'Spodoptera frugiperda', or the fall armyworm (FAW) (Lepidoptera: Noctuidae), is an endemic and important agricultural pest in America. Several outbreaks have occurred with losses estimated at millions of dollars. Insects are affected by climate factors, and climate change may affect geographical range, growth rate, abundance, survival, mortality, number of generations per year and other characteristics. These effects are difficult to project due to the complex interactions among insects, hosts and predators. The aim of the current research is to project the impact of climate change on future suitability for the expansion and final range of FAW as well as highlight the risk of damage due to the pest under current and future conditions. The modelling was carried out using two general circulation models (GCMs), CSIRO Mk3.0 and MIROC-H, for 2050 and 2100 under the A2 Special Report on Emissions Scenarios (SRES), using the known distribution of the species and the CliMond meteorological database. The possible number of generations was estimated to exceed five in the south-eastern USA by 2100. A unique modelling approach linking environmental suitability and number of generations was developed to project the risks of FAW damage. The results show changes in suitability and risk across America, with an increase in the northern hemisphere and decreases or extinction in the southern hemisphere, except for southern Brazil, Uruguay, Paraguay and northern Argentina, which indicate high future levels of risk. The current study highlights the possible extinction of a tropical pest in areas near the Equator. The two GCMs both projected increases in the low-risk category of 40% by 2050 and 23% by 2100, with the medium- and high-risk categories decreasing by >50% by 2050 and >39% by 2100, compared with the current risk. In general, agricultural pest management may become more challenging under future climate change and variation, and thus, understanding and quantifying the possible impacts of FAW under future climate conditions is essential for the future economic production of crops.

'Aim': To model the areas becoming and remaining highly suitable for 'Aspergillus niger' growth over the next ninety years by future climate alteration, in relation to the species' potential enhancement of Low Density Polyethylene (LDPE) biodegradation in soil. 'Location': Global scale 'Methods': Projections of 'A. niger' growth suitability for 2030, 2050, 2070 and 2100 were made using the A2 emissions scenario together with two Global Climate Models (GCMs): the CSIRO-Mk3.0 (CS) model and the MIROC-H (MR) model through CLIMEX software. Subsequently the outputs of the two GCMs were overlaid to extract common areas in each period of time, providing higher certainty concerning areas which will become highly suitable to 'A. niger' in the future. Afterwards, GIS software was employed to extract sustainable regions for this species growth from present time up to 2100. 'Results': Central and eastern Argentina, Uruguay, southern Brazil, eastern United States, southern France, northern Spain, central and southern Italy, southern Hungary, eastern Albania, south western Russia, central and eastern China, eastern Australia, south east of South Africa, central Zambia, Rwanda, Burundi, central Kenya, central Ethiopia and north eastern Oman will be highly suitable for 'A. niger' growth from present time up to 2100. 'Main conclusions': Accurately evaluating the impact of landfilling on land use and predicting future climate are vital components for effective long-term planning of waste management. From a social and economic perspective, utilization of our mapped projections to detect suitable regions for establishing landfills in areas highly sustainable for microorganisms like 'A. niger' growth will allow a significant cost reduction and improve the performance of biodegradation of LDPE over a long period of time, through making use of natural climatic and environmental factors.

This study investigated the importance of the use of appropriate species distribution records in projecting potential distributions under climate change using comparative bioclimatic models and alternative sets of data (native and exotic) to project a species in a new environment. We built bioclimatic models for date palm ('Phoenix dactylifera' L.), using the MaxEnt correlative model and the CLIMEX mechanistic niche model, and fitted the models using three training data sets: native data only, exotic data only and entire data. We compared the ability of the different data sets using the different modelling approaches to project suitable climate envelope for independent records of the species at a global scale. We found that the output of projected species distributions was closely related to the modelling approach as well as the specific categorized distribution of species data used (native data only, exotic data only and entire data).