macroeco.models.mete_sar_iterative

macroeco.models.mete_sar_iterative = <macroeco.models._curves.mete_sar_gen object at 0x10885c210>

A SAR/EAR predicted by the Maximum Entropy Theory of Ecology

The METE SAR/EAR is a special case of the general sampling sar when \(k_{SAD} = 0\) and \(k_{SSAD} = 1\) described in Harte et al. (2009) [1]. See the documentation for sampling_sar for more information.

The METE SAR and EAR may be used either for downscaling, when values of A are less than A0, or upscaling, when values of A are greater than A0. Downscaling creates the traditional SAR known to ecologists, while upscaling is useful for estimating large-scale species richness from small- scale plot data.

Parameters:

x : iterable

Areas at which to calculate SAR (first element is A0)

S0 : float

Species richness at A0

N0 : float

Community abundance at A0

approx : bool (opt)

Approximate the truncated logseries. Default True. The approximation is much faster and not very different than the exact answer for most cases.

References

[1]	Harte, J., Smith, A. B., & Storch, D. (2009). Biodiversity scales from plots to biomes with a universal species-area curve. Ecology Letters, 12(8), 789-797.

Examples

>>> # For a base area of A = 10, downscale species richness using the
>>> # METE SAR
>>> import macroeco.models as md

>>> # Set the areas at which to downscale species richness
>>> areas = [10, 8, 5, 3, 0.5]

>>> # Set the species richness and abundance at the base scale
>>> S0 = 50
>>> N0 = 4356

>>> # Standard METE SAR
>>> md.mete_sar.vals(areas, S0, N0, approx=True)
array([ 50.        ,  47.2541949 ,  42.16880014,  37.32787098,  22.94395771])

>>> # Iterative METE SAR
>>> md.mete_sar_iterative.vals(areas, S0, N0, approx=True)
array([ 50.        ,  42.16880014,  42.16880014,  34.89587044,  16.97747262])

>>> # METE EAR
>>> md.mete_ear.vals(areas, S0, N0, approx=True)
Out[12]: array([ 50.        ,  24.4391921 ,   7.83119986,   3.3844614 ,   0.41615551])

Methods

vals(x, S0, N0, iterative=False)

Calculate SAR given starting values and two models. See notes.