Species distribution modelling (SDM) is a family of statistical methods where species occurrence/density/richness are combined with environmental predictors to create predictive spatial models of species distribution. However, it often turns out that due to complex multi-level interactions between predictors and the response function, different types of models can detect different numbers of important predictors and also vary in their predictive ability. This is why we decided to explore differences in the predictive power of two most common methods, such as the Generalised Additive Model (GAM) and the Random Forest (RF) on the example of the Great Spotted Woodpecker Dendrocopos major and the Great Grey Shrike Lanius excubitor, as well as on the taxonomic and functional species richness. For each of the two bird species’ densities and for two measurements of biodiversity, two sets of SDMs were generated: One based on the GAM, and the other on the RF. According to the out-of-bag, the Akaike Information Criterion (AIC) and an independent evaluation, we demonstrated that the GAM is the best method for predicting density of the Great Spotted Woodpecker and taxonomic species richness, whereas the RF has the lowest prediction error for the density of the Great Grey Shrike and functional species richness. It also becomes apparent that the GAM is responsive to taxonomic species richness and species with broad tolerance to environmental factors, i.e. the Great Spotted Woodpecker, while the RF detects more subtle relationships between density and environmental variables, rendering it more suitable for functional species richness and species with a narrow tolerance range to habitats factors, i.e. the Great Grey Shrike. Thus, effective predictive modelling of animal distribution requires considering several different analytical approaches to produce biologically realistic predictions.

中文翻译：

---

广义加性模型和随机森林法是预测物种密度和功能物种丰富度的有效方法

物种分布模型（SDM）是一系列统计方法，其中物种发生/密度/丰富度与环境预测因子相结合，以创建物种分布的预测性空间模型。但是，通常会发现，由于预测变量和响应函数之间复杂的多级交互作用，不同类型的模型可以检测到不同数量的重要预测变量，并且其预测能力也有所不同。这就是为什么我们决定探索两种最常用的方法，比如在大的例子中，广义加法模型（GAM）和随机森林（RF）预测能力差斑啄木鸟斑啄木鸟主要和灰伯劳伯劳专家以及对分类和功能物种的丰富性。对于两种鸟类的每一种的密度以及两种生物多样性的测量结果，生成了两组SDM：一组基于GAM，另一组基于RF。根据实际价格，Akaike信息准则（AIC）和独立评估，我们证明了GAM是预测大斑啄木鸟密度和分类学物种丰富度的最佳方法，而RF则是预测最低的方法大灰伯劳的密度和功能物种丰富度的误差。同样显而易见的是，GAM对分类物种的丰富性和对环境因子（例如大斑啄木鸟）具有广泛耐受性的物种作出响应，而RF则可以检测到密度与环境变量之间更微妙的关系，使其更适合功能性物种丰富度和对生境因素具有较宽容差范围的物种，例如大灰伯劳。因此，有效的动物分布预测模型需要考虑几种不同的分析方法，以产生生物学上现实的预测。