# Comparing Manual and Automated Spatial Tracking of Captive Spider Monkeys Using Heatmaps

## TL;DR

This study shows that automated tracking using computer vision can reliably monitor spider monkeys' enclosure use and activity, matching manual observations while saving time and reducing bias.

## Contribution

The study demonstrates the reliability of automated pose estimation (SLEAP) for tracking spider monkeys' enclosure use, offering a scalable alternative to manual observation.

## Key findings

- Manual and automated tracking showed strong agreement (83–99% overlap) in identifying core activity areas.
- Both methods produced comparable estimates of time spent being active, with no significant differences.
- Automated tracking using SLEAP is reliable and efficient for monitoring animal welfare in zoos.

## Abstract

Zookeepers and researchers often monitor welfare by recording how animals use their enclosures, as space use and activity levels provide insights into whether animals are stimulated and engaged. Traditionally, this is performed by manually observing and recording positions, but this can be slow and prone to bias. In this study, we tested whether pose estimation, using a tool called SLEAP, could automatically track two spider monkeys and produce comparable results. We compared manual observations with automated tracking by generating heatmaps of space use and measuring time spent being active. Both methods showed strong agreement, with the monkeys spending most of their time around climbing structures. Automated tracking, therefore, offers a reliable way to save time and improve the consistency of welfare monitoring in zoological institutions.

Animal welfare assessments increasingly aim to quantify enclosure use and activity to support naturalistic behavior and improve Quality of Life (QoL). Traditionally, this is achieved through manual observations, which are time-consuming, subject to observer bias, and limited in temporal resolution due to short observation periods. Here, we compared manual tracking using ZooMonitor with automated pose estimation (SLEAP) in a mother–son pair of black-headed spider monkeys (Ateles fusciceps) at Aalborg Zoo. We collected manual observations on six non-consecutive days (median daily duration: 62 min, mean: 66 min, range: 52–90 min) and visualized this as spatial heatmaps. We applied pose estimation to the same video footage, tracking four body parts to generate corresponding heatmaps. Across most days, the methods showed strong agreement (overlap 83–99%, Pearson’s r = 0.93–1.00), with both highlighting core activity areas on the floor near the central climbing structures and by the door with feeding gutters. Both methods also produced comparable estimates of time spent being active, with no significant difference across days (p = 0.952). Our results demonstrate that computer vision technology can provide a reliable and scalable tool for monitoring enclosure use and activity, enhancing the efficiency and consistency of zoo-based welfare assessments while reducing reliance on labor-intensive manual observations.