
Using Technology to Expand Behavioral Husbandry with Joey Golden [Episode 289]
In this forward-thinking conversation, Ryan Cartlidge is joined by Joey Golden — curator of animal behavior programs at the Maryland Zoo in Baltimore, where he oversees behavioral husbandry programs with a focus on extending behavioral husbandry across the full 24-hour cycle. Joey's work centres on building scalable systems for behavioral and environmental data collection, and using data-driven reports to connect that information to welfare decisions. Inspired by Hal Markowitz, he also leads an engineering program developing automated habitat features that allow animals to earn outcomes on their own schedule around the clock.
Together, Ryan and Joey explore how data, technology, engineering and animal behavior can come together to create new opportunities for animal care. Joey shares the evolution of his "observe, interpret, improve" approach at the Maryland Zoo, including the development of camera infrastructure, animal behavior volunteer programs, data collection systems and an engineering lab that has brought around 30 engineers into the zoo to develop automated habitat features. The conversation highlights how these systems can help teams understand what animals are doing when keepers aren't present, identify meaningful patterns in behavior, and use that information to guide environmental changes.
The conversation also dives into some of the practical realities of building these systems, including limited resources, developing new skills, working across departments, learning through failure, and starting small. Joey shares stories from the Maryland Zoo's grizzly bear and lion programs, including how years of data helped identify changes to a grizzly bear habitat that resulted in the bears using a previously neglected pool far more frequently. He also describes the development and testing of the "carcass claw", an automated system designed to allow lions to engage with carcass feeding in a more dynamic way.
Looking towards the future, Ryan and Joey discuss artificial intelligence and its potential to reduce one of the biggest bottlenecks in behavioral data collection: the enormous amount of time required to review footage and turn observations into usable data. Joey explains how the Maryland Zoo is already building the infrastructure and resources that could support this kind of technology in the future, while emphasising the importance of thinking in longer timeframes and developing systems that can grow over time.
Throughout this episode, we discuss:
✅ Joey's journey from the Disney College Program and zookeeping into his current role at the Maryland Zoo
✅ Hal Markowitz's influence on Joey's thinking about technology and dynamic environments
✅ The "observe, interpret, improve" cycle and how it guides decision-making at the Maryland Zoo
✅ Using cameras and behavioral data to understand what animals do across the full 24-hour cycle
✅ How teams can start collecting useful data without needing to tackle an entire organisation at once
✅ Building an engineering volunteer program and developing an engineering lab within the zoo
✅ The development of automated habitat features, including the polar bear device and "carcass claw"
✅ What the Maryland Zoo has learned from both successful projects and failures
✅ Using behavioral data to evaluate habitat configurations, enrichment and husbandry practices
✅ Thinking about technology as a way of applying existing tools in new ways for animal care
✅ The potential role of artificial intelligence in analysing animal behavior and reducing data-collection bottlenecks
✅ Why developing a broader understanding of disciplines such as engineering and technology is more accessible than it might initially seem
Whether you're working in a zoo, shelter, kennel, companion animal setting, or another environment where understanding behavior and welfare matters, this episode offers plenty to think about when it comes to using data, systems and technology to better understand the animals in our care. Joey's experiences also provide a practical reminder that you don't need to know everything before you begin — you can start with the resources available, build your skills as you go, and take the next approximation towards the bigger vision.
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