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Study region
The Atlantic Forest is a global biodiversity hotspot that once covered 1.63 million km2 mostly in Brazil (92% of the total area), but also in Paraguay (6%) and Argentina (2%—Supplementary Fig. 1). It covers a wide range of climatic and edaphic conditions, with forest types ranging from rainforests to seasonal forests, including cloud, swamp, and white-sand forests56. The Atlantic Forest has been suffering from deforestation and degradation for over 500 years. Today, it includes some of the largest cities in South-America, with over 148 million people currently living within the Atlantic Forest limits57. Less than 20% of the original Atlantic Forest remains and the remnants are characterized by small ( More

Rivers are a major source of renewable water, and provide food, jobs and a sense of place and cultural identity for people living in the vicinity. For many Indigenous peoples, rivers are central to how they understand themselves, their origins and their relationships to the rest of nature. As a citizen of the Penobscot Nation in Maine put it1, “The river is us: the river is in our veins.” Writing in Nature, Koning et al.2 report ecological surveys that demonstrate how local Indigenous people in the Salween River basin on the border between Thailand and Myanmar have successfully managed the river for conservation purposes and to protect livelihoods.

Both biodiversity and the people in river-associated communities are under severe stress the world over. Across the globe, 30% of freshwater fish (see go.nature.com/3ixfd9l) are classified as being at risk (in either the critically endangered, endangered or vulnerable categories) in the 2020 Red List of threatened species compiled by the International Union for the Conservation of Nature. Furthermore, it is projected3 that half the human population will live in water-insecure areas by 2050. Principal among the threats to rivers are pollution, climate change, invasive species, changes in surrounding land use, and the construction of dams and infrastructure that affect river flow. These issues need to be addressed on scales ranging from local to global, and solutions should draw on the knowledge, practices and aspirations of those whose lives are most closely entwined with river health.
Koning et al. assessed the outcome of a network of small fishery no-take reserves (areas where fishing is not allowed), and found that there was an average 27% rise in species richness, 124% higher fish density and 2,247% higher fish biomass in the reserve-associated waters compared with the corresponding values for nearby areas open to fishing. The presence of larger species and more individuals in the reserves is what drives the much higher biomass there. The authors suggest that such networks of locally managed, small, protected river areas could be used in other river systems to enhance fisheries and to conserve biodiversity.
The authors’ work highlights the importance of inland waters to food and livelihood systems, demonstrates the value of community-led conservation, and points out commonalities between protected-area conservation strategies in marine and freshwater ecosystems. Marine-protected areas, which are usually created by governments, are used widely in ocean conservation and fisheries, but much less commonly in fresh waters4. The authors characterize the reserves studied as being created by the S’gaw Karen (also known as Pwak’nyaw) Indigenous people who live in the river catchment areas. The paper thus also supports the growing recognition5 among scientists and conservationists of the effectiveness of Indigenous resource-management practices.

Koning and colleagues’ study draws on natural sciences — limnology (the freshwater equivalent of oceanography) and fish ecology — but also discusses how river management operates at a community level. Their natural-sciences disciplinary lens allows them to rigorously evaluate the benefits that protected areas confer on fish conservation and on the sustainability of local fish catches. In the area studied, Indigenous communities had planned and implemented local no-take reserves that complement other community-based conservation initiatives, including the management of adjacent land.
However, the context in which this management system evolved, the knowledge and politics involved in its creation, and how local forms of knowledge and practice can be supported and valued are less in focus in Koning and colleagues’ study. Pwak’nyaw communities have been profoundly transformed as a result of colonization in Myanmar, the arrival of foreign missionaries in Myanmar and Thailand, and state modernization projects in both countries. Supporting river conservation here and elsewhere at locations where other Indigenous peoples live will require a reckoning with such legacies and a willingness to make space for local and Indigenous voices to be heard, alongside those of scientists, in river-basin planning.
One of us (V.C.) is a Pwak’nyaw person, born in Hpa’an, Myanmar, on the banks of the Salween River, and believes that it is crucial that science conducted in Indigenous territory incorporates Indigenous systems of knowledge and beliefs, and for Indigenous people to have ownership over data that involve them. Although, during a period of 8 years of research, Koning et al. worked with local people for more than 18 months when living in the study area, there is scope for furthering these relationships so that Indigenous perspectives have increased visibility. An absence of Indigenous agency and control in the production of knowledge is a key issue, leading to calls for Indigenous data sovereignty and the decolonization of science6.

Koning and colleagues’ study positively recognizes Pwak’nyaw involvement in conservation, and includes some cultural context, although Pwak’nyaw perspectives are lacking. One consequence of this might be the study’s focus on what the Pwak’nyaw would regard as only part of their integrated system of land and water management. For example, Pwak’nyaw don’t commonly identify themselves by categories that are familiar to those in Western culture, such as being a farmer or a fisher. Rather, rotational farming, growing rice, gardening, hunting, gathering and fishing are integrated parts of a Pwak’nyaw livelihood.
Community-based research on Pwak’nyaw livelihoods in northern Thailand has found that fish conservation is also integrated into rotational farming practices. For instance, the concept nya pla htau, meaning fish surface, prohibits the clearing of a field on adjacent sides of a river bank in successive years to conserve fish-breeding grounds, and knowledge about fish is a factor in the selection of farmland7. In this sense, farming cannot be separated from fishing, which cannot be separated from conservation, because they are all part of a whole — and it is beneficial for them to be studied as such.
Future studies, which should involve collaboration with Indigenous researchers, could adopt approaches to integrate Indigenous and scientific knowledge and Indigenous and Western legal and management approaches in ways that recognize and draw on both8. This would help to address some of the unanswered questions in Koning and colleagues’ valuable study on the origins, sustainability and future of this successful network of reserves.
Conflict can arise in Thailand and elsewhere when there is confrontation between Indigenous people and the state, or other groups, regarding competing conservation models. Indigenous lives are in danger — around the world in 2019, more than 200 environmental activists died, 40% of whom were Indigenous people (see go.nature.com/36w68di). In the past decade, the deaths of prominent Pwak’nyaw environmental activists in Myanmar (see go.nature.com/2vspujn) and in Thailand (see go.nature.com/3mwjqm1) have hit the headlines.

Figure 1 | The Salween Peace Park. Pwak’nyaw (also known as S’gaw Karen) people living at this site in Myanmar, located on a tributary of the Salween River, use their Indigenous knowledge to obtain food. For example, the basket-style nets in this image are a traditional way to catch fish and shellfish in shallow waters. Koning et al.2 report that conservation efforts by the Pwak’nyaw community in the Salween River basin area have substantially boosted fish diversity and might increase the yields of fishing catches.Credit: Paul Sein Twa/KESAN

Indigenous resource-management systems can persist despite difficult circumstances. On the Myanmar side of the Salween River, Pwak’nyaw communities, whose livelihoods are affected by ongoing civil war, displacement and militarized development, have created a large-scale conservation project named the Salween Peace Park (Fig. 1), based on kaw (country), a holistic concept that encompasses the localized practice of social and environmental governance, based on Indigenous sovereignty. Pwak’nyaw living there conserve the environment using Indigenous knowledge (see go.nature.com/36tigxg), and are working to revive Indigenous practices lost through decades of conflict.
Without such contextual cultural and political knowledge, it is difficult to say how easily the successes in the Salween River basin, convincingly enumerated by Koning and colleagues’ study, can be achieved elsewhere by trying to transfer this approach. The key insight here may be that the small reserves are potentially useful conservation measures that need to be understood from the perspectives of those who created them. Such reserves should be supported and legitimized where they exist, revived where they existed previously, and perhaps tried out where they haven’t been used before, as part of efforts to meet global river-conservation challenges. This would support a growing movement led by Indigenous peoples to focus on putting rivers at the centre of conservation efforts — including by assigning legal personhood to rivers, as part of a ‘rights of nature’ approach to environmental governance9. More

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An example set of metabolite assemblages and microbial communities
We use metabolite data from the Columbia River corridor to provide an example of how to use a dendrogram-based framework to study the processes influencing metabolite assemblages. In brief, samples of river water and pore water were collected on November 19, 2017 from five locations (Supplementary Fig. 1, Supplementary Table 1) along the mainstem Columbia River in Washington State across a ~1 km transect running along the shoreline. This part of the Columbia River is in an arid region, is dam regulated, is predominantly gravel bedded, experiences significant groundwater-surface water mixing in pore fluids, and has been studied and described extensively36,43,44. At each location, filtered river water and subsurface pore water were collected; one replicate of river water was collected, and three pore water samples were collected from 30 cm depth within a 1 m2 area using 0.25-inch diameter sampling tubes. Samples were analyzed using FTICR-MS at the Environmental Molecular Sciences Laboratory using previously established methods. The raw FTICR-MS data were processed according to established methods to (1) identify peaks from the mass spectra that correspond to unique metabolites identified by their unique mass, (2) calibrate peak/metabolite masses against a standard set of known metabolites, and (3) assign molecular formula based on the Compound Identification Algorithm (CIA)45,46. Further data analyses are described below in the subsections that use the associated analysis. In addition, water samples were analyzed for basic geochemical parameters (i.e., dissolved organic carbon concentration, specific conductivity, and major anions and cations). We extracted DNA from the filters used to collect aqueous samples and characterized associated microbial communities using 16 S rRNA gene sequencing and associated data processing to pick operational taxonomic units and generate a phylogenetic tree.
Building metabolite dendrograms
Tools and metrics in metacommunity ecology often leverage relational information such as among-species evolutionary relatedness or functional trait similarities, allowing researchers to reveal the balance among stochastic and deterministic assembly processes23,35,41,42,47,48. While metabolites do not have genetic sequence information, their characteristics can be approached in a way that is analogous to the functional trait approach in ecological analyses39,49. Unlike multivariate dendrograms typically used within metabolomics studies (e.g., Tfaily et al. 2018)7, these dendrograms represent relationships between metabolites and not samples. To this end, we developed and evaluated three methods of measuring trait-like relational information between different chemical compounds using two different information sets: molecular characteristics and biochemical transformations (Fig. 1, Supplementary Fig. 2, Supplementary Data 1–3).
Fig. 1: Figure summarizing the steps necessary to create the three dendrograms used throughout this manuscript.

The top path (Molecular Characteristics Dendrogram or MCD) demonstrates the relational information provided by molecular properties, like elemental composition and aromaticity index, while the bottom path (Transformation-based Dendrogram or TD) emphasizes the relationships driven by potential biochemical transformation networks. The middle path (Transformation-Weighted Characteristics Dendrogram or TWCD) is a combination of information provided by the top and both paths. All metabolites in the transformation network would have been identified; the numbered metabolites are used to demonstrate the approach. Definition of acronyms under molecular properties: C, H, O, N, S, and P are elemental counts; DBE is double-bond equivalents; AIMod is modified aromaticity index; and kdef is Kendrick defect.

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First, we generated a molecular characteristics dendrogram (MCD) which integrates elemental composition (e.g., C-, H-, O-, N-, S-, P-content) and derived statistics (i.e., aromaticity index, double-bond equivalents, etc.) similar to principles outlined in compound classification studies50,51,52,53,54,55,56 or in NOM functional diversity analyses16,17,18,57. Next, we created a transformation-based dendrogram (TD) using putative biochemical transformations identified by aligning mass differences to a database of known transformations1,2,3,9,51,58,59 (Supplementary Data 4). Finally, we made the transformation-weighted characteristics dendrogram (TWCD), which is a combination of the MCD and TD (Supplementary Fig. 2). Given each dendrogram method incorporates FTICR-MS peaks differently, the number of peaks incorporated into downstream analyses also varies (Fig. 2a, Supplementary Fig. 3; see Supplement for details). For example, while the MCD incorporates all assigned molecular formula (~14% of observed peaks in this dataset), the TD can gain access to a broader range of peaks because formulas are not required (~72.5% of observed peaks) (Supplementary Fig. 3). While there is a discrepancy between these approaches, this is due to inefficient formula assignment of FTICR-MS data and will vary from dataset to dataset, and with improved formula assignment tools60. Detailed differences between these dendrograms are explored in the Supplement, but each resulted in different metabolite clustering patterns that help provide deeper insight into ecosystem assembly. We suggest that while other approaches to estimating dendrograms from metabolite data exist, the MCD, TD, and TWCD provide a complementary set of methods that are useful for studying the spatiotemporal organization of meta-metabolomes.
Fig. 2: Alpha diversity boxplots for the metabolite data.

a Richness (akin to metabolite count). b Dendrogram Diversity (DD) which is analogous to Faith’s Phylogenetic Diversity (PD). c Mean Pairwise Distance (MPD). d Mean Nearest Taxon Distance (MNTD). Two-sided Mann–Whitney U tests (Surface water n = 7, Pore water n = 14) determined that only the TWCD-DD comparison was significant; the p value is indicated within the figure. Each panel represents metrics calculated for the corresponding metabolite dendrogram (e.g., MCD, TD, and TWCD). Boxes represent the 1st and 3rd quartiles, the horizontal line within the box represents the median, the vertical lines represent extreme values calculated based on the interquartile range, and the points are potential outliers.

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Importantly, data collected using an FTICR-MS will include information about any ionizable compound, not just those associated with biological systems61. Despite this potential limitation, previous studies have demonstrated that this type of data still contains biogeochemically relevant information1,2,4,16,17. Therefore, the three dendrograms described above can resolve the potential relationships between molecular formula based upon a point of view, which is agnostic to a molecular formula’s source (MCD), a point of view which encompasses a putative biochemical point of view (TD), and an integrated view (TWCD). As with many of the tools described in this manuscript, the lack of explicit biological information provides two key benefits. First, it embraces the perspective that there is inherent value in investigating the processes, which give rise to all molecular formula, not just those involved in microbiologically mediated reactions. This allows for evaluation of intrinsic metabolite assemblage turnover without requiring potentially inaccurate biological assumptions. Second, it allows for the coupling of meta-metabolome ecology with other multi-omics data types. This approach minimizes errors that could occur by assigning the sources for molecular formula and associated transformations a priori, and allows understanding to be derived a posteriori through coupling to additional data types.
A quick note about phylogenetic signals
In order to ensure that a phylogenetic tree accurately captures the functional trait information of an ecological system, a test for a phylogenetic signal must be first performed13,24,62,63,64. Once a phylogenetic signal is confirmed, a range of ecological null models can be used to infer community assembly processes13. Within many ecosystems, this can be measured by calculating one of many phylogenetic signal metrics using average trait values63; in microbial systems where said trait values are not as readily available, estimated niche values are calculated based upon abundance and environmental data instead13,64. However, when functional trait dendrograms are used instead of a phylogenetic tree, a phylogenetic signal is unnecessary as the trait relationships are already built into the framework39. Given that the three proposed dendrograms are closely aligned to functional trait dendrograms (i.e., molecular formula properties and putative biochemical relationships)16,17, phylogenetic signal is unnecessary when implementing associated null models.
Using metabolite dendrograms to study metabolite diversity and assembly processes
From a practical perspective, the three dendrograms provide a foundation for studying metabolite assemblages with ecological tools that traditionally use phylogenetic or functional trait data. For example, below we show how metabolomes can be studied using metrics associated with richness (Faith’s PD, UniFrac), overall divergence (MPD), and nearest neighbor divergence (MNTD)42,47,48,65. As a parallel to ecological analyses, these metrics can be used to study the spatial and temporal organization of meta-metabolomes.
Many ecological studies track trait dynamics or utilize identity-based (i.e., taxonomic) analyses such as Bray–Curtis dissimilarity to infer ongoing ecosystem processes66,67. There are, however, exciting opportunities to go further by using additional tools from metacommunity ecology that are designed to infer and quantify assembly processes. Null models represent one set of tools that provide additional insight and complement traditional α-diversity and β-diversity analyses. By applying commonly used phylogenetic null models, we can investigate the processes responsible for structuring metabolite assemblages. First, to assess whether α-diversity was more or less structured than would be expected by random chance, we calculated both the net relatedness index (NRI) and nearest taxon index (NTI), which are z-scores quantifying deviation from null models for MPD and MNTD respectively23,65. For both these metrics, positive values indicate clustering within the dendrogram while negative values signify overdispersion65.
Ranging from cold weather adaptation in forests68, labile carbon degradation in bacterial communities69, or host range/soil adaptations in root-associated mycobiomes70, these metrics have revealed patterns in phylogenetic trait conservation through different phylogenetic lineages71. Despite examining different ecosystems and scales, a common framework enabled researchers to develop consistent conceptual conclusions. In turn, these null models should provide a similar framework for metabolite assemblages, with varied interpretations dependent upon the dendrogram. For example, overdispersion observed on the MCD might suggest broadly distributed thermodynamic properties while it could indicate biochemically disconnected peaks on the TD. Such analyses will allow researchers to ask and answer questions regarding the development of meta-metabolomes.
To further explore the ecological assembly processes structuring metabolite profiles, we calculated the β-nearest taxon index (βNTI; detailed extensively in Stegen et al. 2012, 2015). This metric compares the observed β-mean nearest taxon distance (βMNTD) between two communities to a null expectation generated by breaking observed dendrogram associations. While typically informed using abundance data, this null model still produces useful information with presence/absence data. When a comparison between two ecological communities significantly deviates from the null expectation (indicated by |βNTI |  > 2), we infer that some deterministic process is responsible for the observed pattern. These deterministic processes can be further separated into those which drive a divergence between communities, termed ‘variable selection’ (indicated by βNTI  > 2), and those which drive a convergence between communities, termed ‘homogeneous selection’ (indicated by βNTI  More