Microclimate

For the expression profiling experiments, two groups of mosquitoes from the same rearing culture were used per replicate: the test group fed on blood donated by a gametocyte carrier, whereas the control group fed on the same blood which was previously incubated at 42–43°C for 12 min under constant shaking at 500 rpm for gametocyte inactivation. For the gene silencing experiments, we also used two groups of freshly emerged female mosquitoes per gene and per replicate, also separated randomly from the same rearing culture in small containers. The first group was subjected to silencing of the examined gene whereas the second control group was injected with dsRNA of the LacZ gene. In both cases, the two groups were housed under the same microclimate and treated identically, both before and after the blood feeding.
Mosquitoes were allowed to feed via a membrane on blood donated by P. falciparum gametocyte carries. To eliminate transmission blocking immunity factors, the carrier serum was replaced by non-immune AB serum [51] (link). Blood samples (700 µl each) were transferred into pre-warmed (37°C) artificial membrane feeders and exposed to mosquitoes that were previously starved for 12 hours, according to standard procedures [21] (link). To determine the levels of infection, mosquito midguts were dissected 8–10 days post blood feeding and stained with 2% mercurochrome before microscopic examination.

Predictor variables included 19 bioclimatic variables, elevation, slope, and average and standard deviation of normalized difference vegetation indices (NDVI) (see Table 1 for full list of variables). Bioclimatic variables can indirectly affect habitat suitability for ticks, elevation impacts microclimate, host presence, and vegetation [50 (link)], whilst slope provides a proxy for the velocity of subsurface water flow and runoff rate, and thus can represent soil moisture content [51 (link),52 ]. Suitability of tick habitat is also affected by the presence and type of vegetation (e.g., [53 (link),54 (link)]), and vegetation greenness/biomass can be described using NDVI. Two different datasets were used to derive bioclimatic “Bioclim” variables: To train and test the models, we used WorldClim 2.0 at 30 arc seconds resolution [55 (link)], whereas the 4 km resolution parameter-elevation regressions on independent slopes model (PRISM) [56 ] dataset was used to predict potential habitat suitability in California. Both WorldClim and PRISM are a set of climate layers, such as precipitation and temperature, that are interpolated from weather station data [55 (link)], which may impact tick survival (e.g., [57 (link)]). The use of two datasets was necessary as WorldClim data only span the 1950–2000 period. Although WorldClim data geographically and temporally matched species observations, they would not provide recent climatic information. For prediction, monthly average minimum and maximum temperature and cumulative precipitation were downloaded from the PRISM website for 2014–2018. Long term monthly averages were then derived for each variable and used to calculate 2014–2018 Bioclim variables with the r.bioclim module in GRASS GIS 7.6 [58 (link)].
Elevation data were downloaded from the processed Consultative Group for International Agricultural Research-Shuttle Radar Topographic Mission dataset (i.e., data gaps filled by interpolation) available at 90 m resolution. Slope was computed from elevation data using the r.slope.aspect module in GRASS GIS version 7.4 [58 (link),59 ]. No noise (smoothed) NDVI 4 km, 7-day composite data were downloaded from the National Oceanic and Atmospheric Administration STAR dataset for the years 1985–2000 (training and testing) and for 2014–2018 (prediction) (https://www.star.nesdis.noaa.gov/smcd/emb/vci/VH/VH-Syst_10ap30.php). NDVI is the difference between near-infrared light emissions which are reflected by vegetation and visible red light which are absorbed by vegetation. These data were imported into GRASS GIS version 7.64, and the mean and standard deviation were calculated. All training and prediction environmental variables were re-sampled using bilinear spatial interpolation to standardize resolution to 30 arc seconds, imported into R using the raster package (version 2.6-7 [60 ]), and cropped to the extent and shape of the countries from which Amblyomma tick species presence data were obtained.
Predictor variable values were extracted for each presence and background geolocation (30 arc seconds pixel). Many presence and background geolocations near the coast were not well covered by the extent of the environmental predictor raster files; therefore, when a raster did not overlap a geolocation and environmental data could not be extracted a distance matrix was applied to sample the nearest non-empty pixel within a 10 km radius. For data extracted for presence geolocations, collinearity between each pair of predictor variables was assessed using Pearson’s correlation coefficients. When the absolute value of the correlation coefficient was ≥0.80, we retained just one of the two variables in the model. This selection was informed by: the variable with the highest percent contribution to the model (that is, in each iteration of the training algorithm, the increase in regularized gain was added to the contribution of the corresponding variable, or subtracted from it if the change to the absolute value of lambda was negative), and the ecological relevance to the tick species of the variable. Environmental predictor variables were refined until the model consisted of only non-correlated, biologically relevant variables and MaxEnt auto-feature classes were used for all species.

The experimental material comprised two parental maize inbred lines viz. LM 11, heat stress susceptible (HS), and CML 25, heat stress-tolerant (HT). The seedlings of two inbreds were raised during the second week of March in glasshouse conditions at 28°C/23°C and 16 h light (Day)/8 h dark (Night) photoperiod at the School of Agricultural Biotechnology, Punjab Agricultural University, India, during spring 2016. The plants were grown till they reached the reproductive stage (Figure 1). The reproductive phase of maize is categorized into six stages, with the emergence of tasselling and silking; followed by a blister where kernels with clear liquid get secreted and filled with milky fluid; accompanied by doughy consistency and extended kernels and milk line progression towards the kernel tip and finally, a black layer formed at the base of grains. At the reproductive growth stage, from tassel emergence to early grain-filling (lag-phase), maize plants were exposed to natural heat stress and experienced 42°C during the daytime and 35°C during the nighttime. Drought stress is confounded naturally during heat stress. To maintain the microclimate conditions with low RH (<40%) and to avoid the compound effect of drought and heat stress, regular irrigation was applied for at least two weeks during tassel emergence until one week after pollination, which increases the probability of irreversible damage due to heat stress. Both inbreds showed differential responses to heat stress for phenological attributes like top leaf firing, tassel blast, pollen viability and shedding duration, kernel number and weight, and yield (Jagtap, 2020 ). Three different tissue samples, viz. flag leaf, pollen, and ovule from the inbreds, LM 11 and CML 25, were collected in 5 replicates after five days of pollination. Tissue purity was maintained by bagging the tassel and cob. Ovules were isolated from ear florets with a silk length of ~10 cm by removing the silk and ovary wall with forceps and cutting the ovule at its base from the floret under the microscope. Each tissue was pooled (pool of five plants) for each inbred to reduce biological sampling error. A total of six samples (3 tissues x 2 inbreds) were immediately frozen in liquid nitrogen and stored at -80°C until processed for RNA isolations.

The statistical analyses and data visualization were performed using SigmaPlot (ver. 14.0; Systat Software Inc., Chicago, USA) and R statistical software (ver. 4.2; R Core Team, Vienna, Austria). The level of significance in all tests was set at p < 0.05. The maximal daily averages were calculated as averages from the maximal daily Q of all sampled trees for each year and treatment. The data were checked for temporal autocorrelation (identified in Q and TWD) which were considered in the following analyses. Temporal autocorrelation is the way that one variable relates too much to itself in the events occurring in the small subsequent time step. For daily Q and TWD, we assessed trend and seasonal analysis by the Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test and the Ljung-box test, respectively. Autoregressive integrated moving averages (ARIMA, Ripley, 2002 ) were fit to the daily Q and TWD time series using an iterative Box-Jenkins approach where autocorrelation and partial autocorrelation were interrogated and accepted only when detrended models without autocorrelation in residuals were confirmed. ARIMA is the model that explains a given variable based on its own previous values (lags). Autocorrelation and partial autocorrelation were checked visually by acf and pacf functions, accordingly (library stats, Venables and Ripley, 2002), (Supplementary Figure 3), and confirmed by the Breusch-Godfrey test (library lmtest, Johnston, 1984 ). These modeled data were used for the comparison of the daily datasets.
Moreover, the data were tested for normal distribution by Jarque-Bera test and explored by quantile-quantile graphs. The modeled Q values which did not meet normality prerequisites were transformed by logarithm. Differences in annual GRO and daily modeled values of Q and TWD between years and treatments were analyzed by two-way ANOVA and followed by a post-hoc Tukey test for statistical significance among the interactions. Comparison of daily TWD was performed by Analysis of Variance of Aligned Rank Transformed Data (ANOVA of ART, Wobbrock et al., 2011 (link)) also followed by the post-hoc Tukey test. The Student’s t-test was used to test TRW among the treatments in the years 1994-2014 with Hommel’s correction for p-values.
We used linear regression analysis to explore the impact of microclimatic conditions (VPD and GR) and water-soil conditions (SWP) on Q and TWD. The regression was completed with an iterative Cochrane-Orcutt transformation to overcome autocorrelation in the model’s residuals. The residuals were checked for autocorrelation by the Box-Jenkins test and Durbin-Watson Test, besides the visual graphs (Supplementary Figures 4-6). The transformed data were used for the modelling of the linear relationship. Mann-Whitney U Test tested differences in SWP between years and treatments and differences in VPD between the years.