Abundance estimation of unmarked animals based on camera-trap data.

Area of Science:

• Ecology
• Wildlife Conservation
• Biodiversity Monitoring

Background:

• Camera traps have advanced biodiversity monitoring but are underutilized for estimating unmarked animal populations.
• Challenges include distinguishing individuals and defining sampling areas, hindering accurate abundance modeling.
• Existing analytical frameworks for camera trap data are not widely adopted.

Purpose of the Study:

• To summarize challenges in estimating abundance of unmarked animals using camera traps.
• To provide an overview of existing analytical frameworks for abundance estimation.
• To offer guidance for practitioners selecting appropriate methods for camera trap data.

Main Methods:

• Identified 6 analytical approaches for abundance estimation.
• Reviewed 927 camera-trap studies published between 2014 and 2019.
• Assessed the use and prevalence of different abundance estimation methods.

Main Results:

• Only approximately 5% of reviewed studies employed abundance estimation methods.
• Most studies focused on local abundance or covariate relationships, not broader density predictions.
• Data requirements, assumptions, advantages, and disadvantages of each method were compiled.

Conclusions:

• No single method is optimal for all camera trap data; method selection depends on taxa, sampling frame, and data collection possibilities.
• Despite persistent challenges, evolving analytical frameworks will increase the importance of camera traps for conservation.
• Camera traps are crucial tools for informing conservation decision-making through improved abundance estimation.