Physiological Ecology Environmental Entomology, 47(2), 2018, 467–476 doi: 10.1093/ee/nvy020 Advance Access Publication Date: 7 March 2018 Research Thermal Requirements and Development Response to Constant Temperatures by Nesidiocoris tenuis (Hemiptera: Miridae), and Implications for Biological Control Mohammad Ali Mirhosseini,1 Yaghoub Fathipour,1,4 Mahmoud Soufbaf,2 and Gadi V. P. Reddy3 Department of Entomology, Faculty of Agriculture, Tarbiat Modares University, P.O. Box 14115–336, Tehran, Iran, 2Department of Entomology, Agricultural, Medical and Industrial Research School, Karaj, Iran, 3Department of Research Centers, Western Triangle Agricultural Research Center, Montana State University, 9546 Old Shelby Road, P. O. Box 656, Conrad, MT 59425, and 4Corresponding author, e-mail: fathi@modares.ac.ir Subject Editor: Colin Brent Received 23 October 2017; Editorial decision 1 February 2018 Abstract Nesidiocoris tenuis (Reuter) (Hemiptera: Miridae), a common zoophytophagus bug, is a biological control agent for several groups of noxious agricultural pests, including whiteflies, aphids, and leafminers. To improve massrearing of this species and to optimize its application in integrated pest management, nymphal survival and developmental times of this predator were examined at seven constant temperatures ranging from 14 through 34°C. Eggs developed to adulthood at all temperatures tested. Egg-adult developmental time decreased sharply with increasing temperature, except at 34°C (17.21 d), for which developmental time was significantly longer than that obtained at 31°C (15.59 d). The lowest (11.36%) and highest (28.26%) percentage of mortality was found at 28 and 14°C, respectively. To describe the development rate of immature stages of N. tenuis as a function of temperature, two linear and 26 nonlinear models were fitted. The lower temperature threshold (T0) and thermal constant (K) of total immature stages were estimated by the ordinary (10.94°C and 318.37 DD) and Ikemoto (10.28°C and 339.57 DD) linear models. Based on the Akaike information criterion (AIC), Lactin-1, Analytis-1/Allahyari and Janisch/Kontodimas were the best models to describe the temperature-dependent development rate of egg, nymph and whole immature stages of the predator, respectively. Our findings provide information on N. tenuis biology that will improve application of this predator as a biological control agent. Key words: biological control, linear and nonlinear modeling, Miridae, predatory bug, zoophytophagus mirid Several zoophytophagous mirid bugs are part of the predatory communities in vegetable crops (especially the Solanaceae family) in the Mediterranean and other regions with a similar climate when the use of pesticides is reduced (Arno et al. 2006, Castane et al. 2011). These bugs are becoming important biological control agents for pests such as whiteflies, aphids, and the tomato leaf miner, Tuta absoluta (Meyrick) (Lepidoptera: Gelechiidae) (Calvo et al. 2012, Perez-Hedo and Urbaneja 2015). Their ability to survive and reproduce on different non-pest food sources such as plant sap and alternative foods (e.g., eggs of Ephestia kuehniella Zeller) makes them more resilient, which can be an important factor in the conservation of these generalist predators in situations where their main prey (target pest) is lacking or scarce. Among these predators, Nesidiocoris tenuis (Reuter) (Hemiptera: Miridae) is one of the common omnivorous mirid bugs that can be naturally found on both open-field and greenhouse tomato plants when no pesticides are applied (Zappala et al. 2013). Although host plants may be damaged due to oviposition of females inside the stem and also its phytophagous behavior (Arno et al. 2010), this bug behaves mainly as a predator when prey is abundant (Sanchez 2008). In addition, it has been found that oviposition and phytophagous behavior of N. tenuis on tomato plants activates some defensive pathways which make tomato plants less attractive to whiteflies and more attractive to their parasitoids (PerezHedo et al. 2015). Augmentative releases of N. tenuis are widely used in biological control programs in tomato crops in Europe to control mites, whiteflies, thrips, and lepidopteran pests (Molla et al. 2011, Perez-Hedo and Urbaneja 2015). Recently, use of N. tenuis against T. absoluta is increasing because of its high efficiency to control this pest, its ability to use volatiles from T. absoluta-infested tomato plants to find its prey and its capability to significantly reduce populations of this pest in a predator-in-first approach, in which releases of additional agents (e.g., egg parasitoids) are not required (Calvo et al. 2012, Lins et al. 2014). Nevertheless, to improve mass-rearing outcomes and optimize © The Author(s) 2018. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com. 467 Downloaded from https://academic.oup.com/ee/article/47/2/467/4924357 by guest on 17 December 2022 1 468 Environmental Entomology, 2018, Vol. 47, No. 2 Table 1. Linear and nonlinear models for fitting to development rate of Nesidiocoris tenuis as a function of temperature Model Equation Reference Ordinary linear model R (T ) = a + bT (Campbell et al. 1974) Ikemoto linear model DT = K + T0 D (Ikemoto and Takai 2000) Pradhan-Taylor  -1 T - Tm 2  R(T ) = Rm × exp  ( )   2 Tσ  Davidsons logistic R(T ) = Logan-6 T −T ρT −τ R(T ) = ψ e ρT − e ( U )  , τ = U ∆T Hilbert and Logan (Pradhan 1945, Taylor 1981) K 1 + e(a − bT ) (Davidson 1942, 1944) ( (Hilbert and Logan 1983) ) TU − T    ρTU −  ∆  (Lactin et al. 1995) Lactin-1 R(T ) = e ρT − e  Lactin-2 R(T ) = e ρT − e  Logan-10 1   R (T ) = a  − e −τ  , − ρT   1 + Ke Analytis-1 R(T ) = Pδ n (1 − δ )m , δ = Analytis-2 R(T ) = [Pδ n (1 − δ )]m , δ = Analytis-1/Allahyari R(T ) = Pδ n (1 − δ m ), δ = Analytis-3 R(T ) = a(T − T0 )n (TU − T )m TU − T    ρTU −  ∆  (Lactin et al. 1995) +λ τ= TU − T ∆T T − T0 TU − T0 T − T0 TU − T0 T − T0 TU − T0 (Logan et al. 1976) (Analytis 1977, 1980) (Analytis 1977, 1980) (Allahyari 2005, Zahiri et al. 2010) (Analytis 1977, 1980) 1 2 Briere-1 R(T ) = aT (T − T0 )(TU − T ) Briere-2 R(T ) = aT (T − T0 )(TU − T )n (Briere et al. 1999) Analytis-3/Kontodimas R(T ) = a(T − T0 )2 (TU − T ) (Kontodimas et al. 2004) Janisch/Kontodimas R(T ) = Janisch/Rochat R(T ) = (Briere et al. 1999) 1 2 Dmin (e K (T − Topt ) +e − λ (T − Topt ) ) 2c a(T −TU ) + b(TU −T ) ) (Janisch 1932, Kontodimas et al. 2004) (Rochat and Gutierrez 2001) ≠ Sharpe and DeMichele Sh and DeMichele/Schoolfield Sh and DeMichele/Kontodimas Polynomial (cubic) SSI model Performance-1 R(T ) = 1+ e Te(φ − ∆HA / T )/ R + e( ∆SH − ∆HL / T )/ R ( ∆SL − ∆HL / T )/ R  ∆H A ≠  1 1 T −  exp   298 R  298 T    R(T ) =  ∆H H  1  ∆H L  1 1 1 1 + exp  −   + exp  −   T  T R T R T   L H / 1 2 / 1 2    ρ(25°C ) R(T ) = T exp(a − b / T ) 1 + exp(c − d / T ) + exp(f − g / T ) R(T ) = a0T 3 + a1T 2 + a2T + a3  ∆H A  1 T 1 exp  ρΦ  T − T   TΦ R Φ   R(T ) =  ∆H H  1  ∆H L  1 1   1 1 + exp   T − T   + exp  R  T − T   R H L     R(T ) = c(1 − e − K1 (T −T0 ) )(1 − e K2 (T −TU ) ) (Sharpe and DeMichele 1977) (Schoolfield et al. 1981) (Kontodimas et al. 2004) (Harcourt and Yee 1982) (Ikemoto 2005, 2008) (Shi et al. 2011) Downloaded from https://academic.oup.com/ee/article/47/2/467/4924357 by guest on 17 December 2022  T − (T − T0 )  − U  (T − T0 )2   ∆T    R(T ) = ψ  e − 2  (T − T0 ) + D  (Logan et al. 1976) 469 Environmental Entomology, 2018, Vol. 47, No. 2 Table 1. Continued Model Equation Performance-2 R(T ) = m(T − T0 )(1 − e Wang R(T ) = Ratkowsky Beta K2 (T − TU ) Reference ) (Shi et al. 2011) m[1 − exp(K1 (T − T0 ))][1 − exp(K2 (T − TU ))] 1 + exp(−c(T − T0 )) R(T ) = c(T − T0 )(1 − e K (T −TU ) )  T − T   T − T0  R(T ) = rm  U    TU − Topt   Topt − T0  (Wang et al. 1982) (Ratkowsky et al. 1983) Topt − T0 TU − Topt (Yin et al. 1995) the application of N. tenuis in integrated pest management (IPM) programs, studies of its developmental response to temperature and its thermal thresholds are needed. Temperature has an important effect on the biology, survivorship, demographic parameters, prey consumption, and foraging behavior of arthropods (Zamani et al. 2007, Aghdam et al. 2009a, Pakyari et al. 2009, Ganjisaffar et al. 2011). Thus, thermal thresholds and optimum temperature affect all major life processes of these poikilothermic animals due to the limitations imposed by temperature on their biological performance (Roy et al. 2002). Knowledge of insect adaptations to climatic conditions plays a major role in IPM, as it allows for forecasting the occurrence of a pest or natural enemies (Nechols 1999). Mathematical models are powerful tools for describing and predicting the influence of temperature on insect development and population growth. Currently, these models are commonly used to estimate the development of pests and natural enemies (Pakyari et al. 2011; Shi et al. 2016, 2017). For this purpose, a number of linear and nonlinear models have been used to describe the relationship between insect development rate and temperature (Worner 2008). Although a thermal constant (K) or degree-day approach to predict insect development can be achieved only by using linear models, nonlinear models are necessary to estimate the optimum temperature and upper thermal threshold. While the ability and complexity of these models differ, the use of more complex models does not ensure more accuracy and using some statistical parameters to evaluate the performance of mathematical models is required (Mirhosseini et al. 2017). Therefore, given the crucial influence of temperature on the development and performance of natural enemies, the current study was carried out to test the thermal requirement and developmental response of N. tenuis to constant temperatures, as a prerequisite for mass-rearing and use of this omnivorous bug in biological control programs. Although some studies have previously been conducted to determine the effect of temperature on biological parameters of N. tenuis (Sanchez et al. 2009, Martinez-Garcia et al. 2016), this is the first study in which two linear and many nonlinear models have been used to model the effect of temperature on the development of this important biological control agent. The results will contribute to improve application of N. tenuis in future IPM programs. Materials and Methods Insect Colony Adults of N. tenuis were originally collected from tomato fields in the Varamin region of Iran (GPS coordinates 35°19′27″ N 51°38′45″ E, Tehran province) in August 2015. Subsequently, N. tenuis was reared on tobacco plants (Nicotiana tobacum L. cv. White Burley) in gauze-covered wooden-framed cages (1 × 1 × 1 m, 1 mm2 mesh size) under greenhouse conditions (27 ± 5°C, 55 ± 10% RH and natural photoperiod). Eggs of E. kuehniella and cotton soaked in 20% honey/water were placed on tobacco leaves to feed the insect. Eggs of E. kuehniella were obtained from a colony maintained in the insectary of Tarbiat Modares University (25 ± 1°C, 60 ± 5% RH and a 16:8 (L:D) h photoperiod) and were stored in refrigerator (4°C) for less than 1 mo before use. Effect of Temperature on Development Time Developmental time of N. tenuis was determined at seven constant temperatures: 14, 18, 22, 25, 28, 31, and 34 ± 1°C (60 ± 5% RH and a 16:8 (L:D) h photoperiod). For each temperature cohort, at least 20 pairs of less than 10-d-old predators provided with ad libitum eggs of E. kuehniella were kept in a ventilated plastic box (20 × 30 × 10 cm) containing five young shoots of tomato (≈20 cm) for oviposition. The lower portion of tomato shoots were placed in cotton soaked in water to provide moisture. After 24 h, adult predators were removed and plastic boxes were placed in growth chamber (BINDER, KBWF 240, Tuttlingen, Germany) and held at the respective temperatures. Upon hatching, first instar nymphs were individually transferred to 6-cm-diameter Petri dishes and provided with adequate E. kuehniella eggs each day until they developed to adults. At all temperatures, egg mortality was not recorded because eggs inside stem tissue could not be easily observed. Data Analysis and Thermal Modeling Data on developmental times were checked for normality using the Kolmogorov-Smirnov test and were found to be normally distributed. Data were subjected to one-way ANOVA followed by Tukey’s test (α = 0.05) to separate mean values using IBM SPSS software (SPSS 2011). The Student’s t-test was also run and were found that there were no significant difference between sexes within the same temperature. To find a relationship between temperature and nymphal mortality or sex ratio, following parabolic model (equation 1) was used. The logit transformation was applied on sex ratio and mortality data to normalize the residuals before running the equation 1. r = a + bT + cT 2 n (1) Where n, r and T are the number of observations, the number of females or dead individuals, and temperature, respectively; a, b and c are constants. Some of the common linear and nonlinear models (Table 1) were evaluated to describe the development rate (the Downloaded from https://academic.oup.com/ee/article/47/2/467/4924357 by guest on 17 December 2022 T is temperature (Celsius) in all models except Sharpe and DeMichele, Sh and DeMi/Schoolfield, Sh and DeMichele/Kontodimas and SSI models which is absolute temperature or Kelvin. T0, Topt and TU mean the lower temperature threshold (no measurable development is detected), optimum temperature (development rate is highest) and upper temperature threshold (development is zero or life cannot be maintained for long), respectively. 470 Table 2. Developmental time (mean ± SE) of different stages, nymphal mortality, and sex ratios (proportion of females) of Nesidiocoris tenuis at seven constant temperatures Temperature (°C) Egg (day) No. 14 18 22 25 28 31 34 F df P Female Male Nymph (day) Overall No. Male Overall No. Female Male Overall Nymphal mortality (%) 51.93 ± 0.25 a 33.75 ± 0.32 b 16.88 ± 0.27 c 14.87 ± 0.26 d 10.89 ± 0.32 e 8.93 ± 0.25 f 11.33 ± 0.22 e 3053.08 6, 98 <0.001 52.09 ± 0.14 a 33.75 ± 0.23 b 16.81 ± 0.16 c 14.97 ± 0.19 d 10.59 ± 0.21 e 8.86 ± 0.16 f 11.28 ± 0.15 e 7601.83 6, 222 <0.001 33 28 37 34 39 29 29 - 79.84 ± 0.27 a 52.83 ± 0.42 b 28.62 ± 0.26 c 24.79 ± 0.39 d 18.14 ± 0.32 e 15.20 ± 0.34 f 17.18 ± 0.32 e 5332.27 6, 117 <0.001 79.93 ± 0.34 a 52.06 ± 0.42 b 29.06 ± 0.39 c 24.47 ± 0.36 d 18.39 ± 0.41 e 16.00 ± 0.28 f 17.25 ± 0.33 ef 3848.53 6, 98 <0.001 79.88 ± 0.21 a 52.39 ± 0.31 b 28.81 ± 0.23 c 24.65 ± 0.27 d 18.26 ± 0.25 e 15.59 ± 0.23 g 17.21 ± 0.23 f 9192.33 6, 222 <0.001 28.26 26.32 27.45 17.07 11.36 21.62 25.64 - Female 46 27.63 ± 0.19 a 28.00 ± 0.23 a 27.85 ± 0.12 a 33 52.21 ± 0.16 a 38 19.08 ± 0.29 b 18.31 ± 0.33 b 18.66 ± 0.19 b 28 33.75 ± 0.33 b 51 11.86 ± 0.17 c 12.19 ± 0.16 c 11.98 ± 0.10 c 37 16.76 ± 0.21 c 41 9.74 ± 0.20 d 9.60 ± 0.19 d 9.63 ± 0.12 d 34 15.05 ± 0.27 d 44 7.81 ± 0.18 e 7.50 ± 0.19 e 7.80 ± 0.13 e 39 10.33 ± 0.28 e 37 6.40 ± 0.27 f 7.07 ± 0.27 e 6.57 ± 0.18 f 29 8.80 ± 0.22 f 39 5.94 ± 0.20 f 5.92 ± 0.26 f 6.05 ± 0.14 f 29 11.24 ± 0.20 e 1478.36 1080.34 3319.21 4497.51 6, 117 6, 98 6, 289 6, 117 <0.001 < 0.001 <0.001 <0.001 Total (day) 0.58 0.43 0.57 0.56 0.54 0.52 0.59 - Environmental Entomology, 2018, Vol. 47, No. 2 Means followed by the same letter were not significantly different within columns (Tukey, α = 0.05). Sex ratio (females/adults) Downloaded from https://academic.oup.com/ee/article/47/2/467/4924357 by guest on 17 December 2022 471 Environmental Entomology, 2018, Vol. 47, No. 2 reciprocal of developmental time) of N. tenuis as a function of temperature, using ArthroThermoModel (ATM) software (Mirhosseini et al. 2017). To improve estimation of T0 by means of linear models, data obtained at 34°C were excluded from the data analysis since development rate shows a declining trend when the temperature approaches the upper lethal threshold (Ikemoto and Takai 2000). The methods to carry out the linear fitting in ordinary and Ikemoto linear models are a bit different as the former uses ordinary least squares (OLS) and the latter reduced major axis (RMA) approaches, respectively (Campbell et al. 1974, Ikemoto and Takai 2000). However, in the ordinary linear model, relating developmental rate (R(T)) to temperature (T), given as: R(T ) = a + bT (2) The lower temperature threshold (T0) and the thermal constant (K) −a 1 can be found as T0 = and K = . b b Three criteria including Sum of Squared Error (SSE), adjusted coefficient of determination (R2adj) and Akaike Information Criterion (AIC) are presented in Table 4 to evaluate the nonlinear models. All nonlinear models in each stage were ranked using AIC, as the best statistical criterion (Akaike 1974), and the model with the smallest value of AIC was considered to be the best model for describing the temperature-dependent development of N. tenuis. The ATM software calculates these criteria and parameters for all models. According to Burnham et al. (2011), if AICmin denotes the AIC of the best model, then the difference between Stage Egg Nymph Total Method Linear regression Ordinary Ikemoto Ordinary Ikemoto Ordinary Ikemoto Equation R2adj P R= −0.069 + 0.007T DT= 147.85 + 9.26D R= −0.066 + 0.005T DT= 190.67 + 10.95D R= −0.034 + 0.003T DT= 339.57 + 10.28D 0.91 0.94 0.93 0.97 0.95 0.97 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 T0 (°C) K (DD) 9.78 9.26 11.68 10.95 10.94 10.28 140.68 147.85 175.84 190.67 318.37 339.57 T0, K and D are lower temperature threshold (no measurable development is detected), thermal constant (total effective temperature) and development time (day), respectively. Table 4. Goodness of fit of 26 nonlinear models fitted to development rate of different immature stages of Nesidiocoris tenuis Model No. of parameters Egg SSE Pradhan-Taylor Davidsons logistic Logan-6 Hilbert and Logan Lactin-1 Lactin-2 Logan-10 Analytis-1 Analytis-2 Analytis-1/Allahyari Analytis-3 Briere-1 Briere-2 Analytis-3/Kontodimas Janisch/Kontodimas Janisch/Rochat Sharpe and DeMichele Sh and DeMichele/ Schoolfield Sh and DeMichele/ Kontodimas Polynomial (cubic) SSI model Performance-1 Performance-2 Wang Ratkowsky Beta R 2 adj Nymph AIC Rank SSE R 2 adj Total AIC Rank SSE R 2 adj AIC Rank 3 3 4 5 3 4 5 5 5 5 5 3 4 3 4 4 7 7 0.063 0.063 0.063 0.068 0.062 0.065 0.062 0.062 0.063 0.062 0.063 0.066 0.064 0.094 0.062 0.062 0.080 0.071 0.91 0.91 0.91 0.90 0.91 0.90 0.91 0.91 0.91 0.91 0.91 0.90 0.91 0.86 0.91 0.91 0.88 0.89 −2497.7 −2497.41 −2497.17 −2469.24 −2499.68 −2485.16 −2496.46 −2497.02 −2491.34 −2495.6 −2491.88 −2481.30 −2491.11 −2376.86 −2497.87 −2497.87 −2419.89 −2457.27 5 6 7 21 1 17 10 8 14 11 13 18 15 25 4 3 23 22 0.023 0.238 0.014 0.014 0.015 0.016 0.014 0.014 0.023 0.014 0.014 0.024 0.026 0.015 0.015 0.060 0.047 0.90 0.08 0.94 0.94 0.94 0.93 0.94 0.94 0.90 0.94 0.94 0.90 0.89 0.94 0.94 0.74 0.80 −2105.84 −1567.22 −2205.30 −2210.57 −2198.46 −2178.79 −2208.93 −2210.28 −2093.60 −2211.07 −2209.40 −2095.58 −2075.36 −2202.03 −2202.03 −1874.28 −1934.07 17 25 6 2 9 13 5 3 19 1 4 18 20 8 7 23 22 0.004 0.005 0.003 0.003 0.003 0.003 0.003 0.003 0.004 0.003 0.003 0.004 0.003 0.005 0.003 0.003 0.012 0.003 0.95 0.94 0.96 0.96 0.96 0.95 0.96 0.96 0.94 0.96 0.96 0.94 0.96 0.93 0.96 0.96 0.84 0.95 −2501.44 −2463.92 −2566.67 −2559.74 −2565.67 −2532.91 −2566.11 −2568.46 −2487.83 −2565.50 −2568.44 −2480.27 −2567.50 −2455.53 −2561.34 −2561.33 −2246.63 −2534.61 19 22 4 12 6 15 5 1 20 7 2 21 3 23 11 10 24 14 6 0.080 0.88 −2419.75 24 0.061 0.74 −1873.85 24 0.012 0.84 −2246.63 25 4 7 5 4 6 4 4 0.063 0.062 0.066 0.066 0.063 0.106 0.062 0.91 0.91 0.90 0.90 0.91 0.94 0.91 −2496.9 −2493.09 −2476.82 −2479.31 −2490.58 −2340.71 −2498.28 9 12 20 19 16 26 2 0.020 0.016 0.017 0.017 0.015 0.043 0.015 0.92 0.93 0.92 0.93 0.93 0.96 0.94 −2136.27 −2183.14 −2162.15 −2165.00 −2194.70 −1955.82 −2197.74 16 12 15 14 11 21 10 0.003 0.003 0.003 0.003 0.003 0.015 0.003 0.95 0.96 0.95 0.95 0.96 0.97 0.96 −2525.63 −2562.99 −2518.52 −2523.29 −2558.36 −2198.44 −2564.62 16 9 18 17 13 26 8 - Data could not be fitted by the model. Downloaded from https://academic.oup.com/ee/article/47/2/467/4924357 by guest on 17 December 2022 Table 3. Low-temperature threshold (T0) and thermal constant (K) of different immature stages of Nesidiocoris tenuis estimated by two linear models 472 Environmental Entomology, 2018, Vol. 47, No. 2 Table 5. Parameter-values (with 95% confidence bounds) for selected nonlinear models fitted to developmental rates of eggs, nymphs, and the total immature stages of Nesidiocoris tenuis Model Pradhan-Taylor Davidsons logistic Logan-6 Lactin-1 Analytis-1 Analytis-1/Allahyari Janisch/Kontodimas Janisch/Rochat Polynomial (cubic) SSI model Rm Tm (°C) Tб (°C) a b k ∆T ψ ρ TL (°C) ∆ ρ TU (°C) ∆T K ρ TL (°C) a P T0 (°C) TU (°C) m n P T0 (°C) TU (°C) m n Dmin K λ Topt (°C) C TU (°C) a b a0 a1 a2 a3 ∆HA ∆HH ∆HL ρΦ TH (°K) TL (°K) TΦ (°K) Beta Analytis-3 T0 (°C) TU (°C) Topt (°C) rm T0 (°C) TU (°C) a m n Egg Nymph Total 0.1852 (0.1699, 0.2005) 39.71 (36.93, 42.48) 13.98 (12.5, 15.46) 3.735 (3.484, 3.985) 0.1474 (0.13, 0.1648) 0.2184 (0.1971, 0.2397) 4.378 (0.7378, 8.019) 2.702 (1.956, 3.449) 4.208 (2.505, 5.911) 0.0088 (0.0052, 0.0123) 0.00384 (0.002698, 0.004982) 0.002192 (0.001573, 0.002811) 0.106 (0.066, 0.145) 0.1201 (0.1047, 0.1355) 0.1316 (0.09778, 0.1653) 40 (37.03, 42.97) 35.99 (35.6, 36.38) 37.43 (36.8, 38.05) 6.834 (6.433, 7.236) 5.732 (5.564, 5.9) 0.1461 (0.1375, 0.1548) 0.1744 (0.1693, 0.1795) 40.76 (39.63, 41.89) 37.52 (37.24, 37.8) 3.97 (−42.96, 50.9) 1.003 (−0.3914, 2.398) 2.272 (−0.5552, 5.1) 53 (−404.1, 510.1) 77.28 (60.12, 94.45) 76.33 (−36.84, 189.5) 0.1211 (0.04593, 0.1964) 0.1552 (0.1236, 0.1868) 0.1292 (0.08855, 0.1699) 42.95 (−13.59, 99.49) 35.41 (33.48, 37.33) 37.9 (32.41, 43.4) 0.3907 (−3.267, 4.049) 0.1893 (0.1075, 0.2712) 0.1743 (−0.1624, 0.511) 0.5136 (−3.055, 4.082) 0.1925 (0.07043, 0.3145) 0.2744 (−0.419, 0.9679) −28.09 (−230.4, 174.2) −2.937 (−24.05, 18.17) −38.06 (−170.5, 94.41) 37.14 (18.29, 55.99) 34.03 (33.8, 34.26) 35.22 (32.82, 37.62) 0.2787 (−1.58, 2.138) 0.1089 (−0.07983, 0.2976) 0.3457 (−0.2014, 0.8928) 5.422 (−21.78, 32.63) 2.919 (−0.02947, 5.867) 7.806 (−11.75, 27.36) 0.2459 (0.05401, 0.4379) 0.1586 (0.1334, 0.1838) 0.1005 (0.07741, 0.1237) 3 (−8.713, 14.71) 3 (−5.191, 11.19) 3.001 (−5.124, 11.13) 39.46 (27.64, 51.27) 35.32 (34.57, 36.07) 36.85 (35.71, 38) 14.02 (−18.53, 46.56) 22.62 (10.18, 35.06) 13.14 (7.437, 18.84) 1.663 (0.4807, 2.845) 2.021 (1.096, 2.946) 1.935 (0.9931, 2.877) 5.931 (5.236, 6.626) 0.07324 (-0.0354, 0.1819) 0.1112 (0.08197, 0.1405) 33.83 (27.68, 39.97) 0.1686 (0.1485, 0.1886) 0.05854 (0.0556, 0.06149) 33.81 (27.64, 39.98) 34 (33.43, 34.57) 1.076 (0.959, 1.192) 0.8971 (0.8896, 0.9046) 1.118 (1.085, 1.151) 0.7617 (0.7062, 0.8172) −1.582 × 10–5 (−2.415 × 10–5, −7.498 × 10–6) 0.001185 (0.0005852, 0.001785) −0.02127 (−0.03509, −0.007445) 0.145 (0.04384, 0.2462) 1.197 × 104 (−2.735 × 104, 5.129 × 104) 1.494 × 104 (−7024, 3.69 × 104) 7.18 × 104 (−2.081 × 105, 3.517 × 105) 7.999 × 104 (−3.825 × 104, 1.982 × 105) 4 5 5 −3.001 × 10 (−2.31 × 10 , 1.71 × 10 ) −3.049 × 104 (−1.777 × 105, 1.167 × 105) 6 6 0.09 (−4.296 × 10 , 4.296 × 10 ) 0.04225 (−3.62 × 106, 3.62 × 106) 311.2 (308.1, 314.2) 308.1 (305.9, 310.2) 282.3 (243.1, 321.5) 281.9 (262.7, 301.2) 295.1 (−6.578 × 108, 6.578 × 108) 297.2 (−9.684 × 108, 9.684 × 108) 4 7 7 −4.749 × 10 (−2.362 × 10 , 2.353 × 10 ) −1553 (−2.84 × 104, 2.53 × 104) 41.11 (39.46, 42.77) 37.65 (37, 38.31) 34.14 (33.28, 35) 31.83 (31.66, 32) 0.1685 (0.1631, 0.174) 0.06445 (0.06322, 0.06568) 1.433 (−11.95, 14.82) 1.168 34.03 (33.83, 34.22) 34.13 2.888 × 10–5 (−0.0001979, 0.00002625 0.0002557) 0.1001 (−0.06942, 0.2695) 0.1004 2.412 (0.4677, 4.355) 2.266 Downloaded from https://academic.oup.com/ee/article/47/2/467/4924357 by guest on 17 December 2022 Logan-10 Parameter 473 Environmental Entomology, 2018, Vol. 47, No. 2 Table 5. Continued Model Egg Nymph Total T0 (°C) TU (°C) a - - n D ∆T ψ T0 (°C) TU ∆ λ ρ TU (°C) - 57.64 (−2563, 2678) 1.228 (0.3309, 2.125) 0.5275 (−46.94, 47.99) 3.5 (−0.7528, 7.753) 34.01 (28.07, 39.95) - 5 (3.08, 6.92) 34.03 (33.94, 34.12) 7.44 × 10–5 (7.059 × 10–5, 7.82 × 10–5) 14.29 (5.881, 22.7) - Briere-2 Hilbert and Logan Lactin-2 - Data could not be fitted by the model. the best model and the ith model can be expressed by means of ∆ as follows: ∆ = AICi − AICmin (3) Latest researchers also stated that models with ∆ > 7 should be dismissed. Using this argument, the egg models with ranks from 1 to 12 are acceptable. With respect to the nymph models, the models with rank of 1–6 are acceptable and for total development models with rank of 1–10 should not be dismissed (Table 4). The R software (v. 3.4.1; R Core Team) was used to construct the graphs. Results Developmental Rates The predator completed its development from egg to adult at all temperatures examined (Table 2). The duration of the immature stages decreased sharply with increasing temperature except at 34°C, at which the duration of the developmental period increased. The developmental period of egg ranged from 27.63 and 28.00 d at 14°C to 5.94 and 5.92 d at 34°C for females and males, respectively. There was no significant difference between the overall developmental period measured at 31 and 34°C. The nymphal period varied from 52.21 and 51.93 d at 14°C to 8.80 and 8.93 d at 31°C for females and males, respectively. This period increased at 34°C (11.28 d), which was not statistically different from that at 28°C. The shortest (15.59 d) and longest (79.88 d) total immature stages were found at 31 and 14°C, respectively (Table 2). The equation 1 revealed that there is no significant relationship between the temperature and nymphal mortality (P = 0.3513), while the lowest (11.36) and highest (28.26) values of this parameter were obtained at 28 and 14°C, respectively. There was no clear trend in sex ratio (proportion of all progeny that were females) among tested temperatures and parabolic model also showed no significant relationship between them (P = 0.7464), although the smallest (0.43) and largest (0.59) values of this proportion were found at 18 and 34°C, respectively. Thermal Modeling The developmental rate of N. tenuis was positively correlated with all examined temperatures except 34°C. Table 3 shows the lower temperature threshold (T0) and thermal constant (K) values of immature stages of the predator as estimated by two linear models. In both stages, a lower value of T0 and higher value of K were estimated by Ikemoto linear model, which also had a higher R2adj than the other linear model (Table 3). All 26 nonlinear models fitted the developmental rates of different stages of the predator except Bariere-2, in which the developmental rate of the nymphal stage did not fit. Based on the AIC criterion, the Lactin-1, Analytis-1/Allahyari and Analytis-1 models were the best at describing the temperature-dependent developmental rates of the egg, nymphal and total immature stages of N. tenuis, respectively (Table 4). The parameter values of acceptable models for each stage are presented in Table 5. The upper temperature thresholds (Tmax) for egg, nymphal and total immature stages were 40.76, 36.7, and 37.52°C, respectively, as estimated by the Lactin-1 model, which were a bit higher than those estimated by the Analytis-1/Allahyari model (39.46, 35.32, and 36.85°C, respectively). Oddly, the lower temperature threshold for all stages was approximately estimated as 3°C by the Analytis-1/Allahyari model (Table 5). Furthermore, the upper temperature thresholds for egg, nymph and total immature stages of the predator were 37.14, 34.03 and 35.22°C as estimated by the Analytis-1 model, which were a bit lower than those estimated by the Analytis-1/Allahyari. Figure 1 depicts the curves of the influence of temperature on the developmental rate of total immature stages of N. tenuis for the acceptable models. Discussion The effect of temperature on the survival and developmental time of N. tenuis was determined under constant temperatures. Developmental time measured in the current study (Table 2) was slightly different from that estimated by Martinez-Garcia et al. (2016) (76.7, 49.4, 29.3, 23.1, 16.3, 14.0, 12.7 and 14.0 d at 15, 18, 21, 24, 27, 30, 33 and 35°C, respectively) and Sanchez et al. (2009) (86.7, 38.2, 21.8, 17.2, and 14.9 d at 15, 20, 25, 30 and 35°C, respectively). However, Martinez-Garcia et al. (2016) found that sex did not affect the developmental time of the predator, similar to our study in which there was no clear difference between the developmental time of males and females at different temperatures. Percentage of nymphal mortality in both of these studies (58, 18, 10, 14, 18, 23, 30 and 43% in Martinez-Garcia et al. (2016) and 37, 18, 6, 13 and 48% in Sanchez et al. (2009) studies at above mentioned temperatures) were also different from that estimated in the current study. These differences suggest that the thermal characteristics of a species can vary among different populations (Honek 1999). Although there was no clear trend in the proportion of females among different temperature in the current study or that of Sanchez Downloaded from https://academic.oup.com/ee/article/47/2/467/4924357 by guest on 17 December 2022 Parameter 474 Environmental Entomology, 2018, Vol. 47, No. 2 Downloaded from https://academic.oup.com/ee/article/47/2/467/4924357 by guest on 17 December 2022 Fig. 1. Observed total immature stages development rate of Nesidiocoris tenuis (dots) and 10-selected fitted nonlinear models (lines). et al. (2009), the highest proportion of females in both cases were obtained at the highest temperature tested, however, proportion of females was also high (0.58) at 14°C as the lowest temperature of current study. Overall, although developmental time at 28°C (18.26 d) was slightly longer than that obtained at 31°C (15.59 d) (Table 2), it seems that 28°C is a more appropriate temperature for mass rearing of this predator due to lower mortality at 28°C. However, future studies of the life table parameters of N. tenuis at different temperatures, conditions (humidity, photoperiod, etc.) and food sources are needed to make a definitive decision about the best conditions of its mass rearing. Several nonlinear models were used to describe the relationship between the developmental rate of N. tenuis and temperature. Although AIC is the best statistical criterion to validate models due to its adjustability for the number of parameters in a model by giving a penalty to models with many parameters (Akaike 1974) and it was used in many related studies to rank models (Zamani et al. 2007, Aghdam et al. 2009b, Zahiri et al. 2010, Pakyari et al. 2011), Environmental Entomology, 2018, Vol. 47, No. 2 Acknowledgments The financial support of this research by the Department of Entomology, Tarbiat Modares University, is greatly appreciated. References Cited Aghdam, H. R., Y. Fathipour, D. C. Kontodimas, G. Radjabi, and M. Rezapanah. 2009a. Age-specific life table parameters and survivorship of an Iranian population of the codling moth (Lepidoptera: Tortricidae) at different constant temperatures. Ann. Entomol. Soc. Am. 102: 233–240. Aghdam, H. R., Y. Fathipour, G. Radjabi, and M. Rezapanah. 2009b. Temperature-dependent development and temperature thresholds of codling moth (Lepidoptera: Tortricidae) in Iran. Environ. Entomol. 38: 885–895. Akaike, H. 1974. A new look at the statistical model identification. IEEE T. Automat. Contr. 19: 716–723. Allahyari, H. 2005. Decision making with degree-day in control program of Colorado potato beetle. Ph.D. University of Tehran, Tehran, Iran. Analytis, S. 1977. Über die relation zwischen biologischer entwicklung und temperatur bei phytopathogenen Pilzen. J. Phytopathol. 90: 64–76. Analytis, S. 1980. Obtaining of sub-models for modeling the entire life cycle of a pathogen. J. Plant Dis. Protec. 87: 371–382. Arno, J., C. Castane, J. Riudavets, and R. Gabarra. 2006. Characterization of damage to tomato plants produced by the zoophytophagous predator Nesidiocoris tenuis. IOBC wprs Bull. 29: 249–254. Arno, J., C. Castane, J. Riudavets, and R. Gabarra. 2010. Risk of damage to tomato crops by the generalist zoophytophagous predator Nesidiocoris tenuis (Reuter)(Hemiptera: Miridae). Bull. Entomol. Res. 100: 105–115. Briere, J. F., P. Pracros, A. Y. Le Roux, and J. S. Pierre. 1999. A novel rate model of temperature-dependent development for arthropods. Environ. Entomol. 28: 22–29. Burnham, K. P., D. R. Anderson, and K. P. Huyvaert. 2011. AIC model selection and multimodel inference in behavioral ecology: some background, observations, and comparisons. Behav. Ecol. Sociobiol. 65: 23–35. Calvo, F. J., M. J. Lorente, P. A. Stansly, and J. E. Belda. 2012. Preplant release of Nesidiocoris tenuis and supplementary tactics for control of Tuta absoluta and Bemisa tabaci in greenhouse tomato. Entomol. Exp. Appl. 143: 111–119. Campbell, A., B. D. Frazer, N. Gilbert, A. P. Gutierrez, and M. Mackauer. 1974. Temperature requirements of some aphids and their parasites. J. Appl. Ecol. 11: 431–438. Castane, C., J. Arno, R. Gabarra, and O. Alomar. 2011. Plant damage to vegetable crops by zoophytophagous mirid predators. Biol. Control 59: 22–29. Davidson, J. 1942. On the speed of development of insect eggs at constant temperatures. Aust. J. Exp. 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Med. Entomol. 45: 963–969. Ikemoto, T., and K. Takai. 2000. A new linearized formula for the law of total effective temperature and the evaluation of line-fitting methods with both variables subject to error. Environ. Entomol. 29: 671–682. Janisch, E. 1932. The influence of temperature on the life-history of insects. Roy. Entomol. Soc. London 80: 137–168. Kontodimas, D. C., P. A. Eliopoulos, G. J. Stathas, and L. P. Economou. 2004. Comparative temperature-dependent development of Nephus includens (Kirsch) and Nephus bisignatus (Boheman)(Coleoptera: Coccinellidae) preying on Planococcus citri (Risso)(Homoptera: Pseudococcidae): evaluation of a linear and various nonlinear models using specific criteria. Environ. Entomol. 33: 1–11. Lactin, D. J., N. Holliday, D. Johnson, and R. Craigen. 1995. Improved rate model of temperature-dependent development by arthropods. Environ. Entomol. 24: 68–75. Lins, J. C., J. J. van Loon, V. H. Bueno, D. Lucas-Barbosa, M. Dicke, and J. C. van Lenteren. 2014. Response of the zoophytophagous predators Macrolophus pygmaeus and Nesidiocoris tenuis to volatiles of uninfested Downloaded from https://academic.oup.com/ee/article/47/2/467/4924357 by guest on 17 December 2022 our results showed that this criterion cannot really be trusted from a biological point of view. To clarify this discrepancy, the Beta model is ranked second in describing the temperature-dependent developmental rate of the egg stage (Table 4), while the model estimates −47490, 41.11 and 34.14°C for lower and upper temperature thresholds and optimum temperature, respectively (Table 5). Similarly, the lower temperature threshold for nymphal development were 3, 3.5, and −2.937°C as estimated by Analytis-1/Allahyari, Hilbert and Logan and Analytis-1 models, which are the first, second and third best models based on the AIC. These estimates could not be biologically and experimentally valid. On the other hand, although the Ratkowsky model was not well ranked in this study, it was the best model to describe the developmental rate for 10 other insect data sets (Shi et al. 2016). In addition, some researchers believe that models based on physiological and biochemical mechanisms of arthropods (e.g., the SSI model) are more useful and reliable than others (Shi et al. 2017). These contrasts indicate that in addition to statistical validation, biological validation in the laboratory and field is needed to choose the best model for describing the developmental rate of each species as a function of temperature. The results of the current study provide fundamental information on N. tenuis biology which will lead to better implementation of this predator as an effective biological control agent against such pests as whitefly and tomato leafminer. The lower temperature threshold of total immature stages was 10.98 and 10.24°C as estimated by two linear models (Table 3), which is approximately in accordance with results of Martinez-Garcia et al. (2016). The minimum low temperatures for egg-adult development of Bemisia tabaci (Gennadius) and tomato leafminer were reported to be approximately 8.7 and 8.0°C, respectively (Muniz and Nombela 2001, da Silva Krechemer and Foerster 2015). These values suggest that temperature in spring is unsuitable to allow N. tenuis exerting effective control of these pests and also their activities start earlier than that of the predator at the beginning of the cropping period. Likewise, the thermal constant of the predator was estimated to be less than 340 DD (Table 3), which is considerably lower than B. tabaci (≈390 DD) and tomato leaf miner (≈417 DD). Thus, the predator completes each generation more quickly than these pests and it seems that if populations of the predator are supported in the early season, they would be able to provide effective pest control. According to the Analytis-1 model (the best model based on AIC, Table 4), the upper temperature threshold for egg-adult of the predator was estimated to be ≈35°C (Table 5), which is lower than B. tabaci (≈40°C) and tomato leaf miner (≈37°C) (Muniz and Nombela 2001, da Silva Krechemer and Foerster 2015). Taking this into consideration, biological control might be inappropriate when the weather becomes hot (usually towards the end of the season), thus requiring other control methods (e.g., chemical control). In conclusion, it seems that mass rearing and application of N. tenuis against some pests of solanaceous crops in early season (even before pest establishment) can improve its performance and the reliability of the biological control it provides. 475 476 Sanchez, J., A. 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