Efficient algorithms for sampling feasible sets of macroecological patterns

Kj, Locey; Dj, McGlinn

doi:10.7287/peerj.preprints.78v1

Cited by 5 publications

(6 citation statements)

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“…We constructed the feasible set of partitions and the feasible set of compositions for each pair of (Qi, Ni) in the empirical relationship. Given the large size of the feasible set for large values of (Qi, Ni) (Locey and White 2013), we drew 1000 random configurations from the feasible set for each (Qi, Ni) using the algorithms from Locey and McGlinn (2013). We confirmed that 1000 samples were sufficient in this context by testing sample sizes of 4000, which yielded equivalent results (Appendix B, Fig.…”

Section: Analysesmentioning

confidence: 76%

A Process-Independent Explanation for the General Form of Taylor’s Law

Xiao

Locey²,

White³

2015

The American Naturalist

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Taylor's law (TL) describes the scaling relationship between the mean and variance of populations as a power law. TL is widely observed in ecological systems across space and time, with exponents varying largely between 1 and 2. Many ecological explanations have been proposed for TL, but it is also commonly observed outside ecology. We propose that TL arises from the constraining influence of two primary variables: the number of individuals and the number of censuses or sites. We show that most possible configurations of individuals among censuses or sites produce the power-law form of TL, with exponents between 1 and 2. This "feasible set" approach suggests that TL is a statistical pattern driven by two constraints, providing an a priori explanation for this ubiquitous pattern. However, the exact form of any specific mean-variance relationship cannot be predicted in this way, that is, this approach does a poor job of predicting variation in the exponent, suggesting that TL may still contain ecological information.

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Section: Analysesmentioning

confidence: 76%

A Process-Independent Explanation for the General Form of Taylor’s Law

Xiao

Locey²,

White³

2015

The American Naturalist

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“…To choose appropriate patterns of dominance for the species in our dominant communities, we simulated species abundance distributions (SADs) using an algorithm that generates random partitions (i.e., abundance values for each species), constrained by the total sum of individuals (30 for pollinators and 140 for plants) and species richness (five for pollinators and seven for plants; Fig. a, b) using the rpartitions package (Locey and McGlinn ). We then retained a subset of simulated communities that had a SAD dominance skew in the 99th percentile of all simulated communities (i.e., the most dominant communities), and calculated average abundance values for each species in this subset.…”

Section: Methodsmentioning

confidence: 99%

Plant species dominance increases pollination complementarity and plant reproductive function

Stavert

Bartomeus

Beggs

et al. 2019

Ecology

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Worldwide, anthropogenic change is causing biodiversity loss, disrupting many critical ecosystem functions. Most studies investigating the relationship between biodiversity and ecosystem functioning focus on species richness, predominantly within the context of productivity‐related functions. Consequently, there is limited understanding of how other biodiversity measures, such as species evenness (the distribution of abundance among species), affect complex multitrophic functions such as pollination. We explore the effect of species evenness on the ecosystem function of pollination using a controlled experiment with selected plants and insects in flight cages. We manipulated the relative abundances of plant and pollinator species, while holding species richness, composition, dominance order, and total abundance constant. Then, we tested how numerical species evenness affected network structure and consequently, seed production, in our artificial communities. Contrary to our expectation, numerical dominance in plant communities increased complementarity in pollinator use (reduced pollinator sharing) among plant species. As predicted by theory, this increased complementarity resulted in higher seed production for the most dominant and rare plant species in our cages. Our results show that in a controlled experimental setting, numerical species evenness can alter important aspects of plant–pollinator networks and plant reproduction, irrespective of species richness, composition, and total abundance. Extending this understanding of how species evenness affects ecosystem functioning to natural systems is crucial as anthropogenic disturbances continue to alter species’ abundances, likely disrupting ecosystem functions long before extinctions occur.

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“…A lower value of AIC (DAIC) indicates a better model. [46]. Scripts are available in the electronic supplementary material.…”

Section: (C) Analysis Of Aggregationmentioning

confidence: 99%

“…One random configuration corresponds to a potential distribution of P parasites among H hosts. This random partitioning is repeated many times, and the sampled configurations are an estimate of the feasible set given P and H [22,46]. The feasible set approach makes no mechanistic assumptions but predicts the most likely aggregation level given data constraints (see electronic supplementary material), effectively recognizing that many combinations of mechanisms can generate similar levels of aggregation [25,47].…”

Section: (C) Analysis Of Aggregationmentioning

confidence: 99%

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Biological and statistical processes jointly drive population aggregation: using host–parasite interactions to understand Taylor's power law

Johnson

Wilber

2017

Proc. R. Soc. B.

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The macroecological pattern known as Taylor's power law (TPL) represents the pervasive tendency of the variance in population density to increase as a power function of the mean. Despite empirical illustrations in systems ranging from viruses to vertebrates, the biological significance of this relationship continues to be debated. Here we combined collection of a unique dataset involving 11 987 amphibian hosts and 332 684 trematode parasites with experimental measurements of core epidemiological outcomes to explicitly test the contributions of hypothesized biological processes in driving aggregation. After using feasible set theory to account for mechanisms acting indirectly on aggregation and statistical constraints inherent to the data, we detected strongly consistent influences of host and parasite species identity over 7 years of sampling. Incorporation of field-based measurements of host body size, its variance and spatial heterogeneity in host density accounted for host identity effects, while experimental quantification of infection competence (and especially virulence from the 20 most common host-parasite combinations) revealed the role of species-by-environment interactions. By uniting constraint-based theory, controlled experiments and community-based field surveys, we illustrate the joint influences of biological and statistical processes on parasite aggregation and emphasize their importance for understanding population regulation and ecological stability across a range of systems, both infectious and free-living.

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Efficient algorithms for sampling feasible sets of macroecological patterns

Cited by 5 publications

References 0 publications

A Process-Independent Explanation for the General Form of Taylor’s Law

A Process-Independent Explanation for the General Form of Taylor’s Law

Plant species dominance increases pollination complementarity and plant reproductive function

Biological and statistical processes jointly drive population aggregation: using host–parasite interactions to understand Taylor's power law

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