<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 (<em>Salvelinus confluentus</em>). 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 在美国太平洋西北地区，气候变化正导致气温升高、暖季（4月—9月）径流量减少，而冷季（10月—3月）径流量增加。径流量减少与气温升高共同推高了河流水温，这可能会改变洄游性冷水鱼类的栖息环境，例如牛鳟（*Salvelinus confluentus*）。因此，在未来气候不确定的背景下，厘清牛鳟的洄游与存活状况，对该物种的保护与修复工作至关重要。

本研究于1992年至1994年间，在爱达荷州萨蒙河（Salmon River）流域对无线电标记后的河道型牛鳟进行追踪，评估其产卵前与产卵后的洄游行为与存活情况。1992年与1993年的暖季径流量均为近三十年来的极端低值之一，这为我们在潜在气候变化情景下回顾性对比牛鳟的存活与洄游模式提供了独特契机。我们采用科马克-乔利-西伯线性空间捕获-重捕模型（Cormack Jolly-Seber linear spatial capture-recapture model），结合径流量、水温与栖息地协变量，同时对每周及不同河段内的无线电标记产卵前牛鳟（n = 63）与产卵后牛鳟（n = 23）的洄游与存活情况进行建模。

多数标记个体的产卵前洄游行为（5月26日—9月28日）模式较为一致，而产卵后个体（8月12日—次年5月12日）则采取了多种洄游与越冬策略。当周际径流量降幅增大、周均日最大径流量升高、周均日最高水温上升时，产卵前牛鳟的移动距离更大。在径流量偏低的年份，超50%的产卵个体死亡，其周均表观存活率更高（x̄ = 0.97，置信区间0.93—1）；而在径流量更高且波动更大的年份，周均表观存活率为0.91（置信区间0.76—0.98）。在38周的产卵后监测期内，当周均日最高水温最低时，牛鳟的存活率处于最低水平（x̄ = 0.95，置信区间0.90—0.98）。牛鳟的有效检测记录共880条，其检测率通常在栖息地结构更复杂、大型木质碎屑更少且凹岸更少的位点更高。本研究结果加深了学界对牛鳟洄游与存活模式的认知，可为未来气候变化下的物种响应预测提供参考。

研究方法 我们改编了Raabe等人（2014）提出的线性空间捕获-重捕（SCR）模型，将其应用于牛鳟无线电遥测数据，其中河段对应其他鱼类洄游研究中使用的监测阵列（Gardner等人2010）。该空间捕获-重捕模型以乔利-西伯开放式空间捕获-重捕模型的基本框架为基础，采用以首次捕获为条件的科马克-乔利-西伯建模形式（Gardner等人2010）。空间捕获-重捕模型可同时高效、精准且无偏地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等人2010；Raabe等人2014；Harris等人2020），即便针对检测率偏低的物种也同样适用（Blanc等人2013；Royle等人2014；Leuenberger等人2019）。Raabe等人（2014）提出的线性SCR模型是Gardner等人（2010）针对洄游性溪流鱼类提出的SCR模型的扩展版本，其输入数据包括检测坐标、标记个体的捕获-重捕历史与协变量（Leuenberger等人2019）。该空间捕获-重捕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（存活并位于河流系统内、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等人2010；Raabe等人2014）。我们采用贝叶斯框架下的Raabe等人（2014）SCR模型，分析环境与栖息地协变量与牛鳟检测率、存活率及移动距离间的时空关联。该空间捕获-重捕模型需满足三项标准开放式捕获-重捕模型假设：标记操作未对鱼类存活率造成影响、标记个体间的重捕概率一致、标记未脱落或漏检（Williams等人2002）。我们认为本研究满足上述假设：所有接受标记的鱼类均存活，且后续在产卵位点附近被检测到；研究覆盖了急流河与萨蒙河全域；数据集已剔除死亡个体，其中包括停止移动的个体。

搜集汇总

背景与挑战

背景概述

该数据集基于1992-1994年在爱达荷州Salmon River basin的无线电追踪研究，分析了牛鳟在产卵前后的迁移和生存情况，使用线性空间捕获-再捕获模型评估水流、水温和栖息地协变量的影响。研究发现，牛鳟的迁移和生存受水流变化和水温影响显著，例如低水流年份的生存率较高，且检测率与栖息地复杂性相关，为气候变化下的物种保护提供了见解。