Assessing Land-Use Impacts on Bull Trout Using Bayesian Belief Networks

2006 ◽  
pp. 127-147 ◽  
Author(s):  
Danny C. Lee
Keyword(s):  
Land Use ◽  
Bayesian Belief Networks ◽  
Bull Trout ◽  
Belief Networks ◽  
Land Use Impacts

scholarly journals Integrating mixed-cell CA model and Bayesian belief networks to optimize ecosystem services for ecological restoration and conservation

2021 ◽  
Author(s):  
Shuang Zhou ◽  
Li Peng
Keyword(s):  
Land Use ◽  
Ecosystem Services ◽  
Ecological Restoration ◽  
Land Development ◽  
Bayesian Belief Networks ◽  
The State ◽  
Belief Networks ◽  
Mixed Cell ◽  
Development Scenarios ◽  
Ca Model

The complexity and uncertainty of land use and environmental factors pose challenges to the management decisions of ecological restoration and conservation.We integrated the mixed-cell CA model and Bayesian belief networks to develop an innovative method for optimizing ecosystem services under different land development scenarios, including consideration of the uncertainty and variability of factors.The southern region of Sichuan Province, China, was selected as an example.We first established three development scenarios between 2015 and 2035, namely, natural development scenario (NDS), ecological protection scenario (EPS), and cultivated land protection scenario (CLPS).The MCCA model was utilized to simulate the land use pattern in 2035 under different scenarios.We then construced a BBN-based model to investigate the carbon sequestration, grain supply, soil conservation, habitat quality, and water yield in 2035 under uncertain scenarios.After the sensitivity analysis and evaluation of the model, we determined the state combination of influential factors at various ecosystem service levels and the ecological restoration and conservation key areas.The obtained result showed that the key influencing factors impacting the ecosystem services level included NPP, Slope, forestland and ET, and the state combination corresponding to the highest level of ecosystem services was predominantly distributed in regions with the highest NPP, the highest Slope, the highest forestland area and low ET.Based on this finding, we proposed some suggestions for ecological restoration and conservation of key areas.This model considers uncertainties and is capable of providing scientific recommendations on restoration and conservation; therefore, it can serve as an effective tool to assist stakeholders in making decisions.

Using Bayesian network models to incorporate uncertainty in the economic analysis of pollution abatement measures under the water framework directive

2005 ◽  
Vol 5 (6) ◽  
pp. 95-104 ◽  
Author(s):  
D.N. Barton ◽  
T. Saloranta ◽  
T.H. Bakken ◽  
A. Lyche Solheim ◽  
J. Moe ◽  
...  
Keyword(s):  
Water Quality ◽  
At Risk ◽  
Economic Valuation ◽  
Pollution Abatement ◽  
Network Models ◽  
Cost Effective ◽  
Bayesian Belief Networks ◽  
Probabilistic Interpretation ◽  
Belief Networks ◽  
Benefit Cost Analysis

The evaluation of water bodies “at risk” of not achieving the Water Framework Directive's (WFD) goal of “good status” begs the question of how big a risk is acceptable before a programme of measures should be implemented. Documentation of expert judgement and statistical uncertainty in pollution budgets and water quality modelling, combined with Monte Carlo simulation and Bayesian belief networks, make it possible to give a probabilistic interpretation of “at risk”. Combined with information on abatement costs, a cost-effective ranking of measures based on expected costs and effect can be undertaken. Combined with economic valuation of water quality, the definition of “disproportionate cost” of abatement measures compared to benefits of achieving “good status” can also be given a probabilistic interpretation. Explicit modelling of uncertainty helps visualize where research and consulting efforts are most critical for reducing uncertainty. Based on data from the Morsa catchment in South-Eastern Norway, this paper discusses the relative merits of using Bayesian belief networks when integrating biophysical modelling results in the benefit-cost analysis of derogations and cost-effectiveness ranking of abatement measures under the WFD.

scholarly journals Using Bayesian Belief Networks to assess the influence of landscape connectivity on ecosystem service trade-offs and synergies in urban landscapes in the UK

2021 ◽  
Author(s):  
James D. Karimi ◽  
Jim A. Harris ◽  
Ron Corstanje
Keyword(s):  
Functional Connectivity ◽  
Ecosystem Service ◽  
Urban Areas ◽  
Landscape Connectivity ◽  
Structural Characteristics ◽  
Bird Species ◽  
Bayesian Belief Networks ◽  
Belief Networks ◽  
Patch Area ◽  
Trade Offs

Abstract Context Landscape connectivity is assumed to influence ecosystem service (ES) trade-offs and synergies. However, empirical studies of the effect of landscape connectivity on ES trade-offs and synergies are limited, especially in urban areas where the interactions between patterns and processes are complex. Objectives The objectives of this study were to use a Bayesian Belief Network approach to (1) assess whether functional connectivity drives ES trade-offs and synergies in urban areas and (2) assess the influence of connectivity on the supply of ESs. Methods We used circuit theory to model urban bird flow of P. major and C. caeruleus at a 2 m spatial resolution in Bedford, Luton and Milton Keynes, UK, and Bayesian Belief Networks (BBNs) to assess the sensitivity of ES trade-offs and synergies model outputs to landscape and patch structural characteristics (patch area, connectivity and bird species abundance). Results We found that functional connectivity was the most influential variable in determining two of three ES trade-offs and synergies. Patch area and connectivity exerted a strong influence on ES trade-offs and synergies. Low patch area and low to moderately low connectivity were associated with high levels of ES trade-offs and synergies. Conclusions This study demonstrates that landscape connectivity is an influential determinant of ES trade-offs and synergies and supports the conviction that larger and better-connected habitat patches increase ES provision. A BBN approach is proposed as a feasible method of ES trade-off and synergy prediction in complex landscapes. Our findings can prove to be informative for urban ES management.

scholarly journals Changing Land Use and Population Density Are Degrading Water Quality in the Lower Mekong Basin

Water ◽  
2021 ◽  
Vol 13 (14) ◽  
pp. 1948
Author(s):  
Flavia Tromboni ◽  
Thomas E. Dilts ◽  
Sarah E. Null ◽  
Sapana Lohani ◽  
Peng Bun Ngor ◽  
...  
Keyword(s):  
Water Quality ◽  
Land Use ◽  
Population Density ◽  
Local Population ◽  
Reference Conditions ◽  
Mekong River ◽  
Land Use Impacts ◽  
Wet Season ◽  
Mekong Basin ◽  
Lower Mekong Basin

Establishing reference conditions in rivers is important to understand environmental change and protect ecosystem integrity. Ranked third globally for fish biodiversity, the Mekong River has the world’s largest inland fishery providing livelihoods, food security, and protein to the local population. It is therefore of paramount importance to maintain the water quality and biotic integrity of this ecosystem. We analyzed land use impacts on water quality constituents (TSS, TN, TP, DO, NO3−, NH4+, PO43−) in the Lower Mekong Basin. We then used a best-model regression approach with anthropogenic land-use as independent variables and water quality parameters as the dependent variables, to define reference conditions in the absence of human activities (corresponding to the intercept value). From 2000–2017, the population and the percentage of crop, rice, and plantation land cover increased, while there was a decrease in upland forest and flooded forest. Agriculture, urbanization, and population density were associated with decreasing water quality health in the Lower Mekong Basin. In several sites, Thailand and Laos had higher TN, NO3−, and NH4+ concentrations compared to reference conditions, while Cambodia had higher TP values than reference conditions, showing water quality degradation. TSS was higher than reference conditions in the dry season in Cambodia, but was lower than reference values in the wet season in Thailand and Laos. This study shows how deforestation from agriculture conversion and increasing urbanization pressure causes water quality decline in the Lower Mekong Basin, and provides a first characterization of reference water quality conditions for the Lower Mekong River and its tributaries.

Sign in / Sign up

Export Citation Format

Share Document