Estimates Of Production Research Articles

Landscape metrics predict habitat redundancy of restored oyster reefs for recruitment enhancement of dominant estuarine fish

Abstract Structured biogenic habitats in estuarine and coastal landscapes (or seascapes) augment nekton species' production; yet, landscape setting may make restored habitats functionally redundant to co‐occurring habitats for some species. Few relationships between recruitment enhancement and continuous landscape metrics have been quantified, limiting our ability to incorporate functional redundancy into restoration practice. To address this gap, we quantified two landscape metrics, per cent structure of proximal habitat and near distance to co‐occurring structured habitats, for experimentally restored oyster reefs in an intertidal landscape in Middle Marsh, North Carolina, USA. We then examined relationships between each landscape metric and recruitment enhancement of juvenile pinfish (Lagodon rhomboides), a species that uses multiple biogenic habitats and has high site fidelity as juveniles. We estimated that reefs with <33% proximal structured habitat within 15 m and <59% within 50 m contributed to recruitment enhancement, as defined by greater juvenile pinfish abundances at reefs than controls and no overlap in 95% confidence intervals. Additionally, functional redundancy, assigned according to per cent structure of proximal habitat within 15 or 50 m of restored reefs, reduced estimates of nekton recruitment enhancement in this experimental landscape by 58.3% or 33.3%, respectively. Estimates of nekton production augmented by restoration may be inflated if they have not been adjusted to account for habitat redundancy. Synthesis and applications. Landscape metrics offer some predictive capacity to help restoration practitioners avoid habitat redundancy for recruitment enhancement and extend beyond considering individual habitats toward integrating landscape scale processes into predictions and restoration practice. We demonstrated a straightforward process for restoration practitioners to identify where habitats may be redundant and incorporate the landscape into restoration siting decisions: delineate co‐occurring structured habitat with publicly accessible orthoimagery and model juvenile nekton relationships with proximal percent structure and near distance to other structured habitats.

Validating a cassava production spatial disaggregation model in sub-Saharan Africa.

Cassava is a staple in the diet of millions of people in sub-Saharan Africa, as it can grow in poor soils with limited inputs and can withstand a wide range of environmental conditions, including drought. Previous studies have shown that the distribution of rural populations is an important predictor of cassava density in sub-Saharan Africa's landscape. Our aim is to explore relationships between the distribution of cassava from the cassava production disaggregation models (CassavaMap and MapSPAM) and rural population density, looking at potential differences between countries and regions. We analysed various properties of cassava cultivations collected from surveys at 69 locations in Côte d'Ivoire and 87 locations in Uganda conducted between February and March 2018. The relationships between the proportion of surveyed land under cassava cultivation and rural population and settlement data were examined using a set of generalized additive models within each country. Information on rural settlements was aggregated around the survey locations at 2, 5 and 10 km circular buffers. The analysis of the original survey data showed no significant correlation between rural population and cassava production in both MapSPAM and CassavaMap. However, as we aggregate settlement buffers around the survey locations using CassavaMap, we find that at a large scale this model does capture large-scale variations in cassava production. Moreover, through our analyses, we discovered country-specific spatial trends linked to areas of higher cassava production. These analyses are useful for validating disaggregation models of cassava production. As the certainty that existing cassava production maps increases, analyses that rely on the disaggregation maps, such as models of disease spread, nutrient availability from cassava with respect to population in a region, etc. can be performed with increased confidence. These benefit social and natural scientists, policymakers and the population in general by ensuring that cassava production estimates are increasingly reliable.

FLOWERS AEP: An Analytical Model for Wind Farm Layout Optimization

ABSTRACTAnnual energy production (AEP) is commonly used in objective functions for wind farm layout optimization. AEP is proportional to wind farm power production integrated over an annual distribution of free‐stream wind conditions. Physics‐based estimates of wind farm power production typically rely on low‐fidelity engineering wake models that approximate the steady‐state wind farm flow field. AEP estimates are then obtained by performing independent simulations for discrete wind conditions and using rectangular quadrature to account for each condition's expected frequency of occurrence. Depending on the number of simulated discrete wind conditions, this numerical integral could be hampered by poor accuracy or high computational costs. The FLOWERS AEP model instead poses an analytical integral of the engineering wake model over the variable wind conditions, yielding a closed‐form, analytical function for wind farm AEP. This paper derives the analytical functions for FLOWERS AEP and its derivatives with respect to turbine position, which are useful for gradient‐based wind farm layout optimization, in nondimensional form. We then analyze the benefits of the FLOWERS AEP model over conventional reference models, focusing on its low cost, adequate wake loss predictions, and smooth design space. Although the FLOWERS approach is found to predict the exact value of AEP with some error relative to the reference model (within 14% on average), it dramatically reduces computation time by an order of magnitude, produces a qualitatively similar design space at relatively low resolution, and yields comparable optimal layouts. This significant speed improvement is critical in layout optimization applications, where determining an optimal layout in an efficient manner is more important than precise AEP prediction.

Integration of airborne geophysical data for the characterization of the geothermal system in Valle del Cura, high central Andes

The Valle del Cura region, located in the High Central Andes, over the Pampean flat-slab segment, exhibits several geothermal manifestations suggesting structural rather than magmatic controls. However, the structural and favorable conditions of this geothermal system have not been studied in detail yet. By integrating high-resolution airborne geophysical data with existing geological information using GIS tools, we can better characterize the structural controls of the thermal areas. The application of the upward continuation filter and tilt-angle derivative to the reduced to pole magnetic anomaly map allowed us to delineate structural lineaments that correlate well with known faults in the area. Euler deconvolution successfully identified local structures controlling the subsurface water upwelling and provided reliable depth estimations. Results indicate that the Despoblados area is characterized by NW-oriented deep structural controls reaching depths of up to 5 km. In contrast, the Bañitos-Gollete and the northern thermal areas are controlled by the intersection of N-S with E-W shallow structures at less than 3 km deep. Aeroradiometric data provided additional information for the characterization of several lithological units based on the dominance of radioelements (K, Th and U) in near-surface rocks by using individual and ternary maps. In addition, estimations of radiogenic heat production were crucial for assessing the potential energy of geothermal resources in the region. In particular, higher average values were obtained for igneous and metamorphic basement which may contribute to the geothermal gradient and temperature distribution at depth. Further studies, including new geophysical data acquisition and field verification, are still needed to fully describe the regional and local structures involved in these geothermal areas.

Accurate Estimation of Gross Primary Production of Paddy Rice Cropland with UAV Imagery-Driven Leaf Biochemical Model

Accurate estimation of gross primary production (GPP) of paddy rice fields is essential for understanding cropland carbon cycles, yet remains challenging due to spatial heterogeneity. In this study, we integrated high-resolution unmanned aerial vehicle (UAV) imagery into a leaf biochemical properties-based model for improving GPP estimation. The key parameter, maximum carboxylation rate at the top of the canopy (Vcmax,025), was quantified using various spatial information representation methods, including mean (μref) and standard deviation (σref) of reflectance, gray-level co-occurrence matrix (GLCM)-based features, local binary pattern histogram (LBPH), and convolutional neural networks (CNNs). Our models were evaluated using a two-year eddy covariance (EC) system and UAV measurements. The result shows that incorporating spatial information can vastly improve the accuracy of Vcmax,025 and GPP estimation. CNN methods achieved the best Vcmax,025 estimation, with an R of 0.94, an RMSE of 19.44 μmol m−2 s−1, and an MdAPE of 11%, and further produced highly accurate GPP estimates, with an R of 0.92, an RMSE of 6.5 μmol m−2 s−1, and an MdAPE of 23%. The μref-GLCM texture features and μref-LBPH joint-driven models also gave promising results. However, σref contributed less to Vcmax,025 estimation. The Shapley value analysis revealed that the contribution of input features varied considerably across different models. The CNN model focused on nir and red-edge bands and paid much attention to the subregion with high spatial heterogeneity. The μref-LBPH joint-driven model mainly prioritized reflectance information. The μref-GLCM-based features joint-driven model emphasized the role of GLCM texture indices. As the first study to leverage the spatial information from high-resolution UAV imagery for GPP estimation, our work underscores the critical role of spatial information and provides new insight into monitoring the carbon cycle.

Numerical Estimation of Potable Water Production for Single-Slope Solar Stills in the Caspian Region

This study provides a detailed numerical assessment of the productivity of single-slope solar stills across key cities in the Caspian region, including Aktau, Atyrau, Astrakhan, Makhachkala, Baku, Tehran, and Turkmenbashi. Employing a mathematical model based on heat balance equations and the Fourth Order Runge–Kutta method, we simulated the distillation process under various climatic conditions. The results reveal that productivity is significantly influenced by geographic location and local meteorological conditions. Tehran demonstrated the highest productivity across all seasons, with values of 1.75 × 103 kg/(m2·year) and an efficiency of 0.53, due to its optimal solar irradiation and ambient temperature. In contrast, Atyrau and Makhachkala exhibited lower productivity, particularly in colder months, highlighting the effect of ambient temperature on solar still efficiency. The analysis identified the optimal water depth at 2 cm and insulation thickness between 4 and 9 cm for enhancing productivity in continental climates like Aktau. Additionally, the lowest cost of distilled water was USD 0.024 per kilogram in Baku. These findings align with the existing literature, validating the numerical model’s accuracy. Future research will explore integrating solar stills with other renewable and fossil fuel-based technologies.

Hybrid modeling approach for precise estimation of energy production and consumption based on temperature variations

This study introduces an advanced mathematical methodology for predicting energy generation and consumption based on temperature variations in regions with diverse climatic conditions and increasing energy demands. Using a comprehensive dataset of monthly energy production, consumption, and temperature readings spanning ten years (2010–2020), we applied polynomial, sinusoidal, and hybrid modeling techniques to capture the non-linear and cyclical relationships between temperature and energy metrics. The hybrid model, which combines sinusoidal and polynomial functions, achieved an accuracy of 79.15% in estimating energy consumption using temperature as a predictor variable. This model effectively captures the seasonal and non-linear consumption patterns, demonstrating a significant improvement over conventional models. In contrast, the polynomial model for energy production, while yielding partial accuracy (R² = 0.65), highlights the need for more advanced techniques to fully capture the temperature-dependent nature of energy production. The results indicate that temperature variations significantly affect energy consumption, with higher temperatures driving increased energy demand for cooling, while lower temperatures affect production efficiency, particularly in systems like hydropower. These findings underscore the necessity for integrating sophisticated models into energy planning to ensure resilience in energy systems amidst climate variability. The study offers critical insights for policymakers to optimize energy generation and distribution in response to changing climatic conditions.

Remote sensing and computer vision for marine aquaculture.

Aquaculture, the cultivation of aquatic plants and animals, has grown rapidly since the 1990s, but sparse, self-reported, and aggregated production data limit the effective understanding and monitoring of the industry's trends and potential risks. Building on a manual survey of aquaculture production from remote sensing imagery, we train a computer vision model to identify marine aquaculture cages from aerial and satellite imagery and generate a spatially explicit dataset of finfish production locations in the French Mediterranean from 2000 to 2021 including 4010 cages (average cage area, 69 square meters). We demonstrate the value of our method as an easily adaptable, cost-effective approach that can improve the speed and reliability of aquaculture surveys and enables downstream analyses relevant to researchers and regulators. We illustrate its use to compute independent estimates of production and develop a flexible framework to quantify uncertainty in these estimates. Overall, our study presents an efficient, scalable, and adaptable method for monitoring aquaculture production from remote sensing imagery.

A productive friction: Leveraging misalignments between local ecological knowledge and remotely sensed imagery for forest conservation planning

AbstractEarth's forests are continually monitored by both the satellite record and the lived experiences of nearly 2 billion forest‐proximate peoples. Generally, the satellite record summarizes production estimates, such as percent tree cover, at regular, relatively coarse scales. Conversely, local perceptions tend to capture changes at irregular and very fine scales. While the utility of both of these sources of information has been widely demonstrated in isolation, little work has explored in what contexts they are expected to correlate or deviate, or how they might be quantitatively integrated. Here, we collect gridded information on community perceived and remotely sensed mangrove cover change across 719 0.5‐km grids in Pemba Island, Tanzania. We reveal variation in the association between these two data sources across different wards (shehia) and explore the reasons for this variation using interviews and direct observation. We find that shehia with the greatest alignment between perceived and remotely sensed mangrove change tended to have little planting or natural regeneration of mangrove propagules and large areas of complete cover loss. Alternatively, in shehia with the lowest alignment, we find high levels of natural and/or human‐assisted mangrove recolonization and selective harvesting of individual trees and branches. These findings indicate that the alignment between local knowledge and satellite observations of mangrove cover change systematically increases with the scale of change in this system. Finally, we demonstrate a practical workflow for quantitatively leveraging these misalignments by optimizing across both data sources to identify restoration priority areas.

Overlooked branch turnover creates a widespread bias in forest carbon accounting

Most measurements and models of forest carbon cycling neglect the carbon flux associated with the turnover of branch biomass, a physiological process quantified for other organs (fine roots, leaves, and stems). Synthesizing data from boreal, temperate, and tropical forests (184,815 trees), we found that including branch turnover increased empirical estimates of aboveground wood production by 16% (equivalent to 1.9 Pg Cy-1 globally), of similar magnitude to the observed global forest carbon sinks. In addition, reallocating carbon to branch turnover in model simulations reduced stem wood biomass, a long-lasting carbon storage, by 7 to 17%. This prevailing neglect of branch turnover suggests widespread biases in carbon flux estimates across global datasets and model simulations. Branch litterfall, sometimes used as a proxy for branch turnover, ignores carbon lost from attached dead branches, underestimating branch C turnover by 38% in a pine forest. Modifications to field measurement protocols and existing models are needed to allow a more realistic partitioning of wood production and forest carbon storage.

Varying photosynthetic quotients strongly influence net kelp primary production and seasonal differences increase under warming

Reliable net primary production (NPP) estimations of kelp forests are important to evaluate their C-fixation potential. Photosynthetic oxygen measurements can be converted to C-fixation using photosynthetic quotients (PQs). Although there is a known high variability in PQs, the extent and the consequences for NPP is understudied in kelp species. Thus, the present study aimed (i) to quantify the variability of PQs, (ii) to model NPP and (iii) to assess the impact of warming on both. The kelp, Laminaria hyperborea, was studied near the island of Helgoland (North Sea, Germany) along a depth gradient (2, 4, 6 m below mean low water spring tide) across all four seasons. Blade discs were cultivated during at least 6 days per season under simulated ambient photosynthetic photon flux density (PPFD) and temperature conditions and, in parallel, in a warming scenario (+ 4°C). PQs were calculated from parallel oxygen production and 14C-fixation measurements at saturating PPFD at the end of the incubation period. Seasonal PQs varied between 1.7 and 4.4, with highest values in summer due to increased oxygen production. The warming scenario stimulated C-fixation in most seasons, lowering the PQ in comparison to ambient temperature conditions, while collection depth had no significant effect on PQs. The seasonal PQs were used to model daily NPP rates for kelp standing stock at 4 m depth. These daily NPP rates were compared between temperature treatments and with daily NPP rates based on fixed PQs. The warming scenario had a stimulating effect on daily NPP rates in the high-light season spring. In the low-light season autumn, warming resulted in negative daily NPP rates, as the high respiration rates could not be compensated by gross photosynthesis. Overall, annual NPP rate under warming conditions (347 g C m–2 yr–1) was 14% higher than the annual NPP rate under ambient conditions (303 g C m–2 yr–1). Modelling daily NPP with fixed PQs, which neglects the seasonal variation of the PQs, led to a high overestimation of up to 255%. We, therefore, recommend modelling NPP rates not with a fixed PQ, but with seasonal PQs determined under different temperature scenarios in order to obtain reliable future predictions.

Considering Embodied Greenhouse Emissions of Nuclear and Renewable Power Plants for Electrolytic Hydrogen and Its Use for Synthetic Ammonia, Methanol, Fischer-Tropsch Fuel Production.

Recent concerns surrounding climate change and the contribution of fossil fuels to greenhouse gas (GHG) emissions have sparked interest and advancements in renewable energy sources including wind, solar, and hydroelectricity. These energy sources, often referred to as "clean energy", generate no operational onsite GHG emissions. They also offer the potential for clean hydrogen production through water electrolysis, presenting a viable solution to create an environmentally friendly alternative energy carrier with the potential to decarbonize industrial processes reliant on hydrogen. To conduct a full life cycle analysis, it is crucial to account for the embodied emissions associated with renewable and nuclear power generation plants as they can significantly impact the GHG emissions linked to hydrogen production and its derived products. In this work, we conducted a comprehensive analysis of the embodied emissions associated with solar photovoltaic (PV), wind, hydro, and nuclear electricity. We investigated the implications of including plant-embodied emissions in the overall emission estimates of electrolysis hydrogen production and subsequently on the production of synthetic ammonia, methanol, and Fischer-Tropsch (FT) fuels. Results show that average embodied GHG emissions of solar PV, wind, hydro, and nuclear electricity generation in the United States (U.S.) were estimated to be 37, 9.8, 7.2, and 0.3 g CO2 e/kWh, respectively. Life cycle GHG emissions of electrolytic hydrogen produced from solar PV, wind, and hydroelectricity were estimated as 2.1, 0.6, and 0.4 kg of CO2 e/kg of H2, respectively, in contrast to the zero-emissions often used when the embodied emissions in their construction were excluded. Average life cycle emission estimates (CO2 e/kg) of synthetic ammonia, methanol, and FT-fuel from solar PV electricity are increased by 5.5, 16, and 49 times, respectively, compared to the case when embodied emissions are excluded. This change also depends on the local irradiance for solar power, which can result in a further increase of GHG emissions by 35-41% in areas of low irradiance or reduce GHG emissions by 21-25% in areas with higher irradiance.

Evidence for SARS-CoV-2 infected Golden Syrian hamsters (Mesocricetus auratus) reducing daily energy expenditure and body core temperature

Golden Syrian hamsters (Mesocricetus auratus) are a well-established animal model for human infections with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) due to their susceptibility to SARS-CoV-2 infection, robust virus replication and pathological manifestations similar to human COVID-19 pneumonia. To investigate the physiological changes upon infection in this animal model, we explored the alterations in daily energy expenditure (DEE), water turnover, body mass, body temperature, and locomotor activity in non-infected and SARS-CoV-2 infected Golden Syrian hamsters for four days post SARS-CoV-2. DEE was measured using the doubly labelled water method, which allows for the accurate estimation of carbon dioxide production and, consequently, energy expenditure in animals. Additionally, we investigated total water intake (TWI), which comprises drinking water, preformed water in food, and metabolic water. Using intraperitoneally implanted data loggers, we also monitored body core temperature and locomotor activity in some of the animals. Here we provide evidence for infected hamsters exhibiting significantly lower DEE and TWI compared to non-infected animals. We also observed an increase in body weight in the non-infected animals, while infected animals experienced weight loss. Further, infected animals showed a significantly decreased body temperature, indicating a generally lowered metabolic rate.

Modelling and Performance Evaluation of Machine Learning Techniques in Forecasting Cereal Yield for Shri Ganganagar Region, Rajasthan, India

One of agriculture's most difficult issues is predicting crop yield. Crop yield forecasting enables necessary decisions to be made to guarantee food security. The current study looks into the use of statistical and machine learning approaches for wheat and rice yield prediction using long-term weather and yield data of ShriGanganagar, Rajasthan, India. Weather-based models may give accurate crop production estimates, but choosing the right model for agricultural output projections can be difficult. As a result, different models were compared in this study to determine the best model for rice and wheat yield prediction including Multiple linear regression (SMLR), Artificial neural network (ANN), Least absolute shrinkage and selection operator (LASSO), Elastic net (ELNET), Ridge regression, and K-nearest neighbor (K-NN). K-NN outperformed ANN in rice crops and fared better in wheat crops based on the lowest nRMSE value. Ridge regression based on nRMSE was the next best model for Rice yield prediction in the examined region, and KNN was the second best model for Wheat crop.

Seasonal Climatic Variation and Flowering Intensity of Garcinia kola (Heckel) in a Humid Forest Plantation

Aim of study: The study designed to assess seasonal climatic variation and flowering intensity in G. kola in a humid forest plantation. Area of study: The study was conducted in the mono-plantation of Garcinia kola in the Swamp Forest Research station of the Forestry Research Institute of Nigeria, Onne, Rivers state, Nigeria. The plantation consists of 103 trees at 5×5m spacing. Material and methods: Climatic data were sourced from meteoblue.com. Flowering intensity was determined by the estimation of the total flower production per tree: total number of flower buds per inflorescence was determined by visual counting and then extrapolated for the total number of inflorescences per twig, total number of twigs per branch and total number of branches per tree to determine the average flowering intensity per tree. A total of 9 trees and twenty-five inflorescences per tree, were sampled. Data was analysed using covariance and analysis of variance. Main results: Rainfall, wind speed, and flowering intensity varied significantly (p ≤ 0.05) between seasons; seasonal rainfall variation influence on flowering intensity varied with tree gender. Research highlights: Rainfall is the flowering cue of the plantation; we recommend that farmers target low to moderate rainfall seasons for high fruit and seed yields.

Mandatory Notice of Layoff, Job Search, and Efficiency

Abstract In all OECD countries, mandatory notice (MN) policies require firms to inform workers in advance of a layoff. In our theoretical framework, MN helps workers avoid unemployment and find better jobs by encouraging them to search for a new job while still employed, thereby increasing future production. The magnitude of this production gain depends on the relative effectiveness of search while employed versus unemployed. But on-the-job search and diminished work incentives reduce current production. If future gains outweigh current production losses, longer advance notice improves production efficiency. If not, Coasian bargaining predicts that firms offer a larger severance instead of longer notice. With bargaining, the sole efficiency loss of MN is due to delayed separations of unproductive job matches. We test these predictions using novel Swedish administrative data on layoff notifications. Workers eligible for extended MN receive longer notice and larger severance, resulting in less exposure to nonemployment spells and higher-paying jobs. These favorable labor market outcomes are solely due to longer notice; in contrast, larger severance delays job finding and has no impact on wages. We also show that advance notice replaces job search while unemployed with more effective search while employed. On the production side, we document a productivity drop among notified workers and estimate a production loss due to delayed separations. Using our estimates of production gains and losses to evaluate the overall production efficiency, we conclude that the gains of MN seem to outweigh the losses.

A Thermodynamic Comparison of Exergy Production from Sugarcane and Photovoltaic Modules in the Context of Brazilian Energy Transition

This article applies the exergy analysis to the production and use of sugarcane, considering a model published in the literature. In this way, we compute incident solar irradiation, carbohydrate production, water consumption, and the production of stalks and straws. Following the production estimate, we analyze a biorefinery production cycle, from solar irradiation to the biorefinery products on an exergy basis, from birth to production of sugar, electrical energy, and ethanol. The calculated sugarcane production values are 80.7 tons per hectare for a 52-week cycle. As a result, the average exergy efficiency of sugarcane is 4.99%, reaching peaks of 8.3%. When considering only the useful exergy generated in the production of stalks and straw, an annual yield of 17.86 kWh/m2 represents an overall exergy efficiency of 1.31%. Considering the energy conversion processes in the biorefinery, the exergy efficiency from the radiation to the products from the biorefinery was 0.38%. The photovoltaic modules already have a well-established application in the country, though they need to increase their insertion over time, whereby the panels exhibit an average exergy efficiency of 31.6%, resulting in an annual electrical energy production of 255.84 kWh/m2. The results show that photovoltaic modules are a more efficient alternative than sugarcane regarding exergy land use. In conclusion, this study briefly discusses the use of sugarcane and photovoltaic modules in the context of Brazil’s energy transition towards a reduced dependence on fossil fuels, based on the fact that sugarcane already has a low carbon footprint for transportation using ethanol, with supply from more than 40,000 stations, and a similar or lower carbon footprint than electrical vehicles used across the country.

Scaling up taxon-specific microbial traits to predict community-level microbial activity in agricultural systems

Soil microorganisms perform many important ecosystem functions including nitrogen (N) cycling which dictates plant productivity in agricultural ecosystems. Despite the importance of these communities, connecting microbial composition with ecosystem function has been a long standing challenge. Taxon-specific substrate assimilation traits, measured with quantitative stable isotope probing (qSIP), may provide a means to scale from microbial community composition to community-level process rates. To test the potential for scaling up taxon-specific N assimilation to predict community-level rates of carbon mineralization, N mineralization, and N immobilization we measured soil properties, microbial activity, and N assimilation using 15N qSIP in soils from six distinct farms. N assimilation, measured as DNA 15N enrichment, varied among taxa and within taxa across farms. Taxon specific N assimilation was aggregated to calculate a community-weighted mean, which when combined with measures of microbial biomass was used to estimate new microbial biomass N production. This estimate of new microbial biomass production reflects the growth of active microbes over the incubation period and related to microbial activity. The new microbial biomass N produced was predictive of soil C and N mineralization rates, explaining 37-47% of the observed variation across the six farming systems. This approach highlights the ability of trait-based methods to relate microbial community structure data to microbially-mediated functional process rates. Such advances may enhance our ability to understand and manage microbially mediated processes, such as N cycling, in both natural and agricultural ecosystems.

Gross Primary Production of Antarctic Landfast Sea Ice: A Model‐Based Estimate

AbstractMuch of the Antarctic coast is covered by seasonal landfast sea ice (fast ice), which serves as an important habitat for ice algae. Fast‐ice algae provide a key early season food source for pelagic and benthic food webs, and contribute to biogeochemical cycling in Antarctic coastal ecosystems. Summertime fast ice is undergoing a decline, leading to more seasonal fast ice with unknown impacts on interconnected Earth system processes. Our understanding of the spatiotemporal variability of Antarctic fast ice, and its impact on polar ecosystems is currently limited. Evaluating the overall productivity of fast‐ice algae has historically been hampered by limitations in observations and models. By linking new fast‐ice extent maps with a one‐dimensional sea‐ice biogeochemical model, we provide the first estimate of the spatio‐seasonal variability of Antarctic fast‐ice algal gross primary production (GPP) and its annual primary production on a circum‐Antarctic scale. Experiments conducted for the 2005–2006 season provide a mean fast ice‐algal production estimate of 2.8 Tg C/y. This estimate represents about 12% of overall Southern Ocean sea‐ice algae production (estimated in a previous study), with the mean fast‐ice algal production per area being 3.3 times higher than that of pack ice. Our Antarctic fast‐ice GPP estimates are probably underestimated in the Ross Sea and Weddell Sea sectors because the sub‐ice platelet layer habitats and their high biomass are not considered.

Pulsed wind-driven control of phytoplankton biomass at a groundwater-enriched nearshore environment

Along some Mediterranean coastal areas and other world regions, nutrient and chlorophyll concentrations often show gradient increases of up to one order of magnitude perpendicular to the coast. This nearshore stripe, extending a few hundred meters from the coast, is enriched by submarine groundwater discharges (SGD) containing elevated nutrient concentrations that may eventually sustain high biomass phytoplankton blooms. During a survey carried out in the summer of 2018, we examined the short-term (hours) variability of the phytoplankton biomass (measured as chlorophyll; Chl) in response to environmental changes associated with SGD and wind forcing in the nearshore waters of Palma Beach (Mediterranean Sea). Continuous CTD records revealed a general salinity decline indicative of SGD along the shoreline. Large and pulsed salinity fluctuations (i.e. 2–3 psu variations, 1–4 h) were observed each day that were consistent with offshore advection episodes of the lower salinity water retained in the nearshore (peak crosshore velocity 5–6 cm s−1). Chl near the shoreline was markedly higher than offshore (3.55 ± 1.29 and 0.68 ± 0.27 mg m−3 respectively) but recurrently fluctuated in the afternoon to up to >7 mg m−3. Primary production estimations showed that despite the higher production in the nearshore (50.29 ± 10.98 μmol O2 L−1 d−1, 4-fold offshore values) productivity per unit chlorophyll did not significantly vary (p > 0.01) therefore suggesting that, at this time scales, high biomass episodes in the nearshore are driven by an accumulation mechanism. Statistical analysis (CCA) demonstrates that Chl variability is largely explained (93 %) by variations in wind and current velocity. Our results provide evidence that the dynamics of this nearshore environment are modulated by the interplay between the shoreward wind-induced flow and the offshore directed density flow. This mechanism could explain the occurrence and episodic nature of high biomass blooms in the nearshore, as well as be an important factor influencing the microbial community structure at the coastal zone.