IAP-25-129

Evolutionary Trade-offs in Edible Insect Breeding: When Sexual and Artificial Selection Collide

Understanding how different evolutionary processes interact is fundamental to explaining how biological diversity arises and is maintained. While natural and sexual selection often act in concert, they can also impose opposing pressures. For example, in Hawaiian field crickets (Teleogryllus oceanicus), males evolved a mutation that silences their song to evade a lethal parasitoid fly, improving survival but reducing mating success [1]. Conversely, in Hyla versicolor tree frogs, females prefer males with more attractive songs whose offspring also show higher growth and survival [2]. These examples highlight how natural and sexual selection can shape evolution in strikingly different ways.

The rise of intensive commercial insect farming has created a new evolutionary arena in which human management fundamentally reshapes selection. Insect farming is expanding rapidly as a sustainable means of converting organic waste into high-protein animal feed, human food, and biodiesel, contributing to zero hunger and several other UN Sustainable Development Goals [3]. Trillions of insects are reared annually [4], and, as in traditional animal agriculture, breeding programmes are emerging to select for traits such as growth rate and protein content [5,6]. However, unlike in livestock systems, individual matings are rarely controlled. Insects are kept at high densities where mate choice is restricted and sexual conflict may be intensified [6].

We lack a clear understanding of how these artificial mating systems shape sexual selection, and how sexual and artificial selection interact. Are these forces synergistic, accelerating genetic improvement in productivity traits, or do they act at cross-purposes, with sexual conflict and disrupted courtship undermining selection goals? Moreover, how do these processes, together with drift and genetic management, affect genetic variation, inbreeding risk, and long-term fitness, welfare, and economic viability?

This PhD will use the Black Soldier Fly (Hermetia illucens) as a model to investigate how sexual, natural, and artificial selection interact in mass-reared insects. The student will test how rearing environments alter mating behaviour, evolutionary dynamics, and fitness outcomes, and examine whether sexual selection amplifies or constrains artificial selection. They will also explore whether behavioural or physiological indicators can be used to monitor welfare in intensively managed populations.

Objectives:
1. Assess whether mating patterns in farmed BSF enhance or undermine artificial selection goals [6] and erode genetic diversity [7];
2. Test how environmental conditions and cage design influence mating behaviour and outcomes;
3. Evaluate impacts on physiological and behavioural indicators of health and welfare;
4. Develop high-throughput behavioural tools to assess welfare in mass-reared insects.

The project combines behavioural observation with deep-learning video analysis [8,9], linking behavioural metrics to fitness and welfare proxies using molecular parentage tools, productivity measures (e.g. body mass, protein/fat content [5,6]), and physiological assays (e.g. injury rates, VOCs [10], immune and stress markers [11–13]).

The supervisory team provides complementary expertise in sexual selection, behavioural ecology, genomics, and insect welfare, offering a rich interdisciplinary environment with strong cross-sector links and real-world impact in sustainable agriculture and food security.

This project combines experimental insect rearing, behavioural assays, genetic analysis, and physiological health assessment to investigate how commercial breeding protocols influence mating behaviour, productivity, and welfare in the Black Soldier Fly (Hermetia illucens).
The student will rear flies in controlled-environment facilities at the University of Stirling using standard commercial protocols, manipulating key parameters such as cage size, density, temperature, and enrichment. These manipulations will test how rearing conditions affect mating behaviour, productivity traits (e.g. body mass, protein/fat content), genetic diversity, and welfare outcomes. All work will be conducted under controlled laboratory conditions (no fieldwork required).

Advanced statistical modelling (e.g. structural equation modelling and path analysis) will be used to disentangle how environmental factors influence productivity and welfare, whether directly, or indirectly through effects on mating behaviour, sexual selection, and conflict.

Behavioural data:
The student will use a combination of direct observation and video-based analysis to quantify mating success, courtship, harassment, and aggression. Video footage from multiple mating cages will be captured using a fixed camera array and analysed using open-source deep learning tools such as DeepLabCut [8], with support from supervisor Dr Shoko Sugasawa. Behavioural measures will be used to quantify sexual selection and conflict. Experimental adults will be flash-frozen in liquid nitrogen immediately after trials for subsequent physiological and genetic analysis, linking behaviour to health, stress, and reproductive outcomes.

Genetic data:
Offspring from observed adults will be reared under standardised conditions to assess reproductive outcomes. Molecular tools (e.g. microsatellites, SNP panels, or genotyping-by-sequencing) will be used to assign paternity and estimate genetic variation. The student will be trained by supervisors Dr Reuben Nowell and Dr Rebecca Boulton in genomic data handling, parentage analysis (COLONY/CERVUS), population genetics, and quantitative genetics to estimate heritability of productivity traits.

Productivity traits:
Key productivity metrics (including body mass at pupation, growth rate, and crude protein and fat composition) will be measured in offspring. Nutritional analyses will be conducted using analytical services at the Institute of Aquaculture (NAS facility).

Physiological health and welfare:
Health and welfare will be assessed in flash-frozen adults from behavioural trials using selected physiological and behavioural indicators chosen for feasibility and repeatability. Potential measures include external body condition (damage, wing wear, limb loss), oxidative stress biomarkers (e.g. SOD activity, lipid peroxidation via MDA/TBARS), heat shock protein expression (qPCR), haemocyte counts, and biogenic amine levels (ELISA/HPLC). The exact assays will be determined with supervisor Dr Amaya Albalat, who has extensive expertise in invertebrate physiology and welfare.

Data analysis:
Data will be analysed using generalised linear mixed models, structural equation models, and quantitative genetic (animal) models to assess how mating behaviour, environmental variables, and cage design influence productivity, welfare, and artificial selection outcomes.

Placement:
The student will undertake a ~3-month placement with the Insect Café, applying behavioural monitoring and welfare metrics to other insect species (e.g. mealworms, house crickets) and exploring integration with small-scale commercial rearing systems.

Year 1

Training in insect husbandry and experimental design at the University of Stirling, with visits to commercial farms to learn large-scale rearing protocols. Set up pilot experiments to refine housing treatments (e.g. size, density, enrichment) and behavioural variables. Training in video-based behavioural analysis using deep learning tools (with Dr Shoko Sugasawa, Newcastle) will be completed alongside validation of manual and automated scoring schemes. Productivity assays (e.g. body mass, fat/protein content) will be tested and refined.
Milestone: Developed experimental protocols and pilot dataset demonstrating feasibility and repeatability of key variables.

Year 2

Run full-scale experiments testing how environmental and housing conditions influence mating behaviour, productivity, and fitness. Analyse video data using deep learning models, assess reproductive and productivity traits, and link behavioural outcomes to parentage and genetic data. Statistical analyses will quantify the relationships between sexual selection, productivity, and welfare, and the student will present findings at an industry workshop (e.g. Insect IMP COST Action).
Milestone: Complete dataset linking mating behaviour, paternity, fitness, and productivity; initial results figures drafted.

Year 3

Run physiological assays (e.g. stress biomarkers, body condition) and score behavioural welfare indicators. Statistical modelling will test correlations among welfare measures and identify how housing conditions affect health and wellbeing. A short placement (~3 months) with the Insect Cafe will assess the wider applicability of behavioural monitoring and welfare tools to other insect farming systems (e.g. mealworms, crickets).
Milestone: Complete analyses for first two thesis chapters; one manuscript submitted.

Year 3.5

Final statistical analyses, thesis writing, and submission. The student will present findings at conferences and contribute to an industry-facing policy brief.
Milestone: Thesis submitted; at least one additional manuscript ready for submission.

The student will gain broad training in insect husbandry, behavioural ecology, quantitative genetics, genomics and physiology. They will develop core research skills through the IAPETUS and University of Stirling IAS-RDP programmes, covering statistics, programming (R/Python), data visualisation, and scientific communication.

Project-specific training includes deep learning–based behavioural analysis (Dr Sugasawa), insect physiology and welfare (Dr Albalat), molecular and quantitative genetics (Dr Boulton, Dr Nowell), and experimental design. External short courses (e.g. in structural equation modelling, bioinformatics) will be supported as needed.

The Insect Cafe placement will provide hands-on industry training in small-scale insect farming, translating welfare and productivity metrics into real-world IAFF contexts.