TREEasy: An automated workflow to infer gene trees,species trees,and phylogenetic networks from multilocus data

Multilocus genomic data sets can be used to infer a rich set of information about the evolutionary history of a lineage, including gene trees, species trees, and phylogenetic networks. However, user‐friendly tools to run such integrated analyses are lacking, and workflows often require tedious reformatting and handling time to shepherd data through a series of individual programs. Here, we present a tool written in Python—TREEasy—that performs automated sequence alignment (with MAFFT), gene tree inference (with IQ‐Tree), species inference from concatenated data (with IQ‐Tree and RaxML‐NG), species tree inference from gene trees (with ASTRAL, MP‐EST, and STELLS2), and phylogenetic network inference (with SNaQ and PhyloNet). The tool only requires FASTA files and nine parameters as inputs. The tool can be run as command line or through a Graphical User Interface (GUI). As examples, we reproduced a recent analysis of staghorn coral evolution, and performed a new analysis on the evolution of the “WGD clade” of yeast. The latter revealed novel patterns that were not identified by previous analyses. TREEasy represents a reliable and simple tool to accelerate research in systematic biology ( https://github.com/MaoYafei/TREEasy ).     

Colonization history of Galapagos giant tortoises: Insights from mitogenomes support the progression rule

Galapagos giant tortoises (Chelonoidis spp.) are a group of large, long-lived reptiles that includes 14 species, 11 of which are extant and threatened by human activities and introductions of non-native species. Here, we evaluated the phylogenetic relationships of all extant and two extinct species (Chelonoidis abingdonii from the island of Pinta and Chelonoidis niger from the island of Floreana) using Bayesian and maximum likelihood analysis of complete or nearly complete mitochondrial genomes. We also provide an updated phylogeographic scenario of their colonization of the Galapagos Islands using chrono-phylogenetic and biogeographic approaches. The resulting phylogenetic trees show three major groups of species: one from the southern, central, and western Galapagos Islands; the second from the northwestern islands; and the third group from the northern, central, and eastern Galapagos Islands. The time-calibrated phylogenetic and ancestral area reconstructions generally align with the geologic ages of the islands. The divergence of the Galapagos giant tortoises from their South American ancestor likely occurred in the upper Miocene. Their diversification on the Galapagos adheres to the island progression rule, starting in the Pleistocene with the dispersal of the ancestral form from the two oldest islands (San Cristóbal and Española) to Santa Cruz, Santiago, and Pinta, followed by multiple colonizations from different sources within the archipelago. Our work provides an example of how to reconstruct the history of endangered taxa in spite of extinctions and human-mediated dispersal events and provides a framework for evaluating the contribution of colonization and in situ speciation to the diversity of other Galapagos lineages.     

Taxonomic review and phylogenetic inference elucidate the evolutionary history of Mesozoic Procercopidae,with new data from the Cretaceous Jehol Biota of NE China (Hemiptera,Cicadomorpha)

The Mesozoic family Procercopidae is widely treated as the ancient group of Cercopoidea and a transitional unit to recent lineages, but its evolution and diversity are vague due to fragmentary fossil record and confusing taxonomic history. Herein, an extensive taxonomic review of Procercopidae is presented and some new fossils are reported from the Lower Cretaceous Yixian Formation of NE China. As a result, Chengdecercopis Hong, 1983 is transferred from Procercopidae to Sinoalidae; Procercopis longipennis Becker-Migdisova, 1962 and P shawanensis Zhang, Wang and Zhang, 2003 are transferred to Procercopina Martynov, 1937, resulting in Procercopina longipennis (Becker-Migdisova, 1962), comb. n. and P shawanensis (Zhang, Wang and Zhang, 2003), comb. n.; Luanpingia senjituensis Hong, 1984 is transferred to Stellularis Chen, Yao and Ren, 2015, leading to Stellulari senjituensis (Hong, 1984), comb. n.; Anthoscytina macula Hu, Yao and Ren, 2014 is transferred to Sinocercopis Hong, 1982, and Sunoscytinopteris (Scytinopteridae) and Cathaycixius (Cixiidae) are treated as junior homonym names of Sinocercopis, leading to Sinocercopis macula (Hu, Yao and Ren, 2014), comb. n., S lushangfenensis (Hong, 1984), comb. n., S pustulosis (Ren, 1995), comb. n., and S trinervis (Ren, 1995), comb. n. Additionally, two new species are erected: Stellularis bineuris Chen and Wang, sp. n. and S minutus Chen and Wang, sp. n. Our cladistic analysis based on wing (tegmen and hind wing) characteristics recovers the high-level relationships within Cercopoidea: Sinoalidae + (Procercopidae + (Cercopionidae + modern cercopoids)). Within the family Procercopidae, the cladistic analysis reveals that the Middle to Late Jurassic Titanocercopis and Jurocercopis and the Cretaceous Cretocercopis occupy the basal position, and a gradual change in wing venation can be recognized from the Early Jurassic Procercopis and Procercopina to the Jurassic Anthoscytina, and then to the Cretaceous Stellularis and Sinocercopis. The two Cretaceous genera, sharing wing traits with extant cercopoids, likely represent transitional forms between Procercopidae and recent Cercopoidea; however, they are very similar to their Jurassic relatives in body structures, suggesting it is applicable to attribute them to Procercopidae. Furthermore, our analysis suggests that the extinction of Procercopidae and the origin and early diversification of modern Cercopoidea approximately coincided with the rise and explosive radiation of angiosperms in the late Early Cretaceous and onwards.     

生态保护项目绩效评估的技术方法体系

自20世纪90年代以来,以各种形式的生态补偿政策和工程项目为代表的生态保护项目在全球广泛实施。1998年长江大洪水之后,我国也陆续实施了天然林保护工程、退耕还林还草工程、京津风沙源治理工程、森林生态效益补偿基金、退田还湖还湿工程、生态转移支付等一系列生态保护项目。近年来,政府、研究人员和社会各界越来越重视这些项目产生的生态、经济和社会效益。不过,我国生态保护项目绩效评估还处于起步阶段,主要体现在定量化不足、研究设计不严密、基准线选取不规范、评估方法过于简易、因果推理证据不足、评估结果可信度较差。不管是从学术研究、国家战略,还是实际生态管理的角度,都迫切需要推动和完善我国生态保护项目的绩效评估。结合过去十多年的理论和案例研究经验,对绩效评估的研究设计、基准线选取、评估方法、以及评估分析中面临的问题和挑战进行了系统地归纳与梳理,以期为进一步开展生态保护项目绩效评估提供技术参考。     

Recovering speciation and extinction dynamics based on phylogenies

Phylogenetic trees of only extant species contain information about the underlying speciation and extinction pattern. In this review, I provide an overview over the different methodologies that recover the speciation and extinction dynamics from phylogenetic trees. Broadly, the methods can be divided into two classes: (i) methods using the phylogenetic tree shapes (i.e. trees without branch length information) allowing us to test for speciation rate variation and (ii) methods using the phylogenetic trees with branch length information allowing us to quantify speciation and extinction rates. I end the article with an overview on limitations, open questions and challenges of the reviewed methodology.     

Mischaracterising density dependence biases estimated effects of coloured covariates on population dynamics

Environmental effects on population growth are often quantified by coupling environmental covariates with population time series, using statistical models that make particular assumptions about the shape of density dependence. We hypothesized that faulty assumptions about the shape of density dependence can bias estimated effect sizes of temporally autocorrelated covariates. We investigated the presence of bias using Monte Carlo simulations based on three common per capita growth functions with distinct density dependent forms (θ-Ricker, Ricker and Gompertz), autocorrelated (coloured) ‘known’ environmental covariates and uncorrelated (white) ‘unknown’ noise. Faulty assumptions about the shape of density dependence, combined with overcompensatory intrinsic population dynamics, can lead to strongly biased estimated effects of coloured covariates, associated with lower confidence interval coverage. Effects of negatively autocorrelated (blue) environmental covariates are overestimated, while those of positively autocorrelated (red) covariates can be underestimated, generally to a lesser extent. Prewhitening the focal environmental covariate effectively reduces the bias, at the expense of the estimate precision. Fitting models with flexible shapes of density dependence can also reduce bias, but increases model complexity and potentially introduces other problems of parameter identifiability. Model selection is a good option if an appropriate model is included in the set of candidate models. Under the specific and identifiable circumstances with high risk of bias, we recommend prewhitening or careful modelling of the shape of density dependence.