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Validation of a conceptual hierarchical model with data in an oak woodland/annual grassland system: Figuring out where the parts fit.
Shlisky, Ayn1, Allen-Diaz, Barbara2, 1 2
ABSTRACT- This study used multiscale data to build ecological hierarchies for oak woodland/annual grassland ecosystems at the UC Sierra Foothill Research and Extension Center. We asked: can simplified hierarchical models practically describe constraints on community distributions? Do they hold true for multiple community scales? Digitized remote imagery, and 1999 data from 62 permanent, stratified-random 100-m transects were used in analyses. Data-driven predictive models for community and species distributions were devised using classification and regression trees (CART) for multiple community measurement scales. Models for Lolium multiflorum, Taeniatherum caput-medusae and Nassella pulchra abundance were compared at 5 scales, and models for TWINSPAN community types were compared at 3 scales of plant community measurement. Fine scale tree cover was primary in prediction of 10-m community types. Mid-scale aspect was primary otherwise. Mid-scale percent slope primarily predicted LOMU abundance at 10-, 35- and 45-m scales, while coarse slope position or slope class were primary at other scales. Fine or mid-scale tree cover primarily predicted TACA abundance at 10-, 45- and 100-m scales, while mid-scale percent slope and fine scale aspect were important at 35- and 55-m. At larger community scales, fine scale attributes outperformed coarser scale attributes in TACA abundance prediction. For all community measurement scales, fine or mid-scale aspect was the primary predictor of NAPU2 abundance. Scale dependencies in community-, and species-environment relationships complicates identification of a simple hierarchical model for these systems.
KEY WORDS: scale dependency, hierarchical models, oak woodland, classification and regression trees