Abstract

Monitoring insects across space and time is challenging, due to their vast taxonomic and functional diversity. This study demonstrates how nets mounted on rooftops of cars (car nets) and DNA metabarcoding can be applied to sample flying insect richness and diversity across large spatial scales within a limited time period. During June 2018, 365 car net samples were collected by 151 volunteers during two daily time intervals on 218 routes in Denmark. Insect bulk samples were processed with a DNA metabarcoding protocol to estimate taxonomic composition, and the results were compared to known flying insect richness and occurrence data. Insect and hoverfly richness and diversity were assessed across biogeographic regions and dominant land cover types. We detected 15 out of 19 flying insect orders present in Denmark, with high proportions of especially Diptera compared to Danish estimates, and lower insect richness and diversity in urbanized areas. We detected 319 species not known for Denmark and 174 species assessed in the Danish Red List. Our results indicate that the methodology can assess the flying insect fauna at large spatial scales to a wide extent, but may be, like other methods, biased towards certain insect orders.

Highlights

  • Recent studies have highlighted declines in the biomass, abundance and diversity of insects [1,2,3]

  • We examine patterns in insect richness and diversity across biogeographic regions and dominant land covers, and use hoverflies (Syrphidae) as a case taxon to detect patterns in large-scale species distributions

  • We investigated whether the car net samples contained species not registered in the public Danish species database or in the subset of data from the Global Biodiversity Information Facility from Denmark [31] and neighbouring countries Sweden, Norway and Germany [32]

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Summary

Introduction

Recent studies have highlighted declines in the biomass, abundance and diversity of insects [1,2,3]. These declines have come as a surprise, in part because of our poor understanding of spatial and temporal patterns in insect communities [3]. One reason for this dearth is logistic; insects collected via standardized sampling must be sorted manually.

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