Average Daily Minimum Temperature Research Articles

Empirical model development for the estimation of clearness index usingmeteorological parameters

The clearness index is an indispensable parameter required for the design and analysis of solar energy systems. In the absence of measured values for a specific location, the clearness index can be estimated from other measured meteorological variables. In this study three meteorological parameters, sunshine hours, monthly mean values of the temperature difference ($\Delta$T), and cloudiness, are used to develop empirical models for the estimation of clearness index. The empirical models are developed for five major cities in Pakistan (Karachi, Multan, Lahore, Islamabad, and Quetta). For empirical model development, long-term data (1991 to 2010) of monthly average clearness index, sunshine hours, average daily minimum and maximum temperatures, and cloudiness have been used. The accuracy of the models has been tested by statistical indicators that include mean percentage error (MPE), coefficient of determination (R$^2$), mean absolute relative error (MARE), mean bias error (MBE), and root mean square error (RMSE). The error analysis revealed that the proposed models are suitable for the estimation of the clearness index. It is also concluded that multiple regression models give better estimates of clearness index for all the stations (0.80 $\leq$ R$^{2}$ $\leq$ 0.86) compared to single parameter model and therefore are recommended. The study indicated that clear sky conditions prevail throughout the months at all the investigated sites (0.58 $\leq$ K$_T$ $\leq$ 0.68), which is a good indicator for solar energy utilization. The statistical indicators also suggest that multilinear regression model M-3 gives a better representation of the climate system and using three parameters reduces the uncertainties in the developed model.

Persistence and turnover in desert plant communities during a 37‐yr period of land use and climate change

AbstractUnderstanding long‐term changes in ecological communities during global change is a priority for 21st‐century ecology. Deserts, already at climatic extremes, are of unique interest because they are projected to be ecosystems most responsive to global change. Within a 500‐km2 landscape in the Mojave Desert, USA, we measured perennial plant communities at 100 sites three times (1979, 2008, and 2016) during 37 yr to evaluate six hypotheses of community change. These hypotheses encompassed shifts in community measures (e.g., diversity, cover) and species elevational distributions, biotic homogenization, disproportionately large change at the highest elevations, relationships between turnover and species’ responses to disturbance and drought, and that environmental refugia (e.g., moist topographic positions) would receive species during climatic warming and drying. Most community measures changed temporally, such as species density (species/600 m2) increasing 23% and plant cover doubling between 1979 and 2016. There was no increase in nonnative species and minimal evidence for biotic homogenization. High‐elevation communities did not display greater change than low‐elevation communities. Moreover, environmental refugia factored little in species shifts. While species distributional changes were unrelated to affinity for disturbance, the six most persistent species (persisting on >80% of sites) were long‐lived shrubs not associated with disturbance. Overall, seemingly paradoxically, climatic warming and drying was associated with increasing plant abundance. Comparing the 1970s to 2007–2016, precipitation in the study area declined 16% from 17 to 14 cm/yr and average daily minimum temperature rose 13% (1.2°C). The current climate with fewer freezes, together with reduced grazing, could be among the most optimal for desert perennials in the past century, although potential response lags to continuing warming and drying are uncertain. This study of long‐term elevational shifts in communities during global change is among few in deserts, and the average upward elevational shift of 6 m/decade for species in our study is within the range reported for temperate biomes. However, the 41% of species moving downslope is unusually high. We propose that dynamics within desert perennial communities follow a core‐transient species model where a site's species are either highly persistent or transient in approximately equal proportions.

Inner log temperatures vary with log direction and forest cover: Implications for predicting the phenology of saproxylic insects

Ambient temperature is frequently used to model the development of many herbivorous insects. Estimating development rates of cryptic saproxylic species in wood will be biased by the presence of differences between internal log temperatures, where larvae feed, and measured ambient conditions outside of logs. We tested for differences between ambient temperatures at the log surface and internal log temperatures as a function of habitat type (recent clearfell area and pine forest stands), log direction (north- and south-facing), depth (5–20 cm) and season (summer and winter). Internal log temperatures varied from log surface temperatures in all cases and were influenced by depth, depth × habitat, and depth × log direction interactions. A simple constant correction factor could not explain the relationship between log surface and internal log temperatures. Mean internal log temperatures were between 0.88 and 3.68 °C warmer in clearfells compared to forest stands irrespective of log direction or depth. During summer the average daily minimum temperatures were 2.3–4.1 °C warmer in logs compared to the log surface under a conifer canopy and up to 9.8 °C warmer in adjacent recently clearfelled areas. During winter the internal log minimum temperatures were 2.0–2.8 °C warmer under a conifer canopy and 2.6–5.2 °C warmer in recently clearfelled areas compared to log surface temperatures. Differences between log surface and internal minimum temperatures increased consistently with depth into the log. Accurate models that translate observed ambient temperatures to internal log temperatures that are representative of the location of feeding larvae are necessary to model saproxylic insect development at the landscape scale. Results from this study demonstrate that these translation models will have to consider depth, log orientation, and season.

Lycopene Accumulation in Pummelo (Citrus Maxima [Burm.] Merr.) Is Influenced by Growing Temperature

ABSTRACTOne problem with the pink pulp color of the ‘Thong Dee’ (TD) pummelo (Citrus maxima [Burm.] Merr.) is that it becomes pale during some harvesting periods. The cause of the pale pulp is not known. Therefore, the aim of this study was to determine the effect of growing temperature and rainfall on carotenoid contents and pulp color in the TD fruit. The experiments were conducted in 11 harvesting periods over two years from four orchards. The pale pink pulp coincided with low lycopene and was observed when the average daily minimum temperature two months before harvest was less than 20°C. Lycopene content and a*, which indicates pulp ‘redness’, positively correlated with the average daily minimum temperature two months before harvest (r = 0.6644 and r= 0.6013, respectively). In addition, TD pulp mineral nutrients, which include N, P, K, Ca, Mg, Zn, Cu, Fe, B, Mn, and Na, were not significantly related to the lycopene levels or the pale pink color in the pulp (P> .05). It is possible to conclude that the pale pulp of pummelo fruit is caused by the growing temperature during fruit development. This is the first study that comprehensively addresses the effects of environmental conditions on the pulp color of pummelo fruit.Abbreviations : TD: ‘Thong Dee’; MAP: Months after pollination; ABA: Abscisic acid; GAs: Gibberellic acids; MEP: 2-C-methyl-D-erythritol 4-phosphate; TSS: Total soluble solids; TA: Titratable acidity; HPLC: High performance liquid chromatography; N: Nitrogen; P: Phosphorus; K: Potassium; Ca: Calcium; Mg: Magnesium; Cu: Copper; Zn: Zinc; B: Boron; Fe: Iron; Mn: Manganese; Na: Sodium

Regional Decadal Climate Predictions Using an Ensemble of WRF Parameterizations Driven by the MIROC5 GCM

AbstractRegional climate models (RCMs) are expected to provide better representations of the climate dynamics because of their higher spatial resolutions. Here, we generated an ensemble of decadal (2006–36) RCM predictions for the area of Israel, which spans a considerable climatic gradient and comprises complex terrain. We used the WRF Model forced by the MIROC5 global climate model (GCM). The ensemble was generated by choosing different combinations of radiation, microphysics, surface layer, and planetary boundary layer parameterizations. The simulation results were compared with meteorological station data for the first simulated decade. For the minimum surface temperature, all the RCM configurations performed better than the driving GCM, while for the maximum surface temperature, only three out of eight configurations improved the predictions. The RCM configurations had higher errors in predicting the precipitation, but four configurations had comparable errors to the GCM. For the next two decades, the ensemble average predicts an increase of 0.51° and 0.40°C decade−1 for the average daily minimum and maximum surface temperatures, respectively. No significant change is predicted in the precipitation. We found that all the parameterizations affect the predictions of the surface temperatures and precipitation [e.g., the CAM radiation scheme simulates colder temperatures than the RRTM for GCMs (RRTMG)] but the PBL and surface layer scheme has the largest effect on the errors. Spectral nudging was found to have a considerable effect on the deviations of the precipitation predicted by the WRF configurations from the predictions of the GCM and a much smaller effect on the surface temperature predictions.

Sunshine and Temperature Dependent Models for Estimating Global Solar Radiation Across the Guinea Savannah Climatic Zone of Nigeria

This study investigates the most accurate sunshine and temperature dependent models for estimating global solar radiation over Makurdi and Ibadan situated in the Guinea savannah of Nigeria by comparing nine (9) different existing sunshine dependent models. The study also proposed two temperature dependent models that took the form of quadratic logarithmic and quadratic exponential and were compared to three existing temperature dependent models (Chen, Hargreaves and Samani (HS) and Garcia). The measured monthly average daily global solar radiation, sunshine hours, maximum and minimum temperature meteorological parameters during the period of thirty one (1980-2010) years was utilized and the accuracy of the sunshine and temperature dependent models to ascertain the most suitable models in each location were tested using seven various statistical validation indicators of coefficient of determination (R²), Mean Bias Error (MBE), Root Mean Square Error (RMSE), Mean Percentage Error (MPE), t-test, Nash-Sutcliffe Equation (NSE) and Index of Agreement (IA). The results revealed that the exponent sunshine dependent model proposed by Bakirci and the linear exponential sunshine dependent model proposed by Bakirci were found more accurate for estimating global solar radiation in Makurdi and Ibadan respectively. The proposed quadratic logarithmic and quadratic exponential temperature dependent models were found more suitable for estimating global solar radiation in Makurdi and Ibadan respectively. These recommended models can be found appropriate, if properly calibrated in regions with similar climatic information. The HS temperature dependent model evaluated in this study for Ibadan was compared with those available in literatures and was found more suitable. Furthermore, the most suitable sunshine dependent model was found more suitable for global solar radiation estimation when compared to the most suitable temperature dependent model in each of the studied locations and this was testified from the figures of the comparison between the measured and estimated sunshine and temperature dependent models as the sunshine dependent models depicts the best fitting with the measured global solar radiation data.

Developing Empirical Models for Estimating Photo synthetically Active Radiation overAkure, South Western, Nigeria

In this study, the measured monthly average daily global solar radiation and extraterrestrial solar radiation using the generalized 45% and 40% dataset was utilized to estimate the photosynthetically active radiation (PAR) and extraterrestrial photosynthetically active radiation (PAR0) for Akure (Latitude 7.170N, Longitude 5.180E, and 375.0 m above sea level) Ondo State located in South Western, Nigeria. The monthly average daily sunshine hours, maximum and minimum temperature data were used to develop nine (9) new PAR sunshine based models and three (3) PAR temperature-based models. The meteorological parameters used in this study covered a period of thirty-one years (1980 – 2010). The newly developed models were tested using statistical indicators of the coefficient of determination (R2), Mean Bias Error (MBE), Root Mean Square Error (RMSE), Mean Percentage Error (MPE), t-test, Nash – Sutcliffe Equation (NSE) and Index of Agreement (IA). The PAR sunshine based models that took a quadratic form and the linear logarithmic PAR temperature-based models were found more suitable for estimating PAR for the location under study. Comparing the PAR sunshine based and temperature-based models indicated that the PAR sunshine based model is more suitable for PAR estimation in Akure. Furthermore, the results showed that PAR is high during the dry season and low during the rainy season. Based on the measured and estimated PAR models; the minimum values were found in July and August while the maximum values in February, March and November. The descriptive statistical analysis shows that the PAR and all the estimated sunshine based PAR data spread out more to the left of their mean value (negatively skewed). Similarly, they have negative kurtosis which indicates a relatively flat distribution and the possibility of platykurtic distribution. The PAR and the PAR logarithmic temperature-based model (equation 17a) data spread out more to the left of their mean value (negatively skewed), while the PAR linear exponent and linear temperature-based models (equation 17b and 17c) data spread out more to the right of their mean value (positively skewed). The PAR and all the estimated PAR temperature-based data have negative kurtosis which indicates a relatively flat distribution and the possibility of platykurtic distribution.

Models for Estimating Precipitable Water Vapour and Variation of Dew Point Temperature with Other Parameters at Owerri, South Eastern, Nigeria

Precipitable water vapour (PWV) is a vital component of the atmosphere and appreciably controls many atmospheric processes. The PWV is not easy to measure with sufficient spatial and time resolution under all weather conditions. In this paper, three precipitable water vapour models; the Smith, Won and Leckner’s models were evaluated and compared for Owerri (Latitude 5.48°N, Longitude 7.00°E, and 91 m above sea level) using meteorological parameters of monthly average daily maximum temperature, minimum temperature and relative humidity during the period of sixteen years (2000-2015). The Leckner’s model was found most suitable and therefore recommended for estimating PWV for the location with range between 3.253 and 4.662 cm. The highest PWV occurred in June for Won and Leckner’s models while for Smith’s model it occurred in September; the lowest PWV occurred in January for all the evaluated models. The result showed that high values of dew point temperature (Tdew), PWV and relative humidity (RH) were observed during the raining season and low values in the dry season; this is an indication that the dew point temperature is a reflection of the PWV and RH. The dew point temperature is an opposite reflection of the virtual temperature (Tvirtual), potential temperature (Tpotential) and mean temperature (Tmean). The dew point temperature increases and decreases with mean temperature in the months from January to March and in July respectively for the location under investigation. The values of the dew point temperature indicated that the air is stable signifying no development of severe weather condition like thunderstorms. The maximum and minimum virtual temperature correction of 3.3246°C and 2.3371°C occurred in June and January respectively while for the dew point depression, it occurred in the months of January and September with 8.7514°C and 2.1094°C. The descriptive statistical analysis shows that the dew point temperature, potential temperature, mean temperature and virtual temperature correction data spread out more to the left of their mean value (negatively skewed), while the virtual temperature and dew point depression data spread out more to the right of their mean value (positively skewed). The dew point temperature and the virtual temperature correction data have positive kurtosis which indicates a relatively peaked distribution and possibility of a leptokurtic distribution while the virtual temperature, potential temperature, mean temperature and dew point depression data have negative kurtosis which indicates a relatively flat distribution and possibility of platykurtic distribution.

Non-uniform time-lag effects of terrestrial vegetation responses to asymmetric warming

The immediate effects of asymmetric warming (i.e., day- and night-time warming) on terrestrial ecosystems have been well documented, but the time-lag effects remain poorly understood. In this paper, we investigated the global inter-annual hysteretic responses of vegetation to asymmetric warming over the period of 1982–2013. The net primary production (NPP) was employed as the indicator of vegetation growth, and accumulated monthly average daily maximum temperature and minimum temperature (ATmax and ATmin) were used to reflect the asymmetric warming condition. Additionally, partial correlation analyses were conducted to examine the correlations between NPP and ATmax/ATmin on a monthly scale. Furthermore, the best time lags that ATmax/ATmin had on NPP and the optimal correlations between ATmax/ATmin and NPP were analyzed by time-lag analyses. The results showed that (i) vegetation responded to the asymmetric warming with near 12-month delays at a global scale, and vegetation exhibited larger lags in responding to recent warming over temperature-limited areas or semiarid and subhumid regions compared to other places; (ii) compared with ATmin, ATmax had longer time-lag effects on biomes over mid-high latitudes (45 °N–90 °N, 23 °S–60 °S) and high altitudes (i.e., the Tibetan Plateau and the Brazil Plateau) and smaller delay impacts on biomes in other regions; (iii) with hysteretic impacts considered, ATmax correlated positively with vegetation in temperature-limited areas and negatively in heat-sufficient and water-deficit places, and the reverse was mostly true for ATmin. These phenomena may be associated with the intrinsic differences in the mechanisms that day- and night-time temperatures have on vegetation growth. Our paper gives new insights into the non-uniform responses of the terrestrial ecosystem to asymmetric warming. Looking ahead, terrestrial ecosystem models are highly recommended to incorporate such non-uniform time-lag impacts and distinguished correlations so as to improve their performances in future work.

Long-term predictors of dengue outbreaks in Bangladesh: A data mining approach.

The effects of weather variables on the transmission of vector-borne diseases are complex. Relationships can be non-linear, specific to particular geographic locations, and involve long lag times between predictors and outbreaks of disease. This study expands the geographical and temporal range of previous studies in Bangladesh of the mosquito-transmitted viral infection dengue, a major threat to human public health in tropical and subtropical regions worldwide. The analysis incorporates new compound variables such as anomalous events, running averages, consecutive days of particular weather characteristics, seasonal variables based on the traditional Bangla six-season annual calendar, and lag times of up to one year in predicting either the existence or the magnitude of each dengue epidemic. The study takes a novel, comprehensive data mining approach to show that different variables optimally predict the occurrence and extent of an outbreak. The best predictors of an outbreak are the number of rainy days in the preceding two months and the average daily minimum temperature one month prior to the outbreak, while the best predictor of the number of clinical cases is the average humidity six months prior to the month of outbreak. The magnitude of relationships between humidity 6, 7 and 8 months prior to the outbreak suggests the relationship is multifactorial, not due solely to the cyclical nature of prevailing weather conditions but likely due also to the immunocompetence of human hosts.

Developing a growing degree day model for North Dakota and Northern Minnesota soybean

Farmers in North Dakota and Northern Minnesota did not have a model to predict when their soybean (Glycine max L. Merr.) crop will be mature. Soybean plants need to be mature before the first fall freeze. The objectives of this study were to estimate needed accumulated growing degree days (AGDD) for adapted soybean maturity groups (MG) to reach maturity (R8). Research was conducted during 2007–2012 at northern, central, and southern North Dakota, to develop a model to predict the soybean maturity date based on accumulated GDDs and to verify the model using field research data from 2013 to 2015. Based on 1816 data points a regression analysis was performed which predicted that 1666, 1862, 2030 AGDD (with a 50°F base temperature) were needed to reach maturity for 00.7, 0.4, and 1.0 MG soybean cultivars, respectively.The predicted values were used to create a model that allows producers to select the MG of their cultivar, date of planting, and the nearest North Dakota Agricultural Weather Network (NDAWN) station. The output provides the predicted maturity date, as well as the average first day of occurrence of select threshold temperatures (36°, 32° and 28°F) at 50% probability level. This model will help growers to judge the risks of certain frost occurring before the selected cultivar's projected maturity date. The projected maturity date is based on two parts. The first part is the observed period from the planting date to the date the model is queried and is based on daily maximum and minimum temperatures. The second part is the forecast period that is based on normal (1981–2010 average) daily maximum and minimum temperatures from the queried date through the end of the select growing season. The growing degree days (GDD) values are accumulated from the planting date through the end of season based on these daily maximum and minimum temperatures. Input into the model should be for adapted MG soybean for the region and for planting dates between May 15 and 31. Farmers and agriculturist in North Dakota and northwest Minnesota can use the model before planting to determine if a MG would mature before the frost with varying temperature thresholds and monitor the accumulation of GDD and development of the crop during the season and estimated date of maturity.

Global warming and local air pollution have reduced wheat yields in India

We use regression analysis on data from 208 districts over the period 1981–2009 to examine the impact of temperature and solar radiation (affected by pollution from aerosols) on wheat yields in India. We find that a 1 °C increase in average daily maximum and minimum temperatures tends to lower yields by 2–4% each. A 1% increase in solar radiation increases yields by nearly 1%. Yields are estimated to be about 5.2% lower than they would have been if temperatures had not increased during the study period. We combine the estimated impacts of weather on yield with the estimated impacts of aerosol pollution (measured by moderate resolution imaging spectroradiometer sensor in terms of aerosol optical depth, aerosol optical depth (AOD) in 2001–2013) on weather to compute the net impact of reducing aerosol pollution on wheat yields. A one-standard-deviation decrease in AOD is estimated to increase yields by about 4.8%. Our results imply reducing regional pollution and curbing global warming in the coming decades can counter wheat yield losses.

Reduced mobility but high survival: thermal tolerance and locomotor response of the specialist herbivore, Pareuchaetes insulata (Walker) (Lepidoptera: Erebidae), to low temperatures.

Disentangling the responses of insects to variations in their thermal environment is central to our understanding of the evolution of temperature-dependent performance in these species. Here, we report results of experiments examining the effects of high (upper lethal temperature = ULT) and low (lower lethal temperature = LLT) temperature and exposure time on the survival of larvae and adults of a multivoltine, nocturnal moth species, Pareuchaetes insulata, a biological control agent whose impact on an invasive weed, Chromolaena odorata has been variable in South Africa. The influence of temperature and acclimation on locomotion performance of the moth was also investigated. Temperature and duration of exposure significantly affected survival of both adults and larvae of P. insulata with more extreme temperatures and/or longer durations proving to be more lethal. Third instar larvae and adults are both freeze intolerant and had LT50 of -5.9 and -4.7°C, respectively, after a 2 h exposure. Although cold acclimation was beneficial to the nocturnal larvae, temperatures below 10°C significantly reduce their locomotion activities. The average daily minimum temperatures in the coldest months at three locations in South Africa are over 5°C lower than those of Fort Lauderdale, Florida, USA, where P. insulata was originally collected. Our results suggest that lethal high or low temperatures at short timescales are trivial in explaining the variable performance of P. insulata, but reduced locomotion at sub-lethal temperatures may be an important driver of the population dynamics of the biocontrol agent (especially in winter months) and may consequently explain the low population levels of the moth because of possible reduced feeding by larvae during night-time low temperatures.

Greater phenological sensitivity on the higher Tibetan Plateau: new insights from weekly 5 km EVI2 datasets

Plateau vegetation is considered to be highly sensitive to climate change, especially at higher altitudes. Although the Tibetan Plateau has experienced intensive warming over the past few decades, there is much contradictory evidence regarding its phenological variations and the impact of climatic change. In this study, we explored vegetation phenology through the inflexion point-based method with the weekly 0.05° EVI2 datasets from 1982 to 2010. We observed complex spatiotemporal variations in vegetation phenology on the higher Tibetan Plateau from three aspects. From a spatial aspect, the altitudinal gradients of phenological dates, as well as their directions, varied among different altitudes over the past three decades. Compared with delaying with elevation at altitudes below 5000m, the phenological parameters at altitudes above 5000m significantly advanced with increasing altitudes. At higher altitudes, much stronger altitudinal gradients (slope) of phenological dates were observed in the 2000s than in the 1980s and 1990s, i.e., 2.19, 3.47, and 3.68days' advance for start, maximum, and end dates, respectively, compared to less than 1day's change per 100m increase in altitude. From a temporal dynamic aspect, when analyzed at different altitudinal bands, the dynamic trends in phenological dates were generally not significant except the advancing trends in the maximum dates at altitudes above 5000m and the delaying trend in the end dates at altitudes of 4500-5000m in the twenty-first century. Remarkable elevation dependency was also observed at the pixel level: increasing amplitudes of phenological dynamic trends were observed at higher altitudes when obtaining their minimum around 5000m. These spatiotemporal variations of vegetation phenology were due to combined effects from both temperature and precipitation: more abundant rainfall and greater magnitudes of dynamic trends were observed in the average daily minimum temperature (slope=0.08°C/year) and annual precipitation (slope=2.17mm/year) at higher altitudes.

Effect of vegetation change from forest to herbaceous vegetation cover on soil moisture and temperature regimes and soil water chemistry

Objective of this study was to compare the effects of forest and herbaceous vegetation covers on soil temperatures (average daily maximum, minimum, and mean daily temperatures), soil moisture, and chemical content of the soil water. Soil moisture and soil temperature were monitored at three different soil depths (40, 80 and 120cm) and rain water samples and soil water samples from 40cm and 80cm soil depths were collected for 20weeks on a weekly basis depending on precipitation events. Soil water samples were analyzed for total alkalinity, total nitrogen, calcium hardness, chloride, electrical conductivity, organic matter, pH, potassium, sodium, sulfate, and total hardness. Experiment was as a 3-way factorial in a split plot design with whole plots in blocks and repeated measures with two replications. Data were analyzed by using ANOVA and means were separated with Tukey test. Soils under the forest and herbaceous vegetation covers showed significant difference in terms of overall mean daily maximum, minimum, and average daily temperatures. Overall average daily maximum and minimum temperatures were 9.94°C and 9.75°C, respectively for the soils in the forest plot while they were 11.08°C and 10.87°C for those in the herbaceous plot. Woody vegetation removal significantly increased overall mean daily temperature from 9.84°C to 10.98°C and overall mean daily volumetric soil moisture content of the soils from 32% to 48%. Chemical content of the soil water from both study sites were similar except for chloride and sulfate content. Soil water from forestland had a lower chloride but higher sulfate content than the soils from herbaceous vegetation covered area. On the other hand, chemical content of the soil water from forestland did not show significant changes with soil depth while total alkalinity, calcium hardness, electrical conductivity, pH, sodium, sulfate, and total hardness increased significantly with soil depth in the soils under herbaceous vegetation cover.

Optimizing Seeding Dates and Rates for Canola Production in the Humid Eastern Canadian Agroecosystems

Optimum seeding date (OSD) and seeding rate is an important management practice to improve the performance of canola (Brassica napus L.) production. A field study was conducted to investigate the influence of seeding date and rate on plant stand count, yield components, yield, and seed oil and protein concentrations, and to develop a location‐sensitive model for estimating OSD for maximizing canola yield. The factorial experiment of three seeding dates (early, intermediate, and late) and three seeding rates (2.5, 5.0, and 7.5 kg ha−1) was performed in 2011 and 2012 at seven locations across eastern Canada. An independent dataset from an additional 2‐yr field experiment at the Ottawa site was used for model verification. Our data showed that seed yield, seed oil, and pod number per plant were significantly affected by seeding date and seeding rate. The greatest yield and seed oil concentration were obtained with the early seeding in most site‐years. The OSD was a quadratic function of the long‐term (30 yr) average daily minimum air temperature (Tmin) in April and May with R2 = 0.98, P < 0.01 and SE = 2.6 d. Increasing seeding rate from 2.5 to 5.0 kg ha−1 increased seed yield for early‐seeded canola in most site‐years but the yield did not increase with further increases in seeding rate. Early seeding at 5.0 kg ha−1 is therefore recommended as the optimum seeding rate across eastern Canada. Quantitative relationship between optimum seeding date and mean minimum temperature in April and May. A regression model to predict optimum seeding date for spring canola. A suitable seeding rate for canola production in eastern Canada. The impact of seeding date on canola seed oil and protein concentration. Canola yield and yield components as affected by seeding date and rate.

Temperature explains the yield difference of double-season rice between tropical and subtropical environments

Crop yields are largely influenced by air temperature and solar radiation, but the individual effects of these weather variables are difficult to distinguish because they are often not independent. Here, we demonstrate a large effect of temperature on rice grain yield when solar radiation and crop growth duration were not confounding factors for explaining yield difference across two locations in the tropical and subtropical environments. We found that grain yield of double-season rice crops in the subtropical environment of Wuxue County, Hubei Province, China was 9–66% higher than that in the tropical environment of International Rice Research Institute (IRRI), Philippines. Such yield difference was not caused by the difference in crop growth duration from transplanting to maturity. Biomass production rather than harvest index was responsible for the yield difference. Average daily minimum and maximum temperature from transplanting to maturity at IRRI was 3.4 and 1.9°C higher than that at Wuxue, respectively, whereas average daily solar radiation at Wuxue was lower or similar to that at IRRI. Crop’s efficiency in converting solar radiation into biomass (i.e. radiation use efficiency, RUE) at Wuxue was 48% higher than that at IRRI, which was associated with the difference in temperature between the two environments. We concluded that lower rice yield in the tropical environment was associated with lower RUE, which was due to the higher temperature compared with the subtropical environment. Our results suggested the crop adaptation strategies for global climate changes should focus more on future warming.

Altitudinal variation in bumble bee (Bombus) critical thermal limits

Organism critical thermal limits are often tightly linked to current geographic distribution and can therefore help predict future range shifts driven by changing environmental temperatures. Thermal tolerance of diverse organisms often varies predictably with latitude, with upper thermal limits changing little and lower thermal limits decreasing with latitude. Despite similarly steep gradients in environmental temperatures across altitude, few studies have investigated altitudinal variation in critical thermal limits. We estimated critical thermal minimum (CTmin), critical thermal maximum (CTmax) and recovery temperature (Trec) by tracking righting response of three bumble bee species during thermal ramps: Bombus huntii collected from 2180m asl, and Bombus bifarius and Bombus sylvicola collected from 3290m asl in Wyoming, USA. Overall, larger bees could tolerate more extreme temperatures, likely due to a thermal inertia driven lag between core body temperatures and air temperatures. Despite their smaller size, high altitude bumble bees tolerated colder air temperatures: they had ~1°C lower CTmin and recovered from cold exposure at ~3–4°C lower air temperatures. Conversely, low altitude bees tolerated ~5°C hotter air temperatures. These altitudinal differences in thermal tolerance parallel differences in average daily minimum (1.2°C) and maximum (7.5°C) temperatures between these sites. These results provide one of the few measurements of organism thermal tolerance across altitude and the first evidence for geographical differences in tolerance of temperature extremes in heterothermic bumble bees.

Predicting factors for malaria re-introduction: an applied model in an elimination setting to prevent malaria outbreaks.

BackgroundMalaria re-introduction is a challenge in elimination settings. To prevent re-introduction, receptivity, vulnerability, and health system capacity of foci should be monitored using appropriate tools. This study aimed to design an applicable model to monitor predicting factors of re-introduction of malaria in highly prone areas.MethodsThis exploratory, descriptive study was conducted in a pre-elimination setting with a high-risk of malaria transmission re-introduction. By using nominal group technique and literature review, a list of predicting indicators for malaria re-introduction and outbreak was defined. Accordingly, a checklist was developed and completed in the field for foci affected by re-introduction and for cleared-up foci as a control group, for a period of 12 weeks before re-introduction and for the same period in the previous year. Using field data and analytic hierarchical process (AHP), each variable and its sub-categories were weighted, and by calculating geometric means for each sub-category, score of corresponding cells of interaction matrices, lower and upper threshold of different risks strata, including low and mild risk of re-introduction and moderate and high risk of malaria outbreaks, were determined. The developed predictive model was calibrated through resampling with different sets of explanatory variables using R software. Sensitivity and specificity of the model were calculated based on new samples.ResultsTwenty explanatory predictive variables of malaria re-introduction were identified and a predictive model was developed. Unpermitted immigrants from endemic neighbouring countries were determined as a pivotal factor (AHP score: 0.181). Moreover, quality of population movement (0.114), following malaria transmission season (0.088), average daily minimum temperature in the previous 8 weeks (0.062), an outdoor resting shelter for vectors (0.045), and rainfall (0.042) were determined. Positive and negative predictive values of the model were 81.8 and 100 %, respectively.ConclusionsThis study introduced a new, simple, yet reliable model to forecast malaria re-introduction and outbreaks eight weeks in advance in pre-elimination and elimination settings. The model incorporates comprehensive deterministic factors that can easily be measured in the field, thereby facilitating preventive measures.Electronic supplementary materialThe online version of this article (doi:10.1186/s12936-016-1192-y) contains supplementary material, which is available to authorized users.

Gross mismatch between thermal tolerances and environmental temperatures in a tropical freshwater snail: Climate warming and evolutionary implications

The relationship between acute thermal tolerance and habitat temperature in ectotherm animals informs about their thermal adaptation and is used to assess thermal safety margins and sensitivity to climate warming. We studied this relationship in an equatorial freshwater snail (Clea nigricans), belonging to a predominantly marine gastropod lineage (Neogastropoda, Buccinidae). We found that tolerance of heating and cooling exceeded average daily maximum and minimum temperatures, by roughly 20°C in each case. Because habitat temperature is generally assumed to be the main selective factor acting on the fundamental thermal niche, the discordance between thermal tolerance and environmental temperature implies trait conservation following ‘in situ’ environmental change, or following novel colonisation of a thermally less-variable habitat. Whereas heat tolerance could relate to an historical association with the thermally variable and extreme marine intertidal fringe zone, cold tolerance could associate with either an ancestral life at higher latitudes, or represent adaptation to cooler, higher-altitudinal, tropical lotic systems. The broad upper thermal safety margin (difference between heat tolerance and maximum environmental temperature) observed in this snail is grossly incompatible with the very narrow safety margins typically found in most terrestrial tropical ectotherms (insects and lizards), and hence with the emerging prediction that tropical ectotherms, are especially vulnerable to environmental warming. A more comprehensive understanding of climatic vulnerability of animal ectotherms thus requires greater consideration of taxonomic diversity, ecological transition and evolutionary history.