Historical collections as a tool for assessing the global pollination crisis

Abstract

There is increasing concern about the decline of pollinators worldwide. However, despite reports that pollinator declines are widespread, data are scarce and often geographically and taxonomically biased. These biases limit robust inference about any potential pollinator crisis. Non-structured and opportunistic historical specimen collection data provide the only source of historical information which can serve as a baseline for identifying pollinator declines. Specimens historically collected and preserved in museums not only provide information on where and when species were collected, but also contain other ecological information such as species interactions and morphological traits. Here, we provide a synthesis of how researchers have used historical data to identify long-term changes in biodiversity, species abundances, morphology and pollination services. Despite recent advances, we show that information on the status and trends of most pollinators is absent. We highlight opportunities and limitations to progress the assessment of pollinator declines globally. Finally, we demonstrate different approaches to analysing museum collection data using two contrasting case studies from distinct geographical regions (New Zealand and Spain) for which long-term pollinator declines have never been assessed. There is immense potential for museum specimens to play a central role in assessing the extent of the global pollination crisis. This article is part of the theme issue ‘Biological collections for understanding biodiversity in the Anthropocene’.

Evaluating next‐generation sequencing (NGS) methods for routine monitoring of wild bees: Metabarcoding, mitogenomics or NGS barcoding

“…During the last decades, insect pollinators, and especially bees, have declined in several regions of the world (Bartomeus, Stavert, Ward, & Aguado, 2019;Biesmeijer et al, 2006;Burkle, Marlin, & Knight, 2013;Ollerton, Erenler, Edwards, & Crockett, 2014;Potts et al, 2016). While these losses are extensively monitored in managed honeybees (Potts et al, 2010;vanEngelsdorp & Meixner, 2010), less is known on the status, trends and stressors of wild bee populations, as they are more difficult to survey (Goulson, Nicholls, Botías, & Rotheray, 2015;Potts, Biesmeijer, Bommarco, Kleijn, & Scheper, 2015).…”

How old can we go? Evaluating the age limit for effective DNA recovery from historical insect specimens

“…By addressing this question, we can help collection curators develop best practices for selecting which material is suitable for destructive sampling for genotyping, and which material is too old for such purposes and is best preserved intact. Here we focus on mitochondrial genotyping, as it is widely used for species identification (Hebert et al ., ; Janzen et al ., ; Meusnier et al ., ; Pfeiler et al ., ; Bartomeus et al ., ), and because samples that fail to yield mitochondrial genotypes typically also fail to yield nuclear genotypes (Gemmell et al ., ). We have developed a new method that combines advantages of the high copy number of mtDNA with the robustness of fragment‐based methods used for genotyping microsatellites (Gemmell & Marcus, ; Lalonde et al ., ; Lalonde & Marcus, , b).…”

Worldwide occurrence records reflect a global decline in bee species richness

“…Such differential vulnerability might result from a high dependence of bees on flowers for food and a diversity of substrates for nesting, resources that are greatly affected by land conversion to large-scale agriculture, massive urbanization, and other intensive land uses [18][19][20] . However, most studies on "bee decline" to date are based on local-, regional-or country-level datasets, and have a strong bias towards the Northern Hemisphere, particularly North America and Europe, where most long-term research projects capable of generating multidecadal datasets have been conducted 4,12,21,22 .…”

“…GBIF ingests data from a widely diverse range of data sources, localities, recording strategies, geographic areas, sampling intensities, etc., with each data source potentially 50 plagued by both systematic and idiosyncratic biases [24][25][26][27] . Although usage of GBIF data has been strongly criticized due to its inherent biases 21,24,[28][29][30] , most criticisms are usually aimed towards using its occurrence data to reconstruct and model species' distribution ranges.…”