The package provides methods for predicting flight range of birds based on their physiological characteristics. This is an R implementation of Flight program provided by Pennycuick.
Install the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("BMasinde/FlyingR")In brief, the time-marching computation computes the flight range of birds in short time intervals (6-minutes). Within this time period chemical and mechanical powers are held constant during which fat and protein are consumed to sustain flight. Because of the reduction in weight, the chemical and mechanical powers are recalculated for the next time period and the distance flown is incremented to previous time-interval’s distance. And this process iterates until fat mass is depleted giving the maximum possible range under the time-marching computation.
Birds migrate long distances between breeding and wintering grounds. In
the majority of cases, it is not possible to cover these distances at a
go, and therefore, stops are required to rest, feed, and restore both
fat and protein Lindström and Piersma 1993; Klaassen 1996; Kaiser 1999;
Schwilch et al. 2002; Pennycuick 2008). However, research on stopovers
(e.g., Lindström 1991; Lindström and Piersma 1993)
focuses on fuel (fat mass) restoration. As such, the stopover mass
calculator used in the package FlyingR, is based on maximum fuel
deposition rates (FDR), given by Lindström (1991), separately for
passerines and non-passerines (Equations , , and ). This is not an
optimal solution as it does not account for protein restoration. In
addition, there are reservations about using lean body mass; body mass
minus fat mass (Pennycuick 2008).
The package looks for columns named id, name or species.name, bodymass or allupmass, wingspan, ws, wingarea, ordo, order (which is a factored column with levels 1 or 2 passerines and non-passerines respectively) fatmass, fat.mass, fat_mass and lastly muscle_mass.
These two passerine species are trans-Saharan migrants. Lesser
whitethroats winter in the Sahel zone of eastern and north-eastern
Africa (Pearson and Lack 1992), while garden warblers spend winter
further, i.e., in southern Africa (Shirihai, Gargallo, and Helbig 2001).
Data on the Garden Warbler and Lesser Whitethroat are obtained from
various bird ringing stations situated along their SE European flyway
leading from Europe towards African winter quarters. This is split into
four regions: Southern Baltic, Eastern Mediterranean, Northern
Mediterranean and North Eastern Africa. These data include the 1-st year
individuals (immatures): 1,044 garden warblers and 848 lesser
whitethroats, and span the years 2000 to 2006. The sample contains the
following variables: station code, ring number, age, body mass, fat
score, wing and tail measurements. The fat score is used to derive fat
fraction and subsequently fat mass by the procedure described by
Ożarowska (2015). Body mass and fat mass are converted from grams to
kilograms. The wing measurements do not equal the required wingspan as
these are measured differently (i.e., the wingspan is measured from tip
to tip of wings, particularly the primary feathers, in metres). Other
missing variables are the wing area and muscle mass. Bruderer and Boldt
(2001) provide estimates for the wingspan and wing area for several
species. For the Garden Warbler, the wingspan and wing area are 0.223
(m) and 0.0093 (
# ------------------------------------------------------------------------------
# A generic function
# migration function each species at different regions.
# Function returns a list (each species migrated by region)
# ------------------------------------------------------------------------------
region_migrate <- function(data) {
# we need dplyr for filter function
# we need FlyingR for migration
require(dplyr)
require(FlyingR)
# number of regions in dataset
regions <- unique(data$Region)
# regions data in a list
region_data <- list()
for (i in 1:length(regions)) {
# filter data by region
filter_data_region <-data %>% filter(Region == regions[i])
# migrate filter data
user_settings <- list(ipf = 0.9)
results <- migrate(data = filter_data_region[, 3:9], method = "csw",
speed_control = 0,
settings = user_settings,
min_energy_protein = 0.05)
region_data[[i]] <- cbind(filter_data_region, "range" = as.vector(results$range))
}
# return region data as a list
return(region_data)
}# ------------------------------------------------------------------------------
# migrating sylvia borin
# ------------------------------------------------------------------------------
data("garden_wablers")
wablers_migration <- region_migrate(data = garden_wablers)
#> Loading required package: dplyr
#>
#> Attaching package: 'dplyr'
#> The following object is masked from 'package:testthat':
#>
#> matches
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union# ------------------------------------------------------------------------------
# migrating Lesser Whitethroats (sylvia curruca)
# ------------------------------------------------------------------------------
data("lesser_whitethroats")
whitethroats_migration <- region_migrate(data = lesser_whitethroats)# ------------------------------------------------------------------------------
# migration list to one dataframe for Garden Wablers(sylvia_borin)
# ------------------------------------------------------------------------------
wablers_results <- data.frame()
for (i in 1:length(wablers_migration)) {
wablers_results <- rbind(wablers_results, wablers_migration[[i]])
}
cat("number of rows sylvia borin:", nrow(wablers_results), sep = " ")
#> number of rows sylvia borin: 119# ------------------------------------------------------------------------------
# migration list to one dataframe for Lesser Whitethroats
# ------------------------------------------------------------------------------
whitethroats_results <- data.frame()
for (i in 1:length(whitethroats_migration)) {
whitethroats_results <- rbind(whitethroats_results, whitethroats_migration[[i]])
}
cat("number of rows in sylvia curruca:", nrow(whitethroats_results), sep = " ")
#> number of rows in sylvia curruca: 84# ------------------------------------------------------------------------------
# order of region
# rename the regions for clarity
# ------------------------------------------------------------------------------
wablers_results$Region <- factor(wablers_results$Region,
levels = c("S Balt", "N Med",
"E Med"), ordered = TRUE)
levels(wablers_results$Region) <- c("S Baltic", "N Medit",
"E Medit")
whitethroats_results$Region <- factor(whitethroats_results$Region,
levels = c("S Balt", "N Med",
"E Med", "NE Afr"), ordered = TRUE)
levels(whitethroats_results$Region) <- c("S Baltic", "N Medit",
"E Medit", "NE Africa")# ------------------------------------------------------------------------------
# both plots in one
# combine the data sets first
# ------------------------------------------------------------------------------
results <- rbind(wablers_results, whitethroats_results, deparse.level = 0)
# make sure it sums up
nrow(wablers_results) + nrow(whitethroats_results) == nrow(results)
#> [1] TRUE# ------------------------------------------------------------------------------
# new combined plot
# ------------------------------------------------------------------------------
require(ggplot2)
#> Loading required package: ggplot2
results_combined_plot <- ggplot(results, aes(x = Region, y = range,
colour = species)) +
geom_boxplot() +
#ggtitle("Sylvia curruca flight range by region") +
theme_bw()+
labs(y = "Range")+
ylim(500, 4500)
results_combined_plot
Based on these results the two species clearly differ in their potential flight ranges and therefore show species-specific migration strategy, when travelling along the same SE flyway, as was described in the original paper based on Flight (Ożarowska 2015).
The Curlew Sandpiper is a small wader, breeding in Arctic Siberia and wintering in Africa (Cramp and Simmons 1983). A sample of 12 individuals was observed over five days in laboratory conditions (in total a sample of 60 observations) at the University of Gdańsk. Initially, all these individuals had no subcutaneous fat depots (fat score equal to 0) (Meissner 2009). Birds were fed ad-libitum with mealworm larvae Tenebrio molitor. To determine the maximum body mass gain sandpipers were weighed every day in the morning. The data set consists of body mass, fat mass, constant wingspan (0.44 m) and wing area (0.018 m2). Body mass and fat mass are converted from grams to kilograms. On day one, the fat fraction is assumed to be 0.05 according to the observations of closely related species - the Red Knot Calidris canutus (Piersma, Bruinzeel, Drent, Kersten, Van der Meer, and Wiersma 1996). On day 2 the fat fraction increases on average to 0.23, day 3 – 0.34, day 4 – 0.40 and day 5 – 0.44. Data collected in subsequent days were used to calculate the potential flight range of the studied individuals each day (Figure 4). On these data, the constant muscle mass migration simulation is used. The results return a named vector, if the bird identifier is not unique, the id suffix is added after an underscore. The results also return the speed at start and end of migration. All these estimates may be used in the analyses of inter-individual variation of potential flight range with increasing fat fraction in migrating birds.
data("curlew_sandpiper")# needed data for migration columns 2:9
curlew_range <-
migrate(
data = curlew_sandpiper[, 2:9],
method = "cmm",
speed_control = 0,
min_energy_protein = 0.05,
settings = list(ipf = 0.9)
)
# Addin computed range to the dataset
curlew_results <- curlew_sandpiper[, 1:2]
curlew_results$range <- as.vector(curlew_range$range)# migrate curlew sandpiper
require(ggplot2)
require(dplyr)
curlew_plot <- ggplot(data = curlew_results)+
geom_point(aes(x = day, y = range, color = name))+
theme_bw(base_size = 10)+
ggtitle("")+
scale_color_discrete(name = "Individual")+
xlab("Day")+
ylab("Range (km)")
curlew_plot
Data birds is a preset birds data, extracted from Flight Pennycuick(2008). Fat mass percentage generated randomly where zero. In addition, by default muscle mass was derived as 0.17 fraction of the all-up mass.
head(birds)
#> Scientific.name Empty.mass Wing.span Fat.mass Order Wing.area
#> 1 Anser anser 3.77000 1.600 0.84641 2 0.33100
#> 2 Hydrobates pelagicus 0.02580 0.355 0.00591 2 0.01610
#> 3 Pachyptila desolata 0.15500 0.637 0.03886 2 0.04710
#> 4 Regulus regulus 0.00542 0.156 0.00112 1 0.00525
#> 5 Calidris canutus 0.12700 0.538 0.03500 2 0.03320
#> 6 Aegypius monachus 9.90000 3.040 2.02565 2 1.40000
#> Muscle.mass
#> 1 0.6409000
#> 2 0.0043860
#> 3 0.0263500
#> 4 0.0009214
#> 5 0.0215900
#> 6 1.6829999require(FlyingR)
## basic example code
## birds comes with the package
data("birds")
simulation <- migrate(data = birds, method = "cmm", settings = list(airDensity = 0.905))
simulation$range
#> Anser anser Hydrobates pelagicus Pachyptila desolata
#> 3537.296 3060.701 4274.620
#> Regulus regulus Calidris canutus Aegypius monachus
#> 1264.523 4464.400 3979.271
#> Limosa lapponica Anas crecca Hirundo rustica
#> 16652.710 3981.401 3445.013
#> Cygnus cygnus Sylvia borin Luscinia luscinia
#> 3489.445 2663.630 2162.845
#> Corvus monedula Anas penelope Fregata magnificens
#> 2300.933 6436.829 11904.426
#> Larus ridibundus Diomedea exulans Phalacrocorax carbo
#> 6211.954 6271.194 2948.927
#> Gyps rueppellii Torgos tracheliotus Ardeotis kori
#> 7867.072 6901.371 4631.958
#> Sturnus vulgaris Fringilla coelebs Carduelis spinus
#> 4390.668 2821.588 2625.428
#> Turdus philomelos Calidris tenuirostris Buteo swainsoni M
#> 3184.748 7060.782 5979.198
#> Buteo swainsoni F
#> 7868.848The function also returns the mechanical and chemical power during the simulation
This function estimates the range based on Pennycuick (1975) Mechanics of Flight where Breguet set of equations are used.
## when estimating range of a single bird
birds_range <- flysim(data = birds, settings = list(airDensity = 0.905))
birds_range$range
#> Anser anser Hydrobates pelagicus Pachyptila desolata
#> 3193.8 3252.9 4192.0
#> Regulus regulus Calidris canutus Aegypius monachus
#> 1521.6 4240.6 3951.6
#> Limosa lapponica Anas crecca Hirundo rustica
#> 11209.3 3797.8 3898.0
#> Cygnus cygnus Sylvia borin Luscinia luscinia
#> 3296.0 2801.7 2301.9
#> Corvus monedula Anas penelope Fregata magnificens
#> 2452.7 5428.0 10527.4
#> Larus ridibundus Diomedea exulans Phalacrocorax carbo
#> 6113.1 5436.8 2918.8
#> Gyps rueppellii Torgos tracheliotus Ardeotis kori
#> 6808.3 6334.7 4211.5
#> Sturnus vulgaris Fringilla coelebs Carduelis spinus
#> 4197.4 3036.6 2926.6
#> Turdus philomelos Calidris tenuirostris Buteo swainsoni M
#> 3243.8 6053.5 5671.6
#> Buteo swainsoni F
#> 6994.8Lindström Å, Piersma T (1993). “Mass changes in migrating birds: the evidence for fat and protein storage re-examined.” Ibis, 135(1), 70–78. doi:10.1111/j.1474-919X.1993. tb02811.x
Klaassen M (1996). “Metabolic constraints on long-distance migration in birds.” Journal of Experimental Biology, 199(1), 57–64. doi:10.1242/jeb.199.1.57.
Kaiser A (1999). “Stopover strategies in birds: a review of methods for estimating stopover length.” Bird Study, 46(sup1), S299–S308. doi:10.1080/00063659909477257.
Schwilch R, Grattarola A, Spina F, Jenni L (2002). “Protein loss during long-distance migra- tory flight in passerine birds: adaptation and constraint.” Journal of Experimental Biology, 205(5), 687–695. doi:10.1242/jeb.205.5.687.
Pennycuick CJ (2008). MODELLING THE FLYING BIRD, volume 5. First edition edition. Elsevier, New Jersey.
Lindström Å, Piersma T (1993). “Mass changes in migrating birds: the evidence for fat and protein storage re-examined.” Ibis, 135(1), 70–78. doi:10.1111/j.1474-919X.1993. tb02811.x.
Pearson DJ, Lack PC (1992). “Migration patterns and habitat use by passerine and near-passerine migrant birds in eastern Africa.” Ibis, 134(s1), 89–98. doi:10.1111/j. 1474-919X.1992.tb04738.x.
Shirihai H, Gargallo G, Helbig AJ (2001). SYLVIA WARBLERS: IDENTIFICATION, TAX- ONOMY AND PHYLOGENY OF THE GENUS SYLVIA. Christopher Helm Publishers Ltd.
Ożarowska A (2015). “Contrasting fattening strategies in related migratory species: the blackcap, garden warbler, common whitethroat and lesser whitethroat.” Annales Zoologici Fennici, 52(1–2), 115–127. doi:10.5735/086.052.0210.
Bruderer B, Boldt A (2001). “Flight characteristics of birds: I. Radar measurements of speeds.” Ibis, 143(2), 178–204. doi:10.1111/j.1474-919X.2001.tb04475.x.
Cramp S, Simmons K (1983). HANDBOOK OF THE BIRDS OF EUROPE, THE MIDDLE EAST AND NORTH AFRICA. THE BIRDS OF THE WESTERN PALEARCTIC: 3. WADERS TO GULLS. Oxford University Press.
Meissner W (2009). “A classification scheme for scoring subcutaneous fat depots of shore- birds.” Journal of Field Ornithology, 80(3), 289–296. doi:10.1111/j.1557-9263.2009. 00232.x.
Piersma T, Bruinzeel L, Drent R, Kersten M, Van der Meer J, Wiersma P (1996). “Vari- ability in basal metabolic rate of a long-distance migrant shorebird (red knot, Calidris canutus) reflects shifts in organ sizes.” Physiological Zoology, 69(1), 191–217. doi: 10.1086/physzool.69.1.30164207