Biodiversity In Space Research Articles

Untangling the influences of air quality and climate change on biodiversity in space and time.

The article by Hutsemékers and colleagues in this issue reveals the influences of air quality and climate change on biodiversity of epiphytic bryophytes. Biodiversity of this flora has tracked the improving air quality well since the 1980s, and air quality has improved enough so that the influence of current climate can now be seen in species distributions.

Predictability and transferability of local biodiversity environment relationships.

BackgroundBiodiversity varies in space and time, and often in response to environmental heterogeneity. Indicators in the form of local biodiversity measures–such as species richness or abundance–are common tools to capture this variation. The rise of readily available remote sensing data has enabled the characterization of environmental heterogeneity in a globally robust and replicable manner. Based on the assumption that differences in biodiversity measures are generally related to differences in environmental heterogeneity, these data have enabled projections and extrapolations of biodiversity in space and time. However so far little work has been done on quantitatively evaluating if and how accurately local biodiversity measures can be predicted.MethodsHere I combine estimates of biodiversity measures from terrestrial local biodiversity surveys with remotely-sensed data on environmental heterogeneity globally. I then determine through a cross-validation framework how accurately local biodiversity measures can be predicted within (“predictability”) and across similar (“transferability”) biodiversity surveys.ResultsI found that prediction errors can be substantial, with error magnitudes varying between different biodiversity measures, taxonomic groups, sampling techniques and types of environmental heterogeneity characterizations. And although errors associated with model predictability were in many cases relatively low, these results question–particular for transferability–our capability to accurately predict and project local biodiversity measures based on environmental heterogeneity. I make the case that future predictions should be evaluated based on their accuracy and inherent uncertainty, and ecological theories be tested against whether we are able to make accurate predictions from local biodiversity data.

Weed community evolution under diversification managements in a new planted organic apricot orchard

Biodiversity conservation and promotion of agroecology (including organic farming) are focused as priorities by European Commission, as reported in the Biodiversity and Farm to Fork Strategies. In this context, agricultural practices can be considered for their contribution to harnessing ecosystem services to sustain agricultural production, while minimising negative environmental effects. Among these, redesigning agricultural systems through diversification practices can be very promising by exploiting the natural biodiversity in space and time, and connecting farms with natural environments. Being an interface between above and belowground, spontaneous plants (i.e., the weeds) are considered valuable agroecosystem components that, if properly managed, provide ecosystem functions and services, and holding key role for biodiversity conservation within agroecosystems. In this study, we explored the response of spontaneous flora communities in terms of abundance, diversity, and functional traits to different soil and agrobiodiversity management strategies (soil ripping vs reduced tillage at orchard planting, soil tillage vs minimum/no tillage in orchard floor management over 5 years) and cover crop introduction in a new planted organic apricot orchard. Our results showed the strong impact of the planting operation on pre-existing spontaneous flora community structure but a resilient trajectory in the following years in recovering the previous community traits distribution. Interannual variability was the main factor affecting communities. However, the reduced tillage condition showed a conservative trend of the incidence of perennials in the community, while the introduction of cover crops generally decreased species richness and abundance. Understanding the mechanisms by which soil management practices affect weed communities can guide the designing of sustainable agroecosystems, preserving their functionality and biodiversity over time.

Remote sensing of spectral diversity: A new methodological approach to account for spatio-temporal dissimilarities between plant communities

The increasing availability of remote sensing data allows the quantification of biodiversity in space and time. In particular, spectral diversity, defined as the variability of electromagnetic radiation reflected from plants, can be assessed with remote sensing. Plant traits vary diurnally and seasonally due to plant phenology and land management. This results in strong temporal variation of spectral diversity, which cannot be accurately represented by remotely sensed data collected at a single point in time. However, knowledge of how datasets sampled at multiple points in time should best be used to quantify spectral diversity is scarce. To address this issue, we first introduced a new approach using spatio-temporal spectral diversity based on the dissimilarity measure Rao's quadratic entropy index (RaoQ). Thereby, we demonstrated how RaoQ can be used to partition the total spectral diversity of a region (γSD) into additive alpha (αSD, within communities) and spatio-temporal beta (βSD; between communities) components, allowing the calculation of βSD from community mean spectral features, independent from αSD. Second, we illustrated our methodological approach with a case study in which βSD is calculated from Sentinel-2 satellite data at high temporal resolution for managed grasslands which differ across a large gradient of environmental properties. We were able to show differences in βSD and separate its components into phenological and management effects. Furthermore, the contribution of different plant communities to βSD was assessed, and the results were validated against a dataset of in-situ measured β diversity from plant surveys. Compared to spatial dissimilarities from distinct stages of the growing season, using spatio-temporal dissimilarities between communities produced a more accurate estimation of the uniqueness of a community. This study shows how to account for temporal variations in the spectral diversity of plant communities and demonstrates that this improves the estimation of plant biodiversity through remote sensing. Spectral diversity in space and time makes it possible to assess mechanisms that drive biodiversity and identify plant communities relevant for conservation purposes.

Effectively inferring overall spatial distribution pattern of species in a map when exact coordinate information is missing

Abstract Mapping and comparing biodiversity in space is a routine task in ecological research. However, accurate biodiversity mapping requires exact distributional information as inputs. When exact geographical coordinate information is missing or when only coarse distribution of species is available at the quadrat or plot level, how can we effectively infer and compare spatial distributional patterns of species with reasonable accuracy while controlling the confounding impacts of scale dependency? A crossing‐scale coefficient of variation (CCV) is calculated for three simple diversity indices, the Shannon, Gini–Simpson and reciprocal‐Simpson evenness indices, from the quadrat‐based biodiversity data over varying sampling grain sizes for subtly measuring overall spatial distributional pattern of species in a survey map. Extensive numerical simulations showed that, when the overall distribution of organisms was regular, CCV tended to be very small, for all the three evenness indices. By contrast, when the distribution was highly aggregate, CCV tended to be large. Finally, an intermediate CCV was observed when organism distribution was random. To this end, all the three evenness indices can detect the subtle change on aggregate or regular distribution. Among the indices, the Gini–Simpson evenness index is the most insensitive to both the change of population size of species and the number of sampling grain sizes used. Compared to available spatial statistic metrics that requires exact geographical coordinates as inputs, CCV is a useful metric for assessing the overall spatial distribution pattern of a species across sampling plots in which exact spatial coordinates of each individual are unavailable. Empirical applications of the CCV metric showed that plants in habitat patches extensively managed by humans can present very aggregate distributional pattern, being counterintuitive to the usual expectation that plants in cultivated or planted land should present regular distributional pattern. The present study recommends one effective tool, the combination of CCV and evenness indices, for exploring the overall spatial distributional pattern of a single species under the quadrat‐sampling scheme. The proposed method is particularly suitable to handle ecological data for which the finest‐scale distributional coordinate information of each individual of species is missing.

The concerted emergence of well-known spatial and temporal ecological patterns in an evolutionary food web model in space

Ecological systems show a variety of characteristic patterns of biodiversity in space and time. It is a challenge for theory to find models that can reproduce and explain the observed patterns. Since the advent of island biogeography these models revolve around speciation, dispersal, and extinction, but they usually neglect trophic structure. Here, we propose and study a spatially extended evolutionary food web model that allows us to study large spatial systems with several trophic layers. Our computer simulations show that the model gives rise simultaneously to several biodiversity patterns in space and time, from species abundance distributions to the waxing and waning of geographic ranges. We find that trophic position in the network plays a crucial role when it comes to the time evolution of range sizes, because the trophic context restricts the occurrence and survival of species especially on higher trophic levels.

Linking Landscape Ecology and Macroecology by Scaling Biodiversity in Space and Time

Despite the decades-long recognition of the importance of scaling in ecology, our knowledge about many ecological patterns and processes is still largely restricted to particular spatial and temporal scale domains with relatively narrow ranges. There is no exception when it comes to the study of biodiversity, one of the most important and active research fields in ecology. Increasing work suggests that such narrow ranges of scales are in most cases inadequate for addressing conservation challenges associated with biodiversity change. The need for understanding how biodiversity is shaped and will change across different scales is stronger than ever. Here, we review recent progresses of up-scaling and down-scaling biodiversity in the context of global environmental change, with focus on two relatively large spatial scale domains, i.e., the landscape and macroecological scales. Landscape ecology and macroecology are both active, but so-far poorly connected research fields. They share a common central motivation of unraveling spatial patterning of biodiversity and the underlying mechanisms. Our literature review suggests that landscape-scale processes may exert unexpected up-scaling effects to shape biodiversity patterns at macroecological scales, while macroecological processes may generate a range of down-scaling effects on landscape biodiversity. Specifically, although there is a lack of consensus on the underlying mechanisms, it is likely that landscape processes scale up through connectivity and feedback loops within and across landscapes to affect macroecological biodiversity responses. On the other hand, the down-scaling effects of macroecological processes on biodiversity is often confounded with small-scale processes, leading to various responses inconsistent with direct down-scaling extrapolations. In addition, the temporal dimension is indispensable to investigating effects and mechanisms of cross-scale processes. Specifically, long-term (decades and beyond) perspectives are necessary for re-evaluating ecological knowledge obtained from biodiversity responses to short-term environmental changes and recognizing historical legacies of both landscape and macroecological processes on biodiversity at the two spatial scales. Overall, scaling analyses of ecological processes across spatial extents ranging from small habitats to the globe have revealed biodiversity responses to anthropogenic environmental changes as inconsistent with assumptions and extrapolations based on extant ecological knowledge at a few fixed scales. Such analyses are needed to better inform conservation actions and planning practiced mainly at local to macroecological scales. We suggest that elucidating cross-scaling mechanisms and accumulating long-term time series at multiple spatial scales are key to linking landscape ecology and macroecology in terms of biodiversity dynamics. Such efforts would be an important contribution to the ecological basis for managing biodiversity change in the Anthropocene, as these dynamics involve multiple up-scaling and down-scaling processes over time.

Competition and Seasonal Co-Existence of Coprophagous Scarabaeoidea (Coleoptera) in Differently Managed Habitat Patches of Sub-Mediterranean Grasslands in Slovenia

We studied species composition and seasonal co-existence of coprophagous Scarabaeoidea from two study sites in karst meadows in sub-Mediterranean Slovenia. Each site consisted of three habitat patches with different impact of grazing (S1, the active part of the pasture; S2, the overgrown part of the pasture, mainly spiny shrubs; S3, a meadow with some overgrown patches of shrubs outside the fenced pasture). Four pitfall trapping events per month were conducted from March until November to test for temporal, spatial and habitat-related segregation of species. Primary results show a high level of temporal segregation of species within and between the guilds (Aphodiidae – dwellers, Geotrupidae – tunnelers, Scarabaeidae – tunnelers, Scarabaeidae – rollers). Temporal segregation of monthly samples was evident between Aphodiidae – dwellers (most active at the beginning and end of the season, with a month of complete inactivity during hot summer) and Scarabaeidae – tunnelers (present all the time, but with least species and specimens at the beginning and end of season). Intra-guild competition was most prominent for Aphodiidae – dwellers and Geotrupidae – tunnelers, where species show high rate of temporal avoidance to minimize interspecies competition for the same food source. Finally, geographical (site-related) and habitat-related (S1, S2, S3) differences were found in species composition, species richness and abundance, however for the latter two parameters only at habitat level (more species at S1, but higher abundances at S2 and S3). The results imply that the pronounced temporal and spatial segregation facilitate higher biodiversity in space and time, and allow more species to co-exist at the same sites over time.

From environmental DNA sequences to ecological conclusions: How strong is the influence of methodological choices?

AbstractAimEnvironmental DNA (eDNA) is increasingly used for analysing and modelling all‐inclusive biodiversity patterns. However, the reliability of eDNA‐based diversity estimates is commonly compromised by arbitrary decisions for curating the data from molecular artefacts. Here, we test the sensitivity of common ecological analyses to these curation steps, and identify the crucial ones to draw sound ecological conclusions.LocationValloire, French Alps.TaxonVascular plants and fungi.MethodsUsing soil eDNA metabarcoding data for plants and fungi from 20 plots sampled along a 1000‐m elevational gradient, we tested how the conclusions from three types of ecological analyses: (a) the spatial partitioning of diversity, (b) the diversity–environment relationship, and (c) the distance–decay relationship, are robust to data curation steps. Since eDNA metabarcoding data also comprise erroneous sequences with low frequencies, diversity estimates were further calculated using abundance‐based Hill numbers, which penalize rare sequences through a scaling parameter, namely the order of diversity q (Richness with q = 0, Shannon diversity with q ~ 1, Simpson diversity with q = 2).ResultsWe showed that results from different ecological analyses had varying degrees of sensitivity to data curation strategies and that the use of Shannon and Simpson diversities led to more reliable results. We demonstrated that molecular operational taxonomic unit clustering, removal of polymerase chain reaction errors and of cross‐sample contaminations had major impacts on ecological analyses.Main conclusionsIn the Era of Big Data, eDNA metabarcoding is going to be one of the major tools to describe, model and predict biodiversity in space and time. However, ignoring crucial data curation steps will impede the robustness of several ecological conclusions. Here, we propose a roadmap of crucial curation steps for different types of ecological analyses.

How to predict biodiversity in space? An evaluation of modelling approaches in marine ecosystems

AbstractAimBiodiversity prediction becomes increasingly important in the face of global diversity loss, whereas substantial challenges still exist in both conceptual and technical aspects. There exist many predictive models, and an integrative evaluation can help understand their performance in handling the multifacets of biodiversity. This study aims to evaluate the performance of these modelling approaches to predict both α‐ and β‐diversity in diverse ecological contexts.LocationNorth Yellow Sea, China.MethodsThe biodiversity models follow three strategies, “assemble first, predict later”, “predict first, assemble later” and “assemble and predict together”. Hill diversity profile, Fisher's log‐series parameter and the distance decay of similarity are used to measure α‐ and β‐diversity. The evaluation study is conducted based on seasonal bottom trawl surveys from October 2016 to August 2017 in North Yellow Sea, China, allocated to coastal and offshore areas. We evaluate the predictive power of the models using cross‐validation.ResultsFollowing the “assemble first, predict later” approach, macroecological model (MEM) provided the most accurate prediction overall, whereas stacked species distribution model (SSDM) and joint species distribution model (JSDM), following the second and third modelling approaches, tended to overestimate α‐diversity and underestimate β‐diversity. The performances of SSDM and JSDM could be improved by moderately down‐weighting rare species. The relative performances of the three modelling approaches were consistent among seasons and spatial regions.Main conclusionsThe superior performances of MEM in a range of temporal and spatial contexts favour the “assemble first, predict later” approach and imply a tight community assembly in the studied area. The overall predictive powers of varying models suggest that the spatial pattern of marine biodiversity could be fairly well predicted with commonly accessible hydrologic data in a mesoscales. The approach of multi‐model evaluations is applicable to a variety of ecosystems for biodiversity prediction.

Spatiotemporal scaling of plant species richness and functional diversity in a temperate semi‐natural grassland

The accumulation of biodiversity in space and time has been modelled extensively using the species–area relationship and the species–time relationship, respectively. Recently, these models have been combined into time–area curves in order to investigate spatiotemporal scaling of species richness. This study expands on previous research by applying these spatiotemporal models to functional diversity. Understanding spatiotemporal dynamics of ecological traits is important due to their crucial role in ecosystem functioning and mediating species responses to environmental change. We present a new function based on the semi‐logarithmic species–area relationship, which was applied with a power function to vegetation survey data from Scottish machair grassland for both species richness and two measures of functional diversity. When taking a whole‐study approach using non‐linear mixed effects models, the semi‐logarithmic function used here shows a positive time–area interaction for species richness, contrasting with the negative interaction of the power law found in previous investigations. Although there was a negative time–area interaction for functional diversity measures at the whole‐study scale, parameter estimates were inconsistent at the individual site level. Overall, the results reveal differing spatiotemporal dynamics of species and their traits and suggest that the appropriate scale for space‐for‐time substitutions depends on the aspect of biodiversity being investigated. The new model developed in this study, and the novel application to functional diversity, opens up future possible research into spatiotemporal dynamics of biodiversity.

Improving spatial predictions of taxonomic, functional and phylogenetic diversity

Abstract In this study, we compare two community modelling approaches to determine their ability to predict the taxonomic, functional and phylogenetic properties of plant assemblages along a broad elevation gradient and at a fine resolution. The first method is the standard stacking individual species distribution modelling (SSDM) approach, which applies a simple environmental filter to predict species assemblages. The second method couples the SSDM and macroecological modelling (MEM—SSDM‐MEM) approaches to impose a limit on the number of species co‐occurring at each site. Because the detection of diversity patterns can be influenced by different levels of phylogenetic or functional trees, we also examine whether performing our analyses from broad to more exact structures in the trees influences the performance of the two modelling approaches when calculating diversity indices. We found that coupling the SSDM with the MEM improves the overall predictions for the three diversity facets compared with those of the SSDM alone. The accuracy of the SSDM predictions for the diversity indices varied greatly along the elevation gradient, and when considering broad to more exact structure in the functional and phylogenetic trees, the SSDM‐MEM predictions were more stable. SSDM‐MEM moderately but significantly improved the prediction of taxonomic diversity, which was mainly driven by the corrected number of predicted species. The performance of both modelling frameworks increased when predicting the functional and phylogenetic diversity indices. In particular, fair predictions of the taxonomic composition by SSDM‐MEM led to increasingly accurate predictions of the functional and phylogenetic indices, suggesting that the compositional errors were associated with species that were functionally or phylogenetically close to the correct ones; however, this did not always hold for the SSDM predictions. Synthesis. In this study, we tested the use of a recently published approach that couples species distribution and macroecological models to provide the first predictions of the distribution of multiple facets of plant diversity: taxonomic, functional and phylogenetic. Moderate but significant improvements were obtained; thus, our results open promising avenues for improving our ability to predict the different facets of biodiversity in space and time across broad environmental gradients when functional and phylogenetic information is available.

‘Equivalent numbers’ for species, phylogenetic or functional diversity in a nested hierarchy of multiple scales

Summary Many recent studies have searched to integrate species’ functions and phylogenies in the measurement of biodiversity. To obtain easily interpretable measures, some researchers recommended diversity indices expressed in terms of equivalent numbers of species: the number of equally likely and maximally dissimilar species needed to produce the given value of diversity. Then, biodiversity is often calculated at three scales: within communities (α diversity), among communities (β diversity) and in a region (γ diversity). These three scales are, however, insufficient to tackle the organization of biodiversity in space because, for most organisms, there is a nested hierarchy of multiple scales characterized by different patterns and processes, from the small neighbourhood to the biosphere. We developed methodologies for analysing species, functional, taxonomic or phylogenetic diversity in a hierarchy of multiple scales using equivalent numbers of species. As an example, we analysed the taxonomic and functional diversity of macroinvertebrate assemblages in the Loire River, France, at four levels: within sites (α diversity), among sites within geological regions (β1 diversity), among geological regions (β2 diversity) and at the river scale (γ diversity). The new hierarchical approaches of biodiversity revealed very low differences among sites within regions and among regions in terms of taxonomy and functional traits (size and diet), despite moderate, significant species turnover among geological regions. We compare our framework with those other authors have developed. We argue that different definitions of α, β, γ diversities are used in the literature reflecting different points of view on biodiversity. We make recommendations on how to normalize functional (or phylogenetic) dissimilarities among species to render sites and regions comparable, and discuss the pros and cons of our approach. The hierarchical approaches of biodiversity in terms of ‘equivalent numbers’ respond to current demands to obtain intuitive, easily interpretable components of biodiversity. The approaches we propose go beyond current developments by considering a hierarchy of spatial scales and unbalanced sampling design. They will provide powerful tools to detect the ecological and evolutionary processes that act differently at different scales.

Landscape-level predictions of diversity in river networks reveal opposing patterns for different groups of macroinvertebrates

Aquatic biodiversity in rivers and streams is threatened in many regions worldwide. As biodiversity loss has severe consequences on ecosystem functioning, it is important to understand the causes of decline and to predict biodiversity in space and time. In order to achieve this, the identification of the driving factors and the appropriate choice of indicator groups are needed. We developed a spatially explicit habitat distribution model for aquatic macroinvertebrates in Swiss watercourse networks using national biodiversity monitoring data from 410 randomly selected sampling sites. We specifically looked at two worldwide frequently used macroinvertebrate indicator groups. Using generalized linear models, we related firstly species richness of mayfly, stonefly and caddisfly (Ephemeroptera, Plecoptera, Trichoptera; EPT) and secondly richness of all macroinvertebrate families and higher-order taxa (macroinvertebrate family richness) to 38 nationwide available environmental variables. We then predicted richness of both indicator groups at the landscape scale, providing the first nationwide prediction of EPT species and macroinvertebrate family richness. Consistent with previous work, we found that variables describing land use and topology were most important for explaining richness at the landscape level. However, the two indicator groups showed opposing patterns of richness and a different sensitivity to land-use variables. This indicates that the sole use of one of these groups may be misleading with respect to water quality assessments and to the identification of overall diversity hotspots. We conclude that commonly used richness patterns derived from aggregated groups, such as family-level macroinvertebrate richness, may be less appropriate for conservation strategies.

At least some protist species are not ubiquitous

Body size is one of the main regulators of the ecological characteristics of living organisms, including their biogeography. The 'ubiquity hypothesis' for microorganisms states that they are widely distributed, if not cosmopolitan, due to their small size that allows passive dispersal, in contrast to large organisms that are limited by geographical barriers in their active dispersal. Such idea, summarized in the tenet 'Everything is everywhere, but the environment selects', has driven most of the research in biogeography for microscopic organisms in the last decades, spurring a debate on whether there are fundamental differences in the biogeography of small and large organisms or not (Fenchel & Finlay 2004; Foissner 2008; Hortal 2011). The strong focus on the ubiquity hypothesis may have been often abused to provide a rationale for otherwise descriptive work on the spatial distribution of microscopic organisms; nevertheless, such focus also provides a framework to understand the mechanisms originating and maintaining biodiversity in space. The reliability of the analyses on unknown and understudied organisms is improving, and Heger et al. (2013) is a splendid example on small unicellular eukaryotes of what should be done to overcome the major problems and ambiguities that heated the debate on the ubiquity hypothesis.

BIOTA Southern Africa Biodiversity Observatories Vegetation Database

The BIOTA Southern Africa Biodiversity Observatories Vegetation Database (AF-00-003) hosts the project-related data on spatial patterns and time series of biodiversity in southern Africa. Along a 2,000 km long transect, from the northern border of Namibia to the Cape of Good Hope, the plant diversity has been monitored on 37 Biodiversity Observatories for up to ten years (2001– 2010). The design of the Observatories enables the observation of vegetation in nested, permanent plots where standardized measurements can be repeated. Information on species occurrence, cover and abundance has been recorded annually and stored in a database. These vegetation observations form a part of the interdisciplinary approach of BIOTA Southern Africa which assesses and monitors different organisms. In this paper, we describe how the vegetation data of the biodiversity monitoring project BIOTA Southern Africa, are managed. More than 400,000 species observations were recorded and stored in a database, covering a time-series of up to ten years. The Biodiversity Observatories are situated in along a major rainfall gradient, cover six biomes and are subject to different land use. The vegetation monitoring is on-going. The empirical data are a valuable source of information for various research questions ranging from testing theories on patterns, drivers of biodiversity at different spatial scales to studies on changes in biodiversity in space and time. The BIOTA Southern Africa Biodiversity Observatories Vegetation Database is currently explored to answer the following research questions: How many plant species exist at different scale levels? How does plant species composition change over time in relation to seasonal fluctuations, long-term climate change, environmental changes and human influences?

Bitrophic interactions shape biodiversity in space.

Ecologists and conservation biologists often study particular trophic groups in isolation, which precludes an explicit assessment of the impact of multitrophic interactions on community structure and dynamics. Network ecology helps to fill this gap by focusing on species interactions, but it often ignores spatial processes. Here, we are taking a step forward in the integration of metacommunity and network approaches by studying a model of bitrophic interactions in a spatial context. We quantify the effect of bitrophic interactions on the diversity of plants and their animal interactors, and we show their complex dependence on the structure of the interaction network, the strength of interactions, and the dispersal rate. We then develop a method to parameterize our model with real-world networks and apply it to 54 datasets describing three types of interactions: pollination, fungal association, and insect herbivory. In all three network types, bitrophic interactions generally lead to an increase of plant and animal spatial heterogeneity by decreasing local species richness while increasing β-diversity.

An rpoB signature sequence provides unique resolution for the molecular typing of cyanobacteria

The use of morphological characters for the classification of cyanobacteria has often led to ambiguous strain assignment. In the past two decades, the availability of sequences, such as those of the 16S rRNA, nif, cpc and rpoC1 genes, and the use of metagenomics, has steadily increased and has made the reconstruction of evolutionary relationships of some cyanobacterial groups possible in addition to improving strain assignment. Conserved indels (insertions/deletions) are present in all cyanobacterial RpoB (β subunit of RNA polymerase) sequences presently available in public databases. These indels are located in the Rpb2_6 domain of RpoB, which is involved in DNA binding and DNA-directed RNA polymerase activity. They are variable in length (6-44 aa) and sequence, and form part of what appears to be a longer signature sequence (43-81 aa). Indeed, a number of these sequences turn out to be distinctive among several strains of a given genus and even among strains of a given species. These signature sequences can thus be used to identify cyanobacteria at a subgenus level and can be useful molecular markers to establish the taxonomic positions of cyanobacterial isolates in laboratory cultures, and/or to assess cyanobacterial biodiversity in space and time in natural ecosystems.

Hierarchical partitioning of evolutionary and ecological patterns in the organization of phylogenetically‐structured species assemblages: application to rockfish (genus: Sebastes) in the Southern California Bight

AbstractPhylogenetic divergences have recently been included in analyses that aim to elucidate patterns of biodiversity in space and time. We introduce a generalized framework for two widely used phylogenetic diversity (PD) indices: Rao’s quadratic entropy (QE) and Faith’s PD. We demonstrate how this framework can be used to partition diversity simultaneously across evolutionary periods and spatial (e.g. local communities in a region) and/or time units (e.g. a community investigated yearly). From a study of rockfish hotspot diversity from the Southern California Bight, the analysis of PD revealed a recent decrease in the amount of fish caught from six evolutionary deep lineages, with implications for the community structure of this speciose group. This approach, which can also be applied to trees assembled from functional traits, contributes to our understanding of the mechanisms that underpin community organization and to the description of the consequences of human‐driven impacts in the environment.

The International Biogeography Society and the inaugural Alfred Russel Wallace Award

The International Biogeography Society and the inaugural Alfred Russel Wallace Award