Controlling range expansion in habitat networks by adaptively targeting source populations

Controlling the spread of invasive species, pests, and pathogens is often logistically limited to interventions that target specific locations at specific periods. However, in complex, highly connected systems, such as marine environments connected by ocean currents, populations spread dynamically in both space and time via transient connectivity links. This results in nondeterministic future distributions of species in which local populations emerge dynamically and concurrently over a large area. The challenge, therefore, is to choose intervention locations that will maximize the effectiveness of the control efforts. We propose a novel method to manage dynamic species invasions and outbreaks that identifies the intervention locations most likely to curtail population expansion by selectively targeting local populations most likely to expand their future range. Critically, at any point during the development of the invasion or outbreak, the method identifies the local intervention that maximizes the long‐term benefit across the ecosystem by restricting species’ potential to spread. In so doing, the method adaptively selects the intervention targets under dynamically changing circumstances. To illustrate the effectiveness of the method we applied it to controlling the spread of crown‐of‐thorns starfish (Acanthaster sp.) outbreaks across Australia's Great Barrier Reef. Application of our method resulted in an 18‐fold relative improvement in management outcomes compared with a random targeting of reefs in putative starfish control scenarios. Although we focused on applying the method to reducing the spread of an unwanted species, it can also be used to facilitate the spread of desirable species through connectivity networks. For example, the method could be used to select those fragments of habitat most likely to rebuild a population if they were sufficiently well protected.     

Marine pollution originating from purse seine and longline fishing vessel operations in the Western and Central Pacific Ocean, 2003–2015

Ambio - Fisheries observer data recorded between 2003 and 2015 on-board purse seine and longline vessels operating in the Western and Central Pacific Ocean reported more than 10 000 pollution...     

Impact of alternative metrics on estimates of extent of occurrence for extinction risk assessment

In International Union for Conservation of Nature (IUCN) Red List assessments, extent of occurrence (EOO) is a key measure of extinction risk. However, the way assessors estimate EOO from maps of species’ distributions is inconsistent among assessments of different species and among major taxonomic groups. Assessors often estimate EOO from the area of mapped distribution, but these maps often exclude areas that are not habitat in idiosyncratic ways and are not created at the same spatial resolutions. We assessed the impact on extinction risk categories of applying different methods (minimum convex polygon, alpha hull) for estimating EOO for 21,763 species of mammals, birds, and amphibians. Overall, the percentage of threatened species requiring down listing to a lower category of threat (taking into account other Red List criteria under which they qualified) spanned 11–13% for all species combined (14–15% for mammals, 7–8% for birds, and 12–15% for amphibians). These down listings resulted from larger estimates of EOO and depended on the EOO calculation method. Using birds as an example, we found that 14% of threatened and near threatened species could require down listing based on the minimum convex polygon (MCP) approach, an approach that is now recommended by IUCN. Other metrics (such as alpha hull) had marginally smaller impacts. Our results suggest that uniformly applying the MCP approach may lead to a one‐time down listing of hundreds of species but ultimately ensure consistency across assessments and realign the calculation of EOO with the theoretical basis on which the metric was founded.     

An investigation of the kinetic processes influencing mercury emissions from sand and soil samples of varying thickness

Mercury flux from HgCl2-treated sand and untreated soil samples of varying thickness (0.5-15 mm) were measured in dark and light under a Teflon dynamic flux chamber. Mean emissions over a 5.5-d sampling period showed an increase with depth for sand samples between 0.5 and 2 mm, but increasing depth above 2 mm had no effect. First-order kinetic models showed strong goodness of fit to the data and explained a high degree ofvariability in the emissions profile of all sand samples (R = 0.70-0.98). Soil samples showed an initial emissions peak that was not correlated with depth, suggesting a very shallow process at work. However, longer-term "baseline" emissions, measured as mean emissions between days 4.5 and 5.5, did show a relationship with depth. First-order kinetic models showed good fit for soil samples up to 4 mm thick (R2 = 0.66-0.91); however, thicker samples did not show a consistent fit to first- or second-order kinetic models (1 degree R2 = 0.00-0.46; 2 degree R2 = 0.00-0.54). The data suggest that mercury emissions from soil samples may follow a multicomponent model for which more     

Emergy-based fuzzy optimization approach for water reuse in an eco-industrial park

The establishment of an eco-industrial park (EIP) provides opportunity for individual plants to cooperate with each other in order to utilize resources efficiently and thus reduce waste. The goal of an EIP is to “close the loop” through recycling and reuse of material and energy streams. Studies show with current freshwater consumption trends there would be water stress aggravated by global warming in the near future. This paper presents a model to design an EIP water reuse network that considers overall system sustainability as measured with emergy, as well as cost saving desired by individual plants. Case studies from literature are then solved to illustrate the advantage of this method in decision making. The illustrative examples show how the model achieves a compromise among the potentially conflicting fuzzy goals of the various EIP stakeholders.