SummaryThe recognition that the Dark European honey bee, Apis mellifera mellifera, is increasingly threatened in its native range has led to the establishment of conservation programmes and protected areas throughout western Europe. Previous molecular surveys showed that, despite management strategies to preserve the genetic integrity of A. m. mellifera, protected populations had a measurable component of their gene pool derived from commercial C-lineage honey bees. Here we used both sequence data from the tRNA leu -cox2 intergenic mtDNA region and a genome-wide scan, with over 1183 single nucleotide polymorphisms (SNPs), to assess genetic diversity and introgression levels in several protected populations of A. m. mellifera, which were then compared with samples collected from unprotected populations. MtDNA analysis of the protected populations revealed a single colony bearing a foreign haplotype, whereas SNPs showed varying levels of introgression ranging from virtually zero in Norway to about 14% in Denmark. Introgression overall was higher in unprotected (30%) than in protected populations (8%), and is reflected in larger SNP diversity levels of the former, although opposite diversity levels were observed for mtDNA. These results suggest that, despite controlled breeding, some protected populations still require adjustments to the management strategies to further purge foreign alleles, which can be identified by SNPs. 270Pinto et al.

Biodiversity underlies ecosystem resilience, ecosystem function, sustainable economies, and human well-being. Understanding how biodiversity sustains ecosystems under anthropogenic stressors and global environmental change will require new ways of deriving and applying biodiversity data. A major challenge is that biodiversity data and knowledge are scattered, biased, collected with numerous methods, and stored in inconsistent ways. The Group on Earth Observations Biodiversity Observation Network (GEO BON) has developed the Essential Biodiversity Variables (EBVs) as fundamental metrics to help aggregate, harmonize, and interpret biodiversity observation data from diverse sources. Mapping and analyzing EBVs can help to evaluate how aspects of biodiversity are distributed geographically and how they change over time. EBVs are also intended to serve as inputs and validation to forecast the status and trends of biodiversity, and to support policy and decision making. Here, we assess the feasibility of implementing Genetic Composition EBVs (Genetic EBVs), which are metrics of within-species genetic variation. We review and bring together numerous areas of the field of genetics and evaluate how each contributes to global and regional genetic biodiversity monitoring with respect to theory, sampling logistics, metadata, archiving, data aggregation, modeling, and technological advances. We propose four Genetic EBVs: (i) Genetic Diversity; (ii) Genetic Differentiation; (iii) Inbreeding; and (iv) Effective Population Size (N e ). We rank Genetic EBVs according to their relevance, sensitivity to change, generalizability, scalability, feasibility and data availability. We outline the workflow for generating genetic data underlying the Genetic EBVs, and review advances and needs in archiving genetic composition data and metadata. We discuss how Genetic EBVs can be operationalized by visualizing EBVs in space and time across species and by forecasting Genetic EBVs beyond current observations using various modeling approaches. Our review then explores challenges of aggregation, standardization, and costs of operationalizing the Genetic EBVs, as well as future directions and opportunities to maximize their uptake globally in research and policy. The collection, annotation, and availability of genetic data has made major advances in the past decade, each of which contributes to the practical and standardized framework for large-scale genetic observation reporting. Rapid advances in DNA sequencing technology present new opportunities, but also challenges for operationalizing Genetic EBVs for biodiversity monitoring regionally and globally. With these advances, genetic composition monitoring is starting to be integrated into global conservation policy, which can help support the foundation of all biodiversity and species' long-term persistence in the face of environmental change. We conclude with a summary of concrete steps for researchers and policy makers for advancing operationalization of Genetic EBVs. The technical and analytica...

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The distribution and abundance of plants across the world depends in part on their ability to move, which is commonly characterized by a dispersal kernel. For seeds, the total dispersal kernel (TDK) describes the combined influence of all primary, secondary and higher-order dispersal vectors on the overall dispersal kernel for a plant individual, population, species or community. Understanding the role of each vector within the TDK, and their combined influence on the TDK, is critically important for being able to predict plant responses to a changing biotic or abiotic environment. In addition, fully characterizing the TDK by including all vectors may affect predictions of population spread. Here, we review existing research on the TDK and discuss advances in empirical, conceptual modelling and statistical approaches that will facilitate broader application. The concept is simple, but few examples of well-characterized TDKs exist. We find that significant empirical challenges exist, as many studies do not account for all dispersal vectors (e.g. gravity, higher-order dispersal vectors), inadequately measure or estimate long-distance dispersal resulting from multiple vectors and/or neglect spatial heterogeneity and context dependence. Existing mathematical and conceptual modelling approaches and statistical methods allow fitting individual dispersal kernels and combining them to form a TDK; these will perform best if robust prior information is available. We recommend a modelling cycle to parameterize TDKs, where empirical data inform models, which in turn inform additional data collection. Finally, we recommend that the TDK concept be extended to account for not only where seeds land, but also how that location affects the likelihood of establishing and producing a reproductive adult, i.e. the total effective dispersal kernel.

Many models of memory build in a term for encoding variability, the observation that there can be variability in the richness or extensiveness of processing at encoding, and that this variability has consequences for retrieval. In four experiments, we tested the expectation that encoding variability could be driven by the properties of the to-be-remembered item. Specifically, that concepts associated with more semantic features would be better remembered than concepts associated with fewer semantic features. Using feature listing norms we selected sets of items for which people tend to list higher numbers of features (high NoF) and items for which people tend to list lower numbers of features (low NoF). Results showed more accurate free recall for high NoF concepts than for low NoF concepts in expected memory tasks (Experiments 1–3) and also in an unexpected memory task (Experiment 4). This effect was not the result of associative chaining between study items (Experiment 3), and can be attributed to the amount of item-specific processing that occurs at study (Experiment 4). These results provide evidence that stimulus-specific differences in processing at encoding have consequences for explicit memory retrieval.

Significant amounts of cell wall degrading (CWD) enzymes are required to degrade lignocellulosic biomass into its component sugars. One strategy for reducing exogenous enzyme production requirements is to produce the CWD enzymes in planta. For this work, various CWD enzymes were expressed in maize (Zea mays). Following growth and dry down of the plants, harvested maize stover was tested to determine the impact of the expressed enzymes on the production of glucose and xylose using different exogenous enzyme loadings. In this study, a consolidated pretreatment and hydrolysis process consisting of a moderate chemical pretreatment at temperatures below 75°C followed by enzymatic hydrolysis using an in-house enzyme cocktail was used to evaluate engineered transgenic feedstocks. The carbohydrate compositional analysis showed no significant difference in the amounts of glucan and xylan between the transgenic maize plants expressing CWD enzyme(s) and the control plants. Hydrolysis results demonstrated that transgenic plants expressing CWD enzymes achieved up to 141% higher glucose yield and 172% higher xylose yield over the control plants from enzymatic hydrolysis under the experimental conditions. The hydrolytic performance of a specific xylanase (XynA) expressing transgenic event (XynA.2015.05) was heritable in the next generation, and the improved properties can be achieved even with a 25% reduction in exogenous enzyme loading. Simultaneous saccharification and fermentation of biomass hydrolysates from two different transgenic maize lines with yeast (Saccharomyces cerevisiae D5A) converted 65% of the biomass glucan into ethanol, versus only a 42% ethanol yield with hydrolysates from control plants, corresponding to a 55% improvement in ethanol production.

When climatic or environmental conditions change, plant populations must either adapt to these new conditions, or track their niche via seed dispersal. Adaptation of plants to different abiotic environments has mostly been discussed with respect to physiological and demographic parameters that allow local persistence. However, rapid modifications in response to changing environmental conditions can also affect seed dispersal, both via plant traits and via their dispersal agents. Studying such changes empirically is challenging, due to the high variability in dispersal success, resulting from environmental heterogeneity, and substantial phenotypic variability of dispersal-related traits of seeds and their dispersers. The exact mechanisms that drive rapid changes are often not well understood, but the ecological implications of these processes are essential determinants of dispersal success, and deserve more attention from ecologists, especially in the context of adaptation to global change. We outline the evidence for rapid changes in seed dispersal traits by discussing variability due to plasticity or genetics broadly, and describe the specific traits and biological systems in which variability in dispersal is being studied, before discussing some of the potential underlying mechanisms. We then address future research needs and propose a simulation model that incorporates phenotypic plasticity in seed dispersal. We close with a call to action and encourage ecologists and biologist to embrace the challenge of better understanding rapid changes in seed dispersal and their consequences for the reaction of plant populations to global change.

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