nuisancegrowth: nuisancegrowth Bayesian Network

In bnRep: A Repository of Bayesian Networks from the Academic Literature

nuisancegrowth Bayesian Network

Description

Drivers of perceived nuisance growth by aquatic plants.

Format

A discrete Bayesian network approach to integrate the perception of nuisance with the consequences of plant removal. Probabilities were given within the referenced paper (missing entries were given uniform probabilities). The vertices are:

Activity

(Swimming, Boating, Angling, Biodiversity, Aesthetics, Bird-watching);

BenthicFishForaging

(Low, Moderate, High);

Disturbance

(Low, Moderate, High);

Ecosystem

(Standing floating, Standing submerged, Flowing submerged);

EpiphyticInvertebrates

(Low, Medium, High);

Flow

(Low, Medium, High);

Light

(Low, High);

MacrophyteGrowth

(Very low, Low, Medium, High, Very high);

MacrophyteRemoval

(None, Partial Full);

MacrophyteSpecies

(Elodea nuttallii, Pontederia crassipes, Ludwigia, Juncus bulbosus, Sagittaria sagittifolia);

NutrientLoading

(Low, Moderate, High);

Perception

(Nuisance, No nuisance);

Phytoplankton

(Low, Moderate, High);

PiscivorousFish

(Absent, Present);

PiscivorousFishPredation

(Low, High);

PlanktivorousFish

(Low, High);

Resources

(Low, Moderate, High);

RespondentType

(Resident, Visitor);

Zooplankton

(Low, Moderate, High);

Value

An object of class bn.fit. Refer to the documentation of bnlearn for details.

References

Thiemer, K., Immerzeel, B., Schneider, S., Sebola, K., Coetzee, J., Baldo, M., ... & Vermaat, J. E. (2023). Drivers of perceived nuisance growth by aquatic plants. Environmental Management, 71(5), 1024-1036.

bnRep documentation built on April 12, 2025, 1:13 a.m.