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Predicting invasive alien plant distributions: how geographical bias in occurrence records influences model performance

机译:预测外来入侵植物分布:发生记录中的地理偏向如何影响模型性能

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AimTo investigate the impact of geographical bias on the performance of ecological niche models for invasive plant species.LocationSouth Africa and Australia.MethodsWe selected 10 Australian plants invasive in South Africa and nine South African plants invasive in Australia. Geographical bias was simulated in occurrence records obtained from the native range of a species to represent two scenarios. For the first scenario (A, worst-case) a proportion of records were excluded from a specific region of a species' range and for the second scenario (B, less extreme) only some records were excluded from that specific region of the range. Introduced range predictions were produced with the Maxent modelling algorithm where models were calibrated with datasets from these biased occurrence records and 19 bioclimatic variables. Models were evaluated with independent test data obtained from the introduced range of the species. Geographical bias was quantified as the proportional difference between the occurrence records from a control and a biased dataset, and environmental bias was expressed as either the difference in marginality or tolerance between these datasets. Model performance [assessed using the conventional and modified AUC (area under the curve of receiver-operating characteristic plots) and the maximum true skill statistic] was compared between models calibrated with occurrence records from a biased dataset and a control dataset.ResultsWe found considerable variation in the relationship between geographical and environmental bias. Environmental bias, expressed as the difference in marginality, differed significantly across treatments. Model performance did not differ significantly among treatments. Regions predicted as suitable for most of the species were very similar when compared between a biased and control dataset, with only a few exceptions.Main conclusionsThe geographical bias simulated in this study was sufficient to result in significant environmental bias across treatments, but despite this we did not find a significant effect on model performance. Differences in the environmental spaces occupied by the species in their native and invaded ranges may explain why we did not find a significant effect on model performance.
机译:目的调查地理偏向对入侵植物物种生态位模型性能的影响。地点南非和澳大利亚。方法我们选择了10种在南非入侵的澳大利亚植物和9种在澳大利亚入侵的南非植物。在从物种的自然范围获得的出现记录中模拟了地理偏差,以代表两种情况。对于第一种情况(A,最坏的情况),某个物种范围的特定区域中排除了一部分记录;对于第二种情况(B,较不极端),该范围的特定区域中只排除了一些记录。使用Maxent建模算法生成了范围预测,其中使用来自这些偏差发生记录和19个生物气候变量的数据集对模型进行了校准。使用从物种引入范围获得的独立测试数据评估模型。地理偏差被量化为来自控件和偏差数据集的发生记录之间的比例差异,而环境偏差被表示为这些数据集之间的边际差异或容忍度差异。在使用来自偏差数据集和控制数据集的发生记录校准的模型之间,比较了模型性能[使用常规和改进的AUC(在接收器操作的特征图的曲线下的区域)和最大真实技能统计数据进行评估]。结果我们发现差异很大在地理和环境偏差之间的关系。表示为边际差异的环境偏见在不同处理之间差异很大。不同治疗之间的模型表现没有显着差异。在偏倚和对照数据集之间进行比较时,预测适合大多数物种的区域非常相似,只有少数例外。主要结论本研究模拟的地理偏倚足以导致各处理之间的显着环境偏见,尽管如此,并未对模型性能产生重大影响。该物种在其自然和入侵范围内所占据的环境空间的差异可能可以解释为什么我们没有发现对模型性能的重大影响。

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