Environmental Dependency of Inbreeding Depression: Implications for Conservation Biology

Inbreeding depression is environmentally dependent, such that a population may suffer from inbreeding depression in one environment but not another. We examined the phenotypic responses of 35 inbred ( F = 0.672) lineages of the red flour beetle Tribolium castaneum in two different climatic environments. We found a significant environmental effect on males but not females. More important, we found that the rank fitness order of lineages differs between environments; lineages of high fitness in one environment may have low fitness in another environment. This change in rank is evident in a significant genotype-by-environment interaction for inbreeding depression for both females and males. These results suggest that even if we know the average environmental effect of inbreeding depression in a population, for any particular lineage measurements of inbreeding depression in one environment may not predict the level of inbreeding depression in another environment. Conservation biologists need to be aware of the environmental dependency of inbreeding depression when planning wildlife refuges or captive propagation programs for small populations. Ideally, captive propagation programs should maintain separate lineages for release efforts. Refuge design programs should consider maintaining a range of habitat types.     

The fire frequency analysis branch of the pyrostatistics tree: sampling decisions and censoring in fire interval data

Statistical characterization of past fire regimes is important for both the ecology and management of fire-prone ecosystems. Survival analysis—or fire frequency analysis as it is often called in the fire literature—has increasingly been used over the last few decades to examine fire interval distributions. These distributions can be generated from a variety of sources (e.g., tree rings and stand age patterns), and analysis typically involves fitting the Weibull model. Given the widespread use of fire frequency analysis and the increasing availability of mapped fire history data, our goal has been to review and to examine some of the issues faced in applying these methods in a spatially explicit context. In particular, through a case study on the massive Cedar Fire in 2003 in southern California, we examine sensitivities of parameter estimates to the spatial resolution of sampling, point- and area-based methods for assigning sample values, current age surfaces versus historical intervals in generating distributions, and the inclusion of censored (i.e., incomplete) observations. Weibull parameter estimates were found to be roughly consistent with previous fire frequency analyses for shrublands (i.e., median age at burning of ~30–50 years and relatively low age dependency). Results indicate, however, that the inclusion or omission of censored observations can have a substantial effect on parameter estimates, far more than other decisions about specifics of sampling.

Effects of Heating Season on Residential Indoor and Outdoor Polycyclic Aromatic Hydrocarbons, Black Carbon, and Particulate Matter in an Urban Birth Cohort

Exposure to air pollutants has been associated with adverse health effects. However, analyses of the effects of season and ambient parameters such as ozone have not been fully conducted. Residential indoor and outdoor air levels of polycyclic aromatic hydrocarbons (PAH), black carbon (measured as absorption coefficient [Abs]), and fine particulate matter <2.5 μm (PM)(2.5) were measured over two-weeks in a cohort of 5-6 year old children (n=334) living in New York City's Northern Manhattan and the Bronx between October 2005 and April 2010. The objectives were to: 1) characterize seasonal changes in indoor and outdoor levels and indoor/outdoor (I/O) ratios of PAH (gas + particulate phase; dichotomized into Σ(8)PAH(semivolatile) (MW 178-206), and Σ(8)PAH(nonvolatile) (MW 228-278)), Abs, and PM(2.5); and 2) assess the relationship between PAH and ozone. Results showed that heating compared to nonheating season was associated with greater Σ(8)PAH(nonvolatile) (p<0.001) and Abs (p<0.05), and lower levels of Σ(8)PAH(semivolatile) (p<0.001). In addition, the heating season was associated with lower I/O ratios of Σ(8)PAH(nonvolatile) and higher I/O ratios of Σ(8)PAH(semivolatile) (p<0.001) compared to the nonheating season. In outdoor air, Σ(8)PAH(nonvolatile) was correlated negatively with community-wide ozone concentration (p<0.001). Seasonal changes in emission sources, air exchanges, meteorological conditions and photochemical/chemical degradation reactions are discussed in relationship to the observed seasonal trends.     

Arctic marine mammal population status,sea ice habitat loss,and conservation recommendations for the 21st century

Arctic marine mammals (AMMs) are icons of climate change, largely because of their close association with sea ice. However, neither a circumpolar assessment of AMM status nor a standardized metric of sea ice habitat change is available. We summarized available data on abundance and trend for each AMM species and recognized subpopulation. We also examined species diversity, the extent of human use, and temporal trends in sea ice habitat for 12 regions of the Arctic by calculating the dates of spring sea ice retreat and fall sea ice advance from satellite data (1979–2013). Estimates of AMM abundance varied greatly in quality, and few studies were long enough for trend analysis. Of the AMM subpopulations, 78% (61 of 78) are legally harvested for subsistence purposes. Changes in sea ice phenology have been profound. In all regions except the Bering Sea, the duration of the summer (i.e., reduced ice) period increased by 5–10 weeks and by >20 weeks in the Barents Sea between 1979 and 2013. In light of generally poor data, the importance of human use, and forecasted environmental changes in the 21st century, we recommend the following for effective AMM conservation: maintain and improve comanagement by local, federal, and international partners; recognize spatial and temporal variability in AMM subpopulation response to climate change; implement monitoring programs with clear goals; mitigate cumulative impacts of increased human activity; and recognize the limits of current protected species legislation.     

The missing link? Implications of internal,external, and relational attribution combinations for leader–member exchange,relationship work,self‐work,and conflict

Attributions are causal explanations made by individuals in response to important, novel, and/or unexpected events. Numerous attribution theories have examined how people use information to make attributions and how attributions impact an individual's subsequent emotions and outcomes. However, this research has only recently considered the implications of dyadic‐level attributions (i.e., relational attributions), particularly in the context of leader–follower relationships in organizations. Therefore, the purpose of this theoretical paper is threefold. First, we integrate research on attributional biases into the research on relational attributions. Second, we integrate and extend attribution theory to consider the implications of convergent and divergent internal, external‐person, external‐situational, and relational attributions for leader–member exchange (LMX) quality, relationship work, self‐work, and conflict. Third, we make the implicit ranking of attribution combinations and the resultant levels of relationship work explicit. In doing so, we contribute to attribution theory and research by proposing how attribution combinations produce positive and negative outcomes that are both intrapersonal and interpersonal. Further, we contribute to the LMX literature by explicating how leader–follower attribution combinations influence relationship quality.     

Assessing Linkages in Stream Habitat,Geomorphic Condition,and Biological Integrity Using a Generalized Regression Neural Network

Watershed managers often use physical geomorphic and habitat assessments in making decisions about the biological integrity of a stream, and to reduce the cost and time for identifying stream stressors and developing mitigation strategies. Such analysis is difficult since the complex linkages between reach‐scale geomorphic and habitat conditions, and biological integrity are not fully understood. We evaluate the effectiveness of a generalized regression neural network (GRNN) to predict biological integrity using physical (i.e., geomorphic and habitat) stream‐reach assessment data. The method is first tested using geomorphic assessments to predict habitat condition for 1,292 stream reaches from the Vermont Agency of Natural Resources. The GRNN methodology outperforms linear regression (69% vs. 40% classified correctly) and improves slightly (70% correct) with additional data on channel evolution. Analysis of a subset of the reaches where physical assessments are used to predict biological integrity shows no significant linear correlation, however the GRNN predicted 48% of the fish health data and 23% of macroinvertebrate health. Although the GRNN is superior to linear regression, these results show linking physical and biological health remains challenging. Reasons for lack of agreement, including spatial and temporal scale differences, are discussed. We show the GRNN to be a data‐driven tool that can assist watershed managers with large quantities of complex, nonlinear data.