COS 118-5 - Seasonality in population dynamics and environmental drivers alters spatial synchrony

Background/Question/Methods

Spatial synchrony, the tendency for temporal population fluctuations to be correlated across locations, is ubiquitous to population dynamics and is of applied concern because it negatively affects the stability of metapopulations. One common cause of spatial synchrony is due to shared responses to environmental drivers that are correlated over space (i.e., the Moran effect). Prior work has often assumed that the spatial synchrony of environmental fluctuations and their effect on population dynamics are consistent over annual sampling intervals, even though the degree of spatial synchrony in environmental drivers can differ among seasons, and the effects of environmental conditions on population dynamics may be season specific. We used theoretical models to explore the relationship between seasonality and spatial synchrony and the consequences for studying spatial synchrony in empirical populations. We first develop an analytical solution to a linearization of a general population model. We then simulate special cases of the model to assess the robustness of our analytical theory to violations of analytical assumptions, and to explore how seasonal separation of population processes and exposure to multiple environmental drivers interact to shape spatial synchrony.

Results/Conclusions

Our theory predicts that spatial synchrony of populations exposed to season-specific drivers depends on the spatial synchrony of those drivers, and importantly also on “cross-season” synchrony, i.e., the correlations between the drivers in the same location and between locations. Hence, given seasonality, relationships between population spatial synchrony and environmental variation are more complex than often has been assumed. We also found that these effects depended substantially on whether population growth was under- or over-compensatory. Simulation experiments showed that these conclusions were robust to relaxing assumptions of linearity and small noise, that spatial synchrony of the same populations measured in different seasons can differ widely, and that the separation in time of population processes and environmental drivers jointly contribute to seasonality’s influence on spatial synchrony. Our work shows that a simple extension of classical theory on spatial synchrony to improve realism reveals substantial complexity and changes in expected population synchrony. Future work adding realistic nuance to theoretical models will likely strengthen understanding of spatial synchrony and improve empirical inference into factors that produce synchrony and modify its strength, a longstanding challenge.