Terrestrial Ecosystems Research Articles

Panel-based assessment of ecosystem condition as a platform for adaptive and knowledge driven management.

Ecosystems are subjected to increasing exposure to multiple anthropogenic drivers. This has led to the development of national and international accounting systems describing the condition of ecosystems, often based on few, highly aggregated indicators. Such accounting systems would benefit from a stronger theoretical and empirical underpinning of ecosystem dynamics. Operational tools for ecosystem management require understanding of natural ecosystem dynamics, consideration of uncertainty at all levels, means for quantifying driver-response relationships behind observed and anticipated future trajectories of change, and an efficient and transparent synthesis to inform knowledge-driven decision processes. There is hence a gap between highly aggregated indicator-based accounting tools and the need for explicit understanding and assessment of the links between multiple drivers and ecosystem condition as a foundation for informed and adaptive ecosystem management. We describe here an approach termed PAEC (Panel-based Assessment of Ecosystem Condition) for combining quantitative and qualitative elements of evidence and uncertainties into an integrated assessment of ecosystem condition at spatial scales relevant to management and monitoring. The PAEC protocol is founded on explicit predictions, termed phenomena, of how components of ecosystem structure and functions are changing as a result of acting drivers. The protocol tests these predictions with observations and combines these tests to assess the change in the condition of the ecosystem as a whole. PAEC includes explicit, quantitative or qualitative, assessments of uncertainty at different levels and integrates these in the final assessment. As proofs-of-concept we summarize the application of the PAEC protocol to a marine and a terrestrial ecosystem in Norway.

Evaluating the Usefulness of the C-S-R Framework for Understanding AM Fungal Responses to Climate Change in Agroecosystems.

Arbuscular mycorrhizal (AM) fungi play a key role in terrestrial ecosystems by forming symbiotic relationships with plants and may confer benefits for sustainable agriculture, by reducing reliance on harmful fertiliser and pesticide inputs and enhancing plant resilience against insect herbivores. Despite their ecological importance, critical gaps in understanding AM fungal ecology limit predictions of their responses to global change in agroecosystems. However, predicting climate change impacts on AM fungi is important for maintaining crop productivity and ecosystem stability. Efforts to classify AM fungi based on functional traits, such as the competitor, stress-tolerator, ruderal (C-S-R) framework, aim to address these gaps but face challenges due to the obligate symbiotic nature of the fungi. As the framework is still widely used, we evaluate its applicability in predicting global change impacts on AM fungal communities in agroecosystems. Chagnon's adaptation of the C-S-R framework for AM fungi aligns with some study outcomes (e.g., under the context of water limitation) but faces challenges when used in complex climate change scenarios, varying agricultural conditions and/or extreme climatic conditions. The reliance on a limited dataset to classify AM fungal families further limits accurate predictions of AM fungal community dynamics. Trait data collection could support a nuanced understanding of AM fungi and leveraging AM fungal databases could streamline data management and analysis, enhancing efforts to clarify AM fungal responses to environmental change and guide ecosystem management practices. Thus, while the C-S-R framework holds promise, it requires additional AM fungal trait data for validation and improvement of its predictive power. Conclusively, before designing experiments based on life-history strategies and developing new frameworks tailored to AM fungi a critical first step is to gain a comprehensive understanding of their traits.

A novel chitosan/biochar-modified eco-concrete with balanced mechanical, planting, and water purification performance for riparian restoration

The blocking between terrestrial and riverine ecosystems by impermeable concrete can cause negative impacts on plant growth, water quality, and biodiversity in riparian zones. Herein, pursuing a balance among mechanical, planting, and water purification property, chitosan/biochar-modified eco-concrete (CBEC) was prepared as a new sustainable alternative for riparian protection, ecological restoration and water quality improvement. Compressive strength of CBEC with an optimized chitosan/biochar content of 6% could reach up to 14.05 MPa, meeting the requirements for stabilizing riparian slopes. Micromorphology characterization and porosity measurement (29.63%) confirmed the abundantly porous structure of CBEC, facilitating the soil-water nutrient exchange, plant growth and microbial attachment. The 30-d water tank cultivation observed that the physiological parameters of T. orientalis planted in CBEC, including biomass, chlorophyll, protein and starch, were greatly improved compared to unmodified eco-concrete (EC). Moreover, compared to EC, biochar-modified EC and chitosan-modified EC, the planting CBEC could most effectively decrease the levels of TN, NH4+-N, TP, and COD by 53.82%, 62.50%, 88.31%, and 57.95%, respectively. Specially, the planting CBEC could degrade a common but recalcitrant pesticide nitenpyram (NTP) by 32.83% into low-toxic substances, recognized by LC-MS analysis. Microbiological analysis revealed that CBEC greatly promoted the proliferation of both nutrient-transforming bacteria (e.g., Nitrospira and Pseudomonas) and some specific species dominating NTP degradation (e.g., Rhodococcus and Bacillus). Also, PICRUSt2 prediction results identified the enrichment of functional genes related to nitrogen and phosphorus transformation. Our findings can not only develop a superior multi-performance eco-concrete material but also provide a promising strategy for sustainable riparian restoration.

A spatially explicit multi-hazard framework for assessing flood, landslide, wildfire, and drought susceptibilities

Sustainable development goals require evaluating vulnerabilities and examining natural and climatic hazards for effective planning that reduces their impact on economic, social, and developmental efforts. Key hazards like floods, landslides, wildfires, and droughts have significantly affected terrestrial ecosystems and human societies, emphasizing the importance of comprehending these hazards. This study aimed to predict and spatially map multi-hazard, identifying historical and potential risks to inform sustainable development and construction programs that mitigate risks and promote resilience. A 34-year drought magnitude map was generated using long-term data, and ensemble and individual machine learning techniques were used to produce maps of flood, landslide, and wildfire hazards in a northwest region of Iran. Results demonstrated that ensemble learning models outperformed individual models, with the top-performing models being the weighted average (WA) of the two best models, random forest, extreme gradient boosting, WA models with over 80% accuracy, and WA incorporating all models, respectively. The CART model performed best among individual models. Variable importance analysis revealed that slope and precipitation were crucial factors for identifying high-hazard landslide areas, distance from waterways, vegetation cover, and topographic humidity index emerged as the most crucial factor for identifying flood hazard areas, while vegetation, rainfall, and proximity to roads significantly impacted wildfire hazard. The multi-hazard map produced by our study indicated that about 30% of the study area was highly and very highly susceptible to floods, landslides, wildfires, and droughts and the hazards mitigation efforts should be primarily directed to these specific portions of the study area. Our study underscored the importance of integrating long-term data and machine learning techniques in multi-hazard prediction and mapping, ultimately guiding mitigation efforts and promoting resilience in the face of natural and climatic hazards.

Survivability and life support in sealed mini-ecosystems with simulated planetary soils

Establishing a sustainable life-support system for space exploration is a formidable challenge due to the vast distances, high costs, and environmental differences from Earth. Building upon the lessons from the Biosphere 2 experiment, we introduce the novel “Ecosphere” and “Biosealed” systems, self-sustaining ecosystems within customizable, enclosed containers. These systems incorporate terrestrial ecosystems and groundwater layers, offering a potential model for transplanting Earth-like biomes to extraterrestrial environments. Over 4 years, we conducted rigorous experiments and analyses to understand the dynamics of these enclosed ecosystems. We successfully mitigated moisture deficiency, a major obstacle to plant growth, by incorporating groundwater layers. Additionally, we quantified microbial communities proliferating in specific soils, including simulated lunar and Ryugu asteroid regolith, enhance plant cultivation in space environments. Metagenomic analysis of these simulated space soils revealed diverse microbial populations and their crucial role in plant growth and ecosystem stability. Notably, we identified symbiotic relationships between plants and Cyanobacteria, enhancing oxygen production, and demonstrated the potential of LED lighting as an alternative light source for plant cultivation in sun-limited space missions. We also confirmed the survival of fruit flies within these systems, relying on plant-produced oxygen and photosynthetic bacteria. Our research provides a comprehensive framework for developing future space life-support systems. The novelty of our work lies in the unique design of our enclosed ecosystems, incorporating groundwater layers and simulated extraterrestrial soils, and the detailed analysis of microbial communities within these systems. These findings offer valuable insights into the challenges and potential solutions for establishing sustainable human habitats in space, including the importance of microbial management and potential health concerns related to microbial exposure.

Modeling carbon dynamics from a heterogeneous watershed in the mid-Atlantic USA: A distributed-calibration and independent verification (DCIV) approach

The terrestrial ecosystem plays a vital role in regulating regional and global carbon budgets. Ecosystem models are extensively employed to estimate carbon fluxes across different spatial scales. However, there remains a need to reduce the uncertainties associated with model parameterization and input data. To address these limitations, we assessed a distributed-calibration and independent-verification (DCIV) approach that uses (1) remotely sensed net primary production (NPP) and evapotranspiration (ET) data from the Moderate Resolution Imaging Spectroradiometer (MODIS), (2) multi-site eddy covariance net ecosystem exchange (NEE) data; and (3) field sampling of soil organic carbon (SOC) and aboveground biomass (ABG) data to improve the overall predictability of carbon fluxes for the different land use and land cover (LULC) types at a watershed scale. The DCIV approach was applied to an advanced version of the Soil and Water Assessment Tool (SWAT)-Carbon (or SWAT-C), equipped with Century-based SOC algorithms to simulate carbon dynamics for watersheds with heterogeneous vegetation. The objective of the modeling effort was to assess carbon stocks and fluxes under different land management scenarios for a 3000-acre experimental farm and forest preserve in the northeastern United States. Our study showed that a large SOC stock of at least 100 tons ha−1 is stored under mixed forest, deciduous, shrubland, and floodplain (grass). Our study also showed that converting floodplain (grass) to deciduous forest has the potential to increase CO2 uptake (-NEE) by an order of three magnitude and ABG by 77 %, leading to an increased SOC stock of 23 % after twenty years. Similarly, we found that converting ungrazed grassland to grazed pasture leads to a non-statistically decreasing trend of SOC, especially in the 0–30 cm soil layer. Thus, the methodology used in this study can be applied to improve carbon dynamic prediction from a heterogeneous watershed at a regional scale.

Divergent responses of forest canopy height to environmental conditions across China

As major components of terrestrial ecosystems, forest ecosystems play an important role in sequestering carbon and hence mitigating climate change. Canopy height is a crucial factor characterizing the structure and function of forest ecosystems, yet the driving mechanism of forest canopy height receives less attentions in China. Here, we utilize the satellite-based forest canopy height product with several environmental and climate factors (e.g. forest age, temperature, etc.) to delineate the spatial distributions of forest canopy height and its drivers in China at 1 km spatial resolution during the period of 2014 to 2018. The random forest is employed for identifying the dominant factors at province level, while Shapley additive explanations (SHAP) analysis is further incorporated at pixel-level to dig into the specific contributions of each driver. The results show that forest age primarily dominates the spatial distributions of forest canopy height across different forest ecosystems of China, followed by mean annual precipitation, soil type, and aspect. SHAP analysis further indicates that other factors, such as soil moisture and wind speed, also play critical roles to shape the spatial patterns of forest canopy height in China, which could not be revealed from province-level random forest analyses. Such results emphasize the priority of incorporating SHAP analysis with random forest to advance our understanding of forest canopy height distributions and benefit future projections. Our study highlights the necessity to characterize the spatial heterogeneity of forest canopy height, which is critical for accurate estimations of forest and even terrestrial carbon sink in China, facilitating the achievement of the goal of “carbon peak in 2030 and carbon neutrality in 2060”.

Several Mechanisms Drive the Heterogeneity in Browning Across a Boreal Stream Network

AbstractOver the past few decades, many catchments in Northern hemisphere have experienced increases in dissolved organic carbon (DOC) concentrations, resulting in a brownish color of the water, known as aquatic browning. Several mechanisms have been proposed to explain browning, but consensus regarding the relative importance of recovery from acid deposition, climate change, and land management remains elusive. To advance our understanding of browning mechanisms, we explored DOC trends across 13 nested boreal catchments, leveraging concurrent hydrological, chemical, and terrestrial ecosystem data to quantify the contributions of different drivers on observed trends. We first identified the related environmental factors, then attributed the individual trends of DOC to potential drivers across space and time. Our results showed that all catchments exhibited increased DOC trends from 2003 to 2021, but the DOC response rates differed by five‐fold. No single mechanism could fully explain the browning; instead, sulfate deposition, climate‐related factors, and site properties jointly controlled the variation in DOC trends. Specifically, the long‐term increases in DOC were primarily driven by recovery from sulfate deposition, followed by increases in terrestrial productivity, temperature, and discharge. However, catchment area and landcover type also regulated the response rate of DOC to these drivers, creating spatial heterogeneity in browning among sub‐catchments despite similar deposition and climate forcing. Interestingly, browning has weakened in the last decade as sulfate deposition has fully recovered and other current drivers are insufficient to sustain the long‐term increases. Our results highlight that multifaceted, spatially structured, and nonstationary drivers must be accounted for to predict future DOC changes.

Full life cycle assessment of insect biomass allows estimation of bioflows across water, air, and land

AbstractAs global environmental change continues, animals face uncertain habitat availability and quality that influences life cycle phenology and population dynamics. For decades, the population abundance and emergence patterns of burrowing mayflies have been used as a sentinel for water quality changes in large freshwater systems around the world. Despite reduced point source pollutants, evidence is mounting that the interactions among habitat loss, contaminants, and changing climate could be causing declines in mayfly production and shifts in emergence timing. We combined radar observations with traditional field measures to identify changes in mayfly populations from nymph to adult. We studied Hexagenia sp. secondary production in a large reservoir, Lake Seminole, which has contrasting water sources and land use on each arm that could contribute to differences in emergence patterns. We predicted that mayfly secondary production would be higher, and emergence would be earlier in the Chattahoochee arm versus the Flint arm because of differences in available nutrients and temperature. Benthic declines in abundance and biomass followed radar observations of emergence. Mean annual water temperature was similar, with the Flint arm having less seasonal variation. Mayfly growth was similar across the lake, but production was higher in the upper Flint arm, perhaps because of temperature stability, higher nutrient concentrations, and more lotic conditions. The natural abundance of nitrogen‐stable isotopes in mayflies showed distinct patterns between the arms and from nymph to adult. Linking benthic sampling with radar observations verified our capability to track mayfly biomass across the landscape and begin to calibrate previous measures of production with radar‐derived abundance. Coupling radar observations with stable isotope and tissue nutrient measurements allowed us to further quantify the subsidies moving from aquatic to terrestrial ecosystems, setting the framework to examine both historic and future population changes and mayfly contributions to cross‐ecosystem subsidies.

Evaluation of the resistance and resilience of terrestrial ecosystems to drought in southwest China

Evaluation of the resistance and resilience of terrestrial ecosystems to drought in southwest China

Harnessing Information From Shortwave Infrared Reflectance Bands to Enhance Satellite‐Based Estimates of Gross Primary Productivity

AbstractMonitoring gross primary productivity (GPP), the rate at which terrestrial ecosystems fix atmospheric carbon dioxide, is crucial for understanding global carbon cycling. Remote sensing offers a powerful tool for monitoring GPP using vegetation indices (VIs) derived from visible and near‐infrared reflectance (NIRv). While promising, these VIs often suffer from sensitivity to soil background, moisture, and variations in solar and view zenith angle (SZA and VZA). This study investigates the potential of incorporating shortwave infrared (SWIR) reflectance from MODIS and GOES‐R advanced baseline imager (ABI) sensors to improve GPP estimation. We evaluated various formulations for creating SWIR‐enhanced Near‐InfraRed reflectance of Vegetation (sNIRv) by integrating SWIR information into established VIs across 96 Ameriflux and NEON research sites. Our findings reveal that sNIRv improves correlation with GPP for ABI data by up to 0.19 on a half‐hourly basis for normalized difference vegetation index (NDVI) values below 0.25, with diminishing gains as NDVI values rise. Using MODIS data, sNIRv matches r values of NIRv for NDVI above 0.25, with a slight 0.05 increase for NDVI below 0.25. Analyses using SCOPE model simulations further support the ability of sNIRv to capture fractional photosynthetically active radiation, a proxy for GPP, especially for ecosystems with low leaf area index. Results highlight that sNIRv‐based VIs are less sensitive to soil background, SZA, and VZA compared to NIRv. SHapley Additive exPlanations (SHAP) value analysis also identifies sNIRv as the best feature for GPP estimation using machine learning modeling across different land covers, NDVI ranges, and soil water content levels.

Analyzing the impact of phosphorous and nitrogen on Castanopsis sclerophylla early growth stages

Plant growth elements, particularly nitrogen (N) and phosphorus (P) are vital for their growth and development, particularly for understory vegetation and their excess limits the net productivity of terrestrial ecosystems. This study focuses on the understory vegetation responds and adaptation to key essential nutrients under changing climate scenarios in subtropical evergreen broad-leaved forests, still needs research attention. this, we set up an experiment taking four treatments in a 50-year-old Castanopsis sclerophylla secondary forest under (a) control (CK), (b) N, (c) P, and (d) combined N and P addition, applied to natural forest regeneration seedlings of C. sclerophylla attained similar growth parameters of diameter of 3 cm and 10 cm height. In addition, carbon, N, P, and non-structural carbohydrates (NSC) were determined through the anthrone colorimetric approach in different parts of seedlings. Results show that the combined N + P application enhanced the N and P by 14.48 %−140.55 % in the seedlings in both dry and wet seasons, respectively. However, during wet season, the content of NSC in the plant leaves significantly exceeded under P addition. Remarkably, CK showed increased P in the growing season but lower during the dry season. Furthermore, the root starch content of seedlings showed a significant increase under the application of N and P compared to combined N + P, ranging between 45.60 % and 58.70 %. Overall, the plant growth is attributed to N and P intake. The nutrient addition and seasonal variations have a coupled effect on seedling growth as proved in the in the natural open forest experiment. The study outcomes emphasize that the alterations in NSC allocation in the roots and leaves of C. sclerophylla seedlings under N + P addition could enhance their adaptation to future global climate changes, drought conditions, and high N concentrations.

Cambio climático y plaguicidas: el caso del glifosato

esticide pollution, such as that caused by glyphosate, affects marine and terrestrial ecosystems globally. This agrochemical pollution is exacerbated by climate change, which leads to fluctuations in temperatures and increases in greenhouse gases. These factors stress organisms and their microbiomes. Additionally, the stress caused by climate change forces organisms to adapt to changes in precipitation patterns, resulting in droughts and floods. Consequently, the use of pesticides has also changed, often leading to the application of larger quantities than were previously required, due to the growing resistance of some pests. It has been documented that climate change has driven many organisms to migrate geographically. The interaction between pesticide uses and temperature fluctuations promotes plant diseases, reducing the availability and quality of food, while also causing damage to the reproduction of certain organisms, such as insects, amphibians, and fish, among others. In the case of glyphosate, its intensive and widespread use—primarily due to genetically modified seeds—has led to contamination of virtually all ecosystems with this compound, including humans. Keywords: Pesticides, Glyphosate, Climate change.

Diversity of Soil Macro Arthropods in the Arable Land of Cotton Zone, North Cameroon

Arthropods constitute the most diverse and dominant species of biodiversity in terrestrial ecosystems. Despite this great abundance, our understanding of their ecological organization and diversity remains unknown in certain habitats. The present study aimed to evaluate the diversity of soil macro-arthropods in the arable land of six localities in the arable land of cotton zone of Cameroon. For this purpose, collections of soil macro-arthropods were carried out using Barber traps and subpots for two consecutive years 2018-2019. During the entire duration of the study, nearly 33.423 soil macro-arthropods were collected belonging to 67 species divided into 11 orders and 27 families. After classification, the most abundant insect groups were Coleoptera (36.9%), Hymenoptera (33.5%), and Orthoptera (22.9%), while the groups of Neuroptera, Hemiptera and Isoptera practically a low level of relative abundance. The proportions of the different families of soil macro-arthropods sampled varies from one locality to another and three large families were in the majority: Formicidae (32.4%), Acrididae (19.5%) and Tenobrionidae (15.7%). Soil macro-arthropod samples collected in Kodek, Sanguere Njoï and Gashiga, respectively recorded the highest species richness as well as the Shanon-Weaver Diversity and Evenness index. In all the arable soils of the localities studied, the soil arthropods tend to have an Equidistribution of individuals and Equitability varies from 0.5 to 0.8. The soil macro-arthropods collected in the arable land of the cotton zone of North Cameroon were mainly phytophagous and saprophagous with a strong beneficial potential in these different ecosystems.

Nitrogen enrichment stimulates nutrient cycling genes of rhizosphere soil bacteria in the Phoebe bournei young plantations

Anthropogenic nitrogen (N) deposition is expected to increase substantially and continuously in terrestrial ecosystems, endangering the balance of N and phosphorus (P) in P-deficient subtropical forest soil. Despite the widely reported responses of the microbial community to simulated N deposition, there is limited understanding of how N deposition affects the rhizosphere soil processes by mediating functional genes and community compositions of soil bacteria. Here, five levels of simulated N deposition treatments (N0, 0 g m−2·yr−1; N1, 100 g m−2·yr−1; N2, 200 g m−2·yr−1; N3, 300 g m−2·yr−1; and N4, 400 g m−2·yr−1) were performed in a 10-year-old Phoebe bournei plantation. Quantitative microbial element cycling smart chip technology and 16S rRNA gene sequencing were employed to analyze functional gene compositions involved in carbon (C), N, and P cycling, as well as rhizosphere bacterial community composition. N deposition significantly influenced C cycling relative abundance of genes in the rhizosphere soil, especially those involved in C degradation. Low and moderate levels (100–300 g m−2·yr−1) of N deposition promoted the relative abundance of the C decomposition-related genes (e.g., amyA, abfA, pgu, chiA, cex, cdh, and glx), whereas high N deposition (400 g m−2·yr−1) suppressed enzyme (e.g., soil invertase, soil urease, and soil acid phosphatase) activities, affecting the C cycling processes in the rhizosphere. Simulated N deposition affected the functional genes associated with C, N, and P cycling by mediating soil pH and macronutrients. These findings provide new insights into the management of soil C sequestration in P. bournei young plantations as well as the regulation of C, N, and P cycling and microbial functions within ecosystems.

Assessing Microplastic Contamination in Shellfish: Insights from Pantai Remis Kuala Selangor, Strait of Malacca, Malaysia

Introduction: Microplastic (MP) contamination poses a global environmental threat, affecting terrestrial and marine ecosystems, and human health. This study investigates the presence, density, and composition of MPs in three commercially important shellfish species, oriental angel wing clam (Pholas orientalis), bamboo clam (Ensis leei), and blood cockles (Tegillarca granosa) at Pantai Remis Jeram, Kuala Selangor. Methods: Microplastics in shellfish were quantitatively analyzed for their abundance, colour, size, shape, and composition using microscopic techniques and micro-Fourier Transform Infrared (FTIR). Standard experimental protocols were followed. Statistical analysis of the data was performed using SPSS to identify correlations between these parameters. Results and Discussion: Our findings reveal a significant presence of MP particles in shellfish with T. granosa exhibiting the highest density (2.417 particles/cm³) compared to E. leei (0.721 particles/cm³) and P. orientalis (1.449 particles/cm³). Fibers and fragments were the dominant MP morphotypes, primarily in black color. P. orientalis and T. granosa contained a majority of MPs within the 1 - 5 mm size range, totalling 41 and 56 particles, respectively. Shellfish samples contain polymers of cellulase acetate and polyethylene terephthalate, indicating possible origins from plastic bottles and textile fibres. A statistically significant difference in the mean MP densities in the different species of shellfish was found by one-way ANOVA analysis (p = 0.042, p < 0.05). Conclusion: This study provides relevant data on MP pollution in commercially significant shellfish species. To effectively mitigate this environmental concern and comprehend the long-term ecological ramifications of MP intake by shellfish, more research is required.

Unprecedented N2O production by nitrate-ammonifying Geobacteraceae with distinctive N2O isotopocule signatures.

Dissimilatory nitrate reduction to ammonium (DNRA), driven by nitrate-ammonifying bacteria, is an increasingly appreciated nitrogen-cycling pathway in terrestrial ecosystems. This process reportedly generates nitrous oxide (N2O), a strong greenhouse gas with ozone-depleting effects. However, it remains poorly understood how N2O is produced by environmental nitrate-ammonifiers and how to identify DNRA-derived N2O. In this study, we characterize two novel enzymatic pathways responsible for N2O production in Geobacteraceae strains, which are predominant nitrate-ammonifying bacteria in paddy soils. The first pathway involves a membrane-bound nitrate reductase (Nar) and a hybrid cluster protein complex (Hcp-Hcr) that catalyzes the conversion of NO2- to NO and subsequently to N2O. The second pathway is observed in Nar-deficient bacteria, where the nitrite reductase (NrfA) generates NO, which is then reduced to N2O by Hcp-Hcr. These enzyme combinations are prevalent across the domain Bacteria. Moreover, we observe distinctive isotopocule signatures of DNRA-derived N2O from other established N2O production pathways, especially through the highest 15N-site preference (SP) values (43.0‰-49.9‰) reported so far, indicating a robust means for N2O source partitioning. Our findings demonstrate two novel N2O production pathways in DNRA that can be isotopically distinguished from other pathways.IMPORTANCEStimulation of DNRA is a promising strategy to improve fertilizer efficiency and reduce N2O emission in agriculture soils. This process converts water-leachable NO3- and NO2- into soil-adsorbable NH4+, thereby alleviating nitrogen loss and N2O emission resulting from denitrification. However, several studies have noted that DNRA can also be a source of N2O, contributing to global warming. This contribution is often masked by other N2O generation processes, leading to a limited understanding of DNRA as an N2O source. Our study reveals two widespread yet overlooked N2O production pathways in Geobacteraceae, the predominant DNRA bacteria in paddy soils, along with their distinctive isotopocule signatures. These findings offer novel insights into the role of the DNRA bacteria in N2O production and underscore the significance of N2O isotopocule signatures in microbial N2O source tracking.

Co-Occurrence Patterns Do Not Predict Mutualistic Interactions Between Plant and Butterfly Species.

Biotic interactions are crucial for determining the structure and dynamics of communities; however, direct measurement of these interactions can be challenging in terms of time and resources, especially when numerous species are involved. Inferring species interactions from species co-occurrence patterns is increasingly being used; however, recent studies have highlighted some limitations. To our knowledge, no attempt has been made to test the accuracy of the existing methods for detecting mutualistic interactions in terrestrial ecosystems. In this study, we compiled two literature-based, long-term datasets of interactions between butterflies and herbaceous plant species in two regions of Germany and compared them with observational abundance and presence/absence data collected within a year in the same regions. We tested how well the species associations generated by three different co-occurrence analysis methods matched those of empirically measured mutualistic associations using sensitivity and specificity analyses and compared the strength of associations. We also checked whether flower abundance data (instead of plant abundance data) increased the accuracy of the co-occurrence models and validated our results using empirical flower visitation data. The results revealed that, although all methods exhibited low sensitivity, our implementation of the Relative Interaction Intensity index with pairwise null models performed the best, followed by the probabilistic method and Spearman's rank correlation method. However, empirical data showed a significant number of interactions that were not detected using co-occurrence methods. Incorporating flower abundance data did not improve sensitivity but enhanced specificity in one region. Further analysis demonstrated incongruence between the predicted co-occurrence associations and actual interaction strengths, with many pairs exhibiting high interaction strength but low co-occurrence or vice versa. These findings underscore the complexity of ecological dynamics and highlight the limitations of current co-occurrence methods for accurately capturing species interactions.

Characteristics of Climate Change in Poyang Lake Basin and Its Impact on Net Primary Productivity

Climate change exerts substantial impacts on human society and the carbon cycle of terrestrial ecosystems. Studying the spatiotemporal characteristics of regional climate change and its impact on carbon sequestration is an important topic in ecology and environmental science. This study utilized meteorological and land use/cover data to explore these dynamics. Statistical methods such as the Mann–Kendall (M-K) test and wavelet analysis were used to simulate the changes in annual average temperature and precipitation in the Poyang Lake Basin from 1980 to 2020. The Carnegie–Ames–Stanford Approach (CASA) model was used to estimate the interannual variation in net primary productivity (NPP) in the region over the past 40 years. Additionally, the present study examined the influence of various factors on NPP changes. The main results are as follows: (1) Over the past four decades, the average temperature in the Poyang Lake Basin was 17.85 °C, while the average precipitation was 1621.35 mm. The average annual temperature rises at a rate of 0.27 °C per decade. (2) A significant shift in the average annual temperature occurred in the early 21st century, and annual precipitation exhibited multiple abrupt changes during the mid-to-late 1990s. Both temperature and precipitation changes follow a 25-year cycle, with temperature hotspots located in the south and precipitation hotspots in the northeast. (3) The impact of climate change on the change in NPP in the Poyang Lake Basin is about 70%, with the annual average temperature having a significant effect on the increase in NPP. This study can provide a scientific foundation for formulating policies aimed at mitigating climate-related disasters and enhancing carbon cycling in terrestrial ecosystems.

Evaluating tree-ring proxies for representing the ecosystem productivity in India.

Terrestrial ecosystems are one of the major sinks of atmospheric CO2 and play a key role in climate change mitigation. Forest ecosystems offset nearly 25% of the global annual CO2 emissions, and a large part of this is stored in the aboveground woody biomass. Several studies have focused on understanding the carbon sequestration processes in forest ecosystems and their response to climate change using the eddy covariance (EC) technique and remotely sensed vegetation indices. However, very few of them address the linkage of tree-ring growth with the ecosystem-atmosphere carbon exchange, and nearly none have tested this linkage over a long-term (> 100 years) - limited by the short-term (< 50 years) availability of measured ecosystem carbon flux. Nevertheless, tree-ring indices can potentially act as proxies for ecosystem productivity. We utilise the Coupled Climate Carbon Cycle Model Intercomparison Project (C4MIP) model outputs for its 140-year-long simulated records of mean monthly gross primary productivity (GPP) and compare them with the tree-ring growth indices over the northwestern Himalayan region in India. In this study, we examine three coniferous tree species: Pinus roxburghii and Picea smithiana wall. Boiss and Cedrus deodara and find that the strength of the correlation between GPP and tree ring growth indices (RWI) varies among the species.