Bias in the development of health and ecological assessments and potential solutions

The effectiveness and credibility of environmental decisions depend on the information provided by scientific assessments. However, the conflicting assessments provided by government agencies, industries, and environmental advocacy groups suggest that biases occur during assessment processes. Sources of bias include personal bias, regulatory capture, advocacy, reliance on volunteer assessors, biased stakeholder and peer review processes, literature searches, standardization of data, inappropriate standards of proof, misinterpretation, and ambiguity. Assessors can adopt practices to increase objectivity, transparency, and clarity. Decision-makers, managers of assessors, and institutions that commission assessments can adopt other practices that reduce pressures on assessors and reduce opportunities for expression of the personal biases of assessors. Environmental assessment should be recognized as a discipline with its own technical and ethical best practices.     

The mutational structure of metabolism in Caenorhabditis elegans

A properly functioning organism must maintain metabolic homeostasis. Deleterious mutations degrade organismal function, presumably at least in part via effects on metabolic function. Here we present an initial investigation into the mutational structure of the Caenorhabditis elegans metabolome by means of a mutation accumulation experiment. We find that pool sizes of 29 metabolites vary greatly in their vulnerability to mutation, both in terms of the rate of accumulation of genetic variance (the mutational variance, VM) and the rate of change of the trait mean (the mutational bias, ΔM). Strikingly, some metabolites are much more vulnerable to mutation than any other trait previously studied in the same way. Although we cannot statistically assess the strength of mutational correlations between individual metabolites, principal component analysis provides strong evidence that some metabolite pools are genetically correlated, but also that there is substantial scope for independent evolution of different groups of metabolites. Averaged over mutation accumulation lines, PC3 is positively correlated with relative fitness, but a model in which metabolites are uncorrelated with fitness is nearly as good by Akaike's Information Criterion.     

Cophylogenetic patterns are uncorrelated between two lineages of parasites on the same hosts

Free‐living organisms are often host to multiple lineages of closely related parasites. Different lineages of obligate parasites living on the same hosts might potentially be expected to display similar cophylogenetic patterns. However, there are also reasons why these lineages might have different evolutionary histories (e.g. host switching, host geography). In the present study, we use mitochondrial and nuclear DNA sequence data to evaluate the cophylogenetic patterns between doves and their wing and body lice. Previous studies have found that the wing and body lice of doves have different levels of congruence between their phylogenetic histories. However, these studies are limited in scope, either taxonomically or geographically. We used both new and existing data to generate a worldwide and taxonomically diverse data set for doves and two independent groups of lice: wing and body lice. Using event and topology‐based methods, we found that cophylogenetic patterns were not correlated between wing and body lice, even though both groups showed evidence of cospeciation with their hosts. These results indicate that external factors vary in their impact on different groups of parasites and also that broad sampling is critical for identifying patterns in cophylogenetic analyses.     

Distance makes the difference in thermography for ecological studies

Surface temperature drives many ecological processes and infrared thermography is widely used by ecologists to measure the thermal heterogeneity of different species' habitats. However, the potential bias in temperature readings caused by distance between the surface to be measured and the camera is still poorly acknowledged. We examined the effect of distance from 0.3 to 80 m on a variety of thermal metrics (mean temperature, standard deviation, patch richness and aggregation) under various weather conditions and for different structural complexity of the studied surface types (various surfaces with vegetation). We found that distance is a key modifier of the temperature measured by a thermal infrared camera. A non-linear relationship between distance and mean temperature, standard deviation and patch richness led to a rapid under-estimation of the thermal metrics within the first 20 m and then only a slight decrease between 20 and 80 m from the object. Solar radiation also enhanced the bias with increasing distance. Therefore, surface temperatures were under-estimated as distance increased and thermal mosaics were homogenized at long distances with a much stronger bias in the warmer than the colder parts of the distributions. The under-estimation of thermal metrics due to distance was explained by atmospheric composition and the pixel size effect. The structural complexity of the surface had little effect on the surface temperature bias. Finally, we provide general guidelines for ecologists to minimize inaccuracies caused by distance from the studied surface in thermography.     

Super-normal length song preferences of female zebra finches (Taeniopygia guttata) and a theory of the evolution of bird song

Two way choice tests show a preference of female zebra finches for male songs four standard deviations longer than normal song. Further tests show the ontogeny of this preference to parallel song learning in general as well as a preference for songs with entirely heterogeneous notes compared to songs with four note repeats. These findings are discussed in relation to a theory of the evolution of bird song from bird calls due to female preferences for longer, more complex vocalizations. This revised version was published online in July 2006 with corrections to the Cover Date.     

Identifying and correcting spatial bias in opportunistic citizen science data for wild ungulates in Norway

Many publications make use of opportunistic data, such as citizen science observation data, to infer large‐scale properties of species’ distributions. However, the few publications that use opportunistic citizen science data to study animal ecology at a habitat level do so without accounting for spatial biases in opportunistic records or using methods that are difficult to generalize. In this study, we explore the biases that exist in opportunistic observations and suggest an approach to correct for them. We first examined the extent of the biases in opportunistic citizen science observations of three wild ungulate species in Norway by comparing them to data from GPS telemetry. We then quantified the extent of the biases by specifying a model of the biases. From the bias model, we sampled available locations within the species’ home range. Along with opportunistic observations, we used the corrected availability locations to estimate a resource selection function (RSF). We tested this method with simulations and empirical datasets for the three species. We compared the results of our correction method to RSFs obtained using opportunistic observations without correction and to RSFs using GPS‐telemetry data. Finally, we compared habitat suitability maps obtained using each of these models. Opportunistic observations are more affected by human access and visibility than locations derived from GPS telemetry. This has consequences for drawing inferences about species’ ecology. Models naïvely using opportunistic observations in habitat‐use studies can result in spurious inferences. However, sampling availability locations based on the spatial biases in opportunistic data improves the estimation of the species’ RSFs and predicted habitat suitability maps in some cases. This study highlights the challenges and opportunities of using opportunistic observations in habitat‐use studies. While our method is not foolproof it is a first step toward unlocking the potential of opportunistic citizen science data for habitat‐use studies.     

Shrinkage and Pretest Nonparametric Estimation of Regression Parameters from Censored Data with Multiple Observations at Each Level of Covariate

A simple linear regression model is considered where the independent variable assumes only a finite number of values and the response variable is randomly right censored. However, the censoring distribution may depend on the covariate values. A class of noniterative estimators for the slope parameter, namely, the noniterative unrestricted estimator, noniterative restricted estimator and noniterative improved pretest estimator are proposed. The asymptotic bias and mean squared errors of the proposed estimators are derived and compared. The relative dominance picture of the estimators is investigated. A simulation study is also performed to asses the properties of the various estimators for small samples.     

Negative emotional biases in late chronotypes

Increasing evidence suggests evening chronotypes are at increased risk for developing depression. Here, we examined if, similar to acutely depressed patients, evening chronotype individuals display biases in emotional face recognition. Two hundred and twenty-six individuals completed an online survey including measures of sleep quality, depression/anxiety and chronotype followed by a simple emotion recognition task presenting male and female faces morphed in 10 steps between 0 (neutral) and 100% sad or happy. Evening chronotype was associated with increased recognition of sad facial expressions independently of sleep quality, mood, age and gender. The current results extend previous work indicating that negative biases in emotional processing are present in evening chronotypes and may have important implications for the prevention and treatment of depression in these vulnerable individuals.