Species distribution models: A comparison of statistical approaches for livestock and disease epidemics
Species distribution models: A comparison of statistical approaches for livestock and disease epidemics
复制标题
DOI：
10.1371/journal.pone.0183626
复制
发表时间：
2017-08-24
期刊：
影响因子：
3.7
通讯作者：
Burgman, Mark
中科院分区：
文献类型：
--
作者：
Hollings, Tracey;Robinson, Andrew;Burgman, Mark
在畜牧业中，可靠的动物和农场的最新空间分布和丰度记录对于政府管理和应对风险至关重要。然而，几乎没有国家能够保持全面、最新的农业普查数据。统计模型可用作此类数据的替代，但很少对牲畜种群进行比较模型研究。广泛分布的物种，包括牲畜，可能难以有效地建模，因为复杂的空间分布无法对环境梯度做出可预测的反应。我们评估了三种机器学习物种分布模型（SDM）估计财产边界内国家级农场动物种群数量的能力：提升回归树（BRT），随机森林（RF）和K-最近邻（K-NN）。这些模型是根据新西兰的商业牲畜数据库和环境和社会经济预测数据建立的。我们使用了两个空间数据分层来测试（i）对应急响应情况下决策的支持，以及（ii）模型预测新地理区域的能力。三种模型的性能差异很大，但性能最好的模型显示出非常高的准确性。BRT的整体表现最好，但RF在许多模拟中表现同样好或更好; RF在预测除大型商业农场外的所有牲畜数量方面都具有上级优势。在所有模拟中，K-NN相对于RF和BRT表现不佳。多物种和单物种模型对农场和假设的检疫区的预测非常接近观测数据。这些模型普遍适用于牲畜估计，在疾病风险建模、生物安全、政策和规划方面具有广泛的应用。
In livestock industries, reliable up-to-date spatial distribution and abundance records for animals and farms are critical for governments to manage and respond to risks. Yet few, if any, countries can afford to maintain comprehensive, up-to-date agricultural census data. Statistical modelling can be used as a proxy for such data but comparative modelling studies have rarely been undertaken for livestock populations. Widespread species, including livestock, can be difficult to model effectively due to complex spatial distributions that do not respond predictably to environmental gradients. We assessed three machine learning species distribution models (SDM) for their capacity to estimate national-level farm animal population numbers within property boundaries: boosted regression trees (BRT), random forests (RF) and K-nearest neighbour (K-NN). The models were built from a commercial livestock database and environmental and socio-economic predictor data for New Zealand. We used two spatial data stratifications to test (i) support for decision making in an emergency response situation, and (ii) the ability for the models to predict to new geographic regions. The performance of the three model types varied substantially, but the best performing models showed very high accuracy. BRTs had the best performance overall, but RF performed equally well or better in many simulations; RFs were superior at predicting livestock numbers for all but very large commercial farms. K-NN performed poorly relative to both RF and BRT in all simulations. The predictions of both multi species and single species models for farms and within hypothetical quarantine zones were very close to observed data. These models are generally applicable for livestock estimation with broad applications in disease risk modelling, biosecurity, policy and planning.