Centro de Biotecnología y Genómica de Plantas (CBGP, UPM-INIA), Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), and E.T.S.I. Agronómica, Alimentaria y de Biosistemas, Campus de Montegancedo, UPM, Pozuelo de Alarcón, 28223, Madrid, Spain.
Unidad de Botánica, Departamento de Farmacología, Farmacognosia y Botánica, Facultad de Farmacia, Universidad Complutense, Madrid, Spain.
BMC Ecol Evol. 2021 Sep 9;21(1):173. doi: 10.1186/s12862-021-01903-9.
Plant communities of fragmented agricultural landscapes, are subject to patch isolation and scale-dependent effects. Variation in configuration, composition, and distance from one another affect biological processes of disturbance, productivity, and the movement ecology of species. However, connectivity and spatial structuring among these diverse communities are rarely considered together in the investigation of biological processes. Spatially optimised predictor variables that are based on informed measures of connectivity among communities, offer a solution to untangling multiple processes that drive biodiversity.
To address the gap between theory and practice, a novel spatial optimisation method that incorporates hypotheses of community connectivity, was used to estimate the scale of effect of biotic and abiotic factors that distinguish plant communities. We tested: (1) whether different hypotheses of connectivity among sites was important to measuring diversity and environmental variation among plant communities; and (2) whether spatially optimised variables of species relative abundance and the abiotic environment among communities were consistent with diversity parameters in distinguishing four habitat types; namely Crop, Edge, Oak, and Wasteland. The global estimates of spatial autocorrelation, which did not consider environmental variation among sites, indicated significant positive autocorrelation under four hypotheses of landscape connectivity. The spatially optimised approach indicated significant positive and negative autocorrelation of species relative abundance at fine and broad scales, which depended on the measure of connectivity and environmental variation among sites.
These findings showed that variation in community diversity parameters does not necessarily correspond to underlying spatial structuring of species relative abundance. The technique used to generate spatially-optimised predictors is extendible to incorporate multiple variables of interest along with a priori hypotheses of landscape connectivity. Spatially-optimised variables with appropriate definitions of connectivity might be better than diversity parameters in explaining functional differences among communities.
碎片化农业景观中的植物群落受到斑块隔离和尺度相关效应的影响。配置、组成和彼此之间距离的变化会影响干扰、生产力和物种运动生态学等生物过程。然而，在研究生物过程时，很少将这些不同群落之间的连通性和空间结构结合起来考虑。基于群落之间连通性的有根据度量的空间优化预测变量提供了一种解决方法，可以理清驱动生物多样性的多个过程。
为了解决理论与实践之间的差距，采用了一种新的空间优化方法，该方法结合了群落连通性的假设，用于估计区分植物群落的生物和非生物因素的影响规模。我们测试了：(1) 站点之间不同的连通性假设对于测量植物群落之间的多样性和环境变化是否重要；(2) 物种相对丰度和群落之间的非生物环境的空间优化变量是否与区分四种生境类型的多样性参数一致；即作物、边缘、橡树和荒地。不考虑站点之间环境变化的全局空间自相关估计表明，在四种景观连通性假设下存在显著的正自相关。空间优化方法表明，在精细和广泛的尺度上，物种相对丰度的显著正和负自相关取决于连通性和站点之间环境变化的度量。
这些发现表明，群落多样性参数的变化不一定与物种相对丰度的潜在空间结构相对应。用于生成空间优化预测变量的技术可以扩展到包括多个感兴趣的变量以及景观连通性的先验假设。具有适当连通性定义的空间优化变量可能比多样性参数更能解释群落之间的功能差异。
