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Through this mode of dispersion, communities would tend to be at or near the surface instead of underground, even when migration to aquifers can occur.
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While these two measures are still often reported and analyzed, conservationists and community ecologist have become increasingly interested in quantifying the phylogenetic diversity and phylogenetic dispersion of communities [1] [12].
A cartoon illustrating the basic idea behind this result is given in Figure 2. Figure 2 Early dispersion across communities is predictive.
Despite this interest in quantifying the phylogenetic diversity and dispersion of communities, many methodological hurdles remain.
Specifically, it starts by quantifying the phylogenetic diversity and dispersion in communities using a fully resolved phylogeny.
In recent decades ecologists, evolutionists and conservationists have become increasingly interested in quantifying the phylogenetic diversity and phylogenetic dispersion of communities [1] [3], [6], [9], [11] [12], [21] [22].
The present study was designed to quantify the degree to which polytomies in phylogenetic trees influence measures of phylogenetic diversity and dispersion in communities.
Despite this interest, quantifying the phylogenetic diversity and dispersion of communities often necessitates utilizing phylogenetic supertrees that contain multiple unresolved nodes.
Lastly, the analyses indicate that researchers utilizing the metrics analyzed here are generally prone to underestimate the phylogenetic diversity and dispersion in communities when phylogenies are not completely resolved.
When possible, future investigations into the phylogenetic dispersion of communities using phylogenies containing polytomies should generate a distribution of possible results by randomly resolving the polytomies in the phylogeny [6].
Given the increasing interest in quantifying the phylogenetic diversity and dispersion in communities and the increasing use of phylogenetic supertrees to conduct such measurements, it is critical that we quantify the potential biases and the potential loss of statistical power introduced by this approach.
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