How well does presence-only-based species distribution modelling predict assemblage diversity? A case study of the Tenerife flora
How well does presence-only-based species distribution modelling predict assemblage diversity? A case study of the Tenerife flora
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DOI：
10.1111/j.1600-0587.2010.06134.x
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发表时间：
2011-02-01
期刊：
影响因子：
5.9
通讯作者：
Lobo, Jorge M.
中科院分区：
文献类型：
--
作者：
Aranda, Silvia C.;Lobo, Jorge M.
预测误差被认为是物种分布模型中的一个重要问题。为了解决这个问题，我们在这里检查了许多个体物种的基于存在的模型的叠加在表示组合多样性模式方面的准确性。为此，我们使用了一个包含 977 160 个种子植物出现记录的数据库，该数据库位于一个经过深入调查的物种丰富的岛屿（加那利群岛特内里费岛），用于单独模拟其所有 841 种本土植物物种的分布。建模是使用 Maxent（性能最好的仅存在建模技术之一）完成的，使用各种阈值将估计的适宜性值转换为预测的存在或不存在。每个物种的分布模型被叠加以预测物种丰富度和组成，然后与经过充分调查的网格单元的观测值进行比较。当应用预测物种丰富度的最佳适用性阈值时，我们发现成分误差很高。相对于经过充分调查的细胞的观察值，我们的最佳预测是平均物种丰富度误差为 24%，平均成分误差为 60%； > 50% 的所有物种被错误地纳入 > 25% 的经过充分调查的细胞中。因此，大量数据不一定足以获得组合多样性的可靠预测，限制了这种方法在保护规划中的有用性。
Prediction error is considered an important problem in species distribution models. To address this issue, we here examined the accuracy of overlays of presence-only-based models for many individual species in representing patterns of assemblage diversity. For this purpose, we used a database of 977 160 records of seed plant occurrences on an intensively surveyed, species-rich island (Tenerife, Canary Islands) for modelling the distribution of all its 841 native plant species individually. The modelling was done using Maxent, one of the best-performing presence-only modelling techniques, using various thresholds to convert the estimated suitability values into predicted presence or absence. Distribution models for each individual species were overlaid to predict species richness and composition, which were then compared to the observed values for well-surveyed grid cells. We found high levels of compositional error, when the best performing suitability threshold for predicting species richness was applied. Our best prediction had a mean species richness error of 24% and a mean compositional error of 60% relative to the observed values for the well-surveyed cells; > 50% of all species were included erroneously in > 25% of the well-surveyed cells. Hence, large quantities of data are not necessarily enough to obtain reliable predictions of assemblage diversity, limiting the usefulness of this methodology in conservation planning.