Search

The sustainable production of bioenergy is vital to avoiding negative impacts on environmental goods such as climate, soil, water, and especially biodiversity. We propose three key issues that should be addressed in any biodiversity risk-mitigation strategy: conservation of areas of significant biodiversity value; mitigation of negative effects related to indirect land-use change; and promotion of agricultural practices with few negative impacts on biodiversity. Focusing on biodiversity concerns, we compared principles and criteria set to address biodiversity and other environmental and social issues in seven standards (defined here as commodity-based standards or roundtables, or relevant European legislation): five voluntary initiatives related to bioenergy feedstocks, the Renewable Transport Fuel Obligation (United Kingdom), and the European Renewable Energy Source Directive. Conservation of areas of significant biodiversity value was fairly well covered by these standards. Nevertheless, mitigation of negative impacts related to indirect land-use change was underrepresented. Although the EU directive, with its bonus system for the use of degraded land and a subquota system for noncrop biofuels, offered the most robust standards to mitigate potential negative effects, all of the standards fell short in promoting agricultural practices with low negative impacts on biodiversity. We strongly recommend that each standard be benchmarked against related standards, as we have done here, and that efforts should be made to strengthen the elements that are weak or missing. This would be a significant step toward achieving a bioenergy industry that safeguards Earth's living heritage. [ABSTRACT FROM AUTHOR]

Reliable density estimations of plant populations and their statistical comparison are fundamental in many ecological studies. Typical sampling methods are plotless density estimators (PDE) and plot counts. A simulation study was performed to examine the power of these sampling methods for the statistical comparison of the densitiesof two plant populations. The variable area transect estimator, the ordered distance estimator, and Byth’s T-square estimator were considered and compared with the quadrat count estimator. The relative root-mean-squared error and the relative bias were used to explore the quality of the estimators, and robust confidence intervals and standard deviation estimators were given. Tests were developed to compare the population densities of two independent populations, and the performance of the tests was examined. All simulations were run for spatially random and aggregate data patterns. For completely random data, all estimators and all tests behaved well if they were based on the same sampling intensity. For the aggregate pattern, all PDE’s were negatively biased, but the quadrat count estimator was unbiased. Nonparametric confidence intervals had the most robust performance for aggregate data. The results of the presented simulation study demonstrate that plant ecologists should avoid PDE for both density estimation and statistical testing except when the pattern of a population is known to follow a completely random spatial pattern. Instead, the use of the plot count estimator is recommended. [ABSTRACT FROM AUTHOR]