ABSTRACT: Elevation is a major driver of plant ecology and sediment dynamics in tidal wetlands, so accurate and precise spatial data are essential for assessing wetland vulnerability to sea-level rise and making forecasts. We performed survey-grade elevation and vegetation surveys of the Global Change Research Wetland, a brackish microtidal wetland in the Chesapeake Bay estuary, Maryland (USA), to both intercompare unbiased digital elevation model (DEM) creation techniques and to describe niche partitioning of several common tidal wetland plant species. We identified a tradeoff between scalability and performance in creating unbiased DEMs, with more data-intensive methods such as kriging performing better than 3 more scalable methods involving post-processing of light detection and ranging (LiDAR)-based DEMs. The LiDAR Elevation Correction with Normalized Difference Vegetation Index (LEAN) method provided a compromise between scalability and performance, although it underpredicted variability in elevation. In areas where native plants dominated, the sedge Schoenoplectus americanus occupied more frequently flooded areas (median: 0.22, 95% range: 0.09 to 0.31 m relative to North America Vertical Datum of 1988 [NAVD88]) and the grass Spartina patens, less frequently flooded (0.27, 0.1 to 0.35 m NAVD88). Non-native Phragmites australis dominated at lower elevations more than the native graminoids, but had a wide flooding tolerance, encompassing both their ranges (0.19, -0.05 to 0.36 m NAVD88). The native shrub Iva frutescens also dominated at lower elevations (0.20, 0.04 to 0.30 m NAVD88), despite being previously described as a high marsh species. These analyses not only provide valuable context for the temporally rich but spatially restricted data collected at a single well-studied site, but also provide broad insight into mapping techniques and species zonation.

中文翻译：

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量化海拔和潮汐湿地植物群落之间关系的可伸缩性和性能折衷

摘要：海拔是潮汐湿地植物生态学和沉积物动力学的主要驱动力，因此准确而精确的空间数据对于评估湿地对海平面上升的脆弱性并做出预测至关重要。我们对全球变化研究湿地进行了调查级海拔和植被调查，这是美国马里兰州切萨皮克湾河口的微潮微潮湿地，将无偏数字高程模型（DEM）创建技术进行了相互比较，并描述了几种生态位的划分常见的潮汐湿地植物种类。我们确定了在创建无偏DEM时可伸缩性和性能之间的权衡，采用更多的数据密集型方法（例如克里格法）要比基于光检测和测距（LiDAR）的DEM后处理的3种可伸缩性更好。LiDAR高程校正归一化植被指数（LEAN）方法提供了可伸缩性和性能之间的折衷，尽管它低估了高程的变化性。在本地植物占主导地位的地区，莎草Schoenoplectus美洲占用更频繁地洪泛区（中位数：0.22，95％范围：0.09至相对0.31米到1988北美垂直基准[NAVD88]）和草互花米草藓，较不频繁地淹没（0.27，0.1〜0.35米NAVD88） 。非本地的芦苇在较低的海拔上比本地的类动物更占优势，但具有宽泛的洪水耐受性，涵盖了它们的两个范围（0.19，-0.05至0.36 m NAVD88）。本地灌木伊娃（Iva frutescens）尽管先前被描述为高沼泽物种，但在较低海拔（0.20，0.04至0.30 m NAVD88）上也占主导地位。这些分析不仅为在单个经过充分研究的站点收集的时间丰富但空间受限的数据提供了有价值的背景，而且还为制图技术和物种分区提供了广泛的见识。