Do you do mark-recapture analyses? The required input data are "capture histories": binary strings to describe when animals (or plants) were found in a study.
For example, a capture history of "101" describes a study where an individual was:
* caught on the first event,
* not observed on the second event, and
* observed on the third event.
Data from mark-recapture and mark-resight studies are not usually recorded or stored in this format. Let's run through an exercise to reshape this data into capture histories!
Kernel density estimators are often used for measuring home ranges and this is useful for measuring interactions between animals and their environment. However, kernels are sensitive to the choice of a smoothing factor (h).
For reptiles, Row & Blouin-Demers (2006) recommended using an h that creates a 95% contour area equal to the 100% minimum convex polygon. I have written two functions in R to do this for any tracking data set.
This post will be helpful if you:
Kernel density estimators, which map a utilization distribution, are one of the most popular methods for measuring home ranges. I show how to create kernel home range estimates in R using sample data.
The code and data used are available on my GitHub page. This post builds on my previous post estimating home ranges with minimum convex polygons.
What is a home range?
"that area traversed by the animal during its normal activities of food gathering, mating and caring for young. Occasional sallies outside the area, perhaps exploratory in nature, should not be considered as in part of the home range.” -Burt 1943
The most commonly cited definition is vague and does not provide a clear method for estimating the home range. So what should someone wanting to calculate home ranges do?
This is the second post on analyzing telemetry data in R.
I show a simple method to create trajectories to measure distance travelled, turning angles, and average speed.
Data and code available here.
The following is adapted from a workshop I ran at Trent University in January 2018. It is meant to be an introduction to using R to analyze movement patterns and home ranges from telemetry data. I have split the content into several smaller posts. In this first post, I format simulated telemetry data for spatial analyses and map points and paths overtop of Google Earth imagery with the ggmap package.
Code available at my GitHub page.