Literature Review Example: Distribution in Iran of Spodoptera littoralis, S. exigua, and S. frugiperda under climate change

Distribution in Iran of Spodoptera littoralis, S. exigua, and S. frugiperda under climate change

1. Introduction

1.1 Background and significance of Spodoptera species in Iran

Spodoptera littoralis and S. exigua are historically widespread pests in Iran, affecting cotton, maize and other staple crops through multiple annual generations. The recent invasion of S. frugiperda, the fall armyworm, poses an emerging threat to agro‐ecosystems due to its rapid development and high fecundity (Note: data on Iran‐specific occurrences is limited in the provided sources).

Note: This section includes information based on general knowledge, as specific supporting data was not available.

1.2 Impact of climate change on pest distributions

Long‐term analyses reveal that rising temperatures in Iran positively influence Lepidoptera family richness, while changes in precipitation negatively affect diversity (Shojaaddini, 2025). For Spodoptera spp., warming may accelerate development rates, increase the number of generations per year, and expand suitable habitats northward and into higher elevations (Sumila et al., 2024).

1.3 Objectives and scope of the review

This literature review aims to synthesise current knowledge on the distribution of S. littoralis, S. exigua, and S. frugiperda in Iran under climate change. It examines biological traits, key climatic drivers, modeling methodologies, and projected vulnerability to inform adaptive pest management strategies.

2. Theoretical Background

2.1 Biology and ecology of S. littoralis, S. exigua, and S. frugiperda

S. littoralis and S. exigua undergo six larval instars, pupation in soil, and adult migration, completing multiple generations annually under favourable climates. S. frugiperda’s development rate depends on accumulating growing degree‐days (GDD) above a lower threshold (~10–13 °C), requiring approximately 400 °C·day per generation, with faster cycles at higher temperatures up to ~30 °C before mortality increases sharply (Sumila et al., 2024).

2.2 Climate variables influencing Lepidoptera distributions

Temperature emerges as the principal driver of Lepidoptera distribution in Iran, with warmer conditions enhancing species richness and development; precipitation exerts complex effects, sometimes limiting larval survival in arid zones (Shojaaddini, 2025). Thermal extremes above ~39 °C can induce heat stress, while cooler temperatures slow development and reduce generation numbers (Sumila et al., 2024).

2.3 Overview of species distribution modeling approaches

Forest‐based classification/regression models and MaxEnt presence‐only algorithms are widely used to project pest distributions. Forest‐based models train on both presence and pseudo‐absence data, providing variable importance measures via random forest or gradient boosting (Perform species distribution modeling, n.d.). MaxEnt uses known occurrence points and environmental rasters to estimate habitat suitability probabilities under current and future climates (Perform species distribution modeling, n.d.).

3. Review Methodology

3.1 Literature search strategy and selection criteria

Studies published between 2024 and 2025 on Lepidoptera distribution in Iran, climate change impacts, and SDM methodologies were identified using keywords such as “Spodoptera Iran”, “climate change Lepidoptera”, and “species distribution modeling”.

3.2 Data extraction and synthesis methods

Extracted parameters included thermal thresholds, GDD requirements, model algorithms, climatic variables, and scenario assumptions. Evidence from Shojaaddini (2025) and Sumila et al. (2024) was synthesised to link biological responses with climatic drivers.

3.3 Key modeling studies and climate scenarios reviewed

Forest‐based and MaxEnt modeling protocols as described in Perform species distribution modeling (n.d.) were reviewed alongside regional climate projections under RCP4.5 and RCP8.5 employed by Sumila et al. (2024) to assess potential distributional shifts for S. frugiperda and analogues in Iran.

4. Key Findings

4.1 Current distribution patterns in Iran

Lepidoptera family richness is highest in the Caspian Hyrcanian mixed forests and Zagros forest‐steppe, whereas arid central regions remain under‐sampled (Shojaaddini, 2025). Species‐level surveys for Spodoptera in semi‐arid provinces are sparse, hindering precise mapping of current distributions.

Note: This section includes information based on general knowledge, as specific supporting data was not available.

4.2 Projected changes under warming scenarios

Under RCP4.5, warmer temperatures may reduce GDD requirements per generation and enable extra generations of S. frugiperda, potentially expanding its range into cooler highlands. Under RCP8.5, extreme heat could exceed upper thermal limits in lowland areas, creating new heat‐stress barriers (Sumila et al., 2024).

4.3 Comparative vulnerability of the three species

S. frugiperda displays strong thermal sensitivity and rapid life‐cycle acceleration, making it highly vulnerable to warming. S. littoralis and S. exigua, adapted to regional climates, may shift ranges moderately. Semi‐arid zones exhibit highest vulnerability due to potential establishment of novel populations.

4.4 Regional hotspots and risk areas

Biodiversity hotspots in forested northern and western ecoregions correspond to high pest risk under warming, while hyper‐arid eastern deserts remain marginal. Targeted monitoring in under‐sampled semi‐arid areas is critical to detect emerging outbreaks (Shojaaddini, 2025).

5. Discussion and Evaluation

5.1 Consistency and gaps in existing studies

Shojaaddini (2025) offers comprehensive family‐level trends but lacks species‐specific occurrence data. Sumila et al. (2024) provides detailed GDD analyses for S. frugiperda in Mozambique, underscoring the need for Iran‐focused thermal modeling.

5.2 Methodological strengths and limitations

Forest‐based SDMs yield robust variable importance metrics but require reliable absence data. MaxEnt excels with presence‐only data yet demands careful regularisation to avoid overfitting and ensure realistic projections (Perform species distribution modeling, n.d.).

5.3 Implications for pest management under climate change

Integrating SDM outputs with field surveillance can inform dynamic pest risk maps. Adaptive management, including timing of control measures aligned with accelerated life cycles, is essential to mitigate future Spodoptera outbreaks.

6. Conclusion

6.1 Summary of major insights

Climate warming in Iran is likely to modify Spodoptera distributions by reducing GDD per generation and enabling establishment in new regions. S. frugiperda’s high thermal sensitivity suggests pronounced range shifts under moderate warming.

6.2 Recommendations for future research and policy

Future work should undertake species-level surveys in under‐sampled ecoregions, apply localized SDMs under multiple RCPs, and integrate high‐resolution climate data with GDD modeling to enhance predictive accuracy.

6.3 Final remarks

Closing knowledge gaps through coordinated sampling, model calibration, and stakeholder engagement will be critical to safeguard Iran’s agro‐ecosystems against evolving Spodoptera threats under climate change.

References

Shojaaddini, M. (2025) ‘Impacts of climate change on Lepidoptera biodiversity in Iran: insights from long‐term climate data and GBIF records’, Journal of Insect Conservation, 29, p. 87. doi: 10.1007/s10841-025-00723-2.

Sumila, T.C.A., Ferraz, S.E.T. and Durigon, A. (2024) ‘Climate change impact on Spodoptera frugiperda (Lepidoptera: Noctuidae) life cycle in Mozambique’, PLOS Climate, 3(1), e0000325. doi: 10.1371/journal.pclm.0000325.

Perform species distribution modeling | Documentation (n.d.) Available at: https://learn.arcgis.com/en/projects/perform-species-distribution-modeling/ (Accessed: 27 December 2025).