Tundra ecosystems in the Arctic store up to 40% of global below-ground organic carbon but are exposed to the fastest climate warming on Earth. However, accurately monitoring landscape changes in the Arctic is challenging due to the complex interactions among permafrost, micro-topography, climate, vegetation, and disturbance. This complexity results in high spatiotemporal variability in permafrost distribution and active layer depth (ALD). Moreover, these key tundra processes interact at different scales, and an observational mismatch can limit our understanding of intrinsic connections and dynamics between above and below-ground processes. Consequently, this could limit our ability to model and anticipate how ALD will respond to climate change and disturbances across tundra ecosystems. In this paper, we studied the fine-scale heterogeneity of ALD and its connections with land surface characteristics across spatial and spectral scales using a combination of ground, unoccupied aerial system, airborne, and satellite observations. We showed that airborne sensors such as AVIRIS-NG and medium-resolution satellite Earth observation systems like Sentinel-2 can capture the average ALD at the landscape scale. We found that the best observational scale for ALD modeling is heavily influenced by the vegetation and landform patterns occurring on the landscape. Landscapes characterized by small-scale permafrost features such as polygon tussock tundra require high-resolution observations to capture the intrinsic connections between permafrost and small-scale land surface and disturbance patterns. Conversely, in landscapes dominated by water tracks and shrubs, permafrost features manifest at a larger scale and our model results indicate the best performance at medium resolution (5 m), outperforming both higher (0.4 m) and lower resolution (10 m) models. This transcends our study to show that permafrost response to climate change may vary across dominant ecosystem types, driven by different above- and below-ground connections and the scales at which these connections are happening. We thus recommend tailoring observational scales based on landforms and characteristics for modeling permafrost distribution, thereby mitigating the influences of spatial-scale mismatches and improving the understanding of vegetation and permafrost changes for the Arctic region.
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Environmental Research: Ecology is a multidisciplinary, open access journal devoted to addressing important macroscale challenges at the interface of ecology, biodiversity and conservation. The journal bridges scientific progress and methodological advances with assessments of environmental change impacts on ecosystems, and the responses of those ecosystems to change, including resilience, vulnerability and adaptation. For detailed information about subject coverage see the About the journal section.
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Wouter Hantson et al 2025 Environ. Res.: Ecology 4 015001
Aparnna Ravi et al 2025 Environ. Res.: Ecology 4 015004
Significant uncertainties in terrestrial carbon fluxes exist in regions with limited ground-based observations, impacting our understanding of ecosystem carbon dynamics and emission reduction needs. This is particularly true for areas with sparse measurement networks, like India. To address this, we explore the potential of satellite measurements from various missions such as Sentinel-5 Precursor and the Orbiting Carbon Observatory-2 to improve terrestrial biosphere CO2 fluxes of India. We follow a data-driven approach, which simulates spatial and temporal distributions of gross primary productivity (GPP), net ecosystem exchange (NEE), and ecosystem respiration (Reco). We improve these model predictions by additionally using satellite-based solar-induced chlorophyll fluorescence (SIF), soil temperature , and soil moisture specific to the vegetation classes of the domain. Different model refinements were performed to present the improved hourly distributions of terrestrial biospheric CO2 fluxes on a grid from 2012 to 2020. Among them, the best-performing model simulations show reasonable agreement with eddy covariance observations for 2012–2018. For example, our best NEE and GPP predictions are highly correlated with observations with squared correlation coefficient (R2) values of 0.68 (NEE) and 0.74 (GPP) at the monthly scale for 2018. Based on our improved estimations, the annual NEE and GPP show values within the range from −0.38 Pg C yr−1 to −0.53 Pg C yr−1 (land C sink) and 3.39 Pg C yr−1 to 3.88 Pg C yr−1, respectively over India for 2012–2020. Our novel approach and findings highlight the potential of satellite-based SIF measurements to detail the ecosystem-scale vegetation responses across various biomes in India. The use of satellite observations, as demonstrated in this study, offers a scalable solution for regions lacking sufficient ground-based observations to estimate biospheric carbon fluxes reliably.
Johannes Heisig et al 2025 Environ. Res.: Ecology 4 015003
Forest fuels are essential for wildfire behavior modeling and risk assessments but difficult to quantify accurately. An increase in fire frequency in recent years, particularly in regions traditionally not prone to fire, such as central Europe, has increased demands for large-scale remote sensing fuel information. This study develops a methodology for mapping canopy fuels over large areas (Germany) at high spatial resolution, exclusively relying on open remote sensing data. We propose a two-step approach where we first use measurements from NASA's Global Ecosystem Dynamics Investigation (GEDI) instrument to estimate canopy fuel variables at the footprint level, before predicting high-resolution raster maps. Instead of using field measurements, we generate (GEDI-) footprint-level estimates for canopy (Base) height (CH, CBH), cover (CC), bulk density (CBD), and fuel load (CFL) by segmenting airborne Light Detection and Ranging point clouds and processing tree-level metrics with allometric crown biomass models. To predict footprint-level canopy fuels we fit and tune Random Forest models, which are cross-validated using k-fold nearest neighbor distance matching. Predictions at >1.6 M GEDI footprints and biophysical raster covariates are combined with a universal Kriging method to produce countrywide maps at 20 m resolution. Agreement (RMSE/R2) with validation data (from the same population) was strong for footprint-level predictions and moderate for map predictions. A validation with estimates based on National Forest Inventory data revealed low to modest agreement. Better accuracy was achieved for variables related to height (CH, CBH) rather than to cover or biomass (CBD, CFL). Error analysis pointed towards a mixture of biases in model predictions and validation data, as well as underestimation of model prediction standard errors. Contributing factors may be simplification through allometric equations and spatial and temporal mismatch of data inputs. The proposed workflow has the potential to support regions where wildfire is an emerging issue, and fuel and field information is scarce or unavailable.
Jeralyn Poe et al 2025 Environ. Res.: Ecology 4 015007
Land surface models require continuous validation against observations to improve and reduce simulation uncertainty. However, inferred model performance can be heavily influenced by subjective choices made in the selection and application of observational data products. A key area often misrepresented by models is the Arctic–Boreal region, which is a potential tipping point region in Earth's climate system due to large permafrost carbon stocks that are vulnerable to release with climate warming. We use the International Land Model Benchmarking (ILAMB) framework to evaluate how the model skill of TRENDY-v9 models varies based on the choice of observational-based benchmark and how benchmarks are applied in model evaluation. This analysis uses global datasets integrated into ILAMB and new, regionally-specific observational products from the Arctic–Boreal Vulnerability Experiment. Our results cover the overall time period of 1979–2019 and show that model scores can vary substantially depending on the data product applied, with higher model scores indicating better model performance against observations. The lowest model scores occur when benchmarked against regional, compared to global, datasets. We also evaluate observed and modeled functional relationships between ecosystem respiration and air temperature and between gross primary production and precipitation. Here, we find that the magnitude and shape of the responses are strongly impacted by the choice of observational dataset and the approach used to construct the functional relationship benchmark. These results suggest that model evaluation studies could conclude a false sense of model skill if only using a single benchmark data product or if not applying regional data products when performing a regional model analysis. Collectively, our findings highlight the influence of benchmarking choices on model evaluation and point to the need for benchmarking guidelines when assessing model skill.
Ulisse Gomarasca et al 2024 Environ. Res.: Ecology 3 045003
Biodiversity relates to ecosystem functioning by modulating biogeochemical cycles of carbon, water, energy, and nutrients within and between multiple biotic and abiotic components of the ecosystems. However, large-scale, systematic measurements of plant biodiversity are still lacking, and the effects of biodiversity on measured biogeochemical processes are understudied. Here, we combined alpha (α) and beta (β) taxonomic measurements, spectral diversity from satellite observations, structural properties of the vegetation, and climatic drivers to assess the effect of biodiversity on ecosystem functional properties. Ecosystem functional properties were computed from eddy-covariance fluxes at 44 sites of the National Ecological Observatory Network. Based on the spectral variation hypothesis, we used the near-infrared reflectance of vegetation (NIRv) derived from Sentinel-2 satellite imagery to compute Rao's quadratic entropy (Rao Q), a distance metric related to spatial heterogeneity. Using an automatic model averaging technique, we found that biodiversity proxies hold substantial explanatory power when predicting several ecosystem functions related to carbon and water exchange. In particular, NIRv-based Rao Q (RaoQNIRv) reflected positive biodiversity effects on productivity, as expected from the literature. In contrast, traditional taxonomic α-diversity indices were generally not selected as relevant predictors of the ecosystem functional properties. Yet, β-diversity strongly contributed to the prediction of carbon use efficiency, surface conductance, and water use efficiency. We also found that the RaoQNIRv is less affected by issues of saturation and bare soil contribution compared to RaoQNDVI. We show that spectral heterogeneity based on remotely sensed NIRv holds the potential for globally characterizing the biodiversity-ecosystem functioning relationship (BEF). While systematic measurements of taxonomic diversity co-located at biogeochemical measurement stations could reduce the uncertainty surrounding the BEF relationship at whole-ecosystem scale, remotely- sensed metrics characterizing important functional and structural diversity aspects of the landscape will be crucial for continuous spatiotemporal monitoring of biodiversity with relevant implications for ecosystem services to humankind.
Natasha Lutz et al 2024 Environ. Res.: Ecology 3 045004
Worsening climate change impacts are amplifying the need for accurate estimates of vegetation structure and aboveground biomass density (AGBD) to assess changes in biodiversity and carbon storage. In Australia, increasing wildfire frequency and interest in the role of forests in the carbon cycle necessitates biomass mapping across large geographic extents to monitor forest change. The availability of spaceborne Light Detection and Ranging optimised for vegetation structure mapping through the Global Ecosystem Dynamics Investigation (GEDI) provides an opportunity for large-scale forest AGBD estimates of higher accuracy. This study assessed the use of the GEDI canopy height product to predict woody AGBD across five vegetation types in Western Australia: tall eucalypt forests, eucalypt open‒woodlands, low-lying heathland, tropical eucalypt savannas, and tussock and hummock grasslands. Canopy height models were developed using random forest regressions trained on GEDI canopy height discrete point data. Predictor variables included spectral bands and vegetation indices derived from synthetic aperture radar Sentinel‒1 data, and multispectral Landsat and Sentinel‒2 data. AGBD was subsequently estimated using power-law models derived by relating the predicted canopy heights to field AGBD plots. Mapping was conducted for 2020 and 2021. The accuracy of canopy height predictions varied with height quantiles; models underestimated the height of taller trees and overestimated the height of smaller trees. A similar underestimation and overestimation trend was observed for the AGBD estimates. The mean carbon stock was estimated at 69.0 ± 12.0 MgCha−1 in the tall eucalypt forests of the Warren region; 33.8 ± 5.0 MgCha−1 for the open eucalypt woodlands in the South Jarrah region; 7.1 ± 1.4 MgCha−1 for the heathland and shrublands in the Geraldton Sandplains region; 43.9 ± 4.9 MgCha−1 for the Kimberley eucalypt savanna; and 3.9 ± 1.0 MgCha−1 for the Kimberley savanna grasslands. This approach provides a useful framework for the future development of this process for fire management, and habitat health monitoring.
Rajeev Pillay et al 2024 Environ. Res.: Ecology 3 043001
Intact native forests under negligible large-scale human pressures (i.e. high-integrity forests) are critical for biodiversity conservation. However, high-integrity forests are declining worldwide due to deforestation and forest degradation. Recognizing the importance of high-integrity ecosystems (including forests), the Kunming-Montreal Global Biodiversity Framework (GBF) has directly included the maintenance and restoration of ecosystem integrity, in addition to ecosystem extent, in its goals and targets. Yet, the headline indicators identified to help nations monitor forest ecosystems and their integrity can currently track changes only in (1) forest cover or extent, and (2) the risk of ecosystem collapse using the IUCN Red List of Ecosystems (RLE). These headline indicators are unlikely to facilitate the monitoring of forest integrity for two reasons. First, focusing on forest cover not only misses the impacts of anthropogenic degradation on forests but can also fail to detect the effect of positive management actions in enhancing forest integrity. Second, the risk of ecosystem collapse as measured by the ordinal RLE index (from Least Concern to Critically Endangered) makes it unlikely that changes to the continuum of forest integrity over space and time would be reported by nations. Importantly, forest ecosystems in many biodiverse African and Asian nations remain unassessed with the RLE. As such, many nations will likely resort to monitoring forest cover alone and therefore inadequately report progress against forest integrity goals and targets. We concur that monitoring changes in forest cover and the risk of ecosystem collapse are indeed vital aspects of conservation monitoring. Yet, they are insufficient for the specific purpose of tracking progress against crucial ecosystem integrity components of the GBF's goals. We discuss the pitfalls of merely monitoring forest cover, a likely outcome with the current headline indicators. Augmenting forest cover monitoring with indicators that capture change in absolute area along the continuum of forest integrity would help monitor progress toward achieving area-based targets related to both integrity and extent of global forests.
Dedi Yang et al 2024 Environ. Res.: Ecology 3 045007
The Arctic is warming at over twice the rate of the rest of the Earth, resulting in significant changes in vegetation seasonality that regulates annual carbon, water, and energy fluxes. However, a crucial knowledge gap exists regarding the intricate interplay among climate, permafrost, and vegetation that generates high phenology variability across extensive tundra landscapes. This oversight has led to significant discrepancies in phenological patterns observed across warming experiments, long-term ecological observations, and satellite and modeling studies, undermining our ability to understand and forecast plant responses to climate change in the Arctic. To address this problem, we assessed plant phenology across three low-Arctic tundra landscapes on the Seward Peninsula, Alaska, using a combination of in-situ phenocam observations and high-resolution PlanetScope CubeSat data. We examined the patterns and drivers of phenological diversity across the landscape by (1) quantifying phenological diversity among dominant plant function types (PFTs) and (2) modeling the interrelations between plant phenology and fine-scale landscape features, such as topography, snowmelt, and vegetation. Our findings reveal that both spring and fall phenology varied significantly across Arctic PFTs, accounting for about 25%–44% and 34%–59% of the landscape-scale variation in the start of spring [SOS] and start of fall [SOF], respectively. Deciduous tall shrubs (e.g. alder and willow) had a later SOS (∼7 d behind the mean of other PFTs), but completed leaf expansion (within 2 weeks) considerably faster compared to other PFTs. We modeled the landscape-scale variation in SOS and SOF using Random Forest, which showed that plant phenology can be accurately captured by a suite of variables related to vegetation composition, topographic characteristics, and snowmelt timing (variance explained: 53%–68% for SOS and 59%–82% for SOF). Notably, snowmelt timing was a crucial determinant of SOS, a factor often neglected in most spring phenology models. Our study highlights the impact of fine-scale vegetation composition, snow seasonality, and landscape features on tundra phenological heterogeneity. Improved understanding of such considerable intra-site phenological variability and associated proximate controls across extensive Arctic landscapes offers critical insights for representation of tundra phenology in process models and associated impact assessments with climate change.
W Y Lam et al 2025 Environ. Res.: Ecology 4 015006
Beaver ponds and forest harvest are common disturbances in the Canadian boreal forest that result in major changes to catchment hydrology and thus also influence the mobilization and methylation of mercury (Hg). Though both beaver ponds and forest harvest frequently occur in the same watersheds, the possible interactive effects are not well understood. To evaluate the comparative effects of these two disturbances, this study examined in-stream total mercury and methylmercury (MeHg) across 7 stream reaches in the central Canadian boreal forest. Results showed that downstream-to-upstream MeHg concentration ratios were more highly correlated to the presence of beaver ponds than to the presence of forest harvest. However, MeHg concentrations upstream of ponds were higher in streams within harvested watersheds; these streams demonstrated a weaker correlation between beaver pond presence and downstream-to-upstream MeHg concentration ratios. Understanding these comparative and cumulative effects of beaver ponds and forest harvest will allow forest managers to consider how harvest activity could affect downstream MeHg in areas with high beaver activity.
Rowan Jacques-Hamilton et al 2025 Environ. Res.: Ecology 4 015005
Snow cover and snow melt patterns are important features of the Arctic environment, with wide-ranging repercussions for ecology. Datasets based on satellite imaging—often freely available—provide a powerful means for estimating snow cover. However, researchers should be aware of the possible error and bias in such datasets. Here, we quantify measurement error in commonly used data on snow cover, and demonstrate how biases have the potential to alter conclusions of ecological studies. We established 38 quadrats (80 m × 50 m) across a study site of Arctic tundra near Utqiaġvik, Alaska. At each quadrat, we estimated fractional snow cover (FSC) and the timing of snow melt using data from moderate resolution imaging spectroradiometer (MODIS), visible infrared imaging radiometer suite (VIIRS), and Sentinel-2 satellites. We compared satellite-based estimates with data from drone imagery to quantify measurement error and bias. We then evaluated whether the measurement error and bias alter conclusions about the relationship between the timing of snow melt and the breeding phenology of a population of pectoral sandpipers Calidris melanotos. We found that satellite datasets tended to overestimate FSC, leading to late estimates for snow melt dates. The Sentinel-2 dataset gave the most accurate results, followed by VIIRS, with MODIS giving the least accurate results. The degree of error varied substantially with the level of FSC, with biases reaching up to 60% for MODIS and VIIRS datasets at intermediate FSC values. Consequently, these datasets resulted in substantially different conclusions about how snow melt patterns were related to settlement and nesting dates of pectoral sandpipers. Our study indicates that measurement error in FSC can be large with substantial variation in the degree of error among satellite products. We show that these biases can impact conclusions of ecological studies. Therefore, ecologists should be conscious of the limitations of satellite-derived estimates of snow melt, and where possible should consult studies validating snow measurements in environments comparable to that of their study system.
Leandra Merz et al 2025 Environ. Res.: Ecology 4 015008
Recovered and recovering carnivore populations in Europe and North America can pose risks to some human livelihoods like livestock ranching. These risks can motivate wildlife managers to lethally remove carnivores—decisions that are often controversial and poorly understood. We used a 13-year dataset on gray wolves (Canis lupus) in the northwestern United States (Montana, Idaho, Washington, and Oregon) to analyze how social, demographic, and environmental variables influence lethal removal of wolves at the county and state levels. We found that state-level differences are a major driver of lethal removal decisions at the county level. The percentage of federally owned and protected lands was also positively correlated with lethal removal. Predation of livestock by wolves was not significantly correlated with wolf removals in Idaho, but was in Montana, Washington, and Oregon. Our results stress the need to make transparent the process by which recovering populations of carnivores are managed to enhance the legitimacy of management policies.
W Y Lam et al 2025 Environ. Res.: Ecology 4 015006
Beaver ponds and forest harvest are common disturbances in the Canadian boreal forest that result in major changes to catchment hydrology and thus also influence the mobilization and methylation of mercury (Hg). Though both beaver ponds and forest harvest frequently occur in the same watersheds, the possible interactive effects are not well understood. To evaluate the comparative effects of these two disturbances, this study examined in-stream total mercury and methylmercury (MeHg) across 7 stream reaches in the central Canadian boreal forest. Results showed that downstream-to-upstream MeHg concentration ratios were more highly correlated to the presence of beaver ponds than to the presence of forest harvest. However, MeHg concentrations upstream of ponds were higher in streams within harvested watersheds; these streams demonstrated a weaker correlation between beaver pond presence and downstream-to-upstream MeHg concentration ratios. Understanding these comparative and cumulative effects of beaver ponds and forest harvest will allow forest managers to consider how harvest activity could affect downstream MeHg in areas with high beaver activity.
Jeralyn Poe et al 2025 Environ. Res.: Ecology 4 015007
Land surface models require continuous validation against observations to improve and reduce simulation uncertainty. However, inferred model performance can be heavily influenced by subjective choices made in the selection and application of observational data products. A key area often misrepresented by models is the Arctic–Boreal region, which is a potential tipping point region in Earth's climate system due to large permafrost carbon stocks that are vulnerable to release with climate warming. We use the International Land Model Benchmarking (ILAMB) framework to evaluate how the model skill of TRENDY-v9 models varies based on the choice of observational-based benchmark and how benchmarks are applied in model evaluation. This analysis uses global datasets integrated into ILAMB and new, regionally-specific observational products from the Arctic–Boreal Vulnerability Experiment. Our results cover the overall time period of 1979–2019 and show that model scores can vary substantially depending on the data product applied, with higher model scores indicating better model performance against observations. The lowest model scores occur when benchmarked against regional, compared to global, datasets. We also evaluate observed and modeled functional relationships between ecosystem respiration and air temperature and between gross primary production and precipitation. Here, we find that the magnitude and shape of the responses are strongly impacted by the choice of observational dataset and the approach used to construct the functional relationship benchmark. These results suggest that model evaluation studies could conclude a false sense of model skill if only using a single benchmark data product or if not applying regional data products when performing a regional model analysis. Collectively, our findings highlight the influence of benchmarking choices on model evaluation and point to the need for benchmarking guidelines when assessing model skill.
Rowan Jacques-Hamilton et al 2025 Environ. Res.: Ecology 4 015005
Snow cover and snow melt patterns are important features of the Arctic environment, with wide-ranging repercussions for ecology. Datasets based on satellite imaging—often freely available—provide a powerful means for estimating snow cover. However, researchers should be aware of the possible error and bias in such datasets. Here, we quantify measurement error in commonly used data on snow cover, and demonstrate how biases have the potential to alter conclusions of ecological studies. We established 38 quadrats (80 m × 50 m) across a study site of Arctic tundra near Utqiaġvik, Alaska. At each quadrat, we estimated fractional snow cover (FSC) and the timing of snow melt using data from moderate resolution imaging spectroradiometer (MODIS), visible infrared imaging radiometer suite (VIIRS), and Sentinel-2 satellites. We compared satellite-based estimates with data from drone imagery to quantify measurement error and bias. We then evaluated whether the measurement error and bias alter conclusions about the relationship between the timing of snow melt and the breeding phenology of a population of pectoral sandpipers Calidris melanotos. We found that satellite datasets tended to overestimate FSC, leading to late estimates for snow melt dates. The Sentinel-2 dataset gave the most accurate results, followed by VIIRS, with MODIS giving the least accurate results. The degree of error varied substantially with the level of FSC, with biases reaching up to 60% for MODIS and VIIRS datasets at intermediate FSC values. Consequently, these datasets resulted in substantially different conclusions about how snow melt patterns were related to settlement and nesting dates of pectoral sandpipers. Our study indicates that measurement error in FSC can be large with substantial variation in the degree of error among satellite products. We show that these biases can impact conclusions of ecological studies. Therefore, ecologists should be conscious of the limitations of satellite-derived estimates of snow melt, and where possible should consult studies validating snow measurements in environments comparable to that of their study system.
Aparnna Ravi et al 2025 Environ. Res.: Ecology 4 015004
Significant uncertainties in terrestrial carbon fluxes exist in regions with limited ground-based observations, impacting our understanding of ecosystem carbon dynamics and emission reduction needs. This is particularly true for areas with sparse measurement networks, like India. To address this, we explore the potential of satellite measurements from various missions such as Sentinel-5 Precursor and the Orbiting Carbon Observatory-2 to improve terrestrial biosphere CO2 fluxes of India. We follow a data-driven approach, which simulates spatial and temporal distributions of gross primary productivity (GPP), net ecosystem exchange (NEE), and ecosystem respiration (Reco). We improve these model predictions by additionally using satellite-based solar-induced chlorophyll fluorescence (SIF), soil temperature , and soil moisture specific to the vegetation classes of the domain. Different model refinements were performed to present the improved hourly distributions of terrestrial biospheric CO2 fluxes on a grid from 2012 to 2020. Among them, the best-performing model simulations show reasonable agreement with eddy covariance observations for 2012–2018. For example, our best NEE and GPP predictions are highly correlated with observations with squared correlation coefficient (R2) values of 0.68 (NEE) and 0.74 (GPP) at the monthly scale for 2018. Based on our improved estimations, the annual NEE and GPP show values within the range from −0.38 Pg C yr−1 to −0.53 Pg C yr−1 (land C sink) and 3.39 Pg C yr−1 to 3.88 Pg C yr−1, respectively over India for 2012–2020. Our novel approach and findings highlight the potential of satellite-based SIF measurements to detail the ecosystem-scale vegetation responses across various biomes in India. The use of satellite observations, as demonstrated in this study, offers a scalable solution for regions lacking sufficient ground-based observations to estimate biospheric carbon fluxes reliably.
Fabio Carvalho et al 2024 Environ. Res.: Ecology 3 042001
Ground-mounted solar farms are becoming common features of agricultural landscapes worldwide in the move to meet internationally agreed Net Zero targets. In addition to offering low-carbon energy, solar farms in temperate environments can be purposely managed as grasslands that enhance soil carbon uptake to maximise their climate benefits and improve soil health. However, there is little evidence to date on the ecosystem effects of land use change for solar farms, including their impact on soil carbon storage and sequestration potential through land management practices. We review the latest evidence on the associations between grassland management options commonly adopted by solar farms in temperate regions (plant diversity manipulation, mowing, grazing, and nutrient addition) and soil carbon to identify appropriate land management practices that can enhance soil carbon within solar farms managed as grasslands. Soil carbon response to land management intervention is highly variable and context-dependent, but those most likely to enhance soil carbon accrual include organic nutrient addition (e.g. cattle slurry), low-to-moderate intensity sheep grazing, and the planting of legume and plant indicator species. Plant removal and long-term (years to decades) mineral fertilisation are the most likely to result in soil carbon loss over time. These results can inform policy and industry best practice to increase ecosystem service provision within solar farms and help them deliver net environmental benefits beyond low-carbon energy. Regular monitoring and data collection (preferably using standardised methods) will be needed to ensure soil carbon gains from land management practices, especially given the microclimatic and management conditions found within solar farms.
Davide Vione 2023 Environ. Res.: Ecology 2 012001
Reactions induced by sunlight (direct photolysis and indirect photochemistry) are important ecosystem services that aid freshwater bodies in removing contaminants, although they may also exacerbate pollution in some cases. Without photoinduced reactions, pollution problems would be considerably worse overall. The photochemical reaction rates depend on seasonality, depth, water chemistry (which also significantly affects the reaction pathways), and pollutant photoreactivity. Photochemical reactions are also deeply impacted by less studied factors, including hydrology, water dynamics, and precipitation regimes, which are key to understanding the main impacts of climate change on surface-water photochemistry. Climate change is expected in many cases to both exacerbate freshwater pollution, and enhance photochemical decontamination. Therefore, photochemical knowledge will be essential to understand the future evolution of freshwater environments.
Kong et al
Over the past 50 years, nutrient discharge into freshwater ecosystems has significantly increased due to intensive fertilizer applications in China. This has led to frequent environmental issues associated with nutrient enrichment, such as algal blooms, in a number of individual lakes. However, the link between terrestrial nutrient sources and algal bloom occurrences (BO) at large scales remains under-explored. Here, we simulated long-term changes in nitrogen leaching from terrestrial ecosystems at the national scale from 1979 to 2018, and examined its connection to satellite-derived BO in 56 large lakes across China. Our findings reveal that leached nitrogen exhibited significant increasing trends in 74.5% of the national landmass, with an average increase rate of 0.40 kg N ha-1 yr-2 over the past four decades. Using a 95% quantile regression model, we analyzed the linkage between nitrogen leaching and BO from 2003 to 2018. The results indicated significantly positive correlations in the lakes of the Yangtze Plain during autumn and the lakes of the Northern China and Yunnan-Guizhou Plateau during both summer and autumn. These findings suggest that terrestrial nitrogen discharge critically contributes to algal bloom variations in warmer seasons. Our study provides a comprehensive understanding of escalating nitrogen discharge from terrestrial ecosystems and highlights the potential benefits of fertilization management in mitigating and controlling inland water eutrophication in China.
Spiegel et al
Large herbivores regulate ecosystem structure and functioning across Earth's biomes, but vegetation community responses to herbivory depend on complex interactions involving the timing and intensity of herbivory pressure and other, often abiotic, controls on vegetation. Consequently, reindeer-driven vegetation transitions in the Arctic occur heterogeneously between and even within landscapes. Here, we employed drone surveys to investigate drivers of spatial heterogeneity in vegetation responses to reindeer herbivory by mapping change comprehensively across a landscape at the fine scale inherent to plant-herbivore interactions. We conducted our surveys on the Yamal Peninsula, West Siberia in coordination with Indigenous Nenets mobile pastoralists managing a reindeer herd of hundreds of animals, including 13 animals with GPS collars. The surveys mapped the focal landscape immediately before the herd arrived, immediately after they had left the site, and one month after the herd's activity. Using structure-from-motion (SfM) photogrammetry in a novel workflow that accounts for spatially variable uncertainty in the SfM reconstructions, we detected significant decreases in canopy height over 0.4% of the site after the herbivory event and significant increases in canopy height over 3% of the site one month later. Vegetation responses diverged depending on the amount of herbivory pressure, which was derived from the collar GPS data. In areas with higher reindeer activity, there were initial decreases in canopy height strongly suggesting trampling and defoliation, including signs of browsing around the edges of erect shrubs, and subsequent growth instead predominantly in low-lying vegetation one month later. Areas with lower herbivory pressure within the same habitat types showed strikingly little change throughout the study period. Due to our spatially comprehensive approach, we were able to pinpoint immediate and lagged effects of an herbivory pulse, ultimately demonstrating how herbivory can shape the productivity and distribution of vegetation communities within a landscape.