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Visual mimicry is less understood in birds than in other taxa. The interspecific social dominance mimicry (ISDM) hypothesis asserts that subordinate species resemble dominant ones to reduce aggression. Plumage mimicry has also been consistently noted in mixed-species flocks (MSFs), suggesting a connection to grouping behaviour, although it is unclear whether this is linked to ISDM. We studied greater necklaced laughingthrush (GNLT, Pterorhinus pectoralis) and lesser necklaced laughingthrush (LNLT, Garrulax monileger), which were recently placed in different genera. Measurements of 162 museum specimens showed LNLT converging in sympatry with GNLT in necklace colour, but diverging in necklace to body ratio, with proportionally smaller necklaces. The species were closely associated in six of seven MSF systems from Nepal to China. In a study of foraging behaviour in Nepal, aggression was rare between the species, LNLT followed GNLT and had lower foraging rates when further from GNLT. Our data suggest a link between this MSF-associated mimicry and ISDM, and that the subordinate LNLT may be the mimic and gain more from the resemblance. The species spend much time together in dense and poorly lit vegetation, where the LNLTs resemblance to GNLTs potentially allows them to forage closer to GNLTs than would be otherwise possible.

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Although nitrogen (N) enrichment is known to threaten the temporal stability of aboveground net primary productivity, it remains unclear how it alters that of belowground microbial abundance and whether its impact can be regulated by grassland degradation. Using data from N enrichment experiments at temperate grasslands with no, moderate, severe, and extreme degradation degrees, we quantified the temporal stability of soil microbial abundance (hereafter 'microbial community stability') using the ratio of the mean quantitative PCR to its standard deviation over 4 years. Both bacterial and fungal community stability sharply decreased when N input exceeded 30 g N m-2 year-1 in non-degraded grasslands, whereas a reduction in this threshold occurred in degraded grasslands. Microbial species diversity, species asynchrony, and species associations jointly altered microbial community stability. Interestingly, the linkages between plant and microbial community stability were strengthened in degraded grasslands, suggesting that plants and soil microbes might depend on each other to keep stable communities in harsh environments. Our findings highlighted the importance of grassland degradation in regulating the responses of microbial community stability to N enrichment and provided experimental evidence for understanding the relationships between plant and microbial community stability.

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Whale sharks Rhincodontypus frequently appear to interact or associate with other species, which vary depending on the community structure and the demographic of the whale sharks at each location globally. Here, we present the species sighted frequently around whale sharks in the Galapagos Archipelago and reported by dive guides and scientists and also in earlier publications. These associated species include cetacean species: bottlenose dolphins Tursiopstruncatus, other shark species: silky sharks Carcharhinusfalciformis, Galapagos sharks Carcharhinusgalapagensis, scalloped hammerhead sharks Sphyrnalewini, tiger sharks Galeocerdocuvier and teleost fish species: remoras Remora remora, yellowfin tuna Thunnusalbacares, almaco jacks Seriolarivoliana and black jacks Caranxlugubris. The recording of interspecies associations and interactions may lead to better understanding of the natural history of whale sharks and can show important symbiotic relationships or interdependence between different species.

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AIM: The stress-gradient hypothesis (SGH) postulates that species interactions shift from negative to positive with increasing abiotic stress. Interactions between species are increasingly being recognized as important drivers of species distributions, but it is still unclear whether stress-induced changes in interactions affect continental-to-global scale species distributions. Here, we tested whether associations of vascular plant species in dry grasslands in Europe follow the SGH along a climatic water deficit (CWD) gradient across the continent. LOCATION: Dry grasslands in Europe. TIME PERIOD: Present. MAJOR TAXA STUDIED: Vascular plants. METHODS: We built a context-dependent joint species distribution model (JSDM) to estimate the residual associations (i.e., associations that are not explained by the abiotic environment) of 161 plant species as a function of the CWD based on community data from 8,660 vegetation plots. We evaluated changes in residual associations between species for pairs and on the community level, and we compared responses for groups of species with different drought tolerances. RESULTS: We found contrasting shifts in associations for drought-sensitive and drought-tolerant species. For drought-sensitive species, 21% of the pairwise associations became more positive with increasing CWD, whereas 17% became more negative. In contrast, only 17% of the pairwise associations involving drought-tolerant species became more positive, whereas 27% became more negative in areas with a high CWD. Additionally, the incidence of positive associations increased with drought for drought-sensitive species and decreased for drought-tolerant species. MAIN CONCLUSIONS: We found that associations of drought-sensitive plant species became more positive with drought, in line with the SGH. In contrast, associations of drought-tolerant species became more negative. Additionally, changes in associations of single species pairs were highly variable. Our results indicate that stress-modulated species associations might influence the distribution of species over large geographical extents, thus leading to unexpected responses under climate change through shifts in species associations.

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Biodiversity is important for supporting ecosystem functioning. To evaluate the factors contributing to the strength of microbial diversity-function relationships in complex terrestrial ecosystems, we conducted a soil survey over different habitats, including an agricultural field, forest, wetland, grassland, and desert. Soil microbial multidiversity was estimated by the combination of bacterial and fungal diversity. Soil ecosystem functions were evaluated using a multinutrient cycling index (MNC) in relation to carbon, nitrate, phosphorus, and potassium cycling. Significant positive relationships between soil multidiversity and multinutrient cycling were observed in all habitats, except the grassland and desert. Specifically, community compositions showed stronger correlations with multinutrient cycling than α-diversity, indicating the crucial role of microbial community composition differences on soil nutrient cycling. Importantly, we revealed that changes in both the neutral processes (Sloan neutral modeling) and the proportion of negative bacterial-fungal associations were linked to the magnitude and direction of the diversity-MNC relationships. The habitats less governed by neutral processes and dominated by negative bacterial-fungal associations exhibited stronger negative microbial α-diversity-MNC relationships. Our findings suggested that the balance between positive and negative bacterial-fungal associations was connected to the link between soil biodiversity and ecosystem function in complex terrestrial ecosystems. This study elucidates the potential factors influencing diversity-function relationships, thereby enabling future studies to forecast the effects of belowground biodiversity on ecosystem function.IMPORTANCE The relationships between soil biodiversity and ecosystem functions are an important yet poorly understood topic in microbial ecology. This study presents an exploratory effort to gain predictive understanding of the factors driving the relationships between microbial diversity and potential soil nutrient cycling in complex terrestrial ecosystems. Our structural equation modeling and random forest analysis revealed that the balance between positive and negative bacterial-fungal associations was clearly linked to the strength of the relationships between soil microbial diversity and multiple nutrients cycling across different habitats. This study revealed the potential factors underpinning diversity-function relationships in terrestrial ecosystems and thus helps us to manage soil microbial communities for better provisioning of key ecosystem services.

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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.

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Researchers have long viewed patterns of species association as key to understanding the processes that structure communities. Community-level tests of species association have received the most attention; however, pairwise species associations may offer greater opportunity for linking patterns to specific mechanisms. Although several tests of pairwise association have been developed, there remain gaps in our understanding of their performance. Consequently, it is unclear whether these methods reliably detect patterns of association, or if any one method is superior. We maximized association patterns for single species pairs in synthetic community matrices and examined how accurately five pairwise association tests found that pair, while not finding others (i.e., type I and II error rates). All tests are more likely to miss patterns of association than to falsely detect them. When we maximized association for a species pair that included one or more rare or common species, tests were frequently unable to identify that pair as significantly associated. Consequently, these tests are best suited for identifying significant associations between pairs of species that occur in an intermediate number of samples; for such pairs, three of the five tests considered here detected 100% of the pairs for which we maximized associations.

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The difficulty of experimentally quantifying non-trophic species interactions has long troubled ecologists. Increasingly, a new application of the classic "checkerboard distribution" approach is used to infer interactions by examining the pairwise frequency at which species are found to spatially co-occur. However, the link between spatial associations, as estimated from observational co-occurrence, and species interactions has not been tested. Here we used nine common statistical methods to estimate associations from surveys of rocky intertidal communities in the Northeast Pacific Ocean. We compared those inferred associations with a new data set of experimentally determined net and direct species interactions. Although association methods generated networks with aggregate structure similar to previously published interaction networks, each method detected a different set of species associations from the same data set. Moreover, although association methods generally performed better than a random model, associations rarely matched empirical net or direct species interactions, with high rates of false positives and true positives, and many false negatives. Our findings cast doubt on studies that equate species co-occurrences to species interactions and highlight a persistent, unanswered question: how do we interpret spatial patterns in communities? We suggest future research directions to unify the observational and experimental study of species interactions, and discuss the need for community standards and best practices in association analysis.

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Parasite-host and insect-plant research have divergent traditions despite the fact that most phytophagous insects live parasitically on their host plants. In parasitology it is a traditional assumption that parasites are typically highly specialized; cospeciation between parasites and hosts is a frequently expressed default expectation. Insect-plant theory has been more concerned with host shifts than with cospeciation, and more with hierarchies among hosts than with extreme specialization. We suggest that the divergent assumptions in the respective fields have hidden a fundamental similarity with an important role for potential as well as actual hosts, and hence for host colonizations via ecological fitting. A common research program is proposed which better prepares us for the challenges from introduced species and global change.

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Metarhizium Sorokin (Hypocreales: Clavicipitaceae) is a genus of facultative parasites of insects found in soils from various environments and is used for pest management. Understanding the habitat selection of Metarhizium spp. is critical to improve the efficacy and persistence of these fungi as microbial insecticides. This study sought to determine the habitat preferences of Metarhizium spp. in Japan. We identified 302 isolates of Metarhizium spp. as eight species by a combination of PCR-RFLP and phylogenetic analysis of DNA sequences. M. pingshaense was the predominant species in Japan and was most frequently isolated from both forest and agricultural environments. On the other hand, M. brunneum and M. pemphigi were comparatively restricted to forest environments. A similar species association was detected in a small area that was intensively investigated, where 7 species including 14 genotypes were isolated from soil. The results of this study have revealed different habitat preferences among Metarhizium spp. in Japan.

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Inferring species interactions from co-occurrence data is one of the most controversial tasks in community ecology. One difficulty is that a single pairwise interaction can ripple through an ecological network and produce surprising indirect consequences. For example, the negative correlation between two competing species can be reversed in the presence of a third species that outcompetes both of them. Here, I apply models from statistical physics, called Markov networks or Markov random fields, that can predict the direct and indirect consequences of any possible species interaction matrix. Interactions in these models can be estimated from observed co-occurrence rates via maximum likelihood, controlling for indirect effects. Using simulated landscapes with known interactions, I evaluated Markov networks and six existing approaches. Markov networks consistently outperformed the other methods, correctly isolating direct interactions between species pairs even when indirect interactions or abiotic factors largely overpowered them. Two computationally efficient approximations, which controlled for indirect effects with partial correlations or generalized linear models, also performed well. Null models showed no evidence of being able to control for indirect effects, and reliably yielded incorrect inferences when such effects were present.

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