harvest_tolerance: Estimating wildlife sustainability

In NathanWhitmore/harvest: Visualising the Sustainability of a Wildlife Harvest

Estimating wildlife sustainability

Description

Returns a report on the sustainability of harvest using existing knowledge (real data, surrogates, and informed guestimates) using a simple Monte Carlo recombination technique. If safety margins or buffers are required these can be introduced by a threshold value in kilograms. The user can then see the likelihood of sustainability given the knowledge at hand, as well as the minimum intervention required to attain sustainability.

Usage

harvest_tolerance(seed, name, iterations, area, units, growth, density, biomass, offtake)

Arguments

seed	A number which acts as a random seed to allow replication of results
name	Text representing the name of the site
iterations	The number of resampling iterations to generate Monte Carlo estimates, good results are achieved around 100,000
area	The size of the study area
units	Text naming the units of area and density
threshold	User defined net productivity threshold for sustainabililty
growth	Samples representing the distribution of the finite rate of growth of the population
density	Samples representing the number of individuals within a unit area
biomass	Samples representing the biomass (weight) of individuals within the area
offtake	Samples representing the weight of individuals harvested from the area annually

Details

The left graphics panel depicts the distribution function of the gross productivity (grey: background = "no harvest" scenario) and net productivity (foreground: black / red = "harvest" scenario) grey distribution shown in the background of the left graphics panel represents the gross productivity (no harvest). The difference between the two graphical peaks represents the impact of the harvest. If net productivity is unsustainable (i.e. < 0 by default, or the value of a set threshold) those elements display as red.

The right graphics panel depicts the distribution of each parameter contributing to the net productivity standardised by dividing by the median (not mean, as some distributions may be non-linear). The distributions shown represent the intervals between the 1st and 99th percentiles. As a rule of thumb the measurement of offtake is the most important parameter (as it is the value which differentiates net productivity from gross producitivity). Thereafter the most variable (imprecise component) of gross productivity should be prioritised to improve the overall precision of estimate.

Value

Returns a summary report of productivity / sustianability inclusive of graphical depictions of gross and net productivity, and comparative variability of parameters. Sends a data.frame of results "Monte.Carlo" to global environment for further graphing or analysis.

Author(s)

Nathan Whitmore

Examples

harvest_tolerance(
  seed = 0,
  name = "Admiralty cuscus",
  site = "Welei",
  iterations = 100000,
  area = 21,
  units ="ha",
  threshold = 0,
  growth = rnorm(100000, mean=1.15, sd =0.03),
  density = runif(100000, min=3, max=10),
  biomass = rnorm(100000, mean=2.28, sd =0.39),
  offtake = rnorm(100000, mean=52, sd =6)
)

NathanWhitmore/harvest documentation built on Feb. 15, 2023, 2:28 p.m.