Community‐level vs species‐specific approaches to model selection

A topic of particular current interest is community‐level approaches to species distribution modelling (SDM), i.e. approaches that simultaneously analyse distributional data for multiple species. Previous studies have looked at the advantages of community‐level approaches for parameter estimation, but not for model selection – the process of choosing which model (and in particular, which subset of environmental variables) to fit to data. We compared the predictive performance of models using the same modelling method (generalised linear models) but choosing the subset of variables to include in the model either simultaneously across all species (community‐level model selection) or separately for each species (species‐specific model selection). Our results across two large presence/absence tree community datasets were inconclusive as to whether there was an overall difference in predictive performance between models fitted via species‐specific vs community‐level model selection. However, we found some evidence that a community approach was best suited to modelling rare species, and its performance decayed with increasing prevalence. That is, when data were sparse there was more opportunity for gains from “borrowing strength” across species via a community‐level approach. Interestingly, we also found that the community‐level approach tended to work better when the model selection problem was more difficult, and more reliably detected “noise” variables that should be excluded from the model.     

Ordination and significance testing of microbial community composition derived from terminal restriction fragment length polymorphisms: application of multivariate statistics

Terminal restriction fragment length polymorphism (T-RFLP) is increasingly being used to examine microbial community structure and accordingly, a range of approaches have been used to analyze data sets. A number of published reports have included data and results that were statistically flawed or lacked rigorous statistical testing. A range of simple, yet powerful techniques are available to examine community data, however their use is seldom, if ever, discussed in microbial literature. We describe an approach that overcomes some of the problems associated with analyzing community datasets and offer an approach that makes data interpretation simple and effective. The Bray-Curtis coefficient is suggested as an ideal coefficient to be used for the construction of similarity matrices. Its strengths include its ability to deal with data sets containing multiple blocks of zeros in a meaningful manner. Non-metric multi-dimensional scaling is described as a powerful, yet easily interpreted method to examine community patterns based on T-RFLP data. Importantly, we describe the use of significance testing of data sets to allow quantitative assessment of similarity, removing subjectivity in comparing complex data sets. Finally, we introduce a quantitative measure of sample dispersion and suggest its usefulness in describing site heterogeneity. This revised version was published online in June 2006 with corrections to the Cover Date.     

Investigating the causes of fish community change in the Dahan River (Taiwan) using an autecology matrix

Recent work in river restoration and water resources management has seen the need to change the focus of analysis from reach to watershed scales to better define causes of watershed impairment. However, comprehensive investigations at a watershed scale are hindered by difficulties in using reach data that was collected for analysis at small spatial and short temporal scales. This is especially true for ecological and biological data. The approach assembles assessment and monitoring data and uses an autecology matrix to identify the changes in environmental and ecological conditions that may be associated with community change over spatial and temporal scales appropriate for ecosystem analysis in watersheds. The analysis uses a weight-of-evidence approach based on the percent of the community associated with a matrix factor. We have used the autecology matrix to examine historical fish community data from the Dahan River, Taiwan. The results show that the method provides an improved understanding of historical influences on fish community structure and supports a process-based analysis of community change over watershed scales and historic time periods. Further the method helps identify habitat requirements for the fish communities at each sampling site, supporting management and ecological restoration objectives.     

Reconciling empirical interactions and species coexistence

Coexistence in ecological communities is governed largely by the nature and intensity of species interactions. Countless studies have proposed methods to infer these interactions from empirical data, yet models parameterised using such data often fail to recover observed coexistence patterns. Here, we propose a method to reconcile empirical parameterisations of community dynamics with species‐abundance data, ensuring that the predicted equilibrium is consistent with the observed abundance distribution. To illustrate the approach, we explore two case studies: an experimental freshwater algal community and a long‐term time series of displacement in an intertidal community. We demonstrate how our method helps recover observed coexistence patterns, capture the core dynamics of the system, and, in the latter case, predict the impacts of experimental extinctions. Collectively, these results demonstrate an intuitive approach for reconciling observed and empirical data, improving our ability to explore the links between species interactions and coexistence in natural systems.     

Integrating the niche and neutral perspectives on community structure and dynamics

Elucidating the mechanisms underlying the assembly and dynamics of ecological communities is a fundamental goal of ecology. Two conceptual approaches have emerged in this respect: the niche-assembly view and the neutral perspective. The debate as to which approach best explains the biodiversity patterns observed in nature is becoming outdated, as ecologists increasingly agree on the existence of a niche-neutral continuum of community dynamical behaviors. However, attempts to make the continuum idea operational and measurable remain sparse. Here, we propose a model-based approach to achieving this. The proposed methodology consists of separating out fluctuations in species abundances into niche-mediated and stochastic factors, linking the niche configuration to community dynamics through competition, and adding demographic stochasticity. This results in a comprehensive framework including neutrality and strict niche segregation as extreme cases. We develop an index of departure from neutral drift as a surrogate for community position on the niche-neutral continuum. We evaluate the performance of our modeling approach with simulated data, and subsequently use the model to analyze rodent web-trapping data from a real-world system. The model fitting is carried out with a Bayesian approach using Markov chain Monte Carlo simulation methods.     

Beyond ecosystem modeling: A roadmap to community cyberinfrastructure for ecological data‐model integration

In an era of rapid global change, our ability to understand and predict Earth's natural systems is lagging behind our ability to monitor and measure changes in the biosphere. Bottlenecks to informing models with observations have reduced our capacity to fully exploit the growing volume and variety of available data. Here, we take a critical look at the information infrastructure that connects ecosystem modeling and measurement efforts, and propose a roadmap to community cyberinfrastructure development that can reduce the divisions between empirical research and modeling and accelerate the pace of discovery. A new era of data‐model integration requires investment in accessible, scalable, and transparent tools that integrate the expertise of the whole community, including both modelers and empiricists. This roadmap focuses on five key opportunities for community tools: the underlying foundations of community cyberinfrastructure; data ingest; calibration of models to data; model‐data benchmarking; and data assimilation and ecological forecasting. This community‐driven approach is a key to meeting the pressing needs of science and society in the 21st century.     

Egyptian tortoise conservation: A community‐based,field research program developed from a study on a captive population

Local community participation and ex situ conservation has the potential to assist the recovery of the endangered Egyptian tortoise, Testudo kleinmanni. We initiated an in situ community‐based conservation and research program from a captive population of T. kleinmanni. We used a captive population of the Egyptian tortoise to train a member of the local community as a research technician and used his indigenous tracking skills and knowledge of the area to collect activity and dietary data on 28 captive tortoises. We overcame problems with illiteracy by creating a data sheet based on symbols and numbers. This data sheet allowed us to use the indigenous knowledge of various people from the community, and employ them in the future. Our local community approach to data collection, in conjunction with a craft program, made the conservation of the Egyptian tortoise more rewarding to the local community by providing a more sustainable form of income than collecting animals for the pet trade. Our multidimensional approach (local community participation as research technicians, craft program, and trust building) for gaining local support eventually led to the rediscovery of wild Egyptian tortoises in North Sinai, which was significant, as this species was presumed extinct in Egypt. We have now shifted our focus to in situ conservation, using the research and local capacity building template developed from this captive population study. Our template can be used by zoos and conservation organizations with small budgets and collections of native species in natural habitats to create similar captive research programs that can be applied to in situ conservation. Zoo Biol 26:397–406, 2007. © 2007 Wiley‐Liss, Inc.     

Combining α - and β -diversity models to fill gaps in our knowledge of biodiversity

For many taxonomic groups, sparse information on the spatial distribution of biodiversity limits our capacity to answer a variety of theoretical and applied ecological questions. Modelling community-level attributes (α- and β-diversity) over space can help overcome this shortfall in our knowledge, yet individually, predictions of α- or β-diversity have their limitations. In this study, we present a novel approach to combining models of α- and β-diversity, with sparse survey data, to predict the community composition for all sites in a region. We applied our new approach to predict land snail community composition across New Zealand. As we demonstrate, these predictions of metacommunity composition have diverse potential applications, including predicting γ-diversity for any set of sites, identifying target areas for conservation reserves, locating priority areas for future ecological surveys, generating realistic compositional data for metacommunity models and simultaneously predicting the distribution of all species in a taxon consistent with known community diversity patterns.     

Combining counts and incidence data: an efficient approach for estimating the log-normal species abundance distribution and diversity indices

Obtaining accurate estimates of diversity indices is difficult because the number of species encountered in a sample increases with sampling intensity. We introduce a novel method that requires that the presence of species in a sample to be assessed while the counts of the number of individuals per species are only required for just a small part of the sample. To account for species included as incidence data in the species abundance distribution, we modify the likelihood function of the classical Poisson log-normal distribution. Using simulated community assemblages, we contrast diversity estimates based on a community sample, a subsample randomly extracted from the community sample, and a mixture sample where incidence data are added to a subsample. We show that the mixture sampling approach provides more accurate estimates than the subsample and at little extra cost. Diversity indices estimated from a freshwater zooplankton community sampled using the mixture approach show the same pattern of results as the simulation study. Our method efficiently increases the accuracy of diversity estimates and comprehension of the left tail of the species abundance distribution. We show how to choose the scale of sample size needed for a compromise between information gained, accuracy of the estimates and cost expended when assessing biological diversity. The sample size estimates are obtained from key community characteristics, such as the expected number of species in the community, the expected number of individuals in a sample and the evenness of the community.     

Metagenomic abundance estimation and diagnostic testing on species level

One goal of sequencing-based metagenomic community analysis is the quantitative taxonomic assessment of microbial community compositions. In particular, relative quantification of taxons is of high relevance for metagenomic diagnostics or microbial community comparison. However, the majority of existing approaches quantify at low resolution (e.g. at phylum level), rely on the existence of special genes (e.g. 16S), or have severe problems discerning species with highly similar genome sequences. Yet, problems as metagenomic diagnostics require accurate quantification on species level. We developed Genome Abundance Similarity Correction (GASiC), a method to estimate true genome abundances via read alignment by considering reference genome similarities in a non-negative LASSO approach. We demonstrate GASiC’s superior performance over existing methods on simulated benchmark data as well as on real data. In addition, we present applications to datasets of both bacterial DNA and viral RNA source. We further discuss our approach as an alternative to PCR-based DNA quantification.     

Accounting for dispersal and biotic interactions to disentangle the drivers of species distributions and their abundances

Although abiotic factors, together with dispersal and biotic interactions, are often suggested to explain the distribution of species and their abundances, species distribution models usually focus on abiotic factors only. We propose an integrative framework linking ecological theory, empirical data and statistical models to understand the distribution of species and their abundances together with the underlying community assembly dynamics. We illustrate our approach with 21 plant species in the French Alps. We show that a spatially nested modelling framework significantly improves the model's performance and that the spatial variations of species presence-absence and abundances are predominantly explained by different factors. We also show that incorporating abiotic, dispersal and biotic factors into the same model bring new insights to our understanding of community assembly. This approach, at the crossroads between community ecology and biogeography, is a promising avenue for a better understanding of species co-existence and biodiversity distribution.     

应用间接排序识别盐沼植被的光谱特征:以崇明东滩为例

遥感技术已成为大尺度植被分类的重要手段,而地面植物群落特征与其光谱特征之间的关系是解译遥感影像的关键。该研究选择上海崇明东滩自然保护区的盐沼植物群落为对象,应用ASD地物光谱仪测定其植物群落的光谱反射率,并采用10个小型机载成像光谱仪(CASI)默认植被波段组,应用主分量分析法和相关分析分析了不同群落光谱特征与生态环境因子之间的关系。分析结果表明,间接排序法PCA能够识别盐沼植被中光滩、海三棱 草(Scirpus mariqueter)群落、芦苇(Phragmites australis)群落和互花米草(Spartina alterniflora)等群落的光谱特征,绝大多数盐沼湿地植物群落组成与光谱特征之间有显著的相关,识别效果最好的波段组是736~744 nm、746~753 nm、775~784 nm、815~824 nm和860~870 nm;对光谱反射率影响最大的生态环境因子分别是植物群落的高度和盖度,高程和其它环境因子的影响次之。研究成果可为遥感监测崇明东滩自然保护区内入侵种互花米草的空间分布和扩散规律提供技术支撑,为高光谱遥感影像的影像判读和解译分类以及盐沼湿地植被制图提供科学依据。     

A quantitative genetic approach for predicting ecological change in biological communities

Ecological studies of communities have become increasingly focused on the role of genetics. These studies often conclude that genetics and evolution play an important role in community structure and function. For instance, studies have shown that the structure of insect communities associated with a host plant is heritable and therefore can potentially evolve. However, when studying communities of interacting species two problems are faced: (1) the traits that determine the outcomes of these interactions are often unknown, and (2) communities are normally highly multidimensional (n-dimensional for n species). In order to surmount these problems, we adapt a commonly used approach for studying the evolution of multivariate quantitative traits to the study of biological communities. Specifically, we propose utilizing a community-based genetic covariance matrix (G-matrix) and an associated vector of community selection gradients for predicting changes in community composition, where the “traits” under study are the abundances, or other properties, of various interacting species. This approach capitalizes on the relative ease with which data on the abundance of individuals interacting with individuals of a focal species (e.g., abundances of various herbivorous insects on a plant) can be collected and on the utility of the quantitative genetic approach for predicting multidimensional evolution. In order to evaluate the utility and accuracy of the G-matrix approach for predicting the evolution of communities, we develop and analyze numerical simulations of evolving communities. Results of these simulations show that an approach based on community G-matrices and selection gradients provides a rich understanding of how underlying genetics shape community structure and, in many cases, accurately predicts how community structure changes over time.     

Understanding microbial community diversity metrics derived from metagenomes: performance evaluation using simulated data sets

Metagenomics holds the promise of greatly advancing the study of diversity in natural communities, but novel theoretical and methodological approaches must first be developed and adjusted for these data sets. We evaluated widely used macroecological metrics of taxonomic diversity on a simulated set of metagenomic samples, using phylogenetically meaningful protein-coding genes as ecological proxies. To our knowledge, this is the first approach of this kind to evaluate taxonomic diversity metrics derived from metagenomic data sets. We demonstrate that abundance matrices derived from protein-coding marker genes reproduce more faithfully the structure of the original community than those derived from SSU-rRNA gene. We also found that the most commonly used diversity metrics are biased estimators of community structure and differ significantly from their corresponding real parameters and that these biases are most likely caused by insufficient sampling and differences in community phylogenetic composition. Our results suggest that the ranking of samples using multidimensional metrics makes a good qualitative alternative for contrasting community structure and that these comparisons can be greatly improved with the incorporation of metrics for both community structure and phylogenetic diversity. These findings will help to achieve a standardized framework for community diversity comparisons derived from metagenomic data sets.     

Six-year study of meiofaunal dynamics in fish farms in Tenerife (Canary Islands,NE Atlantic Ocean)

In our six-year study, we investigated the dynamics of the meiofaunal community directly under the influence of a fish farm, in adjacent areas and in control areas outside the fish farm influence. Our data showed spatial, seasonal and annual variation in the meiofaunal community under the influence of the fish farm; however, no clear trend is discernible. Copepods are positively associated with mud and very fine sands, which seem to increase with time under the fish farm and adjacent areas, particularly in summer. As shown throughout the study period, copepods could soon take over other components of the community, resulting in a community shift. Our approach to investigate the dynamics of a community with a quick response to environmental changes proves useful to detect early-stage deviations from non-affected areas, which could be critical to distinguish environmental impacts before they cause major shifts in the environment.     

OpenUp! Creating a cross-domain pipeline for natural history data

Multimedia data held by Natural History Museums and Universities are presently not readily accessible, even within the natural history community itself. The EU project OpenUp! is an effort to mobilise scientific biological multimedia resources and open them to a wider audience using the EUROPEANA data standards and portal. The connection between natural history and EUROPEANA is accomplished using well established BioCASe and GBIF technologies. This is complemented with a system for data quality control, data transformation and semantic enrichment. With this approach, OpenUp! will provide at least 1,1 Million multimedia objects to EUROPEANA by 2014. Its lean infrastructure is sustainable within the natural history community and will remain functional and effective in the post-project phase.     

The Lord Howe Island Biodiversity Management Plan: An integrated approach to recovery planning

Summary   The Lord Howe Island Biodiversity Management Plan targeted significant species for the Lord Howe Island Group and formed the recovery plan for 30 threatened species and one endangered ecological community. The plan addressed threats and management actions relevant to the Lord Howe Island Group's overall biodiversity, with a particular focus on rare and significant species and communities. The Biodiversity Management Plan approach enabled holistic and cost-effective planning for the management of biodiversity on Lord Howe Island. We describe the approach, as applied to Lord Howe Island, including the utilization of expert and community knowledge, species research data and GIS innovations.     

Using generalized dissimilarity modelling to analyse and predict patterns of beta diversity in regional biodiversity assessment

Generalized dissimilarity modelling (GDM) is a statistical technique for analysing and predicting spatial patterns of turnover in community composition (beta diversity) across large regions. The approach is an extension of matrix regression, designed specifically to accommodate two types of nonlinearity commonly encountered in large-scaled ecological data sets: (1) the curvilinear relationship between increasing ecological distance, and observed compositional dissimilarity, between sites; and (2) the variation in the rate of compositional turnover at different positions along environmental gradients. GDM can be further adapted to accommodate special types of biological and environmental data including, for example, information on phylogenetic relationships between species and information on barriers to dispersal between geographical locations. The approach can be applied to a wide range of assessment activities including visualization of spatial patterns in community composition, constrained environmental classification, distributional modelling of species or community types, survey gap analysis, conservation assessment, and climate-change impact assessment.     

A Multi Size-Level Assessment of Benthic Marine Communities in a Coastal Environment: Are They Different Sides of the Same Coin?

Organism body size has been demonstrated to be a discriminating element in shaping the response of living beings to environmental factors, thus playing a fundamental role in community structuring. Despite the importance of studies elucidating relations among communities of different size levels in ecosystems, the attempts that have been made in this sense are still very scarce and a reliable approach for these research still has to be defined. We characterized the benthic communities of bacteria, microbial eukaryotes, meiofauna and macrofauna in a coastal environment, encompassing a 10000-fold gradient in body size, testing and discussing a mixed approach of molecular fingerprinting for microbes and morphological observations for meio- and macrofauna. We found no correlation among structures of the different size-level communities: this suggests that community composition at one size-level could have no (or very low) influence on the community composition at other size-levels. Moreover, each community responds in a different way to the environmental parameters and with a degree of sensitivity which seems to increase with organism size. Therefore, our data indicate that the characterization of all the different size levels is clearly a necessity in order to study the dynamics really acting in a system.