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A Bayesian framework to estimate diversification rates and their variation through time and space
(2011)
Background: Patterns of species diversity are the result of speciation and extinction processes, and molecular phylogenetic data can provide valuable information to derive their variability through time and across clades. Bayesian Markov chain Monte Carlo methods offer a promising framework to incorporate phylogenetic uncertainty when estimating rates of diversification.
Results: We introduce a new approach to estimate diversification rates in a Bayesian framework over a distribution of trees under various constant and variable rate birth-death and pure-birth models, and test it on simulated phylogenies. Furthermore, speciation and extinction rates and their posterior credibility intervals can be estimated while accounting for non-random taxon sampling. The framework is particularly suitable for hypothesis testing using Bayes factors, as we demonstrate analyzing dated phylogenies of Chondrostoma (Cyprinids) and Lupinus (Fabaceae). In addition, we develop a model that extends the rate estimation to a meta-analysis framework in which different data sets are combined in a single analysis to detect general temporal and spatial trends in diversification.
Conclusions: Our approach provides a flexible framework for the estimation of diversification parameters and hypothesis testing while simultaneously accounting for uncertainties in the divergence times and incomplete taxon sampling.
Although climate is known to be one of the key factors determining animal species distributions amongst others, projections of global change impacts on their distributions often rely on bioclimatic envelope models. Vegetation structure and landscape configuration are also key determinants of distributions, but they are rarely considered in such assessments. We explore the consequences of using simulated vegetation structure and composition as well as its associated landscape configuration in models projecting global change effects on Iberian bird species distributions. Both present-day and future distributions were modelled for 168 bird species using two ensemble forecasting methods: Random Forests (RF) and Boosted Regression Trees (BRT). For each species, several models were created, differing in the predictor variables used (climate, vegetation, and landscape configuration). Discrimination ability of each model in the present-day was then tested with four commonly used evaluation methods (AUC, TSS, specificity and sensitivity). The different sets of predictor variables yielded similar spatial patterns for well-modelled species, but the future projections diverged for poorly-modelled species. Models using all predictor variables were not significantly better than models fitted with climate variables alone for ca. 50% of the cases. Moreover, models fitted with climate data were always better than models fitted with landscape configuration variables, and vegetation variables were found to correlate with bird species distributions in 26–40% of the cases with BRT, and in 1–18% of the cases with RF. We conclude that improvements from including vegetation and its landscape configuration variables in comparison with climate only variables might not always be as great as expected for future projections of Iberian bird species.
Acanthocephalans are attractive candidates as model organisms for studying the ecology and co-evolutionary history of parasitic life cycles in the marine ecosystem. Adding to earlier molecular analyses of this taxon, a total of 36 acanthocephalans belonging to the classes Archiacanthocephala (3 species), Eoacanthocephala (3 species), Palaeacanthocephala (29 species), Polyacanthocephala (1 species) and Rotifera as outgroup (3 species) were analyzed by using Bayesian Inference and Maximum Likelihood analyses of nuclear 18S rDNA sequence. This data set included three re-collected and six newly collected taxa, Bolbosoma vasculosum from Lepturacanthus savala, Filisoma rizalinum from Scatophagus argus, Rhadinorhynchus pristis from Gempylus serpens, R. lintoni from Selar crumenophthalmus, Serrasentis sagittifer from Johnius coitor, and Southwellina hispida from Epinephelus coioides, representing 5 new host and 3 new locality records. The resulting trees suggest a paraphyletic arrangement of the Echinorhynchida and Polymorphida inside the Palaeacanthocephala. This questions the placement of the genera Serrasentis and Gorgorhynchoides within the Echinorhynchida and not the Polymorphida, necessitating further insights into the systematic position of these taxa based on morphology.
The spider fauna active on the bark of trees in forests on eight sites in different regions in Germany was investigated. Trunk eclectors at about 2-4 meters height on living trees were used in different regions of Germany (SW Bavaria, Hesse, Brandenburg) between 1990 and 2003. In Hesse eclectors were also used on dead beech trees (standing and lying). In this study data, mainly from beech (Fagus sylvatica) and spruce (Picea abies), from May to October are compared – whole year samples (including winter) are only available from Hesse. A total of 334 spider species were recorded with these bark traps, i.e. about one third of the spider species known from Germany. On average, each of the eight regions yielded 140.5 (± 26.2) species, each single tree 40.5 (± 12.2) species and 502 (±452) adult spiders per season (i.e. May to Oct.). The 20 most abundant species are listed and characterised in detail. Six of the 20 species were not known to be abundant on bark, three prefer conifers and three beech/broadleaf. Even in winter (December-March) there was a remarkably high activity on the trunks. However, only a few species occur exclusively or mainly in winter. Finally, the rarity of some bark spider species is discussed and details (all known records in Germany, phenology) of four of them are presented (Clubiona leucaspis, Gongylidiellum edentatum, Kratochviliella bicapitata, Oreonetides quadridentatus). The diversity and importance of the spider fauna on bark in Central Europe is still underestimated.
NeoBiota, Volume 11 (2011)
(2011)
NeoBiota, Volume 10 (2011)
(2011)
NeoBiota, Volume 9 (2011)
(2011)