The effect of excluding diatom taxa and reducing taxonomic resolution on multivariate analyses and stream bioassessment

The goal of the present study was to evaluate whether the Eastern Canadian Diatom Index (IDEC: Indice Diatomées de l’Est du Canada), which was built on fine-scale taxonomic resolution and included all observed diatom taxa, would perform similarly if a number of taxa were excluded from the analyses or if a reduced level of taxonomic resolution was used. The effects of excluding taxa and reducing the taxonomic resolution were evaluated by studying community structure variations in ordination analyses, and more specifically by comparing IDEC values calculated from the original diatom matrix with IDEC values obtained from simplified diatom matrices. The results showed that the exclusion of taxa based on the relative abundance criterion is the most appropriate, while the exclusion of taxa based on the frequency of occurrence criterion greatly affected the structure of the ordination. More specifically, taxa with a maximum relative abundance <2% can be excluded from the CA without markedly affecting the results. As a result, 125 taxa (40%) were excluded from the 311 taxa included in the original matrix without significantly affecting the performance of the IDEC. Excluding a greater number of taxa still allows for the distinction between impacted and reference sites, although subtle changes in the ecological status are lost. Ordinations based on presence/absence or genus-level identification resulted in a loss of information on subtle changes, but gross separation between impacted and reference sites was still possible.     

A program for computer-aided analyses of ecological field data

A program for IBM-compatible microcomputers is introduced whichcombines several complementary analyses of species-station-tablesgenerated in ecological field investigations. The scope of theprogram encompasses table editing Junctions, routines for communitydelimitation by cluster analysis and procedures for the analysisof properties related both to stations (e.g. diversities) andspecies (e.g. abundance statistics and association indices).The essential reasoning behind the application of communitystudies is presented briefly, as well as the multi-step analyticalapproach implemented in the program.     

Comparative ethometrics: Congruence of different multivariate analyses applied to the same ethological data

Five statistically appropriate multivariate analyses were applied to the same data on burrowing in the sea hare Aplysia brasiliana to: (1) identify homogeneous subject-related subgroups within a heterogeneous sample, and (2) compare the extent of congruency among the analyses in terms of the number of extracted subgroups and each subject's placement within the subgroups. Raw scores from 32 subjects on ten burrowing parameters were origin-corrected, standardized to z-scores, and normalized in order to facilitate comparisons among the analyses. One to five identified subgroups were extracted which indicated sensitivity differences to sampling variability among the methods. These results suggested that selecting a biologically interpretable analysis represents the subjective aspect of quantitative data treatment. Q-factor analysis (three subgroups) and linear typal analysis (four subgroups) yielded the most biologically interpretable subgroups for these data. Multidimensional scaling (one group) and principal-components analysis (two subgroups) tended to “lump” subjects, while simple distance-function cluster analysis (five subgroups) tended to “split” subjects into additional groups. As a diagonistic tool, multivariate analyses provide insight into underlying dimensions of individual variation and help generate testable hypotheses for guiding future research.     

The effect of taxonomic resolution on the assessment of ecological water quality classes

Within the ecological assessment of running waters based on benthic macroinvertebrates different levels of taxonomic resolution (species, genus, family and higher) are in use. Although assessment systems are often developed with detailed data on species level, water managers and other end-users could like to use data on higher taxonomic levels to assess the ecological quality of a water body because of limited human or money resources. The question that arises is, if an assessment system built with species level data is also applicable using data with a higher taxonomic resolution. Within the AQEM project a multimetric assessment system was developed to evaluate the ecological quality classes (from bad (1) to high (5) ecological quality) of different stream types throughout Europe. The present study focuses on the question whether the resulting water quality class changes using the AQEM Assessment Software (AAS) with different taxonomic resolutions and if yes, how large the deviations of ecological quality classes from the original classes are and if the deviations are unidirectional. For analyses data from four Austrian and two Dutch stream types were used. It is demonstrated that the assignment of a site to an ecological quality class may change if different taxonomic levels are used. Deviations in both directions (higher/lower ecological quality class) were observed. In most cases the divergence was only one ecological quality class, but also larger deviations occasionally occurred. The causes of changes in the assessment were investigated by separately looking into the underlying metrics of the multimetric system. Some of the evaluated metrics rely on autecological information on species level and are simply not applicable on higher taxonomic levels. Other metrics worked on higher taxonomic levels as well and showed more or less good distinctions between ecological quality classes. It is concluded that the AQEM Assessment Software is not applicable if data on higher taxonomic levels are used. As the deviations were not unidirectional and ranged from one to three ecological quality classes, it is not possible to include a correction factor for using the software with higher taxonomic resolution data.     

A taxonomic and ecological study of candidosis

A total of 450 yeast isolates were obtained from up to 34 sites on 59 human subjects. The yeasts were characterized using morphological features and assimilation tests. Ten species were identified but, of these,Candida albicans andCandida parapsilosis were the most common and accounted for 84% of the isolates. An examination of the biotypes of the various species indicated a much greater diversity in the yeast microflora than that detected by species identifications alone. Fifty-five biotypes were differentiated and it is suggested that these could be regarded as distinct taxonomic or ecological entities.     

A new method of species-reduction for ecological data

Existing strategies for species-reduction in data matrices from species-rich communities are critically reviewed, and a new method is proposed. It is based on a two-parameter model; each species can be associated with an ‘eident value’, which is a measure of the extent to which that species discriminates between site-groups. The method is applied to a small segment of agronomic data, and to a rainforest survey, and in both cases is shown to produce meaningful results. Application of the method promises greatly to reduce the computing time currently needed for two-parameter analyses, and therefore to allow more extensive search techniques to be employed.     

Spatial analyses of ecological count data: A density map comparison approach

Analysing spatial patterns of population distributions may help to infer the decisive underlying ecological processes. Here we propose a method adapted to the spatial analysis of count data. Named MAPCOMP (MAP COMParison), it is based on the calculation of a formal distance, the Hellinger distance, between the density map of counts and the density map of sampling effort. Statistical tests of spatial homogeneity are based on count permutations across sampling sites and on valuable properties of the Hellinger distance. We assessed the efficiency of MAPCOMP by simulating different types and locations of clusters of individuals and compared its performance to the classical red–blue SADIE method, used as a reference. The two methods were also compared with respect to counts of codling moth larvae in orchards. Thanks to its better theoretical properties than SADIE, MAPCOMP was efficient in detecting spatial inhomogeneity when clusters were located on square or elongated spatial domains and more or less close to the edges, even for small sample sizes. It also appeared not very sensitive to edge effects. Another advantage of MAPCOMP is a bandwidth parameter that allows assessing the spatial extent of heterogeneity, if any.     

Application of multivariate analyses of enzymic data to classification of members of the Actinobacillus-Haemophilus-Pasteurella group.

Outer membrane vesicles and fragments from Actinobacillus actinomycetemcomitans, Actinobacillus lignieresii, Actinobacillus ureae, Haemophilus aphrophilus, Haemophilus paraphrophilus, Haemophilus influenzae, Haemophilus parainfluenzae, Pasteurella haemolytica, and Pasteurella multocida were isolated and examined semiquantitatively for 19 enzyme activities by using the API ZYM micromethod. The enzyme contents of vesicles and fragments were compared with the enzyme contents of whole cells of the same organisms. Enzymic data were analyzed by using principal-component analysis and soft independent modeling of class analogy. This technique allowed us to distinguish among the closely related organisms A. actinomycetemcomitans, H. aphrophilus, and H. paraphrophilus. A. actinomycetemcomitans was divided into two groups of strains. A. lignieresii fell outside or on the border of the A. actinobacillus class. A. ureae, H. influenzae, H. parainfluenzae, P. haemolytica, and P. multocida fell outside the A. actinomycetemcomitans, H. aphrophilus, and H. paraphrophilus classes.     

Impact of abundance data errors on the uncertainty of an ecological water quality assessment index

Increased awareness about the uncertainty of ecological water quality (EWQ) assessment tools in river management has led to the identification of the underlying uncertainty sources and the quantification of their effect on assessment. More specifically, with respect to macroinvertebrate-based EWQ assessment, use of erroneous abundance data has been identified as a (possible) source of uncertainty. In this paper, the effect of erroneous abundance data on the uncertainty of an EWQ assessment index was investigated. A model simulation based method, the virtual ecologist approach, was used to estimate the impact of abundance data errors on the uncertainty of the Multimetric Macroinvertebrate Index Flanders (MMIF). The results of this study show that the effects of relative small errors on the MMIF and assessment are limited. Additionally, it is observed that uncertainties due to abundance errors increase with decreasing EWQ (i.e. lower MMIF). This is important, since decision-makers typically formulate management actions for rivers with a low EWQ. In short, the innovative virtual ecologist approach proved to be very successful to research the index uncertainty and present a unique insight in the functioning of the assessment index.     

A note on interobserver error in multivariate analyses of populations

The effect of interobserver error on a principal components analysis of a small sample of human crania is examined. A comparison of individual specimen scores for components is made to find rotated principal components which identify interobserver error. The individual variables which load highly on such components are then tested for interobserver error univariately. Multivariate components which must identify interobserver error contain no high loadings for variables which demonstrate interobserver error in the univariate case. Principal component analysis, in defining new component variables, extracts such error in an easily identified way which makes comparison of samples measured by more than one anthropometrist more reliable.     

Transcending data gaps: a framework to reduce inferential errors in ecological analyses

The analysis of functional diversity (FD) has gained increasing importance due to its generality and utility in ecology. In particular, patterns in the spatial distribution and temporal change of FD are being used to predict locations and functional groups that are immediately vulnerable to global changes. A major impediment to the accurate measurement of FD is the pervasiveness of missing data in trait datasets. While such prevalent data gaps can engender misleading inferences in FD analyses, we currently lack any practical guide to handle missing data in trait datasets. Here, we identify significant mismatches between true FD and values derived from datasets that contain missing data. We demonstrate that imputing missing data with a phylogeny‐informed approach reduces the risk of misinterpretation of FD patterns, and provides baseline information against which central questions in ecology can be evaluated.     

Confidence in ecological indicators: A framework for quantifying uncertainty components from monitoring data

The value of an ecological indicator is no better than the uncertainty associated with its estimate. Nevertheless, indicator uncertainty is seldom estimated, even though legislative frameworks such as the European Water Framework Directive stress that the confidence of an assessment should be quantified. We introduce a general framework for quantifying uncertainties associated with indicators employed to assess ecological status in waterbodies. The framework is illustrated with two examples: eelgrass shoot density and chlorophyll a in coastal ecosystems. Aquatic monitoring data vary over time and space; variations that can only partially be described using fixed parameters, and remaining variations are deemed random. These spatial and temporal variations can be partitioned into uncertainty components operating at different scales. Furthermore, different methods of sampling and analysis as well as people involved in the monitoring introduce additional uncertainty. We have outlined 18 different sources of variation that affect monitoring data to a varying degree and are relevant to consider when quantifying the uncertainty of an indicator calculated from monitoring data. However, in most cases it is not possible to estimate all relevant sources of uncertainty from monitoring data from a single ecosystem, and those uncertainty components that can be quantified will not be well determined due to the lack of replication at different levels of the random variations (e.g. number of stations, number of years, and number of people). For example, spatial variations cannot be determined from datasets with just one station. Therefore, we recommend that random variations are estimated from a larger dataset, by pooling observations from multiple ecosystems with similar characteristics. We also recommend accounting for predictable patterns in time and space using parametric approaches in order to reduce the magnitude of the unpredictable random components and reduce potential bias introduced by heterogeneous monitoring across time. We propose to use robust parameter estimates for both fixed and random variations, determined from a large pooled dataset and assumed common across the range of ecosystems, and estimate a limited subset of parameters from ecosystem-specific data. Partitioning the random variation onto multiple uncertainty components is important to obtain correct estimates of the ecological indicator variance, and the magnitude of the different components provide useful information for improving methods applied and design of monitoring programs. The proposed framework allows comparing different indicators based on their precision relative to the cost of monitoring.     

Getting specific: making taxonomic and ecological sense of large sequencing data sets

Eukaryotic microbes comprise a diverse collection of phototrophic and heterotrophic creatures known to play fundamental roles in ecological processes. Some can be identified by light microscopy, generally the largest and with conspicuous shapes, while the smallest can be counted by epifluorescence microscopy or flow cytometry but remain largely unidentified. Microbial diversity studies greatly advanced with the analysis of phylogenetic markers sequenced from natural assemblages. Molecular surveys began in 1990 targeting marine bacterioplankton (Giovannoni et al. 1990 ) and first approached microbial eukaryotes in three studies published in 2001 (Díez et al. 2001 ; López‐García et al. 2001 ; Moon‐van der Staay et al. 2001 ). These seminal studies, based on cloning and Sanger sequencing the complete 18S rDNA, were critical for obtaining broad pictures of microbial diversity in contrasted habitats and for describing novel lineages by robust phylogenies, but were limited by the number of sequences obtained. So, inventories of species richness in a given sample and community comparisons through environmental gradients were very incomplete. These limitations have been overcome with the advent of high‐throughput sequencing (HTS) methods, initially 454‐pyrosequencing, today Illumina and soon others to come. In this issue of Molecular Ecology, Egge et al. ( 2015 ) show a nice example of the use of HTS to study the biodiversity and seasonal succession of a particularly important group of marine microbial eukaryotes, the haptophytes. Temporal changes were analysed first at the community level, then at the clade level, and finally at the lowest rank comparable to species. Interesting and useful ecological insights were obtained at each taxonomic scale. Haptophyte diversity differed along seasons in a systematic manner, with some species showing seasonal preferences and others being always present. Many of these species had no correspondence with known species, pointing out the high level of novelty in microbial assemblages, only accessible by molecular tools. Moreover, the number of species detected was limited, agreeing with a putative scenario of constrained evolutionary diversification in free‐living small eukaryotes. This study illustrates the potential of HTS to address ecological relevant questions in an accessible way by processing large data sets that, nonetheless, need to be treated with a fair understanding of their limitations.     

A multivariate approach to assess the taxonomic utility of morphometric characters inDoronicum (Asteraceae, Senecioneae)

A multivariate morphometric study based on 22 characters was carried out on samples of the 26 accepted species of the Eurasian and north-African genusDoronicum, to assess whether quantitative, mainly continuous, characters were of use in suggesting, delimiting and structuring natural groups. The study concentrated on three-medium sized to small groups: Mediterranean (D. plantagineum group), European (D. grandiflorum group), and central Asian. Two questions were addressed for each of these groups: species delimitation within them and the inclusion/exclusion of species of uncertain, but putative close affinities. Principal components analysis and discriminant analysis (canonical variates analysis), gave moderately satisfactory results. The addition of doubtfully-related species to presumably natural groups increased cohesiveness of the groups because the between-species differences were minimized when placed in a broader more diverse context. The relative status of the uncertain species, either as outliers or true members, was appropriately depicted by the scatterplots, but more convincingly solved when the species turned out to be outliers. Discrimination between species, which belong to cohesive groups was effective in some cases at least when using discriminant analysis, and the exceptions involved particularly problematic taxa. It was concluded that morphometric characters are a useful complementary source of information in groups where qualitative (in particular, shared) characters are scarce as is the case inDoronicum.     

A new model for parent-of-origin effect analyses applied to Brown Swiss cattle slaughterhouse data

Genomic imprinting is a phenomenon that arises when the expression of genes depends on the parental origin of alleles. Epigenetic mechanisms may induce the full or partial suppression of maternal or paternal alleles, thereby leading to different types of imprinting. However, imprinting effects have received little consideration in animal breeding programmes, although their relevance to some agricultural important traits has been demonstrated. A recently proposed model (imprinting model) with two path-of-transmission (male and female)-specific breeding values for each animal accounts for all types of imprinting simultaneously (paternal, maternal, full and partial). Imprinting effects (or more generally: parent-of-origin effects (POE)) are determined by taking the difference between the two genetic effects in each animal. However, the computation of their prediction error variance (PEV) is laborious; thus, we propose a new model that is equivalent to the aforementioned imprinting model, which facilitates the direct estimation of imprinting effects instead of taking the differences and the PEV is readily obtained. We applied the new model to slaughterhouse data for Brown Swiss cattle, among which imprinting has never been investigated previously. Data were available for up to 173 051 fattening bulls, where the pedigrees contained up to 428 710 animals representing the entire Brown Swiss population of Austria and Germany. The traits analysed comprised the net BW gain, fat score, EUROP class and killing out percentage. The analysis demonstrated that the net BW gain, fat score and EUROP class were influenced significantly by POE. After estimating the POE, the new model yielded estimates with reliabilities ranging between 0.4 and 0.9. On average, the imprinting variances accounted for 9.6% of the total genetic variance, where the maternal gamete was the main contributor. Moreover, our results agreed well with those obtained using linear models when the EUROP class and fat score were treated as categorical traits by applying a GLMM with a logit link function.     

Some contributions to the analysis of multivariate data

In this paper, we provide an overview of recently developed methods for the analysis of multivariate data that do not necessarily emanate from a normal universe. Multivariate data occur naturally in the life sciences and in other research fields. When drawing inference, it is generally recommended to take the multivariate nature of the data into account, and not merely analyze each variable separately. Furthermore, it is often of major interest to select an appropriate set of important variables. We present contributions in three different, but closely related, research areas: first, a general approach to the comparison of mean vectors, which allows for profile analysis and tests of dimensionality; second, non‐parametric and parametric methods for the comparison of independent samples of multivariate observations; and third, methods for the situation where the experimental units are observed repeatedly, for example, over time, and the main focus is on analyzing different time profiles when the number p of repeated observations per subject is larger than the number n of subjects.     

Maps of relative floristic ignorance and virtual floristic lists: An R package to incorporate uncertainty in mapping and analysing biodiversity data

The vast amount of occurrence records currently available offers increasing opportunities for biodiversity data analyses. This amount of data poses new challenges for the reliability and correct interpretation of the results. Indeed, to safely deal with occurrence records, their uncertainty and associated biases should be taken into account. We developed an R package to explicitly include spatial and temporal uncertainties during the mapping and listing of plant occurrence records for a given study area. Our workflow returns two objects: (a) a ‘Map of Relative Floristic Ignorance’ (MRFI), which represents the spatial distribution of the lack of floristic knowledge; (b) a ‘Virtual Floristic List’ (VFL), i.e. a list of taxa potentially occurring in the area with an associated probability of occurrence. The method implemented in the package can manage a large amount of occurrence data and represents relative floristic ignorance across a study area with a sustainable computational effort. Several parameters can be set by the user, conferring high flexibility to the method. Uncertainty is not avoided, but incorporated into biodiversity analyses through appropriate methodological approaches and innovative spatial representations. Our study introduces a workflow that pushes forward the analytical capacities to deal with uncertainty in biological occurrence records, allowing to produce more accurate outputs.