Assessing the value of species: a case study on the willingness to pay for species protection in Chile

We conduct a valuation of species protection in central Chile's Campana National Park (CNP) using the choice experiment (CE) method. The CNP has been recognized as having global relevance for the conservation of biological diversity. Specifically, the aim is to estimate the willingness to pay (WTP) of park visitors for protection of different protected species in the area: popular species of flora and fauna that are known by visitors of the park, inconspicuous species (phytofagous, fungus) that are unknown to visitors, and species with conservation problems of which visitors are unaware. We also investigate the WTP for different levels of species biodiversity protection within the sample as the WTP for biodiversity protection is sensitive to the way in which biodiversity is presented to respondents. The levels of species biodiversity protection are represented using “icon” inconspicuous species and numbers of inconspicuous species protected in La Campana National Park. This methodology allowed us to obtain information on the sensitivity of the participants to the scope of the information provided. Overall, visitors attach positive and significant values to the local conservation of species. These values are derived not only from the desire to preserve popular species in the area but also from the preservation or assured existence of inconspicuous species that are protected in the park. Visitors behave as consumers who are sensitive to changes in the price of park admission as a result of the implementation of specific strategies for wildlife conservation management in the park. Furthermore, the study also elucidates the observations that the public is able to perceive biodiversity conservation in broader terms than a single species and that greater benefits are attached to the conservation of multiple species than single ones. Results also provide insights into methodological considerations regarding the conceptual framework used to assess the valuation of biodiversity changes in developing countries, including the level of biological diversity and the scale of the change.     

Models for preserving species diversity with backup coverage

The problem of selecting land for preservation of species has been a rich and active area of research over the past two decades. Typically, reserve selection models have tried to maximize species diversity by preserving areas that contain the greatest number of species. However, several studies have shown that seldom do these species-rich areas contain the rarest species most in need of protection. Most reserve selection models seek to maximize diversity by choosing parcels so that all species are covered by or represented in at least one parcel. This approach would usually be expected to result in coverage by a single parcel for the rarest species, especially for those that do not coincide with more abundant species. It is precisely these rare species, however, that would be lost or whose survivability would be most challenged, if the single parcels in which they are represented became unavailable due to some unforeseen event. In this paper, we introduce to reserve selection models the concept of secondary, or backup, coverage of species. Briefly stated, a species is said to have backup representation in the system of reserves if it is covered by, or represented in, two or more parcels. Having backup coverage guarantees that every species is still covered in the event that a natural or man-made catastrophe makes a given parcel uninhabitable. The results show that backup coverage can be obtained at little additional cost (as expressed by the number of parcels selected). Bi-objective formulations that trade-off primary with backup coverage show that backup coverage can be guaranteed for larger numbers of species with little reduction in primary coverage.     

The effect of thermodynamic data on computer model predictions of uranium speciation in natural water systems

Computer models have found widespread application in order to help elucidate and predict changes in environmental systems. One such application is the prediction of trace metal speciation in aqueous systems. This is achieved by solving a set of non-linear equations involving equilibrium constants for all the components in the system, within mass and charge balance constraints. In this study a comparison of the predicted uranium speciation from two computer programs, WHAM and PHREEQCI, is used to illustrate the effect variations in thermodynamic data can have on the models produced. Using the original thermodynamic data provided with the models, WHAM predicted the UO2(2+) ion as the major species (84%) while PHREEQCI predicted UO2(HPO4)2(2-) as the major species (86%). Substituting uranium data from the Nuclear Energy Agency Thermochemical Database project (NEA-TDB) into both programs produced similar results from each program, with UO2F+ predicted to dominate (68%) in a groundwater sample. Natural water samples often contain humic substances. The possible interaction of such substances with uranium was also modelled. The WHAM program includes a discreet site electrostatic humic substance model, however in order to use the PHREEQCI program to model humic substance interactions, a 'model fulvic acid' dataset was added to the program. These models predicted 85 to 98% uranium-humic substance species at neutral pH. This indicates that humic substances do need to be taken into account when modelling uranium speciation in natural water samples.     

A Hierarchical Approach to Designing Compact Ecological Reserve Systems

A number of mathematical models and solution techniques have been developed to design systems of reserve sites to protect species and their natural habitats. This paper presents two optimization models for obtaining ecological reserve systems that are spatially compact. Compact reserve systems will increase species persistence relative to more fragmented systems and can reduce the costs associated with reserve boundaries. These optimization models employ a hierarchical approach that incorporates both the boundary length of all clusters and the sum of within-cluster distances. Unlike previous approaches, we argue that measuring the distance between sites within clusters (and not between clusters) is more appropriate. Numerical experiments are conducted on synthetic grid systems and on a real-world hexagonal data set. These experiments confirm that the models produce reasonably compact clusters of reserve sites that respect species availability and budget constraints. Simplifications to the models are developed that significantly reduce the computational effort, while still identifying reasonable sets of reserve clusters. Sensitivity of the results to changes in model parameters (number of allowable sites, number of possible clusters, and rarity of species covered) is also explored.     

LINKING AIR ISSUES WITH DOSE-RESPONSE AND BIOGEOCHEMICAL-CYCLE MODELS

This paper describes the roles that dose-response andbiogeochemical-cycle models play in the comparative and integratedassessment of several atmospheric issues. The two approaches aredescribed briefly, as they are commonly used, and as they might beapplied when multiple stressors and receptors, and when interactingcycles, respectively, are involved. Examples are drawn from thecarbon and sulfur cycles to demonstrate how knowledge of a cycleprovides a useful link among various current pollution issues. Possibleinteractions and some shortcomings of the two approaches are given,leading to suggestions for the appropriate use of the two tools forscientific analysis and policy development for the various air issues.     

Pattern Classification and Recognition of Invertebrate Functional Groups Using Self-Organizing Neural Networks

Self-organizing neural networks can be used to mimic non-linear systems. The main objective of this study is to make pattern classification and recognition on sampling information using two self-organizing neural network models. Invertebrate functional groups sampled in the irrigated rice field were classified and recognized using one-dimensional self-organizing map and self-organizing competitive learning neural networks. Comparisons between neural network models, distance (similarity) measures, and number of neurons were conducted. The results showed that self-organizing map and self-organizing competitive learning neural network models were effective in pattern classification and recognition of sampling information. Overall the performance of one-dimensional self-organizing map neural network was better than self-organizing competitive learning neural network. The number of neurons could determine the number of classes in the classification. Different neural network models with various distance (similarity) measures yielded similar classifications. Some differences, dependent upon the specific network structure, would be found. The pattern of an unrecognized functional group was recognized with the self-organizing neural network. A relative consistent classification indicated that the following invertebrate functional groups, terrestrial blood sucker; terrestrial flyer; tourist (nonpredatory species with no known functional role other than as prey in ecosystem); gall former; collector (gather, deposit feeder); predator and parasitoid; leaf miner; idiobiont (acarine ectoparasitoid), were classified into the same group, and the following invertebrate functional groups, external plant feeder; terrestrial crawler, walker, jumper or hunter; neustonic (water surface) swimmer (semi-aquatic), were classified into another group. It was concluded that reliable conclusions could be drawn from comparisons of different neural network models that use different distance (similarity) measures. Results with the larger consistency will be more reliable.     

Abundance versus presence/absence data for modelling fish habitat preference with a genetic Takagi–Sugeno fuzzy system

This study compared the accuracy of fuzzy habitat preference models (FHPMs) and habitat preference curves (HPCs) obtained from the FHPMs in order to assess the effect of two types of data [log-transformed fish population density (LOG) and presence-absence (P/A) data] on the habitat preference evaluation of Japanese medaka (Oryzias latipes). Three independent data sets were prepared for each type of data. The results differed according to the data sets and the types of data used. The HPCs showed a similar trend, whilst the degrees of preference were different. The model accuracy also differed according to the data sets used. Although almost no statistical difference was observed, on average, the P/A-based models showed a better performance according to the threshold-independent performance measures, whilst the LOG-based models showed better performance in predicting absence of the fish. These results can be explained partly from the different shapes of HPCs. This case study of Japanese medaka demonstrated the effect of different types of data on habitat preference evaluation. Further studies should build on the present finding and evaluate the effects of data characteristics such as the size of data sets and the prevalence for better understanding and reliable assessment of the habitat for target species.     

A Comparison Between Regression Models and Genetic Programming for Predictions of Chlorophyll-a Concentrations in Northern Lakes

Chlorophyll-a (chl-a) concentrations are often used as a proxy for water quality problems as well as phytoplankton blooms. Available chl-a models range from simple phosphorus loading models to complex regression and dynamic models. A comparison of multiple regression models was made with genetic programming (GP) techniques to predict chl-a concentrations over a large range of 104 Swedish lakes. Independent variables used were lake area, mean depth, iron, latitude, ammonium, nitrogen + nitrate, pH, phosphate, secchi depth, silicon, temperature, total phosphorus, total nitrogen and total organic carbon. GP is a method based on the Darwinian evolution theory. This implies that a program will be able to test different mathematical equations, iterating and improving each equation using fundamental ideas from evolution theory to increase the predictive power. A good correspondence was found between the multiple regression and the GP modelling approach. No significant improvement of the predictive power was found using GP, and it is therefore recommended that multiple regression methods should be preferred when predicting chl-a concentrations as these models tend to be less complex and the modelling approach is easier to use. Results from GP were in some cases more accurate compared to multiple regressions; however, the best model was created by multiple regressions which used concentrations of total phosphorus, total nitrogen and latitude as independent variables. These findings will be an important note for limnologists and modelling managers when developing future models of chl-a concentrations in lakes.     

Rabbit Population Landscape-Scale Simulation to Investigate the Relevance of Using Rabbits in Regulatory Environmental Risk Assessment

This paper describes the development and testing of the ALMaSS rabbit model and its baseline, and subsequently its application to the question of lagomorph population vulnerability in environmental risk assessment (ERA). Development and testing following a pattern-oriented modelling protocol resulted in a model able to replicate local and landscape-level rabbit population patterns. We then tested how robust rabbit populations are to an (imaginary) extreme toxic stressor at a landscape level in a variety of landscapes, and to what extremes key uncertain model parameters must be pushed to cause extinctions. This was contrasted with the same (imaginary) toxic stressor applied to the already existing ALMaSS hare model. For EU risk assessment of plant protection products, these results clearly indicate that if the protection goal is population-level impacts, either in abundance and/or distribution, then the hare is a much more vulnerable species than the rabbit under all the conditions tested. Rabbits would only be more vulnerable than hares if the entire population were to be exposed simultaneously, when lower body mass would then be a critical factor. This did not occur even though the toxicant and exposure scenarios tested here were extreme and, in fragmented landscapes at scales used here, will not occur in reality from the use of plant protection products on crop fields. As well as specifically answering the question on rabbit versus hare vulnerability, this study generally illustrates the potential application of models for setting focal species for risk assessments.     

Selecting indicator species to monitor ecological integrity: a review

We review critical issues that must be considered when selectingindicator species for a monitoring program that aims to maintainor restore ecological integrity. First, we examine the pros andcons of different management approaches on which a conservationprogram can be based and conclude that ecosystem management ismost appropriate. We then identify potential indicators ofecological integrity at various levels of the ecosystem, with aparticular emphasis on the species level. We conclude that,although the use of indicator species remains contentious, it canbe useful if (1) many species representing various taxa and lifehistories are included in the monitoring program, (2) theirselection is primarily based on a sound quantitative databasefrom the focal region, and (3) caution is applied wheninterpreting their population trends to distinguish actualsignals from variations that may be unrelated to thedeterioration of ecological integrity. Finally, we present anddiscuss different methods that have been used to select indicatorspecies.