is prohibitive. Spatial count models provided an accurate, but less precise replication of spatial capture–recapture density estimates and may provide consistent insights into spatial variation in density. Mixed samples of camera counts and auxiliary individual detections are likely to be of limited use, but fitting spatial count models to populations with partial visual markings could improve their precision.

Spatially explicit capture–recapture analysis of bobcat (Lynx rufus) density: implications for mesocarnivore monitoring

Context Accurate density estimation is crucial for conservation and management of elusive species. Camera-trapping may provide an efficient method for density estimation, particularly when analysed with recently developed spatially explicit capture–recapture (SECR) models. Although camera-traps are employed extensively to estimate large carnivore density, their use for smaller carnivores has been limited. Moreover, while camera-trapping studies are typically conducted at local scales, the utility of analysing larger-scale patterns by combining multiple camera studies remains poorly known. Aims The goal of the present study was to develop a better understanding of the utility of SECR models and camera-trapping for the estimation of density of small carnivores at local and regional scales. Methods Based on data collected from camera-traps, we used SECR to examine density of bobcats (Lynx rufus) at four study sites in north-central Texas. We then combined our density estimates with previous estimates (from multiple methodologies) across the bobcat’s geographic range, and used linear regression to examine drivers of range-wide density patterns. Key results Bobcat densities averaged 13.2 per 100 km2 across all four study sites, and were lowest at the site in the most heavily modified landscape. Bobcat capture probability was positively related to forest cover around camera-trap sites. At the range-wide scale, 53% of the variation in density was explained by just two factors: temperature and longitude. Conclusions Our results demonstrate the utility of camera-traps, combined with SECR, to generate precise density estimates for mesocarnivores, and reveal the negative effects of landscape disturbance on bobcat populations. The associations revealed in our range-wide analysis, despite variability in techniques used to estimate density, demonstrate how a combination of multiple density estimates for a species can be used for large-scale inference. However, improvement in our understanding of biogeographic density patterns for mesocarnivores could be obtained from a greater number of camera-based density estimates across the range of a species, combined with meta-analytic techniques. Implications Camera-trapping and SECR should be more widely applied to generate local density estimates for many small and medium-sized carnivores, where at least a portion of the individuals are identifiable. If such estimates are more widely obtained, meta-analytic techniques could be used to test biogeographic predictions or for large-scale monitoring efforts.

Optimizing the size of the area surveyed for monitoring a Eurasian lynx (Lynx lynx) population in the Swiss Alps by means of photographic capture–recapture

We studied the influence of surveyed area size on density estimates by means of camera-trapping in a low-density felid population (1-2 individuals/100 km(2) ). We applied non-spatial capture-recapture (CR) and spatial CR (SCR) models for Eurasian lynx during winter 2005/2006 in the northwestern Swiss Alps by sampling an area divided into 5 nested plots ranging from 65 to 760 km(2) . CR model density estimates (95% CI) for models M0 and Mh decreased from 2.61 (1.55-3.68) and 3.6 (1.62-5.57) independent lynx/100 km(2) , respectively, in the smallest to 1.20 (1.04-1.35) and 1.26 (0.89-1.63) independent lynx/100 km(2) , respectively, in the largest area surveyed. SCR model density estimates also decreased with increasing sampling area but not significantly. High individual range overlaps in relatively small areas (the edge effect) is the most plausible reason for this positive bias in the CR models. Our results confirm that SCR models are much more robust to changes in trap array size than CR models, thus avoiding overestimation of density in smaller areas. However, when a study is concerned with monitoring population changes, large spatial efforts (area surveyed ≥760 km(2) ) are required to obtain reliable and precise density estimates with these population densities and recapture rates.

Standardising distance sampling surveys of parrots in New Caledonia

Standardised surveys are essential for monitoring populations and identifying areas that are critical for conservation. With the aim of developing a standardised method of surveying parrots in the rainforests of New Caledonia, we used distance sampling to estimate densities of New Caledonian Parakeets (Cyanoramphus saisseti), Horned Parakeets (Eunymphicus cornutus), Ouvea Parakeets (E. uvaeensis), and New Caledonian Rainbow Lorikeets (Trichoglossus haematodus deplanchii). We carried out surveys in the early morning and late afternoon, when parrots were easiest to detect. To minimise errors associated with estimating distances and flock sizes by ear, we conducted brief searches to locate parrots, then measured their distance from the transect line. We recorded birds in flight and consider these records to be important when estimating parakeet populations. In agreement with existing knowledge on distance sampling, we found line transects to be more efficient than point transects for estimating the density of parakeets. Our results indicate that parrots located beyond 50–70 m from the transect line have little influence upon density estimates. In addition, surveys on roads are likely to underestimate densities if not corrected for road width. We generated relatively stable and precise density estimates (CV < 0.25) with approximately 40–50 detections, yet additional effort may be warranted under different study conditions. Although we aimed to improve parrot surveys in New Caledonia, our suggestions may be useful to other researchers studying rainforest birds, and can be adapted to suit different species or environments.

Evaluation of three methods to estimate density and detectability from roadside point counts

AbstractRoadside point counts are often used to estimate trends of bird populations. The use of aural counts of birds without adjustment for detection probability, however, can lead to incorrect population trend estimates. We compared precision of estimates of density and detectability of whistling northern bobwhites (Colinus virginianus) using distance sampling, independent double‐observer, and removal methods from roadside surveys. Two observers independently recorded each whistling bird heard, distance from the observer, and time of first detection at 362 call‐count stops in Ohio. We examined models that included covariates for year and observer effects for each method and distance from observer effects for the double‐observer and removal methods using Akaike's Information Criterion (AIC). The best model of detectability from distance sampling included observer and year effects. The best models from the removal and double‐observer techniques included observer and distance effects. All 3 methods provided precise estimates of detection probability (CV = 2.4–4.4%) with a range of detectability of 0.44–0.95 for a 6‐min survey. Density estimates from double‐observer surveys had the lowest coefficient of variation (2005 = 3.2%, 2006 = 1.7%), but the removal method also provided precise estimates of density (2005 CV = 3.4%, 2006 CV = 4.8%), and density estimates from distance sampling were less precise (2005 CV = 9.6%, 2006 CV = 7.9%). Assumptions of distance sampling were violated in our study because probability of detecting bobwhites near the observer was &lt;1 or the roadside survey points were not randomly distributed with respect to the birds. Distances also were not consistently recorded by individual members of observer pairs. Although double‐observer surveys provided more precise estimates, we recommend using the removal method to estimate detectability and abundance of bobwhites. The removal method provided precise estimates of density and detection probability and requires half the personnel time as double‐observer surveys. Furthermore, the likelihood of meeting model assumptions is higher for the removal survey than with independent double‐observers. © 2011 The Wildlife Society.

Nested sampling: an improved visual-census technique for studying reef fish assemblages

Our aim was to devise visual sampling protocols to estimate densities in a morphologi- cally and behaviorally diverse assemblage of reef fishes. The focus was on a design that is cost- effective and produces relatively accurate and precise density estimates for the largest number of species. We evaluated the 2 most widely used shapes of sampling units (strip transects and stationary cylinders) of several dimensions on a fringing reef at Fernando de Noronha Archipelago, tropical West Atlantic. Small sampling units produced the best density estimates for small fishes, while large ones produced the best estimates for large fishes, regardless of sampling unit shape. The sampling unit with the lowest cost was the stationary cylinder. We further compared accuracy and precision of density and richness estimates from several combinations of stationary cylinders and fish size. A simple improvement in stationary censuses, i.e. a sampling unit composed of 2 nested cylinders (2 m and 4 m radii, respectively), enhanced the accuracy and precision of estimates. Counts of small fish (≤10 cm) were made in the smaller cylinder and counts of larger fish (>10 cm) were made in the larger. Such nested cylinders reduce the bias towards large and conspicuous species and allow for separate density estimates for adults and juveniles of large species.

A Modified Drop Net for Sampling Fish Communities in Complex Habitats: A Description and Comparison with Other Techniques

AbstractA modified drop net (area = 19.3 m2) was constructed to enhance the collection of fish from within complex freshwater habitats. The net was evaluated in three pools located within the Pilbara region of north Western Australia. The net's efficiency was determined by comparison with gillnetting and beach seining; accuracy of the net was investigated using the toxicant rotenone. In terms of efficiency, the modified drop net and beach seine generated similar descriptions of the fish community (relative abundance, species richness, ordination of a species–abundance matrix); panel gill nets collected a diminished subset of the community. Efficiency of the drop net remained relatively constant among pools, whereas the seine became increasingly easy to use as habitat complexity decreased. In terms of accuracy, the drop net produced estimates of total fish abundance similar to those obtained by use of rotenone and adequately depicted site‐related differences in fish community structure (multivariate ordination in space). The drop net and rotenone collected similar numbers of species, but the drop net missed some species that were present in very low abundance. The drop net also underestimated the abundance of one benthic species. We recommend use of the drop net when studying ephemeral pools where habitat complexity changes through time or when precise estimates of density are required. Beach seining, which has minimal gear requirements, is recommended for situations in which only a general description of the community (species–abundance matrix) or species richness information is required. When the drop net is used, gill nets should also be used to collect large size‐classes that are in low abundance.

CENSUSING BOBCATS USING REMOTE CAMERAS

We estimated bobcat (Lynx rufus) density for 3 different locations in northern California using active infrared-triggered cameras. Using differences in pelage pattern as well as other physical characteristics, we identified individual bobcats from photographs, and used mark-recapture techniques to estimate population density. Camera density affected the precision of population estimates. The same population was estimated using camera densities of 0.5, 1, 2, 4, 6, and 8 cameras ⋅ km-2. Higher camera densities resulted in more captures and recaptures of bobcats and, conse- quently, in more precise density estimates. Similarly, the number of photo-captures and recaptures increased with increasing study duration. Increasing the area sampled resulted in the capture of more individuals but did not increase the percentage of recaptures. While some locations captured multiple bobcat photographs (e.g., 15 at 1 station), these photos tended to be recaptures of the same individual. There were no more than 2 individuals photo-captured at any 1 camera location. Bobcat density varied among habitat types as predicted. We estimated density as 0.27 bobcats ⋅ km-2 (s = 0.16) overall in an area in the northern Sacramento River Valley and as 0.35 bobcats ⋅ km-2 (s = 0.56) in a steep and rocky canyon within the area. At a 3rd site in the Coast Range, the estimate was 0.39 bobcats ⋅ km-2 (s = 1.44). Bobcats were more diurnal where human activity was less common. In addition, photo-capture was significantly higher along roads and trails without an attractant than it was off-trail with an attractant.

MANIPULATING DECAY TIME FOR EFFICIENT LARGE‐MAMMAL DENSITY ESTIMATION: GORILLAS AND DUNG HEIGHT

Large-mammal surveys often rely on indirect signs such as dung or nests. Sign density is usually translated into animal density using sign production and decay rates. In principle, such auxiliary variable estimates should be made in a spatially unbiased manner. However, traditional decay rate estimation methods entail following many signs from production to disappearance, which, in large study areas, requires extensive travel effort. Consequently, decay rate estimates have tended to be made instead at some convenient but unrepresentative location. In this study we evaluated how much bias might be induced by extrapolating decay rates from unrepresentative locations, how much effort would be required to implement current methods in a spatially unbiased manner, and what alternate approaches might be used to improve precision. To evaluate the extent of bias induced by unrepresentative sampling, we collected data on gorilla dung at several central African sites. Variation in gorilla dung decay rate was enormous, varying by up to an order of magnitude within and between survey zones. We then estimated what the effort-precision relationship would be for a previously suggested "retrospective" decay rate (RDR) method, if it were implemented in a spatially unbiased manner. We also evaluated precision for a marked sign count (MSC) approach that does not use a decay rate. Because they require repeat visits to remote locations, both RDR and MSC require enormous effort levels in order to gain precise density estimates. Finally, we examined an objective criterion for decay (i.e., dung height). This showed great potential for improving RDR efficiency because choosing a high threshold height for decay reduces decay time and, consequently, the number of visits that need to be made to remote areas. The ability to adjust decay time using an objective decay criterion also opens up the potential for a "prospective" decay rate (PDR) approach. Further research is necessary to evaluate whether the temporal bias inherent in such an approach is small enough to ignore, given the 10-20-fold increases in precision promised by a PDR approach.

Estimating Detection Probability and Density from Point-Count Surveys: A Combination of Distance and Double-Observer Sampling

AbstractPoint counts are the method most commonly used to estimate abundance of birds, but they often fail to account properly for incomplete and variable detection probabilities. We developed a technique that combines distance and double-observer sampling to estimate detection probabilities and effective area surveyed. We applied this paired-observer, variable circular-plot (POVCP) technique to point-count surveys (n = 753) conducted in closed-canopy forests of southeast Alaska. Distance data were analyzed for each species to model a detection probability for each observer and calculate an estimate of density. We then multiplied each observer's density estimates by a correction factor to adjust for detection probabilities &lt;1 at plot center. We compared analytical results from four survey methods: single-observer fixed-radius (50-m) plot; single-observer, variable circular-plot (SOVCP); double-observer fixed-radius (50-m) plot; and POVCP. We examined differences in detection probabilities at plot center, effective area surveyed, and densities for five bird species: Pacific-slope Flycatcher (Empidonax difficilis), Winter Wren (Troglodytes troglodytes), Golden-crowned Kinglet (Regulus satrapa), Hermit Thrush (Catharus guttatus), and Townsend's Warbler (Dendroica townsendi). Average detection probabilities for paired observers increased ≈8% (SE = 2.9) for all species once estimates were corrected for birds missed at plot center. Density estimators of fixed-radius survey methods were likely negatively biased, because the key assumption of perfect detection was not met. Density estimates generated using SOVCP and POVCP were similar, but standard errors were much lower for the POVCP survey method. We recommend using POVCP when study objectives require precise estimates of density. Failure to account for differences in detection probabilities and effective area surveyed results in biased population estimators and, therefore, faulty inferences about the population in question.Estimaciones de la Densidad y de las Probabilidades de Detección a Partir de Muestreos Utilizando Conteos en Puntos: Una Combinación de Muestreos de Distancia y de Doble Observador