A sensitivity analysis can categorize levels of parameter influence on a model's output. Identifying parameters having the most influence facilitates establishing the best values for parameters of models, providing useful implications in species modelling of crops and associated insect pests. The aim of this study was to quantify the response of species models through a CLIMEX sensitivity analysis. Using open-field Solanum lycopersicum and Neoleucinodes elegantalis distribution records, and 17 fitting parameters, including growth and stress parameters, comparisons were made in model performance by altering one parameter value at a time, in comparison to the best-fit parameter values. Parameters that were found to have a greater effect on the model results are termed “sensitive”. Through the use of two species, we show that even when the Ecoclimatic Index has a major change through upward or downward parameter value alterations, the effect on the species is dependent on the selection of suitability categories and regions of modelling. Two parameters were shown to have the greatest sensitivity, dependent on the suitability categories of each species in the study. Results enhance user understanding of which climatic factors had a greater impact on both species distributions in our model, in terms of suitability categories and areas, when parameter values were perturbed by higher or lower values, compared to the best-fit parameter values. Thus, the sensitivity analyses have the potential to provide additional information for end users, in terms of improving management, by identifying the climatic variables that are most sensitive.

Background: 'Neoleucinodes elegantalis' is one of the major insect pests of 'Solanum lycopersicum'. Currently, 'N. elegantalis' is present only in America and the Caribbean, and is a threat in the world's largest 'S. lycopersicum'-producing countries. In terms of potential impact on agriculture, the impact of climate change on insect invasions must be a concern. At present, no research exists regarding the effects of climatic change on the risk level of 'N. elegantalis'. The purpose of this study was to develop a model for 'S. lycopersicum' and 'N. elegantalis', utilizing CLIMEX to determine risk levels of 'N. elegantalis' in open-field 'S. lycopersicum' cultivation in the present and under projected climate change, using the global climate model CSIRO-Mk3.0. Results: Large areas are projected to be suitable for 'N. elegantalis' and optimal for open-field 'S. lycopersicum' cultivation at the present time. However, in the future these areas will become unsuitable for both species. Conversely, other regions in the future may become optimal for open-field 'S. lycopersicum' cultivation, with a varying risk level for 'N. elegantalis'. Conclusion: The risk level results presented here provide a useful tool to design strategies to prevent the introduction and establishment of 'N. elegantalis' in open-field 'S. lycopersicum' cultivation.

Seasonal variations are important components in understanding the ecology of insect population of crops. Ecological studies through modeling may be a useful tool for enhancing knowledge of seasonal patterns of insects on field crops as well as seasonal patterns of favorable climatic conditions for species. Recently CLIMEX, a semi-mechanistic niche model, was upgraded and enhanced to consider spatio-temporal dynamics of climate suitability through time. In this study, attempts were made to determine monthly variations of climate suitability for 'Neoleucinodes elegantalis' (Guenée) (Lepidoptera: Crambidae) in five commercial tomato crop localities through the latest version of CLIMEX. We observed that N. elegantalis displays seasonality with increased abundance in tomato crops during summer and autumn, corresponding to the first 6 months of the year in monitored areas in this study. Our model demonstrated a strong accord between the CLIMEX weekly growth index (GIw) and the density of 'N. elegantalis' for this period, thus indicating a greater confidence in our model results. Our model shows a seasonal variability of climatic suitability for 'N. elegantalis' and provides useful information for initiating methods for timely management, such as sampling strategies and control, during periods of high degree of suitability for 'N. elegantalis'. In this study, we ensure that the simulation results are valid through our verification using field data.

Palm oil (PO) is a very important commodity used as food, in pharmaceuticals, for cooking and as biodiesel: PO is a major contributor to the economies of many countries, especially Indonesia and Malaysia. Novel tropical regions are being explored increasingly to grow oil palm as current land decreases, whilst recent published modelling studies by the current authors for Malaysia and Indonesia indicate that the climate will become less suitable. Countries that grow the crop commercially include those in Latin America, Africa and Asia. How will climate change (CC) affect the ability to grow oil palm in these countries? Worldwide projections for apt climate were made using Climex software in the present paper and the global area with unsuitable climate was assessed to increase by 6%, whilst highly suitable climate (HSC) decreased by 22% by 2050. The suitability decreases are dramatic by 2100 suggesting regions totally unsuitable for growing OP, which are currently appropriate: the global area with unsuitable climate increased from 154 to 169 million km² and HSC decreased from 17 to 4 million km². This second assessment of Indonesia and Malaysia confirmed the original findings by the current authors of large decreases in suitability. Many parts of Latin America and Africa were dramatically decreased: reductions in HSC for Brazil, Columbia and Nigeria are projected to be 119 000, 35 and 1 from 5 000 000, 219 and 69 km², respectively. However, increases in aptness were observed in 2050 for Paraguay and Madagascar (HSC increases were 90 and 41%, respectively), which were maintained until 2100 (95 and 45%, respectively). Lesser or transient increases were seen for a few other countries. Hot, dry and cold climate stresses upon oil palm for all regions are also provided. These results have negative implications for growing oil palm in countries as: (a) alternatives to Malaysia and Indonesia or (b) economic resources per se. The inability to grow oil palm may assist in amelioration of CC, although the situation is complex. Data suggest a moderate movement of apposite climate towards the poles as previously predicted.

'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.

Tomato ('Solanum lycopersicum' L.) is one of the most important vegetable crops globally and an important agricultural sector for generating employment. Open field cultivation of tomatoes exposes the crop to climatic conditions, whereas greenhouse production is protected. Hence, global warming will have a greater impact on open field cultivation of tomatoes rather than the controlled greenhouse environment. Although the scale of potential impacts is uncertain, there are techniques that can be implemented to predict these impacts. Global climate models (GCMs) are useful tools for the analysis of possible impacts on a species. The current study aims to determine the impacts of climate change and the major factors of abiotic stress that limit the open field cultivation of tomatoes in both the present and future, based on predicted global climate change using CLIMatic indEX and the A2 emissions scenario, together with the GCM Commonwealth Scientific and Industrial Research Organisation (CSIRO)-Mk3·0 (CS), for the years 2050 and 2100. The results indicate that large areas that currently have an optimum climate will become climatically marginal or unsuitable for open field cultivation of tomatoes due to progressively increasing heat and dry stress in the future. Conversely, large areas now marginal and unsuitable for open field cultivation of tomatoes will become suitable or optimal due to a decrease in cold stress. The current model may be useful for plant geneticists and horticulturalists who could develop new regional stress-resilient tomato cultivars based on needs related to these modelling projections.

To investigate the comparative abilities of six different bioclimatic models in an independent area, utilizing the distribution of eight different species available at a global scale and in Australia. Global scale and Australia. We tested a variety of bioclimatic models for eight different plant species employing five discriminatory correlative species distribution models (SDMs) including Generalized Linear Model (GLM), MaxEnt, Random Forest (RF), Boosted Regression Tree (BRT), Bioclim, together with CLIMEX (CL) as a mechanistic niche model. These models were fitted using a training dataset of available global data, but with the exclusion of Australian locations. The capabilities of these techniques in projecting suitable climate, based on independent records for these species in Australia, were compared. Thus, Australia is not used to calibrate the models and therefore it is as an independent area regarding geographic locations. To assess and compare performance, we utilized the area under the receiver operating characteristic (ROC) curves (AUC), true skill statistic (TSS), and fractional predicted areas for all SDMs. In addition, we assessed satisfactory agreements between the outputs of the six different bioclimatic models, for all eight species in Australia. The modeling method impacted on potential distribution predictions under current climate. However, the utilization of sensitivity and the fractional predicted areas showed that GLM, MaxEnt, Bioclim, and CL had the highest sensitivity for Australian climate conditions. Bioclim calculated the highest fractional predicted area of an independent area, while RF and BRT were poor. For many applications, it is difficult to decide which bioclimatic model to use. This research shows that variable results are obtained using different SDMs in an independent area. This research also shows that the SDMs produce different results for different species; for example, Bioclim may not be good for one species but works better for other species. Also, when projecting a "large" number of species into novel environments or in an independent area, the selection of the "best" model/technique is often less reliable than an ensemble modeling approach. In addition, it is vital to understand the accuracy of SDMs' predictions. Further, while TSS, together with fractional predicted areas, are appropriate tools for the measurement of accuracy between model results, particularly when undertaking projections on an independent area, AUC has been proved not to be. Our study highlights that each one of these models (CL, Bioclim, GLM, MaxEnt, BRT, and RF) provides slightly different results on projections and that it may be safer to use an ensemble of models.

At the global level, maize is the third most important crop on the basis of harvested area. Given its importance, an assessment of the variation in regional climatic suitability under climate change is critical. CliMond 10′ data were used to model the potential current and future climate distribution of maize at the global level using the CLIMEX distribution model with climate data from two general circulation models, CSIRO-Mk3.0 and MIROC-H, assuming an A2 emissions scenario for 2050 and 2100. The change in area under future climate was analysed at continental level and for major maizeproducing countries of the world. Regions between the tropics of Cancer and Capricorn indicate the highest loss of climatic suitability, contrary to poleward regions that exhibit an increase of suitability. South America shows the highest loss of climatic suitability, followed by Africa and Oceania. Asia, Europe and North America exhibit an increase in climatic suitability. This study indicates that globally, large areas that are currently suitable for maize cultivation will suffer from heat and dry stresses that may constrain production. For the first time, a model was applied worldwide, allowing for a better understanding of areas that are suitable and that may remain suitable for maize.

Common rust (Puccinia sorghi) and southern rust (Puccinia polysora) are two of the most important foliar corn diseases worldwide. These fungi have caused severe economic loss to corn yields worldwide. The current and future potential distribution of these diseases was modelled with CLIMEX using the known current geographic locations of the rusts, growth and stress indices. The models were run under the A2 scenario using CSIRO-Mk3.0 and MIROC-H for 2050 and 2100. The current projection shows areas with marginal to optimal suitability in all the continents. The models for future projections display a general reduction in the Southern hemisphere and increase in the Northern hemisphere, especially for the southern rust. The overlay of the General Circulation Models produce an estimation of the common areas under risk for future climate conditions for the simultaneous occurrence for both corn rusts, with a reduction of the medium- and high-risk categories by 2100. This study highlights the possible effects of climate change at a global level for common and southern rust, as well as the risk of occurrence of both diseases in common areas for future climate that could be particularly harmful for crops.

Using CLIMEX and the Taguchi Method, a process-based niche model was developed to estimate potential distributions of 'Phoenix dactylifera' L. (date palm), an economically important crop in many counties. Development of the model was based on both its native and invasive distribution and validation was carried out in terms of its extensive distribution in Iran. To identify model parameters having greatest influence on distribution of date palm, a sensitivity analysis was carried out. Changes in suitability were established by mapping of regions where the estimated distribution changed with parameter alterations. This facilitated the assessment of certain areas in Iran where parameter modifications impacted the most, particularly in relation to suitable and highly suitable locations. Parameter sensitivities were also evaluated by the calculation of area changes within the suitable and highly suitable categories. The low temperature limit (DV2), high temperature limit (DV3), upper optimal temperature (SM2) and high soil moisture limit (SM3) had the greatest impact on sensitivity, while other parameters showed relatively less sensitivity or were insensitive to change. For an accurate fit in species distribution models, highly sensitive parameters require more extensive research and data collection methods. Results of this study demonstrate a more cost effective method for developing date palm distribution models, an integral element in species management, and may prove useful for streamlining requirements for data collection in potential distribution modeling for other species as well.