Characteristics that make trophy hunting of giant pandas inconceivable

In November 1928, Theodore Jr. and Kermit Roosevelt led an expedition to China with the expressed purpose of being the first Westerners to kill the giant panda (Ailuropoda melanoleuca). The expedition lasted 8 months and resulted in the brothers shooting a giant panda in the mountains of Sichuan Province. Given the concurrent attention in the popular press describing this celebrated expedition, the giant panda was poised to be trophy hunted much like other large mammals around the world. Today, however, the killing of giant pandas, even for the generation of conservation revenue, is unthinkable for reasons related to the species itself and the context, in time and space, in which the species was popularized in the West. We found that the giant panda's status as a conservation symbol, exceptional charisma and gentle disposition, rarity, value as a nonconsumptive ecotourism attraction, and endemism are integral to the explanation of why the species is not trophy hunted. We compared these intrinsic and extrinsic characteristics with 20 of the most common trophy-hunted mammals to determine whether the principles applying to giant pandas are generalizable to other species. Although certain characteristics of the 20 trophy-hunted mammals aligned with the giant panda, many did not. Charisma, economic value, and endemism, in particular, were comparatively unique to the giant panda. Our analysis suggests that, at present, exceptional characteristics may be necessary for certain mammals to be excepted from trophy hunting. However, because discourse relating to the role of trophy hunting in supporting conservation outcomes is dynamic in both science and society, we suspect these valuations will also change in future.     

Quantifying the impact of vegetation-based metrics on species persistence when choosing offsets for habitat destruction

Developers are often required by law to offset environmental impacts through targeted conservation actions. Most offset policies specify metrics for calculating offset requirements, usually by assessing vegetation condition. Despite widespread use, there is little evidence to support the effectiveness of vegetation-based metrics for ensuring biodiversity persistence. We compared long-term impacts of biodiversity offsetting based on area only; vegetation condition only; area × habitat suitability; and condition × habitat suitability in development and restoration simulations for the Hunter Region of New South Wales, Australia. We simulated development and subsequent offsetting through restoration within a virtual landscape, linking simulations to population viability models for 3 species. Habitat gains did not ensure species persistence. No net loss was achieved when performance of offsetting was assessed in terms of amount of habitat restored, but not when outcomes were assessed in terms of persistence. Maintenance of persistence occurred more often when impacts were avoided, giving further support to better enforce the avoidance stage of the mitigation hierarchy. When development affected areas of high habitat quality for species, persistence could not be guaranteed. Therefore, species must be more explicitly accounted for in offsets, rather than just vegetation or habitat alone. Declines due to a failure to account directly for species population dynamics and connectivity overshadowed the benefits delivered by producing large areas of high-quality habitat. Our modeling framework showed that the benefits delivered by offsets are species specific and that simple vegetation-based metrics can give misguided impressions on how well biodiversity offsets achieve no net loss.     

Assessing and managing freshwater ecosystems vulnerable to environmental change

Freshwater ecosystems are important for global biodiversity and provide essential ecosystem services. There is consensus in the scientific literature that freshwater ecosystems are vulnerable to the impacts of environmental change, which may trigger irreversible regime shifts upon which biodiversity and ecosystem services may be lost. There are profound uncertainties regarding the management and assessment of the vulnerability of freshwater ecosystems to environmental change. Quantitative approaches are needed to reduce this uncertainty. We describe available statistical and modeling approaches along with case studies that demonstrate how resilience theory can be applied to aid decision-making in natural resources management. We highlight especially how long-term monitoring efforts combined with ecological theory can provide a novel nexus between ecological impact assessment and management, and the quantification of systemic vulnerability and thus the resilience of ecosystems to environmental change.     

In situ chemical oxidation: Lessons learned at multiple sites

This paper compiles a detailed set of in situ chemical oxidation (ISCO) lessons learned pertaining to design, execution, and safety based on global experiences over the last 20 years. While the benefits of a “correct” application are known (e.g., cost effectiveness, speed, permanence of treatment), history also provides examples of a variety of “incorrect” applications. These provide an opportunity to highlight recurring themes that resulted in failures. ISCO is, and will continue to provide, an important remedial tool for site remediation, particularly as a component of a multifaceted approach for addressing large and complex sites. Future success, however, requires an objective understanding of both the benefits and the limitations of the technology. The ability to learn from the mistakes of the past provides an opportunity to eliminate, or at least minimize, them in the future. Over the last 25 years of ISCO application, process understanding and knowledge have improved and evolved. This paper combines a thorough discussion of lessons learned through decades of ISCO implementation throughout all aspects of ISCO projects with an analysis of changes to the ISCO remediation market. By discussing the interplay of these two themes and providing recommendations from collective lessons learned, we hope to improve the future of safe, cost‐effective, and successful applications of ISCO.     

Mechanical properties of dewatered sewage sludge

The mechanical properties of dewatered, anaerobically digested sewage sludge were determined from soil laboratory tests. The sludge material is largely composed of organic clay sized-particles, a sizable fraction of which is in an active state of biological digestion which can continue over many years under field conditions. Moderately digested sludge material was found to have a typical specific gravity of solids value of 1.55, and loss on ignition (LOI) value of 70% dry mass. Strongly digested sludge, produced by digesting the liquid sludge further at 35 degrees C in the laboratory, was found to have a lower LOI value of 55% dry mass, and a higher specific gravity of solids value of about 1.72. The maximum dry density of 0.56 tonne/m3 for the dried sludge material was produced using standard Proctor compaction at roughly 85% moisture content (54% solids content). Air-dried, compacted sludge material was tested in quick-undrained triaxial compression and vane shear. Undrained shear strength-moisture content plots are presented. Shear strength values measured in triaxial compression and vane shear were consistent. The effective angle of shearing resistance (phi') was determined from consolidated-undrained, triaxial compression tests on pasteurized, normally consolidated samples of the sludge material. The mechanical properties of the sludge material changed with the level of sludge digestion. The phi' value increased from 32 degrees for moderately digested sludge, to 37 degrees for strongly digested sludge. The effective cohesion of the sludge material remained zero throughout. The shrinkage, swelling and adhesion properties of the sludge material were also studied. Significant shrinkage occurred as the compacted material dried. The sludge material lost its adhesion below about 95% moisture content (51% solids content). Re-hydration of the dry material caused the bulk volume to double.     

Adaptation to climate change and freshwater resources in Vusama village,Viti Levu,Fiji

Regional Environmental Change - Changing precipitation patterns including more intense and prolonged dry periods have become a growing concern for people living in the Pacific Island region. People...     

Monitoring,imperfect detection,and risk optimization of a Tasmanian devil insurance population

Most species are imperfectly detected during biological surveys, which creates uncertainty around their abundance or presence at a given location. Decision makers managing threatened or pest species are regularly faced with this uncertainty. Wildlife diseases can drive species to extinction; thus, managing species with disease is an important part of conservation. Devil facial tumor disease (DFTD) is one such disease that led to the listing of the Tasmanian devil (Sarcophilus harrisii) as endangered. Managers aim to maintain devils in the wild by establishing disease‐free insurance populations at isolated sites. Often a resident DFTD‐affected population must first be removed. In a successful collaboration between decision scientists and wildlife managers, we used an accessible population model to inform monitoring decisions and facilitate the establishment of an insurance population of devils on Forestier Peninsula. We used a Bayesian catch‐effort model to estimate population size of a diseased population from removal and camera trap data. We also analyzed the costs and benefits of declaring the area disease‐free prior to reintroduction and establishment of a healthy insurance population. After the monitoring session in May–June 2015, the probability that all devils had been successfully removed was close to 1, even when we accounted for a possible introduction of a devil to the site. Given this high probability and the baseline cost of declaring population absence prematurely, we found it was not cost‐effective to carry out any additional monitoring before introducing the insurance population. Considering these results within the broader context of Tasmanian devil management, managers ultimately decided to implement an additional monitoring session before the introduction. This was a conservative decision that accounted for uncertainty in model estimates and for the broader nonmonetary costs of mistakenly declaring the area disease‐free.     

Modeling species-habitat relationships with spatially autocorrelated observation data.

Spatial autocorrelation in wildlife observation data arises when extrinsic environmental processes and patterns that influence the spatial distribution of wildlife are themselves spatially structured, or when species are subject to intrinsic population processes, causing contagion or dispersion effects. Territoriality, Allee effects, dispersal limitations, and social clustering are examples of intrinsic processes. Both forms of autocorrelation can violate the assumptions of generalized linear regression models, resulting in biased estimation of model coefficients and diminished predictive performance. Such consequences may be avoided for extrinsic autocorrelation when autocorrelated environmental variables are available for use as model covariates, whereas intrinsic spatial autocorrelation requires an alternative modeling approach. The autologistic model provides an approach suited to the binary observations often obtained in wildlife surveys, but its performance has not been tested across widely varying sampling intensities or strengths of intrinsic spatial structure. Here we use simulated data to test the autologistic model under a range of sampling conditions. The autologistic model obtains better fits and substantially better predictive performance than the standard logistic regression model over the full range of sampling designs and intensities tested. We provide a simple Bayesian implementation of the autologistic model, which until now has not been achieved with standard statistical software alone. A step-by-step procedure is given for characterizing and modeling spatial autocorrelation in binary observation data, along with computer code for fitting autologistic models in WinBUGS, a freeware Bayesian analysis package. This approach avoids normal approximations to the pseudo-likelihood, in contrast to previous Bayesian applications of the autologistic model. We provide a sample application of the autologistic model, fitted to survey data for a gliding marsupial in southeastern Australia.