Large-scale spatial synchrony in red squirrel populations driven by a bottom-up effect

• Published in: Oecologia
• Main Authors: Turkia, Tytti, Jousimo, Jussi, Tiainen, Juha, Helle, Pekka, Rintala, Jukka, Hokkanen, Tatu, Valkama, Jari, Selonen, Vesa
• Other Authors: Biotieteiden ja Ympäristön Tutkimuksen Toimikunta
• Format: Article in Journal/Newspaper
• Language: English
• Published: Springer Science and Business Media LLC 2020
• Subjects: Ecology, Evolution, Behavior and Systematics; Norway; Accipiter gentilis; Northern Goshawk
• Online Access: http://dx.doi.org/10.1007/s00442-019-04589-5; http://link.springer.com/content/pdf/10.1007/s00442-019-04589-5.pdf; http://link.springer.com/article/10.1007/s00442-019-04589-5/fulltext.html

Abstract Spatial synchrony between populations emerges from endogenous and exogenous processes, such as intra- and interspecific interactions and abiotic factors. Understanding factors contributing to synchronous population dynamics help to better understand what determines abundance of a species. This study focuses on spatial and temporal dynamics in the Eurasian red squirrel ( Sciurus vulgaris ) using snow-track data from Finland from 29 years. We disentangled the effects of bottom-up and top-down forces as well as environmental factors on population dynamics with a spatiotemporally explicit Bayesian hierarchical approach. We found red squirrel abundance to be positively associated with both the abundance of Norway spruce ( Picea abies ) cones and the predators, the pine marten ( Martes martes ) and the northern goshawk ( Accipiter gentilis ), probably due to shared habitat preferences. The results suggest that red squirrel populations are synchronized over remarkably large distances, on a scale of hundreds of kilometres, and that this synchrony is mainly driven by similarly spatially autocorrelated spruce cone crop. Our research demonstrates how a bottom-up effect can drive spatial synchrony in consumer populations on a very large scale of hundreds of kilometres, and also how an explicit spatiotemporal approach can improve model performance for fluctuating populations.