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.探明物种在环境中的分布格局，对于自然资源管理而言愈发重要，尤其是关键物种（keystone species）、生境构建物种（habitat forming species）以及受保育关注物种的相关研究。生境适宜性模型（habitat suitability models）是厘清该认知的核心工具；然而由于模型目标往往模糊不清、评估方法存在不足，这类模型在管理实践中的应用仍受局限。在东北太平洋沿岸，冠层褐藻——巨藻（Macrocystis pyrifera）与海囊藻（Nereocystis luetkeana）——可为近岸生态系统提供生物成因生境与可观的初级生产力。本研究针对上述两类物种的分布展开探究，构建了一系列复杂度逐步提升的生境适宜性模型：涵盖基于物种生态学原理的过程驱动模型，以及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（forecast skill）之间的关联，预测能力通过交叉验证（cross-validation）与独立数据评估两种方式进行量化。本研究分析证实了以往沿海褐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；同时还识别出了额外的关键影响因子，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过拟合（over-fitting），而独立数据评估则能清晰筛选出适配生境预测的最优模型复杂度。本研究结果还表明，依据评估数据的不同，简单模型的预测结果可能优于更为复杂的模型。综上，通过多数据集对多模型开展评估所获得的研究结论，有助于对模型预测能力开展整体性评估。持续优化基于独立数据的模型预测评估方法与指标体系，并明确考量模型目标与假设前提，有望进一步提升模型预测结果对决策制定者的实用价值。