Insects as indicators of biodiversity in nature inclusive agricultural development.

Biodiversity in general is under threat. Insects are vital for natural and
agro-ecosystems, and are also under threat. Nature inclusive agriculture
aims to mitigate biodiversity threats. Mitigation needs applicable success
indicators. Insects appear excellent success indicators, because of their
diversity and abundance, and reactivity on small landscape changes.
However, measuring insect biodiversity in quality and quantity is not easy.
We aimed to provide user-friendly and achievable new ways to get
insightful insect measures in nature inclusive agriculture and other
settings. This was done by exploring easy and cheap catching techniques
(pitfall traps, pan traps, sticky traps, moth traps), and by developing userfriendly annotation pipelines, aided by automated recognition using
machine learning. This was developed in an education setting with the aid
of students, while providing data for real life funded applied science
research projects financed by the EU, province of Fryslan, LNV, and
several other funding entities.
Insect diversity was measured on easily discernable taxonomic levels.
Insect order, family and species levels, and biomass profiles were used to
indicate abundance and diversity effects in several projects.
Sticky trap analysis using machine learning was developed to a proof of
concept level for biomass analysis, and is applied in ongoing and planned
biodiversity monitoring projects. This is based on automated
determination of arthropods on sticky traps, followed by automated
estimation of length, and length based biomass calculation. Automated
determination of order, family or species was not successful enough yet.
Automated processing of pitfall trap and pan trap catches was attempted,
but showed too labor intensive. We developed a photo-setup for faster
entry of catches into an annotation pipeline for these catching methods
for future development. Moth traps were not developed further, because
they are maximally aided by automated species recognition (ObsIdentify).
We are developing a tool for interpreting differences between moth
catches.
Our sticky trap biodiversity methods are now used as a standard method
in student projects (internships, BSc theses, education minors), and are
presented in general courses in Diermanagement / Wildlife management,
and in courses and projects of Milieukunde, Dier- en Veehouderij, and
Tuin- en Akkerbouw at VHL in Leeuwarden. Also educational material on
insect measuring and recognition was made (groenkennisnet.nl).

Human kind has a major impact on the state of life on Earth, mainly caused by habitat destruction, fragmentation and pollution related to agricultural land use and industrialization.
Biodiversity is dominated by insects (~50%). Insects are vital for ecosystems through ecosystem engineering and controlling properties, such as soil formation and nutrient cycling, pollination, and in food webs as prey or controlling predator or parasite. Reducing insect diversity reduces resilience of ecosystems and increases risks of non-performance in soil fertility, pollination and pest suppression.
Insects are under threat. Worldwide 41 % of insect species are in decline, 33% species threatened with extinction, and a co-occurring insect biomass loss of 2.5% per year. In Germany, insect biomass in natural areas surrounded by agriculture was reduced by 76% in 27 years. Nature inclusive agriculture and agri-environmental schemes aim to mitigate these kinds of effects.
Protection measures need success indicators. Insects are excellent for biodiversity assessments, even with small landscape adaptations. Measuring insect biodiversity however is not easy.
We aim to use new automated recognition techniques by machine learning with neural networks, to produce algorithms for fast and insightful insect diversity indexes.
Biodiversity can be measured by indicative species (groups). We use three groups: 1) Carabid beetles (are top predators); 2) Moths (relation with host plants); 3) Flying insects (multiple functions in ecosystems, e.g. parasitism).
The project wants to design user-friendly farmer/citizen science biodiversity measurements with machine learning, and use these in comparative research in 3 real life cases as proof of concept: 1) effects of agriculture on insects in hedgerows, 2) effects of different commercial crop production systems on insects, 3) effects of flower richness in crops and grassland on insects, all measured with natural reference situations