Package 'FlyingR'

Sample 28 birds

Description

Preset birds data, extracted from Flight Pennycuick(2008). Fat mass percentage generated randomly where zero.

Usage

birds

Format

A data frame with 28 observations and 5 variables not counting the name.

Scientific.name

Name of bird species

Empty.mass

Body mass in Kg. Includes fuel (fat mass). In this case the crops were empty but otherwise one should always use the all-up mass (body mass + crop)

Wing.span

Length of wings spread out in meters

Fat.mass

Mass of fat that is consumable as fuel in Kg

Order

Order of the species (passerine = 1 vs non-passerine = 2)

Wing.area

Area of both wing projected on a flat surface in meters squared

Muscle.mass

Mass in Kg. of flight muscles

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Range Estimation

Description

Practical range estimation of birds using methods in Pennycuick (1975) Mechanics of Flight. These methods are based on Breguet equations.

Usage

flysim(file, header = TRUE, sep = ",", quote = "\"", dec = ".",
             fill = TRUE, comment.char = "", ..., data = NULL,
             settings = list())

Arguments

file	Arguments for path to data.
header	Logical. If TRUE use first row as column headers
sep	separator
quote	The set of quoting characters. see read.csv
dec	The character used in the file for decimal points.
fill	See read.csv
comment.char	For more details see read.csv
...	further arguments see read.csv
data	A data frame.
settings	A list for re-defining constants. See details.

Details

The option *settings takes the arguments (those particularly required by this function)

• ppc: Profile power constant

• fed: Energy content of fuel from fat

• g: Acceleration due to gravity

• mce: Mechanical conversion efficiency [0,1]

• ipf: Induced power factor

• vcp: Ventilation and circulation power

• airDensity: Air density at cruising altitude

• bdc: Body drag coefficient

• alpha: Basal metabolism factors in passerines and non passerines

• delta: Basal metabolism factors in passerines and non passerines alpha*bodyMass^delta

Value

S3 class object with range estimates based on methods defined and settings used

• range estimates (Km)

• settings used

• data

Author(s)

Brian Masinde

Examples

flysim(data = birds, settings = list(fatEnergy = 3.89*10^7))
flysim(data = birds,  settings = list(airDensity = 0.905))

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Range Estimation

Description

Practical range estimation of birds using methods from Pennycuick (2008).

Usage

migrate(file, header = TRUE, sep = ",", quote = "\"", dec = ".",
               fill = TRUE, comment.char = "", ...,
               data = NULL, settings = list(), method = "cmm",
               speed_control = 1, min_energy_protein = 0.05)

Arguments

file	Path to file where data resides.
header	Logical. If TRUE use first row as column headers
sep	separator
quote	The set of quoting characters. see read.csv
dec	The character used in the file for decimal points
fill	See read.csv
comment.char	For more details see read.csv
...	further arguments see read.csv
data	A data frame with required columns: body mass (Kg), fat mass (Kg), muscle mass (Kg), wing span (m), wing area (m^2), order / taxon (passerines = 1, non passerines = 2).
settings	A list for re-defining constants. See details for these with default values from Pennycuick(2008) and Pennycuick(1998).
method	Methods for protein energy consumption from muscle mass
speed_control	One of two speed control methods. By default 1 is used. 0 is the alternative. The former holds the true airspeed constant while the latter holds the ratio of true airspeed to the minimum power speed constant (V:Vmp constant).
min_energy_protein	Percentage of energy attributed to protein due to metabolism. Default value is 5 percent (0.05). If method "csw" or "csp" is chosen, 2 would be attained from consuming protein in the airframe mass.

Details

The option *control takes the following arguments

• ppc: Profile power constant (8.4).

• fed: Energy content of fuel from fat (3.9E+07).

• ped: Energy content of protein (1.8E+07).

• g: Acceleration due to gravity (9.81).

• mce: Mechanical conversion efficiency [0,1]. Efficiency at which mechanical power is converted to chemical power (0.23).

• ipf: Induced power factor (1.2).

• vcp: Ventilation and circulation power (1.1).

• airDensity: Air density at cruising altitude (1.00).

• bdc: Body drag coefficient (0.1).

• alpha: Basal metabolism factors in passerines and non passerines (6.25, 3.79).

• delta: Basal metabolism factors in passerines and non passerines (0.724, 0.723) alpha*bodyMass^delta.

• mipd: Inverse power density of the mitochondria (1.2E-06).

• speedRatio: True air speed to minimum power speed ratio (1.2).

• muscDensity: Density of the flight muscles (1060).

• phr: Protein hydration ratio (2.2). Whenever protein is consumed from the muscle mass some amount of water is lost in the process. This water is estimated as the mass of dry protein time the phr.

Value

S3 class object with range estimates based on methods defined and settings

• range estimates (Km)

• remaining body mass (Kg)

• remaining fat mass (Kg)

• remaining muscle mass (Kg)

• minimum power speed at start of flight (m/s)

• minimum power speed at end of flight (m/s)

• taxon (order)

Author(s)

Brian Masinde

Examples

migrate(data = birds, settings = list(fed = 3.89*10^7))
migrate(data = birds,  method = "cmm", settings = list(airDensity = 0.905))

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Stopover mass calculator

Description

During stop-overs birds replenish fat mass. Using simplifications from Lindström 1991. The implementation here is simplistic in that muscle mass is not restored as theory and field experiments have shown.

Usage

stopover.mass.calculator(bodyMass, fatMass, taxon, duration)

Arguments

bodyMass	left-over after running function migrate
fatMass	left-over after running function migrate
taxon	(or order) classified into two categories (passerines and non-passerines)
duration	number of hours spent at stop-over site. This must be an integer see example

Value

fat_mass, body_mass

Examples

stopover.mass.calculator(bodyMass = c(2.2, 3.4), fatMass = c(0.34, 0.42),
taxon = c(1,2), duration = 36L)

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