DOI: 10.5061/dryad.h44j0zpx5
title: The pace and sequence of spatial learning: exploration facilitates long-term behavioural refinement

description: Understanding how animals learn in novel environments is crucial for
 predicting behavioral responses to rapid environmental change, yet we lack
 knowledge about how long different behaviors take to develop and refine,
 and how exploration facilitates learning. We tested the
 exploration-refinement hypothesis using movement and diet data from
 GPS-collared bison (Bison bison; n=10) monitored for five years following
 reintroduction to Banff National Park. We examined how exploration
 influenced movement efficiency, habitat selection behaviors, and home
 range establishment. Different behaviors showed distinct learning
 trajectories. Movement efficiency in familiar areas reached an inflection
 point at 384 days, but when facing unfamiliar terrain, efficiency
 decreased as animals needed time to learn the new area. Habitat selection
 behaviors showed rapid initial improvement followed by extended refinement
 periods lasting up to three years. Exploratory movements occurred
 primarily in the first year (57%) but continued throughout the study and
 were positively correlated with improvements in habitat selection
 behaviors and home range stabilization, but not with movement efficiency.
 Our results demonstrate that exploration facilitates spatial learning—the
 process of acquiring and using information about environmental structure,
 distances, and relative positions to navigate effectively, locate
 resources, and avoid threats. This suggests that allowing individuals to
 explore while preventing large dispersal movements may be required for
 successful reintroductions. The extended timeline required for spatial
 behaviors to stabilize (3-4 years) indicates that behavioral refinement
 takes substantially longer than typically assumed, highlighting the need
 to align monitoring periods with the biological timeframes required for
 animals to learn novel environments.
description: Population size, animal handling, and collaring
 The initial population of 16 wild-born bison (10 adult females, 6
 adult males) from Elk Island National Park increased from 34 individuals
 in 2018 to 90 by 2022. Throughout the study period, 30 – 100% (from 100%
 initially to ~30% by study end) of the adult female population was
 monitored via GPS collars(Vectronic Aerospace, Berlin, Germany). We only
 included individuals with &gt; 1 year of data. All data were cleaned
 to only include high quality 2-hour fixes (3 dimensional fixes with a
 Horizontal dilution of precision &lt; 2). While bison initially moved
 as a single group, they exhibited typical fusion-fission dynamics as the
 population grew, regularly splitting into multiple subgroups throughout
 the study period (Figure S2, see Supplementary Methods for details on
 group dynamics). The animal study and capture protocol was approved by
 Parks Canada’s Animal Care Task Force under a 2016 Parks Canada Research
 and Collection Permit. Metrics of spatial
 learning <em>Foraging behavior
 </em> We first evaluated the pace at which naïve
 individuals develop foraging behaviors. Bison are bulk feeders and mainly
 eat grasses (Steuter et al. 1995; Knapp et al. 1999) which predominantly
 grow in meadows within the study area. Thus, we considering foraging
 behaviors as each individual’s exposure to percent herbaceous cover and
 calculating the amount of time spent in meadows. For each individual’s GPS
 location, we extracted percent herbaceous cover from the Vegetation
 Resources Inventory (British Columbia Landcover Classification Scheme
 level 4;
 https://catalogue.data.gov.bc.ca/group/vegetation-resource-inventory [VRI
 level 4]). To quantify time spent in meadows, we first delineated
 potential foraging meadows for bison in this system. We delineated
 potential foraging meadows using a combination of landcover type, percent
 herbaceous cover, and shrub crown closure data. Meadows were required to
 meet minimum size and vegetation quality thresholds to be considered
 viable foraging habitat (full delineation criteria in Supplementary
 Methods). We then intersected GPS locations with the meadow polygons and
 summed the number of locations falling within a meadow over the course of
 each month an individual was monitored.
 <em>Movement behavior</em> To
 evaluate the pace at which individuals develop movement behavior, we
 evaluated how closely individuals followed to predicted least-cost paths
 when moving between heavily used foraging/resting areas. First, we
 delineated the population-level core use areas (i.e., 50% utilization
 distribution polygons) on the landscape by fitting a kernel density
 estimator (adehabitatHR package) to all bison GPS locations (i.e., the
 entire study period July 2018 – September 2023) using a 30 m grid and
 delineating the cells that included the top 50% of volume of use (i.e.,
 core area; Calenge 2006). We analyzed movement
 efficiency by comparing observed travel paths between core areas to
 predicted least-cost paths based on topographic slope. Least-cost paths
 were calculated using elevation-based cost functions optimized for
 mountainous terrain (technical details in Supplementary
 Materials). We compared observed traveling trajectories
 with predicted least-cost paths between core areas over time. To render
 GPS location data to such a framework, we first identified whether each
 bison location was inside or outside the core area and removed locations
 within core areas. We then identified consecutive steps between core areas
 in the movement data and connected those sequential locations to form a
 trajectory. We compared observed paths with least-cost paths using the
 normalized path deviation index. The index compares how far apart the
 observed movement path is from the predicted least-cost path along the
 entirety of the trajectory (the area between the two paths divided by the
 distance of the shortest path to normalize the metric so that indexes can
 be compared across individuals with different path lengths). The
 normalized path deviation index is reported as the percent deviation
 between the two paths (i.e., if normalized path deviation index is 30%,
 the average distance between the two paths is 30% the length of the
 distance of the shortest path; Lewis 2023). 
 <em>Emergent patterns: Home range settlement and diet
 quality</em> We evaluated the pace at which
 emergent patterns such as home range area and overlap and diet stabilize
 over time. First, we estimated individual monthly home ranges using an
 auto-correlated kernel density estimator (Fleming and Calabrese 2017,
 Signer et al. 2019). We removed any individual-month where the individual
 in question had &lt;20 days of GPS locations. As a result of a few
 missing months, we only compared area and overlap for consecutive months.
 We compared monthly home range overlap using Bhattacharyya's affinity
 index which incorporates both the spatial extent and the intensity of
 space use, providing a comprehensive measure of the similarity between the
 utilization distributions of individuals (Fieberg and Kochanny 2005,
 Signer et al. 2019). Second, to estimate diet quality, we analyzed a
 number of diet quality metrics from fecal samples collected
 opportunistically over time (from December 2018 to November 2020) and
 across different areas of the study area. Fecal samples were analyzed for
 diet quality by the Grazing Animal Nutrition (GAN) Lab in Texas (USA).
 Samples were tested for fecal nitrogen, digestible organic matter,
 phosphorus, and nitrogen, all metrics that have been shown to be relevant
 to bison diet and foraging (Geremia et al. 2019).
 Modelling approach To evaluate the pace of
 learning, we plotted learning curves, which have often been used in
 behavioral ecology experiments to test for learning (e.g., Papachristos
 and Gallistel 2006, Dukas 2008) and more recently to quantify the
 development of movement behavior (Hertel et al. 2023). Similar to the
 methods of Hertel et al. (2023), we evaluated the pace of learning and
 whether foraging behavior or movement behaviors are learned more quickly,
 by comparing a set of 3 models for each foraging, movement, and home range
 variable. We evaluated how time influenced: 1) exposure to herbaceous
 cover, 2) time spent in meadows, 3) diet quality, 4) deviation from
 least-cost paths, and 5) home range overlap. If the distribution of our
 explanatory variable was right-skewed, we log transformed it prior to
 fitting the model. We fit each variable as a separate model where time was
 a) a linear predictor, b) a squared predictor, and c) a cubed, polynomial
 predictor and compared models using Akaike information criterion (AIC; Aho
 et al. 2014), allowing the general pace and shape of each learning curve
 to be determined. To account for repeated measures of individuals and
 seasonal effects, each model except for diet quality was fit with a random
 intercept term of animal ID and season. Season was operationally defined
 as spring (March – May), summer (June – August), fall (September –
 November) and winter (December – February). Diet quality samples were not
 tied to individual animals, therefore the random intercept term was only
 season. We then determined changepoints in the
 predicted learning curves (i.e. inflection points) to identify when the
 pace in learning significantly changed (Muggeo et al. 2014). We fit
 segmented regression models to the predicted relationship of each variable
 (e.g., time in meadows, adherence to least-cost paths) to time based on
 the top learning curve model from above. By identifying changepoints, we
 can detect significant behavioral changes, in this case when learning
 peaked or began to slow down. Segmented models are regression models where
 the relationship between the response and predictor variables are
 piecewise linear (i.e., two or more straight lines connected at unknown
 values, the breakpoints or changepoints; Muggeo et al. 2014, Muggeo 2017).
 We set the number of change points to reflect the predicted curve from the
 best fitting model for each variable (e.g., for an asymptotic curve, we
 used one change point, but for a sigmoid or similarly complicated curve,
 we used two change points, etc.). To evaluate whether
 exposure to novel places (i.e., exploration) leads to spatial learning, we
 fit models with each of our foraging and movement behavior variables as a
 function of the number of exploratory events undertaken by the bison
 throughout the study period. We fit each variable as a separate model
 where the number of exploratory events was fit with the same structure as
 the learning curve (i.e., if the top learning curve model was a
 polynomial, we fit exploratory events as a polynomial term). Similar to
 the learning curve models, each model included a random intercept term for
 animal ID and season.
description: # The pace and sequence of spatial learning: exploration facilitates
 long-term behavioural refinement
 [https://doi.org/10.5061/dryad.h44j0zpx5](https://doi.org/10.5061/dryad.h44j0zpx5) ## Description of the data and file structure Data on bison explorations across 5 years after reintroduction. Data files include information on individual metrics of time spent in meadows, exposure to forage biomass, diet quality, adherence to least cost travel paths, home range area, and home range overlap. Code files include scripts to run learning curve models, change point analyses, and regression models to understand how exploration influences spatial learning. Also included in the code are scripts to create the figures in the manuscript. ### Files and variables #### File: DryadData_Code.zip **Description:**  In the DryadData_Code.zip you will find: **CombinedData.rds** is the R data file that contains information on individual metrics of diet quality, adherence to least cost paths, exposure to forage biomass, time spent in meadows, monthly home range area, and monthly home range overlap. id = individual bison ID seas_id = combined season (e.g., fall, spring, etc.) and animal ID. Season was operationally defined as spring (March – May), summer (June – August), fall (September – November) and winter (December – February). value = the numeric value to the variable in question (e.g., home range overlap). Refer to the variable column to see what the value here refers to. ``` herbCover = % biomass (values from 0 - 100) diet quality (CP:DOM [crude protein to digestible organic matter]) = ratio of crude protein to digestible organic matter (values range from 3 - 15) area = home range area in meters squared. Home ranges were estimated monthly using ADKE overlap = percent of overlap between consecutive monthly home ranges (values range from 0 - 1). Home ranges were estimated monthly using ADKE. LCP = normalized path deviation index from predicted least cost path. TimeInMeadows = average number of GPS locations falling within high-quality habitat patches per month (# of points in habitat patches/total # of GPS locations for that month) DistanceTraveled = monthly distance traveled in meters ``` time = number of days since release year_sinceRelease = the number of years since the bison were released (release date = 7/28/2018) variable = different metrics of spatial learning being measured (e.g., adherence to least cost path, monthly home range overlap, etc.) **explorations_noSpatial.rds** is the R data file that has the date and individual animals that explored during the 5 year post-reintroduction period. id = individual bison ID date = date in Mountain Time adjusted for daylight savings time. season = season (e.g., fall, spring, etc.) Season was operationally defined as spring (March – May), summer (June – August), fall (September – November) and winter (December – February). startExpl = the date the exploration began groupedExpl = grouped exploration number (give an ID to each exploration that had the same start date [e.g., if multiple bison explored together, they would all have the same group exploration ID]) time = number of days since release **ModelEvaluation_ChangePoint_LearningCurves.R** - Code for running analyses and creating figures. ## Code/software All analyses are done in program R (version 4.4.1). Packages needed are loaded as Libraries at the beginning of the script. ## Access information Other publicly accessible locations of the data: * None Data was derived from the following sources: * GPS collar data from bison * Diet quality data from analyzed scat samples * GIS layers (described in text) to evaluate time in meadows, exposure to biomass, and least-cost-paths.