Current Climate Scenarios Research Articles

Investigating the impact of temperature on growth rate of the root rot fungus, Gymnopus fusipes.

Gymnopus fusipes is an understudied root rot pathogen associated with multiple tree species and is linked to episodes of oak decline across the United Kingdom and Europe. Although the reported distribution of G. fusipes is broad, many observations rely solely on visual identification of fruiting bodies, which can be unreliable, and lack confirmation by molecular and/or isolation data to verify this broad ecological range. Given the paucity of information regarding the true ecological distribution of G. fusipes, it is difficult to predict and model the potential distribution of the species under both current and future climate scenarios. In this study, to determine the growth capabilities of G. fusipes across a range of ecologically relevant temperatures, five geographically diverse isolates of G. fusipes were grown at five different temperatures ranging from 4-37°C, to determine the optimal temperature for G. fusipes growth, and to establish whether geographically diverse isolates exhibit local adaptation to temperature tolerance. Incubation temperature had a significant effect on G. fusipes growth rate, with 25°C representing the optimum (P<0.001). Isolates had differing growth rates at each of the temperatures, with an isolate from the UK having the highest overall growth rate across all five temperatures tested (P<0.001), and at the optimum, increased by a mean value of over 4915 mm2. Local adaptation to temperature tolerance was not found in the isolates tested. These data demonstrate the optimal incubation temperature for future laboratory studies on G. fusipes and provide the first data on the growth rate of this pathogen across ecologically relevant climate ranges that may inform land managers, modellers, and policy makers in predicting the current and potentially future geographical limits of this widespread root rot pathogen.

Potential impact of climate change on the distribution of Capricornis milneedwardsii, a vulnerable mammal in China.

Climate change significantly impacted on the survival, development, distribution, and abundance of living organisms. The Chinese serow Capricornis milneedwardsii, known as the "four unlike," is a Class II nationally protected species in China. In this study, we predicted the geographical suitability of C. milneedwardsii under current and future climatic conditions using MaxEnt. The model simulations resulted in area under the receiver operating characteristic curve (AUC) values above 0.9 for both current and future climate scenarios, indicating the excellent performance, high accuracy, and credibility of the MaxEnt model. The results also showed that annual precipitation (Bio12), slope, elevation, and mean temperature of wettest quarter (Bio8) were the key environmental variables affecting the distribution of C. milneedwardsii, with contributions of 31.2%, 26.4%, 11%, and 10.3%, respectively. The moderately and highly suitable habitats were mainly located in the moist area of China, with a total area of 34.56 × 104 and 16.61 × 104 km2, respectively. Under future climate change scenarios, the areas of suitability of C. milneedwardsii showed an increasing trend. The geometric center of the total suitable habitats of C. milneedwardsii would show the trend of northwest expansion and southeast contraction. These findings could provide a theoretical reference for the protection of C. milneedwardsii in the future.

Potential planting regions of Pterocarpus santalinus (Fabaceae) under current and future climate in China based on MaxEnt modeling.

This study modeled the habitat distribution of Pterocarpus santalinus, a valuable rosewood species, across China under current and future climate scenarios (SSPs126, SSPs245, and SSPs585) using MaxEnt. Our findings reveal that the current suitable habitat, spanning approximately 409,600 km2, is primarily located in the central and southern parts of Guangdong, Guangxi, Fujian, and Yunnan, as well as in the Hainan provinces, along with the coastal regions of Taiwan, and the Sichuan-Chongqing border. The habitat's distribution is significantly influenced by climatic factors such as temperature seasonality (bio4), mean temperature of the wettest quarter (bio8), annual mean temperature (bio1), and annual precipitation (bio12), while terrain and soil factors play a lesser role. Under future climate scenarios, the suitable habitat for P. santalinus is projected to expand, with a northeastward shift in its distribution center. This research not only sheds light on the geoecological characteristics and geographical distribution of P. santalinus in China but also offers a scientific basis for planning its cultivation areas and enhancing cultivation efficiency under changing climate conditions.

Leveraging climate and remote sensing metrics for predicting forest carbon stock using Bayesian geostatistical modelling under a projected climate warming in Zimbabwe

Climate change, driven by escalating carbon dioxide (CO2) emissions, poses a significant threat to forest ecosystems and the livelihoods of communities reliant on them, especially for the global south countries and regions like the eastern highlands of Zimbabwe. The 2000 land redistribution programme reduced buffer zones between ecologically sensitive forests and land reform beneficiaries near major carbon reservoirs. In light of these challenges, this study aimed to assess the potential effects of climate change on a strategically important plantation forest ecosystem in Zimbabwe's eastern highlands. Using data from the Coupled Model Inter-comparison Project Phase 6 (CMIP6) of the Intergovernmental Panel on Climate Change (IPCC), we modelled and predicted changes in forest carbon (C) stock density under different climate scenarios: current (1970–2000), SSP5–4.5, and SSP5–8.5. Employing a hierarchical Bayesian geostatistical approach, we compared the baseline scenario (1970–2000) with projected scenarios (RCP4.5 and RCP8.5) for 2075 to estimate changes in forest carbon stock distribution. Our results indicated a decline in carbon stock concentration under future climate scenarios, reflecting the adverse impact of greenhouse gas emissions on forest growth. We found that the projected range of forest carbon stock under the RCP8.5 scenario for 2075 is notably lower (2≤MgCha−1≤44.9) than that of the baseline period (1970–2000) (1≤MgCha−1≤97), suggesting a substantial reduction in carbon storage. As the difference in posterior mean C stock (μ̅1−μ̅2), 52.1 MgCha-1 is well above zero, we deduce that the posterior mean C stock distribution of the projected future RCP8.5 2075 climate projection is indeed credibly different from the current (1970–2000) climate scenario. Additionally, there is a high probability (>90%) that forest plantations will be adversely affected by the business-as-usual climate warming projection. Overall, our findings highlight the urgent need for climate change mitigation strategies, such as reforestation programs and careful selection of tree species for plantations, to safeguard forest ecosystems and the communities dependent on them. These insights are crucial for informing effective adaptation measures in the face of future climate uncertainties.

Integrating MODFLOW and machine learning for detecting optimum groundwater abstraction considering sustainable drawdown and climate changes

Detecting optimum groundwater abstraction is a challenging objective. This study proposes a new approach for identifying the optimum pumping rates by integrating MODFLOW with machine learning (ML). The adopted strategy has been applied to the Oligocene and Eocene aquifers in the western desert of Minia, Egypt. MODFLOW-2005 model has been utilized to simulate various management scenarios, including current and expected climate scenarios of two Representative Concentration Pathways (RCPs): RCP 4.5 and RCP 8.5. The steady-state and transient MODFLOW models were calibrated using the groundwater levels of 37 observation wells with a coefficient of correlation (R2) of 0.986 and 0.988, respectively. The developed model has been run for 80 years, from 2020 to 2100. The results of MODFLOW demonstrated a drawdown in the groundwater level in the two aquifers, about 40, 41, and 42 m, owing to current climate conditions and scenarios of RCP 4.5 and RCP 8.5, respectively, in 2100. Generated data based on the calibrated model has been used to develop the ML models. ML models have been applied to predict the pumping rates using the well coordinates, aquifer type, evapotranspiration, the operating period in days, and drawdown as inputs. For this purpose, four ML models, Gaussian Process Regression (GPR), Support Vector Regression (SVR), Tree, and Artificial Neural Network (ANN)) were developed and evaluated. All ML models demonstrated an effective performance during the training and validation processes, particularly the GPR model, which has the best ability to predict the pumping rates with a correlation coefficient, RMSE, and Mean Absolute Error (MAE) equal to 1.0, 6.53, and 8.04, respectively. Therefore, the GPR model has been utilized to identify the optimum pumping rates. The findings of the GPR model revealed that the optimum abstraction of the Oligocene aquifer is 602266, 625329, and 635061 m3/day, while the Eocene aquifer’s optimal groundwater abstraction is 10229801, 1027906, and 102936 m3/day, considering the current climate, RCP 4.5, and RCP8.5, respectively. Anticipated findings are supposed to be instrumental for decision-makers, offering valuable insights into sustainable groundwater management. The novel integration methodology promotes the efficient use of groundwater, contributing to achieving sustainable development goals (SDGs).

Global potential distributions and conservation status of rice wild relatives

Societal Impact StatementRice wild relatives (RWR) provide valuable genetic resources for modern rice breeding, yet knowledge gaps constrain their conservation and further utilization. To address these gaps, the potential distributions of 22 RWR taxa were modeled, and their conservation statuses were assessed. Most taxa were identified as medium priority for further conservation. Further ex‐situ collecting hotspots are in Southeast and South Asia, Northern Australia, West Africa, and tropical Americas, while habitat protection lies in Southeast and South Asia, Northern Australia, and West Africa. Climate change may shift equatorial habitats to higher latitudes and mountain habitats to higher altitudes.Summary Rice wild relatives (RWR) provide valuable genetic resources for modern rice breeding. However, knowledge gaps on their geographic distributions and conservation status constrain their conservation and further utilization. To fill these critical gaps, we modeled the potential distributions of 22 RWR taxa under current climate scenarios, assessed their conservation status, both ex situ (in genebanks or botanical gardens) and in situ (in protected areas), and examined changes in taxa richness of RWR in predicted areas under future climate scenarios. The RWR were primarily distributed in tropical Asia to tropical Australia, tropical Africa, and South and Central America. We identified 4 out of 22 taxa as high priority (HP) for further conservation action and 18 taxa as medium priority (MP). Hotspots requiring further collecting for ex situ conservation are concentrated in Southeast and South Asia, Northern Australia, West Africa, and tropical Americas. Meanwhile, habitat protection should be enhanced in Southeast and South Asia, Northern Australia, and West Africa. Under future climate change, suitable habitats near the equator are expected to shift toward higher latitudes and some in lower‐latitude basins may become unsuitable due to excessive heat, resulting in decreased taxa richness in these areas. Additionally, suitable habitats in high mountain areas may shift to higher altitudes, potentially augmenting taxa richness in the highlands. Our findings provide vital insights to guide future rescue conservation efforts for RWR.

Response of Species to the Impact of Climate Change in the Gum Arabic Belt, Sudan: A Case Study in Acacia senegal

Sustainable management strategies of trees are important for indigenous agroforestry plant species, such as Acacia. Senegal (A. Senegal), due to the impacts of rapid population growth, land use and climate change. The objective of this investigation was to predict the spatio-temporal distribution of A.senegal in the Gum Arabic belt in Sudan in current (1985–2000) and future climate scenarios (2021–2100). Bioclimatic data was used for modeling purposes utilizing Maxent, with the assessment of model precision conducted through the utilization of the Area Under the Curve (AUC) and shown a high goodness-of-it (AUC=0.905±0.003 ). Significant differences were shown in species distribution between current and future periods under selected Shared Socioeconomic Pathways (SSPs) of SSP2-4.5 and SSP5-8.5, climate scenario. Our findings indicated that the main predictors influence the distribution of the species were precipitation of wettest quarter and maximum temperature of warmest month. Under the current potential distribution (25.4%), it is projected that Acacia Senegal would expand 36.2%-87.7% (SSP2-4.5) and 38.9-42.5% (SSP5-8.5). It is expected that A.cacia Senegal will create new environments suitable for it due to expected climate changes. Hence, the research necessitates the formulation of a strategic plan aimed to rehabilitation plantations of Acacia senegal and cultivation these species within existing and prospective habitats conducive to their existence.

Using proxy data and vegetation modelling to predict past, current and future distributional shifts of Butea monosperma, a marker of land degradation in India

Extensive deviations in spatio-temporal social and environmental dynamics currently alter the health of ecosystems and the services they provide. Detecting the causes that contribute to the distribution of a natural forest species capable of restoring the lost ecosystem function and productivity will aid in determining better food security, livelihoods and provision of ecosystem goods and services. We modelled the spatial range of Butea monosperma (B. monosperma) under past, that is, Last Glacial Maximum (LGM), Middle Holocene (MH), current and future (2070) climatic scenarios with Maximum Entropy (MaxEnt) trained on present-day occurrences. We identified areas of suitable habitats for which the estimation of habitat stability is predicted in all the models at different times. To validate the inferred suitable habitat, we tested the model by the current occurrence and fossil pollen data of B. monosperma. Our distribution models agree with the fossil pollen records for the MH (4,500–7,000 yr BP) and predict the prevalence of B. monosperma covering 84.22% of the Indian subcontinent with maximum habitat stability in western and southwestern India (10.95%). The widespread potential distribution of the plant species during the LGM supports the presence of the last remnants of tropical dry deciduous forest in the region. However, a decline in habitat suitability (62.84%) is predicted under current and future climatic scenarios with maximum stability (0.90%–3.09%) along the Western Ghats, Nilgiri hills, Gir range in the western India and north-eastern region covering the Assam Valley and foothills of Tripura and Mizo hills. Temperature seasonality (33.6%) measured in terms of variable contribution in MaxEnt model significantly affects the distribution shift of B. monosperma, along with annual precipitation (22.8%) and annual mean temperature (16.2%). Model results provide evidence of habitat reduction and identify the stability hotspots for B. monosperma for its conservation and establishment of land management policies mainly for the dry tropics.

Direct foliar phosphorus uptake from wildfire ash

Abstract. Atmospheric particles originating from combustion byproducts (burned biomass or wildfire ash) are highly enriched in nutrients such as P, K, Ca, Mg, Fe, Mn, and Zn. Over long timescales, deposited wildfire ash particles contribute to soil fertility by replenishing soil nutrient reservoirs. However, the immediate nutritional effects of freshly deposited fire ash on plants are mostly unknown. Here, we study the influence of fire ash on plant nutrition by applying ash separately on a plant's foliage or to its substrate around the roots. We conducted experiments on chickpea model plants under ambient and elevated CO2 levels, which reflect current and future climate scenarios. We found that plants can utilize fire ash P and Ni through their leaves by direct nutrient uptake from particles captured on their foliage but not via their roots, both under ambient and elevated CO2 levels. These results indicate that over a short timescale, plants effectively uptake P from fire ash only via the foliage rather than the root pathway, which is possibly due to low bioavailability or limited contact between fire ash particles and the roots. According to many previous studies, elevated levels of CO2 will reduce the ionome of plants due to the partial inhibition of the key root uptake mechanism, thus increasing the significance of foliar nutrient uptake in a future climate. Furthermore, the role of fire ash is expected to increase in the future world, thus giving a competitive advantage to plants that can utilize fire ash P from the foliar pathway, as fire ash P is a particularly efficient and important source of P.

Global Atlas of Methane Metabolism Marker Genes in Soil.

Methane, a greenhouse gas, plays a pivotal role in the global carbon cycle, influencing the Earth's climate. Only a limited number of microorganisms control the flux of biologically produced methane in nature, including methane-oxidizing bacteria, anaerobic methanotrophic archaea, and methanogenic archaea. Although previous studies have revealed the spatial and temporal distribution characteristics of methane-metabolizing microorganisms in local regions by using the marker genes pmoA or mcrA, their biogeographical patterns and environmental drivers remain largely unknown at a global scale. Here, we used 3419 metagenomes generated from georeferenced soil samples to examine the global patterns of methane metabolism marker gene abundances in soil, which generally represent the global distribution of methane-metabolizing microorganisms. The resulting maps revealed notable latitudinal trends in the abundances of methane-metabolizing microorganisms across global soils, with higher abundances in the sub-Arctic, sub-Antarctic, and tropical rainforest regions than in temperate regions. The variations in global abundances of methane-metabolizing microorganisms were primarily governed by vegetation cover. Our high-resolution global maps of methane-metabolizing microorganisms will provide valuable information for the prediction of biogenic methane emissions under current and future climate scenarios.

Prediction of the Potential Distribution of Teinopalpus aureus Mell, 1923 (Lepidoptera, Papilionidae) in China Using Habitat Suitability Models

The Golden Kaiser-I-Hind (Teinopalpus aureus Mell, 1923) is the only butterfly among Class I national protected animals in China and is known as the national butterfly. In this study, by accurately predicting the suitable habitat in China under current and future climate scenarios, the potential distribution area of T. aureus was defined, providing a theoretical basis for conservation and management. Based on species distribution records, we utilized the Biomod2 platform to combine climate data from the BCC-CSM2-MR climate model, future shared socio-economic pathways, and altitude data. The potential distribution areas of T. aureus in the current (1970–2000s) and future SSP1_2.6 and SSP5_8.5 climate scenarios in China in 2041–2060 (2050s), 2061–2080 (2070s), and 2081–2100 (2090s) were predicted. The AUC and TSS values of the combined model based on five algorithms were greater than those of the single models, and the AUC value of the receiver operating characteristic curve was 0.990, indicating that the model had high reliability and accuracy. The screening of environmental variables showed that the habitat area of T. aureus in China was mainly affected by annual precipitation, precipitation in the driest month, the lowest temperature in the coldest month, temperature seasonality, elevation, and other factors. Under the current circumstances, the habitat area of T. aureus was mainly located in southern China, including Fujian, Guangdong, Guangxi, Hainan, Zhejiang, Yunnan, Guizhou, Hunan, Taiwan, and other provinces. The suitable area is approximately 138.95 × 104 km2; among them, the highly suitable area of 34.43 × 104 km2 is a priority area in urgent need of protection. Under both SSP1_2.6 and SSP5_8.5, the population centroid tended to shift southward in the 2050s and 2070s, and began to migrate northeast in the 2090s. Temperature, rainfall, and altitude influenced the distribution of T. aureus. In the two climate scenarios, the habitat area of T. aureus declined to different degrees, and the reduction was most obvious in the SSP5_8.5 scenario; climate was the most likely environmental variable to cause a change in the geographical distribution. Climate change will significantly affect the evolution and potential distribution of T. aureus in China and will increase the risk of extinction. Accordingly, it is necessary to strengthen protection and to implement active and effective measures to reduce the negative impact of climate change on T. aureus.

ENHANCING PRE AND POST VEGETATIVE HEAT RESILIENCE AND YIELD POTENTIAL IN THE NEWLY RELEASED MAIZE VARIETY "SAHIWAL GOLD" THROUGH MODIFIED SELECTION

Maize is well-anchored crop across the globe due to its nutritional profile, multipurpose usage, and underlying financial benefits. Growing of open-pollinated maize varieties (OPVs) is a traditional trend and is still followed by a huge community of small farmers in most of the developing world. Improvement in yield potential is always inevitable and requires a purposeful and definite strategy. The current climate scenario loaded with heat escalation and extreme fluctuations is creating uncertainty and unsustainability through acute yield losses in domesticated crops. The breeding strategy of Sahiwal Gold (2008-2018) was comprised of four developmental phases i.e. enrichment phase, half-sib selection, pedigree selection, uniformity phase, and field evaluation at various agro-ecological zones of Pakistan. Each consecutive autumn and spring season was utilized to improve yield potential by exploiting pre and post-vegetative heat resilience. Half sib and pedigree selection improved plant features like height (12%), cob length (4.1%), seeds per cob (7.5%), grain (10%), and stalk yield (11.2%) from enrichment to uniformity phase. Sahiwal Gold maintained a high adaption profile across the tested locations and out-yielded the check variety (MMRI Yellow) by 18% and 26% on average in 6 stations and 16 national yield trials, respectively. National yield testing approved its suitability across provinces like Punjab, Khyber-Pakhtunkhwa (KPK), and Islamabad territory under a diverse range of temperatures. Analysis of variance revealed that highly significant interaction between season and genotypes work for plant height, grain, and stalk yield potential. Higher grain yield up to 95 mounds/acre was observed for spring season crop and batter stalk yield results (650 mounds/acre) were assessed during autumn plantation. Twelve (12) feet tall plants with 4 feet long leaves having 75-90% stay green ability provide a good opportunity fodder and grain yield potential at farmer’s field. This work will provide a better understanding to researchers for designing breeding programs for the development of heat resilience germplasm in crops.

Role of climate change on Oriental spruce (Picea orientalis L.): Modeling and mapping

Global climate change is a process with dramatic consequences for ecosystems, and changes that may occur in the potential distribution of plant communities especially draw attention. This study aimed to reveal the potential distribution modeling and mapping of the Oriental spruce (Picea orientalis L.), distributed in a limited area, using current and future (year 2100) climate scenarios in Turkey. The maximum entropy method for potential distribution and Chelsa V2.1 technical specification IPSL-CM6A-LR scenarios (SSP126-SSP370-SSP585) were preferred to reveal the effect of climate change. Results for the current were in the “excellent” category with training and test data AUC 0.981 and 0.977, respectively. The variables contributing to the model were the precipitation amount of the driest month, mean diurnal air temperature range, annual precipitation amount, and mean annual air temperature. Variables contributing to the current model were analysed using the SSP126, SSP370, and SSP585 scenarios of the year 2100. It was assessed that the potential distribution for 2100 decreases according to SSP126, was fragmented according to SSP370, and decreased according to the SSP585 scenario. As a result, the authors determined that the high potential distribution is reduced 61% when the current mapping of Oriental spruce is compared with the SSP585 mapping.

The contribution of school social work to climate change education and mental health support

ABSTRACT This narrative review outlines how school social workers, as part of multi-professional school teams, can be involved in climate change education and mental health support. Rapid climate change and extreme weather events pose an existential threat to children and adolescents, triggering a range of climate emotions such as worry, anxiety or anger. International research shows that current and future climate scenarios affect young people's mental health. In order to achieve good health and well-being, and to prevent serious mental health problems, it is essential to be aware of their climate emotions and distress. In the climate crisis, schools, as important social spaces for children and adolescents, are therefore challenged to provide appropriate educational responses and health promotion for students of all ages. School social workers can make a professional contribution by providing education and counselling aimed at promoting emotional regulation and coping, empowerment for climate action, building self-efficacy and resilience to climate change, and developing comprehensive climate and health literacy. Finally, new challenges and tasks that arise in this context and that require the professional development of school social work are highlighted.

Characterization based on colorimetry, physiology and sensory attributes among traditional jujube cultivars of Sindh, Pakistan

Jujube (Ziziphus spp.) fruit is attaining incredible attention in current global climatic changing scenario as the fruit has significant nutritional value but remained unexplored in past. Fifteen Jujube cultivars were selected at Jujube Research Station Tandojam, Sindh Pakistan, to exploit their morphological, physiochemical, color and sensory attributes. Measured traits like tee shape diverged as semi erect, erect and spreading whereas leaf shape was also found highly variable. Thorn attachment differed as cadoucous, partial and persistent and fruit shape was set as round, oblong, oval and ovate. Maximum leaf blade length was counted by Soofi sanghar (9.0cm) while lowest was found in Gola soghat (5.1cm). Maximum fruit mass was recorded in cultivar Late gola (31.57 g) whereas least was observed in Khirol mukkhri (5.63 g). Maximum stone weight was estimated in Early gola (10.55 g) whereas minimum was recorded in Khirol desi (6.4 g). Highest glucose level was observed in Soofi local (4.46 g 100 mL−1) while lowest glucose level was recorded in Khirol desi and Khirol Ratam (3.53g 100 mL−1). Best cultivars by appearance were Late gola and Early gola followed by Gola saffina, Gola soghat, Soofi sanghar and Soofi local. Findings of this study are useful for varietal improvement and to run successful breeding programmes. Commercially relevant features evaluated in this study are highly beneficial for jujube varietal identification and germplasm conservation

Adaptive Comfort Potential in Different Climate Zones of Ecuador Considering Global Warming

Ecuador is a country with several climate zones. However, their behaviour is similar throughout the year, with no peaks of extreme temperatures in the various seasons. This paper is a first approach to study the adaptive comfort behaviour in several areas and populations of the country. Considering the ASHRAE 55-2020 model, energy simulation programmes are applied not just to the current climate scenario but also to the climate change scenarios of 2050 and 2100. The results of locations are analysed and compared to determine their performance. Thanks to their climate characteristics, adaptive comfort models could be applied as a passive strategy, using natural ventilation for building indoor comfort improvement, particularly social dwellings. According to previous studies, some prototypes have not considered the climate determinants in each region. Given the geographic situation of the study areas, the adaptive comfort model could be applied in all cases. Percentages of application of natural ventilation and heating and cooling degree hours have similar behaviours according to the climatic region, with a variation greater than 30% among them.

Predicting the current and future distributions of Pinus merkusii in Southeast Asia under climate change

Abstract. Sulton MN, Aurina DM, Muhammad F, Fadzilah FPA, Hanun Z, Indrawan M, Budiharta S, Supriatna J, Nursamsi I, Setyawan AD. 2024. Predicting the current and future distributions of Pinus merkusii in Southeast Asia under climate change. Biodiversitas 25: 1135-1143. Pinus merkusii Jungh. Et de Vriese is a native pine species of Southeast Asia with primary distribution in Indonesia, especially in the mountainous areas of northern Sumatra. The P. merkusii has an important role in the forest ecosystem including maintaining ecosystem stability, reducing soil erosion, and providing habitat for various types of flora and fauna. Climate change is expected to affect the growth, development and distribution of plants, so this study aims to predicting the current dan future distribution of P. merkusii in Southeast Asia under climate change. We used Maxent and Geographic Information System (GIS), which incorporated bioclimatic, edaphic, and UVB radiation variables, to predict the suitable areas of P. merkusii under current and future climate scenarios (RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5) and three time periods (2030, 2050, and 2080). Our findings indicate that compared to current, there will be an increase of suitable areas for P. merkusii in 2030 across all climate scenarios with RCPs 2.6, 4.5, 6.0, and 8.5 represent 9.53%, 9.66%, 9.73%, and 9.91% of Southeast Asia terrestrial area, respectively. In 2050, such increase will continue under all climate scenarios with RCP 4.5 has the largest proportion of suitable area (10.39%). However, in 2080, the suitable areas are likely to reduce compared with 2050 with RCPs 2.6, 4.5, 6.0, and 8.5 have a percentage of 9.21%, 9.69%, 10.29%, and 9.81%, respectively. Our predictions showed that there will be a geographical shift of suitable area of P. merkusii into higher elevation and latitude, migrating east and northeast. Our findings about the potential future distribution of P. merkusii might be used as a reference for cultivation according to predicted suitable areas in the future.

Potential global distribution of Setaria italica, an important species for dryland agriculture in the context of climate change.

Setaria italica (S. italica, Linnaeus, 1753) is a drought-resistant, barren-tolerant, and widely adapted C-4 crop that plays a vital role in maintaining agricultural and economic stability in arid and barren regions of the world. However, the potential habitat of S. italica under current and future climate scenarios remains to be explored. Predicting the potential global geographic distribution of S. italica and clarifying its ecological requirements can help promote sustainable agriculture, which is crucial for addressing the global food crisis. In this study, we predicted the potential global geographic distribution of S. italica based on 3,154 global distribution records using the Maxent model and ArcGIS software. We assessed the constraints on its potential distribution based on the contribution of environmental factors variables. The predictive accuracy of the Maxent model was evaluated using AUC values, TSS values, and Kappa statistics, respectively. The results showed that the Maxent model had a high prediction accuracy, and the simulation results were also reliable; the total suitable habitats of S. italica is 5.54×107 km2, which mainly included the United States (North America), Brazil (South America), Australia (Oceania), China, India (Asia), and the Russian Federation (Europe). The most suitable habitat of S. italica was 0.52×107 km2, accounting for 9.44% of the total areas, mainly in the United States, India, the Russian Federation, and China. Soil and precipitation (driest monthly precipitation, hottest seasonal precipitation) are the most critical factors limiting the potential distribution of S. italica. Compared with the modern potential distribution, we predict that the four future climate change scenarios will result in varying reductions in the possible geographic ranges of S. italica. Overall, climate change may significantly affect the global distribution of S. italica, altering its worldwide production and trade patterns.

Cold temperatures drive the latitudinal range limits and inhibit overwintering survival of the redbanded stink bug (Hemiptera: Pentatomidae).

For non-native insects that are economically damaging, understanding the drivers of range expansions and contractions is important for forecasting pest pressure. The invasion of the redbanded stink bug, Piezodorus guildinii (Westwood) (Hemiptera: Pentatomidae), reached Louisiana, United States, in 2000, after which the northern range limits of this species have fluctuated annually. Low winter temperatures have been implicated as a major driver of this pattern, but the importance of cold temperatures-or other abiotic factors-for the persistence of this pest over large geographic scales are incompletely understood. We coupled occurrence data of P. guildinii with climatic data to investigate trends in P. guildinii presence in relation to winter temperatures and develop species distribution models, forecasting habitat suitability based on current and future climatic scenarios. Our results show that (i) some P. guildinii persisted in locations where ambient temperatures reached -12°C, (ii) overwintering temperatures drive P. guildinii range dynamics, and (iii) with intermediate projections of climatic warming, northward expansion by P. guildinii in North America is likely to be minimal. While the northern extent of P. guildinii's range may now be largely realized in North America, our results suggest that increased frequency of mild winters could reduce interannual fluctuations of P. guildinii and enable it to become a more consistent economic concern for soybean growers throughout the Midsouth region of the United States.

Preparing Colombian coffee production for climate change: Integrated spatial modelling to identify potential robusta coffee (Coffea canephora P.) growing areas

Meeting future demand for coffee under climate change is a challenge. Approaches that can inform where coffee may grow best under current and future climate scenarios are needed. Robusta coffee (Coffea canephora P.) is planted in many tropical areas and makes up around 40% of the world’s coffee supply. However, as the climate shifts, current robusta areas may become less productive, while in other areas new growing regions for robusta may emerge. Colombia is one of the world’s most important Arabica coffee producer, famous for its high-quality coffee. Although robusta coffee is not yet a commercial crop in Colombia, it could be one of the future bastions for robusta coffee in South America contributing to meeting the increasing demand, but this remains unexplored. We aimed to identify areas with highest biophysical and socio-economic potential to grow robusta coffee in Colombia. An integrated modelling approach was used, combining climate suitability and crop-yield modelling for current and future climate scenarios, soil constraints, pest risk assessment and socio-economic constraints to identify the regions with the highest potential productivity and the lowest pest and climate change risks with good market access and low security risks which don’t further expand the agricultural frontier. Our results showed that parts of the foothills along the eastern Andean Mountain ranges, the high plains of the Orinoquía region and the wet parts of the Caribbean region are the best candidates for the potential development of robusta coffee plantations in Colombia. The crop-yield model indicated highest yields of green coffee on the foothills of the eastern Andean Mountain range with an estimated average yield of 2.6 t ha−1 (under rain-fed conditions) which is projected to occur at elevations below 600 m avoiding interference with the traditional and established Arabica coffee regions in Colombia. Under a 2 °C global warming scenario climate change is projected to have the largest impacts on the Caribbean region. Therefore, larger scale irrigated production system could be an appropriate option in the Caribbean region, while diversified smallholder robusta coffee agroforestry systems are considered more favourable in the Orinoquía region.