Mean Diurnal Temperature Range Research Articles

Global Distribution Prediction of Cyrtotrachelus buqueti Guer (Coleoptera: Curculionidae) Insights from the Optimised MaxEnt Model

Cyrtotrachelus buqueti Guer is a major pest affecting bamboo forests economically, causing significant damage to bamboo forests in Sichuan Province, China. To understand how C. buqueti responds to future climate conditions, an optimized Maximum Entropy Model (Maxent) was used to simulate the potential global distribution patterns of C. buqueti under current climate conditions and three different future climate scenarios and to analyze the dominant factors influencing its distribution. The results indicate that Bio18 (precipitation of the warmest quarter), Bio04 (temperature seasonality), Bio06 (minimum temperature of the coldest month), and Bio02 (mean diurnal temperature range) are the main environmental factors affecting the distribution of this species. The global area of high-suitability habitats for C. buqueti is 9.00 × 104 km2, primarily distributed in China. Under three different future climate scenarios, there are varying degrees of expansion in both the total suitable habitat and the medium-suitability areas for C. buqueti. Under the SSP5-8.5 scenario, the medium-suitability area of the species increases the most, reaching 9.83 × 104 km2. Additionally, these findings can serve as a reference for developing and implementing control strategies, assisting relevant authorities in more effectively managing and controlling this pest, and mitigating its potential threats to bamboo forest ecosystems and economies.

Temperature variability regulates the interactive effects of warming and pharmaceutical on aquatic ecosystem dynamics

Climate warming and pharmaceutical contaminants have profound impacts on population dynamics and ecological community structure, yet the consequences of their interactive effects remain poorly understood. Here, we explore how climate warming interacts with pharmaceutical-induced boldness change to affect aquatic ecosystems, built on an empirically well-informed food-chain model, consisting of a size-structured fish consumer, a zooplankton prey, and a fish predator. Climate warming is characterized by both daily mean temperature (DMT) and diurnal temperature range (DTR) in our model. Results show that DMT and high levels of species’ boldness are the primary drivers of community instability. However, their interactive effects can lead to diverse outcomes: from predator collapse to coexistence with seasonality-driven cycles and coexistence with population interaction-driven cycles. The interactive effects are significantly modulated by daily temperature variability, where moderate DTR counteracts the destabilizing interactive effects by increasing consumer reproduction, while large temperature variability considerably reduces consumer biomass, destabilizing the community at high mean temperatures. Our analyses disentangle the respective roles of DMT, DTR and boldness in mediating the response of aquatic ecosystems to the impacts from pharmaceutical contaminants in the context of climate warming. The interactive effects of the environmental stressors reported here underscore the pressing need for studies aimed at quantifying the cumulative impacts of multiple environmental stressors on aquatic ecosystems.

Climate Influence on Leaf Appearance and Ligustroflavone and Rhoifolin Compounds of Turpinia arguta (Lindl.) Seem. from Different Chinese Habitats

The dry leaf of Turpinia arguta (Lindl.) Seem. is used in traditional Chinese medicine as a quick-acting product for sore throat and pharyngitis relief. Samples of T. arguta were collected from 40 different habitats mostly located in Jiangxi Province, and leaf appearance traits and dry matter yield were analyzed. HPLC was used to quantify ligustroflavone and rhoifolin, the pharmacological-quality markers for this species, according to the 2020 Edition of Chinese Pharmacopoeia. The correlations between leaf-measurable traits, ligustroflavone and rhoifolin contents, and climate factors were subsequently assessed using Pearson’s two-tailed correlation test and redundancy analysis. The highest dry matter yields were found in locations S(G-mt), Q(J-t), and A(X-t). Ligustroflavone and rhoifolin contents ranged from 0.023% to 1.336% and 0.008% to 0.962%, respectively; the highest levels of ligustroflavone and rhoifolin compounds were found in locations A(X-t) and Y(B-mt). Leaf morphology was influenced by the mean temperature of the warmest quarter, while leaf thickness was affected by the minimum temperature of the coldest month, precipitation seasonality, and solar radiation. Larger and thicker leaves predicted higher yields of ligustroflavone and rhoifolin compounds in T. arguta under Chinese southern conditions, such as those in Anyuan and Quannan counties. Our findings suggest that enhancing the mean diurnal temperature range and implementing appropriate shading during cultivation can enhance the ligustroflavone and rhoifolin compounds of T. arguta.

Redistribution of Qiongzhuea tumidinoda in Southwest China under Climate Change: A Study from 1987 to 2012

Qiongzhuea tumidinoda stands out as an endemic bamboo species of significant conservation importance in Southwest China, particularly in the upper reaches of the Yangtze River. It holds a pivotal role in poverty alleviation through the commercialization of its wood and bamboo shoots. However, the suitable area of this species is undergoing rapid changes due to climate change, resulting in species redistribution and potential losses for bamboo farmers. We utilized 209 presence records and 25 environmental variables from 1987 to 2012 to predict the potentially suitable habitats for Q. tumidinoda using MaxEnt (version 3.4.1), ArcGIS (version 10.8.2), and R (4.3.3). We rigorously screened the recorded data for reliability and accuracy through expert consultations and observer interviews. We performed pre-processing to select the variables with high contributions for modeling, and 11 variables were selected for the final modeling. Our findings reveal that the top three most influential variables associated with Q. tumidinoda’s distribution were the mean monthly potential evapotranspiration (Pet), annual range of air temperatures (Bio7), and mean diurnal air temperature range (Bio2), and the rates of contributions from 1987 to 2012 were 4.8333, 3.5833, and 1.7000. There was a southeastward shift and an elevation increase in the potentially suitable habitats for Q. tumidinoda. The area of potentially suitable habitats in the study region exhibited fluctuating growth, expanding from 3063.42 km2 to 7054.38 km2. The mean monthly potential evapotranspiration (Pet) emerged as a critical determinant shaping the distribution of potentially suitable habitats for Q. tumidinoda. Our study sheds light on the response of Q. tumidinoda to climate change, offering valuable insights for the development and management of plantation industries associated with this species. In the future, to enhance prediction accuracy, researchers could equally consider both organic and inorganic environmental variables. For better preservation of environment and development, Q. tumidinoda could be introduced into nature restoration projects in areas with a suitable habitat or as a commodity that participates in forest carbon sink trading.

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.

Mapping an endangered living fossil's distribution, threats, and protection coverage

AbstractSpecies Distribution Models have emerged in recent decades as a powerful tool for biodiversity research, as they allow, for example, assessing the status of species in their whole distributions. These models have been particularly helpful in demonstrating that amphibian populations are at high risk, with many declining and others already extinct. Here, we assessed the potential distribution of the helmeted water toad (Calyptocephalella gayi), an endemic Chilean amphibian considered a living fossil and cataloged as vulnerable, which requires significant conservation efforts. We modeled the species' potential distribution using the Maximum Entropy (Maxent) approach and determined the overlap with national protected areas. In addition, we conducted a geospatial risk analysis to estimate the threat level to which this toad is being subjected. The potential distribution of C. gayi ranges from 28°S to 44°S, mainly explained by altitude, mean diurnal temperature range, slope, and distance to water bodies. Protected areas only cover 3.55% of the species' potential geographic distribution, which is of concern, considering that the geospatial risk analysis showed that 60.61% of C. gayi's distribution is subjected to extreme and high risks. We discuss how these results are relevant to focusing and directing efficient protection and conservation efforts for this species shortly.

Global Warming Drives Transitions in Suitable Habitats and Ecological Services of Rare Tinospora Miers Species in China

Tinospora Miers is considered a valuable medicinal herb that is suffering from severe habitat degradation due to climate change and human activities, but the variations in its suitable habitats and ecological service values remain unclear, especially in the context of accelerating global warming. In this study, we employed the MaxEnt model to estimate the suitable habitat changes and ecological service values of three rare Tinospora (T. craveniana, T. yunnanensis, and T. sinensis) species in China under four climate change scenarios (SSP126, SSP245, SSP370, and SSP585) from 2041 to 2100. The results show that the suitable habitats of T. craveniana, T. yunnanensis, and T. sinensis are mainly distributed in Sichuan, Yunnan, and Guangxi, respectively. Under the future climate scenarios, the suitable habitat of T. craveniana and T. sinensis is projected to expand toward the northeast and north, while that of T. yunnanensis will contract toward the northeast. The mean diurnal temperature range is the main environmental factor affecting T. craveniana and T. yunnanensis, while the annual mean temperature is a more important factor affecting T. sinensis. In the SSP245 scenario, T. craveniana and T. yunnanensis are expected to have the highest ecological service values from 2081 to 2100, while they will be relatively consistent in other climate scenarios and chronologies. The case of water protection accounts for the highest proportion of the total ecosystem service values, except for the economic value. This study provides a scientific reference for the diversity conservation of these rare species.

Impacts of Climate Change on the Biogeography and Ecological Structure of Zelkova schneideriana Hand.-Mazz. in China.

This study utilized the platform for ensemble forecasting of species distributions, biomod2, to predict and quantitatively analyze the distribution changes of Zelkova schneideriana Hand.-Mazz. under different climate scenarios (SSP1-2.6 and SSP5-8.5) based on climate and land-use data. This study evaluated the geographic range changes in future distribution areas and the results indicated that, under both SSP1-2.6 and SSP5-8.5 scenarios, the distribution area of Zelkova schneideriana would be reduced, showing a trend towards migration to higher latitudes and elevations. Particularly, in the more extreme SSP5-8.5 scenario, the contraction of the distribution area was more pronounced, accompanied by more significant migration characteristics. Furthermore, the ecological structure within the distribution area of Zelkova schneideriana also experienced significant changes, with an increasing degree of fragmentation. The variables of Bio6 (minimum temperature of the coldest month), Bio2 (mean diurnal temperature range), Bio15 (precipitation seasonality), and elevation exhibited important influences on the distribution of Zelkova schneideriana, with temperature being particularly significant. Changes in land use, especially the conversion of cropland, had a significant impact on the species' habitat. These research findings highlight the distributional pressures faced by Zelkova schneideriana in the future, emphasizing the crucial need for targeted conservation measures to protect this species and similar organisms.

Geographical distribution of two major quarantine fruit flies (Bactrocera minax Enderlein and Bactrocera dorsalis Hendel) in Sichuan Basin based on four SDMs.

Both Bactrocera minax and Bactrocera dorsalis are phytophagous insects, and their larvae are latent feeders, which cause great damage and economic losses to agriculture production and trade. This study aimed to provide a scientific reference for researching and developing the feasible countermeasures against these two pests. Based on the distribution data of B. minax and B. dorsalis in China, obtained from the Chinese herbaria, investigation and literature. Four niche models (Garp, Bioclim, Domain, and Maxent) were used to analyze the key environmental factors affecting the distribution of both pests and to build prediction models of the potential distribution in Sichuan Basin. Combined with two statistical standards, area under the receiver operating characteristic curve (AUC) and Kappa, the validity of prediction models were analyzed and compared. The results show that: the average AUC values of the four models are all above 0.90, and the average Kappa values are all above 0.75, indicating that the four models are suitable for predicting the potential distribution area of B. minax and B.dorsalis. The annual range of temperature, the mean temperature in the driest quarter, the mean temperature in the warmest quarter, the annual precipitation, and the precipitation in driest month are the key environmental factors affecting the distribution of B. minax, while the mean diurnal temperature range, the mean temperature in the driest quarter, the seasonal temperature variations and the precipitation in driest month affect the potential distribution of B. dorsalis. The suitable areas for B. minax are mainly concentrated in the eastern of Sichuan Basin, while the suitable areas for B. dorsalis are concentrated in the southeastern. Except for the Bioclim model, the highly-suitable area for both pests predicted by the other three models are all greater than 15.94 × 104 km2 and the moderately-suitable areas are greater than 13.57 × 104 km2. In conclusion, the suitable areas for both pests in Sichuan Basin are quite wide. Therefore, the relevant authorities should be given strengthened monitoring of both pests, especially in areas with high incursion rates.

Long-term temperature variability and death among stroke patients: A cohort study in central Shandong province, China

Our current understanding of the repercussions stemming from long-term temperature variability (TV) within the broader context of global climate change remains considerably limited. This study aims to investigate the influence of long-term temperature variability on the probability of death and mortality among stroke patients. For this study, a sample of stroke patients aged 40 years and above between 2016 and 2019 was taken from five districts/counties in central Shandong Province. We calculated the standard deviation (SD) of the annual mean daily temperature and the annual mean diurnal temperature range (DTR) obtained from the baseline survey to express the long-term TV. To assess the link between TV exposure and mortality among stroke patients, we utilized the Cox proportional hazards regression method. This statistical method allowed us to estimate the adjusted hazard ratio (HR) and 95 % confidence interval (CI) with regard to the risk of mortality. For stroke patients, there was a correlation between a 1 °C rise in the SD of temperature and an elevated risk of mortality (HR: 1.18, 95 % CI: 1.08–1.29). Additionally, each 1 °C increase in the DTR was associated with a further increase in the risk of death (HR: 1.69, 95 % CI: 1.59–1.79). In patients diagnosed with ischemic and hemorrhagic stroke, DTR was found to be positively associated with the risk of death. Temperature SD was found to be positively associated with the risk of death only in patients with ischemic stroke. Persistent exposure to TV was strongly linked to mortality risk among individuals with stroke. By devoting increased attention to TV, these organizations can develop strategies to mitigate its adverse effects on public health.

All-cause mortality attributable to long-term changes in mean temperature and diurnal temperature variation in China: a nationwide quasi-experimental study

Previous studies have demonstrated an association between short-term exposure to ambient temperature and mortality. However, the long-term effects of elevated temperature and temperature variability on mortality have remained somewhat elusive in epidemiological studies. We conducted a comprehensive epidemiological study utilizing Chinese population census data from 2000 and 2010. Census-derived demographic and socioeconomic factors were paired with temperature data from the European Re-Analysis Land Dataset across 2823 counties. We employed a difference-in-difference approach to quantitatively examine the relationship between all-cause mortality and annual exposure to mean temperature and diurnal temperature range (DTR). Additionally, we evaluated the potential effects of socioeconomic and environmental covariate modifications on this relationship and calculated the attributable mortality. Lastly, we projected excess deaths attributable to annual temperature exposure under various shared socioeconomic pathways (SSPs, e.g. SSP126, SSP370, and SSP585). For each 1 °C rise in annual mean temperature and DTR, the mortality risk could increase by 6.12% (95% CI: 0.84%, 11.69%) and 7.72% (95% CI: 3.75%, 11.84%), respectively. Counties with high labor-force ratios and high NO2 and O3 concentrations appeared to be sensitive to the annual mean temperature and DTR. Climate warming from 2000 to 2010 may have resulted in 5.85 and 14.46 additional deaths per 10 000 people attributable to changes in annual mean temperature and DTR, respectively. The excess mortality related to changes in annual mean temperature and DTR is expected to increase in the future, with special attention warranted for long-term temperature changes in Southwest China. Our findings indicate that long-term mean temperature and DTR could significantly impact mortality rates. Given the spatial heterogeneity of increased mortality risk, the formulation of region-specific strategies to tackle climate change is crucial.

Modelling habitat suitability for Moringa oleifera and Moringa stenopetala under current and future climate change scenarios

Moringa oleifera Lam and Moringa stenopetala (Baker f.) Cufod are being widely promoted as multipurpose trees across the tropics for their nutritional, medicinal and soil health benefits. Different parts of these species are edible, have therapeutic values and their seeds are used for water purification. Although the two species are similar in many ways, they have contrasting distributions. However, their current promotion is not guided by adequate knowledge of the suitability of the target areas. Information is also scanty on the suitability of habitats for these species under the current and future climate change scenarios. Therefore, the objective of this study was to predict the habitat suitability of M. oleifera and M. stenopetala under current and future climate change scenarios using an ensemble of models assuming four shared socio-economic pathways, namely, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 for 2050 and 2070. The results suggest that areas that are highly suitable for M. oleifera will increase by 0.1% and 3.2% under SSP1-2.6 to SSP5-8.5 by 2050, respectively. By 2070, the area suitable for M. oleifera would likely decrease by 5.4 and 10.6% under SSP1-2.6 and SSP5-8.5 scenarios, respectively. The habitat that is highly suitable for M. stenopetala was predicted to increase by 85–98% under SSP3-7.0 and SSP5-8.5 scenarios by 2050 and by 2070, while suitable areas could increase by up to 143.6% under SSP5-8.5. The most influential bioclimatic variables for both species were mean diurnal temperature range, mean temperature of driest quarter, precipitation of wettest month, and isothermality. Additionally, soil pH, elevation and water holding capacity were influential variables in the distribution of M. oleifera, while soil pH, soil salinity and slope were influential in M. stenopetala distribution. This study has provided baseline information on the current distribution and possible future habitat suitability, which will be helpful to guide formulation of good policies and practices for promoting Moringa species outside their current range.

Prediction of Oncomelania hupensis distribution in association with climate change using machine learning models

BackgroundOncomelania hupensis is the sole intermediate host of Schistosoma japonicum. Its emergence and recurrence pose a constant challenge to the elimination of schistosomiasis in China. It is important to accurately predict the snail distribution for schistosomiasis prevention and control.MethodsData describing the distribution of O. hupensis in 2016 was obtained from the Yunnan Institute of Endemic Disease Control and Prevention. Eight machine learning algorithms, including eXtreme Gradient Boosting (XGB), support vector machine (SVM), random forest (RF), generalized boosting model (GBM), neural network (NN), classification and regression trees (CART), k-nearest neighbors (KNN), and generalized additive model (GAM), were employed to explore the impacts of climatic, geographical, and socioeconomic variables on the distribution of suitable areas for O. hupensis. Predictions of the distribution of suitable areas for O. hupensis were made for various periods (2030s, 2050s, and 2070s) under different climate scenarios (SSP126, SSP245, SSP370, and SSP585).ResultsThe RF model exhibited the best performance (AUC: 0.991, sensitivity: 0.982, specificity: 0.995, kappa: 0.942) and the CART model performed the worst (AUC: 0.884, sensitivity: 0.922, specificity: 0.943, kappa: 0.829). Based on the RF model, the top six important variables were as follows: Bio15 (precipitation seasonality) (33.6%), average annual precipitation (25.2%), Bio2 (mean diurnal temperature range) (21.7%), Bio19 (precipitation of the coldest quarter) (14.5%), population density (13.5%), and night light index (11.1%). The results demonstrated that the overall suitable habitats for O. hupensis were predominantly distributed in the schistosomiasis-endemic areas located in northwestern Yunnan Province under the current climate situation and were predicted to expand north- and westward due to climate change.ConclusionsThis study showed that the prediction of the current distribution of O. hupensis corresponded well with the actual records. Furthermore, our study provided compelling evidence that the geographical distribution of snails was projected to expand toward the north and west of Yunnan Province in the coming decades, indicating that the distribution of snails is driven by climate factors. Our findings will be of great significance for formulating effective strategies for snail control.Graphical

Responses of Wheat Protein Content and Protein Yield to Future Climate Change in China during 2041–2060

The nutritional value of wheat is measured by its grain protein content (PC) and is sensitive to climate change. The potential variations of future wheat PC under the influence of global warming varied among studies. Wheat PC data from China since 1980 were collected to explore the relationship between wheat PC and climatic variables, and Coupled Model Intercomparison Project 6 (CMIP6) models were used to project wheat PC and protein yield (PY) in China from 2041–2060. The results show that climatic variables during wheat heading to the maturation period have critical effects on wheat PC. The mean maximum air temperature and mean diurnal temperature range exhibited the greatest positive effects on wheat PC. The mean PC will increase under all shared socioeconomic pathway (SSP) scenarios, with significant rises in North China and the Guanzhong Plain, but a decrease in the Yangtze River Basin. Wheat PY with adaptations will increase, while that without adaptations will decrease. Global warming will increase wheat PC but decrease PY and protein production. These impacts could be mitigated by applying adaptation management. Our results enhance our understanding of wheat PC variation patterns and the possible response of wheat to future climate changes, and highlight the importance of applying suitable adaptations.

Modeling soybean cultivation suitability in China and its future trends in climate change scenarios

Soybean is an important source of oil and vegetable protein and plays a key role in agricultural production and economy. A suitability evaluation of soybean cultivation is important for identifying potential soybean planting areas. Based on the raster data of soybean harvest ratio (FSHA) and climate-soil-topography-socio-economy environmental factors, we used MaxEnt to simulate the soybean planting suitability and potential distribution in China and the future trends of soybean cultivation under climate change. Three shared socio-economic paths (SSPs) that set up in the future climate section were considered, including SSP126 (sustainable path), SSP245 (intermediate path), and SSP585 (fossil fuel dominated development path). The result shows that the suitability of soybean cultivation was primarily influenced by elevation, precipitation of warmest quarter, capacity of the clay fraction, slope, portion of primary industry, topsoil gravel content, mean diurnal temperature range and accumulated temperature ≥10 °C. High-suitability and moderate-suitability area are respectively 26.51 Mha and 41.93 Mha in China. High-suitability areas for soybean are mainly concentrated in the Northeast Plain, the North China Plain and the northern parts of the middle and lower Yangtze River plain. There were many provinces with high soybean planting potential but low development degrees, including Hebei, Henan, Shandong, Tianjin, Jilin, Liaoning, Jiangsu, Hubei and Shaanxi. From 2021 to 2060, the total area highly and moderately suitable for soybean cultivation is projected to increase first and then decrease under both SSP126 and SSP245 scenarios. However, it shows a continued upward trend under SSP585, the rising part accounting for more than 10% in the base of historical data. Specifically, under SSP585, the suitability grade in most parts of Northeast China (eastern Inner Mongolia, northern Heilongjiang and western Jilin and Liaoning) will have a general promotion, opposite to the result under SSP126. Moreover, parts of southwest China (Yunnan, Chongqing, northern Guizhou and eastern Sichuan) may be more suitable for soybean cultivation in both scenarios. This study provides a practical reference for current and future soybean planting layout and relative countermeasures.

Classification of alpine grasslands in the western Tian Shan Mountains (Kyrgyzstan, Middle Asia)

In this paper, we present the first syntaxonomic classification of alpine grasslands in the western Tian Shan Mountains (Kyrgyzstan) with some remarks on its environmental gradients. A total of 251 relevés were sampled in 2015-2022 using the seven-degree cover-abundance scale of the Braun-Blanquet approach. These were classified using the agglomerative hierarchical Ward’s method employing the Euclidean distance measure. Diagnostic species were identified using the <i>phi</i> coefficient as a fidelity measure. NMDS was used to explore the relationships between the distinguished groups. Our classification revealed 13 ecologically meaningful clusters corresponding to 12 associations and one rankless unit of alpine grasslands in Middle Asia. The new communities have been included in three new alliances: <i>Aconito rotundifolii-Potentillion pamiroalaicae</i> – subalpine and alpine mesic forb meadows and pastures in the western Tian Shan and shrubby subalpine chionophilous heaths of Middle Asia, <i>Stipo regelianae-Ptilagrostion malyschevii</i> – <i>Kobresia</i> mats and <i>Artemision dracunculi</i> – subalpine forb steppe vegetation. The main climatic factors differentiating the species composition of the researched vegetation are temperature seasonality, mean diurnal temperature range and a sum of annual precipitation. Among the soil factors the most important are pH, cation exchange capacity and nitrogen availability. Our study has expanded the knowledge of the alpine grasslands on the borderland between vegetation influenced by Irano-Turanian climate in Pamir-Alai and grassland vegetation of the alpine belt in Tian Shan. Their final classification certainly still requires further studies and comparisons, especially with the Alpine grasslands in the Altai region and some of the vegetation units closely related to Karakorum-Himalayan ranges.

The potential global distribution of an emerging forest pathogen, Lecanosticta acicola, under a changing climate

Brown spot needle blight (BSNB), caused by Lecanosticta acicola (Thüm.) Syd., is an emerging forest disease of Pinus species originating from North America and introduced to Europe and Asia. Severity and spread of the disease has increased in the last two decades in North America and Europe as a response to climate change. No modeling work on spread, severity, climatic suitability, or potential distribution has been done for this important emerging pathogen. This study utilizes a global dataset of 2,970 independent observations of L. acicola presence and absence from the geodatabase, together with Pinus spp. distribution data and 44 independent climatic and environmental variables. The objectives were to (1) identify which bioclimatic and environmental variables are most influential in the distribution of L. acicola; (2) compare four modeling approaches to determine which modeling method best fits the data; (3) examine the realized distribution of the pathogen under climatic conditions in the reference period (1971–2000); and (4) predict the potential future global distribution of the pathogen under various climate change scenarios. These objectives were achieved using a species distribution modeling. Four modeling approaches were tested: regression-based model, individual classification trees, bagging with three different base learners, and random forest. Altogether, eight models were developed. An ensemble of the three best models was used to make predictions for the potential distribution of L. acicola: bagging with random tree, bagging with logistic model trees, and random forest. Performance of the model ensemble was very good, with high precision (0.87) and very high AUC (0.94). The potential distribution of L. acicola was computed for five global climate models (GCM) and three combined pathways of Shared Socioeconomic Pathway (SSP) and Representative Concentration Pathway (SSP-RCP): SSP1-RCP2.6, SSP2-RCP4.5, and SSP5-RCP8.5. The results of the five GCMs were averaged on combined SSP-RCP (median) per 30-year period. Eight of 44 studied factors determined as most important in explaining L. acicola distribution were included in the models: mean diurnal temperature range, mean temperature of wettest quarter, precipitation of warmest quarter, precipitation seasonality, moisture in upper portion of soil column of wettest quarter, surface downwelling longwave radiation of driest quarter, surface downwelling shortwave radiation of warmest quarter and elevation. The actual distribution of L. acicola in the reference period 1971–2000 covered 5.9% of Pinus spp. area globally. However, the model ensemble predicted potential distribution of L. acicola to cover an average of 58.2% of Pinus species global cover in the reference period. Different climate change scenarios (five GCMs, three SSP-RCPs) showed a positive trend in possible range expansion of L. acicola for the period 1971–2100. The average model predictions toward the end of the century showed the potential distribution of L. acicola rising to 62.2, 61.9, 60.3% of Pinus spp. area for SSP1-RCP2.6, SSP2-RCP4.5, SSP5-RCP8.5, respectively. However, the 95% confidence interval encompassed 35.7–82.3% of global Pinus spp. area in the period 1971–2000 and 33.6–85.8% in the period 2071–2100. It was found that SSP-RCPs had a little effect on variability of BSNB potential distribution (60.3–62.2% in the period 2071–2100 for medium prediction). In contrast, GCMs had vast impact on the potential distribution of L. acicola (33.6–85.8% of global pines area). The maps of potential distribution of BSNB will assist forest managers in considering the risk of BSNB. The results will allow practitioners and policymakers to focus surveillance methods and implement appropriate management plans.

More people, more cats, more parasites: Human population density and temperature variation predict prevalence of Toxoplasma gondii oocyst shedding in free-ranging domestic and wild felids.

Toxoplasma gondii is a ubiquitous zoonotic parasite that can infect warm-blooded vertebrates, including humans. Felids, the definitive hosts, drive T. gondii infections by shedding the environmentally resistant stage of the parasite (oocysts) in their feces. Few studies characterize the role of climate and anthropogenic factors in oocyst shedding among free-ranging felids, which are responsible for the majority of environmental contamination. We determined how climate and anthropogenic factors influence oocyst shedding in free-ranging domestic cats and wild felids using generalized linear mixed models. T. gondii oocyst shedding data from 47 studies were systematically reviewed and compiled for domestic cats and six wild felid species, encompassing 256 positives out of 9,635 total fecal samples. Shedding prevalence in domestic cats and wild felids was positively associated with human population density at the sampling location. Larger mean diurnal temperature range was associated with more shedding among domestic cats and warmer temperature in the driest quarter was associated with lower oocyst shedding in wild felids. Increasing human population density and temperature fluctuation can exacerbate environmental contamination with the protozoan parasite T. gondii. Management of free-ranging domestic cats could lower the burden of environmental oocysts due to their large population sizes and affinity with human settlements.

Long-term assessments of cotton fiber quality in response to plant population density: Reconciling fiber quality and its temporal stability

Regulation of plant population density is crucial for optimizing cotton fiber quality. However, the relationship between plant density and fiber quality stability under contrasting climatic conditions remains unclear, and the compromise between fiber quality and temporal stability is unknown. In this study, based on a long-term field experiment with various plant densities (1.5–10.5 plants m–2) conducted from 2008 to 2021, cotton fiber quality variability, including fiber length, fiber strength, elongation, uniformity index, and micronaire, and their temporal stability, were evaluated. We determined the optimal plant density for cotton cultivation and elucidated the contributions of plant density and climatic conditions to the variability of cotton fiber quality and its temporal stability in China’s Yellow River Valley. The results revealed that cotton maintained its upper-intermediate quality throughout the study period, with fiber length, strength, elongation, uniformity index, and micronaire values ranging between 28.04 and 30.03 mm, 26.15–30.05 cN tex–1, 5.90–6.83%, 83.85–85.44%, and 4.24–5.14, respectively. An increase in plant density improved fiber quality but impaired temporal stability (P < 0.01), and slight increases in stability at lower plant densities resulted in substantially decreased probabilities of years with major declines in fiber quality class. Plant density and climatic conditions regulated the quality traits (23.5% and 69.3% of the explained variance, respectively), including fiber length, fiber strength, and micronaire, while climate was the most important factor (75.9% of the explained variance) in determining their temporal stability. Photosynthetically active radiation and maximum and mean temperature exhibited significant positive effects on fiber quality, whereas mean diurnal temperature range had the opposite effect. A plant density of 3.3–5.1 plants m–2 ensures the highest temporal stability of cotton fiber quality without declines in the fiber quality class. This study first provides insights into balancing cotton fiber quality and temporal stability through agronomic interventions, with implications for the quantitative prediction of future crop quality changes owing to climatic variation.

Digital mapping of the soil available water capacity: tool for the resilience of agricultural systems to climate change

Soil available water capacity (AWC) is a key function for human survival and well-being. However, its direct measurement is laborious and spatial interpretation is complex. Digital soil mapping (DSM) techniques emerge as an alternative to spatial modeling of soil properties. DSM techniques commonly apply machine learning (ML) models, with a high level of complexity. In this context, we aimed to perform a digital mapping of soil AWC and interpret the results of the Random Forest (RF) algorithm and, in a case study, to show that digital AWC maps can support agricultural planning in response to the local effects of climate change. To do so, we divided this research into two approaches: In the first approach, we showed a DSM using 1857 sample points in a southeastern region of Brazil with laboratory-determined soil attributes, together with a pedotransfer function (PTF), remote sensing and DSM techniques. In the second approach, the constructed AWC digital soil map and weather station data were used to calculate climatological soil water balances for the periods between 1917–1946 and 1991–2020. The result showed the selection of covariates using Shapley values as a criterion contributed to the parsimony of the model, obtaining goodness-of-fit metrics of R2 0.72, RMSE 16.72 mm m−1, CCC 0.83, and Bias of 0.53 over the validation set. The highest contributing covariates for soil AWC prediction were the Landsat multitemporal images with bare soil pixels, mean diurnal, and annual temperature range. Under the current climate conditions, soil available water content (AW) increased during the dry period (April to August). May had the highest increase in AW (∼17 mm m−1) and decrease in September (∼14 mm m−1). The used methodology provides support for AWC modeling at 30 m resolution, as well as insight into the adaptation of crop growth periods to the effects of climate change.