Determining how resource availability changes daily, seasonally and annually, and how wildlife react to these changes, is valuable for managing wildlife. For vegetative resources phenological information can be used to determine availability and model the distribution of available resources. This study develops a set of annually varying species distribution models using maximum entropy modeling for eight grizzly bear key plant-food species using 1635 food occurrences collected between 2001 and 2017. Seasonal availability (phenology) of plant-food species were then estimated daily at a 30 m resolution for the period 2000–2017 using ground data collected at 15 sites in 2015 and 2018 combined with newly created fine scale phenology product DRIVE (Daily Remote Inference of Vegetation). These food availability layers were then used to develop resource selection functions with grizzly bear GPS collar data to describe daily and seasonal habitat selection and to compared how this changes over a three-year period based on changes in mean annual precipitation. Results demonstrated that grizzly bears selected areas where key plant-food species were available and that habitat selection varied between wet and dry years dependant on season and species. In dry spring conditions, selection for root species was stronger and occurred earlier than for an average and wet year. In the wet summer, length of selection for forb species increased, while strength of selection for wet and dry years was species dependant. In the fall, strength of selection for berry species between wet and dry years was species dependant, but overall selection for berry species was prolonged in the dry fall year. This research aids in predicting how inter-annual differences in climate affect grizzly bear habitat selection and provides insights to managers regarding how changes in management practices that encourage growth of understory vegetation could be used to maximize food resources regionally for grizzly bears and other wildlife.

确定资源的可用性每天，季节性和每年如何变化，以及野生生物如何应对这些变化，对于管理野生生物非常重要。对于植物资源，可以使用物候信息来确定可用性并为可用资源的分布建模。这项研究使用2001年至2017年间收集的1635种食物出现的八种灰熊主要植物食物物种，使用最大熵模型开发了一套年度变化的物种分布模型。然后，每天估计植物食物物种的季节性可用性（物候）。使用2015年和2018年在15个地点收集的地面数据，结合新创建的精细物候物候产品DRIVE（植被的每日远程推断），获得2000-2017年的30 m分辨率。这些食物的可利用性层随后用于利用灰熊GPS项圈数据开发资源选择功能，以描述日常和季节性栖息地选择，并根据平均年降水量的变化比较这在三年期间的变化。结果表明，灰熊选择了一些可获得主要植物性食物的地区，并且生境的选择根据季节和物种在干湿年之间有所不同。在干旱的春季条件下，对根物种的选择要强于平均年份和潮湿年份。在潮湿的夏季，对forb物种的选择长度增加，而对于潮湿和干燥年份的选择强度则取决于物种。在秋季，干年和干年之间浆果品种的选择强度取决于物种，但是在干旱的秋季，浆果的总体选择时间有所延长。这项研究有助于预测气候之间的年际差异如何影响灰熊栖息地的选择，并为管理人员提供有关如何利用管理方法的变化来鼓励林下植被生长的见解，这些变化可以被用来最大化灰熊和其他野生动物的区域性食物资源。