Data from: Dynamic balancing of risks and rewards in a large herbivore: Further extending predator-prey concepts to road ecology

Animal behavior is shaped by the ability to identify risks and profitably
balance the levels of risks encountered with the payoffs experienced.
Anthropogenic disturbances like roads generate novel risks and
opportunities that wildlife must accurately perceive and respond to. Basic
concepts in predator-prey ecology are often used to understand responses
of animals to roads (e.g., increased vigilance, selection for cover in
their vicinity). However, prey often display complex behaviors such as
modulating space use given varying risks and rewards, and it is unclear if
such dynamic balancing is used by animals in the context of road
crossings. We tested whether animals dynamically balance risks and rewards
relative to roads using extensive field -based and GPS collar data from
elk in Yoho National Park (British Columbia, Canada) where a major highway
completely bisects their range during most of the year. We analyzed elk
behavior by combining hidden Markov movement models with a step-selection
function framework. Rewards were indexed by a dynamic map of available
forage biomass and risks were indexed by road crossings and traffic
volumes. We found that elk generally selected intermediate and high forage
biomass and avoided crossing the road. Most of the time, elk modulated
their behavior given varying risks and rewards. When crossing the highway
compared with not crossing, elk selected for greater forage biomass and
this selection was stronger as the number of highway crossings increased.
However, with traffic volume, elk only balanced foraging rewards when they
crossed a single time during a travel sequence. Using a road ecology
system, we empirically tested an important component of predator-prey
ecology – the ability to dynamically modulate behavior in response to
varying levels of risks and rewards. Such a test articulates how
decision-making processes that consider the spatiotemporal variation in
risks and rewards allow animals to successfully and profitably navigate
busy roads. Applying well-developed concepts in predator-prey theory helps
understand how animals respond to anthropogenic disturbances and
anticipate the adaptive capacity for individuals and populations to adjust
to rapidly changing environments.

动物的行为模式由其识别风险的能力，以及将所遭遇的风险水平与所获得的收益进行合理权衡的能力共同塑造。道路等人为干扰会为野生动物带来全新的风险与机遇，野生动物必须精准感知并做出应对。捕食者-猎物生态学（predator-prey ecology）中的基础理论常被用于阐释动物对道路的响应模式，例如警惕性提升、偏好选择道路周边的遮蔽区域。然而，猎物往往会表现出复杂行为，例如根据风险与收益的变化调整空间利用策略，目前尚不清楚动物在穿越道路时是否会采用这种动态权衡策略。
本研究借助加拿大不列颠哥伦比亚省幽鹤国家公园（Yoho National Park）内马鹿的大量野外实地数据与GPS项圈（GPS collar）数据开展实验，该区域内一条主要高速公路在一年中的多数时段完全割裂了马鹿的活动范围。本研究将隐马尔可夫运动模型（hidden Markov movement models）与步选择函数（step-selection function）框架相结合，对马鹿的行为进行分析。收益以可利用饲草生物量的动态分布图作为衡量指标，风险则以道路穿越事件与车流量作为衡量指标。
研究结果显示，马鹿通常会选择中等及以上水平的饲草生物量区域，同时规避穿越高速公路。在多数情况下，马鹿会根据风险与收益的变化调整自身行为。相较于不穿越高速公路，马鹿在穿越高速公路时会选择更高水平的饲草生物量，且随着单次移动序列中高速公路穿越次数的增加，这种选择倾向会愈发显著。但就车流量而言，马鹿仅会在单次移动序列中仅穿越一次高速公路的情况下，权衡饲草收益与道路风险。
本研究依托道路生态系统（road ecology）场景，对捕食者-猎物生态学中的一项核心内容——根据风险与收益水平的变化动态调整行为的能力——开展了实证检验。此类研究阐明了：动物如何通过考量风险与收益的时空变异来制定决策，从而得以在交通繁忙的道路区域顺利通行并获得收益。运用捕食者-猎物理论中的成熟概念，有助于我们理解动物对人为干扰的响应机制，并预测个体与种群适应快速变化环境的能力。