Lawrence W. Sheppard’s research while affiliated with Marine Biological Association of the United Kingdom and other places

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Publications (38)


Insights into spatial synchrony enabled by long-term data
  • Preprint

July 2024

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28 Reads

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Lawrence Sheppard

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Spatial synchrony cascades across ecosystem boundaries and up food webs via resource subsidies
  • Article
  • Full-text available

January 2024

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167 Reads

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7 Citations

Proceedings of the National Academy of Sciences

Cross-ecosystem subsidies are critical to ecosystem structure and function, especially in recipient ecosystems where they are the primary source of organic matter to the food web. Subsidies are indicative of processes connecting ecosystems and can couple ecological dynamics across system boundaries. However, the degree to which such flows can induce cross-ecosystem cascades of spatial synchrony, the tendency for system fluctuations to be correlated across locations, is not well understood. Synchrony has destabilizing effects on ecosystems, adding to the importance of understanding spatiotemporal patterns of synchrony transmission. In order to understand whether and how spatial synchrony cascades across the marine-terrestrial boundary via resource subsidies, we studied the relationship between giant kelp forests on rocky nearshore reefs and sandy beach ecosystems that receive resource subsidies in the form of kelp wrack (detritus). We found that synchrony cascades from rocky reefs to sandy beaches, with spatiotemporal patterns mediated by fluctuations in live kelp biomass, wave action, and beach width. Moreover, wrack deposition synchronized local abundances of shorebirds that move among beaches seeking to forage on wrack-associated invertebrates, demonstrating that synchrony due to subsidies propagates across trophic levels in the recipient ecosystem. Synchronizing resource subsidies likely play an underappreciated role in the spatiotemporal structure, functioning, and stability of ecosystems.

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Example of interacting Moran effects. The example, which is based on the very simple model described in the Introduction, shows that interaction effects are possible, and can be substantially positive or negative. We used cov(ε1(1),ε2(1))=cov(ε1(2),ε2(2))=0.7, and the values cov(εi(1),εj(2)), for i,j = 1,2, were all set equal to each other and the common value appears on the horizontal axis – cross synchrony of the environmental variables. See the online version for color renderings of all figures.
Illustration of the main concept of synergistically or antagonistically interacting Moran effects. Interactions require that each environmental variable itself be spatially synchronous; and then the alignment or misalignment of three types of lag determine the sign and strength of the interactions. Solid‐line sinusoids represent the period‐20 components of an environmental variable in three locations (εi(1) for i = 1,2,3) and dashed‐line sinusoids represent the period‐20 components of a different environmental variable in the same locations (εi(2) for i = 1,2,3). Black arrows represent peak positive influences of environmental variables on populations, which are lagged by an amount le1 for εi(1) and by an amount le2 for εi(2), where these lags differ across the scenarios α1 and α2, but are the same in all locations within one of these scenarios. Analogously, red arrows represent maximally negative effects. Due to environmental synchrony, peak positive population effects of the same variable occur at similar times across locations, and likewise for peak negative effects, illustrated with blue and pink rectangles. In the synergistic scenario (α1), the lag between the environmental variables (ln) and the lags of the population effects of the variables (le1 and le2) are aligned, i.e. the environmental effect alignment measure, ln + le1 − le2 (main text), equals zero. So peak positive effects of εi(1) coincide with peak positive effects of εi(2) (the pink rectangles are aligned on a, b), augmenting synchrony. Likewise, negative effects are aligned (blue rectangles). In the antagonistic scenario (α2), lags are misaligned, i.e. ln + le1 − le2 = −σ/2, where σ = 20 is the period. So peak positive effects of εi(1) coincide with maximally negative effects of εi(2), and maximally negative effects of εi(1) coincide with peak positive effects of εi(2) (pink rectangles on c are aligned with blue ones on d, and vice versa), reducing synchrony. See the online version for color renderings of all figures.
Kelp sampling sites and example time series. Sampling sites (a) were from three regions, central California 1 (CCA1, blue points), central California 2 (CCA2, green points) and the region around Santa Barbara (SB, red points). Kelp density in 500 m coastline segments is shown with color intensity in (b)–(d), and those panels correspond to the regions. One example time series from each region is shown in (e)–(g), with locations at which these time series were measured labeled on panels (a)–(d). See the online version for color renderings of all figures.
Results from theoretical case studies A (panels a–b), B (c–d), C1 (e–f) and C2 (g–h). Left panels (a, c, e, g) show the terms on the right side of (5). On those panels, the green line is |fP(1)|2|fB|2ρε(1)ε(1), quantifying population synchrony due to the direct Moran effects of ε⁽¹⁾. The blue line is |fP(2)|2|fB|2ρε(2)ε(2), quantifying population synchrony due to the direct Moran effects of ε⁽²⁾. The dashed line is 2RefP(1)fP(2)¯ρε(1)ε(2)|fB|2, quantifying population synchrony due to interacting Moran effects. The functions plotted on b, d, f, h are those on a, c, e, g, respectively, times |fB|2, plotted to illustrate how autoregressive population effects modulate synchrony [see (5)]. For C1, peaks in the periodic noise process ε⁽²⁾ lagged peaks in the periodic process ε⁽¹⁾ by 1 quarter (e–f), and for C2, ε⁽¹⁾ lagged ε⁽²⁾ by the same amount (g–h). Synch. contrib. = Synchrony contribution refers to contributions to synchrony of the individual terms in our theory; Rel. synch. contrib. = Relative synchrony contribution refers to contributions expressed without accounting for the influence of autoregressive population effects. See the online version for color renderings of all figures.
The new theory as applied to kelp, central CA 1 (CCA1) region (a–c) and Santa Barbara (SB) region (d–f). Panels show the terms on the right side of (5). Green lines show |fP(1)|2|fB|2ρε(1)ε(1), quantifying kelp population synchrony due to the direct Moran effects of ε⁽¹⁾, which, in this context, is nitrates. The blue line is |fP(2)|2|fB|2ρε(2)ε(2), quantifying kelp population synchrony due to the direct Moran effects of ε⁽²⁾, which, in this context, is waves. The dashed green‐blue line is 2RefP(1)fP(2)¯ρε(1)ε(2)|fB|2, which is population synchrony due to interacting Moran effects. The red line is the sum of the green, blue and green‐blue lines, and is the portion of synchrony explained by nitrates, waves, and their interactions. Explained synchrony does not equal total kelp synchrony (black line) because other, unmeasured factors also help synchronize kelp dynamics. The timescale bands 0.5–2, 2–4, and >4 are separated on different panels because of the very different y‐axis ranges. The CCA1 results approximately parallel the results for theoretical case study C scenario 1 (Fig. 4e; text for details). See Supporting information for the central CA 2 (CCA2) region, for which results were substantially the same as for CCA1. This figure used kelp lag n = 4 [see (1)]; see analogue figures in Supporting information for n = 8,12. See the online version for color renderings of all figures.

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How environmental drivers of spatial synchrony interact

August 2023

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135 Reads

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8 Citations

Spatial synchrony, the tendency for populations across space to show correlated fluctuations, is a fundamental feature of population dynamics, linked to central topics of ecology such as population cycling, extinction risk, and ecosystem stability. A common mechanism of spatial synchrony is the Moran effect, whereby spatially synchronized environmental signals drive population dynamics and hence induce population synchrony. After reviewing recent progress in understanding Moran effects, we here elaborate a general theory of how Moran effects of different environmental drivers acting on the same populations can interact, either synergistically or destructively, to produce either substantially more or markedly less population synchrony than would otherwise occur. We provide intuition for how this newly recognized mechanism works through theoretical case studies and application of our theory to California populations of giant kelp. We argue that Moran interactions should be common. Our theory and analysis explain an important new aspect of a fundamental feature of spatiotemporal population dynamics.


How environmental drivers of spatial synchrony interact

May 2023

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72 Reads

Spatial synchrony, the tendency for populations across space to show correlated fluctuations, is a fundamental feature of population dynamics, linked to central topics of ecology such as population cycling, extinction risk, and ecosystem stability. A common mechanism of spatial synchrony is the Moran effect, whereby spatially synchronized environmental signals drive population dynamics and hence induce population synchrony. After reviewing recent progress in understanding Moran effects, we here elaborate a general theory of how Moran effects of different environmental drivers acting on the same populations can interact, either synergistically or destructively, to produce either substantially more or markedly less population synchrony than would otherwise occur. We provide intuition for how this newly recognized mechanism works through theoretical case studies and application of our theory to California populations of giant kelp. We argue that Moran interactions should be common. Our theory and analysis explain an important new aspect of a fundamental feature of spatiotemporal population dynamics.


Figure 3. Exposure indices for: a) fishing capture by size class; b) anthropogenic habitat 697
Habitat use differences mediate anthropogenic threat exposure in white sturgeon

September 2022

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204 Reads

Understanding intraspecific variation in habitat use, particularly of long-lived fishes across multiple life history stages, is core to improved conservation management. Here, we present results from a synthesis of acoustic telemetry data for sub-adult and adult white sturgeon (Acipenser transmontanus) from 2010 to 2017 in the San Francisco Estuary and Sacramento River ecosystems. We focused primarily on uncovering spatial patterns of inferred habitat occupancy across life stages, and on linking habitat use to extant anthropogenic threats. We found substantial differences in habitat use across individuals and over time that was related to fish size classes defined relative to the slot limit (102-152 cm) used to regulate recreational fishing. However, differences in habitat use were not explained by fish sex or water year flow conditions. We also estimated indices of overall exposure for two major threats: capture by anglers and habitat modification. Fish of harvestable size were detected less often than others in areas where many are caught. Future monitoring and management of white sturgeon might benefit from examining multiple phases of white sturgeon life history. For example, additional tracking studies could improve our understanding of juvenile habitat use, adult survival rates, patterns of anadromy, and cross-basin habitat utilization.


Map of the study region in California, USA, showing the distribution of all persistent mainland giant kelp, where each point location represents a 500‐m segment of coastline (n = 361; not to scale). Point Conception divides central California locations (blue) from southern California locations (red). Letters correspond with representative locations plotted in Figure 2
Giant kelp canopy biomass fluctuates synchronously over time, but patterns of synchrony differ by geography and timescale (i.e. period of fluctuations). Top panel shows quarterly giant kelp canopy biomass (log scale) from 1987 through 2019 for all locations (rows) ordered based on alongshore position (see Figure 1). Bottom panel shows biomass time series for three representative locations in each region (indicated by letters in top panel and in Figure 1)
Wavelet mean fields reveal synchrony of giant kelp canopy biomass in central (left) and southern (right) California across time and timescale. Top panels (a–b) show observed synchrony; nearly all features shaded cyan, green, and yellow are highly significant in wavelet phasor mean field testing of phase synchrony (p < 0.001; Appendix S3: Figure S3). Middle panels (c–d) show synchrony predicted by wavelet models based on wave disturbance (maximum wave height), nutrient availability (mean nitrate concentration), and their interaction. Bottom panels (e–f) summarise the top and middle panels by averaging observed (solid line) and model‐predicted (dashed line) timescale‐specific synchrony across all years (i.e. the mean squared synchrony), and comparing these observations and model predictions across timescales. Vertical lines separate timescale bands for annual (<2 y period), short interannual (2–4 y period) and long interannual (4–10 y period) synchrony. Note that the x‐axis shows the timescale of synchrony on a log scale
Wave disturbance, nutrient availability, and their interaction explain synchrony in giant kelp canopy biomass across timescales and regions. The black bar shows the total average percentage of synchrony explained by wavelet models (qall) and coloured bars show the partitioned contributions from maximum wave height (qwaves), mean nitrate concentration (qnutrients), and their interaction (qint). For each panel, coloured bars sum to the black bar. Values for the main effects of waves and nutrients are always positive, but can exceed 100% when offset by negative interactions (antagonism). Such antagonistic interactions indicate that the synchronising effects of waves and nutrients counteract one another to reduce synchrony below that attributable to additive effects alone. Likewise, positive interactions indicate synergy between waves and nutrients that enhance synchrony above that attributable to additive effects alone
Synchrony of giant kelp canopy biomass is partly explained by fluctuations in the North Pacific Gyre Oscillation (NPGO), an oceanographic climate index that corresponds with large‐scale strengthening of wind‐driven upwelling. Lines and axes as in Figure 3. Numbers at the top of each timescale band show the average percentage of synchrony explained by NPGO wavelet models within a given region and timescale band (qall; analogous to black bars for total synchrony explained in Figure 4)
Disturbance and nutrients synchronise kelp forests across scales through interacting Moran effects

June 2022

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261 Reads

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21 Citations

Ecology Letters

Spatial synchrony is a ubiquitous and important feature of population dynamics, but many aspects of this phenomenon are not well understood. In particular, it is largely unknown how multiple environmental drivers interact to determine synchrony via Moran effects, and how these impacts vary across spatial and temporal scales. Using new wavelet statistical techniques, we characterised synchrony in populations of giant kelp Macrocystis pyrifera, a widely distributed marine foundation species, and related synchrony to variation in oceanographic conditions across 33 years (1987–2019) and >900 km of coastline in California, USA. We discovered that disturbance (storm‐driven waves) and resources (seawater nutrients)—underpinned by climatic variability—act individually and interactively to produce synchrony in giant kelp across geography and timescales. Our findings demonstrate that understanding and predicting synchrony, and thus the regional stability of populations, relies on resolving the synergistic and antagonistic Moran effects of multiple environmental drivers acting on different timescales. Spatial synchrony is a ubiquitous feature of population dynamics, but it is largely unknown how multiple environmental drivers interact to determine synchrony via Moran effects, and how these impacts vary across spatial and temporal scales. Using new wavelet statistical techniques, we characterized synchrony in populations of giant kelp, a widely distributed marine foundation species, and related synchrony to variation in oceanographic conditions. We discovered that disturbance and resources—underpinned by climatic variability—act individually and interactively to produce synchrony across geography and timescales, demonstrating that predicting regional population stability relies on resolving the synergistic and antagonistic Moran effects of multiple environmental drivers acting on different timescales.


Positions of receivers throughout the San Francisco Bay, Sacramento River, and San Joaquin River systems during the 2006 through 2018 observation period used in this study.
Cumulative number of fish migration events (maximum = 117) commenced by a given day of a calendar year (journey day). Upriver migration (purple) was used to determine the calendar year migrations began, and this start year was applied to downriver migrations (blue); therefore, journey days >365 indicate a fish that migrated upriver in one calendar year, for example, 2013, and migrated downriver the following calendar year, for example, 2014. The distribution of (b) swim‐up and (c) swim‐down dates is shown with a smoother curve. The dashed line in panel (a) illustrates the cutoff we applied to distinguish between “early” and “late” departing fish based on the downriver migrations summarized in this figure. rkm, river kilometer.
Profiles of Sacramento River discharge rate (in cubic meters per second) and temperature (in degrees Celsius) over a 21‐day period surrounding the day of migration (14 days before and 7 days following migration). Profiles were created for (a) upriver migration dates, (b) “early” downriver migrations, and (c) “late” downriver migrations. Day 0 (dashed line) represents the date identified as the beginning of out‐migration. The black line in each panel represents the mean discharge rate or temperature (across all fish for each day), and each colored line represents an individual fish, over 22 days. Environmental measures were collected from two stations each for the upriver and downriver migrations (see Materials and methods for details).
Comparison of migration timings (journey days) for individual green sturgeon that were tracked making more than one complete migration during the study observation period. Correlations of (a) upriver migration days, (b) downriver migration days, and (c) upriver day and the corresponding downriver date for a given migration are shown. The line of the best fit is shown for correlations determined to be statistically significant. Only individuals with complete detection records for a given year from the time of entry to the Sacramento River were considered; that is, fish tagged mid‐migration in the river system were not included in these correlations because upriver journey days could not be determined (see Materials and methods).
Timing of swim down for fish observed making two downriver migrations during the 2006–2018 observation period. Dashed lines represent the journey day 250 cutoff used to classify early and late swim‐down groups. For fish tagged mid‐migration in the Sacramento River system, the swim‐down journey date is given assuming they began to migrate upriver during the calendar year of capture (see Materials and methods for details).
Intraspecific variation in migration timing of green sturgeon in the Sacramento River system

June 2022

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93 Reads

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9 Citations

Understanding movement patterns of anadromous fishes is critical to conservation and management of declining wild populations and preservation of habitats. Yet, the duration of observations for individual animals can constrain accurate descriptions of movements. In this study, we synthesized over a decade (2006–2018) of acoustic telemetry tracking observations of green sturgeon (Acipenser medirostris) in the Sacramento River system to describe major anadromous movement patterns. We observed that green sturgeon exhibited a unimodal in‐migration during the spring months but had a bimodal distribution of out‐migration timing, split between an “early” out‐migration (32%) group during May–June, or, alternatively, holding in the river until a “late” out‐migration (68%), November–January. Focusing on these out‐migration groups, we found that river discharge, but not water temperature, may cue the timing of migration and that fish showed a tendency to maintain out‐migration timing between subsequent spawning migration events. We recommend that life history descriptions of green sturgeon in this region reflect the distinct out‐migration periods described here. Furthermore, we encourage the continued use of biotelemetry to describe migration timing and life history variation, in not only this population but also other green sturgeon populations and other species.


Periodic synchronisation of dengue epidemics in Thailand over the last 5 decades driven by temperature and immunity

March 2022

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182 Reads

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11 Citations

The spatial distribution of dengue and its vectors (spp. Aedes) may be the widest it has ever been, and projections suggest that climate change may allow the expansion to continue. However, less work has been done to understand how climate variability and change affects dengue in regions where the pathogen is already endemic. In these areas, the waxing and waning of immunity has a large impact on temporal dynamics of cases of dengue haemorrhagic fever. Here, we use 51 years of data across 72 provinces and characterise spatiotemporal patterns of dengue in Thailand, where dengue has caused almost 1.5 million cases over the last 30 years, and examine the roles played by temperature and dynamics of immunity in giving rise to those patterns. We find that timescales of multiannual oscillations in dengue vary in space and time and uncover an interesting spatial phenomenon: Thailand has experienced multiple, periodic synchronisation events. We show that although patterns in synchrony of dengue are similar to those observed in temperature, the relationship between the two is most consistent during synchronous periods, while during asynchronous periods, temperature plays a less prominent role. With simulations from temperature-driven models, we explore how dynamics of immunity interact with temperature to produce the observed patterns in synchrony. The simulations produced patterns in synchrony that were similar to observations, supporting an important role of immunity. We demonstrate that multiannual oscillations produced by immunity can lead to asynchronous dynamics and that synchrony in temperature can then synchronise these dengue dynamics. At higher mean temperatures, immune dynamics can be more predominant, and dengue dynamics more insensitive to multiannual fluctuations in temperature, suggesting that with rising mean temperatures, dengue dynamics may become increasingly asynchronous. These findings can help underpin predictions of disease patterns as global temperatures rise.


Environmental drivers operating on different timescales can create timescale‐specific synchronous and compensatory dynamics. Environmental drivers may operate over (a) short timescales, (b) long timescales, and (c) in combination. (d) Shared species responses to a single, high‐frequency driver result in high species synchrony and unstable total biomass, whereas (e) opposite responses to a single, low‐frequency driver result in compensatory dynamics and stable total biomass. The combination of these responses results in (f) synchrony at short timescales and compensatory dynamics at long timescales. As a result, (g) a timescale‐specific variance ratio differentiates these dynamics, whereas the classic variance ratio does not reflect short timescale synchrony
Applying the timescale‐specific synchrony metric to a case study at Jasper Ridge, California, USA. (a) Averaged timeseries (± SE) of two native annual forbs Plantago erecta and Microseris douglasii before and after gopher disturbance (disturbance occurred during the gray bar at time 1). (b) Short timescale, long timescale, and classic variance ratio for P. erecta and M. douglasii communities (average value of the metric after it was calculated on individual timeseries ± SE). (c) Averaged timeseries (± SE) of a native annual forb P. erecta and native perennial grass Elymus glaucus before and after gopher disturbance (gray band). (d) Short timescale, long timescale, and classic variance ratio for P. erecta and E. glaucus communities (± SE)
Differences in the growth rates of species can alter the timescale of synchrony, even when species share the same directional response to the environment. (a) Growth curves of species with different density‐independent growth rates, r. The black species tracks the environment, the tan species exhibits a lagged response, and the pink species responds rapidly enough to create dampened internal oscillations. (b–d) Population dynamics among species that share directional responses to (e) two timescale‐specific environmental drivers. (f–h) The resulting timescale‐specific and classic variance ratios
Spatial connectivity can alter local and landscape patterns of synchrony. (a) A short timescale driver operates in Patch 1 and (b, c) species dynamics in Patch 1 without (b) and with (c) dispersal from neighboring patch 2. Species 1 (black) and species 2 (gray) both respond to the local driver in similar ways. (d) A long timescale driver operates in Patch 2 and (e, f) species dynamics in Patch 2 without (e) and with (f) dispersal. (g, h) Aggregate species and total biomass dynamics at the landscape scale without (g) and with (h) dispersal. (i, j) Timescale‐specific (short in blue and long in green) and classic variance ratios (teal) for the above communities. (i) Without dispersal, local drivers dominate, leading to synchronous dynamics in patch 1 and compensatory dynamics in patch 2. Within patches, the classic variance ratio tracks these dynamics and, at the landscape scale, it reflects an aggregate of the two patches. (j) With reciprocal dispersal, landscape‐level synchrony remains the same but patch level dynamics vary, with the classic variance ratio representative of the dynamics of the focal patch
Climate change has the potential to alter the strength and timescale of synchrony. (a) A climate driver such as temperature may vary over different timescales, often with both a low frequency (green) and high frequency component (blue). (b–d) The overall environmental driver and species’ sensitivities under a stable, changing, and altered climate. Species 1 responds to all variability (red and purple), whereas species 2 only responds to the driver above a threshold level (red). The environmental threshold is depicted by the dashed gray line. (e–g) The abundance of species 1 (black), species 2 (gray), and their aggregate abundance (red) in response to the above environmental driver. (h–j) Variance ratios for the above communities. With stable climate (b, h), species have different threshold responses, yielding compensatory dynamics on long timescales. Species becomes increasingly synchronized as climate means increase (c, i), causing species 2 to respond to the driver more frequently. Finally, under a stable climate with an elevated mean, both species become synchronized by environmental variability across timescales (d, j)
The long and the short of it: Mechanisms of synchronous and compensatory dynamics across temporal scales

March 2022

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265 Reads

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25 Citations

Synchronous dynamics (fluctuations that occur in unison) are universal phenomena with widespread implications for ecological stability. Synchronous dynamics can amplify the destabilizing effect of environmental variability on ecosystem functions such as productivity, whereas the inverse, compensatory dynamics, can stabilize function. Here we combine simulation and empirical analyses to elucidate mechanisms that underlie patterns of synchronous versus compensatory dynamics. In both simulated and empirical communities, we show that synchronous and compensatory dynamics are not mutually exclusive but instead can vary by timescale. Our simulations identify multiple mechanisms that can generate timescale‐specific patterns, including different environmental drivers, diverse life histories, dispersal, and non‐stationary dynamics. We find that traditional metrics for quantifying synchronous dynamics are often biased toward long‐term drivers and may miss the importance of short‐term drivers. Our findings indicate key mechanisms to consider when assessing synchronous versus compensatory dynamics and our approach provides a pathway for disentangling these dynamics in natural systems.


Tail‐dependent spatial synchrony arises from nonlinear driver–response relationships

March 2022

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316 Reads

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12 Citations

Ecology Letters

Spatial synchrony may be tail‐dependent, that is, stronger when populations are abundant than scarce, or vice‐versa. Here, ‘tail‐dependent’ follows from distributions having a lower tail consisting of relatively low values and an upper tail of relatively high values. We present a general theory of how the distribution and correlation structure of an environmental driver translates into tail‐dependent spatial synchrony through a non‐linear response, and examine empirical evidence for theoretical predictions in giant kelp along the California coastline. In sheltered areas, kelp declines synchronously (lower‐tail dependence) when waves are relatively intense, because waves below a certain height do little damage to kelp. Conversely, in exposed areas, kelp is synchronised primarily by periods of calmness that cause shared recovery (upper‐tail dependence). We find evidence for geographies of tail dependence in synchrony, which helps structure regional population resilience: areas where population declines are asynchronous may be more resilient to disturbance because remnant populations facilitate reestablishment.


Citations (24)


... Macroinvertebrates of the upper beach typically include talitrid amphipods, isopods, flies, and beetles, which can be extremely abundant depending on the availability of macroalgal wrack (Dugan et al. 2003;Lastra et al. 2008;Schooler 2018). The use of upper beach macroinvertebrates by surf zone fish could vary among locations and over time, depending on variability in wrack inputs, beach characteristics, and management (Revell et al. 2011;Liebowitz et al. 2016;Schooler et al. 2019;Walter et al. 2024), and the accessibility of these mobile prey to fish (Dugan et al. 2013;Emery et al. 2022). Macroalgal carbon could also enter the surf zone food web through reefassociated grazers that feed on kelp or kelp detritus (e.g., the isopod, Idotea spp.) exploited by surf zone fish (Crawley and Hyndes 2007;Andrades et al. 2014; Baring et al. 2018). ...

Reference:

Cross-ecosystem trophic subsidies to sandy beaches support surf zone fish
Spatial synchrony cascades across ecosystem boundaries and up food webs via resource subsidies

Proceedings of the National Academy of Sciences

... In masting plants, a major mechanism governing the annual allocation of resources to seed production involves nonlinear responses of seed production to weather variations, known as weather cues (Kelly et al., 2013;Pearse et al., 2016). Consequently, the regional synchronization of masting arises from the Moran effect, that is, spatially correlated fluctuations in environmental drivers of masting (Ascoli et al., 2017;Bogdziewicz, Hacket-Pain, Ascoli, & Szymkowiak, 2021;Koenig & Knops, 2013;LaMontagne et al., 2020;Reuman et al., 2023;Wion et al., 2020). The mechanisms underlying F I G U R E 1 A graphical representation of the hypothetical association of seed production and weather cue and its consequence for spatial synchrony of mast seeding. ...

How environmental drivers of spatial synchrony interact

... Land use change alters deep soil water and carbon, which are jointly regulated by multivariate controls of soil properties, especially within thick loess deposits (Dai et al. 2024). Currently, wavelet analysis has been applied in many fields, such as hydrology, ecology, and earth science (Carey et al. 2013;Castorani et al. 2022). Based on the continuous wavelet transform, the results of bivariate (BWC) and multivariate wavelet coherence (MWC) could reveal the independent and combined effects of multiple variables at various scales. ...

Disturbance and nutrients synchronise kelp forests across scales through interacting Moran effects

Ecology Letters

... water flow discharge (Lazzaro et al. 2017), water temperature (Arevalo et al. 2021), nutrient limitation (Bernthal et al. 2022), environmental heterogeneity or genetic predisposition (Alò et al. 2021), and social interactions (Berdahl, Westley, and Quinn 2017). It is infeasible to fully describe the fish migration mechanism due to a number of random biological phenomena that can affect both inter-and intra-migration events, as suggested in the reported data for diverse case studies (Amtstaetter et al. 2021;Colborne et al. 2022;Fennell et al. 2023;Kaeding and Mogen 2023;Smith et al. 2021;Swanson et al. 2021). A concise-but nonetheless sufficiently realistic-stochastic process model is therefore essential for the analysis of fish migration. ...

Intraspecific variation in migration timing of green sturgeon in the Sacramento River system

... Seasonal climate cycles are a strong predictor of annual cycling for many arboviruses, including dengue ( 10 , 11 ), and climate has been implicated as a possible driver of multiannual dengue periodicity, as well. In Thailand, multiannual dengue cycles demonstrate coherence with El Niño phenomena ( 29 ), and epidemic years exhibit more synchronized dynamics across latitudes ( 29 ), as well as higher correlation with local temperature than do interepidemic periods ( 28 ). The inter action of demography and heterotypic immunity is also thought to play a role in driving multiannual dengue cycles ( 24 , 26 , 30 ), which, in Thailand, show elongated periodicity as a result of declining birth rates and slower build-up of the susceptible pop ulation over the past half-century ( 24 ). ...

Periodic synchronisation of dengue epidemics in Thailand over the last 5 decades driven by temperature and immunity

... Here, we examine this hypothesis. We focused our investigation on giant kelp Macrocystis pyrifera, a broadly distributed marine foundation species that has served as an effective system for studies of synchrony in natural populations Cavanaugh et al., 2013;Reuman et al., 2023;Walter et al., 2022Walter et al., , 2024. Giant kelp is patchily distributed on rocky reefs in shallow coastal seas and demographically linked by ocean currents that disperse kelp spores (Castorani et al., 2015(Castorani et al., , 2017Reed et al., 2006). ...

Tail‐dependent spatial synchrony arises from nonlinear driver–response relationships

Ecology Letters

... Variation in species' responses to temporal changes in the environment can generate compensatory dynamics, allowing aggregate community properties (such as total biomass) to remain constant while the abundances of individual species fluctuate through time (Doak et al., 1998;Schindler et al., 2015). Finally, species with opposite responses to the same variables can coexist if their abundances increase and decrease with differences in the timing of favorable conditions (e.g., wet and dry seasons) (Gonzalez & Loreau, 2009;Shoemaker et al., 2022). Future work will disentangle the relative importance of these mechanisms in driving the patterns we documented here using manipulative experiments or modeling approaches. ...

The long and the short of it: Mechanisms of synchronous and compensatory dynamics across temporal scales

... Synchrony in life history patterns within and among species is a complex phenomenon shaped by ecological factors such as dispersal (Hopson and Fox 2019;Luo et al. 2021), density dependence (Loreau and de Mazancourt 2008), predator-prey relationships (Jarillo et al. 2020), competition (Jarillo et al. 2018) or exploitation (Morrongiello et al. 2021). For example, increased fisheries harvest can truncate demographic structure and increase populations' sensitivity to the environment, amplifying spatial synchrony among targeted stocks (Brander 2007;Morrongiello et al. 2021). ...

The effects of dispersal on spatial synchrony in metapopulations differ by timescale
  • Citing Article
  • August 2021

Oikos

... Intense colors on the plot indicate strong synchrony at the given time and timescale. This method and a suite of closely related and now well developed methods have been applied numerous times to study synchrony of ecological time series 15,[49][50][51][52][53][54][55][56] , and the methods are implemented, open source, in the wsyn package on CRAN 48 . The wsyn package includes a "vignette" which gives a straightforward, operational introduction to the methods implemented therein. ...

Synchronous effects produce cycles in deer populations and deer‐vehicle collisions

Ecology Letters

... The reason is as some extreme heatwave happens (shown by red dashed vertical line) the first three species (i = 1, 2, 3) cannot tolerate that high heat and would show extreme low abundance, whereas the last two species (i = 4, 5) would show extreme high abundances. On the contrary, we can expect a relatively higher value of tail-dependent synchrony during an extreme heat event if a community has species with similar tolerance limits or environmental thresholds [25][26][27] . For example, imagine the community shown in Fig. 1c now comprises the first three species only, i = 1, 2, 3. ...

A new approach to interspecific synchrony in population ecology using tail association