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More than three billion people rely on the ocean to make a living, most of whom are in developing countries. For some 17% of the world’s population, fisheries and aquaculture provide the main source of animal protein. For the least-developed countries, fish contributes about 29% of animal protein intake; in other developing countries, it accounts for 19%1.As the global population increases, the demand for seafood is expected to rise, too. Already, Africa and Asia have seen fish production double over the past few decades. Globally, fish consumption is set to rise by around 15% by 20302.Although ocean ecosystems are strained by climate change, overfishing and more, studies nevertheless suggest that seafood can be expanded sustainably to meet future food demands3. Last year, international efforts promoting this approach included the Blue Food Assessment (a joint initiative of 25 research institutions) and the United Nations Food Systems Summit.Success will depend on small-scale fisheries. Small operations tend to deliver both food and income directly to the people who need them most, and locals have a strong incentive to make their practices sustainable. What’s more, these fisheries can be remarkably efficient. Almost everything that hand-to-mouth fisheries catch is consumed. By contrast, around 20% of the fish caught by industrial fleets is estimated to be wasted, mainly because of unwanted by-catch4. So, whereas large-scale operators land more fish, small-scale fisheries provide a larger share of the fish that is actually consumed.Small fishers rarely have the right resources to expand their operations, or even to survive. If they do scale up, they might lose some of their current advantages or engage in the same harmful practices as do large commercial fisheries. Managed with care, however, small fisheries could provide win–wins for livelihoods and the environment. Making this happen should be high on the agenda at the UN Ocean Conference in Lisbon this month.As someone who has studied food security and policymaking for decades, here I suggest ways to support and strengthen artisanal fishing operations.Small reformsThe potential and importance of small-scale fisheries has been increasingly recognized over the past decade. In 2014, the UN Food and Agriculture Organization (FAO) provided voluntary guidelines to support sustainable small-scale fisheries, aimed at improving food security and eradicating poverty. A forthcoming report by the FAO, Duke University in Durham, North Carolina, and the non-profit organization WorldFish, headquartered in Penang, Malaysia, will conclude a remarkable initiative to collate case studies, questionnaire results and data sets to help get fishers a seat at policymakers’ tables. The UN General Assembly has declared 2022 the International Year of Artisanal Fisheries and Aquaculture.Most nations already have management policies for marine ecosystems that provide for small-scale fisheries. In India, Indonesia, Malaysia and Sri Lanka, for example, there is a ban on trawling within about 8 kilometres of the coastline to prevent industrial fishers from scooping up large catches, which protects those regions for local fishers. Countries such as Costa Rica ease access by exempting small-scale fisheries from licences, and Angola exempts subsistence and artisanal fishers from paying licensing fees5.But this is not enough. Small-scale fishers’ rights to access are often poorly defined, ineffectively enforced or unfairly distributed4. The boundaries of exclusive economic zones (EEZs) — the parts of the coast belonging to a given nation — are often poorly policed, and large-scale vessels regularly swoop in and take sea life through bottom trawling, something that small fishers seldom practice. Large-scale bottom-trawlers account for 26% of the global fisheries catch, with more than 99% of that occurring in the EEZs of coastal countries6. Even when there are well-meaning policies to protect local fishers, foreign vessels can take advantage. For instance, a 2018 investigation by the Environmental Justice Foundation in London found that around 90% of Ghana’s industrial fishing fleet was linked to Chinese ownership, despite Ghanaian laws expressly forbidding foreign ownership or control of its boats. Clearer definitions of the terms fisher, fishing and fishing vessel to make provisions for small-scale operators could help, in part, to avoid such abuse.Government subsidies also require reform. One estimate found that large-scale fishers receive about three-and-a-half times more subsidies than small-scale fishers do7. This widens the existing advantages of large operations in terms of vessels and gear, infrastructure (including cold storage), processing capacity and access to cheap fuel. By giving large-scale fishers the capacity to catch even more, it can have the perverse effect of encouraging overfishing8. Instead, subsidies and other funds should be directed towards small-scale fishers to let them expand their access to markets, while keeping them from adopting the negative practices of large-scale operations.More for consumptionThe total global loss and waste from fisheries is estimated at between 30% and 35% annually1. This could increase as smaller operations broaden their markets. A 2015 estimate of the Volta Basin coast in West Africa attributed 65% of fish-production losses to a lack of technology and good manufacturing practices, and to a lack of infrastructure such as decent roads and cold storage9. The study found that fish were rarely lost to physical damage during the process; most waste resulted from spoilage. Such losses limit the sale of fish locally and to distant markets.Public and private investment in cold-storage facilities and processing equipment (such as for drying, fermentation, pickling or smoking) could help. Current funding for fishery conservation projects comes from development partners, regional banks, the World Bank, private foundations and other agencies — with some entities also providing microloans to small-scale fisheries — but these efforts are uncoordinated and inadequate.One promising strategy is to pair international or national funding with direct contracts for feeding programmes linked to schools, hospitals and similar facilities. Such arrangements would provide small fisheries with large, consistent markets and storage infrastructure that boosts local consumption and does not incentivize overfishing.

Artisanal fishers at a fish-processing cooperative in Santa Rosa de Salinas, Ecuador.Credit: Camilo Pareja/AFP/Getty

Other strategies pair local fishers with conservation efforts. As fishing operations scale up, fish entrails and other waste cannot simply be thrown into the sea: care must be taken not to contaminate the environment. One option is to fund ecosystem-restoration projects that also benefit local fisheries. For example, the Mikoko Pamoja (Mangroves Together) project in Gazi Bay, Kenya, restores and conserves degraded mangrove forests, which act as nurseries for young fish. The restoration thus earns saleable carbon credits while enhancing nearby fishery grounds for the local community.Consumers could support small fisheries by buying local, because shorter supply chains mean more income for the fishers. The use of ecolabels — which seek to promote sustainably managed fisheries by certifying that a product has a reduced environmental impact — could also encourage consumer adoption, and help consumers to make informed choices.However, such certification is costly to obtain and maintain, and requires compliance, monitoring and reporting. Certification can distort market opportunities, effectively excluding small enterprises from entering international markets. These programmes can also have unintended consequences: most certification programmes focus on environmental sustainability and pay less attention to social responsibility elements, such as fairness in access to resources, markets and wages.Instead, simple incentive programmes could be implemented by funders, managers and local governments trying to promote sustainable fisheries. For example, local markets could display a rating system for individual fishers or small entrepreneurs. This could include various elements of sustainability other than environmental ones — such as providing information on the type of fishing gear, location of the catch and freshness. Promoting the rating as a social responsibility concept would inform consumers of the need to support sustainable fisheries. The rating system could be conducted by community members trained in inspection and enforcement.Local controlDiverse efforts are needed to protect small fisheries’ access and to boost local consumption and reduce waste, and must be tailored to local community conditions. The 2021 UN Food Systems Summit was a ‘people’s summit’ that elevated roles for Indigenous peoples and civil-society groups, yet the voice of fishing communities was notably absent.Few governments take an integrated approach to the development, implementation and enforcement of policies. For example, policies governing urban development tend not to consider the implications on the ocean, fish and fishers. In the late 2000s, for instance, fishers were initially denied access to traditional public fishing zones along the beach front in Durban, South Africa, following upgrades to the port and the development of a private marina and hotel. (Fishers later reclaimed some of the zones after protests and engagement with the authorities10.)Cooperatives can help on several fronts: by coordinating fishing activities, sharing information (about weather, sea conditions or fish movement) and advocating effectively for human and social rights. For instance, CoopeSoliDar, a small-scale fisheries management cooperative in San José, Costa Rica, has helped to strengthen collective action to sustainably use molluscs, alleviate poverty and strengthen the representation of women and young people in community decision-making. Governments can help by creating a legal framework to establish cooperatives and include them in decisions to manage marine resources.Local communities can also stand up for themselves. For example, a class action by a group of 5,000 artisanal fishers in South Africa in 2004 argued against a policy they said did not give them recognition or access to food and fishing rights that were established in the country’s constitution. The court ruled in the group’s favour in 2007, and the resulting legal framework granted small-scale fishers collective community fishing rights, recognizing community members as bona fide fishers11.Integrated inputsSmall fisheries do not operate in isolation. Unlike terrestrial resources, the ocean is an extensive, global commons without clear territorial boundaries. Issues as diverse as climate change, ocean acidification, overfishing and pollution by nutrients and plastics and other chemicals all affect local fishers. But such system interactions get scant attention when fisheries policies focus on a single seafood stock or individual fishing area.Whereas the concept of integrated land management has been part of the development agenda for a few decades, integrated marine management is only now emerging. To work, it must involve all relevant stakeholders, including small-scale fishers.A context-specific strategy in the Seychelles is a leading example of such integration. Communities, financing partners and the government worked together to create the Seychelles Marine Spatial Plan Initiative, which protects 30% of the archipelago’s waters and boosts climate resilience. The Seychelles faces significant threats from rising sea levels and warmer air and water temperatures that put fisheries, infrastructure, tourism and its rich biodiversity at risk.In an example in the Coral Triangle region (encompassing Indonesia, Malaysia, Papua New Guinea, the Philippines, the Solomon Islands and East Timor), local communities gave their input to a marine protection plan. This led to a greater understanding of how practices such as overfishing and taking undersized stock sustains marine and coastal resources, and how managing these helps to address food security, climate change and threats to marine biodiversity. Such cooperation between fishing communities and governments in managing marine protected areas is essential to the preservation of future fish stocks (see go.nature.com/3xvkqxj).Fishers should be actively engaged in relevant meetings held by the UN and national and local councils, so that they can weigh in on matters that affect fishing access, their livelihoods and environmental concerns. Both fishers and organizers must help to build empowerment mechanisms to make sure their voices are heard, such as providing translation services and scheduling meetings at accessible locations. This is important not just for the fishers’ human rights, but also because much can be learnt from artisanal fishers’ local knowledge.Moves that would, for instance, restrict the fishing season or areas so that stocks or biodiversity can recover should include compensation mechanisms that will secure fishers’ cooperation and livelihoods. Social-protection measures such as food and income assistance can also help to tide fishers over.When fish swim in schools, they move more efficiently, forage better and are protected from predators. The same might be said for small-scale fishers, but those networks should extend to local and international communities, too. Collaborative problem-solving and an integrated food system can deliver seafood protein, sustainably, to a world that increasingly needs it. More

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In our simulations for the autotrophs, we varied two of the three trade-off properties (level of defence, plasticity costs and defence costs; see Fig. 1b) at a time and kept the third one constant. This results in three constellations reflecting three different trade-offs between these properties (Table 1):

parallel: trade-off between defence and plasticity costs;

crossing: trade-off between defence costs and plasticity costs;

angle: trade-off between defence and defence costs.

Table 1 Description of the three constellations parallel, crossing, and angle defining the position of the four phenotypes in the trait space of defence and growth rate.Full size tableIn all three constellations, the autotrophic species B spanned the entire defence range, i.e. it had a completely undefended phenotype Bu and a maximally defended phenotype Bd. A either had a more limited defence range (in constellations parallel and angle) or spanned the entire range as well (in constellation crossing), representing three distinct ways that the trade-off between defence, growth rate, and plasticity range may play out. For each constellation, we varied the maximum switching rate χmax over 5 orders of magnitude to investigate the effect of plasticity (Table 1, middle row). This parameter determines how rapidly a species can switch between phenotypes (see “Methods”, “Exchange rates”); higher values indicate faster adaptation. These results were also compared with a non-plastic baseline scenario where both phenotypes of each species are presented but χmax = 0 (Table 1, upper row), as well as a rigid scenario where the species have only a single phenotype (Table 1, bottom row). All parameters and their values can be found in Supplementary Table S1.In the following, we give a detailed description of the results for constellation parallel, where the autotroph species A and B have the same defence costs resulting in parallel trade-off lines between defence and growth rate, while varying the level of defence for A and varying the plasticity costs for B (Table 1, left column). We start with examining patterns for the phenotype biomasses, coexistence and community stability in the non-plastic baseline scenario “parallel 0”, and then compare the corresponding scenarios with a low exchange rate (“parallel 0.01”) and a high exchange rate (“parallel 1”). We next discuss the other two constellations (crossing and angle, Table 1) more briefly. Finally, we generalize across all scenarios and focus on the coexistence, the degree of maladaptive switching, and the consumer and total autotroph biomasses.Non-plastic baseline dynamics: scenario parallel 0
In this scenario, four single phenotypes unconnected by exchange compete with each other. Thus, species coexistence here depends entirely on phenotype coexistence: the trade-offs have to be such that for each species, at least one phenotype is a good enough competitor to survive. Which phenotypes survive depend on the two trade-off parameters, defence of the defended phenotype of species A (dAu) and plasticity costs for species B (pcB), which thus determine whether coexistence is possible.The defence costs were kept constant at an intermediate value of 0.3 for both species, resulting in parallel trade-off lines (Table 1, scenario “parallel 0”). The undefended phenotype of A, Au, is a growth-specialist with the highest growth rate of all phenotypes. The defended phenotype of the same species, Ad, has a defence between 0 and 0.9 and a relatively high growth rate, and can be viewed as a generalist. Species B has variable plasticity costs that lower the growth rate of both phenotypes. The defended phenotype of species B, Bd, has the lowest growth rate of all phenotypes but is very well-defended, and thus a defence-specialist. Its undefended phenotype, Bu, is as undefended as Au but has a lower growth rate; it is thus always an inferior competitor and inevitably goes extinct (Fig. 2c).Figure 2Biomasses, coexistence and trait space for scenario parallel 0. Biomasses of the four autotrophic phenotypes (a–d), their coexistence patterns (e), the consumer biomass (f) and the autotrophs’ trait values (g–j) (higher biomasses are shown by darker colours). Lines in (a–f) separate the regions I–III of different coexistence patterns. Note that in (a–f), the y-axis is reversed to show increasing fitness along all axes. An exemplary trait combination for every region is shown in (g–j); larger symbols indicate the surviving phenotypes. Shaded areas in (e) depict oscillating systems (quarter-lag predator–prey cycles in dense shading, antiphase cycles in loose shading).Full size imageAs Bu never survives, coexistence of the autotroph species requires the survival of defence-specialist Bd. Bd can only survive if Ad is not too defended, because Ad has a higher growth rate than Bd and will outcompete Bd in the “defended” niche otherwise (region Ib; Fig. 2d,h). A second criterion is that the plasticity costs for B must not be too high, because then the benefits of the defence of Bd no longer outweigh the costs, and it will go extinct even if there are no other highly defended phenotypes around (region Ia; Fig. 2d,g). In the regions where Bd goes extinct, species coexistence is not possible (Fig. 2e). The generalist Ad either survives by itself (region Ia in Fig. 2b,g) if its defence is low to intermediate, or together with the growth-specialist Au if its defence is high (region Ib in Fig. 2a,b,h). In the regions II and III where Bd survives, it never survives on its own, but always together with one of the phenotypes of A. It coexists with the growth-specialist Au if the plasticity costs are very low (region II in Fig. 2,i), and together with Ad if they are low to intermediate (region III in Fig. 2,j). These two regions do support species coexistence (Fig. 2e).In three of the four regions (Ib, II and III in Fig. 2f), consumer biomass is low, because the final community always contains a well-defended phenotype (Ad in region Ib, and Bd in regions II and III); the overall level of defence of the community is relatively high in these regions (Supplementary Figure S1). Conversely, consumer biomass is relatively high in region Ia, because the only surviving autotroph phenotype is relatively fast-growing and fairly undefended (Fig. 2f,g). The regions where a well-defended phenotype survives often show antiphase cycles (Ib, II and III in Fig. 2e). These cycles do not occur in the region where only Ad survives (Ia in Fig. 2e); but regular quarter-lag predator–prey cycles can be found here if Ad is almost entirely undefended.While the community defence (i.e. mean defence of the autotroph community) depends strongly on the coexisting phenotypes, the community growth rate is roughly constant because over the entire trait space, at least one phenotype with a high growth rate always survives (Supplementary Figure S1). The standing variance of the community defence was high when two phenotypes coexist as they occupy different niches along the defence axis (Fig. 2h–j). In contrast, the variance of the community growth rate was very low and almost constant across all regions.Effect of phenotypic plasticityEven a little bit of plasticity in the scenario parallel 0.01 (χmax = 0.01) can change the above patterns for coexistence, stability, and average consumer biomass (Fig. 3a–d). While the autotrophs are intuitively expected to benefit from being plastic, the effect of plasticity on consumer biomass always turned out to be positive (Fig. 3a). This may be explained by the fact that switching was always, on average, maladaptive (Fig. 3c,d), measured by the adaptation index Φ (see Eqs. (11–13) in “Methods”). This index combines information on the net “flow” of individuals due to switching (i.e. whether more undefended individuals switch to defended or vice versa) with the fitness difference between the two phenotypes, and thus measures whether overall, more individuals switch from a low-fitness to a high-fitness phenotype (adaptive) or the reverse (maladaptive). This index can approach zero, but is always negative at equilibrium (see Appendix B), indicating maladaptive switching.Figure 3Consumer biomass, autotroph coexistence and maladaptive switching for the scenarios parallel 0.01 (a–d) and parallel 1 (e–h). Consumer biomass (a,e), the autotroph coexistence patterns (b,f), and the autotrophs’ maladaptive switching Φ (c,d,g,h) (higher biomasses or more intensive maladaptive switching are shown by darker colours). Lines separate the regions I–III of different autotroph coexistence. The y-axis is reversed to follow the pattern of increasing fitness. Grey areas in (c,d,g,h) depict areas where the species was extinct. Shaded areas in b and f depict oscillating systems (quarter-lag predator–prey cycles in dense shading, antiphase cycles in loose shading).Full size imageThe most striking effect of plasticity was on coexistence, which was affected both positively and negatively by plasticity in different regions of the parameter space (Fig. 3b, Supplementary Figure S4a–d). A negative effect on coexistence is seen in region II, where the autotroph species previously coexisted (Fig. 2e), while with plasticity, B outcompeted A (Fig. 3b). Without plasticity, coexistence was possible in this region because Au and Bd survived; importantly, Au outcompeted Bu due to its higher growth rate, even though the difference between their growth rates is very small in this region (Fig. 2i). Plasticity reverses the competitive exclusion pattern between the two undefended phenotypes: Bu receives a constant flow of biomass from the well-defended Bd, which compensates for its slightly lower growth rate and allows it to outcompete Au. Thus, coexistence is reduced as a direct consequence of maladaptive switching.Plasticity can also promote coexistence, as the coexistence region now extends into former region Ib where the generalist Ad is highly defended (Fig. 2b, Supplementary Figure S1a). This is also an effect of maladaptive switching, though in this case the effect is indirect, mediated through the effect of plasticity on consumer biomass. Without plasticity, coexistence was impossible in region Ib because Bd was always outcompeted by Ad: even though the latter had a slightly lower level of defence, this was outweighed by its higher growth rate, making Ad the superior competitor over Bd. However, plasticity changes this because maladaptive switching increases the consumer biomass, which in turn alters the cost/ benefit balance of defence: Bd derives a stronger benefit from its high level of defence, which now outweighs the cost and allows it to survive. Coexistence through this mechanism is not possible when the plasticity costs for B are too high or when Ad is too well-defended, explaining the narrowing of the coexistence “tail” for high defence of Ad (Fig. 3b).While the patterns of coexistence changed when allowing for plasticity, the patterns in the trait values were nearly indistinguishable from the previous scenario (Supplementary Figure S1, S2). Finally, plasticity had a strong impact on the community dynamics, as most of the antiphase cycles were stabilized (Ib, II, III in Fig. 3b). Their area decreased sharply as these cycles were characterized by asynchronous dynamics between the two prey phenotypes, which were reduced by plasticity. In contrast, the area of the quarter-lag predator–prey cycles remained unaffected by plasticity.All the above patterns were found to a far stronger degree with a higher amount of plasticity (χmax = 1; Fig. 3e–h, Supplementary Figure S4e–h). Consumer biomass increased strongly everywhere (cf. Fig. 3a,e), reflecting the strong increase in the degree of maladaptive switching (cf. Fig. 3c,d,g,h). The higher exchange rates led to more synchronization between the phenotypes, extinguishing the antiphase cycles completely (Fig. 3f). It also decreased the biomass of both defended phenotypes (cf. Supplementary Figure S4b,d,f,h). This in turn led to a lower community defence and a higher community growth rate (Supplementary Figure S3) both contributing to a higher consumer biomass. Finally, there was a sharp decrease in the coexistence region for high plasticity (Fig. 3e). Region II, where B outcompetes A through maladaptive switching, doubled in size due to the much higher degree of maladaptive switching (Fig. 3g,h). Region I, where A outcompetes B, now also increased, when the level of defence of Ad is relatively low (Fig. 3e). This is again an indirect effect of maladaptive switching causing a strong increase in consumer biomass, affecting the cost/ benefit balance of defence: while Bd derives a strong benefit from its high level of defence, Bu is completely undefended, and is at an extra disadvantage because of its low growth rate. Thus, while Bd would have been able to survive by itself, the high exchange rate causes a strong source-sink dynamic that drives B extinct.Effect of plasticity in constellations crossing and angle
In constellation crossing the trade-off lines of both species cross in the trait space, as the level of defence is the same for both defended phenotypes; species B has a lower growth rate for its undefended phenotype than species A due to plasticity costs, while its defence costs are low and thus the growth rate of its defended phenotype is higher than for species A (Table 1, Supplementary Figure S5). Without plasticity the crossing trade-off lines lead to coexistence of both species in all simulations as Au and Bd were always the only survivors, mostly showing antiphase oscillations (Supplementary Figure S5).Allowing for phenotypic plasticity has the same results as were observed for constellation parallel: consumer biomass sharply increases (Fig. 4a,e); antiphase cycles are dampened or absent; and the area of coexistence decreases (Fig. 4b,f). All these changes are more pronounced for higher exchange rates (cf. Fig. 4a,b,e,f). Again, the biomass of the defended phenotypes decreased for high exchange rates (Supplementary Figure S6). Switching was always maladaptive for high exchange rates (Fig. 4g,h), and mostly maladaptive for low exchange rates (Fig. 4c,d). As was seen for constellation parallel, maladaptive switching was the reason for the decrease in coexistence. B can outcompete A when B has low plasticity costs. Bd has a much higher growth rate than Ad, while the undefended phenotypes have similar growth rates. The direction of competitive exclusion between Au and Bu is thus easily reversed by Bd donating biomass to the sink Bu, allowing B to occupy both niches and outcompete A (region II in Fig. 4b,f). The same mechanism happens in reverse for high plasticity and defence costs of B: the differences in growth rate for the undefended phenotypes are high, while the defended phenotypes have very similar growth rates. Au can support Ad, and A outcompetes B (region III in Fig. 4b,f).Figure 4Coexistence and maladaptive switching for scenario crossing 0.01 (a-d) and crossing 1 (e–h). Consumer biomass (a,e), the autotroph coexistence patterns (b,f), and the autotrophs’ maladaptive switching Φ (c,d,g,h) (higher biomasses or more intensive maladaptive switching are shown by darker colours). Lines separate the regions I–III of different autotroph coexistence. The x- and y-axis are reversed to follow the pattern of increasing fitness. Shaded areas in (b) depict antiphase cycles. Grey areas in (c,d,g,h) depict areas where the species was extinct. Shaded grey areas depict areas without simulations (cf. “Methods”). Note that (c,d,g,h) have each a different colour scale.Full size imageIn constellation angle there are no plasticity costs, and thus the undefended phenotypes Au and Bu have identical growth rates. The defended phenotypes take the same places in trait space as in the parallel constellation: Ad is a generalist, with a lower level of defence and a relatively high growth rate due to low defence costs, whereas Bd is a defence-specialist with a high level of defence but a low growth rate. This leads to the trade-off lines forming an angle (see Table 1). Without phenotypic plasticity, the coexistence patterns are the same as in constellation parallel, except that no competitive exclusion occurs between the undefended phenotypes; instead, they (neutrally) coexist in regions Ib, II and III (Supplementary Figure S7; cf. Fig. 2).With plasticity, neutral coexistence vanished: the defended phenotype that survived (Ad in region Ib, Bd in region III) could support the undefended phenotype of its own species, driving the other species extinct (Fig. 5b,f). As in the other constellations, the area of coexistence and the biomasses of the defended phenotypes decreased and antiphase cycles vanished with increasing χmax (Fig. 5b,f, Supplementary Figure S8), while maladaptive switching and the consumer biomass increased (Fig. 5).Figure 5Coexistence and maladaptive switching for scenario angle 0.01 (a–d) and angle 1 (e–h). Consumer biomass (a,e), the autotroph coexistence patterns (b,f), and the autotrophs’ maladaptive switching Φ (c,d,g,h) (higher biomasses or more intensive maladaptive switching are shown by darker colours). Lines separate the regions I–III of different autotroph coexistence. The y-axis is reversed to follow the pattern of increasing fitness. Shaded areas in (b) depict antiphase cycles. Grey areas in (c,d,g,h) depict areas where the species was extinct. Note that (c,d,g,h) have each a different colour scale.Full size imageGeneral resultsAs plasticity had very similar effects across all three constellations, we here generalize our results: we compare the three constellations for exchange rates over 5 orders of magnitude, as well as the non-plastic scenario and the rigid scenario (Table 1). That is, all simulations from one scenario (e.g. parallel 0) were summarized into one bar respective point in Fig. 6.Figure 6General patterns for coexistence, maladapative switching and biomasses. Share of surviving species in percent (A, B, coexistence or neutral coexistence) (a–c), median absolute value of maladaptive switching Φ (d–f) and median of total autotroph biomass (A + B), median consumer biomass C and share of defended phenotypes ((Ad + Bd)/(A + B)) (g–i) for the three constellations and increasing maximum exchange rates χmax. χmax = 0 denotes the non-plastic scenarios; *denotes the rigid scenarios. Maladaptive switching and the share of defended phenotypes do not apply for the rigid scenarios.Full size imageFor all constellations, the fraction of simulation runs leading to coexistence was highest in the non-plastic scenario and decreased with increasing χmax (Fig. 6a–c). In constellation parallel the share of coexistence for increasing χmax continuously decreased from 51 to 3% (Fig. 6a). In crossing, the share decreased from full to no coexistence (Fig. 6b). In angle, the share of coexistence was 88% in the non-plastic scenario when taking also neutral coexistence into account (Fig. 6c). Its share decreased to 9% for a χmax of 10 and increased again to 25% for the rigid scenario. Maladaptive switching increased for both species and all constellations for increasing χmax (Fig. 6d–f). The increased plasticity led to a lower total autotroph biomass and a lower share of defended phenotypes (Fig. 6g–i), which resulted in higher consumer biomass (Fig. 6g–i).Interestingly, and counterintuitively, the above patterns show that increasing the speed of plasticity (by increasing χmax) makes the system behave more like the rigid system. The coexistence patterns in scenarios with high χmax approach those of the rigid scenarios in two of the constellations (Fig. 6a,b). Similarly, the total autotroph and consumer biomasses approach the ones in the rigid scenarios (Fig. 6g–i). Thus, we found the higher χmax make the autotrophs not more adaptive, but behave more like non-adaptive species. More

The conceptual flow chart of the process is provided in Fig. 1. We used seven reanalysis SM data (Table 2) masked with soil temperature (ST) and soil freeze/thaw status to calculate water table depth, i.e. the input of TOPMODEL, given the obvious disagreements between the input datasets. The diagnostic algorithms based on TOPMODEL were used following Stocker et al. (ref. 20) and Xi et al. (ref. 25), where the optimized parameters were calibrated with long-term maximum wetland areas from four observation-based wetland datasets (Table 1). Details about these datasets and computational processing are shown as follows.Fig. 1Diagram of workflow for parameter calibration and the simulation of global wetland dynamics.Full size imageTable 2 Key characteristics of seven global soil moisture reanalysis data used in this study.Full size tableReanalysis soil moisture datasetsSeven long-term reanalysis SM datasets used in this study include NCEP-DOE (National Centers for Environmental Prediction-the Department of Energy)26, MERRA-Land (the Modern-Era Retrospective Analysis for Research and Applications)27, MERRA-2 (ref. 28), GLDAS-Noah v2.0 (the Global Land Data Assimilation System)29, GLDAS-Noah v2.1 (ref. 29), ERA5 (European Environment Agency)30,31, and ERA5-Land30,31. Key characteristics of the seven SM data are listed in Table 2. The datasets differ by their spatial and temporal resolutions, the time-period they cover, as well as the definition of the soil layers. More details are provided for each dataset below.

NCEP-DOE
NCEP-DOE is an updated version of the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) Reanalysis 1 project, which uses a state-of-the-art analysis/forecast system to perform data assimilation with past data from 1948 to the present32. NCEP-DOE features the newer physics and observed SM forcing and also eliminates several previous errors, such as oceanic albedo and snowmelt term during the entire period, and snow cover analysis error from 1974 to 1994 (ref. 26). With a spatial resolution of about 210 km, there are two vertical soil layers in NCEP-DOE for both SM and ST: 0–0.1 and 0.1–2 m.

MERRA-Land and MERRA-2
MERRA-Land soil moisture is generated by driving the Goddard Earth Observing System model version 5.7.2 (GEOS-5.7.2) with meteorological forcing from the MERRA reanalysis product27. The precipitation forcing in MERRA-Land merges MERRA precipitation with a gauge-based data product from the National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center, and the Catchment land surface model used in MERRA-Land is updated to the “Fortuna-2.5” version27. MERRA-2 intends to replace the original MERRA reanalysis and ingests important new data types28. The Catchment model in MERRA-2 has been updated with rainfall interception and snow model parameters of MERRA-Land, and the precipitation correction is a refined version of MERRA-Land. For MERRA-Land and MERRA-2, there is only one layer for SM from the surface to the bedrock, with “depth-to-rock” depending on local conditions. ST is computed on six vertical soil layers: 0–0.10, 0.10–0.29, 0.29–0.68, 0.68–1.44, 1.44–2.95, and 2.95–12.95 m.

ERA5 and ERA5-Land
ERA5 is the fifth generation ECMWF (European Centre for Medium-Range Weather Forecasts) reanalysis of global climate and weather, replacing ERA-Interim30,31. Based on a decade of developments in model dynamics and data assimilation, there is a significantly enhanced horizontal resolution (31 km), temporal resolution (hourly) and uncertainty estimation. ERA5 covers 1979–2020 and continues to be updated in near-real-time. ERA5-Land is produced with a finer horizontal resolution of 9 km by running the land component of the ERA5 climate reanalysis but without data assimilation. By March of 2021, the ERA5-Land outputs are only available since 1981. SM and ST are computed on four vertical soil layers (0–0.07, 0.07–0.28, 0.28–1, and 1–2.89 m) for both ERA5 and ERA5-Land.

GLDAS-Noah v2.0 and GLDAS-Noah v2.1

GLDAS is a global, moderate-resolution (0.25° × 0.25°) offline terrestrial modeling system developed by NASA Goddard Space Flight Center (GSFC) and the NOAA National Centers for Environmental Prediction29, thus similar to ERA5. To produce optimal fields of land surface variables in near-real-time, it incorporates satellite- and ground-based observations. GLDAS-Noah drives the Noah land surface model and has two components: one forced with the Princeton meteorological forcing data (i.e. GLDAS-Noah v2.0) and the other forced with a combination of model and observation (i.e. GLDAS-Noah v2.1). GLDAS-Noah v2.0 covers the period 1948–2014, while GLDAS-Noah v2.1 is available from 2000 to the present. There are four vertical layers in the Noah land surface model for both ST and SM: 0–0.1, 0.1–0.4, 0.4–1, and 1–2 m.Observation-based wetland/flooded area dataIn terms of large uncertainties in current wetland datasets (Table 1) we selected four widely used and available satellite/satellite-based wetland/flooded area data including GIEMS-2 (ref. 14), RFW (the Regularly Flooded Wetland map)10, WAD2M (a global dataset of Wetland Area and Dynamics for Methane Modeling)33, and G2017 (the pantropical wetland extent from an expert system model)9 for parameter calibration. Among them, GIEMS-2 and WAD2M include monthly wetland dynamics, while RFW and G2017 are static. The comparison of the four wetland datasets is shown in Supplementary Fig. 1; details on each data are provided below.

GIEMS-2
The GIEMS-1 is the first global estimate of monthly inundated areas, derived from passive microwave land surface emissivity34. With a 0.25° × 0.25° resolution, GIEMS-1 documents a mean annual maximum inundated area of 9.5 Mkm2 for 1993–2007 (including open water, wetlands, and rice paddies, but excluding large lakes), which shows good agreement with existing independent, static inventories as well as regional high-resolution synthetic aperture radar observations34. Based on similar retrieval principles with GIEMS-1, GIEMS-2 is developed to less depend on ancillary data with an updated microwave emissivity, and correct a known overestimation over low vegetated areas from GIEMS-1 (ref. 14). The period is extended to 1992–2015 for GIEMS-2 and can be updated with the availability of observations. Globally, the mean annual maximum and long-term maximum inundated extent after removing the rice paddies using the Monthly Irrigated and Rainfed Crop Areas dataset (MIRCA2000)35 for the period 1992–2015 are 6.7 and 10.9 million km2 (hereafter Mkm2; sum of mean annual maximum or long-term maximum inundated extent for each grid cell) respectively. The rice paddies are removed here as they are not natural wetlands and cannot be simulated with TOPMODEL.

RFW
RFW is a static, high-resolution map (15 arc-sec) of regularly flooded wetlands, developed by overlapping flooded areas (permanent wetlands and flooded vegetation classes) for 2008–2012 from the ESA-CCI land cover map36, mean annual maximum inundated areas (including wetlands, rivers, small lakes, and irrigated rice) for 1993–2004 from GIEMS-D15 global inundation extent (downscaled using GIEMS-1)37, and long-term maximum surface water areas for 1984–2015 from JRC global surface water bodies product13. The large permanent lakes and reservoirs are distinguished using the HydroLAKES database38. Globally, RFW covers 9.7% of the land surface area (~13.0 Mkm2) including wetlands, river channels, deltas, and flooded lake margins, but excluding large lakes10. Due to the mean annual maximum or long-term maximum inundation/surface water extent for 1984–2016 from the three wetland data is used, we treated RFW as the long-term maximum wetland extent in this study. Besides, given that GIEMS-D15 includes artificial rice paddies, we removed them with MIRCA2000 from RFW (~11.9 Mkm2 after removing rice paddies).

WAD2M
WAD2M dataset used in this study is an improved version of the SWAMPS v3.2 from Jensen et al. (ref. 15), covering the years 2000 to 2018. With a spatial resolution of 25 km × 25 km, this data was used as input wetland area data of phase 2 of the Global Methane Budget33. Given that the initial SWAMPS failed to detect wetlands lacking surface inundation and to differentiate between lakes, wetlands, and other surface water bodies, Zhang et al. (ref. 33) modified it using a series of independent static wetland distribution data7,9,39,40,41 in an attempt to include missing wetlands under dense canopies. Besides, they removed inland waters (lakes, rivers, and ponds) and rice agriculture with JRC and MIRCA2000, respectively. Globally, the mean annual maximum and long-term maximum wetland extent for the period 2000–2018 estimated by WAD2M are 8.1 Mkm2 and 13.2 Mkm2 (sum of mean annual maximum or long-term maximum inundated extent for each grid cell) respectively.

G2017

G2017 (ref. 9) is a static, pantropical wetland and peatland extent map (covering 60°S–40°N) at 232 m × 232 m resolution, derived from a hybrid expert model system. With three biophysical indices related to wetland and peat formation (long-term water supply exceeding atmospheric water demand, annually or seasonally waterlogged soils, and geomorphological position where water is supplied and retained), G2017 identifies not only permanently and seasonally wetland areas, but also soil wetness and topographic conditions that favor waterlogging in the absence of flooding for the end of the 20th century. Given the broad coverage of different types of wetlands, we also treated this map as long-term maximum wetland areas. This ‘pantropical’ data (60°S to 40°N) offers the advantage to include non-flooded wetland areas that are missed in satellite-based wetland products. However, note that not all detected wetlands or peatlands in G2017 have been observed. Rice agriculture was also removed with MIRCA2000 from G2017. The resulting wetland and peatland area for 60°S–40°N is 4.0 Mkm2.The TOPMODEL-based diagnostic modelTOPMODEL as improved by Stocker et al. (ref. 20) and Xi et al. (ref. 25) was used to calculate the inundated fraction from WTD at grid-scale in this study. Based on the assumptions that the local hydraulic gradient is approximated by the local topographic slope and the water table variations can be assimilated to a succession of steady states with uniform recharge, the classical TOPMODEL establishes an analytical relationship between the soil moisture deficit and the distributions of local topographic index within a catchment. At grid-scale, the analytical relationship can be represented as:$$CT{I}_{i}-overline{CT{I}_{x}}=mathrm{-M}left({{Gamma }}_{i}-overline{{{Gamma }}_{x}}right)$$
(1)
where CTI indicates the topographic index, defined as the log of the ratio of contributing area to the local slope. We used the CTI data at 500 m × 500 m resolution from Marthews et al. (ref. 22), where lakes, reservoirs, mountain glaciers, and ice caps are removed using the Global Lakes and Wetlands Database7. The (overline{CT{I}_{x}}) indicates the average of CTIi of all sub-grids (index i) within the grid cell x. M indicates a tunable parameter that describes the exponential decrease of soil transmissivity with depth21. Γi is the water table of the pixel i and (overline{{{Gamma }}_{{x}}}) is the mean water table of the grid x. When Γi is at the soil surface (i.e. Γi = 0), the threshold (CT{I}_{x}^{* }) above which all pixels are flooded for the grid x is derived:$$CT{I}_{x}^{* }=overline{CT{I}_{x}}+{rm{M}}cdot overline{{{Gamma }}_{x}}$$
(2)
The wetlands area is defined as the flooded areas (i.e. Γ ≤ 0), the flooded fraction in the grid x (fx) being the percentage of pixels with CTIi larger than a threshold (CT{I}_{x}^{* }):$${f}_{x}=frac{1}{{A}_{x}}{sum }_{i}{A}_{i}^{* }$$with$${A}_{i}^{* }=left{begin{array}{c}{A}_{i},if,CT{I}_{i}ge CT{I}_{x}^{* }\ 0,if,CT{I}_{i} < CT{I}_{x}^{* }end{array}right.$$ (3) To reduce the computational costs from the high-resolution CTI data for predicting long time series of wetland area, we used the asymmetric sigmoid function from Stocker et al. (ref. 20) to fit the “empirical” relationship (widehat{{Psi }}) between (widehat{f}) and Γ:$${{rm{psi }}}_{x}left({{Gamma }}_{x}right)={left(1+{v}_{x}cdot {e}^{-{k}_{x}left({{Gamma }}_{x}-{q}_{x}right)}right)}^{-1/{v}_{x}}$$ (4) where vx, kx, qx are three parameters of the function. Given a value of parameter M, the three parameters can be derived with a sequence of Γx spanning a plausible range of values (−1 m to 2 m) and corresponding fx from the initial TOPMODEL approach (Eq. (3)). Thus, the wetlands in our study are defined as the flooded area simulated by TOPMODEL. As for the range of parameter M, Stocker et al. (ref. 20) used a global uniform value for M (M = 8) after testing simulated wetland fraction for a range of M (7, 8, 9). Nevertheless, given that distinct topography, soil types, and other intrinsic characteristics in different regions, we considered M as a tunable, spatially heterogeneous, and grid-specific parameter, with a range of 1–15 following Xi et al. (ref. 25). Thus, for each grid cell x there are 15 choices for M, and then 15 sets of (vx, kx, qx). The optimized parameter combination of (vx, kx, qx) is determined by selecting minimum root-mean-square-error (RMSE) between simulated inundated fractions and observations:$$RMSE=sqrt{frac{{sum }_{i=1}^{n}{left({O}_{i}-{P}_{i}right)}^{2}}{n}}$$ (5) where Oi and Pi are observed and simulated wetland fraction, respectively. n represents the time-series length for wetland extent. For simulations calibrated with RFW and G2017, the RMSE was computed with the long-term maximum (hereafter called MAX) monthly wetland area because the two data sets are static and only record the MAX wetland extent. While for simulations calibrated with GIEMS-2 and WAD2M which include temporal variations of wetland area, we calibrated the parameters with all months, mean seasonal cycle, yearly maximum, and MAX wetland area, but only showed the optimal simulations calibrated with MAX wetland area in this work to keep consistency with RFW and G2017. Besides, to provide more choices for users, we combined all of the four wetland datasets (i.e. the union of long-term maximum wetland extent) to generate a new wetland map (hereafter called MAX_all), and then used the MAX_all to calibrate the parameters to produce seven sets of global wetland extent products with seven soil moisture datasets. The simulations calibrated with yearly maximum wetland area from GIEMS-2 and WAD2M and long-term maximum wetland area from MAX_all are also provided in our resulting products.Finally, to avoid unrealistically high wetland fraction output from the function, the simulated maximum wetland fraction fx is constrained by the observed MAX wetland area with a parameter ({f}_{x}^{max}) (Eq. (6)), which is different from Stocker et al. (ref. 20). The determination of ({f}_{x}^{max}) is analyzed in the supplemental material in detail (Supplementary Text 1). Once the value of (vx, kx, qx) are determined, the wetland fraction fx can be directly derived from the monthly water table Γx according to Eqs. (4) and (6).$${f}_{x}=minleft({{Psi }}_{x}left({{Gamma }}_{x}right),{f}_{x}^{max}right)$$ (6) Calculation of water table depthWater table depth is not computed by land surface models, given their coarse soil vertical discretization. We thus used the saturation deficit of soil moisture (θSD) as a surrogate of water table depth, θSD being defined as an index consisting of saturated volumetric water content and the “actual” soil depth modified by soil freeze/thaw status:$${theta }_{SD}={z}_{{l}_{0}}-{sum }_{l=1}^{{l}_{0}}{theta }_{l}cdot frac{Delta {z}_{l}}{{theta }_{S}}$$ (7) Subscript l represents the lth soil layer, l0 is the number of layers above the first frozen soil layer counted from the top (l = 1 at the soil surface), θl is the monthly volumetric water content in the lth soil layer (m3 m−3), (Delta {z}_{l}) is the thickness of the lth soil layer (m), θS is the saturated volumetric water content (in m3 m−3 units, uniform over depth).As formulated in Eq. (7), ({z}_{{l}_{0}}) is the thickness of all soil layers (or depth to bedrock) when there is no frozen soil layer. If there exists at least one frozen layer, ({z}_{{l}_{0}}) is set to the depth of the uppermost frozen soil layer. We excluded the frozen soil layers here given that some important wetland processes such as methane production and transport are insignificant when the soils are frozen. In high latitudes, the presence of frozen soil layers may lead to an overestimation of the wetland fraction due to relatively large θSD values even if there is little liquid soil water above the uppermost frozen soil layer. Hence, we used monthly soil temperature (ST) at 70 cm, the Global Record of Daily Landscape Freeze/Thaw Status data42, and the Köppen climate classification system43 to refine the frozen mask. When the monthly mean ST at 70 cm is below 0 °C, or soil freezing days are more than 5 in a month, or the grid is classified as the Hot desert (BWh) in the Köppen climate classification system, the wetland fraction for the grid is set to zero. However, it should be noted that the algorithm using the ST at 70 cm could omit some unfrozen soil layers above 70 cm, which could lead to bias in estimation of methane emissions from these unfrozen layers. We provided the global wetland maps in our resulting products, but the potential uncertainties in wetland estimation due to the omitted unfrozen layers should be considered, particularly at high latitudes. We used seven reanalysis SM products to compute θSD to provide the uncertainty in SM input (Table 2). All data are re-interpolated to 0.25° × 0.25° resolution.Evaluation against wetland calibration data and independent satellite productsAlthough we calibrated parameters of the TOPMODEL-based diagnostic model with the observation-based wetland data, to what extent the simulations can reproduce the spatial patterns and temporal dynamics of the calibration wetland data must be evaluated. For spatial patterns, we calculated the RMSE of wetland area between our simulations and corresponding wetland calibration data following Eq. (5), and evaluated the spatial patterns of simulated wetland extent in two wetland hotspots including Amazon basin and Western Siberia lowlands with three independent global/regional water products. For Amazon basin, we used the global surface water dataset from JRC13 (optical satellite images) and the wetland map produced using mosaics of Japanese Earth Resources Satellite (JERS-1) L-band SAR imagery from Hess et al. (ref. 44, hereafter H2015). For West Siberian lowlands, we used JRC and the Boreal–Arctic Wetland and Lake Dataset (BAWLD, only covers the north of ~55°N) produced using an expert assessment and extrapolated using random forest modelling from climate, topography, soils, permafrost conditions, vegetation, wetlands, and surface water extents and dynamics45. For temporal dynamics, since we only used the static wetland area (long-term maximum) from all of the four observation-based wetland products to calibrate parameters, the simulated temporal dynamics can be evaluated with the two dynamic wetland products (GIEMS-2 and WAD2M). Besides, we also used the terrestrial water storage (TWS) from the Gravity Recovery and Climate Experiment (GRACE), which retrieves relative change in TWS from the monthly anomalies of the Earth’s gravity field for 2003–2016 measured by the twin GRACE satellites46,47 to evaluate the simulated temporal dynamics. More

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Here we have used existing surveillance to detect an emerging wildlife disease and appraise its impact by combining traditional host and vector screening with utilisation of national datasets generated by citizen scientists. Following the detection of USUV in the UK in 20207, whilst national surveillance identified no further cases of USUV infection in wild birds that year, we discovered a significant cluster of blackbird DIRs and an overlapping regional reduction in reported blackbird observations, possibly indicating disease-mediated population decline. Our investigation also identified mosquito vectors at the index site that were positive for USUV RNA, suggesting that ongoing virus transmission was likely.The most prevalent and notable histological changes in the blackbirds and house sparrow with confirmed USUV infection were those in the liver and spleen, consisting of necrosis and lymphohistiocytic inflammation along with moderate to abundant virus antigen labelling. Whilst neurotropism resulting in brain necrosis and lymphohistiocytic inflammation has been reported in studies which examined large numbers of wild blackbirds with USUV infection in continental Europe4,12, we found minimal evidence of neural lesions in the five wild birds examined in this study. Although, histopathological changes in other tissues were generally non-specific, immunolabelling demonstrated widespread virus antigen distribution in both bird species, which is similar to reports of USUV infection elsewhere4,13,14. Immunolabelling was disproportionately greater in the brain and heart in contrast to the minimal or absent histological changes observed in these organs: similar contrasting results of histological and immunohistochemical examinations of USUV-infected wild birds have previously been reported12. Although only brain and kidney samples were examined using USUV RT-PCR, our findings, together with earlier reports4,14, demonstrate that viral antigen can be detected in abundance in the heart and liver, suggesting that these organs could be useful for molecular diagnostic sampling.A differential for necrotising lesions in European passerines, and a comorbidity detected in blackbirds with USUV infection, is Plasmodium spp. infection4,8,15. DNA of the same Plasmodium spp. as detected in the tissues of USUV-positive blackbirds from the ZSL London Zoo site in 2020 was identified in Cx. pipiens s.l. that fed on blackbird hosts at this site previously in 2015, supporting endemic avian haemoparasite infection of this wild bird species at this location. In contrast to the results reported from USUV-positive blackbirds in the Netherlands4, no exo-erythrocytic stages of haemoprotozoa indicative of avian malaria were observed histologically in the two UK blackbirds positive for Plasmodium DNA. Since histological examination has limited sensitivity, in situ hybridisation could be used to further appraise the clinical significance of this co-infection in the future16.Zoological collections are ideally placed to form part of wildlife disease surveillance networks and have already contributed to flavivirus detection in mainland Europe10,13,17,18. The collection grounds at ZSL London Zoo are well monitored for evidence of morbidity or mortality in synanthropic wildlife; this unusually high level of vigilance is considered the likely explanation for detection of USUV at such a location. Recent import of infected captive birds can be excluded as a potential route of USUV introduction as the COVID-19 pandemic had led to suspension of animal movements into the zoological collection. Following USUV detection in synanthropic wildlife, preemptive management practices were employed to safeguard the health of captive animals (Supplementary Materials 1); there was no evidence of USUV-associated disease in the collection animals.The majority of mosquitoes trapped in 2020 were primarily ornithophagic Cx. pipiens s.l., a known vector for USUV1 and a common species in temperate urban habitats. This mosquito species was also the most frequently detected at the ZSL London Zoo site in 2015, during historical trapping sessions19 and at two zoological collections in northern England20. Bloodmeal analyses from mosquitoes at ZSL London Zoo in 2015 and 2020 demonstrate that this species feeds on both wild and collection birds, as would be expected for a generalist ornithophagic mosquito21. In addition, targeted mosquito surveillance in 2020 confirmed circulating USUV in multiple Cx. pipiens s.l. pools at the index site over a three-week period subsequent to the detection of USUV-associated wild bird mortality. This further demonstrates that local mosquito trapping combined with PCR screening is useful as part of an integrated surveillance programme22 and provides evidence that native vectors in the UK may facilitate the onward transmission of USUV to susceptible hosts following an emergence event.Wild bird flavivirus surveillance in Great Britain integrates submissions from three schemes, each with a different taxonomic focus. These convenience samples inevitably lead to skews in species coverage. Although a common garden bird, the number of blackbirds tested for USUV was modest at 2–8 per annum over the period 2012–2019. A communication programme to raise awareness of blackbirds as a sentinel species for USUV, involving a range of stakeholder communities (e.g. non-governmental organisations, wildlife rehabilitators and veterinary surgeons) could help to increase the volume of submissions and, by extension, the ability to rapidly identify the occurrence of USUV in this species. The potential value of target species as sentinels within wild bird surveillance networks has been highlighted for other pathogens, e.g. highly pathogenic H5N1 avian influenza and West Nile virus23,24. In addition to this passive surveillance focused on disease detection in avian hosts, active targeted serosurveys could be conducted to identify cryptic exposure of subclinically affected birds in the future. Given the logistical challenges around active serosurveys in wild birds, screening of archived samples from captive birds in the zoological collection may provide a means to further appraise the extent of USUV circulation, as has previously been undertaken at other collections in mainland Europe25,26.Local reductions of blackbird populations have been reported following USUV outbreaks in mainland Europe27,28,29, but numbers recorded by the BBS have been stable in the UK and Greater London since 2011 when USUV incursion would be predicted most likely to have occurred on the basis of spatio-temporal patterns of spread in mainland Europe3 until the latest data are available from 2019 (Supplementary Figure 5). Whilst our index site detection of USUV is unlikely to represent the incursion event, and earlier sporadic or localised USUV incidents prior to 2020 may have occurred7, based on historical blackbird population trends it seems plausible that the existing surveillance system enabled rapid detection of this emerging infectious disease.Significant clustering of blackbird DIRs was observed in the Greater London, South East and East of England regions in 2020. These results should be interpreted with care given the potential for biases with these opportunistic data and the absence of confirmed aetiology for the DIRs, however, these findings are consistent with a regional increase in blackbird morbidity and mortality in summer 2020 around the USUV index site. Consequently, it is likely that further blackbirds, in addition to those recovered for PME, were infected and died with USUV. Whilst no evidence of an increase in generalised ill health or neurological disease category blackbird DIRs was found in 2020, particular attention should be paid to early detection of clusters of DIRs of these categories as a potential signal of USUV occurrence in the future.One indicator, the dead bird ringed recovery dataset, did not support increased scale of blackbird mortality in Greater London; however, the dataset is small and vulnerable to variation in observer bias (e.g. related to COVID-19 induced lockdown and travel restrictions). In contrast, using the GBW dataset, we identified a substantial seasonal decline in the blackbird weekly reporting rate which was associated with a concomitant reduction in weekly count in gardens, but not in ecologically similar control species, which was contemporaneous with the period of detected USUV activity in Greater London. These population trends are consistent with a hypothesis of disease-mediated decline. Alternative explanations, such as variation in climate, food availability or bird movement need consideration and are discussed next.Exploration of climate data indicates that, whilst the spring and early summer of 2020 was noteworthy with a high daily temperature average and low rainfall, at the time of USUV detection and the decline in the blackbird reporting rate, these parameters were within historical ranges (Supplementary Table 7). Consequently, while the climate may have been permissive for USUV transmission, there is no evidence to support variation in the weather alone as an explanation for the seasonal pattern of blackbird reporting rate decline; nor were declines observed in the robin or starling data, the control species with similar soil invertebrate diet and therefore similar vulnerability to summer drought. Blackbird, robin and starling populations in the UK are partially migratory; however, birds from mainland Europe do not migrate to overwinter in England until mid-October (i.e. after the decline in blackbird reporting rate occurred): consequently international bird movement does not offer an explanation for the observed regional blackbird decline. During the late summer season, short-distance movement from garden to non-garden habitats typically occurs, during the period of moult; however, the extent of the decline in blackbird reporting rate in gardens that occurred in Greater London in 2020 markedly exceeds that of the historical trend (2011–2019 inclusive; BTO unpubl. data). In summary, despite the fact that surveillance did not confirm further cases of wild bird USUV infection in 2020, and whilst it is not possible to ascribe causality, or exclude the chance that other factors may have contributed to the observed population trend, it remains possible that large-scale blackbird mortality due to USUV occurred in Greater London in summer 2020.Our study and others30 illustrate the need to integrate disease surveillance and long-term population monitoring schemes to evaluate disease impact, and to use control species to explore potential confounding drivers of population change (e.g. climate, food availability). Since GBW reporting rates are generated online in real-time, and nationwide, they offer a tool to rapidly detect changes in species presence (i.e. reporting rate) or flock size in gardens (i.e. weekly maximum count) that can be used to strategically enhance surveillance effort for disease detection. As wild bird ring recovery reports are also submitted online, there is also the potential to develop a complementary system that monitors for trends in occurrence of dead birds that might signal a disease outbreak. The BBS survey provides the most robust available data on population trends to appraise disease impact, however there is a delay of some months until data from this scheme become available. Since repeated incursions have occurred in mainland Europe following first detection1,17,31, it is likely that USUV will emerge in the UK again, either through overwintering or repeat incursion(s). Integrated disease surveillance in combination with bird population monitoring using the various available datasets, as we have capitalised on here, is required to assess whether USUV re-occurs, or becomes endemic, in UK wild birds and to identify any associated population impacts.By combining a range of professional and citizen science datasets our study approach facilitates the rapid detection of an emerging disease in free-living wildlife and enables insights into its incipient impact. We believe this multidisciplinary approach presents a framework for the early detection of disease outbreaks and incursion, thus helping to safeguard animal and public health. Such early warning systems could facilitate prompt mitigation action, for example targeted biosecurity measures and enhanced vigilance by medical and veterinary authorities. In addition, there is opportunity to further develop collaboration with ornithologists through active surveillance of wild birds, as was recently employed to detect West Nile virus in a migratory bird in the Netherlands32. Whilst population monitoring schemes are most developed for wild birds, lessons learned may be applied for the surveillance of diseases affecting other taxa. More