Assessing Community-Level and Single-Species Models Predictions of Species Distributions and Assemblage Composition after 25 Years of Land Cover Change

To predict the impact of environmental change on species distributions, it has been hypothesized that community-level models could give some benefits compared to species-level models. In this study we have assessed the performance of these two approaches. We surveyed 256 bird communities in an agricultural landscape in southwest France at the same locations in 1982 and 2007. We compared the ability of CQO (canonical quadratic ordination; a method of community-level GLM) and GLMs (generalized linear models) to i) explain species distributions in 1982 and ii) predict species distributions, community composition and species richness in 2007, after land cover change. Our results show that models accounting for shared patterns between species (CQO) slightly better explain the distribution of rare species than models that ignore them (GLMs). Conversely, the predictive performances were better for GLMs than for CQO. At the assemblage level, both CQO and GLMs overestimated species richness, compared with that actually observed in 2007, and projected community composition was only moderately similar to that observed in 2007. Species richness projections tended to be more accurate in sites where land cover change was more marked. In contrast, the composition projections tended to be less accurate in those sites. Both modelling approaches showed a similar but limited ability to predict species distribution and assemblage composition under conditions of land cover change. Our study supports the idea that our community-level model can improve understanding of rare species patterns but that species-level models can provide slightly more accurate predictions of species distributions. At the community level, the similar performance of both approaches for predicting patterns of assemblage variation suggests that species tend to respond individualistically or, alternatively, that our community model was unable to effectively account for the emergent community patterns.为预测环境变化对物种分布的影响，已有研究提出假说：相较于物种水平模型（species-level models），群落水平模型（community-level models）可展现出一定优势。本研究对这两种建模方法的性能展开了评估。研究团队于1982年与2007年，在法国西南部某农业景观的相同点位，对256个鸟类群落开展了调查。我们对比了典范二次排序（canonical quadratic ordination, CQO，一种群落水平广义线性模型（generalized linear model, GLM）方法）与广义线性模型（generalized linear models, GLMs）的两类性能：一是解释1982年的物种分布，二是在发生土地覆盖变化（land cover change）后，预测2007年的物种分布、群落组成（community composition）与物种丰富度（species richness）。研究结果显示，考虑物种间共有分布模式的CQO模型，相较于忽略该模式的GLMs，对稀有物种（rare species）分布的解释能力略胜一筹。反之，GLMs的预测性能则优于CQO。在集合群落水平（assemblage level）上，相较于2007年实际观测值，CQO与GLMs均高估了物种丰富度，且预测得到的群落组成仅与2007年实际观测结果存在中等程度的相似性。在土地覆盖变化更显著的样地，物种丰富度的预测结果往往更为准确；反之，此类样地的群落组成预测结果则往往偏差更大。在土地覆盖变化情境下，两种建模方法对物种分布与群落集合组成的预测能力均表现相似，但整体有限。本研究证实了此前的假说：群落水平模型可提升对稀有物种分布模式的认知，而物种水平模型则能提供更为准确的物种分布预测结果。在群落水平上，两种方法在预测群落集合变异模式时表现相近，这提示两种可能：一是物种对环境变化的响应具有个体特异性，二是本研究采用的群落水平模型无法有效捕捉群落的涌现模式。