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Adapting to a changing environment: inspiration for planetary health from east African communities
(2022)
An accurate estimation of crop yield under climate change scenarios is essential to quantify our ability to feed a growing population and develop agronomic adaptations to meet future food demand. A coordinated evaluation of yield simulations from process-based eco-physiological models for climate change impact assessment is still missing for soybean, the most widely grown grain legume and the main source of protein in our food chain. In this first soybean multi-model study, we used ten prominent models capable of simulating soybean yield under varying temperature and atmospheric CO2 concentration [CO2] to quantify the uncertainty in soybean yield simulations in response to these factors. Models were first parametrized with high quality measured data from five contrasting environments. We found considerable variability among models in simulated yield responses to increasing temperature and [CO2]. For example, under a + 3 degrees C temperature rise in our coolest location in Argentina, some models simulated that yield would reduce as much as 24%, while others simulated yield increases up to 29%. In our warmest location in Brazil, the models simulated a yield reduction ranging from a 38% decrease under + 3 degrees C temperature rise to no effect on yield. Similarly, when increasing [CO2] from 360 to 540 ppm, the models simulated a yield increase that ranged from 6% to 31%. Model calibration did not reduce variability across models but had an unexpected effect on modifying yield responses to temperature for some of the models. The high uncertainty in model responses indicates the limited applicability of individual models for climate change food projections. However, the ensemble mean of simulations across models was an effective tool to reduce the high uncertainty in soybean yield simulations associated with individual models and their parametrization. Ensemble mean yield responses to temperature and [CO2] were similar to those reported from the literature. Our study is the first demonstration of the benefits achieved from using an ensemble of grain legume models for climate change food projections, and highlights that further soybean model development with experiments under elevated [CO2] and temperature is needed to reduce the uncertainty from the individual models.
Mediterranean oak woodlands are currently facing unprecedented degradation threats from oak decline. The Iberian oak decline "Seca", related to Phytophthora infection, causes crown defoliation that may adversely affect ecosystem services (ESs). We aim to improve our understanding of how Seca-induced declines in crown foliation affect the provision of multiple ecosystem services from understory vegetation. We selected holm (Quercus ilex) and cork oak (Q. suber) trees in a Spanish oak woodland and evaluated three proxies of canopy effects. One proxy (crown defoliation) solely captured Seca-dependent effects, one proxy solely captured Seca-independent effects (tree dimensions such as diameter and height), while the third proxy (tree vigor) captured overall canopy effects. We then used the best-performing proxies to assess canopy effects on key ecosystem services (ESs) such as aboveground net primary production (ANPP), grass and legume biomass, species diversity, litter decomposition rates, and a combined index of ecosystem multifunctionality. <br /> We found that both types of canopy effects (i.e. Seca-dependent and Seca-independent effects) were related, indicating that ANPP was disproportionally more affected by Seca when defoliated trees were large. Responses of other ESs were mostly not significant, although lower species diversity was found under trees with intermediate vigor. Our results underline that a Seca-related decline in canopy density triggered a homogenization of ecosystem service delivery on the ecosystem scale. The ecosystem functions (EFs) under trees of low vigor are similar to that in adjacent open microsites indicating that the presence of vigorous (i.e. old and vital) trees is critical for maintaining EFs at a landscape level. Our results also highlight the importance of quantifying not only defoliation but also tree dimensions as both factors jointly and interactively modify canopy effects on ecosystem multifunctionality.
In recent years, many efforts have been made to apply image processing techniques for plant leaf identification. However, categorizing leaf images at the cultivar/variety level, because of the very low inter-class variability, is still a challenging task. In this research, we propose an automatic discriminative method based on convolutional neural networks (CNNs) for classifying 12 different cultivars of common beans that belong to three various species. We show that employing advanced loss functions, such as Additive Angular Margin Loss and Large Margin Cosine Loss, instead of the standard softmax loss function for the classification can yield better discrimination between classes and thereby mitigate the problem of low inter-class variability. The method was evaluated by classifying species (level I), cultivars from the same species (level II), and cultivars from different species (level III), based on images from the leaf foreside and backside. The results indicate that the performance of the classification algorithm on the leaf backside image dataset is superior. The maximum mean classification accuracies of 95.86, 91.37 and 86.87% were obtained at the levels I, II and III, respectively. The proposed method outperforms the previous relevant works and provides a reliable approach for plant cultivars identification.
Honey traceability is an important topic, especially for honeydew honeys, due to the increased incidence of adulteration. This study aimed to establish specific markers to quantify proteins in honey. A proteomics strategy to identify marker peptides from bracatinga honeydew honey was therefore developed. The proteomics approach was based on initial untargeted identification of honey proteins and peptides by LC-ESI-Triple-TOF-MS/MS, which identified the major royal jelly proteins (MRJP) presence. Afterwards, the peptides were selected by the in silico digestion. The marker peptides were quantified by the developed targeted LC-QqQ-MS/MS method, which provided good linearity and specificity, besides recoveries between 92 and 100% to quantify peptides from bracatinga honeydew honey. The uniqueness and high response in mass spectrometry were backed by further complementary protein analysis (SDS-PAGE). The selected marker peptides EALPHVPIFDR (MRJP 1), ILGANVK (MRJP 2), TFVTIER (MRJP 3), QNIDVVAR (MRJP 4), FINNDYNFNEVNFR (MRJP 5) and LLQPYPDWSWTK (MRJP 7), quantified by LC-QqQ-MS/MS, highlighted that the content of QNIDVVAR from MRJP 4 could be used to differentiate bracatinga honeydew honey from floral honeys (p < 0.05) as a potential marker for its authentication. Finally, principal components analysis highlighted the QNIDVVAR content as a good descriptor of the analyzed bracatinga honeydew honey samples.
1. Microplastics in soils have become an important threat for terrestrial systems as they may potentially alter the geochemical/biophysical soil environment and can interact with drought. As microplastics may affect soil water content, this could exacerbate the well-known negative effects of drought on ecosystem functionality. Thus, functions including litter decomposition, soil aggregation or those related with nutrient cycling can be altered. Despite this potential interaction, we know relatively little about how microplastics, under different soil water conditions, affect ecosystem functions and multifunctionality.
2. To address this gap, we performed an experiment using grassland plant communities growing in microcosms. Microplastic fibres (absent, present) and soil water conditions (well-watered, drought) were applied in a fully factorial design. At harvest, we measured soil ecosystem functions related to nutrient cycling (beta-glucosaminidase, beta-D-cellobiosidase, phosphatase, beta-glucosidase enzymes), respiration, nutrient retention, pH, litter decomposition and soil aggregation (water stable aggregates). As terrestrial systems provide these functions simultaneously, we also assessed ecosystem multifunctionality, an index that encompasses the array of ecosystem functions measured here.
3. We found that the interaction between microplastic fibres and drought affected ecosystem functions and multifunctionality. Drought had negatively affected nutrient cycling by decreasing enzymatic activities by up to similar to 39%, while microplastics increased soil aggregation by similar to 18%, soil pH by similar to 4% and nutrient retention by up to similar to 70% by diminishing nutrient leaching. Microplastic fibres also impacted soil enzymes, respiration and ecosystem multifunctionality, but importantly, the direction of these effects depended on soil water status. That is, under well-watered conditions, these functions decreased with microplastic fibres by up to similar to 34% while under drought they had similar values irrespective of the microplastic presence, or tended to increase with microplastics. Litter decomposition had a contrary pattern increasing with microplastics by similar to 6% under well-watered conditions while decreasing to a similar percentage under drought.
4. Synthesis and applications. Single ecosystem functions can be positively or negatively affected by microplastics fibres depending on soil water status. However, our results suggest that microplastic fibres may cause negative effects on ecosystem soil multifunctionality of a similar magnitude as drought. Thus, strategies to counteract this new global change factor are necessary.
Consumers are increasingly demanding higher quality and safety standards for the products they consume, and one of this is wheat flour, the basis of a wide variety of processed products. This major component in the diet of many communities can be contaminated by microorganisms before the grain harvest, or during the grain storage right before processing. These microorganisms include several fungal species, many of which produce mycotoxins, secondary metabolites that can cause severe acute and chronic disorders. Yet, we still know little about the overall composition of fungal communities associated with wheat flour. In this study, we contribute to fill this gap by characterizing the fungal microbiome of different types of wheat flour using culture-dependent and -independent techniques. Qualitatively, these approaches suggested similar results, highlighting the presence of several fungal taxa able to produce mycotoxins. In-vitro isolation of fungal species suggest a higher frequency of Penicillium, while metabarcoding suggest a higher abundance of Alternaria. This discrepancy might reside on the targeted portion of the community (alive vs. overall) or in the specific features of each technique. Thus, this study shows that commercial wheat flour hosts a wide fungal diversity with several taxa potentially representing concerns for consumers, aspects that need more attention throughout the food production chain.
Comprehensive untargeted and targeted analysis of root exudate composition has advanced our understanding of rhizosphere processes. However, little is known about exudate spatial distribution and regulation. We studied the specific metabolite signatures of asparagus root exudates, root outer (epidermis and exodermis), and root inner tissues (cortex and vasculature). The greatest differences were found between exudates and root tissues. In total, 263 non-redundant metabolites were identified as significantly differentially abundant between the three root fractions, with the majority being enriched in the root exudate and/or outer tissue and annotated as 'lipids and lipid-like molecules' or 'phenylpropanoids and polyketides'. Spatial distribution was verified for three selected compounds using MALDI-TOF mass spectrometry imaging. Tissue-specific proteome analysis related root tissue-specific metabolite distributions and rhizodeposition with underlying biosynthetic pathways and transport mechanisms. The proteomes of root outer and inner tissues were spatially very distinct, in agreement with the fundamental differences between their functions and structures. According to KEGG pathway analysis, the outer tissue proteome was characterized by a high abundance of proteins related to 'lipid metabolism', 'biosynthesis of other secondary metabolites' and 'transport and catabolism', reflecting its main functions of providing a hydrophobic barrier, secreting secondary metabolites, and mediating water and nutrient uptake. Proteins more abundant in the inner tissue related to 'transcription', 'translation' and 'folding, sorting and degradation', in accord with the high activity of cortical and vasculature cell layers in growth- and development-related processes. In summary, asparagus root fractions accumulate specific metabolites. This expands our knowledge of tissue-specific plant cell function.
Purpose UK guidelines recommend dietary saturated fatty acids (SFAs) should not exceed 10% total energy (%TE) for cardiovascular disease prevention, with benefits observed when SFAs are replaced with unsaturated fatty acids (UFAs). This study aimed to assess the efficacy of a dietary exchange model using commercially available foods to replace SFAs with UFAs. Methods Healthy men (n = 109, age 48, SD 11 year) recruited to the Reading, Imperial, Surrey, Saturated fat Cholesterol Intervention-1 (RISSCI-1) study (ClinicalTrials.Gov n degrees NCT03270527) followed two sequential 4-week isoenergetic moderate-fat (34%TE) diets: high-SFA (18%TE SFAs, 16%TE UFAs) and low-SFA (10%TE SFAs, 24%TE UFAs). Dietary intakes were assessed using 4-day weighed diet diaries. Nutrient intakes were analysed using paired t-tests, fasting plasma phospholipid fatty acid (PL-FA) profiles and dietary patterns were analysed using orthogonal partial least square discriminant analyses. Results Participants exchanged 10.2%TE (SD 4.1) SFAs for 9.7%TE (SD 3.9) UFAs between the high and low-SFA diets, reaching target intakes with minimal effect on other nutrients or energy intakes. Analyses of dietary patterns confirmed successful incorporation of recommended foods from commercially available sources (e.g. dairy products, snacks, oils, and fats), without affecting participants' overall dietary intakes. Analyses of plasma PL-FAs indicated good compliance to the dietary intervention and foods of varying SFA content. Conclusions RISSCI-1 dietary exchange model successfully replaced dietary SFAs with UFAs in free-living healthy men using commercially available foods, and without altering their dietary patterns. Further intervention studies are required to confirm utility and feasibility of such food-based dietary fat replacement models at a population level.
Seed dispersal plays an important role in population dynamics in agricultural ecosystems, but the effects of surrounding vegetation height on seed dispersal and population connectivity on the landscape scale have rarely been studied. Understanding the effects of surrounding vegetation height on seed dispersal will provide important information for land-use management in agricultural landscapes to prevent the spread of undesired weeds or enhance functional connectivity. We used two model species, Phragmites australis and Typha latifolia, growing in small natural ponds known as kettle holes, in an agricultural landscape to evaluate the effects of surrounding vegetation height on wind dispersal and population connectivity between kettle holes. Seed dispersal distance and the probability of long-distance dispersal (LDD) were simulated with the mechanistic WALD model under three scenarios of "low", "dynamic" and "high" surrounding vegetation height. Connectivity between the origin and target kettle holes was quantified with a connectivity index adapted from Hanski and Thomas (1994). Our results show that mean seed dispersal distance decreases with the height of surrounding matrix vegetation, but the probability of long-distance dispersal (LDD) increases with vegetation height. This indicates an important vegetation-based trade-off between mean dispersal distance and LDD, which has an impact on connectivity. Matrix vegetation height has a negative effect on mean seed dispersal distance but a positive effect on the probability of LDD. This positive effect and its impact on connectivity provide novel insights into landscape level (meta-)population and community dynamics - a change in matrix vegetation height by land-use or climatic changes could strongly affect the spread and connectivity of wind-dispersed plants. The opposite effect of vegetation height on mean seed dispersal distance and the probability of LDD should therefore be considered in management and analyses of future land-use and climate change effects.