Species distribution model

Predictive simulation of the geographical distribution of the studied object has become an important tool in agroecology, since it uses the previous information about the spatial distribution of species in the ecosystem, limiting predictive models to the nearest ecological niche, thus generating a forecast of possible areas of occurrence based on environmental conditions that are similar to the identified populated area. The paper considers the approaches and possibilities of using two types of simulation: the species distribution model and the ecological niche model. The study aimed to simulate favorable habitats and the potential spread of nongregarious locust pests in North Kazakhstan based on satellite and ground data for preventive measures. Four regions of North Kazakhstan were selected and covered as the research area, namely, the Akmola, Pavlodar, Kostanay, and North Kazakhstan regions and the analyses were carried out for the period 1999–2021. The MaxEnt software was used to conduct the simulation. According to the species distribution model, high indicators of the habitat are predicted in the Pavlodar and Kostanay regions, on 69.9–100% of the studied territory. With the simulation of ecological niches for non-gregarious locust pests, the following class boundaries were determined for the transition from quantitative to qualitative indicators from I (85–100%) to IV (0–50%), which indicates the zones of the probability of pest attack from a higher indicator to a lower one. According to the fundamental model, high indicators of the area of pest occurrence, that is, zones I and II, are located in the central and northern parts of the Pavlodar region. Here, the probability of non-gregarious locust occurrence of zones I and II with a ratio of 1:1 is observed in a slightly arid, moderately warm agroclimatic zone. In the southern part of the Kostanay region, the simulation predicts the probability of occurrence in zones I and II with a ratio of 1:2 in the moderately arid warm agroclimatic zone of this region. In the southern and southeastern parts of the Akmola region, the model predicts the probability of occurrence in zones I and II with a ratio of 1:3 in a slightly humid, moderately warm agroclimatic zone of the region. These results of studies on the simulation of favorable habitats and the potential spread of nongregarious locust pests may allow prioritizing the areas for risk assessment, monitoring, and early warning measures for the development and spread of pests. The considered species distribution model can be used as a modern tool for long-term forecasting of the spread of nongregarious locust pests since it takes into account the peculiarities of the agricultural landscape. The fundamental niche model can be used in a long-term population forecast since it focuses more on the theoretical conditions of pest habitats. In the future, similar studies can be conducted throughout Kazakhstan to obtain a complete digital map of preferred locations for the spread of non-gregarious locust pests to adequately plan plant protection products.

Author(s) Details:

Kurmet Baibussenov
S. Seifullin Kazakh Agro Technical University, 62 Zhenis Ave., 010011, Nur-Sultan, Republic of Kazakhstan.

Aigul Bekbayeva
S. Seifullin Kazakh Agro Technical University, 62 Zhenis Ave., 010011, Nur-Sultan, Republic of
Kazakhstan

Valery Azhbenov
Zh. Zhyembaev Kazakh Scientific Research Institute of Plant Protection and Quarantine, 1 Kultobe
Str., 050000, Almaty, Republic of Kazakhstan.

Also See :

Recent Global Research Developments in Locust Behavior

Remote Sensing and Satellite Data:

  • Traditional surveys have limitations in covering wide areas, especially in remote and scarcely vegetated regions. Satellite-based remote sensing has emerged as a promising tool for monitoring and forecasting locust species [1].
  • Unmanned aerial vehicles (drones) are also used for locust monitoring. They provide real-time data on locust habitats and can detect green patches suitable for locust infestations.
  • Post-disaster mapping using drones helps assess damage caused by desert locusts.

eLocust:

  • eLocust is an advanced tool for recording and transmitting locust data using electronic devices. It collects information on locust stages, habitat types, vegetation species, treatments used, and safety precautions.
  • This data can be integrated with rainfall and vegetation data for better monitoring and management.

Reconnaissance and Monitoring System of the Environment of Schistocerca (RAMSE Sv4):

  • Developed by the FAO, RAMSE Sv4 explores the biology and ecology of locust populations in countries where outbreaks occur. It complements data generated by eLocust3, enhancing locust management [1].

Application of Remote Sensing Data for Locust Research and Management—A Review

  • Locust outbreaks around the world regularly affect vast areas and millions of people. Mapping and monitoring locust habitats, as well as prediction of locust outbreaks is essential to minimize the damage on crops and pasture. In this context, remote sensing has become one of the most important data sources for effective locust management [2].

References

  1. Qayyum, M. A., Yasin, M., Wakil, W., Hunter, D., Ghazanfar, M. U., Wajid, M., … & Ashfaq, M. (2024). Advanced Technologies for Monitoring and Management of Locusts. In Locust Outbreaks (pp. 103-117). Apple Academic Press.
  2. Klein I, Oppelt N, Kuenzer C. Application of Remote Sensing Data for Locust Research and Management—A Review. Insects. 2021; 12(3):233. https://doi.org/10.3390/insects12030233

To Read the Complete Chapter See Here

By Editor

Leave a Reply