An adaptive composite density estimator for <Emphasis Type="Italic">k</Emphasis>-tree sampling |
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| Authors: | Steen Magnussen Lutz Fehrman William J Platt |
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| Institution: | 1.Natural Resources Canada,Canadian Forest Service,Victoria,Canada;2.Department of Forest Inventory and Remote Sensing, Faculty of Forest Sciences and Forest Ecology,Georg-August-Universit?t G?ttingen,G?ttingen,Germany;3.Department of Biological Sciences 202 LSB,Louisiana State University,Baton Rouge,USA |
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| Abstract: | Density estimators for k-tree distance sampling are sensitive to the amount of extra Poisson variance in distances to the kth tree. To lessen this sensitivity, we propose an adaptive composite estimator (COM). In simulated sampling from 16 test
populations, a three-component composite density estimator (COM)–with weights determined by a multinomial logistic function
of four readily available ancillary variables–was identified as superior in terms of average relative absolute bias. Results
from a different set of nine validation populations–with widely different stem densities and spatial patterns of tree locations—confirmed
that relative root mean squared errors (RRMSE) of COM were, on average, considerably lower than those obtained with the three-component
k-tree density estimators. The RRMSE performance of COM improved with increasing values of k. With k = 6 and sample sizes of 10, 20, and 30, the average relative bias of COM was between −5 and 5% in seven validation populations
but in an open low-density savanna-like population bias reached −12% (1979 data) and 7% (1996 data). For k = 6 and n = 10, the RRMSE of COM was, in six of the nine validation populations, within 3.3 percentage points of the RRMSE for sampling
with fixed-area plots. Jackknife estimates of the precision of COM estimates of density were negatively biased, leading to
under-coverage (7%) of computed 95% confidence intervals. |
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