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Authorship is intended to convey information regarding credit and responsibility for manuscripts. However, while there is general agreement within ecology that the first author is the person who contributed the most to a particular project, there is less agreement regarding whether being last author is a position of significance and regarding what is indicated by someone being the corresponding author on a manuscript. Using an analysis of papers published in American Naturalist, Ecology, Evolution, and Oikos, I found that: (1) the number of authors on papers is increasing over time; (2) the proportion of first authors as corresponding author has increased over time, as has the proportion of last authors as corresponding author; (3) 84% of papers published in 2016 had the first author as corresponding author; and (4) geographic regions differed in the likelihood of having the first (or last) author as corresponding author. I also carried out an online survey to better understand views on last and corresponding authorship. This survey revealed that most ecologists view the last author as the 'senior' author on a paper (i.e., the person who runs the research group in which most of the work was carried out), and most ecologists view the corresponding author as the person taking full responsibility for a paper. However, there was substantial variation in views on authorship, especially corresponding authorship. Given these results, I suggest that discussions of authorship have as their starting point that the first author will be corresponding author and the senior author will be last author. I also suggest ways of deciding author order in cases where two senior authors contributed equally. [ABSTRACT FROM AUTHOR]

Diversifying curricula is of increasing interest in higher education, including in ecology and evolution and allied fields. Yet, many educators may not know where to start. Here we provide a framework for meeting standard curriculum goals while enacting anti‐racist and anti‐colonial syllabi that is grounded in the development of a sustainable network of educators. In addition to highlighting this professional learning process and sharing the list of resources our group has developed, we provide suggestions to help educators highlight contributions of minoritized groups, explore multiple ways of knowing, and perform critical assessments of foundational views of life and environmental science fields. We further discuss the key classroom dynamics that affect the success of such anti‐racist and anti‐colonial initiatives. The retention and success of minoritized students in ecology and evolution depends on whether we address injustices in our fields. Our hope is that our fellow educators will use this paper to catalyze their own efforts to diversify their courses. [ABSTRACT FROM AUTHOR]

As we strive to lift up a diversity of voices in science, it is important for ecologists, evolutionary scientists, and educators to foster inclusive environments in their research and teaching. Academics in science often lack exposure to research on best practices in diversity, equity, and inclusion and may not know where to start to make scientific environments more welcoming and inclusive. We propose that by approaching research and teaching with empathy, flexibility, and a growth mind‐set, scientists can be more supportive and inclusive of their colleagues and students. This paper provides guidance, explores strategies, and directs scientists to resources to better cultivate an inclusive environment in three common settings: the classroom, the research laboratory, and the field. As ecologists and evolutionary scientists, we have an opportunity to adapt our teaching and research practices in order to foster an inclusive educational ecosystem for students and colleagues alike. [ABSTRACT FROM AUTHOR]

Occupancy-detection models that account for imperfect detection have become widely used in many areas of ecology. As with any modelling exercise, it is important to assess whether the fitted model encapsulates the main sources of variation in the data, yet there have been few methods developed for occupancy-detection models that would allow practitioners to do so., In this paper, a new type of residual for occupancy-detection models is developed according to the method of Dunn & Smyth ( Journal of Computational and Graphical Statistics, 5, 1996, 236-244). Residuals are separately constructed to diagnose the occupancy and detection components of the model., Because the residuals are quite noisy, we suggest fitting a smoother through plots of residuals against predictors of fitted values, with 95% confidence bands, to diagnose lack-of-fit., The method is illustrated using Swiss squirrel data, and evaluated using simulations based on that dataset., Plotting residuals against predictors or against fitted values performed reasonably well as methods for diagnosing violations of occupancy-detection model assumptions, particularly plots of residuals against a missing predictor. Relatively high false positive rates were sometimes observed, but this seems to be controlled reasonably well by fitting smoothers to these plots and being guided in interpretation by 95% confidence bands around the smoothers. [ABSTRACT FROM AUTHOR]

An interview with Marcel van der Heijden , the head of the Plant–Soil Interactions Group at the Swiss Federal Research Institute Agroscope in Zurich, Switzerland and a professor in Mycorrhizal Ecology at Utrecht University in the Netherlands, is presented. He mentions things that motivated him and other factors that have influenced his decision to pursue a career in research.

Scientific investigation is of value only insofar as relevant results are obtained and communicated, a task that requires organizing, evaluating, analysing and unambiguously communicating the significance of data. In this context, working with ecological data, reflecting the complexities and interactions of the natural world, can be a challenge. Recent innovations for statistical analysis of multifaceted interrelated data make obtaining more accurate and meaningful results possible, but key decisions of the analyses to use, and which components to present in a scientific paper or report, may be overwhelming., We offer a 10-step protocol to streamline analysis of data that will enhance understanding of the data, the statistical models and the results, and optimize communication with the reader with respect to both the procedure and the outcomes. The protocol takes the investigator from study design and organization of data (formulating relevant questions, visualizing data collection, data exploration, identifying dependency), through conducting analysis (presenting, fitting and validating the model) and presenting output (numerically and visually), to extending the model via simulation. Each step includes procedures to clarify aspects of the data that affect statistical analysis, as well as guidelines for written presentation. Steps are illustrated with examples using data from the literature., Following this protocol will reduce the organization, analysis and presentation of what may be an overwhelming information avalanche into sequential and, more to the point, manageable, steps. It provides guidelines for selecting optimal statistical tools to assess data relevance and significance, for choosing aspects of the analysis to include in a published report and for clearly communicating information. [ABSTRACT FROM AUTHOR]

The role of theory within ecology has changed dramatically in recent decades. Once primarily a source of qualitative conceptual framing, ecological theories and models are now often used to develop quantitative explanations of empirical patterns and to project future dynamics of specific ecological systems. In this essay, I recount my own experience of this transformation, in which accelerating computing power and the widespread incorporation of stochastic processes into ecological theory combined to create some novel integration of mathematical and statistical models. This stronger integration drives theory towards incorporating more biological realism, and I explore ways in which we can grapple with that realism to generate new general theoretical insights. This enhanced realism, in turn, may lead to frameworks for projecting ecological responses to anthropogenic change, which is, arguably, the central challenge for 21st-century ecology. In an era of big data and synthesis, ecologists are increasingly seeking to infer causality from observational data; but conventional biometry provides few tools for this project. This is a realm where theorists can and should play an important role, and I close by pointing towards some analytical and philosophical approaches developed in our sister discipline of economics that address this very problem. While I make no grand prognostications about the likely discoveries of ecological theory over the coming century, you will find in this essay a scattering of more or less far-fetched ideas that I, at least, think are interesting and (possibly) fruitful directions for our field. [ABSTRACT FROM AUTHOR]

Ecology and environmental science graduate degrees often involve fieldwork, frequently led by the graduate student. Few formal resources exist to support graduate students in successfully planning and implementing a data collection field campaign, even though this experience may be fundamental to completing a graduate degree. Graduate fieldwork requires leading and managing a team, often in unique circumstances (including long hours, remote regions, etc.), and therefore can be challenging even for those with previous leadership or field experience. Our objectives were to (1) collect general advice for graduate students on leading fieldwork safely and effectively, (2) solicit specific suggestions on resources and actions to take before, during, and after the field season, and (3) develop a series of recommendations for labs, departments, and universities to better prepare and support their students. We developed a survey to solicit community input and distributed it widely to the ecological sciences community via email LISTSERVs and social media. Here, we present results from the survey responses, including a summary of the perceived challenges that graduate students face while leading their own fieldwork, suggestions for how to prepare and complete fieldwork successfully, and a compilation of resources. Graduate field leaders can improve success via clear communication, risk assessment and procedural planning implemented before, during, and after the field season. Labs, principal investigators, departments, and professional societies can support graduate field leaders by formalizing institutional resources, financial support, and incentivizing skill development. Field leadership is a critical skill to develop during graduate education, and contributes to the success, retention, and advancement of researchers in the natural sciences. [ABSTRACT FROM AUTHOR]

Research into global change ecology is motivated by the need to understand the role of humans in changing biotic systems. Mechanistic understanding of ecological responses requires the separation of different climatic parameters and processes that often operate on diverse spatiotemporal scales. Yet most environmental studies do not distinguish the effects of internal climate variability from those caused by external, natural (e.g. volcanic, solar, orbital) or anthropogenic (e.g. greenhouse gases, ozone, aerosols, land‐use) forcing factors.We suggest extending the climatological concept of 'Detection and Attribution' (DA) to unravel abiotic drivers of ecological dynamics in the Anthropocene. We therefore apply DA to quantify the relative roles of natural versus industrial temperature change on elevational shifts in the outbreak epicentres of the larch budmoth (LBM; Zeiraphera diniana or griseana Gn.); the classic example of a cyclic forest defoliating insect.Our case study shows that anthropogenic warming shifts the epicentre of travelling LBM waves upward, which disrupts the intensity of population outbreaks that occurred regularly over the past millennium in the European Alps. Our findings demonstrate the ability of DA to detect ecological responses beyond internal system variability, to attribute them to specific external climate forcing factors and to identify climate‐induced ecological tipping points.In order to implement the climatological concept of 'Detection and Attribution' successfully into modern global change ecology, future studies should combine high‐resolution paleoenvironmental reconstructions and state‐of‐the‐art climate model simulations to inform inference‐based ecosystem models. A free Plain Language Summary can be found within the Supporting Information of this article. [ABSTRACT FROM AUTHOR]

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The open source and free programming language R is a phenomenal mechanism to address a multiplicity of challenges in ecology and evolution. It is also a complex ecosystem because of the diversity of solutions available to the analyst.Packages for R enhance and specialize the capacity to explore both niche data/experiments and more common needs. However, the paradox of choice or how we select between many seemingly similar options can be overwhelming and lead to different potential outcomes.There is extensive choice in ecology and evolution between packages for both fundamental statistics and for more specialized domain‐level analyses.Here, we provide a checklist to inform these decisions based on the principles of resilience, need, and integration with scientific workflows for evidence.It is important to explore choices in any analytical coding environment—not just R—for solutions to challenges in ecology and evolution, and document this process because it advances reproducible science, promotes a deeper understand of the scientific evidence, and ensures that the outcomes are correct, representative, and robust. [ABSTRACT FROM AUTHOR]

Summary: Feral horse management is a complex social issue. Committed and active stakeholders hold strong beliefs regarding both the level of culling needed and humane approaches to control. Ecological evidence supporting control and expert advice on ethical culling can, in such contexts, have limited effect in policy development and application. Given this complexity, social scientists have an important role to play in developing longer term solutions. Three possible approaches are evident: the use of knowledge exchange and boundary spanning expertise; the proactive development of publicly available evidence‐informed narratives; and the development of influence and leadership at the science–policy interface. Social scientists, ecologists, managers and policymakers need to work together to develop and implement sound management decisions. [ABSTRACT FROM AUTHOR]

In ecology, the true causal structure for a given problem is often not known, and several plausible models and thus model predictions exist. It has been claimed that using weighted averages of these models can reduce prediction error, as well as better reflect model selection uncertainty. These claims, however, are often demonstrated by isolated examples. Analysts must better understand under which conditions model averaging can improve predictions and their uncertainty estimates. Moreover, a large range of different model averaging methods exists, raising the question of how they differ in their behaviour and performance. Here, we review the mathematical foundations of model averaging along with the diversity of approaches available. We explain that the error in model‐averaged predictions depends on each model's predictive bias and variance, as well as the covariance in predictions between models, and uncertainty about model weights. We show that model averaging is particularly useful if the predictive error of contributing model predictions is dominated by variance, and if the covariance between models is low. For noisy data, which predominate in ecology, these conditions will often be met. Many different methods to derive averaging weights exist, from Bayesian over information‐theoretical to cross‐validation optimized and resampling approaches. A general recommendation is difficult, because the performance of methods is often context dependent. Importantly, estimating weights creates some additional uncertainty. As a result, estimated model weights may not always outperform arbitrary fixed weights, such as equal weights for all models. When averaging a set of models with many inadequate models, however, estimating model weights will typically be superior to equal weights. We also investigate the quality of the confidence intervals calculated for model‐averaged predictions, showing that they differ greatly in behaviour and seldom manage to achieve nominal coverage. Our overall recommendations stress the importance of non‐parametric methods such as cross‐validation for a reliable uncertainty quantification of model‐averaged predictions. [ABSTRACT FROM AUTHOR]

The article discusses the Ecological Society of America which will present the 2018 awards that recognize outstanding contributions to ecology in new discoveries, teaching, sustainability, diversity and lifelong commitment to the profession during it's Annual Meeting in New Orleans, Louisiana on August 6, 2018. It states that the award ceremony has several award categories including Eminent Ecologist Award, Robert H. MacArthur Award and Distinguished Service Citation.

Spatial synchrony, defined as correlated temporal fluctuations among populations, is a fundamental feature of population dynamics, but many aspects of synchrony remain poorly understood. Few studies have examined detailed geographical patterns of synchrony; instead most focus on how synchrony declines with increasing linear distance between locations, making the simplifying assumption that distance decay is isotropic. By synthesising and extending prior work, we show how geography of synchrony, a term which we use to refer to detailed spatial variation in patterns of synchrony, can be leveraged to understand ecological processes including identification of drivers of synchrony, a long-standing challenge. We focus on three main objectives: (1) showing conceptually and theoretically four mechanisms that can generate geographies of synchrony; (2) documenting complex and pronounced geographies of synchrony in two important study systems; and (3) demonstrating a variety of methods capable of revealing the geography of synchrony and, through it, underlying organism ecology. For example, we introduce a new type of network, the synchrony network, the structure of which provides ecological insight. By documenting the importance of geographies of synchrony, advancing conceptual frameworks, and demonstrating powerful methods, we aim to help elevate the geography of synchrony into a mainstream area of study and application. [ABSTRACT FROM AUTHOR]

Simplified mechanistic models in ecology have been criticised for the fact that a good fit to data does not imply the mechanism is true: pattern does not equal process. In parallel, the maximum entropy principle (MaxEnt) has been applied in ecology to make predictions constrained by just a handful of state variables, like total abundance or species richness. But an outstanding question remains: what principle tells us which state variables to constrain? Here we attempt to solve both problems simultaneously, by translating a given set of mechanisms into the state variables to be used in MaxEnt, and then using this MaxEnt theory as a null model against which to compare mechanistic predictions. In particular, we identify the sufficient statistics needed to parametrise a given mechanistic model from data and use them as MaxEnt constraints. Our approach isolates exactly what mechanism is telling us over and above the state variables alone. [ABSTRACT FROM AUTHOR]

Scientists studying the energy expenditure of air-breathing divers are interested in developing a more tractable technique to support their endeavour. Accelerometers instrumented to animals can return a tangible measure of those animals' activity levels, which in some situations correlates with their metabolic rate., However, I argue that reported evidence for this correlation in semi-aquatic animals while diving is not always as strong as it seems. This is because some analyses have derived a measure of activity level by calculating the sum of accelerometry-based values and compared those data points to measures of summed (total) energy expenditure. These summations mean that time (duration) has been introduced into both sides of the regression equation. This is likely to generate a correlation between body acceleration and energy expenditure even if the two variables are not mechanistically linked because time will correlate with itself., Thus, a strong relationship between a summed measure of energy expenditure and a summed proxy of energy expenditure indicates only that as time passes an animal burns calories. Issues concerning summation of variables in principle extend to other potential proxies of energy expenditure, such as heart rate. I demonstrate the artefactual effect of regressing summed values with analyses of modelled data using ecologically valid parameters., Unless a summed proxy predicts total energy expenditure substantially better than does time alone, there is little evidence upon which to base a claim that body acceleration is suitably predictive of metabolic rate. Clarity about the predictive power of body acceleration to estimate metabolic rate should be sought by simply presenting a graph of the relationship between these raw values., A is available for this article. [ABSTRACT FROM AUTHOR]

Animals adjust to seasonal challenges in physical, behavioural and spatial ways. Such adjustments are commonly associated with diet changes that often can be characterised isotopically., We introduce the 'double diet switch model', with which the occurrence and timing of two subsequent diet switches of an individual animal can be traced with a single sample assayed for stable isotopes. We demonstrate the model for Sanderling, Calidris alba, a small shorebird that migrates from the Nearctic tundra breeding grounds to the intertidal flats of the Wadden Sea; during this migration some birds may stage in the North Atlantic areas., The 'double diet switch model' successfully predicted the occurrence and timing of two diet switches in 59 Sanderlings captured in the Wadden Sea in July-September. Excluding birds that likely had over-summered at North Atlantic staging areas, the model predicted that Sanderlings departed from the Arctic on 13 July (range: 9-17 July), had a staging duration of 18·6 days in the North Atlantic, and arrived in the Wadden Sea on 1 August (31 July-1 August).The estimated mean Arctic departure dates coincided with the mean hatching date, suggesting that many individuals failed to produce young or left the care to a partner. Estimated mean arrival date matched the main arrival period in the Wadden Sea obtained from observation data. In this study we did not use lipid-free tissues, which may bias model predictions. After correcting for lipid components, the estimated departure date was 11 days later and the staging duration 8·5 days shorter, while arrival date was similar., The 'double diet switch model' successfully identified the occurrence and timing of two subsequent diet switches. The 'double diet switch model' will not only apply to switches between three isotopic levels (as in the case study on Sanderling) but also to scenarios where the second switch reverses to the initial isotopic level. Due to this general applicability, the model can be adapted to a wide range of taxa and situations. Foreseeable applications include changes in habitat and food type, ontogenetic development or drastic phenotypic changes such as the metamorphosis in insects and amphibians. [ABSTRACT FROM AUTHOR]

Model selection is difficult. Even in the apparently straightforward case of choosing between standard linear regression models, there does not yet appear to be consensus in the statistical ecology literature as to the right approach., We review recent works on model selection in ecology and subsequently focus on one aspect in particular: the use of the Akaike Information Criterion (AIC) or its small-sample equivalent [ABSTRACT FROM AUTHOR]

In this article the author comments on the article "The checkered history of checkerboard distributions." The author views that checkerboard distributions in congeneric or ecologically similar bird species pairs on the Bismarck Islands west of New Guinea is exclusive. The author also views that checkerboard distributions are disproportionately common in congeneric pairs of species.

Reproducibility is a key tenet of the scientific process that dictates the reliability and generality of results and methods. The complexities of ecological observations and data present novel challenges in satisfying needs for reproducibility and also transparency. Ecological systems are dynamic and heterogeneous, interacting with numerous factors that sculpt natural history and that investigators cannot completely control. Observations may be highly dependent on spatial and temporal context, making them very difficult to reproduce, but computational reproducibility can still be achieved. Computational reproducibility often refers to the ability to produce equivalent analytical outcomes from the same data set using the same code and software as the original study. When coded workflows are shared, authors and editors provide transparency for readers and allow other researchers to build directly and efficiently on primary work. These qualities may be especially important in ecological applications that have important or controversial implications for science, management, and policy. Expectations for computational reproducibility and transparency are shifting rapidly in the sciences. In this work, we highlight many of the unique challenges for ecology along with practical guidelines for reproducibility and transparency, as ecologists continue to participate in the stewardship of critical environmental information and ensure that research methods demonstrate integrity. [ABSTRACT FROM AUTHOR]