MacroAlgaeIndicator: Macroalgae indicators
In PMassicotte/makroalgaeindicators: MacroalgaeIndicators

Description Usage Arguments Value Examples

View source: R/MacroAlgaeIndicator_CumulativeCover.R

Macroalgae indicators

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MacroAlgaeIndicator(indicator = c("CumulativeCover", "PropOpportunist",
  "NPerennials"), df, boundaries, depth_cutoff, std_depth = 7,
  std_haardsub = 50, n_iter = 10000, Indicator_pred_pct = 0.5)

`indicator`	Name of the indicator to be calculated. Possible values are "CumulativeCover", "PropOpportunist", and "NPerennials"
`df`	A dataframe with monitoring data for macroalgae according to the Danish National Marine Monitoring Program. The dataframe should contain the following variables: kildestationnavn An identifier for the monitoring transect name. dato Date of the sampling. dybde Depth of the observation in meter. hardbund_daekpct Percentage cover of suitable substrate for macroalgae. totcover_daekpct Total cover of macroalgae relative to the suitable substrate. proevetager Identifier of the person carrying out the monitoring transect. steneck The Steneck number of the species. growth_strategy Either "P" for perennial, "O" for opportunist or "C" for crustforming algae.
`boundaries`	A vector of length 4 giving the status class boundary values of the standardized indicator. The boundary values should be given in the order high-good, good-moderate, moderate-poor, poor-bad.
`depth_cutoff`	The cut-off depth for physical exposure. Observations whith depths shallower than this value are not used in the indicator calculation.
`std_depth`	The depth for which the indication is calculated. Default is 7 meters.
`std_haardsub`	The suitable substrate percentage for which the indicator if calculated. Default it 0.5.
`n_iter`	Maximum number of iteration for Montecarlo simulation.
`Indicator_pred_pct`	Probability for predicting the percentile in the simulated distribution. Default is 0.5 for the median.

TODO

1 2	data("alsfjord_2013_2016") indicator <- MacroAlgaeIndicator("CumulativeCover", alsfjord_2013_2016, boundaries = c(74.4, 40.8, 20.4, 13.8), depth_cutoff = 1)