DigiWild – AI and citizen science for monitoring ungulates
KEY POINTS- Combines wildlife cameras, citizen science and artificial intelligence.
- Develops methods for near real-time estimates of reproduction and population density
Project overview
Participants
More related research
Global goals
- 13. Climate action
- 15. Life on land
Short summary
We study how climate change affects Swedish ungulates using wildlife camera images, citizen science, AI and population models. The goal is to develop near real-time estimates of reproduction and population density for more adaptive wildlife management.
Rapid environmental change is affecting wildlife populations and creating new challenges for wildlife management. In Sweden, some ungulate species are declining while others are expanding, but existing monitoring methods often provide information too late for managers to respond rapidly to changes in reproduction or population size.
DigiWild develops digital tools for monitoring Swedish ungulates by combining citizen science, wildlife cameras, artificial intelligence and population modelling. The project builds on Viltbild, a national platform that collects images from wildlife cameras operated by hunters across Sweden.
We will develop computer-vision models that automatically extract information on sex and age and estimate the distance between animals and cameras. These data will then be combined with statistical models to estimate reproduction and population density while accounting for uncertainty in both observations and AI classifications.
The project will also investigate how temperature, precipitation and extreme climatic events affect reproduction and population dynamics across Swedish ungulate species. By linking large-scale camera data with climate data and other monitoring information, we aim to understand how ungulate populations respond to a changing climate.
Ultimately, DigiWild aims to reduce the time between wildlife monitoring and management decisions. The project will provide open and scalable methods that can support adaptive, evidence-based management of Swedish ungulate populations under changing environmental conditions.
