Predicting biodiversity change: outside the climate envelope, beyond the species-area curve

Efforts to anticipate threats to biodiversity take the form of species richness predictions (SRPs) based on simple correlations with current climate and habitat area. We review the major approaches that have been used for SRP, species-area curves and climate envelopes, and suggest that alternative research efforts may provide more understanding and guidance for management. Extinction prediction suffers from a number of limitations related to data and the novelty of future environments. We suggest additional attention to (1) identification of variables related to biodiversity that are diagnostic and potentially more predictable than extinction, (2) constraints on species dispersal and reproduction that will determine population persistence and range shifts, including limited sources or potential immigrants for many regions, and (3) changes in biotic interactions and phenology. We suggest combinations of observational and experimental approaches within a framework available for ingesting heterogeneous data sources. Together, these recommendations amount to a shift in emphasis from prediction of extinction numbers to identification of vulnerabilities and leading indicators of change, as well as suggestions for surveillance tools needed to evaluate important variables and the experiments likely to provide most insight.     

Glyphosate and Its Degradation Product AMPA Occur Frequently and Widely in U.S. Soils,Surface Water,Groundwater, and Precipitation

Glyphosate use in the United States increased from less than 5,000 to more than 80,000 metric tons/yr between 1987 and 2007. Glyphosate is popular due to its ease of use on soybean, cotton, and corn crops that are genetically modified to tolerate it, utility in no‐till farming practices, utility in urban areas, and the perception that it has low toxicity and little mobility in the environment. This compilation is the largest and most comprehensive assessment of the environmental occurrence of glyphosate and aminomethylphosphonic acid (AMPA) in the United States conducted to date, summarizing the results of 3,732 water and sediment and 1,018 quality assurance samples collected between 2001 and 2010 from 38 states. Results indicate that glyphosate and AMPA are usually detected together, mobile, and occur widely in the environment. Glyphosate was detected without AMPA in only 2.3% of samples, whereas AMPA was detected without glyphosate in 17.9% of samples. Glyphosate and AMPA were detected frequently in soils and sediment, ditches and drains, precipitation, rivers, and streams; and less frequently in lakes, ponds, and wetlands; soil water; and groundwater. Concentrations of glyphosate were below the levels of concern for humans or wildlife; however, pesticides are often detected in mixtures. Ecosystem effects of chronic low‐level exposures to pesticide mixtures are uncertain. The environmental health risk of low‐level detections of glyphosate, AMPA, and associated adjuvants and mixtures remain to be determined.     

Tree growth inference and prediction from diameter censuses and ring widths.

Estimation of tree growth is based on sparse observations of tree diameter, ring widths, or increments read from a dendrometer. From annual measurements on a few trees (e.g., increment cores) or sporadic measurements from many trees (e.g., diameter censuses on mapped plots), relationships with resources, tree size, and climate are extrapolated to whole stands. There has been no way to formally integrate different types of data and problems of estimation that result from (1) multiple sources of observation error, which frequently result in impossible estimates of negative growth, (2) the fact that data are typically sparse (a few trees or a few years), whereas inference is needed broadly (many trees over many years), (3) the fact that some unknown fraction of the variance is shared across the population, and (4) the fact that growth rates of trees within competing stands are not independent. We develop a hierarchical Bayes state space model for tree growth that addresses all of these challenges, allowing for formal inference that is consistent with the available data and the assumption that growth is nonnegative. Prediction follows directly, incorporating the full uncertainty from inference with scenarios for "filling the gaps" for past growth rates and for future conditions affecting growth. An example involving multiple species and multiple stands with tree-ring data and up to 14 years of tree census data illustrates how different levels of information at the tree and stand level contribute to inference and prediction.     

A field comparison of BTEX mass flow rates based on integral pumping tests and point scale measurements

Measuring contaminant flow rates at control cross sections is the most accurate method to evaluate natural attenuation processes in the saturated subsurface. In most instances, point scale measurement is the method of choice due to practical reasons and cost factors. However, at many field sites, the monitoring network is too sparse for a reliable estimation of contaminant and groundwater flow rates. Therefore, integral pumping tests have been developed as an alternative. In this study, we compare mass flow rates obtained by integral pumping test results and point scale data. We compare results of both methods with regard to uncertainties due to estimation errors and mass flow estimations based on two different point scale networks. The differences between benzene and groundwater flow rate estimates resulting from point scale samples and integral pumping tests were 6.44% and 6.97%, respectively, demonstrating the applicability of both methods at the site. Point scale-based data, especially with use of cost efficient Direct-Push technique, can be applied to show the contaminant distribution at a site and may be followed by a denser point scale network or an integral method. Nevertheless, a combination of both methods decreases uncertainties.     

Evaluating the sources of potential migrant species: implications under climate change

As changes in climate become more apparent, ecologists face the challenge of predicting species responses to the new conditions. Most forecasts are based on climate envelopes (CE), correlative approaches that project future distributions on the basis of the current climate often assuming some dispersal lag. One major caveat with this approach is that it ignores the complexity of factors other than climate that contribute to a species' distributional range. To overcome this limitation and to complement predictions based on CE modeling we carried out a transplant experiment of resident and potential-migrant species. Tree seedlings of 18 species were planted side by side from 2001 to 2004 at several locations in the Southern Appalachians and in the North Carolina Piedmont (U.S.A.). Growing seedlings under a large array of environmental conditions, including those forecasted for the next decades, allowed us to model seedling survival as a function of variables characteristic of each site, and from here we were able to make predictions on future seedling recruitment. In general, almost all species showed decreased survival in plots and years with lower soil moisture, including both residents and potential migrants, and in both locations, the Southern Appalachians and the Piedmont. The detrimental effects that anticipated arid conditions could have on seedling recruitment contradict some of the projections made by CE modeling, where many of the species tested are expected to increase in abundance or to expand their ranges. These results point out the importance of evaluating the potential sources of migrant species when modeling vegetation response to climate change, and considering that species adapted to the new climate and the local conditions may not be available in the surrounding regions.     

Dynamics and origin of PM2.5 during a three-year sampling period in Beijing, China

Systematic sampling and analysis were performed to investigate the dynamics and the origin of suspended particulate matter smaller than 2.5 μm in diameter (PM(2.5)), in Beijing, China from 2005 to 2008. Identifying the source of PM(2.5) was the main goal of this project, which was funded by the German Research Foundation (DFG). The concentrations of 19 elements, black carbon (BC) and the total mass in 158 weekly PM(2.5) samples were measured. The statistical evaluation of the data from factor analysis (FA) identifies four main sources responsible for PM(2.5) in Beijing: (1) a combination of long-range transport geogenic soil particles, geogenic-like particles from construction sites and the anthropogenic emissions from steel factories; (2) road traffic, industry emissions and domestic heating; (3) local re-suspended soil particles; (4) re-suspended particles from refuse disposal/landfills and uncontrolled dumped waste. Special attention has been paid to seven high concentration "episodes", which were further analyzed by FA, enrichment factor analysis (EF), elemental signatures and backward-trajectory analysis. These results suggest that long-range transport soil particles contribute much to the high concentration of PM(2.5) during dust days. This is supported by mineral analysis which showed a clear imprint of component in PM(2.5). Furthermore, the ratios of Mg/Al have been proved to be a good signature to trace back different source areas. The Pb/Ti ratio allows the distinction between periods of predominant anthropogenic and geogenic sources during high concentration episodes. Backward-trajectory analysis clearly shows the origins of these episodes, which partly corroborate the FA and EF results. This study is only a small contribution to the understanding of the meteorological and source driven dynamics of PM(2.5) concentrations.