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DataPathosystemWMV is widespread in cucurbit crops, mostly in temperate and Mediterranean climatic regions of the world16. WMV has a wide host range including some legumes, orchids and many weeds that can be alternative hosts16. Like other potyviruses, it is non-persistently transmitted by at least 30 aphid species16. In temperate regions, WMV causes summer epidemics on cucurbit crops, and it can overwinter in several common non-cucurbit weeds when no crops are present16,34. WMV has been common in France for more than 40 years, causing mosaics on leaves and fruits in melon, but mostly mild symptoms on zucchini squash. Since 2000, new symptoms were observed in southeastern France on zucchini squash: leaf deformations and mosaics, as well as fruit discoloration and deformations that made them unmarketable. This new agronomic problem was correlated to the introduction of new molecular groups of WMV strains. At least four new groups have emerged since 2000 and they have rapidly replaced the native “classical” strains, causing important problems for the producers35. These new groups, hereafter “emerging strains” (ES) are significantly more related molecularly to worldwide strains than to any other isolates from the French populations36. As emphasised in35, this supports that the new group of emerging strains has arisen through introductions, mostly from Southeastern Asia, rather than through local differentiation.In this study, we focus on the pathosystem corresponding to a classical strain (CS) and four emerging strains (ESk, (k = 1, ldots ,4)) of WMV and their cucurbit hosts.Study area and samplingThe study area, located in Southeastern France, is included in a rectangle of about 25,000 km2 and is bounded on the South by the Mediterranean Sea. Between 2004 and 2008, the presence of WMV had been monitored in collaboration with farmers, farm advisers and seed companies. Each year, cultivated host plants were collected in different fields and at different dates between May 1st and September 30th. In total, more than two thousand plant samples were collected over the entire study area. All plant samples were analyzed in the INRAE Plant Pathology Unit to confirm the presence of WMV and determine the molecular type of the virus strain causing the infection (see35 for detail on field and laboratory protocols). All infected host plants were cucurbits, mostly melon and different squashes (e.g., zucchini, pumpkins).Observations In the absence of individual geographic coordinates, all infected host plants were attributed to the centroid of the municipality (French administrative unit, median size about 10 km2) where they have been collected. Then for one date, one observation corresponded to a municipality in which at least one infected host plant was sampled. Table 1 summarizes for each year, the number of observations (i.e. number of municipalities), the number of infected plants sampled and the proportion of each WMV strain (CS, and ES1 to ES4) found in the infected host plants. Errors in assignment of virus samples to the CS or ES strains was negligible because of the large genetic distance separating them: 5 to 10% nucleotide divergence both in the fragment used in the study and in complete genomes35, also precluding the possibility of local jumps between groups by accumulation of mutations.Table 1 Number of observations and corresponding proportions of classical and emerging strains.Full size tableLandscapeTo approximate the density of WMV host plants over the study area, we used 2006 land use data (i.e. BD Ocsol 2006 PACA and LR) produced by the CRIGE PACA (http://www.crige-paca.org/) and the Association SIG-LR (http://www.siglr.org/lassociation/la-structure.html). Based on satellite images, land use is determined at a spatial resolution of 1/50,000 using an improved three-level hierarchical typology derived from the European Corine Land Cover nomenclature. Here we used the third hierarchical level of the BD Ocsol typology (i.e. 42 land use classes) to classify the entire study area in three habitats: (1) WMV-susceptible crops, (2) habitats unfavorable to WMV host plants (e.g. forests, industrial and commercial units…) and, (3) non-terrestrial habitat (i.e. water). The proportion of WMV-susceptible crops was then computed within all cells of a raster covering the entire study area, with a spatial resolution of (1.4 times 1.4) km2. These proportions were used to approximate host plant density (zleft( {varvec{x}} right)), which was normalized, so that (zleft( {varvec{x}} right) = 0) corresponds to an absence of host plants and (zleft( {varvec{x}} right) = 1) to the maximum density of host plants (Fig. 1).Figure 1Approximated density (zleft( x right)) of the host plants in the study area. The density was normalized, so that (zleft( x right) = zleft( {x_{1} ,x_{2} } right) = 0) corresponds to an absence of cucurbit plants and (zleft( x right) = 1) to the maximum density. The axes (x_{1}) and (x_{2}) correspond to Lambert93 coordinates (in km). The white regions are non-terrestrial habitats (water). Land use data were not available in the gray regions; the host plant density was then computed by interpolation.Full size imageMechanistic-statistical modelThe general modeling strategy is based on a mechanistic-statistical approach12,22,33. This type of approach combines a mechanistic model describing the dynamics under investigation with a probabilistic model conditional on the dynamics, describing how the measurements have been collected. This method that has already proved its theoretical effectiveness in determining dispersal parameters using simulated genetic data12 aims at bridging the gap between the data and the model for the determination of virus dynamics.Here, the mechanistic part of the model describes the spatio-temporal dynamics of the virus strains, given the model parameters (demographic parameters, introduction dates/sites). This allows us to compute the expected proportions of the five types of virus strains (CS and ES1 to ES4) at each date and site of observation. The probabilistic part of the mechanistic-statistical model describes the conditional distribution of the observed proportions of the virus strains, given the expected proportions. Using this approach, it is then possible to derive a numerically tractable formula for the likelihood function associated with the model parameters.Population dynamicsThe model is segmented into two stages: (1) the intra-annual stage describes the dispersal and growth of the five virus strains during the summer epidemics on cucurbit crops, and the competition between them, during a period ranging from May 1st (noted (t = 0)) to September 30 (noted (t = t_{f}), (t_{f} = 153) days); (2) the inter-annual stage describes the winter decay of the different strains when no crops are present and the virus overwinters in weeds. We denote by (c^{n} left( {t,{varvec{x}}} right)) and (e_{k}^{n} left( {t,{varvec{x}}} right)) the densities of classical strain (CS) and emerging strains (ESk, (k = 1, ldots ,4)), at position ({varvec{x}}) and at time (t) of year (n.)Dynamics of the classical strain before the first introduction eventsBefore the introduction of the first emerging strain, the intra-annual dynamics of the population CS is described by a standard diffusion model with logistic growth: (partial_{t} c^{n} = D{Delta }c^{n} + rc^{n} left( {zleft( {varvec{x}} right) – c^{n} } right)). Here, ({Delta }) is the Laplace 2D diffusion operator (sum of the second derivatives with respect to coordinate). This operator describes uncorrelated random walk movements of the viruses, with the coefficient (D) measuring the mobility of the viruses (e.g.,26,37). The term (r zleft( {varvec{x}} right)) is the intrinsic growth rate (i.e., growth rate in the absence of competition) of the population, which depends on the density of host plants (zleft( {varvec{x}} right)) and on a coefficient (r) (intrinsic growth rate at maximum host density). Under these assumptions, the carrying capacity at a position ({varvec{x}}) is equal to (zleft( {varvec{x}} right)), which means that the population densities are expressed in units of the maximum host population density. The model is initialized by setting (c^{1980} left( {0,{varvec{x}}} right) = (1 – m_{c} ) zleft( {varvec{x}} right)), where (m_{c}) is the winter decay rate of the CS (see the description of the inter-annual stage below). In other terms, we assume that the CS density is at the carrying capacity in 1979, i.e., 5 years after its first detection and 20 years before the first detections of ESs38.Introduction eventsThe ESs are introduced during years noted (n_{k} ge 1981), at the beginning of the intra-annual stage (other dates of introduction within the intra-annual stage would lead—at most—to a one-year lag in the dynamics). Their densities are (0) before introduction: (e_{k}^{n} = 0) for (n < n_{k}). Once introduced, the initial density of any ES is assumed to be 1/10th of the carrying capacity at the introduction point (other values have been tested without much effect, see Supplementary Fig. S1), with a decreasing density as the distance from this point increases:$$e_{k}^{{n_{k} }} left( {0,x} right) = frac{{zleft( {varvec{x}} right)}}{10}exp left( { - frac{|{{varvec{x}} - {varvec{X}}_{{varvec{k}}}|^{2} }}{{2sigma^{2} }}} right),$$where ({varvec{X}}_{{varvec{k}}}) is the location of introduction of the strain (k.) In our computations, we took (sigma = 5) km for the standard deviation.Intra-annual dynamics after the first introduction eventIntra-annual dynamics were described by a neutral competition model with diffusion (properties of this model have been analyzed in [54]):$$left{ {begin{array}{*{20}c} {partial_{t} c^{n} left( {t,x} right) = DDelta c^{n} + rc^{n} left( {zleft( {varvec{x}} right) - c^{n} - mathop sum limits_{i = 1}^{4} e_{i}^{n} left( {t,{varvec{x}}} right)} right)} \ {partial_{t} e_{k}^{n} left( {t,x} right) = DDelta e_{k}^{n} + re_{k}^{n} left( {zleft( {varvec{x}} right) - c^{n} - mathop sum limits_{i = 1}^{4} e_{i}^{n} left( {t,{varvec{x}}} right)} right)} \ end{array} } right.,$$for (t = 0 ldots t_{f}) and for all introduced emerging strains, i.e. all (k) such that (n ge n_{k} .) We assume reflecting boundary conditions, meaning that the population flows vanish at the boundary of the study area, due to truly reflecting boundaries (e.g., sea coast in the southern part of the site) or symmetric inward and outward fluxes26. In addition, in order to limit the number of unknown parameters, and in the absence of precise knowledge on the differences between the strains, we assume here that the diffusion, competition and growth coefficients are common to all the strains during the intra-annual stage (see the discussion for more details on this assumption).Inter-annual dynamicsThe population densities at time (t = 0) of year (n) are connected with those of year (n - 1,) at time (t = t_{f} ,) through the following formulas:$$left{ {begin{array}{*{20}c} {c^{n} left( {0,{varvec{x}}} right) = left( {1 - m_{c} } right)c^{n - 1} left( {t_{f} ,{varvec{x}}} right) hbox{ for } n ge 1981} \ {e_{k}^{n} left( {0,{varvec{x}}} right) = left( {1 - m_{e} } right)e_{k}^{n - 1} left( {t_{f} ,{varvec{x}}} right) hbox{ for }n ge n_{k} + 1} \ end{array} } right.$$with (m_{c}) and (m_{e}) the winter decay rates of the CS and ESs strains (note that (m_{e}) is common to all of the ESs). Estimation of CS and ES decay rates provides an assessment of the competitive advantage of one type of strain vs the other.Numerical computationsThe intra-annual dynamics were solved using Comsol Multiphysics time-dependent solver, which is based on a finite element method (FEM). The triangular mesh which was used for our computations is available as Supplementary Fig. S2.Probabilistic model for the observation processDuring the years (n = 2004, ldots ,2008), (I_{n}) observations were made (see Section Observations above and Table 1). They consist in counting data, that we denote by (C_{i}) and (E_{k,i}) for (k = 1, ldots ,4) and (i = 1, ldots ,I_{n}), corresponding to the number of samples infected by the CS and ESs strains, respectively, at each date of observation and location (left( {t_{i} ,{varvec{x}}_{i} } right)). Note that these dates and locations depend on the year of observation (n). More generally, the above quantities should be noted (C_{i}^{n} , E_{k,i}^{n} , t_{i}^{n} , {varvec{x}}_{i}^{n} ;) for simplicity, the index (n) is omitted in the sequel, unless necessary.We denote by (V_{i} = C_{i} + mathop sum nolimits_{k = 1}^{4} E_{k,i}) the total number of infected samples observed at (left( {t_{i} ,{varvec{x}}_{i} } right)). The conditional distribution of the vector (left( {C_{i} ,E_{1,i} ,E_{2,i} ,E_{3,i} ,E_{4,i} } right)), given (V_{i}) can be described by a multinomial distribution ({mathcal{M}}left( {V_{i} ,{varvec{p}}_{i} } right)) with ({varvec{p}}_{i} = left( {p_{i}^{c} ,p_{i}^{{e_{1} }} ,p_{i}^{{e_{2} }} ,p_{i}^{{e_{3} }} ,p_{i}^{{e_{4} }} } right)) the vector of the respective proportions of each strain in the population at (left( {t_{i} ,{varvec{x}}_{i} } right)). We chose to work conditionally to (V_{i}) because the sample sizes are not related to the density of WMV.Statistical inferenceUnknown parametersWe denote by ({{varvec{Theta}}}) the vector of unknown parameters: the diffusion coefficient (D,) the intrinsic growth rate at maximum host density (r), the winter decay rates ((m_{c} , m_{e} )) and the locations ((x_{k} in {mathbb{R}}^{2})) and years ((n_{k})) of introduction, for (k = 1, ldots ,4.) Thus ({{varvec{Theta}}} in {mathbb{R}}^{16} .)Computation of a likelihoodGiven the set of parameters ({{varvec{Theta}}}), the densities (c^{n} left( {t,{varvec{x}}|{{varvec{Theta}}}} right)) and (e_{k}^{n} left( {t,{varvec{x}}|{{varvec{Theta}}}} right)) can be computed explicitly with the mechanistic model for population dynamics. Thus, at a given year (n), at (left( {t_{i} ,x_{i} } right)), the parameter ({varvec{p}}_{i}) of the multinomial distribution ({mathcal{M}}left( {V_{i} ,{varvec{p}}_{i} } right)) writes:$$p_{i}^{c} left( {{varvec{Theta}}} right) = frac{{c^{n} left( {t_{i} ,{varvec{x}}_{i} |{{varvec{Theta}}}} right)}}{{c^{n} left( {t_{i} ,{varvec{x}}_{i} |{{varvec{Theta}}}} right) + mathop sum nolimits_{i = 1}^{4} e_{i}^{n} left( {t_{i} ,{varvec{x}}_{i} {|}{{varvec{Theta}}}} right)}}, p_{i}^{{e_{k} }} left( {{varvec{Theta}}} right) = frac{{e_{k}^{n} left( {t_{i} ,{varvec{x}}_{i} |{{varvec{Theta}}}} right)}}{{c^{n} left( {t_{i} ,{varvec{x}}_{i} |{{varvec{Theta}}}} right) + mathop sum nolimits_{i = 1}^{4} e_{i}^{n} (t_{i} ,{varvec{x}}_{i} |{{varvec{Theta}}})}}.$$The probability (P(C_{i} ,E_{1,i} ,E_{2,i} ,E_{3,i} ,E_{4,i} |{{varvec{Theta}}},{text{V}}_{{text{i}}} )) of the observed outcome (C_{i} ,E_{1,i} ,E_{2,i} ,E_{3,i} ,E_{4,i}) is then$$Pleft( {C_{i} ,E_{1,i} ,E_{2,i} ,E_{3,i} ,E_{4,i} {|}{{varvec{Theta}}},{text{V}}_{{text{i}}} } right) = frac{{left( {V_{i} } right)!}}{{C_{i} ! mathop prod nolimits_{k = 1}^{4} E_{k,i} !}}left( {p_{i}^{c} left( {{varvec{Theta}}} right)} right)^{{C_{i} }} mathop prod limits_{k = 1}^{4} (p_{i}^{{e_{k} }} left( {{varvec{Theta}}} right))^{{E_{k,i} }} .$$Assuming that the observations during each year and at each date/location are independent from each other conditionally on the virus strain proportions, we get the following formula for the likelihood:$${mathcal{L}}left( {{varvec{Theta}}} right) = mathop prod limits_{n = 2004}^{2008} mathop prod limits_{{i = 1, ldots , I_{n} }} Pleft( {C_{i} ,E_{1,i} ,E_{2,i} ,E_{3,i} ,E_{4,i} {|}{{varvec{Theta}}},{text{V}}_{{text{i}}} } right).$$A priori constraints on the parameters By definition and for biological reasons, the parameter vector ({{varvec{Theta}}}) satisfies some constraints. First, (D in left( {10^{ - 4} ,10} right){text{ km}}^{2} /{text{day}}), (r in left( {0.1,1} right) {text{day}}^{ - 1} ,) and (m_{c} , m_{e} in left{ {0,0.1,0.2, ldots ,0.9} right},) (see Supplementary Note S7 for a biological interpretation of these values). Second, we assumed that the locations of introductions ({varvec{X}}_{{varvec{k}}}) belong to the study area. To facilitate the estimation procedure, the points ({varvec{X}}_{{varvec{k}}}) were searched in a regular grid with 20 points (see Supplementary Fig. S3), and the dates of introduction (n_{k}) were searched in (left{ {1985,1990,1995,2000} right}.)Inference procedureDue to the important computation time (4 min in average for one simulation of the model on an Intel(R) Core(R) CPU i7-4790 @ 3.60 GHz), we were not able to compute an a posteriori distribution of the parameters in a Bayesian framework. Rather, we used a simulated annealing algorithm to compute the maximum likelihood estimate (MLE), i.e., the parameter ({{varvec{Theta}}}^{*}) which leads to the highest log-likelihood. This is an iterative algorithm, which constructs a sequence (({{varvec{Theta}}}_{j} )_{j ge 1}) converging in probability towards a MLE. It is based on an acceptance-rejection procedure, where the acceptance rate depends on the current iteration (j) through a "cooling rate" ((alpha )). Empirically, a good trade-off between quality of optimization and time required for computation (number of iterations) is obtained with exponential cooling rates of the type (T_{0} alpha^{j}) with (0 < alpha < 1) and some constant (T_{0} gg 1) (this cooling schedule was first proposed in= 39 = 39). Too rapid cooling ((alpha ll 1)) results in a system frozen into a state far from the optimal one, whereas too slow cooling ((alpha approx 1)) leads to important computation times due to very slow convergence. Here, we ran (6) parallel sequences with cooling rates (alpha in left{ {0.995,0.999,0.9995} right}). For this type of algorithm, there are no general rules for the choice of the stopping criterion [HenJac03], which should be heuristically adapted to the considered optimization problem. Here, our stopping criterion was that ({{varvec{Theta}}}_{j}) remained unchanged during 500 iterations. The computations took about 100 days (CPU time).Confidence intervals and goodness-of-fitTo assess the model’s goodness-of-fit, 95% confidence regions were computed for the observations (left( {C_{i} ,E_{1,i} ,E_{2,i} ,E_{3,i} ,E_{4,i} } right)) at each date/location (left( {t_{i} ,{varvec{x}}_{i} } right),) and for each year of observation. The confidence regions were computed by assessing the probability of each possible outcome of the observation process, at each date/location, based on the computed proportions ({varvec{p}}_{i} = left( {p_{i}^{c} ,p_{i}^{{e_{1} }} ,p_{i}^{{e_{2} }} ,p_{i}^{{e_{3} }} ,p_{i}^{{e_{4} }} } right)), corresponding to the output of the mechanistic model using the MLE ({{varvec{Theta}}}^{user2{*}}) and given the total number of infected samples (V_{i}). Then, we checked if the observations belonged to the 95% most probable outcomes. More

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National breeding bird monitoring schemesFieldworkers record all, or a fixed, pre-defined set, of bird species heard or seen in the main breeding season in 28 European countries on an annual basis (Fig. 1). All observations are recorded following a scheme-specific standardized protocol based on established field methods for counting birds: point count transect, line transect, or territory or spot mapping10,33,34. Here, we provide a short description of the field methods used, as each scheme provides its fieldworkers with specific fieldwork instructions and training.

1.

Point counts: A fieldworker counts all detected birds at census points, often placed along a transect (typically >200 meters apart) during a fixed time period to sample birds in a defined study area. Each point is usually visited twice a year.

2.

Line transects: A fieldworker moves along a transect and records all detected birds along the predefined path to sample birds in a defined study area. Each transect is usually visited twice a year.

3.

Territory or spot mapping: A fieldworker records all birds showing territorial behaviour in a defined study area and marks their positions and their territorial behaviour on a map. The study area is visited multiple times a year (usually 5–12) to map breeding bird territories based on the individual species-specific behaviour recorded. The species counts reflect the number of present territories.

National scheme coordinators provide all fieldworkers with instructions with the prescribed number and timing of survey visits, and information on how to record observations in terms of sampling effort, time of day, seasonality and weather conditions. This ensures the temporal and spatial consistency of data quality within individual national schemes35. The standardization of conditions during counting then enables unbiased comparison of results between years and individual study sites within each country.For the selection of sampling plots, national monitoring schemes use either random, stratified random, systematic selection, or allow a free choice by fieldworkers8,34. Sampling plots are selected randomly within the study boundaries using a random selection method or randomly within the stratum under the stratified random method. Under these methods, study plot selection is conducted by random generators (by computer programs) and stratum is predefined as a region with similar attributes; these might be proportions of habitat types, altitude bands, bird abundance, accessibility of survey sites, or fieldworker density, depending on the local circumstances. Systematic selection predefines a spatial grid for sampling plot selection while free choice enables fieldworkers to select their study areas without restrictions34. The use of a free choice, or stratified random selection of sampling plots may result in a biased sampling of specific habitat types (typically species-rich habitats) and regions (remote areas poorly covered), but post-hoc stratification and weighting procedures are generally used to correct for unequal sampling and reduce sampling bias as long as the number of plots per stratum is sufficient36. Moreover, national coordinators provide fieldworkers with recommendations or oversee the study plot selection to prevent oversampling of specific habitat types and regions. Detailed information on scheme-specific counting protocols, study plot selection and breeding period specification can be found for each national monitoring scheme8.National species indicesA species annual index reflects population size change relative to the population size in the reference year. On an annual basis, coordinators of the national monitoring schemes produce species indices for recorded species using a tailor-made implementation of loglinear regression models (TRIM models – Trends and Indices for Monitoring data) from time series of recorded species counts at the study plots37,38. Species counts from a study plot reflect mean (or maximum) of individuals recorded during visits at the study plot when using point counts or line transects. For some species, only the number of individuals recorded on the second visit is used because the period of the first visit coincides with the migratory period and consequently the mean number of recorded individuals might not reflect the number of breeding individuals. The method to estimate the species counts in a plot is constant within a national scheme.Missing data occur in the species counts at specific sites in individual years for various reasons, such as severe weather conditions during the counting period, abandonment of the study site, restricted access, or where counts are repeated in multi-year intervals. The TRIM model imputes missing data using species counts either from surveyed sites with similar environmental characteristics (stratified imputing) or all other sites with available data37,39. This process is based on the assumption that changes in populations at non-counted sites are similar to those at counted sites within the same stratum. To derive expected between-year changes in species population sizes, the program fits a log-linear regression model assuming Poisson distribution to time series from counted plots. Finally, we use this model to calculate missing species-specific counts for individual years37,39. The resulting time series of species counts with imputed missing values cover the whole period of counts in the national monitoring scheme. These imputed data are then used to estimate annual population sizes from all study plots and to derive population size indices for species11.European species indices and trendsThe individual national indices for a given species are combined to create the European species indices. Subsequently, long-term population size changes (trends) are calculated as the multiplicative linear slopes from species indices and represent an average between-year relative population size change over a predefined period.The European combination process is very similar to the production of national scheme species indices, but with three differences40. Firstly, the indices are calculated using national TRIM output data, consisting of imputed species counts, standard errors per year and covariance matrices. Secondly, species counts are weighted by the most recent species population size estimates (updated every three years) in a given country derived from national bird atlases, official data reports and national experts (http://datazone.birdlife.org/) to account for the country-specific population sizes and thus the unequal contribution of national indices on the European index. Thirdly, missing national time totals due to different start years of the schemes8 are imputed using species counts from a set of countries from the same geographical region6,11. For this purpose, we divided all national schemes into seven geographic regions – Central & East Europe, East Mediterranean, North Europe, South Europe, Southeast Europe, West Balkan and West Europe8. We then use a set of national indices from a given region to impute missing national indices. Therefore, the earliest periods of population size changes are based on data from a reduced number of study plots and schemes.The species trends are then imputed from species indices for four periods: 1980 onwards, 1990 onwards, 2000 onwards and using only the last ten years of data if the data are available. Despite higher uncertainty of the earliest estimates, we do provide the population index estimates for this period as no alternative and continuous measures of bird population size changes exist for this period.The uncertainty estimates of indices and trends are presented by the standard error11,37 allowing a calculation of 95% confidence limits (±1.96 × standard error). The magnitude of the trend estimates together with their 95% confidence intervals are then used for trend classification into six classes facilitating communication and interpretation of the outputs37 (Table 1).Table 1 Classification of the European bird species trends based on the magnitude and uncertainty of the estimates (using 95% confidence intervals).Full size tableFinally, European species indices and trends are presented only for a group of common and widespread bird species (hereafter ‘common bird species’) meeting two criteria:

1.

The estimated breeding population (http://datazone.birdlife.org/) is at least 50 000 pairs in PECBMS Europe (EU countries, Norway, Switzerland and the United Kingdom; Fig. 1). Additionally, Red-billed Chough (Pyrrhocorax pyrrhocorax) and Spotted Redshank (Tringa erythropus) with population sizes below 50 000 pairs are included, as large parts of their breeding populations are covered in the PECBMS Europe.

2.

The estimated breeding population in PECBMS countries providing data for a given species8 covers at least 50% of the whole PECBMS Europe breeding population (http://datazone.birdlife.org/).

The resulting datasets of European population size indices and trends consist of relative population changes for 170 common bird species.UpdatesWe aim to maintain the PECBMS database with annual updates. The annual updates will be available through the PECBMS database deposited at the Zenodo repository8 to ensure long-term public availability of the data. More

Study sites and soil samplingOur major study sites were located in a shrub-dominated bog13 in the Pocosin Lakes National Wildlife Refuge, NC, USA and a Sphagnum-dominated bog47 in the Marcell Experimental Forest, MN, USA (Supplementary Tables 1 and 2). Three sites ( >1 km apart) around Pungo Lake including Pungo West, Pungo Southwest, and Pungo East were selected at the shrub bogs in North Carolina. Ilex glabra and Lyonia lucida cover about 85% and 10%, respectively at Pungo West. Ilex glabra and Lyonia lucida also dominate Pungo Southwest but distribute evenly, also there are many Woodwardia virginica ferns during the growing season. The water level at Pungo Southwest is always higher than at Pungo West. Both Pungo West and Pungo Southwest have prescribed light fire every 4–5 years. There has been no fire disturbance at the Pungo East site over last 30 years, where more dominant plant species exist, including Lyonia lucida, Ilex glabra, Zenobia pulverulenta, Gaylussacia frondosa, Vaccinium formosum.One hollow and one hummock were selected at the Sphagnum-dominated bogs in Minnesota. A lot of mature trees including Picea mariana, Pinus resinosa, Larix laricina with different bryophytes and shrubs grow at both the hollows and the hummocks. S. fallax dominates the bryophyte layer at the hollows, and S. angustifolium and S. magellanicum dominate at the hummocks. The understory has a layer of ericaceous shrubs including Rhododendron groenlandicum, Chamaedaphne calyculata, Vaccinium oxycoccos at the hummocks, however, only scattered shrubs present in the hollows. Other site information is described in Supplementary Tables 1 and 2. We took three soil cores at each sites (with a distance >4 m from each other), and each soil core was sliced to four subsamples (0–5, 5–10, 10–15, and 15–20 cm). Big roots were removed in lab. The hair roots of all plants were included in the soil samples.Additionally, we took three soil cores at depth 0–10 cm in the shrub-dominated area in Dajiuhu peatlands in Shennongjia, China (31°29′N, 109°59′E) in May 2017. The dominant shrub at Dajiuhu is Betula albosinensis and Spiraea salicifolia with a dense Sphagnum layer (detailed plant information is described in Supplementary Tables 1 and 2). The samples were transported to the laboratory in iceboxes. Half of the samples were frozen at −80 °C for DNA isolation; the other half was stored at 4 °C for chemical analysis.Soil chemistry analysisWe used the deionized water extraction of fresh soil for DOC and soluble phenolics measurements. DOC was measured as the difference between total C and inorganic C with a total C analyzer (Shimadzu 5000 A, Kyoto, Japan). Soluble phenolics were measured by following the Folin-Ciocalteu procedure50. Inorganic nitrogen (NH4+–N and NO3−–N + NO2−–N) extract with 2 M KCl was determined colorimetrically on a flow-injection analyzer (Lachat QuikChem 8000, Milwaukee, WI, USA). Total carbon and nitrogen in soil were analyzed with combustion CN soil analyzer equipped with a TCD detector (ThermoQuest Flash EA1112, Milan, Italy). A 1:10 soil/water solution was used to measure soil pH.DNA extraction, PCR, and sequencingGenomic DNA was extracted from 0.25 g (fresh weight) of each homogenized soil sample using the PowerSoil DNA isolation kit (Mo Bio Laboratories, Carlsbad, CA, USA). DNA of each replicate was extracted 3 times and homogenized together as one DNA template. For Pocosin and Minnesota samples, a set of fungus-specific primers, ITS1F (3′-CTTGGTCATTTAGAGGAAGTAA-5′) and ITS4 (3′-TCCTCCGCTTATTGATATGC-5′), were used to amplify the internal transcribed spacer (ITS) region using barcoded ITS1F primers. For Dajiuhu samples, ITS1F and ITS2 (3′-GCTGCGTTCTTCATCGATGC-5′) were used. All PCR reactions were repeated in triplicate, together with the negative controls in which the template DNA was replaced with deionized H2O. The amplicon concentration of each sample was determined after purification using Qubit® 2.0 Fluorometer (Invitrogen, Grand Island, NY, USA), samples pooled at equimolar concentrations, purified using AMPure Bead cleanup. The amplicons from Pocosin, Minnesota and Dajiuhu samples were submitted to the core facility at Duke University (Durham, NC, USA) and Allwegene Tech Beijing (Beijing, China) for sequencing using Illumina MiSeq (Illumina, San Diego, CA, USA), respectively.Bioinformatics processingSequence data of Pocosins and Minnesota samples were obtained from both ITS1 and ITS2 gene regions. ITS sequences were quality filtered and processed using the standard QIIME pipeline, with each fungal taxon represented by an OTU at the 97% sequence similarity level. Singleton OTUs were omitted51, and OTUs classified taxonomically using a QIIME-based wrapper of BLAST against the UNITE database52,53 (see Supplementary Methods for further details). The quality and depth of coverage of both primers’ reads were not significantly different, thus libraries from ITS4 reads were used for further analysis of fungal communities. Taxonomic-based alpha diversity was calculated as the total number of phylotypes (richness) and Shannon’s diversity index (H′). A total of 150,967 ITS sequences from ITS2 region passed quality control criteria in the Pocosin and Minnesota sites. These sorted into 590 OTUs. Following the same procedure, a total of 115,936 ITS1 sequences from Dajiuhu samples were assigned into 307 OTUs. Following the processing procedure described by Wilson et al.47, relative abundance of beta-proteobacteria at the controlled site in the boreal Sphagnum site was recalculated from Wilson and others’ sequence data34 available from the National Center for Biotechnology Information at SRP071256. Relative abundance of fungi from a bog forest at the Calvert Island in Canada was recalculated from the raw amplicon reads in the European Nucleotide Archive, ITS (ERS1798771-ERS1799064).Lab incubationsThe decomposing capability of microbes in the Sphagnum- and shrub-dominated peatlandsWe tested the decomposing capability of microbes in the Sphagnum- and shrub-dominated peatlands by amending peat inocula from both sites in North Carolina and Minnesota to their peats and labile carbon-enriched mineral soil. Fresh Sphagnum- and shrub-formed peat inocula were prepared by mixing 0.5 kg of each type of fresh peat (10–20 cm) with 2 L of deionized water. After 1 h of stirring and 1-day settlement, the suspension liquid inoculum was filtered through a Buchner funnels (without filter, pore size 0.25–0.5 mm). We added 2 g of glucose to 50 g of nutrient-poor mineral soil (initially 0.05% total nitrogen, 0.64% total soil carbon) to produce a mineral soil medium with high labile carbon content. All incubation media (peat and mineral soil) and jars were sterilized by an autoclave before inoculation. About 30-g fresh Sphagnum-formed peat (2.5–2.8 g in dry weight) or shrub-formed peat (9.1–9.3 g in dry weight), or 50-g mineral soil with 2-g glucose was placed in Mason jars (triplicate, 8-cm diameter, 12-cm height, vacuum seal lid with a stainless-steel fitting with sampling septum), then 20 ml of its own or other’s inoculum was added to the peat media, and 5 ml of inoculum from each site was added to the mineral soil. Finally, all samples were aerobically incubated at a constant temperature of 25 °C. We initially used Parafilm M® Laboratory film, which is air permeable but water resistant, to seal the top for 3-day equilibration, afterward we collected gas samples by syringe from the headspace of each jar at the beginning and end of 1-h sealed incubation and used a GC (Varian 450, CA, USA) to analyze CO2 concentration. As microbial biomass itself is a factor regulating soil respiration rates, standardized CO2 emissions at the microbial biomass were calculated based on the elevated CO2 concentration, time, air volume in the jar, and the amount of added MBC from the inoculum. To prevent microbial acclimation to the assay chemistry18,31, we only incubated the soils for a short time. A chloroform fumigation-extraction method (0.5 M K2SO4 to extract biomass C)54 was used to determine soil MBC by the difference in measured carbon contents between fumigated and control replicates of each sample.Temperature sensitivityTo test temperature sensitivity of soil respiration, nine fresh peat samples (30 g) from each site were added to jars and sealed with Parafilm M® Laboratory film. Triplicate samples were incubated at 4, 25, and 44.5 °C. The highest temperature in this incubation does not match the in situ conditions in our sites, but it may happen shortly in tropical wooded peatlands in the future. After 3-day equilibration, we used the same method as above to measure gas emission and calculated soil respiration based on soil dry weight. We conducted regression analyses for soil from each site using R = αeβT, where R is soil respiration, coefficient α is the intercept of soil respiration when temperature is zero, coefficient β represents the temperature sensitivity of soil respiration, and T is soil temperature.The relative contributions of fungi and bacteria to peat decompositionWe subsampled 20 g each of our archived material from the Sphagnum-dominated bog in Minnesota and the shrub-dominated peatland in North Carolina, then subsamples were well mixed to make two composite bulk samples (one for Sphagnum-formed peat, one for shrub-formed peat) for the following incubations.A total of nine broad-spectrum antibiotics were tested either alone or in combination for their inhibition on bacteria or/and fungal respiration using a selective inhibition (SI) technique55 without glucose. The antibiotics include 5 fungicides (cycloheximide, benomyl, nystatin, natamycin, amphotericin B) and 4 bactericides (streptomycin, penicillin, oxytetracycline hydrochloride, neomycin). Both fungicide and bactericide were used alone or combined at concentrations of 0, 10, 20, 100, 500, and 1000 µg g−1 soil for the shrub-formed peat or 0, 35, 71, 357, 1785, and 3571 µg g−1 soil for the Sphagnum-formed peat. Each concentration of antibiotics (triplicate) was added to a 3-g fresh peat placed in a 50-ml tube. Mason jars (8-cm diameter, 12-cm height, vacuum seal lid with a stainless-steel fitting with sampling septum) are used to incubate the treated samples. CO2 accumulation rates over 24 h was measured and calculated as same as testing temperature sensitivity of soil respiration above. We found that all bactericides used in this study increased CO2 emission along with their concentrations. The results suggest that: (1) the contribution of bacteria to peat decomposition in general was simply very little, (2) the bacteria that were inhibited by bactericide contribute negligibly to peat decomposition, (3) the non-targeted bacteria were stimulated after the targeted-bacteria were inhibited, although the bactericides are broad-spectrum antibiotics, they did not inhibit the dominant bacteria at all in our sites, and /or (4) both bacteria and fungi in our sites may utilize these bactericides as a carbon source. As to the fungicide, only cycloheximide at a concentration of 357 µg g−1 soil slightly decrease CO2 emission in the Sphagnum-formed peat, but not the shrub-formed peat. Other fungicides did not suppress the CO2 emission regardless of their concentrations, or increased the CO2 emission along with increase in concentrations of fungicide, which suggest that these fungicides did not inhibit the dominant fungi in our sites. Therefore, we found no evidence that SI technique could detect the relative contribution of bacteria and fungi to peat decomposition in our sites. To further examine our fungal dominance hypothesis, we next used filtration by size to assess dominant decomposers.According to the literature (e.g., refs. 56,57,58), the average size of most bacteria is between 0.2 and 2.0 µm in diameter, with most of them less than 1.5 µm; while most fungi grow as hyphae in soil, which are cylindrical, thread-like structures 1.5–10 µm in diameter and up to several centimeters in length57,59,60. The sizes of most fungal spore are more than 2.0 µm in diameter61,62,63. Theoretically, porous filters could physically separate bacteria from fungi58. Domeignoz-Horta et al.64 used 0.8-μm filter to exclude fungi successfully64. In our test, filters with pore sizes of 0.22, 0.45, 1.2, and 1.5 μm were selected. The filtrates through 1.2- and 1.5-μm filters contain most of the bacteria, in which a small portion of larger bacteria and small fungi may pass through pores due to a lack of rigidity of their cells58.Sphagnum- and shrub-formed peat inocula were made by mixing 50 g of each type of fresh peat with 250 ml of sterilized deionized water. After 1 h of stirring and 1-day settlement, the suspension liquid inoculum was first filtered through a Buchner funnels (without filter, pore size 0.25–0.5 mm). The filtrate, we assumed, contain all bacteria and fungi while removing large decomposers like insects and worms. The 0.25–0.5 mm filtrate was used to make other inocula containing no-bacteria/fungi, nanobacteria and non-fungi, and most bacteria and non-fungi by filtering through 0.22- (nylon), 0.45- (nylon), 1.2- (glass fiber), and 1.5-μm (glass fiber) filters, separately. In total, 6 treatments including 5 inocula (filtrates through 0.22, 0.45, 1.2, 1.5, and 250–500-μm filters) and control (sterilized deionized water) were established. Either inoculum or sterilized deionized water was added to a 3-g sterilized Sphagnum- or shrub-formed peat (triplicate) and incubated at 25 °C. CO2 emission was measured within 24 h.Statistical analysisOne-way ANOVA with Duncan’s multiple-range test was used to compare the means of soil physicochemical parameters. Standard error of the mean was calculated for each mean. The significant level of the test was set at a probability of 0.05. The ANOSIM function in the vegan package in R was used to test statistical significance in fungal composition within and among sites in the shrub- and the Sphagnum-dominated peatlands (999 permutations), which shows that fungal communities were significantly different within sites at the shrub-dominated peatlands (Pungo East, Pungo West, and Pungo Southwest) and at the Sphagnum-dominated peatlands (hollows and hummocks) (Supplementary Fig. 5). Mantel test and redundancy analysis (RDA) were employed to explain the relative roles of soil physicochemical factors in fungal community composition using vegan package in R. The correlation of the redundancy axes with the explanatory matrix was determined with the general permutation test (anova.cca function; 999 permutations). Stepwise regression was further run to test what primarily control the slow-growing versus fast-growing fungi and soil acidity. More