Andersen Erik M, Clapp Justin G, Vinks Milan A, Atwood Todd C, Bjornlie Daniel D, Costello Cecily M, Gustine David D, Haroldson Mark A, Roberts Lori L, Rode Karyn D, van Manen Frank T, Wilson Ryan R
U.S. Fish and Wildlife Service, Marine Mammals Management, 1011 E Tudor Road, Anchorage, AK, 99503, USA.
Wyoming Game and Fish Department, Large Carnivore Section, 260 Buena Vista, Lander, WY, 82520, USA.
Mov Ecol. 2025 Jul 4;13(1):48. doi: 10.1186/s40462-025-00577-y.
Information on reproductive success is crucial to understanding population dynamics but can be difficult to obtain, particularly for species that birth while denning. For grizzly (Ursus arctos) and polar bears (U. maritimus), den visits are impractical because of safety and logistical considerations. Reproduction is typically documented through direct observation, which can be difficult, costly, and often occurs long after den departure. Reproduction could be documented remotely, however, from post-denning movement data if discernable differences exist between females with and without cubs.
We trained support vector machines (SVMs) with eight variables derived from telemetry data of female grizzly (2000-2022) and polar bears (1985-2016) with or without cubs during seven periods with lengths ranging from 5 to 60 days starting at den departure. We assessed SVM classification accuracy by withholding two samples (one cub-present, one cub-absent), training SVMs with the remaining data, predicting classification of the withheld samples, and repeating this process for each sample combination. Additionally, we evaluated how classification accuracy for grizzly bears was influenced by sample size, length of the post-departure period, and frequency of standardized location estimates.
Accuracy of predicting cub presence or absence was 87% for grizzly bears with only 5 days of post-departure data and increased to a maximum of 92% with 20 days of data. For polar bears, accuracy was 86% at 5 days post-departure and increased to a maximum of 93% at 50 days. Classification accuracy for grizzly bears increased from 76 to 90% when sample size increased from 10 to 30 bears while holding period length constant (30 days) but did not increase at larger sample sizes. When sample size was held constant, increasing the length of the post-departure period did not affect classification accuracy markedly.
Presence or absence of grizzly and polar bear cubs can be identified with high accuracy even when SVM models are trained with limited data. Detecting cub presence or absence remotely could improve estimates of reproductive success and litter survival, enhancing our understanding of factors affecting cub recruitment.
繁殖成功率信息对于理解种群动态至关重要，但可能难以获取，尤其是对于在洞穴中产仔的物种。对于灰熊（棕熊，Ursus arctos）和北极熊（U. maritimus）而言，出于安全和后勤方面的考虑，进入洞穴进行观察是不切实际的。繁殖情况通常通过直接观察来记录，这可能困难、成本高昂，而且往往在母熊离开洞穴很久之后才会发生。然而，如果有幼崽和没有幼崽的雌性之间存在可辨别的差异，那么可以从洞穴后的移动数据中远程记录繁殖情况。
我们使用从雌性灰熊（2000 - 2022年）和北极熊（1985 - 2016年）的遥测数据中得出的八个变量，在母熊离开洞穴后开始的七个时长从5天到60天不等的时间段内，对支持向量机（SVM）进行训练，这些母熊有的带着幼崽，有的没有。我们通过保留两个样本（一个有幼崽，一个没有幼崽）、用其余数据训练支持向量机、预测保留样本的分类，并对每个样本组合重复此过程，来评估支持向量机的分类准确性。此外，我们评估了样本大小、离开洞穴后的时间段长度以及标准化位置估计的频率对灰熊分类准确性的影响。
对于灰熊，仅利用离开洞穴后5天的数据，预测幼崽是否存在的准确率为87%，随着数据增加到20天，准确率最高可达到92%。对于北极熊，离开洞穴后5天的准确率为86%，在50天时最高达到93%。当样本大小从10只熊增加到30只熊且保持时间段长度不变（30天）时，灰熊的分类准确率从76%提高到90%，但样本量更大时准确率并未提高。当样本大小保持不变时，增加离开洞穴后的时间段长度对分类准确率没有显著影响。
即使使用有限的数据训练支持向量机模型，也能高精度地识别灰熊和北极熊幼崽的有无。远程检测幼崽的有无可以改进对繁殖成功率和幼崽存活率的估计，增强我们对影响幼崽数量补充因素的理解。
