EDITOR'S CHOICE: REVIEW: Trait matching of flower visitors and crops predicts fruit set better than trait diversity

Understanding the relationships between trait diversity, species diversity and ecosystem functioning is essential for sustainable management. For functions comprising two trophic levels, trait matching between interacting partners should also drive functioning. However, the predictive ability of trait diversity and matching is unclear for most functions, particularly for crop pollination, where interacting partners did not necessarily co-evolve. World-wide, we collected data on traits of flower visitors and crops, visitation rates to crop flowers per insect species and fruit set in 469 fields of 33 crop systems. Through hierarchical mixed-effects models, we tested whether flower visitor trait diversity and/or trait matching between flower visitors and crops improve the prediction of crop fruit set (functioning) beyond flower visitor species diversity and abundance. Flower visitor trait diversity was positively related to fruit set, but surprisingly did not explain more variation than flower visitor species diversity. The best prediction of fruit set was obtained by matching traits of flower visitors (body size and mouthpart length) and crops (nectar accessibility of flowers) in addition to flower visitor abundance, species richness and species evenness. Fruit set increased with species richness, and more so in assemblages with high evenness, indicating that additional species of flower visitors contribute more to crop pollination when species abundances are similar. Synthesis and applications. Despite contrasting floral traits for crops world-wide, only the abundance of a few pollinator species is commonly managed for greater yield. Our results suggest that the identification and enhancement of pollinator species with traits matching those of the focal crop, as well as the enhancement of pollinator richness and evenness, will increase crop yield beyond current practices. Furthermore, we show that field practitioners can predict and manage agroecosystems for pollination services based on knowledge of just a few traits that are known for a wide range of flower visitor species.

Exploring the association of diary product intake with the fatty acids C15:0 and C17:0 measured from dried blood spots in a multi-population cohort: findings from the Food4Me study

Scope: The use of biomarkers in the objective assessment of dietary intake is a high priority in nutrition research. The aim of this study was to examine pentadecanoic acid (C15:0) and heptadecanoic acid (C17:0) as biomarkers of dairy foods intake. Methods and results: The data used in the present study were obtained as part of the Food4me Study. Estimates of C15:0 and C17:0 from dried blood spots and intakes of dairy from an FFQ were obtained from participants (n=1,180) across 7 countries. Regression analyses were used to explore associations of biomarkers with dairy intake levels and receiver operating characteristic (ROC) analyses were used to evaluate the fatty acids. Significant positive associations were found between C15:0 and total intakes of high-fat dairy products. C15:0 showed good ability to distinguish between low and high consumers of high-fat dairy products. Conclusion: C15:0 can be used as a biomarker of high-fat dairy intake and of specific high-fat dairy products. Both C15:0 and C17:0 performed poorly for total dairy intake highlighting the need for caution when using these in epidemiological studies.

Impact of increasing fruit and vegetables and flavonoid intake on the human gut microbiota

Epidemiological studies have shown protective effects of fruits and vegetables (F&V) in lowering the risk of developing cardiovascular diseases (CVD) and cancers. Plant-derived dietary fibre (non-digestible polysaccharides) and/or flavonoids may mediate the observed protective effects particularly through their interaction with the gut microbiota. The aim of this study was to assess the impact of fruit and vegetable (F&V) intake on gut microbiota, with an emphasis on the role of flavonoids, and further to explore relationships between microbiota and factors associated with CVD risk. In the study, a parallel design with 3 study groups, participants in the two intervention groups representing high-flavonoid (HF) and low flavonoid (LF) intakes were asked to increase their daily F&V intake by 2, 4 and 6 portions for a duration of 6 weeks each, while a third (control) group continued with their habitual diet. Faecal samples were collected at baseline and after each dose from 122 subjects. Faecal bacteria enumeration was performed by fluorescence in situ hybridisation (FISH). Correlations of dietary components, flavonoid intake and markers of CVD with bacterial numbers were also performed. A significant dose X treatment interaction was only found for Clostidium leptum-Ruminococcus bromii/flavefaciens with a significant increase after intake of 6 additional portions in the LF group. Correlation analysis of the data from all 122 subjects independent from dietary intervention indicated an inhibitory role of F&V intake, flavonoid content and sugars against the growth of potentially pathogenic clostridia. Additionally, we observed associations between certain bacterial populations and CVD risk factors including plasma TNF-α, plasma lipids and BMI/waist circumference.

CHP toolkit: case study of LAIe sensitivity to discontinuity of canopy cover in fruit plantations

This paper presents an open-source canopy height profile (CHP) toolkit designed for processing small-footprint full-waveform LiDAR data to obtain the estimates of effective leaf area index (LAIe) and CHPs. The use of the toolkit is presented with a case study of LAIe estimation in discontinuous-canopy fruit plantations. The experiments are carried out in two study areas, namely, orange and almond plantations, with different percentages of canopy cover (48% and 40%, respectively). For comparison, two commonly used discrete-point LAIe estimation methods are also tested. The LiDAR LAIe values are first computed for each of the sites and each method as a whole, providing “apparent” site-level LAIe, which disregards the discontinuity of the plantations’ canopies. Since the toolkit allows for the calculation of the study area LAIe at different spatial scales, between-tree-level clumpingcan be easily accounted for and is then used to illustrate the impact of the discontinuity of canopy cover on LAIe retrieval. The LiDAR LAIe estimates are therefore computed at smaller scales as a mean of LAIe in various grid-cell sizes, providing estimates of “actual” site-level LAIe. Subsequently, the LiDAR LAIe results are compared with theoretical models of “apparent” LAIe versus “actual” LAIe, based on known percent canopy cover in each site. The comparison of those models to LiDAR LAIe derived from the smallest grid-cell sizes against the estimates of LAIe for the whole site has shown that the LAIe estimates obtained from the CHP toolkit provided values that are closest to those of theoretical models.