Biomass Data Research Articles

Future trends of marine fish biomass distributions from the North Sea to the Barents Sea

Climate warming is one of the facets of anthropogenic global change predicted to increase in the future, its magnitude depending on present-day decisions. The north Atlantic and Arctic Oceans are already undergoing community changes, with warmer-water species expanding northwards, and colder-water species retracting. However, the future extent and implications of these shifts remain unclear. Here, we fitted a joint species distribution model to occurrence data of 107, and biomass data of 61 marine fish species from 16,345 fishery independent trawls sampled between 2004 and 2022 in the northeast Atlantic Ocean, including the Barents Sea. We project overall increases in richness and declines in relative dominance in the community, and generalised increases in species’ ranges and biomass across three different future scenarios in 2050 and 2100. The projected decline of capelin and the practical extirpation of polar cod from the system, the two most abundant species in the Barents Sea, drove an overall reduction in fish biomass at Arctic latitudes that is not replaced by expanding species. Furthermore, our projections suggest that Arctic demersal fish will be at high risk of extinction by the end of the century if no climate refugia is available at eastern latitudes.

The convex relationship between plant cover and biomass: Implications for assessing species and community properties

AbstractQuestionsCover and biomass serve as common measures of species abundance in plant ecology. However, the underlying relationship between these two measures and its implications remain poorly understood. This makes results based on cover and biomass difficult to compare.LocationsWet meadow, southeast of České Budějovice, Czech Republic (48°57′ N, 14°36′ E).MethodWe developed theoretical expectations for systematic differences in characterizing vegetation using cover and biomass for species and community characteristics, including species diversity, temporal dynamics, and responses to experimental manipulations. We then tested these expectations using cover and biomass data from an experimental study of fertilization and dominant removal spanning 14 years (2001–2014).ResultsConsistent with our expectations, on average, species biomass corresponded to the power of species cover, with a power coefficient slightly below 3/2. Community diversity indices calculated using cover and biomass were tightly correlated but were higher for cover. Temporal variabilities based on cover and biomass for individual species were also correlated, but higher for biomass than cover. Though strongly correlated, cover data show much stronger asynchrony, suggesting higher importance of compensatory dynamics. However, using the sum of individual species' cover values as a measure of total community abundance or productivity is problematic. Such a measure is nearly independent of total biomass and leads to contradictory results when used to characterize temporal variability. Species‐ and community‐level responses to treatments were congruent between the measures.ConclusionsOur study provides theoretical background for a convex relationship between plant cover and biomass. The data analysis confirms the relationship and its consequences for describing species‐ and community‐level properties. Most characteristics are well correlated between cover and biomass, but with one metric systematically shifted higher in many cases. Total abundance is the most sensitive measure and is well characterized by sum of biomass, but not by sum of cover. Understanding these systematic differences allows meaningful comparison of studies based on biomass and cover.

Biomass mapping of fjordic cold-water coral reefs reveals distinct functional role of vertical wall habitat

Fjords provide unique habitats for large cold-water coral (CWC) reefs, typically growing on sills and vertical walls. Fjord reefs are among the most thriving CWC reefs in Norway. Yet, these reefs, especially the wall reefs, are notoriously understudied. Here, we mapped the biomass, total carbon (C) stocks and C turnover (as respiration) of the reef-building coral Lophelia pertusa (syn. Desmophyllum pertusum), and dominant, large CWC reef-associated suspension feeders (the sponges Geodia barretti and Mycale lingua, the CWC Madrepora oculata and the bivalve Acesta excavata) within the Hardangerfjord, Norway. Remotely Operated Vehicle (ROV) recorded videos from wall and sill reefs were used to estimate species-specific biomass. Coupled with high resolution terrain data (2 × 2 m), predictive maps of species biomass were produced using a random forest (RF) model. The resulting biomass data were integrated with species-specific C content and C respiration rates from literature to estimate C stocks and C turnover of wall versus sill reefs. Area-specific results from the RF models reveal that wall reefs had a higher habitat suitability for all species except L. pertusa, which was more dominant on the sills. Accordingly, the wall reefs supported an up to 11 times higher biomass, C stock, and turnover for all species, except for L. pertusa, which had two-fold higher values on the sill reef. As a result, the wall reefs showed a 1.5 to 4.8 times higher total mean C turnover by dominant suspension feeders (all studied species) compared to the sill reefs. With their high C turnover and their presumably wide distribution in Norwegian fjords and globally, benthic wall reef megafauna may have a substantial, but overlooked biomass and functional role within CWC reef systems.

Biomass fast pyrolysis kinetics using curie-point pyrolysis: Comparison with pyroprobe and thermogravimetric analysis

In this study, the kinetics of fast pyrolysis of pine wood biomass at short timescales is evaluated using analytical Curie-point and Pyroprobe® pyrolyzers. Curie-point pyrolysis was highly effective in ensuring that the biomass was heated to the set temperatures at very high heating rates and precise time scales. This is the first time the mass loss data of lignocellulosic biomass using a Curie-point pyrolyzer is being reported at short timescales of the order of 10−1 s. COMSOL Multiphysics® simulations, employed to determine the time required for the sample to heat-up to the final reaction temperature, proved that it was 0.2 s in the Curie-point pyrolyzer and 6–8 s in the Pyroprobe® reactor. Within a time period of 0.5 s for temperatures greater than 400 °C, complete conversion of the biomass was achieved using a Curie-point pyrolyzer. The conventional reaction kinetic models were not able to explain the pyrolysis behavior of the sample in a Curie-point pyrolyzer, especially at short timescales of 0–0.5 s. However, the kinetics until 0.4 s, corresponding to the release of light volatiles, is shown to follow the sigmoidal Avrami model, while the major devolatilization stage in the range of 0.4–0.5 s could not be described by a standard kinetic model. This showed that the reactions at short time scales are highly complex processes governed by the competing decomposition rates of the biomolecules. The isothermal mass loss experiments using the Pyroprobe® showed that pyrolysis followed first-order kinetics, and the rate of decomposition was slower as it took 10–20 s for the conversion to attain a steady value. The kinetic compensation for the pyrolysis of pinewood was constructed using the fast pyrolysis data and the kinetics of slow pyrolysis using isoconversional models, and it was found to be ln(A) = 0.18*Ea – 3.12.

Spatial distribution patterns and drivers of above- and below- biomass in Chinese terrestrial ecosystems

Unraveling the dynamics of the global carbon cycle and assessing the sustainability of terrestrial ecosystems are critically dependent on a comprehensive understanding of vegetation biomass. This exploration delves into the pivotal role of biomass within vegetation communities, emphasizing its impact on ecosystem health, productivity, and community structure development. These insights are invaluable for advancing ecological science and conservation efforts. The synthesis of aboveground (AGB) and belowground (BGB) biomass data from 4485 and 3442 locations across China, respectively, collates a wide range of published sources. Integrating this extensive dataset with environmental parameters and applying advanced machine learning techniques facilitated an in-depth analysis of AGB and BGB spatial patterns within China. Techniques such as variance decomposition analysis and piecewise structural equation modeling were employed to dissect the factors contributing to the spatial variability of vegetation biomass. Significant spatial heterogeneity in biomass distribution was uncovered, with vegetation biomass in the northwest markedly lower than in the southern and northeastern regions. It was observed that AGB consistently surpassed BGB. Climatic conditions, soil characteristics, and soil nutrients were found to significantly explain 53 % and 48 % of the total variance in AGB and BGB, respectively. Specifically, solar radiation and soil total nitrogen were identified as critical factors influencing variations in AGB and BGB. The findings offer profound contributions to the understanding of the global carbon balance and the evaluation of terrestrial ecosystems sustainability. Moreover, they provide essential insights into the ecosystems' response mechanisms to global changes, serving as a fundamental reference for future studies on terrestrial ecosystem carbon cycling and carbon sequestration potentials.

Evaluation of the Compound Effects of the 2022 Drought and Heatwave on Selected Forest Monitoring Sites in Hungary in Relation to Its Multi-Year Drought Legacy

The effects of the changing frequency and severity of drought events in Central Europe may become a growing concern for its forests. In this study, we looked into how Hungary’s forests have been affected by the 2022 compound heatwave and drought, following an arid period from 2018 to 2021. We used our active intensive monitoring plots of the Forest Protection Measuring and Monitoring System (Level II in the ICP Forests) across the country between 2017 and 2022. We analyzed satellite images to support a survey of the large-scale drought utilizing moderate and high-resolution data. The health state of the forest calculated and mapped on the NDVI, ZNDVI, and NDWI indices showed damage and regeneration throughout the period studied. Overall, the forest stands observed tolerated the negative impacts of the drought (126–204 mm water deficit in 2022) based on our biomass data (the summer leaf loss was 14% in each monitoring plot). However, the classified Z-NDVI values of the Sentinel-2 satellite imagery for the period 2017–2022 showed a severe drought in 2022, which was followed by some improvement in 2023.

Performance Evaluation of AquaCrop Model of Tomato under Stage Wise Deficit Drip Irrigation at Southern Ethiopia

Crop modeling is a powerful tool for predicting yield and water productivity. The aim of the study was to calibrate and validate the AquaCrop model for tomato under staged deficit drip irrigation in Ethiopia. The AquaCrop model was calibrated and validated by using the observed data of canopy cover, biomass, dry yield, and soil water content. The results showed that the model was accurate in predicting canopy cover, biomass, and dry yield under different water levels. The overall performance in simulating canopy cover of AquaCrop, biomass, and soil water content showed a good match between measured and simulated data. The calibration results indicated good performance on canopy cover with 0.96 ≤ r ≤ 1.00, 2.9% ≤ RMSE ≤ 8.0%, 7.5% ≤ CV (RMSE) ≤ 21.1%, 0.91 ≤ EF ≤ 0.99, and 0.98 ≤ d ≤ 1.00. On biomass, the model calibration values were 0.99 ≤ r ≤ 1.00, 0.3 t/ha ≤ RMSE ≤ 0.8 t/ha, 8.3% ≤ CV (RMSE) ≤ 25.2%, 0.92 ≤ EF ≤ 0.99, and 0.98 ≤ d ≤ 1.00. Soil water content displayed poor performance on calibration, with performance values of 0.54 ≤ r ≤ 0.82, 3.6 mm ≤ RMSE ≤ 24.4 mm, 1.30% ≤ CV (RMSE) ≤ 9.00%, −4.18 ≤ EF ≤ 0.00, and 0.66 ≤ d ≤ 0.81. The validation results demonstrated that the model performed well on canopy cover with 0.96 ≤ r ≤ 1.00, 3.1% ≤ RMSE ≤ 7.7%, 8.2% ≤ CV (RMSE) ≤ 20.8%, EF ≥ 0.91, and d ≥ 0.9. The model validation correctly predicted biomass with r ≥ 0.98, 0.3 t/ha ≤ RMSE ≤ 0.7 t/ha, 8.9% ≤ CV (RMSE) ≤ 21.8%, EF ≥ 0.94, and d ≥ 0.98. The model validation poorly performed in forecasting soil water content, 0.20 ≤ r ≤ 0.80, 10.2 t/ha ≤ RMSE ≤ 24.0 t/ha, 3.5% ≤ CV (RMSE) ≤ 9.0%, −22.76 ≤ EF ≤ 0.63, and 0.40 ≤ d ≤ 0.87. The AquaCrop model is easy to use, requires less input data, and has high simulation accuracy, making it a useful tool for predicting crop yields and water productivity under staged deficit irrigation in areas with limited data.

Regular widespread aggregations of the oceanic jellyfish Pelagia noctiluca in the northeast Atlantic over 11 years

The jellyfish Pelagia noctiluca form extensive aggregations in the Mediterranean Sea that can exert top-down control on prey and cause mass-mortalities in fish farms. The description of extensive aggregations of P. noctiluca in the northeast Atlantic in 2007 coupled with a subsequent mortality event of farmed salmon in Northern Ireland prompted further investigation of the species in this region. We collected standardised P. noctiluca biomass from 1938 fisheries trawls over 11 years (2008–2018) covering ∼200,000 km2 of northeast Atlantic shelf seas. While a linear trend in P. noctiluca was not identified, the mean annual biomass exhibited significant fluctuations, with particularly elevated levels in 2009, 2013, 2014 and 2016 (0.05, 0.02, 0.01 and 0.02 kg ha−1, respectively). Mean biomass was higher in the northern (0.028 kg ha−1) and western portions (0.005 kg ha−1) of the study area compared to the southwestern (0.003 kg ha−1) and southeastern portions (0.001 kg ha−1). A binomial generalised linear mixed model of P. noctiluca biomass data indicated that higher P. noctiluca occurrence was driven by a positive Eastern Atlantic Pattern index (strongest effect), south-easterly winds, a negative North Atlantic Oscillation index and lower sea-surface temperatures (weakest effect). The fixed effects accounted for 36.81% of the variance in P. noctiluca occurrence data, while a random year effect further explained an additional 24.06% of the variance. These conditions may destabilise the European Slope Current in autumn-winter which promotes the transport of P. noctiluca from oceanic sources onto the northeast Atlantic shelf. Our findings represent a dramatic shift in our understanding of this species in the northeast Atlantic which may have significant ecological impacts, from predation on fish eggs and larvae to providing food for oceanic sunfish.

Role of mangrove afforestation in the high saline zone of Bangladesh: Changes of carbon stock with stand age and species composition

The carbon storage in Bangladeshi mangrove plantations is influenced by site-specific variations and plantation management practices. This study was focused on evaluating the impact of stand age and above-ground factors on the carbon storage of the western-side high-saline mangrove plantations in Bangladesh. A comprehensive field study was conducted, encompassing mangrove plantations aged 3, 5, 7, 10, 12, 15, and 35 years, to assess their potential for ecosystem carbon storage. The biomass data was calculated by estimating the tree height and diameter at breast height (DBH) with common allometric equations and soil samples were collected from every plot for estimating the organic carbon in the soil of the mangrove plantations. Stand structure attributes showed significant differences among all age classes and Sonneratia apetala was found to be dominant among all other species (IV = 147.41). Biomass C storage upsurges with age, ranging from 51.5±3.2–259.9±103.5 Mg C ha−1, while soil C storage averages 33.1±3.1 Mg C ha−1, showing no significant variation on age. Ecosystem carbon storage increases significantly with age and 35-year-old mangrove plantations exhibit the highest storage potential (292.0±105.5 Mg C ha−1). An analysis of species composition highlighted that Sonneratia apetala accumulated 94.1±28.4 Mg C ha−1 of biomass carbon, a figure notably higher than other species. The comparison indicated that, while the ecosystem carbon storage in Bangladesh is higher in mature-aged mangrove plantations, it lags behind young-aged mangrove plantations in Myanmar. Bangladesh exhibits a higher biomass carbon accumulation rate than China and India but falls short by 56% when compared to Myanmar's biomass carbon accumulation. However, these insights from mangrove plantation studies can serve as valuable guidance for policymakers and researchers, aiding in the conservation and management of mangroves and developing a blue carbon framework to meet the required funding for sustainable development of mangrove plantations and local communities in Bangladesh.

Microalgae production in an industrial-scale photobioreactors plant: A comprehensive Life Cycle assessment

Microalgae cultivation provides multiple opportunities to produce valuable bioproducts, but greater clarity must be achieved regarding the real sustainability of current technologies. Numerous life cycle assessment (LCA) studies have been conducted so far. However, most of them were based on literature data and/or extrapolations of lab-scale results, while only a few studies used primary data from pilot or full-scale microalgal plants. Moreover, the obtained results showed great variability, leaving the debate on microalgae sustainability fully open.This work presents a thorough LCA based on primary data from an industrial-scale microalgal facility located in Caltagirone, Italy. The plant is based on vertically-stacked horizontal photobioreactors (total volume of 40.4 m3) installed in a greenhouse and has a capacity of 1200 kgDW/y (Chlorella vulgaris). A cradle-to-gate assessment was performed with the functional unit of 1 kgDW biomass, including operational and infrastructural data. The results emphasized the key role in the generation of potential impacts played by cultivation among process stages and by chemicals (nutrients and cleaning agents) and electricity (mainly for agitation and thermoregulation) among flow types. In comparison with studies from the literature, the analysed microalgal plant has an intermediate environmental performance (e.g., global warming potential of 153 kg CO2,eq/kgDW). This result is encouraging, as it comes from a reliable assessment built on full-scale primary data. On the other hand, it highlights the need to explore alternative strategies (e.g., industrial symbiosis and circular bioeconomy) to reduce the environmental footprint of the process and enhance its economic attractiveness.

Predicting Cover Crop Biomass from Early‐Season Biomass and Weather Data

Predicting Cover Crop Biomass from Early‐Season Biomass and Weather Data

Accounting for spatio-temporal and sampling dependence in survey and CPUE biomass indices: simulation and Bayesian modeling framework

Abstract Estimating changes in the biomass of a fish stock is crucial for successful management. However, fishery assessment may be affected by the quality of the inputs used in stock assessment models. Survey biomass indices derived from fishery-independent and catch per unit effort (CPUE) biomass indices derived from fishery-dependent data are key inputs for model calibration. These indices have biases that could compromise the accuracy of the stock assessment models results. Therefore, there are plenty proposed methods to standardize survey or CPUE biomass data. From simpler models like generalized linear models (GLMs) to more complex models that take into account spatio-temporal correlation, like geostatistical models, and sampling dependence, like marked point processes. But many of them do not consider the underlying spatio-temporal or sampling dependence of these data. Hence, the goal of the study is to present a spatio-temporal simulation and Bayesian modeling framework to assess the impact of applying models that do not consider spatio-temporal and sampling dependence. Results indicate that geostatistical models and marked point processes achieve the lowest measures of error. Hence, to capture the underlying spatio-temporal process of the survey and CPUE biomass indices and data sampling preferentiality, it is essential to apply models that consider the spatio-temporal and sampling dependence.

Semi-continuous mesophilic anaerobic digestion of date palm (Phoenix dactelifyra L.) leaflets of the H'mira cultivar from the Adrar region of Southern Algeria using an alkaline pre-treatment

This research paper aim to investigate the enhancement potential of mesophilic Anaerobic Digestion (AD) of Algerian date palm (Phoenix dactylifera L.) leaflets from H'mira cultivars using an alkaline pretreatment approach. The experiment was conducted for the first time in semi-continuous digesters. NaOH was applied at various percentages (6 %, 12 % and 18 %) to chemically pretreat the substrate for 5 days at 37 °C. The chemical pretreatment results were interpreted for the first time with in-depth FTIR spectroscopic analyses. The Hydraulic Retention Time (HRT) was fixed at 15 days, and the Organic Loading Rate (OLR) at 2 g MO/L/day. The results showed that the methane (CH4) production values were stabilized with a clear distinction at averages of 211.85, 177.26, 143.36 and 123.07 ml of CH4/day, i.e. a CH4 yield of 105.92, 88.63, 71.68 and 61.53 ml of CH4/gVS/day for the reactors containing substrates pre-treated with 12 %, 6 %, 18 % NaOH and the substrate without pre-treatment respectively. Compared to the substrate without pre-treatment, the CH4 production rates were improved 1.72, 1.44 and 1.16 times for the reactors containing 12 %, 6 % and 18 % NaOH respectively. These findings were perfectly in accordance with FTIR spectroscopy results. These findings demonstrate that the semi-continuous AD process can be an attractive energy recovery solution from such waste compared to batch digesters, (on one day, it produce 77–105 % of the CH4 produced during 21–55 days in batch digesters), as well as the importance of adequate pre-treatment prior to the AD. FTIR Spectroscopic analysis technology can also be widely used to analyze pre-treatment data for lignocellulosic biomass, as it gives very encouraging results.

Comparative assessment of eDNA metabarcoding and longline deployments for elasmobranch surveying across a large tropical marine park network

Abstract Medium‐ to large‐scale marine protected areas (MPAs) are playing an increasingly important role in global marine biodiversity conservation. A key question is “how do we collect relevant data on biodiversity and population trends to inform their design and measure success?” This question is particularly relevant for marine taxa that are difficult to survey, such as sharks and rays, and where populations may occur over vast areas. Environmental DNA (eDNA) metabarcoding has proven to be an effective sampling method and may provide a solution to these challenges; however, it remains unclear how its sampling effectiveness compares with traditionally used methods for elasmobranch surveying. Here, we directly compared the efficacy of eDNA metabarcoding and demersal longline deployments to survey elasmobranchs across 31 sites over a 55,000 km2 area of the Kimberley and Roebuck Australian Marine Parks in Australia's North‐west Marine Parks Network. In total, we documented 49 unique elasmobranch taxa, 36 of which were detected by eDNA and 32 (from a total of 815 captured sharks) by longline. A combined approach yielded over 34% more elasmobranch taxa than either method alone. Site community compositions varied between the two survey methods; notably eDNA was able to detect species from outside of the immediate sampling area, although this was still consistent with a detection radius of a few kilometres, highlighting a particular use in rugose reef habitats where it is difficult to deploy longlines. In investigating the quantitative use of eDNA data, we report that eDNA metabarcoding read abundance (count) data was poorly correlated with longline aggregate catch (count and biomass) data across raw, relative and rank abundance measures. However, we found that in multivariate analyses, both binary (presence–absence) and abundance (after square‐root transformation) datasets produced highly similar ordination patterns, largely segregating method (eDNA vs. longline), followed by latitude and depth. Lastly, we identified required levels of eDNA replication and longline deployments to maximize captured elasmobranch diversity. This study sets an integrated, georeferenced baseline and long‐term monitoring approach for the management and conservation of elasmobranch diversity within this unique marine park network.

Generalizing Microbial Parameters in Soil Biogeochemical Models: Insights From a Multi‐Site Incubation Experiment

AbstractIncorporating microbial processes into soil biogeochemical models has received growing interest. However, determining the parameters that govern microbially driven biogeochemical processes typically requires case‐specific model calibration in various soil and ecosystem types. Here each case refers to an independent and individual experimental unit subjected to repeated measurements. Using the Microbial‐ENzyme Decomposition model, this study aimed to test whether a common set of microbially‐relevant parameters (i.e., generalized parameters) could be obtained across multiple cases based on a two‐year incubation experiment in which soil samples of four distinct soil series (i.e., Coland, Kesswick, Westmoreland, and Etowah) collected from forest and grassland were subjected to cellulose or no cellulose amendment. Results showed that a common set of parameters controlling microbial growth and maintenance as well as extracellular enzyme production and turnover could be generalized at the soil series level but not land cover type. This indicates that microbial model developments need to prioritize soil series type over plant functional types when implemented across various sites. This study also suggests that, in addition to heterotrophic respiration and microbial biomass data, extracellular enzyme data sets are needed to achieve reliable microbial‐relevant parameters for large‐scale soil model projections.

Site-Species Allometry Equation for Theobroma cacao L. Biomass Estimation in Agroforestry Systems of Cameroon

The accuracy of biomass estimates through mathematical expressions remains essential for the sustainability of the REDD+ process. The objective of this research was to develop allometric models by site species to evaluate the biomass of Theobroma cacao in agroforestry systems in the Central Region of Cameroon. Biomass data were obtained by the destructive method on a sample of 50 trees (5 cm ≤ D ≤ 27 cm). Allometric models were developed using aboveground (AGB), belowground (BGB), and total biomass (TB) as dependent variables and tree dendrometric parameters as independent variables. Nine linear models were adjusted based on the Akaike information criterion (AIC), residual standard error (RSE), coefficient of determination (R2), and various statistical tests including the normality test, heterogeneity, and autocorrelation for the analysis of residuals. The different results show that only the diameter appears to be a good predictor of biomass with an R2 greater than 0.94, 0.85, and 0.95, respectively, for aboveground biomass (M1: ln B = −1.613 + 1.83 × ln (D)), belowground biomass (M1: ln B = −2.611 + 1.65 × ln (D)), and total biomass (M1: ln B = −1.297 + 1.79 × ln (D)). Incorporating crown diameter and height into the models slightly improved the quality of adjusted. Comparison of the models in this study with pantropical equations previously used to estimate Theobroma cacao biomass shows that the models in this study provide a better estimate. The allometric equations developed in this work to estimate the AGB, BGB, and TB of Theobroma cacao can be used under the same environmental conditions to accurately predict the biomass accumulated in agroforestry systems by this species and thus allow the implementation of activities aimed at reducing emissions from deforestation and degradation (REDD+) for the benefit of local communities through the carbon market.

A new approach for modelling weed suppression of cover crops

AbstractCover cropping plays an important role in Integrated Weed Management (IWM). Weed suppression of cover crops occurs by competition, physical barrier on the soil and allelopathy. Under favourable growing conditions, cover crops can provide similar weed suppression in the autumn‐to‐spring period as repeated stubble tillage and non‐selective herbicides. The objective of this study was to test if cover crop species with higher allelopathic potential can suppress weeds in the field with less shoot biomass than cover crop species with lower allelopathic effects. Data of cover crop shoot biomass, percentage of weed suppression in the field and reduction of weed seed germination in petri‐dish bioassays treated with cover crop extracts were analysed from 11 experiments. Weed suppression in relation to cover crop biomass was better predicted by a Weibul three‐parameter model than with the Cousens model. Raphanus sativus var. oleiferus, Sinapis alba, Fagopyrum esculentum and Phacelia tanacetifolia needed at least three‐fold more biomass than Avena strigosa for 80% weed suppression. Vicia sativa had the lowest weed reduction of only 62%. Aqueous extracts of A. strigosa had the highest inhibiting effect on weed seed germination followed by R. sativus. The lowest effect was observed for P. tanacetifolia. It is assumed that for A. strigosa allelopathy strongly contributes to weed suppression whereas for P. tanacetifolia and other cover crops, competition plays the major role for weed suppression. The model can help growers and consultants to mix cover crop species for the autumn‐to‐spring period. Under dry conditions, species such as A. strigosa can provide high weed suppression with low biomass. Under moist conditions, rapidly growing cover crops such as S. alba suppress most weeds and deliver high biomass.

Seasonal dynamics of the standard test species Lemna sp. in outdoor microcosms.

Lemna L. sp. is a free-floating aquatic macrophyte that plays a key role as a standard test species in aquatic risk assessment for herbicides and other contaminants. Population modeling can be used to extrapolate from laboratory to field conditions. However, there are insufficient data on longer-term seasonal dynamics of this species to evaluate such models. Therefore, several long-term growth experiments were conducted in outdoor microcosms (surface area 0.174 m2). Monitoring parameters included biomass, frond numbers, water parameters, and weather data. Three different datasets were generated: frond numbers and biomass from weekly to monthly destructively sampled microcosms; a year-round dataset of frond numbers from five continuously monitored microcosms; and seasonal growth rates without the effect of density dependence over 1-2 weeks in freshly inoculated microcosms. Lemna sp. reached a maximum of approximately 500 000 fronds m-2 and 190 g dry weight m-2. During the first winter, the microcosms were covered by ice for approximately four weeks, and Lemna sp. populations collapsed. The second winter was warmer, without any ice cover, and Lemna sp. populations maintained high abundance throughout the winter. Dry weight per frond was not constant throughout the year but was highest in autumn and winter. Growth rates without density dependence under outdoor environmental conditions reached 0.29 day-1 for frond number, 0.43 day-1 for fresh weight, and 0.39 day-1 for dry weight. In linear regressions, these growth rates were best explained by water temperature. For the populations continuously monitored throughout a year, the nitrogen-to-phosphorus ratio best explained the growth rate of frond numbers. This study yielded a relevant dataset for testing and refining Lemna population models used in chemical risk assessment as well as for managing ecosystems and combating the effects of eutrophication. Integr Environ Assess Manag 2024;20:1625-1638. © 2024 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Carbon concentration of living tree biomass of Pinus sylvestris, Picea abies, Betula pendula and Betula pubescens in Sweden

ABSTRACT This study examined the carbon (C) concentration of the major tree species (Scots pine, Norway spruce, and Birch) in Sweden based on destructively sampled biomass data. The examination was made using single trees and comprised three components: branches with needles, stemwood with bark, and stump with roots. We developed a weighted C concentration estimator accounting for fresh weights of tree components to infer the C concentration at the population level. Significant differences in C concentration (%) were found among species, with the highest in pine (50.671 ± 0.211), followed by spruce (49.518 ± 0.164) and the lowest in birch (49.347 ± 0.241). Additionally, the C concentration varied among tree components, regardless of tree size, growth rate, and site conditions. At the national level, we applied the estimated species- and component-specific C concentration constants to the measured total biomass of the major tree species from the National Forest Inventory. This extrapolation revealed that the average C concentration of major trees across all forestlands in Sweden was approximately 50.012%. These findings have significant implications for accurate C sequestration reporting in the LULUCF sector.

UAV-RGB-image-based aboveground biomass equation for planted forest in semi-arid Inner Mongolia, China

The acquisition of high-resolution aboveground biomass (AGB) data cost-effectively and expeditiously represents a formidable challenge within the domain of current ecosystem surveillance. Plot-based inventory, the conventional approach for estimating and validating remote sensing data, is nonetheless costly and constrained in terms of spatial coverage. The expeditious advancements in unmanned-aerial-vehicle (UAV) technology furnish the potential to devise AGB equations that transcend traditional diameter-height-based equations alongside techniques for quantifying forest structural parameters through standard RGB aerial imagery. Since the canopy diameter (CD) and tree height (H) can be directly ascertained from UAV-derived datasets, biomass equations parameterized by CD and H may be more valuable. In the present investigation, we established AGB equations predicated on data procured from a UAV outfitted with a high-resolution RGB camera, specifically for planted sparsely Pinus sylvestris forest in central Inner Mongolia, China. Utilizing the aerial imagery, we generated the digital terrain model (DTM), digital surface model (DSM) and the digital orthophoto image (DOM). Then, the canopy height model (CHM) was obtained by subtracting DSM from DTM to extract the H and CD of individual trees. This methodology's CD (R2 = 0.85, RMSE = 0.203 m) and H (R2 = 0.77 and RMSE = 0.671 m) obtained closely mirrored the in-situ measurements. Six prospective AGB equations were constructed for the Pinus sylvestris forest, taking H and CD extracted from UAV aerial survey datasets as the dependent variables. The accuracy of the AGB estimation was appraised by employing extant allometric growth equations, which were parameterized using the ground-measured tree diameter at breast height (DBH) and H. The most efficacious biomass equation, predicated on H and CD data extracted from UAV aerial surveys, was delineated as W=2.3442CD∗H0.9057(R2 = 0.731, RMSE = 2.46 kg), thus presenting a convenient tool for estimating the AGB of sparse Pinus sylvestris forests in semi-arid locales.