Fishes of Indiana streams: current and historic assemblage structure

We examined Indiana fish assemblages using taxonomy and ecological categories to assess temporal shifts in community structure and recent environmental relationships. Historic (1945) and recent (1996–2007) presence/absence data were compiled by subbasin and analyzed with Nonmetric Multidimensional Scaling (NMS) ordination and by species richness. Canonical Correspondence Analysis (CCA) was used to test taxonomic identity and ecological category abundance data for explanation with recent (1996–2007) environmental variables. We found a decrease in assemblage heterogeneity for recent assemblages and an increase in the number of tolerant species per subbasin. Recent Indiana streams are dominated by tolerant fishes with generalist life history strategies and low functional variation. The use of ecological categories resulted in weaker relationships with environmental variables than analyses with taxonomic identities. Analyses using taxonomy resulted in strong assemblage explanation from stream size and flow variation, while analyses using ecological categories resulted in strong assemblage explanation from habitat variation in silt substrates and flow. Analyses of recent assemblage structure using ecological categories resulted in decreased assemblage variation among subbasins than in analyses using taxonomic identities. We found that fish assemblages of Indiana streams are structured primarily by habitat complexity and have been altered during the past 50 years through multiple disturbances including fragmentation, siltation, and species introductions.     

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.     

Dietary variability in fishes: the roles of taxonomic, spatial, temporal and ontogenetic factors

In spite of the general use of diet data in ecological research, still very little is known about the relative roles of spatial, temporal and biotic (e.g. taxonomic identity, size, sex) factors in dietary variability of fishes. Here, we applied canonical correspondence analysis and variation partitioning to examine the roles of taxonomic, annual, seasonal, lake basin, habitat and ontogenetic (standard length, L _S) factors in the dietary variation of fishes in large and shallow Lake Balaton, Hungary. The analyses were performed at the assemblage (15 fish species) and the individual species levels, and based on high (24 fine resource categories) and low resolution (nine broad resource categories) diet data. As hypothesised, most of the explained variation related to interspecific differences, while the roles of sampling year, season, lake area, habitat and L _S proved to be unexpectedly low at the assemblage level. In addition, no regularity was found in how the relative roles of these factors change between fish species. The high ratio of the unexplained variation suggests that individual variations in foraging strategies and resource use of fishes and unascertained stochastic processes had a strong influence on dietary variability both at the assemblage and the individual species levels.     

A global organism detection and monitoring system for non-native species

Harmful invasive non-native species are a significant threat to native species and ecosystems, and the costs associated with non-native species in the United States is estimated at over $120 Billion/year. While some local or regional databases exist for some taxonomic groups, there are no effective geographic databases designed to detect and monitor all species of non-native plants, animals, and pathogens. We developed a web-based solution called the Global Organism Detection and Monitoring (GODM) system to provide real-time data from a broad spectrum of users on the distribution and abundance of non-native species, including attributes of their habitats for predictive spatial modeling of current and potential distributions. The four major subsystems of GODM provide dynamic links between the organism data, web pages, spatial data, and modeling capabilities. The core survey database tables for recording invasive species survey data are organized into three categories: “Where, Who & When, and What.” Organisms are identified with Taxonomic Serial Numbers from the Integrated Taxonomic Information System. To allow users to immediately see a map of their data combined with other user's data, a custom geographic information system (GIS) Internet solution was required. The GIS solution provides an unprecedented level of flexibility in database access, allowing users to display maps of invasive species distributions or abundances based on various criteria including taxonomic classification (i.e., phylum or division, order, class, family, genus, species, subspecies, and variety), a specific project, a range of dates, and a range of attributes (percent cover, age, height, sex, weight). This is a significant paradigm shift from “map servers” to true Internet-based GIS solutions. The remainder of the system was created with a mix of commercial products, open source software, and custom software. Custom GIS libraries were created where required for processing large datasets, accessing the operating system, and to use existing libraries in C++, R, and other languages to develop the tools to track harmful species in space and time. The GODM database and system are crucial for early detection and rapid containment of invasive species.     

The use of coarser taxonomic resolution in studies of predation on marine sedimentary fauna

Given the logistical difficulties, cost, and time involved in species-level identifications, several authors have proposed the use of coarser taxonomic resolution (e.g. family, order) in studies of pollution. The use of surrogates instead of species relies on their sufficiency to detect community responses to the pollution gradient without appreciable loss of information. No studies, however, have applied this approach to experimental studies such as community responses to predation disturbance and evaluated the performance of surrogates at the spatial scales typical of experiments. We addressed both problems by analyzing the results of three predation experiments carried out in Bonne Bay, Newfoundland. We pooled species data into coarser taxonomic categories (family to class) and determined whether effects of predation that were evident at the species level were also evident with the use of each coarser surrogate and increasing data transformation. Our results indicate that non-transformed data at the family level represent a reasonable surrogate of species; however, the ability to discriminate between ambient and (predator) manipulated sediments is gradually lost with data transformation and with the pooling of species into coarser taxonomic categories. Successive data transformation indicates that in this system predation plays a strong role on dominant but not necessarily rare species. Moreover, our results suggest that varying reliability of surrogates precludes the identification of a single general level of taxonomic sufficiency to be used in experimental studies. The use of surrogates, therefore, is suggested only after scrutiny and evaluation, and should be limited to preliminary studies where biodiversity has been well described.     

Signature genes as a phylogenomic tool

Gene content has been shown to contain a strong phylogenetic signal, yet its usage for phylogenetic questions is hampered by horizontal gene transfer and parallel gene loss and until now required completely sequenced genomes. Here, we introduce an approach that allows the phylogenetic signal in gene content to be applied to any set of sequences, using signature genes for phylogenetic classification. The hundreds of publicly available genomes allow us to identify signature genes at various taxonomic depths, and we show how the presence of signature genes in an unspecified sample can be used to characterize its taxonomic composition. We identify 8,362 signature genes specific for 112 prokaryotic taxa. We show that these signature genes can be used to address phylogenetic questions on the basis of gene content in cases where classic gene content or sequence analyses provide an ambiguous answer, such as for Nanoarchaeum equitans, and even in cases where complete genomes are not available, such as for metagenomics data. Cross-validation experiments leaving out up to 30% of the species show that approximately 92% of the signature genes correctly place the species in a related clade. Analyses of metagenomics data sets with the signature gene approach are in good agreement with the previously reported species distributions based on phylogenetic analysis of marker genes. Summarizing, signature genes can complement traditional sequence-based methods in addressing taxonomic questions.     

Taxonomic sufficiency in detecting hydrological changes and reproducing ordination patterns: A test using planktonic ciliates

Ciliates are commonly and successfully used as bioindicators in marine ecosystems, even at low levels of taxonomic resolution. However, the use of these organisms in biomonitoring programs of freshwater ecosystems is less common. Evidence showing the reliability of the taxonomic sufficiency approach for freshwater ciliates is also limited. Demonstrating taxonomic sufficiency would be important to foster the use of ciliates in freshwater biomonitoring programs in a cost-effective way. Here we tested whether richness and multivariate patterns generated by ciliate community data, at species level, were retrieved by analyses carried out with data at lower taxonomic resolutions. Community and environmental datasets were gathered at 36 sites in the Upper Paraná River floodplain (Brazil), during high and low water levels periods of the years 2010 and 2011. We found that, in comparison with species-level data, genus-level identification was sufficient to detect the effects of the environmental changes caused by floods, to retrieve the ordination patterns generated during each hydrological period and to detect relationships with environmental and spatial gradients. We also showed that the use of coarser taxonomic resolutions was not advisable because high relationships with species-level data were found only with genus-level data. We encourage the use of ciliates in biomonitoring programs of freshwater ecosystems and, according to our results, the use of genus-level identification is a reliable strategy, not only to increase cost-efficiency, but also to guarantee temporal comparability of data.     

Characterising functional trait diversity and trait–environment relationships in fish assemblages of boreal lakes

1. Characterisation of biodiversity is typically based on taxonomic approaches, while much less is known about other related aspects. Functional trait diversity is one such component of biodiversity that has not been addressed rigorously in ecological research until recently. We tested the congruence between taxonomic‐ and trait‐based approaches, and examined how spatial configuration, local abiotic environmental factors and biotic effects interact to influence taxonomic‐ and trait‐based characterisation of freshwater fish assemblages. 2. Fish assemblage data were compiled for 124 lakes in southern Finland. Variance partitioning in both linear regression analyses and redundancy analysis was used to quantify the relative contribution of spatial and environmental variables to taxonomic and functional trait diversity and structure. Additionally, a null model analysis was used to test for the potential effects of interspecific segregation and biotic interactions on the co‐occurrence of species. 3. The species pool was relatively poor. However, trait‐based classification of species indicated that most species belonged to unique functional entities, which suggested low redundancy in species composition. Correlation analysis indicated a very strong relationship between species richness (SR) and the number of unique trait combinations (UTC). Ecoregion‐level heterogeneity in SR and UTC were well represented in a relatively small group of randomly selected lakes (c. 30 lakes). Multiple regressions indicated moderate roles for abiotic environmental variables (i.e. lake surface area, depth, total phosphorous, colour and pH) in determining SR, UTC and the distribution of single trait categories, whereas geographical location was not generally influential. 4. Redundancy analysis revealed similar patterns to those of diversity analyses for taxonomic and associated trait‐based structure, emphasising the effect of abiotic environmental variables and the negligible effect of geographical position. 5. Co‐occurrence analysis indicated significant checkerboard distribution at the whole assemblage level, but interspecific segregation proved to be of relatively minor importance in the constrained analyses, where species pair combinations within trait category groups were evaluated. 6. Our results suggest that taxonomic‐ and trait‐based patterns of boreal lake fish assemblages are strongly interrelated. Environmental filtering through the effects of local abiotic variables seems to have the most prominent role in determining trait‐based assemblage patterns among lakes, which may also be secondarily shaped by biotic interactions. 7. From the applied perspective, it may not necessarily matter whether traditional taxonomic or more novel trait‐based approaches are used in characterising spatial patterns in boreal fish assemblages. However, trait‐based approaches may provide complementary information which cannot be directly revealed by taxonomic data.     

Phylogeny of the Asian spiny frog tribe Paini (Family Dicroglossidae) sensu Dubois

The anuran tribe Paini, family Dicroglossidae, is known in this group only from Asia. The phylogenetic relationships and often the taxonomic recognition of species are controversial. In order to stabilize the classification, we used approximately 2100 bp of nuclear (rhodopsin, tyrosinase) and mitochondrial (12S, 16S rRNA) DNA sequence data to infer the phylogenetic relationships of these frogs. Phylogenetic trees reconstructed using Bayesian inference and maximum parsimony methods supported a monophyletic tribe Paini. Two distinct groups (I,II) were recovered with the mtDNA alone and the total concatenated data (mtDNA+nuDNA). The recognition of two genera, Quasipaa and Nanorana, was supported. Group I, Quasipaa, is widespread east of the Hengduan Mountain Ranges and consists of taxa from relatively low elevations in southern China, Vietnam and Laos. Group II, Nanorana, contains a mix of species occurring from high to low elevation predominantly in the Qinghai-Tibetan Plateau and Hengduan Mountain Ranges. The occurrence of frogs at high elevations appears to be a derived ecological condition. The composition of some major species groups based on morphological characteristics strongly conflicts with the molecular analysis. Some possible cryptic species are indicated by the molecular analyses. The incorporation of genetic data from type localities helped to resolve some of the taxonomic problems, although further combined analyses of morphological data from type specimens are required. The two nuDNA gene segments proved to be very informative for resolving higher phylogenetic relationships and more nuclear data should be explored to be more confident in the relationships.     

The bacterial species dilemma and the genomic-phylogenetic species concept

The number of species of Bacteria and Archaea (ca 5000) is surprisingly small considering their early evolution, genetic diversity and residence in all ecosystems. The bacterial species definition accounts in part for the small number of named species. The primary procedures required to identify new species of Bacteria and Archaea are DNA-DNA hybridization and phenotypic characterization. Recently, 16S rRNA gene sequencing and phylogenetic analysis have been applied to bacterial taxonomy. Although 16S phylogeny is arguably excellent for classification of Bacteria and Archaea from the Domain level down to the family or genus, it lacks resolution below that level. Newer approaches, including multilocus sequence analysis, and genome sequence and microarray analyses, promise to provide necessary information to better understand bacterial speciation. Indeed, recent data using these approaches, while meagre, support the view that speciation processes may occur at the subspecies level within ecological niches (ecovars) and owing to biogeography (geovars). A major dilemma for bacterial taxonomists is how to incorporate this new information into the present hierarchical system for classification of Bacteria and Archaea without causing undesirable confusion and contention. This author proposes the genomic-phylogenetic species concept (GPSC) for the taxonomy of prokaryotes. The aim is twofold. First, the GPSC would provide a conceptual and testable framework for bacterial taxonomy. Second, the GPSC would replace the burdensome requirement for DNA hybridization presently needed to describe new species. Furthermore, the GPSC is consistent with the present treatment at higher taxonomic levels.     

Multiple data sets, high homoplasy, and the phylogeny of softshell turtles (Testudines: Trionychidae)

We present a phylogenetic hypothesis and novel, rank-free classification for all extant species of softshell turtles (Testudines:Trionychidae). Our data set included DNA sequence data from two mitochondrial protein-coding genes and a approximately 1-kb nuclear intron for 23 of 26 recognized species, and 59 previously published morphological characters for a complimentary set of 24 species. The combined data set provided complete taxonomic coverage for this globally distributed clade of turtles, with incomplete data for a few taxa. Although our taxonomic sampling is complete, most of the modern taxa are representatives of old and very divergent lineages. Thus, due to biological realities, our sampling consists of one or a few representatives of several ancient lineages across a relatively deep phylogenetic tree. Our analyses of the combined data set converge on a set of well-supported relationships, which is in accord with many aspects of traditional softshell systematics including the monophyly of the Cyclanorbinae and Trionychinae. However, our results conflict with other aspects of current taxonomy and indicate that most of the currently recognized tribes are not monophyletic. We use this strong estimate of the phylogeny of softshell turtles for two purposes: (1) as the basis for a novel rank-free classification, and (2) to retrospectively examine strategies for analyzing highly homoplasious mtDNA data in deep phylogenetic problems where increased taxon sampling is not an option. Weeded and weighted parsimony, and model-based techniques, generally improved the phylogenetic performance of highly homoplasious mtDNA sequences, but no single strategy completely mitigated the problems of associated with these highly homoplasious data. Many deep nodes in the softshell turtle phylogeny were confidently recovered only after the addition of largely nonhomoplasious data from the nuclear intron.     

Implications of ITS sequences and RAPD markers for the taxonomy and biogeography of the Oxytropis campestris and O. arctica (Fabaceae) complexes in Alaska

Taxonomic consensus is lacking on the Oxytropis arctica and O. campestris species complexes, two polyploid complexes found in the interior and arctic areas of Alaska. One classification has emphasized flower size, whereas flower color is considered a key diagnostic character in another classification. Our analyses of internal transcribed spacer (ITS) sequences and random amplified polymorphic DNA (RAPD) markers provided no support for either classification system. The trees generated from ITS sequences and the phenogram derived from RAPD markers suggest that most recognized taxa in the two complexes are probably polyphyletic, including O. arctica var. barnebyana, which is listed as threatened in Alaska. The only consistent pattern detected by both types of molecular markers was a geographic split dividing the northeastern arctic populations from most other populations (48.60-55.03% in AMOVA analyses). This genetic subdivision probably reflects a Pleistocene barrier formed by the northern coastal ice shield. Our molecular data, in conjunction with the previously reported variation of ploidy levels in these groups, suggest a scenario of recent and multiple origins of polyploidy. It is possible that most Alaskan populations of these two complexes are best referred to as a single taxonomic species despite morphological differentiation within the complexes.     

Coexistence and divergence of tropical dry forests and savannas in southern Mexico

Aim The purpose of the study was to assess the degree of floristic differentiation between tropical dry forest (TDF) and savanna occurring in a single landscape. This comparison provides information on the responses of vegetation to the prevailing environmental conditions, while it also allows us to make inferences about large‐scale events and processes, both biogeographical and evolutionary. Our approach included three levels of analysis: (1) taxonomic, (2) morphological and (3) vegetational. Location The seasonal dry tropical landscape in the Nizanda region, Oaxaca State, southern Mexico. The landscape comprises a complex vegetation mosaic in which tropical dry forest and savannas are the most conspicuous components. Methods Comparisons between TDF and savanna were based on inventories for these communities produced after 8 years of botanical survey. At the taxonomic level, the relative representation of taxa of different hierarchical levels in each community was examined. Morphological analyses required the classification of species on each of three criteria: (1) growth form, (2) life form and (3) growth habit. Vegetation level analysis was based on the frequencies of taxa in one hundred 100‐m² composition plots with which matrices of binary data were constructed for species, genera and families. These were subjected to classification analysis with Ward's method and using Euclidean distances as the dissimilarity algorithm. Results The combined flora for both communities comprised 600 species, 375 genera and 94 families; between them they shared 31, 40 and 34 taxa, respectively. The corresponding Sørensen similarity values were 10%, 21% and 72%, respectively. Ranking genera and families according to their species richness displayed large differences between savanna and TDF. Large differences between these communities were observed for Acanthaceae, Cactaceae, Euphorbiaceae and Mimosaceae, whereas Fabaceae and Asteraceae had similar high ranks according to the species richness in the two systems. The growth form spectrum diverged between the two communities, with TDF having more trees, shrubs and climbers. Savanna was characterised by forbs and graminoid herbs. Growth habit spectra revealed a clear dominance of herbaceous and suffruticose plants in savanna, and of woody elements and epiphytes in TDF. Regarding Raunkiaer's life forms, savanna had relatively more hemicryptophytes, and TDF more phanerophytes. Classification analyses showed that savanna and TDF forest samples kept their identities, regardless of taxonomic level (species, genera and families) at which the analyses were performed. Main conclusions The TDF and savanna of Nizanda represent two floristic systems with a large degree of differentiation at all taxonomic levels and patterns of morphological attributes. This suggest that the two floristic sets have evolved independently for extended periods of time, despite their close proximity. One important implication of this floristic differentiation is the large joint contribution made by these communities to the regional flora.     

TACO: Taxonomic prediction of unknown OTUs through OTU co-abundance networks

Background: A main goal of metagenomics is taxonomic characterization of microbial communities. Although sequence comparison has been the main method for the taxonomic classification, there is not a clear agreement on similarity calculation and similarity thresholds, especially at higher taxonomic levels such as phylum and class. Thus taxonomic classification of novel metagenomic sequences without close homologs in the biological databases poses a challenge. Methods: In this study, we propose to use the co-abundant associations between taxa/operational taxonomic units (OTU) across complex and diverse communities to assist taxonomic classification. We developed a Markov Random Field model to predict taxa of unknown microorganisms using co-abundant associations. Results: Although such associations are intrinsically functional associations, we demonstrate that they are strongly correlated with taxonomic associations and can be combined with sequence comparison methods to predict taxonomic origins of unknown microorganisms at phylum and class levels. Conclusions: With the ever-increasing accumulation of sequence data from microbial communities, we now take the first step to explore these associations for taxonomic identification beyond sequence similarity. Availability and Implementation: Source codes of TACO are freely available at the following URL: https://github.com/baharvand/OTU-Taxonomy-Identification implemented in C++, supported on Linux and MS Windows.     

Influence of taxonomic and numerical resolution on the analysis of temporal changes in phytoplankton communities

From December 2003 to November 2005, we analyzed the phytoplankton community of four streams located in Central Brazil (Goiás State) to evaluate if the temporal changes in phytoplankton community structure were dependent on the taxonomic/numerical resolution used to represent the data. Classification based on functional criteria was also contrasted with taxonomic classification to assess whether these classification schemes produce different ordination patterns. Procrustean analyses indicated that ordination patterns generated with data based on the presence or absence of genera correlated significantly with the patterns generated by species density. Temporal trajectories of scores derived from functional groups significantly matched those derived from analyses based on quantitative data (density or biovolume) for genus or family. In general, the results indicated that some simplifications are justifiable, mainly when one takes into account the need for uninterrupted biomonitoring programs over large spatial scales in a continent-sized country with increasing environmental problems and with a relative paucity of scientists.     

GBD-Explorer: Extending open source java GIS for exploring ecoregion-based biodiversity data

Biodiversity and ecosystem data are both geo-referenced and “species-referenced”. Ecoregion classification systems are relevant to basic ecological research and have been increasingly used for making policy and management decisions. There are practical needs to integrate taxonomic data with ecoregion data in a GIS to visualize and explore species distribution conveniently. In this study, we represent the species distributed in an ecoregion as a taxonomic tree and extend the classic GIS data model to incorporate operations on taxonomic trees. A prototype called GBD-Explorer was developed on top of the open source JUMP GIS. We use the World Wildlife Fund (WWF) terrestrial ecoregion and WildFinder species databases as an example to demonstrate the rich capabilities implemented in the prototype.     

A method to incorporate the effect of taxonomic uncertainty on multivariate analyses of ecological data

Researchers in ecology commonly use multivariate analyses (e.g. redundancy analysis, canonical correspondence analysis, Mantel correlation, multivariate analysis of variance) to interpret patterns in biological data and relate these patterns to environmental predictors. There has been, however, little recognition of the errors associated with biological data and the influence that these may have on predictions derived from ecological hypotheses. We present a permutational method that assesses the effects of taxonomic uncertainty on the multivariate analyses typically used in the analysis of ecological data. The procedure is based on iterative randomizations that randomly re‐assign non identified species in each site to any of the other species found in the remaining sites. After each re‐assignment of species identities, the multivariate method at stake is run and a parameter of interest is calculated. Consequently, one can estimate a range of plausible values for the parameter of interest under different scenarios of re‐assigned species identities. We demonstrate the use of our approach in the calculation of two parameters with an example involving tropical tree species from western Amazonia: 1) the Mantel correlation between compositional similarity and environmental distances between pairs of sites, and; 2) the variance explained by environmental predictors in redundancy analysis (RDA). We also investigated the effects of increasing taxonomic uncertainty (i.e. number of unidentified species), and the taxonomic resolution at which morphospecies are determined (genus‐resolution, family‐resolution, or fully undetermined species) on the uncertainty range of these parameters. To achieve this, we performed simulations on a tree dataset from southern Mexico by randomly selecting a portion of the species contained in the dataset and classifying them as unidentified at each level of decreasing taxonomic resolution. An analysis of covariance showed that both taxonomic uncertainty and resolution significantly influence the uncertainty range of the resulting parameters. Increasing taxonomic uncertainty expands our uncertainty of the parameters estimated both in the Mantel test and RDA. The effects of increasing taxonomic resolution, however, are not as evident. The method presented in this study improves the traditional approaches to study compositional change in ecological communities by accounting for some of the uncertainty inherent to biological data. We hope that this approach can be routinely used to estimate any parameter of interest obtained from compositional data tables when faced with taxonomic uncertainty.     

Analysing Extinction Risk in Parrots using Decision Trees

Comparative analysis techniques have been successfully applied in a number of recent attempts to identify the species traits associated with a current threat of extinction although less often to predict which species may become threatened in the future. Although prediction of risk is obviously a priority, such analyses are undermined by the fact that there may be non-linear and non-additive relationships between the species traits used. A Decision Tree analysis can accommodate with such relationships and here it is used to explore factors affecting extinction risk in parrots. The results firstly verify that simple biological and biogeographical traits can separate threatened from non-threatened species. It is also possible to predict which species are likely to become threatened in the future. The utility of the method is not in testing evolutionary-based hypotheses to explain extinction risk, rather it is a simple and practical method of confirming and/or predicting levels of risk. For well known taxonomic groups it could be used to confirm current IUCN threat categories and identify which species should receive closest attention when the group is next reviewed. For poorly known groups it could be used to predict categories of threat for unclassified species from small groups of classified ones.     

Methane indicator values for peatlands: a comparison of species and functional groups

Previous studies have shown a correspondence between the abundance of particular plant species and methane flux. Here, we apply multivariate analyses, and weighted averaging, to assess the suitability of vegetation composition as a predictor of methane flux. We developed a functional classification of the vegetation, in terms of a number of plant traits expected to influence methane production and transport, and compared this with a purely taxonomic classification at species level and higher. We applied weighted averaging and indirect and direct ordination approaches to six sites in the United Kingdom, and found good relationships between methane flux and vegetation composition (classified both taxonomically and functionally). Plant species and functional groups also showed meaningful responses to management and experimental treatments. In addition to the United Kingdom, we applied the functional group classification across different geographical regions (Canada and the Netherlands) to assess the generality of the method. Again, the relationship appeared good at the site level, suggesting some general applicability of the functional classification. The method seems to have the potential for incorporation into large‐scale (national) greenhouse gas accounting programmes (in relation to peatland condition/management) using vegetation mapping schemes. The results presented here strongly suggest that robust predictive models can be derived using plant species data (for use in national‐scale studies). For trans‐national‐scale studies, where the taxonomic assemblage of vegetation differs widely between study sites, a functional classification of plant species data provides an appropriate basis for predictive models of methane flux.