Predicting habitat for rare species at landscape scales is a common goal of environmental monitoring, management, and conservation; however, the ability to meet that objective is often limited by the paucity of location records and availability of spatial predictors that effectively describe their habitat. To address this challenge, we used an adaptive, model-based iterative sampling design to direct four years of rare plant surveys within 0.25 ha plots across 602 sites in northeast Alberta, Canada. We used these location records to model and map rare plant habitats for the region using a suite of geospatial predictors including airborne light detection and ranging (LiDAR) vegetation structure metrics, land cover types, soil pH, and a terrain wetness model. Our results indicated that LiDAR-derived vegetation structural metrics and land cover were the most important individual factors, but all variables contributed to predicting the occurrence of rare plants. For LiDAR variables, rarity was negatively related to maximum canopy height, but positively related to canopy relief ratio. Rarity was therefore more likely in places with shorter canopy heights and greater structural complexity. This included fens, which overall had the highest rates of rare plant occurrence. Model-based allocation of sampling led to detections of uncommon species at nearly all sites, while the rarest species in the region were detected at an average encounter rate of 8%. Landscape predictions of rare plant habitat can improve our understanding of environmental limits in rarity, guide local management decisions and monitoring plans, and provide regional tools for assessing impacts from resource development.

中文翻译：

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加拿大艾伯塔省下阿萨巴斯卡地区稀有维管束植物的景观格局

在景观尺度上预测稀有物种的栖息地是环境监测，管理和保护的共同目标；但是，实现该目标的能力通常受到位置记录的匮乏以及有效描述其栖息地的空间预测因子的可用性的限制。为了应对这一挑战，我们使用了自适应的，基于模型的迭代采样设计，在加拿大阿尔伯塔省东北部602个地点的0.25公顷土地上指导了四年的稀有植物调查。我们使用这些位置记录，使用一套地理空间预测器，对该区域的稀有植物栖息地进行建模和绘图，包括航空光检测和测距（LiDAR）植被结构度量，土地覆盖类型，土壤pH值和地形湿度模型。我们的结果表明，LiDAR得出的植被结构指标和土地覆盖率是最重要的个体因素，但是所有变量都有助于预测稀有植物的发生。对于LiDAR变量，稀有度与最大冠层高度负相关，但与冠层缓解率正相关。因此，在冠层高度较短且结构复杂性较高的地方，稀有度更高。其中包括芬斯，总体上罕见植物的发生率最高。基于模型的采样分配导致几乎所有地点都发现了罕见物种，而该地区最稀有物种的平均发现率为8％。对稀有植物栖息地的景观预测可以增进我们对稀有环境限制的理解，