Becker Elizabeth A, Carretta James V, Forney Karin A, Barlow Jay, Brodie Stephanie, Hoopes Ryan, Jacox Michael G, Maxwell Sara M, Redfern Jessica V, Sisson Nicholas B, Welch Heather, Hazen Elliott L
National Marine Fisheries Service National Oceanic and Atmospheric Administration Ocean Associates, Inc., Under Contract to Southwest Fisheries Science Center La Jolla CA USA.
Institute of Marine Science University of California Santa Cruz Santa Cruz CA USA.
Ecol Evol. 2020 May 11;10(12):5759-5784. doi: 10.1002/ece3.6316. eCollection 2020 Jun.
Species distribution models (SDMs) are important management tools for highly mobile marine species because they provide spatially and temporally explicit information on animal distribution. Two prevalent modeling frameworks used to develop SDMs for marine species are generalized additive models (GAMs) and boosted regression trees (BRTs), but comparative studies have rarely been conducted; most rely on presence-only data; and few have explored how features such as species distribution characteristics affect model performance. Since the majority of marine species BRTs have been used to predict habitat suitability, we first compared BRTs to GAMs that used presence/absence as the response variable. We then compared results from these habitat suitability models to GAMs that predict species density (animals per km) because density models built with a subset of the data used here have previously received extensive validation. We compared both the explanatory power (i.e., model goodness of fit) and predictive power (i.e., performance on a novel dataset) of the GAMs and BRTs for a taxonomically diverse suite of cetacean species using a robust set of systematic survey data (1991-2014) within the California Current Ecosystem. Both BRTs and GAMs were successful at describing overall distribution patterns throughout the study area for the majority of species considered, but when predicting on novel data, the density GAMs exhibited substantially greater predictive power than both the presence/absence GAMs and BRTs, likely due to both the different response variables and fitting algorithms. Our results provide an improved understanding of some of the strengths and limitations of models developed using these two methods. These results can be used by modelers developing SDMs and resource managers tasked with the spatial management of marine species to determine the best modeling technique for their question of interest.
物种分布模型(SDMs)是管理高度洄游海洋物种的重要工具,因为它们能提供有关动物分布的时空明确信息。用于为海洋物种开发物种分布模型的两个普遍建模框架是广义相加模型(GAMs)和增强回归树(BRTs),但很少有比较研究;大多数研究依赖仅存在数据;而且很少有人探讨物种分布特征等因素如何影响模型性能。由于大多数海洋物种的增强回归树已被用于预测栖息地适宜性,我们首先将增强回归树与以存在/不存在为响应变量的广义相加模型进行比较。然后,我们将这些栖息地适宜性模型的结果与预测物种密度(每平方公里动物数量)的广义相加模型进行比较,因为使用此处所用数据子集构建的密度模型此前已得到广泛验证。我们使用加利福尼亚洋流生态系统内一组可靠的系统调查数据(1991 - 2014年),比较了广义相加模型和增强回归树对一系列分类多样的鲸类物种的解释力(即模型拟合优度)和预测力(即在新数据集上的表现)。对于大多数所考虑的物种,增强回归树和广义相加模型都成功地描述了整个研究区域的总体分布模式,但在对新数据进行预测时,密度广义相加模型表现出比存在/不存在广义相加模型和增强回归树都大得多的预测力,这可能是由于响应变量和拟合算法不同所致。我们的结果有助于更好地理解使用这两种方法开发的模型的一些优点和局限性。开发物种分布模型的建模人员和负责海洋物种空间管理的资源管理人员可以利用这些结果,为其感兴趣的问题确定最佳建模技术。