Fig 1.
Scheme of model implementation for estimating temperature-dependent S. litura population growth.
Table 1.
Mean development times (days ± SE) of immature stages and senescence times (days ± SE) of adult life stages of S. litura at different constant temperatures in laboratory.
Table 2.
Distribution of the cumulative development/ senescence time frequencies for different life stages of S. litura at various constant temperatures in laboratory (Fitted function: logit model for all stages).
Fig 2.
Temperature-dependent developmental rates (1/ day) for immature stages of S. litura.
Egg (a), Larva (b), and Pupa (c). Fitted curves: Modified Sharpe and DeMichele model for all immature stages. The bold solid line is the selected model output and dashed lines above and below represents the upper and lower 95% confidence bands. Bars represent standard deviation of the mean.
Table 3.
Estimated parameters of the four parameter Sharpe and DeMichele model fitted to the temperature-dependent development rate of immature life stages of S. litura.
Fig 3.
Temperature-dependent senescence rates (1/ day) for adults of S. litura.
Female(a) and Male (b). Fitted curves: Modified Sharpe and DeMichele model for both sexes. The upper and lower 95% confidence intervals of the model are indicated. Bars represent standard deviation of the mean.
Fig 4.
Temperature-dependent mortality rates of immature life stages S. litura.
Egg (a), Larva (b) and Pupa (c). Fitted curves: Wang model for all immature stages. The upper and lower 95% confidence intervals of the model are indicated. Markers are observed means, bars represent standard deviation.
Table 4.
Estimated parameters of the Wang model fitted to the temperature-dependent mortality rate for immature life stages of S. litura.
Fig 5.
Temperature-dependent reproduction of S. litura.
Total egg production curve, fitted function: exponential polynomial model (a); and Age-related oviposition rate, fitted curve: Gamma distribution function (b). The upper and lower 95% confidence intervals of the model are indicated. The dots are observed data points.
Fig 6.
Life table parameters of S. litura estimated at six constant temperatures.
Intrinsic rate of natural increase (a), Net reproduction rate (b), Gross reproductive rate (c), Mean generation time (d), Finite rate of increase (e), and Doubling time (f).
Table 5.
Comparisons between the developmental effects of diurnal temperature fluctuations predicted from models based on thermal reaction norms designed for constant temperatures with those observed in fluctuating temperatures.
Fig 7.
Observed and simulated life stage frequencies of S. litura. Dots represent observed data points at fluctuating experiments, and the lines represent developmental frequencies simulated at fluctuating temperatures based on thermal reaction norms for constant temperatures.
Fig 8.
Change in establishment and future distribution of S. litura in soybean growing areas of India based on establishment risk index (ERI).
Current climatic conditions (a), Future climatic conditions (b), and Absolute change in ERI (c). Geographical regions having ERI values > 0.6 are associated with the risk of permanent establishment.
Fig 9.
Change in number of generations per year of S. litura in soybean growing areas of India based on generation index (GI).
Current climatic conditions (a), Future climatic conditions (b), and Absolute change in GI (c). Economic damage is most likely to occur in the regions with generation index values > 7.0.
Fig 10.
Change in abundance and damage potential of S. litura in soybean growing areas of India based on activity index (AI).
Current climatic conditions (a), Future climatic conditions (b), and Absolute change in AI (c). An index value of 1 represents 10-fold potential population increase within a year. e.g.: An AI value of 4.0 represents 104 i.e. 10,000 times potential population increase within a year.