An extensive comparison of species-abundance distribution models

Baldridge, Elita; Harris, David J.; Xiao, Xiao; White, Ethan

doi:10.7717/peerj.2823

Cited by 85 publications

(108 citation statements)

References 46 publications

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“…Additionally, visual inspection suggests that there was a slight tendency for the logseries to be favoured when species richness was lower (not shown), and in our analysis logseries was never the model with the best absolute fit (in terms of negative log‐likelihood values only; cf. Baldridge et al ., ). Interestingly, none of the SADs selected as logseries had the largest spatial extent, contrasting with the predictions of neutral theory with point‐mutation speciation (Hubbell, ), which predicts a logseries SAD for the metacommunity.…”

Section: Discussionmentioning

confidence: 65%

Prevalence of multimodal species abundance distributions is linked to spatial and taxonomic breadth

Antão

Connolly

Magurran

et al. 2016

Global Ecol. Biogeogr.

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Aim Species abundance distributions (SADs) are a synthetic measure of biodiversity and community structure. Although typically described by unimodal logseries or lognormal distributions, empirical SADs can also exhibit multiple modes. However, we do not know how prevalent multimodality is, nor do we have an understanding of the factors leading to this pattern. Here we quantify the prevalence of multimodality in SADs across a wide range of taxa, habitats and spatial extents. Location Global. Methods We used the second‐order Akaike information criterion for small sample sizes (AICc) and likelihood ratio tests (LRTs) to test whether models with more than one mode accurately describe the empirical abundance frequency distributions of the underlying communities. We analysed 117 empirical datasets from intensely sampled communities, including taxa ranging from birds, plants, fish and invertebrates, from terrestrial, marine and freshwater habitats. Results We find evidence for multimodality in 14.5% of the SADs when using AICc and LRT. This is a conservative estimate, as AICc alone estimates a prevalence of multimodality of 22%. We additionally show that the pattern is more common in data encompassing broader spatial scales and greater taxonomic breadth, suggesting that multimodality increases with ecological heterogeneity. Main conclusions We suggest that higher levels of ecological heterogeneity, underpinned by larger spatial extent and higher taxonomic breadth, can yield multimodal SADs. Our analysis shows that multimodality occurs with a prevalence that warrants its systematic consideration when assessing SAD shape and emphasizes the need for macroecological theories to include multimodality in the range of SADs they predict.

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Section: Discussionmentioning

confidence: 65%

Prevalence of multimodal species abundance distributions is linked to spatial and taxonomic breadth

Antão

Connolly

Magurran

et al. 2016

Global Ecol. Biogeogr.

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“…We chose these models since they describe well the generally observed excess or rare species in ecological abundance data (Baldridge et al 2016). of machines).…”

Section: Models Of Species-abundance Distribution (Sad)mentioning

confidence: 99%

“…In ecology, species-abundance distributions (Fischer 1943, McGill et al 2007, Nekola and Brown 2007 and distributions of range sizes (Gaston 1996b(Gaston , 2003 are often best described by the same skewed lognormal or logseries model (Baldridge et al 2016). In ecology, species-abundance distributions (Fischer 1943, McGill et al 2007, Nekola and Brown 2007 and distributions of range sizes (Gaston 1996b(Gaston , 2003 are often best described by the same skewed lognormal or logseries model (Baldridge et al 2016).…”

Section: Macroecological Patternsmentioning

confidence: 99%

Macroecological and macroevolutionary patterns emerge in the universe of GNU/Linux operating systems

et al. 2018

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What leads to classically recognized patterns of biodiversity remains an open and contested question. It remains unknown if observed patterns are generated by biological or non-biological mechanisms, or if we should expect the patterns to emerge in nonbiological systems. Here, we employ analogies between GNU/Linux operating systems (distros), a non-biological system, and biodiversity, and we look for a number of wellestablished ecological and evolutionary patterns in the Linux universe. We demonstrate that patterns of the Linux universe generally match macroecological patterns. Particularly, Linux distro commonness and rarity follow a skewed distribution with a clear excess of rare distros, we observed a power law mean-variance scaling of temporal fluctuation, but there is only a weak relationship between niche breadth (number of software packages) and commonness. The diversity in the Linux universe also follows general macroevolutionary patterns: the number of phylogenetic lineages increases linearly through time, with clear per-species diversification and extinction slowdowns, something that has been indirectly estimated, but not directly observed in biology. Moreover, the composition of functional traits (software packages) exhibits significant phylogenetic signal. The emergence of macroecological patterns across Linux suggests that the patterns are produced independently of system identity, which points to the possibility of non-biological drivers of fundamental biodiversity patterns. At the same time, our study provides a step towards using Linux as a model system for exploring macroecological and macroevolutionary patterns.

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“…This gives simply the number of species times the Shannon information of the SAD, familiar to ecologists [3,26]. Its selling feature is its experimental accessibility for real ecosystems.…”

Section: Entropymentioning

confidence: 99%

Entropy in the Tangled Nature Model of Evolution

Roach

Nulton

Sibani

et al. 2017

Entropy

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Abstract:Applications of entropy principles to evolution and ecology are of tantamount importance given the central role spatiotemporal structuring plays in both evolution and ecological succession. We obtain here a qualitative interpretation of the role of entropy in evolving ecological systems. Our interpretation is supported by mathematical arguments using simulation data generated by the Tangled Nature Model (TNM), a stochastic model of evolving ecologies. We define two types of configurational entropy and study their empirical time dependence obtained from the data. Both entropy measures increase logarithmically with time, while the entropy per individual decreases in time, in parallel with the growth of emergent structures visible from other aspects of the simulation. We discuss the biological relevance of these entropies to describe niche space and functional space of ecosystems, as well as their use in characterizing the number of taxonomic configurations compatible with different niche partitioning and functionality. The TNM serves as an illustrative example of how to calculate and interpret these entropies, which are, however, also relevant to real ecosystems, where they can be used to calculate the number of functional and taxonomic configurations that an ecosystem can realize.

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An extensive comparison of species-abundance distribution models

Cited by 85 publications

References 46 publications

Prevalence of multimodal species abundance distributions is linked to spatial and taxonomic breadth

Prevalence of multimodal species abundance distributions is linked to spatial and taxonomic breadth

Macroecological and macroevolutionary patterns emerge in the universe of GNU/Linux operating systems

Entropy in the Tangled Nature Model of Evolution

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