Effects of slope and riparian habitat connectivity on gene flow in an endangered Panamanian frog, Atelopus varius

Aim Understanding how heterogeneous landscapes shape genetic structure not only sheds light on processes involved in population divergence and speciation, but can also guide management strategies to promote and maintain genetic connectivity of populations of endangered species. This study aimed to (1) identify barriers and corridors for gene flow among populations of the endangered frog, Atelopus varius and (2) assess the relative contributions of alternative landscape factors to patterns of genetic variation among these populations in a hypothesis testing framework. Location This study took place in western Panama and included all nine of the remaining known populations of A. varius at the time of study. Methods The influence of landscape variables on gene flow among populations was examined by testing for correlations between alternative landscape‐resistance scenarios and genetic distance. Fifteen alternative hypotheses about the influence of (1) riparian habitat corridors, (2) steep slopes, and (3) climatic suitability on patterns of genetic structure were tested in a causal modelling framework, using Mantel and partial‐Mantel tests, along with an analysis of molecular variation. Results Only the hypothesis attributing resistance to dispersal across steep slopes (genetic isolation by slope distance) was fully supported by the causal modelling approach. However, the analysis of molecular variance and the paths of least‐slope among populations suggest that riparian habitat connectivity may influence genetic structure as well. Main conclusions These results suggest that patterns of genetic variation among A. varius populations are affected by the slope of the landscape such that areas with steep slopes act as barriers to gene flow. In contrast, areas of low slope, such as streams and mountain ridges, appear to be important corridors for gene flow, especially among high elevation populations. These results engender important considerations for the management of this critically endangered species.     

Hierarchical models facilitate spatial analysis of large data sets: a case study on invasive plant species in the northeastern United States

Many critical ecological issues require the analysis of large spatial point data sets – for example, modelling species distributions, abundance and spread from survey data. But modelling spatial relationships, especially in large point data sets, presents major computational challenges. We use a novel Bayesian hierarchical statistical approach, 'spatial predictive process' modelling, to predict the distribution of a major invasive plant species, Celastrus orbiculatus , in the northeastern USA. The model runs orders of magnitude faster than traditional geostatistical models on a large data set of c . 4000 points, and performs better than generalized linear models, generalized additive models and geographically weighted regression in cross-validation. We also use this approach to model simultaneously the distributions of a set of four major invasive species in a spatially explicit multivariate model. This multispecies analysis demonstrates that some pairs of species exhibit negative residual spatial covariation, suggesting potential competitive interaction or divergent responses to unmeasured factors.     

Patterns and causes of species richness: a general simulation model for macroecology

Understanding the causes of spatial variation in species richness is a major research focus of biogeography and macroecology. Gridded environmental data and species richness maps have been used in increasingly sophisticated curve‐fitting analyses, but these methods have not brought us much closer to a mechanistic understanding of the patterns. During the past two decades, macroecologists have successfully addressed technical problems posed by spatial autocorrelation, intercorrelation of predictor variables and non‐linearity. However, curve‐fitting approaches are problematic because most theoretical models in macroecology do not make quantitative predictions, and they do not incorporate interactions among multiple forces. As an alternative, we propose a mechanistic modelling approach. We describe computer simulation models of the stochastic origin, spread, and extinction of species’ geographical ranges in an environmentally heterogeneous, gridded domain and describe progress to date regarding their implementation. The output from such a general simulation model (GSM) would, at a minimum, consist of the simulated distribution of species ranges on a map, yielding the predicted number of species in each grid cell of the domain. In contrast to curve‐fitting analysis, simulation modelling explicitly incorporates the processes believed to be affecting the geographical ranges of species and generates a number of quantitative predictions that can be compared to empirical patterns. We describe three of the ‘control knobs’ for a GSM that specify simple rules for dispersal, evolutionary origins and environmental gradients. Binary combinations of different knob settings correspond to eight distinct simulation models, five of which are already represented in the literature of macroecology. The output from such a GSM will include the predicted species richness per grid cell, the range size frequency distribution, the simulated phylogeny and simulated geographical ranges of the component species, all of which can be compared to empirical patterns. Challenges to the development of the GSM include the measurement of goodness of fit (GOF) between observed data and model predictions, as well as the estimation, optimization and interpretation of the model parameters. The simulation approach offers new insights into the origin and maintenance of species richness patterns, and may provide a common framework for investigating the effects of contemporary climate, evolutionary history and geometric constraints on global biodiversity gradients. With further development, the GSM has the potential to provide a conceptual bridge between macroecology and historical biogeography.     

Ensemble models predict Important Bird Areas in southern Africa will become less effective for conserving endemic birds under climate change

Aim  To examine the impacts of climate change on endemic birds, which are of global significance for conservation, on a continent with few such assessments. We specifically assess projected range changes in relation to the Important Bird Areas (IBAs) network and assess the possible consequences for conservation.
Location  South Africa, Lesotho and Swaziland.
Methods  The newly emerging ensemble modelling approach is used with 50 species, four climate change models for the period 2070–2100 and eight bioclimatic niche models in the statistical package biomod . Model evaluation is done using the receiver operating characteristic and the recently introduced true skill statistic. Future projections are made considering two extreme assumptions: species have full dispersal ability and species have no dispersal ability. A consensus forecast is identified using principal components analysis. This forecast is interpreted in terms of the IBA network. An irreplaceability analysis is used to highlight priority IBAs for conservation attention in terms of climate change.
Results  The majority of species (62%) are predicted to lose climatically suitable space. Five species lose at least 85% of their climatically suitable space. Many IBAs lose species (41%; 47 IBAs) and show high rates of species turnover of more than 50% (77%; 95 IBAs). Highly irreplaceable regions for endemic species become highly localized under climate change, meaning that the endemic species analysed here experience similar range contractions to maintain climate niches.
Main conclusions  The South African IBAs network is likely to become less effective for conserving endemic birds under climate change. The irreplaceability analysis identified key refugia for endemic species under climate change, but many of these areas are not currently IBAs. In addition, many of these high-priority areas that are IBAs fall outside the current formal protected areas network.     

Importance of soil nutrients in the distribution of forest communities on a large geographical scale

Aim Soil nutrient content plays a key role in plant growth through mineral nutrition and toxicity. Its impact on plant species and community distribution is studied on a large geographical scale through surrogates like topography or geology. We investigated the importance of soil pH and C:N ratio, as direct nutritional gradients, to determine, with climatic factors, the spatial distribution of plant communities over large territories. Location We studied the distribution of six beech habitats of the NATURA 2000 network throughout France (550,000 km²). Methods Models were calibrated with 2108 floristic plots classified in the NATURA 2000 system and including climatic and topographic variables and soil nutritional measurements carried out in a laboratory. Logistic regression was used to model habitat distribution according to environmental variables. Climatic layers, a digital elevation model and maps of soil pH and nitrogen content, created using plant indicator values and large floristic databases, were used to map the sites suitable for beech communities. Distribution models were evaluated with an independent set of 2091 phytosociological plots. Results pH and nitrogen supply were the key distribution drivers for four of the six beech communities on a national scale. Their use in the distribution models distinguished within homogeneous climatic territories a gradient of nutritional conditions from acidic areas, suitable for nutrient‐poor beech communities, to calcareous areas suitable for nutrient‐rich ones. Predicted maps of beech habitats fit the spatial distribution of validation plots. Main conclusions Soil pH and nitrogen supply strongly improve predictions of forest community distribution carried out with climatic variables on a broad geographical scale. They allow delineation of areas with nutritional conditions suitable for each community, as well as the realization of predictive high‐resolution maps over large areas useful for sustainable and conservation management. Nomenclature Tutin & Heywood (2001 ) Flora Europaea. Cambridge University Press, Cambridge.     

采用最小二乘支持向量机的青霉素发酵过程建模研究

生化过程通常是严重非线性和时变的复杂动态系统,而且重要过程参数缺少在线测量仪表,对其建立机理模型往往非常耗时和困难。采用最小二乘支持向量机(LS_SVM)并以Pensim仿真平台为例对青霉素发酵这一典型生化过程进行建模研究。给出了LS_SVM参数的调整策略和分析结果,建立了青霉素产物浓度、菌体浓度和底物浓度等重要过程变量的在线预报模型。仿真结果表明用LS_SVM建立的在线预报模型拟合误差小,推广性能好,可以作为发酵过程的进一步控制和优化的参考依据。     

Past tree range dynamics in the Iberian Peninsula inferred through phylogeography and palaeodistribution modelling: A review

The forests in the Iberian Peninsula have been strongly influenced by past climatic changes, but reconstructing their historical distributions and dynamics is very difficult due to the complex climatic characteristics and relief of the region. Research disciplines such as phylogeography and species distribution modelling can describe the past range dynamics of individual tree species in relatively great detail and help elucidate how these species have reacted to climatic changes. Here we review phylogeographical and modelling studies from species representative of the major Iberian forest types and attempt to extract general trends from the diversity of individual species histories in the Peninsula. To date most studies focus on geographical range dynamics during the Pleistocene, but an increasing body of evidence shows that some species have also retained the genetic imprints of much more ancient processes. Many widespread Iberian species show a deep and often remarkably clear-cut divide between populations from the Mediterranean and from the Atlantic regions of the Iberian Peninsula, suggesting that both areas have independently sustained viable populations over extended periods. In fact, phylogeographical studies commonly find that species had several glacial refugia across the Iberian Peninsula. On the other hand, distribution models help identifying further suitable areas that could have sustained so far undetected refugia. Such studies are of interest for species conservation, because refugium populations are high-priority targets due to their long-term persistence and unique evolutionary trajectory. Overall, we conclude that palaeoecology, phylogeography and species distribution modelling have a great potential to inform each other because of their complementary perspectives and results. A true integration of these approaches is therefore fundamental for further progress in our understanding of past Iberian environments and the organisms they harboured.     

Bayesian Semiparametric Multiple Shrinkage

Summary High‐dimensional and highly correlated data leading to non‐ or weakly identified effects are commonplace. Maximum likelihood will typically fail in such situations and a variety of shrinkage methods have been proposed. Standard techniques, such as ridge regression or the lasso, shrink estimates toward zero, with some approaches allowing coefficients to be selected out of the model by achieving a value of zero. When substantive information is available, estimates can be shrunk to nonnull values; however, such information may not be available. We propose a Bayesian semiparametric approach that allows shrinkage to multiple locations. Coefficients are given a mixture of heavy‐tailed double exponential priors, with location and scale parameters assigned Dirichlet process hyperpriors to allow groups of coefficients to be shrunk toward the same, possibly nonzero, mean. Our approach favors sparse, but flexible, structure by shrinking toward a small number of random locations. The methods are illustrated using a study of genetic polymorphisms and Parkinson's disease.     

Biogeography meets conservation: the genetic structure of the endangered lycaenid butterfly Lycaena helle (Denis & Schiffermüller, 1775)

Cold‐adapted species are thought to have had their largest distribution ranges in central Europe during the glacial periods. Postglacial warming caused severe range shifts of such taxa into higher latitudes and altitudes. We selected the boreomontane butterfly Lycaena helle (Denis & Schiffermüller, 1775) as an example to demonstrate the genetic effects of range changes, and to document the recent status of highly fragmented remnant populations. We analysed five polymorphic microsatellite loci in 1059 individuals sampled at 50 different localities scattered over the European distribution area of the species. Genetic differentiation was strong among the mountain ranges of western Europe, but we did not detect similarly distinct genetic groups following a geographical pattern in the more eastern areas. The Fennoscandian populations form a separate genetic group, and provide evidence for a colonization from southern Finland via northern Scandinavia to south‐central Sweden. Species distribution modelling suggests a large extension of the spatial distribution during the last glacial maximum, but highlights strong retractions to a few mountain areas under current conditions. These findings, combined with our genetic data, suggest a more or less continuous distribution of L. helle throughout central Europe at the end of the last ice age. As a consequence of postglacial warming, the species retreated northwards to Fennoscandia and escaped increasing temperatures through altitudinal shifts. Therefore, the species is today restricted to population remnants located at the mountain tops of western Europe, genetically isolated from each other, and evolved into genetically unique entities. Rising temperatures and advancing habitat destruction threaten this wealth of biodiversity. © 2010 The Linnean Society of London, Biological Journal of the Linnean Society, 2010, 101 , 155–168.