Factors determining nest-site selection of surface-nesting seabirds: a case study on the world's largest pelagic bird,the Wandering Albatross (Diomedea exulans)

Several factors may drive bird nest-site selection, including predation risk, resource availability, weather conditions and interaction with other individuals. Understanding the drivers affecting where birds nest is important for conservation planning, especially where environmental change may alter the distribution of suitable nest-sites. This study investigates which environmental variables affect nest-site selection by the Wandering Albatross Diomedea exulans, the world's largest pelagic bird. Here, wind characteristics are quantitatively investigated as a driver of nest-site selection in surface-nesting birds, in addition to several topographical variables, vegetation and geological characteristics. Nest locations from three different breeding seasons on sub-Antarctic Marion Island were modelled to assess which environmental factors affect nest-site selection. Elevation was the most important determinant of nest-site selection, with Wandering Albatrosses only nesting at low elevations. Distance from the coast and terrain roughness were also important predictors, with nests more generally found close to the coast and in flatter terrain, followed by wind velocity, which showed a hump-shaped relationship with the probability of nest occurrence. Nests occurred more frequently on coastal vegetation types, and were absent from polar desert vegetation (generally above c. 500 m elevation). Of the variables that influence Wandering Albatross nest location, both vegetation type and wind characteristics are likely to be influenced by climate change, and have already changed over the last 50 years. As a result, the availability of suitable nest-sites needs to be considered in light of future climate change, in addition to the impacts that these changes will have on foraging patterns and prey distribution. More broadly, these results provide insights into how a wide range of environmental variables, including wind, can affect nest-site selection of surface-nesting seabirds.     

Generalized case-control sampling under generalized linear models

A generalized case-control (GCC) study, like the standard case-control study, leverages outcome-dependent sampling (ODS) to extend to nonbinary responses. We develop a novel, unifying approach for analyzing GCC study data using the recently developed semiparametric extension of the generalized linear model (GLM), which is substantially more robust to model misspecification than existing approaches based on parametric GLMs. For valid estimation and inference, we use a conditional likelihood to account for the biased sampling design. We describe analysis procedures for estimation and inference for the semiparametric GLM under a conditional likelihood, and we discuss problems with estimation and inference under a conditional likelihood when the response distribution is misspecified. We demonstrate the flexibility of our approach over existing ones through extensive simulation studies, and we apply the methodology to an analysis of the Asset and Health Dynamics Among the Oldest Old study, which motives our research. The proposed approach yields a simple yet versatile solution for handling ODS in a wide variety of possible response distributions and sampling schemes encountered in practice.     

中国北方5种栎属树木多度分布及其对未来气候变化的响应

植物分布与气候之间的关系是预估未来气候变化对生态系统影响的实现基础。以往的物种分布模型通常以物种的分布区或者分布点的物种存在数据作为物种分布的响应变量。相较于物种存在数据, 多度反映了一个物种占用资源并把资源分配给个体的能力, 更能衡量物种对区域生态系统的影响。该研究通过野外调查获取了华北及周边地区1 045个样方的栎属树木多度, 利用广义线性模型、广义加性模型和随机森林模型模拟栓皮栎(Quercus variabilis)、麻栎(Q. acutissima)、槲栎(Q. aliena)、锐齿槲栎(Q. aliena var. acuteserrata)和蒙古栎(Q. mongolica) 5个树种多度的地理分布及未来2个不同时期(2050年和2070年)的潜在分布。结果表明: 随机森林模型对5个栎属树种的多度的拟合结果要优于广义线性模型和广义加性模型; 典型浓度路径(RCP) 8.5下的5个栎属树种在未来两个时期的多度变化幅度都要大于RCP 2.6下的变化, 在超过一半面积的区域中麻栎、槲栎、锐齿槲栎和蒙古栎的多度减少, 其中内蒙古东北部和黑龙江北部地区是5种栎属植物多度减少的集中分布地区。未来气候变化背景下, 需要加强对这几个区域的监测与物种保护。     

Effect of species rarity on the accuracy of species distribution models for reptiles and amphibians in southern California

Aim Several studies have found that more accurate predictive models of species’ occurrences can be developed for rarer species; however, one recent study found the relationship between range size and model performance to be an artefact of sample prevalence, that is, the proportion of presence versus absence observations in the data used to train the model. We examined the effect of model type, species rarity class, species’ survey frequency, detectability and manipulated sample prevalence on the accuracy of distribution models developed for 30 reptile and amphibian species. Location Coastal southern California, USA. Methods Classification trees, generalized additive models and generalized linear models were developed using species presence and absence data from 420 locations. Model performance was measured using sensitivity, specificity and the area under the curve (AUC) of the receiver‐operating characteristic (ROC) plot based on twofold cross‐validation, or on bootstrapping. Predictors included climate, terrain, soil and vegetation variables. Species were assigned to rarity classes by experts. The data were sampled to generate subsets with varying ratios of presences and absences to test for the effect of sample prevalence. Join count statistics were used to characterize spatial dependence in the prediction errors. Results Species in classes with higher rarity were more accurately predicted than common species, and this effect was independent of sample prevalence. Although positive spatial autocorrelation remained in the prediction errors, it was weaker than was observed in the species occurrence data. The differences in accuracy among model types were slight. Main conclusions Using a variety of modelling methods, more accurate species distribution models were developed for rarer than for more common species. This was presumably because it is difficult to discriminate suitable from unsuitable habitat for habitat generalists, and not as an artefact of the effect of sample prevalence on model estimation.     

广义模型及分类回归树在物种分布模拟中的应用与比较

比较3个应用较广的模拟物种地理分布模型:广义线性模型(GLM)、广义加法模型(GAM)与分类回归树(CART)对中国树种地理分布模拟的优劣,以提出更为合适的模拟物种地理分布模型,并用于预测气候变化对物种地理分布的影响。3个模型对中国15种树种地理分布的模拟研究表明:除对油松、辽东栎分布的模拟精度稍差外,对其余树种分布的模拟精度均较高,其中以GAM模型最好。结合地理信息系统(GIS),比较分析了这3个模型对青冈、木荷、红松和油松4种树种的地理分布模拟效果,结果亦表明:这3个模型均能很好模拟青冈和木荷的地理分布,而GLM模型对红松分布的模拟结果不太理想,3个模型对油松分布的模拟结果均不甚理想,其中以GLM模型最差。基于3个模型对未来气候变化下青冈与蒙古栎地理分布的预测表明:GLM模型与GAM模型对青冈分布的预测结果较为接近,青冈在未来气候变化情景下向西和向北扩展,而CART模型预测青冈在未来气候变化情景下除有向西、向北扩展趋势外,广东和广西南部的青冈分布区将消失;3个模型均预测蒙古栎在未来气候变化情景下向西扩展,扩展面积的大小为:模型的模拟面积>模型>模型。     

Estimation of capture probabilities using generalized estimating equations and mixed effects approaches

Modeling individual heterogeneity in capture probabilities has been one of the most challenging tasks in capture–recapture studies. Heterogeneity in capture probabilities can be modeled as a function of individual covariates, but correlation structure among capture occasions should be taking into account. A proposed generalized estimating equations (GEE) and generalized linear mixed modeling (GLMM) approaches can be used to estimate capture probabilities and population size for capture–recapture closed population models. An example is used for an illustrative application and for comparison with currently used methodology. A simulation study is also conducted to show the performance of the estimation procedures. Our simulation results show that the proposed quasi‐likelihood based on GEE approach provides lower SE than partial likelihood based on either generalized linear models (GLM) or GLMM approaches for estimating population size in a closed capture–recapture experiment. Estimator performance is good if a large proportion of individuals are captured. For cases where only a small proportion of individuals are captured, the estimates become unstable, but the GEE approach outperforms the other methods.     

Testing Significant Homogeneities Within Binary Information Strings

In a previous paper (Buser, 1982) a nonparametric test has been presented for deciding whether or not a binary coded taxonomic node is significantly homogeneous. In the following tables the corresponding critical values, both for the one and two tailed test, are given.     

Imaging lipid lateral organization in membranes with C-laurdan in a confocal microscope

Lateral organization of biological membranes is frequently studied using fluorescence microscopy. One of the most widely used probes for these studies is 2-dimethylamino-6-lauroylnaphthalene (laurdan). The fluorescence of this probe is sensitive to the environment polarity, and thus laurdan reports the local penetration of water when inserted in membranes. Unfortunately, this probe can only be used under two-photon excitation due to its low photostability. This is a very important limitation, because there are not too many laboratories with capability for two-photon microscopy. In this work, we explored the performance of 6-dodecanoyl-2-[N-methyl-N-(carboxymethyl)amino]naphthalene (C-laurdan), a carboxyl-modified version of laurdan, for imaging biological membranes using a conventional confocal microscopy setup. We acquired generalized polarization (GP) images of C-laurdan inserted in giant unillamelar vesicles composed of binary mixtures of lipids and verified that the probe allows observing the coexistence of different phases. We also tested the performance of the probe for measurement with living cells and registered GP images of melanophore cells labeled with C-laurdan in which we could observe highly ordered regions such as filopodia. These findings show that C-laurdan can be successfully employed for studies of membrane lateral organization using a conventional confocal microscope and can open the possibility of studying a wide variety of membrane-related processes.