Disturbance Regimes Predictably Alter Diversity in an Ecologically Complex Bacterial System

• DOI: 10.1128/mbio.01372-16
• Publication Date: 2016-12-23T18:48:16Z
• Authors: Sean M. Gibbons; Monika Scholz; Alan L. Hutchison; Aaron R. Dinner; Jack A. Gilbert; Maureen L. Coleman
• Keywords: 0301 basic medicine; 570; 0303 health sciences; Ultraviolet Rays; Microbial Consortia; Biodiversity; Environment; Models, Theoretical; 15. Life on land; 551; Microbiology; QR1-502; 6. Clean water; 03 medical and health sciences; Theoretical; Models; 13. Climate action; 14. Life underwater; Ecosystem; Research Article

ABSTRACT Diversity is often associated with the functional stability of ecological communities from microbes to macroorganisms. Understanding how diversity responds environmental perturbations and consequences this relationship for ecosystem function are thus central challenges in microbial ecology. Unimodal diversity-disturbance relationships, which maximum occurs at intermediate levels disturbance, have been predicted ecosystems where life history tradeoffs separate organisms along a disturbance gradient. However, empirical support such peaked relationships macrosystems mixed, few studies explored these systems. Here we use complex microcosm systematically determine over range regimes. We observed reproducible switch between community states, gave rise transient maxima when states were forced mix. Communities showed reduced compositional was highest. To further explore dynamics, formulated simple model that reveals specific regimes under stable. Together, our results show both unimodal non-unimodal can be as system switches two distinct states; process likely across wide spatially temporally heterogeneous ecosystems. IMPORTANCE The linked functioning As humanity continues impact worldwide, diet disease perturb own commensal communities, ability predict will respond critical importance. Using experiments, find different predictable way. Maximum each suited conditions, mixed due disturbance. This except mathematical model, unimodality common feature fluctuating environments.