Seasonality, density dependence, and spatial population synchrony

• Published in: Proceedings of the National Academy of Sciences
• Main Authors: Antunes Lopes Da Silva Nicolau, Pedro Guilherme, Ims, Rolf Anker, Sørbye, Sigrunn Holbek, Yoccoz, Nigel
• Format: Article in Journal/Newspaper
• Language: English
• Published: National Academy of Sciences 2022
• Subjects: Arctic; Climate change
• Online Access: https://hdl.handle.net/10037/28050; https://doi.org/10.1073/pnas.2210144119

Studies of spatial population synchrony constitute a central approach for understanding the drivers of ecological dynamics. Recently, identifying the ecological impacts of climate change has emerged as a new important focus in population synchrony studies. However, while it is well known that climatic seasonality and sequential density dependence influences local population dynamics, the role of season-specific density dependence in shaping large-scale population synchrony has not received attention. Here, we present a widely applicable analytical protocol that allows us to account for both season and geographic context-specific density dependence to better elucidate the relative roles of deterministic and stochastic sources of population synchrony, including the renowned Moran effect. We exemplify our protocol by analyzing time series of seasonal (spring and fall) abundance estimates of cyclic rodent populations, revealing that season-specific density dependence is a major component of population synchrony. By accounting for deterministic sources of synchrony (in particular season-specific density dependence), we are able to identify stochastic components. These stochastic components include mild winter weather events, which are expected to increase in frequency under climate warming in boreal and Arctic ecosystems. Interestingly, these weather effects act both directly and delayed on the vole populations, thus enhancing the Moran effect. Our study demonstrates how different drivers of population synchrony, presently altered by climate warming, can be disentangled based on seasonally sampled population time-series data and adequate population models.