Synchrony is more than its top-down and climatic parts: interacting Moran effects on phytoplankton in British seas

Abstract

Large-scale spatial synchrony is ubiquitous in ecology. We examined 56 years of data representing chlorophyll density in 26 areas in British seas monitored by the Continuous Plankton Recorder survey. We used wavelet methods to disaggregate synchronous fluctuations by timescale and determine that drivers of synchrony include both biotic and abiotic variables. We tested these drivers for statistical significance by comparison with spatially synchronous surrogate data. Identification of causes of synchrony is distinct from, and goes beyond, determining drivers of local population dynamics. We generated timescale-specific models, accounting for 61% of long-timescale (> 4yrs) synchrony in a chlorophyll density index, but only 3% of observed short-timescale (< 4yrs) synchrony. Thus synchrony and its causes are timescale-specific. The dominant source of long-timescale chlorophyll synchrony was closely related to sea surface temperature, through a climatic Moran effect, though likely via complex oceanographic mechanisms. The top-down action of Calanus finmarchicus predation enhances this environmental synchronising mechanism and interacts with it non-additively to produce more long-timescale synchrony than top-down and climatic drivers would produce independently. Our principal result is therefore a demonstration of interaction effects between Moran drivers of synchrony, a new mechanism for synchrony that may influence many ecosystems at large spatial scales.

Scaling‐up biodiversity‐ecosystem functioning research

“…The first is forcing caused by fluxes in energy which varies considerably across scales on land and in the oceans (Carrara & V azquez 2010;Vogt et al 2011;Acevedo-Trejos et al 2018) and is known to be synchronising when driven by strong periodic cycles (Blauw et al 2018), disturbances (Keitt 2008) and autocorrelated random fluctuations (Petchey et al 1997). This environmental variation engenders different compensatory responses among species or functional groups at different scales, reflecting variation in seasonal and interannual phenology (Thackeray et al 2010;Lasky et al 2016) and asynchronous population fluctuations across trophic levels (Fontaine & Gonzalez 2005;Keitt & Fischer 2006;Vasseur & Gaedke 2007;Loreau & de Mazancourt 2008;Fauchald et al 2011;Vasseur et al 2014;Sheppard et al 2019). Second, movement and connectivity across scales can synchronise population fluctuations, even when separated by great distances.…”

The Moran effect revisited: spatial population synchrony under global warming

“…In more recent years, substantial effort has gone into identifying the weather patterns and variables that have a synchronizing effect on population dynamics in specific systems. This covers species from a wide range of taxa, such as feral sheep Ovies aries (Grenfell et al 1998), roe deer Capreolus capreolus (Grøtan et al 2005), caribou and reindeer Rangifer tarandus (Post and Forchhammer 2002, 2004, 2006, Hansen et al 2019), passerine birds (Sæther et al 2007), fishes (Cattanéo et al 2003, Tedesco et al 2004), moths (Allstadt et al 2015), aphid pests (Sheppard et al 2016), plants (Koenig and Knops 1998, 2013, Defriez and Reuman 2017), giant kelp Macrocystis pyrifera (Cavanaugh et al 2013), zooplankton (Defriez et al 2016) and phytoplankton (Sheppard et al 2019). Still, almost two decades after Post and Forchhammer (2002) first pointed out the potential importance of climate change, only a handful of studies have attempted to look at how changes in the climate, weather variables and environment over time might influence spatial population synchrony (Post and Forchhammer 2004, Jepsen et al 2009, Allstadt et al 2015, Defriez et al 2016, Koenig and Liebhold 2016, Sheppard et al 2016, Shestakova et al 2016, Defriez and Reuman 2017, Kahilainen et al 2018).…”

Weather and regional crop composition variation drive spatial synchrony of lepidopteran agricultural pests

“…Significant coherence between pest abundance and climate occurred more often at long timescales (i.e. period lengths > 4 years), suggesting that long‐timescale synchrony may be easier to explain in terms of climate drivers than short‐timescale synchrony, but the same pattern has not necessarily been observed in other systems (Anderson et al ., ; Sheppard et al ., ). Although spring and summer weather influenced dynamics of many pests, the variety of phase differences and timescale‐specific relationships exemplify how species‐specific ecology and physiology can underpin particular responses to climate variability and change (Haynes et al ., ; Walter et al ., ).…”

“…Again, short and long timescales were considered separately. Wavelet linear models extend coherence testing from examining the relationship between a pair of variables to examining effects of multiple predictors on a response variable by finding timescale‐specific, complex‐valued coefficients that maximise the coherence between a response variable and one or more predictors (Sheppard et al ., ). The difference between wavelet spatial coherence and wavelet linear models is analogous to the difference between correlation and multiple linear regression.…”

“…We used spatial wavelet coherences (Sheppard et al ., , ) and wavelet linear models (Sheppard et al ., ) to assess how weather and crop composition drive synchronous, area‐wide pest abundance fluctuations. We tested for spatial wavelet coherence to identify major drivers of pest population dynamics, and used wavelet linear models, in conjunction with the wavelet Moran theorem (Sheppard et al ., ), to determine the percentage of spatial synchrony in pest abundances that was explained by that combination of environmental drivers.…”