Tracking Phenological Status and Ecological Interactions in a Hawaiian Cloud Forest Understory using Low-Cost Camera Traps and Visual Foundation Models

TL;DR

This study employs low-cost camera traps and foundation vision models to monitor plant phenology and ecological interactions in a Hawaiian cloud forest, revealing detailed trends and drivers of ecological change.

Contribution

It introduces a novel, cost-effective method combining camera traps and foundation models for fine-grained phenological and ecological monitoring in tropical forests.

Findings

Detected phenological trends not visible with traditional sampling

Enabled detailed analysis of flora-faunal interactions

Demonstrated effectiveness of foundation models in ecological image analysis

Abstract

Plant phenology, the study of cyclical events such as leafing out, flowering, or fruiting, has wide ecological impacts but is broadly understudied, especially in the tropics. Image analysis has greatly enhanced remote phenological monitoring, yet capturing phenology at the individual level remains challenging. In this project, we deployed low-cost, animal-triggered camera traps at the Pu'u Maka'ala Natural Area Reserve in Hawaii to simultaneously document shifts in plant phenology and flora-faunal interactions. Using a combination of foundation vision models and traditional computer vision methods, we measure phenological trends from images comparable to on-the-ground observations without relying on supervised learning techniques. These temporally fine-grained phenology measurements from camera-trap images uncover trends that coarser traditional sampling fails to detect. When combined with detailed visitation data detected from images, these trends can begin to elucidate drivers of both plant phenology and animal ecology.