Science and Society: Marine Reserve Design for the California Channel Islands

Abstract:  We explored the interaction of science and society in attempts to restore impaired marine ecosystems in Channel Islands National Park and National Marine Sanctuary, California. Deteriorating resource conditions triggered a community's desire to change public policy. Channel Islands National Park, one of 40 marine protected areas in the U.S. National Park System, was proclaimed a national monument in 1938 and expanded substantially in 1980 by an act of Congress. Collapse of marine life populations and loss of 80% of the giant kelp (Macrocystis pyrifera) forests in the park between 1980 and 1998 showed that habitat and water quality protection alone had not secured sustainable ocean ecosystems or fisheries. The failed fishery management strategies and practices prompted formal community and agency requests in 1998 for a network of reserves protected from direct fishing impacts to serve as marine recovery areas. A 2-year attempt to build a community consensus based on science for a reserve network successfully identified recovery goals for fisheries, biodiversity, education, economics, and heritage values. Nevertheless, the community group failed to garner unanimous support for a specific reserve network to achieve those common goals. The group submitted a recommendation, supported by 14 of 16 members, to state and federal authorities in 2001 for action in their respective jurisdictions. California adopted the half of the network in state waters in 2003. This process exposed the socioeconomic factors involved in the design of marine protected areas that can be negotiated successfully among groups of people and factors determined by nature that cannot be negotiated. Understanding the differences among the factors was crucial in reaching consensus and changing public policy.     

Surviving the change to warning colouration: density-dependent polyphenism suggests a route for the evolution of aposematism

Summary. How warning colouration first appeared remains a disputed question in evolutionary biology. A density-dependent transition from crypsis to aposematism that occurs during phase change in the desert locust (Schistocerca gregaria) provides insight into the conditions under which acquiring warning colouration is adaptive. When crowded for only a few hours, solitarious locusts cease avoiding each other and actively aggregate. This occurs well before they acquire warning colouration. We show that accompanying this early behavioural gregarisation is a remarkable shift in feeding behaviour, in which solitarious insects switch from being deterred by a toxic plant alkaloid to feeding avidly upon foods containing it. A computer simulation shows how crypsis ceases to be effective as an anti-predator strategy when solitarious locusts are crowded, how chemical defence becomes essential as conspicuousness increases with local density, and how warning colouration becomes advantageous under these conditions. These findings provide empirical evidence for an adaptive route for the change from a cryptic edible phenotype to a brightly coloured toxic one.     

Linking Wild and Captive Populations to Maximize Species Persistence: Optimal Translocation Strategies

Abstract:  Captive breeding of animals is widely used to manage endangered species, frequently with the ambition of future reintroduction into the wild. Because this conservation measure is very expensive, we need to optimize decisions, such as when to capture wild animals or release captive-bred individuals into the wild. It is unlikely that one particular strategy will always work best; instead, we expect the best decision to depend on the number of individuals in the wild and in captivity. We constructed a first-order Markov-chain population model for two populations, one captive and one wild, and we used stochastic dynamic programming to identify optimal state-dependent strategies. The model recommends unique sequences of optimal management actions over several years. A robust rule of thumb for species that can increase faster in captivity than in the wild is to capture the entire wild population whenever the wild population is below a threshold size of 20 females. This rule applies even if the wild population is growing and under a broad range of different parameter values. Once a captive population is established, it should be maintained as a safety net and animals should be released only if the captive population is close to its carrying capacity. We illustrate the utility of this model by applying it to the Arabian oryx ( Oryx leucoryx ). The threshold for capturing the entire Arabian oryx population in the wild is 36 females, and captive-bred individuals should not be released before the captive facilities are at least 85% full.     

天津城市中心区环境噪声污染及其防治对策

针对城市中心区环境噪声污染的现状及其防治工作中存在的问题，探讨了健全法制、从源头抓起、加强规划和基础设施建设等防治环境噪声污染的对策。     

Estimator bias and efficiency for adaptive cluster sampling with order statistics and a stopping rule

Practical problems facing adaptive cluster sampling with order statistics (acsord) are explored using Monte Carlo simulation for three simulated fish populations and two known waterfowl populations. First, properties of an unbiased Hansen-Hurwitz (HH) estimator and a biased alternative Horvitz-Thompson (HT) estimator are evaluated. An increase in the level of population aggregation or the initial sample size increases the efficiencies of the two acsord estimators. For less aggregated fish populations, the efficiencies decrease as the order statistic parameter r (the number of units about which adaptive sampling is carried out) increases; for the highly aggregated fish and waterfowl populations, they increase with r. Acsord is almost always more efficient than simple random sampling for the highly aggregated populations. Positive bias is observed for the HT estimator, with the maximum bias usually occurring at small values of r. Secondly, a stopping rule at the Sth iteration of adaptive sampling beyond the initial sampling unit was applied to the acsord design to limit the otherwise open-ended sampling effort. The stopping rule induces relatively high positive bias to the HH estimator if the level of the population aggregation is high, the stopping level S is small, and r is large. The bias of HT is not very sensitive to the stopping rule and its bias is often reduced by the stopping rule at smaller values of r. For more aggregated populations, the stopping rule often reduces the efficiencies of the estimators compared to the non-stopping-rule scheme, but acsord still remains more efficient than simple random sampling. Despite its bias and lack of theoretical grounding, the HT estimator is usually more efficient than the HH estimator. In the stopping rule case, the HT estimator is preferable, because its bias is less sensitive to the stopping level.     

Performance of adaptive cluster sampling estimators in a multivariate setting

Adaptive cluster sampling has been proven to perform well in a univariate setting, but it may not perform well when there are several parameters of interest. The efficiency of adaptive sampling when there are several variables of interest depends on the relationship of the variables with one another.     

Markov Chain Monte Carlo on optimal adaptive sampling selections

Under a Bayesian population model with a given prior distribution, the optimal sampling strategy with a fixed sample size n is an n-phase adaptive one. That is, the selection of the next sampling units should sequentially depend on the information obtained from the previously selected units, including the observed values of interest. Such an optimal strategy is in general not executable in practice due to its intensive computation. In many survey sampling situations, an important problem is that one would like to select a set of units in addition to a certain number of sampling units which have been observed. If the optimal strategy is an adaptive one, the selection of the additional units should take both the labels and the observed values of the already selected units into account. Hence, a simpler optimal two-phase adaptive sampling strategy under a Bayesian population model is proposed in this article for practical interest. A Markov chain Monte Carlo method is used to approximate the posterior joint distribution of the unobserved population units after the first phase sampling, for the optimal selection of the second phase sample. This approximation method is found to be successful to select the optimal second-phase sample. Finally, this optimal strategy is applied to a set of data from a study of geothermal CO₂ emissions in Yellowstone National Park as a practical illustrative example.     

Queen dimorphism and reproductive strategies in the fire ant Solenopsis geminata (Hymenoptera: Formicidae)

Alate trapping studies of a monogyne population of the fire ant Solenopsis geminata indicate that two sizes of gynes are produced. Macrogynes, which participate in late spring and summer mating flights, are larger, fattier, and more than twice as heavy as microgynes, which participate in fall mating flights. Three patterns of gyne production were observed in 51 colonies studied: 35 produced macrogynes only, 9 produced microgynes only, and 7 produced both morphs, contributing to both summer and fall mating flights. Behavioral evidence and rearing studies suggest that macrogynes found new colonies independently, whereas microgynes achieve colony queen status by infiltrating or being adopted by established colonies. Of the total number of female alates collected from the trapped colonies, 56% were microgynes. However, because of their smaller size and lower fat content, microgynes made up only one-third of the caloric investment in female alates. By measuring the thorax lengths of queens from mature colonies, we determined that at least 56% were macrogynes and 35% or more were microgynes. These results indicate that as a reproductive strategy, colony investment in microgyne production may have at least as high a payoff as investment in macrogyne production.This is publication #24 of the Fire Ant Research Team