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Background: Tick-borne encephalitis virus (TBEV) can cause severe neurological disease in humans. Its geographical distribution is expanding in Western Europe with unresolved causes and spatial patterns, necessitating enhanced surveillance. Monitoring the virus in the environment is complicated, as it usually relies on destructive sampling of small rodents to test organs for TBEV, which in addition to ethical considerations also raises issues for long-term monitoring or longitudinal studies. Moreover, even when the virus is not detected in the blood or organs of the rodent, TBEV can still be transmitted from an infected tick to uninfected ticks feeding nearby. This is due to the ability of TBEV to replicate and migrate locally within the epidermis of small mammals, including those that do not appear to have systemic infection. This suggests that the virus may be detectable in skin biopsies, which has been confirmed in experimentally infected laboratory rodents, but it remains unknown if this sample type may be a viable alternative to destructively obtained samples in the monitoring of natural TBEV infection. Here we test ear tissue and dried blood spot (DBS) samples from rodents to determine whether TBEV-RNA can be detected in biological samples obtained non-destructively. Methods: Rodents were live-trapped and sampled at three woodland areas in The Netherlands where presence of TBEV has previously been recorded. Ear tissue (n = 79) and DBSs (n = 112) were collected from a total of 117 individuals and were tested for TBEV-RNA by real-time RT-PCR. Results: TBEV-RNA was detected in five rodents (4.3% of tested individuals), all of which had a TBEV-positive ear sample, while only two out of four of these individuals (for which a DBS was available) had a positive DBS. This equated to 6.3% of ear samples and 1.8% of DBSs testing positive for TBEV-RNA. Conclusions: We provide the first evidence to our knowledge that TBEV-RNA can be detected in samples obtained non-destructively from naturally infected wild rodents, providing a viable sampling alternative suitable for longitudinal surveillance of the virus. [ABSTRACT FROM AUTHOR]

Background: Post-harvest physiological deterioration (PPD) poses a significant challenge to the cassava industry, leading to substantial economic losses. This study aims to address this issue by developing a comprehensive framework in collaboration with cassava breeders. Results: Advanced deep learning (DL) techniques such as Segment Anything Model (SAM) and YOLO foundation models (YOLOv7, YOLOv8, YOLOv9, and YOLO-NAS), were used to accurately categorize PPD severity from RGB images captured by cameras or cell phones. YOLOv8 achieved the highest overall mean Average Precision (mAP) of 80.4%, demonstrating superior performance in detecting and classifying different PPD levels across all three models. Although YOLO-NAS had some instability during training, it demonstrated stronger performance in detecting the PPD_0 class, with a mAP of 91.3%. YOLOv7 exhibited the lowest performance across all classes, with an overall mAP of 75.5%. Despite challenges with similar color intensities in the image data, the combination of SAM, image processing techniques such as RGB color filtering, and machine learning (ML) algorithms was effective in removing yellow and gray color sections, significantly reducing the Mean Absolute Error (MAE) in PPD estimation from 20.01 to 15.50. Moreover, Artificial Intelligence (AI)-based algorithms allow for efficient analysis of large datasets, enabling rapid screening of cassava roots for PPD symptoms. This approach is much faster and more streamlined compared to the labor-intensive and time-consuming manual visual scoring methods. Conclusion: These results highlight the significant advancements in PPD detection and quantification in cassava samples using cutting-edge AI techniques. The integration of YOLO foundation models, alongside SAM and image processing methods, has demonstrated promising precision even in scenarios where experts struggle to differentiate closely related classes. This AI-powered model not only effectively streamlines the PPD assessment in the pre-breeding pipeline but also enhances the overall effectiveness of cassava breeding programs, facilitating the selection of PPD-resistant varieties through controlled screening. By improving the precision of PPD assessments, this research contributes to the broader goal of enhancing cassava productivity, quality, and resilience, ultimately supporting global food security efforts. [ABSTRACT FROM AUTHOR]

Background: Invasive management strategies range from preventing new invasive species incursions to eliminating established populations, with all requiring effective monitoring to guide action. The use of DNA sampled from the environment (eDNA) is one such tool that provides the ability to surveille and monitor target invasive species through passive sampling. Technology being developed to eliminate invasive species includes genetic biocontrol in the form of gene drive. This approach would drive a trait through a population and could be used to eliminate or modify a target population. Once a gene drive organism is released into a population then monitoring changes in density of the target species and the spread of the drive in the population would be critical. Results: In this paper, we use invasive Mus musculus as a model for development of an eDNA assay that detects wild-type M. musculus and gene drive M. musculus. We demonstrate successful development of an assay where environmental samples could be used to detect wild-type invasive M. musculus and the relative density of wild-type to gene drive M. musculus. Conclusions: The development of a method that detects both wild-type M. musculus and a gene drive M. musculus (tCRISPR) from environmental samples expands the utility of environmental DNA. This method provides a tool that can immediately be deployed for invasive wild M. musculus management across the world. This is a proof-of-concept that a genetic biocontrol construct could be monitored using environmental samples. [ABSTRACT FROM AUTHOR]

Background: The increasing antimicrobial resistance caused by the improper use of antibiotics poses a significant challenge to humanity. Rapid and accurate identification of microbial species in clinical settings is crucial for precise medication and reducing the development of antimicrobial resistance. This study aimed to explore a method for automatic identification of bacteria using Volatile Organic Compounds (VOCs) analysis and deep learning algorithms. Results: AlexNet, where augmentation is applied, produces the best results. The average accuracy rate for single bacterial culture classification reached 99.24% using cross-validation, and the accuracy rates for identifying the three bacteria in randomly mixed cultures were SA:98.6%, EC:98.58% and PA:98.99%, respectively. Conclusion: This work provides a new approach to quickly identify bacterial microorganisms. Using this method can automatically identify bacteria in GC-IMS detection results, helping clinical doctors quickly detect bacterial species, accurately prescribe medication, thereby controlling epidemics, and minimizing the negative impact of bacterial resistance on society. [ABSTRACT FROM AUTHOR]

Background: The pink bollworm, Pectinophora gossypiella (Saunders), is a devastating global pest of cotton that has caused substantial economic damage to Bt Bollgard-II® cotton plants in recent years due to the evolution of Bt resistance. The associated fitness cost is assumed to be one of the factors delaying the development of resistance against Bt transgenic crops. Hence, the present study was undertaken to assess the biological performance of pink bollworms by comparing the life history and demographic parameters of a resistant (Field-R) and susceptible (Lab-S) population. Results: Prolonged larval duration (23.40 days in Field-R vs 18.80 days in Lab-S population), total life cycle (male = 50.00 vs 42.80 days; female = 53.60 vs 46.20 days), reduced fecundity (100.60 vs 154.20 eggs/female) and fertility (88.00 vs 138.00 fertile eggs/female) was observed. The demographic parameters indicated a significant reduction in the net reproductive rate (184.27 vs 276.72), innate capacity for increase in number (0.11 vs 0.15), finite rate of increase in number (1.12 vs 1.16 female progenies produced/female/day), weekly multiplication rate (2.16 vs 2.86), potential fecundity (545.06 vs 634.11 eggs), number of hypothetical F2 females (33 955.65 vs 76 572.41), but longer mean length of generation (47.54 vs 37.74 days) and population doubling time (6.30 vs 4.62 days) in Field-R compared with Lab-S population. A stage-specific life table demonstrated the differences in survival rates between susceptible and resistant populations at various life stages, with the resistant population having higher generation mortality (0.22 vs 0.19). Conclusions: The study confirms the involvement of fitness costs associated with Bt resistance in P. gossypiella. Despite reduced reproductive fitness, the resistant population tried prolonging the larval stage as a compensatory mechanism to repair the damaged host tissues due to Bt intoxication and for accumulation of enough nutrient reserves for normal pupation and adult emergence. Presence of a high proportion of double Bt-resistant larvae in the field coupled with continued noncompliance with refug planting certainly favours the flaring up of this monophagous pest despite the observed fitness costs. The resistance cannot be effectively reversed unless suitable alternative management strategies are deployed. [ABSTRACT FROM AUTHOR]

Background: Studying the physiological growth status of Pinus yunnanensis Franch and Pinus elliottii Engelm. seedlings under different karst fissure thicknesses and rainfall distributions is of great significance for the management, vegetation restoration, and tree species selection in karst rocky desertification areas. In this study, we used a two-factor block experiment and set different rainfall durations, namely reduced rainfall duration (I3d), natural rainfall duration (I6d), and extended rainfall duration (I9d); Different karst small habitats, i.e., stone-free soil (S0), less stone and more soil (S1/4), and half stone and half soil (S1/2), are simulated at these three levels. Analyze the changes in physiological growth and photosynthetic characteristics in two coniferous seedlings under different treatments with different karst thicknesses. Results: The results showed that with the increase of karst thickness, the growth volumes of height and diameter of P. yunnanensis seedlings, the biomass of various organs, and the accumulation of K+, Ca2+, Na+, and Mg2+ showed a significant change pattern of first increasing and then decreasing (P < 0.05); P. elliottii seedlings show a gradually decreasing trend (except for Ca2+). The biomass accumulation of each organ in two coniferous seedlings showed that leaves > stems > roots. The K+, Ca2+, and Mg2+ content in various organs of P. yunnanensis seedlings showed that leaves > roots > stems, while Na+ shows the order of roots > leaves > stems. The accumulation of mineral elements in various organs of P. elliottii seedlings is manifested as roots > stems > leaves and the accumulation of mineral elements in both coniferous seedlings is manifested as Ca2+ > Mg2+ > K+ > Na+. Root length, root volume, root surface area, root diameter, SOD, POD, SP, photosynthetic pigment content, fluorescence parameters, and gas exchange parameters of P. yunnanensis seedlings gradually increase with the increase of karst thickness (except for the 9-day rainfall duration), while those of P. elliottii seedlings gradually decrease. The light saturation point of P. yunnanensis seedlings is highest under the I6dS1/2 treatment, while that of P. elliottii is highest under the I3dS0 treatment. Conclusions: In summary, prolonging rainfall duration has an inhibitory effect on the growth of two types of coniferous seedlings. Increasing karst thickness inhibits the growth of P. elliottii seedlings, and to some extent, promotes the growth and development of P. yunnanensis seedlings. I6dS1/4 and I3dS0 treatments have the best growth effects on P. yunnanensis and P. elliottii seedlings. Therefore, we give priority to P. yunnanensis as the tree species for vegetation restoration or rocky desertification management in karst areas. Our study reveals the role of limestone-filled different karst fissures in mitigating the effects of drought as "containers" for plant growth. These findings help us understand the response of plants to drought stress and provide valuable insights for vegetation restoration in karst environments affected by global climate change. Therefore, further experiments with various karst fissure sizes are necessary to test the universality of the reactions of various plants under different karst fissures. [ABSTRACT FROM AUTHOR]

Erythropalum scandens Bl. is a medicinal woody vegetable found in southern China and parts of Southeast Asia. Studies have shown improper substrate hindered E. scandens seedling growth, causing water accumulation and nutrient deficiency. In pursuit of an ideal growth medium for E. scandens seedlings during the early stages, this study conducted a pot experiment to identify a mixed substrate with optimal water permeability and fertility. In this study, pure Alfisols soil treatment as the control (CK), and two soilless substrates (peat soil and perlite) were combined with Alfisols soil into different volume ratios, in order to better use soil resources from understory space and balance the texture of mixed substrates. The growth, physiological characteristics and nutrient status of 24-month-old E. scandens seedlings were determined after planting in different mixed ratios. The results showed that as the proportion of peat soil increased in the mix, most indexes exhibited an initial increase followed by a decline, while soluble protein content decreased consistently. Conversely, an increasing perlite ratio resulted in a general decline in most growth and physiological indexes. Root growth, biomass accumulation and chlorophyll content, peaked in the 66.67% Alfisols soil + 33.33% perlite (T4) treatment. Notably, T3 (66.67% Alfisols soil + 33.33% peat soil) showcased the best above-ground growth, while T1 (50.00% Alfisols soil + 50.00% peat soil) excelled in element content accumulation. In conclusion, the cultivation substrate should primarily consist of Alfisols soil, constituting at least 50%. The addition of peat soil enhances above-ground growth and nutrients accumulation, while perlite contributes to robust root development. One third of peat soil and a small amount of perlite can be added to the substrate during E. scandens seedling cultivation, and proper fertilization should also be used in order to increase nutrient accumulation in aboveground and underground parts. This research provides valuable insights into maximizing the potential of E. scandens seedlings through precise cultivation methods. [ABSTRACT FROM AUTHOR]

Background: Studies evaluating the attrition, physical and insecticidal durability of dual active ingredient (AI) insecticide-treated nets (ITNs) are essential for making programmatic decisions regarding their deployment. We performed a prospective study embedded in a cluster randomised controlled trial (cRCT) to evaluate the attrition, fabric integrity and insecticidal durability of Interceptor® G2 (alpha-cypermethrin-chlorfenapyr) and Royal Guard® (alpha-cypermethrin–pyriproxyfen), compared to Interceptor® (alpha-cypermethrin) in Benin. Methods: A total of 2428 study nets in 1093 randomly selected households in five clusters per arm of the cRCT were monitored for ITN attrition and fabric integrity every 6–12 months post-distribution. Householders were further surveyed to investigate non-study net use and their preference for ITN fabric types used in the study nets. A second cohort of 120 nets per ITN type were withdrawn every 12 months and assessed for chemical content and insecticidal activity in laboratory bioassays. Alpha-cypermethrin bioefficacy was investigated using the susceptible Anopheles gambiae Kisumu strain, and chlorfenapyr and pyriproxyfen bioefficacy were investigated using the pyrethroid-resistant Anopheles coluzzii Akron strain. Net pieces were tested in WHO cone bioassays and tunnel tests for alpha-cypermethrin and in tunnel tests for chlorfenapyr; pyriproxyfen activity was assessed in cone bioassays as the reduction in fertility of blood-fed survivors using ovary dissection. Bioefficacy was expressed as the proportion of ITNs passing predetermined WHO criteria, namely knock-down ≥ 95% or 24/72 h mortality ≥ 80% or reduction in fertility ≥ 50%. Results: Overall ITN survivorship was 52% at 24 months and fell to 15% at 36 months. Median ITN survival time was lower with Royal Guard® relative to Interceptor® [1.6 vs 2.3 years; hazard ratio (HR) 1.49, 95% confidence interval (CI) 1.36–1.66; p < 0.001] and Interceptor® G2 (1.6 vs 2.1 years; HR 1.33, 95% CI 1.20–1.47; p < 0.001). Householders overwhelmingly preferred polyester nets over polyethylene nets (96%), and more Royal Guard® nets were replaced with spare polyester nets from previous campaigns. All Royal Guard® nets passed efficacy criteria for alpha-cypermethrin at all time points (100%) while ITN pass rates after 24 months had fallen to < 40% for pyriproxyfen and chlorfenapyr. The chemical content analysis showed a higher loss rate of the non-pyrethroid insecticides relative to the pyrethroids in each dual ingredient AI ITN; 74% vs 47% for Royal Guard® and 85% vs 63% for Interceptor® G2 at 36 months. Conclusions: The median ITN survival time for Interceptor® G2 (2.1 years) and Royal Guard® (1.6 years) in Benin is substantially lower than 3 years. Royal Guard® nets were discarded more quickly by householders, partly due to their low preference for polyethylene nets. The insecticidal activity of the non-pyrethroid insecticides in both dual AI ITNs was short-lived compared to alpha-cypermethrin. The results corroborate the findings from the cRCT conducted in Benin. [ABSTRACT FROM AUTHOR]

Background: To avoid destructive sampling for conservation and genetic assessment, we isolated the DNA of clam Cyclina sinensis from their feces. DNA electrophoresis and PCR amplification were used to determine the quality of fecal DNA. And we analyzed the effects of different conditions on the degradation of feces and fecal DNA. Results: The clear fecal DNA bands were detected by electrophoresis, and PCR amplification using clam fecal DNA as template was effective and reliable, suggesting that clam feces can be used as an ideal material for noninvasive DNA isolation. In addition, by analyzing the effects of different environmental temperatures and soaking times on the degradation of feces and fecal DNA, we found that the optimum temperature was 4 °C. In 15 days, the feces maintained good texture, and the quality of fecal DNA was good. At 28 °C, the feces degraded in 5 days, and the quality of fecal DNA was poor. Conclusions: The clam feces can be used as an ideal material for noninvasive DNA isolation. Moreover, the quality of fecal DNA is negatively correlated with environmental temperature and soaking time. [ABSTRACT FROM AUTHOR]

Key message: We compiled 2,458 biomass equations from 168 destructive sampling studies in Indonesia. Unpublished academic theses contributed the largest share of the biomass equations. The availability of the biomass equations was skewed to certain regions, forest types, and species. Further research is necessary to fill the data gaps in emission factors and to enhance the implementation of climate change mitigation projects and programs. Context: Locally derived allometric equations contribute to reducing the uncertainty in the estimation of biomass, which may be useful in the implementation of climate change mitigation projects and programs in the forestry sector. Many regional and global efforts are underway to compile allometric equations. Aims: The present study compiles the available allometric equations in Indonesia and evaluates their adequacy in estimating biomass in the different types of forest across the archipelago. Methods: A systematic survey of the scientific literature was conducted to compile the biomass equations, including ISI publications, national journals, conference proceedings, scientific reports, and academic theses. The data collected were overlaid on a land use/land cover map to assess the spatial distribution with respect to different regions and land cover types. The validation of the equations for selected forest types was carried out using independent destructive sampling data. Results: A total of 2,458 biomass equations from 168 destructive sampling studies were compiled. Unpublished academic theses contributed the majority of the biomass equations. Twenty-one habitat types and 65 species were studied in detail. Diameter was the most widely used single predictor in all allometric equations. The cumulative number of individual trees cut was 5,207. The islands of Java, Kalimantan, and Sumatra were the most studied, while other regions were underexplored or unexplored. More than half of the biomass equations were for just seven species. The majority of the studies were carried out in plantation forests and secondary forests, while primary forests remain largely understudied. Validation using independent data showed that the allometric models for peat swamp forest had lower error departure, while the models for lowland dipterocarp forest had higher error departure. Conclusion: Although biomass studies are a major research activity in Indonesia due to its high forest cover, the majority of such activities are limited to certain regions, forest types, and species. More research is required to cover underrepresented regions, forest types, particular growth forms, and very large tree diameter classes. [ABSTRACT FROM AUTHOR]

Background: Phytophthora infestans is responsible for late blight, one of the most important potato diseases. Phenazine-1-carboxylic acid (PCA)-producing Pseudomonas fluorescens strain LBUM223 isolated in our laboratory shows biocontrol potential against various plant pathogens. To characterize the effect of LBUM223 on the transcriptome of P. infestans, we conducted an in vitro time-course study. Confrontational assay was performed using P. infestans inoculated alone (control) or with LBUM223, its phzC- isogenic mutant (not producing PCA), or exogenically applied PCA. Destructive sampling was performed at 6, 9 and 12 days and the transcriptome of P. infestans was analysed using RNA-Seq. The expression of a subset of differentially expressed genes was validated by RT-qPCR. Results: Both LBUM223 and exogenically applied PCA significantly repressed P. infestans' growth at all times. Compared to the control treatment, transcriptomic analyses showed that the percentages of all P. infestans' genes significantly altered by LBUM223 and exogenically applied PCA increased as time progressed, from 50 to 61% and from to 32 to 46%, respectively. When applying an absolute cut-off value of 3 fold change or more for all three harvesting times, 207 genes were found significantly differentially expressed by PCA, either produced by LBUM223 or exogenically applied. Gene ontology analysis revealed that both treatments altered the expression of key functional genes involved in major functions like phosphorylation mechanisms, transmembrane transport and oxidoreduction activities. Interestingly, even though no host plant tissue was present in the in vitro system, PCA also led to the overexpression of several genes encoding effectors. The mutant only slightly repressed P. infestans' growth and barely altered its transcriptome. Conclusions: Our study suggests that PCA is involved in P. infestans' growth repression and led to important transcriptomic changes by both up- and down-regulating gene expression in P. infestans over time. Different metabolic functions were altered and many effectors were found to be upregulated, suggesting their implication in biocontrol. [ABSTRACT FROM AUTHOR]

Objective: Environmental DNA (eDNA) methods are crucial for monitoring populations, particularly rare and cryptic species. For confident eDNA application, rigorous assay validation is required including specificity testing with genomic DNA (gDNA). However, this critical step is often difficult to achieve as obtaining fresh tissue samples from at-risk species can be difficult, highly limited, or impossible. Natural history museum collections could serve as a valuable and ethical voucher specimen resource for eDNA assay validation. The present study demonstrates the effectiveness of whole genome amplification (WGA) in providing enough gDNA to assemble high quality mitogenomes from which robust targeted eDNA assays can be designed. Results: Using fresh and historical museum tissue samples from six species spanning fish, birds, and mammals, we successfully developed a WGA method with an average yield of 380 to 1,268 ng gDNA per 20 µL reaction. This gDNA was used for whole genome shotgun sequencing and subsequent assembly of high quality mitogenomes using mtGrasp. These mitogenomes were then used to develop six new robust, targeted quantitative real time polymerase chain reaction-based eDNA assays and 200 ng WGA-enriched yielded satisfactory Cq values and near 100% detection frequencies for all assays tested. This approach offers a cost-effective and non-invasive alternative, streamlining eDNA research processes and aiding in conservation efforts. [ABSTRACT FROM AUTHOR]

Soil microbiome characterization is typically achieved with next-generation sequencing (NGS) techniques. Metabarcoding is very common, and meta-omics is growing in popularity. These techniques have been instrumental in microbiology, but they have limitations. They require extensive time, funding, expertise, and computing power to be effective. Moreover, these techniques are restricted to controlled laboratory conditions; they are not applicable in field settings, nor can they rapidly generate data. This hinders using NGS as an environmental monitoring tool or an in-situ checking device. Biosensing technology can be applied to soil microbiome characterization to overcome these limitations and to complement NGS techniques. Biosensing has been used in biomedical applications for decades, and many successful commercial products are on the market. Given its previous success, biosensing has much to offer soil microbiome characterization. There is a great variety of biosensors and biosensing techniques, and a few in particular are better suited for soil field studies. Aptamers are more stable than enzymes or antibodies and are more ready for field-use biosensors. Given that any microbiome is complex, a multiplex sensor will be needed, and with large, complicated datasets, machine learning might benefit these analyses. If the signals from the biosensors are optical, a smartphone can be used as a portable optical reader and potential data-analyzing device. Biosensing is a rich field that couples engineering and biology, and applying its toolset to help advance soil microbiome characterization would be a boon to microbiology more broadly. [ABSTRACT FROM AUTHOR]

Background: This study explores the use of Unmanned Aerial Vehicles (UAVs) for estimating wheat biomass, focusing on the impact of phenotyping and analytical protocols in the context of late-stage variety selection programs. It emphasizes the importance of variable selection, model specificity, and sampling location within the experimental plot in predicting biomass, aiming to refine UAV-based estimation techniques for enhanced selection accuracy and throughput in variety testing programs. Results: The research uncovered that integrating geometric and spectral traits led to an increase in prediction accuracy, whilst a recursive feature elimination (RFE) based variable selection workflowled to slight reductions in accuracy with the benefit of increased interpretability. Models, tailored to specific experiments were more accurate than those modelling all experiments together, while models trained for broad-growth stages did not significantly increase accuracy. The comparison between a permanent and a precise region of interest (ROI) within the plot showed negligible differences in biomass prediction accuracy, indicating the robustness of the approach across different sampling locations within the plot. Significant differences in the within-season repeatability (w2) of biomass predictions across different experiments highlighted the need for further investigation into the optimal timing of measurement for prediction. Conclusions: The study highlights the promising potential of UAV technology in biomass prediction for wheat at a small plot scale. It suggests that the accuracy of biomass predictions can be significantly improved through optimizing analytical and modelling protocols (i.e., variable selection, algorithm selection, stage-specific model development). Future work should focus on exploring the applicability of these findings under a wider variety of conditions and from a more diverse set of genotypes. [ABSTRACT FROM AUTHOR]

Background: The intensification of the agricultural practices in Europe over the last decades has drastically transformed the agroecosystems. The simplification of the landscape, the loss of semi-natural habitats and the application of chemicals on crops led to biodiversity decline in agricultural landscapes, raising substantial concerns about the loss of essential ecosystem services, such as pollination or pest control. Depending on the location, the scale and the regional context, different indicator species groups (ISGs) are regularly surveyed to assess the state and trend of biodiversity changes in agroecosystems. Although the high diversity of these ISGs allows assessing different biodiversity aspects (e.g., trophic levels, bio-physical compartments, scale of indication), it complicates the interpretation of the results and thus their practical application. In addition, species diversity metrics are various, from simple species counts to more complex measurements of diversity indices, sometimes with antagonistic responses. Here, to meet the pressing need for synthesis in this complex topic, we follow a standardized systematic map protocol to collect and summarize the literature reporting field evidence of the effects of the main agricultural management practices (AMPs) in arable crops, grasslands and ecological infrastructures on a set of ISGs in European lowland farming areas. Methods: Searches of literature were made using online publication databases, search engine and specialist websites in English. Gathered publications were screened for relevance following inclusion/exclusion criteria published in a prior protocol. We extracted and mapped information about experimental design, monitoring methods, ISGs and AMPs studied and the diversity measures presented in each included publication. These parameters are structured in available data coding sheets. Results: The search gathered 20,162 references from which 1208 remained after full text eligibility screening. Main areas studied are in Western Europe, and the number of studies increased exponentially from 1984 to 2022. Most publications are experimental and on-farm studies which assess AMPs effects at the field scale. Main studied AMPs are fertilization, grazing, organic farming, tillage, mowing and herbicide application. Most ISGs used to study their impacts are flora, carabids, spiders, birds, bees and annelids, often combined with other ISGs. The combinations between AMPs and ISGs studied are detailed as well as monitoring methods. The most used diversity measures are abundance, species richness, Shannon index, evenness, and community composition. Conclusions: We identified several knowledge clusters: (1) organic farming, fertilization, tillage, grazing and mowing impact on a wide range of ISGs, (2) flora response to agricultural practices, (3) annelids response to agronomic interventions that impact soil structure (e.g., tillage, fertilization, crop rotation, crop residue management), (4) butterflies and orthopterans response to mowing and grazing effects in grasslands, (5) the use of bird monitoring for the impact for assessing the efficiency of AES implementation at the landscape scale. We highlight that further research should be conducted on ISGs that are until now poorly studied regarding agricultural practices, such as amphibians, reptiles, gastropods, millipedes and centipedes. More field evidence of the effects of diversification practices such as intercropping, undersowing, intermediate cropping, and agroforestry are needed to draw conclusions on their benefits on biodiversity. [ABSTRACT FROM AUTHOR]

Background: Elucidating the intricate structural organization and spatial gradients of biomolecular composition within the rhizosphere is critical to understanding important biogeochemical processes, which include the mechanisms of root-microbe interactions for maintaining sustainable plant ecosystem services. While various analytical methods have been developed to assess the spatial heterogeneity within the rhizosphere, a comprehensive view of the fine distribution of metabolites within the root-soil interface has remained a significant challenge. This is primarily due to the difficulty of maintaining the original spatial organization during sample preparation without compromising its molecular content. Results: In this study, we present a novel approach, RhizoMAP, in which the rhizosphere molecules are imprinted on selected polymer membranes and then spatially profiled using matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI). We enhanced the performance of RhizoMAP by combining the use of two thin (< 20 μm) membranes (polyester and polycarbonate) with distinct MALDI sample preparations. This optimization allowed us to gain insight into the distribution of over 500 different molecules within the rhizosphere of poplar (Populus trichocarpa) grown in rhizoboxes filled with mycorrhizae soil. These two membranes, coupled with three different sample preparation conditions, enabled us to capture the distribution of a wide variety of molecules that included phytohormones, amino acids, sugars, sugar glycosides, polycarboxylic acids components of the Krebs cycle, fatty acids, short aldehydes and ketones, terpenes, volatile organic compounds, fertilizers from the soil, and others. Their spatial distribution varies greatly, with some following root traces, others showing diffusion from roots, some associated with soil particles, and many having distinct hot spots along the plant root or surrounding soil. Moreover, we showed how RhizoMAP can be used to localize the origin of the molecules and molecular transformation during root growth. Finally, we demonstrated the power of RhizoMAP to capture molecular distributions of key metabolites throughout a 20 cm deep rhizosphere. Conclusions: RhizoMAP is a method that provides nondestructive, untargeted, broad, and sensitive metabolite imaging of root-associated molecules, exudates, and soil organic matter throughout the rhizosphere, as demonstrated in a lab-controlled native soil environment. [ABSTRACT FROM AUTHOR]

Background: The history of rare diseases is largely unknown. Research on this topic has focused on individual cases of prominent (historical) individuals and artistic (e.g., iconographic) representations. Medical collections include large numbers of specimens that exhibit signs of rare diseases, but most of them date to relatively recent periods. However, cases of rare diseases detected in mummies and skeletal remains derived from archaeological excavations have also been recorded. Nevertheless, this direct evidence from historical and archaeological contexts is mainly absent from academic discourse and generally not consulted in medical research on rare diseases. Results: This desideratum is addressed by the Digital Atlas of Ancient Rare Diseases (DAARD: https://daard.dainst.org), which is an open access/open data database and web-based mapping tool that collects evidence of different rare diseases found in skeletons and mummies globally and throughout all historic and prehistoric time periods. This easily searchable database allows queries by diagnosis, the preservation level of human remains, research methodology, place of curation and publications. In this manuscript, the design and functionality of the DAARD are illustrated using examples of achondroplasia and other types of stunted growth. Conclusions: As an open, collaborative repository for collecting, mapping and querying well-structured medical data on individuals from ancient times, the DAARD opens new avenues of research. Over time, the number of rare diseases will increase through the addition of new cases from varied backgrounds such as museum collections and archaeological excavations. Depending on the research question, phenotypic or genetic information can be retrieved, as well as information on the general occurrence of a rare disease in selected space–time intervals. Furthermore, for individuals diagnosed with a rare disease, this approach can help them to build identity and reveal an aspect of their condition they might not have been aware of. Thus, the DAARD contributes to the understanding of rare diseases from a long-term perspective and adds to the latest medical research. [ABSTRACT FROM AUTHOR]

Context The pinewood nematode Bursaphelenchus xylophilus, native to North America, is an important pest affecting pine forests throughout Eurasia. In Europe, it has been detected in Portugal and Spain and is primarily associated with Pinus pinaster, an important Mediterranean tree species. Aims We have investigated the differences in susceptibility among several P. pinaster provenances in the Iberian Peninsula and France, as well as their relationship to certain growth traits and physiological parameters. Methods Three independent inoculation tests were performed on 3 to 4-year-old trees, followed by assessment of growth traits and physiological variables, along with time course destructive sampling for nematode quantification. Results The results showed significant differences among provenances for almost all growth traits, wilting, and mortality, though a significant provenance × environment interaction was also detected. Two Atlantic provenances, Noroeste-Litoral and Leiria, displayed the largest susceptibility to pinewood nematode. Changes in susceptibility to B. xylophilus between experiments were influenced by temperature and seasonality. Autumn precipitation and mean maximum temperature during summer at the original provenance sites could be related to provenance susceptibility. Conclusion Noroeste-Litoral and Leiria were the most disease-affected provenances. This study emphasizes the need for further research on how tree growth stage influences susceptibility and on the possibility of cross-breeding among provenances. [ABSTRACT FROM AUTHOR]

Background: The bacterial leaf pathogen Pseudomonas syringae pv tomato (Pst) is the most popular model pathogen for plant pathology research. Previous methods to study the plant-Pst interactions rely on destructive quantification of Pst colonisation, which can be labour- and time-consuming and does not allow for spatial–temporal monitoring of the bacterial colonisation. Here, we describe a rapid and non-destructive method to quantify and visualise spatial–temporal colonisation by Pst in intact leaves of Arabidopsis and tomato. Results: The method presented here uses a bioluminescent Pst DC3000 strain that constitutively expresses the luxCDABE operon from Photorhabdus luminescens (Pst::LUX) and requires a common gel documentation (Gel Doc) system with a sensitive CCD/CMOS camera and imaging software (Photoshop or Image J). By capturing bright field and bioluminescence images from Pst::LUX-infected leaves, we imaged the spatiotemporal dynamics of Pst infection. Analysis of bioluminescence from live Pst bacteria over a 5-day time course after spray inoculation of Arabidopsis revealed transition of the bacterial presence from the older leaves to the younger leaves and apical meristem. Colonisation by Pst:LUX bioluminescence was obtained from digital photos by calculating relative bioluminescence values, which is adjusted for bioluminescence intensity and normalised by leaf surface. This method detected statistically significant differences in Pst::LUX colonisation between Arabidopsis genotypes varying in basal resistance, as well as statistically significant reductions in Pst::LUX colonisation by resistance-inducing treatments in both Arabidopsis and tomato. Comparison of relative bioluminescence values to conventional colony counting on selective agar medium revealed a statistically significant correlation, which was reproducible between different Gel Doc systems. Conclusions: We present a non-destructive method to quantify colonisation by bioluminescent Pst::LUX in plants. Using a common Gel Doc system and imaging software, our method requires less time and labour than conventional methods that are based on destructive sampling of infected leaf material. Furthermore, in contrast to conventional strategies, our method provides additional information about the spatial–temporal patterns of Pst colonisation. [ABSTRACT FROM AUTHOR]

Background: Taxonomic identification of wood specimens provides vital information for a wide variety of academic (e.g. paleoecology, cultural heritage studies) and commercial (e.g. wood trade) purposes. It is generally accomplished through the observation of key anatomical features. Classic methodologies mostly require destructive sub-sampling, which is not always acceptable. X-ray computed micro-tomography (µCT) is a promising non-destructive alternative since it allows a detailed non-invasive visualization of the internal wood structure. There is, however, no standardized approach that determines the required resolution for proper wood identification using X-ray µCT. Here we compared X-ray µCT scans of 17 African wood species at four resolutions (1 µm, 3 µm, 8 µm and 15 µm). The species were selected from the Xylarium of the Royal Museum for Central Africa, Belgium, and represent a wide variety of wood-anatomical features. Results: For each resolution, we determined which standardized anatomical features can be distinguished or measured, using the anatomical descriptions and microscopic photographs on the Inside Wood Online Database as a reference. We show that small-scale features (e.g. pits and fibres) can be best distinguished at high resolution (especially 1 µm voxel size). In contrast, large-scale features (e.g. vessel porosity or arrangement) can be best observed at low resolution due to a larger field of view. Intermediate resolutions are optimal (especially 3 µm voxel size), allowing recognition of most small- and large-scale features. While the potential for wood identification is thus highest at 3 µm, the scans at 1 µm and 8 µm were successful in more than half of the studied cases, and even the 15 µm resolution showed a high potential for 40% of the samples. Conclusions: The results show the potential of X-ray µCT for non-destructive wood identification. Each of the four studied resolutions proved to contain information on the anatomical features and has the potential to lead to an identification. The dataset of 17 scanned species is made available online and serves as the first step towards a reference database of scanned wood species, facilitating and encouraging more systematic use of X-ray µCT for the identification of wood species. [ABSTRACT FROM AUTHOR]

To date, only a limited number of studies have utilized remote sensing imagery to estimate aboveground biomass (AGB) in the Miombo ecoregion using wall-to-wall medium resolution optical satellite imagery (Sentinel-2 and Landsat), localized airborne light detection and ranging (lidar), or localized unmanned aerial systems (UAS) images. On the one hand, the optical satellite imagery is suitable for wall-to-wall coverage, but the AGB estimates based on such imagery lack precision for local or stand-level sustainable forest management and international reporting mechanisms. On the other hand, the AGB estimates based on airborne lidar and UAS imagery have the precision required for sustainable forest management at a local level and international reporting requirements but lack capacity for wall-to-wall coverage. Therefore, the main aim of this study was to investigate the use of UAS-lidar as a sampling tool for satellite-based AGB estimation in the Miombo woodlands of Zambia. In order to bridge the spatial data gap, this study employed a two-phase sampling approach, utilizing Sentinel-2 imagery, partial-coverage UAS-lidar data, and field plot data to estimate AGB in the 8094-hectare Miengwe Forest, Miombo Woodlands, Zambia, where UAS-lidar estimated AGB was used as reference data for estimating AGB using Sentinel-2 image metrics. The findings showed that utilizing UAS-lidar as reference data for predicting AGB using Sentinel-2 image metrics yielded superior results (Adj-R2 = 0.70, RMSE = 27.97) than using direct field estimated AGB and Sentinel-2 image metrics (R2 = 0.55, RMSE = 38.10). The quality of AGB estimates obtained from this approach, coupled with the ongoing advancement and cost-cutting of UAS-lidar technology as well as the continuous availability of wall-to-wall optical imagery such as Sentinel-2, provides much-needed direction for future forest structural attribute estimation for efficient management of the Miombo woodlands. [ABSTRACT FROM AUTHOR]

Key message : Tested on data from Tanzania, both existing species-specific and common biomass models developed elsewhere revealed statistically significant large prediction errors. Species-specific and common above- and belowground biomass models for three mangrove species were therefore developed. The species-specific models fitted better to data than the common models. The former models are recommended for accurate estimation of biomass stored in mangrove forests of Tanzania. Context : Mangroves are essential for climate change mitigation through carbon storage and sequestration. Biomass models are important tools for quantifying biomass and carbon stock. While numerous aboveground biomass models exist, very few studies have focused on belowground biomass, and among these, mangroves of Africa are hardly or not represented. Aims : The aims of the study were to develop above- and belowground biomass models and to evaluate the predictive accuracy of existing aboveground biomass models developed for mangroves in other regions and neighboring countries when applied on data from Tanzania. Methods : Data was collected through destructive sampling of 120 trees (aboveground biomass), among these 30 trees were sampled for belowground biomass. The data originated from four sites along the Tanzanian coastline covering three dominant species: Avicennia marina (Forssk.) Vierh, Sonneratia alba J. Smith, and Rhizophora mucronata Lam. The biomass models were developed through mixed modelling leading to fixed effects/common models and random effects/species-specific models. Results : Both the above- and belowground biomass models improved when random effects (species) were considered. Inclusion of total tree height as predictor variable, in addition to diameter at breast height alone, further improved the model predictive accuracy. The tests of existing models from other regions on our data generally showed large and significant prediction errors for aboveground tree biomass. Conclusion : Inclusion of random effects resulted into improved goodness of fit for both above- and belowground biomass models. Species-specific models therefore are recommended for accurate biomass estimation of mangrove forests in Tanzania for both management and ecological applications. For belowground biomass ( S. alba) however, the fixed effects/common model is recommended. [ABSTRACT FROM AUTHOR]

Background: The thermal plasticity of life-history traits receives wide attention in the recent biological literature. Of all the temperature-dependent traits studied, developmental rates of ectotherms are especially often addressed, and yet surprisingly little is known about embryonic responses to temperature, including changes in the thermal thresholds and thermal sensitivity during early development. Even postembryonic development of many cryptically living species is understood superficially at best. Results: This study is the first to estimate the exact durations of developmental stages in the cowpea seed beetle C. maculatus from oviposition to adult emergence at five permissive constant temperatures from 20 to 32 °C. Early embryonic development was tracked and documented by means of destructive sampling and subsequent confocal imaging of fluorescently stained specimens. Late embryonic and early larval development was studied with the use of destructive sampling and light microscopy. Well-resolved temporal series based on thousands of embryos allowed precise timing of the following developmental events: formation of the blastoderm; formation, elongation, and retraction of the germ band; dorsal closure; the onset and completion of sclerotization of the cuticle; hatching, and penetration of the first-instar larva into the cowpea seed. Pupation and adult eclosion were observed directly through an incision in the seed coat. The thermal phenotype of C. maculatus was found to vary in the course of ontogeny and different stages scaled disproportionately with temperature, but pitfalls and caveats associated with analyses of relative durations of individual stages are also briefly discussed. Conclusion: Disproportionate changes in developmental durations with temperature may have important implications when study design requires a high degree of synchronization among experimental embryos or when the occurrence of particular stages in the field is of interest, as well as in any other cases when development times need to be estimated with precision. This work provides one of the first examples of integration of embryological techniques with ecophysiological concepts and will hopefully motivate similar projects in the future. While experiments with Drosophila continue to be the main source of information on animal development, knowledge on other model species is instrumental to building a broader picture of developmental phenomena. [ABSTRACT FROM AUTHOR]

Background: In soybeans, faster canopy coverage (CC) is a highly desirable trait but a fully covered canopy is unfavorable to light interception at lower levels in the canopy with most of the incident radiation intercepted at the top of the canopy. Shoot architecture that influences CC is well studied in crops such as maize and wheat, and altering architectural traits has resulted in enhanced yield. However, in soybeans the study of shoot architecture has not been as extensive. Results: This study revealed significant differences in CC among the selected soybean accessions. The rate of CC was found to decrease at the beginning of the reproductive stage (R1) followed by an increase during the R2-R3 stages. Most of the accessions in the study achieved maximum rate of CC between R2-R3 stages. We measured Light interception (LI), defined here as the ratio of Photosynthetically Active Radiation (PAR) transmitted through the canopy to the incoming PAR or the radiation above the canopy. LI was found to be significantly correlated with CC parameters, highlighting the relationship between canopy structure and light interception. The study also explored the impact of plant shape on LI and CO2 assimilation. Plant shape was characterized into distinct quantifiable parameters and by modeling the impact of plant shape on LI and CO2 assimilation, we found that plants with broad and flat shapes at the top maybe more photosynthetically efficient at low light levels, while conical shapes were likely more advantageous when light was abundant. Shoot architecture of plants in this study was described in terms of whole plant, branching and leaf-related traits. There was significant variation for the shoot architecture traits between different accessions, displaying high reliability. We found that that several shoot architecture traits such as plant height, and leaf and internode-related traits strongly influenced CC and LI. Conclusion: In conclusion, this study provides insight into the relationship between soybean shoot architecture, canopy coverage, and light interception. It demonstrates that novel shoot architecture traits we have defined here are genetically variable, impact CC and LI and contribute to our understanding of soybean morphology. Correlations between different architecture traits, CC and LI suggest that it is possible to optimize soybean growth without compromising on light transmission within the soybean canopy. In addition, the study underscores the utility of integrating low-cost 2D phenotyping as a practical and cost-effective alternative to more time-intensive 3D or high-tech low-throughput methods. This approach offers a feasible means of studying basic shoot architecture traits at the field level, facilitating a broader and efficient assessment of plant morphology. [ABSTRACT FROM AUTHOR]

Recent advancements in signal processing and computational power have revolutionized computer vision applications in diverse industries such as agriculture, food processing, biomedical, and the military. These developments are propelling efforts to automate processes and enhance efficiency. Notably, computational techniques are replacing labor-intensive manual methods for assessing the maturity indices of fruits and vegetables during critical growth stages. This review paper focuses on recent advancements in computer vision techniques specifically applied to determine the maturity indices of fruits and vegetables within the food processing sector. It highlights successful applications of Nuclear Magnetic Resonance (NMR), Near-Infrared Spectroscopy (NIR), thermal imaging, and image scanning. By examining these techniques, their underlying principles, and practical feasibility, it offers valuable insights into their effectiveness and potential widespread adoption. Additionally, integrating biosensors and AI techniques further improves accuracy and efficiency in maturity index determination. In summary, this review underscores the significant role of computational techniques in advancing maturity index assessment and provides insights into their principles and effective utilization. Looking ahead, the future of computer vision techniques holds immense potential. Collaborative efforts among experts from various fields will be crucial to address challenges, ensure standardization, and safeguard data privacy. Embracing these advancements can lead to sustainable practices, optimized resource management, and progress across industries. Highlights: 1. Recent advancements in signal processing and computation drive interest in computer vision across industries. 2. The review focuses on non-destructive methods in fruits and vegetables. 3. Computational techniques replace manual methods for maturity index determination. 4. The principles of techniques are highlighted, along with their successful applications. 5. The potential of computation techniques in destructive, non-destructive methods, biosensors, and AI summarized. [ABSTRACT FROM AUTHOR]

Background: The final step in the anaerobic decomposition of biopolymers is methanogenesis. Rice field soils are a major anthropogenic source of methane, with straw commonly used as a fertilizer in rice farming. Here, we aimed to decipher the structural and functional responses of the methanogenic community to rice straw addition during an extended anoxic incubation (120 days) of Philippine paddy soil. The research combined process measurements, quantitative real-time PCR and RT-PCR of particular biomarkers (16S rRNA, mcrA), and meta-omics (environmental genomics and transcriptomics). Results: The analysis methods collectively revealed two major bacterial and methanogenic activity phases: early (days 7 to 21) and late (days 28 to 60) community responses, separated by a significant transient decline in microbial gene and transcript abundances and CH4 production rate. The two methanogenic activity phases corresponded to the greatest rRNA and mRNA abundances of the Methanosarcinaceae but differed in the methanogenic pathways expressed. While three genetically distinct Methanosarcina populations contributed to acetoclastic methanogenesis during the early activity phase, the late activity phase was defined by methylotrophic methanogenesis performed by a single Methanosarcina genomospecies. Closely related to Methanosarcina sp. MSH10X1, mapping of environmental transcripts onto metagenome-assembled genomes (MAGs) and population-specific reference genomes revealed this genomospecies as the key player in acetoclastic and methylotrophic methanogenesis. The anaerobic food web was driven by a complex bacterial community, with Geobacteraceae and Peptococcaceae being putative candidates for a functional interplay with Methanosarcina. Members of the Methanocellaceae were the key players in hydrogenotrophic methanogenesis, while the acetoclastic activity of Methanotrichaceae members was detectable only during the very late community response. Conclusions: The predominant but time-shifted expression of acetoclastic and methylotrophic methanogenesis by a single Methanosarcina genomospecies represents a novel finding that expands our hitherto knowledge of the methanogenic pathways being highly expressed in paddy soils. 7k5euw-hvBknz_jds4sXXf Video Abstract [ABSTRACT FROM AUTHOR]

Key message: Evolving societal demands and accelerated ecological dynamics due to global change are rapidly altering forest ecosystems and their services. This has prompted the need for advancing forest inventorying and monitoring initatives to expand their scope, improve data collection, foster scientific understanding, and better inform policy responses. Here, we discuss the collaborative processes followed to develop an Advanced Inventorying and Monitoring (AIM) system for Swiss forests. Further, we provide the key messages that emerged from this process which can be of interest to those involved in similar processes at the national/international level. Forests are under pressure and going through rapid changes. However, current inventorying and monitoring (IM) programs are often either disjointed, too narrow in their scope and/or do not operate at fine enough temporal resolutions, which may hinder scientific understanding, the timely supply of information, fast decision making, and may result in the sub-optimal use of resources. For these reasons, there is an urgent need for Advanced Forest Inventorying and Monitoring (AIM) programs to (i) achieve expanded relevance (by augmenting data/information across ecosystem properties and trophic levels), (ii) have increased temporal resolution (by tailored data collection frequency), and (iii) make use of technological advances (by incorporating novel tools and technologies). The Advanced Inventorying and Monitoring for Swiss Forests (SwissAIM) initiative was launched in 2020 to address these needs. SwissAIM builds upon the foundation offered by the existing programs (e.g., national forest inventory, long-term forest ecosystem research, biodiversity monitoring). It aims to offer a collaborative and adaptive framework to enable integrated data collection, evaluation, interpretation, analysis, and modeling. Ideally, it will result in a more responsive system with respect to current and predicted biotic/abiotic stressors that will challenge Swiss forests. Developing such a system implies identifying the information needs of different stakeholders (e.g., science, policy, practice), related technical requirements, and governance frameworks. Here, we present (i) the main features of the SwissAIM initiative (vision, scientific questions and variables, governance and engagement), (ii) the main outcomes of the participatory design process (measurements, sampling, and plot design), (iii) the potential transferability of AIM initiatives outside Switzerland (timing, relevance, practicability), and (iv) the key messages that emerged (i.e., need for advancement, integration and transdisciplinarity, statistical underpinning). Since similar needs related to forest inventorying and monitoring are emerging throughout Europe and elsewhere, the objective of this opinion paper is to share our experience and promote a dialog with those interested in developing AIM initiatives in other countries and regions. [ABSTRACT FROM AUTHOR]

Background: Herbaria are becoming increasingly important as archives of biodiversity, and play a central role in taxonomic and biogeographic studies. There is also an ongoing interest in functional traits and the way they mediate interactions between a plant species and its environment. Herbarium specimens allow tracking trait values over time, and thus, capturing consequences of anthropogenic activities such as eutrophication. Here, we present an open, reproducible, non-destructive workflow to collect leaf trait data from herbarium specimens using near-infrared spectroscopy (NIRS), and a proof of concept for the reliability of this approach. Results: We carried out three experiments to test the suitability of non-destructive NIRS methods to predict leaf traits both for fresh and dried leaves: (1) With a fertilization experiment, we studied whether NIRS was able to capture changes in leaf N and leaf P during a fertilization experiment and we compared contents predicted by NIRS with results obtained from regular wet lab methods. Calibration models for leaf nitrogen and phosphorus contents had a quality of R2 = 0.7 and 0.5, respectively. We fitted calibration models for NIRS readings on fresh and dried leaf samples, both of which produced equally precise predictions compared to results from wet lab analyses. (2) We tested the effect of herbarium conservation on NIRS readings by simulating them through the application of six treatments combining freezing, drying and pesticide spraying in a factorial scheme and comparing these with untreated samples. No consistent changes were observed in the spectra quality before and after the simulated herbarium conditions. (3) Finally, we studied the effect of specimen storage duration using specimens from a 2018 study which were re-analyzed and compared with spectra obtained in 2021. No consistent changes in spectra were observed after the storage period. Conclusions: The results demonstrate the reliability of NIRS to measure leaf N and P on herbarium samples. Together with the calibration method and dataset presented here, they provide a toolset allowing researchers to study the development of leaf traits and their response to environmental changes over decades and even centuries in a fast and non-destructive manner. [ABSTRACT FROM AUTHOR]

Background: The cryptic nature of pink bollworm Pectinophora gossypiella (Saunders) larvae enables its reduced vulnerability to insecticidal control. Further, the development of resistance against Bacillus thuringiensis (Bt) toxins posed a serious threat to transgenic cotton cultivation. This necessitated determining the critical timing of spray applications on the control effectiveness. This study assessed the influence of egg age (freshly laid vs. three-day-old) and the location of larvae (directly exposed to the insecticide residues on the boll rind vs. burrowed inside the bolls) on insecticide control efficacy. Results: The results revealed a significant decrease in the ovicidal activity of tested insecticides with an increase in the age of eggs from one day old to three days old (paired t-test, P < 0.05). The larvae directly exposed to the insecticide residues on the boll rind were more susceptible (> 80% mortality) than the larvae exposed after they had burrowed inside the bolls (< 49% mortality). The inhibitory effects of tested insecticides on developmental biology were more pronounced in the experiment on pre-larval release insecticide treatment compared with insecticide treatment given post-larval release and entry inside the bolls. Conclusion: Egg age influences the insecticide susceptibility, as does the larval location, directly exposed vs burrowed inside the bolls. Older eggs and the larvae that had burrowed inside the green bolls of cotton were relatively less susceptible to the insecticide treatments. The toxic effects of insecticides on egg and larval stages were primarily ephemeral. These findings are significant for devising a comprehensive strategy for pink bollworm management on a sustainable basis. [ABSTRACT FROM AUTHOR]

Introduction: Tree breeding is giving an increasing attention to wood properties in order to better fit the requirements of the saw, board, pulp and paper industries. In particular, it has been reported that lignin and cellulose content display moderate to high heritabilities making them prime candidates for genetic improvement of wood chemistry. Moreover, these traits have been shown to be negatively correlated at both phenotypic and genetic levels. However, they have generally been evaluated against a narrow genetic background, and little is known about their correlations with mandatory selection criteria such as growth and straightness. Materials and methods: In this study, we first investigated the performance of near-infrared (NIR) spectroscopy combined with a non-destructive sampling method to assess chemical properties of wood in maritime pine. We afterwards estimated genetic parameters of growth, stem form and wood chemistry traits across a large genetic background in a progeny trial and clonally replicated progenies. Results: Our results showed that removal of extractives prior to NIR spectra acquisition is highly recommended for achieving high accuracy in NIRS-PLSR prediction for wood chemistry traits in maritime pine. We further observed moderate heritabilities (0.15-0.55) for the studied traits. Wood chemistry traits were genetically inter-correlated (e.g., negatively between lignin and cellulose), whereas correlations with growth were not significant, indicating that growth and chemical properties could be improved independently. [ABSTRACT FROM AUTHOR]

Background: Fragmentation of terrestrial ecosystems has had detrimental effects on metapopulations of habitat specialists. Maculinea butterflies have been particularly affected because of their specialized lifecycles, requiring both specific food-plants and host-ants. However, the interaction between dispersal, effective population size, and long-term genetic erosion of these endangered butterflies remains unknown. Using non-destructive sampling, we investigated the genetic diversity of the last extant population of M. arion in Denmark, which experienced critically low numbers in the 1980s. Results: Using nine microsatellite markers, we show that the population is genetically impoverished compared to nearby populations in Sweden, but less so than monitoring programs suggested. Ten additional short repeat microsatellites were used to reconstruct changes in genetic diversity and population structure over the last 77 years from museum specimens. We also tested amplification efficiency in such historical samples as a function of repeat length and sample age. Low population numbers in the 1980s did not affect genetic diversity, but considerable turnover of alleles has characterized this population throughout the time-span of our analysis. Conclusions: Our results suggest that M. arion is less sensitive to genetic erosion via population bottlenecks than previously thought, and that managing clusters of high quality habitat may be key for long-term conservation. [ABSTRACT FROM AUTHOR]

Background: Monitoring of sterile to wild insect ratios in field populations can be useful to follow the progress in genetic control programmes such as the Sterile Insect Technique (SIT). Of the numerous methods for marking insects most are not suitable for use in mass rearing and mass release. Suitable ones include dye marking, genetic marking and chemical marking. Methods: The feasibility of using the stable isotope of carbon, 13C, as a potential chemical marker for Anopheles arabiensis was evaluated in the laboratory. Labeled-13C glucose was incorporated into the larval diet in a powder or liquid form. The contribution of adult sugar feeding to the total mosquito carbon pool and the metabolically active carbon pool was determined by tracing the decline of the enrichment of the adult male mosquito as it switched from a labeled larval diet to an unlabeled adult diet. This decline in the adult was monitored by destructive sampling of the whole mosquito and analyzed using isotope ratio mass spectrometry. Results: A two-pool model was used to describe the decline of the 13C-enrichment of adult mosquitoes. The proportion of the total adult carbon pool derived from the adult sugar diet over the life span of mosquitoes was determined and the ratio of structural carbon, with a low turnover rate to metabolically active non-structural carbon was assessed. The uptake and turnover of sugar in the metabolically active fraction suggests that after 3 days >70% of the active fraction carbon is derived from sugar feeding (increasing to >90% by day 7), indicating the high resource demand of male mosquitoes. Conclusion: It was possible to "fix" the isotopic label in adult An. arabiensis and to detect the label at an appropriate concentration up to 21 days post-emergence. The optimum labeling treatment would cost around 250 US$ per million mosquitoes. Stable isotope marking may thus aid research on the fate of released insects besides other population-based ecological studies. [ABSTRACT FROM AUTHOR]

Background: The extrinsic incubation period (EIP), defined as the time it takes for malaria parasites in a mosquito to become infectious to a vertebrate host, is one of the most influential parameters for malaria transmission but remains poorly understood. The EIP is usually estimated by quantifying salivary gland sporozoites in subsets of mosquitoes, which requires terminal sampling. However, assays that allow repeated sampling of individual mosquitoes over time could provide better resolution of the EIP. Methods: We tested a non-destructive assay to quantify sporozoites of two rodent malaria species, Plasmodium chabaudi and Plasmodium berghei, expelled throughout 24-h windows, from sugar-soaked feeding substrates using quantitative-PCR. Results: The assay is able to quantify sporozoites from sugar-soaked feeding substrates, but the prevalence of parasite-positive substrates was low. Various methods were attempted to increase the detection of expelled parasites (e.g. running additional technical replicates; using groups rather than individual mosquitoes), but these did not increase the detection rate, suggesting that expulsion of sporozoites is variable and infrequent. Conclusions: We reveal successful detection of expelled sporozoites from sugar-soaked feeding substrates. However, investigations of the biological causes underlying the low detection rate of sporozoites (e.g. mosquito feeding behaviour, frequency of sporozoite expulsion or sporozoite clumping) are needed to maximise the utility of using non-destructive assays to quantify sporozoite dynamics. Increasing detection rates will facilitate the detailed investigation on infection dynamics within mosquitoes, which is necessary to explain the highly variable EIP of Plasmodium and to improve understanding of malaria transmission dynamics. [ABSTRACT FROM AUTHOR]

Background: Decomposition of plant biomass is vital for carbon cycling in terrestrial ecosystems. In waterlogged soils including paddy fields and natural wetlands, plant biomass degradation generates the largest natural source of global methane emission. However, the intricate process of plant biomass degradation by diverse soil microorganisms remains poorly characterized. Here we report a chemical and metagenomic investigation into the mechanism of straw decomposition in a paddy soil. Results: The chemical analysis of 16-day soil microcosm incubation revealed that straw decomposition could be divided into two stages based on the dynamics of methane, short chain fatty acids, dissolved organic carbon and monosaccharides. Metagenomic analysis revealed that the relative abundance of glucoside hydrolase (GH) encoding genes for cellulose decomposition increased rapidly during the initial stage (3–7 days), while genes involved in hemicellulose decomposition increased in the later stage (7–16 days). The increase of cellulose GH genes in initial stage was derived mainly from Firmicutes while Bacteroidota contributed mostly to the later stage increase of hemicellulose GH genes. Flagella assembly genes were prevalent in Firmicutes but scarce in Bacteroidota. Wood–Ljungdahl pathway (WLP) was present in Firmicutes but not detected in Bacteroidota. Overall, Bacteroidota contained the largest proportion of total GHs and the highest number of carbohydrate active enzymes gene clusters in our paddy soil metagenomes. The strong capacity of the Bacteroidota phylum to degrade straw polymers was specifically attributed to Bacteroidales and Chitinophagales orders, the latter has not been previously recognized. Conclusions: This study revealed a collaborating sequential contribution of microbial taxa and functional genes in the decomposition of straw residues in a paddy soil. Firmicutes with the property of mobility, WLP and cellulose decomposition could be mostly involved in the initial breakdown of straw polymers, while Bacteroidota became abundant and possibly responsible for the decomposition of hemicellulosic polymers during the later stage. [ABSTRACT FROM AUTHOR]

Background: The growth of alfalfa (Medicago sativa L.) is significantly hampered by drought and nutrient deficiencies. The identification of root architectural and anatomical characteristics holds paramount importance for the development of alfalfa genotypes with enhanced adaptation to adverse environmental conditions. In this study, we employed a visual rhizobox system to investigate the variability in root system architecture (including root depth, root length, root tips number, etc.), anatomical features (such as cortical traits, total stele area, number and area of vessel, etc.), as well as nitrogen and phosphorus uptake across 53 alfalfa genotypes during the seedling stage. Results: Out of the 42 traits measured, 21 root traits, along with nitrogen (N) and phosphorus (P) uptake, displayed higher coefficients of variation (CVs ≥ 0.25) among the tested genotypes. Local root morphological and anatomical traits exhibited more significant variation than global root traits. Twenty-three traits with CVs ≥ 0.25 constituted to six principal components (eigenvalues > 1), collectively accounting for 88.0% of the overall genotypic variation. Traits such as total root length, number of root tips, maximal root depth, and others exhibited positive correlations with shoot dry mass and root dry mass. Additionally, total stele area and xylem vessel area showed positive correlations with N and P uptake. Conclusions: These root traits, which have demonstrated associations with biomass and nutrient uptake, may be considered for the breeding of alfalfa genotypes that possess efficient resource absorption and increased adaptability to abiotic stress, following validation during the entire growth period in the field. [ABSTRACT FROM AUTHOR]

Background: Following the World Health Organization (WHO) endorsement of dual active ingredient (AI) nets, an increased uptake of pyrethroid-chlorfenapyr and pyrethroid-pyriproxyfen nets is expected. Studies evaluating their physical and insecticidal durability are essential for making programmatic and procurement decisions. This paper describes the methodology for a prospective study to evaluate the attrition, fabric integrity, insecticidal durability of Interceptor® G2 (alpha-cypermethrin-chlorfenapyr) and Royal Guard® (alpha-cypermethrin-pyriproxyfen), compared to Interceptor® (alpha-cypermethrin), embedded in a 3-arm cluster randomized controlled trial (cRCT) in the Zou Department of Benin. Methods: Ten clusters randomly selected from each arm of the cRCT will be used for the study. A total of 750 ITNs per type will be followed in 5 study clusters per arm to assess ITN attrition and fabric integrity at 6-, 12-, 24- and 36-months post distribution, using standard WHO procedures. A second cohort of 1800 nets per type will be withdrawn every 6 months from all 10 clusters per arm and assessed for chemical content and biological activity in laboratory bioassays at each time point. Alpha-cypermethrin bioefficacy in Interceptor® and Royal Guard® will be monitored in WHO cone bioassays and tunnel tests using the susceptible Anopheles gambiae Kisumu strain. The bioefficacy of the non-pyrethroid insecticides (chlorfenapyr in Interceptor® G2 and pyriproxyfen in Royal Guard®) will be monitored using the pyrethroid-resistant Anopheles coluzzii Akron strain. Chlorfenapyr activity will be assessed in tunnel tests while pyriproxyfen activity will be assessed in cone bioassays in terms of the reduction in fertility of blood-fed survivors observed by dissecting mosquito ovaries. Nets withdrawn at 12, 24 and 36 months will be tested in experimental hut trials within the cRCT study area against wild free-flying pyrethroid resistant An. gambiae sensu lato to investigate their superiority to Interceptor® and to compare them to ITNs washed 20 times for experimental hut evaluation studies. Mechanistic models will also be used to investigate whether entomological outcomes with each dual ITN type in experimental hut trials can predict their epidemiological performance in the cRCT. Conclusion: This study will provide information on the durability of two dual AI nets (Interceptor® G2 and Royal Guard®) in Benin and will help identify suitable methods for monitoring the durability of their insecticidal activity under operational conditions. The modelling component will determine the capacity of experimental hut trials to predict the epidemiological performance of dual AI nets across their lifespan. [ABSTRACT FROM AUTHOR]

Background: Metabolomics measures an intermediate stage between genotype and phenotype, and may therefore be useful for breeding. Our objectives were to investigate genetic parameters and accuracies of predicted breeding values for malting quality (MQ) traits when integrating both genomic and metabolomic information. In total, 2430 plots of 562 malting spring barley lines from three years and two locations were included. Five MQ traits were measured in wort produced from each plot. Metabolomic features used were 24,018 nuclear magnetic resonance intensities measured on each wort sample. Methods for statistical analyses were genomic best linear unbiased prediction (GBLUP) and metabolomic-genomic best linear unbiased prediction (MGBLUP). Accuracies of predicted breeding values were compared using two cross-validation strategies: leave-one-year-out (LOYO) and leave-one-line-out (LOLO), and the increase in accuracy from the successive inclusion of first, metabolomic data on the lines in the validation population (VP), and second, both metabolomic data and phenotypes on the lines in the VP, was investigated using the linear regression (LR) method. Results: For all traits, we saw that the metabolome-mediated heritability was substantial. Cross-validation results showed that, in general, prediction accuracies from MGBLUP and GBLUP were similar when phenotypes and metabolomic data were recorded on the same plots. Results from the LR method showed that for all traits, except one, accuracy of MGBLUP increased when including metabolomic data on the lines of the VP, and further increased when including also phenotypes. However, in general the increase in accuracy of MGBLUP when including both metabolomic data and phenotypes on lines of the VP was similar to the increase in accuracy of GBLUP when including phenotypes on the lines of the VP. Therefore, we found that, when metabolomic data were included on the lines of the VP, accuracies substantially increased for lines without phenotypic records, but they did not increase much when phenotypes were already known. Conclusions: MGBLUP is a useful approach to combine phenotypic, genomic and metabolomic data for predicting breeding values for MQ traits. We believe that our results have significant implications for practical breeding of barley and potentially many other species. [ABSTRACT FROM AUTHOR]

Key message: We provide a set of allometric models for wild cherry trees (Prunus avium L.) established in agroforestry systems. A total of 70 trees in southwestern Germany were surveyed using terrestrial laser scanning and analysed using quantitative structure models. The derived allometric models provide a stable base for biomass estimation in comparable agroforestry systems. Our biomass model, based on volume estimates converted to biomass, shows no significant differences to a previous study in the same region on the same species, although it was conducted on agroforestry trees under a different management regime. Context: Wild cherry (Prunus avium L.) is a common tree species in agroforestry systems (AFS). Utilised for either fruit production or for high-value timber production, it is a highly relevant species, yet even basic allometric models are lacking. Aims: The aim of this study was to develop a set of allometric models for wild cherry trees in AFS. Within this context, we present an innovative non-destructive approach to estimate bark and wood volume separately by applying bark thickness models to 3D models of trees. To assess model applicability to different AFS, we compared our allometric model for above-ground biomass with a previous biomass model for wild cherry trees under different management in the same region. Methods: Wild cherry trees (n = 70) located within AFS in southern Germany were scanned with a terrestrial laser scanner. Quantitative structure models were used to derive tree dimensions and above-ground volume per tree. Using additional auxiliary data, the target variables were derived, and corresponding allometric models were fitted. Results: The allometric models estimating above-ground volume, oven-dry biomass, carbon content and nutrient content based on diameter at breast height (DBH) showed excellent fits (R2adj ≥ 0.97). The comparisons with a similar study conducted in the same region suggested that management practices such as pruning have only a minor influence on the relationship between DBH and above-ground tree biomass. The nutrient content in the trees decreased in the order Ca > N > K > Mg > P. Conclusions: The derived allometric models provide valuable information on this important agroforestry tree species. Our findings can both inform management practices in AFS and advance ecological understanding of these systems. Future research should focus on developing allometric models for other tree species relevant to AFS. [ABSTRACT FROM AUTHOR]

Background: North American bat populations have suffered severe declines over the last decade due to the Pseudogymnoascus destructans fungus infection. The skin disease associated with this causative agent, known as white-nose syndrome (WNS), is specific to bats hibernating in temperate regions. As cultured fungal isolates are required for epidemiological and phylogeographical studies, the purpose of the present work was to compare the efficacy and reliability of different culture approaches based on either skin swabs or wing membrane tissue biopsies for obtaining viable fungal isolates of P. destructans. Results: In total, we collected and analysed 69 fungal and 65 bacterial skin swabs and 51 wing membrane tissue biopsies from three bat species in the Czech Republic, Poland and the Republic of Armenia. From these, we obtained 12 viable P. destructans culture isolates. Conclusions: Our results indicated that the efficacy of cultures based on wing membrane biopsies were significantly higher. Cultivable samples tended to be based on collections from bats with lower body surface temperature and higher counts of UV-visualised lesions. While cultures based on both skin swabs and wing membrane tissue biopsies can be utilised for monitoring and surveillance of P. destructans in bat populations, wing membrane biopsies guided by UV light for skin lesions proved higher efficacy. Interactions between bacteria on the host's skin also appear to play an important role. [ABSTRACT FROM AUTHOR]

Background: Bacterial viruses, phages, play a key role in nutrient turnover and lysis of bacteria in terrestrial ecosystems. While phages are abundant in soils, their effects on plant pathogens and rhizosphere bacterial communities are poorly understood. Here, we used metagenomics and direct experiments to causally test if differences in rhizosphere phage communities could explain variation in soil suppressiveness and bacterial wilt plant disease outcomes by plant-pathogenic Ralstonia solanacearum bacterium. Specifically, we tested two hypotheses: (1) that healthy plants are associated with stronger top-down pathogen control by R. solanacearum-specific phages (i.e. 'primary phages') and (2) that 'secondary phages' that target pathogen-inhibiting bacteria play a stronger role in diseased plant rhizosphere microbiomes by indirectly 'helping' the pathogen. Results: Using a repeated sampling of tomato rhizosphere soil in the field, we show that healthy plants are associated with distinct phage communities that contain relatively higher abundances of R. solanacearum-specific phages that exert strong top-down pathogen density control. Moreover, 'secondary phages' that targeted pathogen-inhibiting bacteria were more abundant in the diseased plant microbiomes. The roles of R. solanacearum-specific and 'secondary phages' were directly validated in separate greenhouse experiments where we causally show that phages can reduce soil suppressiveness, both directly and indirectly, via top-down control of pathogen densities and by alleviating interference competition between pathogen-inhibiting bacteria and the pathogen. Conclusions: Together, our findings demonstrate that soil suppressiveness, which is most often attributed to bacteria, could be driven by rhizosphere phage communities that regulate R. solanacearum densities and strength of interference competition with pathogen-suppressing bacteria. Rhizosphere phage communities are hence likely to be important in determining bacterial wilt disease outcomes and soil suppressiveness in agricultural fields. 9CDhWVdEuw3PsngifdxC8S Video Abstract [ABSTRACT FROM AUTHOR]

Background: Crop breeding based on root system architecture (RSA) optimization is an essential factor for improving crop production in developing countries. Identification, evaluation, and selection of root traits of soil-grown crops require innovations that enable high-throughput and accurate quantification of three-dimensional (3D) RSA of crops over developmental time. Results: We proposed an automated imaging system and 3D imaging data processing pipeline to quantify the 3D RSA of soil-grown individual plants across seedlings to the mature stage. A multi-view automated imaging system composed of a rotary table and an imaging arm with 12 cameras mounted with a combination of fan-shaped and vertical distribution was developed to obtain 3D image data of roots grown on a customized root support mesh. A 3D imaging data processing pipeline was developed to quantify the 3D RSA based on the point cloud generated from multi-view images. The global architecture of root systems can be quantified automatically. Detailed analysis of the reconstructed 3D root model also allowed us to investigate the Spatio-temporal distribution of roots. A method combining horizontal slicing and iterative erosion and dilation was developed to automatically segment different root types, and identify local root traits (e.g., length, diameter of the main root, and length, diameter, initial angle, and the number of nodal roots or lateral roots). One maize (Zea mays L.) cultivar and two rapeseed (Brassica napus L.) cultivars at different growth stages were selected to test the performance of the automated imaging system and 3D imaging data processing pipeline. Conclusions: The results demonstrated the capabilities of the proposed imaging and analytical system for high-throughput phenotyping of root traits for both monocotyledons and dicotyledons across growth stages. The proposed system offers a potential tool to further explore the 3D RSA for improving root traits and agronomic qualities of crops. [ABSTRACT FROM AUTHOR]

Background: Mass distributions of long-lasting insecticidal nets (LLINs) have contributed to large reductions in the malaria burden. However, this success is in jeopardy due in part to the increasing pyrethroid-resistant mosquito population as well as low LLINs coverage in various areas because the lifespan of LLINs is often shorter than the interval between replenishment campaigns. New insecticide-treated nets (ITNs) containing pyrethroid and piperonyl-butoxide (PBO) have shown a greater reduction in the incidence of malaria than pyrethroid LLINs in areas with pyrethroid-resistant mosquitoes. However, the durability (attrition, bio-efficacy, physical integrity and chemical retainment) of pyrethroid-PBO ITNs under operational settings has not been fully characterized. This study will measure the durability of pyrethroid-PBO ITNs to assess whether they meet the World Health Organization (WHO) three years of operational performance criteria required to be categorized as "long-lasting". Methods: A prospective household randomized controlled trial will be conducted simultaneously in Tanzania, India and Côte d'Ivoire to estimate the field durability of three pyrethroid-PBO ITNs (Veeralin®, Tsara® Boost, and Olyset® Plus) compared to a pyrethroid LLIN: MAGNet®. Durability monitoring will be conducted up to 36 months post-distribution and median survival in months will be calculated. The proportion of ITNs: (1) lost (attrition), (2) physical integrity, (3) resistance to damage score, (4) meeting WHO bio-efficacy (≥ 95% knockdown after 1 h or ≥ 80% mortality after 24 h for WHO cone bioassay, or ≥ 90% blood-feeding inhibition or ≥ 80% mortality after 24 h for WHO Tunnel tests) criteria against laboratory-reared resistant and susceptible mosquitoes, and insecticidal persistence over time will be estimated. The non-inferiority of Veeralin® and Tsara® Boost to the first-in-class, Olyset® Plus will additionally be assessed for mortality, and the equivalence of 20 times washed ITNs compared to field aged ITNs will be assessed for mortality and blood-feeding inhibition endpoints in the Ifakara Ambient Chamber Test, Tanzania. Conclusion: This will be the first large-scale prospective household randomized controlled trial of pyrethroid-PBO ITNs in three different countries in East Africa, West Africa and South Asia, simultaneously. The study will generate information on the replenishment intervals for PBO nets. [ABSTRACT FROM AUTHOR]

Cassava is the most cultivated and consumed root crop in the world. One of the major constraints to the cassava value chain is the short shelf life of cassava storage roots which is primarily due to the so-called post-harvest physiological deterioration (PPD). The identification of natural sources of PPD tolerance represents a key approach to mitigating PPD losses by generating farmer- and industry-preferred cassava cultivars with prolonged shelf life. In the present study, a PPD assessment method was developed to screen for PPD tolerance in the cassava germplasm. The proposed PPD assessment method displayed a reduced rate of microbial infection and allowed a rapid and homogenous development of typical PPD symptoms in the cassava storage roots. We successfully used the PPD assessment method in combination with an image-based PPD scoring method to identify and characterize PPD tolerance in 28 cassava cultivars from the Indonesian cassava germplasm. Our analysis showed a significant and positive correlation between PPD score and dry matter content (r = 0.589–0.664, p-value < 0.001). Analysis of additional root parameters showed a significant and positive correlation between PPD scores at 2 days post-harvest (dph) and root length (r = 0.388, p-value < 0.05). Our analysis identified at least 4 cultivars displaying a significantly delayed onset of PPD symptoms as compared to the other selected cultivars. The availability of cassava cultivars contrasting for tolerance to PPD will be particularly instrumental to understanding the molecular mechanisms associated with delayed PPD in cassava roots. [ABSTRACT FROM AUTHOR]

Key message: We studied size distributions of decay-affected Norway spruce trees using cut-to-length harvester data. The harvester data comprised tree-level decay and decay severity recordings from 101 final felling stands, which enabled to analyze relationships between size distributions of all and decay-affected trees. Distribution matching technique was used to transfer the size distribution of all trees into the diameter at breast height (DBH) distribution of decay-affected trees. Context: Stem decay of Norway spruce (Picea abies [L.] Karst.) results in large economic losses in timber production in the northern hemisphere. Forest management planning typically requires information on tree size distributions. However, size distributions of decay-affected trees generally remain unknown impeding decision-making in forest management planning. Aims: Our aim was to analyze and model relationships between size distributions of all and decay-affected Norway spruce trees at the level of forest stands. Methods: Cut-to-length harvester data of 93,456 trees were collected from 101 final felling stands in Norway. For each Norway spruce tree (94% of trees), the presence and severity of stem decay (incipient and advanced) were recorded. The stand-level size distributions (diameter at breast height, DBH; height, H) of all and decay-affected trees were described using the Weibull distribution. We proposed distribution matching (DM) models that transform either the DBH or H distribution of all trees into DBH distributions of decay-affected trees. We compared the predictive performance of DMs with a null-model that refers to a global Weibull distribution estimated based on DBHs of all harvested decay-affected trees. Results: The harvester data showed that an average-sized decay-affected tree is larger and taller compared with an average-sized tree in a forest stand, while trees with advanced decay were generally shorter and thinner compared with trees having incipient decay. DBH distributions of decay-affected trees can be matched with smaller error index (EI) values using DBH (EI = 0.14) than H distributions (EI = 0.31). DM clearly outperformed the null model that resulted in an EI of 0.32. Conclusions: The harvester data analysis showed a relationship between size distributions of all and decay-affected trees that can be explained by the spread biology of decay fungi and modeled using the DM technique. [ABSTRACT FROM AUTHOR]

Background: Fine root production is one of the least well understood components of the carbon cycle in terrestrial ecosystems. Minirhizotrons allow accurate and non-destructive sampling of fine root production. Small and large scale studies across a range of ecosystems are needed to have baseline data on fine root production and further assess the impact of global change upon it; however, the expense and the low adaptability of minirhizotrons prevent such data collection, in worldwide distributed sampling schemes, in low-income countries and in some ecosystems (e.g. tropical forested wetlands). Results: We present EnRoot, a narrow minirhizotron of 25 mm diameter, that is partially 3D printable. EnRoot is inexpensive (€150), easy to construct (no prior knowledge required) and adapted to a range of ecosystems including tropical forested wetlands (e.g. mangroves, peatlands). We tested EnRoot's accuracy and precision for measuring fine root length and diameter, and it yielded Lin's concordance correlation coefficient values of 0.95 for root diameter and 0.92 for length. As a proof of concept, we tested EnRoot in a mesocosm study, and in the field in a tropical mangrove. EnRoot proved its capacity to capture the development of roots of a legume (Medicago sativa) and a mangrove species (seedlings of Rhizophora mangle) in laboratory mesocosms. EnRoot's field installation was possible in the root-dense tropical mangrove because its narrow diameter allowed it to be installed between larger roots and because it is fully waterproof. EnRoot compares favourably with commercial minirhizotrons, and can image roots as small as 56 µm. Conclusion: EnRoot removes barriers to the extensive use of minirhizotrons by being low-cost, easy to construct and adapted to a wide range of ecosystem. It opens the doors to worldwide distributed minirhizotron studies across an extended range of ecosystems with the potential to fill knowledge gaps surrounding fine root production. [ABSTRACT FROM AUTHOR]

Background: The traditional methods for evaluating seeds are usually performed through destructive sampling followed by physical, physiological, biochemical and molecular determinations. Whilst proven to be effective, these approaches can be criticized as being destructive, time consuming, labor intensive and requiring experienced seed analysts. Thus, the objective of this study was to investigate the potential of computer vision and multispectral imaging systems supported with multivariate analysis for high-throughput classification of cowpea (Vigna unguiculata) seeds. An automated computer-vision germination system was utilized for uninterrupted monitoring of seeds during imbibition and germination to identify different categories of all individual seeds. By using spectral signatures of single cowpea seeds extracted from multispectral images, different multivariate analysis models based on linear discriminant analysis (LDA) were developed for classifying the seeds into different categories according to ageing, viability, seedling condition and speed of germination. Results: The results revealed that the LDA models had good accuracy in distinguishing 'Aged' and 'Non-aged' seeds with an overall correct classification (OCC) of 97.51, 96.76 and 97%, 'Germinated' and 'Non-germinated' seeds with OCC of 81.80, 79.05 and 81.0%, 'Early germinated', 'Medium germinated' and 'Dead' seeds with OCC of 77.21, 74.93 and 68.00% and among seeds that give 'Normal' and 'Abnormal' seedlings with OCC of 68.08, 64.34 and 62.00% in training, cross-validation and independent validation data sets, respectively. Image processing routines were also developed to exploit the full power of the multispectral imaging system in visualizing the difference among seed categories by applying the discriminant model in a pixel-wise manner. Conclusion: The results demonstrated the capability of the multispectral imaging system in the ultraviolet, visible and shortwave near infrared range to provide the required information necessary for the discrimination of individual cowpea seeds to different classes. Considering the short time of image acquisition and limited sample preparation, this stat-of-the art multispectral imaging method and chemometric analysis in classifying seeds could be a valuable tool for on-line classification protocols in cost-effective real-time sorting and grading processes as it provides not only morphological and physical features but also chemical information for the seeds being examined. Implementing image processing algorithms specific for seed quality assessment along with the declining cost and increasing power of computer hardware is very efficient to make the development of such computer-integrated systems more attractive in automatic inspection of seed quality. [ABSTRACT FROM AUTHOR]

Background: Above-ground biomass (AGB) is a basic agronomic parameter for field investigation and is frequently used to indicate crop growth status, the effects of agricultural management practices, and the ability to sequester carbon above and below ground. The conventional way to obtain AGB is to use destructive sampling methods that require manual harvesting of crops, weighing, and recording, which makes large-area, long-term measurements challenging and time consuming. However, with the diversity of platforms and sensors and the improvements in spatial and spectral resolution, remote sensing is now regarded as the best technical means for monitoring and estimating AGB over large areas. Results: In this study, we used structural and spectral information provided by remote sensing from an unmanned aerial vehicle (UAV) in combination with machine learning to estimate maize biomass. Of the 14 predictor variables, six were selected to create a model by using a recursive feature elimination algorithm. Four machine-learning regression algorithms (multiple linear regression, support vector machine, artificial neural network, and random forest) were evaluated and compared to create a suitable model, following which we tested whether the two sampling methods influence the training model. To estimate the AGB of maize, we propose an improved method for extracting plant height from UAV images and a volumetric indicator (i.e., BIOVP). The results show that (1) the random forest model gave the most balanced results, with low error and a high ratio of the explained variance for both the training set and the test set. (2) BIOVP can retain the largest strength effect on the AGB estimate in four different machine learning models by using importance analysis of predictors. (3) Comparing the plant heights calculated by the three methods with manual ground-based measurements shows that the proposed method increased the ratio of the explained variance and reduced errors. Conclusions: These results lead us to conclude that the combination of machine learning with UAV remote sensing is a promising alternative for estimating AGB. This work suggests that structural and spectral information can be considered simultaneously rather than separately when estimating biophysical crop parameters. [ABSTRACT FROM AUTHOR]