Advances in the acquisition and dissemination of knowledge over the last decade have dramatically reshaped the way that ecological research is conducted. The advent of large, technology-based resources such as iNaturalist, Genbank, or the Global Biodiversity Information Facility (GBIF) allow ecologists to work at spatio-temporal scales previously unimaginable. This has generated a new approach in ecological research: one that relies on large datasets and rapid synthesis for theory testing and development, and findings that provide specific recommendations to policymakers and managers. This new approach has been termed action ecology, and here we aim to expand on earlier definitions to delineate its characteristics so as to distinguish it from related subfields in applied ecology and ecological management. Our new, more nuanced definition describes action ecology as ecological research that is (1) explicitly motivated by the need for immediate insights into current, pressing problems, (2) collaborative and transdisciplinary, incorporating sociological in addition to ecological considerations throughout all steps of the research, (3) technology-mediated, innovative, and aggregative (i.e., reliant on ‘big data'), and (4) designed and disseminated with the intention to inform policy and management. We provide tangible examples of existing work in the domain of action ecology, and offer suggestions for its implementation and future growth, with explicit recommendations for individuals, research institutions, and ecological societies.

Rigorous statistical methods for estimating thermonuclear reaction rates and nucleosynthesis are becoming increasingly established in nuclear astrophysics. The main challenge being faced is that experimental reaction rates are highly complex quantities derived from a multitude of different measured nuclear parameters (e.g., astrophysical S-factors, resonance energies and strengths, particle and γ-ray partial widths). We discuss the application of the Monte Carlo method to two distinct, but related, questions. First, given a set of measured nuclear parameters, how can one best estimate the resulting thermonuclear reaction rates and associated uncertainties? Second, given a set of appropriate reaction rates, how can one best estimate the abundances from nucleosynthesis (i.e., reaction network) calculations? The techniques described here provide probability density functions that can be used to derive statistically meaningful reaction rates and final abundances for any desired coverage probability. Examples are given for applications to s-process neutron sources, core-collapse supernovae, classical novae, and Big Bang nucleosynthesis.

Much research has established reliable cross-population differences in motivations to invest in one's in-group. We compare two current historical-evolutionary hypotheses for this variation based on (1) effective large-scale institutions and (2) pathogen threats by analyzing cross-national differences (N = 122) in in-group preferences measured in three ways. We find that the effectiveness of government institutions correlates with favoring in-group members, even when controlling for pathogen stress and world region, assessing reverse causality, and providing a check on endogeneity with an instrumental variable analysis. Conversely, pathogen stress shows inconsistent associations with in-group favoritism when controlling for government effectiveness. Moreover, pathogen stress shows little to no association with in-group favoritism within major world regions whereas government effectiveness does. These results suggest that variation in in-group preferences across contemporary nation-states is more consistent with a generalized response to institutions that meet basic needs rather than an evolved response dedicated to pathogens.

Attributing observed CO2 variations to human or natural cause is critical to deducing and tracking emissions from observations. We have used in situ CO2, CO, and planetary boundary layer height (PBLH) measurements recorded during the CalNex-LA (CARB et al., 2008) ground campaign of 15 May-15 June 2010, in Pasadena, CA, to deduce the diurnally varying anthropogenic component of observed CO2 in the megacity of Los Angeles (LA). This affordable and simple technique, validated by carbon isotope observations and WRF-STILT (Weather Research and Forecasting model - Stochastic Time-Inverted Lagrangian Transport model) predictions, is shown to robustly attribute observed CO2 variation to anthropogenic or biogenic origin over the entire diurnal cycle. During CalNex-LA, local fossil fuel combustion contributed up to similar to 50% of the observed CO2 enhancement overnight, and similar to 100% of the enhancement near midday. This suggests that sufficiently accurate total column CO2 observations recorded near midday, such as those from the GOSAT or OCO-2 satellites, can potentially be used to track anthropogenic emissions from the LA megacity.

Introduction: Immune function is a vital physiological process that is often suppressed during times of resource scarcity due to investments in other physiological systems. While energy is the typical currency that has been examined in such trade-offs, limitations of other resources may similarly lead to trade-offs that affect immune function. Specifically, water is a critical resource with profound implications for organismal ecology, yet its availability can fluctuate at local, regional, and even global levels. Despite this, the effect of osmotic state on immune function has received little attention.

Results: Using agglutination and lysis assays as measures of an organism's plasma concentration of natural antibodies and capacity for foreign cell destruction, respectively, we tested the independent effects of osmotic state, digestive state, and energy balance on innate immune function in free-ranging and laboratory populations of the Gila monster, Heloderma suspectum. This desert-dwelling lizard experiences dehydration and energy resource fluctuations on a seasonal basis. Dehydration was expected to decrease innate immune function, yet we found that dehydration increased lysis and agglutination abilities in both lab and field studies, a relationship that was not simply an effect of an increased concentration of immune molecules. Laboratory-based differences in digestive state were not associated with lysis or agglutination metrics, although in our field population, a loss of fat stores was correlated with an increase in lysis.

Conclusions: Depending on the life history of an organism, osmotic state may have a greater influence on immune function than energy availability. Thus, consideration of osmotic state as a factor influencing immune function will likely improve our understanding of ecoimmunology and the disease dynamics of a wide range of species.