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Landscape and biophysical controls of lake productivity to inform evaluation of sockeye salmon (Oncorhynchus nerka) populations in data‐limited regions
Limnology and Oceanography ( IF 4.5 ) Pub Date : 2020-04-27 , DOI: 10.1002/lno.11448
William I. Atlas 1 , Daniel T. Selbie 2 , Carrie A. Holt 3 , Steve Cox‐Rogers 4 , Charmaine Carr‐Harris 4 , Kara J. Pitman 1 , Jonathan W. Moore 1
Affiliation  

Landscape models are increasingly used to classify and predict the structure and productivity of data‐limited aquatic ecosystems. One such suite of ecosystems is on the remote North and Central Coast (NCC) of British Columbia, where sockeye salmon (Oncorhynchus nerka) rear in more than 150 lakes. Given their remoteness and limited resources for assessment, limnological and population monitoring in many of these lakes has been periodic or absent, limiting understanding of the status of populations and their habitats. Lake photosynthetic rate (PR) estimates are foundational to models of sockeye salmon nursery lake productive capacity. Using data from 61 lakes across the NCC, we compared a suite of landscape and lake variables in an information theoretic framework producing a set of models relating these characteristics to lake PR. A categorical variable related to lake biogeochemistry—whether a lake is humic stained, clear, or glacially turbid—was the most important variable predicting lake PR and was included in all models. Lake surface area relative to upstream catchment size and lake perimeter‐to‐surface‐area ratio were also important, with smaller upstream catchments yielding higher production, and high shoreline complexity correlated with lower productivity as measured by limnetic PR. Model‐averaged predictions of PR from the four models with the lowest residual error were created for 96 lakes currently lacking limnological assessments. These landscape models represent a valuable starting point for evaluating lake‐specific carrying capacities for data‐poor sockeye salmon populations under Canada's Wild Salmon Policy.

中文翻译:

对湖泊生产力的景观和生物物理控制,以评估数据受限区域的红鲑(Oncorhynchus nerka)种群

景观模型越来越多地用于分类和预测数据受限的水生生态系统的结构和生产力。其中一套这样的生态系统位于不列颠哥伦比亚省的偏远北部和中部海岸(NCC),那里是鲑鱼(Oncorhynchus nerka))在150多个湖泊中排在后面。由于它们的偏远地区和评估资源有限,许多湖泊中的湖泊学和人口监测一直是定期的或缺乏的,这限制了人们对人口及其栖息地状况的了解。湖泊光合速率(PR)的估计是红鲑鲑鱼苗圃湖泊生产能力模型的基础。利用来自NCC的61个湖泊的数据,我们在信息理论框架中比较了一组景观和湖泊变量,得出了一系列将这些特征与湖泊PR相关的模型。与湖泊生物地球化学有关的分类变量(无论湖泊是腐殖染,透明还是冰川混浊)是预测湖泊PR的最重要变量,并且已包含在所有模型中。相对于上游流域规模和湖泊周长与表面积之比的湖泊表面积也很重要,上游流域越小,产量越高,而沿岸PR测得的高海岸线复杂度与较低生产力相关。针对目前缺乏森林学评估的96个湖泊,从残留误差最低的四个模型中对PR进行模型平均预测。这些景观模型代表了根据加拿大的《野生鲑鱼政策》评估数据贫乏的红鲑鱼种群的湖泊特定承载力的宝贵起点。针对目前缺乏森林学评估的96个湖泊,从残留误差最低的四个模型中对PR进行了模型平均预测。这些景观模型代表了根据加拿大《野生鲑鱼政策》评估数据贫乏的红鲑鱼种群的湖泊特定承载力的宝贵起点。针对目前缺乏森林学评估的96个湖泊,从残留误差最低的四个模型中对PR进行模型平均预测。这些景观模型代表了根据加拿大《野生鲑鱼政策》评估数据贫乏的红鲑鱼种群的湖泊特定承载力的宝贵起点。
更新日期:2020-04-27
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