Title: Conducts Analyses Informing Ecosystem Restoration Decisions
Version: 2.0.0
Description: Three sets of data and functions for informing ecosystem restoration decisions, particularly in the context of the U.S. Army Corps of Engineers. First, model parameters are compiled as a data set and associated metadata for over 300 habitat suitability models developed by the U.S. Fish and Wildlife Service (USFWS 1980, https://www.fws.gov/policy-library/870fw1). Second, functions for conducting habitat suitability analyses both for the models described above as well as generic user-specified model parameterizations. Third, a suite of decision support tools for conducting cost-effectiveness and incremental cost analyses (Robinson et al. 1995, IWR Report 95-R-1, U.S. Army Corps of Engineers).
Depends: R (≥ 3.5.0)
License: CC0
Encoding: UTF-8
LazyData: true
Imports: viridis, stats, graphics, grDevices
RoxygenNote: 7.3.2
NeedsCompilation: no
Packaged: 2024-09-12 19:39:51 UTC; u4eevskm
Author: S. Kyle McKay ORCID iD [aut, cre], Darixa D. Hernandez-Abrams [aut], Kiara C. Cushway [aut]
Maintainer: S. Kyle McKay <kyle.mckay@usace.army.mil>
Repository: CRAN
Date/Publication: 2024-09-12 21:50:02 UTC

Identifies "best buy" actions

Description

BBfinder this analysis examines the slope of the cost-effectiveness frontier to isolate how unit cost (cost/benefit) increases with increasing environmental benefit. Restoration actions with the lowest slope of unit cost are considered "best buys".

Usage

BBfinder(benefit, cost, CE)

Arguments

benefit

a vector of restoration benefits. Typically, these are time-averaged ecological outcomes (e.g., average annual habitat units). Often project benefits are best presented as the "lift" associated with a restoration action (i.e., the benefits of an alternative minus the benefits of a "no action" plan).

cost

a vector of restoration costs. Typically, these are monetary costs associated with a given restoration action such as project first cost or annualized economic cost. Notably, these functions are agnostic to units, so costs could also be non-monetary such as lost political capital or social costs of each alternative.

CE

numeric vector of 0's and 1's indicating whether a plan is cost-effective (1) or non-cost-effective (0). Can be derived from ecorest::CEfinder.

Value

A list with summaries of all restoration actions as well as best buy plans only.

References

Robinson R., Hansen W., and Orth K. 1995. Evaluation of environmental investments procedures manual interim: Cost effectiveness and incremental cost analyses. IWR Report 95-R-1. Institute for Water Resources, U.S. Army Corps of Engineers, Alexandria, Virginia

Examples

#Identify cost-effective actions based on random vectors of benefits and costs
benefit <- runif(50,min=0,max=10)
cost <- runif(50, min=0,max=1000)
CE <- CEfinder(benefit, cost)
BBfinder(benefit, cost, CE)

#Identify cost-effective actions based on a small number of user-specified benefits and costs
restben <- c(0, 10, 5, 20, 20)
restcost <- c(0, 100, 100, 200, 150)
restCE <- CEfinder(restben, restcost)
BBfinder(restben, restcost, restCE)

Plots cost-effectiveness and incremental cost analysis

Description

CEICAplotter Plots Cost-effective Incremental Cost Analysis (CEICA) in *.jpeg format.

Usage

CEICAplotter(altnames, benefit, cost, CE, BB, figure.name)

Arguments

altnames

vector of numerics or characters as unique restoration action identifiers.

benefit

a vector of restoration benefits. Typically, these are time-averaged ecological outcomes (e.g., average annual habitat units). Often project benefits are best presented as the "lift" associated with a restoration action (i.e., the benefits of an alternative minus the benefits of a "no action" plan).

cost

a vector of restoration costs. Typically, these are monetary costs associated with a given restoration action such as project first cost or annualized economic cost. Notably, these functions are agnostic to units, so costs could also be non-monetary such as lost political capital or social costs of each alternative.

CE

numeric vector of 0's and 1's indicating whether a plan is cost-effective (1) or non-cost-effective (0). Can be derived from ecorest::CEfinder.

BB

numeric vector of 0's and 1's indicating whether a plan is a best buy (1) or not (0). Can be derived from ecorest::BBfinder.

figure.name

output figure file name structured as "filename.jpeg".

Value

A multi-panel *.jpeg figure summarizing cost-effectiveness and incremental cost analyses.

References

Robinson R., Hansen W., and Orth K. 1995. Evaluation of environmental investments procedures manual interim: Cost effectiveness and incremental cost analyses. IWR Report 95-R-1. Institute for Water Resources, U.S. Army Corps of Engineers, Alexandria, Virginia

Examples


#Identify cost-effective actions based on random vectors of benefits and costs
altnames<- paste("Alt",seq(1,50), sep="")
benefit <- runif(50,min=0,max=10)
cost <- runif(50, min=0,max=1000)
CE <- CEfinder(benefit, cost)
BB <- BBfinder(benefit, cost, CE)[[1]][,4]
CEICAplotter(altnames, benefit, cost, CE, BB, tempfile("CEICAexample",fileext=".jpeg"))

Finds cost-effective frontier

Description

CEfinder returns cost-effectiveness analysis for a particular set of alternatives.

Usage

CEfinder(benefit, cost)

Arguments

benefit

a vector of restoration benefits. Typically, these are time-averaged ecological outcomes (e.g., average annual habitat units). Often project benefits are best presented as the "lift" associated with a restoration action (i.e., the benefits of an alternative minus the benefits of a "no action" plan).

cost

a vector of restoration costs. Typically, these are monetary costs associated with a given restoration action such as project first cost or annualized economic cost. Notably, these functions are agnostic to units, so costs could also be non-monetary such as lost political capital or social costs of each alternative.

Value

A numeric vector identifying each plan as cost-effective (1) or non-cost-effective (0). The cost-effective actions comprise the Pareto frontier of non-dominated alternatives at a given level of cost or benefit.

References

Robinson R., Hansen W., and Orth K. 1995. Evaluation of environmental investments procedures manual interim: Cost effectiveness and incremental cost analyses. IWR Report 95-R-1. Institute for Water Resources, U.S. Army Corps of Engineers, Alexandria, Virginia

Examples

#Identify cost-effective actions based on random vectors of benefits and costs
CEfinder(runif(50,min=0,max=10), runif(50, min=0,max=1000))

#Identify cost-effective actions based on a small number of user-specified benefits and costs
restben <- c(0, 10, 5, 20, 20)
restcost <- c(0, 100, 100, 200, 150)
CEfinder(restben, restcost)

Computes Habitat Suitability Index with Arithmetic Mean

Description

HSIarimean uses arithmetic mean to combine suitability indices into an overarching habitat suitability index.

Usage

HSIarimean(x)

Arguments

x

a vector of suitability indices.

Value

A value of habitat quality from 0 to 1 ignoring NA values.

References

US Fish and Wildlife Service. (1980). Habitat as a basis for environmental assessment. Ecological Services Manual, 101.

US Fish and Wildlife Service. (1980). Habitat Evaluation Procedures (HEP). Ecological Services Manual, 102.

US Fish and Wildlife Service. (1981). Standards for the Development of Habitat Suitability Index Models. Ecological Services Manual, 103.

Examples

#Determine patch quality based on a vector of four suitability indices.
HSIarimean(c(0.25, 0.25, 0.25, 0.25))

#Determine patch quality based on a vector of suitability indices with an NA.
HSIarimean(c(0.25, 0.25, NA, 0.25))

#Demonstrate error message associated with out of range outcomes.
HSIarimean(c(0.25, 0.25, 10.00, 0.25))

Computes Habitat Suitability Index based on Model-Specified Equation

Description

HSIeqtn computes a habitat suitability index based on equations specified in U.S. Fish and Wildlife Service habitat suitability models contained within ecorest via HSImodels and HSImetadata. Habitat suitability indices represent an overall assessment of habitat quality from combining individual suitability indices for multiple independent variables. The function computes an overall habitat suitability index.

Usage

HSIeqtn(HSImodelname, SIV, HSImetadata, exclude = NULL)

Arguments

HSImodelname

a character string in quotations that must match an existing model name in HSImetadata.

SIV

a vector of suitability index values used in the model specified in HSImodelname.

HSImetadata

a data frame of HSI model metadata within the ecorest package.

exclude

a list of character strings specifying components to be excluded from calculations.

Value

A numeric of the habitat suitability index ranging from 0 to 1.

References

US Fish and Wildlife Service. (1980). Habitat as a basis for environmental assessment. Ecological Services Manual, 101.

US Fish and Wildlife Service. (1980). Habitat Evaluation Procedures (HEP). Ecological Services Manual, 102.

US Fish and Wildlife Service. (1981). Standards for the Development of Habitat Suitability Index Models. Ecological Services Manual, 103.

Examples


#Compute patch quality for the Barred Owl model (no components)
#Allen A.W. 1982. Habitat Suitability Index Models: Barred owl. FWS/OBS 82/10.143.
#U.S. Fish and Wildlife Service. https://pubs.er.usgs.gov/publication/fwsobs82_10_143.
#Suitability indices relate to density of large trees, mean diameter of overstory trees,
#and percent canopy cover of overstory.
#Example suitability vectors
HSIeqtn("barredowl", c(1,1,1), HSImetadata) #c(1,1,1) should result in 1.00
HSIeqtn("barredowl", c(0.5,1,1), HSImetadata) #c(0.5,1,1) should result in 0.707
HSIeqtn("barredowl", c(0,1,1), HSImetadata) #c(0,1,1) should result in 0.00
HSIeqtn("barredowl", c(0,NA,1), HSImetadata) #c(0,NA,1) should return error message
HSIeqtn("barredowl", c(NA,1,1,1), HSImetadata) #c(NA,1,1,1) should return error message

#Compute patch quality for the Juvenile Alewife model (two components)
#Pardue, G.B. 1983. Habitat Suitability index models: alewife and blueback herring.
#U.S. Dept. Int. Fish Wildl. Serv. FWS/OBS-82/10.58. 22pp.
#Suitability indices relate to zooplankton density, salinity, and water temperature
#Example suitability vectors are c(1,1,1), c(0.5,1,1), and c(0,1,1)
HSIeqtn("alewifeJuv", c(1,1,1), HSImetadata) #c(1,1,1) should result in 1.00
HSIeqtn("alewifeJuv", c(0.5,1,1), HSImetadata) #c(0.5,1,1) should result in 0.50
HSIeqtn("alewifeJuv", c(0,1,1), HSImetadata) #c(0,1,1) should result in 0.00
HSIeqtn("alewifeJuv", c(1,NA,1), HSImetadata) #c(1,NA,1) returns error message
HSIeqtn("alewifeJuv", c(1,1,1,NA), HSImetadata) #c(1,1,1,NA) returns error message

#Compute patch quality for Cutthroat trout model for lacustrine habitats (7 components)
#with spawning and lacustrine habitat and with only lacustrine habitat (i.e., 
#embryo component is excluded).
#Hickman, T., and R.F. Raleigh. 1982. Habitat suitability index models: 
#Cutthroat trout. U.S.D.I. Fish and Wildlife Service. FWS/OBS-82/10.5. 38 pp.
#Suitability indices relate to temperature during the warmest period of the year,
#maximum temperature during embryo development, minimum dissolved oxygen during
#the late growing season, average velocity over spawning areas, average size 
#of substrate in spawning areas, annual maximal or minimal pH, and percent fines
#in the spawning area.
#Example suitability vectors are c(1,1,1,1,1,1,1), c(0.5,1,0.5,0,1,1,1) and c(1,NA,0.5,NA,NA,0.5,NA)
#c(1,1,1,1,1,1,1) should result in 1
HSIeqtn("cutthroatLacGenLtoe15C", c(1,1,1,1,1,1,1), HSImetadata) 
#c(0.5,1,0.5,0,1,1,1) should result in 0
HSIeqtn("cutthroatLacGenLtoe15C", c(0.5,1,0.5,0,1,1,1), HSImetadata) 
#c(1,NA,0.5,NA,NA,0.5,NA) should result in 0.63
HSIeqtn("cutthroatLacGenLtoe15C", c(1,NA,0.5,NA,NA,0.5,NA), HSImetadata, exclude=c("CE"))

Habitat Suitability Index with Geometric Mean

Description

HSIgeomen uses geometric mean to combine suitability indices into an overarching habitat suitability index.

Usage

HSIgeomean(x)

Arguments

x

a vector of suitability indices

Value

A value of habitat quality from 0 to 1 ignoring NA values.

References

US Fish and Wildlife Service. (1980). Habitat as a basis for environmental assessment. Ecological Services Manual, 101.

US Fish and Wildlife Service. (1980). Habitat Evaluation Procedures (HEP). Ecological Services Manual, 102.

US Fish and Wildlife Service. (1981). Standards for the Development of Habitat Suitability Index Models. Ecological Services Manual, 103.

Examples

#Determine patch quality based on a vector of four suitability indices.
HSIgeomean(c(0.25, 0.25, 0.25, 0.25))

#Determine patch quality based on a vector of suitability indices with an NA.
HSIgeomean(c(0.25, 0.25, NA, 0.25))

#Determine patch quality based on a vector of suitability indices with a zero-value.
HSIgeomean(c(0.25, 0.25, 0.0, 0.25))

#Demonstrate error message associated with out of range outcomes.
HSIgeomean(c(2, 2, NA, 3))

Habitat suitability index (HSI) model metadata

Description

Metadata for 351 U.S. Fish and Wildlife Service Habitat suitability index (HSI) models

Usage

HSImetadata

Format

A data frame with 351 rows and 85 variables:

model

Model name

submodel

Model specifications

species

Scientific nomenclature of modeled taxa

geography

Geographic range of organism

ecosystem

Type of habitat

documentation

Citation of original model

note

Conditions under which model may be applied

website

Link to individual model source

SIV1

Suitability index values for each organism specific condition

SIV1B

Suitability index values for each organism specific condition

SIV2

Suitability index values for each organism specific condition

SIV2B

Suitability index values for each organism specific condition

SIV3

Suitability index values for each organism specific condition

SIV3B

Suitability index values for each organism specific condition

SIV4

Suitability index values for each organism specific condition

SIV4B

Suitability index values for each organism specific condition

SIV5

Suitability index values for each organism specific condition

SIV5B

Suitability index values for each organism specific condition

SIV6

Suitability index values for each organism specific condition

SIV6B

Suitability index values for each organism specific condition

SIV7

Suitability index values for each organism specific condition

SIV7B

Suitability index values for each organism specific condition

SIV8

Suitability index values for each organism specific condition

SIV8B

Suitability index values for each organism specific condition

SIV9

Suitability index values for each organism specific condition

SIV10

Suitability index values for each organism specific condition

SIV11

Suitability index values for each organism specific condition

SIV12

Suitability index values for each organism specific condition

SIV13

Suitability index values for each organism specific condition

SIV14

Suitability index values for each organism specific condition

SIV15

Suitability index values for each organism specific condition

SIV15B

Suitability index values for each organism specific condition

SIV16

Suitability index values for each organism specific condition

SIV16B

Suitability index values for each organism specific condition

SIV17

Suitability index values for each organism specific condition

SIV18

Suitability index values for each organism specific condition

SIV19

Suitability index values for each organism specific condition

SIV20

Suitability index values for each organism specific condition

SIV21

Suitability index values for each organism specific condition

SIV22

Suitability index values for each organism specific condition

CF

Food component equation

CRF

Food/reproduction component equation

CRN

Roosting-nesting component equation

CC

Cover component equation

CCRO

Cover roosting component equation

CCRF

Cover-reproduction-food component equation

CCF

Cover-food component equation

CCSF

Cover-food shrub component equation

CCHF

Cover-food herbaceous component equation

CWF

Winter food component

CSF

Summer food component

CFF

Fall food component

CW

Water component

CCB

Cover breeding component

CB

Brood component

CN

Nest component

CNBC

Nest-brood cover component

CCN

Cover nesting component

CP

Pair habitat component

CWQ

Water quality component

CR

Reproduction component

CCR

Cover reproduction component

CD

Disturbance component

COT

Other component

CL

Larval component

CEL

Embryo and larval component

CE

Embryo component

CJ

Juvenile component

CFr

Fry component

CS

Spawning component

CA

Adult component

CI

Island component

CIN

Interspersion component

CNI

Non-island component

CWFC

Winter cover food component

CFBS

Summer food brood component

CFSWF

Fall spring winter food component

CSPF

Spring food component

CWC

Winter cover component

CCFS

Fall to spring cover component

CSS

Substrate-suspended solids component

CT

Topography component

CTe

Temperature component

CJA

Juvenile adult component

Eqtn

HSI overarching model equation in R syntax

Source

https://pubs.usgs.gov/

Habitat Suitability Index with Minimum

Description

HSImin uses the minimum of given suitability indices to calculate an overarching habitat suitability index.

Usage

HSImin(x)

Arguments

x

a vector of suitability indices

Value

A value of habitat quality from 0 to 1 ignoring NA values.

References

US Fish and Wildlife Service. (1980). Habitat as a basis for environmental assessment. Ecological Services Manual, 101.

US Fish and Wildlife Service. (1980). Habitat Evaluation Procedures (HEP). Ecological Services Manual, 102.

US Fish and Wildlife Service. (1981). Standards for the Development of Habitat Suitability Index Models. Ecological Services Manual, 103.

Examples

#Determine patch quality based on a vector of four suitability indices.
HSImin(c(0.1, 0.25, 0.25, 0.25))

#Determine patch quality based on a vector of suitability indices with an NA.
HSImin(c(0.1, 0.25, NA, 0.25))

#Demonstrate error message associated with out of range outcomes.
HSImin(c(2, 4, NA, 3))

Habitat suitability index (HSI) models

Description

This list of data frames contains 351 U.S. Fish and Wildlife Service Habitat suitability index (HSI) models. Please note that some of the original HSI documents provide little reference data for constructing suitability curves; hence, some suitability curves are estimated using the authors' best judgement. Users should always cross-reference results with the original documentation.

Usage

HSImodels

Format

An object of class list of length 351.

Details

@format A list with 351 data frames each containing an HSI model with multiple independent variables and associated habitat suitability indices (a 0 to 1 value). Data represent break points in curves with linear extrapolation between. Categorical input variables are coded as letters.

variable1

independent variable for assessing habitat suitability

SIV1

suitability index value relative to variable1

...

additional variables and suitability indices

Source

https://pubs.usgs.gov/

Plots habitat suitability index curves

Description

HSIplotter plots all suitability curves.

Usage

HSIplotter(SI, figure.name)

Arguments

SI

matrix of suitability curves ordered as parameter breakpoints and associated suitability indices for each parameter with appropriate column names.

figure.name

output figure file name structured as "filename.jpeg".

Value

A multi-panel *.jpeg figure showing all suitability curves.

References

US Fish and Wildlife Service. (1980). Habitat as a basis for environmental assessment. Ecological Services Manual, 101.

US Fish and Wildlife Service. (1980). Habitat Evaluation Procedures (HEP). Ecological Services Manual, 102.

US Fish and Wildlife Service. (1981). Standards for the Development of Habitat Suitability Index Models. Ecological Services Manual, 103.

Examples

#Build and define a matrix of the Barred Owl suitability curves
#Allen A.W. 1982. Habitat Suitability Index Models: Barred owl. FWS/OBS 82/10.143.
#U.S. Fish and Wildlife Service. https://pubs.er.usgs.gov/publication/fwsobs82_10_143.
var1 <- cbind(c(0,2,4,NA), c(0.1,1,1,NA)) #Number of trees > 51cm diameter per 0.4 ha plot
var2 <- cbind(c(0,5,20,NA), c(0,0,1,NA)) #Mean diameter of overstory trees
var3 <- cbind(c(0,20,60,100), c(0,0,1,1)) #Percent canopy cover of overstory trees
barredowl <- cbind(var1, var2, var3)
colnames(barredowl)<- c("tree.num", "tree.num.SIV",
  "avg.dbh.in", "avg.dbh.SIV", "can.cov", "can.cov.SIV")

#Create suitability curve summary plot
HSIplotter(barredowl, tempfile("BarredOwl",fileext=".jpeg"))

Habitat Suitability Index with a Weighted Arithmetic Mean

Description

HSIwarimean uses a weighted arithmetic mean to combine suitability indices into an overarching habitat suitability index.

Usage

HSIwarimean(x, w)

Arguments

x

is a vector of suitability indices.

w

is a vector of weights (0 to 1 values that must sum to one).

Value

A value of habitat quality from 0 to 1 ignoring NA values.

References

US Fish and Wildlife Service. (1980). Habitat as a basis for environmental assessment. Ecological Services Manual, 101.

US Fish and Wildlife Service. (1980). Habitat Evaluation Procedures (HEP). Ecological Services Manual, 102.

US Fish and Wildlife Service. (1981). Standards for the Development of Habitat Suitability Index Models. Ecological Services Manual, 103.

Examples

#Determine patch quality based on a vector of four, equal-weight suitability indices.
HSIwarimean(c(1, 0, 0, 0), c(0.25, 0.25, 0.25, 0.25))

#Determine patch quality based on a vector of four, unequal-weight suitability indices.
HSIwarimean(c(1, 0, 0, 0), c(1, 0, 0, 0))

#Determine patch quality based on a vector of four, unequal-weight suitability indices.
HSIwarimean(c(1, 0, 0, 0), c(0, 1, 0, 0))

#Demonstrate error for mismataching inputs.
HSIwarimean(c(1, 0, 0, 0), c(0, 0, 0))

#Demonstrate error for incorrect weighting.
HSIwarimean(c(1, 0, 0, 0), c(1, 1, 0, 0))

#Demonstrate error for out of range output.
HSIwarimean(c(1, 1, 1, 10), c(0.2, 0.3, 0.3, 0.2))

Computes Habitat Quality, Quantity, and Units

Description

HUcalc computes habitat units given a set of suitability indices, a habitat suitability index equation, and habitat quantity.

Usage

HUcalc(SI.out, habitat.quantity, HSIfunc, ...)

Arguments

SI.out

is a vector of application-specific suitability indices, which can be produced from SIcalc.

habitat.quantity

is a numeric of habitat size associated with these suitability indices (i.e., length, area, or volume).

HSIfunc

is a function for combination of the suitability indices.

...

optional arguments to HSIfunc.

Value

A vector of habitat quality, habitat quantity, and index units (quantity times quality).

References

US Fish and Wildlife Service. (1980). Habitat as a basis for environmental assessment. Ecological Services Manual, 101.

US Fish and Wildlife Service. (1980). Habitat Evaluation Procedures (HEP). Ecological Services Manual, 102.

US Fish and Wildlife Service. (1981). Standards for the Development of Habitat Suitability Index Models. Ecological Services Manual, 103.

Examples

#Summarize habitat outcomes based on a vector of two suitability indices
#using multiple combination equations.
HUcalc(c(0.1,1), 100, HSIarimean)
HUcalc(c(0.1,1), 100, HSIgeomean)
HUcalc(c(0.1,1), 100, HSImin)
HUcalc(c(0.1,1), 100, HSIwarimean, c(1,0))
HUcalc(c(0.1,1), 100, HSIwarimean, c(0,1))

#HSIfunc can also represent functions outside of the ecorest package
HUcalc(c(0.1,1), 100, mean)
HUcalc(c(0.1,1), 100, max)

Computes Suitability Indices

Description

SIcalc computes suitability indices given a set of suitability curves and project-specific inputs. Suitability indices may be computed based on either linear interpolation (for continuous variables) or a lookup method (for categorical variables).

Usage

SIcalc(SI, input.proj)

Arguments

SI

matrix of suitability curves ordered as parameter breakpoints and associated suitability indices for each parameter. Note that users should enter NA for excluded variables in HSImodels.

input.proj

numeric or categorical vector of application-specific input parameters associated with the suitability curve data from SI.

Value

A vector of the suitability index values that match given user inputs. Values are returned as equal to the extreme of a range if inputs are outside of model range.

References

US Fish and Wildlife Service. (1980). Habitat as a basis for environmental assessment. Ecological Services Manual, 101.

US Fish and Wildlife Service. (1980). Habitat Evaluation Procedures (HEP). Ecological Services Manual, 102.

US Fish and Wildlife Service. (1981). Standards for the Development of Habitat Suitability Index Models. Ecological Services Manual, 103.

Examples

#Build and define a matrix of the Barred Owl suitability curves
#Allen A.W. 1982. Habitat Suitability Index Models: Barred owl. FWS/OBS 82/10.143.
#U.S. Fish and Wildlife Service. https://pubs.er.usgs.gov/publication/fwsobs82_10_143.
var1 <- cbind(c(0,2,4,NA), c(0.1,1,1,NA)) #Number of trees > 51cm diameter per 0.4 ha plot
var2 <- cbind(c(0,5,20,NA), c(0,0,1,NA)) #Mean diameter of overstory trees
var3 <- cbind(c(0,20,60,100), c(0,0,1,1)) #Percent canopy cover of overstory trees
barredowl <- cbind(var1, var2, var3)
colnames(barredowl)<- c("tree.num", "tree.num.SIV",
  "avg.dbh.in", "avg.dbh.SIV", "can.cov", "can.cov.SIV")

#Set user input variables that should return (1, 0, 0)
input.demo1 <- c(2, 5, 20)
SIcalc(barredowl, input.demo1)

#Set user input variables that should return (1, 1, 1)
input.demo2 <- c(4, 20, 60)
SIcalc(barredowl, input.demo2)

#Set user input variables that should return (1, 1, 0.5)
input.demo3 <- c(4, 20, 40)
SIcalc(barredowl, input.demo3)

#Set user input variables that should return (0.1, 0.5, 0.5)
input.demo4 <- c(0, 12.5, 40)
SIcalc(barredowl, input.demo4)

#Set user input variables that should return (1, 1, 1)
input.demo5 <- c(4, 40, 60)
SIcalc(barredowl, input.demo5)

#Set user input variables that should return (1, NA, 1)
input.demo6 <- c(4, NA, 60)
SIcalc(barredowl, input.demo6)

#Suitability curves may also be drawn from HSImodels (data within ecorest)
#Import Barred Owl suitability curves with HSImodels$barredowl
#The input examples are repeated from above

#Set user input variables that should return (1, 0, 0)
SIcalc(HSImodels$barredowl, input.demo1)

#Set user input variables that should return (1, 1, 1)
SIcalc(HSImodels$barredowl, input.demo2)

#Set user input variables that should return (1, 1, 0.5)
SIcalc(HSImodels$barredowl, input.demo3)

#Set user input variables that should return (0.1, 0.5, 0.5)
SIcalc(HSImodels$barredowl, input.demo4)

#Set user input variables that should return (1, 1, 1)
SIcalc(HSImodels$barredowl, input.demo5)

#Set user input variables that should return (1, NA, 1)
SIcalc(HSImodels$barredowl, input.demo6)

Time-averaged restoration project outcomes

Description

annualizer computes time-averaged quantities based on linear interpolation.

Usage

annualizer(timevec, benefits)

Arguments

timevec

numeric vector of time intervals.

benefits

numeric vector of values to be interpolated.

Value

A time-averaged value over the specified time horizon.

References

Robinson R., Hansen W., and Orth K. 1995. Evaluation of environmental investments procedures manual interim: Cost effectiveness and incremental cost analyses. IWR Report 95-R-1. Institute for Water Resources, U.S. Army Corps of Engineers, Alexandria, Virginia.

Examples

#Constant value through time
annualizer(c(0,50), c(100,100))
annualizer(seq(0,50), rep(100,51))

#Simple time series
annualizer(seq(0,50), seq(0,50))

#User-specified time intervals
demo.timevec <- c(0,2,20,50)
demo.ben <- c(0,100,90,80)
annualizer(demo.timevec, demo.ben)