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Stouffer D.B.,Integrative Ecology Group | Stouffer D.B.,Northwestern University
Functional Ecology | Year: 2010

Food webs, the set of predator-prey interactions in an ecosystem, are a prototypical complex system. Much research to date has concentrated on the use of models to identify and explain the key structural features which characterize food webs. These models often fall into two general categories: (i) phenomenological models which are built upon a set of heuristic rules in order to explain some empirical observation and (ii) population-level models in which interactions between individuals result in emergent properties for the food web. Both types of models have helped to uncover how food-web structure is a product of factors such as foraging behaviour, prey selection and species' body sizes. Historically, the two types of models have followed rather different approaches to the problem. Despite the apparent differences, the overlap between the two styles of models is substantial. Examples are highlighted here. By paying greater attention to both the similarities and differences between the two, we will be better able to demonstrate the ecological insights offered by phenomenological models. This will help us, for example, design experiments which could validate or refute underlying assumptions of the models. By linking models to data, scaling from individuals to networks, we will be closer to understanding the true origins of food-web structure. © 2010 British Ecological Society.

Hughes T.P.,James Cook University | Linares C.,University of Barcelona | Dakos V.,Wageningen University | Dakos V.,Integrative Ecology Group | And 2 more authors.
Trends in Ecology and Evolution | Year: 2013

Regime shifts from one ecological state to another are often portrayed as sudden, dramatic, and difficult to reverse. Yet many regime shifts unfold slowly and imperceptibly after a tipping point has been exceeded, especially at regional and global scales. These long, smooth transitions between equilibrium states are easy to miss, ignore, or deny, confounding management and governance. However, slow responses by ecosystems after transgressing a dangerous threshold also affords borrowed time - a window of opportunity to return to safer conditions before the new state eventually locks in and equilibrates. In this context, the most important challenge is a social one: convincing enough people to confront business-as-usual before time runs out to reverse unwanted regime shifts even after they have already begun. © 2012 Elsevier Ltd.

Guimaraes P.R.,University of California at Santa Cruz | Guimaraes P.R.,University of Sao Paulo | Jordano P.,Integrative Ecology Group | Thompson J.N.,University of California at Santa Cruz
Ecology Letters | Year: 2011

A major current challenge in evolutionary biology is to understand how networks of interacting species shape the coevolutionary process. We combined a model for trait evolution with data for twenty plant-animal assemblages to explore coevolution in mutualistic networks. The results revealed three fundamental aspects of coevolution in species-rich mutualisms. First, coevolution shapes species traits throughout mutualistic networks by speeding up the overall rate of evolution. Second, coevolution results in higher trait complementarity in interacting partners and trait convergence in species in the same trophic level. Third, convergence is higher in the presence of super-generalists, which are species that interact with multiple groups of species. We predict that worldwide shifts in the occurrence of super-generalists will alter how coevolution shapes webs of interacting species. Introduced species such as honeybees will favour trait convergence in invaded communities, whereas the loss of large frugivores will lead to increased trait dissimilarity in tropical ecosystems. © 2011 Blackwell Publishing Ltd/CNRS.

Schupp E.W.,Utah State University | Schupp E.W.,Integrative Ecology Group | Jordano P.,Integrative Ecology Group | Gomez J.M.,University of Granada
New Phytologist | Year: 2010

Growth in seed dispersal studies has been fast-paced since the seed disperser effectiveness (SDE) framework was developed 17 yr ago. Thus, the time is ripe to revisit the framework in light of accumulated new insight. Here, we first present an overview of the framework, how it has been applied, and what we know and do not know. We then introduce the SDE landscape as the two-dimensional representation of the possible combinations of the quantity and the quality of dispersal and with elevational contours representing isoclines of SDE. We discuss the structure of disperser assemblages on such landscapes. Following this we discuss recent advances and ideas in seed dispersal in the context of their impacts on SDE. Finally, we highlight a number of emerging issues that provide insight into SDE. Overall, the SDE framework successfully captures the complexities of seed dispersal. We advocate an expanded use of the term dispersal encompassing the multiple recruitment stages from fruit to adult. While this entails difficulties in estimating SDE, it is a necessary expansion if we are to understand the central relevance of seed dispersal in plant ecology and evolution. © The Authors (2010). Journal compilation © New Phytologist Trust (2010).

Gilarranz L.J.,Integrative Ecology Group | Bascompte J.,Integrative Ecology Group
Journal of Theoretical Biology | Year: 2012

We explore the relationship between network structure and dynamics by relating the topology of spatial networks with its underlying metapopulation abundance. Metapopulation abundance is largely affected by the architecture of the spatial network, although this effect depends on demographic parameters here represented by the extinction-to-colonization ratio (e/. c). Thus, for moderate to large e/. c-values, regional abundance grows with the heterogeneity of the network, with uniform or random networks having the lowest regional abundances, and scale-free networks having the largest abundance. However, the ranking is reversed for low extinction probabilities, with heterogeneous networks showing the lowest relative abundance. We further explore the mechanisms underlying such results by relating a node's incidence (average number of time steps the node is occupied) with its degree, and with the average degree of the nodes it interacts with. These results demonstrate the importance of spatial network structure to understanding metapopulation abundance, and serve to determine under what circumstances information on network structure should be complemented with information on the species life-history traits to understand persistence in heterogeneous environments. © 2011 Elsevier Ltd.

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