DOI: 10.5061/dryad.4qrfj6qm3
title: Data from: Interplay of physical and social drivers of movement in male African savanna elephants

description: Despite extensive research into the behavioral ecology of free-ranging
 animal groups, questions remain about how group members integrate
 information about their physical and social surroundings. This is because
 a) tracking of multiple group members is limited to a few easily
 manageable species; and b) the tools to simultaneously quantify physical
 and social influences on an individual’s movement remain challenging,
 especially across large geographic scales. A relevant example of a
 widely-ranging species with a complex social structure and of conservation
 concern is the African savanna elephant. We evaluate highly synchronized
 GPS tracks from five male elephants in Etosha National Park in Namibia by
 incorporating their dynamic social landscape into an established resource
 selection model. The fitted model predicts movement patterns based
 simultaneously on the physical landscape (e.g., repeated visitation of
 waterholes) and the social landscape (e.g., avoidance of a dominant male).
 Combining the fitted models for multiple focal individuals produces
 landscape-dependent social networks that vary over space (e.g., with
 distance from a waterhole) and time (e.g., as the seasons change). The
 networks, especially around waterholes, are consistent with dominance
 patterns determined from previous behavioral studies.  Models
 that combine physical landscape and social effects based on remote
 tracking can augment traditional methods for determining social structure
 from intensive behavioral observations. More broadly, these models will be
 essential to effective, in-situ conservation and management of
 wide-ranging social species in the face of anthropogenic disruptions to
 their physical surroundings and social connections.
description: <strong><em>Study subjects and the social
 landscape</em>: </strong>The five individuals considered in
 this study belong to a large elephant subpopulation residing in the
 northeastern region of Etosha National Park, Namibia. As a part of a
 different research effort, these individuals were classified into several
 age, dominance, social, and reproductive categories (O’Connell-Rodwell et
 al. 2011; O’Connell et al. 2024a). The age structure in this population
 was determined on the basis of several morphological features and can be
 found in the original publication (O’Connell-Rodwell et al. 2011;
 O’Connell et al. 2022).  The dominance categories are reported from a
 population-level, ordinal dominance hierarchy based on the frequency of
 agonistic dyadic interactions (i.e., displacement) observed during
 all-occurrence sampling, over multiple field seasons (O’Connell-Rodwell et
 al. 2024a). The social categories were approximated using social network
 analysis (i.e., eigenvector centrality—an index expressing how influential
 an individual is based on the frequency of associating with other
 influential conspecifics) (O’Connell-Rodwell et al. 2024a;
 O’Connell-Rodwell et al. 2024b;). The reproductive category expresses
 whether an elephant was in musth at the time of behavioral data
 collection.  <strong><em>Tracking
 data</em>:</strong> In September 2009, ENP personnel fitted
 five elephants with Global Positioning System (GSP) and satellite Global
 System for Mobile Communication (GSM) tracking devices. The trackers
 recorded positional data (i.e., longitude, latitude) every 15 minutes over
 approximately 24 months. Prior to analysis, we converted tracking data to
 Cartesian units (i.e., meters) using the Universal Transverse Mercator
 coordinate system (UTM) projection. We also filtered the data to remove
 outlier movements as follows: we kept only movements (pairs of GPS fixes)
 in which 1) the interval was 15 minutes, 2) the focal individual moved ≤
 300 m in that time, and 3) all four of the other tracked elephants were
 within 20 km. Criterion 1 eliminates missed fixes; criterion 2 eliminates
 a small number of unusually fast movements which could represent startle
 responses to rare stimuli; and criterion 3 ensures that there is at least
 the potential for social interactions between all five elephants. The
 resulting datasets (one for each focal individual) had between 27,397 and
 30,584 movements. <strong><em>The physical
 landscape</em>: </strong>To evaluate tracking data in the
 context of the physical landscape, we constructed a map of vegetation
 productivity using data from the 16-day 250 m Normalized Difference
 Vegetation Index (NDVI) MODIS imagery. We also created a map of the
 perennial waterholes by extracting coordinate information from existing
 geospatial records generated by ENP personnel. Finally, we compiled a map
 of ‘frequently visited areas’ (FVAs) as the centroids of the top 20
 clusters of large turning angles (&gt;90 degrees) in the movement
 data. These locations broadly correlate with the presence of shade and
 proximity to fruiting trees (Kilian, W. personal communication), which in
 other populations affect elephant movement.
 <strong><em>The Social Resource Selection Function
 (SRSF) model:</em> </strong>Our approach extends the existing
 Resource Selection Function framework in which an individual’s location,
 when fixed (by a GPS device or other tracking methodology) is considered a
 choice made from a set of possible locations. This set of locations is
 bounded in space by how far from its previously known location the
 individual could reasonably be expected to move in the time between the
 two fixes. The relative probability of ending up at different
 destinations, relative to one’s current location, is modeled using
 conditional logistic regression (CLR) as a function of various
 environmental parameters that differ between locations (e.g., ‘vegetation
 density’, ‘distance to water’, distance to the previous location). The
 SRSF model adds to the RSF framework by considering the locations of other
 individuals in a moving group as time-varying point features of the
 landscape. One individual (the focal individual) is modeled, and the
 locations of the others (nonfocal individuals) are incorporated as
 ‘distance to neighbor’ values that can be calculated for all the possible
 locations in the CLR. Assuming that each elephant responds differently to
 different conspecifics, we calculate a set of social predictors by
 determining the distance to each neighbor separately.
 For any given movement m, the ‘choice’ is a binary response,
 where a potential location i is either the endpoint at which the
 individual was recorded (yi = 1) or one of the alternatives (yi = 0). For
 convenience, we have labeled the chosen location with the subscript j (j ∈
 i). The probability of a movement is modeled by where X is a matrix of k
 predictors derived from the landscape data; β is a k by 1 matrix of
 parameters to be estimated; c is the total number of locations considered
 (1 being the actual endpoint and c – 1 being randomly sampled within a
 circle of fixed radius); and s is the probability of a stochastic,
 ‘non-choice-type movement’ for which the endpoint is independent to any of
 the included predictors. One example might be a sudden scare that causes a
 flight response. In this case, we assign all possible endpoints the same
 probability 1/c. Including the possibility of non-choice movements is a
 novel addition to the standard CLR model; we found that for these data it
 stabilized the parameter estimates (meaning that we obtained similar
 results with different random subsets of the data when it was included,
 and disparate estimates when it was not). Overall, pm is the predicted
 ‘preference value’ for the chosen location divided by (and therefore
 conditional on) the sum of the preference values for the random sample of
 possible locations. In practice, depending on the resolution of the
 landscape and the boundary of possible distances reached, the denominator
 could include hundreds or even thousands of random locations. This can
 make computation of the expression, which is repeated for every movement
 in a dataset, time-consuming—a challenge that then translates into the
 model fitting. It is thus standard practice to randomly select a fixed
 number of non-chosen alternative locations on the assumption that they
 will comprise a representative sample of the landscape variation available
 to the individual. Given that our landscape features — various distance
 measures plus an interpolated array of NDVI values — vary smoothly and
 continuously within our sampling radius, we used 30 random locations (so c
 = 31). We fit the CLR by maximizing L, the log-likelihood of the entire
 set of n movements, using quasi-Newton nonlinear maximization.
 We performed variable selection by first fitting models with all
 possible subsets of physical and social landscape variables in their
 quadratic forms, except for distance to the previous location, which was
 always included as a linear function as an established proxy for the
 effort required to move to a new location. We ranked the models using
 Akaike’s Information Criterion (AIC) and calculated importance scores for
 each variable as the cumulative Akaike weight of the models in which it
 appeared.  Interpretation of the SRSF model outputs
 depends on the functional form of each variable over the range of its
 values and its importance score. Because a linear cost-of-movement
 function is in every model by design, we exclude it from further reporting
 and discussion. The functional forms of the remaining variables can be
 divided into five categories: 1) monotonically increasing or 2) decreasing
 (indicating a preference for large or smaller values of the variable in
 question); 3) convex with the maximum within the data range (a preference
 for intermediate values); 4) concave with the minimum within the data
 range (a preference for large and small values indicating a back-and-forth
 movement between the locations containing the variable in question); or 5)
 constant over the data range (lack of preference for a specific value)
 (Mashintonio et al. 2014). The SRSF model outputs are
 expressed as the relative preference for movement towards locations
 defined by the physical (e.g., NDVI, a proxy for vegetation productivity)
 and social variables (e.g., distance to a neighbor) (Mashintonio et al.
 2014). <strong><em>Testing the predictions
 about movements in a sociophysical
 landscape:</em></strong> Because one of our goals was to see
 if social interactions previously observed at Mushara waterhole persist in
 the larger landscape, we fit the SRSF model to three different subsets of
 movement data: movements within 2 km of waterholes, movements within 2 km
 of an FVA, and movements more than 2 km away from both kinds of feature.
 To account for potential seasonal changes in the effect of the
 sociophysical landscape on individual movement, we divided each data
 subset into the wet season (November-April) and dry season (May-October).
 Different numbers of movements remained for each subset for each focal
 elephant from 371 to 13,555. To achieve a constant sample size for
 seasonal comparisons, we fit the SRSF model to 350 random movement
 segments for areas near water and far from water or FVAs; and 450 randomly
 selected movement segments for areas near the FVAs. To test for
 consistency, we repeated each model fit ten times using different random
 samples of movements. The physical predictors in each
 fit included time-matched NDVI (vegetation productivity), distance to the
 nearest waterhole, and distance to the nearest FVA. The social predictors
 for each focal animal were distance to each of the four nonfocal elephants
 separately.
 <strong><em>References: </em></strong> O’Connell-Rodwell CE, Wood JD, Kinzley C, Rodwell TC, Alarcon C, Wasser SK, Sapolsky R. (2011). Male African elephants (<em>Loxodonta africana</em>) queue when the stakes are high. Ethol Ecol Evol. 23: 388–397. O’Connell-Rodwell CE, Berezin JL, Kinzley C, Freeman PT, Sandri MN, Kieschnick D, Rodwell TC, Abarca M, and Hayssen V. (2024a Preprint). Dynamics of male African elephant 695 character durability across time and social contexts. bioRxiv 2024.05.24.595367 O’Connell-Rodwell CE, Freeman PT, Kinzley C, Sandri MN, Berezin JL, Wiśniewska M, Jessup K, Rodwell TC. (2022). A novel technique for aging male African elephants (<em>Loxodonta africana</em>) using craniofacial photogrammetry and geometric morphometrics. Mamm Biol. 102: 591-613 O’Connell-Rodwell CE, Berezin JL, Kinzley C, Freeman PT, Sandri MN, Kieschnick D, Rodwell TC, Abarca M, and Hayssen V. (2024a Preprint). Dynamics of male African elephant 695 character durability across time and social contexts. bioRxiv 2024.05.24.595367 O’Connell-Rodwell CE. Berezin JL, Pignatelli A, Rodwell TC. (2024b Preprint). The use of vocal coordination in male African elephant group departures: evidence of active leadership and consensus. bioRxiv 2024.05.31.596833. Mashintonio AF, Pimm SL, Harris GM, van Aarde RJ, Russell GJ. (2014). Data-driven discovery of the spatial scales of habitat choice by elephants. PeerJ. 2: e504.
description: # Data package for evaluating the interplay of physical and social drivers
 of movement in male African savanna elephants
 [https://doi.org/10.5061/dryad.4qrfj6qm3 ](https://doi.org/10.5061/dryad.4qrfj6qm3) ## Description of the data and file structure **READ ME  TEXT FILE for BEHECO-2023-0350.R2 (**[https://doi.org/10.1093/beheco/arae091)](https://doi.org/10.1093/beheco/arae091) This data package is associated with the manuscript BEHECO-2023-0350.R2, titled *Interplay of physical and social drivers of movement in male African savanna elephants.*"  The code in CodeDemo.nb (a.k.a. 'working' notebook), written in Wolfram language, uses functions from a separate code package, Movement.wl, also provided. The CodeDemo notebook demonstrates how these functions were applied to the elephant movement and physical landscape data. Both Movement.wl and the various input files (see below) are assumed to reside in the same directory as CodeDemo.nb. The. The elephant movement data are not provided due to the species' sensitive conservation status but may be requested from the authors (at [maggie.wisniewska@gmail.com](mailto:maggie.wisniewska@gmail.com)). ### Files and variables #### **File: CodeDemo.nb** Description: a notebook demonstrating the analysis in the manuscript; loads the code package Movement.wl as well as physical landscape and movement files. #### **File: Movement.wl** Description: a Wolfram Language package that holds a set of functions for analyzing movement data; this package ist be loaded by the working notebook CodeDemo.nb. #### **File: Waterholes.csv** Description: an input file;a list of coordinates of permanent waterholes visited by elephants; this is an example of a physical landscape point variable. ##### **Variables** * Name: Waterhole name * UTMX: X coordinate in Universal Transverse Mercator projection * UTMY: Y coordinate in Universal Transverse Mercator projection #### **File: FVAs.csv** Description: an input file; a list of coordinates of static centroids of the top 20 clusters of large turning angles (&gt;90 degrees) in the movement data, a.k.a. Frequently Visited Areas (FVAs); this is an example of a physical landscape point variable. ##### Variables * UTMX: X coordinate in Universal Transverse Mercator projection * UTMY: Y coordinate in Universal Transverse Mercator projection #### **File: NDVIStack** Description: an input file in Wolfram format; a 3D array of vegetation productivity values based on data from the 16-day 250 m Normalized Difference Vegetation Index (NDVI) MODIS imagery; an example of a time-varying physical landscape array variable. ##### **Variables** * ndviStack: a Wolfram nested list containing the MODIS values. The first and second levels (dimensions) correspond to 250m ‘pixels’ in the X and Y coordinates respectively of the UTM projection used for the analysis. The third level (dimension) corresponds to successive two-week intervals from September 30, 2009 to December 3, 2011. #### **Files: FitsNearWater\_XXX\_350\_10\_1** Description: output files containing 10 models fitted to different random samples of 350 movements; these data are from movements within 2km of a permanent waterhole; XXX is the season (Wet or Dry). #### **Files: FitsNearFVAs\_XXX\_450\_10\_1** Description: output files containing 10 models fitted to different random samples of 450 movements; these data are from movements within 2km of an identified Frequently Visited Area (see above); XXX is the season (Wet or Dry). #### **Files: FitsIn TheField\_XXX\_350\_10\_1** Description: output files containing 10 models fitted to different random samples of 350 movements; these data are from movements more than 2km from either a waterhole or a Frequency Visited Area; XXX is the season (Wet or Dry). #### **File: Figure\_3.pdf (Zenodo)** Description: graphical representation of model outputs for physical variables (as in the publication). #### **File: Figure\_4.pdf (Zenodo)** Description: graphical representation of model outputs for social variables (as in the publication). ## **Code/software** Please see the information for DemoCode.nb and Movement.wl above. ## **Access information** Other publicly accessible locations of the data: n/a Data were derived from the following sources: * In September 2009, Etosha National Park personnel fitted five elephants with Global Positioning System (GSP) and satellite Global System for Mobile Communication (GSM) tracking devices. The trackers recorded positional data (i.e., longitude, latitude) every 15 minutes over approximately 24 months.