Content uploaded by Richard Bischof
Author content
All content in this area was uploaded by Richard Bischof on Jan 02, 2020
Content may be subject to copyright.
A preview of the PDF is not available
RovQuant final report 2019
ResearchGate has not been able to resolve any citations for this publication.
Many models in population ecology, including spatial capture–recapture (SCR) models, assume that individuals are distributed and detected independently of one another. In reality, this is rarely the case – both antagonistic and gregarious relationships lead to non-independent spatial configurations, with territorial exclusion at one end of the spectrum and group-living at the other. Previous simulation studies suggest that grouping has limited impact on the outcome of SCR analyses. However, group associations entail not only spatial clustering of activity centers but also coordinated space use by group members, potentially impacting both ecological and observation processes underlying SCR analysis. We simulated SCR scenarios with different strengths of aggregation (clustering of individuals into groups with shared activity centers) and cohesion (synchronization of detection patterns of members of a group). We then fit SCR models to the simulated data sets and evaluated the effect of aggregation and cohesion on parameter estimates. Low to moderate aggregation and cohesion did not impact the bias and precision of estimates of density and the scale parameter of the detection function. However, non-independence between individuals led to high levels of overdispersion. Overdispersion strongly decreased the coverage of confidence intervals around parameter estimates, thereby increasing the probability of erroneous predictions. Our results indicate that SCR models are robust to moderate levels of aggregation and cohesion. Nonetheless, spatial dependence between individuals can lead to false inference. We recommend that practitioners 1) test for the presence of overdispersion in SCR data caused by aggregation and cohesion, and, if necessary, 2) correct their variance estimates using the overdispersion factor ĉ . Approaches for doing both are described in this paper. We also urge the development of SCR models that incorporate spatial associations between individuals not only to account for overdispersion but also to obtain quantitative information about social aspects of study populations.
Range declines, habitat connectivity, and trapping have created conservation concern for wolverines throughout their range in North America. Previous researchers used population models and observed estimates of survival and reproduction to infer that current trapping rates limit population growth, except perhaps in the far north where trapping rates are lower. Assessing the sustainability of trapping requires demographic and abundance data that are expensive to acquire and are therefore usually only achievable for small populations, which makes generalization risky. We surveyed wolverines over a large area of southern British Columbia and Alberta, Canada, used spatial capture-recapture models to estimate density, and calculated trapping kill rates using provincial fur harvest data. Wolverine density averaged 2 wolverines/1,000 km 2 and was positively related to spring snow cover and negatively related to road density. Observed annual trapping mortality was >8.4%/year. This level of mortality is unlikely to be sustainable except in rare cases where movement rates are high among sub-populations and sizable un-trapped refuges exist. Our results suggest wolverine trapping is not sustainable because our study area was fragmented by human and natural barriers and few large refuges existed. We recommend future wolverine trapping mortality be reduced by ≥50% throughout southern British Columbia and Alberta to promote population recovery.
The hitherto difficult task of reliably estimating populations of wide-ranging megafauna has been enabled by advances in capture–recapture methodology. Here we combine photographic sampling with a Bayesian spatially-explicit capture–recapture (SCR) model to estimate population parameters for the endangered Asian elephant Elephas maximus in the productive floodplain ecosystem of Kaziranga National Park, India. Posterior density estimates of herd-living adult females and sub-adult males and females (herd-adults) was 0.68 elephants/km2 (95% Credible Intervals, CrI = 0.56−0.81) while that of adult males was 0.24 elephants/km2 (95% CrI = 0.18−0.30), with posterior density estimates highlighting spatial heterogeneity in elephant distribution. Estimates of the space-usage parameter suggested that herd-adults (\({\hat{\sigma }}_{HA}\) = 5.91 km, 95% CrI = 5.18–6.81) moved around considerably more than adult males (\({\hat{\sigma }}_{AM}\) = 3.64 km, 95% CrI = 3.09–4.34). Based on elephant movement and age–sex composition, we derived the population that contributed individuals sampled in Kaziranga to be 908 herd-adults, 228 adult males and 610 young (density = 0.46 young/km2, SD = 0.06). Our study demonstrates how SCR is suited to estimating geographically open populations, characterising spatial heterogeneity in fine-scale density, and facilitating reliable monitoring to assess population status and dynamics for science and conservation.
The Leopard Cat Prionailurus bengalensis is thought to be Asia’s most abundant wild cat. Yet, the species’ status is poorly known due to a lack of rigorous population estimates. Based on the few studies available, Leopard Cats appear to be more abundant in degraded forests, potentially due to increased prey availability. We conducted camera trap surveys, rodent live-trapping, and spatially-explicit capture-recapture analyses to estimate the density of Leopard Cats within a degraded tropical forest fragment (148km2) in northeastern Thailand. A total effort of 12,615 camera trap nights across 65km2 of trapping area resulted in at least 25 uniquely identified individuals. Average rodent biomass (the main prey of Leopard Cats) was highest in the dry evergreen forest (469.0g/ha), followed by dry dipterocarp forest (287.5g/ha) and reforested areas (174.2g/ha). Accordingly, Leopard Cat densities were highest in the dry evergreen forest with 21.42 individuals/100km2, followed by the reforested areas with 7.9 individuals/100km2. Only two detections came from the dry dipterocarp forest despite both an extensive survey effort (4,069 trap nights) and available prey. Although the dipterocarp supported the second highest average rodent biomass, it lacked a key prey species, Maxomys surifer, possibly explaining low encounter rates in that habitat. Our results provide important baseline information concerning the population status of Leopard Cat in southeastern Asia. Further, our findings corroborate with other studies that found a tolerance among Leopard Cats for degraded forests, highlighting the potential for forest fragments to serve as long-term conservation areas for the species.
Spatial capture–recapture (SCR) is an increasingly popular method for estimating ecological parameters. SCR often relies on data collected over relatively long sampling periods. While longer sampling periods can yield larger sample sizes and thus increase the precision of estimates, they also increase the risk of violating the closure assumption, thereby potentially introducing bias. The sampling period characteristics are therefore likely to play an important role in this bias‐precision trade‐off. Yet few studies have studied this trade‐off and none has done so for SCR models.
In this study, we explored the influence of the length and timing of the sampling period on the bias‐precision trade‐off of SCR population size estimators. Using a continuous time‐to‐event approach, we simulated populations with a wide range of life histories and sampling periods before quantifying the bias and precision of population size estimates returned by SCR models.
While longer sampling periods benefit the study of slow‐living species (increased precision and lower bias), they lead to pronounced overestimation of population size for fast‐living species. In addition, we show that both bias and uncertainty increase when the sampling period overlaps the reproductive season of the study species.
Based on our findings, we encourage investigators to carefully consider the life history of their study species when contemplating the length and the timing of the sampling period. We argue that both spatial and non‐spatial capture–recapture studies can safely extend the sampling period to increase precision, as long as it is timed to avoid peak recruitment periods. The simulation framework we propose here can be used to guide decisions regarding the sampling period for a specific situation.
Spatial capture-recapture models (SCR) are used to estimate animal density and to investigate a range of problems in spatial ecology that cannot be addressed with traditional nonspatial methods. Bayesian approaches in particular offer tremendous flexibility for SCR modeling. Increasingly, SCR data are being collected over very large spatial extents making analysis computational intensive, sometimes prohibitively so. To mitigate the computational burden of large-scale SCR models, we developed an improved formulation of the Bayesian SCR model that uses local evaluation of the individual state-space (LESS). Based on prior knowledge about a species' home range size, we created square evaluation windows that restrict the spatial domain in which an individual's detection probability (detector window) and activity center location (AC window) are estimated. We used simulations and empirical data analyses to assess the performance and bias of SCR with LESS. LESS produced unbiased estimates of SCR parameters when the AC window width was ≥5σ (σ: the scale parameter of the half-normal detection function), and when the detector window extended beyond the edge of the AC window by 2σ. Importantly, LESS considerably decreased the computation time needed for fitting SCR models. In our simulations, LESS increased the computation speed of SCR models up to 57-fold. We demonstrate the power of this new approach by mapping the density of an elusive large carnivore-the wolverine (Gulo gulo)-with an unprecedented resolution and across the species' entire range in Norway (> 200,000 km 2). Our approach helps overcome a major computational obstacle to population and landscape-level SCR analyses. The LESS implementation in a Bayesian framework makes the customization and fitting of SCR accessible for practitioners working at scales that are relevant for conservation and management.
With continued global changes, such as climate change, biodiversity loss, and habitat fragmentation, the need for assessment of long‐term population dynamics and population monitoring of threatened species is growing. One powerful way to estimate population size and dynamics is through capture–recapture methods. Spatial capture (SCR) models for open populations make efficient use of capture–recapture data, while being robust to design changes. Relatively few studies have implemented open SCR models, and to date, very few have explored potential issues in defining these models. We develop a series of simulation studies to examine the effects of the state‐space definition and between‐primary‐period movement models on demographic parameter estimation. We demonstrate the implications on a 10‐year camera‐trap study of tigers in India. The results of our simulation study show that movement biases survival estimates in open SCR models when little is known about between‐primary‐period movements of animals. The size of the state‐space delineation can also bias the estimates of survival in certain cases.We found that both the state‐space definition and the between‐primary‐period movement specification affected survival estimates in the analysis of the tiger dataset (posterior mean estimates of survival ranged from 0.71 to 0.89). In general, we suggest that open SCR models can provide an efficient and flexible framework for long‐term monitoring of populations; however, in many cases, realistic modeling of between‐primary‐period movements is crucial for unbiased estimates of survival and density.
Many small mammal populations respond quickly to timber harvest aimed at oak (Quercus) regeneration, which alters microhabitat. We used mark-release–recapture data collected 6–8 years postharvest from the Hardwood Ecosystem Experiment in southern Indiana, United States, to model density and apparent survival of eastern chipmunks (Tamias striatus) and white-footed mice (Peromyscus leucopus) as a function of timber harvest treatments (shelterwood, clearcut, patch cut, and unharvested control). Density, estimated using spatial capture–recapture, increased for chipmunks in all types of harvest openings, but survival was unaffected by harvest. Chipmunk densities in unharvested forest matrix habitat averaged 58% and 71% lower relative to harvest openings and opening edges, respectively. White-footed mouse density was less responsive to timber harvest, but monthly survival rates were reduced by 13% in shelterwoods and 17% in patch cuts relative to control sites. Both rodent species tended to exhibit distance-dependent responses, with higher density of home-range centers near harvest boundaries relative to forest matrix. Structural complexity created at the edges of timber harvest openings can benefit rodents associated with edge habitat 6–8 years after harvest, presumably due to improved foraging efficiency and resource diversity. Cascading effects of rodent demographic responses are likely to affect predation and seed dispersal, which are critical trophic interactions in oak forest ecosystems.
A spatial open‐population capture–recapture model is described that extends both the non‐spatial open‐population model of Schwarz and Arnason (1996) and the spatially explicit closed‐population model of Borchers and Efford (2008). The superpopulation of animals available for detection at some time during a study is conceived as a 2‐dimensional Poisson point process. Individual probabilities of birth and death follow the conventional open‐population model. Movement between sampling times may be modeled with a dispersal kernel using a recursive Markovian algorithm. Observations arise from distance‐dependent sampling at an array of detectors. As in the closed‐population spatial model, the observed data likelihood relies on integration over the unknown animal locations; maximization of this likelihood yields estimates of the birth, death, movement and detection parameters. The models were fitted to data from a live‐trapping study of brushtail possums (Trichosurus vulpecula) in New Zealand. Simulations confirmed that spatial modeling can greatly reduce the bias of capture–recapture survival estimates, and that there is a degree of robustness to misspecification of the dispersal kernel. An R package is available that includes various extensions. This article is protected by copyright. All rights reserved.