Microorganisms are the primary engines of biogeochemical processes and foundational to the provisioning of ecosystem services to human society. Free‐living microbial communities (microbiomes) and their functioning are now known to be highly sensitive to environmental change. Given microorganisms' capacity for rapid evolution, evolutionary processes could play a role in this response. Currently, however, few models of biogeochemical processes explicitly consider how microbial evolution will affect biogeochemical responses to environmental change. Here, we propose a conceptual framework for explicitly integrating evolution into microbiome–functioning relationships. We consider how microbiomes respond simultaneously to environmental change via four interrelated processes that affect overall microbiome functioning (physiological acclimation, demography, dispersal and evolution). Recent evidence in both the laboratory and the field suggests that ecological and evolutionary dynamics occur simultaneously within microbiomes; however, the implications for biogeochemistry under environmental change will depend on the timescales over which these processes contribute to a microbiome's response. Over the long term, evolution may play an increasingly important role for microbially driven biogeochemical responses to environmental change, particularly to conditions without recent historical precedent.
more » « less- PAR ID:
- 10403416
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Ecology Letters
- Volume:
- 26
- Issue:
- S1
- ISSN:
- 1461-023X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
ABSTRACT Microbiomes underpin biogeochemical processes, sustain the bases of food webs, and recycle carbon and nutrients. Thus, microbes are frontline players in determining ecosystem responses to environmental change. My research team and I investigate the causes and consequences of microbiome stability. Our primary objective is to understand the responses of complex microbiomes to stressors associated with environmental change. This work is important because Earth is changing rapidly and drastically, and these changes are expected to have serious consequences for ecosystems, their inhabiting organisms, and their microbiomes. Therefore, we aim to understand the repercussions of alterations to microbiome structure and functions and to use this information to predict the responses of microbiomes to stressors. This research is critical to prepare for, respond to, and potentially moderate environmental change. We anticipate that the results of our research will contribute toward these goals and will broadly inform management or manipulation of microbiomes toward desired functions.more » « less
-
Abstract Soil microbiomes play a key role in driving biogeochemical cycles of the Earth system. As drought frequency and intensity increase due to climate change, soil microbes and the processes they control will be impacted. Even after a drought ends, microbiomes and other systems take time to recover and may display a memory of previous climate conditions. Still, the mechanisms involved in these legacy effects remain unclear, making it difficult to predict climate and biogeochemical rates in the future. Here, we used a trait‐based microbiome model (DEMENTpy) to implement trade‐off‐mediated mechanisms that may lead to drought legacy effects on litter decomposition. Trade‐offs were assumed to follow the Y‐A‐S framework that defines three primary life‐history strategies of microorganisms: high growth Yield, resource Acquisition, and Stress tolerance. We represented cellular trade‐offs between osmolytes required for drought tolerance and investment in enzymes involved in litter decomposition. Simulations were run under varying levels of drought severity and dispersal. With high levels of dispersal, no legacy effects were predicted by DEMENTpy following drought. With limited dispersal, severe drought resulted in a persistent legacy of altered community‐level traits and reduced litter decomposition. Moderate drought resulted in a transient legacy that disappeared after two years, consistent with recent empirical observations in Southern California ecosystems. These results imply that greater movement along the trade‐off between enzyme investment and osmolyte production resulted in stronger legacy effects. More generally, factors that shift the position of a microbiome in YAS space may alter the legacy outcome following drought. Our trait‐based modeling study motivates additional empirical measurements to quantify YAS traits and trade‐offs that are needed to make accurate predictions of soil microbiome resilience and functioning. Also, our study illustrates an emerging approach for representing trait trade‐offs in microbiomes and vegetation that dictate ecosystem responses to drought and other environmental perturbations.
-
Dr Andrea E. A. Stephens (Ed.)Hoffmann and Bridle [ 1. ] describe two processes that the framework introduced by Vinton et al. [ 2. ] did not explicitly consider. These two processes, reversibility of plastic responses and time lags in sensitivity of responses to the environment, can affect how plasticity impacts evolution. These processes are easily incorporated into our framework by adding stage structure and lagged environmental drivers. In Vinton et al. [ 2. ], when discussing the costs of plasticity, we primarily focused on energetic impacts on fitness, and the role of environmental predictability. Hoffmann and Bridle [ 1. ] are correct that differential impacts of plasticity across an individual’s lifetime might determine its response to different types of environmental change.more » « less
-
Abstract Climate change is affecting how energy and matter flow through ecosystems, thereby altering global carbon and nutrient cycles. Microorganisms play a fundamental role in carbon and nutrient cycling and are thus an integral link between ecosystems and climate. Here, we highlight a major black box hindering our ability to anticipate ecosystem climate responses: viral infections within complex microbial food webs. We show how understanding and predicting ecosystem responses to warming could be challenging—if not impossible—without accounting for the direct and indirect effects of viral infections on different microbes (bacteria, archaea, fungi, protists) that together perform diverse ecosystem functions. Importantly, understanding how rising temperatures associated with climate change influence viruses and virus-host dynamics is crucial to this task, yet is severely understudied. In this perspective, we (i) synthesize existing knowledge about virus-microbe-temperature interactions and (ii) identify important gaps to guide future investigations regarding how climate change might alter microbial food web effects on ecosystem functioning. To provide real-world context, we consider how these processes may operate in peatlands—globally significant carbon sinks that are threatened by climate change. We stress that understanding how warming affects biogeochemical cycles in any ecosystem hinges on disentangling complex interactions and temperature responses within microbial food webs.
-
Abstract In recent times, interest has grown in understanding how microbiomes – the collection of microorganisms in a specific environment – influence the survivability or fitness of their plant and animal hosts. The profound diversity of bacterial and fungal species found in certain environments, such as soil, provides a large pool of potential microbial partners that can interact in ways that reveal patterns of associations linking host–microbiome traits developed over time. However, most microbiome sequence data are reported as a community fingerprint, without analysis of interaction networks across microbial taxa through time.
To address this knowledge gap, more robust tools are needed to account for microbiome dynamics that could signal a beneficial change to a plant or animal host. In this paper, we discuss applying mathematical tools, such as dynamic network modelling, which involves the use of longitudinal data to study system dynamics and microbiomes that identify potential alterations in microbial communities over time in response to an environmental change. In addition, we discuss the potential challenges and pitfalls of these methodologies, such as handling large amounts of sequencing data and accounting for random processes that influence community dynamics, as well as potential ways to address them.
Ultimately, we argue that components of microbial community interactions can be characterized through mathematical models to reveal insights into complex dynamics associated with a plant or animal host trait. The inclusion of interaction networks in microbiome studies could provide insights into the behaviour of complex communities in tandem with host trait modification and evolution.
A free
Plain Language Summary can be found within the Supporting Information of this article.