Trends in single trait dispersion between early-mid successional stages: the importance of species pool extension and habitat scale

Aims Are there trends of increasing/decreasing dispersion of single, categorical traits related to early/late-successional species between stages of community development? If yes, are these trends dependent on species pool extension and habitat scale? Is there a consistent reduction in single trait convergence or divergence in any seral stage when scaling down from ecological to local species pool?Methods Presence of all vascular species rooted within plots of 5 × 5 m was recorded in assemblages of exposed mining spoils (EMS) and heathlands (HTL), which form a chronosequence on two abandoned ore tailing heaps located close to each other in the south-eastern Carpathians (Romania). Fifteen nominal, trait attributes of plant species co-occurring in the two seral assemblages were collected from available databases and subsequently classified as either successionally 'pioneer' or 'mature'. The strength of single trait convergence or divergence was estimated by comparison with null plant assemblages at patch type (meta-community) level by reference to the ecological or local species pool, and at community level.Important findings At patch type level, all pioneer and mature trait attributes (apart from short life span), with significant variation between the two seral stages, increased and, respectively, decreased in dispersion irrespective of species pool extension. However, these trends were more conspicuous when using the ecological species pool, very likely due to relaxation in abiotic filtering and dispersal limitation. At community level, no consistent trends were observed between EMS and HTL assemblages, probably because most trait attributes were sorted by microenvironmental filters displaying high variation, like topography or habitat patch geometry. In both seral stages, there was a general weakening of trait convergence or divergence at patch type level when scaling down from the ecological to the local species pool, which was due to niche space contraction. At community level, there was a trend of rise in dispersion of pioneer attributes along the observed chronosequence, presumably imputable to increasing competition for light and underground water, but an opposite trend of dispersion drop in mature attributes was not so evident. Based on these findings, we proposed two rules of thumb concerning the expected changes in dispersion of trait attributes at patch level along successions and between levels of species pool extension. In conclusion, trends in the successional dynamics of pioneer and mature trait dispersion are clearly detectable at meta-community level, especially by reference to the ecological species pool. Habitat scale and species pool extension are key factors to consider and report when estimating the magnitude of single trait dispersion.     

Immunomic Identification of Malaria Antigens Associated With Protection in Mice

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Highlights

• •Production of sera with different levels of protection against rodent Plasmodium.
• •Generation of immunomic and proteomic data sets enriched in protective antigens.
• •Prediction of the most likely protective antigens using a weighted scoring system.

     

The best of both worlds: Phylogenetic eigenvector regression and mapping

Eigenfunction analyses have been widely used to model patterns of autocorrelation in time, space and phylogeny. In a phylogenetic context, Diniz-Filho et al. (1998) proposed what they called Phylogenetic Eigenvector Regression (PVR), in which pairwise phylogenetic distances among species are submitted to a Principal Coordinate Analysis, and eigenvectors are then used as explanatory variables in regression, correlation or ANOVAs. More recently, a new approach called Phylogenetic Eigenvector Mapping (PEM) was proposed, with the main advantage of explicitly incorporating a model-based warping in phylogenetic distance in which an Ornstein-Uhlenbeck (O-U) process is fitted to data before eigenvector extraction. Here we compared PVR and PEM in respect to estimated phylogenetic signal, correlated evolution under alternative evolutionary models and phylogenetic imputation, using simulated data. Despite similarity between the two approaches, PEM has a slightly higher prediction ability and is more general than the original PVR. Even so, in a conceptual sense, PEM may provide a technique in the best of both worlds, combining the flexibility of data-driven and empirical eigenfunction analyses and the sounding insights provided by evolutionary models well known in comparative analyses.     

Statistical shape and appearance models for fast and automated estimation of proximal femur fracture load using 2D finite element models

Estimating the risk of osteoporotic fractures is an important diagnostic step that needs to be taken before medicinal treatment. Densitometry-based criteria are normally used in clinical practice for this purpose. However, densitometry-based techniques could not explain all low-energy fractures. As patient-specific finite element (FE) models allow for consideration of other parameters (e.g. load conditions) that are known to be associated with fracture, they are considered promising candidates for more accurate fracture risk estimation. Nevertheless, they are often time consuming, expensive, and complex to build and may need the type of expertise that is not normally available in clinical settings. In this study, we report the development of an automated platform for estimating proximal femur fracture loads using patient-specific 2D FE models generated using dual-energy x-ray absorptiometry (DXA) scans. First, a statistical shape and appearance model (SSAM) is built using DXA scans of patients screened for osteoporosis following a low energy fracture. SSAM is then used together with Active Appearance Models (AAM) for automated segmentation of the proximal femur from new unseen DXA scans. The mean point-to-curve error of the automated procedure, i.e. 1.2–1.4 mm, is shown to be only slightly larger than the intra-observer variability of manual segmentation, i.e. 1.0 mm. Moreover, the developed platform automatically meshes the segmented shape, assigns density-based mechanical properties, assigns loads and boundary conditions, submits the 2D FE model for solution, and performs post-processing of the 2D FE simulation data to determine fracture loads. The fracture loads predicted using the manually generated and automatically generated 2D FE models are shown to be very close with a mean difference of around 8.8%. Repeated measures ANOVA showed no significant differences between the fracture loads calculated using FE models manually generated by three independent observers and those calculated using the automatically generated FE models (p>0.05).     

Ecosystem engineering in space and time

The ecosystem engineering concept focuses on how organisms physically change the abiotic environment and how this feeds back to the biota. While the concept was formally introduced a little more than 10 years ago, the underpinning of the concept can be traced back to more than a century to the early work of Darwin. The formal application of the idea is yielding new insights into the role of species in ecosystems and many other areas of basic and applied ecology. Here we focus on how temporal, spatial and organizational scales usefully inform the roles played by ecosystem engineers and their incorporation into broader ecological contexts. Two particular, distinguishing features of ecosystem engineers are that they affect the physical space in which other species live and their direct effects can last longer than the lifetime of the organism – engineering can in essence outlive the engineer. Together, these factors identify critical considerations that need to be included in models, experimental and observational work. The ecosystem engineering concept holds particular promise in the area of ecological applications, where influence over abiotic variables and their consequent effects on biotic communities may facilitate ecological restoration and counterbalance anthropogenic influences.