Animals disperse the seeds of 60-90% of trees in tropical rainforests, which are among the most structurally complex ecosystems on Earth. Here, we investigated how 3D rainforest structure influences the movements of large, frugivorous birds and resulting spatial patterns of seed dispersal. We GPS-tracked white-thighed (Bycanistes albotibialis) and black-casqued hornbills (Ceratogymna atrata) in southern Cameroon and found that both species preferred areas of greater canopy height, and white-thighed hornbill preferred areas of greater vertical complexity. In addition, 33% of the hornbills preferred areas close to canopy gaps, while 16.7% and 27.8% avoided large and small gaps, respectively. White-thighed hornbills avoided swamp habitats, while black-casqued preferred them during the hottest temperatures. We mapped spatial probabilities of seed dispersal by hornbills, showing that 3D structural attributes shape this ecological process by influencing hornbill behavior. These results provide evidence of a possible feedback loop between rainforest vegetation structure and seed dispersal by animals.

Ecosystems function in a series of feedback loops that can change or maintain vegetation structure. Vegetation structure influences the ecological niche space available for animals to partition, shaping many aspects of behavior and reproduction. In turn, animals perform ecological functions that shape vegetation structure. However, most studies concerning 3D vegetation structure consider only one of these relationships. Here, we review these separate lines of research and integrate them into a single concept that describes a feedback mechanism. We also show how remote sensing and animal tracking technologies are now available at the global scale to describe feedback loops and their consequences for ecosystem functioning. An improved understanding of how animals interact with vegetation structure in feedback loops is needed to conserve ecosystems that face major disruptions in response to climate and land use change.

During the 21st century, human–environment interactions will increasingly expose both systems to risks, but also yield opportunities for improvement as we gain insight into these complex coupled-systems. Human–environment interactions operate over multiple spatial and temporal scales, requiring large data volumes of multi-resolution information for analysis. Climate change, land-use change, urbanization, and wildfires, for example, can affect regions differently depending on ecological and socioeconomic structures. The relative scarcity of data on both humans and natural systems at the relevant extent can be prohibitive when pursuing inquiries into these complex relationships. We explore the value of multitemporal, high-density, and high-resolution LiDAR, imaging spectroscopy, and digital camera data from the National Ecological Observatory Network’s Airborne Observation Platform (NEON AOP) for Socio-Environmental Systems (SES) research. We outline specific applications for addressing SES questions, highlight current challenges, and provide recommendations for the SES research community to improve and expand its use of this platform for SES research. The coordinated, nationwide AOP remote sensing data, collected annually over the next 30 years, offer exciting opportunities for cross-site analyses and comparison, upscaling metrics derived from LiDAR and hyperspectral datasets across larger spatial extents, and addressing questions across diverse scales. Integrating AOP data with other SES datasets will allow researchers to investigate complex systems and provide urgently needed policy recommendations for socio-environmental challenges. We urge the research community to further explore interdisciplinary questions and theories that might leverage NEON AOP data, and present a new Research Coordination Network aimed at supporting these efforts.