Quantitative and qualitative differences in morphological traits revealed between diploid Fragaria species

Background and Aims: The aims of this investigation were to highlight the qualitative and quantitative diversity apparent between nine diploid Fragaria species and produce interspecific populations segregating for a large number of morphological characters suitable for quantitative trait loci analysis. Methods: A qualitative comparison of eight described diploid Fragaria species was performed and measurements were taken of 23 morphological traits from 19 accessions including eight described species and one previously undescribed species. A principal components analysis was performed on 14 mathematically unrelated traits from these accessions, which partitioned the species accessions into distinct morphological groups. Interspecific crosses were performed with accessions of species that displayed significant quantitative divergence and, from these, populations that should segregate for a range of quantitative traits were raised. Key Results: Significant differences between species were observed for all 23 morphological traits quantified and three distinct groups of species accessions were observed after the principal components analysis. Interspecific crosses were performed between these groups, and F2 and backcross populations were raised that should segregate for a range of morphological characters. In addition, the study highlighted a number of distinctive morphological characters in many of the species studied. Conclusions: Diploid Fragaria species are morphologically diverse, yet remain highly interfertile, making the group an ideal model for the study of the genetic basis of phenotypic differences between species through map-based investigation using quantitative trait loci. The segregating interspecific populations raised will be ideal for such investigations and could also provide insights into the nature and extent of genome evolution within this group.

Characterization of a highly biodiverse floodplain meadow using hyperspectral remote sensing within a plant functional trait framework

We assessed the potential for using optical functional types as effective markers to monitor changes in vegetation in floodplain meadows associated with changes in their local environment. Floodplain meadows are challenging ecosystems for monitoring and conservation because of their highly biodiverse nature. Our aim was to understand and explain spectral differences among key members of floodplain meadows and also characterize differences with respect to functional traits. The study was conducted on a typical floodplain meadow in UK (MG4-type, mesotrophic grassland type 4, according to British National Vegetation Classification). We compared two approaches to characterize floodplain communities using field spectroscopy. The first approach was sub-community based, in which we collected spectral signatures for species groupings indicating two distinct eco-hydrological conditions (dry and wet soil indicator species). The other approach was “species-specific”, in which we focused on the spectral reflectance of three key species found on the meadow. One herb species is a typical member of the MG4 floodplain meadow community, while the other two species, sedge and rush, represent wetland vegetation. We also monitored vegetation biophysical and functional properties as well as soil nutrients and ground water levels. We found that the vegetation classes representing meadow sub-communities could not be spectrally distinguished from each other, whereas the individual herb species was found to have a distinctly different spectral signature from the sedge and rush species. The spectral differences between these three species could be explained by their observed differences in plant biophysical parameters, as corroborated through radiative transfer model simulations. These parameters, such as leaf area index, leaf dry matter content, leaf water content, and specific leaf area, along with other functional parameters, such as maximum carboxylation capacity and leaf nitrogen content, also helped explain the species’ differences in functional dynamics. Groundwater level and soil nitrogen availability, which are important factors governing plant nutrient status, were also found to be significantly different for the herb/wetland species’ locations. The study concludes that spectrally distinguishable species, typical for a highly biodiverse site such as a floodplain meadow, could potentially be used as target species to monitor vegetation dynamics under changing environmental conditions.

High-throughput root phenotyping screens identify genetic loci associated with root architectural traits in Brassica napus under contrasting phosphate availabilities

Background and Aims: Phosphate (Pi) deficiency in soils is a major limiting factor for crop growth worldwide. Plant growth under low Pi conditions correlates with root architectural traits and it may therefore be possible to select these traits for crop improvement. The aim of this study was to characterize root architectural traits, and to test quantitative trait loci (QTL) associated with these traits, under low Pi (LP) and high Pi (HP) availability in Brassica napus. Methods: Root architectural traits were characterized in seedlings of a double haploid (DH) mapping population (n = 190) of B. napus 'Tapidor' x 'Ningyou 7' (TNDH) using high-throughput phenotyping methods. Primary root length (PRL), lateral root length (LRL), lateral root number (LRN), lateral root density (LRD) and biomass traits were measured 12 d post-germination in agar at LP and HP. Key Results: In general, root and biomass traits were highly correlated under LP and HP conditions. 'Ningyou 7' had greater LRL, LRN and LRD than 'Tapidor', at both LP and HP availability, but smaller PRL. A cluster of highly significant QTL for LRN, LRD and biomass traits at LP availability were identified on chromosome A03; QTL for PRL were identified on chromosomes A07 and C06. Conclusions: High-throughput phenotyping of Brassica can be used to identify root architectural traits which correlate with shoot biomass. It is feasible that these traits could be used in crop improvement strategies. The identification of QTL linked to root traits under LP and HP conditions provides further insights on the genetic basis of plant tolerance to P deficiency, and these QTL warrant further dissection.

Simple measures of climate, soil properties and plant traits predict national scale grassland soil carbon stocks

1. Soil carbon (C) storage is a key ecosystem service. Soil C stocks play a vital role in soil fertility and climate regulation, but the factors that control these stocks at regional and national scales are unknown, particularly when their composition and stability are considered. As a result, their mapping relies on either unreliable proxy measures or laborious direct measurements. 2. Using data from an extensive national survey of English grasslands we show that surface soil (0-7cm) C stocks in size fractions of varying stability can be predicted at both regional and national scales from plant traits and simple measures of soil and climatic conditions. 3. Soil C stocks in the largest pool, of intermediate particle size (50-250 µm), were best explained by mean annual temperature (MAT), soil pH and soil moisture content. The second largest C pool, highly stable physically and biochemically protected particles (0.45-50 µm), was explained by soil pH and the community abundance weighted mean (CWM) leaf nitrogen (N) content, with the highest soil C stocks under N rich vegetation. The C stock in the small active fraction (250-4000 µm) was explained by a wide range of variables: MAT, mean annual precipitation, mean growing season length, soil pH and CWM specific leaf area; stocks were higher under vegetation with thick and/or dense leaves. 4. Testing the models describing these fractions against data from an independent English region indicated moderately strong correlation between predicted and actual values and no systematic bias, with the exception of the active fraction, for which predictions were inaccurate. 5. Synthesis and Applications: Validation indicates that readily available climate, soils and plant survey data can be effective in making local- to landscape-scale (1-100,000 km2) soil C stock predictions. Such predictions are a crucial component of effective management strategies to protect C stocks and enhance soil C sequestration.

Morphological and fecundity traits of Culex mosquitoes caught in gravid traps in urban and rural Berkshire, UK

Culex pipiens s.l. is one of the primary vectors of West Nile Virus in the USA and Continental Europe. The seasonal abundance and eco-behavioural characteristics of the typical form, Cx. pipiens pipiens, make it a key putative vector in Britain. Surveillance of Culex larvae and adults is essential to detect any changes to spatial and seasonal activity or morphological traits that may increase the risk of disease transmission. Here we report the use of the modified Reiter gravid box trap, which is commonly used in the USA but scarcely used in the UK, to assess its suitability as a tool for British female Culex mosquito surveillance. Trapping was carried out at 110 sites in urban and rural gardens in Berkshire in May, July and September 2013. We tested if reproductively active adult female Culex are more abundant in urban than rural gardens and if wing characteristic traits and egg raft size are influenced by location and seasonal variations. Gravid traps were highly selective for Culex mosquitoes, on average catching significantly more per trap in urban gardens (32.4 ± 6.2) than rural gardens (19.3 ± 4.0) and more in July than in May or September. The majority of females were caught alive in a good condition. Wing lengths were measured as an indicator of size. Females flying in September were significantly smaller than females in May or July. Further non-significant differences in morphology and fecundity between urban and rural populations were found that should be explored further across the seasons.

Testing projected wild bee distributions in agricultural habitats: predictive power depends on species traits and habitat type

Species distribution models (SDM) are increasingly used to understand the factors that regulate variation in biodiversity patterns and to help plan conservation strategies. However, these models are rarely validated with independently collected data and it is unclear whether SDM performance is maintained across distinct habitats and for species with different functional traits. Highly mobile species, such as bees, can be particularly challenging to model. Here, we use independent sets of occurrence data collected systematically in several agricultural habitats to test how the predictive performance of SDMs for wild bee species depends on species traits, habitat type, and sampling technique. We used a species distribution modeling approach parametrized for the Netherlands, with presence records from 1990 to 2010 for 193 Dutch wild bees. For each species, we built a Maxent model based on 13 climate and landscape variables. We tested the predictive performance of the SDMs with independent datasets collected from orchards and arable fields across the Netherlands from 2010 to 2013, using transect surveys or pan traps. Model predictive performance depended on species traits and habitat type. Occurrence of bee species specialized in habitat and diet was better predicted than generalist bees. Predictions of habitat suitability were also more precise for habitats that are temporally more stable (orchards) than for habitats that suffer regular alterations (arable), particularly for small, solitary bees. As a conservation tool, SDMs are best suited to modeling rarer, specialist species than more generalist and will work best in long-term stable habitats. The variability of complex, short-term habitats is difficult to capture in such models and historical land use generally has low thematic resolution. To improve SDMs’ usefulness, models require explanatory variables and collection data that include detailed landscape characteristics, for example, variability of crops and flower availability. Additionally, testing SDMs with field surveys should involve multiple collection techniques.

The role of life history traits in mammalian invasion success

Why some organisms become invasive when introduced into novel regions while others fail to even establish is a fundamental question in ecology. Barriers to success are expected to filter species at each stage along the invasion pathway. No study to date, however, has investigated how species traits associate with success from introduction to spread at a large spatial scale in any group. Using the largest data set of mammalian introductions at the global scale and recently developed phylogenetic comparative methods, we show that human-mediated introductions considerably bias which species have the opportunity to become invasive, as highly productive mammals with longer reproductive lifespans are far more likely to be introduced. Subsequently, greater reproductive output and higher introduction effort are associated with success at both the establishment and spread stages. High productivity thus supports population growth and invasion success, with barriers at each invasion stage filtering species with progressively greater fecundity.

Efficient evaluation of highly oscillatory acoustic scattering surface integrals

We consider the approximation of some highly oscillatory weakly singular surface integrals, arising from boundary integral methods with smooth global basis functions for solving problems of high frequency acoustic scattering by three-dimensional convex obstacles, described globally in spherical coordinates. As the frequency of the incident wave increases, the performance of standard quadrature schemes deteriorates. Naive application of asymptotic schemes also fails due to the weak singularity. We propose here a new scheme based on a combination of an asymptotic approach and exact treatment of singularities in an appropriate coordinate system. For the case of a spherical scatterer we demonstrate via error analysis and numerical results that, provided the observation point is sufficiently far from the shadow boundary, a high level of accuracy can be achieved with a minimal computational cost.

Measuring root traits in barley (Hordeum vulgare ssp vulgare and ssp spontaneum) seedlings using gel chambers, soil sacs and X-ray microtomography

Root characteristics of seedlings of five different barley genotypes were analysed in 2D using gel chambers, and in 3D using soil sacs that were destructively harvested and pots of soil that were assessed non-invasively using X-ray microtomography. After 5 days, Chime produced the greatest number of root axes (similar to 6) and Mehola significantly less (similar to 4) in all growing methods. Total root length was longest in GSH01915 and shortest in Mehola for all methods, but both total length and average root diameter were significantly larger for plants grown in gel chambers than those grown in soil. The ranking of particular growth traits (root number, root angular spread) of plants grown in gel plates, soil sacs and X-ray pots was similar, but plants grown in the gel chambers had a different order of ranking for root length to the soil-grown plants. Analysis of angles in soil-grown plants showed that Tadmore had the most even spread of individual roots and Chime had a propensity for non-uniform distribution and root clumping. The roots of Mehola were less well spread than the barley cultivars supporting the suggestion that wild and landrace barleys tend to have a narrower angular spread than modern cultivars. The three dimensional analysis of root systems carried out in this study provides insights into the limitations of screening methods for root traits and useful data for modelling root architecture.

AtTRB1, a telomeric DNA-binding protein from Arabidopsis, is concentrated in the nucleolus and shows highly dynamic association with chromatin

AtTRB1, 2 and 3 are members of the SMH (single Myb histone) protein family, which comprises double-stranded DNA-binding proteins that are specific to higher plants. They are structurally conserved, containing a Myb domain at the N-terminus, a central H1/H5-like domain and a C-terminally located coiled-coil domain. AtTRB1, 2 and 3 interact through their Myb domain specifically with telomeric double-stranded DNA in vitro, while the central H1/H5-like domain interacts non-specifically with DNA sequences and mediates protein–protein interactions. Here we show that AtTRB1, 2 and 3 preferentially localize to the nucleus and nucleolus during interphase. Both the central H1/H5-like domain and the Myb domain from AtTRB1 can direct a GFP fusion protein to the nucleus and nucleolus. AtTRB1–GFP localization is cell cycle-regulated, as the level of nuclear-associated GFP diminishes during mitotic entry and GFP progressively re-associates with chromatin during anaphase/telophase. Using fluorescence recovery after photobleaching and fluorescence loss in photobleaching, we determined the dynamics of AtTRB1 interactions in vivo. The results reveal that AtTRB1 interaction with chromatin is regulated at two levels at least, one of which is coupled with cell-cycle progression, with the other involving rapid exchange.

Decentralized load balancing for highly irregular search problems

In this paper, we present a distributed computing framework for problems characterized by a highly irregular search tree, whereby no reliable workload prediction is available. The framework is based on a peer-to-peer computing environment and dynamic load balancing. The system allows for dynamic resource aggregation, does not depend on any specific meta-computing middleware and is suitable for large-scale, multi-domain, heterogeneous environments, such as computational Grids. Dynamic load balancing policies based on global statistics are known to provide optimal load balancing performance, while randomized techniques provide high scalability. The proposed method combines both advantages and adopts distributed job-pools and a randomized polling technique. The framework has been successfully adopted in a parallel search algorithm for subgraph mining and evaluated on a molecular compounds dataset. The parallel application has shown good calability and close-to linear speedup in a distributed network of workstations.

Towards an assessment of multiple ecosystem processes and services via functional traits

Managing ecosystems to ensure the provision of multiple ecosystem services is a key challenge for applied ecology. Functional traits are receiving increasing attention as the main ecological attributes by which different organisms and biological communities influence ecosystem services through their effects on underlying ecosystem processes. Here we synthesize concepts and empirical evidence on linkages between functional traits and ecosystem services across different trophic levels. Most of the 247 studies reviewed considered plants and soil invertebrates, but quantitative trait–service associations have been documented for a range of organisms and ecosystems, illustrating the wide applicability of the trait approach. Within each trophic level, specific processes are affected by a combination of traits while particular key traits are simultaneously involved in the control of multiple processes. These multiple associations between traits and ecosystem processes can help to identify predictable trait–service clusters that depend on several trophic levels, such as clusters of traits of plants and soil organisms that underlie nutrient cycling, herbivory, and fodder and fibre production. We propose that the assessment of trait–service clusters will represent a crucial step in ecosystem service monitoring and in balancing the delivery of multiple, and sometimes conflicting, services in ecosystem management.

Ecological and life-history traits predict bee species responses to environmental disturbances

The ability to predict the responses of ecological communities and individual species to human-induced environmental change remains a key issue for ecologists and conservation managers alike. Responses are often variable among species within groups making general predictions difficult. One option is to include ecological trait information that might help to disentangle patterns of response and also provide greater understanding of how particular traits link whole clades to their environment. Although this ‘‘trait-guild” approach has been used for single disturbances, the importance of particular traits on general responses to multiple disturbances has not been explored. We used a mixed model analysis of 19 data sets from throughout the world to test the effect of ecological and life-history traits on the responses of bee species to different types of anthropogenic environmental change. These changes included habitat loss, fragmentation, agricultural intensification, pesticides and fire. Individual traits significantly affected bee species responses to different disturbances and several traits were broadly predictive among multiple disturbances. The location of nests – above vs. below ground – significantly affected response to habitat loss, agricultural intensification, tillage regime (within agriculture) and fire. Species that nested above ground were on average more negatively affected by isolation from natural habitat and intensive agricultural land use than were species nesting below ground. In contrast below-ground-nesting species were more negatively affected by tilling than were above-ground nesters. The response of different nesting guilds to fire depended on the time since the burn. Social bee species were more strongly affected by isolation from natural habitat and pesticides than were solitary bee species. Surprisingly, body size did not consistently affect species responses, despite its importance in determining many aspects of individuals’ interaction with their environment. Although synergistic interactions among traits remain to be explored, individual traits can be useful in predicting and understanding responses of related species to global change.