Ecosystem Indicators Research Articles

A novel method to estimate the 3D chlorophyll a distribution in the South China Sea surface waters using hydrometeorological parameters

Chlorophyll a (Chl-a) is a key indicator of marine ecosystems, and certain hydro-meteorological parameters (HMPs) are highly correlated with its fluctuations. Here, relevant and accessible HMPs were used as inputs, combined with machine learning (ML) algorithms for estimating 3D Chl-a in the South China Sea (SCS). With the inputs of temperature, salinity, depth, wind speed, wind direction, sea surface pressure, and relative humidity, the LightGBM-based model performed well, achieving high R2 values of 0.985 and 0.789 in validation and testing sets, respectively. Based on a large number of in situ measurements, this model enables the estimation of the 3D distribution of summer Chl-a in the SCS over the past fifteen years using a 3D hydrographic dataset combined with surface meteorological parameters. The results show that the 3D distribution of the model estimated Chl-a is characterized similarly to the previous studies and can capture the effect of hydro-meteorological conditions on Chl-a distribution. The environmental variables affecting Chl-a were considered more comprehensively in this study, and the methodological framework has the potential to be applied to the low-cost monitoring of the remaining water quality parameters.

Emergy-Based Evaluation of the Sustainability of Agricultural Ecosystem in Dazhou, China, from 2002 to 2022

Our aim is to analyze the emergy evaluation indicators of the agricultural ecosystem in Dazhou, northeastern Sichuan, and provide practical and effective recommendations for sustainable agricultural development. Using emergy analysis, the emergy inputs and outputs of an agricultural ecosystem from 2002 to 2022 were calculated. Five emergy indicators were selected for evaluation: emergy yield ratio (EYR), emergy self-sufficiency ratio (ESR), emergy input ratio (EIR), environmental load ratio (ELR), and emergy sustainable indices (ESI). The total emergy input of the agricultural ecosystem showed an upward trend from 2002 to 2017, thus the industrial auxiliary emergy input decreased, somewhat curbing its continued rise from 2017 to 2022. The structure of emergy inputs, in descending order, is as follows: industrial auxiliary > organic emergy > renewable environmental resources > non-renewable environmental resources. The total emergy output of the agricultural ecosystem was highest in 2007, reaching 2.31 × 1022 Sej, and lowest in 2012, at 1.83 × 1022 Sej. The structure of emergy outputs, in descending order, is as follows: livestock > planting > fishery > forestry. The emergy yield ratio fluctuated down from 3.12 to 2.51, with an average of 2.88, below the provincial average of 3.07. The emergy self-sufficiency ratio fluctuated down from 0.30 to 0.26, with an average of 0.27, above the provincial average of 0.13. The emergy input ratio fluctuated up from 2.31 to 2.91, with an average of 2.66, above the provincial average of 1.86. The environmental load ratio fluctuated from 3.8 to 4.75, with an average of 4.40, which is higher than the provincial average of 1.68. The emergy sustainable indices fluctuated down from 0.81 to 0.53, with an average of 0.67, below the provincial average of 1.17. The efficiency of resource utilization in the agricultural ecosystem of Dazhou has decreased, economic inputs have increased, and it is in a consumptive production process. The pressure on the local natural environment is increasing, and the capacity for sustainable development remains at a low level over the long term.

A Study on the Diversity of Planktons of Urban and Rural Ponds of Kokrajhar District, Assam, India

Due to rapid urbanization, many aquatic habitats are now under severe eutrophication because of waste contaminated soil. Therefore, it is essential to assess the status of aquatic ecosystem surrounding us. The present study was conducted for the study of diversity of planktons which are indicator of aquatic ecosystem of two urban and two rural ponds of Kokrajhar district, Assam. The present study reveals a total of 37 species of planktons of which 23 species represents phytoplankton and 14 species represent zooplankton. The most pollution-tolerant species of Euglena, Navicula, Nitzschia, Ankistrodesmus, Phacus,Pinnularia and Scenedesmus were recorded indicating the highest degree of organic pollution. This plankton study explore that water quality has reached its threshold level and therefore, it needs some corrective measures to maintain the water quality from further deterioration in the study area.

Predicting Gross Primary Productivity under Future Climate Change for the Tibetan Plateau Based on Convolutional Neural Networks

Gross primary productivity (GPP) is vital for ecosystems and the global carbon cycle, serving as a sensitive indicator of ecosystems’ responses to climate change. However, the impact of future climate changes on GPP in the Tibetan Plateau, an ecologically important and climatically sensitive region, remains underexplored. This study aimed to develop a data-driven approach to predict the seasonal and annual variations in GPP in the Tibetan Plateau up to the year 2100 under changing climatic conditions. A convolutional neural network (CNN) was employed to investigate the relationships between GPP and various environmental factors, including climate variables, CO2 concentrations, and terrain attributes. This study analyzed the projected seasonal and annual GPP from the Coupled Model Intercomparison Project Phase 6 (CMIP6) under four future scenarios: SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5. The results suggest that the annual GPP is expected to significantly increase throughout the 21st century under all future climate scenarios. By 2100, the annual GPP is projected to reach 1011.98 Tg C, 1032.67 Tg C, 1044.35 Tg C, and 1055.50 Tg C under the four scenarios, representing changes of 0.36%, 4.02%, 5.55%, and 5.67% relative to 2021. A seasonal analysis indicates that the GPP in spring and autumn shows more pronounced growth under the SSP3–7.0 and SSP5–8.5 scenarios due to the extended growing season. Furthermore, the study identified an elevation band between 3000 and 4500 m that is particularly sensitive to climate change in terms of the GPP response. Significant GPP increases would occur in the east of the Tibetan Plateau, including the Qilian Mountains and the upper reaches of the Yellow and Yangtze Rivers. These findings highlight the pivotal role of climate change in driving future GPP dynamics in this region. These insights not only bridge existing knowledge gaps regarding the impact of future climate change on the GPP of the Tibetan Plateau over the coming decades but also provide valuable guidance for the formulation of climate adaptation strategies aimed at ecological conservation and carbon management.

Ichthyofaunal diversity of Banshelki Dam Udgir, Dist. Latur (M.S.)

Ichthyofaunal diversity is good indicator of aquatic ecosystem. A diversity of fishes represents the balanced ecosystem. By taking this into consideration the ichthyofaunal diversity of Banshelki dam is studied during present investigation. The Banshelki Dam spread over area about 2290 m. The Dam is old and constructed on Manmod river. Total 20 species of fishes belonging to 13 genera, 06 families and 05 orders were identified from the Dam. The order cypriniformes was dominant among fishes.

Ecosystem links: Anthropogenic activities, environmental variables, and macrophytes structure snail preferences in man-made waterbodies

Freshwater snails act as obligate intermediate hosts for trematode parasites that cause trematodiases threatening public and veterinary health, and biodiversity conservation. While interest in snail control for trematodiases has re-emerged, their ecology remains poorly understood. We examined the relationship between ecosystem indicators – such as environmental variables, macroinvertebrates, macrophytes, and land use – and their correlation with snail abundance, diversity, and infection prevalence in 19 man-made ponds in eastern Zimbabwe. In total, 926 freshwater snails from 10 species were collected, with 547 individuals belonging to five schistosome-competent species: Bulinus tropicus, Bulinus truncatus, Bulinus globosus, Bulinus forskalii, and Biomphalaria pfeifferi. The remaining 379 snails comprised Radix natalensis, Gyraulus sp., and the exotic invasive species Melanoides tuberculata, Pseudosuccinea columella, and Physella acuta. Six cercarial types – mammalian schistosomes, avian schistosomes, longifurcate pharyngeates, echinostomes, amphistomes, and xiphidiocercariae – were isolated from 104 out of 926 snails (11.2 %). PCR revealed a significantly higher infection rate, with 70.2 % of snails testing positive for trematodes. Snail taxon diversity and infection rate significantly varied across land use types, with the lowest values observed in the commercial tobacco farm section, highlighting the potential adverse effects of agriculture on biodiversity. Ponds with extensive Lagarosiphon major (oxygen weed) coverage appeared to facilitate the presence and abundance of P. acuta and P. columella. Schistosome-competent snails such as B. truncatus and B. tropicus seemed to favor shallow water depths and more eutrophic sites characterized by high levels of nitrates, phytoplankton biomass, turbidity, and phycocyanin. These ponds were predominantly associated with the emergent macrophyte Cladium mariscus, revealing a potential association with important intermediate snail hosts. In conclusion, our study emphasizes the complex interplay among environmental factors, macrophyte composition, land use, and the abundance, diversity, and infection prevalence of freshwater snails, offering insights into potential strategies for targeted snail control and disease management in man-made waterbodies.

Application of Fisheries Acoustics: A Review of the Current State in Mexico and Future Perspectives

In Mexico, marine acoustics research still faces technical and scientific challenges. For the past decade, the country has made a sustained effort to implement acoustic techniques to generate time series of standardized information; however, these data have been underutilized. Marine acoustics research has been used mainly for small pelagic species and has contributed to improving fishery management and to advising stakeholders. The Mexican scientific community has perceived marine acoustic techniques as expensive tools that are only used for industrial fishing purposes. Marine acoustics can provide information on the variability and interactions between species, their physical environment, and other communities of species, but this approach has not yet been integrated into interdisciplinary research programs or ecosystem models. Additionally, acoustic data provide estimates of biomass and indices of relative abundance, and they have suitable statistical properties for use in integrated catch-at-age models. In summary, to consolidate marine acoustic techniques in Mexico, it is necessary, at a minimum, to maintain the current infrastructure for acoustic studies, to increase the budget for the development of monitoring programs that collect ecosystem indicator data, to promote the training of human resources, and to encourage peer review of the information generated and reported in gray literature.

First Red List of Ecosystems assessment of a tropical glacier ecosystem to diagnose the pathways towards imminent collapse

Abstract Tropical glaciers are rapidly disappearing, particularly in isolated mountain peaks below 5,000 m elevation. These glaciers are fundamental substrates for unique cryogenic ecosystems in high tropical environments where the ice, melting water and rocky substrate sustain microbiological communities and other meso- and macro-biota. This study uses the Red List of Ecosystems guidelines to diagnose the collapse of the tropical glacier ecosystem of the Cordillera de Mérida, Venezuela. We undertook the assessment with existing estimates of glacier ice extent, indirect historical estimates of ice mass balance and global mechanistic models of future ice mass balance. We complemented these with additional statistical analysis of trends and bioclimatic suitability modelling to calculate and predict rates of decline and relative severity of degradation in selected ecosystem indicators. The evidence suggests an extreme risk of collapse (Critically Endangered) because of a prolonged and acute reduction in ice extent and changes in climatic conditions that are leading to the complete loss of ice mass. The ice substrate has declined 90% in the last 20 years, and observed acceleration of the rate of decline suggests it will probably disappear within the next 5 years. Loss of ice substrate will trigger an immediate loss of supraglacial, englacial and subglacial biotic compartments and initiate a decades-long succession of forefield vegetation. However, ongoing inventories of native biota and monitoring of ecosystem transitions can provide valuable insights and lessons for other ecosystems facing similar risks. The Red List of Ecosystems assessment protocol provides a useful framework for comparative analysis of cryogenic ecosystems.

Exploring the ecosystem structure of Zanzibar archipelago, trophic flows and fisheries interaction by using a mass balance modelling approach

The marine ecosystem in the Zanzibar archipelago has great economic and ecological importance due to high fisheries productivity. Yet, limited information is available on its status and possible impacts of fishing on it. Ecological models have been recognized as powerful tools for assessing the structure and function of ecosystems and forecasting the impacts of human activities on marine ecosystems. However, the use of ecological models in understanding ecosystems in the Western Indian Ocean region is inadequate. This study constructed a model to examine the ecosystem functioning, trophic flows, and fisheries interactions of the marine region affected by fishing gears in the Zanzibar archipelago using Ecopath with Ecosim software. The model includes 34 functional groups and 10 most common fishing gears, the input parameters were gathered from published reports. The ecosystem indicators showed that the Zanzibar ecosystem is in the early developmental stage contributed by strong fishing pressure that reduces the stability and maturity of the ecosystem. The total catch estimated at 5.538 t km−2year−1 showed a sign of the high exploitation rate of fisheries resources. The catch is dominated by higher trophic level fish as reflected in the mean trophic level of the fishery (3.2). The keystoneness analysis identified pelagic cephalopods, sharks and rays, turtles, tuna and tuna like species and toothed whales for their importance in the Zanzibar marine ecosystem. Fishing pressure on the ecosystem of Zanzibar is high, hence for better management, the focus should be for coral reef and pelagic species that are highly targeted by number of gears while protecting keystone species as important groups of the ecosystem. Fisheries harvest data collection system should be improved to collect information at species level and focus on all target species, including invertebrates.

Evaluating models of expert judgment to inform assessment of ecosystem viability and collapse.

Expert judgment underpins assessment of threatened ecosystems. However, experts are often narrowly defined, and variability in their judgments may be substantial. Models built from structured elicitation with large diverse expert panels can contribute to more consistent and transparent decision-making. We conducted a structured elicitation under a broad definition of expertise to examine variation in judgments of ecosystem viability and collapse in a critically endangered ecosystem. We explored whether variation in judgments among 83 experts was related to affiliation and management expertise and assessed performance of an average model based on common ecosystem indicators. There were systematic differences among individuals, much of which were not explained by affiliation or expertise. However, of the individuals affiliated with government, those in conservation and environmental departments were more likely to determine a patch was viable than those in agriculture and rural land management. Classification errors from an average model, in which all individuals were weighted equally, were highest among government agriculture experts (27%) and lowest among government conservation experts (12%). Differences were mostly cases in which the average model predicted a patch was viable but the individual thought it was not. These differences arose primarily for areas that were grazed or cleared of mature trees. These areas are often the target of restoration, but they are also valuable for agriculture. These results highlight the potential for conflicting advice and disagreement about policies and actions for conserving and restoring threatened ecosystems. Although adoption of an average model can improve consistency of ecosystem assessment, it can fail to capture and convey diverse opinions held by experts. Structured elicitation and models of ecosystem viability play an important role in providing data-driven evidence of where differences arise among experts to support engagement and discussion among stakeholders and decision makers and to improve the management of threatened ecosystems.

Habitat shifts of the vulnerable crinoid Leptometra phalangium under climate change scenarios

Crinoid beds of Leptometra phalangium (Müller, 1841) have been identified as sensitive habitats by the General Fisheries Commission for the Mediterranean (GFCM) due to their high vulnerability to bottom trawl fisheries. Poorly resilient to physical damage, L. phalangium has been listed as a potential indicator of Vulnerable Marine Ecosystems (VMEs) in the Mediterranean Sea. If fishing activities represent the main cause of habitat destruction for this species, the ongoing changes in climate conditions may rapidly exacerbate the process. In this study, we developed an ensemble Species Distribution Modeling framework to predict the potential habitat of L. phalangium for present-days in the Mediterranean Sea, and used the model to infer potential changes in its spatial distribution by 2050 under two different climate scenarios (IPCC Representative Concentration Pathways RCP2.6 and RCP8.5). True presence-absence records were used and correlated to a parsimonious set of environmental predictors considered as important drivers of benthic species distribution. In present conditions, L. phalangium seems to be widely distributed along the continental slopes of the western and central Mediterranean. This crinoid is often described as confined to the continental shelf-break (100–200 m), but our results show that it can be found over a wider depth range, between 100 and 500 m. Our predictions obtained for the mid-21st century indicate an important habitat loss for L. phalangium under future climate conditions, mainly in the central and southern basins. Declines of 50 to 70 % in its suitable habitat were predicted under RCP2.6 and RCP8.5 compared to present-day predictions. Climate refugia (i.e., areas where environmental conditions remain suitable for the species in the future) were restricted to the northwestern basin (e.g., Gulf of Lion, the Catalan Sea, the Balearic Sea, Ligurian Sea) and in the southern Adriatic Sea. Provided by a robust statistical framework, climate refugia predictions, along with uncertainty assessments, must support the identification of priority areas for the conservation of VME indicator species by governmental bodies and regional management organizations.

TERRITORY'S INNOVATION ECOSYSTEM: TOOLS FOR MANAGING DEVELOPMENT IN THE DIGITAL ENVIRONMENT

Purpose of the study. The innovation ecosystem of the territory was set to be the format of adaptation of the regional innovation system to the digital environment. This management model is rapidly developing, based on the generalization of empirical experience and multidirectional theoretical research. However, the diversity of management concepts requires building a logical relationship in terms of their application to ensure the successful operation of the ecosystem. Therefore, the purpose of this article is to substantiate management approaches and tools that contribute to improving the performance of the territory's innovation ecosystem in the digital environment. Conclusions. It is proposed to use the model for assessing its maturity as an information basis for managing the development of the territory's innovation ecosystem. A comparative analysis of approaches to the assessment of ecosystem maturity has been carried out. The key aspects of assessment are defined, and the expediency of building a structured dynamic complex of assessment models is substantiated. Promising models of ecosystem management (hybrid orchestration, multi-orchestration, multi-tier orchestration) are identified, which allow for non-hierarchical coordination of relatively independent partners in the process of creating a common value. The landscape of trajectories of possible development of the innovation ecosystem of the territory is constructed, which defines a controlled transition between possible states depending on the ratio of maturity levels of each of the participants and the ecosystem as a whole. The importance of taking risks into account for the possibility of realizing the optimal development trajectory is emphasized. A methodological approach to ecosystem risk assessment is proposed, combining: 1) a map of risks of the territory's innovation ecosystem, structured by life cycle stages and management level (an individual participant and the ecosystem as a whole), 2) risk ranking based on the assessment of their impact on the performance indicators of the territory's innovation ecosystem.

Leveraging ecological indicators to improve short term forecasts of fish recruitment

AbstractForecasting the recruitment of fish populations with skill has been a challenge in fisheries for over a century. Previous large‐scale meta‐analyses have suggested linkages between environmental or ecosystem drivers and recruitment; however, applying this information in a management setting remains underutilized. Here, we use a well‐studied database of groundfish assessments from the West Coast of the USA to ask whether environmental variables or ecosystem indicators derived from long‐term monitoring datasets offer an improvement in our ability to skilfully forecast fish recruitment. A secondary question is which types of modelling approaches (ranging from linear models to non‐parametric methods) yield the best forecast skill. Third, we examine whether simultaneous forecasting of multiple species offers an advantage over generating species‐specific forecasts. We find that for approximately one third of the 29 assessed stocks, ecosystem indicators from juvenile surveys yields the highest out of sample predictive skill compared to other covariates (including environmental variables from Regional Ocean Modeling System output) or null models. Across modelling approaches, our results suggest that simpler linear modelling approaches do as well or better than more complicated approaches (reducing out of sample Root Mean Square Error by ~40% compared to null models), and that there appears to be little benefit to performing multispecies forecasts instead of single‐species forecasts. Our results provide a general framework for generating recruitment forecasts in other species and ecosystems, as well as a benchmark for future analyses to evaluate skill. The most promising applications are likely for species that are short lived, have relatively high recruitment variability, and moderate amounts of age or length data. Forecasts using our approach may be useful in identifying covariates or mechanisms to include in operational assessments but also provide qualitative advice to managers implementing ecosystem based fisheries management.

Using joint species distribution modelling to predict distributions of seafloor taxa and identify vulnerable marine ecosystems in New Zealand waters

Effective ecosystem-based management of bottom-contacting fisheries requires understanding of how disturbances from fishing affect seafloor fauna over a wide range of spatial and temporal scales. Spatial predictions of abundance for 67 taxa were developed, using an extensive dataset of faunal abundances collected using a towed camera system and spatially explicit predictor variables including bottom-trawl fishing effort, using a Joint Species Distribution Model (JSDM). The model fit metrics varied by taxon: the mean tenfold cross-validated AUC score was 0.70 ± 0.1 (standard deviation) for presence–absence and an R2 of 0.11 ± 0.1 (standard deviation) for abundance models. Spatial predictions of probability of occurrence and abundance (individuals per km2) varied by taxon, but there were key areas of overlap, with highest predicted taxon richness in areas of the continental shelf break and slope. The resulting joint predictions represent significant advances on previous predictions because they are of abundance, allow the exploration of co-occurrence patterns and provide credible estimates of taxon richness (including for rare species that are often not included in more commonly used single-species distribution modelling). Habitat-forming taxa considered to be Vulnerable Marine Ecosystem (VME) indicators (those taxa that are physically or functionally fragile to anthropogenic impacts) were identified in the dataset. Spatial estimates of likely VME distribution (as well as associated estimates of uncertainty) were predicted for the study area. Identifying areas most likely to represent a VME (rather than simply VME indicator taxa) provides much needed quantitative estimates of vulnerable habitats, and facilitates an evidence-based approach to managing potential impacts of bottom-trawling.

Information and Logic Model Development Natural Engineering System Environmental Monitoring

Environmental monitoring is the main source of information on the current environment state and its changes associated with man-made and anthropogenic impact. The system of environmental monitoring of increased danger technogenic objects includes state observations of air and water environments, soil snow cover, as well as monitoring of flora and fauna, including biotesting and bioindication. An example of an integrated approach to organizing the environment conditionobserving system is environmental monitoring in the zone of chemical weapons destruction facilities. The results of monitoring activities are numerical arrays of quantitative indicators of the environment state related to specific monitoring objects: concentrations of pollutants in natural environments (air, water, and soil); indicators of the state of flora and fauna; bioindication results. The complexity of the monitoring result integrated analysis is primarily associated with the diversity of the structure and number of indicators for individual natural environments, different dimensions of indicators, as well as the sparseness of the matrices of data arrays. Taking into account the above-mentioned information, it is relevant to develop methods for storing and updating environmental monitoring data arrays, as well as algorithms for operational analysis of the results of monitoring studies. The paper presents an information and logical model that allows to form databases for storing and replenishing data on the state of the natural engineering system. The developed information and logic model is focused on building OLAP cubes for subsequent analysis of environmental monitoring data. The presented information and logical model is the main stage of developing a system for storing, updating and analyzing the results of environmental monitoring of the TCP. Systematization and structuring of indicators of natural environments and ecosystems makes it possible to formulate a number of practical tasks for assessing and predicting the state of the environment in the presence of technogenic impact.

A mapped dataset of surface ocean acidification indicators in large marine ecosystems of the United States

Mapped monthly data products of surface ocean acidification indicators from 1998 to 2022 on a 0.25° by 0.25° spatial grid have been developed for eleven U.S. large marine ecosystems (LMEs). The data products were constructed using observations from the Surface Ocean CO2 Atlas, co-located surface ocean properties, and two types of machine learning algorithms: Gaussian mixture models to organize LMEs into clusters of similar environmental variability and random forest regressions (RFRs) that were trained and applied within each cluster to spatiotemporally interpolate the observational data. The data products, called RFR-LMEs, have been averaged into regional timeseries to summarize the status of ocean acidification in U.S. coastal waters, showing a domain-wide carbon dioxide partial pressure increase of 1.4 ± 0.4 μatm yr−1 and pH decrease of 0.0014 ± 0.0004 yr−1. RFR-LMEs have been evaluated via comparisons to discrete shipboard data, fixed timeseries, and other mapped surface ocean carbon chemistry data products. Regionally averaged timeseries of RFR-LME indicators are provided online through the NOAA National Marine Ecosystem Status web portal.

THEORETICAL AND METHODOLOGICAL PRINCIPLES OF RESEARCH MONITORING OF THE EUROPEAN TOURISM ECOSYSTEM GREEN AND DIGITAL TRANSITION

Objective. The objective of the article is to generalize and analyze the theoretical and methodological basis of the green and digital transition of the European tourism ecosystem in the context of the implementation of the renewed industrial strategy of the EU. Methods. To achieve the outlined goal, such methods of empirical and theoretical research were used: literature review, theoretical generalization (to identify, collect and analyze modern research on the digitalization and greening of tourism), a descriptive method (to highlight the methodology of tracking ecological, digital and socio-economic aspects of tourism according to using harmonized data and indicators for the European tourism ecosystem), data visualization (for visualization and grouping of material). Results. Multi-annual EU Work Plan of the European Agenda for Tourism 2030 is characterized in five priority areas, namely: "Enabled policy and governance frameworks", "Green transition", "Digital transition", "Sustainability and inclusion", "Skills and transition support". The methodology of monitoring the green and digital transition of the European tourism ecosystem using the online tool "EU Tourism Dashboard" is highlighted. The indicators of the panel, which are grouped according to the priority areas: "Green transition", "Digital transition" as well as the blocks "Socio-economic indicators" and "Main descriptors of tourism" are summarized, the sources of information for the calculation of indicators are highlighted. The main tools for financing the green and digital transition and increasing the sustainability of EU tourism are substantiated, namely: private investments and loans, sources of EU funding for tourism, EU technical support for the transformation of the tourism ecosystem, programs at the national and regional levels within the framework of cohesion policy.

Soil functional indicators in a semi-arid environment change patchy under the influence of shrub species

The presence of mountain shrublands within semi-arid regions is of great importance due to their function in the preservation of water and soil resources and their contribution to the conservation of biodiversity. The study of the effects of semi-arid highland shrubs on soil functional indicators has not yet received sufficient attention. To fill this knowledge gap, we conducted a study to investigate the effects of different shrub species specifically Carpinus orientalis Miller, Berberis integerrima Bunge, Crataegus microphylla C. Koch, Prunus spinosa L., and Rhamnus pallasii Fisch. and C. A. Mey) on soil physical, chemical, and biological properties. For this study, 15 individuals of each of the above-mentioned shrub species were selected. Soil samples were collected at a depth of 10 cm and an area of 30 cm × 30 cm beneath the canopy of these particular species. The examination of the influence of various independent factors on the variability of soil microclimate and biological characteristics indicates that the presence of shrub covers is the primary determinant of changes in water content, fauna and microflora population, microbial respiration and biomass, whereas soil temperature, MBN/MBP and qCO2 are more affected by season factor. Based on the biological fertility index, all the studied shrub covers are in the medium class with the maximum score under Carpinus shrub. Results indicate that soil biological characteristics play a more significant role in distinguishing in terms of soil functional indicators. Based on hot spots output, the soil functional indicators decreased in order of Carpinus > Crataegus > Berberis > Prunus > Rhamnus. As a conclsuion, Carpinus create hot spots (areas) of soil functional indicators in the mountainous ecosystems that claimed the potentially role of nutrient-rich soil cycles.

Deep learning insights into suspended sediment concentrations across the conterminous United States: Strengths and limitations

Suspended sediment concentration (SSC) is a crucial indicator for aquatic ecosystems and reservoir management but is challenging to predict at large scales. It is unclear whether SSC is predictable using macroscopic environmental attributes and forcings. This study tested the feasibility of deep-network-based models to predict daily SSC at basin outlets given only basin-averaged forcings, readily-available physiographic attributes, and streamflow (from observation or model). We trained long short-term memory (LSTM) deep networks both separately for each of the 377 sites across the conterminous United States (CONUS) (termed “local models”), and on all the sites collectively (Whole-CONUS). The Whole-CONUS and local models presented median coefficient of determination (R2) values of 0.63 and 0.52, respectively. This comparison agrees with previously acknowledged “data synergy” effects for LSTM models, where more data from more sites can help improve predictions overall. Furthermore, the continental-scale analysis provided a wealth of insights about SSC patterns. Both local and Whole-CONUS models tended to be more successful where SSC-streamflow correlations (Rs-q) were high − typically in the humid Eastern US − and with lower SSC. Low Rs-q basins were often found in the arid Southwest with higher SSC. The highly-nonlinear SSC-streamflow relationships seem related to seasonality, basin size, and heterogeneity of land cover and rainfall within the basins, suggesting these basins need to be simulated at higher spatial resolution and may require additional inputs related to SSC induced by seasonality. The Whole-CONUS model also performed well for spatial extrapolation (basins not included in the training dataset, median R2 = 0.55), supporting large-scale modeling efforts. These state-of-the-art results using only minimal inputs suggest data-driven approaches can exploit the natural coevolution of sediment processes and the environment to support sediment modeling.

Spatial pattern and attribution of ecosystem drought recovery in China

Under the backdrop of global climate change, frequent drought events pose a severe and persistent threat to the normal functioning of ecosystems. Drought recovery time, the time it takes for an ecosystem to return to its pre-drought state, is a crucial metric for drought impact and ecosystem stability. However, most previous studies have focused on the drought recovery time of the region as a whole or a specific type of ecosystem. The differences in drought recovery time among different ecosystems and their driving factors are largely unknown. Therefore, this study utilizes multi-source fused Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage (TWS) data and Gross primary productivity (GPP) data to construct drought and ecosystem indicators. Subsequently, the drought recovery time under different climates and ecosystem types in China from 2002 to 2017 is analyzed. Finally, the factors influencing the differences in drought recovery time among ecosystems are discussed in detail. The results indicate that there is significant spatial heterogeneity in drought recovery time among ecosystems in China, with the forest ecosystems in the Northeast and Southwest regions having the longest recovery time. The recovery time of forest ecosystems (4.32 months) is longer than that of cropland (4.07 months) and grassland (3.79 months) ecosystems, but there are also significant differences in the recovery time for the same ecosystems under different climate types. The differences in drought recovery time among different ecosystem types are primarily influenced by temperature and precipitation during drought recovery, and the response of ecosystems to drought. These results provide scientific support for adopting differentiated management strategies for different ecosystem types to cope with future climate change.