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首页> 外文期刊>Canadian Journal of Fisheries and Aquatic Sciences >Scalable population estimates using spatial-stream-network (SSN) models, fish density surveys, and national geospatial database frameworks for streams
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Scalable population estimates using spatial-stream-network (SSN) models, fish density surveys, and national geospatial database frameworks for streams

机译：使用空间流 - 网络（SSN）模型，鱼密度调查和国家地理空间数据库框架进行可扩展的人口估计

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摘要

Population size estimates for stream fishes are important for conservation and management, but sampling costs limit the extent of most estimates to small portions of river networks that encompass 100s-10 000s of linear kilometres. However, the advent of large fish density data sets, spatial-stream-network (SSN) models that benefit from nonindependence among samples, and national geospatial database frameworks for streams provide the components to create a broadly scalable approach to population estimation. We demonstrate such an approach with density surveys for trout species from 108 sites in a 735 km river network. Universal kriging was used to predict a continuous map of densities among survey locations, and block kriging (BK) was used to summarize discrete map areas and make population estimates at stream, river, and network scales. The SSN models also accommodate covariates, which facilitates hypothesis testing and provides insights about factors affecting patterns of abundance. The SSN-BK population estimator can be applied using free software and geospatial resources to develop valuable information at low cost from many existing fisheries data sets.
机译：溪流鱼类的种群规模估计对保护和管理很重要，但采样成本将大多数估计的范围限制在河流网络的一小部分，包括100-10000线性公里。然而，大型鱼类密度数据集、得益于样本之间不依赖性的空间流网络（SSN）模型以及国家地理空间数据库框架的出现，为创建一种广泛可扩展的种群估计方法提供了组件。我们通过对735公里河网中108个地点的鳟鱼物种进行密度调查来证明这种方法。通用克里格法用于预测调查地点之间的连续密度图，而块克里格法（BK）用于总结离散地图区域，并在河流、河流和网络尺度上进行人口估计。SSN模型还包含协变量，这有助于假设检验，并提供了有关影响丰度模式的因素的见解。SSN-BK人口估计器可以使用自由软件和地理空间资源，从许多现有渔业数据集中以低成本开发有价值的信息。

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