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The stability of mutualism.

ABSTRACT: Positive interactions are observed at high frequencies in nearly all living systems, ranging from human and animal societies down to the scale of microbial organisms. However, historically, detailed ecological studies of mutualism have been relatively unrepresented. Moreover, while ecologists have long portrayed competition as a stabilizing process, mutualism is often deemed destabilizing. Recently, several key modelling studies have applied random matrix methods, and have further corroborated the instability of mutualism. Here, I reassess these findings by factoring in species densities into the "community matrix," a practice which has almost always been ignored in random matrix analyses. With this modification, mutualistic interactions are found to boost equilibrium population densities and stabilize communities by increasing their resilience. By taking into account transient dynamics after a strong population perturbation, it is found that mutualists have the ability to pull up communities by their bootstraps when species are dangerously depressed in numbers.

SUBMITTER: Stone L

PROVIDER: S-EPMC7253468 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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The stability of mutualism.

Stone Lewi L

Nature communications 20200527 1

Positive interactions are observed at high frequencies in nearly all living systems, ranging from human and animal societies down to the scale of microbial organisms. However, historically, detailed ecological studies of mutualism have been relatively unrepresented. Moreover, while ecologists have long portrayed competition as a stabilizing process, mutualism is often deemed destabilizing. Recently, several key modelling studies have applied random matrix methods, and have further corroborated t ...[more]

PMID: 32461545

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Project description:BACKGROUND:Recent research indicates that the best-fitting structural model of psychopathology includes a general factor capturing comorbidity (p) and several more specific, orthogonal factors. Little is known about the stability of these factors, although two opposing developmental processes have been proposed: dynamic mutualism suggests that symptom-level interaction and reinforcement may lead to a strengthening of comorbidity (p) over time, whereas p-differentiation suggests a general vulnerability to psychopathology that gives way to increasingly distinct patterns of symptoms over time. In order to test both processes, we examine two forms of developmental stability from ages 2 to 14 years: strength (i.e., consistency in the amount of variance explained by general and specific factors) and phenotypic stability (i.e., homotypic and heterotypic continuity). METHODS:Data are from the NICHD Study of Early Child Care and Youth Development. Psychopathology symptoms were assessed nine times between ages 2 and 14 years (n = 1,253) using the Child Behavior Checklist completed by mothers. Confirmatory bifactor modeling was used to test structural models of psychopathology at each age. Consistency in strength was examined by calculating the Explained Common Variance (ECV) and phenotypic stability was investigated with cross-lagged modeling of the general and specific factors. RESULTS:Bifactor models fit the data well across this developmental period. ECV values were reasonably consistent across development, with the general factor accounting for the majority of shared variance (61%-71%). Evidence of both homotypic and heterotypic continuity emerged, with most heterotypic continuity involving the general factor, as it both predicted and was predicted by specific factors. CONCLUSIONS:A bifactor model effectively captures psychopathological comorbidity from early childhood through adolescence. The longitudinal associations between the general and specific factors provide evidence for both the hypothesized processes (dynamic mutualism and p-differentiation) occurring through development.

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The stability of mutualism.

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The stability of mutualism.

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