Sensitivity of predictive species distribution models to change in grain size
Sensitivity of predictive species distribution models to change in grain size
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DOI：
10.1111/j.1472-4642.2007.00342.x
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发表时间：
2007-05-01
影响因子：
4.6
通讯作者：
Huettmann, Falk
中科院分区：
文献类型：
--
作者：
Guisan, Antoine;Graham, Catherine H.;Huettmann, Falk
预测物种分布模型（SDM）已成为生物多样性保护和管理的重要工具。在建模中使用的环境层粒度（分辨率）的选择是可能影响预测的一个重要因素。我们采用10种不同的建模技术对5个不同地区的50个物种的仅存在数据进行了分析，以测试：(1)分辨率粗化10倍是否会影响SDMs的预测性能，以及(2)任何观察到的效果取决于区域类型、建模技术或所考虑的物种。结果表明，10倍的粒度变化不会严重影响物种分布模型的预测结果。总体趋势是模型性能的下降，但也可以观察到改进。改变颗粒大小对不同地区、技术和物种类型的模型的影响并不相同。对区域和物种类型的影响最大，定位精度最高的数据集（区域）中的树种受影响最大。改变颗粒大小对技术的排名影响不大：增强回归树在两种分辨率下都是最好的。用于模型训练的出现次数具有重要的影响，样本量越大，模型越好，对粒度越敏感。只有当模型达到足够的性能和/或初始数据的固有误差小于粗粒度时，晶粒变化的影响才会被注意到。
Predictive species distribution modelling (SDM) has become an essential tool in biodiversity conservation and management. The choice of grain size (resolution) of environmental layers used in modelling is one important factor that may affect predictions. We applied 10 distinct modelling techniques to presence-only data for 50 species in five different regions, to test whether: (1) a 10-fold coarsening of resolution affects predictive performance of SDMs, and (2) any observed effects are dependent on the type of region, modelling technique, or species considered. Results show that a 10 times change in grain size does not severely affect predictions from species distribution models. The overall trend is towards degradation of model performance, but improvement can also be observed. Changing grain size does not equally affect models across regions, techniques, and species types. The strongest effect is on regions and species types, with tree species in the data sets (regions) with highest locational accuracy being most affected. Changing grain size had little influence on the ranking of techniques: boosted regression trees remain best at both resolutions. The number of occurrences used for model training had an important effect, with larger sample sizes resulting in better models, which tended to be more sensitive to grain. Effect of grain change was only noticeable for models reaching sufficient performance and/or with initial data that have an intrinsic error smaller than the coarser grain size.