Connecting the ruminant microbiome to climate change: insights from current ecological and evolutionary concepts

Abstract

Ruminant livestock provide meat, milk, wool, and other products required for human subsistence. Within the digestive tract of ruminant animals, the rumen houses a complex and diverse microbial ecosystem. These microbes generate many of the nutrients that are needed by the host animal for maintenance and production. However, enteric methane (CH₄) is also produced during the final stage of anaerobic digestion. Growing public concern for global climate change has driven the agriculture sector to enhance its investigation into CH₄ mitigation. Many CH₄ mitigation methods have been explored, with varying outcomes. With the advent of new sequencing technologies, the host-microbe interactions that mediate fermentation processes have been examined to enhance ruminant enteric CH₄ mitigation strategies. In this review, we describe current knowledge of the factors driving ruminant microbial assembly, how this relates to functionality, and how CH₄ mitigation approaches influence ecological and evolutionary gradients. Through the current literature, we elucidated that many ecological and evolutionary properties are working in tandem in the assembly of ruminant microbes and in the functionality of these microbes in methanogenesis. Additionally, we provide a conceptual framework for future research wherein ecological and evolutionary dynamics account for CH₄ mitigation in ruminant microbial composition. Thus, preparation of future research should incorporate this framework to address the roles ecology and evolution have in anthropogenic climate change.

Keywords: cattle; ecology; enteric methane; evolution; greenhouse gases; inhibition; methanogenesis; rumen microbiome.

Figure 1

Global methane emissions. In panel A, the global CH₄ emissions from 2021 are displayed measured in tons of CO₂-equivalents. Data was sourced from Jones et al. (2024) and the graph was generated from ourworldindata.org/co2-and-greenhouse-gas-emissions (accessed 8/18/2023). Panel B displays the methane emissions by sector as measured in tons of CO₂-equivalents and was generated from the Climate Analysis Indicators Tool from ourworldindata.org/co2-and-greenhouse-gas-emissions (accessed 8/18/2023).

Figure 2

The first microbial colonizers are driven by stochastic processes such as historical contingency, drift, and dispersal. In this example, two scenarios are explored. In scenario I, the early arriving species introduce niche preemption. Here, the blue bacteria inhibit the growth of the green bacteria generating a high CH₄, low efficiency animal. In scenario 11, the early arriving species introduce niche modification. Here, the yellow bacteria create an environment suitable for colonization and establishment for other microbial members generating a low CH₄, high efficiency animal phenotype (Adapted from Mizrahi and Jami, 2021).

Figure 3

Metapopulation, metacommunity, and island biogeography theories are of interest in ruminant microbial ecology. In panel A, we visualize a metapopulation as three “patches” of microbes (denoted by the red box). The dashed black arrows demonstrate the movement of the yellow bacterial species between the different patches on a spatial gradient – in this case, different areas of the rumen. Additionally, if we zoom into a single patch and can consider interspecies interactions, we incorporate metacommunity theory as the movement of microbes within and between populations. In panel B, we consider the host as the population and the movement of microbes between each host represents metapopulation theory while considering the holobiont. In panel C, we consider a group of hosts as “islands” or “patches” and consider the movement of microbes within and between each island or patch (i.e., island biogeography and/or metacommunity).

Figure 4

Volatile fatty acid production involves many different microbial species that occupy three different trophic levels. Green represents trophic level 1. Red boxes indicate products involved in level two as end-products and in level three substrates. Teal boxes represent products involved in levels two and three as end-products. Yellow boxes represent known microbial species intricately involved in VFA production (Adapted from Hassan et al., 2020, Mizrahi et al., 2021, and Besharati et al., 2022).

Figure 5

The blurry lines between taxonomy and function. Functional redundancy in the rumen ecosystem arises due to variability in microbial species and their taxonomic identity. Taxonomic analysis could reveal that Species 1 (yellow) is greater in relative abundance compared to Species 2 (pink) and Species 3 (green). Functional analysis could mask the species in lower abundance making taxonomic analysis of the microbiome difficult. Meanwhile, applying the functional group concept enables the various species to be grouped together based on functional genes. In doing so, Species 1, Species 2, and Species 3 are now considered to be “the same” according to function for Gene A. This process can further be applied by grouping microbes for Gene B and Gene C. Another consideration regarding function is that a stable community could be acted upon by positive selection resulting in a loss of genes to reduce functional redundancy (i.e., BQH). Here in this example, Species 2 and Species 3 would lose Gene A because Species 1, which is more abundant, carries the gene. Additionally, Species 3 would lose Gene 2 as Species 2 is more abundant. Therefore, the function encoded by Gene A would still be carried out by Species 1 and the remaining community members are now free to perform other community functions that could be beneficial to its host. Indeed, the same logic goes for gene loss in Species 2 and Species 3. The functional group concept and the BQH are important ecological and evolutionary concepts for understanding microbial community dynamics that could factor into methanogenesis inhibition interventions.

Figure 6

Dispersion of microbial communities can be initiated by many factors. Deterministic factors such as dietary interventions and stochastic forces such as environmental stressors can act to alter or disrupt stable microbiomes. Anna Karenina principles demonstrate that these variability changes are often stochastic in nature as deterministic forces are more localized.