Hollings Tracey, Robinson Andrew, van Andel Mary, Jewell Chris, Burgman Mark
Centre of Excellence for Biosecurity Risk Analysis, University of Melbourne, Melbourne, Australia.
Ministry for Primary Industries, Wellington, New Zealand.
PLoS One. 2017 Aug 24;12(8):e0183626. doi: 10.1371/journal.pone.0183626. eCollection 2017.
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.
在畜牧业中，动物和农场可靠的最新空间分布及数量记录对于政府管理和应对风险至关重要。然而，几乎没有哪个国家能够负担得起维护全面、最新的农业普查数据。统计建模可作为此类数据的替代方法，但针对牲畜种群的比较建模研究却很少进行。包括牲畜在内的广泛分布物种，由于其复杂的空间分布对环境梯度没有可预测的响应，因此难以有效地进行建模。我们评估了三种机器学习物种分布模型（SDM），以估算财产边界内的国家级农场动物数量：增强回归树（BRT）、随机森林（RF）和K近邻（K-NN）。这些模型是根据新西兰的商业牲畜数据库以及环境和社会经济预测数据构建的。我们使用了两种空间数据分层方法来测试：（i）在应急响应情况下对决策的支持，以及（ii）模型预测新地理区域的能力。三种模型类型的性能差异很大，但表现最佳的模型显示出非常高的准确性。总体而言，BRT性能最佳，但RF在许多模拟中表现同样出色或更好；除了非常大的商业农场外，RF在预测牲畜数量方面表现更优。在所有模拟中，K-NN相对于RF和BRT表现较差。农场以及假设检疫区内的多物种和单物种模型预测与观测数据非常接近。这些模型通常适用于牲畜估计，在疾病风险建模、生物安全、政策和规划等方面有广泛应用。
