The Concept of Taxon Invariance in Ecology: Do Diversity Patterns Vary with Changes in Taxonomic Resolution?

Diversity patterns cannot be properly interpreted without a theory providing criteria for their evaluation. We propose a concept to prevent artifictions caused by improper consideration of changes in observed patterns due to variation in taxon delimitation. Most biodiversity patterns concern assemblages of species of given higher taxon (e.g. class). Some patterns seem to be universal, e.g., body size distribution, species-abundance distribution, species-area relationship, or the relationship between diversity and energy availability. However, truly universal patterns should not change when we change taxonomic scope by focusing on subtaxa or when we merge several sister taxa together and analyze patterns in resulting higher taxon. Similarly, some patterns may not change when changing the basic unit of the study e.g., when replacing species by genera or families (or any monophyletic clades), although other patterns may not be invariant against the variation of the basic unit. In fact, there are only two possibilities: biodiversity patterns are either taxon-invariant or they vary systematically with taxonomic resolution, which would indicate some fundamental taxonomic level with interesting implications for biological processes behind those patterns. Here we develop the concept of taxon invariance of diversity patterns and apply it on the abovementioned patterns. We show that simple theoretical considerations markedly constrain the set of possible patterns, as some of them cannot be simultaneously valid for both a taxon and its subtaxa – frequency distributions of abundances cannot be simultaneously lognormal for a given taxon and all its subtaxa, the taxa-area relationship cannot follow a power-law for all levels of taxonomic resolution, and energy availability cannot affect diversity of all taxonomic units in the same way. Analyses of the variation in the form of biodiversity patterns with changing taxonomic resolution thus provide an extremely useful tool for revealing properties of respective patterns, their universality and logical consistency.     

Conditionally autoregressive models improve occupancy analyses of autocorrelated data: An example with environmental DNA

Site occupancy‐detection models (SODMs) are statistical models widely used for biodiversity surveys where imperfect detection of species occurs. For instance, SODMs are increasingly used to analyse environmental DNA (eDNA) data, taking into account the occurrence of both false‐positive and false‐negative errors. However, species occurrence data are often characterized by spatial and temporal autocorrelation, which might challenge the use of standard SODMs. Here we reviewed the literature of eDNA biodiversity surveys and found that most of studies do not take into account spatial or temporal autocorrelation. We then demonstrated how the analysis of data with spatial or temporal autocorrelation can be improved by using a conditionally autoregressive SODM, and show its application to environmental DNA data. We tested the autoregressive model on both simulated and real data sets, including chronosequences with different degrees of autocorrelation, and a spatial data set on a virtual landscape. Analyses of simulated data showed that autoregressive SODMs perform better than traditional SODMs in the estimation of key parameters such as true‐/false‐positive rates and show a better discrimination capacity (e.g., higher true skill statistics). The usefulness of autoregressive SODMs was particularly high in data sets with strong autocorrelation. When applied to real eDNA data sets (eDNA from lake sediment cores and freshwater), autoregressive SODM provided more precise estimation of true‐/false‐positive rates, resulting in more reasonable inference of occupancy states. Our results suggest that analyses of occurrence data, such as many applications of eDNA, can be largely improved by applying conditionally autoregressive specifications to SODMs.     

Beyond Trivial Relationships: on the Hidden Aspects of Biodiversity

The strong correlation found between detailed and simple measures of biodiversity at landscape-scale may suggest that patterns of detailed diversity measures are artifacts (sensu Palmer et al. 2008, this issue). For example the regional pattern of Compositional Diversity (a measure of within-stand structural complexity) can be considered as a correct but trivial result because this pattern is almost completely determined (at least statistically) by the regional pattern of species richness. However, I argue that this interpretation is misleading. Using spatially-explicit individual-based simulations I demonstrate that fine-scale patterns and assembly rules (between a dominant grass and some annual species) significantly affect the dynamics of diversity, and fine-scale constraints are able to generate regional diversity patterns. Simulations illustrate that patterns that seem to be artifacts in a static view might acquire functional relevance in a temporal-dynamic approach. While a static view suggests that variation of fine-scale community attributes (e.g. Compositional Diversity) is explained by the variation of species richness, a dynamic approach shows the opposite. In the long term, variation of species richness is explained by the patterns of fine-scale community organization. I propose the use of mechanistic simulations coupled with the analyses of within-stand variability and spatial dependence of species combinations to reveal otherwise hidden spatiotemporal aspects of biodiversity.     

Scale Dependency in the Species-Area Relationship of Plant Communities

The species-area relationship (SAR) is one of the most frequently described patterns in biodiversity; however, it is seldom applied in conservation practice. Awareness of some shortcomings such as scale dependency is the basis for a better understanding of biodiversity patterns and a way to cope with artifacts occurring in empirical data. We demonstrate effects of scale dependency within plant communities deriving from different scale ranges, extreme values and curve intersections on empirical data of forest and grassland communities and propose to reduce the effect of scale dependence and artifacts of the SAR within plant communities by using limited scale ranges. Due to scale dependence, an appropriate limited scale range should be chosen, depending on vegetation type and realized scale range of the considered data set. Species-area curves based on the standard Whittaker plots for plant communities could act as references to assess species richness of the large pool of vegetation records of different sizes and provide a tool for ranking species diversity for grassland and forest communities, where vegetation records differ considerably in plot size.     

Towards an integrative understanding of soil biodiversity

Soil is one of the most biodiverse terrestrial habitats. Yet, we lack an integrative conceptual framework for understanding the patterns and mechanisms driving soil biodiversity. One of the underlying reasons for our poor understanding of soil biodiversity patterns relates to whether key biodiversity theories (historically developed for aboveground and aquatic organisms) are applicable to patterns of soil biodiversity. Here, we present a systematic literature review to investigate whether and how key biodiversity theories (species–energy relationship, theory of island biogeography, metacommunity theory, niche theory and neutral theory) can explain observed patterns of soil biodiversity. We then discuss two spatial compartments nested within soil at which biodiversity theories can be applied to acknowledge the scale‐dependent nature of soil biodiversity.     

On the relationship between a resource based measure of geodiversity and broad scale biodiversity patterns

Geodiversity, (diversity of the geosphere) incorporates many of the environmental patterns and processes that are considered drivers of biodiversity. Components of geodiversity (climate, topography, geology and hydrology) can be considered in terms of their resource giving potential, where resources are taken as energy, water, space and nutrients. The total amount of these resources, along with their spatial and temporal variation, is herein proposed as a compound index of geodiversity that has the potential to model broad scale biodiversity patterns. This paper outlines potential datasets that could be used to represent geodiversity, and then reviews the theoretical links between each element of the proposed compound index of geodiversity (overall resource availability, temporal variation and spatial variation in those resources) and broad-scale patterns of biodiversity. Support for the influence of each of the elements of geodiversity on overall biodiversity patterns was found in the literature, although the majority of relevant research focuses on resource availability, particularly available energy. The links between temporal and spatial variation in resources and biodiversity have been less thoroughly investigated in the literature. For the most part, it was reported that overall resource availability, temporal variation and spatial variation in those resources do not act in isolation in terms of controlling biodiversity. Overall there are sufficient datasets to calculate the proposed compound index of geodiversity, and evidence in the literature for links between the geographical distribution of biodiversity and each of the elements of the compound index defined. Since data for measuring geodiversity is more spatially consistent and widely available (thanks to satellite remote sensing) geodiversity has potential as a conservation planning tool, especially where biological data are not available or sparsely distributed.     

Diversity Over Time

Temporal turnover is a fundamental feature of ecological communities. Darwin 1859 noted the ecological and evolutionary significance of turnover, Fisher and Preston acknowledged its role in their models of species abundance, while this ongoing and entirely natural rearrangement of species underpins key ecological concepts such as MacArthur and Wilson’s theory of island biogeography. However, the current focus on spatial patterns of diversity means that temporal changes are often overlooked. Here I argue that failure to take heed of the time frame over which data are collected can lead to both artefacts and artifictions. There are also deeper issues, such as the consequences for species richness estimation and rarefaction methods of a constantly changing community. Moreover, some of the confusion surrounding species abundance distributions may be resolved by taking account of time. A better appreciation of temporal turnover is essential for accurate diversity measurement and assessment, and, more importantly, will also lead to improved understanding of the processes that underpin community structure.     

Précautions,pièges et artefacts en TEP/TDM au 18FDG : généralités et cas particulier des pathologies de la tête et du cou

The PET/CT imaging with 18F-FDG is an essential diagnostic tool in the management of many diseases today. However, because of its functional and metabolic nature, it requires for its realization the respect of certain precautions to avoid abnormal images or false negative tests. Moreover, in the context of head and neck diseases, knowledge of anatomy and physiological uptakes appears essential for quality interpretation. In this paper, we illustrate with several personal circumstances or particular clinical situations from literature, the potential pitfalls and artifacts that could lead to misinterpretation.     

Genomic biodiversity, phylogenetics and coevolution in proteins

Comprehensive sampling of genomic biodiversity is fast becoming a reality for some genomic regions and complete organelle genomes. Genomic biodiversity is defined as large genomic sequences from many species, and here some recent work is reviewed that demonstrates the potential benefits of genomic biodiversity for molecular evolutionary analysis and phylogenetic reconstruction. This work shows that using likelihood-based approaches, taxon addition can dramatically improve phylogenetic reconstruction. Features or dynamics of the evolutionary process are much more easily inferred with large numbers of taxa, and large numbers are essential for discriminating differences in evolutionary patterns between sites. Accurate prediction of site-specific patterns can improve phylogenetic reconstruction by an amount equivalent to quadrupling sequence length. Genomic biodiversity is particularly central to research relating patterns of evolution, adaptation and coevolution to structural and functional features of proteins. Research on detecting coevolution between amino acid residues in proteins demonstrates a clear need for much greater numbers of closely related taxa to better discriminate site-specific patterns of interaction, and to allow more detailed analysis of coevolutionary interactions between subunits in protein complexes. It is argued that parsing out coevolutionary and other context-dependent substitution probabilities is essential for discriminating between coevolution and adaptation, and for more realistically modelling the evolution of proteins. Also reviewed is research that argues for increasing the efficiency of acquiring genomic biodiversity, and suggests that this might be done by simultaneously shotgun cloning and sequencing genomic mixtures from many species. Increased efficiency is a prerequisite if genomic biodiversity levels are to rapidly increase by orders of magnitude, and thus lead to dramatically improved understanding of interactions between protein structure, function and sequence evolution.     

Are we selecting appropriate metrics to assess human impacts on biodiversity?

Biased and subjective choices of metrics to be used in ecological studies could lead researchers to reach misleading conclusions regarding patterns of biodiversity response to human disturbances. Nevertheless, little attention has been given to the choices of variables in the majority of studies published to date. Here, we used the literature concerning land use change effects on dung beetles to assess the extent to which variables commonly employed in ecological studies correspond to those deemed to be most important by researchers of the same studies. Specifically, we examined both biodiversity (response) and environmental (explanatory) metrics from a comprehensive literature review and compared their use with their relative importance, according to a survey of the authors of the studies. Our results highlight marked disparities between researchers opinion expressed in our survey and their choice of variables in published papers. We suggest that these disparities are due to the high costs of sampling and processing some variables, logistical constraints and different perceptions of importance amongst researchers. We highlight the importance of these issues for our understanding of the biodiversity consequences of land use change, and highlight some recommendations for alleviating this issue.     

Heterogeneous CD8+ T Cell Migration in the Lymph Node in the Absence of Inflammation Revealed by Quantitative Migration Analysis

The three-dimensional positions of immune cells can be tracked in live tissues precisely as a function of time using two-photon microscopy. However, standard methods of analysis used in the field and experimental artifacts can bias interpretations and obscure important aspects of cell migration such as directional migration and non-Brownian walk statistics. Therefore, methods were developed for minimizing drift artifacts, identifying directional and anisotropic (asymmetric) migration, and classifying cell migration statistics. These methods were applied to describe the migration statistics of CD8+ T cells in uninflamed lymph nodes. Contrary to current models, CD8+ T cell statistics are not well described by a straightforward persistent random walk model. Instead, a model in which one population of cells moves via Brownian-like motion and another population follows variable persistent random walks with noise reproduces multiple statistical measures of CD8+ T cell migration in the lymph node in the absence of inflammation.     

Contrasting Diversity and Host Association of Ectomycorrhizal Basidiomycetes versus Root-Associated Ascomycetes in a Dipterocarp Rainforest

Root-associated fungi, including ectomycorrhizal and root-endophytic fungi, are among the most diverse and important belowground plant symbionts in dipterocarp rainforests. Our study aimed to reveal the biodiversity, host association, and community structure of ectomycorrhizal Basidiomycota and root-associated Ascomycota (including root-endophytic Ascomycota) in a lowland dipterocarp rainforest in Southeast Asia. The host plant chloroplast ribulose-1,5-bisphosphate carboxylase/oxygenase large subunit (rbcL) region and fungal internal transcribed spacer 2 (ITS2) region were sequenced using tag-encoded, massively parallel 454 pyrosequencing to identify host plant and root-associated fungal taxa in root samples. In total, 1245 ascomycetous and 127 putative ectomycorrhizal basidiomycetous taxa were detected from 442 root samples. The putative ectomycorrhizal Basidiomycota were likely to be associated with closely related dipterocarp taxa to greater or lesser extents, whereas host association patterns of the root-associated Ascomycota were much less distinct. The community structure of the putative ectomycorrhizal Basidiomycota was possibly more influenced by host genetic distances than was that of the root-associated Ascomycota. This study also indicated that in dipterocarp rainforests, root-associated Ascomycota were characterized by high biodiversity and indistinct host association patterns, whereas ectomycorrhizal Basidiomycota showed less biodiversity and a strong host phylogenetic preference for dipterocarp trees. Our findings lead to the working hypothesis that root-associated Ascomycota, which might be mainly represented by root-endophytic fungi, have biodiversity hotspots in the tropics, whereas biodiversity of ectomycorrhizal Basidiomycota increases with host genetic diversity.     

Strong observer effect on tree microhabitats inventories: A case study in a French lowland forest

Validating biodiversity indicators requires an analysis of their applicability, their range of validity and their degree of correlation with the biodiversity they are supposed to represent. In this process, assessing the magnitude of observer effect is an essential step, especially if non-specialist observers are involved. Tree microhabitats – woodpecker cavities, cracks and bark characteristics – are reputed to be easily detected by non-specialists as microhabitat observation does not require prior forestry or ecology knowledge. We therefore quantified the probabilities of true and false positive detections made by observers during inventories.Within two 0.5 ha plots in a forest reserve that has not been harvested for at least 150 years, 14 observers with various backgrounds visually inventoried microhabitats on 106 oak (Quercus petraea and Quercus robur) and beech (Fagus sylvatica) trees. We used parametric and Bayesian statistics to compare these observers’ recorded observations with results from an independent census.The mean number of microhabitats per tree varied widely among observers – from 1.4 to over 3. Only five observers reported a mean number of microhabitats per tree that was statistically equivalent to the reference census. The probability of true detection also varied among observers for each microhabitat (from to 0 to 1) as did the probability of false positive detection (from 0 to 0.7). These results show that microhabitat inventories are particularly prone to observer effects.Such strong observer effects weaken the usefulness of microhabitats as biodiversity indicators. If microhabitat inventories are to be developed, we recommend controlling for observer effects by (i) defining standard operating procedures and multiplying the number of observer training sessions and of consensual standardization censuses; (ii) using pairs of observers to record microhabitats whenever possible (though the efficiency of this method remains to be tested); (iii) planning fieldwork so that the factors of interest are not confused with observer effects; and (iv) integrating observer profiles into the statistical models used to analyze the data.     

Can unified theories of biodiversity explain mammalian macroecological patterns?

The idea of a unifying theory of biodiversity linking the diverse array of macroecological patterns into a common theoretical framework is very appealing. We explore this idea to examine currently proposed unified theories of biodiversity (UTBs) and their predictions. Synthesizing the literature on the macroecological patterns of mammals, we critically evaluate the evidence to support these theories. We find general qualitative support for the UTBs' predictions within mammals, but rigorous testing is hampered by the types of data typically collected in studies of mammals. In particular, abundance is rarely estimated for entire mammalian communities or of individual species in multiple locations, reflecting the logistical challenges of studying wild mammal populations. By contrast, there are numerous macroecological patterns (especially allometric scaling relationships) that are extremely well characterized for mammals, but which fall outside the scope of current UTBs. We consider how these theories might be extended to explain mammalian biodiversity patterns more generally. Specifically, we suggest that UTBs need to incorporate the dimensions of geographical space, species' traits and time to reconcile theory with pattern.     

Ghost QTL and hotspots in experimental crosses: novel approach for modeling polygenic effects

Ghost quantitative trait loci (QTL) are the false discoveries in QTL mapping, that arise due to the “accumulation” of the polygenic effects, uniformly distributed over the genome. The locations on the chromosome that are strongly correlated with the total of the polygenic effects depend on a specific sample correlation structure determined by the genotypes at all loci. The problem is particularly severe when the same genotypes are used to study multiple QTL, e.g. using recombinant inbred lines or studying the expression QTL. In this case, the ghost QTL phenomenon can lead to false hotspots, where multiple QTL show apparent linkage to the same locus. We illustrate the problem using the classic backcross design and suggest that it can be solved by the application of the extended mixed effect model, where the random effects are allowed to have a nonzero mean. We provide formulas for estimating the thresholds for the corresponding t-test statistics and use them in the stepwise selection strategy, which allows for a simultaneous detection of several QTL. Extensive simulation studies illustrate that our approach eliminates ghost QTL/false hotspots, while preserving a high power of true QTL detection.     

Investigating the Effects of Chemical Gradients on Performance and Reliability within Perovskite Solar Cells with TOF‐SIMS

Time‐of‐flight secondary‐ion mass spectrometry (TOF‐SIMS), a powerful analytical technique sensitive to all components of perovskite solar cell (PSC) materials, can differentiate between the various organic species within a PSC absorber or a complete device stack. The ability to probe chemical gradients through the depth of a device (both organic and inorganic), with down to 100 nm lateral resolution, can lead to unique insights into the relationships between chemistry in the absorber bulk, at grain boundaries, and at interfaces as well as how they relate to changes in performance and/or stability. In this review, the technique is described; then, from the literature, several examples of how TOF‐SIMS have been used to provide unique insight into PSC absorbers and devices are covered. Finally, the common artifacts that can be introduced if the data are improperly collected, as well as methods to mitigate these artifacts are discussed.     

Inferring macro‐ecological patterns from local presence/absence data

Biodiversity provides support for life, vital provisions, regulating services and has positive cultural impacts. It is therefore important to have accurate methods to measure biodiversity, in order to safeguard it when we discover it to be threatened. For practical reasons, biodiversity is usually measured at fine scales whereas diversity issues (e.g. conservation) interest regional or global scales. Moreover, biodiversity may change across spatial scales. It is therefore a key challenge to be able to translate local information on biodiversity into global patterns. Many databases give no information about the abundances of a species within an area, but only its occurrence in each of the surveyed plots. In this paper, we introduce an analytical framework (implemented in a ready‐to‐use R code) to infer species richness and abundances at large spatial scales in biodiversity‐rich ecosystems when species presence/absence information is available on various scattered samples (i.e. upscaling). This framework is based on the scale‐invariance property of the negative binomial. Our approach allows to infer and link within a unique framework important and well‐known biodiversity patterns of ecological theory, such as the species accumulation curve (SAC) and the relative species abundance (RSA) as well as a new emergent pattern, which is the relative species occupancy (RSO). Our estimates are robust and accurate, as confirmed by tests performed on both in silico‐generated and real forests. We demonstrate the accuracy of our predictions using data from two well‐studied forest stands. Moreover, we compared our results with other popular methods proposed in the literature to infer species richness from presence to absence data and we showed that our framework gives better estimates. It has thus important applications to biodiversity research and conservation practice.     

Pitfalls in Small-Scale Species-Area Sampling and Analysis

Analyses of the dependency of species richness (S) on area (A), the so-called species-area relationships (SARs), are widespread approaches to characterize and compare biodiversity patterns. This article highlights – with a focus on small-scale SARs of plants in continuous ecosystems – how inappropriate sampling methods or theoretical misconceptions can create artifacts and thus may lead to wrong conclusions. Most of these problems have been recognized before but continue to appear regularly in the scientific literature. The following main points are reviewed and discussed: i) Species richness values and SARs depend on the measurement method (any-part vs. grid-point system); ii) Species-richness values depend on the shape of the analyzed plots; iii) Many published SARs are not true SARs but instead represent species sampling curves or their data points consist of richness totals for incontiguous subplots; iv) Nested-plot design is the preferred sampling method for SARs (the claim that this approach would cause pseudoreplication is erroneous); v) SARs should be constructed using mean values of several counts for the smaller areas; vi) SAR functions can be fitted and selected both in the S- and the log S-space but this must be done consistently for all compared function types. It turns out that the finding of non-power function SARs in many studies is due to a lack of awareness of one or several of the named points. Thus, power-function SARs are even more widespread than a recent review would suggest. I therefore propose to use the power law as a universal model for all types of SARs but to test whether the slope z varies with spatial scale. Finally, I urge readers to be aware of the many pitfalls in SAR studies, to fully disclose methodology, and to apply a meaningful and consistent terminology, especially by restricting the terms “species-area relationship” and “species density” to situations in which each data point represents a contiguous area.     

Are Water Beetles Good Indicators of Biodiversity in Mediterranean Aquatic Ecosystems? The Case of the Segura River Basin (SE Spain)

Water beetles were examined for use as potential biodiversity indicators in continental aquatic ecosystems in a semiarid Mediterranean region, the Segura river basin (SE Spain). The indicator value of water beetles was investigated by examining the degree to which their species richness patterns was correlated with other groups (Plecoptera, Trichoptera, Mollusca, Heteroptera and Ephemeroptera), and the efficiency of water beetle area networks (selected by complementarity) in conserving overall groups richness. The species richness patterns of Coleoptera, Ephemeroptera, Plecoptera and Trichoptera were significantly correlated with the Remaining Richness value (RR), defined as the total number of species found at a site (of all six groups examined) minus the number of species belonging to the considered indicator group. Area networks for Coleoptera selected by complementarity represented the highest RR percentage (84.46%) and contained more than 78% species of each group. Furthermore, water beetles meet most of the criteria proposed in the literature for choosing biodiversity indicator taxa. In our study, the correlation values and the percentage of species represented by family, genus and species complementary networks were similar and we suggest that the higher taxa of water beetles (genera or families) can be used as biodiversity surrogates for cost-effective practical surveys.