Data from: Multiresponse algorithms for community-level modeling: review of theory, applications, and comparison to species distribution models

1.Community-level models (CLMs) consider multiple, co-occurring species in model fitting and are lesser known alternatives to species distribution models (SDMs) for analyzing and predicting biodiversity patterns. CLMs simultaneously model multiple species, including rare species, while reducing overfitting and implicitly considering drivers of co-occurrence. Many CLMs are direct extensions of well-known SDMs and therefore should be familiar to ecologists. However, CLMs remain underutilized, and there have been few tests of their potential benefits and no systematic reviews of their assumptions and implementations. Here we review this emerging field and provide examples in R to fit common CLMs. Our goal is to introduce CLMs to a broader audience, and discuss their attributes, benefits, and limitations relative to SDMs.

2.We review i) statistical implementations and applications of CLMs, ii) their advantages and limitations, and iii) comparative analyses of CLMs and SDMs. We also suggest directions for future research.

3.We identify seven CLM algorithms with similar data structures and predictive outputs as SDMs that should be most accessible to ecologists familiar with species-level modeling, including five methods that predict assemblage composition and individual species distributions and two methods that model compositional turnover along environmental gradients. CLMs have been applied to numerous taxa, regions, and spatial scales, and a variety of topics (e.g., studying drivers of community structure or assessing relationships between community composition and functional traits). Studies suggest that the relative benefits of CLMs and SDMs may be case specific, especially in terms of predicting species distributions and community composition. However, CLMs may offer advantages in terms of computational efficiency, modeling rare species, and projecting to no-analog climates. A major shortcoming of CLMs is their reliance on presence-absence community composition data.

4.Studies are needed to assess the relative merits of SDMs and CLMs, and different CLM algorithms, with a focus on three key areas: i) under which circumstances CLMs improve predictions for rare species, ii) how CLMs perform under different community compositions (e.g. relative abundance of rare vs. common species), including the extent to which co-occurrence patterns are structured by biotic interactions, and iii) ability to project across time/space.

1. 群落级模型（Community-level Models，CLMs）在模型拟合过程中纳入多个共生物种，是用于分析和预测生物多样性格局的物种分布模型（Species Distribution Models，SDMs）的一类鲜为人知的替代方法。CLMs可同时对包括珍稀物种在内的多个物种进行建模，同时能够降低过拟合风险，并隐性考量物种共现的驱动因素。多数CLMs是经典SDMs的直接拓展，因此生态学家通常对其较为熟悉。但目前CLMs的应用仍未得到充分推广，相关研究极少对其潜在优势开展验证，也尚未有针对其假设前提与实现方式的系统性综述。本文对这一新兴领域展开综述，并提供在R语言中拟合常见CLMs的示例。本文旨在向更广泛的受众介绍CLMs，并探讨其相较于SDMs的属性、优势与局限。
2. 本文综述内容涵盖三部分：① CLMs的统计实现方式与应用场景；② 其优势与局限；③ CLMs与SDMs的对比分析。此外，本文还将提出未来研究的方向。
3. 本文梳理出7种与SDMs数据结构、预测输出高度相似的CLM算法，这类算法对于熟悉物种水平建模的生态学家而言最为易用。其中5种方法可用于预测群落组成与单个物种的分布，另外2种方法则用于模拟沿环境梯度的群落组成更替。CLMs已被应用于众多类群、区域与空间尺度，涵盖各类研究主题（例如探究群落结构的驱动因素，或评估群落组成与功能性状之间的关联）。现有研究表明，CLMs与SDMs的相对优势可能因研究场景而异，尤其是在预测物种分布与群落组成方面。不过，CLMs在计算效率、珍稀物种建模以及向无类似气候场景外推方面可能具备优势。CLMs的一个主要缺陷是其依赖于有无记录的群落组成数据。
4. 未来仍需开展研究以评估SDMs与CLMs以及不同CLM算法的相对优劣，重点关注三个核心方向：① CLMs在何种场景下能够提升珍稀物种的预测精度；② CLMs在不同群落组成（例如珍稀物种与常见物种的相对丰度）下的表现，其中包括生物相互作用对共现格局的塑造程度；③ CLMs在跨时间与空间场景下的外推能力。