Large-scale spatiotemporal variation in vital rates and population dynamics of an alpine bird

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Chloé R. Nater , James A. Martin, Erlend Birkeland Nilsen 


Quantifying temporal and spatial variation in animal population size and demography is a central theme in ecological research and important for directing management and policy. However, this requires field sampling at large spatial extents and over long periods of time, which is not only prohibitively costly but often politically untenable. Participatory monitoring programs (also called citizen science programmes) can alleviate these constraints by recruiting stakeholders and the public to increase the spatial and temporal resolution of sampling effort and hence resulting data. While the majority of participatory monitoring programs are limited by opportunistic sampling designs, we are starting to see the emergence of structures citizen science programs that employ trained volunteers to collect data according to standardized protocols. Simultaneously, there is much ongoing development of statistical models that are increasingly more powerful and able to make more efficient use of field data. Integrated population models (IPMs), for example, are able to use multiple streams of data from different field monitoring programmes and/or multiple aspects of single datasets to estimate population sizes and key vital rates. Here, we developed a multi-area version of a recently developed integrated distance sampling model (IDSM) and applied it to data from a large-scale participatory monitoring program – the “Hønsefuglportalen” – to study spatio-temporal variation in population dynamics of willow ptarmigan (Lagopus lagopus) in Norway. We constructed an open and reproducible workflow for exploring temporal, spatial (latitudinal, longitudinal, altitudinal), and residual variation in recruitment, survival, and population density, as well as relationships between vital rates and relevant covariates and signals of density dependence. Recruitment rates varied more across space than over time, while the opposite was the case for survival. Slower life history patterns (higher survival, lower recruitment) appeared to be more common at higher latitudes and altitudes, portending differential effects of climate change on ptarmigan across their range. While there was variation in the magnitude of the effect small rodent occupancy had on recruitment, the relationships were predominantly positive and thus consistent with the alternative prey hypothesis. Notably, the accurate estimation of covariate effect was only made possible by integrating data from several monitoring areas for analysis. Our study highlights the potential of participatory monitoring and integrated modelling approaches for estimating and understanding spatio-temporal patterns in species abundance and demographic rates, and showcases how corresponding workflows can be set up in a reproducible and semi-automated way that increases their usefulness for informing management and regular reporting towards national and international biodiversity frameworks.



Applied Statistics, Ecology and Evolutionary Biology, Population Biology, Statistical Models


population dynamics, workflow, Vital rates, Temporal variation, survival, spatiotemporal variation, spatial variation, recruitment, ptarmigan, Alpine, Pipeline, modelling, large-scale, Lagopus lagopus, integrated distance sampling model, IDSM, distance sampling, detection


Published: 2024-02-02 03:51

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CC BY Attribution 4.0 International

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Data and Code Availability Statement:
The raw data from the line transect surveys is deposited on GBIF and can be accessed freely via the Living Norway Data Portal ( The work presented above is based on versions 1.7, 1.8, and 1.12 for the datasets from Fjellstyrene (Nilsen, Vang, Kjønsberg, and J. 2022), Statskog (Nilsen, Vang, and I. 2022), and FeFo (Nilsen, Vang, Kjønsberg, and E. 2022), respectively. The auxiliary radio-telemetry data, rodent occupancy data, posterior summaries, and supplementary figures are archived on OSF (Nater, Nilsen, and Martin 2024). All code including the “targets pipeline” can be found in the project’s repository on GitHub: The results presented in this paper were created using version 2.0 of the code.