1,720,979 research outputs found
Lognormal MAD of regional migration treatment [Dataset]
No full textIn Fig 2d the MAD of each treatment were separately rescaled to facilitate comparison, meaning that a single lognormal was fit. The lognormal parameters ( and s) slightly vary from treatment-to-treatment, so lognormal fits were examined for separate treatments. Here we present the MAD with fitted lognormal for the regional migration treatment at transfer 12, the treatment with the highest number of ASVs. We see that the empirical MAD is non-linear on a log-log scale, suggesting that a power law would not serve as an appropriate descriptor.Peer reviewe
Taylor’s Law intercept simulations [Dataset]
No full textThe equivalent analysis as shown in S9 Fig for the intercept of Taylor’s Law. Similar to S10 Fig the errors tend to cluster together for regional migration, implying that the SLM is performing adequately for adjacent combinations of parameter regimes.Peer reviewe
The effect of migration on the AFD when modeled as a perturbation of initial conditions vs. a constant rate
No full textWe examined how migration as a perturbation of initial conditions compared to a commonly assumed form of migration where it occurs at a constant rate per-unit time. The AFD of a form of the SLM with a constant rate of migration at stationarity was derived (S4 Text) and the time-dependent solution of the SLM was obtained from a prior study (S5 Text) . The following parameters were used: , , , and . We have rescaled time using the timescale of growth to arrive at a dimensionless parameter . The AFD with no experimentally-imposed migration is represented by Eq 2 (i.e., a gamma distribution).Peer reviewe
Simulations of global migration statistics [Dataset]
No full textThe equivalent analysis as shown in S9 Fig for statistics that capture the change in the fluctuations around the typical abundance caused by global migration.Peer reviewe
Mean change in under global and no migration [Dataset]
No full textFor both a) no and b) global migration the mean of is initially higher than the stationary value of zero, though the mean relaxes to zero by transfer six for both treatments and does not appear to change after the cessation of global migration. This result is consistent with predicted consequences of global migration.Peer reviewe
Attractor status [Dataset]
No full textThe percent of communities belonging to a given attractor for each migration treatment.Peer reviewe
The distribution of abundances in the progenitor community is consistent across treatment status [Dataset]
No full texta–d) The abundances of ASVs in the progenitor that are present in a given treatment are consistently shifted to the right, implying that the probability of an ASV surviving is conditional on its initial abundance. Permutation-based two-sample Kolmogorov—Smirnov tests were performed for each treatment.Peer reviewe
Deriving the stationary AFD for the SLM with a constant rate of migration [Dataset]
No full textDerivation of the stationary distribution of abundance for the SLM with a constant rate of migration.Peer reviewe
Error estimates [Dataset]
No full textEcology has historically benefited from the characterization of statistical patterns of biodiversity within and across communities, an approach known as macroecology. Within microbial ecology, macroecological approaches have identified universal patterns of diversity and abundance that can be captured by effective models. Experimentation has simultaneously played a crucial role, as the advent of high-replication community time-series has allowed researchers to investigate underlying ecological forces. However, there remains a gap between experiments performed in the laboratory and macroecological patterns documented in natural systems, as we do not know whether these patterns can be recapitulated in the lab and whether experimental manipulations produce macroecological effects. This work aims at bridging the gap between experimental ecology and macroecology. Using high-replication time-series, we demonstrate that microbial macroecological patterns observed in nature exist in a laboratory setting, despite controlled conditions, and can be unified under the Stochastic Logistic Model of growth (SLM). We found that demographic manipulations (e.g., migration) impact observed macroecological patterns. By modifying the SLM to incorporate said manipulations alongside experimental details (e.g., sampling), we obtain predictions that are consistent with macroecological outcomes. By combining high-replication experiments with ecological models, microbial macroecology can be viewed as a predictive discipline.Peer reviewe
The sampling form of the gamma distribution predicts ASV occupancy [Dataset]
No full texta) The fraction of replicates harboring a given ASV (i.e., occupancy) can be predicted using a form of the gamma distribution that accounts for sampling across experimental treatments and transfers. b) The distribution or relative errors exhibited a similar form across treatments and transfers, suggesting that the predictions of the SLM are broadly applicable. c) By comparing ASVs that were present in both migration and no migration treatments, we can see that the errors are generally similar between treatments, if only slightly higher in the no migration treatment.Peer reviewe
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