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Improving performance and transferability of small mammal species distribution models
Transactions of the Royal Society of South Australia ( IF 0.8 ) Pub Date : 2018-07-03 , DOI: 10.1080/03721426.2018.1513770
Nerissa A. Haby 1 , Steven Delean 2 , Barry W. Brook 3
Affiliation  

ABSTRACT In theory, interpretation and transferability of species distribution models (SDMs) should be improved by including abiotic and biotic factors that directly influence a species’ fundamental niche. We investigated whether adding topographic, soil and vegetation variables to a climate-only model improved model performance and predictive capacity for four coastal small mammal species. Adding landscape variables improved the structural goodness of fit for all four species (e.g. 2.6–47.6% increase in deviance explained), and the information-theoretic rankings (based on AICc, BIC and DIC) for the wet-heath specialist (Muridae, Rattus lutreolus lutreolus) and peramelid (Peramelidae, Isoodon obesulus obesulus). For the latter species, improved model performance successfully coincided with improved predictive capacity in the out-of-region validation (increase in the area under the curve, AUC). However, this result was poorly supported by trends in the successful classification of absences (specificity) indicating a modelling bias caused by low prevalence of species occurrence. Across all SDMs, additional abiotic and biotic landscape variables contributed between 3.7 and 14.9% of accumulative deviance explained. Our results illustrate increased model fit and transferability for select species, highlighting the potential for landscape variables that represent resources to better represent the fundamental niche in SDMs.

中文翻译:

提高小型哺乳动物物种分布模型的性能和可转移性

摘要 从理论上讲,物种分布模型 (SDM) 的解释和可转移性应该通过包括直接影响物种基本生态位的非生物和生物因素来改进。我们调查了将地形、土壤和植被变量添加到纯气候模型是否可以提高四种沿海小型哺乳动物物种的模型性能和预测能力。添加景观变量提高了所有四种物种的结构拟合优度(例如解释的偏差增加了 2.6-47.6%),以及湿地专家(鼠科、鼠科)的信息理论排名(基于 AICc、BIC 和 DIC) lutreolus lutreolus) 和 peramelid (Peramelidae, Isoodon obesulus obesulus)。对于后一物种,改进的模型性能与改进的区域外验证预测能力相吻合(曲线下面积增加,AUC)。然而,这一结果没有得到成功分类缺席(特异性)的趋势的支持,表明由于物种发生率低导致建模偏差。在所有 SDM 中,额外的非生物和生物景观变量贡献了 3.7% 到 14.9% 的累积偏差。我们的结果表明,选择物种的模型拟合度和可转移性增加,突出了代表资源的景观变量的潜力,以更好地代表 SDM 的基本生态位。这一结果没有得到成功分类缺席(特异性)的趋势的支持,表明由于物种出现率低导致建模偏差。在所有 SDM 中,额外的非生物和生物景观变量贡献了 3.7% 到 14.9% 的累积偏差。我们的结果表明,选择物种的模型拟合度和可转移性增加,突出了代表资源的景观变量的潜力,以更好地代表 SDM 的基本生态位。这一结果没有得到成功分类缺席(特异性)的趋势的支持,表明由于物种出现率低导致建模偏差。在所有 SDM 中,额外的非生物和生物景观变量贡献了 3.7% 到 14.9% 的累积偏差。我们的结果表明,选择物种的模型拟合度和可转移性增加,突出了代表资源的景观变量的潜力,以更好地代表 SDM 的基本生态位。
更新日期:2018-07-03
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