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Ecological Dynamics and Forecasting' is a semester-long course to introduce students to the fundamentals of ecological dynamics and forecasting. This course implements paper-based discussion to introduce students to concepts and ideas and R-based tutorials for hands-on application and training. The course material includes a reading list with prompting questions for discussions, teachers notes for guiding discussions, lecture notes for live coding demonstrations, and video presentations of all R tutorials. This course material can be used either as self-directed learning or as all or part of a college or university course. Individual learners have access to all of the necessary material - including discussion questions and instructor notes - on the website. The course focuses on papers with an open-access or free-to-read version where possible, though some materials still rely on access to closed-access papers. The course is structured around two sessions per week, with most weeks consisting of a one hour paper discussion session and a 1-2 hour session focused on applications in R. R tutorials use publicly available ecological datasets to provide realistic applications. Because the material is organized around content themes, instructors can modify and remix materials based on their course goals and student levels of background knowledge. These course materials have been taught for several years at the authors’ university and have also generated significant online engagement with course videos tens of thousands of times. 

A pair of Y-organs (YOs) synthesize ecdysteroids that initiate and coordinate molting processes in decapod crustaceans. The YO converts cholesterol to secreted products through a biosynthetic pathway involving a Rieske oxygenase encoded by Neverland (Nvd) and cytochrome P450 monooxygenases encoded by Halloween genes Spook (Spo; Cyp307a1), Phantom (Phm; Cyp306a1), Disembodied (Dib; Cyp302a1), and Shadow (Sad; Cyp315a1). NAD kinase (NADK) and 5-aminolevulinic acid synthase (ALAS) support ecdysteroid synthesis in insects. A 20-hydroxylase, encoded by Shed in decapods and Shade in insects, converts ecdysone to the active hormone 20-hydroxyecdysone (20E). 20E is inactivated by cytochrome P450 26-hydroxylase (Cyp18a1). Contigs encoding these eight proteins were extracted from a Gecarcinus lateralis YO transcriptome and their expression was quantified by quantitative polymerase chain reaction. mRNA levels of Gl-Spo and Gl-Phm were four orders of magnitude higher in YO than those in nine other tissues, while mRNA levels of Gl-NADK and Gl-ALAS were similar in all ten tissues. In G. lateralis induced to molt by multiple leg autotomy, YO mRNA levels of Gl-Nvd, Gl-Spo, Gl-Phm, Gl-NADK, and Gl-ALAS were highest in intermolt and premolt stages and lower in postmolt. Gl-Dib mRNA level was not affected by molt stage. mRNA level of Gl-Sad, which converts 2-deoxyecdysone to ecdysone, was higher in mid-and late premolt stages, when YO ecdysteroidogenic capacity is greatest. Gl-Cyp18a1 mRNA level was highest in intermolt, decreased in premolt stages, and was lowest in postmolt. In animals induced to molt by eyestalk ablation, YO mRNA levels of all eight genes were not correlated with increased hemolymph 20E titers. These results suggest that YO ecdysteroidogenic genes are differentially regulated at transcriptional and translational levels. 

Abstract AimThe frequency of different body sizes in an ecological community (the individual size distribution, or ISD) is a key link between the number of individual organisms present in a community and community function—total biomass or total energy use. If the ISD changes over time, the dynamics of community function may become decoupled from trends in abundance. Understanding how, and how often, the ISD modulates the relationship between abundance, biomass and energy use is of critical importance to understand biodiversity trends in the Anthropocene. Here, we conduct the first macroecological‐scale analysis of this type for avian communities. LocationNorth America, north of Mexico. Time Period1989–2018. Major Taxa StudiedBreeding birds. MethodsWe used species' traits to generate annual ISDs for bird communities in the North American Breeding Bird Survey. We compared the long‐term trends in total biomass and energy use to the trends generated from a null model of an unchanging ISD. ResultsTrends in biomass have been evenly split between increases and decreases, but the trends predicted by the null model were dominated by decreases. A substantial number of communities have undergone a shift in the ISD favouring larger bodied species, resulting in a less negative trend in biomass than would be expected had the ISD remained static. Trends in energy use more closely paralleled the null model. Main ConclusionsTaking changes in the ISD into account qualitatively changes the continental‐scale picture of how biomass and energy use have changed over the past 30 years. For North American breeding birds, shifts in species composition favouring larger bodied species may have partially offset declines in standing biomass driven by losses of individuals over the past 30 years. 

Abstract For many species, a well documented response to anthropogenic climate change is a shift in various aspects of its life history, including its timing or phenology. Often, these phenological shifts are associated with changes in abiotic factors used as proxies for resource availability or other suitable conditions. Resource availability, however, can also be impacted by competition, but the impact of competition on phenology is less studied than abiotic drivers. We fit generalized additive models (GAMs) to a long‐term experimental dataset on small mammals monitored in the southwestern United States and show that altered competitive landscapes can drive shifts in breeding timing and prevalence, and that, relative to a dominant competitor, other species exhibit less specific responses to environmental factors. These results suggest that plasticity of phenological responses, which is often described in the context of annual variation in abiotic factors, can occur in response to biotic context as well. Variation in phenological responses under different biotic conditions shown here further demonstrates that a more nuanced understanding of shifting biotic interactions is useful to better understand and predict biodiversity patterns in a changing world. 

Abstract Exploring and accounting for the emergent properties of ecosystems as complex systems is a promising horizon in the search for general processes to explain common ecological patterns. For example the ubiquitous hollow‐curve form of the species abundance distribution is frequently assumed to reflect ecological processes structuring communities, but can also emerge as a statistical phenomenon from the mathematical definition of an abundance distribution. Although the hollow curve may be a statistical artefact, ecological processes may induce subtle deviations between empirical species abundance distributions and their statistically most probable forms. These deviations may reflect biological processes operating on top of mathematical constraints and provide new avenues for advancing ecological theory. Examining ~22,000 communities, we found that empirical SADs are highly uneven and dominated by rare species compared to their statistical baselines. Efforts to detect deviations may be less informative in small communities—those with few species or individuals—because these communities have poorly resolved statistical baselines. The uneven nature of many empirical SADs demonstrates a path forward for leveraging complexity to understand ecological processes governing the distribution of abundance, while the issues posed by small communities illustrate the limitations of using this approach to study ecological patterns in small samples. 

Abstract Probabilistic near‐term forecasting facilitates evaluation of model predictions against observations and is of pressing need in ecology to inform environmental decision‐making and effect societal change. Despite this imperative, many ecologists are unfamiliar with the widely used tools for evaluating probabilistic forecasts developed in other fields. We address this gap by reviewing the literature on probabilistic forecast evaluation from diverse fields including climatology, economics, and epidemiology. We present established practices for selecting evaluation data (end‐sample hold out), graphical forecast evaluation (times‐series plots with uncertainty, probability integral transform plots), quantitative evaluation using scoring rules (log, quadratic, spherical, and ranked probability scores), and comparing scores across models (skill score, Diebold–Mariano test). We cover common approaches, highlight mathematical concepts to follow, and note decision points to allow application of general principles to specific forecasting endeavors. We illustrate these approaches with an application to a long‐term rodent population time series currently used for ecological forecasting and discuss how ecology can continue to learn from and drive the cross‐disciplinary field of forecasting science.