Measuring impacts on species with models and metrics of varying ecological and computational complexity

Approaches to assess the impacts of landscape disturbance scenarios on species range from metrics based on patterns of occurrence or habitat to comprehensive models that explicitly include ecological processes. The choice of metrics and models affects how impacts are interpreted and conservation decisions. We explored the impacts of 3 realistic disturbance scenarios on 4 species with different ecological and taxonomic traits. We used progressively more complex models and metrics to evaluate relative impact and rank of scenarios on the species. Models ranged from species distribution models that relied on implicit assumptions about environmental factors and species presence to highly parameterized spatially explicit population models that explicitly included ecological processes and stochasticity. Metrics performed consistently in ranking different scenarios in order of severity primarily when variation in impact was driven by habitat amount. However, they differed in rank for cases where dispersal dynamics were critical in influencing metapopulation persistence. Impacts of scenarios on species with low dispersal ability were better characterized using models that explicitly captured these processes. Metapopulation capacity provided rank orders that most consistently correlated with those from highly parameterized and data-rich models and incorporated information about dispersal with little additional computational and data cost. Our results highlight the importance of explicitly considering species’ ecology, spatial configuration of habitat, and disturbance when choosing indicators of species persistence. We suggest using hybrid approaches that are a mixture of simple and complex models to improve multispecies assessments.     

From expectation to experience: Graduates entering a large corporation

The paper reports a longitudinal study of new and recently recruited graduates to an oil company, to evaluate how initial expectations compared with experience, and how needs, interests and work activities changed over time post-entry. Results showed that new entrants had unrealistic expectations, especially of having more managerial job content than they were assigned initially, but this increased with subsequent experience to reach expected levels. Socialization effects were evident, especially by job function, but not by gender. The work orientations and job experiences of commercial and technical graduates developed on divergent paths over time, suggesting more positive personal growth over their early careers for the commercial graduates.     

A guide to representing variability and uncertainty in biodiversity indicators

Biodiversity indicators are used to inform decisions and measure progress toward global targets, such as the United Nations Sustainable Development Goals. Indicators aggregate and simplify complex information, so underlying information influencing its reliability and interpretation (e.g., variability in data and uncertainty in indicator values) can be lost. Communicating uncertainty is necessary to ensure robust decisions and limit misinterpretations of trends, yet variability and uncertainty are rarely quantified in biodiversity indicators. We developed a guide to representing uncertainty and variability in biodiversity indicators. We considered the key purposes of biodiversity indicators and commonly used methods for representing uncertainty (standard error, bootstrap resampling, and jackknife resampling) and variability (quantiles, standard deviation, median absolute deviation, and mean absolute deviation) with intervals. Using 3 high-profile biodiversity indicators (Red List Index, Living Planet Index, and Ocean Health Index), we tested the use, suitability, and interpretation of each interval method based on the formulation and data types underpinning the indicators. The methods revealed vastly different information; indicator formula and data distribution affected the suitability of each interval method. Because the data underpinning each indicator were not normally distributed, methods relying on normality or symmetrical spread were unsuitable. Quantiles, bootstrapping, and jackknifing provided useful information about the underlying variability and uncertainty. We built a decision tree to inform selection of the appropriate interval method to represent uncertainty or variation in biodiversity indicators, depending on data type and objectives. Our guide supports transparent and effective communication of biodiversity indicator trends to facilitate accurate interpretation by decision makers.     

Simultaneous-count models to estimate abundance from counts of unmarked individuals with imperfect detection

We developed a method to estimate population abundance from simultaneous counts of unmarked individuals over multiple sites. We considered that at each sampling occasion, individuals in a population could be detected at 1 of the survey sites or remain undetected and used either multinomial or binomial simultaneous-count models to estimate abundance, the latter being equivalent to an N-mixture model with one site. We tested model performance with simulations over a range of detection probabilities, population sizes, growth rates, number of years, sampling occasions, and sites. We then applied our method to 3 critically endangered vulture species in Cambodia to demonstrate the real-world applicability of the model and to provide the first abundance estimates for these species in Cambodia. Our new approach works best when existing methods are expected to perform poorly (i.e., few sites and large variation in abundance among sites) and if individuals may move among sites between sampling occasions. The approach performed better when there were >8 sampling occasions and net probability of detection was high (>0.5). We believe our approach will be useful in particular for simultaneous surveys at aggregation sites, such as roosts. The method complements existing approaches for estimating abundance of unmarked individuals and is the first method designed specifically for simultaneous counts.     

Photocatalytic oxidation of NOx gases using TiO2: a surface spectroscopic approach

The bandgap of solid-state TiO2 (3.2 eV) enables it to be a useful photocatalyst in the ultraviolet (lambda < 380 nm) region of the spectrum. A clean TiO2 surface in the presence of sunlight therefore enables the removal of harmful NOx gases from the atmosphere by oxidation to nitrates. These properties, in addition to the whiteness, relative cheapness and non-toxicity, make TiO2 ideal for the many de-NOX catalysts that are currently being commercially exploited both in the UK and Japan for concrete paving materials in inner cities. There is need, however, for further academic understanding of the surface reactions involved. Hence, we have used surface specific techniques, including X-ray photoelectron spectroscopy and Raman spectroscopy, to investigate the NOx adsorbate reaction at the TiO2 substrate surface.     

Degradation of 4-fluorobiphenyl in soil investigated by 19F NMR spectroscopy and 14C radiolabelling analysis

The incubation of the model pollutant [U-14C]'-4-fluorobiphenyl (4FBP) in soil, in the presence and absence of biphenyl (a co-substrate), was carried out in order to study the qualitative disposition and fate of the compound using 14C-HPLC and 19F NMR spectroscopy. Components accounted for using the radiolabel were volatilization, CO2 evolution, organic solvent extractable and bound residue. Quantitative analysis of these data gave a complete mass balance. After sample preparation. 14C-HPLC was used to establish the number of 4FBP related components present in the organic solvent extract. 19F NMR was also used to quantify the organic extracts and to identify the components of the extract. Both approaches showed that the composition of the solvent extractable fractions comprised only parent compound with no metabolites present. As the 14C radiolabel was found to be incorporated into the soil organic matter this indicates that metabolites were being generated, but were highly transitory as incorporation into the SOM was rapid. The inclusion of the co-substrate biphenyl was to increase the overall rate of degradation of 4FBP in soil. The kinetics of disappearance of parent from the soil using the data obtained were investigated from both techniques. This is the first report describing the degradation of a fluorinated biphenyl in soil.     

Effects of air filtration at small SO2 and NO2 concentrations on the yield of barley

Two cultivars of Igri and Gerbel winter barley Horteum vulgare L. were grown in open-top chambers in filtered and unfiltered air at a site with approximately 10 nl litre(-1) SO2 and 12 nl litre(-1) NO2 (seasonal mean). The experiment ran for three consecutive seasons 1982-1983, 1983-1984, 1984-1985, and significant effects of filtration were observed for each crop. In years 1982-1983 and 1984-1985, the crops in unfiltered air yielded larger grain dry matter, 9% in 1982-1983, and 8% in 1984-1985. For both crops, the differences were statistically significant at the 5% level. Differences were also observed for the remaining above-ground dry matter, and these were consistent in direction in each year but statistically significant only in 1984-1985. In both growing seasons (1982-1983 and 1984-1985), there were no major pest infestations and no long-term water stress or photochemical ozone episodes. In the remaining experiment (1983-1984) similar air concentrations of SO2 and NO2 produced effects of the opposite sign to those observed in 1982-1983 and 1984-1985. Significant reductions in grain yield (13%) were obtained in unfiltered air. The only major environmental difference for the 1983-1984 crop was a notable dry period in May and June 1984 with marked water stress in the crop, requiring irrigation. These results suggest that the relationship between yield and pollutant concentration may be confounded by additional stresses, many of which are a common component of the growing season for major crops.