A central focus of my research is understanding how dispersal shapes forest regeneration and successional dynamics. I investigate how seed rain, disturbance, and landscape connectivity influence community assembly and recovery in tropical forests. I developed the concept of seed rain–successional feedbacks, demonstrating that regenerating forests increasingly reflect their own seed rain over time, reinforcing successional trajectories and driving divergence in species composition (Huanca-Núñez et al. 2021, Ecology). In related work in Amazonian floodplain forests, we showed that disturbance and dispersal filters interact to generate diversity during forest regeneration (Terborgh et al. 2017; Terborgh et al. 2020, Ecology). Together, this research highlights how dispersal processes shape the pace and pathways of tropical forest recovery.

Another major component of my research examines how species interactions regulate diversity during the seedling stage. Using long-term seedling datasets from tropical forest plots, I study how conspecific negative density dependence (CNDD) varies among species and contributes to species coexistence. Using 18 years of spatially explicit seedling data from the Barro Colorado Island forest dynamics plot, we found that the strength of density-dependent mortality aligns with broader life-history strategies. In particular, fast-growing and long-lived pioneer species experience stronger conspecific density dependence than slower-growing species (Huanca-Núñez et al. 2026, Journal of Ecology). This work links demographic processes to broader ecological theory on species coexistence and community stability (LaManna et al. 2024, Ecology Letters; Du et al. 2025, Oikos).

A third theme of my research explores how functional traits shape demographic trade-offs and species strategies during forest regeneration. By integrating trait measurements with demographic data, I investigate how variation in plant structure, resource acquisition, and biomass allocation influences seedling performance, species interactions, and successional dynamics. In tropical secondary forests, our work showed that allocation traits—such as biomass partitioning among leaves, stems, and roots—can better predict seedling performance than individual organ-level traits (Huanca-Núñez et al. 2024, Plants). Building on this work, I am expanding trait-based approaches to include belowground functional traits, examining how root strategies contribute to species differences in survival, growth, and responses to biotic interactions such as conspecific density dependence. In ongoing work, I integrate trait data across ontogeny—from seedlings to saplings to adult trees—to examine how functional strategies scale during development. Our analyses indicate that structural traits tend to remain conserved across life stages, maintaining consistent species rankings, whereas chemical traits show greater variability, suggesting stronger environmental and physiological influences on functional variation.

Identifying tropical seedlings and measuring traits at large scales remains a major challenge in forest ecology. To address this limitation, I am developing approaches that integrate ecological knowledge with machine learning and computer vision. Current projects combine large datasets of seedling images, hyperspectral reflectance measurements, and species descriptions to improve automated species identification and trait inference. These approaches aim to build scalable tools that accelerate biodiversity monitoring and enable trait-based frameworks for forest restoration and conservation. This research bridges field ecology, plant functional biology, and data science to develop new tools for understanding and managing tropical forest ecosystems.