Project WildMap: Putting wildlife population dynamics on the map through spatially explicit estimation of abundance and demographic rates

What if we could map the density and dynamics of wildlife populations? And what if, akin to weather maps, we could then forecast the dynamics of these populations across space and time? Since 2019, project WildMap has been working with ecologists, computer scientists, and wildlife managers on changing how we quantify information about the status and future of populations of wild animals. The project has developed efficient computational methods that can take non-invasively collected monitoring data, such as DNA left behind by animals, and turn them into maps of population density and vital rates. We applied these methods to map the status and dynamics of wolves, bears, and wolverines in Scandinavia, as well as wildlife species in other European countries. The WildMap team was made up of researchers from Norway, Sweden, France, and the US. During the project we also collaborated closely with researchers in Germany, Czechia, Italy, and Australia. Breaking through computational barriers: During project WildMap, we developed and used powerful ecological models – so-called spatial-capture recapture or “SCR” models. These models estimate population density and dynamics in space and time. Until WildMap, the millions of calculations required made such analyses prohibitive across vast landscapes, such as entire countries. Through a series of conceptual developments and novel algorithms, we succeeded in breaking through the computational barriers and performed the most extensive population-dynamic analyses and mapping of wildlife populations to date. As a result of this work, an analysis that took 40 days before WildMap can now be done in 5 minutes. An important outcome of this effort was an open-source software package for building and fitting efficient SCR models, which is now being used by researchers in Norway, France, Spain, Germany, Italy, Canada, and the USA. Efficient wildlife monitoring at large scales: Monitoring elusive species across large areas – such as entire countries – is supremely challenging and costly. During project WildMap, we developed a framework for evaluating the impact of alternative wildlife monitoring designs and data analysis approaches. We then used this framework to compare approaches and ultimately improve data collection and analysis in terms of cost efficiency and robustness. Insights gained during the project are being integrated into the large carnivore monitoring programs in Scandinavia, wolf monitoring in Italy, and ungulate inventory in Germany. Wildlife populations in space and time: WildMap produced the first comprehensive maps and estimates of wolf, bear, and wolverine population densities across Scandinavia. In addition, together with our international collaborators, we generated maps and estimates of population densities of wolves across the Italian alps, brown bears in the French Pyrenees, red deer and chamois in Germany, and red deer in Czechia. Applications of the analytical framework has helped us reveal ecological patterns and processes, such as high spatial variability in wolf mortality, the role of historic persecution and current management on the spatial distribution and abundance wolverines, and the environmental and anthropogenic drivers of red deer density across the landscape, to mention a few. Forecasting: We have completed and published the first empirical demonstration of population forecasting using the analytical framework developed in the project, with the wolverine in Scandinavia as an example. Following this major milestone, we have expanded the analytical framework to allow mapping of survival and recruitment. This model forms the foundation for generating realistic and actionable forecasts of wildlife population dynamics across both space and time, akin to weather forecast maps. Applied perspective: Close collaboration with wildlife managers was a core feature of the project. Applied perspectives provided the motivation behind research questions, presented technical and conceptual challenges, and ultimately yielded a rigorous testing ground for theory, framework, and tools. In turn, methods and results from the project have now been incorporated into wildlife monitoring programs in Norway, Sweden, Germany, Italy, and France. As such, the project has and will continue to support wildlife policy decision making for the foreseeable future. WildMap has boosted our ability to quantify wildlife population dynamics and provide decision makers with informative maps and population estimates at relevant spatial scales. Knowledge exchange: The project attracted visits from researchers interested in learning and applying our methods for large scale estimation of wildlife population density and dynamics. During the project, we received visits lasting from scientists from the USA, Canada, Italy, and Germany to learn from and work with team members at NMBU. In addition, we have conducted a workshop teaching other researchers how to use the tool we developed.

Project WildMap has overcome significant conceptual and computational barriers to our understanding of wildlife population dynamics in space and time. With the framework and tools developed during the project, and given available data, we are now able to map and estimate the population dynamics of entire populations across vast landscapes. Technical advancements during project were integrated in a new software package (nimbleSCR) and are already benefiting researchers beyond our team, including groups in Germany, Italy, France, the USA, and Canada. In addition to mapping the density and dynamics of wild populations, our comprehensive framework can be used to obtain quantitative answers ecological questions at hitherto unprecedented scales. In the future, we expect to see more studies disentangling the drivers of wildlife population dynamics, both by our team and other groups. Close collaboration between ecologists, statisticians, and computer scientists has been mutually beneficial. Advances in statistical modelling and computation efficiency made during the project have contributed to improvements in a statistical modelling program (NIMBLE) which has become increasingly popular among ecologists and other scientists working with complex problems. During WildMap, we generated annual estimates of large carnivore populations, shared with Norwegian and Swedish management authorities via a technical report series and maps. Continuous communication and collaboration with managers have facilitated the generation of actionable results, as well as improved guidelines for large scale and transnational wildlife monitoring. This includes information about the status of carnivore populations across their entire range in Scandinavia, as well as quantitative support of management agencies in their efforts to develop fairer carnivore damage compensation schemes. Spreading beyond Scandinavia, the work has also begun and will continue to support wildlife management agencies, and thus policy, in Germany, France, and Italy. WildMap has grown from a comparatively small group of partners into an international collaborative initiative, with a commensurate increase in its scientific and applied scope, as well as geographic and societal reach. In a human-dominated and rapidly changing world, informed conservation and management of nature is no longer optional but a necessity. WildMap has made a significant contribution to further our understanding of the spatial-temporal dynamics of wildlife populations and thus support their sustainable management.

What if, akin to weather maps, we could forecast the dynamics of wildlife populations across space and time? During project WildMap, ecologists and computational scientists will join 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 natural resource 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. We will use a novel analytical tool, spatial capture-recapture, and apply it to two decades-worth of non-invasive monitoring data on some of the most emblematic and controversial large carnivore species: wolverine, wolf, and brown bear. This will be a complex undertaking with an immense scope covering entire populations in two countries, Norway and Sweden. To accomplish its goals, the project team will pair innovations in ecological data analysis with advances in computation that allow processing of massive amounts of information and performing millions of calculations. Project WildMap will improve our ability to quantify environmental effects on wildlife population dynamics, match ecological processes and interventions at relevant scales, and communicate results in intuitive form to decision makers.