Using geography to infer the importance of dispersal for the synchrony of freshwater plankton

Abstract

Spatial synchrony in population dynamics is a ubiquitous ecological phenomenon that can result from predator–prey interactions, synchronized environmental variation (Moran effects), or dispersal. Of these, dispersal historically has been the least well studied in natural systems, partly because of the difficulty in quantifying dispersal in situ. We hypothesized that dispersal routes of plankton were based on the major and consistent water current movements in Kentucky Lake, a large reservoir in western Kentucky, USA. Then, using 26‐year time series collected at 16 locations, we used matrix regression techniques to test whether spatial heterogeneity in strengths of hypothesized dispersal predicted spatial patterns of synchrony of phytoplankton and zooplankton, thereby testing for evidence of dispersal as a possible mechanism of synchrony in this system. Nearly all taxa showed significant spatial synchrony that did not decline with increasing linear distance between locations. All taxa also showed substantial geographic structure in synchrony that was not explained by linear distance. Matrix regression revealed that our hypothesized matrix of dispersal pathways, which differed substantially from linear distance, was a significant predictor of spatial variability in synchrony in phytoplankton biomass, and Bosmina longirostris and Daphnia lumholtzi densities. Thus dispersal was a likely mechanism of synchrony for these taxa. Our hypothesized dispersal matrix was a significant predictor of spatial patterns of synchrony for these taxa even after accounting for numerous alternative possible mechanisms, including possible Moran effects through any of ten physical/abiotic constraints. Our findings indicate that statistically comparing hypothesized or measured dispersal pathway information to synchrony data via matrix regressions can provide valuable evidence for the importance of dispersal as a mechanism of spatial synchrony.

The dependence of synchrony on timescale and geography in freshwater plankton

“…Thus, pairs for which dispersal was easy in one direction (0, e.g., upstream to downstream in the main channel) and hard in the other (2) have an intermediate final matrix entry value (1, the mean of 0 and 2), whereas sites on opposite sides of the main channel, which were characterized by difficult dispersal (2) in both directions even for sites directly across the lake from each other, received a final matrix entry value of 2. A previous analysis (Anderson et al ) found that our proxy matrix for dispersal connectivity was still an important predictor of spatial patterns of synchrony, even after controlling for the patterns of spatial synchrony of numerous environmental variables, providing indirect support that the matrix sufficiently characterizes broad movement patterns in KY Lake.…”

Spatial synchrony in sub‐arctic geometrid moth outbreaks reflects dispersal in larval and adult life cycle stages

“…Indeed, most studies of synchrony are based on time series that have been sampled for other purposes. Second, if processes that are linked to dispersal can be related to synchrony, a synchronizing effect of dispersal may be inferred (Anderson et al., ). For example, dispersal in many insect species is aided by wind (Straussfogel, Lindgren, Mitchell, Murphy, & Jackson, ).…”

Similarity in seasonal flow regimes, not regional environmental classifications explain synchrony in brown trout population dynamics in France

“…Analysing synchrony (i.e. The main reasons for synchrony between populations include correlated environmental variables (Ranta, Kaitala, Lindstrom, & Linden, 1995), trophic interactions (Ranta, Kaitala, & Lundberg, 1997), dispersal of individuals (Anderson et al, 2018;Ims & Andreassen, 2005), and commercial exploitation (Frank, Petrie, Leggett, & Boyce, 2016). The main reasons for synchrony between populations include correlated environmental variables (Ranta, Kaitala, Lindstrom, & Linden, 1995), trophic interactions (Ranta, Kaitala, & Lundberg, 1997), dispersal of individuals (Anderson et al, 2018;Ims & Andreassen, 2005), and commercial exploitation (Frank, Petrie, Leggett, & Boyce, 2016).…”