Data from: Why less complexity produces better forecasts: an independent data evaluation of kelp habitat models

Understanding how species are distributed in the environment is increasingly important for natural resource management, particularly for keystone and habitat forming species, and those of conservation concern. Habitat suitability models are fundamental to developing this understanding; however their use in management continues to be limited due to often-vague model objectives and inadequate evaluation methods. Along the Northeast Pacific coast, canopy kelps (Macrocystis pyrifera and Nereocystis luetkeana) provide biogenic habitat and considerable primary production to nearshore ecosystems. We investigated the distribution of these species by examining a series of increasingly complex habitat suitability models ranging from process-based models based on species' ecology to complex Generalised Additive Models applied to purpose-collected survey data. Seeking limits on model complexity, we explored the relationship between model complexity and forecast skill, measured using both cross-validation and independent data evaluation. Our analysis confirmed the importance of predictors used in models of coastal kelp distributions developed elsewhere (i.e., depth, bottom type, bottom slope, and exposure); it also identified additional important factors including salinity, and interactions between exposure and salinity, and slope and tidal energy. Comparative results showed that cross-validation can lead to over-fitting, while independent data evaluation clearly identified the appropriate model complexity for generating habitat forecasts. Our results also illustrate that, depending on the evaluation data, predictions from simpler models can out-perform those from more complex models. Collectively, the insights from evaluating multiple models with multiple data sets contribute to the holistic assessment of model forecast skill. The continued development of methods and metrics for evaluating model forecasts with independent data, and the explicit consideration of model objectives and assumptions, promise to increase the utility of model forecasts to decision makers.

厘清物种在环境中的分布格局，对自然资源管理而言愈发重要，尤其是关键物种（keystone species）、造境物种以及受保护关注的物种。生境适宜性模型（Habitat Suitability Models）是构建此类认知的核心工具，但由于模型目标常模糊不清、评估方法不足，其在管理实践中的应用仍受局限。在东北太平洋沿岸，巨藻（Macrocystis pyrifera）和海囊藻（Nereocystis luetkeana）这类冠层海藻可形成生物生境，并为近岸生态系统贡献可观的初级生产力。本研究针对上述两类海藻的分布展开探究，构建了一系列复杂度逐步提升的生境适宜性模型：从基于物种生态学的过程驱动模型，到针对专项采集的调查数据构建的复杂广义可加模型（Generalised Additive Models）。为探究模型复杂度的合理边界，本研究分析了模型复杂度与预测能力之间的关联，预测能力通过交叉验证（cross-validation）与独立数据评估（independent data evaluation）两种方式进行量化。分析结果证实了过往沿海海藻分布模型中所采用的预测因子的重要性，即水深、底质类型、底坡与暴露度；同时还识别出了其他关键影响因子，包括盐度，以及暴露度与盐度、底坡与潮汐能之间的交互效应。对比分析结果显示，交叉验证可能会导致模型过拟合，而独立数据评估则能清晰筛选出适用于生境预测的最优模型复杂度。本研究结果还表明，基于不同的评估数据集，简单模型的预测效果可能优于复杂模型。综上，通过多数据集对多模型开展评估所获得的认知，有助于全面量化评估模型的预测能力。未来若能持续优化基于独立数据的模型预测评估方法与指标，并明确考量模型目标与假设前提，将有望进一步提升模型预测结果对决策者的实用价值。