California is home to more native animal and plant species than any other state in the nation. It also hosts the most endemic species—species that occur nowhere else in the world. However, our incredibly diverse native wildlife is facing an intensifying array of stressors stemming from human activity: habitat loss, new land uses like cannabis cultivation, invasive species, wildfires, drought and so many others. Wildlife managers can mitigate these threats through actions like conserving and restoring habitat, building relationships with private landowners and managing ecosystems for resilience to wildfire and climate change. But, to effectively target management actions, managers need to have high-quality information on wildlife populations across the state.
In two studies recently published in the California Fish and Wildlife Journal, Vol. 107-2 (PDF), researchers with CDFW’s Cannabis Program and Wildlife Diversity Program focused on this need for effective wildlife data collection.
One study focused on monitoring small terrestrial vertebrates, like small mammals, reptiles and amphibians. Traditionally, researchers have monitored these species through live-trapping and visual encounter surveys. But such time-intensive methods are not always feasible. Recently developed methods that use automatic cameras are one alternative. To determine how well cameras perform compared to more traditional methods, CDFW researchers tested two methods alongside each other: 1) visual encounter surveys, where they searched for reptiles and amphibians in a study area, and 2) camera traps, which combined small strips of fencing with close-focus cameras pointed at the ground. They found that the camera system detected far more species of small animals compared to the traditional surveys.
In a second study, researchers compared different methods for monitoring birds. Traditionally, researchers have used point counts, where trained observers identify every bird they hear or see at a location. Researchers are also increasingly using acoustic devices to automatically record bird sounds. Recently, machine learning tools have enabled computers to identify bird sounds from these recordings, allowing people to indirectly identify birds while saving much time and effort. In their study, the CDFW researchers found that low-cost recorders performed comparably to expensive ones, and that a machine learning tool accurately identified high numbers of bird species from the recordings.
The researchers will apply what they have learned and shared to a new statewide monitoring effort, which is being developed by CDFW’s Cannabis Program. These advancements will enable a more efficient wildlife monitoring effort that saves money and time. And most importantly, with the information gained from improved monitoring, CDFW staff and other wildlife managers will be able to make more informed decisions to help our native California wildlife cope with current and future challenges.