Despite the advocacy for non‐timber forest product (NTFP) extraction as a form of sustainable development, the population ecology of many NTFPs remains unstudied, making it difficult to assess the ecological impacts of extraction. We investigated the demography and population dynamics of the harvested, understory palm, Chamaedorea radicalis in the El Cielo Biosphere Reserve, Mexico. Our objectives were: (1) to describe patterns of C. radicalis abundance and population size structure, (2) to document C. radicalis demography, (3) to test experimentally how this demography was affected by different leaf harvest regimes and livestock browse intensities, and (4) to project their effects on transient and long‐term population dynamics. Data on palm abundance and population size structure were collected from belt‐transects along hillsides. We also exposed 100 adult palms to each of five leaf harvest treatments (N = 500): control, harvest once per year, harvest twice per year, harvest four times per year, and a modified four times per year harvest where only one leaf was removed each harvest. Browse experiments were conducted to assess the effect of burro browse on demography. Experiments were monitored over two years, and results were incorporated with other demographic data to parameterize stage‐based (Lefkovitch) matrices for each year × treatment combination. Topographic position influenced both population size structure and density, with a gradient from valleys (727 palms/ha) to upper slopes (5513 palms/ha). Palm demography was characterized by low mortality, low reproductive activity, and high seed germination rates. Leaf harvest increased adult mortality and reduced fecundity, and it was projected to reduce λ (finite rate of increase). However, λ for harvested populations did not differ significantly from 1. Browsing increased mortality of seedlings, juveniles, and small adults, resulting in populations projected to decline (λ < 1). These findings indicate that browsing by free‐range livestock impacts C. radicalis populations more than leaf harvest and could explain the low density and skewed size structure in valleys. The modest impact of leaf harvest treatments is due in part to the reduction in the availability of marketable leaves. Detection of such feedbacks exemplifies how the incorporation of human management practices enhances the insights that experiments bring to studies of the population ecology of NTFPs.
Experimental animal studies often serve as the basis for predicting risk of adverse responses in humans exposed to occupational hazards. A statistical model is applied to exposure-response data and this fitted model may be used to obtain estimates of the exposure associated with a specified level of adverse response. Unfortunately, a number of different statistical models are candidates for fitting the data and may result in wide ranging estimates of risk. Bayesian model averaging (BMA) offers a strategy for addressing uncertainty in the selection of statistical models when generating risk estimates. This strategy is illustrated with two examples: applying the multistage model to cancer responses and a second example where different quantal models are fit to kidney lesion data. BMA provides excess risk estimates or benchmark dose estimates that reflects model uncertainty.
Variable rate technology enables management of individual soil types within fields. However, correct classification of soil types for mid‐Atlantic coastal plain soils are currently impractically expensive using an Order I Soil Survey, yet variable rate fertilizer application based on soil type can be highly effective. The objectives of this study were to determine if apparent electromagnetic conductivity (ECa) alone or combined with previous year crop yields using global positioning system technology can provide a useful alternative to detailed soil mapping. The site contained alluvial soils ranging from Bojac 1 and 2 (coarse‐loamy, mixed, thermic, Hapludults) to Wickham 3 and 4 (fine‐loamy, mixed, thermic, Ultic Hapludalfs). The two fields totaled approximately 24 ha. A statistical nonparametric classification method, called recursive binary classification trees, was used to determine how well soil types could be classified. Electromagnetic conductivity readings and crop yields were positively correlated. Broad patterns in the relationship between soil types and ECa readings and crop yields existed for all crop combinations considered. Lower ECa readings and crop yields corresponded to the Bojac soils, while higher ECa readings and crop yields were categorized as Wickham soils. Electromagnetic induction alone correctly classified the soils into broad categories of Bojac or Wickham with over 85% accuracy. When ECa was combined with crop yield data, correct classification rose to over 90%. More precise classification into Bojac 1, Bojac 2, and Wickham soils yielded slightly lower correct classifications ranging from 62.6 to 81.2% for ECa alone, and 80.3 to 91.5% when combined with various crop yields.
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