Dispersal, Environmental Correlation, and Spatial Synchrony in Population Dynamics

Kendall,; Bjørnstad,; Bascompte,; Keitt,; Fagan,

doi:10.2307/3078985

Cited by 127 publications

(190 citation statements)

References 28 publications

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“…The spatial scales of alternative stable states can also increase with the spatial extent of environmental variation via environmentally induced synchrony (Fig. Analogous dynamics occur in stochastic population models with single equilibria (Kendall et al 2000), highlighting how extensive research on population synchrony might inform the spatial scales of alternative stable ecosystem states. However, largerscale disturbances can decrease the spatial extent of both FIG .…”

Section: Discussionmentioning

confidence: 93%

At what spatial scales are alternative stable states relevant in highly interconnected ecosystems?

Karatayev

Baskett

2019

Ecology

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Citation: Karatayev, V. A., and M. L. Baskett. 2020. At what spatial scales are alternative stable states relevant in highly interconnected ecosystems? Ecology 101(2):Abstract. Whether ecosystems recover from disturbance depends on the presence of alternative stable states, which are theoretically possible in simple models of many systems. However, definitive empirical evidence for this phenomenon remains limited to demographically closed ecosystems such as lakes. In more interconnected systems such as temperate rocky reefs, the local relevance of alternative stable states might erode as immigration overwhelms local feedbacks and produces a single stable state. At larger spatial scales, dispersal might counter localized disturbance and feedbacks to synchronize states throughout a region. Here, we quantify how interconnectedness affects the relevance of alternative stable states using dynamical models of California rocky reef communities that incorporate observed environmental stochasticity and feedback loops in kelp-urchin-predator interactions. Our models demonstrate the potential for localized alternative states despite high interconnectedness likely due to feedbacks affecting dispersers as they settle into local communities. Regionally, such feedbacks affecting settlement can produce a mosaic of alternative stable states that span local (10-20 km) scales despite the synchronizing effect of long-distance dispersal. The specific spatial scale and duration of each state predominantly depend on the scales of environmental variation and on local dynamics (here, fishing). Model predictions reflect observed scales of community states in California rocky reefs and suggest how alternative states co-occur in the wide array of marine and terrestrial systems with settlement feedbacks.

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Section: Discussionmentioning

confidence: 93%

At what spatial scales are alternative stable states relevant in highly interconnected ecosystems?

Karatayev

Baskett

2019

Ecology

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“…High densities in the core then result in elevated growth rates in and dispersal to the periphery. Dispersal between patches potentially synchronize population dynamics among different habitat patches [62], [63]. The result is the appearance of density-dependent regulation in the periphery.…”

Section: Discussionmentioning

confidence: 99%

Density-Dependent Regulation of Brook Trout Population Dynamics along a Core-Periphery Distribution Gradient in a Central Appalachian Watershed

Huntsman

Petty

2014

PLoS ONE

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Spatial population models predict strong density-dependence and relatively stable population dynamics near the core of a species' distribution with increasing variance and importance of density-independent processes operating towards the population periphery. Using a 10-year data set and an information-theoretic approach, we tested a series of candidate models considering density-dependent and density-independent controls on brook trout population dynamics across a core-periphery distribution gradient within a central Appalachian watershed. We sampled seven sub-populations with study sites ranging in drainage area from 1.3–60 km2 and long-term average densities ranging from 0.335–0.006 trout/m. Modeled response variables included per capita population growth rate of young-of-the-year, adult, and total brook trout. We also quantified a stock-recruitment relationship for the headwater population and coefficients of variability in mean trout density for all sub-populations over time. Density-dependent regulation was prevalent throughout the study area regardless of stream size. However, density-independent temperature models carried substantial weight and likely reflect the effect of year-to-year variability in water temperature on trout dispersal between cold tributaries and warm main stems. Estimated adult carrying capacities decreased exponentially with increasing stream size from 0.24 trout/m in headwaters to 0.005 trout/m in the main stem. Finally, temporal variance in brook trout population size was lowest in the high-density headwater population, tended to peak in mid-sized streams and declined slightly in the largest streams with the lowest densities. Our results provide support for the hypothesis that local density-dependent processes have a strong control on brook trout dynamics across the entire distribution gradient. However, the mechanisms of regulation likely shift from competition for limited food and space in headwater streams to competition for thermal refugia in larger main stems. It also is likely that source-sink dynamics and dispersal from small headwater habitats may partially influence brook trout population dynamics in the main stem.

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“…Theoretical analysis of dynamic models of temporal and spatial fluctuations has demonstrated that increasing migration leads to increasing scale of spatial covariance in population density , Kendall et al 2000, Engen 2001, Ranta et al 2006. As the slope of ln V vs. ln m is expected to be at its maximum value for areas of the order of l 2 l, the slope is expected to increase from 1 to 2 for ln m .…”

Section: Discussionmentioning

confidence: 99%

A General Model for Analyzing Taylor's Spatial Scaling Laws

Engen¹,

Lande

Sæther

2008

Ecology

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Taylor's spatial scaling law concerns the relation between the variance and the mean population counts within areas of a given size. For a range of area sizes, the log of the variance often is an approximately linear function of the mean with a slope between 1 and 2, depending on the range of areas considered. In this paper, we investigate this relationship theoretically for random quadrat samples within a large area. The model makes a distinction between the local point process determining the position of each individual and the population density described by a spatial covariance function. The local point process and the spatial covariance of population density both contribute to the general relationship between the mean and the variance in which the slope may begin at 1, increase to 2, and decrease to 1 again. It is demonstrated by an example that the slope theoretically may exceed 2 by a small amount for very regular patterns that generate spatial covariance functions that increase in certain intervals. We also show how properties of population dynamics in space and time determine this relationship.

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Dispersal, Environmental Correlation, and Spatial Synchrony in Population Dynamics

Cited by 127 publications

References 28 publications

At what spatial scales are alternative stable states relevant in highly interconnected ecosystems?

At what spatial scales are alternative stable states relevant in highly interconnected ecosystems?

Density-Dependent Regulation of Brook Trout Population Dynamics along a Core-Periphery Distribution Gradient in a Central Appalachian Watershed

A General Model for Analyzing Taylor's Spatial Scaling Laws

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