Native insect herbivory overwhelms context dependence to limit complex invasion dynamics of exotic weeds

Understanding the role of consumers in density‐dependent plant population dynamics is a long‐standing goal in ecology. However, the generality of herbivory effects across heterogeneous landscapes is poorly understood due to the pervasive influence of context‐dependence. We tested effects of native insect herbivory on the population dynamics of an exotic thistle, Cirsium vulgare, in a field experiment replicated across eight sites in eastern Nebraska. Using hierarchical Bayesian analysis and density‐dependent population models, we found potential for explosive low‐density population growth (λ > 5) and complex density fluctuations under herbivore exclusion. However, herbivore access drove population decline (λ < 1), suppressing complex fluctuations. While plant–herbivore interaction outcomes are famously context‐dependent, we demonstrated that herbivores suppress potentially invasive populations throughout our study region, and this qualitative outcome is insensitive to environmental context. Our novel use of Bayesian demographic modelling shows that native insect herbivores consistently prevent hard‐to‐predict fluctuations of weeds in environments otherwise susceptible to invasion.     

Linking macroecology and community ecology: refining predictions of species distributions using biotic interaction networks

Macroecological models for predicting species distributions usually only include abiotic environmental conditions as explanatory variables, despite knowledge from community ecology that all species are linked to other species through biotic interactions. This disconnect is largely due to the different spatial scales considered by the two sub‐disciplines: macroecologists study patterns at large extents and coarse resolutions, while community ecologists focus on small extents and fine resolutions. A general framework for including biotic interactions in macroecological models would help bridge this divide, as it would allow for rigorous testing of the role that biotic interactions play in determining species ranges. Here, we present an approach that combines species distribution models with Bayesian networks, which enables the direct and indirect effects of biotic interactions to be modelled as propagating conditional dependencies among species’ presences. We show that including biotic interactions in distribution models for species from a California grassland community results in better range predictions across the western USA. This new approach will be important for improving estimates of species distributions and their dynamics under environmental change.     

Detecting rare introgression of domestic dog genes into wild wolf (Canis lupus) populations by Bayesian admixture analyses of microsatellite variation

Hybridization with free-ranging dogs isthought to threat the genetic integrity ofwolves in Europe, although available mtDNA dataevidenced only sporadic cases of crossbreeding.Here we report results of population assignmentand genetic admixture analyses in 107wild-living Italian wolves, 95 dogs including30 different breeds and feral dogs, andcaptive-reared wolves of unknown or hybridorigins, which were genotyped at 18microsatellites. Two Italian wolves showedunusually dark coats (``black wolves'), and oneshowed a spur in both hindlegs (``fifth fingerwolf'), suggesting hybridization. Italianwolves showed significant deficit ofheterozygotes, positive F_IS values anddeviations from Hardy-Weinberg equilibrium.Genetic variability was significantlypartitioned between groups, suggesting thatwolves and dogs represent distinct gene pools.Multivariate ordination of individual genotypesand clustering of inter-individual geneticdistances split wolves and dogs into twodifferent clusters congruent with the priorphenotypic classification, but hybrids andwolves of unknown origin were not identifiedfrom genetic information alone. By contrast, aBayesian admixture analysis assigned all theItalian wolves and dogs to two differentclusters, independent of any prior phenotypicinformation, and simultaneously detected theadmixed gene composition of the hybrids, whichwere assigned to more than one cluster.Captive-reared wolves of unknown origin wereprevalently assigned to the Italian wolfpopulation. Admixture analyses showed that one``black wolf' had mixed ancestry in the dog genepool and could be a hybrid, while the other twowolves with unusual phenotypes were assigned tothe Italian wolf population.     

Evolution in Apiales: nuclear and chloroplast markers together in (almost) perfect harmony

Relationships within the angiosperm order Apiales have long been difficult to interpret. Traditionally, the order comprised two families, Apiaceae and Araliaceae. Recent studies, however, suggest three additional lineages should also be recognized in the order (Pittosporaceae plus two tribes segregated from Araliaceae, Mackinlayeae and Myodocarpeae), and that one taxon (Apiaceae subfamily Hydrocotyloideae) is polyphyletic. Nuclear data also support the placement of five enigmatic genera ( Aralidium , Griselinia , Melanophylla , Pennantia and Torricellia ) within an expanded Apiales. To date, detailed molecular studies of Apiales have relied largely on data derived from plastid sequences, especially mat K and rbc L. To test and complement the results of these studies, the 26S (large subunit) of nuclear ribosomal DNA was sequenced and analysed phylogenetically. Results from this study confirm that Apiales comprise five major lineages: core Apiaceae, core Araliaceae, Pittosporaceae, the Mackinlaya group and the Myodocarpus group. Moreover, using an expanded sampling of members of subfamily Hydrocotyloideae, the nature and extent of the polyphyly is confirmed, with members of this taxon found among four distinct clades within Apiales. © 2004 The Linnean Society of London, Botanical Journal of the Linnean Society , 2004, 144 , 123–147.     

Comparative influence of spatial scale on beta diversity within regional assemblages of birds and butterflies

Aim We examined whether variation in species composition of breeding birds and resident butterflies in the Great Basin of North America depended on sampling grain (the smallest resolvable unit of study) and on the relative proximity of sampling units across the landscape. We also compared patterns between the two taxonomic groups with reference to their life‐history characteristics. Location Data for our analyses were collected from 1996 to 2003 in three adjacent mountain ranges in the central Great Basin (Lander and Nye counties, Nevada, USA): the Shoshone Mountains, Toiyabe Range and Toquima Range. Methods Data on species composition for both taxonomic groups were collecting using standard inventory methods for birds and butterflies in temperate regions. Data were compiled at three sampling grains, sites (average 12 ha), canyons (average 74 ha) and mountain ranges. For each sampling grain in turn, we calculated similarity of species composition using the Jaccard index. First, we investigated whether mean similarity of species composition among the three ranges differed as a function of the grain size at which data were compiled. Secondly, we explored whether mean similarity of species composition was greater for canyons within the same mountain range than for canyons within different mountain ranges. Thirdly, we examined whether mean similarity of species composition at the site level was different for sites within the same canyon, sites within different canyons in the same mountain range, and sites within canyons in different mountain ranges. We used a Bayesian model to analyse these comparisons. Results For both taxonomic groups, mean similarity of species composition increased as the sampling grain increased. The effect of spatial grain was somewhat greater for birds than for butterflies, especially when the intermediate sampling grain was compared with the smallest sampling grain. Similarity of species composition of butterflies at each sampling grain was greater than similarity of species composition of birds at the same grain. Mean similarity of species composition of both birds and butterflies at the canyon level and site level was affected by relative proximity of sampling locations; beta diversity increased as the relative isolation of sampling locations increased. Main conclusions The sensitivity of beta diversity to sampling grain likely reflects the effect of local environmental heterogeneity: as sampling grain increases, biotic assemblages appear more homogeneous. Although breeding birds in our study system have larger home ranges than resident butterflies, birds may have more specialized resource requirements related to vegetation structure and composition, especially at small sampling scales. The degree of variation in species composition of both taxonomic groups suggests that spatially extensive sampling will be more effective for drawing inferences about regional patterns of species diversity than intensive sampling at relatively few, smaller sites.     

Multivariate Bayesian analysis of Gaussian,right censored Gaussian,ordered categorical and binary traits using Gibbs sampling

A fully Bayesian analysis using Gibbs sampling and data augmentation in a multivariate model of Gaussian, right censored, and grouped Gaussian traits is described. The grouped Gaussian traits are either ordered categorical traits (with more than two categories) or binary traits, where the grouping is determined via thresholds on the underlying Gaussian scale, the liability scale. Allowances are made for unequal models, unknown covariance matrices and missing data. Having outlined the theory, strategies for implementation are reviewed. These include joint sampling of location parameters; efficient sampling from the fully conditional posterior distribution of augmented data, a multivariate truncated normal distribution; and sampling from the conditional inverse Wishart distribution, the fully conditional posterior distribution of the residual covariance matrix. Finally, a simulated dataset was analysed to illustrate the methodology. This paper concentrates on a model where residuals associated with liabilities of the binary traits are assumed to be independent. A Bayesian analysis using Gibbs sampling is outlined for the model where this assumption is relaxed.     

基于贝叶斯模型平均的蒸散遥感产品集成——以三江源国家公园为例

蒸散发(ET)是陆表水热过程的一个基础通量,不同模型基于的概念、假设、应用尺度等诸多差异给ET的准确模拟带来了多种不确定性.本研究以三江源国家公园为例,应用贝叶斯模型平均(BMA)方法,通过通量塔观测值对模型进行训练,并综合PT-JPL、ARTS-GIMMS3g、ARTS-MODIS、MOD16和SSEBo 5个模型结...