23The ability of species to mutually invade from rare is the defining measure of species 24 coexistence. However, it is unknown whether invasion growth rates predict any characteristic 25 of long-term community dynamics. Here, we use a model five-species microbial community 26 to investigate the link between short-term growth rate and long-term relative abundance. We 27 manipulated diversity and tested the ability of species to coexist in different combinations. 28Across all diversity levels and species combinations, populations re-established from rare in 29 71 of 75 combinations and all combinations were stable in long-term culture. Moreover, short-30 term relative invader growth rate was positively associated with long-term equilibrium 31 proportion, despite large variation in interactions between species and communities. This 32 finding was confirmed using a modelling approach and suggests that the short-term invasion 33 growth rate can predict long-term relative abundance within that community. 34 56 both support (Martínez-Meyer et al. 2013) and refute (Dallas & Hastings 2018) the niche 57 centrality hypothesis across animal and plant communities. Recent theory has highlighted why 58 this macroecological relationship may not be ubiquitous, with spatial and temporal variation 59 3 and metapopulation dynamics potentially causing this relationship to break down (Holt 2019; 60

The potential for antibiotics to affect the ecology and evolution of the human gut microbiota is well recognised and has wide-ranging implications for host health. Here, we review the findings of key studies that surveyed the human gut microbiota during antibiotic treatment. We find several broad patterns including the loss of diversity, disturbance of community composition, suppression of bacteria in the Actinobacteria phylum, amplification of bacteria in the Bacteroidetes phylum, and promotion of antibiotic resistance. Such changes to the microbiota were often, but not always, recovered following the end of treatment. However, many studies reported unique and/or contradictory results, which highlights our inability to meaningfully predict or explain the effects of antibiotic treatment on the human gut microbiome. This problem arises from variation between existing studies in three major categories: differences in dose, class, and combinations of antibiotic treatments used; differences in demographics, lifestyles, and locations of subjects; and differences in measurements, analyses, and reporting styles used by researchers. To overcome this, we suggest two integrated approaches: (i) a top-down approach focused on building predictive models through large sample sizes, deep metagenomic sequencing, and effective collaboration; and (ii) a bottom-up reductionist approach focused on testing hypotheses using model systems.