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Freshwater fish are important contributors to human livelihoods, food and nutrition, recreation, ecosystem services, and biological diversity. Yet, they inhabit some of the most threatened ecosystems globally1, face higher declines relative to marine and terrestrial species2, and are disproportionally understudied3,4. Inland fisheries are subjected to a suite of anthropogenic stressors across aquatic-terrestrial landscapes5, including flow alterations, dams, invasive species, sedimentation, drought, and pollution6,7,8. Evaluating stressors and their impacts on global inland fisheries is essential for effective management, monitoring, and conservation6, but unlike marine fisheries, there is no standardized method to assess inland fisheries9.Data inputs for a fisheries threat assessment typically include baseline information, such as species-specific landings or in situ population data (volume and composition), size (population and landings), and biomass. In addition, multi-stressor interactions (e.g., synergistic, additive) across complex habitats often warrant cross-ecosystem and cross-sector evaluations at multiple scales10,11. However, in the case of inland fisheries, these data inputs are severely deficient and often disparate in many regions12,13, which challenges the development of a global assessment. Thus, evaluating stressors and their impacts on inland fisheries necessitates the use of additional data sources (e.g., expert knowledge) beyond those typically derived directly from fish or fish habitats12,14. Local and subject-matter expertise can provide contextualized insights where spatial data are limited or unattainable (e.g., emerging threats15) and where empirical evidence is incomplete (e.g., multi-stressor interactions).Expert elicitation (i.e., expert opinion synthesis, where opinion is the preliminary state of knowledge of an individual) is increasingly used to inform ecological evaluations and guide water infrastructure, development, food security, and conservation decision-making and assessments, especially in data-poor scenarios14,16. While spatial data can be integrated as a suite of individual stressors (i.e., input variables) within ranking systems for the development of vulnerability or habitat assessments for conservation purposes14,17, the utilization of spatial variables is limited by the method for determining relative impacts (i.e., value judgment)18. Cumulate impact scores and systematic weighted ranking of threats are often based on geographically biased, small sized, or non-representative subsets of experts’ opinions (e.g., global weight determination from eight experts5). Thus, data collection for this study was motivated by the development of a global assessment of threats to major inland fisheries, and the overarching need for tractable freshwater indicators. The data generated contribute essential relative influence scores for the assessment and provide a timely snapshot of inland fisheries as perceived by fisheries professionals. Threat composition and influence have broader potential applications to inform vulnerability and adaptation components of freshwater conservation and management targets (e.g., United Nations (UN) Sustainable Development Goals, UN International Decade “Water for Sustainable Development,” Convention on Biological Diversity, Ramsar Convention on Wetlands).This paper introduces a dataset that can help address a knowledge gap in understanding natural and human influences on inland fisheries with local, contextualized fishery evaluations. Derived from an electronic survey, data comprise perceptions from fisheries professionals (n = 536) on the relative influence and spatial associations of fishery threats, recent successes, and adaptive capacity measures within the respondent’s fishery of expertise.In the context of the survey, we use the term “threat” as a proximate human activity or process (“direct threat”) causing degradation or impairment (“stress”; e.g., reduced population size, fragmented riparian habitat) to ecological targets (e.g., species, communities, ecosystems; in this case, fishery)19. We considered only the threats most proximate and direct to the target (fishery) and excluded stresses (i.e., symptoms, degraded key attributes) and contributing factors (i.e., root causes, underlying factors). For example, we considered pollutants (direct threat) rather than the pollution source (contributing factor) or the resulting contaminated water (stress, effect). We addressed the ambiguity of the term ‘fishery’20 by allowing respondents to indicate a geographic location (specific point) within their fishery area. This allows for spatial attribution with an inclusive use of ‘fishery’ as it pertains to threats (e.g., threats to a fish population of fishery-targeted species, catch characteristics, or the habitat in which the fishery operates).We structured survey questions about the occurrence and relative influence of threats to the production and health of inland fisheries using 29 specified individual threats derived from well-studied pressures to inland fisheries in addition to pressures emerging as threats to fisheries (e.g., climate change, plastics15). We categorized individual threats into five well-established categories: habitat degradation, pollution, overexploitation, species invasion, and climate change1,7 for organizational context in the survey. We also designed survey questions specifically to understand the social adaptive capacity of fishers using five major community-level domains: fisher access to assets (e.g., financial, technological, service), fisher and institutional flexibility to adapt to changing conditions (e.g., livelihood alternatives, adaptive management), social capital and organization to enable cooperation and collective action (e.g., co-management), learning and problem-solving for responding to threats, and fishers’ sense of agency to influence and shape actions and outcomes21.This dataset can be useful as an overview assessment, on which future assessments may expand for specific temporal or spatial interests. Some data in this dataset (e.g., microplastics, invasive species disturbances) are otherwise unattainable at relevant scales from geospatial information and therefore provide novel information. Potential uses include demographic influences on threat perceptions, spatial distribution of adaptive capacity measures paired with climate change or other threats, external factors driving multi-stressor interactions, and paired geospatial and expert-derived threat analysis. These data can provide insights on fisheries as a coupled human-natural system and inform regional and global freshwater assessments. More

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From SSN to PFCsWe analyzed the 1,914,171 proteins from 885 MAGs from marine plankton, recovered from 12 geographically bound assemblies of metagenomic sets corresponding to a total of 93 Tara Oceans samples from the 0.2 to 3 µm and 0.2 to 1.6 µm size fractions21. A flowchart of our bioinformatic pipeline is available in Supplementary Fig. 1. 39.6% of the MAGs’ proteins (757,457) were involved in our SSN, i.e., they had at least one similarity relationship with another protein that satisfied the chosen threshold of 80% similarity and 80% coverage (see “Methods”). In total, 51.1% of the network proteins could be annotated to 4922 unique molecular function IDs in the KEGG database37, associated with 327 distinct metabolic pathways (a full list of these pathways is available in Supplementary Data 1). In total, 85.2% of the network proteins were annotated to 17,009 eggNOG functional descriptions38,39.The SSN involved 233,756 connected components (CCs), i.e., groups of nodes (here proteins) connected together by at least one path and disconnected from the rest of the network. According to KEGG and eggNOG databases, 15.3% and 48.5% of the CCs remained without any functional annotation (i.e., all sequences from the CC were unmatched in the databases, or had a match but were not yet linked to any biological function, Table 1), and 14.8% were functionally unannotated for both databases. We ranked the functional homogeneity of CCs involving at least one functional annotation from 0 (all annotations in the CC are different) to 1 (all annotations in the CC are the same) and found mean homogeneity scores of 0.99 over 1 for KEGG annotations and 0.94 over 1 for eggNOG ones (see “Methods” for score calculation details). Only 88 (0.04%) CCs had a homogeneity score below 0.5 in both annotation databases, all with sizes below five proteins. 177 CCs (0.07%) had a score below 0.8 in both databases, all under 12 proteins in size. These CCs were kept in the analysis while tagged as poorly homogenous. We thereafter considered each CC as a PFC, numbered from #1 to #233,756.Table 1 Metrics computed on the 233,756 protein functional clusters (PFC) from the sequence similarity network of MAGs proteins.Full size tableTo check for the influence of taxonomic relationships between the MAGs on our PFCs, we computed different metrics based on MAGs taxonomic annotations provided by Delmont et al.21. (Table 1). This taxonomic annotation based on 43 single-copy core genes allowed to annotate 100% of the MAGs at the domain level, and 95% of the MAGs at the phylum level, the remaining 5% corresponding to Bacteria MAGs of unidentified phyla21. Only 1330 PFCs (0.6%) mixed proteins from the Archaea and Bacteria domains. PFCs were very homogeneous at the phylum level, then the homogeneity decreased at lower taxonomic rank, meaning that PFCs studied here were generally not specific from a single class, order, family, genus, or MAG (Table 1). In total, 7834 PFCs (3.4%) were only composed of proteins with no functional annotation in KEGG and eggNOG databases, and no taxonomic annotation under the phylum level. Their sizes ranged from 2 to 30 proteins (mean of 2.62). Their 20,552 proteins came from Euryarchaeota MAGs (12,458; 60.6%), Bacteria MAGs of unidentified phylum (2742; 13.3%), Candidatus Marinimicrobia MAGs (2451; 11.9%), Proteobacteria MAGs (1528; 7.4%), Acidobacteria MAGs (1031; 5%), Verrucomicrobia MAGs (103; 0.5%), Planctomycetes MAGs (89; 0.4%), Bacteroidetes MAGs (79; 0.4%), Chloroflexi MAGs (59; 0.3%) and Candidate Phyla Radiation MAGs (12; 0.05%). We hereafter considered these functionally and taxonomically unknown PFCs as “dark” PFCs40,41. Their nucleotidic sequences are available in separate supplementary files (see “Data availability”). The abundance of dark PFCs was significantly different from the abundance of other PFCs in 85 samples over 93 (two-sided Wilcoxon rank-sum test, p-value  More