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Who interacts with whom is a key question in community and network ecology. The concept that these interactions may be driven by a match between the traits of consumer and resource species is known as trait‐matching. If trait‐matching would allow for general predictions of interaction structure based on sufficiently few and easily‐measurable traits, then this approach could replace the laborious description of each individual pairwise interaction. To resolve imprints of trait‐matching in a species‐rich tri‐trophic Salix–galler–parasitoid network, and to identify the most relevant traits, we applied five different methods, each approaching the same phenomenon from a different perspective. As traits, we used, body sizes, gall type (position on plant, structure of gall) and phenology, among others, as well as phylogenetic proxies. When jointly applied, the methods demonstrate distinctly different imprints of traits within the two bipartite network elements (Salix–galler versus galler–parasitoid interactions). Of the galler–parasitoid sub‐network's interactions, approximately half were explainable by the species traits used; of the Salix–galler sub‐network's interactions, traits explained at most two‐fifths. Gall type appeared to be the most important structuring trait in both networks. Phylogeny explained as much, or more than did our tested traits, suggesting that traits may be conserved and phylogeny therefore an effective proxy. Overall, the more specialized structure of the Salix–galler network versus the more nested structure of the galler–parasitoid network meant that different methods were more effective at capturing interactions and interaction structure in the different sub‐networks. Thus, our analysis reveals how structuring impacts may vary even between levels within the same multitrophic network, and calls for comparative analyses of trait matching across a wide set of systems and methods. [ABSTRACT FROM AUTHOR]

Aim: Food webs are essential for understanding how ecosystems function, but empirical data on the interactions that make up these ecological networks are lacking for most taxa in most ecosystems. Trait‐based models can fill these data gaps, but their ability to do so has not been widely tested. We test how well these models can extrapolate to new ecological communities both in terms of pairwise predator–prey interactions and higher level food web attributes (i.e. species position, food web‐level properties). Location: Canada, Europe, Tanzania. Time Period: Current. Major Taxa Studied: Terrestrial vertebrates. Methods: We train trait‐based models of pairwise trophic interactions on four independent vertebrate food webs (Canadian tundra, Serengeti, alpine south‐eastern Pyrenees and Europe) and evaluate how well these models predict pairwise interactions and network properties of each food web. Results: We find that, overall, trait‐based models predict most interactions and their absence correctly. Performance was best for training and testing on the same food web (AUC > 0.90) and declined with environmental and phylogenetic distances with the strongest loss of performance for the tundra‐Serengeti ecosystems (AUC > 0.75). Network metrics were less well‐predicted than single interactions by our models with predicted food webs being more connected, less modular, and with higher mean trophic levels than observed. Main Conclusions: Theory predicts that the variability observed in food webs can be explained by differences in trait distributions and trait‐matching relationships. Our finding that trait‐based models can predict many trophic interactions, even in contrasting environments, adds to the growing body of evidence that there are general constraints on interactions and that trait‐based methods can serve as a useful first approximation of food webs in unknown areas. However, food webs are more than the sum of their parts, and predicting network attributes will likely require models that simultaneously predict individual interactions and community constraints. [ABSTRACT FROM AUTHOR]

The three‐dimensional rarity typology proposed by Rabinowitz in 1981, based on geographic range, habitat specificity, and local abundance, is among the most widely used frameworks for describing rarity in ecological and conservation research. While this framework is descriptive and is not meant to explain the causes of rarity, recent advances in ecology may be leveraged to add explanatory power. Here we present a macroecological exploration of rarity and its underlying causes. We propose a modification of Rabinowitz's typology to better distinguish between the dimensions of rarity and the ecological processes that drive them, and explore the conservation implications of our modified framework. We propose to add occupancy (the proportion of occupied sites within a species' range) as a rarity axis, and recast habitat specificity as a cause of rarity, thus yielding a modified classification based on range size, occupancy, and local abundance. Under our framework, habitat specialists are no longer considered rare if they are widespread and abundant; we argue that this modification more accurately identifies truly rare species, as habitat specialists may be common if their habitat is abundant. Finally, we draw on the macroecological and theoretical literature to identify the key processes and associated traits that drive each rarity axis. In this respect, we identify four processes (environmental filtering, movement, demography and interactions), and hypothesise that range size and occupancy are primarily driven by environmental filtering and movement, whereas local abundance is more strongly influenced by demography and interactions. We further use ecological theory to hypothesise the conservation concerns associated with each rarity axis, and propose conservation measures that may be suitable for conserving different types of rare species. Our work may provide a basis for developing hypotheses about the causes of rarity of particular focal taxa or groups, and inform the development of targeted conservation strategies. [ABSTRACT FROM AUTHOR]

The integration of ecosystem processes over large spatial extents is critical to predicting whether and how local and global changes may impact biodiversity and ecosystem functions. Yet, there remains an important gap in meta‐ecosystem models to predict multiple functions (e.g. carbon sequestration, elemental cycling, trophic efficiency) across ecosystem types (e.g. terrestrial‐aquatic, benthic‐pelagic). We derive a flexible meta‐ecosystem model to predict ecosystem functions at landscape extents by integrating the spatial dimension of natural systems as spatial networks of different habitat types connected by cross‐ecosystem flows of materials and organisms. We partition the physical connectedness of ecosystems from the spatial flow rates of materials and organisms, allowing the representation of all types of connectivity across ecosystem boundaries. Through simulating a forest‐lake‐stream meta‐ecosystem, our model illustrates that even if spatial flows induced significant local losses of nutrients, differences in local ecosystem efficiencies could lead to increased secondary production at regional scale. This emergent result, which we dub the 'cross‐ecosystem efficiency hypothesis', emphasizes the importance of integrating ecosystem diversity and complementarity in meta‐ecosystem models to generate empirically testable hypotheses for ecosystem functions. [ABSTRACT FROM AUTHOR]

Summary: Knowledge about the distribution and local diversity patterns of arbuscular mycorrhizal (AM) fungi are limited for extreme environments such as the Arctic, where most studies have focused on spore morphology or root colonization. We here studied the joint effects of plant species identity and elevation on AM fungal distribution and diversity.We sampled roots of 19 plant species in 18 locations in Northeast Greenland, using next generation sequencing to identify AM fungi. We studied the joint effect of plant species, elevation and selected abiotic conditions on AM fungal presence, richness and composition.We identified 29 AM fungal virtual taxa (VT), of which six represent putatively new VT. Arbuscular mycorrhizal fungal presence increased with elevation, and as vegetation cover and the active soil layer decreased. Arbuscular mycorrhizal fungal composition was shaped jointly by elevation and plant species identity.We demonstrate that the Arctic harbours a relatively species‐rich and nonrandomly distributed diversity of AM fungi. Given the high diversity and general lack of knowledge exposed herein, we encourage further research into the diversity, drivers and functional role of AM fungi in the Arctic. Such insight is urgently needed for an area with some of the globally highest rates of climate change. [ABSTRACT FROM AUTHOR]

An alternative novel lateral double diffused MOS transistors (LDMOS) device architecture to minimize the feedback capacitance and improve the LDMOS small-signal and power performances is discussed. The optimized device exhibits good radio frequency (RF) and power performances with a high [F.sub.T] and [F.sub.max].

Weather and land use can significantly impact mosquito abundance and presence, and by consequence, mosquito-borne disease (MBD) dynamics. Knowledge of vector ecology and mosquito species response to these drivers will help us better predict risk from MBD. In this study, we evaluated and compared the independent and combined effects of weather and land use on mosquito species occurrence and abundance in Eastern Ontario, Canada. Data on occurrence and abundance (245,591 individuals) of 30 mosquito species were obtained from mosquito capture at 85 field sites in 2017 and 2018. Environmental variables were extracted from weather and land use datasets in a 1-km buffer around trapping sites. The relative importance of weather and land use on mosquito abundance (for common species) or occurrence (for all species) was evaluated using multivariate hierarchical statistical models. Models incorporating both weather and land use performed better than models that include weather only for approximately half of species (59% for occurrence model and 50% for abundance model). Mosquito occurrence was mainly associated with temperature whereas abundance was associated with precipitation and temperature combined. Land use was more often associated with abundance than occurrence. For most species, occurrence and abundance were positively associated with forest cover but for some there was a negative association. Occurrence and abundance of some species (47% for occurrence model and 88% for abundance model) were positively associated with wetlands, but negatively associated with urban (Culiseta melanura and Anopheles walkeri) and agriculture (An. quadrimaculatus, Cs. minnesotae and An. walkeri) environments. This study provides predictive relationships between weather, land use and mosquito occurrence and abundance for a wide range of species including those that are currently uncommon, yet known as arboviruses vectors. Elucidation of these relationships has the potential to contribute to better prediction of MBD risk, and thus more efficiently targeted prevention and control measures. [ABSTRACT FROM AUTHOR]

Current analyses of metacommunity data largely focus on global attributes across the entire metacommunity, such as mean alpha, beta, and gamma diversity, as well as the partitioning of compositional variation into single estimates of contributions of space and environmental effects and, more recently, possible contributions of species interactions. However, this view neglects the fact that different species and sites in the landscape can vary widely in how they contribute to these metacommunity‐wide attributes. We argue for a new conceptual framework with matched analytics with the goals of studying the complex and interactive relations between process and pattern in metacommunities that is focused on the variation among species and among sites which we call the 'internal structure' of the metacommunity. To demonstrate how the internal structure could be studied, we create synthetic data using a process‐based colonization–extinction metacommunity model. We then use joint species distribution models to estimate how the contributions of space, environment, and biotic interactions driving metacommunity assembly differ among species and sites. We find that this approach to the internal structure of metacommunities provides useful information about the distinct ways that different species and different sites contribute to metacommunity structure. Although it has limitations, our work points at a more general approach to understand how other possible complexities might affect internal structure and might thus be incorporated into a more cohesive metacommunity theory. [ABSTRACT FROM AUTHOR]

Summary: Leaves interact with a wealth of microorganisms. Among these, fungi are highly diverse and are known to contribute to plant health, leaf senescence and early decomposition. However, patterns and drivers of the seasonal dynamics of foliar fungal communities are poorly understood.We used a multifactorial experiment to investigate the influence of warming and tree genotype on the foliar fungal community on the pedunculate oak Quercus robur across one growing season.Fungal species richness increased, evenness tended to decrease, and community composition strongly shifted during the growing season. Yeasts increased in relative abundance as the season progressed, while putative fungal pathogens decreased. Warming decreased species richness, reduced evenness and changed community composition, especially at the end of the growing season. Warming also negatively affected putative fungal pathogens. We only detected a minor imprint of tree genotype and warming × genotype interactions on species richness and community composition.Overall, our findings demonstrate that warming plays a larger role than plant genotype in shaping the seasonal dynamics of the foliar fungal community on oak. These warming‐induced shifts in the foliar fungal community may have a pronounced impact on plant health, plant–fungal interactions and ecosystem functions. [ABSTRACT FROM AUTHOR]

There is an increasingly widespread use of biomarkers in network physiology to evaluate an organism's physiological state. A recent study showed that albumin variability increases before death in chronic hemodialysis patients. We hypothesized that a multivariate statistical approach would better allow us to capture signals of impending physiological collapse/death. We proposed a Moving Multivariate Distance (MMD), based on the Mahalanobis distance, to quantify the variability of the multivariate biomarker profile as a whole from one visit to the next. Biomarker profiles from a visit were used as the reference to calculate MMD at the subsequent visit. We selected 16 biomarkers (of which 11 are measured every 2 weeks) from blood samples of 763 chronic kidney disease patients hemodialyzed at the CHUS hospital in Quebec, who visited the hospital regularly (∼every 2 weeks) to perform routine blood tests. MMD tended to increase markedly preceding death, indicating an increasing intraindividual multivariate variability presaging a critical transition. In survival analysis, the hazard ratio between the 97.5th percentile and the 2.5th percentile of MMD reached as high as 21.1 [95% CI: 14.3, 31.2], showing that higher variability indicates substantially higher mortality risk. Multivariate approaches to early warning signs of critical transitions hold substantial clinical promise to identify early signs of critical transitions, such as risk of death in hemodialysis patients; future work should also explore whether the MMD approach works in other complex systems (i.e., ecosystems, economies), and should compare it to other multivariate approaches to quantify system variability. [ABSTRACT FROM AUTHOR]

There is a rich amount of information in co‐occurrence (presence–absence) data that could be used to understand community assembly. This proposition first envisioned by Forbes (1907) and then Diamond (1975) prompted the development of numerous modelling approaches (e.g. null model analysis, co‐occurrence networks and, more recently, joint species distribution models). Both theory and experimental evidence support the idea that ecological interactions may affect co‐occurrence, but it remains unclear to what extent the signal of interaction can be captured in observational data. It is now time to step back from the statistical developments and critically assess whether co‐occurrence data are really a proxy for ecological interactions. In this paper, we present a series of arguments based on probability, sampling, food web and coexistence theories supporting that significant spatial associations between species (or lack thereof) is a poor proxy for ecological interactions. We discuss appropriate interpretations of co‐occurrence, along with potential avenues to extract as much information as possible from such data. [ABSTRACT FROM AUTHOR]

Ecologists seek to understand the fitness consequences of variation in physiological markers, under the hypothesis that physiological state is linked to variability in individual condition and life history.Thus, ecologists are often interested in estimating correlations between entire suites of correlated traits, or biomarkers, but sample size limitations often do not allow us to do this properly when large numbers of traits or biomarkers are considered.Latent variables are a powerful tool to overcome this complexity. Recent statistical advances have enabled a new class of multivariate models—multivariate hierarchical modelling (MHM) with latent variables—which allow to statistically estimate unstructured covariances/correlations among traits with reduced constraints on the number of degrees of freedom to account in the model. It is thus possible to highlight correlated structures in potentially very large numbers of traits.Here, we apply MHM to evaluate the relative importance of individual differences and environmental effects on milk composition and identify the drivers of this variation. We ask whether variation in bighorn sheep milk affects offspring fitness.We evaluate whether mothers show repeatable individual differences in the concentrations of 11 markers of milk composition, and we investigate the relative importance of annual variability, maternal identity and morphological traits in structuring milk composition. We then use variance estimates to investigate how a subset of repeatable milk markers influence lamb summer survival.Repeatability of milk markers ranged from 0.05 to 0.64 after accounting for year‐to‐year variations. Milk composition was weakly but significantly associated with maternal mass in June and September, summer mass gain and winter mass loss. Variation explained by year‐to‐year fluctuations ranged from 0.07 to 0.91 suggesting a strong influence of environmental variability on milk composition. Milk composition did not affect lamb survival to weaning.Using joint models in ecological, physiological or behavioural contexts has the major advantage of decomposing a (co)variance/correlation matrix while being estimated with fewer parameters than in a "traditional" mixed‐effects model. The joint models presented here complement a growing list of tools to analyse correlations at different hierarchical levels separately and may thus represent a partial solution to the conundrum of physiological complexity. [ABSTRACT FROM AUTHOR]

Pathogens are embedded in a complex network of microparasites that can collectively or individually alter disease dynamics and outcomes. Endemic pathogens that infect an individual in the first years of life, for example, can either facilitate or compete with subsequent pathogens thereby exacerbating or ameliorating morbidity and mortality. Pathogen associations are ubiquitous but poorly understood, particularly in wild populations. We report here on 10 years of serological and molecular data in African lions, leveraging comprehensive demographic and behavioural data to test if endemic pathogens shape subsequent infection by epidemic pathogens. We combine network and community ecology approaches to assess broad network structure and characterise associations between pathogens across spatial and temporal scales. We found significant non‐random structure in the lion‐pathogen co‐occurrence network and identified both positive and negative associations between endemic and epidemic pathogens. Our results provide novel insights on the complex associations underlying pathogen co‐occurrence networks. [ABSTRACT FROM AUTHOR]

Community ecology aims to understand what factors determine the assembly and dynamics of species assemblages at different spatiotemporal scales. To facilitate the integration between conceptual and statistical approaches in community ecology, we propose Hierarchical Modelling of Species Communities ( HMSC) as a general, flexible framework for modern analysis of community data. While non-manipulative data allow for only correlative and not causal inference, this framework facilitates the formulation of data-driven hypotheses regarding the processes that structure communities. We model environmental filtering by variation and covariation in the responses of individual species to the characteristics of their environment, with potential contingencies on species traits and phylogenetic relationships. We capture biotic assembly rules by species-to-species association matrices, which may be estimated at multiple spatial or temporal scales. We operationalise the HMSC framework as a hierarchical Bayesian joint species distribution model, and implement it as R- and Matlab-packages which enable computationally efficient analyses of large data sets. Armed with this tool, community ecologists can make sense of many types of data, including spatially explicit data and time-series data. We illustrate the use of this framework through a series of diverse ecological examples. [ABSTRACT FROM AUTHOR]

A fundamental goal of ecology is to understand the determinants of species' distributions (i.e., the set of locations where a species is present). Competition among species (i.e., interactions among species that harms each of the species involved) is common in nature and it would be tremendously useful to quantify its effects on species' distributions. An approach to studying the large-scale effects of competition or other biotic interactions is to fit species' distributions models ( SDMs) and assess the effect of competitors on the distribution and abundance of the species of interest. It is often difficult to validate the accuracy of this approach with available data. Here, we simulate virtual species that experience competition. In these simulated datasets, we can unambiguously identify the effects that competition has on a species' distribution. We then fit SDMs to the simulated datasets and test whether we can use the outputs of the SDMs to infer the true effect of competition in each simulated dataset. In our simulations, the abiotic environment influenced the effects of competition. Thus, our SDMs often inferred that the abiotic environment was a strong predictor of species abundance, even when the species' distribution was strongly affected by competition. The severity of this problem depended on whether the competitor excluded the focal species from highly suitable sites or marginally suitable sites. Our results highlight how correlations between biotic interactions and the abiotic environment make it difficult to infer the effects of competition using SDMs. [ABSTRACT FROM AUTHOR]