Identifying extremely large values using composite sample data

Quantifying a composite sample results in a loss of information on the values of the constituent individual samples. As a consequence of this information loss, it is impossible to identify individual samples having large values, based on composite sample measurements alone. However, under certain circumstances, it is possible to identify individual samples having large values without exhaustively measuring all individual samples. In addition to composite sample measurements, a few additional measurements on carefully selected individual samples are sufficient to identify the individual samples having large values. In this paper, we present a statistical method to recover extremely large individual sample values using composite sample measurements. An application to site characterization is used to illustrate the method.The paper has been prepared with partial support from the United States Environmental Protection Agency Number CR815273. The contents have not been subject to Agency review and therefore do not necessarily reflect the views or policies of the Agency and no official endorsement should be inferred.     

Identifying and mapping local bycatch hotspots of loggerhead sea turtles using a GIS-based method: implications for conservation

Fisheries bycatch of marine megafauna is a worldwide conservation issue. In this study, we propose a method for generating reliable maps of sea turtle bycatch hotspots. Based on a well-defined area of fishing effort determined from the longline sets monitored in 2007 and 2010, we calculated the fishing area with the highest turtle bycatch density for both years. Our results show that it is important to consider all the components of fishing effort (area, number of hooks and soak time) in order to standardize the bycatch events, and thus the spatial density of the captures can be considered an index of abundance and aggregation of the species. Moreover, the high-resolution level of the analyses was useful for investigating the cumulative effect of the longline sets, which often overlap, and made it possible to correctly map the capture density and the intensity of the fishing effort at any given location.     

Predicting occurrence of juvenile shark habitat to improve conservation planning

Fishing and habitat degradation have increased the extinction risk of sharks, and conservation strategies recognize that survival of juveniles is critical for the effective management of shark populations. Despite the rapid expansion of marine protected areas (MPAs) globally, the paucity of shark‐monitoring data on large scales (100s–1000s km) means that the effectiveness of MPAs in halting shark declines remains unclear. Using data collected by baited remote underwater video systems (BRUVS) in northwestern Australia, we developed generalized linear models to elucidate the ecological drivers of habitat suitability for juvenile sharks. We assessed occurrence patterns at the order and species levels. We included all juvenile sharks sampled and the 3 most abundant species sampled separately (grey reef [Carcharhinus amblyrhynchos], sandbar [Carcharhinus plumbeus], and whitetip reef sharks [Triaenodon obesus]). We predicted the occurrence of juvenile sharks across 490,515 km² of coastal waters and quantified the representation of highly suitable habitats within MPAs. Our species‐level models had higher accuracy (? ≥ 0.69) and deviance explained (≥48%) than our order‐level model (? = 0.36 and deviance explained of 10%). Maps of predicted occurrence revealed different species‐specific patterns of highly suitable habitat. These differences likely reflect different physiological or resource requirements between individual species and validate concerns over the utility of conservation targets based on aggregate species groups as opposed to a species‐focused approach. Highly suitable habitats were poorly represented in MPAs with the most restrictions on extractive activities. This spatial mismatch possibly indicates a lack of explicit conservation targets and information on species distribution during the planning process. Non‐extractive BRUVS provided a useful platform for building the suitability models across large scales to assist conservation planning across multiple maritime jurisdictions, and our approach provides a simple for method for testing the effectiveness of MPAs.     

The value of flexibility in conservation financing

Land‐acquisition strategies employed by conservation organizations vary in their flexibility. Conservation‐planning theory largely fails to reflect this by presenting models that are either extremely inflexible—parcel acquisitions are irreversible and budgets are fixed—or extremely flexible—previously acquired parcels can readily be sold. This latter approach, the selling of protected areas, is infeasible or problematic in many situations. We considered the value to conservation organizations of increasing the flexibility of their land‐acquisition strategies through their approach to financing deals. Specifically, we modeled 2 acquisition‐financing methods commonly used by conservation organizations: borrowing and budget carry‐over. Using simulated data, we compared results from these models with those from an inflexible fixed‐budget model and an extremely flexible selling model in which previous acquisitions could be sold to fund new acquisitions. We then examined 3 case studies of how conservation organizations use borrowing and budget carry‐over in practice. Model comparisons showed that borrowing and budget carry‐over always returned considerably higher rewards than the fixed‐budget model. How they performed relative to the selling model depended on the relative conservation value of past acquisitions. Both the models and case studies showed that incorporating flexibility through borrowing or budget carry‐over gives conservation organizations the ability to purchase parcels of higher conservation value than when budgets are fixed without the problems associated with the selling of protected areas.     

Identifying fishing trip behaviour and estimating fishing effort from VMS data using Bayesian Hidden Markov Models

Recent advances in technologies have lead to a vast influx of data on movements, based on discrete recorded position of animals or fishing boats, opening new horizons for future analyses. However, most of the potential interest of tracking data depends on the ability to develop suitable modelling strategies to analyze trajectories from discrete recorded positions. A serious modelling challenge is to infer the evolution of the true position and the associated spatio-temporal distribution of behavioural states using discrete, error-prone and incomplete observations. In this paper, a Bayesian Hierarchical Model (HBM) using Hidden Markov Process (HMP) is proposed as a template for analyzing fishing boats trajectories based on data available from satellite-based vessel monitoring systems (VMS). The analysis seeks to enhance the definition of the fishing pressure exerted on fish stocks, by discriminating between the different behavioural states of a fishing trip, and also by quantifying the relative importance of each of these states during a fishing trip. The HBM approach is tested to analyse the behaviour of pelagic trawlers in the Bay of Biscay. A hidden Markov chain with a regular discrete time step is used to model transitions between successive behavioural states (e.g., fishing, steaming, stopping (at Port or at sea)) of each vessel. The parameters of the movement process (speed and turning angles) are defined conditionally upon the behavioural states. Bayesian methods are used to integrate the available data (typically VMS position recorded at discrete time) and to draw inferences on any unknown parameters of the model. The model is first tested on simulated data with different parameters structures. Results provide insights on the potential of HBM with HMP to analyze VMS data. They show that if VMS positions are recorded synchronously with the instants at which the process switch from one behavioural state to another, the estimation method provides unbiased and precise inferences on behavioural states and on associated movement parameters. However, if the observations are not gathered with a sufficiently high frequency, the performance of the estimation method could be drastically impacted when the discrete observations are not synchronous with the switching instants. The model is then applied to real pathways to estimate variables of interest such as the number of operations per trip, time and distance spent fishing or travelling.     

Distribution of salmon-habitat potential relative to landscape characteristics and implications for conservation.

The geographic distribution of stream reaches with potential to support high-quality habitat for salmonids has bearing on the actual status of habitats and populations over broad spatial extents. As part of the Coastal Landscape Analysis and Modeling Study (CLAMS), we examined how salmon-habitat potential was distributed relative to current and future (+100 years) landscape characteristics in the Coastal Province of Oregon, USA. The intrinsic potential to provide high-quality rearing habitat was modeled for juvenile coho salmon (Oncorhynchus kisutch) and juvenile steelhead (O. mykiss) based on stream flow, valley constraint, and stream gradient. Land ownership, use, and cover were summarized for 100-m analysis buffers on either side of stream reaches with high intrinsic potential and in the overall area encompassing the buffers. Past management seems to have concentrated nonindustrial private ownership, agriculture, and developed uses adjacent to reaches with high intrinsic potential for coho salmon. Thus, of the area in coho salmon buffers, 45% is either nonforested or recently logged, but only 10% is in larger-diameter forests. For the area in steelhead buffers, 21% is either non-forested or recently logged while 20% is in larger-diameter forests. Older forests are most extensive on federal lands but are rare on private lands, highlighting the critical role for public lands in near-term salmon conservation. Agriculture and development are projected to remain focused near high-intrinsic-potential reaches for coho salmon, increasing the importance of effectively addressing nonpoint source pollution from these uses. Percentages of larger-diameter forests are expected to increase throughout the province, but the increase will be only half as much in coho salmon buffers as in steelhead buffers. Most of the increase is projected for public lands, where policies emphasize biodiversity protection. Results suggest that widespread recovery of coho salmon is unlikely unless habitat can be improved in high-intrinsic-potential reaches on private lands. Knowing where high-intrinsic-potential stream reaches occur relative to landscape characteristics can help in evaluating the current and future condition of freshwater habitat, explaining differences between species in population status and risk, and assessing the need for and feasibility of restoration.     

自然保护区经济价值评价探讨

概括论述自然保护区社会经济价值的表现形式、类型及作用等，对资源直接、间接和潜在用途的社会经济价值估算方式、方法及评价进行了探讨。     

The value of virtual conferencing for ecology and conservation

The objectives of conservation science and dissemination of its research create a paradox: Conservation is about preserving the environment, yet scientists spread this message at conferences with heavy carbon footprints. Ecology and conservation science depend on global knowledge exchange—getting the best science to the places it is most needed. However, conference attendance from developed countries typically outweighs that from developing countries that are biodiversity and conservation hotspots. If any branch of science should be trying to maximize participation while minimizing carbon emissions, it is conservation. Virtual conferencing is common in other disciplines, such as education and humanities, but it is surprisingly underused in ecology and conservation. Adopting virtual conferencing entails a number of challenges, including logistics and unified acceptance, which we argue can be overcome through planning and technology. We examined 4 conference models: a pure‐virtual model and 3 hybrid hub‐and‐node models, where hubs stream content to local nodes. These models collectively aim to mitigate the logistical and administrative challenges of global knowledge transfer. Embracing virtual conferencing addresses 2 essential prerequisites of modern conferences: lowering carbon emissions and increasing accessibility for remote, time‐ and resource‐poor researchers, particularly those from developing countries.     

The occurrence and need for conservation of metallophytes on mine wastes in Europe

Metalliferous substrates are important in the distribution of various plants such asArmeria maritima, Minuartia verna, Thlaspi alpestre, Silene maritima andCochlearia officinalis, in parts of the United Kingdom. Metalliferous substrates are similarly important for a slightly different assemblage of metallophytes in the Aachen-Liège orefield in Belgium and Germany. The significance of metalliferous substrates in the distribution of the metallophytes varies from orefield to orefield and this is investigated. Certain sites, by virtue of their environmental and species diversity, are outstanding as habitats for the metallophytes and some of these are described. Observations on the importance and problems of the conservation of the metallophyte habitats are included.     

The preservation and re-creation of conservation value in mineral workings

The importance of abandoned mineral workings to nature conservation is discussed, together with the conflicts posed by proposed new developments. Four ways of preserving, recreating and/or enhancing conservation value are presented and considered. The need for additional research is emphasised.     

Predictive vegetation modeling for conservation: impact of error propagation from digital elevation data.

The effect of digital elevation model (DEM) error on environmental variables, and subsequently on predictive habitat models, has not been explored. Based on an error analysis of a DEM, multiple error realizations of the DEM were created and used to develop both direct and indirect environmental variables for input to predictive habitat models. The study explores the effects of DEM error and the resultant uncertainty of results on typical steps in the modeling procedure for prediction of vegetation species presence/absence. Results indicate that all of these steps and results, including the statistical significance of environmental variables, shapes of species response curves in generalized additive models (GAMs), stepwise model selection, coefficients and standard errors for generalized linear models (GLMs), prediction accuracy (Cohen's kappa and AUC), and spatial extent of predictions, were greatly affected by this type of error. Error in the DEM can affect the reliability of interpretations of model results and level of accuracy in predictions, as well as the spatial extent of the predictions. We suggest that the sensitivity of DEM-derived environmental variables to error in the DEM should be considered before including them in the modeling processes.     

Multi-scale sustainability evaluation of natural resource management systems: Quantifying indicators for different scales of analysis and their trade-offs using linear programming

The purposes of this paper are: (a) to describe a framework designed for multi-scale sustainability evaluation of Natural Resource Management Systems (NRMS), and (b) to illustrate its application for quantitative analysis using linear programming. The framework described here is intended to contribute to the operationalisation of the concept of sustainability by supporting the processes of design, evaluation and implementation of alternative NRMS at different scales. In this paper, Linear Programming is used for the quantitative analysis of indicators and their trade-offs; using a schematised example, the basic characteristics of the Multi-scale Multiple Goal Linear Programming (M-MGLP) method are described. In M-MGLP, indicators pertaining to different scales of analysis can be set as objectives or constraints for the optimisation. In this way, stakeholders interacting in a specific region can be made aware of the consequences of alternative NRMS in terms of the different indicators at the same scale and/or for indicators at other scales of analysis. The paper ends with a discussion of the main strengths and limitations of the framework and, specifically, of linear programming.     

Direct estimation of natural mortality rates for littoral marine fishes using populational data from a marine reserve

 Visual censuses conducted in a marine reserve (Medas Islands) were used to estimate the natural mortality rates (M) for five common fish species in the NW Mediterranean Sea (Coris julis, Diplodus annularis, D. sargus, Serranus cabrilla and Symphodus roissali). Visual censuses of these same five species were also performed at three sites in unprotected areas of the coast where both commercial and sport fishing activity was normal. Censuses were conducted over a 3 year period. Estimates of M in the 3 years displayed scant seasonal or interannual variation, which may mean that the populations were in equilibrium during that period. The results of this study showed that the relationships between M and the growth parameters and maximum life span were unclear, and considerable caution is therefore recommended when using indirect methods of estimating M based on those parameters. For certain species the values of M were equal to or greater than the estimated total mortality in the exploited areas. The virtual absence of piscivorous predators in the unprotected area as a consequence of the high level of fishing in that area contrasts with the high abundance of such predators in the marine reserve. Since predation is the main contributor to M, estimated mortality in the unprotected areas is attributable nearly entirely to fishing. It is suggested that M may vary according to alterations taking place in conditions in the ecosystem inhabited by a species and thus that use of a value of M for a pristine population cannot be extrapolated to exploited areas. Received: 18 January 2000 / Accepted: 14 July 2000     

Extreme value analysis of Munich air pollution data

We present three different approaches to modelling extreme values of daily air pollution data. We fitted a generalized extreme value distribution to the monthly maxima of daily concentration measures. For the exceedances of a high threshold depending on the data, the parameters of the generalized Pareto distribution were estimated. Accounting for autocorrelation, clusters of exceedances were used. To obtain information about the relationship of the exceedance of the air quality standard and possible predictors we applied logistic regression. Results and their interpretation are given for daily average concentrations of ozone and nitrogen dioxide at two monitoring sites within the city of Munich.     

Identification of the largest individual sample value using composite sample data and certain modifications of the sweep-out method

Cost-effective hotspot identification is an important issue in hazardous waste site characterization and evaluation. Composite sampling techniques are known to be cost effective when the cost of measurement is substantially higher than the cost of sampling. Although compositing incurs no loss of information on the means, information on individual sample values is lost due to compositing. In particular, if the interest is in identifying the largest individual sample value, the composite sampling techniques are not able to do so. Under certain assumptions, it may be possible to satisfactorily predict individual sample values using the composite sample data, but it is not generally possible to identify the largest individual sample value. In this paper, we propose two methods of identifying the largest individual sample value with some additional measurement effort. Both methods are modifications of the simple sweep-out method proposed earlier. Since analytical results do not seem to be feasible, performance of the proposed methods is assessed via simulation. The simulation results show that both the proposed methods, namely the locally sequential sweep-out and the globally sequential sweep-out, are better than the simple sweep-out method.Prepared with partial support from the Statistical Analysis and Computing Branch, Environmental Statistics and Information Division, Office of Policy, Planning, and Evaluation, United States Environmental Protection Agency, Washington, DC under a Cooperative Agreement Number CR-821531. The contents have not been subjected to Agency review and therefore do not necessarily reflect the views of the Agency and no official endorsement should be inferred.     

Understanding the effects of different social data on selecting priority conservation areas

Conservation success is contingent on assessing social and environmental factors so that cost‐effective implementation of strategies and actions can be placed in a broad social–ecological context. Until now, the focus has been on how to include spatially explicit social data in conservation planning, whereas the value of different kinds of social data has received limited attention. In a regional systematic conservation planning case study in Australia, we examined the spatial concurrence of a range of spatially explicit social values and land‐use preferences collected using a public participation geographic information system and biological data. We used Zonation to integrate the social data with the biological data in a series of spatial‐prioritization scenarios to determine the effect of the different types of social data on spatial prioritization compared with biological data alone. The type of social data (i.e., conservation opportunities or constraints) significantly affected spatial prioritization outcomes. The integration of social values and land‐use preferences under different scenarios was highly variable and generated spatial prioritizations 1.2–51% different from those based on biological data alone. The inclusion of conservation‐compatible values and preferences added relatively few new areas to conservation priorities, whereas including noncompatible economic values and development preferences as costs significantly changed conservation priority areas (48.2% and 47.4%, respectively). Based on our results, a multifaceted conservation prioritization approach that combines spatially explicit social data with biological data can help conservation planners identify the type of social data to collect for more effective and feasible conservation actions.     

Positives and pathologies of natural resource management on private land‐conservation areas

In managed natural resource systems, such as fisheries and rangelands, there is a recognized trade‐off between managing for short‐term benefits and managing for longer term resilience. Management actions that stabilize ecological attributes or processes can improve productivity in the supply of ecosystem goods and services in the short term but erode system resilience at longer time scales. For example, fire suppression in rangelands can increase grass biomass initially but ultimately result in an undesirable, shrub‐dominated system. Analyses of this phenomenon have focused largely on how management actions influence slow‐changing biophysical system attributes (such as vegetation composition). Data on the frequency of management actions that reduce natural ecological variation on 66 private land‐conservation areas (PLCAs) in South Africa were used to investigate how management actions are influenced by manager decision‐making approaches, a largely ignored part of the problem. The pathology of natural resource management was evident on some PLCAs: increased focus on revenue‐generation in decision making resulted in an increased frequency of actions to stabilize short‐term variation in large mammal populations, which led to increased revenues from ecotourism or hunting. On many PLCAs, these management actions corresponded with a reduced focus on ecological monitoring and an increase in overstocking of game (i.e., ungulate species) and stocking of extralimitals (i.e., game species outside their historical range). Positives in natural resource management also existed. Some managers monitored slower changing ecological attributes, which resulted in less‐intensive management, fewer extralimital species, and lower stocking rates. Our unique, empirical investigation of monitoring‐management relationships illustrates that management decisions informed by revenue monitoring versus ecological monitoring can have opposing consequences for natural resource productivity and sustainability. Promoting management actions that maintain resilience in natural resource systems therefore requires cognizance of why managers act the way they do and how these actions can gradually shift managers toward unsustainable strategies.     

Toxicity assays of ladybirds using natural predators

Nestling blue titsParus caeruleus L. were given diets containing homogenized ladybirds, to assess the effects of their chemical defences. The 2spot, 10spot and water ladybirds produced no apparent toxic effects when small numbers were given at regular intervals.The pine and kidney-spot ladybirds slightly inhibit growth and may be toxic to very young nestlings. The Water ladybird is extremely distasteful to this predator, despite its lack of toxicity. The results are discussed with reference to the proposed Müllerian and Batesian mimetic relationships between the ladybird species.