Physical and biological constraints on the capacity for life-history expression of anadromous salmonids: an Eel River, California, case study

Recovery of anadromous salmonid populations is complicated by the fact that these fish have complex life-histories. Habitat valuation and capacity methods need to account for spatiotemporal variability in temperature, geomorphic features, and a species’ thermal sensitivity mediated by biological interactions. We examined this interplay in a case study of steelhead trout (Oncorhynchus mykiss) and Chinook Salmon (O. tshawytscha) in California’s Eel River watershed. We estimated habitat suitability and fish capacity for each salmonid run and freshwater life stage during average, cool, and warm years in each of the watershed’s subbasins, including a high-elevation subbasin upstream of an impassable dam. Our estimates varied depending on whether we accounted for exposure to the Sacramento pikeminnow (Ptychocheilus grandis), an introduced predator/competitor. Our results indicate that the dammed subbasin could provide an important cool-water refuge during warm years and from pikeminnow, potentially improving the productivity and resilience of multiple anadromous salmonid populations. Our approach can be applied in any setting where spatially explicit habitat metrics can be estimated and population specific and life-stage specific habitat criteria can be specified.

Methods
Summary

The attached shapefile contains information used to assess salmonid habitat suitability and fish capacity for over 10,000 stream segments in the Eel River watershed, California. Specifically, the shapefile contains spatial and/or temporal data for stream temperature, geomorphic channel type, salmonid accessibility and potential occupancy, and stream segment characteristics needed to estimate capacity (e.g. stream segment length and wetted width. Each stream segment is ~1 km in length. Below, we summarize each dataset briefly. For full details, refer to our full paper.

Stream temperature

Mean monthly stream temperature for each reach was obtained from FitzGerald et al. (2021). Briefly, FitzGerald et al. (2021) predicted stream temperature using a spatial stream network (SSN) model, a specialized statistical regression model that accounts for spatial autocorrelation in temperatures due to stream-network structure and geographic proximity Additional modeling details can be found in FitzGerald et al. (2021). The model was then used to predict monthly mean stream temperatures for every river km in the Eel River Basin for every year in the study time period (attribute column "Month_Year"). Reaches classified as manmade lakes and reservoirs were removed because they involve different thermal dynamics that are not well-represented by the SSN model. The temperature predictions and habitat suitability analyses therefore do not include reaches that are currently inundated (e.g. by Lake Pillsbury, created by Scott Dam). A single river km in the Eel River Basin showed abnormally high predictions (sometimes > 10°C higher than the next highest stream temperature in the Basin), and this outlier was removed from all subsequent analyses.

Geomorphic channel types

We classified each reach by geomorphic channel type. To do this, we generated a fine-grained hydrography with channel gradients and catchment areas from a 10 m DEM. We then spatially joined the finer-grained hydrography to the stream network that was used in the temperature modeling, summarizing the mean gradient and catchment area for each 1 km reach. Then, channel morphology types were assigned using channel gradient and catchment area from a classification tree ("ChanType"). 

Potentially accessible streams for salmonids

We defined the potential spatial distribution for each run/life stage from historical population boundaries, accessibility of reaches, and channel type. First, historical population boundaries were defined from a study on salmonid biogeographic breaks that showed that steelhead trout and Chinook Salmon in the Eel River Basin are divided into historical populations that generally reflect watershed subbasins ("Pop_SW", "Pop_SS", and "Pop_CF"). We did not analyze any subbasins that were historically uninhabited for a given run. Next, we removed reaches beyond the limits of anadromy for each species ("STL_access" and "CHK_access"). Anadromous limits were defined as upstream of physical impassable barriers (e.g. large waterfalls) or upstream of species-specific barriers inferred from stream gradient; we excluded the currently impassable Scott Dam. The remaining reaches, including those in the currently blocked Upper Mainstem, are hereafter referred to as ‘potentially accessible’ (e.g., "Pop_SW" == 'Upper Mainstem' & "STL_access" == 1).

Estimating capacity

To estimate juvenile rearing capacity, we expanded the Unit Characteristic Method (UCM), applied by Cooper et al. (2020) to the Upper Mainstem, to all subbasins in the Eel River Basin. The UCM is a capacity model that multiplies baseline fish density by unit area of stream habitat, then adjusts the density by habitat scalar values based on parameters describing local conditions (e.g. cover, depth, pH) for each habitat unit type, such as fastwater, flatwater, and pools. In the Eel River Basin, empirical measures of local conditions (excluding stream segment length, stream temperature, and wetted width; see below) were only available for reaches throughout the Upper Mainstem (Cooper et al. 2020). Cooper et al. (2020) categorized each reach surveyed by channel gradient and upstream watershed area and measured habitat characteristics for each reach to estimate the appropriate scalar for local conditions in the Upper Mainstem. Following their approach, we first applied the same reach categorization scheme throughout the Eel River Basin for each stream segment (attribute "ReachCateg"), and, assuming that local conditions in the Upper Mainstem are representative of the entire Eel River Basin, we then assigned the averaged habitat values by reach category (Cooper et al. 2020) to the appropriate stream segment. Stream segment length ("length_m") and monthly temperature ("Month_Year") were extracted from our stream temperature modeling.

The absolute capacity of a reach is given by the product of its capacity density and the reach area, the product of average wetted width and channel length of stream segment. We modeled wetted width each month in order to better predict how reach area changes throughout the year. We then fit linear models for each month from observed wetted widths and bankfull widths. Monthly models generally performed well (r2 range: 0.61-0.84) and better than an annual model (r2 = 0.60), so we used the fitted monthly models to predict wetted width each month throughout the Basin ("width_Month").

洄游性鲑科鱼类（anadromous salmonid）种群的恢复工作往往极为复杂，这是因为该类群拥有复杂的生活史。生境评估与种群容纳量测算方法，需要纳入温度、地貌特征以及由生物相互作用介导的物种热敏感性等要素的时空变异性。我们以加利福尼亚州埃尔克河流域的硬头鳟（Oncorhynchus mykiss）与奇努克鲑（O. tshawytscha）为案例研究对象，探究了上述要素间的相互作用。我们针对该流域各子流域（包括一座无法通行鱼类的拦河坝（impassable dam）上游的高海拔子流域），分别在平水年、冷水年与暖水年情境下，估算了每种鲑科鱼类洄游类群以及淡水生活史阶段的生境适宜性与鱼类容纳量。我们的估算结果会因是否纳入外来捕食者/竞争者萨克拉门托梭子鱼（Ptychocheilus grandis）的影响而产生差异。研究结果表明，该拦河坝所在的子流域可在暖水年成为重要的冷水庇护所，同时能规避萨克拉门托梭子鱼的威胁，有望提升多种洄游性鲑科鱼类种群的生产力与恢复力。本研究方法可推广至所有可估算空间显性生境指标、且能明确种群与生活史特异性生境标准的研究场景。

方法
摘要
附件中的形状文件（shapefile）包含了用于评估加利福尼亚州埃尔克河流域1万余个河段的鲑科鱼类生境适宜性与鱼类容纳量的数据。具体而言，该形状文件包含了用于估算容纳量所需的河道水温、地貌河道类型、鲑科鱼类可栖息性与潜在分布，以及河段特征（如河段长度与湿周）的空间/时间数据。单个河段长度约为1 km。下文将对各数据集进行简要概述，完整细节请参见研究全文。

河道水温
各河段的月均河道水温数据来源于FitzGerald等人（2021）的研究。简而言之，该团队采用空间河道网络模型（spatial stream network, SSN）预测河道水温——这是一类专门的统计回归模型，可考虑由河道网络结构与地理邻近性导致的水温空间自相关性。更多建模细节可参见FitzGerald等人（2021）的研究。我们利用该模型对研究时段内埃尔克河流域每一公里河道的月均水温进行了预测（对应属性列"Month_Year"）。由于人工湖泊与水库的热动力学特征无法通过空间河道网络模型很好地体现，我们将这类河段从数据中剔除。因此，水温预测与生境适宜性分析未包含当前被淹没的河段（如由斯科特坝形成的皮尔斯湖）。埃尔克河流域有一处河道公里段的水温预测值异常偏高（有时比流域内第二高水温高出10℃以上），该异常值已从所有后续分析中移除。

地貌河道类型
我们依据地貌特征对各河段进行了河道类型分类。具体流程为：基于10 m分辨率的数字高程模型（DEM）生成包含河道坡度与集水区信息的高精度水文地形数据；将该高精度水文地形数据与水温建模所用的河道网络进行空间连接，汇总得到每1 km河段的平均坡度与集水区面积；随后利用分类树基于河道坡度与集水区面积完成河道形态类型的赋值，对应属性为"ChanType"。

鲑科鱼类潜在可栖息河道
我们基于历史种群分布边界、河段可达性与河道类型，定义了每种洄游类群/生活史阶段的潜在空间分布范围。首先，基于一项关于鲑科鱼类生物地理分界的研究，我们将埃尔克河流域的硬头鳟与奇努克鲑的历史种群划分为大致对应流域子流域的单元（对应属性为"Pop_SW"、"Pop_SS"与"Pop_CF"），我们未对某一洄游类群历史上无分布的子流域开展分析。随后，我们移除了超出各物种溯河洄游上限的河段（对应属性"STL_access"与"CHK_access"）。溯河洄游上限被定义为物理性无法通行障碍物（如大型瀑布）上游，或基于河道坡度推断的物种专属障碍物上游；我们未纳入当前无法通行鱼类的斯科特坝。剩余河段（包括当前被阻断的上游干流河段）即被称为"潜在可栖息河段"，例如满足"Pop_SW == 'Upper Mainstem' 且 STL_access == 1"的河段。

容纳量估算
为估算幼鱼育幼容纳量，我们将Cooper等人（2020）针对上游干流河段提出的单位特征法（Unit Characteristic Method, UCM）推广至埃尔克河流域的所有子流域。单位特征法是一类容纳量模型，其核心逻辑为：以基准鱼类密度乘以河道生境单位面积，再基于描述局地生境条件（如遮蔽物、水深、pH值）的参数，对各生境单元类型（如急流区、缓流区与深潭）的密度进行生境标量值调整。在埃尔克河流域，仅上游干流河段拥有局地生境条件的实测数据（不包含河段长度、河道水温与湿周，详见下文；Cooper等人，2020）。Cooper等人（2020）依据河道坡度与上游集水区面积对调查河段进行分类，并通过实测各河段的生境特征，为上游干流河段的局地生境条件估算了适配的标量值。我们沿用其研究思路，首先为埃尔克河流域的每一个河段采用相同的河段分类方案（对应属性"ReachCateg"），并假设上游干流河段的局地生境条件可代表整个埃尔克河流域，随后将Cooper等人（2020）基于河段分类得到的平均生境标量值赋值至对应河段。河段长度（"length_m"）与月均水温（"Month_Year"）数据来源于我们的河道水温建模工作。

河段的绝对容纳量等于其容纳量密度与河段面积的乘积，而河段面积为平均湿周与河道河段长度的乘积。为更精准地预测全年河段面积的变化，我们对各月的湿周进行了建模。随后，我们基于实测湿周与满槽宽度为每个月份拟合了线性模型。各月模型的拟合效果整体良好（决定系数r²范围为0.61~0.84），优于年度模型（r²=0.60），因此我们利用拟合得到的月模型对流域内各河段的每月湿周进行了预测，对应属性为"width_Month"。