Using food-web theory to conserve ecosystems

© 2016, Nature Publishing Group. All rights reserved.Food-web theory can be a powerful guide to the management of complex ecosystems. However, we show that indices of species importance common in food-web and network theory can be a poor guide to ecosystem management, resulting in significantly more extinctions than necessary. We use Bayesian Networks and Constrained Combinatorial Optimization to find optimal management strategies for a wide range of real and hypothetical food webs. This Artificial Intelligence approach provides the ability to test the performance of any index for prioritizing species management in a network. While no single network theory index provides an appropriate guide to management for all food webs, a modified version of the Google PageRank algorithm reliably minimizes the chance and severity of negative outcomes. Our analysis shows that by prioritizing ecosystem management based on the network-wide impact of species protection rather than species loss, we can substantially improve conservation outcomes

Determination of Chlorinated organic compounds in aqueous matrices

Thirteen pure volatile, semi-volatile and non-volatile chlorinated organic compounds of molecular weights ranging from trichloroethylene (MW = 131.39 g mole -¹) to hexachlorobenzene (MW = 284.78 g mole-¹) were determined in aqueous matrices by GC-ECD. After 10% salt addition, different extraction tests were performed using fibres whose adsorbing phase was based on microsphere carbon particles characterized by a constant size. Five experimental parameters were optimized: extraction temperature and time, position of the fibre in the GC injector port, desorption temperature and time. The optimized analytical protocol was employed to determine the efficiency of a real activated carbon adsorption plant to remove organic chlorinated pollutants from an industrial wastewater at ng l-¹ levels

Optimal Management of a Multispecies Shorebird Flyway under Sea-Level Rise

Every year, millions of migratory shorebirds fly through the East Asian-Australasian Flyway between their arctic breeding grounds and Australasia. This flyway includes numerous coastal wetlands in Asia and the Pacific that are used as stopover sites where birds rest and feed. Loss of a few important stopover sites through sea-level rise (SLR) could cause sudden population declines. We formulated and solved mathematically the problem of how to identify the most important stopover sites to minimize losses of bird populations across flyways by conserving land that facilitates upshore shifts of tidal flats in response to SLR. To guide conservation investment that minimizes losses of migratory bird populations during migration, we developed a spatially explicit flyway model coupled with a maximum flow algorithm. Migratory routes of 10 shorebird taxa were modeled in a graph theoretic framework by representing clusters of important wetlands as nodes and the number of birds flying between 2 nodes as edges. We also evaluated several resource allocation algorithms that required only partial information on flyway connectivity (node strategy, based on the impacts of SLR at nodes; habitat strategy, based on habitat change at sites; population strategy, based on population change at sites; and random investment). The resource allocation algorithms based on flyway information performed on average 15% better than simpler allocations based on patterns of habitat loss or local bird counts. The Yellow Sea region stood out as the most important priority for effective conservation of migratory shorebirds, but investment in this area alone will not ensure the persistence of species across the flyway. The spatial distribution of conservation investments differed enormously according to the severity of SLR and whether information about flyway connectivity was used to guide the prioritizations. With the rapid ongoing loss of coastal wetlands globally, our method provides insight into efficient conservation planning for migratory species

Population Cycling in Space-Limited Organisms Subject to Density-Dependent Predation

We present a population model with density-dependent disturbance. The model is motivated by, and is illustrated with, data on the percentage of space covered by barnacles on quadrats of rock in the intertidal zone. The autocorrelation function observed indicates population cycling. This autocorrelation function is predicted qualitatively and quantitatively by the detailed model we present. The general version of the model suggests the following rules regarding cycling in space-limited communities subject to density-dependent disturbances. These rules may apply to any space-limited community where a density-dependent disturbance reduces population densities to very low levels, like fire or wind for plant communities. We propose that the period of the cycle will be approximately equal to the time it takes the community to reach a critical density plus the average time between disturbance events when the density is above that critical density. The cycling will only be clear from autocorrelation data if the growth process is relatively consistent, there is a critical density (which the sessile organism reaches and passes) above which the probability of disturbance increases rapidly, and the time to reach the critical density is at least twice the average time between disturbance events

The value of migration information for conservation prioritization of sea turtles in the Mediterranean

Aim: Conservation plans often struggle to account for connectivity in spatial prioritization approaches for the protection of migratory species. Protection of such species is challenging because their movements may be uncertain and variable, span vast distances, cross international borders and traverse land and sea habitats. Often we are faced with small samples of information from various sources and the collection of additional data can be costly and time-consuming. Therefore it is important to evaluate what degree of spatial information provides sufficient results for directing management actions. Here we develop and evaluate an approach that incorporates habitat and movement information to advance the conservation of migratory species. We test our approach using information on threatened loggerhead sea turtles (Caretta caretta) in the Mediterranean. Location: The Mediterranean Sea. Methods: We use Marxan, a spatially explicit decision support tool, to select priority conservation areas. Four approaches with increasing amounts of information about the loggerhead sea turtle are compared, ranging from (1) the broad distribution, (2) multiple habitat types that represent foraging, nesting and inter-nesting habitats, (3) mark-recapture movement information to (4) telemetry-derived migration tracks. Results: We find that spatial priorities for sea turtle conservation are sensitive to the information used in the prioritization process. Setting conservation targets for migration tracks altered the location of conservation priorities, indicating that conservation plans designed without such data would miss important sea turtle habitat. We discover that even a small number of tracks make a significant contribution to a spatial conservation plan if those tracks are substantially different. Main conclusions: This study presents a novel approach to improving spatial prioritization for conserving migratory species. We propose that future telemetry studies tailor their efforts towards conservation prioritization needs, meaning that spatially dispersed samples rather than just large numbers should be obtained. This work highlights the valuable information that telemetry research contributes to the conservation of migratory species

Ocean zoning within a sparing versus sharing framework

The land-sparing versus land-sharing debate centers around how different intensities of habitat use can be coordinated to satisfy competing demands for biodiversity persistence and food production in agricultural landscapes. We apply the broad concepts from this debate to the sea and propose it as a framework to inform marine zoning based on three possible management strategies, establishing: no-take marine reserves, regulated fishing zones, and unregulated open-access areas. We develop a general model that maximizes standing fish biomass, given a fixed management budget while maintaining a minimum harvest level. We find that when management budgets are small, sea-sparing is the optimal management strategy because for all parameters tested, reserves are more cost-effective at increasing standing biomass than traditional fisheries management. For larger budgets, the optimal strategy switches to sea-sharing because, at a certain point, further investing to grow the no-take marine reserves reduces catch below the minimum harvest constraint. Our intention is to illustrate how general rules of thumb derived from plausible, single-purpose models can help guide marine protected area policy under our novel sparing and sharing framework. This work is the beginning of a basic theory for optimal zoning allocations and should be considered complementary to the more specific spatial planning literature for marine reserve as nations expand their marine protected area estates

Making robust policy decision using global biodiversity indicators

In order to influence global policy effectively, conservation scientists need to be able to provide robust predictions of the impact of alternative policies on biodiversity and measure progress towards goals using reliable indicators. We present a framework for using biodiversity indicators predictively to inform policy choices at a global level. The approach is illustrated with two case studies in which we project forwards the impacts of feasible policies on trends in biodiversity and in relevant indicators. The policies are based on targets agreed at the Convention on Biological Diversity (CBD) meeting in Nagoya in October 2010. The first case study compares protected area policies for African mammals, assessed using the Red List Index; the second example uses the Living Planet Index to assess the impact of a complete halt, versus a reduction, in bottom trawling. In the protected areas example, we find that the indicator can aid in decision-making because it is able to differentiate between the impacts of the different policies. In the bottom trawling example, the indicator exhibits some counter-intuitive behaviour, due to over-representation of some taxonomic and functional groups in the indicator, and contrasting impacts of the policies on different groups caused by trophic interactions. Our results support the need for further research on how to use predictive models and indicators to credibly track trends and inform policy. To be useful and relevant, scientists must make testable predictions about the impact of global policy on biodiversity to ensure that targets such as those set at Nagoya catalyse effective and measurable change

Risk‐sensitive planning for conserving coral reefs under rapid climate change

Coral reef ecosystems are seriously threatened by changing conditions in the ocean. Although many factors are implicated, climate change has emerged as a dominant and rapidly growing threat. Developing a long‐term strategic plan for the conservation of coral reefs is urgently needed yet is complicated by significant uncertainty associated with climate change impacts on coral reef ecosystems. We use Modern Portfolio Theory to identify coral reef locations globally that, in the absence of other impacts, are likely to have a heightened chance of surviving projected climate changes relative to other reefs. Long‐term planning that is robust to uncertainty in future conditions provides an objective and transparent framework for guiding conservation action and strategic investment. These locations constitute important opportunities for novel conservation investments to secure less vulnerable yet well‐connected coral reefs that may, in turn, help to repopulate degraded areas in the event that the climate has stabilized