• Figure 1.
    Download figureOpen in new tabFigure 1. Combining three-dimensional habitat reconstruction with baboon tracking data to determine which habit and social features predict individual movement decisions.

    (A) Example of baboon trajectories overlaid on a high-resolution three-dimensional habitat reconstruction. Colored lines show trajectories of each baboon. (B) Predictive accuracy for step selection models using habitat features only (red point), social features only (blue point), or both social and habitat features (purple point), as compared to a null model (black point). Y-axis shows the negative log loss of out-of-sample data; points farther up the y-axis indicate better model predictions. (C) AIC weights associated with each feature based on multi-model inference. Each point shows the feature weights for a particular baboon individual, and black bars show the median feature weight across all individuals. Features are ranked from highest median feature weight (top) to lowest mean feature weight (bottom). See also Videos 12.

    DOI: http://dx.doi.org/10.7554/eLife.19505.002

    Figure 2.
    Download figureOpen in new tabFigure 2. Visualizing the preference landscape underlying individual movement decisions.

    (A) Example of a single step taken by a focal individual. Background image shows overhead view of 3D habitat reconstruction. White marker shows the location of the focal baboon (whose next step is being modeled), and white circle shows a radius of 5 m (the specified step size) around the focal individual. White arrow shows the step actually taken by the individual. Red lines show recent locations of other baboons, and red points show locations of other baboons at the end of the step. (B –I) Visualizations of the influence of each habitat and social factor (the ‘preference landscape’) based on the fitted step selection model - lighter yellow areas represent locations that are more preferred, and darker blue areas are less preferred. Each panel represents the influence of a particular factor (ignoring all others) as predicted by the model: (B) vegetation density, (C) sleep site direction, (D) roads, (E) animal paths, (F) social density, (G) fraction of visible neighbors, (H) locations that have ever been used by another baboon, (I) number of baboons that have recently (in the past 4.5 minutes) used a location. (JL) Last three panels represent overall preference landscapes, combining information from all habitat features only (J), all social features only (K), and all features, i.e. the full model prediction (L). For another example of preference landscapes (from a case where the focal individual started on a road), see Appendix 1—figure 14.

    DOI: http://dx.doi.org/10.7554/eLife.19505.006

    Figure 3.
    Download figureOpen in new tabFigure 3. The interplay between habitat and social features in shaping individual movement decisions.

    We compare each real location where a baboon moved to an alternative location that it could have moved. We randomly select one of these two locations and denote it location 1, denoting the other location 2. Each plot shows the probability that location 1 was the true location actually chosen by the baboon, as a function of the difference between the numbers of other baboons that had recently (within the past 4.5 min) occupied location 1 and the number that had recently occupied location 2. (A) Across all data, the location chosen by the focal baboon is more likely to be the one recently occupied by more of its group mates. Moreover, the greater the difference between the number of baboons to have occupied each location, the stronger the effect (sigmoidal shape of curve). (B,C) Movement decisions are altered by the influence of roads. Here, data are shown from when the focal baboon started on a road (B) and when it started off a road (C), in three different cases: neither location was on a road (black line), both locations were on a road (blue line) or location 1 was on a road whereas location 2 was not (red line). (DF) Movement decisions are influenced by the direction of the sleep site in the morning (D) and evening (F), but less in the midday (E). Each colored line shows data from instances when the difference in the steps’ directedness toward the sleep site between location 1 and location 2 fell into a different bin (given in the legend), with lighter (more yellow) colors indicating a greater difference. When there is little difference (dark purple lines), the curve resembles that shown in panel A. As the difference increases, the location that is in the direction away from (in the morning) or towards (in the evening) the sleep site becomes more likely to be chosen by the baboon. Shaded regions denote 95% confidence intervals (based on Clopper-Pearson intervals). See also Appendix 1—figure 12 (other environmental influences) and Appendix 1—figure 13 (10 m steps rather than 5 m steps).

    DOI: http://dx.doi.org/10.7554/eLife.19505.007

    Figure 4.
    Download figureOpen in new tabFigure 4. Illustration of the six measures used to characterize group-level properties.

    For further information, see Supplementary methods.

    DOI: http://dx.doi.org/10.7554/eLife.19505.008

    Figure 5.
    Download figureOpen in new tabFigure 5. At the group level, troop structure and movement changes based on roads and vegetation density.

    (A) Two-dimensional histograms of the speed and polarization (top panel), spread and directedness (middle panel), and elongation and tilt (bottom panel) of the baboon troop across all data. Lighter areas represent more likely group configurations. (BC) Difference between the distributions within a given context and the overall distribution across all data. Redder areas represent group configurations that are over-represented within a given context (relative to the rest of the data), and bluer areas represent under-represented configurations. Each column represents a different context: (B) on-road and off-road, and (C) open, medium, and dense vegetation. See also Appendix 1—figures 1718 for the influence of path density and time of day respectively, as well as Appendix 1—figures 1922 for one-dimensional histograms of each group-level property within each context.

    DOI: http://dx.doi.org/10.7554/eLife.19505.009

    Figure 6.
    Download figureOpen in new tabFigure 6. Results of fitting linear models to predict group-level properties as a function of features of the habitat occupied by the group and the time of day.

    Colors show feature weights, with lighter colors indicating habitat/temporal features (columns) that were more supported by multi-model inference to predict each group-level property (rows). Arrows indicate the direction of the effects for each fit, with upward (downward) pointing arrows indicating a positive (negative) effect. For time of day column, arrows correspond to morning (left), midday (middle), and evening (right), with the upward (downward) pointing arrow indicating the time of day associated with the largest (smallest) value of the group-level property in each row.

    DOI: http://dx.doi.org/10.7554/eLife.19505.010

  • Table 1.

    Features (predictor variables) used in conditional logistic regression models to predict baboon movement decisions.

    DOI: http://dx.doi.org/10.7554/eLife.19505.003

    Environment densityFraction of non-ground (vegetated) area within a 2.5 m* radius of a potential locationHabitat Feature
    Social densityFraction of all troop mates within a 4.25 m* radius of a potential locationSocial Feature
    Sleep site directionDirection of a potential location relative to the sleep site, ranges from −1 (directly away) to 1 (directly toward), fit as interaction with time of dayHabitat Feature
    RoadsWhether a potential location is on a road (1) or not (0). Fit as an interaction with whether the baboon’s previous location was on a roadHabitat Feature
    Recently-used spaceNumber of other baboons (not including focal individual) that have occupied a potential location within the past 4.5 min*Social Feature
    Ever-used spaceWhether a potential location was ever occupied by another baboon (not including focal individual) across the entire datasetBoth
    Animal pathsWhether a potential location is on an animal path (1) or not (0), fit as interaction with whether baboon’s previous location was on a pathHabitat Feature
    Visible neighborsFraction of other group members visible from a potential location (i.e. direct line-of-sight does not pass through any vegetated areas)Social Feature
    SlopeChange in elevation to a potential location from the baboon’s previous locationHabitat Feature
    • *Spatial and temporal scales were determined using maximum likelihood in a preliminary analysis. See Supplementary methods and Appendix 1—table 1 for further details.

  • Appendix 1—table 1.

    Features (predictor variables) used in conditional logistic regression models to predict baboon movement decisions.

    DOI: http://dx.doi.org/10.7554/eLife.19505.019

    FeatureDescriptionRangeConsidered as:
    Environment densityFraction of raster pixels containing non-ground points within a specified radius (R = 2.5 m) from the potential location (a proxy for vegetation density)[0, 1]

    Habitat Feature
    Social densityFraction of baboons currently tracked within a specified radius (Rsoc = 4.25 m) of the potential location[0, 1]

    Social Feature
    Sleep site directionThe dot product of the direction vector from the current location to the potential location and the vector from the current location to the sleep site location. This quantity ranges from −1 (potential step is in the direct opposite direction from the sleep site) to 1 (potential step is directly toward the sleep site). The influence of the sleep site is expected to vary with time of day, therefore it is fit as an interaction with time of day, yielding three parameters. Times of day included Morning (9 am–12 pm), Midday (12–3 pm), and Evening (3–6 pm)[−1, 1]

    Habitat Feature
    RoadsA binary predictor indicating whether the potential location is on a road or not. The influence of roads is expected to vary with whether an individual is currently on the road or not, therefore it is fit as an interaction with whether the baboon’s current location is on a road (also a binary value), yielding two parameters{0, 1}

    Habitat Feature
    Recently-used spaceAn integer indicating how many other baboons (not including the focal individual) have recently (within the past 4.5 min) occupied the potential location, where a baboon is considered to be 'occupying a location' if its position is within 1 m of that location{0, …, N}

    Social Feature
    Ever-used spaceA binary variable that is defined as 1 if the location, was ever occupied by another baboon (not the focal baboon) throughout the entire dataset, (past, present, and future){0, 1}

    Animal pathsA binary variable indicating whether a potential location is on an animal path (1) or not (0). It is fit as an interaction with whether the baboon’s current location is on a path (also a binary value), yielding two parameters{0, 1}

    Habitat feature
    Visible neighborsA continuous variable that indicates the fraction of other group members that are visible from a given location at the time of the step. Group members were defined as visible if a line drawn from the potential location to their location at the relevant time does not pass through any raster cells containing non-ground points.[0, 1]

    Social feature
    SlopeThe change in elevation (in meters) from the starting location to a potential location(−∞, ∞)

    Habitat feature
  • Video 1. Animation of 3-dimensional habitat reconstruction (point cloud data).

    DOI: http://dx.doi.org/10.7554/eLife.19505.004

  • Video 2. Example of baboon trajectories overlaid on 3-dimensional habitat reconstruction.

    Each point shows the movement of a single baboon within the troop, with color indicating the baboon’s age and sex (red: adult female, dark blue: adult male, orange: subadult female, light blue: subadult male, gray: juvenile male).

    DOI: http://dx.doi.org/10.7554/eLife.19505.005

  • The following dataset was generated:

    Strandburg-Peshkin A, Farine DR, Crofoot MC, Couzin ID, 2015,Data from: Habitat structure shapes individual decisions and emergent group structure in collectively moving wild baboons, http://dx.doi.org/10.5061/dryad.6h5b7, Available at Dryad Digital Repository under a CC0 Public Domain Dedication

    The following previously published dataset was used:

    Crofoot MC, Kays R, Wikelski M, 2015,Data from: Shared decision-making drives collective movement in wild baboons, https://www.datarepository.movebank.org/handle/10255/move.405, Data publicly available at Movebank Data Repository (www.datarepository.movebank.org)