Patterns of parasite community dissimilarity: the significant role of land use and lack of distance‐decay in a bat–helminth system

Increasing community dissimilarity across geographic distance has been described for a wide variety of organisms and understanding its underlying causes is key to understanding mechanisms driving patterns of biodiversity. Both niche‐based and neutral processes may produce a distance decay relationship; however, disentangling their relative influence requires simultaneous examination of multiple potential drivers. Parasites represent a unique opportunity in which to study distance decay because community dissimilarity may depend on environmental requirements and dispersal capability of parasites as well also those of their hosts. We used big brown bats Eptesicus fuscus and their intestinal helminths to investigate: 1) independent contributions of geographic and environmental distances on dissimilarity of intestinal helminth component communities between populations of big brown bats; 2) which environmental variables best explained variation in community dissimilarity; and 3) whether similar patterns of decay with geographic or environmental distance were observed for within‐host population and within‐individual host parasite communities. We used compositional measures of community dissimilarity to examine how parasite communities may change with geographic distance and varying environmental conditions. Non‐spatial variables strongly influenced compositional parasite community dissimilarity over multiple community scales, and we observed little evidence for spatial processes such as distance decay. Environment surrounding roost sites better predicted helminth community dissimilarity than any other class of variables and landcover classes representing anthropogenic modification consistently explained variation in community structure. Our results indicate that human disturbance drives significant patterns of parasite community dissimilarity, most likely by changing the presence or abundance of intermediate hosts in an area.     

Geographical patterns of turnover and nestedness-resultant components of allelic diversity among populations

The analysis of geographical patterns in population divergence has always been a powerful way to infer microevolutionary processes involved in population differentiation, and several approaches have been used to investigate such patterns. Most frequently, multivariate spatial patterns of population differentiation are analyzed by computing pairwise genetic distances or F_ST (or related statistics, such as ?_ST from AMOVA), which are then correlated with geographical distances or landscape features. However, when calculating distances, especially based on presence-absence of alleles in local populations, there would be a confounding effect of allelic richness differences in the population differentiation. Moreover, the relative magnitude of these components and their spatial patterns can help identifying microevolutionary processes driving population differentiation. Here we show how recent methodological advances in ecological community analyses that allows partitioning dissimilarity into turnover (turnover) and richness differences, or nestedness-resultant dissimilarity, can be applied to allelic variation data, using an endemic Cerrado tree (Dipteryx alata) as a case study. Individuals from 15 local populations were genotyped for eight microsatellite loci, and pairwise dissimilarities were computed based on presence-absence of alleles. The turnover of alleles among populations represented 69?% of variation in dissimilarity, but only the richness difference component shows a clear spatial structure, appearing as a westward decrease of allelic richness. We show that decoupling richness difference and turnover components of allelic variation reveals more clearly how similarity among populations reflects geographical patterns in allelic diversity that can be interpreted in respect to historical range expansion in the species.     

A pathway for multivariate analysis of ecological communities using copulas

We describe a new pathway for multivariate analysis of data consisting of counts of species abundances that includes two key components: copulas, to provide a flexible joint model of individual species, and dissimilarity‐based methods, to integrate information across species and provide a holistic view of the community. Individual species are characterized using suitable (marginal) statistical distributions, with the mean, the degree of over‐dispersion, and/or zero‐inflation being allowed to vary among a priori groups of sampling units. Associations among species are then modeled using copulas, which allow any pair of disparate types of variables to be coupled through their cumulative distribution function, while maintaining entirely the separate individual marginal distributions appropriate for each species. A Gaussian copula smoothly captures changes in an index of association that excludes joint absences in the space of the original species variables. A permutation‐based filter with exact family‐wise error can optionally be used a priori to reduce the dimensionality of the copula estimation problem. We describe in detail a Monte Carlo expectation maximization algorithm for efficient estimation of the copula correlation matrix with discrete marginal distributions (counts). The resulting fully parameterized copula models can be used to simulate realistic ecological community data under fully specified null or alternative hypotheses. Distributions of community centroids derived from simulated data can then be visualized in ordinations of ecologically meaningful dissimilarity spaces. Multinomial mixtures of data drawn from copula models also yield smooth power curves in dissimilarity‐based settings. Our proposed analysis pathway provides new opportunities to combine model‐based approaches with dissimilarity‐based methods to enhance understanding of ecological systems. We demonstrate implementation of the pathway through an ecological example, where associations among fish species were found to increase after the establishment of a marine reserve.     

β‐diversity scaling patterns are consistent across metrics and taxa

β‐diversity (variation in community composition) is a fundamental component of biodiversity, with implications for macroecology, community ecology and conservation. However, its scaling properties are poorly understood. Here, we systematically assessed the spatial scaling of β‐diversity using 12 empirical large‐scale datasets including different taxonomic groups, by examining two conceptual types of β‐diversity and explicitly considering the turnover and nestedness components. We found highly consistent patterns across datasets. Multiple‐site β‐diversity (i.e. variation across multiple sites) scaling curves were remarkably consistent, with β‐diversity decreasing with sampled area according to a power law. For pairwise dissimilarities, the rates of increase of dissimilarity with geographic distance remained largely constant across scales, while grain size (or scale level) had a stronger effect on overall dissimilarity. In both analyses, turnover was the main contributor to β‐diversity, following total β‐diversity patterns closely, while the nestedness component was largely insensitive to scale changes. Our results highlight the importance of integrating both inter‐ and intraspecific aggregation patterns across spatial scales, which underpin substantial differences in community structure from local to regional scales.     

Compositional dissimilarity as a robust measure of ecological distance

The robustness of quantitative measures of compositional dissimilarity between sites was evaluated using extensive computer simulations of species' abundance patterns over one and two dimensional configurations of sample sites in ecological space. Robustness was equated with the strength over a range of models, of the linear and monotonic (rank-order) relationship between the compositional dissimilarities and the corresponding Euclidean distances between sites measured in the ecological space. The range of models reflected different assumptions about species' response curve shape, sampling pattern of sites, noise level of the data, species' interactions, trends in total site abundance, and beta diversity of gradients.The Kulczynski, Bray-Curtis and Relativized Manhattan measures were found to have not only a robust monotonic relationship with ecological distance, but also a robust linear (proportional) relationship until ecological distances became large. Less robust measures included Chord distance, Kendall's coefficient, Chisquared distance, Manhattan distance, and Euclidean distance.A new ordination method, hybrid multidimensional scaling (HMDS), is introduced that combines metric and nonmetric criteria, and so takes advantage of the particular properties of robust dissimilarity measures such as the Kulczynski measure.We thank M. P. Austin for encouraging this study, and I. C. Prentice, E. Van der Maarel, and an anonymous reviewer for helpful comments. E. M. Adomeit provided technical assistance.     

Nonparametric One‐way Analysis of Variance of Replicated Bivariate Spatial Point Patterns

A common problem in neuropathological studies is to assess the spatial patterning of cells on tissue sections and to compare spatial patterning between disorder groups. For a single cell type, the cell positions constitute a univariate point process and interest focuses on the degree of spatial aggregation. For two different cell types, the cell positions constitute a bivariate point process and the degree of spatial interaction between the cell types is of interest. We discuss the problem of analysing univariate and bivariate spatial point patterns in the one‐way design where cell patterns have been obtained for groups of subjects. A bootstrapping procedure to perform a nonparametric one‐way analysis of variance of the spatial aggregation of a univariate point process has been suggested by Diggle, Lange and Bene? (1991). We extend their replication‐based approach to allow the comparison of the spatial interaction of two cell types between groups, to include planned comparisons (contrasts) and to assess whole groups against complete spatial randomness and spatial independence. We also accommodate several replicate tissue sections per subject. An advantage of our approach is that it can be applied when processes are not stationary, a common problem in brain tissue sections since neurons are arranged in cortical layers. We illustrate our methods by applying them to a neuropathological study to investigate abnormalities in the functional relationship between neurons and astrocytes in HIV associated dementia. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Wrangling Phosphoproteomic Data to Elucidate Cancer Signaling Pathways

The interpretation of biological data sets is essential for generating hypotheses that guide research, yet modern methods of global analysis challenge our ability to discern meaningful patterns and then convey results in a way that can be easily appreciated. Proteomic data is especially challenging because mass spectrometry detectors often miss peptides in complex samples, resulting in sparsely populated data sets. Using the R programming language and techniques from the field of pattern recognition, we have devised methods to resolve and evaluate clusters of proteins related by their pattern of expression in different samples in proteomic data sets. We examined tyrosine phosphoproteomic data from lung cancer samples. We calculated dissimilarities between the proteins based on Pearson or Spearman correlations and on Euclidean distances, whilst dealing with large amounts of missing data. The dissimilarities were then used as feature vectors in clustering and visualization algorithms. The quality of the clusterings and visualizations were evaluated internally based on the primary data and externally based on gene ontology and protein interaction networks. The results show that t-distributed stochastic neighbor embedding (t-SNE) followed by minimum spanning tree methods groups sparse proteomic data into meaningful clusters more effectively than other methods such as k-means and classical multidimensional scaling. Furthermore, our results show that using a combination of Spearman correlation and Euclidean distance as a dissimilarity representation increases the resolution of clusters. Our analyses show that many clusters contain one or more tyrosine kinases and include known effectors as well as proteins with no known interactions. Visualizing these clusters as networks elucidated previously unknown tyrosine kinase signal transduction pathways that drive cancer. Our approach can be applied to other data types, and can be easily adopted because open source software packages are employed.     

Spatial eigenfunction analyses in stream networks: do watercourse and overland distances produce different results?

1. The use of spatial variables is a common procedure in ecological studies. The technique is based on the definition of a connectivity/distance matrix that conceptually defines the dispersal of organisms. The shortest distance between two points is a straight line. Despite the fact that a straight line may not represent the easiest dispersal path for many kinds of organisms, straight‐line distances are often used to detect patterns. We argue that other types of connectivity/distance matrices will better represent dispersal paths, such as the watercourse distance for aquatic organisms (e.g. fish, shrimps). 2. We used empirical and simulated community data to evaluate the usefulness of spatial variables generated from watercourse and overland (straight‐line) distances. 3. Spatial variables based on watercourse distances captured patterns that straight‐line distances did not, and provided better representations of the spatial patterns generated by dispersal along a dendritic network.     

Optimizing the trade‐off between spatial and genetic sampling efforts in patchy populations: towards a better assessment of functional connectivity using an individual‐based sampling scheme

Genetic data are increasingly used in landscape ecology for the indirect assessment of functional connectivity, that is, the permeability of landscape to movements of organisms. Among available tools, matrix correlation analyses (e.g. Mantel tests or mixed models) are commonly used to test for the relationship between pairwise genetic distances and movement costs incurred by dispersing individuals. When organisms are spatially clustered, a population‐based sampling scheme (PSS) is usually performed, so that a large number of genotypes can be used to compute pairwise genetic distances on the basis of allelic frequencies. Because of financial constraints, this kind of sampling scheme implies a drastic reduction in the number of sampled aggregates, thereby reducing sampling coverage at the landscape level. We used matrix correlation analyses on simulated and empirical genetic data sets to investigate the efficiency of an individual‐based sampling scheme (ISS) in detecting isolation‐by‐distance and isolation‐by‐barrier patterns. Provided that pseudo‐replication issues are taken into account (e.g. through restricted permutations in Mantel tests), we showed that the use of interindividual measures of genotypic dissimilarity may efficiently replace interpopulation measures of genetic differentiation: the sampling of only three or four individuals per aggregate may be sufficient to efficiently detect specific genetic patterns in most situations. The ISS proved to be a promising methodological alternative to the more conventional PSS, offering much flexibility in the spatial design of sampling schemes and ensuring an optimal representativeness of landscape heterogeneity in data, with few aggregates left unsampled. Each strategy offering specific advantages, a combined use of both sampling schemes is discussed.     

Towards understanding the crosstalk between protein post‐translational modifications: Homo‐ and heterotypic PTM pair distances on protein surfaces are not random

Post‐translational modifications (PTMs) represent an important regulatory layer influencing the structure and function of proteins. With broader availability of experimental information on the occurrences of different PTM types, the investigation of a potential “crosstalk” between different PTM types and combinatorial effects have moved into the research focus. Hypothesizing that relevant interferences between different PTM types and sites may become apparent when investigating their mutual physical distances, we performed a systematic survey of pairwise homo‐ and heterotypic distances of seven frequent PTM types considering their sequence and spatial distances in resolved protein structures. We found that actual PTM site distance distributions differ from random distributions with most PTM type pairs exhibiting larger than expected distances with the exception of homotypic phosphorylation site distances and distances between phosphorylation and ubiquitination sites that were found to be closer than expected by chance. Random reference distributions considering canonical acceptor amino acid residues only were found to be shifted to larger distances compared to distances between any amino acid residue type indicating an underlying tendency of PTM‐amenable residue types to be further apart than randomly expected. Distance distributions based on sequence separations were found largely consistent with their spatial counterparts suggesting a primary role of sequence‐based pairwise PTM‐location encoding rather than folding‐mediated effects. Our analysis provides a systematic and comprehensive overview of the characteristics of pairwise PTM site distances on proteins and reveals that, predominantly, PTM sites tend to avoid close proximity with the potential implication that an independent attachment or removal of PTMs remains possible. Proteins 2016; 85:78–92. © 2016 Wiley Periodicals, Inc.     

Upward shift of alpine plants increases floristic similarity of mountain summits

Question: Does the upward shift of species and accompanied increase in species richness, induced by climate change, lead to homogenization of Alpine summit vegetation? Location: Bernina region of the Swiss Alps. Methods: Based on a data set from previous literature we expand the analysis from species richness to beta‐diversity and spatial heterogeneity. Species compositions of mountain summits are compared using a two‐component heterogeneity concept including the mean and the variance of Sørensen similarities calculated between the summits. Non‐metric multidimensional scaling is applied to explore developments of single summits in detail. Results: Both heterogeneity components (mean dissimilarity and variance) decrease over time, indicating a trend towards more homogeneous vegetation among Alpine summits. However, the development on single summits is not strictly unidirectional. Conclusions: The upward shift of plant species leads to homogenization of alpine summit regions. Thus, increasing alpha‐diversity is accompanied by decreasing beta‐diversity. Beta‐diversity demands higher recognition by scientists as well as nature conservationists as it detects changes which cannot be described using species richness alone.     

Different roles of elevational and local environmental factors on abundance‐based beta diversity of the soil Enchytraeidae on the Changbai Mountain

The elevational alpha biodiversity gradient in mountain regions is one of the well‐known ecological patterns, but its beta diversity pattern remains poorly known. Examining the beta diversity and its components could enhance the understanding of community assembly mechanism. We studied the beta diversity pattern of the soil enchytraeids along a distinct elevational gradient (705–2,280 m) on the Changbai Mountain, the best‐preserved mountain in northeastern China. The overall abundance‐based community dissimilarity was relatively high (ca. 0.70), largely due to the balanced‐variation component (85%). The overall dissimilarity and its balanced‐variation (substitution) component were related to both local environmental heterogeneity and elevational distance, with the environmental relationships being stronger. In contrast, the abundance‐gradient (subsets) component was not related to the two gradients. The same important spatial and environmental variables were detected in structuring overall dissimilarity and substitution component, different from that in subsets component. Variation partitioning analysis showed that environmental control played a more important role than spatial (vertical and horizontal) factors in structuring the patterns of overall beta diversity and its two components. The predictive power of multivariate analysis was higher for the substitution component (nearly 50%) and overall dissimilarity (35%), but much lower for subsets components (<4%). These findings implied that abundance‐based beta diversity patterns of the soil enchytraeids were the results of different ecological processes (e.g., environmental sorting and dispersal limitation), operating in the two antithetic components. Our study showed the substitution and loss of individuals reflecting different ecological processes and highlights the importance of partitioning beta diversity in assessing biodiversity patterns and their causes.     

Deconstructing spatial patterns in species composition of ectoparasite communities: the relative contribution of host composition,environmental variables and geography

Aim We determined whether dissimilarity in species composition between parasite communities depends on geographic distance, environmental dissimilarity or host faunal dissimilarity, for different subsets of parasite species with different levels of host specificity. Location Communities of fleas parasitic on small mammals from 28 different regions of the Palaearctic. Method Dissimilarities in both parasite and host species composition were computed between each pair of regions using the Bray–Curtis index. Geographic distances between regions were also calculated, as were measures of environmental dissimilarity consisting of the pairwise Euclidean distances between regions derived from elevation, vegetation and climatic variables. The 136 flea species included in the dataset were divided into highly host‐specific species (using 1–2 host species per region, on average), moderately host‐specific species (2.2–4 hosts per region) and generalist species (>4 hosts per region). The relative influence of geographic distance, host faunal dissimilarity and environmental dissimilarity on dissimilarity of flea species composition among all regions was analysed for the entire set of flea species as well as for the three above subsets using multiple regressions on distance matrices. Results When including all flea species, dissimilarity in flea species composition was affected by all three independent variables, although the pure effect of dissimilarity in host species composition was the strongest. Results were different when the subsets of fleas differing in host specificity were treated separately. In particular, dissimilarity in species composition of highly host‐specific fleas increased solely with environmental dissimilarity, whereas dissimilarity for both moderately specific and non‐specific fleas increased with both geographic distance and dissimilarity in host species composition. Main conclusions Host specificity seems to dictate which of the three factors considered is most likely to affect the dissimilarity between flea communities. Counter‐intuitively, environmental dissimilarity played a key role in determining dissimilarity in species composition of highly host‐specific fleas, possibly because, although their presence in a region relies on the occurrence of particular host species, their abundance is itself mostly determined by climatic conditions. Our results show that deconstructing communities into subsets of species with different traits can make it easier to uncover the mechanisms shaping geographic patterns of diversity.     

Are the assemblages of tree pollination modes being recovered by tropical forest restoration?

Questions

Do the assemblages of pollination modes in restored (tree plantings) and secondary (naturally regenerated) forests change in comparison to primary forests, and how do these assemblages relate to species turnover at regional scale?

Location

Southeast region of Brazil.

Methods

We classified tree species found in a total of 40 forest sites (18 primary, 11 restored, 11 secondary) according to pollination mode, based on the literature. We calculated and compared functional dissimilarity distances, amounts of species and accumulated abundance of pollination modes, and functional indices of richness and evenness between forest types.

Results

Functional dissimilarity distances were much smaller than species dissimilarity distances within forest types (mean <20%, >80%, respectively), indicating a small variation in pollination modes between sites. Functional indices of richness and evenness did not differ between forest types. However, significant changes were found in the species and abundance proportions of several pollination modes. Primary forests were characterized by the predominance of generalized insect‐pollinated species, followed by secondary proportions of bee, wind and moth pollination; other pollination modes were underrepresented. In restored forests, reductions were found in generalized insect, moth, wind, fly, pollen‐consuming insect and very‐small insect pollination, whereas the species pollinated by bees and bats more than doubled. Smaller changes were found among secondary forests, including reductions in moth, fly and fig‐wasp pollination, whereas there were incremental changes in bee, beetle, big animal and small insect pollination.

Conclusions

Our results indicate a rather stable assemblage of pollination modes and also high ecological redundancy among trees regardless of the species replacement at the regional scale. Major changes among restored forests are probably in response to larger disturbance effects and/or restoration practices conducted in these sites. In contrast, smaller changes among secondary forests could be in response to smaller disturbance effects and natural selection processes, and also seem to suggest that highly resilient degraded areas are more likely to recuperate their functional diversity through natural regeneration alone. In both cases, however, efforts to recover such patterns should be encouraged to avoid possible negative effects in plant–pollinator interactions.     

Environmental drivers of ant species richness and composition across the Argentine Pampas grassland

Understanding the underlying mechanisms causing diversity patterns is a fundamental objective in ecology and science‐based conservation biology. Energy and environmental‐heterogeneity hypotheses have been suggested to explain spatial changes in ant diversity. However, the relative roles of each one in determining alpha and beta diversity patterns remain elusive. We investigated the main factors driving spatial changes in ant (Hymenoptera, Formicidae) species richness and composition (including turnover and nestedness components) along a 500 km longitudinal gradient in the Pampean region of Argentina. Ants were sampled using pitfall traps in 12 sample sites during the summer. We performed a model selection approach to analyse responses of ant richness and composition dissimilarity to environmental factors. Then, we computed a dissimilarity partitioning of the contributions of spatial turnover and nestedness to total composition dissimilarity. Temporal habitat heterogeneity and temperature were the primary factors explaining spatial patterns of epigean ant species richness across the Pampas. The distance decay in species composition similarity was best accounted by temperature dissimilarity, and turnover had the greatest contribution to the observed beta diversity pattern. Our findings suggest that both energy and environmental‐heterogeneity‐related variables are key factors shaping richness patterns of ants and niche‐based processes instead of neutral processes appear to be regulating species composition of ant assemblages. The major contribution of turnover to the beta diversity pattern indicated that lands for potential reconversion to grassland should represent the complete environmental gradient of the Pampean region, instead of prioritizing a single site with high species richness.     

Modelling niche and neutral dynamics: on the ecological interpretation of variation partitioning results

Many studies have attempted to disentangle the effects of neutral and niche‐mediated processes on community composition using partial Mantel tests and variance partitioning on dissimilarity matrices. Recently, doubts about the reliability of these methods have emerged. Here we explore how the results are affected by three confounding factors that may affect ecological data to different degrees: spatial autocorrelation of the environmental variables, length of the compositional gradient, and sampling noise. We document that the statistical hypotheses tested in these methods may or may not correspond to the ecological hypotheses of interest. A major discrepancy emerges if a large proportion of sampling units in the analysed dataset share no species, in which case compositional dissimilarities saturate to a fixed maximum value although explanatory dissimilarities do not. With increasing dissimilarity saturation, the explanatory power of regression models decrease, which may lead to the erroneous conclusion that the ecological processes represented by the explanatory variables are not operating. A survey of recent literature suggests that there is a general lack of awareness of this problem, although it appears to affect > 10% of relevant studies. Our simulations show that if dissimilarity saturation is due to a long ecological gradient, extended dissimilarities essentially solve the problem for any degree of saturation. Using distances from a hybrid multidimensional scaling alleviates the saturation problem when the degree of saturation is < 60%. However, neither correction method can provide a solution to problems caused by insufficient sampling. How the presence of multiple explanatory gradients in combination with sampling noise affects overall analysis performance remains to be clarified.     

A guide through a family of phylogenetic dissimilarity measures among sites

Ecological studies have now gone beyond measures of species turnover towards measures of phylogenetic and functional dissimilarity. This change of perspective has a main objective: disentangling the processes that drive species distributions from local to broad scales. A fundamental difference between phylogenetic and functional analyses is that phylogeny is intrinsically dependent on a tree‐like structure whereas functional data can, most of time, only be forced to adhere a tree structure, not without some loss of information. When the branches of a phylogenetic tree have lengths, then each evolutionary unit on these branches can be considered as a basic entity on which dissimilarities among sites should be measured. Several of the recent measures of phylogenetic dissimilarities among sites thus are traditional dissimilarity indices where species are replaced by evolutionary units. The resulting indices were named PD‐dissimilarity indices, in reference to early work on the phylogenetic diversity (PD) measure. Here I review and compare indices and ordination approaches that, although first developed to analyse the differences in the species compositions of sites, can be adapted to describe PD‐dissimilarities among sites. Using simulations of species distributions along environmental gradients, I compare indices, associated with permutation tests and null models, in their ability to reveal existing phylogenetic patterns along the gradients. As an illustration, I show that the amount of bat PD‐dissimilarities along a disturbance gradient in Selva Lacandona of Chiapas, Mexico is dependent on whether species' abundance is considered, and on the PD‐dissimilarity index used. Overall, the family of PD‐dissimilarity indices has a critical potential for future analyses of phylogenetic diversity as it benefits from decades of research on the measure of species dissimilarity. I provide clues to help to choose among many potential indices, identifying which indices satisfy minimal basic properties, and analysing their sensitivity to abundance, size, diversity and joint absences.     

Adopting a spatially explicit perspective to study the mysterious fairy circles of Namibia

The mysterious ‘fairy circles’ are vegetation‐free discs that cover vast areas along the pro‐Namib Desert. Despite 30 yr of research their origin remains unknown. Here we adopt a novel approach that focuses on analysis of the spatial patterns of fairy circles obtained from representative 25‐ha aerial images of north‐west Namibia. We use spatial point pattern analysis to quantify different features of their spatial structures and then critically inspect existing hypotheses with respect to their ability to generate the observed circle patterns. Our working hypothesis is that fairy circles are a self‐organized vegetation pattern. Finally, we test if an existing partial‐differential‐equation model, that was designed to describe vegetation pattern formation, is able to reproduce the characteristic features of the observed fairy circle patterns. The model is based on key‐processes in arid areas such as plant competition for water and local resource‐biomass feedbacks. The fairy circles showed at all three study areas the same regular spatial distribution patterns, characterized by Voronoi cells with mostly six corners, negative correlations in their size up to a distance of 13 m, and remarkable homogeneity over large spatial scales. These results cast doubts on abiotic gas‐leakage along geological lines or social insects as causal agents of their origin. However, our mathematical model was able to generate spatial patterns that agreed quantitatively in all of these features with the observed patterns. This supports the hypothesis that fairy circles are self‐organized vegetation patterns that emerge from positive biomass‐water feedbacks involving water transport by extended root systems and soil‐water diffusion. Future research should search for mechanisms that explain how the different hypotheses can generate the patterns observed here and test the ability of self‐organization to match the birth‐ and death dynamics of fairy circles and their regional patterns in the density and size with respect to environmental gradients.     

The biogeography of non-marine molluscs in the Tuscan Archipelago reveals combined effects of current eco-geographical drivers and paleogeography

We investigated the role of present (Recent) and historical (Pleistocene, i.e., Würmian Last Glacial Maximum) eco-geographical variables on the richness and diversity of non-marine molluscs in the Tuscan Archipelago, as well as inter-island faunal dissimilarity and relationships with source pools (Sardinia and Corsica, Tuscany). The association between species richness and present and historical eco-geographical variables were assessed with Spearman’s rank correlation test, while faunal dissimilarity both between islands and with their source pools was analyzed through beta-diversity partitioning (Sørensen index and its nestedness and turnover component) with UPGMA clustering tested with a multiscale bootstrap procedure. Non-metric multidimensional scaling in RGB color space was also used. Multiple regressions on distance matrices were then applied to explain assemblage composition between islands. Analyses were performed on all species and on all species except aliens. The overall framework showed the combined effects of current eco-geographical and paleogeographical imprints on non-marine malacofauna in the Tuscan Archipelago. However, excluding aliens, differences in species spatial turnover showed a clear correlation with Pleistocene inter-island distances, evidence of stronger historical biogeographical relationships between islands. This may indicate that widespread native species established their distribution during the Pleistocene, while alien species spread into the Tuscan Archipelago through stochastic and human-mediated dispersion events in recent times. Interestingly, Giglio’s relationships do not agree with the most accepted paleogeographical model, suggesting that this island might have been connected to the Tuscan mainland during the Würmian Last Glacial Maximum. An in-depth revision of the paleogeographic framework of the northern Tyrrhenian is therefore called for.     

No evidence for MHC class I‐based disassortative mating in a wild population of great tits

Genes of the major histocompatibility complex (MHC) are regarded as a potentially important target of mate choice due to the fitness benefits that may be conferred to the offspring. According to the complementary genes hypothesis, females mate with MHC dissimilar males to enhance the immunocompetence of their offspring or to avoid inbreeding depression. Here, we investigate whether selection favours a preference for maximally dissimilar or optimally dissimilar MHC class I types, based on MHC genotypes, average amino acid distances and the functional properties of the antigen‐binding sites (MHC supertypes); and whether MHC type dissimilarity predicts relatedness between mates in a wild great tit population. In particular, we explore the role that MHC class I plays in female mate choice decisions while controlling for relatedness and spatial population structure, and examine the reproductive fitness consequences of MHC compatibility between mates. We find no evidence for the hypotheses that females select mates on the basis of either maximal or optimal MHC class I dissimilarity. A weak correlation between MHC supertype sharing and relatedness suggests that MHC dissimilarity at functional variants may not provide an effective index of relatedness. Moreover, the reproductive success of pairs did not vary with MHC dissimilarity. Our results provide no support for the suggestion that selection favours, or that mate choice realizes, a preference for complimentary MHC types.