Estimating competition in metacommunities: accounting for biases caused by dispersal

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Abstract Estimating the strength of interactions among species in natural communities has always been a challenge for empirical ecologists. Sessile organisms, like plants or corals, often occur in metacommunities where they compete only with their immediate neighbours but disperse propagules over a wider area. To estimate the strength of competitive interactions, ecologists often count abundances in cells on a spatial grid for at least two time‐points. This data is then analysed using regression, by modelling the change in population size as a function of local densities, using cells as independent data‐points: a technique known as space‐for‐time substitution. These analyses generate estimates of competition coefficients; however, the method ignores dispersal among cells. To determine the impact of ignoring dispersal, we derived the bias that would arise when we apply regression methods to a metacommunity in which a fraction of seeds disperse beyond their natal cells but this dispersal is ignored in the model fitting process. We present results from a range of population models that make different assumptions about the nature of competition and assess the performance of our bias formulae by analysing data from simulated metacommunities. We reveal that: estimates of competition coefficients are biased when dispersal is not properly accounted for; and the resulting bias is often correlated with abundance, with rare species suffering the greatest overestimation. We also provide a standardized metric of competition that allows the bias to be calculated for a broad range of other population models. Our study suggests that regression methods that ignore dispersal produce biased estimates of competition coefficients when using space‐for‐time substitution. Our analytical bias formula allows empirical ecologists to potentially correct for biases, but it requires either tailored experiments in controlled conditions or an estimate of the average dispersal rate in a natural community, so may be challenging to apply to real datasets.

Highlights

  • In population models with n species competitors, the eect of species interaction is often described by the interaction matrix:

  • A positive value of αiGj,t denotes positive eect from species j on species i, which is known as mutualism or facilitation

  • With the general denition of the interaction matrix (Eqn 2) we can write the metric as a function of competition coecients of a broad range of population models that use the interaction matrix

Read more Highlights Expand/Collapse icon

Summary

The general denition of the strength of species interactionExpand/Collapse icon

In population models with n species competitors, the eect of species interaction is often described by the interaction matrix: α11 α12 · · · α1n α21 α22 · · · α2n. The interaction matrix describes the strength of pairwise interaction between species. Αij denotes 7 the competitive eect from species j on species i (Adler et al 2018). With dierent functional forms of population models, the expression of the interaction matrix diers. It is dicult to understand the biological implications of the estimated coecients from empirical data when using dierent population models. ∂Nj,t where Ni,t denotes the density of species i at time t. A positive value of αiGj,t denotes positive eect from species j on species i, which is known as mutualism or facilitation.

Interconversion of the interaction matrix between population modelsExpand/Collapse icon
Estimate the pairwise interaction matrix from empirical dataExpand/Collapse icon
The simulation modelExpand/Collapse icon
Seeds stay and dispersalExpand/Collapse icon
Seeds fallExpand/Collapse icon
Seeds growExpand/Collapse icon
The bias correction approachExpand/Collapse icon
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Sampling effort required for fitting spatially explicit models of species distribution dynamics
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Sampling effort required for fitting spatially explicit models of species distribution dynamics

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Density dependence promotes species coexistence and provides a unifying explanation for distinct productivity-diversity relationships
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  • Liang Xu + 2 more

Abstract Understanding diversity patterns in complex communities, such as microbial consortia, requires a mechanistic framework appropriate for many species. Negative density dependence is often utilized in complex ecosystem models, typically as a density-dependent mortality term for a population, but its full impact on community structure remains unclear. Here we use mechanistic population models of resource consumption to examine the effects of negative density dependence and develop a tractable framework for understanding diversity patterns in complex systems. To provide mechanistic grounding, we quantify how density-dependent mortality expands coexistence zones along resource gradients in simple communities using graphical analysis. We then derive an analytical, ecologically insightful formula predicting species abundances in subsets (guilds) of complex communities, in which many species share a resource or predator. Finally, we use the formula to explain how distinct relationships between productivity and diversity emerge from the resulting mechanistic framework, providing insights into previously unreconciled observed patterns.

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