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Terrestrial wildlife is the primary source of meat for hundreds of millions of people throughout the developing world. Despite widespread human reliance on wildlife for food, the impact of wildlife depletion on human health remains poorly understood. Here we studied a prospective longitudinal cohort of 77 preadolescent children (under 12 y of age) in rural northeastern Madagascar and show that consuming more wildlife was associated with significantly higher hemoglobin concentrations. Our empirical models demonstrate that removing access to wildlife would induce a 29% increase in the numbers of children suffering from anemia and a tripling of anemia cases among children in the poorest households. The well-known progression from anemia to future disease demonstrates the powerful and far-reaching effects of lost wildlife access on a variety of human health outcomes, including cognitive, motor, and physical deficits. Loss of access to wildlife could arise either from the diligent enforcement of existing conservation policy or from unbridled unsustainable harvest, leading to depletion. Conservation enforcement would enact a more rapid restriction of resources, but self-depletion would potentially lead, albeit more slowly, both to irrevocable local wildlife extinctions and loss of the harvested resource. Our research quantifies costs of reduced access to wildlife for a rural community in Madagascar and illuminates pathways that may broadly link reduced natural resource access to declines in childhood health.

Numerous techniques have been proposed to estimate carnivore abundance and density, but few have been validated against populations of known size. We used a density estimate established by intensive monitoring of a population of radiotagged leopards (Panthera pardus) with a detection probability of 1.0 to evaluate efficacy of track counts and camera-trap surveys as population estimators. We calculated densities from track counts using 2 methods and compared performance of 10 methods for calculating the effectively sampled area for camera-trapping data. Compared to our reference density (7.33 ± 0.44 leopards/100 km2), camera-trapping generally produced more accurate but less precise estimates than did track counts. The most accurate result (6.97 ± 1.88 leopards/100 km2) came from camera-trap data with a sampled area buffered by a boundary strip representing the mean maximum distance moved by leopards outside the survey area (MMDMOSA) established by telemetry. However, contrary to recent suggestions, the traditional method of using half the mean maximum distance moved from photographic recaptures did not result in gross overestimates of population density (6.56 ± 1.92 leopards/100 km2) but rather displayed the next best performance after MMDMOSA. The only track-count method comparable to reference density employed a capture–recapture framework applied to data when individuals were identified from their tracks (6.45 ± 1.43 leopards/100 km2) but the underlying assumptions of this technique limit more widespread application. Our results demonstrate that if applied correctly, camera-trap surveys represent the best balance of rigor and cost-effectiveness for estimating abundance and density of cryptic carnivore species that can be identified individually.

Indices to population size have come under increasing criticism in recent years, on the grounds that indices might not faithfully represent the entire population. Most criticisms involve surveys of birds, particularly those based on point counts, which is my focus here. A variety of quantitative methods have been developed to reduce the bias of point counts, such as distance sampling, multiple-observer surveys, and time-of-detection methods. I argue that these developments are valuable, in that they enhance understanding of the detection process, but that their practical application may well be limited, likely to intensive studies focusing on a small number of species. These quantitative methods are not generally applicable to extensive, multiple-species surveys. Although criticism of the thoughtless use of indices is welcome, their wholesale rejection is not.

Mountain lions (Puma concolor) are often difficult to monitor because of their low capture probabilities, extensive movements, and large territories. Methods for estimating the abundance of this species are needed to assess population status, determine harvest levels, evaluate the impacts of management actions on populations, and derive conservation and management strategies. Traditional mark—recapture methods do not explicitly account for differences in individual capture probabilities due to the spatial distribution of individuals in relation to survey effort (or trap locations). However, recent advances in the analysis of capture—recapture data have produced methods estimating abundance and density of animals from spatially explicit capture—recapture data that account for heterogeneity in capture probabilities due to the spatial organization of individuals and traps. We adapt recently developed spatial capture—recapture models to estimate density and abundance of mountain lions in western Montana. Volunteers and state agency personnel collected mountain lion DNA samples in portions of the Blackfoot drainage (7,908 km 2 ) in west-central Montana using 2 methods: snow back-tracking mountain lion tracks to collect hair samples and biopsy darting treed mountain lions to obtain tissue samples. Overall, we recorded 72 individual capture events, including captures both with and without tissue sample collection and hair samples resulting in the identification of 50 individual mountain lions (30 females, 19 males, and 1 unknown sex individual). We estimated lion densities from 8 models containing effects of distance, sex, and survey effort on detection probability. Our population density estimates ranged from a minimum of 3.7 mountain lions/100 km 2 (95% CI 2.3—5.7) under the distance only model (including only an effect of distance on detection probability) to 6.7 (95% CI 3.1—11.0) under the full model (including effects of distance, sex, survey effort, and distance × sex on detection probability). These numbers translate to a total estimate of 293 mountain lions (95% CI 182—451) to 529 (95% CI 245—870) within the Blackfoot drainage. Results from the distance model are similar to previous estimates of 3.6 mountain lions/100 km 2 for the study area; however, results from all other models indicated greater numbers of mountain lions. Our results indicate that unstructured spatial sampling combined with spatial capture—recapture analysis can be an effective method for estimating large carnivore densities. Published 2012. This article is a U.S. Government work and is in the public domain in the USA.

Estimating densities of cougar (Puma concolor) is important for managing cougars and their prey but remains challenging because of cougar's elusive and solitary behavior. To evaluate a non-invasive, genetic capture-recapture method to estimate cougar population size and density, we surveyed a 220-km² area using conservation detection dogs trained to locate scat over a 4-week sampling period in northeast Oregon. We collected 272 scat samples and conducted DNA analysis on 249 samples from which we determined individual identification from 73 samples that represented 21 cougars (9 males and 12 females). We evaluated 4 models to estimate cougar densities: Huggins closed population capture-recapture (Huggins), CAPWIRE, multiple detections with Poisson (MDP), and spatially explicit capture-recapture (SECR). Population estimates for cougars using our study area were 26 (95% CI = 22-35, 9 males and 17 females) from Huggins models, 24 (95% CI = 21-30, 9 males and 15 females) from CAPWIRE, and 27 (95% CI = 24-42, 9 males and 18 females) from the MDP model. We accounted for the edge effect in density estimates caused by individuals whose home ranges included only a portion of the survey grid by buffering the study area using the mean home range radius of 8 cougars equipped with global positioning system collars on or near the study area. We estimated densities of 4.6 cougars/100 km² (95% CI = 3.8-8.3) for the Huggins model, 4.8 cougars/100 km² (95% CI = 4.2-7.8) for the MDP model, 4.2 cougars/100 km² (95% CI = 3.3-5.3) for the CAPWIRE model, and 5.0 cougars/100 km² (95% CI = 3.2-7.7) for the SECR model. Our results suggested estimating cougar densities using scat detection dogs could be feasible at a broader scale with less effort than other methods currently being used.

We flew aerial line-transect surveys to estimate the range-wide population size of lesser prairie-chickens (Tympanuchus pallidicinctus) in the Great Plains, United States in 2012 and 2013. We also estimated the number of lesser prairie-chicken leks, the number of mixed-species leks that contained both lesser and greater prairie-chickens (T. cupido) and the number of hybrid lesser–greater prairie-chickens where these species' ranges overlap. The study area included the 2011 estimated occupied lesser prairie-chicken range in 5 states and was divided into 4 ecoregions. We created a sampling frame over the study area, consisting of 536 15- × 15-kmgrid cells. We flew 512 transects within a probabilistic sample of 256 cells totaling 7,680 km in 2012 and 566 transects within a probabilistic sample of 283 cells totaling 8,490 km in 2013. We estimated a total of 34,440 individual lesser prairie-chickens in 2012 (17,615 in 2013) and 350 hybrid lesser–greater prairie-chickens in 2012 (342 in 2013) in the study area. We estimated a total of 2,930 lesser prairie-chicken leks in 2012 (2,037 in 2013) and 453 lesser and greater prairie-chicken mixed leks in 2012 (356 in 2013) in the study area. We discuss the implications of alternative sampling designs with regard to conservation questions to be addressed.

Estimation of animal density is fundamental to wildlife research and management, but estimation via mark-recapture is often complicated by lack of geographic closure of study sites. Contemporary methods for estimating density using mark-recapture data include (1) approximating the effective area sampled by an array of detectors based on the mean maximum distance moved (MMDM) by animals during the sampling session, (2) spatially explicit capture-recapture (SECR) methods that formulate the problem hierarchically with a process model for animal density and an observation model in which detection probability declines with distance from a detector, and (3) a telemetry estimator (TELEM) that uses auxiliary telemetry information to estimate the proportion of animals on the study site. We used simulation to compare relative performance (percent error) of these methods under all combinations of three levels of detection probability (0.2, 0.4, 0.6), three levels of occasions (5, 7, 10), and three levels of abundance (10, 20, 40 animals). We also tested each estimator using five different models for animal home ranges. TELEM performed best across most combinations of capture probabilities, sampling occasions, true densities, and home range configurations, and performance was unaffected by home range shape. SECR outperformed MMDM estimators in nearly all comparisons and may be preferable to TELEM at low capture probabilities, but performance varied with home range configuration. MMDM estimators exhibited substantial positive bias for most simulations, but performance improved for elongated or infinite home ranges.

Changes in vertebrate populations in tropical ecosystems are often understood to occur at large spatial and temporal scales. Understanding these dynamics and developing management responses when they are affected by hunting and land-use change require research and monitoring at large spatial scales. Data collection at such scales can be accomplished only through the participation of locally resident nonscientists. To assess the feasibility of rigorous, scientifically valid data collection under such conditions, we describe the design and management of a three-year study of the relationships among socioeconomic factors, hunting behavior, and wildlife population dynamics in a 48,000-square-kilometer, predominantly indigenous region of Amazonia. All of the data in the study were collected by locally recruited and trained indigenous technicians. We describe data collection and verification systems adapted to the culturally influenced data-collection practices of these technicians and propose protocols and improvements on our methodology to guide future large-scale research-and-monitoring projects.