Submerged aquatic vegetation (SAV) improves environmental conditions by acting as a sediment stabilizer and nutrient retention tool; therefore, reintroduction of SAV is a common freshwater restoration goal. Initial plant establishment is often difficult in suboptimal conditions, and planting material with specific traits may increase establishment rates. Here we evaluate the variability in plant traits based on collection location. We find consistent differences in traits of plants collected from different natural water bodies, and those differences persist in plants grown from seeds under common garden greenhouse conditions—presumably because of genetic differentiation. In three separate mesocosm experiments, we tested the interactive impacts of collection location and environmental condition (control conditions, reduced light, elevated nutrients, or a combination of reduced light and elevated nutrients) on plant reproduction and on traits that might indicate future restoration success (plant height, number of leaves, and rhizome diameter). In most cases, plant traits at the end of the experiments varied by collection location, environmental condition, and an interaction between the two. The best performing plants also depended on response variable (e.g., plant height or number of new shoots produced). Together these results suggest that unpredictable environmental conditions at restoration sites will make selection of a single high-performing plant source difficult, so we suggest incorporating a diverse set of collection locations to increase the probability of incorporating desirable traits.

沉水植被 (SAV) 通过充当沉积物稳定剂和养分保留工具来改善环境条件；因此，重新引入 SAV 是一个常见的淡水恢复目标。在次优条件下，最初的植物建立通常很困难，具有特定性状的种植材料可能会提高建立率。在这里，我们根据收集位置评估植物性状的可变性。我们发现从不同天然水体采集的植物性状存在一致差异，而这些差异在普通花园温室条件下从种子生长的植物中持续存在——大概是因为遗传分化。在三个独立的中宇宙实验中，我们测试了收集位置和环境条件（控制条件、光线减少、营养物质增加、或减少光照和增加营养的组合）对植物繁殖和可能表明未来恢复成功的性状（植物高度、叶数和根茎直径）的影响。在大多数情况下，实验结束时的植物性状因采集地点、环境条件以及两者之间的相互作用而异。表现最好的植物还取决于响应变量（例如，植物高度或产生的新芽的数量）。这些结果共同表明，恢复地点不可预测的环境条件将使选择单一的高性能植物来源变得困难，因此我们建议合并一组不同的收集地点，以增加合并所需特征的可能性。