Sentence examples for species estimation from inspiring English sources

*BEAST uses a multilocus species estimation by the multispecies coalescent for estimating the species tree, and hence can incorporate multilocus data from multiple individuals.

In this study, approaches for leading species estimation based upon very high spatial resolution (pixel sided <1 m) have been developed and implemented, with opportunities for improving attribute accuracy using data fusion methods.

As far as we are aware, this is the first report that compares the GCPSR method to other newly developed tree species estimation methods.

Using incident-based methods of species estimation, we extrapolate there to be approximately 940 fish species in Bootless Bay, one of the lowest reported numbers in Papua New Guinea.

Consequently, assumed errors for species estimations with a few sampled nests could be relatively higher than that expected from the number of nestlings sampled.

Another important consideration with species-tree estimation is that species are defined a priori and the coalescent model assumes species are monophyletic.

An alternative approach for coalescent-based species tree estimation has been proposed that avoids estimating trees on each locus, and hence bypasses the issue of gene tree estimation error.

Thus, SVDquartets+PAUP* (the implementation of SVDquartets within PAUP*) is a species tree estimation method that could, conceivably, provide much improved species tree estimation accuracy compared to standard summary methods and concatenation.

In particular, ASTRAL-2, a species tree estimation method that was initially designed to estimate species trees in the presence of ILS, had excellent accuracy and generally gave somewhat more accurate results than the other methods we explored.

Using bootstrap replicate gene trees instead of best ML gene trees did not improve species tree estimation accuracy on the simulated mixed mammalian dataset and in fact made species tree estimations less accurate for MRP, MP-EST and ASTRAL.

This paper deals with the state (chemical species concentration) estimation in stirred tank reactors when only reactor temperature is measured.