Pesticides in urban multiunit dwellings: hazard identification using classification and regression tree (CART) analysis

Many units in public housing or other low-income urban dwellings may have elevated pesticide residues, given recurring infestation, but it would be logistically and economically infeasible to sample a large number of units to identify highly exposed households to design interventions. Within this study, our aim was to devise a low-cost approach to identify homes in public housing with high levels of pesticide residues, using information that would allow the housing authority and residents to determine optimal strategies to reduce household exposures. As part of the Healthy Public Housing Initiative, we collected environmental samples from 42 public housing apartments in Boston, MA, in 2002 and 2003 and gathered housing characteristics; for example, household demographics and self-reported pesticide use information, considering information available with and without a home visit. Focusing on five organophosphate and pyrethroid pesticides, we used classification and regression tree analysis (CART) to disaggregate the pesticide concentration data into homogenous subsamples according to housing characteristics, which allowed us to identify households and associated networks impacted by the mismanagement of pesticides. The CART analysis demonstrated reasonable sensitivity and specificity given more extensive household information but generally poor performance using only information available without a home visit. Apartments with high concentrations of cyfluthrin, a pyrethroid of interest given that it is a restricted use pesticide, were more likely to be associated with Hispanic residents who resided in their current apartment for more than 5 yr, consistent with documented pesticide usage patterns. We conclude that using CART as an exploratory technique to better understand the home characteristics associated with elevated pesticide levels may be a viable approach for risk management in large multiunit housing developments.     

Potential responses of terrestrial biodiversity in Southern Africa to anthropogenic climate change

Key studies supported by species-level data collection have provided early indications of the potential implications of unmitigated change for the ecosystems and biodiversity of southern Africa. These suggest a significant threat to biodiversity, both from changing bioclimatic suitability and changing atmospheric CO₂ level that seems to affect the competitive balance between woody and herbaceous plants in the dominant savanna biome of this region. Modeling efforts suggest significant implications of unmitigated climate change for this region, but assumptions underpinning methods such as bioclimatic modeling must be recognized, some of which might lead to over estimates of the rate and extent of the potential impacts. General trends and level of coincidence between various types of studies do support a high degree of concern for a substantial portion of southern African biodiversity under unmitigated climate-change scenarios. The most significant changes in ecosystem structure (both increases and decreases in woody plant cover), and associated faunal diversity changes, are projected in the dominant savanna vegetation type in this region, while the most significant biodiversity loss is projected for the winter rainfall region. Follow-up work to detect early signs of climate change identify regions of high- and low-potential impacts, and experimental work to test some important hypotheses relating to the future evolution of climate-change impacts across the region are very few and urgently required.     

Plant-trait-based modeling assessment of ecosystem-service sensitivity to land-use change

Evidence is accumulating that the continued provision of essential ecosystem services is vulnerable to land-use change. Yet, we lack a strong scientific basis for this vulnerability as the processes that drive ecosystem-service delivery often remain unclear. In this paper, we use plant traits to assess ecosystem-service sensitivity to land-use change in subalpine grasslands. We use a trait-based plant classification (plant functional types, PFTs) in a landscape modeling platform to model community dynamics under contrasting but internally consistent land-use change scenarios. We then use predictive models of relevant ecosystem attributes, based on quantitative plant traits, to make projections of ecosystem-service delivery. We show that plant traits and PFTs are effective predictors of relevant ecosystem attributes for a range of ecosystem services including provisioning (fodder), cultural (land stewardship), regulating (landslide and avalanche risk), and supporting services (plant diversity). By analyzing the relative effects of the physical environment and land use on relevant ecosystem attributes, we also show that these ecosystem services are most sensitive to changes in grassland management, supporting current agri-environmental policies aimed at maintaining mowing of subalpine grasslands in Europe.     

Remote Antarctic feeding ground important for east Australian humpback whales

Understanding the dynamics of population recovery is particularly complex when an organism has multiple, remote breeding and feeding grounds separated by one of the longest known migration routes. This study reports on the most comprehensive assessment of humpback whale (Megaptera novaeangliae) movements between remote Antarctic waters south of New Zealand and east Australia (EA), and the migratory corridors and breeding grounds of Australia and Oceania. A total of 112 individual whales were identified; 57 from microsatellites and 61 by fluke with 23 % (n = 26) matched to sites outside Antarctica. Despite large datasets from other southern regions being included in the comparison, the whales were predominantly linked to EA (n = 24). Only two matches to the Oceania catalogues directly north was surprising; therefore the primary feeding grounds of these endangered whales still remain unknown. The confirmation of the Balleny Islands as an important feeding ground for EA whales could provide an insight into reasons behind the rapid recovery of this population. Determining the feeding grounds of Oceania’s whales may explain whether prey energetics or migration length are limiting factors to their recovery and will allow an understanding of future ecosystem changes in these whales.     

The performance of state-of-the-art modelling techniques depends on geographical distribution of species

We explored the effects of prevalence, latitudinal range and clumping (spatial autocorrelation) of species distribution patterns on the predictive accuracy of eight state-of-the-art modelling techniques: Generalized Linear Models (GLMs), Generalized Boosting Method (GBM), Generalized Additive Models (GAMs), Classification Tree Analysis (CTA), Artificial Neural Network (ANN), Multivariate Adaptive Regression Splines (MARS), Mixture Discriminant Analysis (MDA) and Random Forest (RF). One hundred species of Lepidoptera, selected from the Distribution Atlas of European Butterflies, and three climate variables were used to determine the bioclimatic envelope for each butterfly species. The data set consisting of 2620 grid squares 30′ × 60′ in size all over Europe was randomly split into the calibration and the evaluation data sets. The performance of different models was assessed using the area under the curve (AUC) of a receiver operating characteristic (ROC) plot. Observed differences in modelling accuracy among species were then related to the geographical attributes of the species using GAM. The modelling performance was negatively related to the latitudinal range and prevalence, whereas the effect of spatial autocorrelation on prediction accuracy depended on the modelling technique. These three geographical attributes accounted for 19–61% of the variation in the modelling accuracy. Predictive accuracy of GAM, GLM and MDA was highly influenced by the three geographical attributes, whereas RF, ANN and GBM were moderately, and MARS and CTA only slightly affected. The contrasting effects of geographical distribution of species on predictive performance of different modelling techniques represent one source of uncertainty in species spatial distribution models. This should be taken into account in biogeographical modelling studies and assessments of climate change impacts.     

Accounting for Climate Change: Introduction

The assessment of greenhouse gases (GHGs) emitted to and removed from the atmosphere is high on both political and scientific agendas internationally. As increasing international concern and cooperation aim at policy-oriented solutions to the climate change problem, several issues have begun to arise regarding verification and compliance under both proposed and legislated schemes meant to reduce the human-induced global climate impact. The approaches to addressing uncertainty introduced in this article attempt to improve national inventories or to provide a basis for the standardization of inventory estimates to enable comparison of emissions and emission changes across countries. Authors of the accompanying articles use detailed uncertainty analyses to enforce the current structure of the emission trading system and attempt to internalize high levels of uncertainty by tailoring the emissions trading market rules. Assessment of uncertainty can help improve inventories and manage risk. Through recognizing the importance of, identifying and quantifying uncertainties, great strides can be made in the process of Accounting for Climate Change.     

Spatial predictions of phylogenetic diversity in conservation decision making

Considering genetic relatedness among species has long been argued as an important step toward measuring biological diversity more accurately, rather than relying solely on species richness. Some researchers have correlated measures of phylogenetic diversity and species richness across a series of sites and suggest that values of phylogenetic diversity do not differ enough from those of species richness to justify their inclusion in conservation planning. We compared predictions of species richness and 10 measures of phylogenetic diversity by creating distribution models for 168 individual species of a species-rich plant family, the Cape Proteaceae. When we used average amounts of land set aside for conservation to compare areas selected on the basis of species richness with areas selected on the basis of phylogenetic diversity, correlations between species richness and different measures of phylogenetic diversity varied considerably. Correlations between species richness and measures that were based on the length of phylogenetic tree branches and tree shape were weaker than those that were based on tree shape alone. Elevation explained up to 31% of the segregation of species rich versus phylogenetically rich areas. Given these results, the increased availability of molecular data, and the known ecological effect of phylogenetically rich communities, consideration of phylogenetic diversity in conservation decision making may be feasible and informative.