Camera traps are one of the fastest growing tools for collecting non-invasive ecological data. They are powerful for monitoring and researching animal behavior, occurrence, and abundance. Cameras allow ecologists to collect data 24 hours a day; this produces huge datasets, even on rare and elusive species. Camera trap studies are producing millions of photos annually, contributing to massive datasets that allow ecologists around the globe to answer questions never before thought possible. With these gigantic datasets comes an increasing demand for easy-to-use tools that enable biologists to analyze these data.
One of the key goals across many ecological studies is to estimate abundance. However, most camera trap abundance estimate techniques rely on a traditional capture-recapture framework that requires individually identifiable animals. Although some animals have natural stripe or spot patterns that allow individual identification, most species do not have such natural markings. In these cases, biologists must mark animals with artificial marks, a process that is often invasive, stressful, and potentially dangerous to animals and their observers.
To harness the incoming ecological data from camera traps, we developed three new methods for estimating abundance of unmarked animals, and we created a free tool for analyzing the data.
Results/Conclusions
We present an easy-to-use, free R package for estimating abundance of unmarked animals from camera trap data. The package spaceNtime includes functions to take the user from raw data to abundance estimates, using three novel methods of estimating abundance. Estimating abundance is an essential part of understanding population dynamics and monitoring populations. The camera trap revolution has changed the way we think about ecological data; we can now collect non-invasive data in quantities that allow for rigorous statistical estimation of population parameters. The spaceNtime package allows biologists to harness and utilize more information than ever before from camera data.