Conservation and Management for Multiple Species: Integrating Field Research and Modeling into Management Decisions

Multiple-species reserves aim at supporting viable populations of selected species. Population viability analysis (PVA) is a group of methods for predicting such measures as extinction risk based on species-specific data. These methods include models that simulate the dynamics of a population or a metapopulation. A PVA model for the California gnatcatcher in Orange County was developed with landscape (GIS) data on the habitat characteristics and requirements and demographic data on population dynamics of the species. The potential applications of this model include sensitivity analysis that provides guidance for planning fieldwork, designing reserves, evaluating management options, and assessing human impact. The method can be extended to multiple species by combining habitat suitability maps for selected species with weights based on the threat faced by each species, and the contribution of habitat patches to the persistence of each species. These applications and extensions, together with the ability of the model to combine habitat and demographic data, make PVA a powerful tool for the design, conservation, and management of multiple species reserves.     

Detecting Extinction Risk from Climate Change by IUCN Red List Criteria

Anthropogenic climate change is a key threat to global biodiversity. To inform strategic actions aimed at conserving biodiversity as climate changes, conservation planners need early warning of the risks faced by different species. The IUCN Red List criteria for threatened species are widely acknowledged as useful risk assessment tools for informing conservation under constraints imposed by limited data. However, doubts have been expressed about the ability of the criteria to detect risks imposed by potentially slow‐acting threats such as climate change, particularly because criteria addressing rates of population decline are assessed over time scales as short as 10 years. We used spatially explicit stochastic population models and dynamic species distribution models projected to future climates to determine how long before extinction a species would become eligible for listing as threatened based on the IUCN Red List criteria. We focused on a short‐lived frog species (Assa darlingtoni) chosen specifically to represent potential weaknesses in the criteria to allow detailed consideration of the analytical issues and to develop an approach for wider application. The criteria were more sensitive to climate change than previously anticipated; lead times between initial listing in a threatened category and predicted extinction varied from 40 to 80 years, depending on data availability. We attributed this sensitivity primarily to the ensemble properties of the criteria that assess contrasting symptoms of extinction risk. Nevertheless, we recommend the robustness of the criteria warrants further investigation across species with contrasting life histories and patterns of decline. The adequacy of these lead times for early warning depends on practicalities of environmental policy and management, bureaucratic or political inertia, and the anticipated species response times to management actions. Detección del Riesgo de Extinción a partir del Cambio Climático por medio del Criterio de la Lista Roja de la UICNKeith et al.     

Fire Management,Managed Relocation,and Land Conservation Options for Long‐Lived Obligate Seeding Plants under Global Changes in Climate,Urbanization, and Fire Regime

Most species face multiple anthropogenic disruptions. Few studies have quantified the cumulative influence of multiple threats on species of conservation concern, and far fewer have quantified the potential relative value of multiple conservation interventions in light of these threats. We linked spatial distribution and population viability models to explore conservation interventions under projected climate change, urbanization, and changes in fire regime on a long‐lived obligate seeding plant species sensitive to high fire frequencies, a dominant plant functional type in many fire‐prone ecosystems, including the biodiversity hotspots of Mediterranean‐type ecosystems. First, we investigated the relative risk of population decline for plant populations in landscapes with and without land protection under an existing habitat conservation plan. Second, we modeled the effectiveness of relocating both seedlings and seeds from a large patch with predicted declines in habitat area to 2 unoccupied recipient patches with increasing habitat area under 2 projected climate change scenarios. Finally, we modeled 8 fire return intervals (FRIs) approximating the outcomes of different management strategies that effectively control fire frequency. Invariably, long‐lived obligate seeding populations remained viable only when FRIs were maintained at or above a minimum level. Land conservation and seedling relocation efforts lessened the impact of climate change and land‐use change on obligate seeding populations to differing degrees depending on the climate change scenario, but neither of these efforts was as generally effective as frequent translocation of seeds. While none of the modeled strategies fully compensated for the effects of land‐use and climate change, an integrative approach managing multiple threats may diminish population declines for species in complex landscapes. Conservation plans designed to mitigate the impacts of a single threat are likely to fail if additional threats are ignored. Manejo de Incendios, Reubicación Administrada y Opciones de Conservación de Suelo para Plantas de Vida Larga con Sembrado Obligado bajo los Cambios Globales en el Clima, la Urbanización y el Régimen de Incendios     

Development of fuzzy air quality index using soft computing approach

Proper assessment of air quality status in an atmosphere based on limited observations is an essential task for meeting the goals of environmental management. A number of classification methods are available for estimating the changing status of air quality. However, a discrepancy frequently arises from the quality criteria of air employed and vagueness or fuzziness embedded in the decision making output values. Owing to inherent imprecision, difficulties always exist in some conventional methodologies like air quality index when describing integrated air quality conditions with respect to various pollutants parameters and time of exposure. In recent years, the fuzzy logic-based methods have demonstrated to be appropriated to address uncertainty and subjectivity in environmental issues. In the present study, a methodology based on fuzzy inference systems (FIS) to assess air quality is proposed. This paper presents a comparative study to assess status of air quality using fuzzy logic technique and that of conventional technique. The findings clearly indicate that the FIS may successfully harmonize inherent discrepancies and interpret complex conditions.     

Quantification of Extinction Risk: IUCN's System for Classifying Threatened Species

Abstract: The International Union for Conservation of Nature (IUCN) Red List of Threatened Species was increasingly used during the 1980s to assess the conservation status of species for policy and planning purposes. This use stimulated the development of a new set of quantitative criteria for listing species in the categories of threat: critically endangered, endangered, and vulnerable. These criteria, which were intended to be applicable to all species except microorganisms, were part of a broader system for classifying threatened species and were fully implemented by IUCN in 2000. The system and the criteria have been widely used by conservation practitioners and scientists and now underpin one indicator being used to assess the Convention on Biological Diversity 2010 biodiversity target. We describe the process and the technical background to the IUCN Red List system. The criteria refer to fundamental biological processes underlying population decline and extinction. But given major differences between species, the threatening processes affecting them, and the paucity of knowledge relating to most species, the IUCN system had to be both broad and flexible to be applicable to the majority of described species. The system was designed to measure the symptoms of extinction risk, and uses 5 independent criteria relating to aspects of population loss and decline of range size. A species is assigned to a threat category if it meets the quantitative threshold for at least one criterion. The criteria and the accompanying rules and guidelines used by IUCN are intended to increase the consistency, transparency, and validity of its categorization system, but it necessitates some compromises that affect the applicability of the system and the species lists that result. In particular, choices were made over the assessment of uncertainty, poorly known species, depleted species, population decline, restricted ranges, and rarity; all of these affect the way red lists should be viewed and used. Processes related to priority setting and the development of national red lists need to take account of some assumptions in the formulation of the criteria.     

Determination of Path Coefficients and Its Application in Road Traffic Noise Annoyance – a Theoretical Approach

This paper elucidates the basic approach of determining the path coefficients and its significance in the road traffic noise annoyance. Path model not only outline the direct effect of the traffic noise on the nearby residents but also indicate the indirect effect via other variables. In this study seven variables were considered for determining road traffic noise annoyance. However the same would be equally applicable for other situations like aircraft noise, rail noise, and industry noise with the different variables. At the outset a priori path model was designed and then on the basis of the partial regression coefficient values for the different paths, the revised path model was developed. The standardized partial regression coefficients known as path coefficients, determine the strength of the linkage among variables. Some of the paths in the model were not statistically significant. Revised path models were developed by deleting the insignificant paths whose values were found above 5% level. In the revised path model, thus the direct and indirect effect due to a particular variable causing the road traffic noise annoyance could be observed.     

Design of fuzzy synthetic evaluation model for air quality assessment

Little attention was paid to growing air quality concerns until about a decade earlier in India. Indian Government started continuous monitoring of the urban air quality in Taj corridor area to protect the heritage monuments like Agra Fort, Fatehpur Sikri, the bird sanctuary at Bharatpur National Park and also the human health associated with air pollution. The aim of this study was to address air quality assessment using fuzzy synthetic evaluation model. The model was designed for four air pollutants (sulphur dioxide, nitrogen dioxide, suspended particulate matters and respirable suspended particulate matter). In the present paper, an approach is demonstrated for the determination of fuzzy air quality index by aggregating the four pollutants. The model also considers the weights of individual pollutants during aggregation. The weights of individual pollutants were determined using analytical hierarchical process. The model was applied for air quality assessment in four monitoring stations situated in Taj Trapezium Zone.