Application Of Uncertainty Analysis To Ecologic... [VERIFIED]
Primarily, the book draws together a series of important case studies to provide a comprehensive review and synthesis of the most recent concepts, theories and methods in scaling and uncertainty analysis. It compares current definitions and ideas concerning scale within a coherent framework, and examines two key scaling approaches: similarity-based scaling, which is rooted in the idea of similitude or self-similarity; and dynamic model-based scaling, which emphasizes processes and mechanisms.
Application of Uncertainty Analysis to Ecologic...
AbstractIntroductionThe Importance of EnvisioningFour Visions of the FutureStar Trek: the default technological optimist visionMad Max: the skeptic's nightmareBig Government: Reagan's worst nightmareEcotopia: the low-consumption sustainable visionDealing with Uncertainty at the Level of Future VisionsA Two-tier Decision Process: Value Formation and Decision MakingPayoff Matrix Formulation and SurveyWorst Case AnalysisConclusionsResponses to this ArticleAcknowledgmentsLiterature CitedAppendix 1ABSTRACTThe most critical task facing humanity today is the creation of a shared visionof a sustainable and desirable society, one that can provide permanent prosperitywithin the biophysical constraints of the real world in a way that is fairand equitable to all of humanity, to other species, and to future generations.Recent work with businesses and communities indicates that creating a sharedvision is the most effective engine for change in the desired direction, yetmost effort in "futures modeling" has focused on extrapolating past trendsrather than envisioning alternative futures. Science and economics as appliedto policy are in conflict more often over alternative visions of the worldthan purely "scientific" disagreements. Likewise, governance has gotten boggeddown in mediating short term conflicts between special interests rather thanits more basic role of creating broadly shared visions that can guide disputeresolution.This paper addresses the question of what policies are most appropriate forsociety now, given alternative visions of the future and the enormous uncertaintyabout the reality of the assumptions underlying these visions. Four specificvisions are laid out as being representative of the major alternatives. Foreach vision the benefits of achieving the vision, the assumptions that wouldhave to be true in order for it to be achieved, and the implications of itbeing attempted but not achieved are explored. It is argued that dealing withuncertainty about the nature of the world, its carrying capacity for humans,the impacts of climate change, and other aspects of its future can best bedone at this level of future visions and assumptions, not at more detailedlevels (like the parameter uncertainty in models). Application of this vision/uncertaintyanalysis can help us both to design the future society we want and to maximizethe chances of our getting there safely.KEY WORDS: alternative futures, change process, envisioning, public judgment, public policy analysis, uncertainty.
DSS frameworks are systems for building DSS applications, as opposed to the conventional notion of a DSS as a one-off application designed to support solutions for very a specific problem type. Frameworks are a higher order abstraction of the DSS concept, with the advantage that they are suitable for developing DSS solutions for a very broad range of problems.Workflow engines and interfaces extend the capabilities of DSS frameworks with graphical programming tools that support data analysis, data processing (especially complex geoprocessing tasks), data transformation, scenario modelling, and system extensibility (by calling analytical engines external to the framework when needed). Ontologies (or knowledge graphs) similarly are a way to enhance the power of DSS frameworks, in this case by invoking an ontology engine to query an ontology for relevant entities and relations formalized within it. For example, what are some alternative analytical sequences that be used to solve decision problems of type X? A bit out on the horizon, combining the capabilities of ontology engines and workflow engines in DSS frameworks opens up intriguing possibilities for querying for solution methods and then implementing them in workflows.Parsimonious multiscale nonlinear models are difficult to develop given the computational challenges and hierarchical structure innate to forest ecosystems and landscapes. However, it is paramount to develop accurate description of the processes characterizing the status and dynamic of the forest.Spatially explicit models for the development of tree components became the one of the central part of the current forest modelling, as the focuses shifted historically from stands to trees and now to branches and leaves.Integration of remote sensing data into landscape modelling. Data acquired with remote sensing technologies became the source of almost unlimited amount of information that must be analysed. However, remote sensing brought a new challenge in modelling, namely wall to wall data that contains errors. Therefore, the models developed from remote sensing data must include now an assessment of error, or error propagation is now a part of the forest modelling process.Machine learning modelling for the exploration and prediction of landscape disturbance patterns. The adaptability of machine learning to particular situation while considering the general trend in data made them suitable for solving a variety of forest problems, from landslides to growth and yield and inventory.
Spatial conservation prioritisation (SCP) is a set of computational tools designed to support the efficient spatial allocation of priority areas for conservation actions, but it is subject to many sources of uncertainty which should be accounted for during the prioritisation process. We quantified the sensitivity of an SCP application (using software Zonation) to possible sources of uncertainty in data-poor situations, including the use of different surrogate options; correction for sampling bias; how to integrate connectivity; the choice of species distribution modelling (SDM) algorithm; how cells are removed from the landscape; and two methods of assigning weights to species (red-list status or prediction uncertainty). Further, we evaluated the effectiveness of the Egyptian protected areas for conservation, and spatially allocated the top priority sites for further on-the-ground evaluation as potential areas for protected areas expansion.
The robustness of conservation planning applications is subject to many sources of uncertainty which should be accounted for during the prioritisation process. Conservation planning in data-poor situations is sensitive to the selection of the surrogate group, correction for sampling bias, connectivity parameters, and the choice of modelling algorithm; collectively, these reflect data quality issues. This underlines the urgent need to improve data quality in the data-poor countries to enhance the usefulness of SDMs and conservation planning applications for long-term biodiversity conservation. We recommend the use of data on as many species groups as possible and more than one modelling algorithm to obtain a robust and stable conservation planning. Sampling bias can highly affect the efficiency of SCP output and therefore should be corrected for. Maintaining connectivity between top priority sites is essential for the effective long-term conservation of many species and therefore should be carefully integrated into conservation planning. However, as species-specific responses to habitat loss represents an important knowledge gap. We highlight the need for studies elaborating the response to habitat loss for less-studied species. Using currently available data on the Egyptian butterflies, reptiles, and mammals, the Egyptian protected areas network seems to be inefficient for wildlife conservation. We determined the top priority sites for further on-the-ground field evaluation as potential areas for protected areas expansion.
The summary estimate and confidence interval from a random-effects meta-analysis refer to the centre of the distribution of intervention effects, but do not describe the width of the distribution. Often the summary estimate and its confidence interval are quoted in isolation and portrayed as a sufficient summary of the meta-analysis. This is inappropriate. The confidence interval from a random-effects meta-analysis describes uncertainty in the location of the mean of systematically different effects in the different studies. It does not describe the degree of heterogeneity among studies, as may be commonly believed. For example, when there are many studies in a meta-analysis, we may obtain a very tight confidence interval around the random-effects estimate of the mean effect even when there is a large amount of heterogeneity. A solution to this problem is to consider a prediction interval (see Section 10.10.4.3).
Veroniki AA, Jackson D, Viechtbauer W, Bender R, Bowden J, Knapp G, Kuss O, Higgins JPT, Langan D, Salanti G. Methods to estimate the between-study variance and its uncertainty in meta-analysis. Research Synthesis Methods 2016; 7: 55-79.
We demonstrate the applications of the analytical expression of the uncertainty through two examples, Dengue virus and Aedes Aegypti mosquito, and compare the analytical results with the uncertainty calculated using the bootstrap method and Poission PPM approach. The analytic method results aligned well with bootstrap method results, but Poisson PPM approach gave much larger standard deviations. We only show the results and comparison of analytic and bootstrap below but include results and comparison of Poisson PPM in S1 Figs. The resolution of the Dengue virus example is at county level while the resolution of the Aedes Aegypti mosquito is at 1 km2 area level through Texas. 041b061a72