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Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.This is a summary of: Needham, D. M. et al. The microbiome of a bacterivorous marine choanoflagellate contains a resource-demanding obligate bacterial associate. Nat. Microbiol. https://doi.org/10.1038/s41564-022-01174-0 (2022). More

The generalist accumulates extracellular nitriteWe first tested whether the generalist accumulates substantial extracellular nitrite under our experimental conditions, and thus creates a niche for the specialist. To accomplish this, we grew the generalist alone in bioreactors with anoxic ACS medium amended with 12 mM nitrate as the growth-limiting substrate and measured the extracellular concentrations of nitrate and nitrite over time. We performed these experiments at pH 6.5 (strong nitrite toxicity) and 7.5 (weak nitrite toxicity).We observed a substantial accumulation of extracellular nitrite regardless of the pH (Fig. 3A, B). When grown at pH 6.5 (strong nitrite toxicity), extracellular nitrite accumulated to a concentration comparable to the initial nitrate concentration (measured maximum extracellular nitrite concentration, 11.8 mM; measured initial nitrate concentration, 12.0 mM) and was subsequently consumed to below the detection limit (Fig. 3A). When grown at pH 7.5 (weak nitrite toxicity), extracellular nitrite again accumulated to a concentration comparable to the initial nitrate concentration (measured maximum extracellular nitrite concentration, 11.7 mM; measured initial nitrate concentration, 12.9 mM) and was subsequently consumed to below the detection limit (Fig. 3B). During growth at pH 6.5, substantial nitrite consumption did not begin until a prolonged period of time after nitrate consumption was complete, resulting in a relatively long duration of nitrite availability (Fig. 3A). During growth at pH 7.5, in contrast, substantial nitrite consumption began immediately after nitrate consumption was complete, resulting in a relatively short duration of nitrite availability (Fig. 3B). The longer duration of nitrite availability at pH 6.5 indicates that the duration of the niche created by the generalist for the specialist depends on pH.Fig. 3: Growth and nitrogen oxide dynamics of the generalist in batch culture.We grew the generalist alone in a bioreactor at A pH 7.5 (weak nitrite toxicity) or B pH 6.5 (strong nitrite toxicity) under anoxic conditions with nitrate as the growth-limiting substrate. Blue squares are measured extracellular nitrate concentrations, yellow triangles are measured extracellular nitrite concentrations, and black circles are measured cell densities. We measured extracellular nitrate and nitrite concentrations with IC and cell densities with FC. C Measured durations of nitrite availability for the generalist growing in batch culture. We grew the generalist alone in 96-well microtiter plates under anoxic conditions with nitrate as the growth-limiting substrate. Open symbols are durations of nitrite availability at pH 6.5 and closed symbols are durations of nitrite availability at pH 7.5. Each symbol is an independent biological replicate.Full size imageTo routinely quantify the duration of nitrite availability, we grew the generalist alone with varying amounts of nitrate as the growth-limiting substrate. We then quantified the length of time from when the growth rate with nitrate was maximum to when the growth rate with nitrite was maximum. This cell density-based proxy measure is valid because the growth of the generalist is directly linked to the consumption of nitrate and nitrite (Fig. 3A, B). The cell density of the generalist was initially linearly correlated with nitrate consumption at both pH 6.5 (strong nitrite toxicity) (two-sided Pearson correlation test; r = −0.96, p = 1.5 × 10–8, n = 15) (Fig. 3A) and 7.5 (weak nitrite toxicity) (two-sided Pearson correlation test; r = −1.00, p = 2.2 × 10–16, n = 30) (Fig. 3B). After nitrate was depleted, the cell density of the generalist became linearly correlated with nitrite consumption at both pH 6.5 (strong nitrite toxicity) (two-sided Pearson correlation test; r = −0.97, p = 3 × 10–4, n = 7) (Fig. 3A) and 7.5 (weak nitrite toxicity) (two-sided Pearson correlation test; r = −0.97, p = 6.8 × 10–10, n = 16) (Fig. 3B). We further validated our cell density-based approach by testing for concordance with our IC-based direct measures of the duration of nitrite availability. We observed a significant positive and linear relationship between the cell density- and IC-based measures (two-sided Pearson correlation test; r = 0.999, p = 0.023, n = 3) (linear regression model; slope = 1.19, intercept = −2.31, r2 = 0.99) (Supplementary Fig. S2), which further validates our cell density-based approach to routinely estimate the duration of nitrite availability.Using our cell density-based approach, we found that the duration of nitrite availability was significantly longer at pH 6.5 (strong nitrite toxicity) than at 7.5 (weak nitrite toxicity) regardless of the initial nitrate concentration (two-sample two-sided t-tests; Holm-adjusted p  0.92, Holm-adjusted p  0.6), and thus followed model predictions (Fig. 4A). However, when the specialist was initially rare (measured initial log rS/Gs of –3.19, –2.65, and –0.88), the relative abundances of the specialist continuously decreased between the third and twelfth transfers (Mann–Kendall trend tests; tau = –0.61 to –0.89, p  0 were dominated by phenotype C (dominant ancestral phenotype with a long time delay between nitrate and nitrite consumption), while generalist isolates from co-cultures with initial rS/Gs  More

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DataWe used temperature data, rainfall data, and data on the incidence of malaria collected from 1901 to 2015 for 43 African countries to construct networks to determine the relationships between transmission of malaria and climate change elements, especially temperature and rainfall. Data resolution is given by the latitude and longitude of the capital city for every country in Africa. Temperature and rainfall data are provided in terms of monthly averages in the country wise. The nodes in the network represent the country, and the edges in the network represent the relationship between countries. We collected malaria data from Harvard Dataverse35 and the world malaria report from the WHO31. Data for temperature and rainfall were obtained from the Climate Change Knowledge Portal of the World Bank Group36.Network generation and analysisThe networks were constructed by using the threshold method where the network depends on the mean, standard deviation, and the real number ((n)) used to control the features of the network. Therefore, data for temperature, rainfall, and the incidence of malaria were divided into six groups mostly comprising ranges of 20 years (1900–1920, 1921–1940, 1941–1960, 1961–1980, 1981–2000) as well as the period from 2001 to 2015. The missing data in Malaria incidence data are filled by the average amount of malaria incidence collected per year.In Table S1, a malaria report from the World Health Organization shows that the rate of death is directly proportional to the incidence of malaria35. The death toll in Africa from malaria is about 98% of world deaths from malaria. Such deaths in African regions decrease thanks to efforts the WHO, governments, and the private sector have been conducting to prevent them. Weather and climate are among the factors that drive increases in malaria infections in different areas.We consider networks based on the threshold method (see the “Methods and Materials” section below). First, we fill the missing malaria incidence data, and we calculate normalized Pearson correlation coefficients of three-time series between two countries. Then, we obtain a correlation matrix for the countries. We estimate the average value of the correlation coefficients from the time intervals 1901–1920, 1921–1940, 1941–1960, 1961–1980, 1981–2000, and 2001–2015 for three time series: temperature, rainfall, and incidence of malaria. We summarize the averages and standard deviations of the correlation coefficients, as shown in Table S2. The mean values from the correlation in temperature are high, compared to those for rainfall and the incidence of malaria. The standard deviations in temperature and rainfall are large, but the standard deviation for the incidence of malaria is small.We chose an ad hoc threshold value of the correlation coefficients to generate sparse networks. The characteristic values for (n) of the threshold are given in Table S3. We consider three types of thresholds in order to observe changes in the networks according to the threshold.Let us define the normalized variance of each time series. We considered time series (T_{i} left( t right)), (M_{i} left( t right)), and (R_{i} left( t right)) in country (i) for temperature, the incidence of malaria, and rainfall, respectively. We define normalized variance as$$r_{ij} = frac{{x_{i} left( t right)x_{j} left( t right) – x_{i} left( t right)x_{j} left( t right)}}{{sigma_{i} sigma_{j} }}$$
(1)
where (x_{i} left( t right)) = (T_{i} left( t right)), (M_{i} left( t right)), (R_{i} left( t right)). We obtained a Pearson correlation matrix for each time series as follows:$$R_{S} = left[ {begin{array}{*{20}c} {r_{11} } & cdots & {r_{1N} } \ vdots & {r_{ij} } & vdots \ {r_{N1} } & cdots & {r_{NN} } \ end{array} } right]$$
(2)
where (S = T, M, R).We calculated the average value, (overline{r }), and the standard deviation, (sigma), for the correlation coefficients of the matrix. We applied the threshold method to generate a sparse network from the correlation matrix. Two countries are connected in the correlation network if and only if the value of the correlation coefficient is greater than, or equal to, the threshold value:$$r_{{ij}} = left{ {begin{array}{*{20}c} 1 & {{text{if}};r_{{ij}} ge bar{r}{text{ + n}}sigma } \ 0 & {{text{otherwise}}} \ end{array} } right.$$
(3)
where (r_{ij}) is the correlation coefficient between two countries, and (n) is an element of real numbers ((n in {mathbb{R}})). The value of (n) determines whether the network is sparsely or densely connected.We use Python programming language, packages, numpy for mathematical functions and random number generator, pandas for data analysis and manipulations, networkx for creation, manipulation, and studying the structure of the complex network, matplotlib for visualization and plotting graph and base map for map projection and visualization of geographic information. More

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