CONTROL ID: 1805444

TITLE: Empirical Mining of Large Data Sets Already Helps to Solve Practical Ecological Problems; A Panoply of Working Examples (Invited)

AUTHORS (FIRST NAME, LAST NAME): William Walter Hargrove¹, Forrest M. Hoffman³, Jitendra Kumar³, Joseph Spruce², Steve Norman¹

INSTITUTIONS (ALL): 1. Eastern Threat Center, USDA Forest Service, Asheville, NC, United States.
2. Computer Sciences Corporation, NASA Stennis Space Center, Stennis, MS, United States.
3. Oak Ridge National Laboratory, Oak Ridge, TN, United States.

ABSTRACT BODY: Here we present diverse examples where empirical mining and statistical analysis of large data sets have already been shown to be useful for a wide variety of practical decision-making problems within the realm of large-scale ecology. Because a full understanding and appreciation of particular ecological phenomena are possible only after hypothesis-directed research regarding the existence and nature of that process, some ecologists may feel that purely empirical data harvesting may represent a less-than-satisfactory approach. Restricting ourselves exclusively to process-driven approaches, however, may actually slow progress, particularly for more complex or subtle ecological processes. We may not be able to afford the delays caused by such directed approaches.

Rather than attempting to formulate and ask every relevant question correctly, empirical methods allow trends, relationships and associations to emerge freely from the data themselves, unencumbered by a priori theories, ideas and prejudices that have been imposed upon them. Although they cannot directly demonstrate causality, empirical methods can be extremely efficient at uncovering strong correlations with intermediate "linking" variables. In practice, these correlative structures and linking variables, once identified, may provide sufficient predictive power to be useful themselves. Such correlation "shadows" of causation can be harnessed by, e.g., Bayesian Belief Nets, which bias ecological management decisions, made with incomplete information, toward favorable outcomes. Empirical data-harvesting also generates a myriad of testable hypotheses regarding processes, some of which may even be correct.

Quantitative statistical regionalizations based on quantitative multivariate similarity have lended insights into carbon eddy-flux direction and magnitude, wildfire biophysical conditions, phenological ecoregions useful for vegetation type mapping and monitoring, forest disease risk maps (e.g., sudden oak death), global aquatic ecoregion risk maps for aquatic invasives, and forest vertical structure ecoregions (e.g., using extensive LiDAR data sets). Multivariate Spatio-Temporal Clustering, which quantitatively places alternative future conditions on a common footing with present conditions, allows prediction of present and future shifts in tree species ranges, given alternative climatic change forecasts.

ForWarn, a forest disturbance detection and monitoring system mining 12 years of national 8-day MODIS phenology data, has been operating since 2010, producing national maps every 8 days showing many kinds of potential forest disturbances. Forest resource managers can view disturbance maps via a web-based viewer, and alerts are issued when particular forest disturbances are seen. Regression-based decadal trend analysis showing long-term forest thrive and decline areas, and individual-based, brute-force supercomputing to map potential movement corridors and migration routes across landscapes will also be discussed. As significant ecological changes occur with increasing rapidity, such empirical data-mining approaches may be the most efficient means to help land managers find the best, most-actionable policies and decision strategies.

http://www.geobabble.org/phenoregions

INDEX TERMS: 1914 INFORMATICS Data mining, 1916 INFORMATICS Data and information discovery, 0439 BIOGEOSCIENCES Ecosystems, structure and dynamics , 1637 GLOBAL CHANGE Regional climate change.
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Additional Details

Previously Presented Material: none

Contact Details

CONTACT (NAME ONLY): William Hargrove
CONTACT (E-MAIL ONLY): hnw@geobabble.org
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