Department of Bioscience, Aarhus University, Frederiksborgvej 399, 4000 Roskilde, Denmark.
Biol Rev Camb Philos Soc. 2013 Feb;88(1):15-30. doi: 10.1111/j.1469-185X.2012.00235.x. Epub 2012 Jun 12.
Predicting which species will occur together in the future, and where, remains one of the greatest challenges in ecology, and requires a sound understanding of how the abiotic and biotic environments interact with dispersal processes and history across scales. Biotic interactions and their dynamics influence species' relationships to climate, and this also has important implications for predicting future distributions of species. It is already well accepted that biotic interactions shape species' spatial distributions at local spatial extents, but the role of these interactions beyond local extents (e.g. 10 km(2) to global extents) are usually dismissed as unimportant. In this review we consolidate evidence for how biotic interactions shape species distributions beyond local extents and review methods for integrating biotic interactions into species distribution modelling tools. Drawing upon evidence from contemporary and palaeoecological studies of individual species ranges, functional groups, and species richness patterns, we show that biotic interactions have clearly left their mark on species distributions and realised assemblages of species across all spatial extents. We demonstrate this with examples from within and across trophic groups. A range of species distribution modelling tools is available to quantify species environmental relationships and predict species occurrence, such as: (i) integrating pairwise dependencies, (ii) using integrative predictors, and (iii) hybridising species distribution models (SDMs) with dynamic models. These methods have typically only been applied to interacting pairs of species at a single time, require a priori ecological knowledge about which species interact, and due to data paucity must assume that biotic interactions are constant in space and time. To better inform the future development of these models across spatial scales, we call for accelerated collection of spatially and temporally explicit species data. Ideally, these data should be sampled to reflect variation in the underlying environment across large spatial extents, and at fine spatial resolution. Simplified ecosystems where there are relatively few interacting species and sometimes a wealth of existing ecosystem monitoring data (e.g. arctic, alpine or island habitats) offer settings where the development of modelling tools that account for biotic interactions may be less difficult than elsewhere.
预测哪些物种将来会在何处共存仍然是生态学中最大的挑战之一,需要深入了解非生物和生物环境如何在不同尺度上与扩散过程和历史相互作用。生物相互作用及其动态影响物种与气候的关系,这对预测物种未来分布也具有重要意义。人们已经普遍认识到,生物相互作用在局部空间范围内塑造了物种的空间分布,但这些相互作用在局部范围之外(例如 10 平方公里到全球范围)的作用通常被认为不重要。在这篇综述中,我们整合了有关生物相互作用如何在局部范围之外塑造物种分布的证据,并回顾了将生物相互作用纳入物种分布模型工具的方法。通过借鉴关于个别物种范围、功能组和物种丰富度模式的当代和古生态学研究的证据,我们表明生物相互作用显然在所有空间范围内都对物种分布和实际物种组合留下了痕迹。我们通过来自营养级内和跨营养级的例子来说明这一点。有一系列的物种分布模型工具可用于量化物种与环境的关系并预测物种的出现,例如:(i)整合成对依赖性,(ii)使用综合预测因子,以及(iii)将物种分布模型(SDM)与动态模型混合。这些方法通常仅应用于同一时间相互作用的成对物种,需要关于哪些物种相互作用的先验生态知识,并且由于数据稀缺性,必须假定生物相互作用在空间和时间上是恒定的。为了更好地在空间尺度上为这些模型的未来发展提供信息,我们呼吁加快收集具有空间和时间明确性的物种数据。理想情况下,这些数据应采样以反映大空间范围内基础环境的变化,并具有精细的空间分辨率。简化的生态系统中相互作用的物种相对较少,并且有时存在丰富的现有生态系统监测数据(例如,北极、高山或岛屿栖息地),这些环境为开发考虑生物相互作用的模型工具提供了条件,这可能比其他地方更容易。