README.md

In KiranLDA/migflow: Calculates the maximum flow through a network

Package migflow

Turnestones, Copyright (c) Kiran Dhanjal-Adams

The methods from this package are based on a paper published in Conservation Biology. The packages provides a set of functions to set up a migratory connectivity matrix, and then use this matrix to calculate the maximum flow of birds/animal through the migratory network, and then prioritise sites for conservation.

Prerequisites

This package relies on igraph, fields, sp and stats. If there are any problem installing migflow, ensure these are working.

Installing

To install this package from github, make sure you first have devtools installed.

install.packages("devtools")

Once devtools is installed, use:

devtools::install_github("KiranLDA/migflow")

Load and test

The package relies on there being a little tracking data (start & stop latitude and longitude of a migratory movement) and a list of potential stopover sites that animals can use with counts. The method then estimates the likelihood of individuals moving between sites based on the distance between those sites and the relative number of individuals recorded at each site and allocates the population through this network.

# load library
library(maxflow)

# Simulate 10 fake tracks with a mean distance of 500km
tracks <- rnorm(10, 500, 200)
rgamma(10, 500, 200)

# Create a fake list of sites where animals were seen at, with latitude, longitude and number of anumals seen there 
dta <- data.frame(Site= LETTERS[1:5], Lat= 1:5, Lon= 6:10, Pop=100:104)

# create a distance matrix based on these data
dist <- point2DIST(dta)

# calculate the probability of going between these sites given the distance the animal can travel
Dist_P <- distPROB(tracks, dist, adjust=2, plot=TRUE)

# Calculate proportion of population using a site
Pop_P <- nodePopPROP(dta, 300000)

#Calculate the azimuth angle
Azi_P <- absAZIMUTH(dist, lonlats=dta)

# make birds/animals prefer sites which a larger proportion of the population has been seen and where the distance is better
network <- Dist_P * Pop_P * Azi_P

# Make the network directed
network <- directedNET(network, include_diagonal = TRUE)

#estimate number of birds entering and exiting sites based on distance, population count and azimuth
network <- popPROP(network, 300000)

#Add supersource and sink nodes
network <- addSUPERNODE(network, sources=c("A","B"), sinks= c("D", "E"))
network

Creating a random network prioritising sites

It's also possible to generate random networks

pop=100000
rand_net = randomNET(nsites=15,pop=pop)

Prioritising sites

This uses a reverse greedy approch to find sites which contribute least to population flow, remove them, and reallocate population flow through the network based on the carrying capacity of remaining sites. This process is done iteratively until no sites are left.

# priotise sites according to flow through network
prioritiseFLOW(rand_net$network, rand_net$sites)

This allows us to compare with networks, with for instance fewer edges

# with fewer edges
pop=100000
rand_net = randomNET(nsites=15,pop=pop, nedges=40)

# priotise sites according to flow through network
prioritiseFLOW(rand_net$network, rand_net$sites)

Describing networks using curve shape

pop=100000
rand_net = randomNET(nsites=100,nbreeding=3, nwintering=3,pop=pop, mean_dist = 8000, sd_dist=9000, plot=F)

# priotise sites according to flow through network
prioritisation = prioritiseFLOW(rand_net$network, rand_net$sites, plot=F)

y=(prioritisation$prioritisation$Pop_Flow/pop)
x=(1:length(prioritisation$prioritisation$Pop_Flow))/length(prioritisation$prioritisation$Pop_Flow)

# calculate area under curve
AUC = curvCALC(x,y)
par(mfrow=c(1,1), mar=c(4,4,2,1))
plot(x,y,type="o",pch=20, 
      xlab = "proportion of sites lost",
      ylab = "proportion of population lost",
      main = paste0("Curve = ",AUC))

Authors

Kiran Dhanjal-Adams

Citation

Dhanjal‐Adams, K. L., Klaassen, M. , Nicol, S. , Possingham, H. P., Chadès, I. and Fuller, R. A. (2017), Setting conservation priorities for migratory networks under uncertainty. Conservation Biology, 31: 646-656. doi:10.1111/cobi.12842

License

This project is licensed under the GNU General Public License version 3 - see the LICENSE file for details

KiranLDA/migflow documentation built on Aug. 8, 2019, 8:55 p.m.