Wolverine density distribution reflects past persecution and current management in Scandinavia

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A flexible and efficient Bayesian implementation of point process models for spatial capture‐recapture data

Spatial capture–recapture (SCR) is now routinely used for estimating abundance and density of wildlife populations. A standard SCR model includes sub-models for the distribution of individual activity centers (ACs) and for individual detections conditional on the locations of these ACs. Both sub-models can be expressed as point processes taking place in continuous space, but there is a lack of accessible and efficient tools to fit such models in a Bayesian paradigm. Here, we describe a set of custom functions and distributions to achieve this. Our work allows for more efficient model fitting with spatial covariates on population density, offers the option to fit SCR models using the semi-complete data likelihood (SCDL) approach instead of data augmentation, and better reflects the spatially continuous detection process in SCR studies that use area searches. In addition, the SCDL approach is more efficient than data augmentation for simple SCR models while losing its advantages for more complicated models that account for spatial variation in either population density or detection. We present the model formulation, test it with simulations, quantify computational efficiency gains, and conclude with a real-life example using non-invasive genetic sampling data for an elusive large carnivore, the wolverine (Gulo gulo) in Norway. area search, binomial point process, continuous sampling, NIMBLE, non-invasive genetic sampling, Poisson point process, spatial capture–recapture, wolverinepublishedVersio

Small rodent monitoring at Birkebeiner road, Norway

Background. Northern small mammal populations are renowned for their multi-annual population cycles. Population cycles are multi-faceted and have extensive impacts on the rest of the ecosystem. In 2011, we started a student-based research activity to monitor the variation of small rodent density along an elevation gradient following the Birkebeiner Road, in southeast Norway. Fieldwork was conducted by staff and students at the University campus Evenstad, Inland Norway University of Applied Sciences, which has a long history of researching cyclic population dynamics. The faculty has a strong focus on engaging students in all parts of the research activities, including data collection. Small rodents were monitored using a set of snap trap stations. Trapped animals were measured (e.g. body mass, body length, sex) and dissected to assess their reproductive status. We also characterised the vegetation at trapping sites. New information. We provide a dataset of small rodent observations that show fluctuating population dynamics across an elevation gradient (300 m to 1,100 m a.s.l) and in contrasting habitats. This dataset encompasses three peaks of the typical 3-4-year vole population cycles; the number of small rodents and shrews captured show synchrony and peaked in years 2014, 2017 and 2021. The bank vole Myodes glareolus was by far (87%) the most common species trapped, but also other species were observed (including shrews). We provide digital data collection forms and highlight the importance of long-term data collection.publishedVersio

Spatial variation in red deer density in a transboundary forest ecosystem

Forests in Europe are exposed to increasingly frequent and severe disturbances. The resulting changes in the structure and composition of forests can have profound consequences for the wildlife inhabiting them. Moreover, wildlife populations in Europe are often subjected to differential management regimes as they regularly extend across multiple national and administrative borders. The red deer Cervus elaphus population in the Bohemian Forest Ecosystem, straddling the Czech-German border, has experienced forest disturbances, primarily caused by windfalls and bark beetle Ips typographus outbreaks during the past decades. To adapt local management strategies to the changing environmental conditions and to coordinate them across the international border, reliable estimates of red deer density and abundance are highly sought-after by policymakers, wildlife managers, and stakeholders. Covering a 1081-km2 study area, we conducted a transnational non-invasive DNA sampling study in 2018 that yielded 1578 genotyped DNA samples from 1120 individual red deer. Using spatial capture-recapture models, we estimated total and jurisdiction-specific abundance of red deer throughout the ecosystem and quantified the role of forest disturbance and differential management strategies in shaping spatial heterogeneity in red deer density. We hypothesised that (a) forest disturbances provide favourable habitat conditions (e.g., forage and cover), and (b) contrasting red deer management regimes in different jurisdictions create a differential risk landscape, ultimately shaping density distributions. Overall, we estimated that 2851 red deer (95% Credible Interval = 2609–3119) resided in the study area during the sampling period, with a relatively even overall sex ratio (1406 females, 95% CI = 1229–1612 and 1445 males, 95% CI = 1288–1626). The average red deer density was higher in Czechia (3.5 km−2, 95% CI = 1.2–12.3) compared to Germany (2 km−2, 95% CI = 0.2–11). The effect of forest disturbances on red deer density was context-dependent. Forest disturbances had a positive effect on red deer density at higher elevations and a negative effect at lower elevations, which could be explained by partial migration and its drivers in this population. Density of red deer was generally higher in management units where hunting is prohibited. In addition, we found that sex ratios differed between administrative units and were more balanced in the non-intervention zones. Our results show that the effect of forest disturbances on wild ungulates is modulated by additional factors, such as elevation and ungulate management practices. Overall density patterns and sex ratios suggested strong gradients in density between administrative units. With climate change increasing the severity and frequency of forest disturbances, population-level monitoring and management are becoming increasingly important, especially for wide-ranging species as both wildlife and global change transcend administrative boundaries.publishedVersio

Habitat segregation between brown bears and grey wolves in a human-dominated landscape

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A multidisciplinary approach to estimating wolf population size for long-term conservation

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GPS collars have an apparent positive effect on the survival of a large carnivore

Are instrumented animals representative of the population, given the potential bias caused by selective sampling and the influence of capture, handling and wearing bio-loggers? The answer is elusive owing to the challenges of obtaining comparable data from individuals with and without bio-loggers. Using non-invasive genetic data of a large carnivore, the wolverine (Gulo gulo) in Scandinavia, and an open-population spatial capture–recapture model, we found a 16 (credible interval: 4–30) percentage points lower mortality probability for GPS-collared individuals compared with individuals without GPS collars. While the risk of dying from legal culling was comparable for collared and non-collared wolverines, the former experienced lower probability of mortality due to causes other than legal culling. The aforementioned effect was pronounced despite a potentially lower age—and therefore likely higher natural mortality—of collared individuals. Reports of positive effects of bio-loggers on the survival of individuals are uncommon and we argue that GPS collars could shield animals from poaching. Our results highlight the challenges of drawing population-level inferences for populations subjected to poaching when using data from instrumented individuals. ecology, population level, representativeness, population dynamicspublishedVersio

Consequences of ignoring group association in spatial capture–recapture analysis

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Consequences of reduced sampling intensity for estimating population size of wolves in Scandinavia with spatial capture-recapture models

Every winter, the Scandinavian wolf population is surveyed using non-invasive genetic sampling (NGS) and snow-tracking to assess its annual status and trends. During 2017/18, search intensity and the proportion of samples genotyped was unusually high, resulting in more than 3 000 samples associated with 375 individuals. The boost in sampling was realized in part through intensified structured searches by the management authorities and in part by encouraging hunters and the general public to collect samples opportunistically. Such a high effort is not sustainable for long-term monitoring, but presents an opportunity to evaluate the consequences of reduced sampling intensity. We artificially thinned the number of genetic samples available and evaluated the consequences of thinning on population size estimates derived with spatial capture-recapture (SCR) models. The original aim of this study was to identify sampling strategies (sampling intensity and spatial configuration) that increased the cost-efficiency of wolf monitoring in Scandinavia for population size estimation using SCR. However, discovery of an apparent bias in population size estimates in response to sample reduction led to a shift in focus to identifying the possible causes of this pattern. We found population size estimates obtained after sample reduction to be sensitive to different thinning strategies and model specifications. Aside from the expected increase in uncertainty around parameter estimates due to a reduction in sample size, removal of detections collected during structured sampling led to a reduction in mean abundance estimates. Further testing revealed that the apparent negative bias was especially pronounced 1) for the model that included separate submodels for opportunistic and structured sampling, 2) for abundance estimates of females that were not adult scent-marking individuals, 3) for single-season SCR models, as opposed to open-population SCR models (OPSCR), and 4) when thinning was conducted only on samples collected during structured searches. Our analysis helped us hone in on the conditions under which bias is most prevalent, but further work is needed to identify the mechanisms causing it. As a next step towards a more applicable observation process model and/or data thinning scheme, we recommend a thorough characterization of the data accumulation process, including the potential link between opportunistic and structured sampling and the spatio-temporal relationship between track logs and samples.Den Skandinaviska vargpopulationen inventeras varje vinter, främst genom spårning på snö och analys av DNA-prov som samlas in under spårningarna, för att bland annat uppskatta antal och fördelning familjegrupper och revirmarkerande par. Under vintersäsongen 2017/18 utökades insatsen att söka efter och analysera DNA-prov från varg, vilket resulterade i mer än 3000 prov från 375 individer. Insamlingen av prov under vintern utökades dels genom mer intensifierade strukturerade sökinsatser av de förvaltande myndigheterna, dels genom att uppmuntra jägare och allmänheten att opportunistiskt samla in prov. En sådan insats är inte ekonomiskt hållbar för inventeringar på lång sikt, men med det erhållna datamaterialet är möjligt att undersöka och jämföra vad som händer om insatsen skulle varit mindre. Här tunnade vi artificiellt ut antalet tillgängliga prov från vintern 2017/2018 och undersökte konsekvenserna av uttunningen på uppskattningen av populationsstorlek från rumsliga fångst-återfångstmodeller (SCR). Målet med studien var ursprungligen att identifiera provtagningsstrategier (m.a.p. antal prov och rumslig sammansättning) för att en mer kostnadseffektiv uppskattning av den Skandinaviska vargpopulationens storlek med SCR. Vi upptäckte emellertid att med uttunnade provmaterial fick snedfördelade uppskattningar av populationsstorleken med SCR. Detta gjorde att vi bytte fokus i studien för att undersöka orsaken bakom snedfördelningarna. Vi fann att uppskattningar av populationsstorlek var känsliga för olika typer av uttunningar av provmaterialet samt olika modelltyper. Som förväntat ökade osäkerheten kring uppskattningarna av parametrarna i modellen med minskat antal prov. Då observationer från den strukturerade insamlingen av prov reducerades blev de uppskattade populationsstorlekarna dessutom lägre. Efter fortsatta undersökningar visade det sig att den negativa avvikelsen i populationsstorlek blev tydligare 1) med modeller som byggde på skilda förklaringsmodeller för opportunistiska och strukturerade provinsamlingar 2) för det uppskattade antalet tikar som inte var revirmarkerande, 3) för SCR-modeller enbart baserade data insamlat inom säsongen till skillnad från fångst-återfångstmodeller som även bygger på data från andra säsonger och 4) när endast prover från den strukturerade insamlingen tunnades ut. Denna studie hjälpte oss att identifiera de förhållanden som generades de mest snedfördelade uppskattningarna av antalet individer i populationen, men mer analyser behöves för att ta reda mekanismerna bakom dessa resultat. För en mer tillämpbar modell över vargobservationerna och/eller uttunning av befintligt datamaterial, rekommenderar vi en noggrann karakterisering av hur data tas fram, däribland den möjliga kopplingen mellan opportunistisk och strukturerad insamling samt det rumsliga och tidsmässiga sambandet mellan spårningar och insamlade prov

Using partial aggregation in spatial capture recapture

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