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RovQuant final report 2019
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Kleven, O., Andersskog, I. P. Ø., Brandsegg, H., Eriksen, L. B., Spets, M. H., Königsson, H., Spong, G., Milleret, C., Dupont, P., Bischof, R., Flagstad, Ø. & Brøseth, H. 2022. DNA-based monitoring of the Scandinavian wolverine population 2021. NINA Report 2111. Norwegian Institute for Nature Research.
Abstract: Genetic analysis is an important tool for monitoring large carnivores in Scandinavia, where DNA analyses of scats, hair and urine are extensively used. Over the last decade, wolverine DNA samples have been routinely collected and analysed over large parts of the distribution range in Norway and Sweden. Identification of individuals from DNA profiles of the collected samples has provided an increased knowledge of population size, reproduction, population structure, and immigration. Here, we report the number of individuals identified in Norway, Sweden and northern Finland during the winter of 2021. In addition, we present population size estimates for Norway and Sweden based on spatial capture-mark-recapture models.
From a total of 2446 DNA samples of sufficient genotyping quality, we identified 737 wolverines in Norway, Sweden, and Finland in 2021. The corresponding figure from last winter was 707 DNA-identified individuals from 2234 samples. In total, 322 wolverines were registered with one or more samples in Norway in 2021, compared to 339 individuals in 2020. The corresponding figure from Sweden is 421 individuals in 2021 and 381 in 2020. In Scandinavia, each of the identified wolverines was represented with an average of 3.3 samples. The geographic representation of samples seems to be good for most regions and counties with wolverine presence in Scandinavia. The only exception is the Norrbotten county, that dedicated less effort to DNA sampling during the last two years.
Based on the spatial capture-recapture modelling approach, the Scandinavian wolverine population size was likely between 1013 and 1126 individuals (95% credible interval) in 2021, of which 358 to 418 were attributed to Norway and 639 to 724 individuals to Sweden. These population size estimates correspond well to the extrapolation of individuals from the monitoring of active natal dens. High agreement between the two methodological approaches is satisfactory, implying that we have robust estimates of the size of the Scandinavian wolverine population.
In Norwegian with English abstract
The estimation of population size remains one of the primary goals and challenges in ecology and provides a basis for debate and policy in wildlife management. Despite the development of efficient noninvasive sampling methods and robust statistical tools to estimate abundance, the maintenance of field sampling is still subject to economic and logistic constraints. These can result in intentional or unintentional interruptions in sampling and cause gaps in data time series, posing a challenge to abundance estimation, and ultimately conservation and management decisions. We applied an open population spatial capture–recapture (OPSCR) model to simulations and a real‐life case study to test the reliability of abundance inference to interruptions in data collection. Using individual detections occurring over consecutive sampling occasions, OPSCR models allow the estimation of abundance while accounting for lack of demographic and geographic closure between occasions. First, we simulated sampling data with interruptions in field sampling of different lengths and timing and checked the performance of an OPSCR model in deriving abundance for species with slow and intermediate life‐history strategies. Next, we introduced artificial sampling interruptions of various magnitudes and timing to a five‐year noninvasive monitoring data set of wolverines (Gulo gulo) in Norway and quantified the consequences for OPSCR model predictions. Inferences from OPSCR models were reliable even with temporal interruptions in monitoring. Interruption did not cause systematic bias, but increased uncertainty. Interruptions occurring at occasions near the beginning and the end of the sampling period caused higher uncertainty. The loss in precision was more severe for species with a faster life‐history strategy. OPSCR allows monitoring studies to provide contiguous abundance estimates to managers, stakeholders, and policy makers even when data are noncontiguous. OPSCR models do not only help cope with unintentional interruptions during sampling but also offer opportunities for using intentional sampling interruptions during the design of cost‐effective population surveys.
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 ( = 5.91 km, 95% CrI = 5.18–6.81) moved around considerably more than adult males ( = 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.
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.