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Field-measured variables included forest floor depth and mass, tree cluster (canopy overlap), distance to nearest tree, and resin available nutrients.
We carry out rigorous simulation experiments on the proposed algorithm and compare the results with two existing algorithms namely, tree cluster based data gathering algorithm (TCBDGA) and energy aware sink relocation (EASR).
Both the single tree and tree cluster models were statistically similar and a combined model to predict average stem DBH yielded R2 = 0.71 with a mean prediction error (average DBH per stem) of ±13 cm within the range of 0.28 0.84 m.
The projection of Structure assignation for K = 5 on the NJ tree was in excellent agreement with the clusters identified, including for those genotypes from enzymatic group 5 which had appeared in an unexpected NJ tree cluster.
Dominant species in the surrounding regeneration had an adult tree within a cluster, such as Celtis africana, Rapanea melanophloeos, Vepris lanceolata or Maytenus acuminata; Dominant species in a cluster was not dominant or was absent in the surrounding regeneration; Some species were present in the regeneration zone outside a cluster and were absent from the tree cluster.
The relatedness of the bacterial communities in the five dietary treatments was measured using a hierarchical tree cluster analysis on the proportion of individuals in each treatment possessing each bacterial tRF, where distances are Euclidean and complete linkages were used to determine relatedness [56].
Similar(39)
The main dependent parameter is the size of the maximal clique as generated by tree clustering.
We present a new tree clustering algorithm based on combinatorial statistics on trees.
A forest consists of multi-scale branches, tree crowns, and tree clusters.
One of the main approaches to performing computation in Bayesian networks (BNs) is clique tree clustering and propagation.
Those vast plains are covered with grasses and sedges, but tree clusters (mainly palms) also grow, especially along streams.
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