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To defend against predators, insects often modify their morphology, flexibly, to enhance survival and reproductive advantages. Here, we report that predation risks from either isolated predator or predator odor cues, induce a higher proportion of nymphs to developed into long-winged females among the parent generation, as well as among F1 generation offspring. Surprisingly, these previously threatened long-winged adults survived better when attacked by a predator owing to the enhanced agility level gained from risk experience. The long wing, and increased agility level, provide adaptive benefits for SBPHs to escape from predation and so are able to go on to reproduce.SBPHs responded more strongly to the caged predators (visual + odor risk cues) and predator odor cues, than just the visual cue of the predator. Different risk cues can elicit different levels of responses in prey33,34,35,36. For example, in the case of the Colorado potato beetle, volatile odor cues from the predator stronger reduced the beetle feeding on plants than predator visual and tactile cues35. But a visual cue has been shown to be crucial for insect pollinators detecting and avoiding flowers with predators37. Insect herbivores frequently communicate via chemical odors33,38. Exploiting the odor cue to perceive the presence of predators should have advantages, because the odor cue can be sensed from a long distance and penetrate the blocking effect of foliage or canopy structure39, enabling the prior detection of risks and the preparation of antipredation behaviors.In densely planted rice paddies, the active foraging behavior of rove beetle may serve as a selective pressure favouring the development in SBPHs of a chemical instead of visual pathway to detect the approach of a rove beetle. However, in the F1 generation, the influence of a predator odor cue on the proportion of winged forms was weaker than that of caged predators, indicating the combined effects of odor and visual cues might be stronger than only an odor cue, suggesting that visual cues cannot be ignored. In our experiments, sealed predator cadavers may have weakened the visual cue of the rove beetles, because the lack of motion did not fully represent the normal visual cue.SBPHs frequently exhibit wing plasticity in response to population density and food quality28,29. When nymph density is higher, or food has deteriorated, a higher proportion of macropters will arise28,29. The development of the winged form is thought to be a strategy for SBPHs to emigrate from inhospitable environments. However, we assumed, predation risks could also induce the occurrence of the winged form, because long wings might enable SBPHs to escape from predation. As expected, the results presented here show that a higher proportion of long-winged females and their offspring arose when nymphs or adults were previously exposed to predation risk, demonstrating that SBPHs can express morphologically plastic defenses in response to prior predation risk. Additionally, the higher proportion of wing forms was not only due to the increasing number of winged females (see Fig. 1, the number of winged females in “caged rove beetle” treatment was lower), but also the increasing proportion of winged females among female groups (the decreasing numbers and proportions of wingless females, Fig. 1). To date, similar patterns have only been shown in pea aphids, in which when predation risk (foot prints from lady beetles) is higher during the parent generation, a higher proportion of winged morphs arise in the offspring40,41. In our experiments, we tested the risk effects passing from nymphs to adults and from parents to their offspring with combined risk cues, an odor cue or a visual cue, which better reveals the capacity for flexible defense strategies within SBPHs and the nature of predation risks in the perpetual ‘arms race’ against predation. This is the first example of how insects can express both within- generational and transgenerational morphological plasticity as a defense strategy in response to prior predator threat, and we suggest that this phenomenon is likely to occur more widely.However, SBPHs do not only face a single lethal pressure from their environment as we discussed above. Nymph density, food quality, even the temperatures or photoperiods may play or interplay roles in the induction of wing plasticity in SBPHs28,29. In these situations, the responses of SBPHs may differ from present results, or opposite results can occur. As an example, the growth rates of snails vary depending on snail densities, food supply and the strength of predation risks. Growth rates were higher when snails were reared on high nutrients and in low densities, but decreased steeply as the predation risk increased. Conversely, the growth rate was lower at high densities and with high predation risk, but increased as nutrient availability increased42. As for SBPHs, the proportion of winged adults may be higher if we reared in higher densities combined with high predation risk, or may be lower if the nutrient condition of the rice plants increases (for example, higher fertilizer inputs benefit the development of planthoppers43) and predators are removed. This hypothesis needs to be tested. Further, the rice plant phenotypes (resistant or sensitive phenotypes) are important to the development of planthoppers or leafhoppers44,45,46, and tests of the interactive effects of plant phenotype, plant quality/quantity, nymph density and predation risk on the wing plasticity of SBPHs should provide insights into the evolution of insects within changing environments.Induced transgenerational defense plasticity as shown in SBPHs may be common in many organisms20,47. It allows parents to transfer their risk experience to offspring and promotes their evolutionary fitness20. When SBPH nymphs are exposed to predation risk, they are likely to develop into long-winged females, because it is an advantageous form for them in the current risk environment. However, such predation risk is variable in time and space, and SBPH parents cannot predict when or whether the predators will disappear. Thus, an appropriate strategy to enhance the survival rate of offspring in an unpredictable environment is to continue producing a higher proportion of long-winged forms. Within-generational and transgenerational plasticity of defense should be a successful adaptive defense strategy for SBPHs, given that rove beetle and other groups of predators such as predatory spiders are abundant all around the year in rice paddies.The higher mortality of SBPH nymphs when they experience predation risk, has been broadly addressed before24,48,49. Reduced food intake during risk periods may contribute to this poorer survival outcome, because insects are likely to alter their feeding behavior50,51, or shift from a high-risk host to a safer, but nutritionally inferior, one52, when they detect the presence of predators. However, we did not observe an apparent behavior change in threatened nymphs in our experiments, even those going on to be macropters, compared to the non-threatened ones. For example, changing feeding location, non-feeding related motility, an increase in jump frequency, etc. did not occur in threatened nymphs. Thus, behavior plasticity seems not to be invoked to explain this phenomenon. However, considering the food consumption of sap-sucking SBPHs is difficult to determine, experiments employing electrical penetration graph (EPG) techniques should be conducted to quantify the amounts of sap consumption during risk periods53. This will help to explain whether the higher mortality is due to a change of feeding behavior (less food intake). Furthermore, some obscure internal physiological plasticity may also cause the higher mortality of SBPH nymphs at risk. For example, increased oxidative damage and decreased assimilation efficiency during the risk period may weaken the survival success of SBPH nymphs. Unfortunately, few studies have verified this assumption, although it has been shown that different assimilation efficiencies may arise under predation risk17, or oxidative damage may be induced by predation risk resulting in a slower growth rate54 and decreased escape performance55.SBPHs exhibit sexual differences in both with- and trans- generational morphological plasticity in relation to defense, i.e., threatened nymphs/parents are more likely to develop into long-winged females, due to the different vulnerability of females and males to predation. This predation difference is particularly acute between short-winged females and males, given that the proportion of short-winged females is lower than that seen in control settings (Fig. 1), and we assume the level of vulnerability may depend on their body size and reproductive role. The body sizes of short-winged females are larger than those of long-winged females or males, causing them to be more vulnerable to predation because they are more highly preferred targets for predator. Also, the short-winged female needs to stay and deposit eggs in the bare rice stem, which increases the time window of exposure to predators while, by contrast, long-winged males are slim and are not required to lay eggs, and so should be not be heavily predated. It follows that short-winged females should be more vulnerable to predation than long-winged females or males. Hence, in SHPBs, increasing the proportion of long-wing females in a population creates greater opportunities to migrate to predator-free habitats for reproduction, while at the same time reducing their vulnerability to predation. We hypothesize that the sexual difference in responses should be adaptive, and might be inheritable if predation pressure frequently favors the long-winged forms among populations over multiple generations.Results presented here also show that previously threatened long-winged offspring survived better than previosuly non-threatened ones when attacked by P. fuscipes. Studies suggest prey-altered morphology in response to predation risks should confer a survival advantage (fitness gained), i.e., a better-developed defensive structure13,24, or refuge in having a larger size that increase survival success57. However, wings themselves are without protective functions for SBPHs, as seen in pea aphids41. Thus, we setup behavioral experiments to reveal how threatened long-winged adults may increase their survival when attacked by a predator. Results show threatened long- winged offspring (but not parents) are more active, and respond more quickly, than unthreatened ones, i.e., a higher number of attacks are needed for P. fuscipes to capture a previously threatened long-winged offspring than one that has not been threatened before. We suggest the increased agility level is not because of the long wing itself, but due to the enhanced muscle strength in the legs of long-winged adults, because in our observation, long-winged adults avoid attack mainly by jumping but not by flight, probably because a jump needs less reaction time than flight.We only observed transgenerational plasticity of induced behavioral defense in SBPHs. This generational difference (within- and trans-generational) in behavioral defense in SBPHs may reflect potential carry-over effects from parents. To our knowledge, the generational difference in defense has rarely been shown in insects, though in pea aphids a fluctuating expression of transgenerational defensive traits (long wing) over generations when predation risk was present or absent has been reported58. We also expect there will be cumulative effects59 accumulated by SBPHs from the parent generation to the F1 generation. However, we are not certain whether these effects exist in our experiments. To determine this, experiments examining defensive traits across multigeneration should be conducted.However, if predation risk increases the number of agile, long-winged SBPH adults, which are of benefit in respect of dispersal, migration, and thus spreading rice viruses, the application of P. fuscipes in biological control appears ultimately to weaken the control effectiveness. Also, a study with field experiments found that predatory ladybugs increase the number of dispersed aphid nymphs, especially in plants with lower resistance. However, surprising results show that the higher number of dispersed aphid nymphs will not necessarily translate into population growth because dispersed aphids are weak (less food intake) and more easily predated by predators60. Thus, the benefits of anti-predator defense in aphids will, over time, translate into negative developmental costs that suppress the aphid population. As for SBPHs, threatened long-winged females perform well in dispersal and defense, but worse in development and reproduction. Recent experiments reveal that previously threatened long-winged females have a longevity that is three days shorter, and produces about 60 fewer eggs per female, than non-threatened long-winged females (unpublished data). Consequently, these negative effects would eventually translate into lower population growth rates within SBPHs. Thus, the introduction of the predation risk from P. fuscipes to control SBPHs is workable, since field experiments in controlling western flower thrips and grasshoppers by exposure to predation risk have been successful49,61, and the main purpose of biological control is to suppress the pest population beneath the relevant economic threshold, and reduce plant mass loss without necessarily eliminating the pest altogether.This study advances the importance of predation risk on the induction of flexible anti-predation defenses in insect parents and their offspring, uncovers the mediating mechanisms, shows how this anti-predation defense expresses differently between sexes, and further explores the adaptation significance of these defense traits for insects exposed to unpredictable environments. These findings should prove important for predicting SBPH migration or dispersal, conducting effective pest control, and better understanding prey-predator interactions. However, future work should examine the effects of predation risks from other groups of predators or parasites on the physiological and behavioral plasticity of SBPHs. More

WORLD VIEW
18 August 2021

Great Barrier Reef: accept ‘in danger’ status, there’s more to gain than lose

The Australian government must embrace UNESCO’s assessment to marshal the resources needed to protect the unique coral ecosystem.

Tiffany H. Morrison

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Tiffany H. Morrison

Tiffany H. Morrison is a political geographer specializing in marine interventions at James Cook University in Townsville, Australia.

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No one denies the cascade of climate-induced coral bleaching that devastated huge portions of the Great Barrier Reef in 2016, nor the subsequent bleaching. No one questions the Queensland government’s 2019 report (see go.nature.com/3ckg) that the reef’s condition near the shore is poor.Yet last month, the World Heritage Committee of the United Nations organization UNESCO caved to lobbying from the Australian government — pressured by fossil-fuel, agricultural and mining interests — and kept the Great Barrier Reef off its list of ecosystems ‘in danger’. In my view, this decision is wrong, factually and strategically. It leaves both UNESCO and Australia weaker against the climate crisis.I study the governance of approximately 250 ecosystems with World Heritage status because of their outstanding value to humanity — including attempts to curtail runaway industrial development of Vietnam’s Ha Long Bay and overzealous urbanization along Florida’s Everglades wetlands.There are benefits to an in-danger listing: the Belize Barrier Reef Reserve System was placed on the list in 2009. The World Heritage Fund then provided technical and financial assistance for its restoration. By 2018, mangrove coverage was restored nearly to 1996 levels, with clearing in protected areas almost entirely curtailed. The whole maritime zone was under a moratorium on oil and gas production. Restoration work is ongoing, but the Belize reef is no longer on the list.
Save reefs to rescue all ecosystems
This July, UNESCO proposed to list the Great Barrier Reef as in danger owing to severe coral bleaching, poor water quality and inaction on climate change.In arguing against the listing, the Australian government did not directly deny the reef’s parlous state, but did play down its condition. The government also argued that the listing would decrease tourism revenues, that Australia had too little time to respond and should not be held responsible for global change, and that UNESCO should not supersede national sovereignty on climate-change policy.Australian environment minister Sussan Ley lobbied committee members from more than a dozen countries to override UNESCO’s recommendation. Australia avoided an in-danger listing in 2015 using similar tactics and by touting a sustainability plan. The following year saw the worst coral bleaching in the world’s history.But changes are in the wind. After back-to-back coral bleaching in 2016–17 and the tragic 2020 bush fires, more Australian voters, industries and even conservative politicians are calling for strong efforts against climate change.Accepting an in-danger listing for the reef could tip the balance past gridlock. More than 70% of Australians think that formally acknowledging the reef’s endangered state would spur action. In 1993, former US president Bill Clinton’s administration requested that UNESCO certify Florida’s Everglades as in danger. This helped to bring industry opponents on board to better manage coastal development. Had the Great Barrier Reef been listed as in danger in 2015, fossil-fuel developments in the catchment areas draining into the reef would have struggled to get approval.Australia’s most conservative politicians will argue that avoiding an in-danger listing in 2022 is necessary to boost economic development. But this will embarrass Australia later. As more marine heating occurs globally, Australia will struggle to defend its inaction on climate to the UN climate-change conference in November and to the World Heritage Committee next year. Even the Queensland Tourism Industry Council has said keeping the reef’s status under the spotlight is a “call to the world to do more on climate change”.
Fevers are plaguing the oceans — and climate change is making them worse
And undercutting the listing undermines the purpose of the World Heritage Committee. Since 1972, 41 ecosystems have been considered for the in-danger list — 27 of them more than once — but not officially inscribed, even though UNESCO and its advisory body had assessed these ecosystems as threatened, or more threatened than those already listed. The number of sites on the list has declined by almost one-third since 2001, although threats continue to grow and there are more ecosystems on the overall World Heritage List.However, destabilizing strategies are mainly due to a small group of nations — including countries in the Organisation for Economic Co-operation and Development, such as Australia and Spain. World Heritage status and in-danger listings often work as intended: the managers of 73% of sites do comply with their responsibilities.Concerned observers are helping the World Heritage Committee to protect itself from political manipulation. In February 2020, a consortium of 76 organizations and individuals petitioned UNESCO to consider climate change in its World Heritage decisions. A nascent international network known as World Heritage Watch hopes to provide more oversight and monitoring of self-interested states. Ecologists and non-profit organizations are using remote sensing and citizen science to track and expose degradation of protected areas (see go.nature.com/2xn1) and hold governments accountable.UNESCO and its World Heritage Committee grasp the stakes. A new draft policy clearly states that climate-related degradation of a World Heritage Area can be used as the basis for in-danger listing; it will probably be ratified later this year at the UNESCO General Assembly. This policy will shine a harsh light on the intensifying geopolitics of climate change. Advanced economies, such as Australia, with high per-capita emissions but limited climate action, will need to find alternative ways to protect resources and jobs.

Nature 596, 319 (2021)
doi: https://doi.org/10.1038/d41586-021-02220-3

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A. Strain sampling and isolationBradyrhizobium is a commonly occurring genus in soil [21]. Closely related Bradyrhizobium diazoefficiens (previously Bradyrhizobium japonicum) strains were isolated from soil, as previously described [20, 22]. In brief, Bradyrhizobium isolates that formed symbiotic associations with a foundational legume species in the censused region, Acacia acuminata, were isolated from soil sampled along a large region spanning ~300,000 km2 in South West Australia, a globally significant biodiversity hotspot [23]. In total 60 soil samples were collected from twenty sites (3 soil samples per site; Supplementary Fig. S1) and 380 isolates were sequenced (19 isolates per site, 5 or 6 isolates per soil sample, each isolate re-plated from a single colony at least 2 times). Host A. acuminata legume plants were inoculated with field soil in controlled chamber conditions and isolates were cultured on Mannitol Yeast agar plates from root nodules (see [20, 22] for details). A total of 374 strains were included in this study after removing 5 contaminated samples and one sample that was a different Bradyrhizobium species; non- Bradyrhizobium diazoefficiens sample removal was determined from 16S rRNA sequences extracted from draft genome assemblies (Method C) using RNAmmer [24].B. Environmental variation among sampled sitesIn this study, I focus on environmental factors (temperature, rainfall, soil pH and salinity) previously identified to impact either rhizobia growth performance, functional fitness or persistence in soil [25,26,27,28] and where a directionality of rhizobial stress response could be attributed with respect to environmental variation present in the sampled region (i.e. stress occurs at high temperatures, low rainfall, high acidity and high salinity). Each environmental factor was standardised to a mean of 0 and a standard deviation of 1, and pH and rainfall scales were reversed to standardise stress responses directions so that low stress is at low values and high stress is at high values for all factors (Supplementary Fig. S2). Additionally, salinity was transformed using a log transformation (log(x + 0.01) to account for some zeroes) prior to standardisation.C. Isolate sequencing and pangenome annotationIllumina short reads (150 bp paired-end) were obtained and draft genome assemblies were generated using Unicycler from a previous study [29]. Resulting assemblies were of good assembly quality (99.2% of all strains had >95.0% genome completeness score according to BUSCO [30]; Table S1; assembled using reads that contained nominal 0.016 ± 0.00524% non-prokaryotic DNA content across all 374 isolates, according to Kraken classification [31]). Protein coding regions (CDS regions) were identified using Prokka [32] and assembled into a draft pangenome using ROARY [33], which produced a matrix of counts of orthologous gene clusters (i.e. here cluster refers to a set of protein-coding sequences containing all orthologous variants from all the different strains, grouped together and designated as a single putative gene). Gene clusters that occurred in 99% of strains were designated as ‘core genes’ and used to calculate the ‘efficiency of selection’ [34, 35] (measured as dN/dS, Method G.2) and population divergence measured as Fixation Index ‘Fst’, Method H) across each environmental stress factor. The identified gene clusters were then annotated using eggNOG-mapper V2 [36] and the strain by gene cluster matrix was reaggregated using the Seed ortholog ID returned by eggNOG-mapper as the protein identity. Out of the total 2,744,533 CDS regions identified in the full sample of 374 strains, eggNOG-mapper was able to assign 2,612,345 of them to 91,230 unique Seed orthologs. These 91,230 protein coding genes constituted the final protein dataset for subsequent analyses.D. Calculation and statistical analysis of gene richness and pangenome diversity along the stress gradientGene richness was calculated as the total number of unique seed orthologues for each strain (i.e. genome). Any singleton genes that occurred in only a single strain, as well as ‘core’ genes that occurred in every strain (for symmetry, and because these are equally uninformative with respect to variation between strains) were removed, leaving 74,089 genes in this analysis. Gene richness (being count data) was modelled on a negative binomial distribution (glmer.nb function) as a function of each of the four environmental stressors as predictors using the lme4 package in R [37], also accounting for hierarchical structure in the data by including site and soil sample as random effects.To rule out potentially spurious effects of assembly quality (i.e. missed gene annotations due to incomplete draft genomes) on key findings, I confirmed no significant association between gene richness and genome completeness (r = 0.042, p = 0.4224, Fig. S3).Finally, pangenome diversity was calculated as the total number of unique genes that occurred in each soil sample (since multiple strains were isolated from a single soil sample). Pangenome diversity was modelled the same as gene richness, except here soil sample was not included as a random effect.E. Calculation of network and duplication traits for each geneI used the seed orthologue identifier from eggNOG-mapper annotations to query matching genes within StringDB ([38]; https://string-db.org/), which collects information on protein-protein interactions. Out of 91,230 query seed orthologues, 73,126 (~80%) returned a match in STRING. For matching seed orthologue hits, a network was created by connecting any proteins that were annotated as having pairwise interactions in the STRING database using the igraph package in R [39]. Two vertex-based network metrics were calculated for each gene: betweenness centrality, which measures a genes tendency to connect other genes in the gene network, and mean cosine similarity, which is a measure of how much a gene’s links to other genes are similar to other genes.Betweenness centrality was calculated using igraph (functional betweenness). For mean cosine similarity, a pairwise cosine similarity was first calculated between all genes. To do this, the igraph network object was converted into a (naturally sparse yet large) adjacency matrix and the cosSparse function in qlcMatrix in R [40] was used to calculate cosine similarity between all pairs of genes. To obtain an overall cosine similarity trait value for each gene, the average pairwise cosine similarity to all other genes in the network was calculated.Finally, gene duplication level was calculated for each gene as one additional measure of ‘redundancy’, by calculating the average number of gene duplicates found within the same strain. Duplicates were identified as CDS regions with the same Seed orthologue ID.F. Gene distribution modelsTo determine how gene traits predict accessory genome distributions patterns along the stress gradients, I first calculated a model-based metric (hereafter and more specifically a standardised coefficient, ‘z-score’) of the relative tendency of each gene to be found in different soil samples across the four stress gradients (heat, salinity, acidity, and aridity). This was achieved by modelling each gene’s presence or absence in a strain as a function of the four stress gradients, with site and soil sample as a random effect, using a binomial model in lme4 (the structure of the model being the same as the gene richness model, only the response is different). To reduce computational overhead, these models were only run for the set of genes that were used in the gene richness analysis (e.g. after removing singletons and core genes), and which had matching network traits calculated (e.g. they occurred in the STRING database; n = 64,867 genes). Distribution models were run in tandem for each gene using the manyany function in the R package mvabund [41]. Standardised coefficients, or z-scores (coefficient/standard error) indicating the change in the probability of occurrence for each gene across each of the stress gradients were extracted. More negative coefficients correspond to genes that are more likely to be absent in high stress (and vice versa for positive coefficients).To determine how network and duplication traits influence the distribution of genes across the stress gradient, I performed a subsequent linear regression model where the gene’s z-scores was the response and gene traits as predictors. The environmental stress type (i.e. acidity, aridity, heat and salinity) was included as a categorical predictor, and the interaction between stress category and gene function traits were used to infer the influence of gene function traits on gene distributions in a given stress type (see Supplementary Methods S1 for z-score transformation).G. Quantifying molecular signals of natural selection on accessory and core genesTo examine molecular signatures of selection in accessory and core genes, I calculated dN/dS for a subsample of the total pool (n=74,089 genes), which estimates the efficiency of selection [34, 35]. Two major questions relevant to dN/dS that are addressed here require a different gene subsampling approach:(1) Do variable environmental stress responses lead to different dN/dS patterns among accessory genes?Here, I subsampled accessory genes (total accessory gene pool across 374 strains, 74,089) to generate and compare dN/dS among 3 categorical groups, each representing a different level of stress response based on their z-scores (accessory genes that either strongly increase, decrease or have no change in occurrence as stress increases; n = 1000 genes/category; see Supplementary Methods S2 for subsample stratification details).For each gene, sequences were aligned using codon-aware alignment tool, MACSE v2 [42]. dN/dS was estimated by codon within each gene using Genomegamap’s Bayesian model-based approach [43], which is a phylogeny-free method optimised for within bacterial species dN/dS calculation (see Supplementary Methods S3 for dN/dS calculation/summarisation; S9 for xml configuration). The proportion of codons with dN/dS that were credibly less than 1 (purifying selection) and those credibly greater than 1 (positive selection) were analysed, with respect to the genes’ occurrence response to stress. Specifically, I modelled the proportion of codons with dN/dS  1 was overall too low to analyse in this way, so the binary outcome (a gene with any codons with dN/dS  > 1 or not) was modelled using a binomial response model with the response categories as predictors (see Supplementary Methods S4 for details of both models).(2) Does dN/dS among microbial populations vary across environmental stress?Here, I compared the average change in dN/dS in core genes present across all environments at the population level (i.e. all isolates from one soil sample), which is used here to measure the change in the efficiency of selection across each stress gradient. Core genes were used since they occur in all soil samples, allowing a consistent set and sample size of genes to be used in the population-level dN/dS calculation. This contrasts with the previous section, which quantifies gene-level dN/dS on extant accessory genes that intrinsically have variable presence or absence across soil samples. For computational feasibility, 500 core genes were subsampled (total core 1015 genes) and, for each gene, individual strain variants were collated into a single fasta file based on soil sample membership, such that dN/dS could be calculated separately for each gene within each soil sample (i.e. 60 soil samples × 500 genes = 30,000 fasta files). Each fasta file was then aligned in MACSE and dN/dS calculated using the same methodology for accessory genes (Supplementary Method S3). This enabled the average dN/dS in a sample to be associated with soil-sample level environmental stress variables. Specifically, I modelled the mean proportion of codons with dN/dS  1 due to overall rarity of positive selection (average proportion of genes where at least 1 codon with dN/dS  > 1 was ~0.006). This low level of positive selection is expected for core genes which tend to be under strong selective constraint.H. Calculation and analysis of Fixation index (Fst) along stress gradientsUsing the core genome alignment (all SNPs among 1015 core genes) generated previously with ROARY, I computed pairwise environmentally-stratified Fst. Each soil sample (n = 60) was first placed into one of 5 bins based on their distances in total environmental stress space (using all four stress gradients), with the overall goal of generating roughly evenly sized bins such that the environmental similarity of stress was greater within bins than between (see Supplementary Methods S6 and Fig. S4 for clustering algorithm details). Next, fasta alignments were converted to binary SNPs using the adegenet package. Pairwise Fst between all strains (originating from a particular soil sample) within a single bin was calculated using StAMPP in R [44]. For each strain pair, the average of the two stress gradient values was assigned.The relationship between pairwise Fst and the average stress value was evaluated using a linear regression model with each of the four stress values as predictors. Since the analysis uses pairwise data (thus violating standard independence assumptions), the significance of the relationship was determined using a permutation test (see Supplementary Methods S7 for details).I. Chromosomal structure analysis of gene loss patternsTo gain insight into structural variation and test for regional hotspots in gene loss along the chromosome, I mapped each gene’s stress response (i.e. probability of loss or gain indicated by each genes z-score) onto a completed Bradyrhizobium genome (strain ‘36_1’ from the same set of 374 strains (Genbank CP067102.1; [45]). Putative CDS positions on the complete genome were determined using Prokka and annotated with SEED orthologue ID’s using eggNOG-mapper. Matching accessory genes derived from the full set of 374 incomplete draft genomes (n = 74,089) were mapped to positions on the complete genome (6274 matches). The magnitude of gene loss or gain (as measured by their standardised z-scores for each environment from the gene distribution models; see Method F) was then modelled across the genome using a one-dimensional spatial smoothing model. This model was implemented in R INLA [46] (www.r-inla.org), and models a response in a one-dimensional space using a Matern covariance-based random effect. The method uses an integrated nested Laplace approximation to a Bayesian posterior distribution, with a cyclical coordinate system to accommodate circular genomes. The model accounts for spatial non-independence among sites and produces a continuous posterior distribution of average z-score predictions along the entire genome, which was then used to visualise potential ‘hotspots’ of gene loss or gain. The modelling procedure was repeated, instead with gene network traits, such that model predictions of similarity and betweenness could be visualised on the reference chromosome. More

State Key Laboratory of Tree Genetics and Breeding, Chinese Academy of Forestry, Beijing, ChinaZhilin Yuan, Huanshen Wei & Long PengResearch Institute of Subtropical Forestry, Chinese Academy of Forestry, Hangzhou, ChinaZhilin Yuan, Xinyu Wang, Huanshen Wei & Long PengFungal Genomics Laboratory (FungiG), College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing, ChinaIrina S. Druzhinina & Feng CaiDepartment of Food Science, University of Massachusetts, Amherst, MA, USAJohn G. GibbonsState Key Laboratory of Ecological Pest Control for Fujian and Taiwan Crops, College of Plant Protection, Fujian Agriculture and Forestry University, Fuzhou, ChinaZhenhui ZhongDepartment of Molecular, Cell and Developmental Biology, University of California, Los Angeles, CA, USAZhenhui ZhongDepartment of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, BelgiumYves Van de PeerVIB Center for Plant Systems Biology, Ghent, BelgiumYves Van de PeerCentre for Microbial Ecology and Genomics, Department of Biochemistry, Genetics and Microbiology, University of Pretoria, Hatfield, South AfricaYves Van de PeerAdaptive Symbiotic Technologies, University of Washington, Seattle, WA, USARussell J. RodriguezKey Laboratory of National Forestry and Grassland Administration for Orchid Conservation and Utilization at College of Landscape Architecture, Fujian Agriculture and Forestry University, Fuzhou, ChinaZhongjian LiuState Key Laboratory of Mycology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, ChinaQi Wu & Guohui ShiKey Laboratory of Plant Resources Conservation and Sustainable Utilization, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou, ChinaJieyu WangBeijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing, ChinaFrancis M. MartinUniversité de Lorraine, INRAE, UMR Interactions Arbres/Micro-Organismes, Centre INRAE Grand Est Nancy, Champenoux, FranceFrancis M. Martin More

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Detecting alternative attractors in ecosystem dynamicsWe use empirical dynamical modeling, a set of equation-free tools for analyzing non-linear time series (for a review and assumptions see25,26, respectively), to test if the temporal dynamics of alternative dynamical regimes are qualitatively different. Empirical dynamic modeling builds fundamentally on Takens embedding theorem, which shows that attractors of multi-dimensional dynamical systems can be reconstructed using higher order lags of its embedded time series27. However, if a dynamical system has gone through a bifurcation, or switched to an alternative basin of attraction, attractors are qualitative dissimilar in the two regimes. Theoretically, this infers that it should be possible to reconstruct the attractor of one regime using information from the same regime, but not from the other regime. In practice, this implies that if a model (attractor reconstruction) based on one dynamical regime is used to predict the dynamics of variables from the same dynamical regime predictions should be accurate (i.e. low prediction errors), whereas if an attractor reconstruction based on one dynamical regime is used to predict the dynamics of variables of another attractor predictions should be less accurate (i.e. high prediction errors). We make use of this idea by specifically testing if prediction errors of across and within regime predictions are different. As explained below this idea can be used for both univariate and multivariate time series data.Univariate approachUnivariate attractor reconstructions can be found using the simplex algorithm28,29. First, for a given dynamical regime, a time series can be split into a library of vectors, and each vector is described by$${underline{y}}_{A}(t)= < {Y}_{A}(t),{Y}_{A}(t-1),{Y}_{A}(t-2),ldots ,{Y}_{A}(t-(E-1)) > ,$$
(1)
where ({Y}_{A}(t)) is an observation of variable Y at time t in dynamical regime A and E is the reconstructed attractors embedding dimension. Using the simplex projection algorithm, a one-step ahead forecast is produced as follows:$${hat{Y}}_{A}(t+1)|{{{{{{boldsymbol{M}}}}}}}_{B}=mathop{sum}limits_{m=1ldots E+1}{w}_{m}{Y}_{B}({t}_{m}+1),$$
(2)
where tm is a time index of an observation in dynamical regime B, E is the embedding dimension of regime B, and wm is an exponential weighting described by:$${w}_{m}={u}_{m}/mathop{sum}limits_{n=1,ldots ,E+1}{u}_{n},$$
(3)
where n and m belongs to the set of the E+1 nearest neighbors of vector ({underline{y}}_{A}(t)) in the set of vectors ({{underline{y}}_{B}({t}_{m})}), ({u}_{m}=exp {-d[{underline{y}}_{A}(t),{underline{y}}_{B}({t}_{m})]/d[{underline{y}}_{A}(t),{underline{y}}_{B}({t}_{1})]}), and (d[{underline{y}}_{A}(t),{underline{y}}_{B}({t}_{1})],)is the Euclidean distance between the prediction vector ({underline{y}}_{A}(t)) and its nearest neighbor ({underline{y}}_{B}({t}_{1})) in the set ({{underline{y}}_{B}({t}_{m})}).The only parameter that is estimated using the simplex algorithm is the embedding dimension E. This parameter is estimated by optimizing the correlation between observations (({Y}_{A}(t+1))) and predictions (({hat{Y}}_{A}(t+1)|{{{{{{boldsymbol{M}}}}}}}_{A})) using a leave-one-out cross validation approach (See Supplementary Discussion). The embedding dimension E and its corresponding set of E-dimensional vectors (Eq. 1) constitutes the reconstructed attractor, MA, of a given dynamical regime A. This reconstructed attractor (MA) is then used to predict data for both the same dynamical regime (({hat{Y}}_{A}(t+1)|{{{{{{boldsymbol{M}}}}}}}_{A})), and the contrasting dynamical regime ({hat{Y}}_{B}(t+1)|{{{{{{boldsymbol{M}}}}}}}_{A}). Likewise, the reconstructed attractor MB can be used to predict time series dynamics from both dynamical regimes; that is, ({hat{Y}}_{A}(t+1)|{{{{{{boldsymbol{M}}}}}}}_{B}) and ({hat{Y}}_{B}(t+1)|{{{{{{boldsymbol{M}}}}}}}_{B}), respectively.Multivariate approachA multivariate time series describes a number of simultaneously evolving variables. For example, a bivariate time series can be described by variables X and Y. For such time series, Sugihara et al.30 developed an approach for testing if two variables (time series) are dynamically coupled. Their methodology builds on the fact that a reconstructed attractor should map 1:1 to the original attractor on which the reconstruction is based. This infers that two attractor reconstructions (based on two different variables) should also map 1:1 to each other30. Practically, this means that if two variables are dynamically coupled one-time series should be predictable based on an attractor reconstruction of another variable. However, if a dynamical system has gone through a bifurcation, or potentially switched to an alternative basin of attraction, a new set of rules will govern the dynamics of the system. Hence, a new attractor should have emerged. Now, since this new attractor is most likely governed by a new set of rules it should be difficult to predict the dynamics of this new alternative attractor based on information from the former attractor. Thus, if one variable in one dynamical regime is used to predict another variable in another dynamical regime, predictions should be biased. Yet, if one variable from one dynamical regime is used to predict another variable from the same regime predictions should be more accurate.The simplex algorithm can be used to make predictions of a variable Y using a time series of another variable X30. Predictions are produced as follows:$${hat{Y}}_{{{{{{boldsymbol{A}}}}}}}(t)|{{{{{{boldsymbol{M}}}}}}}_{B}=mathop{sum}limits_{m=1ldots E+1}{w}_{m}{Y}_{B}({t}_{m}),$$
(4)
where tm is the time series index of a vector of variable X of dynamical regime B, wm is an exponential weighting based on variable X:$${w}_{m}={u}_{m}/mathop{sum}limits_{n=1,ldots ,E+1}{u}_{n},$$
(5)
where n and m belongs to the set of the E+1 nearest neighbors of ({underline{x}}_{A}(t)) in ({{underline{x}}_{B}({t}_{m})}), ({u}_{m}=exp {-d[{underline{x}}_{A}(t),{underline{x}}_{B}({t}_{m})]/d[{underline{x}}_{A}(t),{underline{x}}_{B}({t}_{1})]}), and (d[{underline{x}}_{A}(t),{underline{x}}_{B}({t}_{1})],)is the Euclidean distance between the prediction vector(,{underline{x}}_{A}(t)) and its nearest neighbor ({underline{x}}_{B}({t}_{1})) in dynamical regime (B).The reconstructed attractors, MA and MB, for each variable and regime are found using the univariate simplex algorithm described above28,29,30. Similar to the univariate case, the reconstructed attractor (MA) is used to predict data from the same dynamical regime (({hat{Y}}_{{{{{{boldsymbol{A}}}}}}}(t)|{{{{{{boldsymbol{M}}}}}}}_{A})), and to predict time series of a contrasting dynamical regime (({hat{Y}}_{{{{{{boldsymbol{A}}}}}}}(t)|{{{{{{boldsymbol{M}}}}}}}_{B})). Yet, it is important to stress that MA here reflects an attractor reconstruction based on a variable that is not being predicted (that is, variable X is used to predict variable Y). This prediction approach thus infers that predictions are made on data that was not used to fit the model (X predicts Y and vice versa). Thus, neither across nor within regime predictions are made on data used to fit a model.Test statisticWe used mean absolute prediction errors to test for difference between across and within regime predictions. Alternative metrics, such as mean sum of square errors, can also be used. However, since our approach gives skewed prediction errors we used mean absolute prediction errors to reduce the impact of extreme values. Further, since the absolute prediction errors are non-normally distributed we used a permutation test. The null hypothesis that is tested reads:$$H0:{{{{{rm{MAP{E}}}}}}}_{A} < {{{{{rm{MAP{E}}}}}}}_{w},$$ (6) where MAPEA is the mean absolute prediction error for across regime predictions (that is, ({{{{{rm{MAP{E}}}}}}}_{A}=frac{1}{n}mathop{sum}limits_{t=1:n}{{{{{rm{abs}}}}}}({hat{Y}}_{{{{{{{boldsymbol{M}}}}}}}_{{{{{{boldsymbol{A}}}}}}}}(t)|{{{{{{boldsymbol{M}}}}}}}_{B}-{Y}_{{{{{{{boldsymbol{M}}}}}}}_{{{{{{boldsymbol{A}}}}}}}}(t))), and ({{{{{rm{MAP{E}}}}}}}_{w}) is the mean absolute prediction error for within regime predictions (that is, ({{{{{rm{MAP{E}}}}}}}_{w}=frac{1}{n}mathop{sum}limits_{t=1:n}{{{{{rm{abs}}}}}}({hat{Y}}_{{{{{{{boldsymbol{M}}}}}}}_{{{{{{boldsymbol{A}}}}}}}}(t)|{{{{{{boldsymbol{M}}}}}}}_{A}-{Y}_{{{{{{{boldsymbol{M}}}}}}}_{{{{{{boldsymbol{A}}}}}}}}(t))). A test is consider significant if observed difference in across and within regime mean prediction errors is larger than the 95th percentile of 1000 permuted data sets.Food-chain modelWe used a food-chain model parameterized as in McCann and Yodzis31 to simulate food-chain dynamics:$$frac{{{{{{rm{d}}}}}}R}{{{{{{rm{d}}}}}}t}=Rleft(1-frac{R}{K}right)-frac{{x}_{c}{y}_{c}CR}{R+{R}_{0}}$$ (7) $$frac{{{{{{rm{d}}}}}}C}{{{{{{rm{d}}}}}}t}={x}_{c}Cleft(-1+frac{{y}_{C}R}{R+{R}_{0}}right)-frac{{x}_{P}{y}_{P}PC}{C+{C}_{0}}$$$$,frac{{{{{{rm{d}}}}}}P}{{{{{{rm{d}}}}}}t}={x}_{P}Pleft(-1+frac{{y}_{P}C}{C+{C}_{0}}right),$$where R is the resource density, C consumer density, and P predator density. All parameters, except half-saturation constants R0 (here set to 0.16129) and C0 (here set to 0.5), and resource carrying capacity K, are derived from bioenergetics and body size allometry30 (xc = 0.4, yc = 2.009, yp = 2.876, R0, r = 1, xp = 0.08).This model can display a rich set of dynamics depending on parameter values31. Here we alter resource carrying capacity K in order to simulate the dynamics (using the deSolve package32 in R) of qualitatively different attractors (See Supplementary Fig. 1; K = 0.78, equilibrium; K = 0.85; two-point limit cycle; K = 0.92, four-point limit cycle; K = 0.997, chaotic dynamics). Every fifth time step of the simulated dynamics, corresponding to a sampling frequency of ≈10 samples per cycle for the 2-point limit cycle, was sampled. Observation noise was thereafter added to the deterministic dynamics produced by the model:$${N}_{l}(t)={N}_{l}^{prime}(t)+rho * e(t);e(t) sim N(0,{sigma }_{N^{prime_{l}}}),$$ (8) where (N_{l}^{prime}(t)) is the abundance of species l (P, C or R) simulated by the food-chain model at time point t, (rho) is the level of observation noise and ({sigma }_{N_{l}^{prime}}) is the standard deviation of the deterministic dynamics of species l produced by the food chain model.In order to investigate how time series length and observation noise affects the probability of detecting alternative attractors we derived probability landscapes. These were derived by testing the null-hypothesis (H0:(|{hat{{{{{{boldsymbol{Y}}}}}}}}_{{{{{{boldsymbol{i}}}}}}}|{{{{{{boldsymbol{M}}}}}}}_{{{{{{boldsymbol{i}}}}}}}-{{{{{{boldsymbol{Y}}}}}}}_{{{{{{boldsymbol{i}}}}}}}| > |{hat{{{{{{boldsymbol{Y}}}}}}}}_{{{{{{boldsymbol{i}}}}}}}|{{{{{{boldsymbol{M}}}}}}}_{{{{{{boldsymbol{j}}}}}}}-{{{{{{boldsymbol{Y}}}}}}}_{{{{{{boldsymbol{i}}}}}}}|); See Test statistic above) across 100 replicates for each combination of time series length and level of observation noise, (rho). Time-series length was varied from 10 to 100 in steps of 10, and observation noise, (rho), was varied from 0.01 to 0.3 in steps of 0.01, in total yielding 300 combinations of observation noise and time series length, for each combination of dynamical regimes i and j. Predator dynamics was used to predict consumer and resource dynamics using the multivariate approach described above (results for the cases where consumer or resource dynamics are used to predict the other species´ dynamics are presented in Supplementary Figs. 2, 3). All time series were standardized ((mu =0;sd=1)) prior testing for dynamical difference.Experimental data setThe experimental data set was given by Fussman et al.7. This data set contains 14 time series of a predator Brachionus calyciflorus and its prey Chlorella vulgaris derived from chemostat experiments. Time series for different dilution rates were produced by keeping the dilution rate fixed in different chemostats (Supplementary Figs. 3–11). Brachionus calyciflorus and Chlorella vulgaris time series were used to predict Chlorella vulgaris and Brachionus calyciflorus time series, respectively, using the multivariate approach described above. We tested for qualitative difference in the temporal dynamics across all time series, which were standardized ((mu =0;sd=1)) prior testing.Alternative stable state modelWe used a stochastic version of a well-known alternative stable state model4,33 to produce alternative stochastic dynamical regimes. The model is described by:$${{{{{rm{d}}}}}}x=left(xleft(1-frac{x}{{{{{{rm{K}}}}}}}right)+frac{c{x}^{2}}{1-{x}^{2}}right){{{{{rm{d}}}}}}t+sigma {{{{{rm{d}}}}}}w,$$
(9)
where K is the carrying capacity (here set to 11), c is a harvest rate, and σ (here set to 0.01) is the magnitude of noise which is described by a Wiener process (dw).The model was simulated for fixed harvest rates (c) assuming that the system state resides in either of its two basins of attraction. The initial value for the simulation was set to the equilibrium of the noise-free model skeleton for fixed harvest rates c, and σ is set low in order to avoid stochastic flips, so-called flickering, between alternative basins of attraction. Dynamics was integrated (Δt = 0.01) using the matlab-package SDE-Tools34.In order to investigate how time-series length and harvest rate, c, affects the probability of detecting alternative attractors in stochastic regimes we derived probability landscapes.These were derived by testing the null-hypothesis H0:(|{hat{{{{{{boldsymbol{Y}}}}}}}}_{{{{{{boldsymbol{i}}}}}}}|{{{{{{boldsymbol{M}}}}}}}_{{{{{{boldsymbol{i}}}}}}}-{{{{{{boldsymbol{Y}}}}}}}_{{{{{{boldsymbol{i}}}}}}}| > |{hat{{{{{{boldsymbol{Y}}}}}}}}_{{{{{{boldsymbol{i}}}}}}}|{{{{{{boldsymbol{M}}}}}}}_{{{{{{boldsymbol{j}}}}}}}-{{{{{{boldsymbol{Y}}}}}}}_{{{{{{boldsymbol{i}}}}}}}|) (permutation test p = 0.05) across 100 simulated data sets for each combination of time series length and harvest rate, c. Time-series length was varied between 50 and 150 in steps of 10, and c was varied between 1.83 and 2.73 in steps of 0.05, in total yielding 209 combinations of time series length and harvest rate. Each time series was standardized ((mu =0;sd=1)) prior testing for difference in temporal dynamics of contrasting regimes.Natural time-series dataIn a previous study on early warning signals of impending regime shifts, Gsell et al.18 used breakpoint analysis to identify two potential alternative dynamical regimes. We here test if these two-time series segments constitute alternative dynamical attractors. Prior analysis, we imputed a few missing observations (n = 24) using a kalman smoother35. The two time series segments, i.e. pre- and post-breakpoint time series, were standardized ((mu =0;sd=1)) prior testing for dynamical difference.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More