Using photographic image analysis to assess ground cover: a case study of forest road cutbanks

Road prisms, including cutbanks, road surfaces, and fillslopes, can be important contributors of sediment to streams in forested watersheds. Following road construction, cutbanks and fillslopes are often seeded, mulched, and sometimes fertilized to limit erosion and sedimentation. Assessing the success of vegetation establishment on cutbanks and fillslopes is a common task of forested land managers. This study developed and applied a photographic image analysis method to assess percent ground cover along an entire cutbank of a cut-and-fill haul road in the Monongahela National Forest in Tucker County, West Virginia. Variable-sized sections were employed to quantify the vegetative cover. Measurements obtained by this technique were similar to more commonly applied fixed-area plots, and it proved to be a useful tool for land managers who require a more repeatable quantification of ground cover than is possible through visual assessments. Cutbank slope and aspect also were analyzed to determine their potential impact on cutbank vegetation establishment. Slope was not a significant variable in explaining differences in vegetation cover; however, aspect did affect vegetation establishment. South-facing aspects had significantly lower percent vegetation cover than northeast, east, northwest, and north northwest aspects after the first year following seeding and throughout the entire study. Mean percent cover on the south-facing cutbanks was 32% over all time periods, compared to 60% to 73% for the other represented aspects. This result was expected since south-facing slopes generally are drier in the growing season and are subject to more freeze–thaw cycles in the winter. Timber felled onto the cutbank also decreased vegetative cover in the short term on north and north northwest aspects, but vegetation quickly became reestablished on these aspects with their favorable growing conditions.     

Training carbon management engineers: Removing a major hurdle for geologic CO2 storage by increasing educational capacity

Implementing geologic storage of CO₂ at a material scale (ca. 1 Gt C/year) will require an industry comparable in size to the current oil and gas industry and a workforce trained in subsurface engineering. Since the same technologies that apply to hydrocarbon production apply to the subsurface storage of CO₂, petroleum engineering (PE) graduates will be valuable candidates to work in the carbon storage industry. We expect however that the demand for PEs from the oil and gas industry will increase, and that already strained educational capacity will not be sufficient to supply both industries. Thus we advocate building new targeted educational infrastructure. We present a model curriculum based on an existing accredited multidisciplinary degree program. This program combines the fundamentals of petroleum engineering with the subsurface architecture emphasis of geology and the environmental perspective of hydrogeology. We indicate key elements of this program that could be integrated with other, more traditional undergraduate engineering majors that also deal with the subsurface.     

Ten Key Questions About the Management of Water in the Yellow River Basin

Water is scarce in many regions of the world, clean water is difficult to find in most developing countries, there are conflicts between irrigation needs and urban demands, and there is wide debate over appropriate means of resolving these problems. Similarly, in China, there is limited understanding of the ways in which people, groups, and institutions contribute to, are affected by, and respond to changes in water quantity and quality. We use the example of the Yellow River basin to argue that these social, managerial, and policy dimensions of the present water problems are significant and overshadow the physical ones. Despite this, they receive relatively little attention in the research agenda, particularly of the lead agencies in the management of the Yellow River basin. To this end, we ask ten research questions needed to address the policy needs of water management in the basin, split into two groups of five. The first five relate to the importance of water in this basin and the changes that have affected water problems and will continue to do so. The second five questions represent an attempt to explore possible solutions to these problems.     

COMPOSITE VS. DISTRIBUTED CURVE NUMBERS: EFFECTS ON ESTIMATES OF STORM RUNOFF DEPTHS1

ABSTRACT: The U.S. Department of Agriculture Curve Number (CN) method is one of the most common and widely used techniques for estimating surface runoff and has been incorporated into a number of popular hydrologic models. The CN method has traditionally been applied using compositing techniques in which the area weighted average of all curve numbers is calculated for a watershed or a small number of sub-watersheds. CN compositing was originally developed as a time saving procedure, reducing the number of runoff calculations required. However, with the proliferation of high speed computers and geographic information systems, it is now feasible to use distributed CNs when applying the CN method. To determine the effect of using composited versus distributed CNs on runoff estimates, two simulations of idealized watersheds were developed to compare runoff depths using composite and distributed CNs. The results of these simulations were compared to the results of similar analyses performed on an urbanizing watershed located in central Indiana and show that runoff depth estimates using distributed CNs are as much as 100 percent higher than when composited CNs are used. Underestimation of runoff due to CN compositing is a result of the curvilinear relationship between CN and runoff depth and is most severe for wide CN ranges, low CN values, and low precipitation depths. For larger design storms, however, the difference in runoff computed using composite and distributed CNs is minimal.     

Predicting the conservation status of data‐deficient species

There is little appreciation of the level of extinction risk faced by one‐sixth of the over 65,000 species assessed by the International Union for Conservation of Nature. Determining the status of these data‐deficient (DD) species is essential to developing an accurate picture of global biodiversity and identifying potentially threatened DD species. To address this knowledge gap, we used predictive models incorporating species’ life history, geography, and threat information to predict the conservation status of DD terrestrial mammals. We constructed the models with 7 machine learning (ML) tools trained on species of known status. The resultant models showed very high species classification accuracy (up to 92%) and ability to correctly identify centers of threatened species richness. Applying the best model to DD species, we predicted 313 of 493 DD species (64%) to be at risk of extinction, which increases the estimated proportion of threatened terrestrial mammals from 22% to 27%. Regions predicted to contain large numbers of threatened DD species are already conservation priorities, but species in these areas show considerably higher levels of risk than previously recognized. We conclude that unless directly targeted for monitoring, species classified as DD are likely to go extinct without notice. Taking into account information on DD species may therefore help alleviate data gaps in biodiversity indicators and conserve poorly known biodiversity. Predección del Estado de Conservación de Especies con Deficiencia de Datos     

Identifying impediments to long‐distance mammal migrations

In much of the world, the persistence of long‐distance migrations by mammals is threatened by development. Even where human population density is relatively low, there are roads, fencing, and energy development that present barriers to animal movement. If we are to conserve species that rely on long‐distance migration, then it is critical that we identify existing migration impediments. To delineate stopover sites associated with anthropogenic development, we applied Brownian bridge movement models to high‐frequency locations of pronghorn (Antilocapra americana) in the Greater Yellowstone Ecosystem. We then used resource utilization functions to assess the threats to long‐distance migration of pronghorn that were due to fences and highways. Migrating pronghorn avoided dense developments of natural gas fields. Highways with relatively high volumes of traffic and woven‐wire sheep fence acted as complete barriers. At crossings with known migration bottlenecks, use of high?quality forage and shrub habitat by pronghorn as they approached the highway was lower than expected based on availability of those resources. In contrast, pronghorn consistently utilized high?quality forage close to the highway at crossings with no known migration bottlenecks. Our findings demonstrate the importance of minimizing development in migration corridors in the future and of mitigating existing pressure on migratory animals by removing barriers, reducing the development footprint, or installing crossing structures. Identificación de los Impedimentos para las Migraciones de Larga Distancia de Mamíferos     

Using average autonomy to test whether behavioral syndromes constrain evolution

Genetic correlations between behaviors underlying a behavioral syndrome may constrain the capacity of a population to respond to selection on these behaviors. Average autonomy quantifies the extent in which estimated genetic (co)variances constrain the rate of evolutionary change of behavioral traits forming a syndrome when these traits are under selections in all possible directions of multivariate trait-space. However, it is not clear whether a calculated average autonomy value of an observed syndrome constitutes a significant evolutionary constraint or not. I here outline an approach for testing evolutionary constraint in a syndrome, which is based on comparing the observed genetic (co)variance structure to the one where the genetic covariances are assumed to be zero and taking onboard the uncertainty in the (co)variances between behaviors into the calculations of average autonomy. The approach can be implemented in the context of parametric bootstrap or Bayesian statistics, and I provide a worked example of the latter. I further highlight that when genetic (co)variances are unattainable, the between-individual (co)variances act as an interesting proxy, which is within reach for many behavioral studies. I provide R code for all calculations.