Medicinal plants of the Argentine Yungas plants of the Las Yungas biosphere reserve, Northwest of Argentina, used in health care

We have compared the species richness of medicinal plants and the differential patterns of use amongst settlements in the Andean communities of Northwest Argentina which have differing levels of isolation. About 259 ethnoespecies, belonging to 74 plant families, were included, representing between 70 and 80% of the total estimate. The results indicate that Coronopus didymus is the most relevant and important species. The method of use of medicinal plants and the ailments treated by rural doctors compared to those of the layperson is different. Native and exotic plants are used differently according to the body system treated. There are some relationships between internal and external use and body systems and recipes. The greater medicinal species richness found in the less isolated locations is due to external enriching cultural influences.     

Constructing more informative plant–pollinator networks: visitation and pollen deposition networks in a heathland plant community

Interaction networks are widely used as tools to understand plant–pollinator communities, and to examine potential threats to plant diversity and food security if the ecosystem service provided by pollinating animals declines. However, most networks to date are based on recording visits to flowers, rather than recording clearly defined effective pollination events. Here we provide the first networks that explicitly incorporate measures of pollinator effectiveness (PE) from pollen deposition on stigmas per visit, and pollinator importance (PI) as the product of PE and visit frequency. These more informative networks, here produced for a low diversity heathland habitat, reveal that plant–pollinator interactions are more specialized than shown in most previous studies. At the studied site, the specialization index was lower for the visitation network than the PE network, which was in turn lower than for the PI network. Our study shows that collecting PE data is feasible for community-level studies in low diversity communities and that including information about PE can change the structure of interaction networks. This could have important consequences for our understanding of threats to pollination systems.     

地衣特殊性、多样性及其重要性

本文从地衣二元性和地衣的真菌本质、地衣物种、生活环境和生命力、生长速度、代谢产物等方面阐述地衣的特殊性与多样性及其在共生进化研究、监测大气污染、测年、药用诸方面表现出的科学意义与实用价值。并在地衣的开发利用中,提出地衣多样性的保护与永续利用的建议。     

Seed morphology in some European aconites (Aconitum,Ranunculaceae)

The seed coat morphology, investigated in taxa representative of the main European groups ofAconitum, are in good agreement with the current taxonomy of the genus. The seed coat microcharacteristics (warty epidermal cells) are very constant. There is a trend for the reduction of longitudinal wings on the edges concomitant with the development of ridges and transverse wings on the faces. Another morphological progression leads from smooth to rugulose and eventually to transverse wing-bearing seed faces. A working hypothesis suggests an ecological adaptative significance to these changes.     

Revisiting the dimensionality of biological diversity

Biodiversity can be represented by different dimensions. While many diversity metrics try to capture the variation of these dimensions they also lead to a ‘fragmentation’ of the concept of biodiversity itself. Developing a unified measure that integrates all the dimensions of biodiversity is a theoretical solution for this problem, however, it remains operationally impossible. Alternatively, understanding which dimensions better represent the biodiversity of a set of communities can be a reliable way to integrate the different diversity metrics. Therefore, to achieve a holistic understand of biological diversity, we explore the concept of dimensionality. We define dimensionality of diversity as the number of complementary components of biodiversity, represented by diversity metrics, needed to describe biodiversity in an unambiguously and effective way. We provide a solution that joins two components of dimensionality – correlation and the variation – operationalized through two metrics, respectively: evenness of eigenvalues (EE) and importance values (IV). Through simulation we show that considering EE and IV together can provide information that is neglected when only EE is considered. We demonstrate how to apply this framework by investigating the dimensionality of South American small mammal communities. Our example evidenced that, for some representations of biological diversity, more attention is needed in the choice of diversity metrics necessary to effectively characterize biodiversity. We conclude by highlighting that this integrated framework provides a better understanding of dimensionality than considering only the correlation component.     

Repeated measures random forests (RMRF): Identifying factors associated with nocturnal hypoglycemia

Nocturnal hypoglycemia is a common phenomenon among patients with diabetes and can lead to a broad range of adverse events and complications. Identifying factors associated with hypoglycemia can improve glucose control and patient care. We propose a repeated measures random forest (RMRF) algorithm that can handle nonlinear relationships and interactions and the correlated responses from patients evaluated over several nights. Simulation results show that our proposed algorithm captures the informative variable more often than naïvely assuming independence. RMRF also outperforms standard random forest and extremely randomized trees algorithms. We demonstrate scenarios where RMRF attains greater prediction accuracy than generalized linear models. We apply the RMRF algorithm to analyze a diabetes study with 2524 nights from 127 patients with type 1 diabetes. We find that nocturnal hypoglycemia is associated with HbA1c, bedtime blood glucose (BG), insulin on board, time system activated, exercise intensity, and daytime hypoglycemia. The RMRF can accurately classify nights at high risk of nocturnal hypoglycemia.     

Seasonal and annual variations in the pollination efficiency of a pollinator community of Dictamnus albus L.

The interplay between insect and plant traits outlines the patterns of pollen transfer and the subsequent plant reproductive fitness. We studied the factors that affect the pollination efficiency of a pollinator community of Dictamnus albus L. by evaluating insect behaviour and morphological characteristics in relation to flowering phenology. In order to extrapolate the pollinator importance of single taxa and of the whole pollinator guild, we calculated an index distinguishing between potential (PPI) and realized (RPI) pollinator importance. Although the pollinator species spectrum appeared rather constant, we found high intra‐ and inter‐annual variability of pollinator frequency and importance within the insect community. Flower visitation rate strictly depended on insect abundance and on the overlap between their flying period and flower blooming. All the pollinators visited flowers from the bottom to the top of the racemes, excluding intra‐plant geitonogamous pollination, and most of them showed high pollen fidelity. Only medium large‐sized bees could contact the upward bending stiles while feeding on nectar, highlighting a specialisation of the plant towards bigger pollinators. Moreover, we found evidence of functional specialisation, since all pollinators were restricted to a single taxonomic group (order: Hymenoptera; superfamily: Apoidea). Both the PPI and RPI indices indicate Habropoda tarsata as the most important pollinator of D. albus. Following hand cross‐pollination experiments we revealed the presence of pollination limitation in 1 of the 3 years of field study. We discuss this result in relation to flowering abundance and to possible mismatches of phenological periods between plants and insects.     

Economic weights of maternal and direct traits of pigs calculated by applying gene flow methods

Multiple trait selection indexes in pig breeding programmes should take into account the population structure and time delay between parent selection and expressions of traits in all production levels next to the trait impacts on economic efficiency of production systems. Gene flow procedures could be used for the correct evaluation of maternal and direct traits of pig breeds involved in breeding or crossbreeding systems. Therefore, the aim of this study was to expand a previously developed bioeconomic model and computer program to calculate the marginal economic values by including a gene flow procedure to calculate the economic weights for maternal and direct traits in pig breeds. The new program was then applied to the three-way crossbreeding system of the Czech National Programme for Pig Breeding. Using this program, the marginal economic values of traits for dam breeds Czech Large White in the dam position and Czech Landrace in the sire position, and for the sire breed Pietrain were weighted by the number of discounted gene expressions of selected parents of each breed summarised within all links of the crossbreeding system during the 8-year investment period. Economic weights calculated in this way were compared with the approximate economic weights calculated previously without a gene flow procedure. Taking into account the time delay between parent selection and trait expression (using discounting with half-year discount rates of 2% or 5%) and including more than one generation of parent progeny had little impact on the relative economic importance of maternal and direct traits of breeds involved in the evaluated three-way crossbreeding system. These results indicated that this gene-flow method could be foregone when estimating the relative economic weights of traits in pig crossbreeding systems applying artificial insemination at all production levels.     

Climatic impact,future biomass production,and local adaptation of four switchgrass cultivars

Previous studies have shown that switchgrass has a wide range of genetic variation and that productivity is linked to local adaptation to the location of origin for many cultivars. In this meta‐analysis, we compiled and analyzed 900 observations associated with 41 field trials for four switchgrass cultivars (two lowlands, Alamo and Kanlow, and two uplands, Cave‐In‐Rock and Shelter). This extensive dataset and machine learning were used to identify the most influential variables impacting switchgrass productivity, to search for evidence of local adaptation to each cultivar's location of origin, and to predict change in productivity under future climate for each cultivar. In general, variables associated with climate and management are more important predictors of productivity relative to soil variables. Three climatic variables, annual mean temperature, annual precipitation, and precipitation in the wettest month, are identified as key environmental variables for productivity of all cultivars. Productivity under future climate (2041–2060) is predicted to stay stable for all cultivars relative to the prediction under current climate (1986–2005) across all trial locations and over a 20‐year simulation period. However, the productivity of each cultivar varies from location to location and from year to year, although productivity varies more between locations than between years. Additionally, we observe shifts in the most productive cultivar at the local field scale depending on the combination of management practice and climates. The shape of the relationship between productivity and the annual mean temperature relative to the cultivar's location of origin is a bell‐shaped curve for Kanlow, Cave‐in‐Rock, and Shelter, indicative of local adaptation. Identifying influential environmental variables and their relationships to productivity with respect to cultivar's location of origin help predicting productivity on the local field scale, and will help with the biofuel production planning through the selection of suitable cultivars for different locations under climate changes.