Drawing ecological inferences from coincident patterns of population‐ and community‐level biodiversity

Biodiversity is comprised of genetic and phenotypic variation among individual organisms, which might belong to the same species or to different species. Spatial patterns of biodiversity are of central interest in ecology and evolution for several reasons: to identify general patterns in nature (e.g. species–area relationships, latitudinal gradients), to inform conservation priorities (e.g. identifying hotspots, prioritizing management efforts) and to draw inferences about processes, historical or otherwise (e.g. adaptation, the centre of origin of particular clades). There are long traditions in ecology and evolutionary biology of examining spatial patterns of biodiversity among species (i.e. in multispecies communities) and within species, respectively, and there has been a recent surge of interest in studying these two types of pattern simultaneously. The idea is that examining both levels of diversity can materially advance the above‐stated goals and perhaps lead to entirely novel lines of inquiry. Here, we review two broad categories of approach to merging studies of inter‐ and intraspecific variation: (i) the study of phenotypic trait variation along environmental gradients and (ii) the study of relationships between patterns of molecular genetic variation within species and patterns of distribution and diversity across species. For the latter, we report a new meta‐analysis in which we find that correlations between species diversity and genetic diversity are generally positive and significantly stronger in studies with discrete sampling units (e.g. islands, lakes, forest fragments) than in studies with nondiscrete sampling units (e.g. equal‐area study plots). For each topic, we summarize the current state of knowledge and key future directions.     

Comparison of microbial community assays for the assessment of stream biofilm ecology

We investigated a range of microbiological community assays performed on scrapes of biofilms formed on artificial diffusing substrates deployed in 8 streams in eastern Scotland, with a view to using them to characterize ecological response to stream water quality. The assays considered were: Multiplex Terminal Restriction Fragment Length Polymorphism or M-TRFLP (a molecular method), Phospholipid Fatty Acid or PLFA analysis (a biochemical method) and MICRORESP^™ (a physiological method) alongside TDI, diatom species, and chlorophyll a content. Four of the streams were classified as of excellent status (3-6 μg/L Soluble Reactive Phosphorus (SRP)) with respect to soluble P content under the EU Water Framework Directive and four were of borderline good/moderate or moderate status (43-577 μg/L SRP). At each site, 3 replicates of 3 solute diffusion treatments were deployed in a Latin square design. Solute diffusion treatments were: KCl (as a control solute), N and P (to investigate the effect of nutrient enrichment), or the herbicide isoproturon (as a “high impact” control, which aimed to affect biofilm growth in a way detectable by all assays). Biofilms were sampled after 4 weeks deployment in a low flow period of early summer 2006.The chlorophyll a content of biofilms after 4 weeks was 2.0 ± 0.29 mg/m² (mean ± se). Dry matter content was 16.0 ± 13.1 g/m². The M-TRFLP was successfully used for generating community profiles of cyanobacteria, algae and bacteria and was much faster than diatom identification. The PFLA and TDI were successful after an increase in the sample size, due to low counts. The MICRORESP^™ assays were often below or near detection limit. We estimated the per-sample times for the successful assays as follows: M-TRFLP: 20 min, PLFA 40 min, TDI 90 min. Using MANOVA on the first 5 principal co-ordinates, all the assays except MICRORESP^™ showed significant differences between sites, but none of the assays showed a significant effect of either initial stream trophic status (as classified by the EU Water Framework Directive using chemical standards for soluble P), or of the diffusing solute treatment. Multiple Procrustes analysis on the ordination results showed that the diatom and M-TRFLP data sets hold distinct, though as yet unexplored, information about the ecological factors affecting stream biofilms. The diatom data were subjected to principal components analysis, to identify which taxa were more strongly influenced by site variables, trophic status or treatment effects. These were Acnanthes lanceolata, A. minutissimma, Nitzchia spp., Coccineis spp. and Navicula spp. Further experimentation and data analysis on a larger number of sites, to identify specific M-TRFLP bands that could be used as indicators linked to specific taxa, are desirable. Results highlight the need for a multifactorial approach to understanding controls on stream ecology.     

Making more sense of the order: A review of Canoco for Windows 4.5, PC‐ORD version 4 and SYN‐TAX 2000

Abstract. Personal computers of ever‐increasing speed have motivated programmers of multivariate software to adapt their programs to be run in Microsoft Windows and Macintosh platforms. Updated versions of these multivariate programs appear more and more frequently and are marketed intensively. In this review we provide a comparative analysis of the most recent versions of three analytical software packages –Canoco for Windows 4.5, PC‐ORD version 4 and SYN‐TAX 2000. The three packages share two characteristics. First, the most recent versions are now compatible with the most recent Windows platforms and should therefore be accessible for use by virtually all vegetation scientists. Second, they have capabilities for numerous multivariate techniques, although each package has some unique techniques. Thus, any one of the packages will have much to offer the user.     

Measures of precision for dissimilarity‐based multivariate analysis of ecological communities

Ecological studies require key decisions regarding the appropriate size and number of sampling units. No methods currently exist to measure precision for multivariate assemblage data when dissimilarity‐based analyses are intended to follow. Here, we propose a pseudo multivariate dissimilarity‐based standard error (MultSE) as a useful quantity for assessing sample‐size adequacy in studies of ecological communities. Based on sums of squared dissimilarities, MultSE measures variability in the position of the centroid in the space of a chosen dissimilarity measure under repeated sampling for a given sample size. We describe a novel double resampling method to quantify uncertainty in MultSE values with increasing sample size. For more complex designs, values of MultSE can be calculated from the pseudo residual mean square of a permanova model, with the double resampling done within appropriate cells in the design. R code functions for implementing these techniques, along with ecological examples, are provided.     

Phylogenetic and ecological structure of Mediterranean caddisfly communities at various spatio‐temporal scales

Aim The evolutionary processes structuring the composition of communities remain unclear due to the complexity of factors active at various spatial and temporal scales. Here, we conducted ecological and evolutionary analyses of communities of caddisflies in the genus Hydropsyche (Insecta: Trichoptera) composed of ecomorphologically differentiated species. Location River ecosystems in the Iberian Peninsula and northern Morocco. Methods Nineteen environmental variables were assessed at 180 local study sites and species presence/absence at these sites was used to determine their ecological niche. The evolutionary framework for all 19 species of Hydropsyche encountered was generated by phylogenetic analysis of the mitochondrial cytochrome c oxidase subunit I gene and three nuclear genes: wingless, elongation factor 1‐alpha and 28S RNA. The phylogenetic tree was used: (1) to assess evolutionary niche conservatism by ecological trait correlation with the tree; and (2) to analyse the phylogenetic relatedness of community member species, at three spatial scales (local stream reaches, drainage basins, biogeographical regions). Results Ecological measurements grouped most species into either headwater, mid‐stream or lowland specialists, and traits presumably relevant to river zonation were found to be phylogenetically conservative. Species assemblages at local stream reaches were mostly mono‐ or dispecific. Species diversity increased at larger spatial scales, by adding species with non‐overlapping ecological niches at the level of river basins and by turnover of anciently differentiated lineages at the level of biogeographical regions. This indicates the effects of competition and niche filtering on community structure locally, and ancient ecological diversification and allopatric speciation, respectively, in building up the species pool at basin and biogeographical scales. Main conclusions The study demonstrates the importance of scale (grain size) in studying what determines community composition. Current ecological factors (i.e. competitive exclusion) in Hydropsyche were evident only when studying narrow local sites, while studies of assemblages at larger spatial scales instead demonstrated the roles of ecological niche differentiation, phylogenetic history of trait diversification and allopatric speciation. Increasing the grain size of investigation reveals different portions of correlated spatial and evolutionary processes.     

Stump‐harvesting for bioenergy probably has transient impacts on abundance,richness and community structure of beetle assemblages

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Improving the assessment of species compositional dissimilarity in a priori ecological classifications: evaluating map scale,sampling intensity and improvement in a hierarchical classification

Question: Can species compositional dissimilarity analyses be used to assess and improve the representation of biodiversity patterns in a priori ecological classifications? Location: The case study examined the northern‐half of the South‐east Queensland Bioregion, eastern Australia. Methods: Site‐based floristic presence–absence data were used to construct species dissimilarity matrices (Kulczynski metric) for three levels of Queensland's bioregional hierarchy – subregions (1:500 000 scale), land zones (1:250 000 scale) and regional ecosystems (1:100 000 scale). Within‐ and between‐class dissimilarities were compiled for each level to elucidate species compositional patterns. Randomized subsampling was used to determine the minimum site sampling intensity for each hierarchy level, and the effects of lumping and splitting illustrated for several classes. Results: Consistent dissimilarity estimates were obtained with five or more sites per regional ecosystem, 10 or more sites per land zone, and more than 15 sites per subregion. On average, subregions represented 4% dissimilarity in floristic composition, land zones approximately 10%, and regional ecosystems over 19%. Splitting classes with a low dissimilarity increased dissimilarity levels closer to average, while merging ecologically similar classes with high dissimilarities reduced dissimilarity levels closer to average levels. Conclusions: This approach demonstrates a robust and repeatable means of analysing species compositional dissimilarity, determining site sampling requirements for classifications and guiding decisions about ‘lumping’ or ‘splitting’ of classes. This will allow more informed decisions on selecting and improving classifications and map scales in an ecologically and statistically robust manner.     

Value of long‐term ecological studies

Long‐term ecological studies are critical for providing key insights in ecology, environmental change, natural resource management and biodiversity conservation. In this paper, we briefly discuss five key values of such studies. These are: (1) quantifying ecological responses to drivers of ecosystem change; (2) understanding complex ecosystem processes that occur over prolonged periods; (3) providing core ecological data that may be used to develop theoretical ecological models and to parameterize and validate simulation models; (4) acting as platforms for collaborative studies, thus promoting multidisciplinary research; and (5) providing data and understanding at scales relevant to management, and hence critically supporting evidence‐based policy, decision making and the management of ecosystems. We suggest that the ecological research community needs to put higher priority on communicating the benefits of long‐term ecological studies to resource managers, policy makers and the general public. Long‐term research will be especially important for tackling large‐scale emerging problems confronting humanity such as resource management for a rapidly increasing human population, mass species extinction, and climate change detection, mitigation and adaptation. While some ecologically relevant, long‐term data sets are now becoming more generally available, these are exceptions. This deficiency occurs because ecological studies can be difficult to maintain for long periods as they exceed the length of government administrations and funding cycles. We argue that the ecological research community will need to coordinate ongoing efforts in an open and collaborative way, to ensure that discoverable long‐term ecological studies do not become a long‐term deficiency. It is important to maintain publishing outlets for empirical field‐based ecology, while simultaneously developing new systems of recognition that reward ecologists for the use and collaborative sharing of their long‐term data sets. Funding schemes must be re‐crafted to emphasize collaborative partnerships between field‐based ecologists, theoreticians and modellers, and to provide financial support that is committed over commensurate time frames.     

Eco‐evolutionary partitioning metrics: assessing the importance of ecological and evolutionary contributions to population and community change

Interest in eco‐evolutionary dynamics is rapidly increasing thanks to ground‐breaking research indicating that evolution can occur rapidly and can alter the outcome of ecological processes. A key challenge in this sub‐discipline is establishing how important the contribution of evolutionary and ecological processes and their interactions are to observed shifts in population and community characteristics. Although a variety of metrics to separate and quantify the effects of evolutionary and ecological contributions to observed trait changes have been used, they often allocate fractions of observed changes to ecology and evolution in different ways. We used a mathematical and numerical comparison of two commonly used frameworks – the Price equation and reaction norms – to reveal that the Price equation cannot partition genetic from non‐genetic trait change within lineages, whereas the reaction norm approach cannot partition among‐ from within‐lineage trait change. We developed a new metric that combines the strengths of both Price‐based and reaction norm metrics, extended all metrics to analyse community change and also incorporated extinction and colonisation of species in these metrics. Depending on whether our new metric is applied to populations or communities, it can correctly separate intraspecific, interspecific, evolutionary, non‐evolutionary and interacting eco‐evolutionary contributions to trait change.     

Assignment of relevés to pre‐defined classes by supervised clustering of plant communities using a new composite index

Question: How does a newly designed method of supervised clustering perform in the assignment of relevé (species composition) data to a previously established classification. How do the results compare to the assignment by experts and to the assignment using a completely different numerical method? Material: Relevés analysed represent 4186 Czech grassland plots and 4990 plots from a wide variety of vegetation types (359 different associations or basal communities) in The Netherlands. For both data sets we had at our disposal an expert classification, and for the Czech data we also had available a numerical classification as well as a classification based on a neural network method (multi‐layer perceptron). Methods: Two distance indices, one qualitative and one quantitative, are combined into a single index by weighted multiplication. The composite index is a distance index for the dissimilarity between relevés and vegetation types. For both data sets the classifications by the new method were compared with the existing classifications. Results: For the Czech grasslands we correctly classified 81% of the plots to the classes of an expert classification at the alliance level and 71% to the classes of the numerical classification. Correct classification rates for the Dutch relevés were 64, 78 and 83 % for the lowest (subassociation or association), association, and alliance level, respectively. Conclusion: Our method performs well in assigning community composition records to previously established classes. Its performance is comparable to the performance of other methods of supervised clustering. Compared with a multi‐layer perceptron (a type of artificial neural network), fewer parameters have to be estimated. Our method does not need the original relevé data for the types, but uses synoptic tables. Another practical advantage is the provision of directly interpretable information on the contributions of separate species to the result.     

Ecological biogeography of Malagasy non‐volant mammals: community structure is correlated with habitat

Aim To examine the relationship between ecoregions, as a proxy for regional climate and habitat type, and mammalian community structure, defined by species composition and richness (e.g. taxonomic structure) and ecological diversity (e.g. ecological structure) of non‐volant species. Location Madagascar. Methods Faunal lists of non‐volant mammal species occurring in 35 communities from five World Wildlife Fund ecoregions were collected from published and unpublished sources. Species were assigned to ecological groups defined by trophic status, locomotor habits, activity cycle and body mass. We used Mantel tests, cluster analysis and principal coordinates analysis to evaluate geographic patterning in taxonomic composition and species richness. We used stepwise multiple discriminant analysis to characterize patterns in the ecological diversity of the mammalian communities from each ecoregion. Communities from transitional habitats (e.g. representing more than one ecoregion) were used to test the predictive power of the analyses. Results Non‐volant mammal communities divided into clusters that correspond to ecoregions. There was a strong distance effect in the taxonomic structure of communities across the island and within both humid and dry forest communities, but this effect was weak within humid forest communities. Mammalian species richness was significantly lower in dry forest than in humid forest communities. The ecological structure of communities was also correlated with ecoregions. Changes in the relative percentages of omnivory, arboreal quadrupedalism, terrestrial/arboreal quadrupedalism and two body mass classes accounted for 98.1% of the variation in ecological structure. Transitional communities were projected in intermediate positions by the discriminant model. Main conclusions Our analysis demonstrates that the broad‐scale habitat and climate variables captured by the ecoregion model have shaped the assembly of non‐volant mammal communities in Madagascar over evolutionary time. The spatial pattern is consistent with ecological sorting of species ranges along environmental gradients. Historical processes, such as recent extinction and migration, may have also affected the structure of mammal communities, although these factors have played a secondary role.     

OptimClass: Using species‐to‐cluster fidelity to determine the optimal partition in classification of ecological communities

Question: Community ecologists are often confronted with multiple possible partitions of a single set of records of species composition and/or abundances from several sites. Different methods of numerical classification produce different results, and the question is which of them, and how many clusters, should be selected for interpretation. We demonstrate a new method for identifying the optimal partition from a series of partitions of the same set of sites, based on number of species with high fidelity to clusters in a partition (faithful species). Methods: The new method, OptimClass, has two variants. OptimClass 1 searches the partition with the maximum number of faithful species across all clusters, while OptimClass 2 searches the partition with the maximum number of clusters that contain at least a preselected minimum number of faithful species. Faithful species are determined based on the P value of the Fisher's exact test, as a measure of fidelity. OptimClass was tested on three vegetation datasets that varied in species richness and internal heterogeneity, using several classification algorithms, resemblance measures and cover transformations. Results: Results from both variants of OptimClass depended on the preselected threshold P value for faithful species: higher P gave higher probability that a partition with more clusters was selected as optimal. Good partitions, in terms of OptimClass criteria, involved flexible beta clustering, and also ordinal clustering. Good partitions were also obtained with TWINSPAN when the required number of clusters was small, or UPGMA when the required number of clusters was large. Poor partitions usually resulted from classifications that used resemblance measures and cover transformations emphasizing differences in species cover; this is not unexpected because OptimClass uses a presence/absence‐based fidelity measure. Conclusions: If the aim of a classification is to obtain clusters rich in faithful species, which can be subsequently used as diagnostic species for identification of community types, OptimClass is a suitable method for simultaneous choice of the optimal classification algorithm and optimal number of clusters. It can be computed in the JUICE program.     

Species co‐occurrence patterns and dietary resource competition in primates

Diamond (Assembly of species communities. In: Cody ML, Diamond JM, editors. Ecology and evolution of communities. Cambridge: Belknap. p 342–444 ( 1975 )) argued that interspecific competition between species occupying similar niches results in a nonrandom pattern of species distributions. In particular, some species pairs may never be found in the same community due to competitive exclusion. Rigorous analytical methods have been developed to investigate the possible role that interspecific competition has on the evolution of communities. Many studies that have implemented these methods have shown support for Diamond's assembly rules, yet there are numerous exceptions. We build on this previous research by examining the co‐occurrence patterns of primate species in 109 communities from across the world. We used EcoSim to calculate a checkerboard (C) score for each region. The C score provides a measure of the proportion of species pairs that do not co‐occur in a set of communities. High C scores indicate that species are nonrandomly distributed throughout a region, and interspecific competition may be driving patterns of competitive exclusion. We conducted two sets of analyses. One included all primate species per region, and the second analysis assigned each species to one of four dietary guilds: frugivores, folivores, insectivores, and frugivore‐insectivores. Using all species per region, we found significantly high C scores in 9 of 10 regions examined. For frugivores, we found significantly high‐C scores in more than 50% of regions. In contrast, only 23% of regions exhibited significantly high‐C scores for folivores. Our results suggest that communities are nonrandomly structured and may be the result of greater levels of interspecific competition between frugivores compared to folivores. Am J Phys Anthropol, 2010. © 2010 Wiley‐Liss, Inc.     

GeoChip 4: a functional gene‐array‐based high‐throughput environmental technology for microbial community analysis

Micro‐organisms play critical roles in many important biogeochemical processes in the Earth's biosphere. However, understanding and characterizing the functional capacity of microbial communities are still difficult due to the extremely diverse and often uncultivable nature of most micro‐organisms. In this study, we developed a new functional gene array, GeoChip 4, for analysing the functional diversity, composition, structure, metabolic potential/activity and dynamics of microbial communities. GeoChip 4 contained approximately 82 000 probes covering 141 995 coding sequences from 410 functional gene families related to microbial carbon (C), nitrogen (N), sulphur (S), and phosphorus (P) cycling, energy metabolism, antibiotic resistance, metal resistance/reduction, organic remediation, stress responses, bacteriophage and virulence. A total of 173 archaeal, 4138 bacterial, 404 eukaryotic and 252 viral strains were targeted, providing the ability to analyse targeted functional gene families of micro‐organisms included in all four domains. Experimental assessment using different amounts of DNA suggested that as little as 500 ng environmental DNA was required for good hybridization, and the signal intensities detected were well correlated with the DNA amount used. GeoChip 4 was then applied to study the effect of long‐term warming on soil microbial communities at a Central Oklahoma site, with results indicating that microbial communities respond to long‐term warming by enriching carbon degradation, nutrient cycling (nitrogen and phosphorous) and stress response gene families. To the best of our knowledge, GeoChip 4 is the most comprehensive functional gene array for microbial community analysis.     

Input‐Output‐based Life Cycle Inventory

An input‐output‐based life cycle inventory (IO‐based LCI) is grounded on economic environmental input‐output analysis (IO analysis). It is a fast and low‐budget method for generating LCI data sets, and is used to close data gaps in life cycle assessment (LCA). Due to the fact that its methodological basis differs from that of process‐based inventory, its application in LCA is a matter of controversy. We developed a German IO‐based approach to derive IO‐based LCI data sets that is based on the German IO accounts and on the German environmental accounts, which provide data for the sector‐specific direct emissions of seven airborne compounds. The method to calculate German IO‐based LCI data sets for building products is explained in detail. The appropriateness of employing IO‐based LCI for German buildings is analyzed by using process‐based LCI data from the Swiss Ecoinvent database to validate the calculated IO‐based LCI data. The extent of the deviations between process‐based LCI and IO‐based LCI varies considerably for the airborne emissions we investigated. We carried out a systematic evaluation of the possible reasons for this deviation. This analysis shows that the sector‐specific effects (aggregation of sectors) and the quality of primary data for emissions from national inventory reporting (NIR) are the main reasons for the deviations. As a rule, IO‐based LCI data sets seem to underestimate specific emissions while overestimating sector‐specific aspects.