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RovQuant - Integrated analysis for management of large carnivores in Scandinavia

RovQuant provides quantitative information and tools for improved management of large carnivores in Scandinavia.

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The Swedish Environmental Agency (50%) The Norwegian Environment Agency (50%)

Large carnivores are rare, elusive, and controversial. Their populations are difficult to monitor and manage. For over 10 years, Swedish and Norwegian authorities have been accumulating monitoring data in Rovbase, an international large carnivore database.

Today, the database contains records from thousands of genetic captures, observations, and dead recoveries of bears, wolves, and wolverines, collected by managers, researchers, hunters, and other members of the public in Scandinavia. This international research project uses these data to estimate population sizes, survival and reproduction of carnivores in Sweden and Norway.

Methods

For over 10 years, Swedish and Norwegian authorities have been accumulating monitoring data in Rovbase, an international large carnivore database. Today, the database contains records from thousands of genetic captures, observations, and dead recoveries of bears, wolves, lynx and wolverines, collected by managers, researchers, hunters, and other members of the public in Scandinavia.

Each sample, once the DNA it contains has been extracted and analyzed, represents evidence of an individual animal in space and time. We use this information to estimate population sizes, survival and reproduction of carnivores in Sweden and Norway.

We focus primarily on spatial capture-recapture models (SCR), a suite of analytical tools that take into account the spatial configuration of individual detections, and can deal with number of important challenges in wildlife studies, including imperfect detection and transboundary animal movements. Carnivores have comparatively high life expectancies and individual animals are often detected over multiple years. We have developed SCR models that incoprorate population dynamics, thereby making better use of the available data and estimating important parameters such as recruitment and mortality, in addition to abundance.

Objectives

Monitoring design and develop protocols

We research monitoring design and help authorities develop protocols for the cost-efficient collection of non-invasive sampling data. 

Properly designed and executed monitoring leads to reliable data, and reliable data are a pre-requisite for producing trustworthy estimates and conclusions. This is particularly important when scientific results inform policy decisions and wildlife management.

RovQuant uses existing dataset in combination with simulations to develop protocols that allow managers to collect reliable data, while at the same time making monitoring more cost-efficient. This work is done in collaboration with those leading the data collection effort in Scandinavia, including Rovdata, Wildlife Damage Centre (VSC), The Swedish Museum of Natural History (NRM), and other organizations. 

Carnivore monitoring at large spatial scales

We develop and test statistical methods for analysis of carnivore monitoring data at very large spatial scales. 

In collaboration with the University of California, Berkeley and Williams College, we have developed nimbleSCR, a software package for efficient analysis of large-scale spatial capture-recapture data. The package is freely available to anyone and can be run through R.  

Spatial capture-recapture (SCR) models use the spatial information of detections, for example DNA samples left behind by wildlife, to estimate the location of activity centers of individuals. SCR models produce density estimates and density surfaces, accounting for the fact that not all individuals are detected.

Furthermore, abundance estimates produced by SCR models take into account that animals move across the landscape and may be detected in multiple location – for example, a bear living near the Swedish-Norwegian border may contribute partially to population size estimates in both countries.

Maps of carnivore densities

We produce maps of carnivore densities across Norway and Sweden.

RovQuant generates density surfaces - maps of the density of wolverines, wolves, and bears across Scandinavia. These maps and the underlying data are freely available at the GITHUB page (external link)


Estimated population densities for Wolverine (Gulo gulo).

 

Carnivore population sizes

We estimate carnivore population sizes at regional and national levels, as well as for subnational jurisdictions such as carnivore management units and counties.

“How many are there?” This is one of the main questions asked by wildlife managers and policy makers. RovQuant provides population size estimates for wolves, bears, and wolverines throughout Scandinavia. Estimates are available at regional and national levels, as well as for subnational jurisdictions.

Population vital rates

Population vital rates drive the change in wildlife population over time. These dynamics are the result of a combination of mortality, recruitment, immigration, and emigration. Monitoring conducted over multiple years allows RovQuant to estimate vital rates of large carnivores in Scandinavia. These estimates, in turn, are a prerequisite for population forecasting.

Collaboration with managers

Since its beginnings, RovQuant has collaborated closely with large carnivore managers in Norway and Sweden. This is to ensure that the results produced by the project are useful for moving large carnivore monitoring and management forward.

Results from the project are published in technical reports annually and periodic meetings and workshops ensure that RovQuant remains up-to-date on current and emerging challenges in large carnivore monitoring and management.

Foto: Shutterstock

Publications

Technical reports

Estimates of wolf density, abundance, and population dynamics in Scandinavia, 2013–2022 (2022)
Milleret, C., Dupont, P., Åkesson, M., Brøseth, H., Svensson, L., Kindberg, J., and Bischof, R
Estimates of wolverine density, abundance, and population dynamics in Scandinavia, 2013–2021 - MINA fagrapport 74 (2022)
Milleret, C., Dupont, P., Brøseth, H., Flagstad, Ø, Kindberg, J., and Bischof, R.
Estimates of wolf density, abundance, and population dynamics in Scandinavia, 2012 - 2021 - MINA fagrapport 72 (2021)
Milleret, C., Dupont, P., Åkesson, M., Brøseth, H., Kindberg, J., and Bischof, R.
Consequences of reduced sampling intensity for estimating population size of wolves in Scandinavia with spatial capture-recapture models - MINA fagrapport 65 (2020)
Milleret, C., Dupont, P., Åkesson, M., Svensson, L., Brøseth, H., and Bischof, R.
RovQuant: Estimating density, abundance and population dynamics of bears, wolverines, and wolves in Scandinavia - MINA fagrapport 63 (2019)
Bischof, R., Milleret, C., Dupont, P., Chipperfield, J., Brøseth, H., and Kindberg, J.
Estimating the size of the Scandinavian wolf population with spatial capture-recapture and conversion factors - MINA fagrapport 57 (2019)
Bischof, R., Milleret, C., Dupont, P., Chipperfield, J., Åkesson, M., Brøseth, H., and Kindberg, J.

Peer-reviewed

Does the punishment fit the crime? Consequences and diagnosis of misspecified detection functions in Bayesian spatial capture–recapture modeling. Ecology and Evolution 12(2). (2022)
Dey, S., Dupont, P., Bischof, R., Milleret, C.,
Integrating dead recoveries in open-population spatial capture–recapture models. Ecosphere 12(7) (2021)
Dupont, P., Milleret, C., Tourani, M., Brøseth, H., Bischof, R.
GPS collars have an apparent positive effect on the survival of a large carnivore Biology Letters 17 (2021)
Milleret, C., Bischof, R., Dupont, P., Brøseth, H., Odden, J., and Mattison, J.
Consequences of ignoring variable and spatially autocorrelated detection probability in spatial capture-recapture Landscape Ecology 12 (2021)
Moqanaki, E., Milleret, C., Tourani, M., Dupont, P., Bischof, R.
Efficient estimation of large‐scale spatial capture–recapture models. Ecosphere 12(2) (2021)
Turek, D., Milleret, C., Ergon, T., Brøseth, H., Dupont, P., Bischof, R., de Valpine P.
Estimating and forecasting spatial population dynamics of apex predators using transnational genetic monitoring Proceedings of the National Academy of Sciences (2020)
Turek, D., Milleret, C., Ergon, T., Brøseth, H., Dupont, P., Bischof, R., de Valpine P.
Estimating abundance with interruptions in data collection using open population spatial capture–recapture models Ecosphere 11(6) (2020)
Milleret, C., Dupont, P., Chipperfield, J., Turek, D., Brøseth, H., Gimenez, O., de Valpine, P., Bischof, R.
Consequences of ignoring group association in spatial capture–recapture analysis. Wildlife Biology (2020)
Bischof, R., Dupont, P., Milleret, C., Chipperfield, J., Royle J.A.
Population closure and the bias‐precision trade‐off in spatial capture–recapture. Methods in Ecology and Evolution (2019)
Dupont, P., Milleret, C., Gimenez, O. and Bischof, R.
A local evaluation of the individual state-space to scale up Bayesian spatial capture recapture. Ecology and Evolution (2019)
Milleret C., Dupont P., Bonenfant C., Brøseth H., Øystein F., Sutherland C., Bischof R.
Using partial aggregation in spatial capture recapture Methods in Ecology and Evolution (2018)
Milleret C., Dupont P., Brøseth H., Kindberg J., Royle J.A., Bischof R.