Dynamical evolution of ecosystems

Azaele, Sandro; Pigolotti, Simone; Banavar, Jayanth R.; Maritan, Amos

doi:10.1038/nature05320

Cited by 124 publications

(159 citation statements)

References 26 publications

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“…Different works have recently analyzed this type of models, showing that time-dependent habitat preference greatly improves predictions of empirical ecological patterns with respect to purely neutral theories [31,[85][86][87]. In particular, it has been claimed that these models provides more realistic estimates of dynamical quantities, such as average species persistence times and distributions of species turnover [88], compared with their neutral counterparts.…”

Section: E Temporally-dependent Habitat Preferencesmentioning

confidence: 99%

Stochastic Spatial Models in Ecology: A Statistical Physics Approach

et al. 2017

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Ecosystems display a complex spatial organization. Ecologists have long tried to characterize them by looking at how different measures of biodiversity change across spatial scales. Ecological neutral theory has provided simple predictions accounting for general empirical patterns in communities of competing species. However, while neutral theory in well-mixed ecosystems is mathematically well understood, spatial models still present several open problems, limiting the quantitative understanding of spatial biodiversity. In this review, we discuss the state of the art in spatial neutral theory. We emphasize the connection between spatial ecological models and the physics of non-equilibrium phase transitions and how concepts developed in statistical physics translate in population dynamics, and vice versa. We focus on non-trivial scaling laws arising at the critical dimension D = 2 of spatial neutral models, and their relevance for biological populations inhabiting two-dimensional environments. We conclude by discussing models incorporating non-neutral effects in the form of spatial and temporal disorder, and analyze how their predictions deviate from those of purely neutral theories.

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Section: E Temporally-dependent Habitat Preferencesmentioning

confidence: 99%

Stochastic Spatial Models in Ecology: A Statistical Physics Approach

et al. 2017

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“…To make more powerful inferences from neutral theory, an important step will be to exploit this ability and combine fitting of species abundances with temporal dynamics of abundance [21,22], SARs [23,24], or phylogenetic data [25]. It is sometimes found that the classic neutral model fails to match patterns; for example, spatially explicit patterns.…”

Section: Reviewmentioning

confidence: 99%

The Unified Neutral Theory of Biodiversity and Biogeography at Age Ten

Rosindell

Hubbell

Etienne

2011

Trends in Ecology & Evolution

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535

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“…The characterization of biodiversity similarity among different LCs of the river network is carried out within a stochastic ecological framework (25)(26)(27)(28)(29) that allows us to formulate an analytically tractable model. We focus mainly on the study of a random variable, J(d), the relative fraction of common species between 2 LCs at distance d ϭ 0, 1, 2, … in link units.…”

Section: Modeling Spatial Biodiversity Similaritymentioning

confidence: 99%

Predicting spatial similarity of freshwater fish biodiversity

Azaele

Muneepeerakul²,

Maritan

et al. 2009

Proc. Natl. Acad. Sci. U.S.A.

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A major issue in modern ecology is to understand how ecological complexity at broad scales is regulated by mechanisms operating at the organismic level. What specific underlying processes are essential for a macroecological pattern to emerge? Here, we analyze the analytical predictions of a general model suitable for describing the spatial biodiversity similarity in river ecosystems, and benchmark them against the empirical occurrence data of freshwater fish species collected in the Mississippi-Missouri river system. Encapsulating immigration, emigration, and stochastic noise, and without resorting to species abundance data, the model is able to reproduce the observed probability distribution of the Jaccard similarity index at any given distance. In addition to providing an excellent agreement with the empirical data, this approach accounts for heterogeneities of different subbasins, suggesting a strong dependence of biodiversity similarity on their respective climates. Strikingly, the model can also predict the actual probability distribution of the Jaccard similarity index for any distance when considering just a relatively small sample. The proposed framework supports the notion that simplified macroecological models are capable of predicting fundamental patterns-a theme at the heart of modern community ecology. macroecology ͉ river networks ͉ Jaccard index ͉ average annual runoff production ͉ Mississippi-Missouri basin T he fundamental mechanisms that underlie emergent biodiversity patterns are the fabric of the large canvas of ecological complexity (1-5), whose functioning across spatial and temporal scales is still challenging modern ecologists (6, 7).River ecosystems, like tropical forests, constitute an invaluable source of species diversity whose study can shed light on these puzzling issues (8), and whose insights will in turn resonate in the conservation biology of freshwater fauna suffering extinction rates comparable to the species decline in tropical rainforests (9). Riverine ecology has recognized the importance of geomorphology for biodiversity (10): on the one hand, ecologists have investigated the role of branches (11-13) and riparian zones (14) as primary habitats as well as the importance of tributaries (15) in sculpting species diversity (16, 17); on the other hand, theoretical studies have looked into possible implications of dendritic networks (18) on population persistence (19), species richness, and spatial turnover (20). Recent advances have also pointed out that simplifying neutral assumptions can reproduce important macroecological patterns in such dendritic structures (21).Indeed, despite the fact that biodiversity similarity among habitat patches is crucial to dispersal, speciation, and adaptation to climate diversity at large scales (22) and can also be related to species richness (23, 24), it is not well understood yet. Here, we develop an analytical model suggesting that essential features of biodiversity similarity in river ecosystems may be captured by using only the occurrence...

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Dynamical evolution of ecosystems

Cited by 124 publications

References 26 publications

Stochastic Spatial Models in Ecology: A Statistical Physics Approach

Stochastic Spatial Models in Ecology: A Statistical Physics Approach

The Unified Neutral Theory of Biodiversity and Biogeography at Age Ten

Predicting spatial similarity of freshwater fish biodiversity

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