cff-version: 1.2.0
abstract: "Wildlife
crime is one of the most profitable illegal industries worldwide. Current
actions to reduce it are far from effective and fail to prevent population
declines of many endangered species, pressing the need for innovative
anti-poaching solutions. Here, we propose and test a poacher early warning
system that is based on the movement responses of non-targeted sentinel animals,
which naturally respond to threats by fleeing and changing herd topology. We
analyzed human-evasive movement patterns of 135 mammalian savanna herbivores of
four different species, using an internet-of-things architecture with wearable
sensors, wireless data transmission and machine learning algorithms. We show
that the presence of human intruders can be accurately detected (86.1% accuracy)
and localized (less than 500m error in 54.2% of the experimentally staged
intrusions) by algorithmically identifying characteristic changes in sentinel movement.
These behavioral signatures include, among others, an increase in movement
speed, energy expenditure, body acceleration, directional persistence and herd
coherence, and a decrease in suitability of selected habitat. The key to successful
identification of these signatures lies in identifying systematic deviations
from normal behavior under similar conditions, such as season, time of day and
habitat. We also show that the indirect costs of predation are not limited to
vigilance, but also include 1) long, high-speed flights; 2) energetically
costly flight paths; and 3) suboptimal habitat selection during flights. The
combination of wireless biologging, predictive analytics and sentinel animal
behavior can benefit wildlife conservation via early poacher detection, but
also solve challenges related to surveillance, safety and health."
authors:
  - family-names: Data Librarian
    given-names: WUR
  - family-names: de Knegt
    given-names: Henrik J.
title: "Data and scripts underlying the publication: Timely poacher detection and localization using sentinel animal movement"
keywords:
version: 1
identifiers:
  - type: doi
    value: 10.4121/13900106.v1
license: CC BY 4.0
date-released: 2021-02-15