Acceleration data reveal behavioural responses to hunting risk in Scandinavian brown bears

Predation may indirectly influence prey’s fitness and population dynamics through behavioural adjustments in response to perceived predation risk. These non-consumptive effects of predation can also arise from hunting by humans, but they remain less documented. Advances in biologging allow detailed assessments of the activity budgets of elusive wildlife, increasing the potential to uncover the non-consumptive effects of human activities on animals. We used tri-axial accelerometry to record the daily activity of 24 Scandinavian brown bears (20 females and four males) from a heavily hunted population in Sweden, for a total of 29 bear-years (2015-2022). We used a random forest algorithm trained with observations of captive brown bears to classify the accelerometry data into four behaviours, running, walking, feeding, and resting, with an overall precision of 95%. We then used these classifications to evaluate changes in bear activity budgets before and during the hunting season. Bears exhi..., There are two main analyses associated with the datasets and scripts provided in this submission. Here, we provide an overview of both analyses but invite readers to read the associated manuscript for further details.
Behavioural classification algorithm
We classified the behaviours of captive brown bears using a random forest algorithm. To do so we used raw acceleration data on three axes recorded at a frequency of 8Hz. Raw acceleration data were divided into 3s samples, and for each sample, we calculated summary statistics (dataset \"random_forest_algorithm_dataset.csv\"). These statistics included measures like mean, standard deviation, minimum, maximum, skewness, kurtosis, correlations between axes, and dominant power spectrum. We then trained the random forest using known behaviour labels, obtained through visual observations of the captive bears. We build multiple decision trees and combined their results (script \"random_forest_bear_behaviour.R\"). 
Wild brown bear activity patt..., , # Acceleration data reveal behavioural responses to hunting risk in Scandinavian brown bears

[https://doi.org/10.5061/dryad.pc866t214](https://doi.org/10.5061/dryad.pc866t214)

## Description of the data and file structure

Updated 2025-07-18

Clermont et al. 2025 - Acceleration data reveal behavioural responses to hunting risk in Scandinavian brown bears, Ecology and Evolution, [https://doi.org/10.1002/ece3.71489](https://doi.org/10.1002/ece3.71489)

## Code/software

R codes stored at Zenodo: [https://doi.org/10.5281/zenodo.14884698](https://doi.org/10.5281/zenodo.14884698)

### Files and variables

**Description of datasets and scripts included, by analysis (see Methods):**

**1. Behavioural classification algorithm analysis:**

**Data:** random_forest_training_dataset.csv

**R Script:** random_forest_bear_behaviour.R

**Description of variables:**

\- start_UTC_timestamp: timestamp in UTC at the start of the 3s sequence

\- Subject: the name of the bear. Eternity is the young femal...,