Seasonal Production Patterns Research Articles

Climatic and edaphic controls of root-tip production and mortality in five temperate tree species

Root tips are the main components of absorptive fine roots, but their seasonal dynamics and relationship to environmental factors remain unclear due to the difficulties in methodology. In this study, we explored the temporal patterns of root-tip production and mortality in monoculture plantations of five temperate tree species at a common site in northeastern China, and identified the general environmental controls on such processes. We made monthly in-situ assessments of root tip length (RTL) production and mortality in two hardwood and three coniferous species with a minirhizotron (MR) method during the growing seasons of 2008 and 2009. Air temperature, rainfall, soil temperature and water content at 10 cm depth were determined concurrently. RTL production in all species exhibited consistent peaks in summer (June–August) in two growing seasons. RTL mortality showed substantial interannual and interspecific variability, with peaks in autumn and winter in 2008, but various patterns in 2009. RTL production positively correlated with monthly soil and air temperature across all species, and with monthly rainfall in three coniferous species. However, there was no significant correlation between RTL production and soil water content. By contrast, RTL mortality was weakly related to environmental factors, showing positive correlations with soil temperature in Korean spruce, and with rainfall in Korean pine and Korean spruce. Our findings suggest that the seasonal patterns of RTL production are convergent across the five temperate tree species due to the overlapped distribution of heat and rainfall, which can conduce roots to maximizing the acquisition of nutrient resources in the soil.

Analysing Market Integration and Causality of Oilseed Crops: Key Insights and Challenges in Major Tamil Nadu Markets and Interstate Regions of India

Oilseed crops like Sesame and Groundnut experience significant price fluctuations due to factors like seasonal production patterns, their perishable nature, and risk involved in production and marketing of output. The farmers are further complicated by a lack of information about market conditions, including the timing of arrivals and prices. Market integration, which helps stabilize prices and improve the efficiency of the marketing system. This study focuses on analysing the market integration of major oilseed crops— Sesame, and Groundnut in India from January 2013 to January 2024. Johansen's cointegration test and Granger Causality test were applied to examine how prices in different markets are influence one another. The stationarity of the prices tested using the Augmented Dickey-Fuller test. The results confirmed that the prices are cointegrated, showing a strong interdependence between them. The analysis also revealed causal relationships between regions, such as bidirectional causality in the case of Sesame and Groundnut. The findings underscore the importance of further research to address production challenges, improve technical methods, and develop informed policies to manage the issues faced by oilseed crop growing farmers. This will help overcome obstacles in production and ensure a more efficient marketing system.

Earlier Spring-Summer Phenology and Higher Photosynthetic Peak Altered the Seasonal Patterns of Vegetation Productivity in Alpine Ecosystems

Carbon uptake of vegetation is controlled by phenology and photosynthetic carbon uptake capacity. However, our knowledge of the seasonal responses of vegetation productivity to phenological and physiological changes in alpine ecosystems is still weak. In this study, we quantified the spatio-temporal variations of vegetation phenology and gross primary productivity (GPP) across the source region of the Yellow River (SRYR) by analyzing MODIS-derived vegetation phenology and GPP from 2001 to 2019, and explored how vegetation phenology and maximum carbon uptake capacity (GPPmax) affected seasonal GPP over the region. Our results showed that the SRYR experienced significantly advanced trends (p < 0.05) for both start (SOS) and peak (POS) of the growing season from 2001 to 2019. Spring GPP (GPPspr) had a significantly increasing trend (p < 0.01), and the earlier SOS had obvious positive effects on GPPspr. Summer GPP (GPPsum) was significantly and negatively correlated to POS (p < 0.05). In addition, GPPmax had a significant and positive correlation with GPPsum and GPPann (p < 0.01), respectively. It was found that an earlier spring-summer phenology and higher photosynthetic peak enhanced the photosynthetic efficiency of vegetation in spring and summer and altered the seasonal patterns of vegetation productivity in the SRYR under warming and wetting climates. This study indicated that not only spring and autumn phenology but also summer phenology and maximum carbon uptake capacity should be regarded as crucial indicators regulating the carbon uptake process in alpine ecosystems. This research provides important information about how changes in phenology affect vegetation productivity in alpine ecosystems under global climate warming.

Storm-driven hydrological, seasonal, and land use/land cover impact on dissolved organic matter dynamics in a mid-Atlantic, USA coastal plain river system characterized by 21 T FT-ICR mass spectrometry

Introduction: Dissolved organic matter (DOM) as primary and secondary energy sources can be transported via rivers to estuaries and impact coastal biogeochemical cycles. Storm-induced high discharge events can alter the dominant river flow paths and enhance leaching of shallow organic-rich soil layers, leading to elevated terrestrial DOM export. Land use/land cover (LULC) and associated anthropogenic interventions (including artificial reservoirs and agricultural irrigation) can control sources and transformation processes of exported DOM along with hydrologic factors. The relative significance of LULC, hydrological factors, and temperature variations with seasons will differ depending on geographical locations and complicate their incorporation in biogeochemical models of DOM dynamics. This study investigates the role of LULC, seasonality, and storm events on DOM concentrations and molecular composition in the Murderkill River system.Method: Surface water samples were collected seasonally and before/after storm events from 6 sites representing forested, agricultural, and developed LULC units. The DOM was characterized via parallel factor analysis of excitation-emission matrix data and electrospray ionization 21 T Fourier-transform ion cyclotron resonance mass spectrometry to determine potential DOM sources and enable the development of a conceptual model for DOM dynamics in rivers impacted by anthropogenic reservoirs.Result and Discussion: Our results suggest that storm-induced shallow and overland flow paths can increase surface-vegetation/plant-litter derived DOM based on atomic ratios associated with specific biogenic precursors (i.e., lignin, tannins, and/or oxygenated aromatic DOM), particularly in winter when autochthonous production was suppressed due to reduced temperatures. We further demonstrate that the damming effects of artificial reservoirs enhance the role of seasonal patterns of autochthonous production, disrupting storm-shunt process and stimulating significantly more bio-produced DOM export during spring and summer (i.e., tryptophan/tyrosine-like. N- and S- containing, phytoplankton-derived compounds). Collectively, these results demonstrate how artificial reservoirs alter the characteristics of DOM exported from rivers with implications for understanding carbon export and fate at river-estuary interfaces.

Data-driven analysis and prediction of wastewater treatment plant performance: Insights and forecasting for sustainable operations

This study presents a comprehensive performance and forecasting analysis of the As-Samra wastewater treatment plant (WWTP) in Jordan, with two main objectives. Firstly, a thorough evaluation of the plant’s performance is conducted. The analysis involves independently assessing historical operational conditions, plant production, and their statistical correlations using various statistical techniques. The second objective focuses on developing a data-driven forecasting approach to predict the plant's production one month in advance, using multiple machine learning models. The results highlight the effectiveness of principal component analysis (PCA) in simplifying operational data, revealing distinct operational clusters, and identifying seasonal production patterns while showing correlations between operational conditions and overall power production. The support vector machine (SVM) forecasting model emerged as the top performer, showcasing the potential of a hybrid forecasting approach. The findings offer valuable perspectives for enhancing operational efficiency, refining production planning, and ultimately improving the environmental impact of the plant.

A time series analysis of milk productivity in US dairy states

As US dairy cow production evolves, it is important to characterize trends and seasonal patterns to project amounts and fluctuations in milk and milk components by states or regions. Hence, this study aimed to (1) quantify historical trends and seasonal patterns of milk and milk components production associated with calving date by parities and states; (2) classify parities and states with similar trends and seasonal patterns into clusters; and (3) summarize the general pattern for each cluster for further application in simulation models. Our data set contained 9.18 million lactation records from 5.61 million Holstein cows distributed in 17 states during the period January 2006 to December 2016. Each record included a cow's total milk, fat, and protein yield during a lactation. We used time series decomposition to obtain each state's annual trend and seasonal pattern in milk productivity for each parity. Then, we classified states and parities with agglomerative hierarchical clustering into groups according to 2 methods: (1) dynamic time warping on the original time series and (2) Euclidean distance on extracted features of trend and seasonality from the decomposition. Results showed distinguishable trends and seasonality for all states and lactation numbers for all response variables. The clusters and cluster centroid pattern showed a general upward trend for all yields [energy-corrected milk (ECM), milk, fat, and protein] and a steady trend for fat and protein percent for all states except Texas. We also found a larger seasonality amplitude for all yields (ECM, milk, fat, and protein) from higher lactation numbers and a similar amplitude for fat and protein percent across lactation numbers. The results could be used for advising management decisions according to farm productivity goals. Furthermore, the trend and seasonality patterns could be used to adjust the production level in a specific state, year, and season for farm simulations to accurately project milk and milk components production.

Seasonal beta‐diversity of dry grassland vegetation: Divergent peaks of above‐ground biomass and species richness

AbstractQuestionTemperate grasslands are known for their high plant diversity and distinct seasonality. However, their intra‐annual community dynamics are still largely overlooked by ecologists. Therefore, we explored the seasonal alpha‐ and beta‐diversity patterns of vascular plants and their relationships to above‐ground biomass in a rocky steppe (Festucion valesiacae).LocationPavlov Hills, SE Czech Republic.MethodsFor one year, we monitored the plant community of the rocky steppe at monthly intervals in 42 permanent plots of 0.25 m2. We examined seasonal changes in above‐ground biomass (estimated from the cover and height of living plant parts) and seasonal beta‐diversity, which we partitioned into turnover and nestedness components and their quantitative counterparts: balanced changes and abundance gradients.ResultsWe identified a pronounced seasonal pattern of above‐ground biomass, species richness and composition. Total above‐ground biomass was highest in June (summer), with a peak representing only 60% of total annual production (sum of individual species' maxima). However, the observed peak in species richness occurred in March (early spring), with 80% of the total species number recorded throughout the year. Accordingly, nestedness and abundance gradient patterns differed in the spring months, while seasonal turnover and balanced changes in abundance were generally congruent. Annual, short‐lived, and perennial species exhibited different seasonal patterns of species richness and biomass production, although a sharp increase in biomass and a peak in species richness in spring were universal across the community.ConclusionsSeasonal climatic constraints on plant growth are key determinants of primary production dynamics. Plants adapt to these constraints by adjusting their life cycles in different ways. In dry grasslands, the complexity of plant responses to climatic seasonality can result in seasonal beta‐diversity patterns with divergent peaks in biomass and species richness.

Seasonal variability of satellite-derived primary production in the South China Sea from an absorption-based model

Seasonal patterns of marine net primary production (NPP) are crucial for understanding the marine ecosystem and biogeochemical cycles. Uncoupling of seasonal variations between NPP and phytoplankton chlorophyll-a over different areas has attracted much attention. In this study, following a review of previous studies, monthly climatological NPP data from 2003 to 2020, estimated using the Size-fractioned Phytoplankton Pigment Absorption (aph)-based NPP Model (SABPM), were selected to study the seasonal variability of NPP in the South China Sea (SCS). Results showed the spatial differences of NPP seasonality and its departures from climatology in extreme El Niño-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) years. Cluster analysis for climatological monthly data identified significant differences of NPP seasonality in five typical regions. In coastal regions along the northern SCS and off eastern Vietnam, NPP exhibited the most obvious seasonal cycle with maximum (minimum) values in summer (winter), attributable mainly to river discharge and summer upwelling. In regions off northwestern Luzon and coast of southern SCS, NPP showed peaks in winter, which were related to strong mixing and upwelling. In northwestern SCS, NPP was high during May–September in phase with sea surface temperature and the primary controlling factors were found to be shallow nutricline depth and wind-driven mixing. Owing to the deep nutricline depth in the central basin, NPP exhibited little seasonal variability; only a weak signal was observed in spring in phase with photosynthetically active radiation. Local dynamics on regulating the nutrient supply and light availability contribute to these regional differences in NPP seasonality, which could also be affected by extreme climate events. The largest anomalies of the NPP seasonal cycle coincide with 2015/2016 ENSO and super IOD in 2020. During these events, enhanced (weakened) westerly winds caused fall (rise) of SLA and increase (decrease) of NPP in coastal regions along the northern SCS and that off eastern Vietnam. Overall, theaph-based model shows a new perspective to study the spatiotemporal variations of NPP in the SCS.

Predicting seasonal patterns of energy production: A grey seasonal trend least squares support vector machine

This paper proposed a hybrid ensemble forecasting technique that incorporates the merits of the accumulation generation operation (AGO), least-square support vector regression (LSSVR), dummy variable, and time trend item to forecast the seasonal time series characterized by nonlinearity and uncertainty. This proposed framework, a grey seasonal trend least squares support vector machine (GSTLSSVR), can make it easy to add arbitrary seasonality to any feature, which improves model realism significantly. Furthermore, the grid search method and the last block validation are developed to identify the optimal hyperparameter, making it possible for the developed method to capture seasonal and nonlinear patterns of the original data. For illustration and verification, the quarterly petroleum coke production and monthly crude oil production in China are employed to construct diverse competing models, including the seasonal fluctuation grey model (SGM(1,1)), seasonal autoregressive integrated moving average (SARIMA), Holt-Winters (HW), backpropagation neural network (BPNN), a grey seasonal least squares support vector machine (GSLSSVR), long short term memory (LSTM) and LSSVR. The forecasting values of quarterly petroleum coke production are 668.74, 682.68, 679.99, 704.35 for 2019 and 717.59, 734.99, 727.16, 741.53 for 2020, with the lowest MAPEP value (3.7566%). Moreover, the monthly predicted crude oil production for 2019 are 3113.9, 1645, 1562.2, 1625.4, 1599.3, 1619.1, 1627, 1576.9, 1629.6, 1583.6, and 1615.6, while those counterparts for 2020 are 3093.2, 1625, 1547.5, 1621.5, 1610.6, 1644.9, 1662.9, 1615.4, 1667.4, 1615, 1667.4, 1615, and 1640.2, with the lowest MAPEP value (1.0034%). Generally, the empirical results show that the proposed model obtains superior overall performance to other benchmarks in both case studies. Therefore, this proposed model exhibits promising prospects for identifying seasonal patterns in energy production sequences.

Temporal dynamics of fine root production, mortality and turnover deviate across branch orders in a larch stand.

Fine roots play a key role in carbon, nutrient, and water biogeochemical cycles in forest ecosystems. However, inter-annual dynamics of fine root production, mortality, and turnover on the basis of long-term measurement have been less studied. Here, field scanning rhizotrons were employed for tracking fine root by branch order over a 6years period in a larch plantation. For total fine roots, from the first- to the fifth-order roots, annual root length production, length mortality, standing crops, and turnover rate varied up to 3.4, 2.3, 1.5, and 2.3-folds during the study period, respectively. The inter-annual variability of those roots indices in the first-order and the second-order roots were greater than that of the higher order (third- to fifth-order) roots. The turnover rate was markedly larger for the first-order roots than for the higher order roots, showing the greatest variability up to 20times. Seasonal dynamics of root length production followed a general concentrated pattern with peak typically occurring in June or July, whereas root length mortality followed a general bimodal mortality pattern with the dominant peak in May and the secondary peak in August or October. Furthermore, the seasonal patterns of root length production and mortality were similar across years, especially for the first-order and the second-order roots. These results from long-term observation were beneficial for reducing uncertainty of characterizing fine root demography in consideration of large variation among years. Our findings highlight it is important for better understanding of fine root dynamics and determining root demography through distinguishing observation years and root branch orders.

Trends in ANPP Response to Temperature in Wetland Meadows across a Subcontinental Gradient in Patagonia

Due to ongoing global warming, seasonal patterns of aboveground net primary production (ANPP) may be altered by temperature trends. Of particular interest is the seasonal association between ANPP and temperature at the beginning of the growing season (the period encompassing minimum to maximum ANPP), where two contrasting mechanisms are in tension. On the one hand, the restrictions exerted by low temperatures in winter may be relaxed, increasing the slope of seasonal association between ANPP and temperature over the years. On the other hand, increases in temperature may increase water demand, reducing the slope over time. Across 543 wetland meadow areas in Patagonia, we estimated ANPP and obtained temperature records on a monthly basis from 2001 to 2019. The seasonal association between ANPP and temperature, tested through linear regression, was statistically significant in 96% of the cases (9921/10317 (543 wetland areas × 19 growing seasons)). The fitted linear models explained, on average, 84% of ANPP seasonal (June–December) variations. Through regression trees, we found out that the two tested mechanisms, the relaxation of temperature restriction and the increase in water demand, showed clear spatial patterns. The relaxation due to temperature increase took place at higher latitudes, but the water-limiting mechanism increased over mid-latitude areas.

Seasonal hydrogen storage decisions under constrained electricity distribution capacity

We consider a profit-maximizing renewable energy producer operating in a rural area with limited electricity distribution capacity to the grid. While maximizing profits, the energy producer is responsible for the electricity supply of a local community that aims to be self-sufficient. Energy storage is required to deal with the energy productions' uncertain and intermittent character. A promising, new solution is to use strategic hydrogen reserves. This provides a long-term storage option to deal with seasonal mismatches in energy production and the local community's demand. Using a Markov decision process, we provide a model that determines optimal daily decisions on how much energy to store as hydrogen and buy or sell from the power grid. We explicitly consider the seasonality and uncertainty of production, demand, and electricity prices. We show that ignoring seasonal demand and production patterns is suboptimal and that introducing hydrogen storage transforms loss-making operations into profitable ones. Extensive numerical experiments show that the distribution capacity should not be too small to prevent local grid congestion. A higher storage capacity increases the number of buying actions from the grid, thereby causing more congestion, which is problematic for the grid operator. We conclude that a profit-maximizing hydrogen storage operation alone is not an alternative to grid expansion to solve congestion, which is essential knowledge for policy-makers and grid operators.

Mapping the long‐term influence of river discharge on coastal ocean chlorophyll‐a

AbstractAlthough the potential of river discharge to support ocean productivity and marine ecosystems is known, the specifics of this relationship are poorly understood in many regions of the world. Global estimates of river flow indicate that river discharge is decreasing due to the increasing fragmentation, extraction and regulation of rivers. This likely means that the contribution of river flow to coastal productivity and water quality is changing, potentially leading to fewer and smaller magnitude ocean fertilisation events. We developed a simple analysis method, based on Earth observation data, to investigate where coastal ocean chlorophyll‐a is most strongly influenced by river discharge. The per‐pixel spatiotemporal correlation technique (implemented using Python) correlates chlorophyll‐a concentration (a proxy for phytoplankton biomass and indicator of primary productivity) from MODIS ocean colour data with river discharge data. The method was tested globally on 11 different rivers discharging into coastal ocean regions. Our findings suggest some of the world's largest river systems, such as the Amazon River, have zones of elevated coastal chl‐a that extend hundreds to thousands of km from the river mouth. These findings suggest the influence of river discharge may have been underestimated in many coastal regions of the world. The method appears more effective for larger river systems discharging to ocean waters with less complex nutrient dynamics and weaker seasonal productivity patterns, most notably in temperate regions. Increasing our understanding of the specific areas influenced by river discharge, and the degree of influence over space and time, is an important step towards the improved river and coastal management. This method will increase the capacity of researchers to monitor how, when and where coastal waters are affected as river discharge continues to change into the future.

Seasonal and Spatial Production Patterns of Dissolved Inorganic Carbon and Total Alkalinity in a Shallow Beach Aquifer

Dissolved inorganic carbon (DIC) and total alkalinity (AlkT) fluxes to the nearshore ocean can directly impact the rates of primary production, coral reef formation, coastal ocean acidification, and continental shelf ecology. Current understanding of the transformations that DIC and AlkT undergo as they move from land to sea are limited, leading to difficulties in estimating future DIC and AlkT export that may be altered under a changing climate. While much research has focused on carbon fluxes in carbon-rich mangroves and coastal wetlands, DIC and AlkT transformations and distributions in sandy beach aquifers, which are comparatively carbon-poor, have not been studied as extensively. We monitored DIC and AlkT concentrations in a sandy beach system over six sampling events spanning two years. Substantial changes to DIC and AlkT occurred along subsurface flowpaths due to aerobic respiration and anoxic reactions, resulting in an additional mean flux to the ocean of 191 and 134 mmol/d per meter length of shoreline, respectively. The chemical alterations occurred within the saltwater-freshwater mixing zone beneath the beach surface. Both aerobic and anaerobic reactions actively contributed to DIC and AlkT production within the system, as indicated by DIC: AlkT and dDIC:dAlkT ratios relative to the theoretical dilution line. The work indicates that beach aquifers support active transformation of inorganic carbon and highlights a potentially important and overlooked source of DIC and AlkT to coastal systems.

Price analysis of the Indonesian carrageenan seaweed industry

Indonesia is the world's largest producer of carrageenan seaweeds, an industry that supports the livelihoods of over 250,000 coastal households. Seaweed farming brings a range of positive benefits to household livelihoods, including cash income. However, the industry is subject to volatile price and production cycles to which seaweed farmers are vulnerable. Despite the central importance of seaweed farming to Indonesian coastal livelihoods, no formal analysis of prices has been undertaken. This paper addresses this gap by drawing on fortnightly price data for 13 locations in Indonesia over a 10-year period. It examines the social, economic and ecological drivers of price trends, seasonality, volatility and transmission. The paper offers four key findings. Firstly, that demand from international carrageenan processors drives long-term price trends. Secondly, that prices follow a consistent intra-year pattern across the country despite differing seasonal production patterns. Thirdly, that prices are not significantly related to product attributes such as moisture, sand and salt content, suggesting low price-grade differentials. Finally, that price levels are determined by key central aggregation and transport hubs and transmitted to remote areas. This indicates that there is substantial opportunity to upgrade the industry through improved coordination between value chain actors.

Distinct Seasonal Primary Production Patterns in the Sub-Polar Gyre and Surrounding Seas

Primary production (PP) in the sub-polar region appears to be important for ocean carbon uptake but how the different water masses contribute to the PP occurring here has not yet been described. Using two models based on satellite observations of surface chlorophyll, light and temperature, seasonal patterns in the distribution of PP are shown here to differ in the sub-polar gyre south of the Greenland-Scotland Ridge (GSR) and surrounding water masses. Monthly averages of PP (2003–2013) were determined. Total and seasonal PP were similar in both models. Average PP in five of the domains (0.47–0.77 g C m–2 d–1) was well above the global average (0.37 g C m–2 d–1). Over the East Greenland shelf, however, total annual PP was estimated to be only 0.19 g C m–2 d–1. The Norwegian shelf was the most productive of the regions studied. “Spring blooms” appear sporadically as spikes in the annual distribution of PP in some regions/years, but do not emerge as a dominant feature in the average annual development of PP in any of the domains. For all regions, ∼25% of the annual PP takes place in the period January-May. PP peaked over most of the study area at or around maximum insolation or temperature. PP in the study region as a whole appears to be more related to latitude or water masses than to bathymetry. In waters over the East Greenland shelf, the Norwegian shelf, and north of the GSR up to 50% of annual PP had taken place when ∼50% of the annual flux of light has reached the surface. In contrast, only about 35% of annual PP had taken place in the sub-polar gyre and waters over the southern open shelf by this time. Light-use efficiency differences may be explained by differences in mixed layer depth (MLD). Multi-model Earth System model studies have indicated that climate change may decrease the MLD in the sub-polar gyre and suggest this may lead to a decrease in the PP occurring here. The results presented here, however, suggest that a shallower MLD could lead to an increase in PP.

Crop traits enabling yield gains under more frequent extreme climatic events

Climate change (CC) in central China will change seasonal patterns of agricultural production through increasingly frequent extreme climatic events (ECEs). Breeding climate-resilient wheat (Triticum aestivum L.) genotypes may mitigate adverse effects of ECEs on crop productivity. To reveal crop traits conducive to long-term yield improvement in the target population of environments, we created 8,192 virtual genotypes with contrasting but realistic ranges of phenology, productivity and waterlogging tolerance. Using these virtual genotypes, we conducted a genotype (G) by environment (E) by management (M) factorial analysis (G×E×M) using locations distributed across the entire cereal cropping zone in mid-China. The G×E×M invoked locally-specific sowing dates under future climates that were premised on shared socioeconomic pathways SSP5-8.5, with a time horizon centred on 2080. Across the simulated adaptation landscape, productivity was primarily driven by yield components and phenology (average grain yield increase of 6-69% across sites with optimal combinations of these traits). When incident solar radiation was not limiting carbon assimilation, ideotypes with higher grain yields were characterised by earlier flowering, higher radiation-use efficiency and larger maximum kernel size. At sites with limited solar radiation, crops required longer growing periods to realise genetic yield potential, although higher radiation-use efficiency and larger maximum kernel size were again prospective traits enabling higher rates of yield gains. By 2080, extreme waterlogging stress in some regions of mid-China will impact substantially on productivity, with yield penalties of up to 1,010 kg ha−1. Ideotypes with optimal G×M could mitigate yield penalty caused by waterlogging by up to 15% under future climates. These results help distil promising crop trait by best management practice combinations that enable higher yields and robust adaptation to future climates and more frequent extreme climatic events, including flash flooding and soil waterlogging.

A first assessment of particle flux over the South Brazil Bight continental slope

The vertical export of particulate organic carbon (POC) is a key component of the oceanic biological pump, which regulates the distribution of elements associated with primary production in the upper euphotic zone. To better understand how oceanic biogeochemical processes will respond in climate change scenarios, it is necessary to comprehend the mechanisms that regulate the magnitude and variations of POC flux among distinct oceanic domains, including the boundaries between continental margins and oceanic basins. This work is a first assessment of particle flux over the South Brazil Bight continental slope, a tropical western boundary system dominated by the Brazil Current. Total mass flux, and specific carbon, nitrogen and biogenic silica fluxes were measured periodically by two sediment traps installed at 450 m deep over the 900 m isobath, and at 750 m deep over the 2000 m isobath, from November 2016 to September 2017. Due to strong contribution of advected particles, high and extremely irregular fluxes were registered at the 900 m isobath site (mean = 1.1 g m-2 d− 1, stand. dev. ± 2.9 g m-2 d-1). The captured material suggests this sediment trap was affected by turbidity flows, which precludes the estimation of the biological pump contribution over this isobath. Over the 2000 m isobath, POC flux (3.7 mg m-2 d-1; ±2.5 mg m-2 d− 1) followed the seasonal pattern of primary production measured by satellite. POC flux was strongly correlated with biogenic silica content (r = 0.79; p < 0.01). This ballast mineral was more efficient than CaCO3 in transferring carbon from the base of the euphotic zone to 750 m. We suggest intra-seasonal variations found in the POC flux over the 2000 m isobath to be related to Brazil Current instabilities. Measurements of δ15N suggest that higher POC flux occurred during subsurface nitrate-based production regimes. A regional exponential export model is proposed, with a mean exponent (b value) of 1.4 (±0.3). Although initial, the results of this work allow us to estimate that 2.2 × 1012 g C is exported annually over the South Brazil Bight continental slope.

Multifunctions of fine root phenology in vegetative and reproductive growth in mature beech forest ecosystems

AbstractMasting, a large interannual variation of reproductive investment spatially synchronized within perennial plants, is an important factor for understanding the use and storage of carbon and nutrient resources in various forest ecosystems. Fine roots play essential roles in water and nutrient uptake but also consume a large amount of net primary production (NPP) during growth. Understanding masting mechanisms requires knowledge of how masting trees succeed in abundant fructification by adjusting resource acquisition via fine roots and how mast seeding influences fine root dynamics. The aims of the present study were to estimate interannual variations in NPP, including belowground production, over several years while evaluating fine root phenology and to suggest possible relationships between fine root growth and the production of nuts, leaves, and woody biomass in mature forest ecosystems. Nut and leaf production was quantified using litter traps in two beech forests located at different altitudes of 550 and 1500 m, and woody biomass increment was estimated using allometric equations. Seasonal and mass‐based annual fine root production was determined using minirhizotrons and soil cores. Correlations between seasonal fine root and aboveground production were analyzed using the state‐space model, which can verify masting effects on fine root production between flower bud formation and mast seeding. NPP estimates exhibited almost biennial or irregular masting behavior. Nut production was positively correlated with fine root production in autumn of the previous year in the lower site but showed negative correlations with that in the early growing season of the same year in the upper site. The former indicated enhanced fine root production to absorb nutrients prior to mast seeding, and the latter suggested the suppression of fine root production due to resource depletion during masting. Fine root production increased with increasing leaf production in the early growing season in both sites, indicating the corresponding water transport between them. Thus, fine root phenology has distinct multiple functions to ensure water and nutrient resources in supporting aboveground vegetative and reproductive growth, resulting in variable seasonal patterns of fine root production.

Phenology and Fraser River sockeye salmon marine survival

Inspired by the pioneering work of Dr. Bill Peterson who demonstrated the utility of ocean indicators at predicting survival of coho and chinook salmon in the Columbia River, we investigated whether the phenology of primary productivity could explain variable marine survival of Fraser River sockeye salmon. Building on a study that had found a strong correlation between satellite-derived spring chlorophyll concentrations in Queen Charlotte Sound (British Columbia) and smolt survival, we hypothesized that smolt migration phenology could help to explain interannual survival differences among years. Applying a new migration model to 18 years of smolt migration data from Chilko Lake demonstrated that interannual differences in smolt migration timing were organized in up to 3 pulses of abundance with a general trend by the largest peak toward earlier peak migration dates over the time series (1998–2016). Analysis of satellite-derived fluorescence line height data within Queen Charlotte Sound identified 4 productivity domains through which most young sockeye salmon would migrate. Each domain had distinct seasonal productivity patterns. With these data, we were unable to demonstrate significant correlations between spring bloom dates in these domains and smolt marine survival, or between smolt migration timing and marine survival. Having separate survival estimates for each pulse and phenological indicators of the sockeye salmon prey base might improve our ability to test the hypothesis that phenology matters to sockeye salmon in the Queen Charlotte Sound region.