Insect biodiversity loss is an increasing concern to scientists, stakeholders, and societies. Approximately 40 % of the world’s insects are threatened with extinction mainly due to habitat loss and agro-chemical pollutants. Pollination contributes to the world’s food security but there is still a loss of insects around the world. In order to stop the loss of insects, scientist have developed several conservation strategies including restoring previous pollinators habitats and changing farming methods to enhance insect rich areas. However, it is important to monitor insects in order to see how pollinator abundance change over time, provide a baseline on their status, and to evaluate the effect of conservation strategies.
Citizen science is well established in monitoring of invertebrates and citizens can be trained quickly to collect samples of species to higher taxon as well as getting similar results scientists. It is possible for citizens to detect insect orders, families, and specific species, however, the data quality differs.
The overall aim was to assess the quality of citizen generated data in pollinator monitoring. This was done by comparing data collected by volunteers with data collected from two experts to see if some pollinators are easier to identify than other. In this thesis I did also investigate if cognitive load affect citizen generated data quality and if data quality different depending on age-group (children or adult).