best linear unbiased prediction
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CitationRodríguez Iglesias, R. M., & Kothmann, M. M. (1998). Best linear unbiased prediction of herbivore preferences. Journal of Range Management, 51(1), 19-28.
PublisherSociety for Range Management
JournalJournal of Range Management
AbstractGeneralized linear mixed models were used to obtain best linear unbiased predictions (BLUP's) of herbivore preferences for range plant species from expert knowledge contained in range site descriptions produced by the USDA Soil Conservation Service (currently Natural Resources Conservation Service). A total of 4,558 assessments of preference for cattle, deer, goats, and sheep on 167 plant species were available from 55 range site descriptions for the Edwards Plateau (Texas). Consistency of predicted preferences was evaluated through intraclass correlation estimated by restricted maximum likelihood. Predictions in observed (3-level ordinal) and logit scales were very similar; rank correlations between predictions in different scales ranged from 0.994 (P < 0.0001) for cattle to 0.998 (P < 0.0001) for sheep. Estimated intraclass correlations were also high (0.74 to 0.84 in observed scale and 0.82 to 0.92 in logit scale) suggesting consistent rankings of plant species across range sites. Metric multidimensional scaling and principal components analysis showed distinct patterns among the 4 herbivores. Grasses and browse were the most informative forage classes for discriminating preferences among herbivores. Deer and cattle exhibited the least similar diet preferences. Sheep and goats were intermediate, with sheep closer to cattle and goats most similar to deer. The pair deer-goat showed the most similar pattern of preferences. BLUP's of plant species preferences showed good agreement with published research on both individual plant species and forage classes. Optimal properties of mixed model procedures can be exploited to predict animal preferences at the range site scale from standardized expert opinion. These estimated preferences may be useful for modeling grazing effects at spatial scales compatible with management decisions.