Tsyrlin Edward, Carew Melissa, Hoffmann Ary A, Linke Simon, Coleman Rhys A
School of BioSciences, The University of Melbourne, Bio21, Parkville, Victoria, 3052, Australia.
School of BioSciences, The University of Melbourne, Bio21, Parkville, Victoria, 3052, Australia.
J Environ Manage. 2023 Apr 1;331:117186. doi: 10.1016/j.jenvman.2022.117186. Epub 2023 Jan 23.
Family-level identification of freshwater macroinvertebrates is often used to monitor the health of streams due to the lower cost and higher accuracy of identification compared to identifying species. While data on the presence of families from stream monitoring programs can also be used for biodiversity conservation planning, the ability of family-level datasets to accurately reflect regional biodiversity patterns for freshwater macroinvertebrates in Australia remains untested. This study compares family-level and species-level datasets for freshwater insects identified using morphological features and collected over 16 years from 140 sites in Greater Melbourne, Australia. Similar to the results of other studies, our results show a strong positive relationship between family- and species-level taxon richness. However, using the planning software Marxan to compare conservation priorities in our study region, we found that a data analysis of the family-level dataset underestimated the minimum sampling effort required to accurately reflect species diversity. It also identified sub-optimal conservation priority sites and overlooked regionally rare species. We recommend that aquatic macroinvertebrate monitoring programs aimed at understanding regional biodiversity patterns and conservation priorities should routinely include species-level identification, which is now becoming feasible with advances in molecular methods.
由于与物种鉴定相比,淡水大型无脊椎动物的科级鉴定成本更低且准确性更高,因此常被用于监测溪流的健康状况。虽然来自溪流监测项目的科级数据也可用于生物多样性保护规划,但科级数据集能否准确反映澳大利亚淡水大型无脊椎动物的区域生物多样性模式仍未得到验证。本研究比较了利用形态特征鉴定的、在16年时间里从澳大利亚大墨尔本地区140个地点收集的淡水昆虫科级和物种级数据集。与其他研究结果相似,我们的结果表明科级和物种级分类群丰富度之间存在很强的正相关关系。然而,使用规划软件Marxan来比较我们研究区域的保护优先级时,我们发现科级数据集的数据分析低估了准确反映物种多样性所需的最小采样量。它还识别出了次优的保护优先级地点,并忽略了区域罕见物种。我们建议,旨在了解区域生物多样性模式和保护优先级的水生大型无脊椎动物监测项目应常规性地包括物种级鉴定,随着分子方法的进步,现在这已变得可行。