Background: The oak tree (Quercus aegilops) comprises ~ 70% of the oak forests in the Kurdistan Region of Iraq (KRI). Besides its ecological importance as the residence for various endemic and migratory species,Q. aegilopsforest also has socio-economic values—for example, as fodder for livestock, building material, medicine, charcoal, and firewood. In the KRI,Q. aegilopshas been degrading due to anthropogenic threats (e.g., shifting cultivation, land use/land cover changes, civil war, and inadequate forest management policy) and these threats could increase as climate changes. In the KRI and Iraq as a whole, information on current and potential future geographical distributions ofQ. aegilopsis minimal or not existent. The objectives of this study were to (i) predict the current and future habitat suitability distributions of the species in relation to environmental variables and future climate change scenarios (Representative Concentration Pathway (RCP) 2.6 2070 and RCP8.5 2070); and (ii) determine the most important environmental variables controlling the distribution of the species in the KRI. The objectives were achieved by using the MaxEnt (maximum entropy) algorithm, available records ofQ. aegilops, and environmental variables.Results: The model demonstrated that, under the RCP2.6 2070 and RCP8.5 2070 climate change scenarios, the distribution ranges ofQ. aegilopswould be reduced by 3.6% (1849.7 km²) and 3.16% (1627.1 km²), respectively. By contrast, the species ranges would expand by 1.5% (777.0 km²) and 1.7% (848.0 km²), respectively. The distribution of the species was mainly controlled by annual precipitation. Under future climate change scenarios, the centroid of the distribution would shift toward higher altitudes.Conclusions: The results suggest (i) a significant suitable habitat range of the species will be lost in the KRI due to climate change by 2070 and (ii) the preference of the species for cooler areas (high altitude) with high annual precipitation. Conservation actions should focus on the mountainous areas (e.g., by establishment of national parks and protected areas) of the KRI as climate changes. These findings provide useful benchmarking guidance for the future investigation of the ecology of the oak forest, and the categorical current and potential habitat suitability maps can effectively be used to improve biodiversity conservation plans and management actions in the KRI and Iraq as a whole.

The hawthorns Crataegus azarolus L. and Crataegus monogyna Jacq are two ecologically and medicinally important endemic tree species that occur only in forests of oak in the mountain ranges of the Kurdistan region of Iraq (KRI). These species have been degrading across the mountain ranges at an alarming pace due to climate-related factors (e.g., wildfire events and drought) and anthropogenic drivers. Nevertheless, there is a gap in understanding their distributions today and in the future under a changing climate in Iraq. To address the species’ knowledge gap and thus establish a baseline for a future management and conservation strategy, this study used field observation records, species distribution modeling integrated with GIS techniques, and relevant environmental predictors to (i) estimate the species’ potential distributions and map their current known distributions across unsurveyed areas; (ii) model the species’ possible response under several scenarios for a weather change in the future; (iii) map the species’ overlap ranges and the direction of the distributions. Results suggest that under two global climatic models (GCMs), BCC-CSM2-MR and CNRM-CM6-1, the overall habitat expansion magnitude for the two species would be less than the overall habitat reduction magnitude. For C. azarolus, the habitat range would contract by 3714.64 km² (7.20%) and 3550.47 km² (6.89%), whereas it would expand by 2415.90 km² (4.68%) and 1627.76 km² (3.16%) for the GCMs, respectively. Modeling also demonstrated a similar pattern for C. monogyna. The species overlap by 7626.53 km² (14.80%) and 7351.45 km2 (14.27%) for the two GCMs. The two species’ habitat ranges would contract significantly due to the changing climate. The direction of the species’ potential distribution would be mostly toward the KRI’s east and southeast mountain forests. Our results, for the first time, provide new data on the species’ present and future distributions and outline the advantages of distribution modeling combined with geospatial techniques in areas where species data are limited, such as Iraq.

Crop production is a major source of food and livelihood for many people in arid and semi-arid (ASA) regions across the world. However, due to irregular climatic events, ASA regions are affected commonly by frequent droughts that can impact food production. In addition, ASA regions in the Middle East and Africa are often characterised by political instability, which can increase population vulnerability to hunger and ill health. Remote sensing (RS) provides a platform to improve the spatial prediction of crop production and food availability, with the potential to positively impact populations. This paper, firstly, describes some of the important characteristics of agriculture in ASA regions that require monitoring to improve their management. Secondly, it demonstrates how freely available RS data can support decision-making through a cost-effective monitoring system that complements traditional approaches for collecting agricultural data. Thirdly, it illustrates the challenges of employing freely available RS data for mapping and monitoring crop area, crop status and forecasting crop yield in these regions. Finally, existing approaches used in these applications are evaluated, and the challenges associated with their use and possible future improvements are discussed. We demonstrate that agricultural activities can be monitored effectively and both crop area and crop yield can be predicted in advance using RS data. We also discuss the future challenges associated with maintaining food security in ASA regions and explore some recent advances in RS that can be used to monitor cropland and forecast crop production and yield.