Unraveling power-law scaling through exponential cell division dynamics

A primary objective of biology is the development of universal laws that define how organic form develops and how it evolves as a function of size, both ontogenetically and across evolutionary time. Scaling theory has been essential in reaching this goal by giving a complete perspective point, particularly in illuminating the fundamental biological features produced within scaling exponents defining families of equations. Nonetheless, the theoretical basis of the allometric equation within scaling theory are inadequately explained, particularly when it comes to establishing links between micro-level processes at the cellular level and macro-level phenomena. We proposed an unlimited cell bipartition, resulting in an exponential growth in cell numbers during an individual's lifespan, to bridge this conceptual gap between cellular processes and allometric scaling. The power-law scaling between body mass and organ weight was produced by the synchronous exponential increments and the allometric exponent is rate of logarithmic cell proliferation rate. Substituting organ weight for erythrocyte weight aided in the development of a power-law scaling relationship between body mass and metabolic rate. Furthermore, it is critical to understand how cell size affects the exponent in power-law scaling. We find that a bigger exponent will result from an increase in the average weight of organ cells or a decrease in the average weight of all cells. Furthermore, cell proliferation dynamics showed a complex exponential scaling between body mass and longevity, defying the previously reported power-law scaling. We discovered a quadratic link between longevity and logarithmic body mass. Notably, all of the parameters included in these relationships are explained by indices linked to cell division and embryonic development. This research adds to our understanding of the complex interaction between cellular processes and overarching scaling phenomena in biology.

in the bimodal rainfall forest of Cameroon

Over the last two decades, bamboo has received increasing attention owing to its socio-economic and environmental importance. Environmentally, bamboo plays an important role in carbon sequestration, thus enhancing climate change mitigation. In Cameroon, knowledge about the importance of Bambusa vulgaris Schrad. ex J.C.Wendl. to climate change mitigation is deficient, despite the fact that it is the most abundant bamboo species in Cameroon's Bimodal rainforest agroecological zone (Agroecological zone 5 - AEZ5). This study was initiated to develop allometric equations and estimate carbon stocks of B. vulgaris in Cameroon's AEZ5. The destructive, clump-based method was used for bamboo biomass data collection on 40 clumps and 86 culms. Regression analyses were performed to obtain allometric models for B. vulgaris biomass prediction which were used for B. vulgaris carbon stocks estimation in AEZ5. The best allometric model for culms was obtained when all predictive variables including age, diameter and height were considered into the model. For clump, the best model was obtained when the number of culms per clump, girth and average diameter were considered in the model. Model quality adjustment was better for clump aboveground biomass (AGB) compared to those of culm AGB. The model of B. vulgaris of the evergreen rainfall forest was validated with a bias of 45.5 %. Bamboo aboveground biomass proportions were 77 %, 15 % and 8 %, respectively for culms, branches and leaves. The mean density and carbon stocks of B. vulgaris were estimated at 2,0679 culms.ha-1, 257 clumps.ha-1, and 61.65 tC ha-1. B. vulgaris has a veritable carbon sequestration capacity which policymakers should consider in climate change mitigation strategies like those linked to payments for ecosystem services, voluntary carbon stocks market, Bonn Challenge, AFR100 initiative, and the Paris agreement ratified by the government of Cameroon.

A protocol for monitoring plant responses to changing nitrogen deposition regimes in Alberta bogs

Bogs are nutrient poor, acidic ecosystems that receive their water and nutrients entirely from precipitation (= ombrogenous) and as a result are sensitive to nutrient loading from atmospheric sources. Bogs occur frequently on the northern Alberta landscape, estimated to cover 6% of the Athabasca Oil Sands Area. As a result of oil sand extraction and processing, emissions of nitrogen (N) and sulfur (S) to the atmosphere have led to increasing N and S deposition that have the potential to alter the structure and function of these traditionally nutrient-poor ecosystems. At present, no detailed protocol is available for monitoring potential change of these sensitive ecosystems. We propose a user-friendly protocol that will monitor potential plant and lichen responses to future environmental inputs of nutrients and provide a structured means for collecting annual data. The protocol centers on measurement of five key plant/lichen attributes, including changes in (1) plant abundances, (2) dominant shrub annual growth and primary production, (3) lichen health estimated through chlorophyll/phaeophytin concentrations, (4) Sphagnum annual growth and production, and (5) annual growth of the dominant tree species (Picea mariana). We placed five permanent plots in each of six bogs located at different distances from the center of oil sand extraction and sampled these for 2 years (2018 and 2019). We compared line intercept with point intercept plant assessments using NMDS ordination, concluding that both methods provide comparable data. These data indicated that each of our six bog sites differ in key species abundances. Structural differences were apparent for the six sites between years. These differences were mostly driven by changes in Vaccinium oxycoccos, not the dominant shrubs. We developed allometric growth equations for the dominant two shrubs (Rhododendron groenlandicum and Chamaedaphne calyculata). Equations developed for each of the six sites produced growth values that were not different from one another nor from one developed using data from all sites. Annual growth of R. groenlandicum differed between sites, but not years, whereas growth of C. calyculata differed between the 2 years with more growth in 2018 compared with 2019. In comparison, Sphagnum plant density and stem bulk density both had strong site differences, with stem mass density higher in 2019. When combined, annual production of S. fuscum was greater in 2019 at three sites and not different at three of the sites. Chlorophyll and phaeophytin concentrations from the epiphytic lichen Evernia mesomorpha also differed between sites and years. This protocol for field assessments of five key plant/lichen response variables indicated that both site and year are factors that must be accounted for in future assessments. A portion of the site variation was related to patterns of N and S deposition.

The genetic control of leaf allometry in the common bean, Phaseolus vulgaris

BACKGROUND

To maximize photosynthetic efficiency, plants have evolved a capacity by which leaf area scales allometrically with leaf mass through interactions with the environment. However, our understanding of genetic control of this allometric relationship remains limited.

RESULTS

We integrated allometric scaling laws expressed at static and ontogenetic levels into genetic mapping to identify the quantitative trait loci (QTLs) that mediate how leaf area scales with leaf mass and how such leaf allometry, under the control of these QTLs, varies as a response to environment change. A major QTL detected by the static model constantly affects the allometric growth of leaf area vs. leaf mass for the common bean (Phaseolus vulgaris) in two different environments. The ontogenetic model identified this QTL plus a few other QTLs that determine developmental trajectories of leaf allometry, whose expression is contingent heavily upon the environment.

CONCLUSIONS

Our results gain new insight into the genetic mechanisms of how plants program their leaf morphogenesis to adapt to environmental perturbations.

Aboveground carbon of community-managed Chirpine (Pinus roxburghii Sarg.) forests of Nepal based on stand types and geographic aspects

On a global scale, about 15.5% of forests are administered through community-based forestry programs that offer the opportunity for enhanced carbon sequestration while maintaining the supply of more traditional goods and services such as cooking fuels, animal fodder and bedding. A challenge in community forest (CF) management is to realize their carbon value without compromising their role in the provision of these traditional goods and services. In this study of CF dominated by Pinus roxburghii in the Phalebas region of Nepal, the impacts of stand composition and geographic aspect on aboveground forest carbon is investigated as a means to optimize CF management for both traditional values and for emerging carbon market values. The aboveground carbon of mixed and monospecific stands of Pinus roxburghii was estimated using a combination of destructive sampling and species-specific allometric equations. On average, monospecific stands contained 106.2 Mg C ha⁻¹ in aboveground tree biomass, significantly more than mixed stands at 73.1 Mg C ha⁻¹ (p = 0.022). Similarly, stands growing on northern aspects (northeast 124.8 Mg C ha⁻¹, northwest 100.9 Mg C ha⁻¹) stored significantly more carbon (p = 0.002) than southern aspects (southeast 75.3 Mg C ha⁻¹, southwest 57.6 Mg C ha⁻¹), reflecting the more favorable growing conditions of northern aspects. These results suggest monospecific stands planted on northern aspects may be best suited for management to achieve carbon benefits, whilst mixed-species stands on southern aspects may be better suited for biodiversity conservation and supporting livelihoods. To maintain and increase carbon value, community forestry may need to implement nutrient return practices to limit the impact of sustained nutrient removals on stand productivity.

A new model for simulating microbial cyanide production and optimizing the medium parameters for recovering precious metals from waste printed circuit boards

Bioleaching is a green recycling technology for recovering precious metals from waste printed circuit boards (WPCBs). However, this technology requires increasing cyanide production to obtain desirable recovery efficiency. Luria-Bertani medium (LB medium, containing tryptone 10 g/L, yeast extract 5 g/L, NaCl 10 g/L) was commonly used in bioleaching of precious metal. In this study, results showed that LB medium did not produce highest yield of cyanide. Under optimal culture conditions (25 °C, pH 7.5), the maximum cyanide yield of the optimized medium (containing tryptone 6 g/L and yeast extract 5 g/L) was 1.5 times as high as that of LB medium. In addition, kinetics and relationship of cell growth and cyanide production was studied. Data of cell growth fitted logistics model well. Allometric model was demonstrated effective in describing relationship between cell growth and cyanide production. By inserting logistics equation into allometric equation, we got a novel hybrid equation containing five parameters. Kinetic data for cyanide production were well fitted to the new model. Model parameters reflected both cell growth and cyanide production process.

Aboveground biomass estimation at different scales for subtropical forests in China

BACKGROUND

The accurate estimation of forest biomass at different scales is the critical step in the assessment of forest carbon stocks. We used three models at increasing scales: allometric model at ecoregional scale (model 1), dummy variable allometric model at both ecoregion and regional scales (model 2), and allometric model at regional scale (model 3) to estimate the aboveground biomass of six subtropical forests in China. Furthermore, we also tested whether wood density can improve the accuracy of the allometric model at regional scale.

RESULTS

Aboveground biomass estimates for six subtropical forests were significantly affected by the ecoregions (p < 0.05). Model 1 and model 2 had good fitness with higher values of R ², lower RSE (residual standard error) and MPSE (mean percent standard error) than model 3. The values of MPSE for model 1, model 2, and model 3 ranged from 2.79 to 30.40%, 5.15 to 40.94%, and 13.25 to 80.81% at ecoregion scale, respectively. At regional scale, MPSE of model 2 was very similar to that of model 1, and was less than model 3. New allometric models with wood density had greater R ², lower RSE and MPSE than the traditional allometric models without wood density variable for six subtropical forests at regional scale.

CONCLUSION

The dummy variable allometric models have better performances to estimate aboveground biomass for six subtropical forests in China, which provided an effective approach to improve the compatibility of forest biomass estimations from different scales. New allometric models with wood density substantially improved accuracies of aboveground biomass estimation for subtropical forests at regional scale.

An alternative approach for estimating above ground biomass using Resourcesat-2 satellite data and artificial neural network in Bundelkhand region of India

Determination of above ground biomass (AGB) of any forest is a longstanding scientific endeavor, which helps to estimate net primary productivity, carbon stock and other biophysical parameters of that forest. With advancement of geospatial technology in last few decades, AGB estimation now can be done using space-borne and airborne remotely sensed data. It is a well-established, time saving and cost effective technique with high precision and is frequently applied by the scientific community. It involves development of allometric equations based on correlations of ground-based forest biomass measurements with vegetation indices derived from remotely sensed data. However, selection of the best-fit and explanatory models of biomass estimation often becomes a difficult proposition with respect to the image data resolution (spatial and spectral) as well as the sensor platform position in space. Using Resourcesat-2 satellite data and Normalized Difference Vegetation Index (NDVI), this pilot scale study compared traditional linear and nonlinear models with an artificial intelligence-based non-parametric technique, i.e. artificial neural network (ANN) for formulation of the best-fit model to determine AGB of forest of the Bundelkhand region of India. The results confirmed the superiority of ANN over other models in terms of several statistical significance and reliability assessment measures. Accordingly, this study proposed the use of ANN instead of traditional models for determination of AGB and other bio-physical parameters of any dry deciduous forest of tropical sub-humid or semi-arid area. In addition, large numbers of sampling sites with different quadrant sizes for trees, shrubs, and herbs as well as application of LiDAR data as predictor variable were recommended for very high precision modelling in ANN for a large scale study.