Recent progresses in gene delivery-based bone tissue engineering

Gene therapy has converged with bone engineering over the past decade, by which a variety of therapeutic genes have been delivered to stimulate bone repair. These genes can be administered via in vivo or ex vivo approach using either viral or nonviral vectors. This article reviews the fundamental aspects and recent progresses in the gene therapy-based bone engineering, with emphasis on the new genes, viral vectors and gene delivery approaches.     

Grassland plant species occurring in extensively managed road verges are filtered by urban environments

Background: Urbanisation filters species in communities depending on their adaptability to conditions in built-up areas, especially in semi-natural habitats. Roadside vegetation is widespread along urban-rural gradients and is therefore a good place to study landscape-scale factors influencing plant community composition.

Aim: Our study aimed to assess how plant species distributions vary between urban and rural landscape contexts and to identify biological traits favoured in urban areas.

Methods: Presence/absence data for 63 indigenous common species were collected in 296 road verge patches distributed along the urban–rural gradient in three French cities. We investigated the effects of landscape composition on species assemblages and related individual species responses to urbanisation to functional traits associated with dispersal and persistence capacity.

Results: Many grassland species were negatively affected by increasing proportion of built-up areas in the landscape. Insect-pollination and high seed production appeared to be key traits favoured in grassland communities in urban areas, whereas dispersal modes were less related to plant distribution.

Conclusions: This study has demonstrated that urban filters affect common species of widespread, managed road verges. Better knowledge of the flora of these herbaceous roadsides may contribute to the conservation of common biodiversity within other grassland habitats found in urban areas.     

基于GAM模型的阿拉伯海鲐鱼渔场分布与环境关系

基于2016-2017年中国印度洋围拖网生产数据以及同期的海表温度、叶绿素、表层海流和海面高度数据,采用广义加性模型(Generalized Additive Model,GAM)建立了围网单位捕捞努力量渔获量(catch per unit effort,CPUE)对海洋环境的非线性响应模型,分析了阿拉伯海鲐鱼(Scomber australasicus)渔场分布与海洋环境关系.结果表明:空间因子和环境因子对阿拉伯海鲐鱼渔场有显著影响,GAM模型的方差解释率为30.1%;印度洋季风对鲐鱼CPUE影响大,鲐鱼CPUE在印度洋东北季风高,在夏季季风低;全年阿拉伯海鲐鱼围网渔场主要分布在60°E、13°N-15°N斜向椭圆区域;模型表明,鲐鱼渔场适宜海表温度为26~28℃,叶绿素浓度0.2~0.5 mg·m^-3,海面高度0.2~ 0.4 m;影响鲐鱼渔场的因子按重要性依次为海面高度、经纬度、海表温度和叶绿素浓度.     

Spatial genetic structure in two congeneric epiphytes with different dispersal strategies analysed by three different methods

Three different approaches were used to assess the kinship structure of two epiphytic bryophytes, Orthotrichum speciosum and O. obtusifolium, that have different dispersal strategies. The two species were sampled in a 200 ha landscape where species occurrence and host trees had been mapped previously. Local environmental conditions at sampled trees were recorded and kinship between individuals was calculated based on amplified fragment length polymorphism (AFLP)-marker data. We did not detect any association between AFLP-markers and investigated environmental conditions. In both species, significant kinship coefficients were found between individuals up to 300-350 m apart which shows that both species have a restricted dispersal range. The spatial kinship structure was detected with both autocorrelation analysis and generalized additive models (GAMs), but linear regression failed to detect any structure in O. speciosum. Although the dioecious O. obtusifolium is currently the more common species it may, none the less, due to its restricted dispersal range and reproduction mode, become threatened in the future by current silvicultural practices which enhance the distance between host trees and decrease their life span. Finally, GAMs seem most appropriate for analysing spatial genetic structure because the effects of local environmental conditions and spatial structure can be analysed simultaneously, no assumption of a parametric form between kinship coefficient and distance is required, and spatial data resolution is not lost in the arbitrary choice of distance classes characterizing autocorrelation analysis.     

Assessing the effect of prevalence on the predictive performance of species distribution models using simulated data

Aim The proportion of sampled sites where a species is present is known as prevalence. Empirical studies have shown that prevalence can affect the predictive performance of species distribution models. This paper uses simulated species data to examine how prevalence and the form of species environmental dependence affect the assessment of the predictive performance of models. Methods Simulated species data were based on various functions of simulated environmental data with differing degrees of spatial correlation. Seven model performance measures – sensitivity, specificity, class‐average (CA), overall prediction success, kappa (κ), normalized mutual information (NMI) and area under the receiver operating characteristic curve (AUC) – were applied to species models fitted by three regression methods. The response of the performance measures to prevalence was then assessed. Three probability threshold selection methods used to convert fitted logistic model values to presence or absence were also assessed. Results The study shows that the extent to which prevalence affects model performance depends on the modelling technique and its degree of success in capturing dominant environmental determinants. It also depends on the statistic used to measure model performance and the probability threshold method. The response based on κ generally preferred models with medium prevalence. All performance measures were least affected by prevalence when the probability threshold was chosen to maximize predictive performance or was based directly on prevalence. In these cases, the responses based on AUC, CA and NMI generally preferred models with small or large prevalence. Main conclusions The effect of prevalence on the predictive performance of species distribution models has a methodological basis. Relevant factors include the success of the fitted distribution model in capturing the dominant environmental determinant, the model performance measure and the probability threshold selection method. The fixed probability threshold method yields a marked response of model performance to prevalence and is therefore not recommended. The study explains previous empirical results obtained with real data.     

A global population redistribution in a migrant shorebird detected with continent‐wide qualitative breeding survey data

Aim Over the last two decades, thousands of northward migrating ruffs (Philomachus pugnax) have disappeared from western European staging sites. These migratory ruffs were partly temperate breeding birds, but most individuals head towards the Eurasian Arctic tundras where 95% of the global population breeds. This regional decline may represent either: (1) local loss of breeding birds in western Europe, (2) a global decline, (3) shift(s) in distribution or (4) a combination of these. Location Northern Eurasia. Methods To put the declines in western Europe in context, we analysed Arctic monitoring data from the last two decades ( Soloviev & Tomkovich, 2009 ) to detect changes in regional breeding densities across northern Eurasia. We used a novel approach applying generalized additive modelling (GAM) and generalized estimations equations (GEE). Results We show that the global breeding population of ruffs has made a significant eastwards shift into the Asian part of the breeding range. In the European Arctic, ruffs decreased during the last 18 years. At the same time, in western Siberia, ruffs increased. In eastern Siberia, no significant population changes could be detected. These changes corroborate the finding that during northward migration, growing numbers of ruffs avoided staging areas in the Netherlands and Sweden and started migrating along a more easterly route leading into western Siberia. Main conclusions We detected an unprecedented large‐scale population redistribution of ruffs and suggest that this is a response to loss of habitat quality at the traditional staging site in the Netherlands.     

Spatially modelling native vegetation condition

Summary   The assessment of vegetation condition is seen as an increasingly important requirement for effective biodiversity conservation in Australia. Condition assessments that operate at the scale of the site are well established. However, there is a need for mapped representations of vegetation condition at regional scales to: (i) assist with regional planning and target setting; (ii) provide regional context for site-based assessment; and (iii) monitor the change in vegetation condition at multiple scales. This paper describes a methodology for converting site condition data collected in plots into maps of vegetation condition across entire regions using a predictive statistical modelling framework (Generalized Additive Modelling) combined with a GIS. The research demonstrates how explanatory variables including topographic position, terrain roughness, landscape connectivity and remote sensing derived indices can be used to map the condition of native vegetation at the scale of a subcatchment. The inclusion of indices derived from remotely sensed imagery (SPOT4) as explanatory variables in the modelling is a novel component of this research. Although the methodology generates statistically and ecologically plausible models of vegetation condition, there are nevertheless limitations associated with the way plot data were collected and some of the explanatory variables, which impacts upon model utility. We discuss how these problems can be minimized when embarking upon studies of this type. We demonstrate how maps produced from exercises such as this could be used for conservation planning and discuss the limitations of these data for monitoring.     

Spatial segregation of specialists and generalists in bird communities

Each species generally has a close relationship with one or more habitats and can therefore be classified as either specialist or generalist. We studied whether specialist and generalist species are spatially distributed independently of each other. Repeating the analysis for 100 of the most frequent terrestrial bird species recorded over the 10 000 sampled sites of the French Breeding Bird survey, we found that specialists were more abundant if the rest of the community was specialized, and that the inverse was also true. This pattern was far subtler than just a simple dichotomy: most species actually presented a maximum abundance at a value of community specialization similar to their own level of specialization. Bird communities appear very well defined along a specialist–generalist gradient. We believe this pattern becomes more apparent with habitat degradation. The consequences on both ecological services and community resilience may well be considerable.     

The potential of generalized additive modelling for the prediction of radial growth of Norway spruce from Central Germany

During the past decades managed forest ecosystems in Central Europe underwent vast changes, induced by extreme climate conditions and occasionally adverse forest management. Tree ring width patterns mirror these changes and thus have been widely examined as environmental archives and reliable empirical data sources in ‘tree growth modelling’. Dendrochronologists often suppose linear co-variation among the covariates, variable independence and homoscedasticity. Conventionally, these assumptions were achieved by eliminating biological age trends (detrending) and removing the autocorrelation from the time series (pre-whitening). Particularly detrending might be biased according to the scientific problem and sometimes inflexible age models. In this study, we tackle these issues and examine the suitability of a flexible Generalized Additive Model (GAM) on recently developed tree ring width time series of 30 Norway spruce stands (Picea abies [L.] H. Karst) from Central Germany.The model was established to simultaneously cope with the mentioned detrending issue, to unravel nonlinear climate-growth relationships and to predict mean ring width time series for spruce stands in the region. Particularly the latter was of primary interest, since recent forest planning relies on static yield tables that often underestimate the actual growth.The model reliably captured the empirical data, indicated by a small Generalized Cross Validation criterion (GCV = 0.045) and a deviance explained of 88.6 %. The flexible additive smoothers accounted for the social status of individual trees, captured low frequency variations of changing growth conditions adequately and displayed a rather flat biological age trend. The radial increment responded positively to summer season precipitation of the current and previous year. Positive temperature responses were found during the early vegetation period, whereas high summer season temperatures negatively affected the radial growth. The seasonal transition from spring to summer in June induced a shift in the climate response of the linear predictor, leading to a distinct negative effect of temperature and a no-role of precipitation on the linear predictor.Most important, utilizing the calibrated GAM for the purely climate-driven prediction of mean ring width time series from five independent spruce sites revealed proper coherencies. Herein, the mean ring width for sites located within the climatic-optimum for spruce growth were more exactly predicted than for sites with adverse spruce growth conditions. In addition, large mean ring widths were systematically underestimated, whereas small mean ring widths were precisely predicted. Overall, we strongly recommend GAMs as a powerful tool for the investigation of nonlinear climate-growth relationships and for the prediction of radial growth in managed forest ecosystems.