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1. Many animal social structures are organized hierarchically, with some individuals monopolizing resources. Dominance hierarchies have received great attention from behavioural and evolutionary ecologists. 2. There are many methods for inferring hierarchies from social interactions. Yet, there are no clear guidelines about how many observed dominance interactions (i.e. sampling effort) are necessary for inferring reliable dominance hierarchies, nor are there any established tools for quantifying their uncertainty. 3. We simulate interactions (winners and losers) in scenarios of varying steepness (the probability that a dominant defeats a subordinate based on their difference in rank). Using these data, we (1) quantify how the number of interactions recorded and the steepness of the hierarchy affect the performance of five methods for inferring hierarchies, (2) propose an amendment that improves the performance of a popular method, and (3) suggest two easy procedures to measure uncertainty and steepness in the inferred hierarchy. 4. We find that the ratio of interactions to individuals required to infer reliable hierarchies is surprisingly low, but depends on the steepness of the hierarchy and the method used. We show that David's score and our novel randomized Elo-rating are the best methods when hierarchies are not extremely steep, where the original Elorating, the I&SI and the recently described ADAGIO perform less well. In addition, we show that two simple methods can be used to estimate uncertainty at the individual and group level, and that the randomized Elo-rating repeatability provides researchers with a standardized measure valid for comparing the steepness of different hierarchies. We provide several worked examples to guide researchers interested in studying dominance hierarchies. 5. Methods for inferring dominance hierarchies are relatively robust. We recommend that a ratio of observed interactions to individuals of at least 10 (for steep hierarchies), and ideally 20 serves as a good benchmark. Our simple procedures for estimating uncertainty in the observed data will facilitate evaluating whether sufficient data have been collected, while plotting the shape of the hierarchy will provide new insights into the social structure of the study organism.

Statistical models supporting inferences about species occurrence patterns in relation to environmental gradients are fundamental to ecology and conservation biology. A common implicit assumption is that the sampling design is ignorable and does not need to be formally accounted for in analyses. The analyst assumes data are representative of the desired population and statistical modeling proceeds. However, if data sets from probability and non-probability surveys are combined or unequal selection probabilities are used, the design may be non-ignorable. We outline the use of pseudo-maximum likelihood estimation for site-occupancy models to account for such non-ignorable survey designs. This estimation method accounts for the survey design by properly weighting the pseudo-likelihood equation. In our empirical example, legacy and newer randomly selected locations were surveyed for bats to bridge a historic statewide effort with an ongoing nationwide program. We provide a worked example using bat acoustic detection/non-detection data and show how analysts can diagnose whether their design is ignorable. Using simulations we assessed whether our approach is viable for modeling data sets composed of sites contributed outside of a probability design. Pseudo-maximum likelihood estimates differed from the usual maximum likelihood occupancy estimates for some bat species. Using simulations we show the maximum likelihood estimator of species–environment relationships with non-ignorable sampling designs was biased, whereas the pseudo-likelihood estimator was design unbiased. However, in our simulation study the designs composed of a large proportion of legacy or non-probability sites resulted in estimation issues for standard errors. These issues were likely a result of highly variable weights confounded by small sample sizes (5% or 10% sampling intensity and four revisits). Aggregating data sets from multiple sources logically supports larger sample sizes and potentially increases spatial extents for statistical inferences. our results suggest that ignoring the mechanism for how locations were selected for data collection (e.g., the sampling design) could result in erroneous model-based conclusions. Therefore, in order to ensure robust and defensible recommendations for evidence-based conservation decision-making, the survey design information in addition to the data themselves must be available for analysts. Details for constructing the weights used in estimation and code for implementation are provided.

Trophic ecology has benefitted from the use of stable isotopes for the last three decades. However, during the last 10 years, there has been a growing awareness of the isotopic biases associated with some pre-analytical procedures that can seriously hamper the interpretation of food webs. We have assessed the extent of such biases by: (1) reviewing the literature on the topic, and (2) compiling C and N isotopic values of marine invertebrates reported in the literature with the associated sample preparation protocols. The factors considered were: acid-washing, distilled water rinsing (DWR), sample type (whole individuals or pieces of soft tissues), lipid content, and gut contents. Two-level ANOVA revealed overall large and highly significant effects of acidification for both δ¹³C values (up to 0.9[per thousand] decrease) and δ¹⁵ N values (up to 2.1[per thousand] decrease in whole individual samples, and up to 1.1[per thousand] increase in tissue samples). DWR showed a weak overall effect with δ¹³C increments of 0.6[per thousand] (for the entire data set) or decrements of 0.7[per thousand] in δ¹⁵ N values (for tissue samples). Gut contents showed no overall significant effect, whereas lipid extraction resulted in the greatest biases in both isotopic signatures (δ¹³C, up to -2.0[per thousand] in whole individuals; δ¹⁵N, up to +4.3[per thousand] in tissue samples). The study analyzed separately the effects of the various factors in different taxonomic groups and revealed a very high diversity in the extent and direction of the effects. Maxillopoda, Gastropoda, and Polychaeta were the classes that showed the largest isotopic shifts associated with sample preparation. Guidelines for the standardization of sample preparation protocols for isotopic analysis are proposed both for large and small marine invertebrates. Broadly, these guidelines recommend: (1) avoiding both acid washing and DWR, and (2) performing lipid extraction and gut evacuation in most cases.

Microorganisms play key roles in transforming soil carbon into greenhouse gases. As Arctic soils warm as a result of climate change, greater depths and expanses of permanently frozen soil are experiencing seasonal thaw. Despite the importance of active soil microorganisms in transforming soil carbon, the seasonal freezing and thawing of Arctic soils and associated dormancy and re-activation of microbial populations are not well constrained. Here, we thawed and incubated active layer (i.e., seasonally thawing) Arctic soil with a stable isotope to directly label the DNA of growing soil microorganisms. We found that half of the microbial diversity did not grow after thaw and that some groups, including the Bacteroidota and predatory bacteria, grew disproportionately. The growing microbial community shifted over time, and bacteria capable of oxidizing methane grew more after prolonged thaw. These findings highlight that dormancy, predation, and variable growth dynamics are important factors determining ecological and biogeochemical processes in thawing Arctic soil.

Stable isotope analysis is increasingly being utilised across broad areas of ecology and biology. Key to much of this work is the use of mixing models to estimate the proportion of sources contributing to a mixture such as in diet estimation. By accurately reflecting natural variation and uncertainty to generate robust probability estimates of source proportions, the application of Bayesian methods to stable isotope mixing models promises to enable researchers to address an array of new questions, and approach current questions with greater insight and honesty. We outline a framework that builds on recently published Bayesian isotopic mixing models and present a new open source R package, SIAR. The formulation in R will allow for continued and rapid development of this core model into an all-encompassing single analysis suite for stable isotope research.

Two foundational questions about sustainability are “How are ecosystems and the services they provide going to change in the future?” and “How do human decisions affect these trajectories?” Answering these questions requires an ability to forecast ecological processes. Unfortunately, most ecological forecasts focus on centennial-scale climate responses, therefore neither meeting the needs of near-term (daily to decadal) environmental decision-making nor allowing comparison of specific, quantitative predictions to new observational data, one of the strongest tests of scientific theory. Near-term forecasts provide the opportunity to iteratively cycle between performing analyses and updating predictions in light of new evidence. This iterative process of gaining feedback, building experience, and correcting models and methods is critical for improving forecasts. Iterative, near-term forecasting will accelerate ecological research, make it more relevant to society, and inform sustainable decision-making under high uncertainty and adaptive management. Here, we identify the immediate scientific and societal needs, opportunities, and challenges for iterative near-term ecological forecasting. Over the past decade, data volume, variety, and accessibility have greatly increased, but challenges remain in interoperability, latency, and uncertainty quantification. Similarly, ecologists have made considerable advances in applying computational, informatic, and statistical methods, but opportunities exist for improving forecast-specific theory, methods, and cyberinfra-structure. Effective forecasting will also require changes in scientific training, culture, and institutions. The need to start forecasting is now; the time for making ecology more predictive is here, and learning by doing is the fastest route to drive the science forward.