Unable to connect to database - 11:52:45 Unable to connect to database - 11:52:45 SQL Statement is null or not a SELECT - 11:52:45 SQL Statement is null or not a DELETE - 11:52:45 Botany & Mycology 2009 - Abstract Search
Unable to connect to database - 11:52:45 Unable to connect to database - 11:52:45 SQL Statement is null or not a SELECT - 11:52:46

Abstract Detail

Conservation Biology

Schultz, Joanna [1], Matthews, Jeffrey [2].

A chance-based methodology for predictive modeling of at-risk plant species using a cellular automata approach.

Predictive modeling of species distributions is an increasingly powerful tool in the conservation and management of special status plant species. Advances in quantitative methodologies have enabled more robust predictions of species occurrences, and serve as a tool to monitor short and long term population change. We used simulated and empirical data to test a chance of adequacy/probability of occurrence approach to predict species distributions. Data were obtained from publicly available Digital Elevation Models (DEM), regional climate databases and field collection. A multi-element chance-based methodology of predictive modeling was applied to terrain and climate data using a grid based cellular automata approach. Terrain features (slope, aspect, elevation, curvature and roughness) and climatic elements (temperature and precipitation) were given chance of adequacy (COA) values from a simple classification matrix ranging from zero to one, where zero indicated the element had no chance of adequacy and one was fully adequate. GIS was used to examine input values on a cell-by-cell basis and assign values (0 to 1) based on pre-defined chance matrices. The chance values were subsequently multiplied to produce an aggregate chance of adequacy value for each grid cell in the model. The result was a COA surface that was examined using traditional visualization and analysis techniques. An optional aggregation/smoothing function was subsequently applied to reduce high frequency variation. Finally, variation in climate parameters (change modeling) was introduced to predict the consequences of climate change on the future viability of at-risk plant species.

Log in to add this item to your schedule

1 - Earth Information Systems, PO Box 174, Troy, ID, 83871, USA
2 - ExxonMobil Exploration Company, Global Studies, 233 Benmar, Houston, TX, 77060, USA

Plant conservation
Spatial predictive modeling
Cellular Automata

Presentation Type: Oral Paper:Papers for Topics
Session: 58
Location: Wasatch A/Cliff Lodge - Level C
Date: Wednesday, July 29th, 2009
Time: 9:30 AM
Number: 58007
Abstract ID:914