ABSTRACT
The migratory locust, Locustamigratoria (L.), a significant grasshopper species known for its ability to form large swarms and cause extensive damage to crops and vegetation, is subject to the influence of climate change. This research paper employs geographic information system (GIS) and MaxEnt ecological modelling techniques to assess the impact of climate change on the distribution patterns of L.migratoria. Occurrence data and environmental variables are collected and analysed to create predictive models for the current and future distribution of the species. The study highlights the crucial role of climate factors, particularly temperature and precipitation, in determining the locust's distribution. The MaxEnt models exhibit high-performance indicators, accurately predicting the potential habitat suitability of L.migratoria. Additionally, specific bioclimatic variables, such as mean temperature and annual precipitation, are identified as significant factors influencing the species' presence. The generated future maps indicate how this species will invade new regions especially in Europe. Such results predict the risk of this destructive species for many agriculture communities as a direct result of a warming world. The research provides valuable insights into the complex relationship between locust distribution and environmental factors, enabling the development of effective strategies for locust management and early warning systems to mitigate the impact on agriculture and ecosystems.
ABSTRACT
The Egyptian cotton leafworm, Spodoptera littoralis is a highly invasive insect pest that causes extensive damage to many of the primary food crops. Considering the recent challenges facing global food production including climate change, knowledge about the invasive potential of this pest is essential. In this study, the maximum entropy model (MaxEnt) was used to predict the current global spatial distribution of the pest and the future distribution using two representative concentration pathways (RCPs) 2.6 and 8.5 in 2050 and 2070. High AUC and TSS values indicated model accuracy and high performance. Response curves showed that the optimal temperature for the S. littoralis is between 10 and 28 °C. The pest is currently found in Africa and is widely distributed across the Middle East and throughout Southern Europe. MaxEnt results revealed that the insect will shift towards Northern Europe and the Americas. Further, China was seen to have a suitable climate. We also extrapolated the impact of these results on major producing countries and how this affects trade flow, which help decision makers to take the invasiveness of such destructive pest into their account.
Subject(s)
Climate Change , Animals , Spodoptera/physiology , Egypt , China , AfricaABSTRACT
BACKGROUND: Obesity is a widely prevalent medical and socioeconomic problem. Bariatric surgery is indicated for patients with clinically severe obesity. Reduction of gastric volume is an important factor that contributes to weight loss after laparscopic sleeve gastrectomy (LSG). The impact of the gastric volume on weight after LSG has been studied. AIM OF THE STUDY: This study was designed to assess the gastric volume in patients with obesity prior to LSG and in the normal-weight patients, using three-dimensional multi-detector computer tomography (3D-MDCT), and to evaluate the potential correlation of the gastric volume with body mass index (BMI). PATIENTS AND METHODS: A total of 100 patients were equally enrolled in two groups: one group for patients with obesity scheduled for LSG and another one for normal-weight patients scheduled for non-bariatric surgery. The study patients underwent 3D-MDCT gastric volumetry. RESULTS: The gastric volume ranged from 525 to 1170 mL in patients with obesity and from 312 to 676 mL in the normal-weight group. Statistically significant difference was found between the two groups. Age, weight, and BMI were found to be predictors for the gastric volume in normal-weight patients only. CONCLUSION: MDCT gastric volumetry is a feasible method to assess the stomach volume. Higher volumes were evident in patients with obesity. Age, weight, and BMI are predictors for the gastric volume in normal-weight patients with linear regression equations that could help during the preoperative planning of bariatric surgeries.
