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1. Linking leaf dark respiration to leaf traits and reflectance spectroscopy across diverse forest types

   https://doi.org/10.1111/nph.20267  

   Wu, Fengqi; Liu, Shuwen; Lamour, Julien; Atkin, Owen K; Yang, Nan; Dong, Tingting; Xu, Weiying; Smith, Nicholas G; Wang, Zhihui; Wang, Han; et al (April 2025, New Phytologist)

   Summary Leaf dark respiration (R_dark), an important yet rarely quantified component of carbon cycling in forest ecosystems, is often simulated from leaf traits such as the maximum carboxylation capacity (V_cmax), leaf mass per area (LMA), nitrogen (N) and phosphorus (P) concentrations, in terrestrial biosphere models. However, the validity of these relationships across forest types remains to be thoroughly assessed.Here, we analyzedR_darkvariability and its associations withV_cmaxand other leaf traits across three temperate, subtropical and tropical forests in China, evaluating the effectiveness of leaf spectroscopy as a superior monitoring alternative.We found that leaf magnesium and calcium concentrations were more significant in explaining cross‐siteR_darkthan commonly used traits like LMA, N and P concentrations, but univariate trait–R_darkrelationships were always weak (r² ≤ 0.15) and forest‐specific. Although multivariate relationships of leaf traits improved the model performance, leaf spectroscopy outperformed trait–R_darkrelationships, accurately predicted cross‐siteR_dark(r² = 0.65) and pinpointed the factors contributing toR_darkvariability.Our findings reveal a few novel traits with greater cross‐site scalability regardingR_dark, challenging the use of empirical trait–R_darkrelationships in process models and emphasize the potential of leaf spectroscopy as a promising alternative for estimatingR_dark, which could ultimately improve process modeling of terrestrial plant respiration. 

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2. Soybean prediction using computationally efficient Bayesian spatial regression models and satellite imagery

   https://doi.org/10.1002/agj2.21670  

   Fischer, Richard_J; Rekabdarkolaee, Hossein_Moradi; Joshi, Deepak_R; Clay, David_E; Clay, Sharon_A (September 2024, Agronomy Journal)

   Abstract Preharvest yield estimates can be used for harvest planning, marketing, and prescribing in‐season fertilizer and pesticide applications. One approach that is being widely tested is the use of machine learning (ML) or artificial intelligence (AI) algorithms to estimate yields. However, one barrier to the adoption of this approach is that ML/AI algorithms behave as a black block. An alternative approach is to create an algorithm using Bayesian statistics. In Bayesian statistics, prior information is used to help create the algorithm. However, algorithms based on Bayesian statistics are not often computationally efficient. The objective of the current study was to compare the accuracy and computational efficiency of four Bayesian models that used different assumptions to reduce the execution time. In this paper, the Bayesian multiple linear regression (BLR), Bayesian spatial, Bayesian skewed spatial regression, and the Bayesian nearest neighbor Gaussian process (NNGP) models were compared with ML non‐Bayesian random forest model. In this analysis, soybean (Glycine max) yields were the response variable (y), and spaced‐based blue, green, red, and near‐infrared reflectance that was measured with the PlanetScope satellite were the predictor (x). Among the models tested, the Bayesian (NNGP;R²‐testing = 0.485) model, which captures the short‐range correlation, outperformed the (BLR;R²‐testing = 0.02), Bayesian spatial regression (SRM;R²‐testing = 0.087), and Bayesian skewed spatial regression (sSRM;R²‐testing = 0.236) models. However, associated with improved accuracy was an increase in run time from 534 s for the BLR model to 2047 s for the NNGP model. These data show that relatively accurate within‐field yield estimates can be obtained without sacrificing computational efficiency and that the coefficients have biological meaning. However, all Bayesian models had lowerR²values and higher execution times than the random forest model. 

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3. Assessing the utility of SoilGrids250 for biogeographic inference of plant populations

   https://doi.org/10.1002/ece3.10986  

   Miller, Tony; Blackwood, Christopher B.; Case, Andrea L. (March 2024, Ecology and Evolution)

   Abstract Inclusion of edaphic conditions in biogeographical studies typically provides a better fit and deeper understanding of plant distributions. Increased reliance on soil data calls for easily accessible data layers providing continuous soil predictions worldwide. Although SoilGrids provides a potentially useful source of predicted soil data for biogeographic applications, its accuracy for estimating the soil characteristics experienced by individuals in small‐scale populations is unclear. We used a biogeographic sampling approach to obtain soil samples from 212 sites across the midwestern and eastern United States, sampling only at sites where there was a population of one of the 22 species inLobeliasect.Lobelia. We analyzed six physical and chemical characteristics in our samples and compared them with predicted values from SoilGrids. Across all sites and species, soil texture variables (clay, silt, sand) were better predicted by SoilGrids (R²: .25–.46) than were soil chemistry variables (carbon and nitrogen,R² ≤ .01; pH,R²: .19). While SoilGrids predictions rarely matched actual field values for any variable, we were able to recover qualitative patterns relating species means and population‐level plant characteristics to soil texture and pH. Rank order of species mean values from SoilGrids and direct measures were much more consistent for soil texture (Spearmanr_S = .74–.84; allp < .0001) and pH (r_S = .61,p = .002) than for carbon and nitrogen (p > .35). Within the speciesL. siphilitica, a significant association, known from field measurements, between soil texture and population sex ratios could be detected using SoilGrids data, but only with large numbers of sites. Our results suggest that modeled soil texture values can be used with caution in biogeographic applications, such as species distribution modeling, but that soil carbon and nitrogen contents are currently unreliable, at least in the region studied here. 

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4. Core excitation in ⁹² Mo up to high spin

   https://doi.org/10.1088/1361-6471/ad9343  

   Malik, Vishal; Palit, R; Srivastava, P C; Dey, P; Patel, Deepak; Das, Biswajit; Negi, D; Garg, U; Raut, R; Goel, Nidhi; et al (December 2024, Journal of Physics G: Nuclear and Particle Physics)

   Abstract An in-beam gamma-ray spectroscopy study of the even–even nucleus⁹²Mo has been carried out using the³⁰Si +⁶⁵Cu,¹⁸O +⁸⁰Se reactions at beam energies of 120 and 99 MeV, respectively. Angular distribution from the oriented state ratio (R_ADO) and linear polarization (Δ_asym) measurements have fixed most of the tentatively assigned spin-parity of the high-energy levels. A large-scale shell-model calculation using the GWBXG interaction has been carried out to understand the configuration and structure of both positive and negative parity states up to the highest observed spin. The high-spin states primarily originate from the coupling of excited proton- and neutron-core structures in an almost stretched manner. The systematics of the energy required to form a neutron particle-hole pair excitation,νg_9/2→νd_5/2, is discussed. The lifetimes of a few high-spin states have been measured using the Doppler shift attenuation method. Additionally, a qualitative argument is proposed to explain the comparatively strong E1 transition feeding the 7310.9 keV level. 

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5. Hyperspectral leaf reflectance of grasses varies with evolutionary lineage more than with site

   https://doi.org/10.1002/ecs2.70257  

   Pau, Stephanie; Slapikas, Ryan; Ho, Che‐Ling; Bayliss, Shannon_L J; Donnelly, Ryan C; Abdullahi, Adam; Helliker, Brent R; Nippert, Jesse B; Riley, William J; Still, Christopher J; et al (April 2025, Ecosphere)

   Abstract To predict ecological responses at broad environmental scales, grass species are commonly grouped into two broad functional types based on photosynthetic pathway. However, closely related species may have distinctive anatomical and physiological attributes that influence ecological responses, beyond those related to photosynthetic pathway alone. Hyperspectral leaf reflectance can provide an integrated measure of covarying leaf traits that may result from phylogenetic trait conservatism and/or environmental conditions. Understanding whether spectra‐trait relationships are lineage specific or reflect environmental variation across sites is necessary for using hyperspectral reflectance to predict plant responses to environmental changes across spatial scales. We measured hyperspectral leaf reflectance (400–2400 nm) and 12 structural, biochemical, and physiological leaf traits from five grass‐dominated sites spanning the Great Plains of North America. We assessed if variation in leaf reflectance spectra among grass species is explained more by evolutionary lineage (as captured by tribes or subfamilies), photosynthetic pathway (C₃or C₄), or site differences. We then determined whether leaf spectra can be used to predict leaf traits within and across lineages. Our results using redundancy analysis ordination (RDA) show that grass tribe identity explained more variation in leaf spectra (adjustedR² = 0.12) than photosynthetic pathway, which explained little variation in leaf spectra (adjustedR² = 0.00). Furthermore, leaf reflectance from the same tribe across multiple sites was more similar than leaf reflectance from the same site across tribes (adjustedR² = 0.12 and 0.08, respectively). Across all sites and species, trait predictions based on spectra ranged considerably in predictive accuracies (R² = 0.65 to <0.01), butR²was >0.80 for certain lineages and sites. The relationship between Vc_max, a measure of photosynthetic capacity, and spectra was particularly promising. Chloridoideae, a lineage more common at drier sites, appears to have distinct spectra‐trait relationships compared with other lineages. Overall, our results show that evolutionary relatedness explains more variation in grass leaf spectra than photosynthetic pathway or site, but consideration of lineage‐ and site‐specific trait relationships is needed to interpret spectral variation across large environmental gradients. 

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