UPstream Regional LiDAR Model for Extent of Trout (UPRLIMET) model training and prediction data

We present a novel model development and evaluation framework, wherein we compare 26 models to predict upper distribution limits of trout in streams in Oregon using observational data collected in 2017. The models used machine learning, logistic regression, and a sophisticated nested spatial cross-validation routine to evaluate predictive performance while accounting for spatial autocorrelation. The model resulting in the best predictive performance, termed UPstream Regional LiDAR Model for Extent of Trout (UPRLIMET), is a two-stage model that uses a logistic regression algorithm calibrated to observations of Coastal Cutthroat Trout (Oncorhynchus clarkii clarkii) occurrence and variables representing hydro-topographic characteristics of the landscape. We predict trout presence along reaches throughout a stream network and include a stopping rule to identify a discrete upper limit point above which all stream reaches are classified as fishless. This data publication contains the geospatial data used for training, validation, and prediction by UPRLIMET (UPstream Regional LiDAR Model for Extent of Trout). Data are provided as two geodatabases with streamline (flowline) hydrography and include spatially explicit full-detail (predictions + covariates) prediction features separated by HUC12 watersheds and layers with pertinent prediction outputs merged into single spatial data layers for rapid rendering. Additionally, tabular data files are included that provide definitions of the covariates used in the model as well as the location and habitat barrier information for each stream and mainstem or tributary.Predicting the edges of species distributions is fundamental for species conservation, ecosystem services, and management decisions. In North America, the location of the upstream limit of fish in forested streams receives special attention, because fish-bearing portions of streams have more protections during forest management activities than fishless portions. The purpose of this study was to develop a model that would predict upper distribution limits of trout in streams in Oregon.For more information about these data, see Penaluna et al. (2022).

本研究提出了一种新颖的模型开发与评估框架，该框架中我们对比了26种模型，用以预测俄勒冈州溪流中鲑鱼的分布上限。这些模型采用了机器学习、逻辑回归以及复杂嵌套的空间交叉验证程序来评估预测性能，并在考虑空间自相关性的同时进行。表现最佳的模型，即称为UPstream Regional LiDAR Model for Extent of Trout（UPRLIMET）的模型，是一种两阶段模型，它使用逻辑回归算法对沿海红点鲑（Oncorhynchus clarkii clarkii）的分布观测和代表景观水文特征的变量进行校准。我们预测了整个溪流网络各段中鲑鱼的存在，并包含一个停止规则，以识别一个离散的上限点，在此点之上，所有溪流段均被归类为无鱼区域。本数据出版物包含了UPRLIMET（UPstream Regional LiDAR Model for Extent of Trout）用于训练、验证和预测的地理空间数据。数据以两个地理数据库的形式提供，包含流线（流线）水文地理信息，并包括空间明确的详细预测特征（预测+协变量），这些特征按HUC12流域和合并了相关预测输出的单层空间数据层分开。此外，还包括了表格数据文件，其中提供了模型中使用的协变量的定义以及每条溪流和主干或支流的地理位置和栖息地障碍信息。预测物种分布的边界对于物种保护、生态系统服务和管理工作决策至关重要。在北美，森林溪流中鱼类上游极限的位置受到特别关注，因为森林管理活动中的鱼类承载体部分比无鱼部分享有更多的保护。本研究旨在开发一个能够预测俄勒冈州溪流中鲑鱼分布上限的模型。有关这些数据的更多信息，请参阅Penaluna等（2022）。