259 research outputs found
Data‐driven counterfactual evaluation of management outcomes to improve emergency conservation decisions
No full textMonitoring is needed to assess conservation success and improve management, but naïve or simplistic interpretation of monitoring data can lead to poor decisions. We illustrate how to counter this risk by combining decision-support tools and quantitative counterfactual analysis. We analyzed 20 years of egg rescue for tara iti (Sternula nereis davisae) in Aotearoa New Zealand. Survival is lower for rescued eggs; however, only eggs perceived as imminently threatened by predators or weather are rescued, so concluding that rescue is ineffective would be biased. Equally, simply assuming all rescued eggswould have died if left in situ is likely to be simplistic. Instead, we used the monitoring data itself to estimate statistical support for a wide space of uncertain counterfactuals about decisions and fate of rescued eggs. Results suggest under past management, rescuing and leaving eggs would have led to approximately the same overall fledging rate, because of likely imperfect threat assessment and low survival of rescued eggs to fledging. Managers are currently working to improve both parameters. Our approach avoids both naïve interpretation of observed outcomes and simplistic assumptions thatmanagement is always justified, using the same data to obtain unbiased quantitative estimates of counterfactual support
Statistical Development of Animal Density Estimation Using Random Encounter Modelling
No full textCamera trapping is widely used in ecological studies to estimate animal density, although these studies are largely restricted to animals that can be identified to the individual level. The random encounter model, developed by Rowcliffe et al. (J Anal Ecol 45(4):1228–1236, 2008), estimates animal density from camera-trap data without the need to identify animals. Although the REM can provide reliable density estimates, it lacks the potential to account for the multiple sources of variance in the modelling process. The density estimator in REM is a ratio, and since the variance of a ratio estimator is intractable, we examine and compare the finite sample performance of many approaches for obtaining confidence intervals via simulation studies. We also propose an integrated random encounter model as a parametric alternative, which is flexible and can incorporate covariates and random effects. A data example from Whipsnade Wild Animal Park, Bedfordshire, south England, is used to demonstrate the application of these methods
Economical crowdsourcing for camera trap image classification
No full textCamera trapping is widely used to monitor mammalian wildlife but creates large image datasets that must be classified. In response, there is a trend towards crowdsourcing image classification. For high‐profile studies of charismatic faunas, many classifications can be obtained per image, enabling consensus assessments of the image contents. For more local‐scale or less charismatic communities, however, demand may outstrip the supply of crowdsourced classifications. Here, we consider MammalWeb, a local‐scale project in North East England, which involves citizen scientists in both the capture and classification of sequences of camera trap images. We show that, for our global pool of image sequences, the probability of correct classification exceeds 99% with about nine concordant crowdsourced classifications per sequence. However, there is high variation among species. For highly recognizable species, species‐specific consensus algorithms could be even more efficient; for difficult to spot or easily confused taxa, expert classifications might be preferable. We show that two types of incorrect classifications – misidentification of species and overlooking the presence of animals – have different impacts on the confidence of consensus classifications, depending on the true species pictured. Our results have implications for data capture and classification in increasingly numerous, local‐scale citizen science projects. The species‐specific nature of our findings suggests that the performance of crowdsourcing projects is likely to be highly sensitive to the local fauna and context. The generality of consensus algorithms will, thus, be an important consideration for ecologists interested in harnessing the power of the crowd to assist with camera trapping studies
Food aquisition and predator avoidance in a Neotropical rodent
No full textForaging activity in animals reflects a compromise between acquiring food and avoiding predation. The Risk Allocation Hypothesis predicts that prey animals optimize this balance by concentrating their foraging activity at times of relatively low predation risk, as much as their energy status permits, but empirical evidence is scarce. We used a unique combination of automated telemetry, manual radio telemetry and camera trapping to test whether activity at high-risk times declined with food availability, as predicted, in a Neotropical forest rodent, the Central American agouti (Dasyprocta punctata). We found that the relative risk of predation by the main predator, the Ocelot (Leopardus pardalis), estimated as the ratio of ocelot to agouti activity on camera trap footage, was up to four orders of magnitude higher between sunset and sunrise than during the rest of the day. Kills of radio-tracked agoutis by ocelots during this high-risk period far exceeded expectations given agouti activity. Both telemetric monitoring of radio-tagged agoutis and camera monitoring of burrow entrances indicated that agoutis exited their burrows later at dawn, entered their burrows earlier at dusk, and had lower overall activity levels, as they lived in areas with higher food abundance. Thus, agoutis avoided activity during the high-risk period more strongly as access to food was higher. Our study provides quantitative empirical evidence of prey animals concentrating their activity at times of relatively low predation risk
Quantifying the sensitivity of camera traps:an adapted distance sampling approach
No full text1. 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
Compromised survivorship in zoo elephants
No full textKeeping elephants in zoos is extremely costly, yet does not yield self-sustaining 16 populations. In Europe, which holds c. half the global zoo elephant population, a long17 term decline of c.10% per year is expected in both species, if reliant on zoo-bred animals 18 under historically prevailing conditions. Fitness in zoos is compromised in several ways. 19 Compared with protected in situ populations (Burmese working Asians; Kenyan free20 living Africans), zoo elephants show premature reproductive senescence and -- despite 21 improving adult survivorship for Africans -- die earlier in adulthood than expected. In 22 Asian elephants, infant survivorship in zoos is also greatly reduced relative to Burmese 23 elephants, and furthermore, zoo-born animals die earlier in adulthood than wild-caught 24 conspecifics kept in zoos, via effects ‘programmed’ peri-natally. In this species, being 25 transferred between zoos also increases mortality rates. Both survival and fecundity 26 would need to improve to attain self-sustaining zoo populations. Our findings 27 demonstrate deficits in zoo elephant management, particularly for Asians, and implicate 28 stress and obesity as likely problems
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Application of the random encounter model in citizen science projects to monitor animal densities
No full textAbundance and density are vital metrics for assessing a species’ conservation status and for developing effective management strategies. Remote-sensing cameras are being used increasingly as part of citizen science projects to monitor wildlife, but current methodologies to monitor densities pose challenges when animals are not individually recognisable. We investigate the use of camera traps and the Random Encounter Model (REM) for estimating the density of West European hedgehogs (Erinaceus europaeus) within a citizen science framework. We evaluate the use of a simplified version of the REM in terms of the parameters’ estimation (averaged versus survey-specific) and asses it’s potential application as part of a large-scale, long-term citizen science project. We compare averaged REM estimates to those obtained via Spatial Capture-Recapture (SCR) using data from nocturnal spotlight surveys. There was a high degree of concordance in REM-derived density estimates from averaged parameters versus those derived from survey-specific parameters. Averaged REM density estimates were also comparable to those produced by SCR at 8 out of 9 sites; hedgehog density was 7.5 times higher in urban (32.3 km-2) versus rural (4.3 km2) sites. Power analyses indicated that the averaged REM approach would be able to detect a 25% change in hedgehog density in both habitats with >90% power. Furthermore, despite the high start-up costs associated with the REM method, it would be cost-effective in the long term. The averaged REM approach is a promising solution to the challenge of large-scale and long-term species monitoring. We suggest including the REM as part of a citizen science monitoring project, where participants collect data and researchers verify and implement the required analysis
Foraging inequalities in large groups: quantifying depletion experienced by individuals in goose flocks
No full textThe costs of carnivory.
No full textMammalian carnivores fall into two broad dietary groups: smaller carnivores (20 kg) that specialize in feeding on large vertebrates. We develop a model that predicts the mass-related energy budgets and limits of carnivore size within these groups. We show that the transition from small to large prey can be predicted by the maximization of net energy gain; larger carnivores achieve a higher net gain rate by concentrating on large prey. However, because it requires more energy to pursue and subdue large prey, this leads to a 2-fold step increase in energy expenditure, as well as increased intake. Across all species, energy expenditure and intake both follow a three-fourths scaling with body mass. However, when each dietary group is considered individually they both display a shallower scaling. This suggests that carnivores at the upper limits of each group are constrained by intake and adopt energy conserving strategies to counter this. Given predictions of expenditure and estimates of intake, we predict a maximum carnivore mass of approximately a ton, consistent with the largest extinct species. Our approach provides a framework for understanding carnivore energetics, size, and extinction dynamics
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