A general framework for quantitatively assessing ecological stochasticity

Proceedings of the National Academy of Sciences, Journal Year: 2019, Volume and Issue: 116(34), P. 16892 - 16898

Published: Aug. 7, 2019

Understanding the community assembly mechanisms controlling biodiversity patterns is a central issue in ecology. Although it generally accepted that both deterministic and stochastic processes play important roles assembly, quantifying their relative importance challenging. Here we propose general mathematical framework to quantify ecological stochasticity under different situations which factors drive communities more similar or dissimilar than null expectation. An index, normalized ratio (NST), was developed with 50% as boundary point between (<50%) (>50%) assembly. NST tested simulated by considering abiotic filtering, competition, environmental noise, spatial scales. All approaches showed limited performance at large scales very high noise. However, all of other scenarios, accuracy (0.90 1.00) precision (0.91 0.99), averages 0.37 higher (0.1 0.7) 0.33 (0.0 1.8) previous approaches. also applied estimate succession groundwater microbial response organic carbon (vegetable oil) injection. Our results shifted from (NST = 21%) 70%) right after input. As vegetable oil consumed, gradually returned be 27%). In addition, our demonstrated model algorithms similarity metrics had strong effects on stochasticity.

Language: Английский

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Estimating and mapping ecological processes influencing microbial community assembly

Frontiers in Microbiology, Journal Year: 2015, Volume and Issue: 6

Published: May 1, 2015

Ecological community assembly is governed by a combination of (i) selection resulting from among-taxa differences in performance; (ii) dispersal organismal movement; and (iii) ecological drift stochastic changes population sizes. The relative importance nature these processes can vary across environments. Selection be homogeneous or variable, while rate, we conceptualize extreme rates as two categories; limitation results limited exchange organisms among communities, homogenizing high levels organism exchange. To estimate the influence spatial variation each process extend recently developed statistical framework, use simulation model to evaluate accuracy extended framework examine subsurface microbial communities over geologic formations. For degree which it influenced selection, variable limitation, dispersal. Our analyses revealed that influences substantially even within formation. We further identify environmental features associated with process, allowed mapping ecological-process-influences. maps provide new lens through systems understood; system investigated here they was rate at redox conditions change depth.

Language: Английский

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Stochasticity, succession, and environmental perturbations in a fluidic ecosystem

Proceedings of the National Academy of Sciences, Journal Year: 2014, Volume and Issue: 111(9)

Published: Feb. 18, 2014

Significance The study of ecological succession remains at the core ecology. Understanding trajectories and mechanisms controlling is crucial to predicting responses ecosystems environmental change projecting their future states. By definition, deterministic expected under homogeneous abiotic biotic starting conditions. This study, however, shows that groundwater microbial communities in response nutrient amendment primarily stochastic, but drivers biodiversity are dynamic rather than static. identifying community assembly succession, this makes fundamental contribution mechanistic understanding essential for a predictive ecology many systems ranging from microbiomes humans plants natural managed ecosystems.

Language: Английский

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Community ecology in the age of multivariate multiscale spatial analysis

Ecological Monographs, Journal Year: 2012, Volume and Issue: 82(3), P. 257 - 275

Published: March 14, 2012

Species spatial distributions are the result of population demography, behavioral traits, and species interactions in spatially heterogeneous environmental conditions. Hence composition assemblages is an integrative response variable, its variability can be explained by complex interplay among several structuring factors. The thorough analysis variation may help infer processes shaping ecological communities. We suggest that studies would benefit from combined use classical statistical models community data, such as constrained or unconstrained multivariate analyses site‐by‐species abundance tables, with rapidly emerging diversifying methods pattern analysis. Doing so allows one to deal explicit beta diversity a biogeographic context through multiscale patterns original data including characterization fitted residual models. summarize here recent progress for specifying features weighting matrices eigenfunctions order define scale‐explicit analyses. Through worked example on tropical tree communities, we also show potential overall approach identify significant could arise omission important unmeasured explanatory variables processes.

Language: Английский

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Advances, challenges and a developing synthesis of ecological community assembly theory

Philosophical Transactions of the Royal Society B Biological Sciences, Journal Year: 2011, Volume and Issue: 366(1576), P. 2403 - 2413

Published: July 18, 2011

Ecological approaches to community assembly have emphasized the interplay between neutral processes, niche-based environmental filtering and species sorting in an interactive milieu. Recently, progress has been made terms of aligning our vocabulary with conceptual advances, assessing how trait-based functional parameters differ from expectation traits vary along gradients. Experiments confirmed influence these processes on addressed role dispersal shaping local assemblages. Community phylogenetics forged common ground ecologists biogeographers, but it is not a proxy for approaches. theory need comparative synthesis that addresses relative importance niche varies among taxa, gradients, across scales. Towards goal, we suggest set probably confer increasing neutrality regionality review influences stress, disturbance scale assembly. We advocate complexity experiments order assess multiple processes. As example, provide evidence dispersal, trait interdependencies about equal experimental grassland.

Language: Английский

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