2015
DOI: 10.1111/geb.12341
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Community assembly on isolated islands: macroecology meets evolution

Abstract: Aim Understanding how ecological and evolutionary processes together determine patterns of biodiversity remains a central aim in biology. Guided by ecological theory, we use data from multiple arthropod lineages across the Hawaiian archipelago to explore the interplay between ecological (population dynamics, dispersal, trophic interactions) and evolutionary (genetic structuring, adaptation, speciation, extinction) processes. Our goal is to show how communities develop from the dynamic feedbacks that operate at… Show more

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Cited by 68 publications
(73 citation statements)
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“…Thus, we can measure ecological metrics at different time slices of the community assembly process to find how properties (species diversity, abundance, body size distributions, trophic interactions) change over time (104,105), and how the origin of new species affects these properties. Moreover, as genomic data become available across multiple lineages that appear to follow a progression in a given system (9), we are gaining insight into how taxa differ in mode, rates, and patterns of establishment and diversification (13). Integrating multidimensional datasets across stages of the progression will allow us to understand how interactions develop and evolve and the importance of such interactions in dictating properties of stability, turnover, and the evolution of biotic resistance in a community.…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…Thus, we can measure ecological metrics at different time slices of the community assembly process to find how properties (species diversity, abundance, body size distributions, trophic interactions) change over time (104,105), and how the origin of new species affects these properties. Moreover, as genomic data become available across multiple lineages that appear to follow a progression in a given system (9), we are gaining insight into how taxa differ in mode, rates, and patterns of establishment and diversification (13). Integrating multidimensional datasets across stages of the progression will allow us to understand how interactions develop and evolve and the importance of such interactions in dictating properties of stability, turnover, and the evolution of biotic resistance in a community.…”
Section: Discussionmentioning
confidence: 99%
“…Likewise, ecologists have long recognized the value of islands as microcosms of the processes of community assembly (10,11). In recent years, there has been growing interest in combining these elements to study the evolution of community assembly, with particular focus on islands within (and beyond) the "radiation zone" (10), where in situ speciation can be a major contributor to the origin of ecological communities (12,13). Under such circumstances, rules for community assembly can be illuminated by comparative phylogeographic approaches, revealing common evolutionary histories of codistributed, endemic taxa both within and between island archipelagos.…”
mentioning
confidence: 99%
“…Amounting to just 5.3% of the global landmass (Weigelt et al 2013), islands hold 25 a disproportionate amount of the world's biodiversity (Whittaker and Fernández-Palacios 2007;Kier et al 2009), and this is manifest at taxonomic, functional and phylogenetic levels of diversity (Jønsson and Holt 2015;Warren et al 2015;Rominger et al 2016). High levels of in situ speciation and endemism are an attribute of the most remote islands, where strong dispersal effects leads to the absence of some and the overrepresentation of other taxonomic groups (Carlquist 1974;Whittaker and Fernández-Palacios 2007;Weigelt et al 2015).…”
Section: Introductionmentioning
confidence: 99%
“…Newman et al () showed that it equally well describes meadow vegetation (Figure b). Rominger et al () and Harte () showed that it applies to arthropods (Figure c). It is noteworthy that the open‐canopy meadow vegetation and the arthropod communities in Figure b,c would not be considered light limited.…”
Section: Metabolic Rate Distributions Can Be Predicted Without Invokimentioning
confidence: 90%