pineforest

<p>A dataset containing two traits for species in a pine forest.
The variables are as follows:</p>

datasets

The objective of these functions is to derive a species assemblage
that satisfies a functional trait profile. Restoring resilient ecosystems
requires a flexible framework for selecting assemblages that are based on the
functional traits of species. However, current trait-based models have been
limited to algorithms that can only select species by optimising specific trait
values, and could not elegantly accommodate the common desire among restoration
ecologists to produce functionally diverse assemblages. We have solved this
problem by applying a non-linear optimisation algorithm that optimises Rao Q,
a closed-form functional trait diversity index that incorporates species
abundances, subject to other linear constraints. This framework generalises
previous models that only optimised the entropy of the community, and can
optimise both functional diversity and entropy simultaneously. This package
can also be used to generate experimental assemblages to test the effects
of community-level traits on community dynamics and ecosystem function. The
method is based on theory discussed in Laughlin (2014, Ecology Letters)
<doi.org/10.1111/ele.12288>.

Daniel Laughlin 

Select

Determines Species Probabilities Based on Functional Traits

Daniel Laughlin

Loic Chalmandrier

pineforest function

<p>dataframe of 46 species and 2 traits</p>

pineforest: Functional trait data for understory species in ponderosa pine forest

Description

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Details