Does adding community science observations to museum records improve distribution modeling of a rare endemic plant?
Does adding community science observations to museum records improve distribution modeling of a rare endemic plant?
复制标题
DOI：
10.1002/ecs2.4419
复制
发表时间：
2023-03-01
期刊：
影响因子：
2.7
通讯作者：
Resasco, Julian
中科院分区：
文献类型：
--
作者：
Gaier, Andrew G.;Resasco, Julian
了解稀有和濒危物种的范围是保护人类世生物多样性的核心。物种分布模型（SDM）已成为分析地理空间物种-环境关系的常用和强大的工具。虽然评估稀有物种的分布是其保护不可或缺的，但当可用的分布数据有限时，这可能很困难。社区科学平台，如iNaturalist，已成为物种发生数据的替代来源。虽然这些意见往往被认为是较低的质量比自然历史收藏，他们可能有潜力改善SDMs的物种发生记录从集合。在这里，我们调查的效用iNaturalist数据开发SDM的一种罕见的高海拔植物，Telesonix jamesii。由于稀有物种的建模方法在文献中有限，因此考虑了五种不同的建模技术，包括剖面方法，统计模型和机器学习算法。包含iNaturalist数据使T. jamesii。我们发现，使用集合训练数据的随机森林（RF）模型在所有模型中表现最好（曲线下面积= 0.98）。然后，我们比较了仅使用自然历史训练数据的RF模型和使用自然历史（植物标本）和iNaturalist训练数据组合的RF模型的性能。所有模型都严重依赖于气候数据（最干燥季度的平均温度和最温暖季度的降水量），这表明随着气候的持续变化，该物种正受到威胁。验证数据集也影响模型拟合。仅使用植物标本数据的模型在交叉验证评估时表现略差，而不是在外部验证时使用iNaturalist数据。这项研究可以作为一个模型，为未来的SDM研究的物种具有类似的数据限制。
Understanding the ranges of rare and endangered species is central to conserving biodiversity in the Anthropocene. Species distribution models (SDMs) have become a common and powerful tool for analyzing species-environment relationships across geographic space. Although evaluating the distribution of rare species is integral to their conservation, this can be difficult when limited distribution data are available. Community science platforms, such as iNaturalist, have emerged as alternative sources for species occurrence data. Although these observations are often thought to be of lower quality than those of natural history collections, they may have potential for improving SDMs for species with few occurrence records from collections. Here, we investigate the utility of iNaturalist data for developing SDMs for a rare high-elevation plant, Telesonix jamesii. Because methods for modeling rare species are limited in the literature, five different modeling techniques were considered, including profile methods, statistical models, and machine learning algorithms. The inclusion of iNaturalist data doubled the number of usable records for T. jamesii. We found that a random forest (RF) model using ensemble training data performed the highest of any model (area under curve = 0.98). We then compared the performance of RF models that use only natural history training data and those that use a combination of natural history (herbarium specimens) and iNaturalist training data. All models heavily relied on climate data (mean temperature of driest quarter, and precipitation of the warmest quarter), indicating that this species is under threat as climate continues to change. Validation datasets affected model fits as well. Models using only herbarium data performed slightly poorer when evaluated with cross-validation than when validated externally with iNaturalist data. This study can serve as a model for future SDM studies of species with similar data limitations.