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Rediscovering the species in community-wide predictive modeling

机译：在社区范围的预测模型中重新发现物种

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Broadening the scope of conservation efforts to protect entire communities provides several advantages over the current species-specific focus, yet ecologists have been hampered by the fact that predictive modeling of multiple species is not directly amenable to traditional statistical approaches. Perhaps the greatest hurdle in community-wide modeling is that communities are composed of both co-occurring groups of species and species arranged independently along environmental gradients. Therefore, commonly used "short-cut" methods such as the modeling of so-called "assemblage types" are problematic. Our study demonstrates the utility of a multiresponse artificial neural network (MANN) to model entire community membership in an integrative yet species-specific manner. We compare MANN to two traditional approaches used to predict community composition: (1) a species-by-species approach using logistic regression analysis (LOG) and (2) a "classification-then-i-.nodeling" approach in which sites are classified into assemblage "types" (here we used two-way indicator species analysis and multiple discriminant analysis [MDA]). For freshwater fish assemblages of the North Island, New Zealand, we found that the MANN outperformed all other methods for predicting community composition based on multiscaled descriptors of the environment. The simple-matching coefficient comparing predicted and actual species composition was, on average, greatest for the MANN (91%), followed by MDA (85%), and LOG (83%). Mean Jaccard's similarity (emphasizing model performance for predicting species' presence) for the MANN (66%) exceeded both LOG (47%) and MDA (46%). The MANN also, correctly predicted community composition (i.e., a significant proportion of the species membership based on a randomization procedure) for 82% of the study sites compared to 54% (MDA) and 49% (LOG), resulting in the MANN correctly predicting community composition in a total of 311 sites and an additional 117 sites (n = 379), on average, compared to LOG and MDA. The MANN also provided valuable explanatory power by simultaneously quantifying the nature of the relationships between the environment and both individual species and the entire community (composition and richness), which is not readily available from traditional approaches. We discuss how the MANN approach provides a powerful quantitative tool for conservation planning and highlight its potential for biomonitoring programs that currently depend on modeling discrete assemblage types to assess aquatic ecosystem health.
机译：拓宽保护范围以保护整个社区比当前针对特定物种的重点提供了多个优势，然而，生态学家因多种物种的预测模型不直接适用于传统的统计方法而受到阻碍。社区范围建模中最大的障碍可能是，社区是由物种的共生群体和沿环境梯度独立排列的物种组成。因此，常用的“捷径”方法（例如所谓的“组合类型”的建模）是有问题的。我们的研究证明了多响应人工神经网络（MANN）能够以综合但物种特定的方式对整个社区成员进行建模。我们将MANN与两种用于预测群落组成的传统方法进行比较：（1）使用Logistic回归分析（LOG）的逐种方法，以及（2）在“地点-地点-地点”分类方法归类为集合“类型”（此处我们使用了双向指标种类分析和多重判别分析[MDA]）。对于新西兰北岛的淡水鱼群，我们发现MANN优于基于环境的多尺度描述符来预测群落组成的所有其他方法。比较预测物种和实际物种组成的简单匹配系数平均最高，其中MANN（91％），其次是MDA（85％）和LOG（83％）。 MANN（66％）的平均Jaccard相似度（强调模型性能以预测物种的存在）超过了LOG（47％）和MDA（46％）。 MANN还正确预测了82％的研究地点的群落组成（即基于随机化程序的很大一部分物种隶属关系），而54％（MDA）和49％（LOG）则正确地预测了MANN预测与LOG和MDA相比，平均共有311个站点和另外117个站点（n = 379）的社区组成。通过同时量化环境与单个物种与整个社区之间的关系的性质（组成和丰富度），MANN还提供了宝贵的解释能力，而传统方法不容易获得这种解释力。我们将讨论MANN方法如何为保护规划提供强大的量化工具，并强调其在目前依赖离散组合类型建模以评估水生生态系统健康的生物监测程序中的潜力。

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