Thompson Ian D
Great Lakes Forestry Centre, Canadian Forest Service, Natural Resources Canada, 1219 Queen St. East, Sault Ste. Marie, Ontario Canada, P6A 2E5.
Environ Monit Assess. 2006 Oct;121(1-3):263-73. doi: 10.1007/s10661-005-9119-z. Epub 2006 Jun 3.
The general principles of scale and coarse and fine filters have been widely accepted, but management agencies and industry are still grappling with the question of what to monitor to detect changes in forest biodiversity following forest management. Part of this problem can be attributed to the lack of focused questions for monitoring including absence of null models and predicted effects, a certain level of disconnect between research and management, and recognition that monitoring can be designed as a research question. Considerable research from the past decade has not been adequately synthesized to answer important questions, such as which species or forest attributes might be the best indicators of change. A disproportionate research emphasis has been placed on community ecology, and mostly on a few groups of organisms including arthropods, amphibians, migratory songbirds, and small mammals, while other species, including soil organisms, lichens, bats, raptors, some carnivores, and larger mammals remain less well-known. In most studies of community ecology, the question of what is the importance, if any, of the regularly observed subtle changes in community structures, and causes of observed changes is usually not answered. Hence, our ability to deal with questions of persistence is limited, and demographic research on regionally-defined key species (such as species linked to processes, species whose persistence may be affected, species with large home ranges, species already selected as indicators, and rare and threatened species) is urgently needed. Monitoring programs need to be designed to enable managers to respond to unexpected changes caused by forest management. To do this, management agencies need to articulate null models for monitoring that predict effects, focus fine-scale monitoring on key species (defined by local and regional research) in key habitats (rare, declining, important) across landscapes, and have a protocol in place to adapt management strategies to changes observed. Finally, agencies must have some way to determine and define when a significant change has occurred and to predict the persistence of species; this too should flow from a well-designed null model.
尺度以及粗滤器和细滤器的一般原则已被广泛接受,但管理机构和行业仍在努力解决这样一个问题:在森林管理之后,监测什么才能发现森林生物多样性的变化。这个问题的部分原因可归结为缺乏针对性的监测问题,包括缺乏零模型和预测效应,研究与管理之间存在一定程度的脱节,以及认识到监测可以设计成一个研究问题。过去十年的大量研究尚未得到充分整合,无法回答重要问题,比如哪些物种或森林属性可能是变化的最佳指标。研究重点过度集中在群落生态学上,且主要集中在少数几类生物上,包括节肢动物、两栖动物、候鸟鸣禽和小型哺乳动物,而其他物种,包括土壤生物、地衣、蝙蝠、猛禽、一些食肉动物和大型哺乳动物,仍然鲜为人知。在大多数群落生态学研究中,通常没有回答这样的问题:定期观察到的群落结构细微变化的重要性(如果有的话)以及观察到的变化原因是什么。因此,我们应对持续性问题的能力有限,迫切需要对区域界定的关键物种(如与过程相关的物种、其持续性可能受到影响的物种、活动范围大的物种、已被选为指标的物种以及珍稀和受威胁物种)进行种群统计学研究。监测计划的设计应使管理者能够应对森林管理引发的意外变化。要做到这一点,管理机构需要阐明用于监测的零模型,预测效应,将精细尺度的监测重点放在景观中关键栖息地(稀有、数量减少、重要)的关键物种(由当地和区域研究界定)上,并制定一个方案,以便根据观察到的变化调整管理策略。最后,各机构必须有某种方法来确定和界定何时发生了重大变化,并预测物种的持续性;这也应该源于一个精心设计的零模型。