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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

Bagworm walking method using a ladder-like silk footholdWhen bagworms are reared in a plastic or glass cage, they walk not only on the floor but also on the walls or ceiling using only their three pairs of thoracic legs. The method by which they achieve this was clarified by placing a bagworm on black paper. Where the bagworm had walked, a ladder-like silk trace was observed on the black paper (Fig. 2a). Scanning electron microscopy (SEM) observation of one of the steps (or rungs) of the ladder-like trace revealed that each step was made up of a zigzag pattern of silk threads (Fig. 2b). Further magnified SEM observations revealed that the folded parts of the zigzag-spun thread were glued selectively to the substrate with adhesive whereas the remaining straight parts (hereafter, termed ‘bridges’ or ‘bridge threads’) were unglued (Fig. 2c–e).Figure 2Architecture of the ladder-like foothold. (a) A typical ladder-like foothold constructed by a bagworm on black paper, (b) an enlarged image showing one of the steps in the foothold and (c) a scanning electron microscopy image of the step shown in (b). The unglued bridge threads and a glued turn in the step shown in (c) are magnified in (d) and (e), respectively. (f) An enlarged image of four continuous steps in the foothold shown in (a). The neighbouring steps are connected via a single thread indicated by the arrows. (g) A schematic depiction of the basic architecture of the foothold; blue lines and green circles correspond to the silk thread and glued parts, respectively. (h) A photograph of a bagworm constructing a foothold on a transparent plastic board.Full size imageNotably, the steps of the foothold were not independent but rather always connected with neighbouring steps via a single thread (Fig. 2f). The overall basic construction of the foothold is schematically depicted in Fig. 2g. We found that the foothold was constructed in one continuous movement and always made of a single thread regardless of walking distance or time; therefore, a continuous thread exceeding a length of 100 m could be collected from one foothold14. We also observed bagworm climbing behaviour on a transparent plastic board, which clarified the important role of the silk trace as a foothold (Fig. 2h). During this behaviour, the bagworm used its sickle claws (Fig. 1e) to hook its second and third pairs of thoracic legs onto the first and second newest steps, respectively, and constructed the next step by spinning silk with a zigzag motion of the head and the skilful use of the first pair of thoracic legs. When the bagworm advanced one step, it always first shifted its third pair of thoracic legs to the next step before then shifting its second pair of thoracic legs to the newest step to avoid overloading this step, which may not yet be fully adhered to the surface (see Supplementary Movie S1). Because of this construction method, the interval distance between neighbouring steps is automatically determined by the interval between the thoracic legs. By repeating this process, the bagworm can advance forward slowly but steadily. This walking method was commonly observed on a horizontal floor surface, vertical wall, or horizontal ceiling. Although we have mainly described and shown observations from E. variegate here, with the exception of Supplementary Fig. S4 and Movie S1, we also observed instances of walking behaviour in other species, namely Eumeta minuscula, Mahasena aurea, Nipponopsyche fuscescens and Bambalina sp. (for a movie on E. minuscula walking behaviour, wherein it climbs a vertical wall, see Supplementary Movie S2). For at least 100 individuals of these bagworm species, we observed essentially identical walking behaviour to that described in the present study without exceptions for locomotion on substrates with slippery surfaces.Based on our observations, we asked the following question: how do bagworms selectively glue the folded parts of the foothold onto the substrate? Real-time observation of the tip of the spinneret (i.e. the spigot) through a transparent plastic board during the construction of the foothold revealed that adhesive was selectively discharged to attach the folded parts to the substrate; this process could be distinguished from the continuous spinning of the silk thread (for a movie showing construction behaviour, see Supplementary Movie S3). Figure 3a–g shows a time-sequence of foothold construction with enlarged images in the vicinity of the spinneret provided, whereas Fig. 3h depicts a schematic trace of the construction process. It was clearly noted that the bagworm discharged the adhesive only at the folded parts (shown in Fig. 3a–c,e,f; termed the ‘glued turn’) and not at the straight bridge parts (shown in Fig. 3d,g; termed the ‘unglued bridge thread’). From these time-sequence observations, we concluded that the bagworm controls the discharge of adhesive in an ‘on and off’ manner as necessary (essentially the same construction behaviours were confirmed for at least 20 individuals).Figure 3Foothold construction. (a–g) (left side) Time-sequence images taken during foothold construction and (right side) enlarged images of the vicinity of the spinneret (corresponding to the yellow rectangular area in each left-side image). The time-sequence images correspond to the parts of the schematic trace of foothold construction depicted by the red line in (h). In each right-side image and the schematic trace, the part of silk thread at which the adhesive was discharged is traced with a light-blue line. Green arrows in the right-side images show the direction of travel of the spinneret.Full size imagePassages of fibroin brins and adhesiveWe next investigated the spinning mechanism that enables continuous spinning of silk thread together with the selective discharge of adhesive via a single spigot. To this end, we observed the morphology of the bagworm from the silk gland to the spigot. Figure 4a shows the area in the vicinity of the spinneret, dissected and isolated from an E. variegata bagworm, which included a pair of silk glands and plural adhesive glands. As we previously reported21, the exterior shape of the silk gland in E. variegata (see Supplementary Fig. S1) is almost the same shape as that in the silkworm Bombyx mori and it is subdivided into three parts: the anterior (ASG), middle (MSG) and posterior (PSG) silk glands. We also previously confirmed that fibroin heavy chain (h-fib), fibroin light chain (l-fib) and fiboinhexamerin genes are expressed dominantly in the PSG, while sericin is expressed in the MSG, which strongly suggests that division-selective production of each protein exists in E. variegata (as has been shown in B. mori22). Figure 4b shows a magnified image of the spinneret including the end of the ASG. Beyond the pair of ASGs, which are merged into a common tube, a silk press and spinning tube appear before the spigot. This basic passage of silk fibroin from the ASG to the spigot is essentially the same as the passage observed in B. mori23. However, more detailed morphological observations of the inner structure of the passage revealed several obvious differences between E. variegata and B. mori.Figure 4Structural examination of the passages of fibroin brins and adhesive. (a) An optical microscope image of the area in the vicinity of a spinneret isolated from a female bagworm in the final instar stage. Indicated by arrows is a pair of silk glands (SG), one of the adhesive glands (ADG) and the spinneret (SP). (b) An optical microscope image of the passage including the (1) end of the anterior SGs (ASGs), (2) common tube, (3) silk press, (4) spinning tube and (5) spigot. (c–j) Optical microscope images showing cross-sections of the passage of fibroin brins obtained from the corresponding positions (c–j) in image (b). To focus on the fibroin brins and its passage, the surrounding outer part was removed so that a pair of fibroin brins was revealed in each image (except for image (c), which shows only one side of the ASG). Unmagnified images of (f–j), including the outer part, are shown in Supplementary Fig. S2. (k–n) 3D X-ray CT images of the spinneret: (k) overview, (l) cross-sectional top view, (m) cross-sectional side view and (n) passage of the fibroin brins and corresponding cross-sectional images at various positions. In the cross-sectional side view (m), the sheath and core parts are coloured blue and pink, respectively. (o) Image of the tip of a spigot from which adhesive is overflowing and a silk thread is emerging.Full size imageCross-sectional images along the spinneret are shown in Fig. 4c–j; these focus on the silk brins and their passage (unmagnified versions of the images in Fig. 4f–j are shown in Supplementary Fig. S2). The fibroin brins have an approximately round cross-sectional shape at the end of the ASG (Fig. 4c) and are merged at a common tube, which deforms their round shape slightly (Fig. 4d). The fibroin brins seem to be coated with a thin layer of sericin after the MSG, similar to B. mori; however, we omit the presence of the sericin layer here for convenience. The paired brins are gradually pressed between the ventral and dorsal hard cuticle plates at the silk press, and a gradual diameter decrease and shape deformation follows (Fig. 4e,f). At the exit of the silk press, each brin becomes elliptic and the diameter in the major axis decreases. Interestingly, the elliptical shape and 1.7-axial ratio for the major and minor axes of the fibroin brin cross-section in bagworm silk, which we previously reported14, are already determined at this stage in the silk press; afterwards, the diameter decreases without any change in the axial ratio of the elliptical cross-section. Notably, the two elliptical fibroin brins are aligned side-by-side so that their major axes are in line horizontally (to resemble a figure of ‘∞’) at the spinning press, and these are followed by the spinning tube (Fig. 4e–h). However, the alignment is twisted by 90° in one direction (to resemble a figure of ‘8’) before the brins are spun from the spigot (Fig. 4i,j).We found that the spinning tube was surrounded by a hard exoskeleton. Using 3D-X-ray CT observations, we produced clear images of the exterior and interior morphologies of the spinning tube enveloped by exoskeleton (Fig. 4k–m; the exterior shape observed from the dorsal-, ventral- and lateral-sides by optical microscopy is provided in Supplementary Fig. S3). The spigot was not cut perpendicularly to the spinning tube but rather with a slope of around 20°; consequently, it was elliptic. X-ray CT clearly showed the core-sheath structure of the spinneret and a wide expanse of sheath parts (Fig. 4m) between the exterior shell and interior spinning tube (Fig. 4l,m). Using optical microscope observations of the cross-sections, we found that at least three pairs of adhesive ducts were running in the sheath space (Supplementary Fig. S2E). Therefore, while the silk brins pass through the central narrow spinning tube, the plural adhesive ducts pass through the outer space independently of the silk thread. Finally, the adhesive enters a ladle-like reservoir located at the spigot and is released together with the silk thread (Fig. 4o). The presence of definitive routes connecting the adhesive passage and the spigot were not clearly observed in our X-ray CT images, probably due to the small structural scale relative to the space resolution used in our analysis (i.e. 0.31 μm). We speculate that the adhesive merges into the spigot via a fine, porous sponge-like structure, and we indicate assumed routes in Fig. 4l,m. X-ray CT observations also revealed a sophisticated structural design involving gradual twists in the silk brins by 90° from ‘∞’ to ‘8’ (Fig. 4n and Supplementary Movie S4). Essentially identical spinneret structures were observed by X-ray CT images for all of eight observed individuals from the third to final instars of E. variegata. More

NEWS
17 August 2021

Brazilian road proposal threatens famed biodiversity hotspot

Scientists and environmentalists say the road, slated to pass through Iguaçu National Park, could harm research projects and precious ecosystems.

Meghie Rodrigues

Meghie Rodrigues

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Protesters oppose the Caminho do Colono at Iguaçu Falls.Credit: Marcos Labanca

Brazil’s National Congress could soon vote on a bill proposing to construct a road through the country’s Iguaçu National Park. If the proposal moves ahead, researchers fear that it will threaten the park’s lush forest, a biodiversity hotspot that is home to almost 1,600 animal species, including endangered animals such as the purple-winged ground dove.Environmentalists and researchers have fought off construction of the 17.5-kilometre road for years, arguing that it will not only bring pollution to the park, but also poachers, who would threaten animals such as jaguars and tapirs. Even research in the park could be affected. In a portion of the park that dips into Argentina, for example, “poachers often steal our cameras”, says Julia Pardo, a mammal conservation and ecology researcher at the Subtropical Biology Institute in Misiones, Argentina.
‘Apocalyptic’ fires are ravaging the world’s largest tropical wetland
Under the leadership of President Jair Bolsonaro, Brazil’s government has weakened protection of the country’s forests in favour of industries such as mining, logging and ranching. The lower house of Brazil’s Congress, the Chamber of Deputies, put the bill on a fast track in June, allowing it to skip regular debate among its committees and head straight for a vote — a move that has researchers worried.If passed, the legislation would establish a dangerous precedent that could weaken environmental law in Brazil, says Sylvia Torrecilha, a biologist at the Secretariat of Environment, Economic Development, Production and Family Agriculture in Mato Grosso do Sul. In addition to cutting Iguaçu Park in two with a road that will connect towns to its north and south (see ‘Contested route’), the bill seeks to create a new type of protected area — the estrada-parque, or park road — within Brazil’s System of Natural Conservation Units, which regulates environmentally protected areas. Approving the construction of the ‘Caminho do Colono’ (the Settler’s Road) in Iguaçu could literally pave the way for creating through-ways in other parks and conservation areas in Brazil, says Torrecilha.Normally, the idea of a park road is to preserve the green areas along an already-existing scenic route, she says, not to bring commercial or economic advancement to a state — the argument lawmakers have made in favour of the road. The proposal, from its very beginning, is “inappropriate”, she adds.A historical routeEstablished in 1939, Iguaçu National Park is famous for the waterfall — one of the world’s largest — on the border with Argentina along its southwestern tip. But it is also notable because it contains the largest remaining patch of Atlantic Forest in southern Brazil. Although less well-known than the Amazon rainforest, the Atlantic Forest is rich in plant and animal species, and originally stretched along the coast of southeastern Brazil and down to Argentina and Paraguay. However, the forest is rapidly disappearing: it has lost almost 90% of its tree cover, accelerated by deforestation from urbanization, and agricultural and industrial activities in the twentieth century. Because of these attributes, the park was designated as a World Heritage site by the United Nations cultural organization UNESCO in 1986.

If the legislation is successful, it would actually enable the creation of the Caminho do Colono for the second time. The government of Paraná, the state where Iguaçu National Park is located, transformed an existing walking path into an unpaved version of the road during the 1950s. “Nobody cared much at the time because there wasn’t much difference between the inside and the outside of the park, as the Atlantic Forest stretched all over the place,” says former park chief Ivan Baptiston. “With all the deforestation of the last decades, nowadays, the scenario is a lot different.”In 1986 — the same year the park received its UNESCO World Heritage Site designation — Brazil’s Federal Prosecutor’s Office filed a civil suit to close the road, and the following year, a federal judge officially closed it. Since then, vegetation has overtaken the route, and some local residents have tried and failed to force it back open, claiming economic hardships associated with not being able to travel efficiently through the area.
‘We are being ignored’: Brazil’s researchers blame anti-science government for devastating COVID surge
The new bill states that re-establishing the road would offer a “solution to a logistical problem in Paraná state”. Sponsored by Nelsi Coguetto Maria, a member of the Chamber of Deputies, the proposal also says it “answers a decades-old outcry of Paraná inhabitants, salvaging the region’s history and its socioeconomic, environmental and tourism relations.”Environmentalists have criticized Coguetto Maria for backing the bill. And local media outlets have reported that his family stands to potentially gain from the Caminho do Colono; two of his sons are partners in construction companies that could pave the road. Coguetto Maria’s office did not respond to Nature’s queries about this, or about researchers’ concerns over the road. When the Chamber of Deputies approved fast-tracking of the bill, he argued that the Brazil of today is “responsible”, and has the “competence and capacity to build an ecologically correct road”, pointing out that the road existed as a walking path before the park was even created.Research interruptedFor many conservationists and researchers, the economic argument to open the road doesn’t hold water. The damage caused to the park’s highly valued Atlantic Forest would far outweigh the potential economic gains for the surrounding towns1, they say. Furthermore, the species protected by the park are irreplaceable, they add. Iguaçu is the only location in the world where the jaguar population is increasing instead of declining. If the road opens, says Pardo, pressure on the animals will skyrocket. “Easy access is the main enabler for poachers,” she says.

Iguaçu Falls is located along the border of Argentina and Brazil, on the Iguaçu River.Credit: Thiago Trevisan/Alamy

Cars using the road will also cause air, soil, water and even sound pollution, says Victor Prasniewski, a conservation biologist at the Federal University of Mato Grosso in Brazil. Sound pollution, in particular, changes communication patterns among a number of species. “Birds that attract females by singing will be forced to sing louder or longer to get noticed,” says Prasniewski, who published a paper last year2 listing the potential negative impacts of the Caminho do Colono.“These changes can affect the reproduction and even the evolution of some birds,” says Carlos Araújo, a bioacoustics ecologist at Argentina’s Subtropical Biology Institute. “The building of a road would be catastrophic to research in my field,” he says.He works on a large-scale monitoring project looking for the purple-winged ground-dove, the last confirmed sighting of which was more than three decades ago. “It’s a rare animal, and we leave recorders spread over the forest to try and catch her singing. We often capture helicopter noise, which disturbs our work.” Cars and trucks on the road would create similar low-frequency noise, he says. “It will be a lot harder to find birds like this dove.”
Brazil’s lawmakers renew push to weaken environmental rules
For some, the argument that the road will enhance tourism in Paraná doesn’t make sense either. Reopening the road, says Carmel Croukamp Davies, chief executive of Parque das Aves, a private bird sanctuary and shelter near the park, could threaten Iguaçu’s UNESCO World Heritage title if it damages the park’s biodiversity and severs the Atlantic Forest. Visitors come because they want to experience nature, she adds: “Whoever doesn’t understand the impact of a proposal like this doesn’t understand an inch of tourism nor biodiversity.”With Brazil’s Congress having returned from holiday earlier this month, the bill could soon be put to a vote. And when it is, environmentalists worry it will be passed, given how many representatives within the Chamber of Deputies currently align with Bolsonaro. Then it would face the Senate, and finally, Bolsonaro, who is expected to ultimately approve it.

doi: https://doi.org/10.1038/d41586-021-02199-x

References1.Ortiz, R. A. Ambientalia 1, 141–160 (2009).
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2.Prasniewski, V. M. et al. Ambio 49, 2061–2067 (2020).
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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

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