Department of Geography, Memorial University of Newfoundland, St. John's, Newfoundland, Canada.
Department of Fisheries and Aquatic Sciences, School of Forest Resources and Conservation, University of Florida, Gainesville, Florida, United States of America.
PLoS One. 2018 Feb 28;13(2):e0193647. doi: 10.1371/journal.pone.0193647. eCollection 2018.
Benthic habitat maps, including maps of seabed sediments, have become critical spatial-decision support tools for marine ecological management and conservation. Despite the increasing recognition that environmental variables should be considered at multiple spatial scales, variables used in habitat mapping are often implemented at a single scale. The objective of this study was to evaluate the potential for using environmental variables at multiple scales for modelling and mapping seabed sediments. Sixteen environmental variables were derived from multibeam echosounder data collected near Qikiqtarjuaq, Nunavut, Canada at eight spatial scales ranging from 5 to 275 m, and were tested as predictor variables for modelling seabed sediment distributions. Using grain size data obtained from grab samples, we tested which scales of each predictor variable contributed most to sediment models. Results showed that the default scale was often not the best. Out of 129 potential scale-dependent variables, 11 were selected to model the additive log-ratio of mud and sand at five different scales, and 15 were selected to model the additive log-ratio of gravel and sand, also at five different scales. Boosted Regression Tree models that explained between 46.4 and 56.3% of statistical deviance produced multiscale predictions of mud, sand, and gravel that were correlated with cross-validated test data (Spearman's ρmud = 0.77, ρsand = 0.71, ρgravel = 0.58). Predictions of individual size fractions were classified to produce a map of seabed sediments that is useful for marine spatial planning. Based on the scale-dependence of variables in this study, we concluded that spatial scale consideration is at least as important as variable selection in seabed mapping.
底栖生境图，包括海底沉积物图，已成为海洋生态管理和保护的关键空间决策支持工具。尽管越来越多的人认识到环境变量应该在多个空间尺度上考虑，但生境图中使用的变量通常在单一尺度上实施。本研究的目的是评估在多个尺度上使用环境变量来模拟和绘制海底沉积物的潜力。从加拿大努纳武特地区的吉奇塔鲁阿克附近采集的多波束回声测深仪数据中提取了 16 个环境变量，这些变量的空间尺度范围从 5 米到 275 米不等，然后将这些变量作为预测变量来模拟海底沉积物的分布。利用从抓斗样本中获得的粒度数据，我们测试了每个预测变量的哪些尺度对沉积物模型的贡献最大。结果表明，默认尺度并不总是最佳的。在 129 个潜在的尺度相关变量中，有 11 个变量被选择用于在五个不同的尺度上对泥砂的加对数比进行建模，有 15 个变量被选择用于在五个不同的尺度上对砾石和砂的加对数比进行建模。解释了 46.4%至 56.3%统计离差的 Boosted Regression Tree 模型生成了与交叉验证测试数据相关的多尺度泥、砂和砾石预测（Spearman's ρmud = 0.77，ρsand = 0.71，ρgravel = 0.58）。对个别粒度分数的预测进行分类，生成了一张海底沉积物图，该图可用于海洋空间规划。基于本研究中变量的尺度依赖性，我们得出结论，空间尺度考虑至少与海底测绘中的变量选择一样重要。
