Biomass Distribution Research Articles

Night Interruption with Red and Far-Red Light Optimizes the Phytochemical Composition, Enhances Photosynthetic Efficiency, and Increases Biomass Partitioning in Italian Basil

Controlled environment agriculture is a promising solution to address climate change and resource limitations. Light, the primary energy source driving photosynthesis and regulating plant growth, is critical in optimizing produce quality. However, the impact of specific light spectra during night interruption on improving phytochemical content and produce quality remains underexplored. This study investigated the effects of red (peak wavelength at 660 nm) and far-red night interruption (peak wavelength at 730 nm) on photosynthetic efficiency, biomass distribution, and phytochemical production in Italian basil (Ocimum basilicum L.). Treatments included red light, far-red light, a combination of both, and a control without night interruption. Red light significantly increased chlorophyll a by 16.8%, chlorophyll b by 20.6%, and carotenoids by 11%, improving photosynthetic efficiency and nutritional quality. Red light also elevated anthocyanin levels by 15.5%, while far-red light promoted flavonoid production by 43.56%. Although red light enhanced biomass, the primary benefit was improved leaf quality, with more biomass directed to leaves over roots. Far-red light reduced transpiration, enhancing post-harvest water retention and shelf life. These findings demonstrate that red and far-red night interruption can optimize phytochemical content, produce quality, and post-harvest durability, offering valuable insights for controlled environment agriculture. Future research should focus on refining night interruption light strategies across a broader range of crops to enhance produce quality and shelf life in controlled environment agriculture.

ОЦЕНКА ВЕРТИКАЛЬНОГО ПОТОКА УГЛЕРОДА, СВЯЗАННОГО С ДЫХАНИЕМ МИГРИРУЮЩИХ КОПЕПОД CALANUS EUXINUS И PSEUDOCALANUS ELONGATUS, В ЧЕРНОМ МОРЕ

Diel vertical migrations of zooplankton make a significant contribution to the functioning of the biological pump by providing active transport of carbon from surface layers to depth. In the Black Sea, quantitative assessments of this process have not been carried out until now. In this work, for the first time, calculated values of the carbon flux associated with the respiration of two mass species of migrating copepods, Calanus euxinus and Pseudocalanus elongatus, were obtained. The study was conducted at 13 daily stations in the northern and northeastern parts of the Black Sea from April to September 2020–2021 (cruises No. 114, 116, 118 of the R/V “Professor Vodyanitsky”). To estimate the carbon flux, we used: (1) expedition data on the daily dynamics of the vertical distribution of copepod abundance and biomass; (2) the Vinogradov’s migration coefficients obtained on their basis, characterizing the intensity of vertical movements of organisms; (3) calculated values of the intensity of their respiration, taking into account the water temperature and the time spent by them at depth during daylight hours. It was shown that the main contribution (up to 90 %) to the studied carbon flux was made by females and fifth-stage copepodites of C. euxinus. The values of the total flux increased from the spring minimum of 0.14 mmol C·m-2·day-1 in April to a maximum (0.46 mmol C·m-2·day-1) in September. The results obtained may indicate an important role of diel vertical migrations of zooplankton in the functioning of the biological pump of the Black Sea. The relevance of further studies of the seasonal and interannual dynamics of zooplankton migrations and associated fluxes, as well as their response to changes in climate and hydrological conditions in the Black Sea, is emphasized

Thermal engineering and chemical kinetics of wood pellets torrefication

Relevance. The widespread distribution and availability of biomass in the regions of Russian Federation make it possible to consider it as a renewable energy source with a lower negative impact on the environment compared to fossil fuels, since biomass is recognized as a carbon-neutral fuel. The use of biomass as a fuel in the form of pellets is increasing, which provides increased density and heat value. At the same time, pellets as a fuel or as a technological raw material have a significant disadvantage – low hydrophobicity. This problem is solved by torrefaction – low-temperature pyrolysis at a temperature of 200–300°C to produce bio-carbon. The study of the wood pellets torrefaction in order to improve it is an urgent task. Aim. To identify the impact of the parameters of the torrefaction of wood pellets on the thermal characteristics of the product (the highest heat value of bio-coal) and the regime parameters of the process (mass and energy yields), as well as to determine the parameters of the chemical kinetics of low-temperature pyrolysis as the basis for mathematical modeling and development of efficient torrefaction reactors. Object. Torrefied coniferous wood pellets of domestic production (Moscow region). Methods. Experimental study of the low-temperature pyrolysis thermal characteristics; synchronous thermal analysis in an inert atmosphere. Results. It was found that during the torrefaction of wood pellets in an inert atmosphere, a change in the holding time in the range of 30–60 minutes does not significantly affect the higher heat value, mass and energy yield of bio-coal, while an increase in temperature from 250 to 300°C reduces mass and energy yield, and the higher heat value increases by 26%. The dynamics of mass loss and thermal effects when heated to 600°C at a rate of 10 K/min are determined by means of the synchronous thermal analysis. It is established that the chemical kinetics of the process is described by a generalized reaction of the order n=1.507, in the Arrhenius equation the activation energy E=71.25 kJ/mol, the pre-exponential factor A=4772 s–1.

Internal tidal dynamics and associated processes at highly supercritical slopes in Banda Sea: Lessons from the oceanic island of Ambon, eastern Indonesia

Interactions between the deep sea and steep-sloping oceanic islands generate physical processes with potential impacts on sediment resuspension and ocean productivity. While studies have been conducted in the open oceans, those focusing on oceanic islands of the deep Banda Sea in eastern Indonesia are lacking. Here, we present the first observational evidence of vertical mechanisms (i.e. internal tidal reflection, internal hydraulic jumps) at the highly supercritical slope in outer Ambon Bay (OAB) in Ambon Island – an oceanic island in the Banda Sea. A 23-h CTD yoyo experiment combined with ADCP measurements conducted during spring flood and ebb tides demonstrated tidally-varying vertical temperature, salinity and density profiles. During spring flood tide, incoming internal tides were reflected by OAB's highly supercritical slope back to the deep sea with isopycnals and isotherms showing sharp downward plunges (downward vertical velocity = 6.5–8.3 × 10−3 m/s), and the internal tidal amplitude reaching 90–110 m. The reflection during flood tide caused seaward overturning flow at deeper depths despite the prevailing landward flow at the upper layers. During spring ebb tide, internal hydraulic jumps (upward vertical velocity = 6.1–6.48 × 10−3 m/s) occurred to rebound the downward plunges of isopycnals and isotherms as flood tide relaxed with the observed amplitude of internal tides of 90 m. We also observed weakened seaward ebb flow during the isothermal uplifting (when hydraulic jumps occurred), and subsequent intensified landward flow at the end of spring ebb tide indicating strong upslope flow when isotherms reached the maximum shoaling depths. Taken together, the observed vertical mechanisms indicate the conservation of energy at the highly supercritical slope of OAB evident by the comparable vertical velocities. An embedded turbidity-chlorophyll profiler in the CTD reveals that internal tidal activities at the highly supercritical slopes OAB may induce bottom nepheloid layers, and influence ocean productivity by regulating the vertical distribution of phytoplankton biomass.

Elevation, Soil and Environmental Factors Determine the Spatial and Quantitative Distribution of Qinghai Spruce Recruitment Biomass in Mountainous (Alpine) Watersheds

Soil heterogeneity observed in the alpine environment plays a very important role in the growth of forest recruitment. However, the mechanisms by which the biomass accumulation and allocation patterns of forest recruitment respond to such environmental differences are unclear, which hinders a thorough understanding of climate change’s impact on forest biomass. We hypothesized that soil heterogeneity influences the distribution of Qinghai spruce recruitment biomass along with elevation. In the frame of this study, carried out in the northern Tibetan Plateau, forest Qinghai spruce recruitment data were combined with soil data derived from 24 sample plots, while permutation multifactor ANOVA and multiple linear regression were utilized to reveal the characteristics of forest recruits’ above- and below-ground biomass and their allocation patterns in response to soil heterogeneity. According to the results, the soil heterogeneity mainly affected the distribution characteristics of recruits’ above- and below-ground biomass at different elevations, while the recruits’ root–shoot ratio variability was influenced by a combination of soil and other environmental factors. Soil organic carbon (SOC) had the greatest effect on the variability of the above- and below-ground biomass of spruce recruits, with R2 of 0.280 and 0.257, respectively. Soil organic carbon and soil moisture content (SMC) had a significant effect on the variability of the root–shoot ratio, with R2 of 0.168 and 0.165, respectively. Soil total nitrogen (TN) and soil organic carbon were the main influencing factors of the above-ground biomass of forest recruits, with contribution rates of 43.15% and 35.28%, respectively. Soil total nitrogen and soil organic carbon were also the main factors influencing the below-ground biomass of forest recruits, with contribution rates of 42.52% and 37.24%, respectively, and both of them had a positive effect on biomass accumulation, and the magnitude of the influence varied with the elevation gradient. Soil moisture content was the main influence factor of spruce recruits’ root–shoot ratio, with a contribution rate of 54.12%. Decreasing soil moisture content would significantly increase the root–shoot ratio of spruce recruits and promote plants to allocate more biomass to root growth. Changes in elevation not only affected the intensity of the effect of soil factors on spruce recruitment biomass and its allocation pattern but even led to a change in the positive and negative effects.

Estimation of above-ground biomass in dry temperate forests using Sentinel-2 data and random forest: a case study of the Swat area of Pakistan

Accurate mapping of above-ground biomass (AGB) is essential for carbon stock quantification and climate change impact assessment, particularly in mountainous areas. This study applies a random forest (RF) regression model to predict the spatial distribution of AGB in Usho (site A) and Utror (site B) forests located in the northern mountainous region of Pakistan. The predicted maps elucidate AGB variations across these sites, with non-forest areas excluded based on an normalized difference vegetation index (NDVI) threshold value of <0.4. Three different combinations of input datasets were used to predict the biomass, including spectral bands (SBs) only, vegetation indexes (VIs) only, and a combination of both spectral bands and vegetation indexes (SBVIs). Utilizing SBs, the biomass ranged between 150 and 286 mg/ha in site A and 99 and 376 mg/ha in site B. Meanwhile, using VIs indicated a biomass range of 163 Mg/ha–337 Mg/ha and 131–392 Mg/ha for sites A and B, respectively. The combination of spectral bands and vegetation indexes yielded AGB values of 145–290 Mg/ha in site A and 116–389 Mg/ha in site B. The northern and western regions of site A, characterized by higher altitudes and lower forest density, notably showed lower biomass values than other regions. Conversely, similar regions in site B, situated at lower latitudes, demonstrated different biomass ranges. The RF model exhibited robust accuracy, with R2 values of 0.74 and 0.83 for spectral bands and vegetation indexes, respectively. However, with a combination of both, an R2 of 0.79 was achieved. Furthermore, altitudinal gradients significantly influence the biomass distribution across both sites, with specific elevation ranges yielding optimal results. The AGB variation along the slope further corroborated these findings. In both sites, the western aspects showed the highest biomass across all combinations of input datasets. The variable importance analysis highlighted that ARVI8a, NDI45, Band12, Band11, TSAVI8, and ARVI8a are significant predictors in sites A and B. This comprehensive analysis enhances our understanding of AGB distribution in the mountainous forests of Pakistan, offering valuable insights for forest management and ecological studies.

The global distribution and drivers of wood density and their impact on forest carbon stocks.

The density of wood is a key indicator of the carbon investment strategies of trees, impacting productivity and carbon storage. Despite its importance, the global variation in wood density and its environmental controls remain poorly understood, preventing accurate predictions of global forest carbon stocks. Here we analyse information from 1.1 million forest inventory plots alongside wood density data from 10,703 tree species to create a spatially explicit understanding of the global wood density distribution and its drivers. Our findings reveal a pronounced latitudinal gradient, with wood in tropical forests being up to 30% denser than that in boreal forests. In both angiosperms and gymnosperms, hydrothermal conditions represented by annual mean temperature and soil moisture emerged as the primary factors influencing the variation in wood density globally. This indicates similar environmental filters and evolutionary adaptations among distinct plant groups, underscoring the essential role of abiotic factors in determining wood density in forest ecosystems. Additionally, our study highlights the prominent role of disturbance, such as human modification and fire risk, in influencing wood density at more local scales. Factoring in the spatial variation of wood density notably changes the estimates of forest carbon stocks, leading to differences of up to 21% within biomes. Therefore, our research contributes to a deeper understanding of terrestrial biomass distribution and how environmental changes and disturbances impact forest ecosystems.

Phytoplankton Spring Bloom Inhibited by Marine Heatwaves in the North‐Western Mediterranean Sea

AbstractMarine heatwaves (MHWs) represent anomalously warm temperature conditions of seawater that may affect marine life and ocean biogeochemistry. Under such conditions, phytoplankton communities may modify their structure and functions, and their resilience is not assured. This study characterizes the impact of MHWs on the phytoplankton spring bloom in the North‐Western Mediterranean Sea. Here, we synergistically combine autonomous observations from BioGeoChemical‐Argo floats, satellite‐based and marine ecosystem model data, and show that MHW events occurring during winter drastically inhibit phytoplankton carbon biomass in spring by up to 70%. Such reduction is related to the enhanced stratification of the water column under MHWs which hinders the renewal of nutrients from deep‐ocean reservoirs, thus preventing surface phytoplankton from blooming. This process negatively impacts particulate organic carbon stocks within the mixed layer, while severe events cause an earlier shift of phytoplankton phenology that provokes changes in zooplankton biomass distribution.

Greenhouse gas emission from prescribed fires is influenced by vegetation types in West African Savannas

Greenhouse gas (GHG) emissions from prescribed fires are poorly investigated, resulting in a high uncertainty in GHG budgets. Using, a carbon mass balance approach and experimental prescribed fires in 80 plots, this study assessed carbon emissions and established emission factors (EFs) for carbon dioxides (CO2), carbon monoxide (CO), and methane (CH4) across climate zones and vegetation types. In grass and shrub savannas, fires could burn intensely due to the lower moisture content and continuous spatial distribution of biomass fuel, causing greater carbon emissions with 1.61 ± 0.13 t C ha−1 and 1.01 ± 0.13 t C ha−1, respectively. Despite their low carbon emissions, tree savannas (1658.17 ± 11.13 g kg−1) and woodlands (1629.94 ± 12.23 g kg−1) have the highest EFs, which can be attribute to the high carbon content of biomass fuel in these vegetation types. Vegetation types and their interaction with climate zones have a substantial impact on carbon emissions and carbon species EFs, and should therefore be considered in assessing GHG emissions from fires. The findings from this study provide a useful basis for improving the national measurement, reporting, and verification of GHG emissions and for improving the measurement of the global balance of GHG emissions from fires.

Geoinformatics and Modeling Approaches for Estimating Above-Ground Biomass in the Moist Deciduous Forests of Uttara Kannada, Karnataka, India

The present study aims to estimate Above-Ground Biomass (AGB) in the moist deciduous forests of Mundgod Taluk, Uttara Kannada district, Karnataka, India using a combination of field sampling and remote sensing techniques. AGB was assessed using Sentinel-2A satellite imagery with a spatial resolution of 10 meters. Field data were collected using the Point-Centered Quarter (PCQ) method, it includes measuring tree girth at breast height (GBH), tree height, and canopy density. AGB was calculated using artificial form factor and specific gravity of tree species and further modelled using the Normalized Difference Vegetation Index (NDVI) derived from satellite data. The results showed a significant correlation between canopy density and biomass, with very dense forests exhibiting the highest AGB values. The field-measured AGB was 436.96 t/ha for very dense forests, 259.89 t/ha for moderately dense forests, and 161.67 t/ha for open forests. A linear regression model developed between AGB and NDVI showed a high R² value of 0.91 for open forests, 0.87 for moderately dense forests, and 0.85 for very dense forests. The total area-weighted biomass for the study area was estimated at 8.87 million tons, with 4.79 million tons in very dense forests, 3.44 million tons in moderately dense forests, and 0.76 million tons in open forests. The regression model validation indicated low Root Mean Square Error (RMSE) values, with the moderately dense forests 8.81 t/ha and the in very dense forests 7.54 t/ha. This study highlights the effectiveness of integrating field measurements with remote sensing for rapid, reliable AGB estimation. The approach offers valuable insights into biomass distribution and carbon sequestration potential, providing a scalable method for carbon inventories at state and national levels, contributing to forest management and climate change mitigation efforts.

The role of evolutionary processes in determining trophic structure

There are two contrasting hypotheses regarding the drivers of biomass‐distribution among trophic levels within ecosystems. Energetic or bottom–up models propose control by supply of energy, either from autotrophy or from the underlying trophic level. Dynamical or top–down models propose control by predators in the overlying trophic level. Although multiple approaches have been used to reconcile these hypotheses, they have rarely considered the evolutionary pressures created by different distributions of biomass. Here, we study the effects of these evolutionary processes using an agent‐based, spatially‐explicit, eco‐evolutionary model. We demonstrate that, when ecosystems are simple and predator–prey relationships between species are fixed and do not evolve, primary productivity limits the number of trophic levels. In this case, the abundance in the top trophic level is always limited by productivity. However, productivity‐limited trophic levels subject those below them to limitation by predation, and predation‐limited trophic levels allow trophic levels below them to grow until limited by productivity. These results match the expectations of the exploitation ecosystems hypothesis (EEH), which predicts the same pattern of alternating top–down and bottom–up control on trophic levels. When species are able to evolve to adaptively adjust their trophic levels, however, selection is liable to lead to collapse of the trophic pyramid through prey switching. Under these conditions, all trophic levels experience bottom–up control. When the evolution of prey switching is restricted, higher trophic levels are evolutionarily stable and the predictions of the EEH are once again met; the stability of long food chains is thus dependent on the difficulty associated with prey switching. These results suggest that, at least under the combinations of model parameters explored in this study, evolutionary limitation is key to maintaining trophic structure.

Genome-wide analysis of the WRKY gene family and their response to low-temperature stress in elephant grass.

Elephant grass (Cenchrus purpureus) is a perennial forage grass characterized by tall plants, high biomass and wide adaptability. Low-temperature stress severely limits elephant grass biomass and geographic distribution. WRKY is one of the largest families of plant-specific transcription factors and plays important roles in plant resistance to low-temperature. However, the understanding of the WRKY family in grasses is limited. In this study, we conducted a genome-wide characterization of WRKY proteins in elephant grass, including gene structure, phylogeny, expression, conserved motif organization, and functional annotation, to identify key CpWRKY candidates involved in cold tolerance. In this study, a total of 176 WRKY genes were identified in elephant grass. It was found that 172 were unevenly distributed across its 14 chromosomes, while the remaining 4 genes were not anchored to any chromosome. The genes were classified into three groups based on their WRKY conserved domains and zinc finger motifs. There were 12, 8, 19, 27, 12, 18 and 80 CpWRKYs belonging to group I, group IIa, group IIb, group IIc, group IId, group IIe and group III, respectively. We hypothesized that the ancient subgroup IIc WRKY gene is the ancestor of all WRKY genes in elephant grass. Most CpWRKYs in the same group have similar structure and motif composition. A total of 169 duplicate gene pairs were identified, suggesting that segmental duplication might have contributed to the expansion of the CpWRKY gene family. Ka/Ks analysis revealed that most of the CpWRKYs were subjected to purifying selection during the evolution. It was also found that six genes (CpWRKY51, CpWRKY81, CpWRKY100, CpWRKY101, CpWRKY140 and CpWRKY143) exhibited higher expression in roots compare to leaves, and were significantly induced by low temperature stress. Among them, CpWRKY81 had the highest expression under low-temperature stress, and its over-expression significantly enhanced the cold tolerance in yeast. In this study, we characterized WRKY genes in elephant grass and further investigated their physicochemical properties, evolution, and expression patterns under low-temperature stress. This research provides valuable resources for identifying key CpWRKY genes that contribute to cold tolerance in elephant grass.

Distribution of Non-Structural Carbohydrates and Root Structure of Plantago lanceolata L. under Different Defoliation Frequencies and Intensities.

Plantago lanceolata L. (plantain) increases herbage dry matter (DM) production and quality during warm and dry conditions due to its deep roots and drought tolerance and reduces nitrogen losses in grazing systems compared to traditional pastures. However, plantain density usually declines after the third growing season, mainly due to defoliation management. The effects of defoliation frequency and intensity on water-soluble carbohydrate (WSC) reserves and below-ground plant responses need further research to optimize grazing strategies for improved productivity and sustainability of grazing systems. Our study investigated the effects of defoliation frequencies (15, 25, and 35 cm of extended leaf length, ELL) and intensities (5 and 8 cm of residual heights) on morphological traits and WSC concentrations in plantain biomass under controlled environmental conditions. Defoliation frequency significantly influenced morphological and chemical characteristics and biomass distribution more than residual height. Less frequent defoliations promoted above-ground herbage DM production, reproductive stems, and root biomass. Root architecture showed adaptations in response to defoliation frequency, optimizing resource acquisition efficiency. Frequent defoliation reduced high molecular weight WSC concentrations in leaves, affecting regrowth capacity and DM mass. A defoliation frequency of 25 cm ELL (~15 days) balances herbage production and root development, promoting long-term pasture sustainability.

Estimation and Spatial Distribution of Individual Tree Aboveground Biomass in a Chinese Fir Plantation in the Dabieshan Mountains of Western Anhui, China

Understanding aboveground biomass (AGB) and its spatial distribution is key to evaluating the productivity and carbon sink effect of forest ecosystems. In this study, a 123-year-old Chinese fir forest in the Dabieshan Mountains of western Anhui Province was used as the research subject. Using AGB data calculated from field measurements of individual Chinese fir trees (diameter at breast height [DBH] and height) and spectral vegetation indices derived from unmanned aerial vehicle (UAV) remote sensing images, a random forest regression model was developed to predict individual tree AGB. This model was then used to estimate the AGB of individual Chinese fir trees. Combined with digital elevation model (DEM) data, the effects of topographic factors on the spatial distribution of AGB were analyzed. We found that remote sensing spectral vegetation indices obtained by UAVs can be used to predict the AGB of individual Chinese fir trees, with the normalized difference vegetation index (NDVI) and the optimized soil-adjusted vegetation index (OSAVI) being two important predictors. The estimated AGB of individual Chinese fir trees was 339.34 Mg·ha−1 with a coefficient of variation of 23.21%. At the local scale, under the influence of elevation, slope, and aspect, the AGB of individual Chinese fir trees showed a distribution pattern of decreasing from the middle to the northwest and southeast along the northeast-southwest trend. The effect of elevation on AGB was influenced by slope and aspect; AGB on steep slopes was higher than on gentle slopes, and the impact of slope on AGB was influenced by aspect. Additionally, AGB on north-facing slopes was higher than on south-facing slopes. Our results suggest that local environmental factors such as elevation, slope, and aspect should be considered in future Chinese fir plantation management and carbon sink assessments in the Dabieshan Mountains of western Anhui, China.

Phytoplankton morphological traits and biomass outline community dynamics in a coastal ecosystem (Gulf of Trieste, Adriatic Sea)

AbstractTrait-based ecology has recently gained increasing importance in phytoplankton research. In particular, the taxonomic and morphological traits, such as size and shape of phytoplankton cells, can help to unveil the ecological processes and their drivers in the pelagic domain. Our study aims to shed light on the trophodynamics of phytoplankton communities in a coastal ecosystem in the northern Adriatic Sea (Gulf of Trieste) using data on individual traits such as biomass, size and shape of phytoplankton taxa during a one-year study. The phytoplankton parameters were investigated at the levels of the whole community, groups, and individual cells, analysing also the probability distributions of biomass and size of the latter level. The results showed good agreement between abundance and biomass data, as well as individual size and biomass with differences partly explained by cell shapes. We have emphasized the role of the local freshwater source in bottom-up control, alternating with top-down control of phytoplankton dynamics through taxonomic and morphological diversity. The predominant bimodal and non-power law distribution, especially during and around the biomass peaks, confirmed the importance of nano- and microphytoplankton size classes and the role of blooms in destabilizing the trophic webs. We suggest that the analyses of distribution types of individual cell size and biomass can be appropriate to spot ecological processes driving to unconstrained phytoplankton proliferation or to periods of trophic web stability.

Modelling global mesozooplankton biomass using machine learning

Mesozooplankton are a crucial link between primary producers and higher trophic levels and play a vital role in marine food webs, biological carbon pumps, and sustaining fishery resources. However, the global distribution of mesozooplankton biomass and the relevant controlling mechanisms remain elusive. We compared four machine learning algorithms (Boosted Regression Trees, Random Forest, Artificial Neural Network, and Support Vector Machine) to model the spatiotemporal distributions of global mesozooplankton biomass. These algorithms were trained on a compiled dataset of published mesozooplankton biomass observations with corresponding environmental predictors from contemporaneous satellite observations (temperature, chlorophyll, salinity, and mixed layer depth). We found that Random Forest achieved the best predictive accuracy with R2 and RMSE (Root Mean Standard Error) of 0.57 and 0.39, respectively. Also, the global distribution of mesozooplankton biomass predicted by the Random Forest model was more consistent with the observational data than other models. We used the Random Forest model to create a global map of mesozooplankton biomass which serves as a reference for validating process-based ecosystem models. The model outputs confirm that environmental factors, especially surface Chl a, a proxy for prey availability, significantly correlate with the spatiotemporal distribution of mesozooplankton biomass. The scaling relationship between the mesozooplankton biomass and Chl a can be used as an emergent constraint for model validation and development. Moreover, our model predicts that the global mesozooplankton biomass will decrease by 3% by the end of this century under the “business-as-usual” scenarios, potentially reducing fishery production and carbon sequestration. Our study contributes to predicting global mesozooplankton biomass and provides deep insights into the underlying environmental impacts on the distribution of mesozooplankton biomass.

Autonomous data sampling for high-resolution spatiotemporal fish biomass estimates

Many key ecological dynamics such as biomass distributions are only detectable on a fine spatiotemporal scale. Autonomous data collection with Unmanned Surface Vehicles (USV) creates new possibilities for cost efficient and high-resolution aquatic data sampling. However, the spatial coverage and sampling resolution remain uncertain due to the novelty of the technology. Further, there is no established method for analysing such fine-scale autocorrelated data without aggregation, potentially compromising data resolution. We here used a USV with an echosounder, a conductivity-temperature sensor and a flourometer to collect data from April–July 2019–2023 in a 60x80km area in the central Baltic Sea. The USV covered a total distance of 8000 nmi, over 42–81 days per year, with an average speed of 0.5 m/s. We combined the hydroacoustic data with publicly available oceanographic data from Copernicus Marine Service Information (CMSI) to describe seasonal distribution dynamics of a small pelagic fish community. Key oceanographic variables collected by the USV were correlated with CMSI estimates at daily/monthly resolution, respectively, to test for suitability to scale (Temperature 0.99/0.97; Salinity −0.77/−0.26; Chlorophyll-a 0.12/0.28). We investigated two approaches of Species Distribution Models (SDMs): generalized additive models (GAM) versus spatiotemporal generalized linear mixed effect models (GLMM). The GLMMs explained the observed data better than the GAMs (R2 0.31 and 0.20, respectively). The addition of environmental variables increased the explanatory capability of GAM and GLMM by 25 % and ~ 3 %, respectively. Due to the high data resolution, we found significant amounts of positive autocorrelation (R: 0.05–0.30) across more than 50 lags of observation. However, we found that diel patterns in fish detection strongly affected the abundance estimates due to diel vertically migrating species hiding in the ‘acoustic dead zone’ near the seabed. Such dynamics could only be estimated and corrected for in predictions on the high-resolution data, complicating the trade-off between autocorrelation and high-resolution for SDMs even further. We compared estimates and effect sizes/directions in identical SDMs on 2x2km/month aggregated (i.e non-autocorrelated) observations and non-aggregated (i.e. autocorrelated) observations, and found relatively little difference in spatiotemporal estimates (r = 0.80). For the first time, we predicted the distribution of a small pelagic fish community at a high spatial resolution, in an area essential to breeding top predators, opening up for new applications in ecological studies locally and globally.

Latitudinal variation in zooplankton over the Emperor Seamounts (34°–44° N, 170°–171° E) during the summer of 2019

During a research expedition to the Northwest Pacific between July 13 and August 11, 2019, we collected oceanic zooplankton samples over the Emperor Seamounts (Koko, Jingu, Nintoku, and Suiko guyots). We analyzed the horizontal and vertical distributions of abundance and biomass and also the structure of the zooplankton in the highly productive but poorly studied ocean waters between 34° and 44° N. The zooplankton was represented by four genera of amphipods, seven genera of pteropods, 39 genera of copepods, and larvae of benthic invertebrates and fish. The abundance and biomass of zooplankton increased towards higher latitudes along the series Koko < Jingu < Nintoku < Suiko guyots. The highest index of diversity was recorded over the Koko and Jingu guyots; the highest species richness occurred over the Jingu and Nintoku guyots. Small-sized copepods, appendicularians, chaetognaths, euphausiids, and fish eggs and larvae concentrated in the epipelagic; large copepods and ostracods concentrated in the mesopelagic. We identified three types of zooplankton assemblages: a Subtropical assemblage between 34°–35° N characterized by tropical/subtropical forms; a Transition assemblage characterized by subtropical, subarctic, and widespread species between 38°–41° N; and a Subarctic assemblage characterized by subarctic and widespread species between 43°–44° N. The spatial distribution of zooplankton was a function of environmental variables such as surface salinity, temperature at 200-m, and, to a certain extent, by surface Chl-a concentrations.

Resilience underground: Understanding earthworm biomass responses to land use changes in the tropics

Soil biodiversity, like terrestrial biodiversity, is currently under threat by changes in land use. Intensively managed farming activities with agrochemicals have degraded both soil biodiversity and health. However, little is known about how these changes in land use affect the distribution of earthworm biomass in Southeast Asia. We conducted earthworm sampling across multiple habitats, including lowland forests, exotic monoculture plantations (e.g., oil palm and rubber tree), and agroforestry orchards. To survey earthworm populations, we excavated the top 30 cm of soil at 18 sites encompassing 399 plots distributed across natural and human-modified ecosystems in Selangor and Negeri Sembilan, Peninsular Malaysia. We found that earthworm abundance was negatively related to increasing soil compaction, leaf litter weight, soil pH, and undergrowth height, whereas it was positively associated with increasing undergrowth and canopy cover. Our findings demonstrated that agroforestry orchards, rubber tree plantations, and mature oil palm plantations had higher earthworm abundance than those in logged lowland forests. Earthworm abundance in unlogged lowland forests and young oil palm plantations, on the other hand, was lower than in logged lowland forests. Overall earthworm weight was greater in rubber tree plantations, agroforestry orchards, mature oil palm plantations, and unlogged lowland forests than those in logged lowland forests, while young oil palm plantations exhibited lower earthworm weight than logged lowland forests. Our data indicate that increases in soil compaction and leaf litter weight were associated with decreased earthworm weight. These results demonstrate the importance of site-level habitat management for maintaining healthy earthworm populations and soil biodiversity.

Forest structure explains spatial heterogeneity of decadal carbon dynamics in a cool-temperate forest

Abstract Accurate evaluation of forest biomass distribution and its long-term change over wide areas is required for effective forest carbon management and prediction of landscape-scale forest dynamics. We evaluated a landscape-scale (225 km2) decadal forest carbon budget at a 1 ha spatial resolution in a cool-temperate forest, by repeating airborne laser observations 10 years apart and partitioning net forest biomass change (FBC) into growth and mortality. Using &gt;10 000 samples, we revealed that naturally regenerated forests have large spatial heterogeneity in net biomass change, and 3/4 of the photosynthetically acquired carbon stock moved to necromass even without anthropogenic disturbances. Actual carbon residence time as living tree biomass was estimated by dividing biomass by growth or mortality rates. The residence time was 107 and 106 years, respectively with large spatial variation among stands (48 and 42 years, respectively, as the difference between 25 and 75 percentile), although studied forest stands have small variation in the forest functional type in a landscape-scale. The best predictors of subsequent decadal biomass changes were two forest structural factors, mean canopy height and canopy height variation in addition to one environmental factor, elevation. Considering the long lifetime of trees, these structural factors may be an indicator of forest soundness rather than a cause of forest growth or mortality. However, in any cases, these structural factors can be powerful predictors of subsequent FBC.