Über die Phasenprobe und die nächsten Abkömmlinge des Chlorophylls

The Science of Nature -     

Building phenological models from presence/absence data for a butterfly fauna.

Species phenology is increasingly being used to explore the effects of climate change and other environmental stressors. Long-term monitoring data sets are essential for understanding both patterns manifest by individual species and more complex patterns evident at the community level. This study used records of 78 butterfly species observed on 626 days across 27 years at a site in northern California, USA, to build quadratic logistic regression models of the observation probability of each species for each day of the year. Daily species probabilities were summed to develop a potential aggregate species richness (PASR) model, indicating expected daily species richness. Daily positive and negative contributions to PASR were calculated, which can be used to target optimum sampling time frames. Residuals to PASR indicate a rate of decline of 0.12 species per year over the course of the study. When PASR was calculated for wet and dry years, wet years were found to delay group phenology by up to 17 days and reduce the maximum annual expected species from 32.36 to 30. Three tests to determine how well the PASR model reflected the butterfly fauna dynamics were all positive: We correlated probabilities developed with species presence/absence data to observed abundance by species, tested species' predicted phenological patterns against known biological characteristics, and compared the PASR curve to a spline-fitted curve calculated from the original species richness observations. Modeling individual species' flight windows was possible from presence/absence data, an approach that could be used on other similar records for butterfly communities with seasonal phenologies, and for common species with far fewer dates than used here. It also provided a method to assess sample frequency guidelines for other butterfly monitoring programs.     

Modeling joint production of wildlife and timber

Production of marketed commodities and protection of natural systems often conflict. A focus on only one goal can result in large losses in other goals and may result in inefficient and unsustainable outcomes. In this paper, we develop a method that combines economic and ecological models in a dynamic and spatial analysis to evaluate land use decisions and find cost-effective alternatives for which it is not possible to do better on one objective without harming another objective. The method is demonstrated using timber production and species conservation on a forested landscape over a 100-year planning horizon. Current land use strategies are simulated and compared with cost-effective alternatives. The approach is compared to a static reserve approach, similar to previous studies, and found to produce substantially more efficient management strategies for the case study landscape.     

Measurements of personal exposure to nitrogen dioxide in four Mexican cities in 1996

Nitrogen dioxide is a ubiquitous pollutant in urban areas. Indoor NO2 concentrations are influenced by penetration of outdoor concentrations and by indoor sources. The objectives of this study were to evaluate personal exposure to NO2, taking into account human time-activity patterns in four Mexican cities. Passive filter badges were used for indoor, outdoor, and personal NO2 measurements over 48 hr and indoor workplace measurements over 16 hr. Volunteers completed a questionnaire on exposure factors and a time-activity diary during the sample period. An unpaired t test, an analysis of variance (ANOVA), and a linear regression were performed to compare differences among cities and mean personal NO2 concentrations involving housing characteristics, as well as to determine which variables predicted the personal NO2 concentration. Sampling periods were in April, May, and June 1996 in Mexico City, Guadalajara, Cuernavaca, and Monterrey. All 122 volunteers in the study were working adults, with a mean age of 34 (SD +/- 7.38); 64% were female, and the majority worked in public offices and universities. The highest NO2 concentrations were found in Mexico City (36 ppb for outdoor, 57 ppb for indoor, and 39 ppb for personal concentration) and the lowest in Monterrey (19 ppb for outdoor, 24 ppb for indoor, and 24 ppb for personal concentration). Significant differences in NO2 concentrations were found among the cities in different microenvironments. During the sampling period, volunteers spent 85% of their time indoors. The highest personal NO2 concentration was found when volunteers kept their windows closed (p = 0.03). In the regression model adjusted by city and gender, the best predictors of personal NO2 concentration were outdoor levels and time spent outdoors (R2 = 0.68). These findings suggest that outdoor NO2 concentrations were an important influence on the personal exposure to NO2, due to the specific characteristics and personal behavior of the people in these Mexican cities.