Partitioning the effects of species loss on community variability using multi‐level selection theory

The temporal variability of ecological communities may depend on species richness and composition due to a variety of statistical and ecological mechanisms. However, ecologists currently lack a general, unified theoretical framework within which to compare the effects of these mechanisms. Developing such a framework is difficult because community variability depends not just on how species vary, but also how they covary, making it unclear how to isolate the contributions of individual species to community variability. Here I develop such a theoretical framework using the multi‐level Price equation, originally developed in evolutionary biology to partition the effects of group selection and individual selection. I show how the variability of a community can be related to the properties of the individual species comprising it, just as the properties of an evolving group can be related to the properties of the individual organisms comprising it. I show that effects of species loss on community variability can be partitioned into effects of species richness (random loss of species), effects of species composition (non‐random loss of species with respect to their variances and covariances), and effects of context dependence (post‐loss changes in species’ variances and covariances). I illustrate the application of this framework using data from the Biodiversity II experiment, and show that it leads to new conceptual and empirical insights. For instance, effects of species richness on community variability necessarily occur, but often are swamped by other effects, particularly context dependence.     

Integrating trait and phylogenetic distances to assess scale‐dependent community assembly processes

Biodiversity is structured by multiple mechanisms that are dependent, at least in part, on ecological similarities and differences among species. Integrating traits and phylogenies in diversity metrics may provide deeper insight into community assembly processes across spatial scales. However, different traits are influenced by processes at different spatial scales, and it is not clear how trait‐spatial scale mismatches skew our ability to detect assembly patterns. An additional complexity is how phylogenetic distances, which might capture unmeasured traits, reflect spatially dependent processes. Here we analyze a freshwater zooplankton dataset from 91 ponds and show that different traits are associated with processes at different spatial scales. We first assessed the response of individual traits to processes at both α‐ and β‐scales, and then quantified the power of different combinations of traits and phylogenetic distances to reveal environmental and spatial drivers of α‐ and β‐diversity. We found that explanatory power was maximised when we accounted for environmental and spatial drivers with single, but different traits for α‐ and β‐diversity. Using the most appropriate trait for each spatial scale outperformed phylogenetic information, but phylogenetic information outperformed the same traits when these were used at the wrong spatial scale, and all outperformed taxonomic analyses that ignore trait and phylogenetic information. We demonstrate that accounting for species’ similarities and differences provides important information about dominant assembly mechanisms at different spatial scales, and that phylogeny is especially useful when measured traits are uninformative at a given spatial scale or when there is lack of trait data. Our study also indicates, however, that trait‐scale mismatches among phylogenetically conserved traits may affect the performance of phylogenetic indices compared to indices that account only for the best single trait at each spatial scale.     

微生物物种多样性、群落构建与功能性状研究进展

微生物主要包括细菌、真菌、古菌、病毒等类群, 是地球上出现时间最早、分布最广泛、个体数量最多, 以及物种和基因多样性十分丰富的生物类群。为了适应各种生境, 微生物衍生出腐生、寄生、共生等多样的生存策略, 在生物地球化学循环、生态系统演替与稳定性、环境修复以及人类健康等方面发挥着重要作用。传统的微生物监测方法限制了我们对微生物多样性的认知; 但是, 近年来高通量测序技术和生物信息学的发展极大推动了微生物多样性的研究进展。本文概述了近年来在微生物多样性分布格局与维持、群落构建以及功能属性多样性的最新进展; 总结分析了细菌、古菌、真菌的多样性纬度分布格局及其驱动因子, 选择、扩散、成种、漂变等过程对细菌、古菌、真菌的群落构建的贡献, 以及细菌和真菌的形态、生理生化、生长繁殖、扩散、基因组等功能性状的多样性; 提出了未来微生物多样性研究的重要领域: 环境宏真菌组研究, 微生物多样性与生态系统多功能性的关系研究, 以及微生物互作网络的生态功能研究。     

StAMPP: an R package for calculation of genetic differentiation and structure of mixed‐ploidy level populations

Statistical Analysis of Mixed‐Ploidy Populations (StAMPP) is a freely available R package for calculation of population structure and differentiation based on single nucleotide polymorphism (SNP) genotype data from populations of any ploidy level, and/or mixed‐ploidy levels. StAMPP provides an advance on previous similar software packages, due to an ability to calculate pairwise F_ST values along with confidence intervals, Nei's genetic distance and genomic relationship matrixes from data sets of mixed‐ploidy level. The software code is designed to efficiently handle analysis of large genotypic data sets that are typically generated by high‐throughput genotyping platforms. Population differentiation studies using StAMPP are broadly applicable to studies of molecular ecology and conservation genetics, as well as animal and plant breeding.     

ACGCA: An R package for simulating tree growth and mortality based on functional traits

The Allometrically Constrained Growth and Carbon Allocation (ACGCA) model is an individual-based model of tree growth and mortality, and it is a unique tool for investigating how tree functional traits influence growth and mortality. Several studies have used the ACGCA model to investigate tree traits, but the model is not readily accessible to the wider scientific community. Thus, we developed an R package that provides a greatly simplified interface to the ACGCA model. The package contains a single function, runacgca, with the capability of simulating tree growth under constant light conditions, variable light, or within a simple forest gap dynamics scenario. The ACGCA package can accommodate temporally varying parameters (tree traits), and its modular structure allows for users to swap out the current carbon gain (photosynthesis) algorithm for alternative versions that may incorporate more physiological detail. We provide examples demonstrating how the package can be used to investigate the effects of tree traits on growth and survival under constant light and under different gap dynamic scenarios. Though not explicitly shown, this model can be easily adapted to include more mechanistic detail or integrated into larger modeling frameworks.     

funrar: An R package to characterize functional rarity

Emphasis has been put in recent ecological research on investigating phylogenetic, functional and taxonomic facets of biological diversity. While a flourishing number of indices have been proposed for assessing functional diversity, surprisingly few options are available to characterize functional rarity. Functional rarity can play a key role in community and ecosystem dynamics. We introduce here the funrar R package to quantify functional rarity based on species trait differences and species frequencies at local and regional scales. Because of the increasing availability of big datasets in macroecology and biogeography, we optimized funrar to work with large datasets of thousands of species and sites. We illustrate the use of the package to investigate the functional rarity of North and Central American mammals.     

Revisiting the Holy Grail: using plant functional traits to understand ecological processes

One of ecology's grand challenges is developing general rules to explain and predict highly complex systems. Understanding and predicting ecological processes from species' traits has been considered a ‘Holy Grail’ in ecology. Plant functional traits are increasingly being used to develop mechanistic models that can predict how ecological communities will respond to abiotic and biotic perturbations and how species will affect ecosystem function and services in a rapidly changing world; however, significant challenges remain. In this review, we highlight recent work and outstanding questions in three areas: (i) selecting relevant traits; (ii) describing intraspecific trait variation and incorporating this variation into models; and (iii) scaling trait data to community‐ and ecosystem‐level processes. Over the past decade, there have been significant advances in the characterization of plant strategies based on traits and trait relationships, and the integration of traits into multivariate indices and models of community and ecosystem function. However, the utility of trait‐based approaches in ecology will benefit from efforts that demonstrate how these traits and indices influence organismal, community, and ecosystem processes across vegetation types, which may be achieved through meta‐analysis and enhancement of trait databases. Additionally, intraspecific trait variation and species interactions need to be incorporated into predictive models using tools such as Bayesian hierarchical modelling. Finally, existing models linking traits to community and ecosystem processes need to be empirically tested for their applicability to be realized.     

Exploring the community assembly of subalpine meadow communities based on functional traits and community phylogeny

Aims The community assembly has been a prominent issue in community ecology. This work was intended to explore the mechanisms of the species coexistence and biodiversity in communities. Our objective was to explore the mechanisms of community assembly in subalpine meadow plant communities along slope gradients in Gannan Tibetan Autonomous Prefecture, Gansu Province, Northwest China.Methods We selected five slope-oriented plots to construct a super-tree representing the species pool. We surveyed the leaf functional traits and soil environmental factors in different slopes. Then we tested the phylogenetic signal of leaf dry matter content (LDMC), specific leaf area (SLA), leaf nitrogen content (LNC) and leaf phosphorus content (LPC).Important findings The changes of slope aspect had significant influence on soil water content (SWC) and soil nutrient content. Most of the plants leaf functional traits had significant difference along different slope aspects. The LDMC was higher in south and southwest slope than north slope, while SLA, LNC and LPC were relatively high in north and northwest slope. The LPC showed feeble phylogenetic signal, while LDMC, SLA, LNC did not have a significant phylogenetic signal. With changes in the slope aspect from south to north, community phylogenetic structure shifted from over-dispersion to clustered dispersion. In south and southwest slope, habitat filtering was the driving force for community assembly. Interspecific competition was the main driving factor for community assembly in north and northwest slope aspects. But in west slope, two indices showed contrary consequence. This means the process of community assembly in west slope was more complicated and its phylogenetic index may be the result of several mechanisms working together.     

Plant functional traits and community assembly along interacting gradients of productivity and fragmentation

Quantifying the association of plant functional traits to environmental gradients is a promising approach for understanding and projecting community responses to land use and climatic changes. Although habitat fragmentation and climate are expected to affect plant communities interactively, there is a lack of empirical studies addressing trait associations to fragmentation in different climatic regimes.In this study, we analyse data on the key functional traits: specific leaf area (SLA), plant height, seed mass and seed number. First, we assess the evidence for the community assembly mechanisms habitat filtering and competition at different spatial scales, using several null-models and a comprehensive set of community-level trait convergence and divergence indices. Second, we analyse the association of community-mean traits with patch area and connectivity along a south–north productivity gradient.We found clear evidence for trait convergence due to habitat filtering. In contrast, the evidence for trait divergence due to competition fundamentally depended on the null-model used. When the null-model controlled for habitat filtering, there was only evidence for trait divergence at the smallest sampling scale (0.25 m × 0.25 m). All traits varied significantly along the S–N productivity gradient. While plant height and SLA were consistently associated with fragmentation, the association of seed mass and seed number with fragmentation changed along the S–N gradient.Our findings indicate trait convergence due to drought stress in the arid sites and due to higher productivity in the mesic sites. The association of plant traits to fragmentation is likely driven by increased colonization ability in small and/or isolated patches (plant height, seed number) or increased persistence ability in isolated patches (seed mass).Our study provides the first empirical test of trait associations with fragmentation along a productivity gradient. We conclude that it is crucial to study the interactive effects of different ecological drivers on plant functional traits.     

Predicting coral community recovery using multi‐species population dynamics models

Predicting whether, how, and to what degree communities recover from disturbance remain major challenges in ecology. To predict recovery of coral communities we applied field survey data of early recovery dynamics to a multi‐species integral projection model that captured key demographic processes driving coral population trajectories, notably density‐dependent larval recruitment. After testing model predictions against field observations, we updated the model to generate projections of future coral communities. Our results indicated that communities distributed across an island landscape followed different recovery trajectories but would reassemble to pre‐disturbed levels of coral abundance, composition, and size, thus demonstrating persistence in the provision of reef habitat and other ecosystem services. Our study indicates that coral community dynamics are predictable when accounting for the interplay between species life‐history, environmental conditions, and density‐dependence. We provide a quantitative framework for evaluating the ecological processes underlying community trajectory and characteristics important to ecosystem functioning.     

hzar: hybrid zone analysis using an R software package

We present a new software package (hzar ) that provides functions for fitting molecular genetic and morphological data from hybrid zones to classic equilibrium cline models using the Metropolis–Hastings Markov chain Monte Carlo (MCMC) algorithm. The software applies likelihood functions appropriate for different types of data, including diploid and haploid genetic markers and quantitative morphological traits. The modular design allows flexibility in fitting cline models of varying complexity. To facilitate hypothesis testing, an autofit function is included that allows automated model selection from a set of nested cline models. Cline parameter values, such as cline centre and cline width, are estimated and may be compared statistically across clines. The package is written in the R language and is available through the Comprehensive R Archive Network (CRAN; http://cran.r-project.org/ ). Here, we describe hzar and demonstrate its use with a sample data set from a well‐studied hybrid zone in western Panama between white‐collared (Manacus candei) and golden‐collared manakins (M. vitellinus). Comparisons of our results with previously published results for this hybrid zone validate the hzar software. We extend analysis of this hybrid zone by fitting additional models to molecular data where appropriate.     

Cell culture monitoring via an auto‐sampler and an integrated multi‐functional off‐line analyzer

Mammalian cell‐based bioprocesses are used extensively for production of therapeutic proteins. Off‐line monitoring of such cultivations via manual sampling is often labor‐intensive and can introduce operator‐dependent error into the process. An integrated multi‐functional off‐line analyzer, the BioProfile FLEX (NOVA Biomedical, Waltham MA) has been developed, which combines the functionality of three off‐line analyzers (a cell counter, an osmometer, and a gas/electrolyte & nutrient/metabolite bio‐profile analyzer) into one device. In addition, a novel automated sampling system has also been developed that allows the BioProfile FLEX to automatically analyze the culture conditions in as many as ten bioreactors. This is the first report on the development and function of this integrated analyzer and an auto‐sampler prototype for monitoring of mammalian cell cultures. Evaluation of the BioProfile FLEX was conducted in two separate laboratories and involved two BioProfile FLEX analyzers and two sets of reference analyzers (Nova BioProfile 400, Beckman‐Coulter Vi‐Cell AS, and Advanced Instruments Osmometer 3900), 13 CHO cell lines and over 20 operators. In general, BioProfile FLEX measurements were equivalent to those obtained using reference analyzers, and the auto‐sampler did not alter the samples it provided to the BioProfile FLEX. These results suggest that the system has the potential to dramatically reduce the manual labor involved in monitoring mammalian cell bioprocesses without altering the quality of the data obtained, and integration with a bioreactor control system will allow feedback control of parameters previously available only for off‐line monitoring. © 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2010     

archiDART: an R package for the automated computation of plant root architectural traits

Background and aims

In order to analyse root system architectures (RSAs) from captured images, a variety of manual (e.g. Data Analysis of Root Tracings, DART), semi-automated and fully automated software packages have been developed. These tools offer complementary approaches to study RSAs and the use of the Root System Markup Language (RSML) to store RSA data makes the comparison of measurements obtained with different (semi-) automated root imaging platforms easier. The throughput of the data analysis process using exported RSA data, however, should benefit greatly from batch analysis in a generic data analysis environment (R software).

Methods

We developed an R package (archiDART) with five functions. It computes global RSA traits, root growth rates, root growth directions and trajectories, and lateral root distribution from DART-generated and/or RSML files. It also has specific plotting functions designed to visualise the dynamics of root system growth.

Results

The results demonstrated the ability of the package’s functions to compute relevant traits for three contrasted RSAs (Brachypodium distachyon [L.] P. Beauv., Hevea brasiliensis Müll. Arg. and Solanum lycopersicum L.).

Conclusions

This work extends the DART software package and other image analysis tools supporting the RSML format, enabling users to easily calculate a number of RSA traits in a generic data analysis environment.

     

driftsel: an R package for detecting signals of natural selection in quantitative traits

Approaches and tools to differentiate between natural selection and genetic drift as causes of population differentiation are of frequent demand in evolutionary biology. Based on the approach of Ovaskainen et al. (2011), we have developed an R package (driftsel ) that can be used to differentiate between stabilizing selection, diversifying selection and random genetic drift as causes of population differentiation in quantitative traits when neutral marker and quantitative genetic data are available. Apart from illustrating the use of this method and the interpretation of results using simulated data, we apply the package on data from three‐spined sticklebacks (Gasterosteus aculeatus) to highlight its virtues. driftsel can also be used to perform usual quantitative genetic analyses in common‐garden study designs.     

Assembling an ant community: species functional traits reflect environmental filtering

Species should only persist in local communities if they have functional traits that are compatible with habitat-specific environmental conditions. Consequently, pronounced regional environmental gradients should produce environmental filtering, or a trait-based spatial segregation of species. It is critical to quantify the links between species' functional traits and their environment in order to reveal the relative importance of this process to community assembly and promote understanding of the impacts of ongoing environmental changes. We investigated this relationship using epigaeic ants in an environmentally heterogeneous region of Florida. We found evidence for environmental filtering as environmental conditions such as groundcover, surface temperature, vapor pressure deficit, and plant diversity were strongly correlated with assemblage composition. Certain species traits appeared particularly important to persistence: (1) ants in environments with less groundcover have relatively longer legs but do not differ in size, (2) ants in hotter environments exhibit greater thermal tolerances, and (3) ants in hotter and drier environments do not exhibit greater desiccation resistance. These findings show surface complexity and temperature may interact with morphology and physiology to impact the spatial distribution of ants and underscore the importance of climate change. Climate warming is predicted to alter assemblage composition, competitive dynamics, and consequently impact ecosystem processes. We suggest environmental filters acting at regional scales, as shown here, act in tandem with more frequently studied local-scale competitive interactions to delimit ant community assemblages.     

ConR: An R package to assist large‐scale multispecies preliminary conservation assessments using distribution data

The Red List Categories and the accompanying five criteria developed by the International Union for Conservation of Nature (IUCN) provide an authoritative and comprehensive methodology to assess the conservation status of organisms. Red List criterion B, which principally uses distribution data, is the most widely used to assess conservation status, particularly of plant species. No software package has previously been available to perform large‐scale multispecies calculations of the three main criterion B parameters [extent of occurrence (EOO), area of occupancy (AOO) and an estimate of the number of locations] and provide preliminary conservation assessments using an automated batch process. We developed ConR, a dedicated R package, as a rapid and efficient tool to conduct large numbers of preliminary assessments, thereby facilitating complete Red List assessment. ConR (1) calculates key geographic range parameters (AOO and EOO) and estimates the number of locations sensu IUCN needed for an assessment under criterion B; (2) uses this information in a batch process to generate preliminary assessments of multiple species; (3) summarize the parameters and preliminary assessments in a spreadsheet; and (4) provides a visualization of the results by generating maps suitable for the submission of full assessments to the IUCN Red List. ConR can be used for any living organism for which reliable georeferenced distribution data are available. As distributional data for taxa become increasingly available via large open access datasets, ConR provides a novel, timely tool to guide and accelerate the work of the conservation and taxonomic communities by enabling practitioners to conduct preliminary assessments simultaneously for hundreds or even thousands of species in an efficient and time‐saving way.     

FELLA: an R package to enrich metabolomics data

Background

Pathway enrichment techniques are useful for understanding experimental metabolomics data. Their purpose is to give context to the affected metabolites in terms of the prior knowledge contained in metabolic pathways. However, the interpretation of a prioritized pathway list is still challenging, as pathways show overlap and cross talk effects.

Results

We introduce FELLA, an R package to perform a network-based enrichment of a list of affected metabolites. FELLA builds a hierarchical representation of an organism biochemistry from the Kyoto Encyclopedia of Genes and Genomes (KEGG), containing pathways, modules, enzymes, reactions and metabolites. In addition to providing a list of pathways, FELLA reports intermediate entities (modules, enzymes, reactions) that link the input metabolites to them. This sheds light on pathway cross talk and potential enzymes or metabolites as targets for the condition under study. FELLA has been applied to six public datasets –three from Homo sapiens, two from Danio rerio and one from Mus musculus– and has reproduced findings from the original studies and from independent literature.

Conclusions

The R package FELLA offers an innovative enrichment concept starting from a list of metabolites, based on a knowledge graph representation of the KEGG database that focuses on interpretability. Besides reporting a list of pathways, FELLA suggests intermediate entities that are of interest per se. Its usefulness has been shown at several molecular levels on six public datasets, including human and animal models. The user can run the enrichment analysis through a simple interactive graphical interface or programmatically. FELLA is publicly available in Bioconductor under the GPL-3 license.

     

Using functional trait diversity to infer community assembly mechanisms: an exclosure experiment as an example

AimsThe Qinghai-Tibetan Plateau has a mean altitude exceeding 4000 m and covers about 2.5 million km2. More than 60% of this area is alpine grassland. Exclosures have been widely used in this region to study the sustainable use of grassland resources. We used patterns of functional trait diversity to infer the effects of exclosures on community assembly in alpine meadows.