Do grazers alter nitrogen dynamics on grazing lawns in a South African savannah?

The presence of grazers on grazing lawns in East Africa and North America often alters nitrogen cycling and availability. Grazing lawns can be defined as areas where grasses are kept in a short, actively growing, palatable state by the action of grazers. Our aim was to test whether lawns have enhanced leaf nitrogen (N) concentrations, total soil N and δ¹⁵N when compared to tall grass areas in a South African savannah. Previous studies have used ecosystem δ¹⁵N as a proxy of N availability, and enriched δ¹⁵N values have been suggested to indicate higher N availability or higher N transformation rates. Across all sites, foliar N concentrations (but not soil N) were higher when compared to tall grass areas, and evidence of enriched foliar and soil δ¹⁵N values was found on the lawns. These results suggest that grazers may be involved in altering the rates of N transformations directly on grazing lawns. Regardless of whether these N transformations included increased net N mineralization, higher N concentrations in above‐ground foliage attract grazers back to the lawns, encouraging their maintenance.     

Reconciling the influence of predictiveness and uncertainty on stimulus salience: a model of attention in associative learning

Theories of selective attention in associative learning posit that the salience of a cue will be high if the cue is the best available predictor of reinforcement (high predictiveness). In contrast, a different class of attentional theory stipulates that the salience of a cue will be high if the cue is an inaccurate predictor of reinforcement (high uncertainty). Evidence in support of these seemingly contradictory propositions has led to: (i) the development of hybrid attentional models that assume the coexistence of separate, predictiveness-driven and uncertainty-driven mechanisms of changes in cue salience; and (ii) a surge of interest in identifying the neural circuits underpinning these mechanisms. Here, we put forward a formal attentional model of learning that reconciles the roles of predictiveness and uncertainty in salience modification. The issues discussed are relevant to psychologists, behavioural neuroscientists and neuroeconomists investigating the roles of predictiveness and uncertainty in behaviour.     

Uncertainties in the governance of animal disease: an interdisciplinary framework for analysis

Uncertainty is an inherent feature of strategies to contain animal disease. In this paper, an interdisciplinary framework for representing strategies of containment, and analysing how uncertainties are embedded and propagated through them, is developed and illustrated. Analysis centres on persistent, periodic and emerging disease threats, with a particular focus on cryptosporidiosis, foot and mouth disease and avian influenza. Uncertainty is shown to be produced at strategic, tactical and operational levels of containment, and across the different arenas of disease prevention, anticipation and alleviation. The paper argues for more critically reflexive assessments of uncertainty in containment policy and practice. An interdisciplinary approach has an important contribution to make, but is absent from current real-world containment policy.     

Evaluating uncertainty to improve a common monitoring method and guide management decisions for diamond-backed terrapins

Designing monitoring with adequate certainty to evaluate management actions can be challenging, especially for elusive species in relatively inaccessible habitats. The diamond-backed terrapin (Malaclemys terrapin) is considered a high priority species for management in all states within its range. Among key threats to terrapin populations is bycatch mortality in crab pots, prompting states throughout the species' range to consider crabbing regulations and creating a need for a monitoring approach able to evaluate the success of any new crabbing regulations to benefit terrapins. Because terrapins occupy extensive and often difficult to access estuarine habitat, it is hard to collect sufficient data to estimate population responses accurately and precisely for management. To assist state managers with decisions regarding monitoring bycatch reduction regulations, we modeled 12 years of terrapin capture-recapture data. We used estimates from those models to simulate capture-recapture data pre- and post-implementation of bycatch reduction that resulted in a 0.20 absolute increase in mean apparent survival probability. Results indicated weak reliability of a monitoring approach using only manual seining of tidal creeks to detect a real management effect, with a positive management effect detected at the 95% certainty level only 34% of the time. When we considered 85% and 75% certainty thresholds, we detected a positive effect on survival among 61% and 75% of simulations, respectively. Low within-year recapture probability and the effect of tide amplitude on terrapin availability indicate there is low feasibility of improving monitoring precision at a single site, requiring monitoring of more sites to improve confidence in the detection of the management effect. The number of sites monitored depends on the acceptable level of certainty. We recommend that researchers and management entities that use seining assess the level of certainty they require to evaluate management actions and increase the number of sites sampled to meet that level of certainty. Additionally, the use of multiple monitoring methods and integrated models should be explored to reduce uncertainty and to allow for easier monitoring of more populations over broader spatial scales.     

Dynamics of the active site architecture in plant-type ferredoxin-NADP reductases catalytic complexes

Kinetic isotope effects in reactions involving hydride transfer and their temperature dependence are powerful tools to explore dynamics of enzyme catalytic sites. In plant-type ferredoxin-NADP⁺ reductases the FAD cofactor exchanges a hydride with the NADP(H) coenzyme. Rates for these processes are considerably faster for the plastidic members (FNR) of the family than for those belonging to the bacterial class (FPR). Hydride transfer (HT) and deuteride transfer (DT) rates for the NADP⁺ coenzyme reduction of four plant-type FNRs (two representatives of the plastidic type FNRs and the other two from the bacterial class), and their temperature dependences are here examined applying a full tunnelling model with coupled environmental fluctuations. Parameters for the two plastidic FNRs confirm a tunnelling reaction with active dynamics contributions, but isotope effects on Arrhenius factors indicate a larger contribution for donor–acceptor distance (DAD) dynamics in the Pisum sativum FNR reaction than in the Anabaena FNR reaction. On the other hand, parameters for bacterial FPRs are consistent with passive environmental reorganisation movements dominating the HT coordinate and no contribution of DAD sampling or gating fluctuations. This indicates that active sites of FPRs are more organised and rigid than those of FNRs. These differences must be due to adaptation of the active sites and catalytic mechanisms to fulfil their particular metabolic roles, establishing a compromise between protein flexibility and functional optimisation. Analysis of site-directed mutants in plastidic enzymes additionally indicates the requirement of a minimal optimal architecture in the catalytic complex to provide a favourable gating contribution.     

Codon usage bias in human cytomegalovirus and its biological implication

Human cytomegalovirus (HCMV) infection, a worldwide contagion, causes a serious disorder in infected individuals. Analysis of codon usage can reveal much molecular information about this virus. The effective number of codon (ENC) values, relative synonymous codon usage (RSCU) values, codon adaptation index (CAI), and nucleotide contents was investigated in approximately 160 coding sequences (CDS) among 17 human cytomegalovirus genomes using the software CodonW. Linear regression analysis and logistic regression were performed to explore the preliminary data. The results showed that, overall, HCMV genomes had low codon usage bias (mean ENC = 47.619). However, the ENC of individual CDS varied widely and was distributed unevenly between host-related genes and viral-self-function genes (P = 0.002, odds ratio (OR) = 3.194), as did the GC content (P = 0.016, OR = 2.178). The ENC values correlated with CAI, GC content, and the nucleotide composing at the 3rd codon position (GC3s) (P < 0.001). There was a significant variation in the codon preference that depended on the RSCU data. The predicted ENC curve suggested that mutational pressure, rather than natural selection, was one of the main factors that determined the codon usage bias in HCMV. Among 123 genes with known function, the genes related to viral self-replication and viral–host interaction showed different ENC and CAI values, and GC and GC3s contents. In conclusion, the detailed codon usage bias theoretically revealed information concerning HCMV evolution and could be a valuable additional parameter for HCMV gene function research.     

How is Scientific Credibility Affected by Communicating Uncertainty? The Case of Endocrine Disrupter Effects on Male Fertility

Using the case of endocrine disrupter effects on male fertility, we explored how communicating uncertainty influences the credibility of the information that laypeople receive from scientists and how laypeople form judgments about the relationship between uncertainty and credibility. We found that laypeople assess the credibility of scientific information—whether or not it is accompanied by uncertainty—by referencing their “science model” and using non-scientific references (i.e., situations encountered in one's daily life, information received from other sources, one's own observations of the world, and one's education or professional experience). Scientific credibility is a mixture of (sometimes contradictory) considerations along these different axes. Previous studies have found that some scientists assume that communicating uncertainty will lower public credibility of science. Our results contradict this assumption for situations in which academic scientists communicate uncertainty, which is perceived as additional knowledge bringing a new perspective on certain information. People expect scientists to provide practical solutions and feel disillusionment when scientists lack straight answers. However, they accepted uncertainty as an intrinsic characteristic of science and a consequence of the limits to human beings’ capacity to understand the world. Further, the low credibility of industry scientists is further reinforced when they communicate uncertainty.     

Using Attributional Life Cycle Assessment to Estimate Climate‐Change Mitigation Benefits Misleads Policy Makers

Life cycle assessment (LCA) is generally described as a tool for environmental decision making. Results from attributional LCA (ALCA), the most commonly used LCA method, often are presented in a way that suggests that policy decisions based on these results will yield the quantitative benefits estimated by ALCA. For example, ALCAs of biofuels are routinely used to suggest that the implementation of one alternative (say, a biofuel) will cause an X% change in greenhouse gas emissions, compared with a baseline (typically gasoline). However, because of several simplifications inherent in ALCA, the method, in fact, is not predictive of real‐world impacts on climate change, and hence the usual quantitative interpretation of ALCA results is not valid. A conceptually superior approach, consequential LCA (CLCA), avoids many of the limitations of ALCA, but because it is meant to model actual changes in the real world, CLCA results are scenario dependent and uncertain. These limitations mean that even the best practical CLCAs cannot produce definitive quantitative estimates of actual environmental outcomes. Both forms of LCA, however, can yield valuable insights about potential environmental effects, and CLCA can support robust decision making. By openly recognizing the limitations and understanding the appropriate uses of LCA as discussed here, practitioners and researchers can help policy makers implement policies that are less likely to have perverse effects and more likely to lead to effective environmental policies, including climate mitigation strategies.