Gray Clare, Baird Donald J, Baumgartner Simone, Jacob Ute, Jenkins Gareth B, O'Gorman Eoin J, Lu Xueke, Ma Athen, Pocock Michael J O, Schuwirth Nele, Thompson Murray, Woodward Guy
School of Biological and Chemical Sciences, Queen Mary University of London London, E1 4NS, UK ; Department of Life Sciences, Silwood Park, Imperial College London Buckhurst Road, Ascot, Berkshire, SL5 7PY, UK.
Department of Biology, Environment Canada @ Canadian Rivers Institute, University of New Brunswick 10 Bailey Drive, P.O. Box 4400, Fredericton, NB, E3B 5A3, Canada.
J Appl Ecol. 2014 Oct;51(5):1444-1449. doi: 10.1111/1365-2664.12300. Epub 2014 Jul 28.
Monitoring anthropogenic impacts is essential for managing and conserving ecosystems, yet current biomonitoring approaches lack the tools required to deal with the effects of stressors on species and their interactions in complex natural systems.Ecological networks (trophic or mutualistic) can offer new insights into ecosystem degradation, adding value to current taxonomically constrained schemes. We highlight some examples to show how new network approaches can be used to interpret ecological responses.. Augmenting routine biomonitoring data with interaction data derived from the literature, complemented with ground-truthed data from direct observations where feasible, allows us to begin to characterise large numbers of ecological networks across environmental gradients. This process can be accelerated by adopting emerging technologies and novel analytical approaches, enabling biomonitoring to move beyond simple pass/fail schemes and to address the many ecological responses that can only be understood from a network-based perspective.
监测人为影响对于管理和保护生态系统至关重要,但目前的生物监测方法缺乏应对压力源对复杂自然系统中物种及其相互作用影响所需的工具。生态网络(营养或互利共生)可以为生态系统退化提供新的见解,为当前受分类学限制的方案增添价值。我们突出一些例子来说明新的网络方法如何用于解释生态响应。用从文献中获取的相互作用数据增强常规生物监测数据,并在可行的情况下辅以直接观测的实地验证数据,使我们能够开始描绘跨越环境梯度的大量生态网络。采用新兴技术和新颖的分析方法可以加速这一过程,使生物监测超越简单的通过/失败方案,并解决许多只能从基于网络的角度理解的生态响应。