Bacterial communities are ubiquitous [1], dynamic [2], and sensitive to environmental change [3, 4]. A wide range of literature explores microbiome responses to rapid environmental change in different environments [3], consistently revealing that microbial communities are affected by disturbance, and generally do not recover to their pre-disturbance composition [5]. Historically, experimental procedures, designs, and hypotheses regarding the recovery of microbiomes following disturbance have developed in a largely field-specific manner (e.g., medical microbiology, soil microbiology, aquatic microbiology). Consequently, a comparison of community disturbance responses across microbial environments is lacking. Whether microbiomes from different environments exhibit responses to disturbance, and whether these responses are consistent with extant conceptual frameworks [6, 7] is a major gap in knowledge, especially considering growing anthropogenic pressures on microbial systems (e.g., pollutants, antibiotics, and climate extremes).
Properties of the microbial environment likely affect the dominant responses of microbiomes to disturbance, but empirical comparisons of recovery across environments are scarce [4]. Different microbial habitats have varying degrees of spatial and temporal heterogeneity, microbial species pool sizes, connectivity, and resource availability, all of which may affect community assembly processes [6], and likely result in different disturbance responses among environments. For example, animal gut microbiomes have relatively low diversity [1] and are dispersal-limited due to selective pressures associated with host physiology that likely influence the recovery of the resident microbial diversity. In contrast, soil microbiomes are extremely diverse, but poorly connected [8], likely affecting recolonization following disturbance. The lack of host-driven selection in these systems, combined with high diversity may result in communities composed of different taxon when compared to their pre-disturbance state.
Assessments of microbiome recovery often rely on indicator measurements that are environment-specific (e.g., host health in host-associated microbiomes or plant productivity in soil microbiomes), hindering the comparison of microbial disturbance responses across environments. By considering changes in diversity at multiple spatial scales (i.e., within and among samples) and the role of spatial connectivity in these responses, the metacommunity framework [9] can help to synthesize and explicitly compare microbial community responses to disturbance across environments, and in turn provide new insights into the role of the environment in shaping these responses [4]. To this end, publicly available 16S rRNA gene amplicon sequences can be leveraged to assess bacterial community responses as changes in bacterial richness (the number of taxa present in a sample) and composition (variation in taxon relative abundance between samples). Generally, we expect that across environments, community richness will decrease (Fig. 1a), and community composition will change immediately after the disturbance, due for example, to differential mortality and an altered competitive landscape [5]. However, environmental change does not consistently result in decreased richness [10]. Additionally, in microbes, disturbances may involve the addition of novel taxa (e.g., with sewage sludge amendments to soil [11]), which may result in richness increases. Over longer time scales following disturbance, richness may either fail to fully recover (at least within the period observed; e.g., [12]), recover fully [13], or even be higher following disturbance [14].
Community composition is often a more robust indicator of biodiversity change than richness [10]. Compositional changes can be assessed in terms of compositional variation among local communities, or dispersion, and the extent to which the community recovers to its pre-disturbance composition, or turnover (Fig. 1b). Following disturbance, dispersion can decrease, for example, if a stressor is selective and leaves only tolerant taxa to persist. Alternatively, dispersion can increase, for example, if specific taxa are favored due to differences in which taxa persist following disturbances [15]. Given enough time, we expect the same taxa that dominated prior to a disturbance to recover to their original abundances [4], especially in host-associated microbiomes, which can be modulated by the host [16]. However, under some circumstances (e.g., strong or long disturbances, or invasion by novel taxa [17, 18]), it is also possible that the disturbance could permanently alter relative abundance patterns in the community [19, 20], resulting in communities that tend away from their pre-disturbance composition over time. Across environments, microbiomes have been shown to recover towards (negative turnover, e.g., [13, 21]), or to drift away from (positive turnover, e.g., [22], their pre-disturbance compositions. Importantly, both changes in dispersion and turnover can arise from changes in richness alone and null models have been developed that allow for the measurement of compositional change independent of changes in community richness [23].
Meta-analyses focusing on the undisturbed temporal dynamics of microbial communities have shown consistent patterns across systems [2, 5, 24], but temporal disturbance responses have received less attention [4]. To this end, we performed a synthetic analysis of time series of disturbed aquatic, mammal-associated, and soil microbiomes. Across environments, we compared the initial response and subsequent recovery from disturbance in terms of community richness, dispersion, and turnover, and used null models to disentangle whether the observed changes in dispersion and turnover were due to changes in richness. Given the rapid rates of compositional turnover in microbiomes [25], we focused on 29 studies that repeatedly sampled the microbiomes within 50 days post disturbance.