Global methane emissions from rivers and streams.

Methane (CH4) is a potent greenhouse gas and its concentrations have tripled in the atmosphere since the industrial revolution. There is evidence that global warming has increased CH4 emissions from freshwater ecosystems1,2, providing positive feedback to the global climate. Yet for rivers and streams, the controls and the magnitude of CH4 emissions remain highly uncertain3,4. Here we report a spatially explicit global estimate of CH4 emissions from running waters, accounting for 27.9 (16.7-39.7) Tg CH4 per year and roughly equal in magnitude to those of other freshwater systems5,6. Riverine CH4 emissions are not strongly temperature dependent, with low average activation energy (EM = 0.14 eV) compared with that of lakes and wetlands (EM = 0.96 eV)1. By contrast, global patterns of emissions are characterized by large fluxes in high- and low-latitude settings as well as in human-dominated environments. These patterns are explained by edaphic and climate features that are linked to anoxia in and near fluvial habitats, including a high supply of organic matter and water saturation in hydrologically connected soils. Our results highlight the importance of land-water connections in regulating CH4 supply to running waters, which is vulnerable not only to direct human modifications but also to several climate change responses on land.

The recovery of European freshwater biodiversity has come to a halt.

Owing to a long history of anthropogenic pressures, freshwater ecosystems are among the most vulnerable to biodiversity loss1. Mitigation measures, including wastewater treatment and hydromorphological restoration, have aimed to improve environmental quality and foster the recovery of freshwater biodiversity2. Here, using 1,816 time series of freshwater invertebrate communities collected across 22 European countries between 1968 and 2020, we quantified temporal trends in taxonomic and functional diversity and their responses to environmental pressures and gradients. We observed overall increases in taxon richness (0.73% per year), functional richness (2.4% per year) and abundance (1.17% per year). However, these increases primarily occurred before the 2010s, and have since plateaued. Freshwater communities downstream of dams, urban areas and cropland were less likely to experience recovery. Communities at sites with faster rates of warming had fewer gains in taxon richness, functional richness and abundance. Although biodiversity gains in the 1990s and 2000s probably reflect the effectiveness of water-quality improvements and restoration projects, the decelerating trajectory in the 2010s suggests that the current measures offer diminishing returns. Given new and persistent pressures on freshwater ecosystems, including emerging pollutants, climate change and the spread of invasive species, we call for additional mitigation to revive the recovery of freshwater biodiversity.

Long-term trends in crayfish invasions across European rivers.

Europe has experienced a substantial increase in non-indigenous crayfish species (NICS) since the mid-20th century due to their extensive use in fisheries, aquaculture and, more recently, pet trade. Despite relatively long invasion histories of some NICS and negative impacts on biodiversity and ecosystem functioning, large spatio-temporal analyses of their occurrences are lacking. Here, we used a large freshwater macroinvertebrate database to evaluate what information on NICS can be obtained from widely applied biomonitoring approaches and how usable such data is for descriptions of trends in identified NICS species. We found 160 time-series containing NICS between 1983 and 2019, to infer temporal patterns and environmental drivers of species and region-specific trends. Using a combination of meta-regression and generalized linear models, we found no significant temporal trend for the abundance of any species (Procambarus clarkii, Pacifastacus leniusculus or Faxonius limosus) at the European scale, but identified species-specific predictors of abundances. While analysis of the spatial range expansion of NICS was positive (i.e. increasing spread) in England and negative (significant retreat) in northern Spain, no trend was detected in Hungary and the Dutch-German-Luxembourg region. The average invasion velocity varied among countries, ranging from 30 km/year in England to 90 km/year in Hungary. The average invasion velocity gradually decreased over time in the long term, with declines being fastest in the Dutch-German-Luxembourg region, and much slower in England. Considering that NICS pose a substantial threat to aquatic biodiversity across Europe, our study highlights the utility and importance of collecting high resolution (i.e. annual) biomonitoring data using a sampling protocol that is able to estimate crayfish abundance, enabling a more profound understanding of NICS impacts on biodiversity.

Invasion impacts and dynamics of a European-wide introduced species.

Globalization has led to the introduction of thousands of alien species worldwide. With growing impacts by invasive species, understanding the invasion process remains critical for predicting adverse effects and informing efficient management. Theoretically, invasion dynamics have been assumed to follow an "invasion curve" (S-shaped curve of available area invaded over time), but this dynamic has lacked empirical testing using large-scale data and neglects to consider invader abundances. We propose an "impact curve" describing the impacts generated by invasive species over time based on cumulative abundances. To test this curve's large-scale applicability, we used the data-rich New Zealand mud snail Potamopyrgus antipodarum, one of the most damaging freshwater invaders that has invaded almost all of Europe. Using long-term (1979-2020) abundance and environmental data collected across 306 European sites, we observed that P. antipodarum abundance generally increased through time, with slower population growth at higher latitudes and with lower runoff depth. Fifty-nine percent of these populations followed the impact curve, characterized by first occurrence, exponential growth, then long-term saturation. This behaviour is consistent with boom-bust dynamics, as saturation occurs due to a rapid decline in abundance over time. Across sites, we estimated that impact peaked approximately two decades after first detection, but the rate of progression along the invasion process was influenced by local abiotic conditions. The S-shaped impact curve may be common among many invasive species that undergo complex invasion dynamics. This provides a potentially unifying approach to advance understanding of large-scale invasion dynamics and could inform timely management actions to mitigate impacts on ecosystems and economies.

The importance of hydrology in routing terrestrial carbon to the atmosphere via global streams and rivers.

SignificanceStream/river carbon dioxide (CO2) emission has significant spatial and seasonal variations critical for understanding its macroecosystem controls and plumbing of the terrestrial carbon budget. We relied on direct fluvial CO2 partial pressure measurements and seasonally varying gas transfer velocity and river network surface area estimates to resolve reach-level seasonal variations of the flux at the global scale. The percentage of terrestrial primary production (GPP) shunted into rivers that ultimately contributes to CO2 evasion increases with discharge across regions, due to a stronger response in fluvial CO2 evasion to discharge than GPP. This highlights the importance of hydrology, in particular water throughput, in terrestrial-fluvial carbon transfers and the need to account for this effect in plumbing the terrestrial carbon budget.

A machine learning approach to integrating genetic and ecological data in tsetse flies (Glossina pallidipes) for spatially explicit vector control planning.

Vector control is an effective strategy for reducing vector-borne disease transmission, but requires knowledge of vector habitat use and dispersal patterns. Our goal was to improve this knowledge for the tsetse species Glossina pallidipes, a vector of human and animal African trypanosomiasis, which are diseases that pose serious health and socioeconomic burdens across sub-Saharan Africa. We used random forest regression to (i) build and integrate models of G. pallidipes habitat suitability and genetic connectivity across Kenya and northern Tanzania and (ii) provide novel vector control recommendations. Inputs for the models included field survey records from 349 trap locations, genetic data from 11 microsatellite loci from 659 flies and 29 sampling sites, and remotely sensed environmental data. The suitability and connectivity models explained approximately 80% and 67% of the variance in the occurrence and genetic data and exhibited high accuracy based on cross-validation. The bivariate map showed that suitability and connectivity vary independently across the landscape and was used to inform our vector control recommendations. Post hoc analyses show spatial variation in the correlations between the most important environmental predictors from our models and each response variable (e.g., suitability and connectivity) as well as heterogeneity in expected future climatic change of these predictors. The bivariate map suggests that vector control is most likely to be successful in the Lake Victoria Basin and supports the previous recommendation that G. pallidipes from most of eastern Kenya should be managed as a single unit. We further recommend that future monitoring efforts should focus on tracking potential changes in vector presence and dispersal around the Serengeti and the Lake Victoria Basin based on projected local climatic shifts. The strong performance of the spatial models suggests potential for our integrative methodology to be used to understand future impacts of climate change in this and other vector systems.

Global riverine nitrous oxide emissions: The role of small streams and large rivers.

Nitrous oxide, N2O, is the leading cause of stratospheric ozone depletion and one of the most potent greenhouse gases (GHG). Its concentration in the atmosphere has been rapidly increasing since the green revolution in the 1950s and 1960s. Riverine systems have been suggested to be an important source of N2O, although their quantitative contribution has been estimated with poor precision, ranging between 32.2 and 2100 GgN2O - N/yr. Here, we quantify reach scale N2O emissions by integrating a data-driven machine learning model with a physically-based upscaling model. The application of this hybrid modeling approach reveals that small streams (those with widths less than 10 m) are the primary sources of riverine N2O emissions to the atmosphere. They contribute nearly 36 GgN2O - N/yr; almost 50% of the entire N2O emissions from riverine systems (72.8 Gg2O - N/yr), although they account for only 13% of the total riverine surface area worldwide. Large rivers (widths wider than 175 m), such as the main stems of the Amazon River (~ 6 GgN2O - N/yr), the Mississippi River (~ 2 GgN2O - N/yr), the Congo River (~ 1 GgN2O - N/yr) and the Yang Tze River (~ 0.7 GgN2O - N/yr), only contribute 26% of global N2O emissions, which primarily originate from their water column. This study identifies, for the first time, near-global N2O emission and NO3 removal hot spots within watersheds and thus can aid the development of local- to global-scale management and mitigation strategies for riverine systems with respect to N2O emissions. The presented framework can be extended to quantified biogeochemical, besides N2O emissions, processes at the global scale.

A machine-learning approach to map landscape connectivity in Aedes aegypti with genetic and environmental data.

Mapping landscape connectivity is important for controlling invasive species and disease vectors. Current landscape genetics methods are often constrained by the subjectivity of creating resistance surfaces and the difficulty of working with interacting and correlated environmental variables. To overcome these constraints, we combine the advantages of a machine-learning framework and an iterative optimization process to develop a method for integrating genetic and environmental (e.g., climate, land cover, human infrastructure) data. We validate and demonstrate this method for the Aedes aegypti mosquito, an invasive species and the primary vector of dengue, yellow fever, chikungunya, and Zika. We test two contrasting metrics to approximate genetic distance and find Cavalli-Sforza-Edwards distance (CSE) performs better than linearized FST The correlation (R) between the model's predicted genetic distance and actual distance is 0.83. We produce a map of genetic connectivity for Ae. aegypti's range in North America and discuss which environmental and anthropogenic variables are most important for predicting gene flow, especially in the context of vector control.

Groundwater as a limited carbon dioxide source in a large river (the Yangtze River).

Groundwater discharge to river networks makes up a major source of riverine CO2 emission, available evidence however comes mainly from headwater streams which are directly connected to terrestrial ecosystems and spatially limited in terms of system size. Here relying on coupled water and CO2 mass balances, we quantified the groundwater-mediated CO2 input to the Yangtze River mainstem on an annual basis, where the mass balance of water provided physical constraints on CO2 exchange between the river and groundwater. A landscape topographic control of the groundwater-river interaction was proposed where mountain reaches preferentially receive water and CO2 discharge from the groundwater while plain alluvial reaches predominantly lose water to the aquifers. Groundwater CO2 inputs were however small in magnitude on all reaches (0.3-14% of the total CO2 emission and transport by the river) and unable to account for the discrepancy between surface evasion and internal metabolism in the river. Minor direct groundwater discharge to the reaches in comparison to smaller streams (negative to < 3.5% of the surface water flows) was concluded to be the main reason for low groundwater-sourced CO2 in the large river reaches.

Geomorpho90m, empirical evaluation and accuracy assessment of global high-resolution geomorphometric layers.

Topographical relief comprises the vertical and horizontal variations of the Earth's terrain and drives processes in geomorphology, biogeography, climatology, hydrology and ecology. Its characterisation and assessment, through geomorphometry and feature extraction, is fundamental to numerous environmental modelling and simulation analyses. We, therefore, developed the Geomorpho90m global dataset comprising of different geomorphometric features derived from the MERIT-Digital Elevation Model (DEM) - the best global, high-resolution DEM available. The fully-standardised 26 geomorphometric variables consist of layers that describe the (i) rate of change across the elevation gradient, using first and second derivatives, (ii) ruggedness, and (iii) geomorphological forms. The Geomorpho90m variables are available at 3 (~90 m) and 7.5 arc-second (~250 m) resolutions under the WGS84 geodetic datum, and 100 m spatial resolution under the Equi7 projection. They are useful for modelling applications in fields such as geomorphology, geology, hydrology, ecology and biogeography.

Estimating nitrogen and phosphorus concentrations in streams and rivers, within a machine learning framework.

Nitrogen (N) and Phosphorus (P) are essential nutritional elements for life processes in water bodies. However, in excessive quantities, they may represent a significant source of aquatic pollution. Eutrophication has become a widespread issue rising from a chemical nutrient imbalance and is largely attributed to anthropogenic activities. In view of this phenomenon, we present a new geo-dataset to estimate and map the concentrations of N and P in their various chemical forms at a spatial resolution of 30 arc-second (∼1 km) for the conterminous US. The models were built using Random Forest (RF), a machine learning algorithm that regressed the seasonally measured N and P concentrations collected at 62,495 stations across the US streams for the period of 1994-2018 onto a set of 47 in-house built environmental variables that are available at a near-global extent. The seasonal models were validated through internal and external validation procedures and the predictive powers measured by Pearson Coefficients reached approximately 0.66 on average.

A spatial genetics approach to inform vector control of tsetse flies (Glossina fuscipes fuscipes) in Northern Uganda.

Tsetse flies (genus Glossina) are the only vector for the parasitic trypanosomes responsible for sleeping sickness and nagana across sub-Saharan Africa. In Uganda, the tsetse fly Glossina fuscipes fuscipes is responsible for transmission of the parasite in 90% of sleeping sickness cases, and co-occurrence of both forms of human-infective trypanosomes makes vector control a priority. We use population genetic data from 38 samples from northern Uganda in a novel methodological pipeline that integrates genetic data, remotely sensed environmental data, and hundreds of field-survey observations. This methodological pipeline identifies isolated habitat by first identifying environmental parameters correlated with genetic differentiation, second, predicting spatial connectivity using field-survey observations and the most predictive environmental parameter(s), and third, overlaying the connectivity surface onto a habitat suitability map. Results from this pipeline indicated that net photosynthesis was the strongest predictor of genetic differentiation in G. f. fuscipes in northern Uganda. The resulting connectivity surface identified a large area of well-connected habitat in northwestern Uganda, and twenty-four isolated patches on the northeastern margin of the G. f. fuscipes distribution. We tested this novel methodological pipeline by completing an ad hoc sample and genetic screen of G. f. fuscipes samples from a model-predicted isolated patch, and evaluated whether the ad hoc sample was in fact as genetically isolated as predicted. Results indicated that genetic isolation of the ad hoc sample was as genetically isolated as predicted, with differentiation well above estimates made in samples from within well-connected habitat separated by similar geographic distances. This work has important practical implications for the control of tsetse and other disease vectors, because it provides a way to identify isolated populations where it will be safer and easier to implement vector control and that should be prioritized as study sites during the development and improvement of vector control methods.

A suite of global, cross-scale topographic variables for environmental and biodiversity modeling.

Topographic variation underpins a myriad of patterns and processes in hydrology, climatology, geography and ecology and is key to understanding the variation of life on the planet. A fully standardized and global multivariate product of different terrain features has the potential to support many large-scale research applications, however to date, such datasets are unavailable. Here we used the digital elevation model products of global 250 m GMTED2010 and near-global 90 m SRTM4.1dev to derive a suite of topographic variables: elevation, slope, aspect, eastness, northness, roughness, terrain roughness index, topographic position index, vector ruggedness measure, profile/tangential curvature, first/second order partial derivative, and 10 geomorphological landform classes. We aggregated each variable to 1, 5, 10, 50 and 100 km spatial grains using several aggregation approaches. While a cross-correlation underlines the high similarity of many variables, a more detailed view in four mountain regions reveals local differences, as well as scale variations in the aggregated variables at different spatial grains. All newly-developed variables are available for download at Data Citation 1 and for download and visualization at http://www.earthenv.org/topography.

Potentially dangerous consequences for biodiversity of solar geoengineering implementation and termination.

Solar geoengineering is receiving increased policy attention as a potential tool to offset climate warming. While climate responses to geoengineering have been studied in detail, the potential biodiversity consequences are largely unknown. To avoid extinction, species must either adapt or move to track shifting climates. Here, we assess the effects of the rapid implementation, continuation and sudden termination of geoengineering on climate velocities-the speeds and directions that species would need to move to track changes in climate. Compared to a moderate climate change scenario (RCP4.5), rapid geoengineering implementation reduces temperature velocities towards zero in terrestrial biodiversity hotspots. In contrast, sudden termination increases both ocean and land temperature velocities to unprecedented speeds (global medians >10 km yr-1) that are more than double the temperature velocities for recent and future climate change in global biodiversity hotspots. Furthermore, as climate velocities more than double in speed, rapid climate fragmentation occurs in biomes such as temperate grasslands and forests where temperature and precipitation velocity vectors diverge spatially by >90°. Rapid geoengineering termination would significantly increase the threats to biodiversity from climate change.

Near-global freshwater-specific environmental variables for biodiversity analyses in 1 km resolution.

The lack of freshwater-specific environmental information at sufficiently fine spatial grain hampers broad-scale analyses in aquatic biology, biogeography, conservation, and ecology. Here we present a near-global, spatially continuous, and freshwater-specific set of environmental variables in a standardized 1 km grid. We delineate the sub-catchment for each grid cell along the HydroSHEDS river network and summarize the upstream climate, topography, land cover, surface geology and soil to each grid cell using various metrics (average, minimum, maximum, range, sum, inverse distance-weighted average and sum). All variables were subsequently averaged across single lakes and reservoirs of the Global lakes and Wetlands Database that are connected to the river network. Monthly climate variables were summarized into 19 long-term climatic variables following the 'bioclim' framework. This new set of variables provides a basis for spatial ecological and biodiversity analyses in freshwater ecosystems at near global extent, yet fine spatial grain. To facilitate the generation of freshwater variables for custom study areas and spatial grains, we provide the 'r.stream.watersheds' and 'r.stream.variables' add-ons for the GRASS GIS software.

Estimating future burned areas under changing climate in the EU-Mediterranean countries.

The impacts of climate change on forest fires have received increased attention in recent years at both continental and local scales. It is widely recognized that weather plays a key role in extreme fire situations. It is therefore of great interest to analyze projected changes in fire danger under climate change scenarios and to assess the consequent impacts of forest fires. In this study we estimated burned areas in the European Mediterranean (EU-Med) countries under past and future climate conditions. Historical (1985-2004) monthly burned areas in EU-Med countries were modeled by using the Canadian Fire Weather Index (CFWI). Monthly averages of the CFWI sub-indices were used as explanatory variables to estimate the monthly burned areas in each of the five most affected countries in Europe using three different modeling approaches (Multiple Linear Regression - MLR, Random Forest - RF, Multivariate Adaptive Regression Splines - MARS). MARS outperformed the other methods. Regression equations and significant coefficients of determination were obtained, although there were noticeable differences from country to country. Climatic conditions at the end of the 21st Century were simulated using results from the runs of the regional climate model HIRHAM in the European project PRUDENCE, considering two IPCC SRES scenarios (A2-B2). The MARS models were applied to both scenarios resulting in projected burned areas in each country and in the EU-Med region. Results showed that significant increases, 66% and 140% of the total burned area, can be expected in the EU-Med region under the A2 and B2 scenarios, respectively.