Light and temperature are strong drivers of ecosystem processes that are seasonally coupled in most ecosystems (i.e., highest during summer and lowest in winter). Due to this near-ubiquitous correlation, it is difficult to isolate individual effects of light and temperature under natural conditions. Arctic spring-streams provide an exception due to their relatively constant water temperatures and extreme annual light fluctuations, which effectively uncouples their annual light and temperature regimes. We sought to assess nutrient responses of dominant organic matter compartments (i.e., epilithic biofilms and aquatic bryophytes) to light and temperature through semi-monthly sampling from five spring-streams in arctic Alaska (USA) that vary in source temperature ( 1 to 12°C). We measured
Stable isotope analysis is a powerful tool that is widely used in modern marine ecology. However, its utility depends on researchers accounting for spatial and temporal variability in baseline values, e.g., for the correct interpretation of food web dynamics or animal movement in marine ecosystems. Here, we present the first observation-based δ13C and δ15N isoscapes for particulate organic matter (POM) in the southeastern Pacific, spanning 2300 km of the Chilean coastline. POM samples were collected during four research cruises (2017–2018) across three latitudinal zones. Using a Bayesian hierarchical modeling framework, we predicted spatial variation in δ13CPOM and δ15NPOM values based on in situ and satellite-derived environmental data. Because temporal overlap among stations was limited, these predictions represent integrated isotopic baseline estimates for 2017–2018 rather than season-specific surfaces. Results revealed a latitudinal gradient, with δ13C values increasing and δ15N values decreasing with latitude (i.e., southward). Best-fit models explained 61
Understanding how urbanization affects vegetation growth within global megacities remains underexplored, particularly with respect to intra-urban intensity gradients, temporal instability, and the climatic and socioeconomic factors that condition indirect effects. We compared urban expansion, enhanced vegetation index (EVI) trends, and potential driving factors across ten global megacities from 2000 to 2018. All cities expanded from 2000 to 2018; newly impervious area ranged from 313.14 km2 in São Paulo to 2377.78 km2 in Shanghai, and the impervious area growth rate ranged from 13.41
Belowground carbon allocation regulates soil carbon inputs and ecosystem resilience, yet global evidence for how management alters allocation across resource gradients remains limited. I used a global terrestrial net primary production archive to test whether human management changes the fraction of total net primary production allocated belowground and whether that effect is comparable to climatic and edaphic controls on total net primary production. I aggregated repeated field measurements to site means across 456 terrestrial sites spanning forests, grasslands, peatlands, shrublands, tundra, and croplands, calculated belowground production fractions for 293 sites, and fitted weighted least-squares models that incorporated archived methodological uncertainty. Unmanaged grasslands allocated substantially more production belowground than managed grasslands (median difference = 0.182, 95 p < 0.001 ), whereas forests showed no detectable management contrast (median difference = 0.003, 95 -0.057 to 0.059, p = 0.967 ). In the final allocation model, the unmanaged-by-open-ecosystem interaction was positive (estimate = 0.154, 95 p = 0.003 ), and cross-validated R^2 increased from 0.160 in the climate baseline to 0.243. For total net primary production, climate and resource covariates improved cross-validated R^2 from 0.223 to 0.269, with positive effects of annual mean temperature, aridity index, and atmospheric nitrogen deposition, whereas the management term weakened in the conservative subset excluding croplands and treatment experiments. These results show that management more strongly reorganizes carbon allocation than total productivity, especially in open ecosystems, providing a field-based benchmark for ecosystem science and model evaluation.
Networks of drainage ditches play a crucial role in processing N loads along the land–river–sea system. This study investigated the N removal capacity of ditch sediments in a reclaimed area with paleo-marsh peat lenses releasing geogenic N to surface waters (Po River Delta, Italy). Sediment–water fluxes of O2, inorganic N and N2O, as well as denitrification and DNRA (dissimilatory nitrate reduction to ammonium) rates, were measured in spring and summer via incubations of intact sediment cores sampled at two sites having different pedological characteristics (silty clay loam and organic silty clay). The NO3− removal rate (247–534 µmol N m−2 h−1) was primarily due to denitrification in both seasons, with a low amount recycled into NH4+ by DNRA (6–11
Hydrology is a key variable of ecosystem development in wetlands, and climatic and anthropogenic disturbances are altering hydrologic dynamics worldwide. Freshwater restoration is widely implemented to rehydrate degraded ecosystems; however, verifying ecological recovery and guiding adaptive management require long-term monitoring. Quantifying changes in marsh primary productivity, species composition, and phosphorus (P) retention can characterize recovering wetland trajectories and inform restoration. We quantified long-term (2006–2023) changes in vegetation and P retention during freshwater rehydration in Everglades National Park, Florida, USA. Specifically, we measured aboveground net primary productivity (ANPP) of Cladium jamaicense (sawgrass, the dominant marsh species), density of Eleocharis spp. (spikerush, an indicator of wetter conditions), and soil and plant P retention patterns. Our study spanned shallower marl marshes and deeper peat marshes with historically shorter and longer hydroperiods, respectively. Mean ANPP was lower in marl marshes than in peat marshes. Models quantifying basin-wide temporal trends in ANPP, site-specific deviations, and effects of soil total P and water depth explained 61.5–74.2
Fine-root decomposition plays a critical role in regulating the carbon (C) cycle in terrestrial ecosystems, however, the underlying microbial-mediated mechanisms under global change remain poorly understood. To address this knowledge gap, we conducted an 18-month field manipulation experiment using the litterbag method in the Tibetan alpine meadow to investigate how warming and nitrogen (N) addition affected fine-root decomposition through changes in litter-associated microbial communities. The results showed that N addition promoted the degradation of cellulose and hemicellulose, but suppressed lignin degradation. In contrast, warming enhanced the decomposition of hemicellulose and lignin. Changes in carbon chemistry composition during fine-root decomposition were significantly correlated with shifts in litter bacterial community composition and enzymatic activities, indicating that litter bacterial communities were more responsive to warming and N addition than fungal communities. Specifically, N addition increased the relative abundance of Proteobacteria and hydrolase activities, but reduced the relative abundance of Actinobacteria, Basidiomycota, as well as oxidase activities during 12–18 months period. Warming did not alter bacterial relative abundance at the phylum level (> 1
The improvement of forest biomass estimates is of outstanding interest to get more detailed information on the global carbon cycle. Forest biomass is usually derived from allometric biomass functions, which rely on the outer shape of the trees (i.e., diameter at breast height [dbh] and tree height). We tested the influence of biomass loss due to internal stem decay and of topography on the aboveground biomass in southern boreal forests of Mongolia. We selected this study area, because the Mongolian boreal forest includes evergreen and deciduous forests (called dark and light taiga) with all gymnosperm genera globally dominating the circumboreal forests. Furthermore, our study could be based on a detailed published carbon stock estimate from this region that was based on allometric regression. We found that internal stem decay and tree cavities caused a significant reduction of the aboveground biomass on the stand level by 5.3
Conservation translocations require careful assessment of dietary resources to ensure population viability. Here, we used environmental DNA metabarcoding to characterise fungal assemblages across three major habitat types on Dirk Hartog Island, Western Australia, prior to the planned reintroduction of two threatened mycophagous species: the boodie (Bettongia lesueur) and woylie (Bettongia ogilbyi). We detected 243 fungal ASVs, of which 75 represented taxa that are likely to produce macroscopic fruiting bodies accessible to mycophagous mammals. Ectomycorrhizal fungi dominated all habitats, with Pezizaceae being the most abundant family—a pattern consistent with known dietary preferences of both species across multiple Australian locations. Fungal community composition differed significantly among habitat types, with birrida (saltpan), dwarf scrub, and shrub-steppe each supporting distinct assemblages. Comparative analysis with recent dietary studies revealed approximately 50
Despite growing scientific evidence of the important role that wood-decomposing fungi serve in ecosystem function, we know little about how fungal community dynamics relate to decomposition outcomes. We deployed 42 standardized (same dimensions and source) wood stakes from sugar maple (Acer saccharum) and white ash (Fraxinus americana) on the forest floor of a northern US hardwood forest. After four years, we collected the stakes for analysis of fungal biomass (via ergosterol), community composition (DNA sequencing), and nutrient concentrations. We found large variability in mass loss (9–95 X/X_0 ) were associated with greater mass loss. We found consistently stronger correlations of fungal community composition, compared to fungal biomass, with C/nutrient ratios in sugar maple stakes. White ash stakes showed consistent correlations of both fungal community composition and fungal biomass with C/nutrient ratios. Lastly, redundancy and cluster analyses showed that fungal communities clustered into taxonomically distinct groups that also corresponded to differences in mass loss. Together, our results suggest that communities with low biomass but efficient decomposers are associated with faster decomposition, while communities with high biomass but less efficient decomposers may not necessarily correlate with high mass loss. Our results suggest that wood decomposition and forest ecosystem models could be improved by including fungal community composition and total fungal biomass.
The Tibetan Plateau, often called the “Third Pole,” hosts ecosystems that are highly sensitive to extreme climatic events. Although research on climate change and ecological processes in this region has advanced, our understanding of ecosystem productivity in terms of resistance and resilience to extreme climate disturbances remains limited. Using remote sensing data, this study systematically quantified the resistance and resilience of plateau ecosystems to extreme temperature events, revealing diverse carbon-cycle responses. The results indicate that approximately 73
Wetlands are critical ecosystems that regulate hydrology, support biodiversity, and sustain livelihoods, yet they are increasingly threatened by land use change, hydrological alteration, and fire. Fire can act as both a natural process and a destructive force in wetlands, shaping vegetation dynamics and the provision of ecosystem services. Despite their ecological importance, few studies have assessed how human activities, hydroclimatic variability, and policy interventions jointly influence fire regimes in wetlands. We evaluated how land use, hydrology, and public policies interact to shape fire dynamics in the Paraná River Delta in Argentina, one of South America’s most important wetlands and among those with the highest fire density over the past two decades. We analyzed burned-area patterns from 2001 to 2020 using satellite data (Fire_CCI) combined with hydrological and socioeconomic indicators. Over 40
The introduction of pines in Southern America to supply the wood industry has promoted interactions with native fauna, including the black-horned capuchin (Sapajus nigritus [Goldfuss, 1809]). In southern Brazil, this species feeds on pine sap, causing significant economic losses. We used the maximum entropy algorithm (MaxEnt) to model the current and future potential distribution of S. nigritus under different climate change scenarios and conducted spatial analyses to evaluate land cover changes and the overlap between pine-producing areas and the species’ range. Current climatic suitable conditions cover about 1 million km2, mainly in southern and southeastern Brazil, but projected to decline by 31.7 to 89.8
The decomposition of dead plant matter (leaf litter and deadwood) is a key process for carbon (C) and nutrient cycles in forests. Increased nitrogen (N) deposition due to human activity may affect this process, with important consequences on forest biogeochemistry. Although there is extensive work on leaf litter, the research about the effect of N deposition on deadwood decomposition is still limited. Moreover, most N manipulation studies rely on ground N fertilization, neglecting the canopy, which may influence the quantity and the form of N reaching the forest soil. This study assesses how litter and deadwood decomposition can be influenced by N deposition, using an innovative experimental design, in which N (20 kg N ha−1 year−1) is applied above the canopy (treatment “above”) or on the forest floor (treatment “below”) during a three-year (36-month) field experiment. Litter decomposition was studied with the litterbag method; deadwood decomposition was determined by incubating blocks of deadwood (sapwood and heartwood) at different decay stages. Neither N treatments influenced the decomposition rates or carbon (C) loss patterns of litter and deadwood. Remaining N in litter and deadwood was not affected by treatments, except for decay class IV of the sapwood, where N decreased (release) under the below-canopy treatment but showed a net increase in total N content under the above-canopy treatment and in the control. The dynamics of Na, P, and S in the litter differed between the treatment below and the other treatments. Together, despite unchanged mass loss and C loss these treatment-specific nutrient responses suggest that canopy-mediated N delivery can modulate early biogeochemical trajectories of decomposing litter and deadwood even when bulk decay rates remain unaffected over the short term. However, further research is needed to assess the effect of N deposition on deadwood decomposition in the long term, especially considering the slow decay of heartwood.
Identifying the coordinated responses and the biotic and abiotic drivers of aquatic ecosystem dynamics across space and time is the key to understanding and predicting ecosystem trajectories under global change. Here, we applied the concept of ecosystem synchrony (that is, similarities in temporal fluctuations of ecosystem functions across space) to evaluate: (1) the coordinated dynamics of 16 French gravel pit lakes and (2) how geographic proximity, abiotic and biotic similarities explain their level of synchrony. We quantified both multi-year (two-year period) and seasonal (warm vs. cold seasons) levels of synchrony in dissolved oxygen saturation using high-frequency (10 min) measurements. We showed that the two-year ecosystem synchrony was driven by the environmental similarities in abiotic (for example, nutrient levels, hydromorphology) and biotic (for example, fish biomass) conditions between lakes yet the relative influence of these two processes varied seasonally. Indeed, ecosystem synchrony during warm seasons was primarily driven by abiotic environmental conditions, likely reflecting the stronger control of physical and chemical conditions on ecosystem functioning. Conversely, biotic similarity was more influential on ecosystem synchrony during cold seasons, suggesting a greater influence of biological structure under reduced ecosystem productivity. Geographic distance had a negligible effect, likely due to the relatively limited spatial extent of the lake network. These findings highlight the value of ecosystem synchrony as a spatiotemporal integrator of ecosystem dynamics and emphasize the need to account for both the temporal scale and local biotic and abiotic context dependencies when assessing the ecological trajectories of ecosystems.
Mowing and grazing are among the most common grassland management practices, and the frequency and intensity with which they are applied can reduce soil nutrient availabilities. These alterations may drive microorganisms to increase the production of enzymes associated with the acquisition of limiting nutrients. This study aimed to investigate how different land uses—and their associated management practices—affect microbial strategies for soil nutrient acquisition and ecosystem responses to nutrient limitation. Thus, in the Matese mountain (Southern Italy), adjacent areas under different land uses (forest-FO, meadow-ME, pasture-PA) were selected, and the soils sampled at four times along the year (T1, T2, T3 and T4), corresponding to specific management practices in ME and PA. Total soil nutrient (seven macro- and four micronutrient) concentrations, and twelve soil enzymatic activities involved in carbon, nitrogen, and phosphorus acquisition by the microbial community were analyzed. Enzyme stoichiometric ratios (ECN, ECP, ENP) as well as length and angle of a vector combining ECN and ECP were calculated to assess soil nutrient acquisition strategies. ECN and ECP values highlighted a greater microbial investment in nitrogen and phosphorus acquisition across all land uses, whereas the higher vector length value observed at T2 and T3 in PA and at T3 in ME suggested increased microbial allocation toward carbon acquisition. ENP and the vector angle values highlighted a greater phosphorus acquisition in FO, nitrogen in PA, and balanced nitrogen and phosphorus acquisition in ME. These patterns suggest that land use and management practices influence microbial resource-allocation strategies and ecosystem responses to nutrient limitation by altering extracellular enzyme production. Our findings further show that practices such as annual mowing and low grazing pressure have helped preserve the balance of soil microbial nutrients, while also indicating the value of enzymatic stoichiometry as an effective useful approach for understanding microbial adaptation to nutrient limitation and ecosystem functioning under different land uses.
Forests and savannas frequently co-occur as patches within tropical landscape mosaics, yet the mechanisms controlling their spatial configuration remain unclear. The presence of both vegetation states under similar climatic conditions is often attributed to fire–vegetation feedbacks, but could also reflect variation in overlooked external drivers. In Central Africa, forest–savanna mosaics become more common with increasing topographic roughness, but how well topographic heterogeneity explains the forest–savanna configuration within mosaic landscapes is unknown. Here we address this question and examine the role of individual topographic variables that may influence tree cover by, for instance, changing water availability and fire spread. We identify mosaic landscapes from remotely sensed tree cover data and derive topographic variables from a digital elevation model. We use these variables to develop machine learning algorithms predicting vegetation state within mosaic landscapes. Models achieved an average prediction accuracy of 0.75, with local elevation (relative to the surrounding 500 m or 5000 m) emerging as the strongest predictor of vegetation state. Both model accuracy and the role of topographic predictors varied strongly among landscapes, reflecting the diverse pathways by which topography can influence tree cover. Overall, our findings indicate that topographic heterogeneity is a major driver of forest–savanna mosaics in Central Africa. Mosaic landscapes are more deterministic than previously assumed, suggesting that their response to disturbances and climate change will be spatially heterogeneous, thereby reducing the likelihood of abrupt large-scale shifts between forest and savanna states.
Southwest China (SWC), a pivotal carbon sink region, has experienced increasingly frequent and intense droughts under climate change. Yet, the relative roles of soil moisture (SM) and vapor pressure deficit (VPD) in regulating ecosystem productivity remain unclear. This study combines Solar-Induced Chlorophyll Fluorescence (SIF) with a two-dimensional copula framework to quantify SM and VPD dominance and critical thresholds across the SWC from 2001 to 2024, using the 40th percentile of SIF values as the threshold for productivity reduction. Results show that SM predominantly drives SIF reduction across 90.8
Estimating forest soil organic carbon density (SOCD) is particularly challenging in dry tropical valleys (DTVs) due to their complex topography, highly variable soils, diverse vegetation types, and unique climatic conditions. In this respect, this study incorporates multi-source remote sensing data and multiple machine learning algorithms to improve the accuracy and reliability of forest SOCD estimation. This study integrates 47 variables, including optical remote sensing, environmental factors, and topographic factors, and employs the Boruta algorithm for variable selection. Eight machine learning models are constructed to estimate the forest SOCD in the DTVs of Yuanmou County, Yunnan Province, China. The results indicate that: (1) Regarding the remote sensing estimation of DTV forest SOCD, the soil factors are relatively critical, followed by the vegetation index factors, while the climatic factors have relatively little effect. (2) There are significant differences in the performances of the various models. The extreme gradient boosting algorithm achieves the highest estimation accuracy with an R2 of 0.68 and a root mean square error (RMSE) of 1.29 kg C m−2, followed by the multilayer perceptron model with an R2 of 0.67 and an RMSE of 1.30 kg C m−2. The Bayesian regularized neural network and multilayer perceptron have largely equal estimation performance, with the elastic net model having the lowest fitting accuracy with an R2 of 0.32 and RMSE of 1.89 kg C m−2. The most applicable machine learning models for DTV forest SOCD estimation are extreme gradient boosting, multilayer perceptron, and Bayesian regularized neural network, with extreme gradient boosting showing the best performance. This study provides valuable insights into selecting data sources and models for remote sensing-based SOCD estimation in DTVs.
