When is the best time to sample aquatic macroinvertebrates in ponds for biodiversity assessment?

Abstract

Ponds are sites of high biodiversity and conservation value, yet there is little or no statutory monitoring of them across most of Europe. There are clear and standardised protocols for sampling aquatic macroinvertebrate communities in ponds, but the most suitable time(s) to undertake the survey(s) remains poorly specified. This paper examined the aquatic macroinvertebrate communities from 95 ponds within different land use types over three seasons (spring, summer and autumn) to determine the most appropriate time to undertake sampling to characterise biodiversity. The combined samples from all three seasons provided the most comprehensive record of the aquatic macroinvertebrate taxa recorded within ponds (alpha and gamma diversity). Samples collected during the autumn survey yielded significantly greater macroinvertebrate richness (76 % of the total diversity) than either spring or summer surveys. Macroinvertebrate diversity was greatest during autumn in meadow and agricultural ponds, but taxon richness among forest and urban ponds did not differ significantly temporally. The autumn survey provided the highest measures of richness for Coleoptera, Hemiptera and Odonata. However, richness of the aquatic insect order Trichoptera was highest in spring and lowest in autumn. The results illustrate that multiple surveys, covering more than one season, provide the most comprehensive representation of macroinvertebrate biodiversity. When sampling can only be undertaken on one occasion, the most appropriate time to undertake surveys to characterise the macroinvertebrate community biodiversity is during autumn, although this may need to be modified if other floral and faunal groups need to be incorporated into the sampling programme.

<b>Stream macro-invertebrate diversity of the Phobjikha Valley, Bhutan</b>

“…At this time of the year insects attain bigger size that can be best identified (Hill et al 2016). The study of macro-invertebrates in different seasons is essential for estimating gross diversity, as species are well adapted to the seasonal cycle of flood and drought (Li et al 2012).…”

Bioassessment of Mandakini River with the help of aquatic macroinvertebrates in the vicinity of Kedarnath Wildlife Sanctuary

“…"Handpicking method" was adopted over the conventional method of "kick sampling" because most of the riparian bed of Mandakini River is composed of cobbles and boulders. Substrates were picked up quickly to collect the relatively fast swimmers (Flotemersch et al 2017;Hill et al 2016). The dominant substratum subsamples were pooled to get composite samples.…”

Partitioning the impact of environmental drivers and species interactions in dynamic aquatic communities

“…Efforts to manage human impacts are to date still primarily governed by trial and error (Terborgh 2015, Hill et al 2016. This requires quantification of the effects of environmental drivers on the community during the period when population growth dominates, as well as during the period when intra-and interspecific interaction dominates.…”

“…It could, for instance, reveal time windows in which human disturbance is most damaging or when indicator species for specific human impacts should be monitored. Efforts to manage human impacts are to date still primarily governed by trial and error (Terborgh 2015, Hill et al 2016.…”

“…Thus far, the high diversity of ecological communities and the associated number of potential interactions (K efi et al 2015) have hampered quantitative analysis. As a consequence, field studies of seasonality in aquatic systems have determined changes in productivity, biomass, species richness, and interactions in a limited number of taxa or functional groups (Odum 1969, Sommer et al 1986, Carpenter et al 1987, Hill et al 2016, Leslie and Lamp 2017, Ovaskainen et al 2017, Little and Altermatt 2018, but rarely in all species interactions, including interspecific competition, of a complete community. An exception is the study of Lima-Mendez et al (2015) who used a relatively new classifying technique, random forest, developed for machine learning (Strobl et al 2009), to show that, in addition to environmental conditions, species interactions are important drivers of in marine plankton communities.…”