In the intricate dance of the planet’s carbon cycle and the protection of terrestrial vertebrate species, tropical forests take center stage. These lush ecosystems are not just vital for carbon sequestration and climate mitigation; they are also guardians of global biodiversity, hosting an astounding 62% of terrestrial vertebrate species.
Thus, restoring these forests holds the key to addressing two of this era’s most pressing challenges: climate change and biodiversity loss.
Tropical Forest Restoration and the Quest for Effective Biodiversity Monitoring
Ambitious initiatives like the New York and Glasgow Leaders’ Declarations on Forests are propelling large-scale restoration projects into the limelight.
These endeavors aim to restore carbon storage and forest structure within a few decades, provided the right species are planted, and deforestation drivers are addressed. However, restoring the diverse fauna of tropical forests is a far more intricate and unpredictable feat. Complex ecosystem processes, species diversity, and the ghosts of past land use make precise restoration predictions elusive. Monitoring the performance of individual restoration projects is thus essential for adaptive management and informed conservation funding.
To support conservation efforts effectively, robust and cost-efficient biodiversity monitoring is essential. Unfortunately, biodiversity monitoring lags behind carbon measurement due to a lack of scalable, reproducible, and cost-effective sampling methods. Market-based conservation mechanisms such as payments for ecosystem services and biodiversity offsets urgently require transparent and generalizable tools for biodiversity measurement. Such tools are crucial for aligning with United Nations targets and preventing greenwashing practices that could undermine true biodiversity restoration.
AI: Listening to the Sounds of Biodiversity
Acoustic monitoring has emerged as a promising tool for monitoring biodiversity, particularly in taxonomic groups that communicate through vocalization. Soundscapes can reveal how forest health changes, as evidenced by studies in Papua New Guinea, Borneo, Puerto Rico, and Southeast Asia. These findings suggest that soundscapes can track forest loss and degradation, but their effectiveness in monitoring the restoration of faunal biodiversity in tropical forests remains uncertain.
Recent advances in artificial intelligence, including Convolutional Neural Networks (CNN), offer new possibilities for identifying vocalizing species like birds, bats, and amphibians, according to a recent study from the University of Würzburg in Germany and Fundación Jocotoco published in Nature Communications.
These AI models are flexible and require fewer human hours, making them invaluable tools. Although the need for extensive training datasets remains a bottleneck, progress in acoustic datasets and AI tools like Arbimon and BirdNET is making it increasingly feasible to identify entire vocalizing communities at the species level. This innovative approach holds promise for comprehensive biodiversity assessments across multiple sites.
Case Study: Ecuador’s Choco Region
A pioneering biodiversity recovery assessment was conducted in the Ecuadorian lowland Choco. Soundscapes from 43 plots along a recovery gradient were sampled using consistent devices and protocols. These plots spanned active cacao plantations, abandoned cacao and pastures with forest recovery ranging from 1 to 34 years, and pristine old-growth forests.
Beyond Time: Rethinking Biodiversity Recovery Metrics
The study explored how well the total vocalizing vertebrate species community, as identified by experts, represented the forest recovery gradient. It also delved into community composition, species richness, old-growth species, and the composition of nocturnal insects using data derived from soundscapes, including various acoustic indices and CNN models.
The results revealed a clear gradient in the community of vocalizing vertebrates. Notably, abandoned cacao plantations initially recovered vertebrate diversity more rapidly than abandoned pastures, but convergence occurred in later stages. Old-growth forests stood out in community composition, although one pasture regenerating for 34 years exhibited similarities. While total species richness decreased along the recovery gradient, the richness of old-growth-specific species increased over time.
The study emphasized that time alone cannot serve as a proxy for biodiversity recovery. The early and late recovery stages exhibited significant species overlap, with the most profound changes occurring in the early phase. Variations in forest cover in the surrounding area also influenced early-stage recovery, indicating the need for a more nuanced approach. Moreover, anthropogenic impacts like logging or hunting can significantly affect local fauna but are not accounted for by recovery age.
In conclusion, the study in Ecuador’s Choco Region illustrates the complexity of biodiversity recovery in tropical forests. It emphasizes the need for multifaceted monitoring approaches that go beyond simple time-based metrics. As we navigate the challenging terrain of restoring tropical forests, which can unlock the potential of carbon sequestration, monitoring tools like acoustic monitoring and AI-driven biodiversity assessments are lighting the way toward a more biodiverse and resilient future.