Linking bayesian belief networks and GIS to assess the ecosystem integrity in the brazilian Amazon.

Deforestation and climate change heavily impact the ecosystem of the Amazon rainforest threatening its resilience and the sustainability of many human activities. Land protection may prevent ecosystems and their services to deteriorate from the pressures of agricultural expansion, population growth and wood harvesting. In the Brazilian Amazon land protection occurs in several forms such as environmental conservation, setting biodiversity priority areas and the delineation of indigenous lands. Still, the effects are not clear as understanding of the ecosystems is incomplete and responses to human actions are highly uncertain. Bayesian Belief Networks (BBN) are models that probabilistically represent correlative and causal relationships among variables. BBNs have been successfully applied to natural resource management to address environmental management problems and to assess the impact of alternative management measures. By training the probabilistic relationships using field data, Remote Sensing data and GIS data the BBN can provide information on the ecosystems: the ecosystem integrity and their likely response to climate change or alternative management actions. An increasing number of studies train and apply BBNs with evidence originating from GIS data; a cumbersome and error prone soft-linking method requiring manual conversion of data files between the BBN and GIS software systems. This paper presents the full integration of a BBN software system within an existing GIS based Discussion Support System (DSS) illustrated by the case of the ecosystem integrity of the Brazilian amazon. The full integration speeds up the processing and thereby allows doing multiple runs within a short period of time such as a stakeholder workshop. Each consecutive run is based upon insights from a previous one. Furthermore, the DSS provides the management of different options, visualize spatial summaries and trade-offs between different impact indicators and see regional differences

Water requirements of floodplain rivers and fisheries: existing decision support tools and pathways for development

Fisheries / Rivers / Flood plains / Hydrology / Ecology / Models / Decision support tools / Environmental impact assessment / Methodology / Databases

Use of remote sensing to assess ecosystem integrity of the Brazilian Amazon rainforest: a Bayesian approach.

Biodiversity supports many ecosystem services that are very important for climate change mitigation and adaptation. There is a functional link between the tropical forest ecosystem biodiversity and their capacity for carbon uptake and storage as well as regulation of evapotranspiration flux. Nevertheless, land use changes and agriculture expansion reduce the ecosystems integrity modifying the functions related directly to the ecosystem services. The relationship between biodiversity loss and the impacts on ecosystem services of tropical forests, in face of the ongoing global climate change needs to be better quantified. In this work, we considered the concept of Ecosystem Integrity (EI), which represents the connection of biodiversity with the ability of ecosystems to sustain the processes of self-organization. Bayesian Networks (BBN-Bayesian Belief Network) can provide metrics for the generation of Ecosystem Integrity Index, from the training of probabilistic relationships of evidence obtained through Remote Sensing data. The objective of this work is to present the methodological approach and the results of EI mapping, elaborated at the regional scale for different patterns of phyto-ecologic landscape of the Brazilian Amazon. The modelling was based on learning from the parameters (data-driven model) through the use of the Expectation Maximization algorithm. For the validation of this probabilistic model, an evaluation was carried out in controlled areas with field observation by experts. Results showed that it is possible to generate an Ecosystem Integrity Index at regional scale using a probabilistic model based on Bayesian Belief Networks (BBN), and totally free web-available satellite products

Decision support systems for large dam planning and operation in Africa

Decision support systems/ Dams/ Planning/ Operations/ Social impact/ Environmental effects

Using Bayesian Belief Networks to Identify Potential Compatibilities and Conflicts Between Development and Landscape Conservation

Experts with different land use interests often use differing definitions of land suitability that can result in competing land use decisions. We use Bayesian belief networks linked to GIS data layers to integrate empirical data and expert knowledge from two different land use interests (development and conservation) in Maine’s Lower Penobscot River Watershed. Using ground locations and digital orthoquads, we determined the overall accuracy of the resulting development and conservation suitability maps to be 82% and 89%, respectively. Overlay of the two maps show large areas of land suitable for both conservation protection and economic development and provide multiple options for mitigating potential conflict among these competing land users. The modeling process can be adapted to help prioritize and choose among different alternatives as new information becomes available, or as land use and land-use policies change. The current model structure provides a maximal coverage strategy that allows decision makers to target and prioritize several areas for protection or development and to set specific strategies in the face of changing ecological, social, or economic processes. Having multiple options can generate new hypotheses and decisions at more local scales or for more specific conservation purposes not yet identified by stakeholders and decision makers in the region. Subsequently, new models can be developed using the same process, but with higher resolution data, thereby helping a community evaluate the impacts of alternative land uses between different prioritized areas at finer scales

Investigating the relationships between environmental stressors and stream condition using Bayesian belief networks

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/92037/1/j.1365-2427.2011.02683.x.pd

On how religions could accidentally incite lies and violence: Folktales as a cultural transmitter

This research employs the Bayesian network modeling approach, and the Markov chain Monte Carlo technique, to learn about the role of lies and violence in teachings of major religions, using a unique dataset extracted from long-standing Vietnamese folktales. The results indicate that, although lying and violent acts augur negative consequences for those who commit them, their associations with core religious values diverge in the final outcome for the folktale characters. Lying that serves a religious mission of either Confucianism or Taoism (but not Buddhism) brings a positive outcome to a character (βT_and_Lie_O= 2.23; βC_and_Lie_O= 1.47; βT_and_Lie_O= 2.23). A violent act committed to serving Buddhist missions results in a happy ending for the committer (βB_and_Viol_O= 2.55). What is highlighted here is a glaring double standard in the interpretation and practice of the three teachings: the very virtuous outcomes being preached, whether that be compassion and meditation in Buddhism, societal order in Confucianism, or natural harmony in Taoism, appear to accommodate two universal vices—violence in Buddhism and lying in the latter two. These findings contribute to a host of studies aimed at making sense of contradictory human behaviors, adding the role of religious teachings in addition to cognition in belief maintenance and motivated reasoning in discounting counterargument

Cyanobacterial Harmful Algal Blooms: Chapter 38: Integrating Human and Ecological Risk Assessment: Application to the Cyanobacterial Harmful Algal Bloom Problem

Environmental and public health policy continues to evolve in response to new and complex social, economic and environmental drivers. Globalization and centralization of commerce, evolving patterns of land use (e.g., urbanization, deforestation), and technological advances in such areas as manufacturing and development of genetically modified foods have created new and complex classes of stressors and risks (e.g., climate change, emergent and opportunist disease, sprawl, genomic change). In recognition of these changes, environmental risk assessment and its use are changing from stressor-endpoint specific assessments used in command and control types of decisions to an integrated approach for application in communitybased decisions. As a result, the process of risk assessment and supporting risk analyses are evolving to characterize the human-environment relationship. Integrating risk paradigms combine the process of risk estimation for humans, biota, and natural resources into one assessment to improve the information used in environmental decisions (Suter et al. 2003b). A benefit to this approach includes a broader, system-wide evaluation that considers the interacting effects of stressors on humans and the environment, as well the interactions between these entities. To improve our understanding of the linkages within complex systems, risk assessors will need to rely on a suite of techniques for conducting rigorous analyses characterizing the exposure and effects relationships between stressors and biological receptors. Many of the analytical techniques routinely employed are narrowly focused and unable to address the complexities of an integrated assessment. In this paper, we describe an approach to integrated risk assessment, and discuss qualitative community modeling and Probabilistic Relational Modeling techniques that address these limitations and evaluate their potential for use in an integrated risk assessment of cyanobacteria

The role of economics in ecosystem based management:The case of the EU Marine Strategy Framework Directive; first lessons learnt and way forward

The EU Marine Strategy Framework Directive (MSFD) sets out a plan of action relating to marine environmental policy and in particular to achieving ‘good environmental status’ (GES) in European marine waters by 2020. Article 8.1 (c) of the Directive calls for ‘an economic and social analysis of the use of those waters and of the cost of degradation of the marine environment’. The MSFD is ‘informed’ by the Ecosystem Approach to management, with GES interpreted in terms of ecosystem functioning and services provision. Implementation of the Ecosystem Approach is expected to be by adaptive management policy and practice. The initial socio-economic assessment was made by maritime EU Member States between 2011 and 2012, with future updates to be made on a regular basis. For the majority of Member States, this assessment has led to an exercise combining an analysis of maritime activities both at national and coastal zone scales, and an analysis of the non-market value of marine waters. In this paper we examine the approaches taken in more detail, outline the main challenges facing the Member States in assessing the economic value of achieving GES as outlined in the Directive and make recommendations for the theoretically sound and practically useful completion of the required follow-up economic assessments specified in the MSFD