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Abstract Individual body size distributions (ISD) within communities are remarkably consistent across habitats and spatiotemporal scales and can be represented by size spectra, which are described by a power law. The focus of size spectra analysis is to estimate the exponent () of the power law. A common application of size spectra studies is to detect anthropogenic pressures.Many methods have been proposed for estimating most of which involve binning the data, counting the abundance within bins, and then fitting an ordinary least squares regression in log–log space. However, recent work has shown that binning procedures return biased estimates of compared to procedures that directly estimate using maximum likelihood estimation (MLE). While it is clear that MLE produces less biased estimates of site‐specificλ's, it is less clear how this bias affects the ability to test for changes inλacross space and time, a common question in the ecological literature.Here, we used simulation to compare the ability of two normalised binning methods (equal logarithmic and log₂bins) and MLE to (1) recapture known values of , and (2) recapture parameters in a linear regression measuring the change in across a hypothetical environmental gradient. We also compared the methods using two previously published body size datasets across a natural temperature gradient and an anthropogenic pollution gradient.Maximum likelihood methods always performed better than common binning methods, which demonstrated consistent bias depending on the simulated values of . This bias carried over to the regressions, which were more accurate when was estimated using MLE compared to the binning procedures. Additionally, the variance in estimates using MLE methods is markedly reduced when compared to binning methods.The error induced by binning methods can be of similar magnitudes as the variation previously published in experimental and observational studies, bringing into question the effect sizes of previously published results. However, while the methods produced different regression slope estimates, they were in qualitative agreement on the sign of those slopes (i.e. all negative or all positive). Our results provide further support for the direct estimation of and its relative variation across environmental gradients using MLE over the more common methods of binning. 

Abstract Understanding the drivers of food chain length in natural communities has intrigued ecologists since Elton publicized “food cycles” in the early 20th century. Proposed drivers of food chain length have included productivity, disturbance regime, ecosystem size, and trophic omnivory. However, current theories have largely assumed simple, two‐dimensional habitat architectures and may not be adequate to predict food chain length in ecosystems with a complex, branching structure. Here, we develop a spatially explicit theoretical model that provides an integrated framework for understanding variation in food chain length in branching networks. We show independent, positive influences of ecosystem size and complexity (as indicated by branching properties) on food chain length. However, the effects of ecosystem size and complexity were contingent upon other factors, appearing more clearly in high‐disturbance and high‐productivity regimes. Our results suggest that ecosystem complexity is an important yet overlooked driver of food chain length that may increase the resilience to anthropogenic environmental changes. 

Abstract Bayesian data analysis is increasingly used in ecology, but prior specification remains focused on choosing non‐informative priors (e.g., flat or vague priors). One barrier to choosing more informative priors is that priors must be specified on model parameters (e.g., intercepts, slopes, and sigmas), but prior knowledge often exists on the level of the response variable. This is particularly true for common models in ecology, like generalized linear mixed models that have a link function and potentially dozens of parameters, each of which needs a prior distribution. We suggest that this difficulty can be overcome by simulating from the prior predictive distribution and visualizing the results on the scale of the response variable. In doing so, some common choices for non‐informative priors on parameters can easily be seen to produce biologically impossible values of response variables. Such implications of prior choices are difficult to foresee without visualization. We demonstrate a workflow for prior selection using simulation and visualization with two ecological examples (predator–prey body sizes and spider responses to food competition). This approach is not new, but its adoption by ecologists will help to better incorporate prior information in ecological models, thereby maximizing one of the benefits of Bayesian data analysis. 

Abstract Parameters describing the negative relationship between abundance and body size within ecological communities provide a summary of many important biological processes. While it is considered to be one of the few consistent patterns in ecology, spatiotemporal variation of this relationship across continental scale temperature gradients is unknown. Using a database of stream communities collected across North America (18–68°N latitude, −4 to 25°C mean annual air temperature) over 3 years, we constructed 160 individual size distribution (ISD) relationships (i.e. abundance size spectra). The exponent parameter describing ISD’s decreased (became steeper) with increasing mean annual temperature, with median slopes varying by ~0.2 units across the 29°C temperature gradient. In addition, total community biomass increased with increasing temperatures, contrary with theoretical predictions. Our study suggests conservation of ISD relationships in streams across broad natural environmental gradients. This supports the emerging use of size‐spectra deviations as indicators of fundamental changes to the structure and function of ecological communities.