Sensitivity analysis of CLIMEX parameters in modeling potential distribution of Phoenix dactylifera L

Spatio-Temporal Distribution of Digitaria insularis: Risk Analysis of Areas with Potential for Selection of Glyphosate-Resistant Biotypes in Eucalyptus Crops in Brazil

The objective of this study was to model the spatio-temporal distribution of Digitaria insularis (D. insularis) and analyze the risk of selection of glyphosate-resistant biotypes in eucalyptus cultivation in Brazil. Global data on the distribution of the specie were collected and associated with their ideal growth characteristics. The models were generated using Climex software, providing a predictive modeling technique. Biological data, species distribution, and climatic parameters were used to predict and map potential areas for the species of interest through the combination of growth and stress indices, giving rise to the Ecoclimatic Index (EI). The spatial distribution of D. insularis is predominantly in South and Central America and southern North America. The model had a good fit with the collected data and predicted higher EI values for tropical and subtropical regions, as was the case in Brazil. Species growth can occur throughout the year, with lower rates in winter, mainly in the country’s southern regions. Brazil has high climatic suitability for the occurrence of Digitaria insularis. Due to the climate suitability evidenced by the models and the expressive use of the same active ingredient, there is a risk of selecting glyphosate-resistant Digitaria insularis biotypes in eucalyptus cultivation areas.

Spatio-temporal dynamic climate model for Neoleucinodes elegantalis using CLIMEX

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 (GI_w) 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.

A comparison of absolute performance of different correlative and mechanistic species distribution models in an independent area

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.

Projected future distribution of date palm and its potential use in alleviating micronutrient deficiency

BACKGROUND

Micronutrient deficiency develops when nutrient intake does not match nutritional requirements for maintaining healthy tissue and organ functions which may have long-ranging effects on health, learning ability and productivity. Inadequacy of iron, zinc and vitamin A are the most important micronutrient deficiencies. Consumption of a 100 g portion of date flesh from date palm (Phoenix dactylifera L.) has been reported to meet approximately half the daily dietary recommended intake of these micronutrients. This study investigated the potential distribution of P. dactylifera under future climates to address its potential long-term use as a food commodity to tackle micronutrient deficiencies in some developing countries.

RESULTS

Modelling outputs indicated large shifts in areas conducive to date palm cultivation, based on global-scale alteration over the next 60 years. Most of the regions suffering from micronutrient deficiencies were projected to become highly conducive for date palm cultivation.

CONCLUSIONS

These results could inform strategic planning by government and agricultural organizations by identifying areas to cultivate this nutritionally important crop in the future to support the alleviation of micronutrient deficiencies.

Improving the use of species distribution models in conservation planning and management under climate change

Choice of variables, climate models and emissions scenarios all influence the results of species distribution models under future climatic conditions. However, an overview of applied studies suggests that the uncertainty associated with these factors is not always appropriately incorporated or even considered. We examine the effects of choice of variables, climate models and emissions scenarios can have on future species distribution models using two endangered species: one a short-lived invertebrate species (Ptunarra Brown Butterfly), and the other a long-lived paleo-endemic tree species (King Billy Pine). We show the range in projected distributions that result from different variable selection, climate models and emissions scenarios. The extent to which results are affected by these choices depends on the characteristics of the species modelled, but they all have the potential to substantially alter conclusions about the impacts of climate change. We discuss implications for conservation planning and management, and provide recommendations to conservation practitioners on variable selection and accommodating uncertainty when using future climate projections in species distribution models.