Life in 2.5D: Animal Movement in the Trees

Abstract

The complex, interconnected, and non-contiguous nature of canopy environments present unique cognitive, locomotor, and sensory challenges to their animal inhabitants. Animal movement through forest canopies is constrained; unlike most aquatic or aerial habitats, the three-dimensional space of a forest canopy is not fully realized or available to the animals within it. Determining how the unique constraints of arboreal habitats shape the ecology and evolution of canopy-dwelling animals is key to fully understanding forest ecosystems. With emerging technologies, there is now the opportunity to quantify and map tree connectivity, and to embed the fine-scale horizontal and vertical position of moving animals into these networks of branching pathways. Integrating detailed multi-dimensional habitat structure and animal movement data will enable us to see the world from the perspective of an arboreal animal. This synthesis will shed light on fundamental aspects of arboreal animals’ cognition and ecology, including how they navigate landscapes of risk and reward and weigh energetic trade-offs, as well as how their environment shapes their spatial cognition and their social dynamics.

Smarter foragers do not forage smarter: a test of the diet hypothesis for brain expansion

“…There could also be significant differences in travel costs between the four species. We assume that coati terrestrial locomotion expends less energy than arboreal movements of capuchins, which include substantial vertical leaping and climbing [59], although arboreal species can reduce locomotor costs in hilly areas by traveling at consistent altitudes within the canopy [60]. How these differences in locomotor costs affect foraging efficiency is not known.…”

Multi-scale movement syndromes for comparative analyses of animal movement patterns

“…This suggests that the unique sensory and locomotor adaptations of these species were not different enough to result in consistently large differences in these most basic components of movement: step lengths or turning angles. However, step-level metrics are the most likely to be affected by sampling rate GPS error, and potentially even vertical movement (which we didn’t study, but see [ 20 ]), which could have obscured species differences. Other studies comparing step metrics across species have found differences in step length associated with phylogeny, ecology, and human disturbance for terrestrial species, but fewer differences across marine taxa [ 44 , 47 , 48 ].…”

Beyond the horizon: immersive developments for animal ecology research

“…• Investigation of movement and movement patterns on different levels of scale [53][54][55] and individual or collective movement [24], such as home-range, territorial behavior, and swarm movement • Interaction between individuals or groups [56,57], e.g., in movement, predation, and decision making [58] • Impact of environmental conditions, e.g., on decision making, social dynamics, or survival [18,19,59, 60] • Differences in species and groups, e.g., based on phenotypic variation [61] or related to evolutionary relation • Cognitive processes underlying behavioral patterns, e.g., foraging or mate choice [62] • Prediction and modeling of behavior [13,63] The available information on animal behavior and movement is usually collected by sensors, imaging, and subsequent processing of the results. Analysis methods need to exhibit a certain level of robustness toward incomplete and noisy data and be capable of coping with the uncertainty associated with it.…”

“…In a holistic approach, the enriched recreation of the environment and information on animal behavior can be combined with access to analysis methods and pipelines, supporting an immersive interactive experience and analysis. For example, Harel et al [59] discussed the representation of arboreal animal movements and decision making in VR. They mapped the 2.5D setting of canopy environments into a S3D environment for detailed analysis.…”

“…They mapped the 2.5D setting of canopy environments into a S3D environment for detailed analysis. As the ability to precisely measure movement and position is improving, more fine-grained options for representation are becoming available, which may support a better understanding of animal decisionmaking, for example, regarding the trade-off between risk and reward [59]. However, as Harel et al mention, current technologies used to collect data, such as sensor collars or drones, may still have an impact on the available decision options of the animal and the decisions taken.…”