<strong>Bull trout streamflow and temperature linear SCR model</strong>

Abstract In the Pacific Northwestern United States, climate change is increasing air temperatures, decreasing warm season (April–September), and increasing cool season (October–March) streamflow. Warmer water temperatures produced by both reduced streamflow and warmer air temperatures may alter conditions for migratory, cold-water fishes like bull trout (Salvelinus confluentus). Consequently, understanding bull trout migration and survival is critical for species conservation and restoration in an uncertain future. We evaluated pre- and post- spawning migrations and survival of fluvial bull trout radiotagged and tracked in the Salmon River basin, Idaho from 1992–1994. Both 1992 and 1993 recorded two of the most extreme warm season streamflows during the last three decades. These extremes provided a unique opportunity to retrospectively compare bull trout survival and migration under potential climate change scenarios. We used a Cormack Jolly-Seber linear spatial capture-recapture model to simultaneously model migration and survival of radio-tagged pre-spawning (n = 63) and post-spawning (n = 23) bull trout among weeks and river reaches with streamflow, water temperature, and habitat covariates. Most individual pre-spawning migrations (May 26–September 28) were similar among tagged fish, whereas post-spawning fish (August 12–May 12) adopted multiple migration and overwintering strategies. Movements of pre-spawning bull trout were larger when between-weekly changes in streamflow decreased, weekly average daily maximum streamflow increased, and weekly average daily maximum water temperature increased. More than 50% of spawners died and mean weekly pre-spawning apparent survival was higher in the low streamflow year (x̄ = 0.97, CI 0.93–1), compared to the higher and more variable streamflow year (x̄ = 0.91, CI 0.76–0.98). Survival during the 38-week post-spawning period was lowest (x̄ = 0.95, CI 0.90–0.98) when weekly maximum average daily water temperatures were coldest. Bull trout detections (n = 880 detections) were generally higher in sites with more complex habitats, less large woody debris, and fewer undercut banks. Our results increase knowledge of bull trout migration and survival and offer insights into changes that might be expected under future climate. Methods We adapted the Raabe et al. (2014) linear spatial capture-recapture (SCR) model for use with the bull trout radio-telemetry data where reaches correspond to arrays used in other fish migration studies (Gardner et al. 2010). The SCR model uses the basic framework of the Jolly-Seber open spatial capture-recapture model (Gardner et al. 2010) with a Cormack Jolly-Seber formulation that is conditional on first capture. SCR models are an efficient, precise, and unbiased method for estimating the spatial scale of detection rates, and effects of environmental covariates on detection rates, movements, and survival simultaneously (Gardner et al. 2010; Raabe et al. 2014; Harris et al. 2020), even for species with low detection rates (Blanc et al. 2013; Royle et al. 2014; Leuenberger et al. 2019). The Raabe et al. (2014) linear SCR model is an extension of the Gardner et al. (2010) SCR model for migrating stream fishes that requires detection coordinates, capture-recapture histories of marked individuals, and covariates (Leuenberger et al. 2019). The main components of the SCR model are an observation model based on detections (λijt), a state model (zit), based on whether the fish was alive and in the river system, dead, or had emigrated, and latent individual activity (or home range) centers (Si; Gardner et al. 2010; Raabe et al. 2014). We used Raabe et al.’s (2014) SCR model in a Bayesian framework to evaluate the relationship between environmental and habitat covariates and bull trout detection rates, survival, and movement distances over time. The SCR model requires three standard open capture-recapture model assumptions: tagging did not influence survival, relocation probability was similar among tagged individuals, and tags were not lost or missed (Williams et al. 2002). We believe we have met these assumptions because fish survived tagging and were later detected near spawning sites, we detected fish throughout Rapid River and the Salmon River, and mortalities were removed from the dataset and included fish that stopped moving.摘要 美国太平洋西北地区的气候变化正导致气温升高，暖季（4月至9月）径流量减少，而冷季（10月至3月）径流量增加。水流减少与气温升高共同引发的水温升高，可能会改变洄游性冷水鱼类公牛鳟（*Salvelinus confluentus*）的生存环境。因此，在未来气候充满不确定性的背景下，了解公牛鳟的洄游与存活情况，对于该物种的保护与恢复工作至关重要。本研究于1992年至1994年间，在爱达荷州鲑鱼河流域对经无线电标记并追踪的河流型公牛鳟的产卵前后洄游与存活情况进行了评估。1992年与1993年的暖季径流量均为近三十年来最为极端的水平之一，这些极端径流量事件为我们在潜在气候变化情景下回溯对比公牛鳟的存活与洄游情况提供了独特契机。我们采用科马克-乔利-西伯线性空间捕获-再捕获模型（Cormack Jolly-Seber linear spatial capture-recapture model），结合径流量、水温和生境协变量，对每周及各河段内经无线电标记的产卵前（n=63）与产卵后（n=23）公牛鳟的洄游与存活情况进行联合建模。多数标记个体的产卵前洄游（5月26日至9月28日）模式较为一致，而产卵后个体（8月12日至次年5月12日）则采用了多种洄游与越冬策略。当周际径流量变化幅度减小、周平均日最大径流量升高以及周平均日最高水温上升时，产卵前公牛鳟的移动距离更大。产卵个体中有超过50%死亡；相较于径流量更高且波动更大的年份（均值=0.91，置信区间0.76~0.98），低径流量年份的产卵前周表观存活率均值更高（均值=0.97，置信区间0.93~1）。在产卵后38周的时段内，当周平均日最高水温最低时，公牛鳟的存活率最低（均值=0.95，置信区间0.90~0.98）。公牛鳟的检测记录（共880次检测）在生境更复杂、大型木质碎屑更少以及凹岸更少的监测点普遍更多。本研究结果增进了我们对公牛鳟洄游与存活情况的认知，并为未来气候情景下可能出现的种群变化提供了参考见解。 方法 我们将Raabe等人（2014）提出的线性空间捕获-再捕获（spatial capture-recapture, SCR）模型应用于公牛鳟的无线电遥测数据，其中河段对应其他鱼类洄游研究中使用的监测阵列（Gardner et al. 2010）。该SCR模型以乔利-西伯开放式空间捕获-再捕获模型（Gardner et al. 2010）的基本框架为基础，采用了以首次捕获为条件的科马克-乔利-西伯建模形式。SCR模型是一种高效、精准且无偏的方法，可同时估算检测率的空间尺度以及环境协变量对检测率、移动行为与存活率的影响（Gardner et al. 2010; Raabe et al. 2014; Harris et al. 2020），即便对于检测率较低的物种亦是如此（Blanc et al. 2013; Royle et al. 2014; Leuenberger et al. 2019）。Raabe等人（2014）提出的线性SCR模型是Gardner等人（2010）提出的SCR模型针对洄游性河流鱼类的扩展版本，该模型需要检测坐标、标记个体的捕获-再捕获历史以及协变量数据（Leuenberger et al. 2019）。SCR模型的核心组成部分包括基于检测记录的观测模型（λ_ijt）、基于鱼类存活状态（存活且位于河流系统内、死亡或已迁出）的状态模型（z_it），以及个体潜在活动（或家域）中心（S_i; Gardner et al. 2010; Raabe et al. 2014）。我们采用贝叶斯框架下的Raabe等人（2014）SCR模型，评估了环境与生境协变量与公牛鳟检测率、存活率以及随时间变化的移动距离之间的关联。SCR模型需满足三项标准的开放式捕获-再捕获模型假设：标记操作不会影响个体存活率、标记个体间的重捕概率一致，以及标记未丢失或未被漏检（Williams et al. 2002）。我们认为本研究满足上述假设：经标记的鱼类均存活且后续在产卵场附近被检测到；我们在急流河与鲑鱼河流域全境均检测到了鱼类；数据集已剔除死亡个体，其中包括停止移动的个体。