Barrier island geomorphology and shorebird habitat metrics: Four sites in New York, New Jersey, and Virginia, 2010-2014

Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated into predictive models and the training data used to parameterize those models. This data release contains the extracted metrics of barrier island geomorphology and spatial data layers of habitat characteristics that are input to Bayesian networks for piping plover habitat availability and barrier island geomorphology. These datasets and models are being developed for sites along the northeastern coast of the United States. This work is one component of a larger research and management program that seeks to understand and sustain the ecological value, ecosystem services, and habitat suitability of beaches in the face of storm impacts, climate change, and sea-level rise.

理解海平面上升如何影响海岸地貌及其支撑的物种与栖息地，对于制定兼顾人类需求与本土物种保护的应对方案至关重要。鉴于当前对近、长期海平面上升对障壁岛影响的预测需求日益增长，我们正开发贝叶斯网络（Bayesian networks），以评估并预测海平面上升对海岸线变化、障壁岛状态以及笛鸻（piping plover）栖息地适宜性的连锁效应。我们采用激光雷达（lidar）、正射影像（orthophotography）以及由此衍生的地貌特征集等公开可用的数据产品，以统一采样距离提取障壁岛特征的量化指标。随后将这些量化指标纳入预测模型，并利用训练数据对模型进行参数化处理。本次发布的数据包含障壁岛地貌量化提取指标，以及用于笛鸻栖息地适宜性和障壁岛地貌分析的贝叶斯网络输入所需的栖息地特征空间数据图层。上述数据集与模型均针对美国东北海岸沿线的站点开发。本研究是一项大型研究与管理计划的组成部分，该计划旨在应对风暴影响、气候变化与海平面上升，探究并维持海滩的生态价值、生态系统服务功能与栖息地适宜性。