A Spatially Explicit Estimate of Avoided Forest Loss

2011 ◽  
Vol 25 (5) ◽  
pp. 1032-1043 ◽  
Author(s):  
JORDI HONEY-ROSÉS ◽  
KATHY BAYLIS ◽  
M. ISABEL RAMÍREZ
Keyword(s):  
Forest Loss ◽  
Spatially Explicit ◽  
Explicit Estimate

scholarly journals Using a Coproduction Approach to Map Future Forest Retention Likelihood in the Southeastern United States

2019 ◽  
Vol 118 (1) ◽  
pp. 28-43
Author(s):  
Rachel E Greene ◽  
Kristine O Evans ◽  
Michael T Gray ◽  
D Todd Jones-Farrand ◽  
William G Wathen
Keyword(s):  
United States ◽  
High Risk ◽  
Southeastern United States ◽  
Southern United States ◽  
Forest Loss ◽  
Spatially Explicit ◽  
High Likelihood ◽  
Land Use Conversion ◽  
Southern States ◽  
Very High

Abstract Forestlands in the Southern United States provide important ecological and socioeconomic services that are under increasing pressure from development and other stressors. We used a coproduction approach with 50+ stakeholders to create a qualitative, spatially explicit Forest Retention Index to provide a gradient of future forest retention likelihood on presently forested lands. An estimated 17.7 million acres are at high risk of forest loss by 2060. These losses are largely driven by urbanization, but sea-level rise plays a key role in some coastal areas. Approximately 59 percent of southern forest is projected to be retained with High or Very High likelihood but is unevenly distributed among southern states. Approximately 8 percent of highly biodiverse forest is at high risk of land-use conversion. This tool provides a collaborative, transparent, and defensible mapping product that can aid in identification of key areas where retaining forest is critical to maintaining ecological and socioeconomic integrity.

A spatially explicit estimate of the prewhaling abundance of the endangered North Atlantic right whale

2016 ◽  
Vol 30 (4) ◽  
pp. 783-791 ◽  
Author(s):  
Sophie Monsarrat ◽  
M. Grazia Pennino ◽  
Tim D. Smith ◽  
Randall R. Reeves ◽  
Christine N. Meynard ◽  
...  
Keyword(s):  
North Atlantic ◽  
Spatially Explicit ◽  
Explicit Estimate ◽  
North Atlantic Right Whale ◽  
Right Whale

Global deforestation patterns: comparing recent and past forest loss processes through a spatially explicit analysis

2017 ◽  
Vol 19 (3) ◽  
pp. 350-368 ◽  
Author(s):  
M. Sandker ◽  
Y. Finegold ◽  
R. D'Annunzio ◽  
E. Lindquist
Keyword(s):  
Forest Loss ◽  
Spatially Explicit ◽  
Explicit Analysis

Spatially explicit models to analyze forest loss and fragmentation between 1976 and 2020 in southern Chile

2008 ◽  
Vol 212 (3-4) ◽  
pp. 439-449 ◽  
Author(s):  
Cristian Echeverria ◽  
David A. Coomes ◽  
Myrna Hall ◽  
Adrian C. Newton
Keyword(s):  
Forest Loss ◽  
Spatially Explicit ◽  
Southern Chile ◽  
Spatially Explicit Models

Potential Forest Loss and Biodiversity Risks from Road Improvement in Lao PDR

2018 ◽  
Author(s):  
Stephen Danyo ◽  
Susmita Dasgupta ◽  
David Wheeler
Keyword(s):  
Lao Pdr ◽  
Forest Loss

Agent-Based Computational Model

2017 ◽  
Author(s):  
Joshua M. Epstein
Keyword(s):  
Computational Model ◽  
Bounded Rationality ◽  
Time Scales ◽  
Spatially Explicit ◽  
Agent Based ◽  
Affective Component ◽  
Elementary Type ◽  
Social Component ◽  
Different Time Scales

This part describes the agent-based and computational model for Agent_Zero and demonstrates its capacity for generative minimalism. It first explains the replicability of the model before offering an interpretation of the model by imagining a guerilla war like Vietnam, Afghanistan, or Iraq, where events transpire on a 2-D population of contiguous yellow patches. Each patch is occupied by a single stationary indigenous agent, which has two possible states: inactive and active. The discussion then turns to Agent_Zero's affective component and an elementary type of bounded rationality, as well as its social component, with particular emphasis on disposition, action, and pseudocode. Computational parables are then presented, including a parable relating to the slaughter of innocents through dispositional contagion. This part also shows how the model can capture three spatially explicit examples in which affect and probability change on different time scales.

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