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

  1. Chloé R. Nater
  2. Francesco Frassinelli
  3. James A. Martin
  4. Erlend B. Nilsen

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Abstract

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 structured 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 2integrated 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 reproducible and semi-automated ways that increase their usefulness for informing management and regular reporting towards national and international biodiversity frameworks.

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  1. Version published to 10.24072/pcjournal.494
  2. Peer Community in Ecology

    Population ecologists are in the business of decrypting the drivers of variation in the abundance of organisms across space and time (Begon et al. 1986). Comprehensive studies of wild vertebrate populations which provide the necessary information on variations in vital rates in relation to environmental conditions to construct informative models of large-scale population dynamics are rare, ostensibly because of the huge effort required to monitor individuals across ecological contexts and over generations. In this current aim, Nater et al. (2024) are leading the way forward by combining distance sampling data collected through a large-scale citizen science (Fraisl et al. 2022) programme in Norway with state-of-the-art modelling approaches to build a comprehensive overview of the population dynamics of willow ptarmigan. Their work enhances our fundamental understanding of this system and provides evidence-based tools to improve its management (Williams et al. 2002). Even better, they are working for the common good, by providing an open-source workflow that should enable ecologists and managers together to predict what will happen to their favourite model organism when the planet throws its next curve ball. In the case of the ptarmigan, for example, it seems that the impact of climate change on their population dynamics will differ across the species’ distributional range, with a slower pace of life (sensu Stearns 1983) at higher latitudes and altitudes. 

    On a personal note, I have often mused whether citizen science, with its inherent limits and biases, was just another sticking plaster over the ever-deeper cuts in the research budgets to finance long-term ecological research. Here, Nater et al. are doing well to convince me that we would be foolish to ignore such opportunities, particularly when citizens are engaged, motivated, with an inherent capacity for the necessary discipline to employ common protocols in a standardised fashion. A key challenge for us professional ecologists is to inculcate the next generation of citizens with a sense of their opportunity to contribute to a better understanding of the natural world.

    References

    Begon, Michael, John L Harper, and Colin R Townsend. 1986. Ecology: individuals, populations and communities. Blackwell Science.

    Fraisl, Dilek, Gerid Hager, Baptiste Bedessem, Margaret Gold, Pen-Yuan Hsing, Finn Danielsen, Colleen B Hitchcock, et al. 2022. Citizen Science in Environmental and Ecological Sciences. Nature Reviews Methods Primers 2 (1): 64. https://doi.org/10.1038/s43586-022-00144-4

    Chloé R. Nater, Francesco Frassinelli, James A. Martin, Erlend B. Nilsen (2024) Large-scale spatio-temporal variation in vital rates and population dynamics of an alpine bird. EcoEvoRxiv, ver.4 peer-reviewed and recommended by PCI Ecology https://doi.org/10.32942/X2VP6J

    Stearns, S.C. 1983. The influence of size and phylogeny of covariation among life-history traits in the mammals. Oikos, 41, 173–187. https://doi.org/10.2307/3544261

    Williams, Byron K, James D Nichols, and Michael J Conroy. 2002. Analysis and Management of Animal Populations. Academic Press.

  3. Version published to 10.32942/x2vp6j