Ecology

Callie Veelenturf measured the pH, conductivity and temperature near a leatherback sea turtle’s nest during research in Equatorial Guinea.Credit: Jonah Reenders

This picture of marine conservation biologist Callie Veelenturf won the Nature Careers photo competition in 2018 — an event Veelenturf credits with kick-starting her career. She went on to assist in drafting a law that will help to protect species and habitats in Panama.Since 2021, editors at Nature have been tracking instances such as this, in which our journalism and opinion articles have had an impact. Here, we look at three times when content on biodiversity affected researchers, communities or policies. As well as shaping Veelenturf’s conservation work, Nature articles have raised the profile of a proposal to protect part of the Antarctic Ocean and fuelled discussions of carbon-tax proposals to fund tropical-forest conservation.Protect PanamaIn the prize-winning photo, Veelenturf was pictured with a leatherback sea turtle (Dermochelys coriacea) in Equatorial Guinea, where she was collecting data for her master’s degree at Purdue University Fort Wayne, Indiana, in 2016. She and biologist Jonah Reenders, now a photographer based in San Francisco, California, spent nearly half a year there, living in tents on Bioko Island, and Reenders took the picture of her as she measured the pH, conductivity and temperature of the sand near the leatherback’s nest.After the photo was published, a deluge of e-mails and messages “gave me this network, almost overnight, of other sea-turtle conservationists doing similar things around the world”, says Veelenturf, who is now based in Arraiján, Panama. “All of a sudden I was an ‘us’.”The photo award also validated her hard work, Veelenturf says, contradicting a common assumption that sea-turtle research just meant relaxing on the beach. Karla Barrientos-Muñoz, a Colombian sea-turtle conservationist at the Fundación Tortugas del Mar, based in Medellín, wrote that Veelenturf’s win was for all women in sea-turtle conservation. “It made me feel part of this community,” Veelenturf says.Inspired, she founded a non-profit organization called the Leatherback Project, based in Norfolk, Massachusetts, and later won a National Geographic Explorers grant, allowing her to perform the first scientific survey of sea turtles in Panama’s Pearl Islands archipelago. Here, her team worked with local communities to study the nesting sites and foraging grounds of olive ridley (Lepidochelys olivacea), green (Chelonia mydas), hawksbill (Eretmochelys imbricata) and eastern Pacific leatherback sea turtles.While doing fieldwork, Veelenturf read David Boyd’s book The Rights of Nature (2017), which described how some lawyers had fought to earn legal rights for nature. Such laws, which now exist in at least nine countries, make it easier to conserve the environment, because organizations can sue to protect a rainforest or stream. She went on to work with environmentally minded congress member Juan Diego Vásquez Gutiérrez and Panamanian legal advisers to draft a similar law for Panama, which is especially rich in biodiversity. Vásquez sponsored the legislation, and after more than a year of debate and revision by the public and in the national assembly, it was signed into law on 24 February 2022.Protect the AntarcticIn October 2020, a Comment article argued that the seas around the western Antarctic Peninsula should be designated a marine protected area. Overfishing there is removing large numbers of shrimp-like crustaceans called Antarctic krill (Euphausia superba), affecting the region’s entire web of species, including penguins, whales and seals, which feed on krill. The peninsula is also one of the fastest-warming ecosystems on the planet.A proposal for a marine protected area in the Antarctic must be approved by the groups of governments that make up the Commission for the Conservation of Antarctic Marine Living Resources (CCAMLR). Cassandra Brooks, a marine scientist at University of Colorado Boulder who co-authored the Nature piece and sits on CCAMLR’s non-voting science delegation, says that the Comment was sent to all the commission’s government delegations and observer groups. “If we can raise the issue in the public,” Brooks says, “it does help raise the issue within that diplomatic space.”The western Antarctic Peninsula proposal is one of three on the table for the next CCAMLR meeting in October 2022. It took ten years for CCAMLR to declare the Ross Sea a marine protected area. “The Antarctic does not have ten years,” says Comment co-author Carolyn Hogg, a conservation biologist at the University of Sydney in Australia.News stories about the article were published globally, including in China, India, South Korea and Malaysia. Hogg says it increased her visibility and further raised her profile with the Australian government. She is working with the government to ensure that the country’s threatened-species policy is informed by the latest genomic research. The goal is to give endangered populations the best chance of survival by preserving as much genetic diversity as possible.Hogg and Brooks wrote the piece with other women, some of whom were part of Homeward Bound, a global leadership programme for women in science, technology, engineering, mathematics and medicine. Many Homeward Bound participants and alumnae — 288 women from at least 30 countries — co-signed it and worked to translate it into many languages, “showing CCAMLR that this large community of women scientists from all over the world is watching, and going to hold them accountable”, Brooks says.Antarctica tends to be “both diplomatically and scientifically dominated by men”, she notes, and the impact of this global community of women was inspiring.Carbon tax for tropical forestsTropical countries should adopt a carbon tax, urged another Comment in February 2020, creating a levy on fossil fuels that should be used to conserve tropical forests. Costa Rica and Colombia had already adopted such a tax, and several other countries, including Indonesia, Brazil and Peru, are now considering implementing one, says Sebastian Troëng, executive vice-president of conservation partnerships at Conservation International who is based in Brussels and co-authored the piece.After the article was published, the authors made sure it was widely discussed. One of them, environmental economist Edward Barbier at Colorado State University in Fort Collins, presented the proposal at major meetings. These included the World Bank–International Monetary Fund forum in April 2022 and the Global Peatlands Initiative of the United Nations Framework Convention on Climate Change, at the 2021 climate summit COP26, in Glasgow, UK. The carbon-pricing proposal can be applied to any ecosystem, Barbier says. “Peatlands are ideal, because you’re saving probably the most carbon-dense ecosystem on our planet.”Meanwhile, Troëng’s colleagues presented the proposal to representatives from the finance and environment ministries of Chile, Mexico, Peru, Ecuador, Colombia and Costa Rica. “Since then, we’ve been working directly with government ministries,” he says, to strengthen the existing carbon-tax system in Colombia and to establish similar systems in Peru and Singapore. “I think what people appreciate the most is the fact that two countries have already done it, so it’s not just a theory or a wild idea, but it’s actually working,” Barbier says.“It’s always challenging to say, was it this paper that made something happen?” notes Troëng, on the impact of the article. “But it’s part of this growing consensus that nature plays an extremely important role in how we address climate change.” More

Fly population and maintenanceWe used an outbred population of Drosophila melanogaster established from 160 Wolbachia-infected fertilised females collected in Azeitão, Portugal54, and given to us by Élio Sucena. For at least 13 generations prior to the start of the experiments the flies were maintained on standard sugar yeast agar medium (SYA medium: 970 ml water, 100 g brewer’s yeast, 50 g sugar, 15 g agar, 30 ml 10% Nipagin solution and 3 ml propionic acid; ref. 61), in a population cage containing at least 5000 flies, with non-overlapping generations of 15 days. They were maintained at 24.3 ± 0.2 °C, on a 12:12 h light-dark cycle, at 60–80 % relative humidity. The experimental flies were kept under the same conditions. No ethical approval or guidance is required for experiments with D. melanogaster.Bacterial speciesWe used the Gram positive Lactococcus lactis (gift from Brian Lazzaro), Gram negative Enterobacter cloacae subsp. dissolvens (hereafter called E. cloacae; German collection of microorganisms and cell cultures, DSMZ; type strain: DSM-16657), Providencia burhodogranariea strain B (gift from Brian Lazzaro, DSMZ; type strain: DSM-19968) and Pseudomonas entomophila (gift from Bruno Lemaitre). L. lactis43, Pr. burhodogranariea44 and Ps. entomophila45 were isolated from wild-collected D. melanogaster and can be considered as opportunistic pathogens. E. cloacae was isolated from a maize plant, but has been detected in the microbiota of D. melanogaster46. All bacterial species were stored in 34.4% glycerol at −80 °C and new cultures were grown freshly for each experimental replicate.Experimental designFor each bacterial species, flies were exposed to one of seven treatments: no injection (naïve), injection with Drosophila Ringer’s (injection control) or injection with one of five concentrations of bacteria ranging from 5 × 106 to 5 × 109 colony forming units (CFUs)/mL, corresponding to doses of approximately 92, 920, 1,840, 9200 and 92,000 CFUs per fly. The injections were done in a randomised block design by two people. Each bacterial species was tested in three independent experimental replicates. Per experimental replicate we treated 252 flies, giving a total of 756 flies per bacterium (including naïve and Ringer’s injection control flies). Per experimental replicate and treatment, 36 flies were checked daily for survival until all flies were dead. A sub-set of the dead flies were homogenised upon death to test whether the infection had been cleared before death or not. To evaluate bacterial load in living flies, per experimental replicate, four of the flies were homogenised per treatment, for each of nine time points: one, two, three, four, seven, 14, 21, 28- and 35-days post-injection.Infection assayBacterial preparation was performed as in Kutzer et al.24, except that we grew two overnight liquid cultures of bacteria per species, which were incubated overnight for approximately 15 h at 30 °C and 200 rpm. The overnight cultures were centrifuged at 2880 × g at 4 °C for 10 min and the supernatant removed. The bacteria were washed twice in 45 mL sterile Drosophila Ringer’s solution (182 mmol·L-1 KCl; 46 mol·L-1 NaCl; 3 mmol·L-1 CaCl2; 10 mmol·L-1 Tris·HCl; ref. 62) by centrifugation at 2880 × g at 4 °C for 10 min. The cultures from the two flasks were combined into a single bacterial solution and the optical density (OD) of 500 µL of the solution was measured in a Ultrospec 10 classic (Amersham) at 600 nm. The concentration of the solution was adjusted to that required for each injection dose, based on preliminary experiments where a range of ODs between 0.1 and 0.7 were serially diluted and plated to estimate the number of CFUs. Additionally, to confirm post hoc the concentration estimated by the OD, we serially diluted to 1:107 and plated the bacterial solution three times and counted the number of CFUs.The experimental flies were reared at constant larval density for one generation prior to the start of the experiments. Grape juice agar plates (50 g agar, 600 mL red grape juice, 42 mL Nipagin [10% w/v solution] and 1.1 L water) were smeared with a thin layer of active yeast paste and placed inside the population cage for egg laying and removed 24 h later. The plates were incubated overnight then first instar larvae were collected and placed into plastic vials (95 × 25 mm) containing 7 ml of SYA medium. Each vial contained 100 larvae to maintain a constant density during development. One day after the start of adult eclosion, the flies were placed in fresh food vials in groups of five males and five females, after four days the females were randomly allocated to treatment groups and processed as described below.Before injection, females were anesthetised with CO2 for a maximum of five minutes and injected in the lateral side of the thorax using a fine glass capillary (Ø 0.5 mm, Drummond), pulled to a fine tip with a Narishige PC-10, and then connected to a Nanoject II™ injector (Drummond). A volume of 18.4 nL of bacterial solution, or Drosophila Ringer’s solution as a control, was injected into each fly. Full controls, i.e., naïve flies, underwent the same procedure but without any injection. After being treated, flies were placed in groups of six into new vials containing SYA medium, and then transferred into new vials every 2–5 days. Maintaining flies in groups after infection is a standard method in experiments with D. melanogaster that examine survival and bacterial load (e.g. refs. 22, 63, 64). At the end of each experimental replicate, 50 µL of the aliquots of bacteria that had been used for injections were plated on LB agar to check for potential contamination. No bacteria grew from the Ringer’s solution and there was no evidence of contamination in any of the bacterial replicates. To confirm the concentration of the injected bacteria, serial dilutions were prepared and plated before and after the injections for each experimental replicate, and CFUs counted the following day.Bacterial load of living fliesFlies were randomly allocated to the day at which they would be homogenised. Prior to homogenisation, the flies were briefly anesthetised with CO2 and removed from their vial. Each individual was placed in a 1.5 mL microcentrifuge tube containing 100 µL of pre-chilled LB media and one stainless steel bead (Ø 3 mm, Retsch) on ice. The microcentrifuge tubes were placed in a holder that had previously been chilled in the fridge at 4 °C for at least 30 min to reduce further growth of the bacteria. The holders were placed in a Retsch Mill (MM300) and the flies homogenised at a frequency of 20 Hz for 45 s. Then, the tubes were centrifuged at 420 × g for one minute at 4 °C. After resuspending the solution, 80 µL of the homogenate from each fly was pipetted into a 96-well plate and then serially diluted 1:10 until 1:105. Per fly, three droplets of 5 μL of every dilution were plated onto LB agar. Our lower detection limit with this method was around seven colony-forming units per fly. We consider bacterial clearance by the host to be when no CFUs were visible in any of the droplets, although we note that clearance is indistinguishable from an infection that is below the detection limit. The plates were incubated at 28 °C and the numbers of CFUs were counted after ~20 h. Individual bacterial loads per fly were back calculated using the average of the three droplets from the lowest countable dilution in the plate, which was usually between 10 and 60 CFUs per droplet.D. melanogaster microbiota does not easily grow under the above culturing conditions (e.g. ref. 42) Nonetheless we homogenised control flies (Ringer’s injected and naïve) as a control. We rarely retrieved foreign CFUs after homogenising Ringer’s injected or naïve flies (23 out of 642 cases, i.e., 3.6 %). We also rarely observed contamination in the bacteria-injected flies: except for homogenates from 27 out of 1223 flies (2.2 %), colony morphology and colour were always consistent with the injected bacteria (see methods of ref. 65). Twenty one of these 27 flies were excluded from further analyses given that the contamination made counts of the injected bacteria unreliable; the remaining six flies had only one or two foreign CFUs in the most concentrated homogenate dilution, therefore these flies were included in further analyses. For L. lactis (70 out of 321 flies), P. burhodogranaeria (7 out of 381 flies) and Ps. entomophila (1 out of 71 flies) there were too many CFUs to count at the highest dilution. For these cases, we denoted the flies as having the highest countable number of CFUs found in any fly for that bacterium and at the highest dilution23. This will lead to an underestimate of the bacterial load in these flies. Note that because the assay is destructive, bacterial loads were measured once per fly.Bacterial load of dead fliesFor two periods of time in the chronic infection phase, i.e., between 14 and 35 days and 56 to 78 days post injection, dead flies were retrieved from their vial at the daily survival checks and homogenised in order to test whether they died whilst being infected, or whether they had cleared the infection before death. The fly homogenate was produced in the same way as for live flies, but we increased the dilution of the homogenate (1:1 to 1:1012) because we anticipated higher bacterial loads in the dead compared to the live flies. The higher dilution allowed us more easily to determine whether there was any obvious contamination from foreign CFUs or not. Because the flies may have died at any point in the 24 h preceding the survival check, and the bacteria can potentially continue replicating after host death, we evaluated the infection status (yes/no) of dead flies instead of the number of CFUs. Dead flies were evaluated for two experimental replicates per bacteria, and 160 flies across the whole experiment. Similar to homogenisation of live flies, we rarely observed contamination from foreign CFUs in the homogenate of dead bacteria-injected flies (3 out of 160; 1.9 %); of these three flies, one fly had only one foreign CFU, so it was included in the analyses. Dead Ringer’s injected and naïve flies were also homogenised and plated as controls, with 6 out of 68 flies (8.8%) resulting in the growth of unidentified CFUs.Statistical analysesStatistical analyses were performed with R version 4.2.166 in RStudio version 2022.2.3.49267. The following packages were used for visualising the data: “dplyr”68, “ggpubr”69, “gridExtra”70, “ggplot2”71, “plyr”72, “purr”73, “scales”74, “survival”75,76, “survminer”77, “tidyr”78 and “viridis”79, as well as Microsoft PowerPoint for Mac v16.60 and Inkscape for Mac v 1.0.2. Residuals diagnostics of the statistical models were carried out using “DHARMa”80, analysis of variance tables were produced using “car”81, and post-hoc tests were carried out with “emmeans”82. To include a factor as a random factor in a model it has been suggested that there should be more than five to six random-effect levels per random effect83, so that there are sufficient levels to base an estimate of the variance of the population of effects84. In our experimental designs, the low numbers of levels within the factors ‘experimental replicate’ (two to three levels) and ‘person’ (two levels), meant that we therefore fitted them as fixed, rather than random factors84. However, for the analysis of clearance (see below) we included species as a random effect because it was not possible to include it as a fixed effect because PPP is already a species-level predictor. Below we detail the statistical models that were run according to the questions posed. All statistical tests were two-sided.Do the bacterial species differ in virulence?To test whether the bacterial species differed in virulence, we performed a linear model with the natural log of the maximum hazard as the dependent variable and bacterial species as a factor. Post-hoc multiple comparisons were performed using “emmeans”82 and “magrittr”85, using the default Tukey adjustment for multiple comparisons. Effect sizes given as Cohen’s d, were also calculated using “emmeans”, using the sigma value of 0.4342, as estimated by the package. The hazard function in survival analyses gives the instantaneous failure rate, and the maximum hazard gives the hazard at the point at which this rate is highest. We extracted maximum hazard values from time of death data for each bacterial species/dose/experimental replicate. Each maximum hazard per species/dose/experimental replicate was estimated from an average of 33 flies (a few flies were lost whilst being moved between vials etc.). To extract maximum hazard values we defined a function that used the “muhaz” package86 to generate a smooth hazard function and then output the maximum hazard in a defined time window, as well as the time at which this maximum is reached. To assess the appropriate amount of smoothing, we tested and visualised results for four values (1, 2, 3 and 5) of the smoothing parameter, b, which was specified using bw.grid87. We present the results from b = 2, but all of the other values gave qualitatively similar results (see Supplementary Table 2). We used bw.method = “global” to allow a constant smoothing parameter across all times. The defined time window was zero to 20 days post injection. We removed one replicate (92 CFU for E. cloacae infection) because there was no mortality in the first 20 days and therefore the maximum hazard could not be estimated. This gave final sizes of n = 14 for E. cloacae and n = 15 for each of the other three species.$${{{{{rm{Model}}}}}},1:,{{log }}left({{{{{rm{maximum}}}}}},{{{{{rm{hazard}}}}}}right), sim ,{{{{{rm{bacterial}}}}}},{{{{{rm{species}}}}}}$$Are virulence differences due to variation in pathogen exploitation or PPP?To test whether the bacterial species vary in PPP, we performed a linear model with the natural log of the maximum hazard as the dependent variable, bacterial species as a factor, and the natural log of infection intensity as a covariate. We also included the interaction between bacterial species and infection intensity: a significant interaction would indicate variation in the reaction norms, i.e., variation in PPP. The package “emmeans”82 was used to test which of the reaction norms differed significantly from each other. We extracted maximum hazard values from time of death data for each bacterial species/dose/experimental replicate as described in section “Do the bacterial species differ in virulence?”. We also calculated the maximum hazard for the Ringer’s control groups, which gives the maximum hazard in the absence of infection (the y-intercept). We present the results from b = 2, but all of the other values gave qualitatively similar results (see results). We wanted to infer the causal effect of bacterial load upon host survival (and not the reverse), therefore we reasoned that the bacterial load measures should derive from flies homogenised before the maximum hazard had been reached. For E. cloacae, L. lactis, and Pr. burhodogranariea, for all smoothing parameter values, the maximum hazard was reached after two days post injection, although for smoothing parameter value 1, there were four incidences where it was reached between 1.8- and 2-days post injection. Per species/dose/experimental replicate we therefore calculated the geometric mean of infection intensity combined for days 1 and 2 post injection. In order to include flies with zero load, we added one to all load values before calculating the geometric mean. Geometric mean calculation was done using the R packages “dplyr”68, “EnvStats”88, “plyr”72 and “psych”89. Each mean was calculated from the bacterial load of eight flies, except for four mean values for E. cloacae, which derived from four flies each.For Ps. entomophila the maximum hazard was consistently reached at around day one post injection, meaning that bacterial sampling happened at around the time of the maximum hazard, and we therefore excluded this bacterial species from the analysis. We removed two replicates (Ringer’s and 92 CFU for E. cloacae infection) because there was no mortality in the first 20 days and therefore the maximum hazard could not be estimated. One replicate was removed because the maximum hazard occurred before day 1 for all b values (92,000 CFU for E. cloacae) and six replicates were removed because there were no bacterial load data available for day one (experimental replicate three of L. lactis). This gave final sample sizes of n = 15 for E. cloacae and n = 12 for L. lactis, and n = 18 for Pr. burhodogranariea.$${{{{{rm{Model}}}}}},2 :,{{log }}({{{{{rm{maximum}}}}}},{{{{{rm{hazard}}}}}}), sim ,{{log }}({{{{{rm{geometric}}}}}},{{{{{rm{mean}}}}}},{{{{{rm{bacterial}}}}}},{{{{{rm{load}}}}}}),\ times ,{{{{{rm{bacterial}}}}}},{{{{{rm{species}}}}}}$$To test whether there is variation in pathogen exploitation (infection intensity measured as bacterial load), we performed a linear model with the natural log of infection intensity as the dependent variable and bacterial species as a factor. Similar to the previous model, we used the geometric mean of infection intensity combined for days 1 and 2 post injection, for each bacterial species/dose/experimental replicate. The uninfected Ringer’s replicates were not included in this model. Post-hoc multiple comparisons were performed using “emmeans”, using the default Tukey adjustment for multiple comparisons. Effect sizes given as Cohen’s d, were also calculated using “emmeans”, using the sigma value of 2.327, as estimated by the package. Ps. entomophila was excluded for the reason given above. The sample sizes per bacterial species were: n = 13 for E. cloacae, n = 10 for L. lactis and n = 15 for Pr. burhodogranariea.$${{{{{rm{Model}}}}}},3:,{{log }}({{{{{rm{geometric}}}}}},{{{{{rm{mean}}}}}},{{{{{rm{bacterial}}}}}},{{{{{rm{load}}}}}}), sim ,{{{{{rm{bacterial}}}}}},{{{{{rm{species}}}}}}$$Are persistent infection loads dose-dependent?We tested whether initial injection dose is a predictor of bacterial load at seven days post injection22,25. We removed all flies that had a bacterial load that was below the detection limit as they are not informative for this analysis. The response variable was natural log transformed bacterial load at seven days post-injection and the covariate was natural log transformed injection dose, except for P. burhodogranariea, where the response variable and the covariate were log-log transformed. Separate models were carried out for each bacterial species. Experimental replicate and person were fitted as fixed factors. By day seven none of the flies injected with 92,000 CFU of L. lactis were alive. The analysis was not possible for Ps. entomophila infected flies because all flies were dead by seven days post injection.$${{{{{rm{Model}}}}}},4:,{{log }}({{{{{rm{day}}}}}},7,{{{{{rm{bacterial}}}}}},{{{{{rm{load}}}}}}), sim ,{{log }}({{{{{rm{injection}}}}}},{{{{{rm{dose}}}}}}),+,{{{{{rm{replicate}}}}}},+,{{{{{rm{person}}}}}}$$Calculation of clearance indicesTo facilitate the analyses of clearance we calculated clearance indices, which aggregate information about clearance into a single value for each bacterial species/dose/experimental replicate. All indices were based on the estimated proportion of cleared infections (defined as samples with a bacterial load that was below the detection limit) of the whole initial population. For this purpose, we first used data on bacterial load in living flies to calculate the daily proportion of cleared infections in live flies for the days that we sampled. Then we used the data on fly survival to calculate the daily proportion of flies that were still alive. By multiplying the daily proportion of cleared flies in living flies with the proportion of flies that were still alive, we obtained the proportion of cleared infections of the whole initial population – for each day on which bacterial load was measured. We then used these data to calculate two different clearance indices, which we used for different analyses. For each index we calculated the mean clearance across several days. Specifically, the first index was calculated across days three and four post injection (clearance index3,4), and the second index was calculated from days seven, 14 and 21 (clearance index7,14,21).Do the bacterial species differ in clearance?To test whether the bacterial species differed in clearance, we used clearance index3,4, which is the latest timeframe for which we could calculate this index for all four species: due to the high virulence of Ps. entomophila we were not able to assess bacterial load and thus clearance for later days. The distribution of clearance values did not conform to the assumptions of a linear model. We therefore used a Kruskal-Wallis test with pairwise Mann-Whitney-U post hoc tests. Note that the Kruskal-Wallis test uses a Chi-square distribution for approximating the H test statistic. To control for multiple testing we corrected the p-values of the post hoc tests using the method proposed by Benjamini and Hochberg90 that is implemented in the R function pairwise.wilcox.test.$${{{{{rm{Model}}}}}},5:,{{{{{{rm{clearance}}}}}},{{{{{rm{index}}}}}}}_{3,4}, sim ,{{{{{rm{bacterial}}}}}},{{{{{rm{species}}}}}}$$Do exploitation or PPP predict variation in clearance?To assess whether exploitation or PPP predict variation in clearance we performed separate analyses for clearance index3,4 and clearance index7,14,21. As discussed above, this precluded analysing Ps. entomophila. For each of the two indices we fitted a linear mixed effects model with the clearance index as the response variable. As fixed effects predictors we used the replicate-specific geometric mean log bacterial load and the species-specific PPP. In addition, we included species as a random effect.In our analysis we faced the challenge that many measured clearance values were at, or very close to zero. In addition, clearance values below zero do not make conceptual sense. To appropriately account for this issue, we used a logit link function (with Gaussian errors) in our model, which restricts the predicted clearance values to an interval between zero and one. Initial inspections of residuals indicated violations of the model assumption of homogenously distributed errors. To account for this problem, we included the log bacterial load and PPP as predictors of the error variance, which means that we used a model in which we relaxed the standard assumption of homogenous errors and account for heterogenous errors by fitting a function of how errors vary. For this purpose, we used the option dispformula when fitting the models with the function glmmTMB91.$${{{{{rm{Model}}}}}},6 :,{{{{{{rm{clearance}}}}}},{{{{{rm{index}}}}}}}_{3,4},{{{{{rm{or}}}}}},{{{{{{rm{clearance}}}}}},{{{{{rm{index}}}}}}}_{7,14,21}, \ sim ,{{log }}({{{{{rm{geometric}}}}}},{{{{{rm{mean}}}}}},{{{{{rm{bacterial}}}}}},{{{{{rm{load}}}}}}),+,{{{{{rm{PPP}}}}}}+{{{{{{rm{bacterial}}}}}},{{{{{rm{species}}}}}}}_{{{{{{rm{random}}}}}}}$$Does longer-term clearance depend upon the injection dose?In contrast to the analyses described above, we additionally aimed to assess the long-term dynamics of clearance based on the infection status of dead flies collected between 14 and 35 days and 56 to 78 days after injection. Using binomial logistic regressions, we tested whether initial injection dose affected the propensity for flies to clear an infection with E. cloacae or Pr. burhodogranariea before they died. The response variable was binary whereby 0 denoted that no CFUs grew from the homogenate and 1 denoted that CFUs did grow from the homogenate. Log-log transformed injection dose was included as a covariate as well as its interaction with the natural log of day post injection, and person was fitted as a fixed factor. Replicate was included in the Pr. burhodogranariea analysis only, because of unequal sampling across replicates for E. cloacae. L. lactis injected flies were not analysed because only 4 out of 39 (10.3%) cleared the infection. Ps. entomophila infected flies were not statistically analysed because of a low sample size (n = 12). The two bacterial species were analysed separately.$${{{{{rm{Model}}}}}},7 :,{{{{{rm{CFU}}}}}},{{{{{{rm{presence}}}}}}/{{{{{rm{absence}}}}}}}_{{{{{{rm{dead}}}}}}}, sim ,{{log }}({{log }}({{{{{rm{injection}}}}}},{{{{{rm{dose}}}}}})),\ times ,{{log }}({{{{{rm{day}}}}}},{{{{{rm{post}}}}}},{{{{{rm{injection}}}}}}),+,{{{{{rm{replicate}}}}}},+,{{{{{rm{person}}}}}}$$To test whether the patterns of clearance were similar for live and dead flies we tested whether the proportion of live uninfected flies was a predictor of the proportion of dead uninfected flies. We separately summed up the numbers of uninfected and infected flies for each bacterial species and dose, giving us a total sample size of n = 20 (four species × five doses). For live and for dead homogenised flies we had a two-vector (proportion infected and proportion uninfected) response variable, which was bound into a single object using cbind. The predictor was live flies, and the response variable was dead flies, and it was analysed using a generalized linear model with family = quasibinomial.$${{{{{rm{Model}}}}}},8:,{{{{{rm{cbind}}}}}}({{{{{rm{dead}}}}}},{{{{{rm{uninfected}}}}}},,{{{{{rm{dead}}}}}},{{{{{rm{infected}}}}}}), sim ,{{{{{rm{cbind}}}}}}({{{{{rm{live}}}}}},{{{{{rm{uninfected}}}}}},,{{{{{rm{live}}}}}},{{{{{rm{infected}}}}}})$$Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

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To derive the relationship among macro-level ecological variables, which would constitute an ecological analog of the thermodynamic equation of state, we introduce a fourth state variable, B, the total biomass in the community. The ecological analog of the thermodynamic equation of state, an expression for biomass, B, in terms of S, N, and E, arises if we combine METE with a scaling result from the metabolic theory of ecology (MTE)18,21. In particular, we assume the MTE scaling relationship between the metabolic rate, (varepsilon ,) of an individual organism and its mass, m: (varepsilon sim {m}^{3/4}). Without loss of generality22, units are normalized such that the smallest mass and the smallest metabolic rate within a censused plot are each assigned a value of 1. With this units convention, the proportionality constant in this scaling relationship can be assigned a value of 1. From the definition of the structure-function, it follows23 that averaging the biomass of individuals times the abundance of species, nε4/3, over the distribution R and multiplying by the number of species gives the total ecosystem biomass as a function of S, N, and E. Explicitly:$$B=Smathop{sum}limits_{n}nint dvarepsilon ,{varepsilon }^{4/3}R(n,varepsilon {{{{{rm{|}}}}}}S,N,E)$$
(1)
Both the sum and integral in the above equation can be calculated numerically, and Python code to do so for a given set of state variables S, N, and E, is available at github.com/micbru/equation of_ state/.We can also approximate the solution to Eq. 1 analytically (Supplementary Note 2) to reveal the predicted functional relationship among the four state variables. If E > > N > > S > > 1:$$B=cfrac{{E}^{4/3}}{{S}^{1/3}{{{{{rm{ln}}}}}}(1/beta )}$$
(2)
where (capprox (7/2)Gamma (7/3)) ≈ 4.17 and (beta) = ({lambda }_{1}+{lambda }_{2}) is estimated13,22 from the relationship (beta {{{{{rm{ln}}}}}}(1/beta )approx S/N). Equation 2 approximates the numerical result to within 10% for 5 of the 42 datasets analyzed here, corresponding to N/S greater than ~100 and E/N greater than ~25. Multiplying the right-hand side of Eq. 2 by (1-1.16{beta }^{1/3}) approximates the numerical result to within 10% for 33 of the 42 datasets analyzed here, corresponding to N/S greater than ~3 and E/N greater than ~5. The inequality requirements are not necessary for the numerical solution of Eq. 1, which is what is used below to test the prediction.Empirical values of E and B can be estimated from the same data. In particular, if measured metabolic rates of the individuals are denoted by ({varepsilon }_{i},) where i runs from 1 to N, then E is given by the sum over the ({varepsilon }_{i}) and B is given by the sum over the ({{varepsilon }_{i}}^{4/3}.) Similarly, if the mass, mi, of each individual is measured, then B is the sum over the mi and E is the sum over the mi3/4. In practice, for animal data, metabolic rate is often estimated by measuring mass and then using metabolic scaling, while for tree data, metabolic rate is estimated from measurements of individual tree basal areas, which are estimators5 of the ({varepsilon }_{i}).With E and B estimated from the same measurements, the question naturally arises as to whether a simple mathematical relationship holds between them, such as E = B3/4. If all the measured m’s, are identical, then all the calculated individual (varepsilon {{hbox{‘}}}s) are identical, and with our units convention we would have E = B. More generally, with variation in masses and metabolic rates, the only purely mathematical relationship we can write is inequality between E and B3/4: (E=sum {varepsilon }_{i}ge (sum {{{varepsilon }_{i}}^{4/3}})^{3/4}={B}^{3/4}). Our derived equation of state (Eq. 2) can be interpreted as expressing the theoretical prediction for the quantitative degree of inequality between E and B3/4 as a function of S and N.A test of Eq. 1 that compares observed and predicted values of biomass with data from 42 censused plots across a variety of habitats, spatial scales, and taxa is shown in Fig. 1. The 42 plots are listed and described in Table S2 and Supplementary Note 3. The communities censused include arthropods and plants, the habitats include both temperate and tropical, and the census plots range in area from 0.0064 to 50 ha. As seen in the figure, 99.4% of the variance in the observed values of B is explained by the predicted values of B.Fig. 1: A test of the ecological equation of state.Observed biomass is determined by either summing empirical masses of individuals or summing empirical metabolic rates raised to the ¾ power of each individual. Predicted biomass is determined from Eq. 1 using observed values of S, N, and E. The quantity ln(predicted biomass) explains 99.4% of the variance in observed biomass. Units of mass and metabolism are chosen such that the masses of the smallest individuals in each dataset are set to 1 and those individuals are also assigned a metabolic rate of 1. The shape of the marker indicates the type of data, and the lighter color corresponds to higher species richness. Data for all analyses come from tropical trees39,40,41,42,43,44,45, temperate trees30,31,32,33,46,47,48, temperate forest communities27,49, subalpine meadow flora28, and tropical island arthropods50.Full size imageFigure 2 addresses the possible concern that the success of Eq. 1 shown in Fig. 1 might simply reflect an approximate constancy, across all the datasets, of the ratio of E to B3/4. If that ratio were constant, then S and N would play no effective role in the equation of state. Equation 1 predicts that variation in the ratio depends on S and N in the approximate combination S1/4ln3/4(1/(beta (N/S))). In Fig. 2, the observed and predicted values of E/B3/4 calculated from Eq. 1, are compared, showing a nearly fourfold variation in that ratio across the datasets. The equation of state predicts 60% of the variance in the ratio.Fig. 2: The explanatory power of diversity and abundance.The observed ratio E/B3/4 is plotted against the ratio predicted by Eq. 1. Of the fourfold variability across ecosystems in that ratio, 60% is explained by the variability in the predicted combination of diversity and abundance. The shape of the marker indicates the type of data, and the lighter color corresponds to higher species richness. Data for all analyses come from tropical trees39,40,41,42,43,44,45, temperate trees30,31,32,33,46,47,48, temperate forest communities27,49, subalpine meadow flora28, and tropical island arthropods50.Full size imageFigure 3 shows the dependence on S and N of the predicted ratio E/B3/4 over empirically observed values of S, N, and E. We examined the case in which S is varied for two different fixed values of each of N and E (Fig. 3a) and N is varied for two different fixed values of S and E (Fig. 3b). The value of E does not have a large impact on the predicted ratio, particularly when E > > N. On the other hand, the predicted ratio depends more strongly on N and S.Fig. 3: The theoretical prediction for the ratio E/B3/4 as a function of S and N.The biomass B is predicted by holding E fixed along with one other state variable. In a N is fixed and S is varied, and in b S is fixed and N is varied. The fixed values are chosen to be roughly consistent within a range of the data considered. The color of the lines represents the corresponding fixed value of N or S, while the solid and dashed lines represent different fixed values of E.Full size imageThe total productivity of an ecological community is a focus of interest in ecology1, as a possible predictor of species diversity24 and more generally as a measure of ecosystem functioning25. By combining the METE and MTE frameworks, we can now generate explicit predictions for certain debated ecological relationships, including one between productivity and diversity. Interpreting total metabolic rate E in our theory as gross productivity, then in the limit 1 More

Here we provide a unified analysis of predator-prey biomass scaling in complex food webs. Doing so reveals a consistent sub-linear scaling pattern across levels of organization – from populations within webs to whole ecosystems – for freshwater, marine and terrestrial systems. This regularity in sub-linear predator-prey scaling among complex food webs from diverse ecosystem types has important implications for understanding energy flows in natural systems across large spatial gradients.Within food webs, predator-prey biomass scaling was characterised by a near three-quarter power scaling relationship ((bar{k}) = 0.71 across ecosystem types), revealing an approximately three-fold increase in predator biomass for every five-fold increase in prey biomass. When summing all predator and prey biomasses within a food web (Fig. 4), predator-prey scaling across webs followed a similar sub-linear scaling regime, with k ranging from 0.65 to 0.67 between ecosystem types. That is, biomass pyramids became systematically more bottom-heavy as pyramid size increased along a biomass gradient (Fig. 1a). These ecosystem-level patterns are quantitatively consistent with previous analysis of predator-prey biomass scaling among distinct trophic groups, which also found sub-linear scaling with k values between 0.66 to about 0.768,17,18. The approach we introduce here permits expanding these analyses to more complex omnivorous feeding relations both among populations within webs and across webs in diverse ecosystems. The similarity in the scaling exponents (and overlap in confidence intervals) of within- and across-web scaling suggest the existence of a general sub-linear scaling pattern, possibly signifying that fundamental constraints apply across levels of biological organization.These results beg the question: where do these sub-linear scaling patterns originate? We are not aware of any ecological theory that is sufficiently general to encompass the diversity of community types in which sub-linear biomass scaling is observed (Appendix S2). Size spectrum theory, which aims to explain the observation that, for whole ecosystems, biomass is approximately evenly distributed across logarithmic body size classes19,20 would appear to be particularity relevant. However, static size spectrum models typically assume that the predator-prey body mass ratio (PPmR) and trophic transfer efficiency (ratio of predator to prey production), whilst inherently variable21,22, do not vary systematically with prey biomass19,23. These measures indicate from which size class energy is obtained relative to predator body mass, and how efficiently that energy is utilized by any given predator to maintain its biomass. While these variables are thought to drive size spectra scaling3, they do not appear to be consistent with predator-prey biomass scaling observed in natural communities. Assuming both an even distribution of biomass across size classes, and a constant PPmR or transfer efficiency across a prey biomass gradient suggests an unchanging trophic biomass pyramid (all else being equal; Appendix S2), Therefore it is not clear how current size-spectrum models might account for sub-linear predator-prey biomass scaling.Predator-prey theory, on the other hand, which models the dynamics of feeding interactions, has traditionally focused on two distinct trophic levels, rather than on networks of highly omnivorous food webs24. Equilibrium predictions from a range of simple predator-prey models are also not consistent with sub-linear predator-prey scaling without additional and likely questionable assumptions (Appendix S2). Although predator-prey theory can be made to accord with our observed patterns, it requires tuning the scaling of prey growth or other terms of the model. Furthermore, questions remain about how best to simulate a biomass gradient as well as how models should be generalized to multi-trophic food webs (Appendix S2).Despite the lack of any general mechanism, it is reasonable to assume that predator biomass, at steady state, is maintained in proportion to prey production8,10. This would suggest that as prey biomass increases, their total production should scale near ~¾ to match the predator biomass they support. Density-dependent processes, such as competition for resources and other negative interactions among prey species, could thus cause per capita growth to decline sub-exponentially. We observed that changes in prey biomass were primarily driven by changes in prey density, rather than average prey body size, consistent with density dependent effects driving the sub-linear nature of predator-prey biomass relations, rather than allometric body mass effects. Clearly, however, ecological theory has further work yet to knit together the various patterns and processes to explain the consistency and generality of predator-prey scaling patterns.Addressing predator-prey biomass scaling from a food web perspective allowed us to assess which node properties were associated with greater predator-prey biomass ratios. Our results go beyond prior theoretical studies6,7 to provide empirical evidence that populations of highly omnivorous predators, as well as predator populations that feed down the food web on smaller, more productive, prey (i.e. a high predator-to-prey body mass ratio), tend to attain higher biomass stocks than predicted by their prey biomass alone. Interestingly, the role of these variables in driving predator biomass deviations appear to vary between ecosystem types: predator biomass increases more strongly with PPmR in rock pool webs, whereas predator omnivory only proved to correlate with predator biomass residuals in soil webs (Fig. 3). Further research would be instructive to understand if these are general patterns across different types of terrestrial and aquatic ecosystems. For instance, whilst rock pool webs display very similar network topology and PPmR scaling as open marine webs25,26, we might expect different scaling patterns in pelagic marine webs where trophic interactions take place in three dimensions21, where ontogenetic diet shifts are common27, and where food chains are long13. Adapting our food-web approach to quantify biomass scaling among size classes would likely be informative for tackling these additional complexities. Whilst predator biomass was associated with PPmR and omnivory (in soil webs), the consistent sub-linear predator-prey scaling regime within ecosystem types and across levels of organization, suggests that density dependent population growth might be the overriding driver of predator-prey biomass scaling.The regularity in predator-prey scaling we observed could provide insight into baselines for the biomass structure of natural communities, which could be informative for assessing the effects of environmental impacts within ecological communities and ecological status. For instance within webs, deviations away from these baselines in the form of smaller power-law exponents (shallower slopes) could reflect local perturbations (e.g. acidification, warming, over-exploitation) which have a disproportionate impact among larger organisms at higher trophic levels28. Predator-prey biomass scaling could therefore offer a complementary approach to body size distributions and size spectra for evaluating ecosystem health29. A similar approach could be applied for scaling relations within species, where the same species occur in multiple webs. Doing so could reveal how the biomass of a given predator species responds to variation in prey availability. For instance, among the stream food webs studied here, two common fish species displayed the characteristic near ¾-power scaling pattern, whilst the biomass of salmonids, and particularly brown trout (Salmo trutta), was invariant with prey biomass across webs (Fig. S4). These results are consistent with previous work in these sites which has highlighted the importance of terrestrial prey for subsidizing the biomass production of these apex predators30,31. Deviations from the expected general scaling pattern could therefore be valuable for identifying the importance of environmental factors that permit some species an ‘escape’ from the predator-prey power law (see also32), and offers promising avenues for future research.Our study, which takes a first step towards investigating predator-prey biomass scaling in complex food webs, has some notable limitations. First, information on the weighting of feeding links was not available for the food webs studied here, and a more comprehensive investigation should require specific interactions strengths and vulnerabilities of each species, data that is, as yet, unavailable. Although our results are robust to alternative assumptions in how these factors are treated (Table S5), any systematic variation in feeding interactions could play an important role. Second, information on the biomass of all basal resources was also not generally available, so our analysis focused on higher trophic predators feeding on (animal) prey. While our approach may equally apply more generally to consumers and resources (e.g. aquatic snails feeding on biofilm), further work is required to test the generality of the empirical patterns we observed using more detailed datasets where this information, and data on interaction strengths, is widely available.Overall, our study reveals a consistent sub-linear predator-prey scaling regime in complex food webs and makes a strong case for the existence of a systematic form of density-dependent population growth that governs the biomass structure of freshwater, marine and terrestrial ecosystems. The highly conserved predator-prey scaling we observed within and across food webs implies a relatively simple scaling-up of predator and prey population biomasses across levels of biological organization. These general patterns in energy flow between predator and prey could facilitate improvements in modelling trophic structure and community dynamics, as well as the corresponding ecosystem functions4,5. Our findings suggest sub-linear predator-prey biomass scaling holds within complex omnivorous food webs, urging ecologists to understand the origin of this large scale, cross-system pattern. More

Different proxies for changes in structure and/or function of microbiomes have been developed, allowing assessing microbiome dynamics at multiple levels. However, the lack and differences in understanding the microbiome dynamics are due to the differences in the choice of proxies in different studies and the limitations of proxies themselves. Here, using both amplicon and metatranscriptomic sequencings, we compared four different proxies (16/18S rRNA genes, 16/18S rRNA transcripts, mRNA taxonomy and mRNA function) to reveal the impact of a severe summer drought in 2018 on prokaryotic and eukaryotic microbiome structures and functions in two rewetted fen peatlands in northern Germany. We found that both prokaryotic and eukaryotic microbiome compositions were significantly different between dry and wet months. Interestingly, mRNA proxies showed stronger and more significant impacts of drought for prokaryotes, while 18S rRNA transcript and mRNA taxonomy showed stronger drought impacts for eukaryotes. Accordingly, by comparing the accuracy of microbiome changes in predicting dry and wet months under different proxies, we found that mRNA proxies performed better for prokaryotes, while 18S rRNA transcript and mRNA taxonomy performed better for eukaryotes. In both cases, rRNA gene proxies showed much lower to the lowest accuracy, suggesting the drawback of DNA based approaches. To our knowledge, this is the first study comparing all these proxies to reveal the dynamics of both prokaryotic and eukaryotic microbiomes in soils. This study shows that microbiomes are sensitive to (extreme) weather changes in rewetted fens, and the associated microbial changes might contribute to ecological consequences. More