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  1. Artikel: Untangling drivers of species distributions: Global sensitivity and uncertainty analyses of MaxEnt

    Convertino, M / G.A. Kiker / I. Linkov / M.L. Chu-Agor / R. Muñoz-Carpena

    Environmental modelling & software. 2014 Jan., v. 51

    2014  

    Abstract: Untangling drivers of systems and uncertainty for species distribution models (SDMs) is important to provide reliable predictions that are useful for conservation campaigns. This is particularly true for species whose habitat is threatened by climate ... ...

    Abstract Untangling drivers of systems and uncertainty for species distribution models (SDMs) is important to provide reliable predictions that are useful for conservation campaigns. This is particularly true for species whose habitat is threatened by climate change that enhances the uncertainty in future species distributions. Global sensitivity and uncertainty analyses (GSUA) is a robust method to globally investigate the uncertainty of SDMs and the importance of species distributions' drivers in space and time.Here we apply GSUA to MaxEnt that is one of the popular presence-only SDMs. We consider the Snowy Plover (Charadrius alexandrinus nivosus) (SP) in Florida that is a shorebird whose habitat is affected by sea level rise due to climate change. The importance of intrinsic and exogenous input factors to the uncertainty of the species distribution is evaluated for MaxEnt. GSUA is applied for three projections of the habitat (2006, 2060, and 2100) according to the A1B sea level rise scenario. The large land cover variation determines a moderate decrease in habitat suitability in 2060 and 2100 prospecting a low risk of decline for the SP. The regularization parameter for the environmental features, the uncertainty into the classification of salt-marsh, transitional marsh, and ocean beach, and the maximum number of iterations for the model training are in this order the most important input factors for the average habitat suitability. These results are related to the SP but, in general MaxEnt appears as a very non-linear model where uncertainty mostly derives from the interactions among input factors.The uncertainty of the output is a species-specific variable. Thus, GSUA need be performed for each case considering local exogenous input factors of the model. GSUA allows quantitative informed species-management decisions by providing scenarios with controlled uncertainty and confidence over factors' importance that can be used by resource managers.
    Schlagwörter biogeography ; Charadrius alexandrinus ; climate change ; computer software ; environmental factors ; environmental models ; habitats ; land cover ; managers ; nonlinear models ; prediction ; risk ; salt marshes ; sea level ; uncertainty ; uncertainty analysis ; Florida
    Sprache Englisch
    Erscheinungsverlauf 2014-01
    Umfang p. 296-309.
    Erscheinungsort Elsevier Ltd
    Dokumenttyp Artikel
    ISSN 1364-8152
    DOI 10.1016/j.envsoft.2013.10.001
    Datenquelle NAL Katalog (AGRICOLA)

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  2. Artikel: A simplified approach for simulating changes in beach habitat due to the combined effects of long-term sea level rise, storm erosion, and nourishment

    Chu, M.L / G.A. Kiker / I. Linkov / J.A. Guzman / R. Muñoz-Carpena

    Environmental modelling & software. 2014 Feb., v. 52

    2014  

    Abstract: Better understanding of the vulnerability of coastal habitats to sea level rise and major storm events are aided by the use of simulation models. Since coastal habitats also undergo frequent nourishment restoration works in order to maintain their ... ...

    Abstract Better understanding of the vulnerability of coastal habitats to sea level rise and major storm events are aided by the use of simulation models. Since coastal habitats also undergo frequent nourishment restoration works in order to maintain their viability, vulnerability models must be able to assess the combined effects of sea level rise, storm surge, and beach nourishment. The Sea Level Affecting Marshes Model (SLAMM) was modified and applied to quantify the changes in the beach area in a 5-km stretch of beach in Santa Rosa Island, Florida due to these combined effects. A new methodology to estimate spatial erosion patterns was developed based on measured erosion during three historic storm events representing a wide range of storm intensities over the study area (named storms Ivan (H5), Dennis (H4), and Katrina (TS)). Future major storms over the 2012-2100 period were generated based on the frequency distribution of historic storms using 4000 simulations to account for uncertainty in the storms temporal distribution. Potential effects of individual, successive, and random storms occurring over the area under 0-1.5 m nourishment schemes were evaluated. The risk of losing the beach habitat in 90 years for different scenarios is studied based on probability distribution contours constructed with the model results. Simulation results suggest that without nourishment, a major storm with a category of tropical storm or higher will reduce the beach at the end of the period by 97-100%. This loss can be reduced to 60% by maintaining a 1-m beach elevation and can further be reduced to 34% with 1.5 m beach nourishment.
    Schlagwörter computer software ; environmental models ; habitats ; long term effects ; marshes ; methodology ; probability distribution ; risk ; sea level ; simulation models ; storms ; uncertainty ; viability ; Florida
    Sprache Englisch
    Erscheinungsverlauf 2014-02
    Umfang p. 111-120.
    Erscheinungsort Elsevier Ltd
    Dokumenttyp Artikel
    ISSN 1364-8152
    DOI 10.1016/j.envsoft.2013.10.020
    Datenquelle NAL Katalog (AGRICOLA)

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  3. Artikel ; Online: From comparative risk assessment to multi-criteria decision analysis and adaptive management

    I. Linkov / F.K. Satterstrom / G. Kiker / C. Batchelor / T. Bridges / E. Ferguson

    Environment International, Vol 32, Iss 8, Pp 1072-

    Recent developments and applications

    2006  Band 1093

    Abstract: Environmental risk assessment and decision-making strategies over the last several decades have become increasingly more sophisticated, information-intensive, and complex, including such approaches as expert judgment, cost–benefit analysis, and ... ...

    Abstract Environmental risk assessment and decision-making strategies over the last several decades have become increasingly more sophisticated, information-intensive, and complex, including such approaches as expert judgment, cost–benefit analysis, and toxicological risk assessment. One tool that has been used to support environmental decision-making is comparative risk assessment (CRA), but CRA lacks a structured method for arriving at an optimal project alternative. Multi-criteria decision analysis (MCDA) provides better-supported techniques for the comparison of project alternatives based on decision matrices, and it also provides structured methods for the incorporation of project stakeholders' opinions in the ranking of alternatives. We argue that the inherent uncertainty in our ability to predict ecosystem evolution and response to different management policies requires shifting from optimization-based management to an adaptive management paradigm. This paper brings together a multidisciplinary review of existing decision-making approaches at regulatory agencies in the United States and Europe and synthesizes state-of-the-art research in CRA, MCDA, and adaptive management methods applicable to environmental remediation and restoration projects. We propose a basic decision analytic framework that couples MCDA with adaptive management and its public participation and stakeholder value elicitation methods, and we demonstrate application of the framework to a realistic case study based on contaminated sediment management issues in the New York/New Jersey Harbor. Keywords: Comparative risk assessment, Decision analysis, Adaptive management, Risk analysis, Sediments
    Schlagwörter Environmental sciences ; GE1-350
    Thema/Rubrik (Code) 650
    Sprache Englisch
    Erscheinungsdatum 2006-12-01T00:00:00Z
    Verlag Elsevier
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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