The Project
What if, akin to weather maps, we could measure and forecast the dynamics of wildlife populations across space and time? With project WildMap, ecologists and computational scientists have joined forces to make this a reality. Estimates of abundance and vital rates of wildlife populations help reconstruct their past, assess their present status, and predict their future. "How many?" and "What happens if...?" are the questions that many inquiries from ecologists and wildlife managers boil down to.
Project WildMap takes the leap from overwhelmingly aggregate answers to these questions – point estimates and time series - towards scale-transcending maps of abundance and vital rates. As part of the project, we produce population estimates for carnivores (brown bear, wolf, wolverine) and ungulates (red deer, roe deer, chamois) in five European countries (Norway, Sweden, Gemany, Italy, and Czechia).
In the long run, the foundation laid by project WildMap will improve our ability to quantify environmental effects on wildlife population dynamics, as well as match ecological processes and interventions at relevant scales.
WildMap advances the theory and methods for mapping and forecasting wildlife population dynamics in space and time.
As part of this work, we:
1. challenge the computational barriers to large-scale mapping of population dynamics.
2. develop general rules for efficient wildlife monitoring at the level of landscapes and populations.
3. Quantify spatio-temporal patterns in population dynamics and identify their drivers at multiple scales.
4. Generate spatially-explicit forecasts of wildlife population dynamics under alternative management scenarios.
NMBU Team Members
Former NMBU Team Members
Mahdieh Tourani
University of Montana
Jospeh Chipperfield
Norsk institutt for naturforskning
External Team Members
Andrew Royle
United States Geological Survey
Jonas Kindberg
Norsk institutt for naturforskning
Henrik Brøseth
Norsk institutt for naturforskning
Wei Zhang
University of Glasgow
Perry de Valpine
UC Berkeley
Daniel Turek
Williams College
Olivier Gimenez
Centre D'Ecologie Fonctionnelle & Evolutive
Collaborators
Publications
- Milleret, Cyril, Dey, Soumen, Dupont, Pierre, Brøseth, Henrik, Turek, Daniel, de Valpine, Perry, and Bischof, Richard. 2023. “ Estimating Spatially Variable and Density-Dependent Survival Using Open-Population Spatial Capture–Recapture Models.” Ecology 104(2): e3934.
- Edelhoff, H., Milleret, C., Ebert, C., Dupont, P., Kudernatsch, T., Zollner, A., … & Peters, W. (2023). Sexual segregation results in pronounced sex-specific density gradients in the mountain ungulate, Rupicapra rupicapra. Communications Biology, 6(1), 979.
- Moqanaki, E., Milleret, C., Dupont, P., Brøseth, H. & Bischof, R. (2023) Wolverine density distribution reflects past persecution and current management in Scandinavia. Ecography, e06689.
- Zhang, W., Chipperfield, J. D., Illian, J. B., Dupont, P., Milleret, C., de Valpine, P., & Bischof, R. (2023). A flexible and efficient B ayesian implementation of point process models for spatial capture–recapture data. Ecology, 104(1), e3887.
- Dey, S., Bischof, R., Dupont, P. P., & Milleret, C. (2022). Does the punishment fit the crime? Consequences and diagnosis of misspecified detection functions in Bayesian spatial capture–recapture modeling. Ecology and Evolution, 12(2), e8600.
- Dupont, P., Milleret, C., Tourani, M., Brøseth, H., & Bischof, R. (2021). Integrating dead recoveries in open‐population spatial capture–recapture models. Ecosphere, 12(7), e03571.
- Milleret, C., Bischof, R., Dupont, P., Brøseth, H., Odden, J., & Mattisson, J. (2021). GPS collars have an apparent positive effect on the survival of a large carnivore. Biology Letters, 17(6), 20210128.
- Moqanaki, E. M., Milleret, C., Tourani, M., Dupont, P., & Bischof, R. (2021). Consequences of ignoring variable and spatially autocorrelated detection probability in spatial capture-recapture. Landscape Ecology, 36(10), 2879-2895.
- Turek, D., Milleret, C., Ergon, T., Brøseth, H., Dupont, P., Bischof, R., & De Valpine, P. (2021). Efficient estimation of large‐scale spatial capture–recapture models. Ecosphere, 12(2), e03385.
- Bischof, R., Milleret, C., Dupont, P., Chipperfield, J., Tourani, M., Ordiz, A., … & Kindberg, J. (2020). Estimating and forecasting spatial population dynamics of apex predators using transnational genetic monitoring. Proceedings of the National Academy of Sciences, 117(48), 30531-30538.
- Milleret, C., Dupont, P., Chipperfield, J., Turek, D., Brøseth, H., Gimenez, O., … & Bischof, R. (2020). Estimating abundance with interruptions in data collection using open population spatial capture–recapture models. Ecosphere, 11(7), e03172.
- Bischof, R., Dupont, P., Milleret, C., Chipperfield, J., & Royle, J. A. (2020). Consequences of ignoring group association in spatial capture–recapture analysis. Wildlife Biology, 2020(1), 1-10.
- Dupont, P., Milleret, C., Gimenez, O., & Bischof, R. (2019). Population closure and the bias‐precision trade‐off in spatial capture–recapture. Methods in Ecology and Evolution, 10(5), 661-672.
- Milleret, C., Dupont, P., Bonenfant, C., Brøseth, H., Flagstad, Ø., Sutherland, C., & Bischof, R. (2019). A local evaluation of the individual state‐space to scale up Bayesian spatial capture–recapture. Ecology and evolution, 9(1), 352-363.
- Milleret, C., Dupont, P., Brøseth, H., Kindberg, J., Royle, J. A., & Bischof, R. (2018). Using partial aggregation in spatial capture recapture. Methods in Ecology and Evolution, 9(8), 1896-1907.
