Evolving ecological networks and the emergence of biodiversity patterns across temperature gradients

Stegen, James C.; Ferrière, Régis; Enquist, Brian J.

doi:10.1098/rspb.2011.1733

Cited by 43 publications

(59 citation statements)

References 48 publications

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“…For example, several studies have investigated the drivers of richness patterns between regions (Goldberg et al 2005;Roy and Goldberg 2007;Yoder and Nuismer 2010). Others have studied community assembly along environmental gradients (McPeek 2008;Stegen et al 2009Stegen et al , 2012aStegen et al , 2012b, and Birand et al (2012) examined speciation, extinction, and range sizes. Similar to these studies, we simulate assembly processes such as competition and coevolution of multiple species (see also Nuismer et al 2010) and analyze the emergent biogeographical patterns.…”

Section: The Model Captures the Essentialsmentioning

confidence: 99%

The Biogeography of Adaptive Radiations and the Geographic Overlap of Sister Species

Pontarp

Ripa

Lundberg

2015

The American Naturalist

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Originally published at: Pontarp, Mikael; Ripa, Jörgen; Lundberg, Per (2015). The biogeography of adaptive radiations and the geographic overlap of sister species. The American Naturalist, 186(5):565-581. DOI: https://doi.org/10.1086/683260The University of Chicago Online enhancement: appendix.abstract: The biogeography of speciation and what can be learned about the past mode of speciation from current biogeography of sister species are recurrent problems in evolution. We used a trait-and individual-based, eco-evolutionary model to simulate adaptive radiations and recorded the geographical overlap of species during and after evolutionary branching (speciation). We compared the spatial overlap among sister species in the fully saturated community with the overlap at the speciation event. The mean geographic overlap at speciation varied continuously from complete (sympatry) to none (allopatry), depending on local and regional environmental heterogeneity and the rate of dispersal. The distribution of overlap was, however, in some cases considerably bimodal. This tendency was most expressed at large values of regional heterogeneity, corresponding to sharp environmental contrasts. The mean geographic overlap also varied during the course of a radiation, sometimes with a consistent negative trend over time. The speciations that resulted in currently observable end community sister species were therefore not an unbiased sample of all speciations throughout the radiation. Postspeciation range shifts (causing increased overlap) occurred most frequently when dispersal was high or when local habitat heterogeneity was low. Our results help us understand how the patterns of geographic mode of speciation emerge. We also show the difficulty in inferring the geographical speciation mode from phylogenies and the biogeography of extant species.

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Section: The Model Captures the Essentialsmentioning

confidence: 99%

The Biogeography of Adaptive Radiations and the Geographic Overlap of Sister Species

Pontarp

Ripa

Lundberg

2015

The American Naturalist

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“…In addition, ecological network models have shown the importance of adaptive behavior at the component level for organization, stability, and function at the system level [27]. Such models have been used to identify key constraints on the assembly and evolution of persistent complex networks [28][29][30][31]. Similar modeling approaches may prove useful in a PRN context.…”

Section: Ecological and Evolutionary Implications Of Prnsmentioning

confidence: 99%

Physiological regulatory networks: ecological roles and evolutionary constraints

Cohen

Martin

Wingfield

et al. 2012

Trends in Ecology & Evolution

182

160

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Ecological and evolutionary physiology has traditionally focused on aspects of physiology one at a time. Here, we discuss the implications of considering physiological regulatory networks (PRNs) as integrated wholes, a perspective that reveals novel roles for physiology in organismal ecology and evolution. For example, evolutionary response to changes in resource abundance might be constrained by the role of dietary micronutrients in immune response regulation, given a particular pathogen environment. Because many physiological components impact more than one process, organismal homeostasis is maintained, individual fitness is determined, and evolutionary change is constrained (or facilitated) by interactions within PRNs. We discuss how PRN structure and its system-level properties could determine both individual performance and patterns of physiological evolution. GlossaryAlternative physiological structures -Different PRN structures that would produce equivalent functional and fitness outcomes for a given species in a given environment. Which structure evolves might be largely random, but might have consequences for long-term evolution of PRN structure.Cytokines -Small cell-signalling proteins involved in intercellular communication. Regulatory cascades of cytokines are particularly critical in the immune system. Dysregulation -A breakdown over time in the ability of an individual's PRN to maintain homeostasis; possibly in response to chronic stress or infection, and possibly a root cause of aging.3 Homeostasis -A state of healthy physiological equilibrium, including appropriate anticipation of and responses to changing conditions.Integrator -A PRN molecule that has a particularly crucial role in synthesizing information (internal or external) and thereby determining multiple aspects of PRN functioning (e.g., a 'hub' or 'keystone' PRN molecule) Physiological Regulatory Network (PRN) -The network of molecules and their regulatory relationships that maintain and adjust homeostasis and facilitate performance at the wholeorganism level.PRN Molecule -PRN molecules are the subset of molecules that maintain homeostasis at the organism level via their regulatory relationships with other molecules. In most cases, these molecules are the nodes (vertices) in a PRN, and regulatory relationships between them are the links (edges). In some cases, several related molecules (e.g., circulating steroid, receptors, binding globulins) may be grouped together as a single node for measurement purposes, or gene activation may be a node in some contexts. Flexibility of node definition is common in other biological networks-for example, nodes in ecological networks can be species, groups of species, life stages, or other categories (e.g., detritus). PRN state -The concentrations of all PRN molecules at a given moment for a given individual in a particular context (e.g. age, time of year, life history stage). More generally, "changes in PRN state" describe joint adjustments in molecule concentrations in anticipation of or respo...

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“…Indeed, whether the thermal sensitivities of biological traits truly reflect an activation energy remains a contentious issue (Clarke 2004;Gillooly et al 2006;del Rio 2008;Hobbs et al 2013;Corkrey et al 2014). Use of the BA model in ecology has also been criticized, because recent empirical studies have revealed a much wider range of E values (Irlich et al 2009; Knies and Kingsolver 2010;Dell et al 2011;Englund et al 2011) than the 0.6 eV (or sometimes 0.65 eV) originally suggested (Gillooly et al 2001) and which has subsequently so often been used in theoretical studies Vasseur and McCann 2005;Wolfshaar et al 2008;Petchey et al 2010;Rall et al 2010;O'Connor et al 2011;Stegen et al 2012). Understanding the sources of the variation in E within and across species is therefore important, especially because such variation is likely to have nontrivial effects on ecological and evolutionary dynamics (Vasseur and McCann 2005;Dell et al 2011Dell et al , 2014.…”

mentioning

confidence: 99%

Real versus Artificial Variation in the Thermal Sensitivity of Biological Traits

Pawar

Dell

Savage

et al. 2016

The American Naturalist

136

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Whether the thermal sensitivity of an organism's traits follows the simple Boltzmann-Arrhenius model remains a contentious issue that centers around consideration of its operational temperature range and whether the sensitivity corresponds to one or a few underlying rate-limiting enzymes. Resolving this issue is crucial, because mechanistic models for temperature dependence of traits are required to predict the biological effects of climate change. Here, by combining theory with data on 1,085 thermal responses from a wide range of traits and organisms, we show that substantial variation in thermal sensitivity (activation energy) estimates can arise simply because of variation in the range of measured temperatures. Furthermore, when thermal responses deviate systematically from the Boltzmann-Arrhenius model, variation in measured temperature ranges across studies can bias estimated activation energy distributions toward higher mean, median, variance, and skewness. Remarkably, this bias alone can yield activation energies that encompass the range expected from biochemical reactions (from ∼0.2 to 1.2 eV), making it difficult to establish whether a single activation energy appropriately captures thermal sensitivity. We provide guidelines and a simple equation for partially correcting for such artifacts. Our results have important implications for understanding the mechanistic basis of thermal responses of biological traits and for accurately modeling effects of variation in thermal sensitivity on responses of individuals, populations, and ecological communities to changing climatic temperatures.

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Evolving ecological networks and the emergence of biodiversity patterns across temperature gradients

Cited by 43 publications

References 48 publications

The Biogeography of Adaptive Radiations and the Geographic Overlap of Sister Species

The Biogeography of Adaptive Radiations and the Geographic Overlap of Sister Species

Physiological regulatory networks: ecological roles and evolutionary constraints

Real versus Artificial Variation in the Thermal Sensitivity of Biological Traits

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