Explore the vast range of titles available.

Co-occurrence methods are increasingly utilized in ecology to infer networks of species interactions where detailed knowledge based on empirical studies is difficult to obtain. Their use is particularly common, but not restricted to, microbial networks constructed from metagenomic analyses. In this study, we test the efficacy of this procedure by comparing an inferred network constructed using spatially intensive co-occurrence data from the rocky intertidal zone in central Chile to a well-resolved, empirically based, species interaction network from the same region. We evaluated the overlap in the information provided by each network and the extent to which there is a bias for co-occurrence data to better detect known trophic or non-trophic, positive or negative interactions. We found a poor correspondence between the co-occurrence network and the known species interactions with overall sensitivity (probability of true link detection) equal to 0.469, and specificity (true non-interaction) equal to 0.527. The ability to detect interactions varied with interaction type. Positive non-trophic interactions such as commensalism and facilitation were detected at the highest rates. These results demonstrate that co-occurrence networks do not represent classical ecological networks in which interactions are defined by direct observations or experimental manipulations. Co-occurrence networks provide information about the joint spatial effects of environmental conditions, recruitment, and, to some extent, biotic interactions, and among the latter, they tend to better detect niche-expanding positive non-trophic interactions. Detection of links (sensitivity or specificity) was not higher for well-known intertidal keystone species than for the rest of consumers in the community. Thus, as observed in previous empirical and theoretical studies, patterns of interactions in co-occurrence networks must be interpreted with caution, especially when extending interaction-based ecological theory to interpret network variability and stability. Co-occurrence networks may be particularly valuable for analysis of community dynamics that blends interactions and environment, rather than pairwise interactions alone.

Aim Species are responding to climate warming by shifting their distributions toward historically cooler regions, but the degree to which expansions at cool range limits are balanced by contractions at warm limits is unknown. We synthesized published data documenting shifts at species’ warm versus cool range limits along elevational gradients to (a) test classic ecological theory that predicts temperature more directly influences species’ cool range limits than their warm range limits, and (b) determine how warming‐associated shifts have changed the extent and area of species’ elevational distributions. Location Global. Time period 1802–2012. Major taxa studied Vascular plants, endotherms, ectotherms. Methods We compiled a dataset of 975 species from 32 elevational gradients for which range shifts have been measured at both warm and cool range limits. We compared the magnitude and variance of shifts at species’ warm versus cool limits, and quantified how range shifts have impacted species’ elevational extents and areas. Results On average species have shifted upslope associated with temperature increases at both warm and cool limits (warm limit: 92 ± 455 m/C; cool limit: 131 ± 465 m/C; overall mean ± SD). There was no systematic difference in the magnitude or variance of shifts at warm versus cool limits and thus no indication that cool limits are more directly controlled by temperature. Species’ elevational extents and available area significantly decreased for mountaintop species. Main conclusions Our results do not support the long‐standing hypothesis that cool limits are more sensitive or responsive to temperature. We find that, across the globe, mountaintop species’ ranges are significantly shrinking as they shift upslope, supporting predictions that high elevation species are especially vulnerable to temperature increases. Our synthesis highlights the extreme variation in species’ distributional responses to warming, which may indicate that biotic interactions play a more prominent role in setting range limits than previously thought.

Aims The 50th anniversary of the publication of the seminal book, The Theory of Island Biogeography, by Robert H. MacArthur and Edward O. Wilson, is a timely moment to review and identify key research foci that could advance island biology. Here, we take a collaborative horizon-scanning approach to identify 50 fundamental questions for the continued development of the field. Location Worldwide. Methods We adapted a well-established methodology of horizon scanning to identify priority research questions in island biology, and initiated it during the Island Biology 2016 conference held in the Azores. A multidisciplinary working group prepared an initial pool of 187 questions. A series of online surveys was then used to refine a list of the 50 top priority questions. The final shortlist was restricted to questions with a broad conceptual scope, and which should be answerable through achievable research approaches. Results Questions were structured around four broad and partially overlapping island topics, including: (Macro)Ecology and Biogeography, (Macro)Evolution, Community Ecology, and Conservation and Management. These topics were then subdivided according to the following subject areas: global diversity patterns (five questions in total); island ontogeny and past climate change (4); island rules and syndromes (3); island biogeography theory (4); immigration–speciation–extinction dynamics (5); speciation and diversification (4); dispersal and colonization (3); community assembly (6); biotic interactions (2); global change (5); conservation and management policies (5); and invasive alien species (4). Main conclusions Collectively, this cross-disciplinary set of topics covering the 50 fundamental questions has the potential to stimulate and guide future research in island biology. By covering fields ranging from biogeography, community ecology and evolution to global change, this horizon scan may help to foster the formation of interdisciplinary research networks, enhancing joint efforts to better understand the past, present and future of island biotas.

Trait-based approaches are increasingly being used to test mechanisms underlying species assemblages and biotic interactions across a wide range of organisms including terrestrial arthropods and to investigate consequences for ecosystem processes. Such an approach relies on the standardized measurement of functional traits that can be applied across taxa and regions. Currently, however, unified methods of trait measurements are lacking for terrestrial arthropods and related macroinvertebrates (terrestrial invertebrates hereafter). Here, we present a comprehensive review and detailed protocol for a set of 29 traits known to be sensitive to global stressors and to affect ecosystem processes and services. We give recommendations how to measure these traits under standardized conditions across various terrestrial invertebrate taxonomic groups. We provide considerations and approaches that apply to almost all traits described, such as the selection of species and individuals needed for the measurements, the importance of intraspecific trait variability, how many populations or communities to sample and over which spatial scales. The approaches outlined here provide a means to improve the reliability and predictive power of functional traits to explain community assembly, species diversity patterns, and ecosystem processes and services within and across taxa and trophic levels, allowing comparison of studies and running meta-analyses across regions and ecosystems. This handbook is a crucial first step towards standardizing trait methodology across the most studied terrestrial invertebrate groups, and the protocols are aimed to balance general applicability and requirements for special cases or particular taxa. Therefore, we envision this handbook as a common platform to which researches can further provide methodological input for additional special cases.

Aim: The number of studies investigating the nestedness and turnover components of beta diversity has increased substantially, but our general understanding of the drivers of turnover and nestedness remains elusive. Here, we examined the effects of species traits, spatial extent, latitude and ecosystem type on the nestedness and turnover components of beta diversity. Location: Global. Time period: 1968–2017. Major taxa studied: From bacteria to mammals. Methods: From the 99 studies that partition total beta diversity into its turnover and nestedness components, we assembled 269 and 259 data points for the pairwise and multiple site beta-diversity metrics, respectively. Our data covered a broad variation in species dispersal type, body size and trophic position. The data were from freshwater, marine and terrestrial realms, and encompassed geographical areas from the tropics to near polar regions. We used linear modelling as a meta-regression tool to analyse the data. Results: Pairwise turnover, multiple site turnover and total beta diversity all decreased significantly with latitude. In contrast, multiple site nestedness showed a positive relationship with latitude. Beta-diversity components did not generally differ among the realms. The turnover component and total beta diversity increased with spatial extent, whereas nestedness was scale invariant for pairwise metrics. Multiple site beta-diversity components did not vary with spatial extent. Surprisingly, passively dispersed organisms had lower turnover and total beta diversity than flying organisms. Body size showed a relatively weak relationship with beta diversity but had important interactions with trophic position, thus also affecting beta diversity via interactive effects. Producers had significantly higher average pairwise turnover and total beta diversity than carnivores. Main conclusions: The present results provide evidence that species turnover, being consistently the larger component of total beta diversity, and nestedness are related to the latitude of the study area and intrinsic organismal features. We showed that two beta-diversity components had generally opposing patterns with regard to latitude. We highlight that beta-diversity partition may give additional insights into the underlying causes of spatial variability in biotic communities compared with total beta diversity alone.

Aim: Recent studies increasingly use statistical methods to infer biotic interactions from co-occurrence information at a large spatial scale. However, disentangling biotic interactions from other factors that can affect co-occurrence patterns at the macroscale is a major challenge. Approach: We present a set of questions that analysts and reviewers should ask to avoid erroneously attributing species association patterns to biotic interactions. Our questions relate to the appropriateness of data and models, the causality behind a correlative signal, and the problems associated with static data from dynamic systems. We summarize caveats reported by macroecological studies of biotic interactions and examine whether conclusions on the presence of biotic interactions are supported by the modelling approaches used. Findings: Irrespective of the method used, studies that set out to test for biotic interactions find statistical associations in species' co-occurrences. Yet, when compared with our list of questions, few purported interpretations of such associations as biotic interactions hold up to scrutiny. This does not dismiss the presence or importance of biotic interactions, but it highlights the risk of too lenient interpretation of the data. Combining model results with information from experiments and functional traits that are relevant for the biotic interaction of interest might strengthen conclusions. Main conclusions: Moving from species- to community-level models, including biotic interactions among species, is of great importance for process-based understanding and forecasting ecological responses. We hope that our questions will help to improve these models and facilitate the interpretation of their results. In essence, we conclude that ecologists have to recognize that a species association pattern in joint species distribution models will be driven not only by real biotic interactions, but also by shared habitat preferences, common migration history, phylogenetic history and shared response to missing environmental drivers, which specifically need to be discussed and, if possible, integrated into models.

Biotic interactions drive key ecological and evolutionary processes and mediate ecosystem responses to climate change. The direction, frequency, and intensity of biotic interactions can in turn be altered by climate change. Understanding the complex interplay between climate and biotic interactions is thus essential for fully anticipating how ecosystems will respond to the fast rates of current warming, which are unprecedented since the end of the last glacial period. We highlight episodes of climate change that have disrupted ecosystems and trophic interactions over time scales ranging from years to millennia by changing species' relative abundances and geographic ranges, causing extinctions, and creating transient and novel communities dominated by generalist species and interactions. These patterns emerge repeatedly across disparate temporal and spatial scales, suggesting the possibility of similar underlying processes. Based on these findings, we identify knowledge gaps and fruitful areas for research that will further our understanding of the effects of climate change on ecosystems.

It is increasingly recognized that species distributions are driven by both abiotic factors and biotic interactions. Despite much recent work incorporating competition, predation, and mutualism into species distribution models (SDMs), the focus has been confined to aboveground macroscopic interactions. Biotic interactions between plants and soil microbial communities are understudied as potentially important drivers of plant distributions. Some soil bacteria promote plant growth by cycling nutrients, while others are pathogenic; thus they have a high potential for influencing plant occurrence. We investigated the influence of soil bacterial clades on the distributions of bryophytes and 12 vascular plant species in a high elevation talus-field ecosystem in the Rocky Mountain Front Range, Colorado, USA. We used an informationtheoretic criterion (AICc) modeling approach to compare SDMs with the following different sets of predictors: abiotic variables, abiotic variables and other plant abundances, abiotic variables and soil bacteria clade relative abundances, and a full model with abiotic factors, plant abundances, and bacteria relative abundances. We predicted that bacteria would influence plant distributions both positively and negatively, and that these interactions would improve prediction of plant species distributions. We found that inclusion of either plant or bacteria biotic predictors generally improved the fit, deviance explained, and predictive power of the SDMs, and for the majority of the species, adding information on both other plants and bacteria yielded the best model. Interactions between the modeled species and biotic predictors were both positive and negative, suggesting the presence of competition, parasitism, and facilitation. While our results indicate that plant–plant co-occurrences are a stronger driver of plant distributions than plant–bacteria co-occurrences, they also show that bacteria can explain parts of plant distributions that remain unexplained by abiotic and plant predictors. Our results provide further support for including biotic factors in SDMs, and suggest that belowground factors be considered as well.

Aims: The choice of environmental predictor variables in correlative models of plant species distributions (hereafter SDMs) is crucial to ensure predictive accuracy and model realism, as highlighted in multiple earlier studies. Because variable selection is directly related to a model's capacity to capture important species' environmental requirements, one would expect an explicit prior consideration of all ecophysiologically meaningful variables. For plants, these include temperature, water, soil nutrients, light, and in some cases, disturbances and biotic interactions. However, the set of predictors used in published correlative plant SDM studies varies considerably. No comprehensive review exists of what environmental predictors are meaningful, available (or missing) and used in practice to predict plant distributions. Contributing to answer these questions is the aim of this review. Methods: We carried out an extensive, systematic review of recently published plant SDM studies (years 2010-2015; n = 200) to determine the predictors used (and not used) in the models. We additionally conducted an in-depth review of SDM studies in selected journals to identify temporal trends in the use of predictors (years 2000-2015; n = 40). Results: A large majority of plant SDM studies neglected several ecophysiologically meaningful environmental variables, and the number of relevant predictors used in models has stagnated or even declined over the last 15 yr. Conclusions: Neglecting ecophysiologically meaningful predictors can result in incomplete niche quantification and can thus limit the predictive power of plant SDMs. Some of these missing predictors are already available spatially or may soon become available (e.g. soil moisture). However, others are not yet easily obtainable across whole study extents (e.g. soil pH and nutrients), and their development should receive increased attention. We conclude that more effort should be made to build ecologically more sound plant SDMs. This requires a more thorough rationale for the choice of environmental predictors needed to meet the study goal, and the development of missing ones. The latter calls for increased collaborative effort between ecological and geo-environmental sciences.

Macroecology and biogeography are concerned with understanding biodiversity patterns across space and time. In the past, the two disciplines have addressed this question mainly with correlative approaches, despite frequent calls for more mechanistic explanations. Recent advances in computational power, theoretical understanding, and statistical tools are, however, currently facilitating the development of more system-oriented, mechanistic models. We review these models, identify different model types and theoretical frameworks, compare their processes and properties, and summarize emergent findings. We show that ecological (physiology, demographics, dispersal, biotic interactions) and evolutionary processes, as well as environmental and human-induced drivers, are increasingly modelled mechanistically; and that new insights into biodiversity dynamics emerge from these models. Yet, substantial challenges still lie ahead for this young research field. Among these, we identify scaling, calibration, validation, and balancing complexity as pressing issues. Moreover, particular process combinations are still understudied, and so far models tend to be developed for specific applications. Future work should aim at developing more flexible and modular models that not only allow different ecological theories to be expressed and contrasted, but which are also built for tight integration with all macroecological data sources. Moving the field towards such a ‘systems macroecology’ will test and improve our understanding of the causal pathways through which eco-evolutionary processes create diversity patterns across spatial and temporal scales.