Plant phenological development is orchestrated through subtle changes in photoperiod, temperature, soil moisture and nutrient availability. Presently, the exact timing of plant development stages and their response to climate and management practices 5 are crudely represented in land surface models. As visual observations of phenology are laborious, there is a need to supplement long-term observations with automated techniques such as those provided by digital repeat photography at high temporal and spatial resolution. We present the first synthesis from a growing observational network of digital cameras installed on towers across Europe above deciduous and evergreen 10 forests, grasslands and croplands, where vegetation and atmosphere CO2 fluxes are measured continuously. Using colour indices from digital images and using piecewise regression analysis of time-series, we explored whether key changes in canopy phenology could be detected automatically across different land use types in the network. The piecewise regression approach could capture the start and end of the growing 15 season, in addition to identifying striking changes in colour signals caused by flowering and management practices such as mowing. Exploring the dates of green up and senescence of deciduous forests extracted by the piecewise regression approach against dates estimated from visual observations we found that these phenological events could be detected adequately (RMSE< 8 and 11 days for leaf out and leaf fall 20 respectively). We also investigated whether the seasonal patterns of red, green and blue colour fractions derived from digital images could be modelled mechanistically using the PROSAIL model parameterised with information of seasonal changes in canopy leaf area and leaf chlorophyll and carotenoid concentrations. From a model sensitivity analysis we found that variations in colour fractions, and in particular the late spring 25 “green hump” observed repeatedly in deciduous broadleaf canopies across the network, are essentially dominated by changes in the respective pigment concentrations. Using the model we were able to explain why this spring maximum in green signal is often observed out of phase with the maximum period of canopy photosynthesis in ecosystems across Europe. Coupling such quasi-continuous digital records of canopy colours with co-located CO2 flux measurements will improve our understanding of how changes in growing season length are likely to shape the capacity of European ecosystems to sequester CO2 in the future

Isotopic data provide powerful constraints on regional and global methane emissions and their source profiles. However, inverse modeling of spatially resolved methane flux is currently constrained by a lack of information on the variability of source isotopic signatures. In this study, isotopic signatures of emissions in the Fennoscandian Arctic have been determined in chambers over wetland, in the air 0.3 to 3 m above the wetland surface and by aircraft sampling from 100 m above wetlands up to the stratosphere. Overall, the methane flux to atmosphere has a coherent δ 13 C isotopic signature of À71 ± 1‰, measured in situ on the ground in wetlands. This is in close agreement with δ 13 C isotopic signatures of local and regional methane increments measured by aircraft campaigns flying through air masses containing elevated methane mole fractions. In contrast, results from wetlands in Canadian boreal forest farther south gave isotopic signatures of À67 ± 1‰. Wetland emissions dominate the local methane source measured over the European Arctic in summer. Chamber measurements demonstrate a highly variable methane flux and isotopic signature, but the results from air sampling within wetland areas show that emissions mix rapidly immediately above the wetland surface and methane emissions reaching the wider atmosphere do indeed have strongly coherent C isotope signatures. The study suggests that for boreal wetlands (>60°N) global and regional modeling can use an isotopic signature of À71‰ to apportion sources more accurately, but there is much need for further measurements over other wetlands regions to verify this.

Russia's forests play an important role in the global carbon cycle. Because of their scale and interannual variability, forest fires can change the direction of the net carbon flux over Eurasia. 2002 and 2003 were the first two consecutive years in the atmospheric record in which the carbon content rose by more than 2 ppm per year. Northern Hemisphere fires could be the reason. We show that 2002 and 2003 were the two years with the largest fire extent in Central Siberia since 1996 using new measurements of burned forest area in Central Siberia derived from remote sensing. To quantify the relationship between Siberian forest fires and climate variability, we compare these measurements with time‐series of large‐scale climatic indices for the period 1992–2003. This paper is amongst the first studies that analyse statistical relationships between interannual variability of forest fires in Russia and climate indices. Significant relationships of annual burned forest area with the Arctic Oscillation, summer temperatures, precipitation, and the El Niño index NINO4 were found (p < 0.1). In contrast, we find no significant relation with the El Niño indices NINO1, NINO3 or SOI (p > 0.1). Interannual forest fire variability in Central Siberia could best be explained by a combination of the Arctic Oscillation index and regional summer temperatures (r2 = 0.80).

Zoonotic diseases affect resource-poor tropical communities disproportionately, and are linked to human use and modification of ecosystems. Disentangling the socio-ecological mechanisms by which ecosystem change precipitates impacts of pathogens is critical for predicting disease risk and designing effective intervention strategies. Despite the global "One Health" initiative, predictive models for tropical zoonotic diseases often focus on narrow ranges of risk factors and are rarely scaled to intervention programs and ecosystem use. This study uses a participatory, co-production approach to address this disconnect between science, policy and implementation, by developing more informative disease models for a fatal tick-borne viral haemorrhagic disease, Kyasanur Forest Disease (KFD), that is spreading across degraded forest ecosystems in India. We integrated knowledge across disciplines to identify key risk factors and needs with actors and beneficiaries across the relevant policy sectors, to understand disease patterns and develop decision support tools. Human case locations (2014-2018) and spatial machine learning quantified the relative role of risk factors, including forest cover and loss, host densities and public health access, in driving landscape-scale disease patterns in a long-affected district (Shivamogga, Karnataka State). Models combining forest metrics, livestock densities and elevation accurately predicted spatial patterns in human KFD cases (2014-2018). Consistent with suggestions that KFD is an "ecotonal" disease, landscapes at higher risk for human KFD contained diverse forest-plantation mosaics with high coverage of moist evergreen forest and plantation, high PLOS NEGLECTED TROPICAL DISEASES

Forest fires are frequent in the Siberian taiga and are predicted to increase in frequency as a result of increased fire risk under drought conditions, and prolonged fire seasons caused by climate change. There is, however, some uncertainty as to the extent to which drought influences forest fire frequency at a regional scale. Here, we present an analysis of satellite derived soil moisture anomaly data from ERS-1/2 (ERS: Earth Resources Satellite) scatterometer data and burned area maps from MODIS/AVHRR/ATSR (Moderate Resolution Imaging Spectroradiometer/Advanced Very High Resolution Radiometer/Along-Track Scanning Radiometer) over Central Siberia for the years 1992-2000. The purpose of this study is to investigate the relationship of remotely sensed soil moisture deviations from the long-term mean and fire within the boreal biome on a sub-continental scale.Results show that wet surface soil moisture conditions limit the extent of burned area. They can prevent the outbreak of fires but the magnitude of a negative (dry) deviation does not determine the maximum size of fire affected areas. It is known from the literature, however, that an ignition is more likely to occur under low surface wetness conditions, such as those that we observed during July and August in both permafrost and non-permafrost regions. Although the burned area under drier conditions in July is lowest over non-permafrost, the actual number of fires is as high as over continuous permafrost. Approximately 80% of all events occurred under such conditions during that month. The fire size was below 50 km 2 under moist conditions. Larger burned areas have in general not been detected when the surface wetness deviation exceeded +5%.

Abstract. Airborne and ground-based measurements of methane (CH 4 ), carbon dioxide (CO 2 ) and boundary layer thermodynamics were recorded over the Fennoscandian landscape (67-69.5 • N, 20-28 • E) in July 2012 as part of the MAMM (Methane and other greenhouse gases in the Arctic: Measurements, process studies and Modelling) field campaign. Employing these airborne measurements and a simple boundary layer box model, net regional-scale (∼ 100 km) fluxes were calculated to be 1.2 ± 0.5 mg CH 4 h −1 m −2 and −350 ± 143 mg CO 2 h −1 m −2 . These airborne fluxes were found to be relatively consistent with seasonally averaged surface chamber (1.3 ± 1.0 mg CH 4 h −1 m −2 ) and eddy covariance (1.3 ± 0.3 mg CH 4 h −1 m −2 and −309 ± 306 mg CO 2 h −1 m −2 ) flux measurements in the local area. The internal consistency of the aircraft-derived fluxes across a wide swath of Fennoscandia coupled with an excellent statistical comparison with local seasonally averaged ground-based measurements demonstrates the potential scalability of such localised measurements to regional-scale representativeness. Comparisons were also made to longerterm regional CH 4 climatologies from the JULES (Joint UK Land Environment Simulator) and HYBRID8 land surface models within the area of the MAMM campaign. The average hourly emission flux output for the summer period Based on these observations both models were found to significantly underestimate the CH 4 emission flux in this region, which was linked to the under-prediction of the wetland extents generated by the models.

An 18-yr time series of the fraction of absorbed photosynthetically active radiation (fAPAR) taken in by the green parts of vegetation data from the NOAA Advanced Very High Resolution Radiometer (AVHRR) instrument series was analyzed for interannual variations in the start, peak, end, and length of the season of vegetation photosynthetic activity in central and east Siberia. Variations in these indicators of seasonality can give important information on interactions between the biosphere and atmosphere. A second-order local moving window regression model called the "camelback method" was developed to determine the dates of phenological events at subcontinental scale. The algorithm was validated by comparing the estimated dates to phenological field observations. Using spatial correlations with temperature and precipitation data and climatic oscillation indices, two geographically distinct mechanisms in the system of climatic controls of the biosphere in Siberia are postulated: central Siberia is controlled by an "Arctic Oscillation-temperature mechanism," while east Siberia is controlled by an "El Niño-precipitation mechanism." While the analysis of data from 1982 to 1991 indicates a slight increase in the length of the growing season for some land-cover types due to an earlier beginning of the growing season, the overall trend from 1982 to 1999 is toward a slightly shorter season for some land-cover types caused by an earlier end of season. The Arctic Oscillation tended toward a more positive phase in the 1980s leading to enhanced high pressure system prevalence but toward a less positive phase in the 1990s. The results suggest that the two mechanisms also control the fire regimes in central and east Siberia. Several extreme fire years in central Siberia were associated with a highly positive Arctic Oscillation phase, while several years with high fire damage in east Siberia occurred in El Niño years. An analysis of remote sensing data of forest fire partially supports this hypothesis.

Natural succession of vegetation on abandoned farmland provides opportunities for passive rewilding to re-establish native woodlands, but in Western Europe the patterns and outcomes of vegetation colonisation are poorly known. We combine time series of field surveys and remote sensing (lidar and photogrammetry) to study woodland development on two farmland fields in England over 24 and 59 years respectively: the New Wilderness (2.1 ha) abandoned in 1996, and the Old Wilderness (3.9 ha) abandoned in 1961, both adjacent to ancient woodland. Woody vegetation colonisation of the New Wilderness was rapid, with 86% vegetation cover averaging 2.9 m tall after 23 years post-abandonment. The Old Wilderness had 100% woody cover averaging 13.1 m tall after 53 years, with an overstorey tree-canopy (≥ 8 m tall) covering 91%. By this stage, the structural characteristics of the Old Wilderness were approaching those of neighbouring ancient woodlands. The woody species composition of both Wildernesses differed from ancient woodland, being dominated by animal-dispersed pedunculate oak Quercus robur and berry-bearing shrubs. Tree colonisation was spatially clustered, with wind-dispersed common ash Fraxinus excelsior mostly occurring near seed sources in adjacent woodland and hedgerows, and clusters of oaks probably resulting from acorn hoarding by birds and rodents. After 24 years the density of live trees in the New Wilderness was 132/ha (57% oak), with 390/ha (52% oak) in the Old Wilderness after 59 years; deadwood accounted for 8% of tree stems in the former and 14% in the latter. Passive rewilding of these ‘Wilderness’ sites shows that closed-canopy woodland readily re-established on abandoned farmland close to existing woodland, it was resilient to the presence of herbivores and variable weather, and approached the height structure of older woods within approximately 50 years. This study provides valuable long-term reference data in temperate Europe, helping to inform predictions of the potential outcomes of widespread abandonment of agricultural land in this region.