Seasonal Coverage Research Articles

Prevalence of asymptomatic parasitaemia among household members of children under seasonal malaria chemoprevention coverage and comparison of the performance of standard rapid diagnostic tests versus ultrasensitive RDT for the detection of asymptomatic parasitaemia in Nanoro, Burkina Faso

Asymptomatic carriers of Plasmodium falciparum represent important parasite reservoirs maintaining malaria transmission in the community. This study aimed on the one hand to screen the other household members living with children under seasonal malaria chemoprevention (SMC) coverage in order to determine the level of malaria infection in this population and on the other hand to determine the appropriate type of rapid diagnostic test (RDT) for this screening to detect these asymptomatic carriers in the community. During the 2022 SMC campaign (July to October), a cross-sectional survey was carried out in 745 participants who were screened by ultrasensitive rapid diagnostic test (usRDT), standard rapid diagnostic test (rRDT) and microscopy. Out of them, 395 had microscopy results available and were included in the data analysis. The prevalence of asymptomatic carriers of asexual forms of Plasmodium falciparum was 26.58% (105/395) while sexual forms were found in 5.32% (21/395) of the study population. Children from 5 to 15 years had the highest prevalence of P. falciparum asexual forms 35.76% (59/165) compared with older participants. Malaria positivity rate for rRDT and usRDT was 29.40% (219/745) and 40.49% (305/745) respectively. The usRDT had a higher sensitivity than the rRDT (72.38% (95% CI 62.8–80.66) vs. 60.95% (95% CI 50.94–70.33)). In terms of specificity, rRDT had a higher specificity 82.41% (95% CI 77.53–86.62) versus 69.66% (95% CI 64.01–74.89) for usRDT. This study reports a high prevalence of parasite carriers in household members of children under SMC coverage in Nanoro, Burkina Faso. In conclusion, usRDT seems more appropriate for strategies based on detection and treatment of parasite carriers within the community.

Winter arctic sea ice volume decline: uncertainties reduced using passive microwave-based sea ice thickness

Arctic sea ice volume (SIV) is a key climate indicator and memory source in sea ice predictions and projections, yet suffering from large observational and model uncertainty. Here, we test whether passive microwave (PMW) data constrain the long-term evolution of Arctic SIV, as recently hypothesized. We find many commonalities in Arctic SIV changes from a PMW sea ice thickness (SIT) 1992-2020 time series reconstructed with a neural network algorithm trained on lidar altimetry, and the reference PIOMAS reanalysis: relatively low differences in SIV mean (4615 km3, 37%), SIV trends (46 km3, 17%), and phased variability (r2=0.55). Key to reduced differences is the consistent evolution of many SIV contributors: seasonal and perennial ice coverage, their SIT contrast, whereas perennial SIT provides the largest remaining uncertainty source. We argue that PMW includes useful SIT information, reducing SIV uncertainty. We foresee progress from sea ice reanalyses combining dynamical models and data assimilation of PMW SIT estimates, in addition to the already assimilated PWM sea ice concentration.

Evolving Arctic maritime hazards: Declining sea ice and rising waves in the Northwest Passage

The ongoing and projected retreat of Arctic sea ice has garnered international interest toward the utilization of Arctic maritime corridors for shipping, tourism, and development. Yet, with potential for increasing traffic in Arctic regions, it's important to consider additional environmental variables affected by climate change which may threaten maritime operations. Here, we use four climate model projections to produce ocean wave simulations and investigate the future magnitude and seasonality of sea ice risk coupled with wave hazards. Analyzing the potential 5 mo shipping season spanning July to November along the Northwest Passage maritime route between 2020 and 2070, our results show a substantial decline in sea ice risk over the analysis time period, resulting in near open-water conditions along the route for a 5 mo period by 2070. However, as seasonal ice coverage retreats, there is a significant upward trend in wave heights along the route during July and November, with the timing of the greatest wave height shifting away from September toward later in the season. This result is pertinent as the possibility of seasonally unprecedented extreme waves coupled with subfreezing late fall temperatures makes for an especially hazardous environment, thus emphasizing the importance of considering the interaction between evolving sea ice and interdependent hazards when predicting the risks and challenges faced by Arctic maritime operations.

Floodplain carbon dioxide emissions strongly exceed those of the main river stem: A case study of the Ob River, western Siberia

The importance of floodplains in carbon (C) evasion from the lotic systems is especially important in continental plains of low runoff such as the organic-rich Western Siberian Lowland (WSL). To quantify the relative importance of the floodplain compared to main stem CO2 emissions, we monitored a large region of the Ob River’s middle course (permafrost-free zone) over 3 months from spring to summer. We calculated seasonal water coverage using remote sensing, GIS and hydrologically-based approaches and measured CO2 emissions using floating chambers. There was a strongly pronounced seasonality in the water area’s extent of the floodplain with water covering > 40 % of land during the ∼ 30 days of the most intensive spring flood (May – June) and subsequently declining to ≤ 10 % during summer (July-August). Maximal CO2 emissions were recorded in most shallow water bodies of the floodplain, notably in temporary flooded fens and birch forests. The CO2 emissions during the study period ranged from 0.2 ± 0.2 to 0.9 ± 0.2 g Cm−2 d-1 for the floodplain and 0.03 ± 0.34 g C m−2 d-1 for the Ob’s main channel.CO2 emissions from the floodplain were ∼ 163 ± 20 t C per km for the river’s main stem during the 95 day study period. The partial contributions of temporary flooded zones, main stem, and permanent lakes / secondary channels to total emissions (1820 km² area) were 70, 16, and 14 %, respectively. Over spring and summer seasons, contributions from flooded zones ranged from 43 to 99 % of total CO2 emissions from water surfaces of the Ob River’s middle course. Extrapolation of obtained results to the entire territory of the Ob River floodplain indicates that not accounting for the floodplain emissions may sizably—up to an order of magnitude—underestimate the CO2 emissions from riverine systems in Western Siberia during open water period. Future work on the Ob River floodplain in the permafrost-bearing zone should be prioritized and would allow adequate upscaling of C emission from this environmentally important territory.

Assessing improvements in global ocean pCO2 machine learning reconstructions with Southern Ocean autonomous sampling

Abstract. The Southern Ocean plays an important role in the exchange of carbon between the atmosphere and oceans and is a critical region for the ocean uptake of anthropogenic CO2. However, estimates of the Southern Ocean air–sea CO2 flux are highly uncertain due to limited data coverage. Increased sampling in winter and across meridional gradients in the Southern Ocean may improve machine learning (ML) reconstructions of global surface ocean pCO2. Here, we use a large ensemble test bed (LET) of Earth system models and the “pCO2-Residual” reconstruction method to assess improvements in pCO2 reconstruction fidelity that could be achieved with additional autonomous sampling in the Southern Ocean added to existing Surface Ocean CO2 Atlas (SOCAT) observations. The LET allows for a robust evaluation of the skill of pCO2 reconstructions in space and time through comparison to “model truth”. With only SOCAT sampling, Southern Ocean and global pCO2 are overestimated, and thus the ocean carbon sink is underestimated. Incorporating uncrewed surface vehicle (USV) sampling increases the spatial and seasonal coverage of observations within the Southern Ocean, leading to a decrease in the overestimation of pCO2. A modest number of additional observations in Southern Hemisphere winter and across meridional gradients in the Southern Ocean leads to an improvement in reconstruction bias and root-mean-squared error (RMSE) of as much as 86 % and 16 %, respectively, as compared to SOCAT sampling alone. Lastly, the large decadal variability of air–sea CO2 fluxes shown by SOCAT-only sampling may be partially attributable to undersampling of the Southern Ocean.

Increasing seasonal variation in the extent of rivers and lakes from 1984 to 2022

Abstract. Knowledge of the spatial and temporal distribution of surface water is important for water resource management, flood risk assessment, monitoring ecosystem health, constraining estimates of biogeochemical cycles and understanding our climate. While global-scale spatiotemporal change detection of surface water has significantly improved in recent years due to planetary-scale remote sensing and computing, it has remained challenging to distinguish the changing characteristics of rivers and lakes. Here we analyze the spatial extent of permanent and seasonal rivers and lakes globally over the past 38 years based on new data of river system extents and surface water trends. Results show that while the total permanent surface area of both rivers and lakes has remained relatively constant, the areas with intermittent seasonal coverage have increased by 12 % and 27 % for rivers and lakes, respectively. The increase is statistically significant in over 84 % of global water catchments based on Spearman's rank correlations (rho) above 0.05 and p values less than 0.05. The seasonal river extent is nearly 32 % larger than the previously observed annual mean river extent, suggesting large seasonal variations that impact not only ecosystem health but also estimations of terrestrial biogeochemical cycles of carbon. The outcomes of our analysis are shared as the Surface Area of Rivers and Lakes (SARL) database, serving as a valuable resource for monitoring and research of hydrological cycles, ecosystem accounting, and water management.

Increasing coastal exposure to extreme wave events in the Alaskan Arctic as the open water season expands

Declining Arctic sea ice over recent decades has been linked to growth in coastal hazards affecting the Alaskan Arctic. In this study, climate model projections of sea ice are utilized in the simulation of an extratropical cyclone to quantify how future changes in seasonal ice coverage could affect coastal waves caused by this extreme event. All future scenarios and decades show an increase in coastal wave heights, demonstrating how an extended season of open water in the Chukchi and Beaufort Seas could expose Alaskan Arctic shorelines to wave hazards resulting from such a storm event for an additional winter month by 2050 and up to three additional months by 2070 depending on climate pathway. Additionally, for the Beaufort coastal region, future scenarios agree that a coastal wave saturation limit is reached during the sea ice minimum, where historically sea ice would provide a degree of protection throughout the year.

Comparison of factors associated with seasonal influenza and COVID-19 booster vaccination coverage among healthcare personnel working at acute care hospitals during 2021–2022 influenza season, National Healthcare Safety Network, United States

The simultaneous circulation of seasonal influenza virus and SARS-CoV-2 variants will likely pose unique challenges to public health during the future influenza seasons. Persons who are undergoing treatment in healthcare facilities may be particularly at risk. It is important for healthcare personnel to protect themselves and patients by receiving vaccines. The purpose of this study is to assess coverage of the seasonal influenza vaccine and COVID-19 monovalent booster among healthcare personnel working at acute care hospitals in the United States during the 2021–22 influenza season and to examine the demographic and facility characteristics associated with coverage. A total of 3260 acute care hospitals with over 7 million healthcare personnel reported vaccination data to National Healthcare Safety Network (NHSN) during the 2021–22 influenza season. Two separate negative binomial mixed models were developed to explore the factors associated with seasonal influenza coverage and COVID-19 monovalent booster coverage. At the end of the 2021–2022 influenza season, the overall pooled mean seasonal influenza coverage was 80.3%, and the pooled mean COVID-19 booster coverage was 39.5%. Several demographic and facility-level factors, such as employee type, facility ownership, and geographic region, were significantly associated with vaccination against influenza and COVID-19 among healthcare personnel working in acute care hospitals. Our findings highlight the need to increase the uptake of vaccination among healthcare personnel, particularly non-employees, those working in for-profit and non-medical school-affiliated facilities, and those residing in the South.

Urban-rural differences in seasonal malaria chemoprevention coverage and characteristics of target populations in nine states of Nigeria: a comparative cross-sectional study.

Differences between urban and rural contexts in terms of sociodemographic characteristics, geographical features and risk perceptions may lead to disparities in coverage and related outcomes of community-based preventive interventions, such as seasonal malaria chemoprevention (SMC). This study investigated urban-rural differences in SMC coverage and other programme outcomes, as well as child and caregiver characteristics of target populations in nine implementing states in Nigeria during the 2022 SMC round. This is a comparative cross-sectional study based on comprehensive end-of-round household surveys conducted in nine states where SMC was delivered in Nigeria in 2022. Data of 11,880 caregiver-child pairs were included in the analysis. Rural-urban differences in SMC outcomes and child and caregiver characteristics were assessed, first by using Pearsons' chi-square test for independence for categorical variables. Univariate multilevel mixed-effect logistic regression models, with random intercepts for cluster units, were used to quantify the strength of association between location and each SMC coverage and related outcomes. Significant urban-rural differences were observed in caregivers' sociodemographic characteristics, such as age, gender, level of education, occupation status and health-seeking behaviour for febrile childhood illnesses. Disparities were also seen in terms of SMC coverage and related outcomes, with lower odds of the receipt of Day 1 dose direct observation of the administration of Day 1 dose by community distributors, receipt of the full three-day course of SMC medicines and receipt of SMC in all cycles of the annual round among children residing in urban areas, compared with those residing in rural areas. Similarly, urban-dwelling caregivers had lower odds of being knowledgeable of SMC and believing in the protective effect of SMC than rural-dwelling caregivers. Findings highlight observable urban-rural disparities in SMC programme delivery and related outcomes, as well as target population characteristics, underscoring the need for context-specific strategies to ensure optimal delivery of SMC and improve programme implementation outcomes in urban settings.

Global Snow Seasonality Regimes from Satellite Records of Snow Cover

Abstract Snow cover provides distinct seasonal controls on the exchange of energy between Earth’s surface and atmosphere, and on hydrologic cycling, and holds considerable importance to communities and ecosystems worldwide. In this work, we tackle a comprehensive review of existing snow classification approaches and the development of new globally applicable snow cover–based rules for delineating snow seasonality classes. Snow classification rules are defined using machine learning approaches, which are then applied to the 22-yr record of snow cover (2000–22) from the Moderate Resolution Imaging Spectroradiometer (MODIS) on a 0.01° global grid. For the MODIS period of record, we find the global land surface can be effectively partitioned into five snow seasonality classes: no snow, ephemeral, transitional, seasonal, and perennial snow regimes which on average cover extents of approximately 76 (52% of global land areas), 19 (13%), 16 (11%), 18 (13%), and 16 million km2 (11%), respectively. Using the multidecadal dataset, we explore changes within snow regimes and find significant increases in the areal extent of no snow (approximately +70 000 km2 yr−1) as well as apparent losses in perennial (−3600 km2 yr−1) and seasonal snow regime coverage (−38 000 km2 yr−1). The resulting classification maps have strong agreement with in situ snow depth observations and present similar patterns to existing snow and climate classifications with notable discrepancies in cold arid regions. The framework’s ability to accurately capture variations in snow persistence, snow accumulation, and melt cycling is shown, providing a reference to the current state of global snow seasonality. Significance Statement Following a review of existing snow classification approaches, this study focuses on improving our understanding of snow-cover variability on Earth by using satellite-based observations of snow-covered area to derive a new snow classification. Satellite observations provide the best means of measuring snow cover at a global scale, helping to identify regions where its influence on energy, water, and climate is changing. The results are compared to existing climate and snow summaries, ground observations of snow depth, and include trends in snow cover over recent decades (2000–22). The resulting datasets are also made available to the broader scientific community.

Improved Acoustic Tracking of RAFOS-Enabled Profiling Floats through the New Software Package artoa4argo

Abstract In sea ice–covered polar oceans, profiling Argo floats are often unable to surface for 9 months or longer, rendering acoustic RAFOS tracking the only method to obtain unambiguous under-ice positions. Tracking RAFOS-enabled floats has historically relied on the ARTOA3 software, which had originally been tailored toward nonprofiling floats in regions featuring the sound fixing and ranging (SOFAR) channel with acoustic ranges of approximately 1000 km. However, in sea ice–covered regions, RAFOS tracking is challenged due to (i) reduced acoustic ranges of RAFOS signals, and (ii) enhanced uncertainties in float and sound source clock offsets. A new software, built on methodologies of previous ARTOA versions, called artoa4argo, has been created to overcome these issues by exploiting additional float satellite fixes, resolving ambiguous float positions when tracking with only two sources and systematically resolving float and sound source clock offsets. To gauge the performance of artoa4argo, 21 RAFOS-enabled profiling floats deployed in the Weddell Sea during 2008–12 were tracked. These have previously been tracked in independent studies with a Kalman smoother and a multiconstraint method. The artoa4argo improves tracking by automating and streamlining methods. Although artoa4argo does not necessarily produce positions for every time step, which the Kalman smoother and multiconstraint methods do, whenever a track location is available, it outperforms both methods. Significance Statement Argo is an international program that collects oceanic data using floats that drift with ocean currents and sample the water column from 2000-m depth to the surface every 7–10 days. Upon surfacing, the float acquires a satellite position and transmits its data via satellite. In polar regions, with extensive seasonal sea ice coverage, floats are unable to surface for many months. Thus, any under-ice samples collected are missing positions, hampering their use in scientific endeavors. Since monitoring of polar regions is imperative to better understand and predict the effects of climate change, hydroacoustic tracking is employed there. Here a new acoustic tracking software, artoa4argo, is introduced, which improves tracking of these floats.

MtDNA data reveal disparate population structures and High Arctic colonization patterns in three intertidal invertebrates with contrasting life history traits

IntroductionPost-glacial climate variation is known to have influenced the distribution of marine species in the North Atlantic. In particular, the Atlantic side of the Arctic has experienced strong fluctuations in both atmospheric and sea surface temperature, as well as seasonal ice coverage since the last glacial maximum (LGM). Here, we aim to unveil the phylogeography and historical demography of three rocky intertidal marine invertebrates showing a trans-Atlantic distribution and presently inhabiting the Arctic: Gammarus oceanicus, Littorina saxatilis and Semibalanus balanoides.MethodsWe used a large amount of mitochondrial DNA barcode data, both newly-obtained and stored in public databases. We performed phylogeographic and demographic analyses on 1119 G. oceanicus, 205 L. saxatilis, and 884 S. balanoides sequences.ResultsOur results show that all three of these boreal species have expanded their effective population sizes in the high Arctic Svalbard Archipelago since the LGM. Analyses investigating the origin of all these populations point to the eastern Atlantic.DiscussionBased on our results we conclude that the expansion of these boreal species to the Arctic possibly happened during an earlier warm cycle of the Holocene era, and is probably not the result of the recent ‘Atlantification’ of the Arctic. We also discuss the effects of dispersal potential on population structure as an important aspect of comparative biogeographical studies.

Zooplankton of the Eastern Kara Sea: Response to a Short Ice-Free Period

—A characteristic feature of the eastern Kara Sea, which is separated from its central part by the shallow Central Kara Rise, is a later ice breakup, a shorter ice-free period, and lower water temperature. Zooplankton in this part of the sea is virtually unstudied. The first data on the composition, spatial distribution of zooplankton, age structure and feeding of the dominant species, and their grazing impact on phytoplankton were obtained in the cruise 125 of the R/V Professor Shtokman (September 2013) and cruise 63 of the R/V Akademik Mstislav Keldysh (September 2015) to the eastern Kara Sea. 2015 was a typical year in terms of the ice regime for this region, while in 2013, seasonal ice coverage lasted longer than usual. Zooplankton biomass (17–112 mg dry weight m–3), to which copepods Calanus glacialis contributed the main portion, were close to the values in the central Kara Sea in September. The seasonal development of the C. glacialis population began much later than in the central Kara Sea, but development of Metridia longa was not influenced by the specifics of the ice regime and hydrophysical conditions in the eastern Kara Sea. The interannual differences in the timing of ice retreat had almost no effect on the zooplankton abundance, seasonal development of the dominant copepod populations, feeding activity, and grazing impact on phytoplankton.

New insights into the Barents Sea Calanus glacialis population dynamics and distribution

Arctic copepods are major grazers and vital food for planktivores in polar ecosystems but challenging to observe due to remoteness and seasonal sea ice coverage. Models offer higher spatio-temporal resolution, and individual-based models (IBMs) are useful since they incorporate individual variability which characterizes most copepod populations. Here, we present an IBM of the Arctic copepod Calanus glacialis, a key secondary producer in polar regions of the Barents Sea. The model is coupled to a three-dimensional physical-biological model, and an IBM for the Atlantic congener C. finmarchicus. We use the model to fill seasonal “gaps” between discontinuous spatio-temporal sampling for studying the spatial and seasonal population dynamics. Our simulations suggest that, across the Atlantic and Arctic domains of this ecosystem, total population egg production peaks in July, and copepodid 3 is the main overwintering stage descending to deeper overwintering depths between July and September. Total population biomass peaks at 5 times higher carbon mass than the seasonal minimums and is driven by the seasonal build-up of biomass in stages C4, C5 and adults. Ocean currents spreads the population over a large area, though with a clear spatial separation between C. glacialis and C. finmarchicus in the northern and southern Barents Sea, respectively. There is a mixture between 1- and 2-years life cycles in the model population, and those who require two diapause phases to reach maturity have spent a larger part of their life north of 77°N, where temperatures are colder and the growth season shorter than further south. A remaining question is where the source population of C. glacialis in this ecosystem resides, and whether the population relies on local survival and reproduction or continuous supply from a source population outside the Barents Sea.

Predicting Carbonate Chemistry on the Northwest Atlantic Shelf Using Neural Networks

AbstractThe Northwest Atlantic Shelf (NAS) region has experienced accelerated warming, heatwaves, and is susceptible to ocean acidification, yet also suffers from a paucity of carbonate chemistry observations, particularly at depth. We address this critical data gap by developing three different neural network models to predict dissolved inorganic carbon (DIC) and total alkalinity (TA) in the NAS region from more readily available hydrographic and satellite data. The models predicted DIC withr2between 0.913–0.963 and root mean square errors (RMSE) between 15.4–23.7 (μmol kg−1) and TA withr2between 0.986–0.983 and RMSE between 9.0–10.4 (μmol kg−1) on an unseen test data set that was not used in training the models. Cross‐validation analysis revealed that all models were insensitive to the choice of training data and had good generalization performance on unseen data. Uncertainty in DIC and TA were low (coefficients of variation 0.1%–1%). Compared with other predictive models of carbonate system variables in this region, a larger and more diverse data set with full seasonal coverage and a more sophisticated model architecture resulted in a robust predictive model with higher accuracy and precision across all seasons. We used one of the models to generate a reconstructed seasonal distribution of carbonate chemistry fields based on DIC and TA predictions that shows a clear seasonal progression and large spatial gradients consistent with observations. The distinct models will allow for a range of applications based on the predictor variables available and will be useful to understand and address ocean sustainability challenges.

Baseline malarial and nutritional profile of children under seasonal malaria chemoprevention coverage in the health district of Nanoro, Burkina Faso.

Seasonal Malaria chemoprevention (SMC) is one of the large-scale life-saving malaria interventions initially recommended for the Sahel subregion, including Burkina Faso and recently extended to other parts of Africa. Initially, SMC was restricted to children 3 to 59 months old, but an extension to older children in some locations was recently recommended. Further characterization of SMC population profile beyond age criterion is necessary for understanding factors that could negatively impact the effectiveness of the intervention and to define complementary measures that could enhance its impact. Children were assessed through a cross-sectional survey during the first month of the 2020 SMC campaign (July-August 2020) as part of the SMC-NUT project in the health district of Nanoro. Parameters such as body temperature, weight, height, mid-upper arm circumference (MUAC) were assessed. In addition, blood sample was collected for malaria diagnosis by rapid diagnostic tests (RDT) and microscopy, and for haemoglobin measurement. A total of 1059 children were enrolled. RDT positivity rate (RPR) was 22.2%, while microscopy positivity rate (MPR) was 10.4%, with parasitaemia levels ranging from 40 to 70480/μL. RPR and MPR increased as patient age increased. Wasting was observed in 7.25% of children under SMC coverage while the prevalence of stunting and underweight was 48.79% and 23.38%, respectively. As the age of the children increased, an improvement in their nutritional status was observed. Finally, undernourished children had higher parasite densities than children with adequate nutritional status. In the health district of Nanoro, children who received Seasonal Malaria Chemoprevention (SMC) were mostly undernourished during the period of SMC delivery, suggesting the need for combining the SMC with synergistic interventions against malnutrition to achieve best impact.

Detection of Water Hyacinth (Eichhornia crassipes) in Lake Tana, Ethiopia, Using Machine Learning Algorithms

Lake Tana is Ethiopia’s largest lake and is infested with invasive water hyacinth (E. crassipes), which endangers the lake’s biodiversity and habitat. Using appropriate remote sensing detection methods and determining the seasonal distribution of the weed is important for decision-making, water resource management, and environmental protection. As the demand for the reliable estimation of E. crassipes mapping from satellite data grows, comparing the performance of different machine learning algorithms could help in identifying the most effective method for E. crassipes detection in the lake. Therefore, this study aimed to examine the ability of random forest (RF), support vector machine (SVM), and classification and regression tree (CART) machine learning algorithms to detect E. crassipes and estimating seasonal spatial coverage of the weed on the Google Earth Engine (GEE) platform using Landsat 8 and Sentinel 2 images. Cloud-masked monthly median composite Landsat 8 and Sentinel 2 data from October 2021 and 2022, January 2022 and 2023, March 2022, and June 2022 were used to represent autumn, winter, spring, and summer, respectively. Four spectral indices were derived and used in combination with spectral bands to improve the E. crassipes detection accuracy. All methods achieved greater than 95% and 90% overall accuracy when using Sentinel 2 and Landsat 8 images, respectively. Using both data sets, all methods achieved a greater than 93% F1 score for E. crassipes detection. Though the difference in performance between the methods was small, the RF was the most accurate, while the SVM and CART methods had the same accuracy. The maximum E. crassipes coverage area was observed in autumn (22.4 km2), while the minimum (2.2 km2) was observed in summer. Based on Sentinel 2 data, the E. crassipes area coverage decreased significantly by 62.5% from winter to spring and increased significantly by 81.7% from summer to autumn. The findings suggested that the RF classifier was the most accurate E. crassipes detection algorithm, and autumn was an appropriate season for E. crassipes detection in Lake Tana.

Riverine CO2 variations in permafrost catchments of the Yangtze River source region: Hot spots and hot moments

Rivers and streams are pivotal modulators in regional and global carbon cycles, but riverine CO2 flux is still uncertain for permafrost watersheds. Here we present the seasonal CO2 partial pressure (pCO2) and CO2 emission flux (FCO2) of 8 rivers and streams in the Yangtze River source region (YRSR), which have high permafrost coverage and seasonally thawed active layer. The YRSR rivers and streams are generally supersaturated with CO2, although there are a few sites with CO2 undersaturation during spring. The small headwater streams are CO2 hot spots that show significantly higher pCO2 (52 % higher) and FCO2 (792 % higher) than larger rivers. Both pCO2 and FCO2 show distinct seasonality across the study sites. pCO2 and FCO2 peak in summer and exhibit much lower levels in autumn and spring, indicating that hot moments of riverine CO2 occur in summer. Seasonal pCO2 and FCO2 variations are jointly controlled by hydrology, active layer dynamics and associated processes. The warm summer causes active layer thaw and highly active soil respiration, which release a large quantity of soil carbon and increase the CO2 sources via strengthened hydrologic connectivity. The high rainfall and more thaw-released water in summer bring high discharge, which can increase the water velocity and gas exchange rate and thus CO2 emission flux. Most of the variances of seasonal FCO2 (95 %) can be explained by hydrology and active layer thaw depth. Nevertheless, the hydrological process and seasonally thawed active layer over Qinghai-Tibet Plateau (QTP) play crucial roles in riverine carbon export due to the summer monsoon-dominated climate in QTP. Our results suggest that full seasonal coverage of CO2 dynamics is essential to quantify the annual CO2 flux accurately. Changing climate and warming permafrost may alter the annual CO2 emission due to deeper flow paths, hydrology changes, and longer emission windows throughout the year.

Temporal and Spatial Variation of Land Use and Vegetation in the Three–North Shelter Forest Program Area from 2000 to 2020

The Three–North Shelter Forest Program Area (TNSFPA), covering the three subregions of Northwest, North and Northeast China, is an important green ecological barrier in northern China. Research on spatiotemporal variation of land use and vegetation in this area can help us understand the evolution of vegetation recovery. Based on MODIS image data, we built a dataset including land use/cover, annual and seasonal vegetation coverage and vegetation productivity in the TNSFPA from 2000 to 2020, then analyzed their spatiotemporal dynamics’ characteristics and further explored the driving factors. The results demonstrate that 90.05% of land area in the TNSFPA remained unchanged from 2001 to 2020. The area of barren decreased, whereas the area of all other land use types increased. From 2000 to 2020, the vegetation coverage generally presented a spatial pattern of high in the east and low in the west, and the interannual fluctuation of high coverage area was small. More than 90% of the TNSFPA had an increasing vegetation coverage (0.41%·a−1), with the change rates of 0.51%·a−1, 0.54%·a−1 and 0.37%·a−1 in Northeast, North and Northwest China, respectively. The spatial distribution of vegetation productivity was generally consistent with the vegetation coverage, and the interannual fluctuation in areas with low productivity was small. The area with an increasing vegetation productivity (3.41 gC·m−2·a−1) occupied 51.66% of the TNSFPA, mainly in the basic stable state and significantly increased state. The change rates in Northeast, North and Northwest China were 7.34 gC·m−2·a−1, 6.72 gC·m−2·a−1 and 2.10 gC·m−2·a−1, respectively. The vegetation coverage and productivity were positively correlated with climate factors, and the correlation with precipitation is significantly higher than that with temperature. The ecological protection and restoration activities in the TNSFPA have accelerated the recovery of the vegetation ecosystem. Meanwhile, the rapid growth of population has led to the acceleration of urbanization. The intensification of the interaction between humans and land has led to the reclamation and development of barren, which has a great impact on the small–scale vegetation ecosystem.

Impact of climate change on Arctic macroalgal communities

The Arctic region faces a warming rate that is more than twice the global average. Sea-ice loss, increase in precipitation and freshwater discharge, changes in underwater light, and amplification of ocean acidification modify benthic habitats and the communities they host. Here, we synthesize existing information on the impacts of climate change on the macroalgal communities of Arctic coasts. We review the short- and long-term changes in environmental characteristics of shallow hard-bottomed Arctic coasts, the floristics of Arctic macroalgae (description, distribution, life-cycle, adaptations), the responses of their biological and ecological processes to climate change, the resulting winning and losing species, and the effects on ecosystem functioning. The focus of this review is on fucoid species, kelps, and coralline algae which are key ecosystem engineers in hard-bottom shallow areas of the Arctic, providing food, substrate, shelter, and nursery ground for many species. Changes in seasonality, benthic functional diversity, food-web structure, and carbon cycle are already occurring and are reshaping Arctic benthic ecosystems. Shallow communities are projected to shift from invertebrate- to algal-dominated communities. Fucoid and several kelp species are expected to largely spread and dominate the area with possible extinctions of native species. A considerable amount of functional diversity could be lost impacting the processing of land-derived nutrients and organic matter and significantly altering trophic structure and energy flow up to the apex consumers. However, many factors are not well understood yet, making it difficult to appreciate the current situation and predict the future coastal Arctic ecosystem. Efforts must be made to improve knowledge in key regions with proper seasonal coverage, taking into account interactions between stressors and across species.