Table of contents

Tropical peatland is a complex and globally important wetland ecosystem, storing an enormous amount of the Earth's terrestrial carbon from centuries of organic material accumulation. In this ecosystem, peat swamp forests developed over an ombrogenous peat where hydrology influences its physico-chemical properties, one of which is fluctuation of the peat surface. While several studies of tropical peatland surface fluctuation have been reported, most are based on relatively short measurement periods or focused on drained areas. Hence, the objective of this study is to determine the long-term dynamics of peat surface fluctuation from an undrained peat swamp forest in relation to its water table depth. Peat surface level, water table, and rainfall were measured monthly at three experimental sites in a peat swamp forest in Sarawak, Malaysia over a period of about 10 years (2011–2020). The sites were different in soil structure and vegetation community; namely mixed peat swamp, Alan Batu, and Alan Bunga forests. Throughout the measurement period the peat surface in all sites exhibited consistent oscillating movement that generally follow the fluctuation of water table, with swelling and subsidence occurring after water table increase following high rainfall and receding water during dry spells respectively. Positive linear relationships were also found between peat surface level and water table (p < 0.05). Both the surface level and water table at all sites fell to their lowest during an intense dry period in 2019. Surface fluctuation at the Alan Batu site was most affected by seasonal changes in water table, which may be due to presence of vacant zones in the peat profile.

Soil fumigation is a well-established method for controlling soil-borne diseases. Better quantification of soil available nitrogen and trace elements may provide recommendations on the safe and effective application of soil fumigants. The effects of four fumigants on the available soil nitrogen and trace elements (including Mn, Co, Ni, Cu, Zn and Cd) were investigated in the laboratory incubation with agricultural soil. The results showed that soil fumigation increased soil mineral nitrogen and available Mn, Co, Ni. A significant increase of soil NH₄⁺-N and available Mn was observed in all fumigation treatments. Chloropicrin treatments showed the largest increase of soil NH₄⁺-N. In addition, chloropicrin and dazomet increased Mn and Co to a greater extent than dimethyl disulfide and 1,3-dichloropropene in soil. Available Mn content after chloropicrin fumigation has increased by 2.2 times compared with control treatments, and available Co content in dazomet treatments is also 2.8 times more than untreated control. In fumigated soil, the steady increase of soil available elements is beneficial to nutrient uptake by plants. Soil fumigation created an activation effect on soil mineral nitrogen and available trace elements, which may increase their bioavailability to plants and promote plant growth.

The SARS-CoV-2 pandemic dramatically shifted daily life in the United States during 2020. The release of high spatial and temporal resolution cell phone data presented a unique opportunity to study the correlation between mobility, electricity use, and tropospheric NO₂. While many studies have identified trends of changes in air pollution in locations with reduced mobility due to Covid-19-related restrictions, these studies fall short of assessing whether these trends are statistically significant. Here we analyze if, and how much, mobility, nighttime light and NO₂ concentrations correlate. We analyze seven geographically diverse metropolitan areas in the United States (Atlanta, Austin, Boston, Denver, Los Angeles, New York City, Phoenix) in the first half of 2020. Using statistical tests of significance, we find that there is a strong correlation between NO₂ levels and nighttime light during February-July 2020 in all urban centers. Mobility and NO₂ are correlated strongly in four of the seven urban areas investigated. Together, these results suggest that policies aimed at limiting anthropogenic emissions from fixed (power generation) and mobile (traffic) NO₂ sources have an effect on air quality but additional factors should also be considered.

We tested the capabilities of urban greenhouse gas (GHG) measurement networks to detect abrupt changes in emissions, such as those caused by the roughly 6-week COVID-19 lockdown in March 2020 using hourly in situ GHG mole fraction measurements from six North American cities. We compared observed changes in CO₂, CO, and CH₄ for different mole fraction metrics (diurnal amplitude, vertical gradients, enhancements, within-hour variances, and multi-gas enhancement ratios) during 2020 relative to previous years for three periods: pre-lockdown, lockdown, and ongoing recovery. The networks showed decreases in CO₂ and CO metrics during the lockdown period in all cities for all metrics, while changes in the CH₄ metrics were variable across cities and not statistically significant. Traffic decreases in 2020 were correlated with the changes in GHG metrics, whereas changes in meteorology and biology were not, implying that decreases in the CO₂ and CO metrics were related to reduced emissions from traffic and demonstrating the sensitivity of these tower networks to rapid changes in urban emissions. The enhancements showed signatures of the lockdowns more consistently than the three micrometeorological methods, possibly because the urban measurements are collected at relatively high altitudes to be sensitive to whole-city emissions. This suggests that urban observatories might benefit from a mixture of measurement altitudes to improve observational network sensitivity to both city-scale and more local fluxes.

The rise of spatial heterogeneity in surface temperature (T_s) over India cannot be explained by only the direct effect of enhanced greenhouse gas forcing. Here we propose an analytical framework to estimate the impact of the net cloud radiative forcing (CRF) trend on the observed T_s trend for the duration of 2000–2019. The cloud sensitivity () estimated from the satellite observations vary in the range −0.180 to 0.241 K per W m⁻² across the seven T_s homogeneous zones in India. Net average (±1σ) CRFs over the Indian landmass during 2000–2019 in the shortwave and longwave are −15.5 ±11.3 and 12.4 ± 7.4 W m⁻², −28.1 ± 22.4 and 24.6 ± 9.8 W m⁻², −75.1 ± 20.4 and 55.0  ± 13.8 W m⁻² and −23.3 ± 14.9 and 21.6 ± 14.8 W m⁻², respectively for the winter (Dec-Feb), pre-monsoon (Mar–May), monsoon (Jun–Sep) and post-monsoon (Oct–Nov) seasons. We find that in some of the T_s homogeneous regions, seasonal T_s trends are suppressed, whereas in some other T_s homogeneous regions, seasonal T_s trends are accelerated by the net CRF. Our framework can be useful in studying the role of clouds in observed surface warming trends.

Globally, wetlands in many places have been at risk by natural and anthropogenic threats including climate change and land use and land cover change. Because of their significant contribution to providing various ecosystem services, understanding the vulnerability to various threats and the effects of their loss on various scales and aspects is an imminent issue for wetland conservation. On a landscape scale, these wetlands can be distributed in a variety of forms (e.g., by size, bathymetry, geology, etc.) and interconnected by dispersal of inhabiting species. Here, we use the network modeling approach associated with wetland hydrology to analyze potential shifts in an ecological network caused by hydro-climatic and anthropogenic forcings. We focus on the role of small wetlands which are often easily ignored in assessing landscape function because of their minor occupancy in an overall area. Specifically, by manipulating the hydrological status of the small wetlands, an area of which only contributes 0.82%, we observed the degrading effects on the characteristics (mean degree and network efficiency) of resulting ecological networks. Our results suggest that wetland size does not necessarily correlate with network centralities, and the loss of small wetlands acting as high centrality nodes induce a critical regime shift in network structure and function. Although hypothetically tested, because of their high sensitivity to hydro-climatic conditions and vulnerability to land use and land change along with climate change effects, the persisting functional loss of small wetlands is highly expected which eventually leads to trapping in the undesirable state of an ecological network. Our study is expected to provide a framework to evaluate the importance of small wetlands that can be easily ignored from an area-based point of view in a landscape.

Climate change, manifest via rising temperatures, extreme drought, and associated anthropogenic activities, has a negative impact on the health and development of tropical dryland forests. Southern Africa encompasses significant areas of dryland forests that are important to local communities but are facing rapid deforestation and are highly vulnerable to biome degradation from land uses and extreme climate events. Appropriate integration of remote sensing technologies helps to assess and monitor forest ecosystems and provide spatially explicit, operational, and long-term data to assist the sustainable use of tropical environment landscapes. The period from 2010 onwards has seen the rapid development of remote sensing research on tropical forests, which has led to a significant increase in the number of scientific publications. This review aims to analyse and synthesise the evidence published in peer review studies with a focus on optical and radar remote sensing of dryland forests in Southern Africa from 1997–2020. For this study, 137 citation indexed research publications have been analysed with respect to publication timing, study location, spatial and temporal scale of applied remote sensing data, satellite sensors or platforms employed, research topics considered, and overall outcomes of the studies. This enabled us to provide a comprehensive overview of past achievements, current efforts, major research topics studies, EO product gaps/challenges, and to propose ways in which challenges may be overcome. It is hoped that this review will motivate discussion and encourage uptake of new remote sensing tools (e.g., Google Earth Engine (GEE)), data (e.g., the Sentinel satellites), improved vegetation parameters (e.g., red-edge related indices, vegetation optical depth (VOD)) and methodologies (e.g., data fusion or deep learning, etc.), where these have potential applications in monitoring dryland forests.

Ecotourism is gaining traction as a veritable approach to biodiversity conservation and livelihood sustenance in sub-Saharan Africa (SSA). The success of this approach rests in part, on the governance architecture of most states in SSA. However, empirical evidence on this subject is fragmented. This shades possibilities to frame conceptual and methodological questions to advance ecotourism governance literature. This study undertakes a review of the literature on ecotourism governance in SSA, using the PROFOR governance assessment framework as analytical lens. Content analysis, descriptive and inferential statistical methods were employed to analyse 54 published empirical articles on ecotourism governance. The results indicated that studies conducted in Southern Africa (38%) and East Africa (30%) account for close to 70% of the literature on ecotourism governance in SSA. Participation was the most studied ecotourism governance principle (96%), while efficiency was the least (15%). Kruskal-Wallis test statistic showed no significant variation in the study of ecotourism governance principles in the different sub regions of SSA. Methodologically, most of the studies have either employed qualitative (50%) or quantitative (33%) approaches, with few studies employing mixed methods (17%). Future studies need to prioritize mixed-method approaches to study principles such as efficiency and equity in the analysis of ecotourism governance. Equally more empirical research studies should be undertaken in the West and Central Africa sub-regions in order to paint a better picture of the state of ecotourism governance across sub-Saharan Africa in general.

Runoff prediction is an important basis for rational allocation of basin water resources and plays a very important role in regional water resources management. In this study, a hybrid short-term runoff prediction model based on long short-term memory network (LSTM), improved Harris hawks optimization algorithm (IHHO) and optimal variational mode decomposition (OVMD) are proposed. Firstly, the original runoff data is decomposed into several sub-modes by OVMD, and then the sub-modes are reconstructed by phase space reconstruction (PSR). Secondly, the Harris hawks optimization algorithm is improved by the chaos map and the hill climbing algorithm. Then, the LSTM model is established for each sub-mode, and the improved Harris hawks optimization algorithm (IHHO) is used to optimize the number of hidden layer neurons and learning rate of the LSTM network. Finally, the results of all sub-modes are combined to obtain the finally runoff prediction result. In this study, seven control models are constructed and compared with the proposed model to verify the effectiveness of the proposed model in runoff prediction.

Global climate change will change the temporal and spatial distribution of precipitation, as well as the intensity and frequency of extreme precipitation. The Yangtze River basin is one of the world's largest basins, and understanding the future precipitation changes should be vital to flood control, water resources supply, and hydropower electricity generation in this basin. In this study, projected future characteristics of precipitation are analyzed in the upper Yangtze river basin (UYRB). To this end, based on the observed data from national meteorological stations, the bias correction spatial downscaling (BCSD) of five models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) is carried out. Then, based on the results of multi model ensemble (MME), we find that, relative to the historical period (1988–2014), the mean annual precipitation in the whole UYRB during 2015–2064 increases by 4.23%, 1.11%, 1.24% under SSP1-2.6, SSP2-4.5, SSP5-8.5, respectively, and it increases more in the long term (2040-2064) than that in the near term (2015–2039). Under SSP1–2.6, the precipitation will increase more significantly, which means lower emission of aerosols and greenhouse gases may increase the risk of flood disaster in the future over the UYRB. Interdecadal precipitation variability is more intense than interannual precipitation variability. Future precipitation changes in four seasons are spatially heterogeneous under three scenarios. Three extreme precipitation indices, including R95p, Rx1day and R10 mm, generally increase in the UYRB. R95p and Rx1day increase more in the WR and YBYCR basins with relatively high mean annual precipitation than that in other three sub-basins. R10 mm changes slightly in all sub-basins. The results reveal that the lower region of the UYRB may face greater risk of extreme precipitation. This study provides a timely updated finding about future changes in precipitation in the UYRB based on more accurate climate projections and ground-based observation.

Assessing the effectiveness of surface ozone air quality regulations is complicated by non-local sources and inhibited by sparse in situ observational networks. Here, we harness satellite measurements of ozone and its precursors over Asia to identify the Asian contribution to background ozone over the United States (US) from 2006-2016 using a state-of-the-art chemical data assimilation system. Our results indicate that Chinese NOx emissions reached an apex in 2011, increasing daily average ozone concentrations over Los Angeles by up to 3.8 ppb at 750 hPa and 0.7 ppb at the surface in 2012 relative to 2006, with somewhat larger impacts in the high altitude regions of the Western US (assuming constant VOC emissions). Decreasing Chinese NOx emissions after 2011 contributed to a reduction of similar magnitude in Western US background ozone from 2012-2016. We also assess the impact of direct assimilation of satellite-based tropospheric ozone profile measurements over Asia and find that it substantially modifies global ozone, including US surface concentrations. These results suggest that measurements of both ozone and its precursors over Asia play a substantial role in evaluating not only local but also global air quality. The simultaneous assimilation of ozone and its precursors proffers a powerful way to constrain the vertical profile of ozone and improve understanding of ozone variations. In the future, a more extensive satellite observing system has great potential to better constrain both local pollution and background ozone globally when employed in conjunction with chemical data assimilation.

With the ongoing global warming, the occurrence and amplitude of extreme weather events have increased over the West African Sahel. The increasing frequency of heavy rain events, can negatively affect the lowland crops' growth and production. Two-season field experiments were conducted near Ouagadougou (Burkina Faso) to test the effects of temporary flooding and surface water stagnation on maize (Zea mays L.) growth and productivity. The treatments were organized into a split-split plot design. Three factors were monitored, including aboveground flooding levels (i.e., 0 cm, 2–3 cm, and 7–8 cm), flooding duration (i.e., three days and six days), and growth stages (i.e., six-leaf stage (V6), tasseling stage (VT) and milky stage (R3)). Optimal crop management was practiced to Obatanpa cultivar planted during the rainy season and flooding was induced by over-irrigation. The results show that three days and six days of flooding, reduced grain yield by at least 35% when they occurred at the tasseling stage. Only 4–6 days of flooding reduced grain yield by 21% at the six-leaf stage. Further scrutiny, using the stress day index (SDI), revealed that the penalty on yield increases exponentially under flooding conditions as the value of the stress day index increases. Considering the new characteristics of the rainfall regime in the West African Sahel, dominated by a high frequency of heavy rain events and wet spells, temporary floods, and water stagnation are tremendously contributing to yield loss of on-farm maize. As the region's climate changes, we hypothesize that excess water stress will become the next cause of food insecurity in the area.

Under calm and steady weather conditions with low wind speeds, turbulent intermittency frequently occurs in the atmospheric boundary layer (ABL), which can significantly weaken the turbulent diffusion of matter and energy between the surface and atmosphere. The turbulence barrier effect is defined as the phenomenon in which turbulence may disappear at certain heights, and during periods of heavy haze, creating what can seem like a barrier layer that hinders vertical transmissions. Although the turbulence barrier effect can explain the physical mechanisms behind the rapid accumulation of PM_2.5 (fine particulate matter with diameters smaller than 2.5 μm) and the influence of turbulent diffusion conditions on the vertical distribution of PM_2.5, more direct perspectives such as turbulent flux is still required for quantitative verification. Due of challenges in the acquisition of PM_2.5 turbulent flux, carbon dioxide (CO₂), which has relatively mature flux acquisition technology, was used as a substitute means of verifying and quantifying this phenomenon. The turbulence data collected during heavy haze events, at from five levels of a 255 m meteorological tower located in Tianjin, were analyzed and used to quantitatively verify the influence of the turbulent barrier effect on PM_2.5. The results also revealed that the vertical changes in the turbulent barrier effect were consistent with those of the concentrations and flux of CO₂. This means that this knowledge about the turbulent barrier effect can be extended to other mass-transfer processes. The analysis also found that the proportion of counter-gradient transport increases when the occurrences of the turbulent barrier effect are frequent. This work validates the presence of the turbulent barrier effect and is an important foundation for its future parameterization, which will help to accurately identify the matter transport processes in the stable boundary layer and under extreme weather conditions, such as intense pollution events.

As a large agricultural country, China should pay more attention to the carbon emission in agriculture in the context of achieving the goal of 'peak carbon and carbon neutrality'. This paper measures the agricultural carbon emissions and analyzes its temporal characteristics in Jiangxi from 2000 to 2019, examines the influencing factors using Kaya's constant equation. The study shows that: (1) from 2000 to 2019, Jiangxi's agricultural carbon emissions show a general upward trend, but the growth rate tends to slow down gradually; (2) the factors of agricultural production efficiency, agricultural industrial structure and agricultural labor force have a negative driving effect on carbon emissions, while the factors of agricultural economic development and urbanization level have a positive driving effect on agricultural carbon emissions.

The Tibetan Plateau (TP) plays a critical role in Earth's climate system and is highly sensitive to global warming. However, comprehensive analysis of the interaction between various climatic factors and vegetation growth across the TP is still limited. Using daily normalized difference vegetation index (NDVI) series interpolated from the 16-day satellite measurements and climatic data during 1982–2018, we investigated the spatiotemporal changes in growing season NDVI (NDVI_GS) and associated climatic drivers over the TP and analyzed the responses of NDVI_GS to climatic drivers for different vegetation types. Our results show that NDVI_GS of the TP as a whole exhibits a significant rising trend (0.0011 year⁻¹; P < 0.01) from 1982 to 2018. However, trends in NDVI_GS show apparent spatial heterogeneity over the TP with higher growth rates in forests (trend = 0.012 de⁻¹; P < 0.01) and shrubs (trend = 0.009 de⁻¹; P < 0.01) in the east and southeast than in alpine steppe (trend = 0.003 de⁻¹; P < 0.01) and alpine meadow (trend = 0.006 de⁻¹; P < 0.01) in the west and north. Air temperature, precipitation, and VPD serve as the dominant climatic factor affecting the NDVI_GS trends in 62%, 19%, and 12% of the TP, respectively. Additionally, climatic factors show differential impacts on NDVI_GS among different vegetation types. Air temperature has a predominantly positive correlation with NDVI_GS for all vegetation types, while precipitation has a negative impact on plant growth in the eastern humid forest region but a generally positive impact in the other areas. Our results also highlight that the effect of VPD on NDVI_GS varies among different vegetation types. These findings contribute to a systematic understanding of the possible mechanisms underlying the responses of vegetation growth to various climatic drivers across the TP.

The use of grass cultivation in the restoration of degraded ecosystems is widespread, in order to reveal the effect of different grass cultivation patterns on the community structure of soil mites in the integrated management area of rocky desertification. In April and July 2021, a total of 2782 soil mites belonging to 3 orders, 42 families, and 73 genera were captured from three typical grass cultivation, Lolium perenne, Dactylis glomerata and Trifolium repens, and the traditional Zea mays as a control sample, in the integrated management area of potential-light rocky desertification in the karst plateau mountains of Salaxi, Guizhou Province. The soil mite community structure was analyzed using number of taxa (genera), number of individuals, diversity index, community similarity index, MI index of predatory mites, and MGP analysis of oribatid mite ecological taxa. The results showed that: (1) The summer has a more prosperous composition and diversity of soil mites across habitats, while the dominant genera of soil mites show a differential distribution across habitats; the number of soil mite genera, individuals and individual densities was significantly higher in the three grass cultivation habitats than the Zea mays habitat, and surface aggregation of soil mites is more pronounced. (2) There were differences in soil mite community structure among the three grass cultivation patterns, with a regularity of Lolium perenne > Dactylis glomerata > Trifolium repens in the composition of soil mites genera and the number of individuals, and the proportion of shared genera was not high. (3) The diversity of soil mites varied according to the environment and season, with the highest diversity of soil mites in Lolium perenne habitats; both the community similarity analysis and the CCA analysis showed that Lolium perenne and Dactylis glomerata habitats had the highest similarity of soil mite communities (4) Predatory mites were dominated by r-selective ecotypes, and oribatida were dominated by O and P type ecotypes under the three habitat patterns. (5) Soil nutrient conditions were more favorable in Trifolium repens habitats, while soil water content was higher in Dactylis glomerata environment, and correlation analysis indicated that TK was a key environmental factor influencing soil mite community composition and diversity. Based on the above results, it is further elaborated that the artificial grass restoration model not only improves the nutrient supply of soil N, P, and K but also significantly increases the composition and diversity of soil mite species, which is beneficial to the restoration of soil mite communities and is very helpful in terms of achieving self-sustainability and restoration of soil functions in rocky desertification areas.

The delineation of ecological red lines (ERLs) is of great significance to ensure ecological security and improve the quality of the ecological environment. However, most of the current ERL delineation only focuses on the natural environment, lacks consideration of the spatial pattern of ecological landscape, and there is no scientific standard for ERL delineation. From the perspective of ecological security patterns (ESPs), this study proposed an ERL delineation method based on gray relational analysis (GRA) and the minimum cumulative resistance (MCR) model. Then taking Shawan District as an example, the effectiveness of the method was verified by comparing the delineated ERL in this study with that of traditional evaluation methods. A total of 321.43 km² of ERL was delineated, accounting for 67.75% of the total area of Shawan District, including 69.70 km² of Grade I ERL zones, 251.73 km² of Grade II ERL zones. The results show that ERLs based on GRA and MCR exhibit better connectivity and contain more ecological land. The method has guiding significance and reference value for regional ERL delineation, which is conducive to the decision-making of environmental protection departments. Conservation measures for different landscapes are more targeted to constrain people's productive activities precisely.

The coronavirus pandemic has severely impacted our day-to-day activities and brought about significant change in all major sectors, especially surface passenger transport. Lockdowns and stay-at-home restrictions have significantly reduced energy demand and consequently CO₂ emissions of surface passenger transport. The change in CO₂ emissions is calculated from near-real-time activity change data as a function of 3 confinement levels. The activity change and related emission trends reflect changes in the mode of transport during different waves, this can be used to understand mobility trends and patterns when stringent measures are imposed. Consequently, constructive use of this data can help prepare and develop the transport sector in case of another epidemic outbreak or other unprecedented calamities and to build a resilient transport infrastructure post-COVID-19. This study estimates and analyzes the changes in CO₂ emissions associated with the public (bus and rail) and private surface passenger transport from March 1^st, 2020 to Jan 31^st, 2021 in 21 countries. The research period covers the 1^st and the 2^nd waves of COVID-19 in these countries. A higher activity reduction and consequently CO₂ emission reduction is displayed during the 1^st wave compared to the 2^nd for most countries despite implementing stringent measures during both waves. This is in line with countries adapting to the 'new normal' and restarting socio-economic activities. Similarly, public transport recovery is slower than private transport recovery, making it essential to focus on reinforcement and adaptation of public transport infrastructure for the future. The results show that a cumulative 510 Mt CO₂ has been reduced over 11 months in 21 countries, compared to pre-pandemic levels. This reduction brings about a 6% drop in transport CO₂ emissions and a 1.5% drop in global CO₂ emissions. This analysis sheds light on mobility trends and travel behavior of surface passenger transport modes and related CO₂ emissions in different countries which can be used to exemplify the path to recovery based on near-real-time data.

The River Ganga is reeling from pressures of rapid urbanization and resulting anthropogenic forcings. In this study, phytoplankton community assemblages were deduced from the Dakshineswar site located in the lower stretch of River Ganga to quantify and understand the health status of this river. Surface water samples were collected from six pre-defined stations of Dakshineswar spanning across monsoon and post-monsoon seasons of 2019 and 2020. Stations were categorized into point source and surface water based on proximity to municipal discharges. Measurement of in situ environmental parameters showed significant differences in values for dissolved oxygen, total dissolved solids, electrical conductivity and suspended particulate matter between the two seasons during the study period. In particular, concentrations of dissolved nitrate and silicate were found to be higher in point source stations compared to surface water stations. The concentration of Chlorophyll-a (Chl-a) was found to be higher in post-monsoon compared to monsoon seasons. Phytoplankton communities consisted of 23 diatom taxa and 14 green algal taxa and they showed distinct seasonal and spatial variations in the study site. Phytoplankton communities were dominated by diatom taxa namely Aulacoseira, Bacillaria, Coscinodiscus, and green algal taxa such as Ulothrix, Chlorella, and Scenedesmus. There was a dramatic increase in cell abundance of Aulacoseira spp. in post-monsoon seasons indicating a bloom-like scenario. Moreover, the rapid increase in cell abundance of Aulacoseira spp. also coincided with an increase in Chl-a and a sharp fall in the concentration of dissolved silicate. Some of the encountered phytoplankton taxa such as Tetraedron, Cosmarium, Nitzschia and Scenedesmus showed strong co-occurrence patterns indicating possible association at ecological scales. Four distinct clusters were formed in nMDS ordination plot based on the influences of environmental variables on encountered phytoplankton taxa. Network analysis revealed evidence of co-occurrence patterns between several diatoms and green algal taxa.

Understanding drivers of water quality in local watersheds is the first step for implementing targeted restoration practices. Nutrient inventories can inform water quality management decisions by identifying shifts in nitrogen (N) and phosphorus (P) balances over space and time while also keeping track of the likely urban and agricultural point and nonpoint sources of pollution. The Chesapeake Bay Program's Chesapeake Assessment Scenario Tool (CAST) provides N and P balance data for counties throughout the Chesapeake Bay watershed, and these data were leveraged to create a detailed nutrient inventory for all the counties in the watershed from 1985–2019. This study focuses on three primary watershed nutrient balance components—agricultural surplus, atmospheric deposition, and point source loads—which are thought to be the leading anthropogenic drivers of nutrient loading trends across the watershed. All inputs, outputs, and derived metrics (n=53) like agricultural surplus and nutrient use efficiency, were subjected to short- and long-term trend analyses to discern how sources of pollution to surface water have changed over time. Across the watershed from 1985–2019, downward trends in atmospheric deposition were ubiquitous. Though there are varying effects, long-term declines in agricultural surplus were observed, likely because nutrients are being managed more efficiently. Multiple counties' point source loads declined, primarily associated with upgrades at major cities that discharge treated wastewater directly to tidal waters. Despite all of these positive developments, recent increases in agricultural surpluses from 2009–2019 highlight that water quality gains may soon be reversed in many agricultural areas of the basin. Besides tracking progress and jurisdictional influence on pollution sources, the nutrient inventory can be used for retrospective water quality analysis to highlight drivers of past improvement/degradation of water quality trends and for decision makers to develop and track their near- and long-term watershed restoration strategies.