Dispersion and Population Interactions

Levin, Simon A.

doi:10.1086/282900

Cited by 1,022 publications

(655 citation statements)

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“…Moreover, we assume that population regulation operates locally. In this case, the regulating variables (i.e., the resource exploitations) in the different patches must be considered as different variables (Levin, 1974), each of them has a contribution to the metapopulation-level regulation. There is a single regulating variable in each patch.…”

Section: Analytic Resultsmentioning

confidence: 99%

“…They argued that the symmetry between habitat and functional niche segregation translates to a conceptual symmetry between allopatric and parapatric niche segregation. The purely ecological study of heterogeneity-maintained coexistence was initiated by Levin (1974); see Amarasekare (2003) for a recent review.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

“…This way, the notion of regulating variables plays the role of the unifying concept in the case a functional and habitat type niche segregation. As pointed out by Levin (1974), the concentrations of a given kind of resource in different habitats are different variables in the feedback loop. Therefore they should be counted as different regulating variables.…”

mentioning

confidence: 99%

“…Still, development of coexistence theory has remained consistent with the original concept of competitive exclusion and niche segregation. Both Levins's understanding on the role of regulating variables (Levin, 1970(Levin, , 1974) and Chesson's one on the need to be averaged differently point to the necessity for ecological differentiation. The expectation that space-time heterogeneity can weaken competition and de-emphasize the importance of segregation for coexistence was falsified (Chesson, 1991;Chesson and Huntly, 1997).…”

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confidence: 99%

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Limiting similarity and niche theory for structured populations

Szilágyi

Meszéna

2009

Journal of Theoretical Biology

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We develop the theory of limiting similarity and niche for structured populations with finite number of individual states (i-state). In line with a previously published theory for unstructured populations, the niche of a species is specified by the impact and sensitivity niche vectors. They describe the population's impact on and sensitivity towards the variables involved in the population regulation. Robust coexistence requires sufficient segregation of the impact, as well as of the sensitivity niche vectors. Connection between the population-level impact and sensitivity and the impact/sensitivity of the specific i-states is developed. Each i-state contributes to the impact of the population proportional to its frequency in the population. Sensitivity of the population is composed of the sensitivity of the rates of demographic transitions, weighted by the frequency and by the reproductive value of the initial and final i-states of the transition, respectively. Coexistence in a multi-patch environment is studied. This analysis is interpreted as spatial niche segregation.

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Section: Analytic Resultsmentioning

confidence: 99%

Section: Accepted Manuscriptmentioning

confidence: 99%

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confidence: 99%

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Limiting similarity and niche theory for structured populations

Szilágyi

Meszéna

2009

Journal of Theoretical Biology

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“…[12,13]). At one extreme, models have been developed to represent fragmented landscapes [14] while at the other, many researchers have treated landscapes as homogeneous [15,16,17,18].…”

Section: Introductionmentioning

confidence: 99%

Spreading speeds for plant populations in landscapes with low environmental variation

Gilbert¹,

Gaffney²,

Bullock

et al. 2014

Journal of Theoretical Biology

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Article (refereed) -postprintGilbert, Mark A.; Gaffney, Eamonn A.; Bullock, James M.; White, Steven M. 2014. Spreading speeds for plant populations in landscapes with low environmental variation.Contact CEH NORA team at noraceh@ceh.ac.ukThe NERC and CEH trademarks and logos ('the Trademarks') are registered trademarks of NERC in the UK and other countries, and may not be used without the prior written consent of the Trademark owner. AbstractCharacterising the spread of biological populations is crucial in responding to both biological invasions and the shifting of habitat under climate change. Spreading speeds can be studied through mathematical models such as the discrete-time integro-difference equation (IDE) framework. The usual approach in implementing IDE models has been to ignore spatial variation in the demographic and dispersal parameters and to assume that these are spatially homogeneous. On the other hand, real landscapes are rarely spatially uniform with environmental variation being very important in determining biological spread. This raises the question of under what circumstances spatial structure need not be modelled explicitly. Recent work has shown that spatial variation can be ignored in models in the specific case where the scale of landscape variation is much smaller than the spreading population's dispersal scale. We consider more general types of landscape, where the spatial scales of environmental variation are arbitrarily large, but the maximum change in environmental parameters is relatively small. We find that the difference between the wave-speeds of populations spreading in a spatially structured periodic landscape and its homogenisation is, in general, proportional to 2 , where governs the degree of environmental variation. For stochastically generated landscapes we numerically demonstrate that the error decays faster than . In both cases, this means that for sufficiently small , the homogeneous approximation is better than might be expected. Hence, in many situations, the precise details of the landscape can be ignored in favour of spatially homogeneous parameters. This means that field ecologists can use the homogeneous IDE as a relatively simple modelling tool -in terms of both measuring parameter values and doing the modelling itself. However, as increases, this homogeneous approximation loses its accuracy. The change in wave-speed due to the extrinisic (landscape) variation can be positive or negative, which is in contrast to the reduction in wave-speed caused by intrinsic stochasticity. To deal with the loss of accuracy as increases, we formulate a second order approximation to the wave-speed for periodic landscapes and compare both approximations against the results of numerical simulation and show that they are both accurate for the range of landscapes considered.

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The role of local‐scale heterogeneities in terrestrial ecosystem modeling

et al. 2015

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The coarse‐grained spatial representation of many terrestrial ecosystem models hampers the importance of local‐scale heterogeneities. To address this issue, we combine a range of observations (forest inventories, eddy flux tower data, and remote sensing products) and modeling approaches with contrasting degrees of abstraction. The following models are selected: (i) Lund‐Potsdam‐Jena (LPJ), a well‐established, area‐based, dynamic global vegetation model (DGVM); (ii) LPJ‐General Ecosystem Simulator, a hybrid, individual‐based approach that additionally considers plant population dynamics in greater detail; and (iii) distributed in space‐LPJ, a spatially explicit version of LPJ, operating at a fine spatial resolution (100 m × 100 m), which uses an enhanced hydrological representation accounting for lateral connectivity of surface and subsurface water fluxes. By comparing model simulations with a multivariate data set available at the catchment scale, we argue that (i) local environmental and topographic attributes that are often ignored or crudely represented in DGVM applications exert a strong control on terrestrial ecosystem response; (ii) the assumption of steady state vegetation and soil carbon pools at the beginning of simulation studies (e.g., under “current conditions”), as embedded in many DGVM applications, is in contradiction with the current state of many forests that are often out of equilibrium; and (iii) model evaluation against vegetation carbon fluxes does not imply an accurate simulation of vegetation carbon stocks. Having gained insights about the magnitude of aggregation‐induced biases due to smoothing of spatial variability at the catchment scale, we discuss the implications of our findings with respect to the global‐scale modeling studies of carbon cycle and we illustrate alternative ways forward.

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Dispersion and Population Interactions

Cited by 1,022 publications

References 24 publications

Limiting similarity and niche theory for structured populations

Limiting similarity and niche theory for structured populations

Spreading speeds for plant populations in landscapes with low environmental variation

The role of local‐scale heterogeneities in terrestrial ecosystem modeling

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