Hance Dalton J, Moriarty Katie M, Hollen Bruce A, Perry Russell W
US Geological Survey, Western Fisheries Research Center, Columbia River Research Laboratory, Cook, WA, 98605, USA.
National Council for Air and Stream Improvement, Inc., Corvallis, OR, USA.
Mov Ecol. 2021 Apr 6;9(1):17. doi: 10.1186/s40462-021-00256-8.
Studies of animal movement using location data are often faced with two challenges. First, time series of animal locations are likely to arise from multiple behavioral states (e.g., directed movement, resting) that cannot be observed directly. Second, location data can be affected by measurement error, including failed location fixes. Simultaneously addressing both problems in a single statistical model is analytically and computationally challenging. To both separate behavioral states and account for measurement error, we used a two-stage modeling approach to identify resting locations of fishers (Pekania pennanti) based on GPS and accelerometer data.
We developed a two-stage modelling approach to estimate when and where GPS-collared fishers were resting for 21 separate collar deployments on 9 individuals in southern Oregon. For each deployment, we first fit independent hidden Markov models (HMMs) to the time series of accelerometer-derived activity measurements and apparent step lengths to identify periods of movement and resting. Treating the state assignments as given, we next fit a set of linear Gaussian state space models (SSMs) to estimate the location of each resting event.
Parameter estimates were similar across collar deployments. The HMMs successfully identified periods of resting and movement with posterior state assignment probabilities greater than 0.95 for 97% of all observations. On average, fishers were in the resting state 63% of the time. Rest events averaged 5 h (4.3 SD) and occurred most often at night. The SSMs allowed us to estimate the 95% credible ellipses with a median area of 0.12 ha for 3772 unique rest events. We identified 1176 geographically distinct rest locations; 13% of locations were used on > 1 occasion and 5% were used by > 1 fisher. Females and males traveled an average of 6.7 (3.5 SD) and 7.7 (6.8 SD) km/day, respectively.
We demonstrated that if auxiliary data are available (e.g., accelerometer data), a two-stage approach can successfully resolve both problems of latent behavioral states and GPS measurement error. Our relatively simple two-stage method is repeatable, computationally efficient, and yields directly interpretable estimates of resting site locations that can be used to guide conservation decisions.
利用位置数据对动物活动进行研究通常面临两个挑战。第一，动物位置的时间序列可能源自多种无法直接观测到的行为状态（例如，定向移动、休息）。第二，位置数据可能受到测量误差的影响，包括定位失败。在单一统计模型中同时解决这两个问题在分析和计算上都具有挑战性。为了分离行为状态并考虑测量误差，我们采用了两阶段建模方法，基于全球定位系统（GPS）和加速度计数据来识别渔貂（Pekania pennanti）的休息位置。
我们开发了一种两阶段建模方法，以估计在俄勒冈州南部9只个体上进行的21次独立项圈部署期间，佩戴GPS项圈的渔貂何时何地处于休息状态。对于每次部署，我们首先将独立的隐马尔可夫模型（HMM）拟合到加速度计得出的活动测量值和视在步长的时间序列，以识别移动和休息时段。将状态分配视为已知条件，接下来我们拟合一组线性高斯状态空间模型（SSM）来估计每次休息事件的位置。
不同项圈部署的参数估计相似。隐马尔可夫模型成功识别出休息和移动时段，所有观测值中97%的后验状态分配概率大于0.95。平均而言，渔貂63%的时间处于休息状态。休息事件平均持续5小时（标准差为4.3），且最常发生在夜间。状态空间模型使我们能够为3772个独特的休息事件估计95%可信椭圆，中位数面积为0.12公顷。我们识别出1176个地理上不同的休息位置；13%的位置被多次使用，5%的位置被不止一只渔貂使用。雌性和雄性渔貂每天平均移动6.7公里（标准差为3.5）和7.7公里（标准差为6.8）。
我们证明，如果有辅助数据（例如加速度计数据），两阶段方法可以成功解决潜在行为状态和GPS测量误差这两个问题。我们相对简单的两阶段方法具有可重复性、计算效率高，并且能直接得出可解释的休息地点位置估计值，可用于指导保护决策。
