Non-random walks in monkeys and humans

Principles of self-organization play an increasingly central role in models of human activity. Notably, individual human displacements exhibit strongly recurrent patterns that are characterized by scaling laws and can be mechanistically modelled as self-attracting walks. Recurrence is not, however, unique to human displacements. Here we report that the mobility patterns of wild capuchin monkeys are not random walks and exhibit recurrence properties similar to those of cell phone users, suggesting spatial cognition mechanisms shared with humans. We also show that the highly uneven visitation patterns within monkey home ranges are not entirely self-generated but are forced by spatio-temporal habitat heterogeneities. If models of human mobility are to become useful tools for predictive purposes, they will need to consider the interaction between memory and environmental heterogeneities.Comment: 18 pages, 3 figure

Using optimal foraging theory to infer how groups make collective decisions

A growing body of evidence emerging from the analysis of advanced animal tracking data shows that moving groups make shared decisions about where to go, with each group member influencing the outcome. How groups coordinate departure decisions (when to go), however, remains poorly understood. Classic models from optimal foraging theory, specifically the marginal value theorem (MVT), are well-established tools that can generate quantitative predictions about when individuals should prefer to leave a food patch, given patch quality and the distribution of patches in the environment. Integrating optimal foraging theory into studies of animal collectives provides rich opportunities for gaining new insights from both empirical and theoretical studies. Specifically, the MVT can be used to make predictions about conflict of interests among group members, how consensus costs vary under different models of collective decision-making, and under what environmental conditions shared decision-making may be favored or disfavored

Arboreal monkeys facilitate foraging of terrestrial frugivores

Terrestrial animals feed on fruit dropped by arboreal frugivores in tropical forests around the world, but it remains unknown whether the resulting spatial associations of these animals are coincidental or intentionally maintained. On Barro Colorado Island, Panama, we used a combination of acoustic playback experiments, remote camera monitoring, and GPS tracking to quantify the frequency of such interactions, determine who initiates and maintains spatial associations, and test whether terrestrial animals adopt a strategy of acoustic eavesdropping to locate fruit patches created by foraging primates. Indeed, 90% of fruits collected in fruit fall traps had tooth marks of arboreal frugivores, and terrestrial frugivores visited fruit trees sooner following visits by GPS-collared monkeys. While our play back experiments were insufficient to support the hypothesis that terrestrial frugivores use auditory cues to locate food dropped by arboreal primates, analyses of movement paths of capuchin monkeys (Cebus capucinus), spider monkeys (Ateles geoffroyi), and coatis (Nasua narica) reveal that observed patterns of interspecific attraction are not merely a byproduct of mutual attraction to shared resources. Coatis were significantly more likely to initiate close encounters with arboreal primates than vice versa and maintained these associations by spending significantly longer periods at fruiting trees when collared primates were present. Our results demonstrate that terrestrial frugivores are attracted to arboreal primates, likely because they increase local resource availability. Primates are often among the first species in a habitat to be extirpated by hunting; our results suggest that their loss may have unanticipated consequences for the frugivore community

Life in 2.5D: Animal Movement in the Trees

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

Estimating encounter location distributions from animal tracking data

1. Ecologists have long been interested in linking individual behaviour with higher level processes. For motile species, this ‘upscaling’ is governed by how well any given movement strategy maximizes encounters with positive factors and minimizes encounters with negative factors. Despite the importance of encounter events for a broad range of ecological processes, encounter theory has not kept pace with developments in animal tracking or movement modelling. Furthermore, existing work has focused primarily on the relationship between animal movement and encounter rates while the relationship between individual movement and the spatial locations of encounter events in the environment has remained conspicuously understudied. 2. Here, we bridge this gap by introducing a method for describing the long-term encounter location probabilities for movement within home ranges, termed the conditional distribution of encounters (CDE). We then derive this distribution, as well as confidence intervals, implement its statistical estimator into open-source software and demonstrate the broad ecological relevance of this distribution. 3. We first use simulated data to show how our estimator provides asymptotically consistent estimates. We then demonstrate the general utility of this method for three simulation-based scenarios that occur routinely in biological systems: (a) a population of individuals with home ranges that overlap with neighbours; (b) a pair of individuals with a hard territorial border between their home ranges; and (c) a predator with a large home range that encompassed the home ranges of multiple prey individuals. Using GPS data from white-faced capuchins Cebus capucinus, tracked on Barro Colorado Island, Panama, and sleepy lizards Tiliqua rugosa, tracked in Bundey, South Australia, we then show how the CDE can be used to estimate the locations of territorial borders, identify key resources, quantify the potential for competitive or predatory interactions and/or identify any changes in behaviour that directly result from location-specific encounter probability. 4. The CDE enables researchers to better understand the dynamics of populations of interacting individuals. Notably, the general estimation framework developed in this work builds straightforwardly off of home range estimation and requires no specialized data collection protocols. This method is now openly available via the ctmm R package

Perspectives in machine learning for wildlife conservation

Data acquisition in animal ecology is rapidly accelerating due to inexpensive and accessible sensors such as smartphones, drones, satellites, audio recorders and bio-logging devices. These new technologies and the data they generate hold great potential for large-scale environmental monitoring and understanding, but are limited by current data processing approaches which are inefficient in how they ingest, digest, and distill data into relevant information. We argue that machine learning, and especially deep learning approaches, can meet this analytic challenge to enhance our understanding, monitoring capacity, and conservation of wildlife species. Incorporating machine learning into ecological workflows could improve inputs for population and behavior models and eventually lead to integrated hybrid modeling tools, with ecological models acting as constraints for machine learning models and the latter providing data-supported insights. In essence, by combining new machine learning approaches with ecological domain knowledge, animal ecologists can capitalize on the abundance of data generated by modern sensor technologies in order to reliably estimate population abundances, study animal behavior and mitigate human/wildlife conflicts. To succeed, this approach will require close collaboration and cross-disciplinary education between the computer science and animal ecology communities in order to ensure the quality of machine learning approaches and train a new generation of data scientists in ecology and conservation

Modeling the Spatial Distribution and Fruiting Pattern of a Key Tree Species in a Neotropical Forest: Methodology and Potential Applications

Damien Caillaud is with UT Austin and Max Planck Institute for Evolutionary Anthropology; Margaret C. Crofoot is with the Smithsonian Tropical Research Institute, Max Planck Institute for Ornithology, and Princeton University; Samuel V. Scarpino is with UT Austin; Patrick A. Jansen is with the Smithsonian Tropical Research Institute, Wageningen University, and University of Groningen; Carol X. Garzon-Lopez is with University of Groningen; Annemarie J. S. Winkelhagen is with Wageningen University; Stephanie A. Bohlman is with Princeton University; Peter D. Walsh is with VaccinApe.Background -- The movement patterns of wild animals depend crucially on the spatial and temporal availability of resources in their habitat. To date, most attempts to model this relationship were forced to rely on simplified assumptions about the spatiotemporal distribution of food resources. Here we demonstrate how advances in statistics permit the combination of sparse ground sampling with remote sensing imagery to generate biological relevant, spatially and temporally explicit distributions of food resources. We illustrate our procedure by creating a detailed simulation model of fruit production patterns for Dipteryx oleifera, a keystone tree species, on Barro Colorado Island (BCI), Panama. Methodology and Principal Findings -- Aerial photographs providing GPS positions for large, canopy trees, the complete census of a 50-ha and 25-ha area, diameter at breast height data from haphazardly sampled trees and long-term phenology data from six trees were used to fit 1) a point process model of tree spatial distribution and 2) a generalized linear mixed-effect model of temporal variation of fruit production. The fitted parameters from these models are then used to create a stochastic simulation model which incorporates spatio-temporal variations of D. oleifera fruit availability on BCI. Conclusions and Significance -- We present a framework that can provide a statistical characterization of the habitat that can be included in agent-based models of animal movements. When environmental heterogeneity cannot be exhaustively mapped, this approach can be a powerful alternative. The results of our model on the spatio-temporal variation in D. oleifera fruit availability will be used to understand behavioral and movement patterns of several species on BCI.The National Center For Ecological Analysis is supported by NSF Grant DEB-0553768, the University of California Santa Barbara and the State of California. The Forest Dynamics Plots were funded by NSF Grants to Stephen Hubbell DEB-0640386, DEB-0425651, DEB-0346488, DEB-0129874, DEB-00753102, DEB-9909347, DEB-9615226, DEB-9615226, DEB-9405933, DEB-9221033, DEB-9100058, DEB-8906869, DEB-8605042, DEB-8206992, DEB-7922197, and by the Center for Tropical Forest Science, the Smithsonian Tropical Forest Research Institute, The John D. and Catherine T. MacArthur Foundation, the Mellon Foundation and the Celera Foundation. DC is supported by NSF grant DEB-0749097 to L.A. Meyers. SS is supported by an NSF Graduate Research Fellowship. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Biological Sciences, School o