Use of standardized methods to improve extinction-risk classification

Standardized classification methods based on quantifiable risk metrics are critical for evaluating extinction threats because they increase objectivity, consistency, and transparency of listing decisions. Yet, in the United States, neither federal nor state agencies use standardized methods for listing species for legal protection, which could put listing decisions at odds with the magnitude of the risk. We used a recently developed set of quantitative risk metrics for California herpetofauna as a case study to highlight discrepancies in listing decisions made without standardized methods. We also combined such quantitative metrics with classification tree analysis to attempt to increase the transparency of previous listing decisions by identifying the criteria that had inherently been given the most weight. Federally listed herpetofauna in California scored significantly higher on the risk-metric spectrum than those not federally listed, whereas state-listed species did not score any higher than species that were not state listed. Based on classification trees, state endemism was the most important predictor of listing status at the state level and distribution trend (decline in a species’ range size) and population trend (decline in a species’ abundance at localized sites) were the most important predictors at the federal level. Our results emphasize the need for governing bodies to adopt standardized methods for assessing conservation risk that are based on quantitative criteria. Such methods allow decision makers to identify criteria inherently given the most weight in determining listing status, thus increasing the transparency of previous listing decisions, and produce an unbiased comparison of conservation threat across all species to promote consistency, efficiency, and effectiveness of the listing process.     

Validation of the stomatal flux approach for the assessment of ozone visible injury in young forest trees. Results from the TOP (transboundary ozone pollution) experiment at Curno, Italy

This paper summarises some of the main results of a two-year experiment carried out in an Open-Top Chambers facility in Northern Italy. Seedlings of Populus nigra, Fagus sylvatica, Quercus robur and Fraxinus excelsior have been subjected to different ozone treatments (charcoal-filtered and non-filtered air) and soil moisture regimes (irrigated and non-irrigated plots). Stomatal conductance models were applied and parameterised under South Alpine environmental conditions and stomatal ozone fluxes have been calculated.The flux-based approach provided a better performance than AOT40 in predicting the onset of foliar visible injuries. Critical flux levels, related to visible leaf injury, are proposed for P. nigra and F. sylvatica (ranging between 30 and 33 mmol O₃ m⁻²). Soil water stress delayed visible injury appearance and development by limiting ozone uptake. Data from charcoal-filtered treatments suggest the existence of an hourly flux threshold, below which may occur a complete ozone detoxification.     

Forecasting the Spread of Invasive Rainbow Smelt in the Laurentian Great Lakes Region of North America

Abstract:  Rainbow smelt ( Osmerus mordax ) have invaded many North American lakes, often resulting in the extirpation of native fish populations. Yet, their invasion is incipient and provides the rationale for identifying ecosystems likely to be invaded and where management and prevention efforts should be focused. To predict smelt presence and absence, we constructed a classification-tree model based on habitat data from 354 lakes in the native range for smelt in southern Maine. Maximum lake depth, lake area, and Secchi depth (surrogate measure of lake productivity) were the most important predictors. We then used our model to identify lakes vulnerable to invasion in three regions outside the smelt's native range: northern Maine (52 of 244 lakes in the non-native range), Ontario (4447 of 8110), and Wisconsin (553 of 5164). We further identified a subset of lakes with a strong potential for impact (potential–impact lakes) based on the presence of fish species that are affected by rainbow smelt. Ninety-four percent of vulnerable lakes in the non-native range in Maine are also potential–impact lakes, as are 94% and 58% of Ontario and Wisconsin's vulnerable lakes, respectively. Our modeling approach can be applied to other invaders and regions to identify invasion-prone ecosystems, thus aiding in the management of invasive species and the efficient allocation of invasive species mitigation and prevention resources.     

Environmental benefits from the use of the residual biomass in nurseries

Every year, nurseries waste about 40 t of residual biomass for each ha of potted plants cultivation. The European nursery sector deals with about 90,000 ha of cultivated land and 120,000 ha of nurseries, with a turnover of 19.8 billion Euros in 2011. In recent years, a number of Italian projects highlighted that GHG (greenhouse gas) emissions for the nursery sector range between 37 tCO₂eq/ha/year and 45 tCO₂eq/ha/year for potted plants, mainly due to the consumption of electric energy, plastics and peat. Moreover, other studies analyse the impacts associated to nurseries, recommending best practices for energy reductions and waste recycle or reuse. Therefore, the present work focused its attention to the possible environmental benefits associated to the reuse of residues (wood and substrate) of potted plants that are discarded from the nursery production chain. GHG emissions and fossil energy requirement were quantified by considering the CO₂eq (CO₂ equivalent) and the CER (cumulative energy requirement) respectively, in order to assess the environmental impacts of two different scenarios proposed for the materials recovery. Final results highlighted that the solutions which are able to recover the substrate and the wood allow impact reductions compared to landfill disposal. In particular, the scenario consisting in the immediate separation of the substrate from the root-plant system and the successive chipping of wood for energetic reuse, allows higher savings than those obtainable through shredding and subsequent wind separation. Moreover, for what concerns the CO₂eq, an adequate use of the residual biomass make it possible to compensate the GHG emissions of the nurseries up to 15%.     

Data mining of tree-based models to analyze freeway accident frequency

INTRODUCTION: Statistical models, such as Poisson or negative binomial regression models, have been employed to analyze vehicle accident frequency for many years. However, these models have their own model assumptions and pre-defined underlying relationship between dependent and independent variables. If these assumptions are violated, the model could lead to erroneous estimation of accident likelihood. Classification and Regression Tree (CART), one of the most widely applied data mining techniques, has been commonly employed in business administration, industry, and engineering. CART does not require any pre-defined underlying relationship between target (dependent) variable and predictors (independent variables) and has been shown to be a powerful tool, particularly for dealing with prediction and classification problems. METHOD: This study collected the 2001-2002 accident data of National Freeway 1 in Taiwan. A CART model and a negative binomial regression model were developed to establish the empirical relationship between traffic accidents and highway geometric variables, traffic characteristics, and environmental factors. RESULTS: The CART findings indicated that the average daily traffic volume and precipitation variables were the key determinants for freeway accident frequencies. By comparing the prediction performance between the CART and the negative binomial regression models, this study demonstrates that CART is a good alternative method for analyzing freeway accident frequencies. IMPACT ON INDUSTRY: By comparing the prediction performance between the CART and the negative binomial regression models, this study demonstrates that CART is a good alternative method for analyzing freeway accident frequencies.     

Analyzing pedestrian crash injury severity under different weather conditions

Objective: Pedestrians are the most vulnerable road users due to the lack of mass, speed, and protection compared to other types of road users. Adverse weather conditions may reduce road friction and visibility and thus increase crash risk. There is limited evidence and considerable discrepancy with regard to impacts of weather conditions on injury severity in the literature. This article investigated factors affecting pedestrian injury severity level under different weather conditions based on a publicly available accident database in Great Britain.

Method: Accident data from Great Britain that are publicly available through the STATS19 database were analyzed. Factors associated with pedestrian, driver, and environment were investigated using a novel approach that combines a classification and regression tree with random forest approach.

Results: Significant severity predictors under fine weather conditions from the models included speed limits, pedestrian age, light conditions, and vehicle maneuver. Under adverse weather conditions, the significant predictors were pedestrian age, vehicle maneuver, and speed limit.

Conclusions: Elderly pedestrians are associated with higher pedestrian injury severities. Higher speed limits increase pedestrian injury severity. Based on the research findings, recommendations are provided to improve pedestrian safety.     

Analyzing injury severity of bus passengers with different movements

Objective: Though public transport vehicles are rarely involved in mass casualty accidents, when they are, the number of injuries and fatalities is usually high due to the high passenger capacity. Of the few studies that have been conducted on bus safety, the majority focused on vehicle safety features, road environmental factors, as well as driver characteristics. Nevertheless, few studies have attempted to investigate the underlying risk factors related to bus occupants. This article presents an investigation aimed at identifying the risk factors affecting injury severity of bus passengers with different movements.

Method: Three different passenger movement types including standing, seated, and boarding/alighting were analyzed individually using classification and regression tree (CART) method based on publicly available accident database of Great Britain.

Results: According to the results of exploratory analyses, passenger age and vehicle maneuver are associated with passenger injury severity in all 3 types of accidents. Moreover, the variable “skidding and overturning” is associated with injury severity of seated passengers and driver age is correlated with injury severity of standing and boarding/alighting passengers.

Conclusions: The CART method shows its ability to identify and easily explain the complicated patterns affecting passenger injury severity. Several countermeasures to reduce bus passenger injury severity are recommended.     

Spatial and temporal analysis of vegetation change in agricultural landscapes: A case study of two brigalow (Acacia harpophylla) landscapes in Queensland, Australia

The majority of landscapes around the world have been modified or transformed by human activities to meet the needs of human societies. The loss of native vegetation for agricultural development affects the sustainability of growing proportion of the world's ecosystems. Factors such as land tenure, roads and agricultural intensification, together with biophysical properties, have been cited as drivers of deforestation. This paper combined analysis of the historical drivers of change with analysis of the trends of deforestation since 1945 in two brigalow landscapes (100,000 ha) in sub-tropical Australia. A selection of these drivers were then applied at a property-level (1000 ha) to test their influence on native vegetation retention. Regression trees were used to identify significant human drivers and biophysical properties, and then a generalised linear modelling approach was used to quantify the effect of these factors on the proportion of remnant native vegetation. Results showed that until the mid-20th century, government policies to intensify settlement did not result in increased agricultural production, but since this time, landscape change has been rapid, and has particularly affected ecosystems on fertile clay soils. Although socio-economic factors were critical in driving deforestation, after 60 years of agricultural intensification by far the most significant explanatory variable determining the proportion of native vegetation retained at a property scale was the suitability of the soil for agriculture. Property size was an important secondary influence. The results were not, by and large, consistent with other studies of landscape change and suggest that generalised principles explaining deforestation may be elusive. Solutions to the problem of over-clearance of native vegetation, therefore, need to be tailored to the specific regional situations encountered.