Behavioural plasticity of a pest species may aggravate global wheat yield loss under climate change

Domain: biology
Published: 2025-12-16
Editorial outcome: major_revision

Abstract
Extreme heat events are expected to increase under global climate change, which may depress the abundance of most insect species, including agricultural pests. However, here our experimental observations show that the behavioural plasticity within fine-scale microhabitats, which is often neglected in pest damage and crop loss predictions, can buffer the exposure to simulated extreme high temperatures in a small pest aphid species. As expected, this buffering effect promotes aphid population growth in our lab experiments. Our model predictions further suggest that this buffering effect can aggravate pest damage and wheat yield losses globally during 1977-2017 (8.7 million tons/year, accounting for ~1.2% of global production), assuming a moderate initial pest density if no pesticides are applied. This estimated yield-loss aggravation may even worsen faster (+2.6% per year) than the reported increase of global wheat production (+2.0% per year) under warming. More than 4/5 countries may undergo increasingly aggravated losses, including most of the countries with the largest wheat production, particularly serious for less developed countries. Global food security will be greatly challenged, considering many other pest insects may also cause aggravated crop loss via buffering climate impacts through behavioural plasticity.

Introduction
Global food production has to grow by more than 70% by 2050 to meet the demand of world population growth1. However, pests and pathogens have caused more than 40% of crop losses every year across the globe2. Importantly, the crop losses by pests are predicted to increase under global climate change3–5. Therefore, the crop yield loss caused by pests is of great significance to global food security in the context of ongoing climate change and world population increase4–7.

Climate change leads to an increase in both mean temperature and frequency of extreme events8. All life activities of ectothermic pests, from metabolic rate to population growth, depend largely on ambient temperatures. Increase in mean temperature is predicted to aggravate the pest damage and yield loss4,7,9–11, while extreme events are generally thought to inhibit pest populations12. Insects can actively initiate mitigation approaches, such as behavioural, physiological and evolutionary mechanisms, to buffer stress and increase their survival probability under extreme events12–14. However, the true roles of these processes in pest population dynamics and crop damage have rarely been identified and modelled. Behavioural plasticity is one of the most important processes of insects to cope with extreme high temperature13,15–19, implying an important role in enabling pest population growth. Nevertheless, no studies have incorporated behavioural plasticity in pest damage and crop loss predictions so far.

When predicting pest damage under climate change, mean temperature increase is usually treated as the main driver in models for metabolic rate3 and feeding rate10, phenology and voltinism8,9, population growth rate3 of pest insects, or disease infection risk6. The data used for establishing these models are commonly derived from constant temperature experiments. However, field insects experience temperature fluctuations through space and time20–22 within their microhabitats12,13,15,23. Constant temperatures failed to reflect the irreplaceable role of daily thermal extremes in driving insect performances12,24,25. Moreover, in studies on the impact of fluctuating temperatures and thermal extremes on pests, insects are often confined in limited spaces such as leaf clip cages26,27 or containers28. As a result, these studies overlooked the buffering role of pests’ behavioural thermoregulation12,13,15–18 by exploiting microclimate heterogeneity in nature21–23,29–31. Importantly, since crop yield loss is often a function of pest density32, neglecting insects’ buffering activity will likely hide the true effects of daily thermal fluctuations and thereby lead to inaccurate predictions of insect density due to the accumulation of daily errors during the whole crop growing season. Unfortunately, despite a few predictions incorporating behavioural thermoregulation of rather large ectotherms at the landscape scales or across kilometres16,33, fine-scale behavioural thermoregulation (for instance, by moving across a single plant) has not been considered in predictions of pest population growth and the consequent crop yield losses. Thus, it remains unclear if and to what extent pest insects can use microclimate heterogeneity and behavioural thermoregulation to buffer climate impacts13,23, and whether this buffering effect will cause more abundant pests and more serious crop yield losses13.

In the present study, by using wheat Triticum spp. and the grain aphid Sitobion avenae as a worldwide pest-crop model system34, we quantify the effect of fine-scale thermoregulation on global abundance of a pest and a crop yield loss under climate change. First, we conducted a series of experiments (Fig. S1) to understand if and to what extent aphid fine-scale behavioural thermoregulation (FSBT) can buffer the impact of extreme high temperatures (EHTs). Then, we estimated and compared the global abundance of S. avenae and wheat yield loss across spatial-temporal scales with and without aphid thermoregulation in 697 locations during 1977–2017 (Fig. S2). Meanwhile, we illustrated how pest thermoregulation aggravated wheat yield loss (AYL, in %) in different countries/regions and we linked these results to global food security. Our study allows for an understanding of the importance of fine-scale thermoregulation of pest insects when assessing global food security and pest status at the macro-scales, although some other main factors, such as agronomic practices, crop genetics/varieties, other pest insects and diseases also need to be considered in future estimations.

Results
Behavioural plasticity of aphids and its effects on survival and population growth

Behaviour experiment–First, we observed S. avenae behaviour under simulated daily fluctuating temperatures involving natural EHTs to determine if the aphids displayed FSBT at the individual plant level (surface of leaves and the soil surface below the plant). Overall, aphids can exploit spatial temperature heterogeneity within microhabitats and avoid overheating through thermoregulation (Fig. 1a, b). In the presence of daytime EHT, temperature was the lowest on the soil surface, intermediate on the leaf and the highest in the air (Fig. 1a). The temperature difference between soil surface and air increased with air temperature, and could be greater than 12 °C at midday. As a result, the aphids began to move from plant to soil surface as the temperature increased, resulting in a decreased proportion of the aphids residing on the host plant. When the temperature decreased after midday, aphids went back to leaves to a large extent. By contrast, in the absence of EHT, aphids did not leave the plant and the proportion of host plant residence remained high through time (Fig. 1b).

Then, we conducted two parallel experiments (clip cage and soil moisture experiments) to test if aphid FSBT can reduce the negative effects of EHT on key life-history traits and population growth of the aphids.

Demographic experiment (clip cage)–We used clip cages to fix the aphids on the plant to limit their FSBT (Fig. 1c, d). We designed a full factorial experiment of FSBT and EHT to investigate the impact of S. avenae thermoregulation on survival and population growth (Fig. 1c, d) and other important life-history traits (Fig. S3, Table S1 & S2). The aphids in the two treatments without EHT experienced similar mild temperatures, survived similarly well (χ²=0.28, df=1, p=0.5987), and had a relatively high intrinsic rate of increase (rm) (Fig. 1d) due to faster development, higher fecundity, etc (Fig. S3). However, in the two treatments with EHT, aphid survival decreased. Importantly, FSBT increased survival significantly (χ²=12.83, df=1, p=0.0003) and reversed the negative rm observed in the treatment without FSBT to a positive index, by exploiting a wider range of temperatures within the microhabitats (~28–40 °C at midday).

Abundance experiment (soil moisture)–In this experiment, the aphids were allowed to move freely. We used moist soil to maximize the thermal heterogeneity across the plant and the soil surface, allowing FSBT of aphids. By contrast, we used dry soil to decrease thermal heterogeneity as much as possible to limit the efficiency of FSBT. Since these soil moisture conditions did not affect S. avenae performance (Figs. S4 & S5), this experiment allows us to understand if FSBT of aphids can contribute to their population growth under EHT. The aphids in the different treatments also experienced different fine-scale microhabitat temperatures (Fig. 1e). As a result, in the two treatments without EHT, aphid abundance increased rapidly (Fig. 1f). In the presence of EHT, the aphids with limited FSBT (dry soil) underwent high maximal microhabitat temperature (~35–40 °C), leading to a population decline. By contrast, the aphids with FSBT (moist soil) during EHT exploited a larger microhabitat temperature range (~29–40 °C), resulting in a population increase.

EHT-survival experiment and survival models–Finally, we conducted an experiment to determine daily maximum temperature-dependent survival under conditions with and without FSBT. We built two survival models with these experimental data to quantify S. avenae survival during EHT with and without FSBT (Fig. 1g, h). Aphid survival decreased with daily maximum air temperature, following an inverse sigmoid curve. The aphids survived better when they had the possibility to thermoregulate behaviourally. However, as daily maximum air temperature continues to increase, the potential for aphid FSBT may decline due to the simultaneous increase in temperatures at the soil and plant surfaces.

Global abundance of S. avenae and wheat yield loss with and without FSBT

FSBT is powerful to buffer aphids against exposure to deleterious temperatures during EHTs. Thus, FSBT will allow a continuous increase in S. avenae abundance despite EHT, potentially contributing to wheat yield loss. Here, we calculated the aggravated yield loss (AYL) in wheat production caused by FSBT globally by following these steps (Fig. S2). First, to estimate the global aphid abundance and wheat yield loss across spatial-temporal scales, we collected the global distribution data of the aphid and the global daily temperature data during the past 41 years (1977–2017). We used these data to estimate the following variables during wheat growing season under the conditions with and without aphid FSBT: (i) the number of days with EHT which can depress aphid survival based on the EHT-survival models (Fig. 1h), (ii) the aphid peak density which was determined by the EHT frequency-dependent survival and the temperature-dependent relative growth rate of the aphid population in field conditions according to Ciss et al (2014)‘s model35 assuming a given initial aphid density (1 aphid/tiller at the decimal code = 45 of wheat phenology) and (iii) the relative wheat yield loss (%) which was derived from aphid peak density using a yield loss model (Fig. S6). To quantify the effects of FSBT, we calculated the differences between the above paired (with and without FSBT) estimations, respectively. Then, we quantified the role of aphid FSBT in assessing global food security. We first estimated the global yearly aggravated wheat production loss caused by aphid FSBT (Fig. S7). Then, we compared the speed (% per year) of this production loss with the speed of the observed increase for global wheat production.

The results showed that FSBT reduces EHTs depressing aphid survival during the wheat growing season globally, and thereby increases aphid peak density and wheat yield loss by aphids (Fig. 2a–c). Specifically, the increase in aphid peak density and wheat yield loss (AYL) could be 0.15 aphid/tiller and 0.04%, respectively, for each EHT day buffered by aphid FSBT (Fig. S8). Aphid thermoregulation caused an aggravated loss of global wheat production of 8.7 million tons/year, and this aggravated loss increased at a rate of 0.15 million tons/year during 1977–2017 (Fig. 3a). Importantly, this increased rate of aggravated global production loss (2.6% per year) was faster than the increase of global wheat production (2.0% per year) (Fig. 3b), indicating that aphid thermoregulation not only greatly aggravates wheat yield loss but also generates a hidden risk for global food security that was never anticipated before.

Furthermore, we calculated the mean AYL (1977-2017) for each country and compared the AYL between various geographical regions and between the countries with different income levels. The results showed that the AYL due to FSBT by S. avenae is greater for underdeveloped countries in Africa, Asia, and South America than other developed countries (Fig. 4a, b). Among them, Eastern Africa (1.91%), Southern Asia (1.84%), Western Asia (1.75%), Central Asia (1.62%) and South America (1.21%) had higher AYL than the global annual average (1.18%). Moreover, wheat yield loss due to aphid FSBT varied according to the countries’ income levels (Fig. 4c). The AYL increased as the income level decreased, with much larger losses in low-income countries (2.10%) and low-income and food-deficit countries (2.27%).

Global trends of aggravated wheat yield loss with climate warming

To understand how past climate warming has altered the trends in AYL (annual change rate) caused by FSBT in S. avenae, we calculated the slopes (% per year) of the linear regressions between AYL and year for the globe and for each country. Overall, under the global warming scenarios for wheat planting areas in 1977-2017 (0.03 °C/year) (Fig. 5a), aphid FSBT led to an accelerated global AYL (0.08%/°C) (Fig. 5b). We also found that the change rate differed largely between countries (Fig. 5c). More than 4/5 of the countries (61/71) showed increased aggravation with warming, including most of the largest wheat production countries (15/19) and all the low-income and food-deficient countries (11/11) (Fig. 5d).

Methods
Thermoregulation experiments in the laboratory

Here, we designed four experiments to clarify if and to what extent S. avenae can behaviourally buffer the impact of extreme high temperatures (Fig. S1).

Experiment 1: Fine-scale behavioural thermoregulation under EHT.

Insect preparation. The aphids of Sitobion avenae were collected from a winter wheat field near Beijing (39o48′N, 116o28′E). The aphids were reared in the laboratory on 5–20 cm high wheat seedlings in screen cages (30×30×25 cm) under standard rearing conditions at constant 20 °C, 50–70% relative humidity and 16:8 h (light: dark) photoperiod. The winter wheat seeds were sown evenly in plastic pots (diameter = 10 cm, height = 8 cm) containing nutrient soil to provide essential nourishment. The wheat seedlings were used to feed the aphids and were renewed once a week.

Generating EHTs, microclimate heterogeneity and aphid FSBT. EHTs – To simulate the ecologically relevant EHTs during growing season in nature, we used a “8h-fluctuating EHTs” thermal regime: during daytime, the temperature increased linearly from 20 °C at 08:00 and reached the maximum of 40 °C (EHT) at 12:00, then, decreased linearly to 20 °C at 16:00; in the remaining 16 hours, the temperature was set to constant 20 °C (Fig. 1a, c, e). Here, the ramping rate was 0.08 °C/min, which is very close to the rate of temperature change in nature66,67. We chose 40 °C as the highest temperature because this temperature was often recorded as extremely high temperatures in the field when S. avenae occurs24,25,68. The daily average temperature was kept at 24 °C, which is within the suitable temperature range of S. avenae24,25. A constant 24 °C was used as the thermal regime without EHT (Fig. 1b, c, e) to eliminate the possible influence of the mean temperature difference between treatments. While we cannot test all natural thermal fluctuations, the constant temperature serves as a simplified representation of optimal conditions for insect performance. Previous research shows that mild temperatures (22–25 °C) with moderate daily fluctuations (±5 °C) are common in wheat fields69. Under these moderate conditions, the effects of constant and variable temperatures are similar70. Therefore, using a constant temperature as a control is reasonable.

Microclimate heterogeneity – We generated the microclimate heterogeneity by manipulating the soil surface moisture (see below). When the ambient temperature gradually increased from 20 °C to 40 °C, the moist soil surfaces were cooler than the ambient air and plant leaf surfaces, and the maximum temperature of the moist soil surface was <28 °C (Fig. 1a). Therefore, the soil surface can provide a cooler microsite for aphids to avoid the detrimental impacts of EHT. This manipulation simulated the thermal landscape in wheat fields, whereby the temperature at different heights of wheat plants differs greatly at midday, showing an obvious trend of higher temperature at the upper part and lower temperature at the lower part71,72, which provides a temperature gradient for the aphids to regulate their body temperature by moving downward.

Fine-scale behavioural thermoregulation (FSBT) – In our study arena (see below), the aphids need to move from their host plant to the moist soil surface to avoid EHT. Here, we recorded the positions of the aphids and the moving behaviour of aphids between the seedlings and the soil surface to characterize their behavioural thermoregulation. Since aphids rarely transfer between host plants and soil surface in the absence of biotic and abiotic environmental disturbances34, we can use the transfer behaviour as an indicator of the behavioural thermoregulation.

Experimental design – We quantified thermoregulation behaviour (the movements and the microsite choice of aphids) under the conditions with EHT (“8h-fluctuating EHT”, Fig. 1a) and without EHT, i.e., constant 24 °C (Fig. 1b). We tested the thermoregulatory behaviour for each nymphal stage (1st, 2nd, 3rd and 4th nymphal instars) and adults of the aphid separately. For each development stage, we tested 30 aphids placed individually on different wheat seedlings.

Aphid preparation for different development stages – To obtain the 30 test aphids of a certain development stage, and to ensure that the tested aphids were not related directly to each other, we randomly selected ~60 adult aphids from the stock rearing and put them individually into tubes (diameter = 1.5 cm, length = 7.0 cm). The number of aphids prepared was twice that of aphids tested to ensure enough test individuals. Inside each tube, a hydrated wheat seedling was provided for the adult aphid to feed and produce progeny for 12 hours. After that, only one newborn aphid from each tube was kept for testing the behaviour of 1st nymphal instars or for continuing to rear with a new tube containing a new wheat seedling until the aphid developed into 2nd, 3rd, 4th nymphal instars or adult stage. Different development stages of the aphids were obtained based on their respective development time at a constant temperature of 20 °C24,25.

Experimental manipulation – Each test aphid was placed individually in an experimental arena. For this, we planted five wheat seedlings in a plastic pot (diameter = 10 cm, height = 8 cm), one in the centre and the other four around the centre at a distance of 3~4 cm, and then placed them in a rearing room with a constant 24 °C and 16L:8D photoperiod. When the seedlings grew to about 10 cm height, they were used to identify the thermoregulation process of the aphids, i.e., leaving their original feeding site to search for other microsites. An aphid was transferred to the central seedling and allowed to settle down with a clip cage 12 hours before the experiment. The clip cage was removed gently when the experiment started. Each pot was covered by a transparent plastic cylinder (diameter = 9 cm, height = 21 cm), and the top of the cylinder was strapped using a piece of nylon gauze for ventilation. We recorded the aphid position every 10 minutes by eye from 08:00 to 16:00 hours – a procedure that was eased by the fact that only one aphid was present in the arena at a time. The temperature of different microhabitats (air, leaf surface of the central seedling, and soil surface) was recorded using thermocouples (type T) and data loggers during the experiment.

Response variables – A preliminary test indicated that aphids moved only rarely when feeding on their host plant under mild temperatures (20~30 °C). By contrast, the proportion of moving individuals increased at high temperatures. Therefore, the proportion of aphids displaying dispersal behaviour in response to the EHT can reflect the frequency of thermoregulation. Here, we took the changes in the proportion of aphids residing on their host plant as the response variables for thermoregulatory behaviour of the aphid across micro-sites (air, leaf and soil surface).

Experiment 2 (clip cage): Effects of FSBT on demographic responses to EHTs. Experimental design – To determine how EHTs affect key life-history traits and population parameters of the aphids and how FSBT can mitigate these negative effects, we designed a full factorial experiment of EHT and FSBT. The treatments were (1) EHT(+)FSBT(−), with EHT but without FSBT; (2) EHT(+)FSBT(+), with both EHT and FSBT; (3) EHT(−)FSBT(−), without EHT and FSBT; (4) EHT(−)FSBT(+), without EHT but allowing FSBT. The EHT was controlled as “8h-fluctuating EHTs” and without EHT was the constant 24 °C (Fig. 1c). We used clip cages to restrict the movement of aphids to create the treatments without FSBT.

Experimental manipulation – Here, we used the same experimental arena (a pot with 5 plants) as in experiment 1. When the wheat seedlings grew to about 10 cm height, one wingless adult aphid was gently transferred from the stock rearing to the central wheat seedling with a fine wool brush and then covered with a clip cage. After 12 hours, only one newborn nymphal aphid from the adult on the central wheat seedling was kept as the tested aphid by removing the adult aphid and other newborn nymphs. In the two treatments without FSBT, the test aphid was constrained on the central wheat seedlings with a clip cage (diameter = 5 cm, with ventilated windows on both sides) so that they could not move to the cooler soil surface during the whole experiment. In the two treatments with FSBT, each pot of wheat seedlings was covered by a transparent plastic cylinder (diameter = 9 cm, height = 21 cm), and the top of the cylinder was strapped using a piece of nylon gauze for ventilation. In this case, the aphid could move freely inside the cylinder from the host plant to the cooler soil surface, while it could not escape from the experiment arena.

We used four climate chambers (RG-400B, Hefei Yoke Instrument Co., Ltd., China, accuracy: ±1.0 °C) to conduct the four treatments of EHT and FSBT (temperatures were recorded in the four chambers with hobo loggers to validate conditions). The pots with wheat seedlings and aphids were placed in the climate chambers with the corresponding treatment. The survival, development (moulting or not) and reproduction of the aphids were recorded twice a day at 8:00 am and 20:00 pm, and the deaths and offspring of the aphid were removed. When the aphids developed into adults, their body mass, body length and body width were measured by an analytical balance (MS105DU, METTLER TOLEDO International Trade (Shanghai) Co., Ltd., USA, accuracy: 0.01 mg) and a micro-object measurement and analysis system (V 1.01, Beijing Dongfang Nongren Biotechnology Co., Ltd., China, resolution: 768×576). Then, the aphids were returned to the experiment arena and their survival and reproduction were recorded twice a day, and the offspring of the aphid were then removed. For each treatment, 50–60 aphids were tested individually from birth to death. During the experiment, data loggers (U23-002, Onset HOBO, USA, accuracy: ± 0.1 °C) were used to measure the temperature of various microhabitats (i.e., ambient air temperature inside the cylinder, the leaf temperature within clip cage and soil surface temperature) every 5 minutes.

Response variables – Aphid survival (days) was selected as the key variable to identify the effects of EHT and FSBT on life-history traits of the aphids. Survival curves were estimated with the Kaplan-Meier method and compared using the log-rank test; pairwise log-rank tests were further conducted with Holm-Bonferroni adjusted P-values. The intrinsic rate of increase (rm) was calculated as the indicator to reflect how EHT and FSBT affected the aphid population.

For other important life-history traits (Supplementary Data 1), We identified error distributions of different traits, i.e., development time, nymphal growth rate, body size at maturity (length, width, and mass), reproductive time, adult fecundity, and longevity, by using ‘fitdistr’ and ‘AIC’ functions from the ‘car’ and ‘MASS’ packages in R (version 4.0.0). We used generalized linear models (GLMs) with gamma distributed errors (for development time, growth rate, body mass, body length, body width, longevity) or normal distributed errors (for reproductive time, fecundity) to analyse the effect of 2 fixed factors (EHT and FSBT) and their interactions on these traits, with the ‘glm’ function and ‘car’ package. The multiple comparisons based on the GLMs between different treatments were conducted by using the ‘Tukey’ method, in the ‘emmeans’ function from the ‘emmeans’ package. The results are provided in the Supplementary Information (Fig. S3, Table S1 & S2).

Experiment 3 (soil moisture): Effects of FSBT on aphid abundance under EHTs. Experimental design – To test if aphid FSBT can contribute to promote their population, we tested the effects of EHTs and FSBT on aphid abundance across time. We used the same experimental design that in experiment 2: (EHT(+)FSBT(−), EHT(+)FSBT(+), EHT(−)FSBT(−) and EHT(−)FSBT(+)). To make our experiment more ecologically relevant, we allowed the aphids to move freely within the microhabitats, but we manipulated soil moisture to adjust the microclimatic gradient: a dry soil surface reflected a situation without FSBT (small temperature gradient), while a moist soil surface generated a situation with FSBT (large temperature gradient). Although this manipulation can potentially lead to changes in the relative humidity (RH) within microhabitats (on the leaf surface and on soil surface) under both dry and moist soil surface conditions (ranging from 40% to 90%), according to a preliminary test (see Supplementary Methods: Effects of EHT and RH on S. avenae performance), these RH conditions may not necessarily affect aphid performance (Fig. S4 & S5).

Experimental manipulation – We transferred 10 adult aphids from our stock rearing into a pot with 25 plants 12 h prior to the beginning of the experiment, allowing them to reproduce. Here, we increased the number of plants to provide sufficient host plants for the aphids, especially in the treatments without EHT (high growth rate). The pot was covered by a transparent plastic cylinder to avoid the aphids’ escape. After 12 h, we recorded the number of aphids in each pot as the initial aphid abundance, and then moved the pots together with aphids to the four climate chambers for the designed treatments, respectively. We recorded the number of aphids in each pot at 08:00 on days 0, 1, 3, 6, 9, 12, 15 and 18. We replicated 10 times each treatment. We used data loggers to measure the air temperature inside the cylinder and used thermocouples to measure various microhabitat temperatures (each of the random five leaves, soil surface and ambient air) every 5 minutes during the 8h-fluctuating EHTs (Fig. 1e).

Response variables – We calculated and used the number of aphids on each observation day for each pot as the aphid abundance to show the population trends in each treatment. We used a linear regression model to estimate the relationship between aphid abundance and experimental time for each treatment. The numbers of aphids on each observation day were log-transformed to avoid large dimensional differences between treatments. Meanwhile, we used the General Linear Model to test the differences among treatments and used Tukey’s method for multiple pairwise comparisons of different treatments.

Experiment 4: Aphid survival at different EHTs with and without FSBT. To quantify to what extent aphid FSBT can buffer the impact of different EHTs on their survival, we tested the 24-h aphid survival with and without FSBT (by restricting the movement of aphids using clip cages) under a constant 20 °C (control) and seven “8h-fluctuating EHT” regimes with maximum daytime temperatures of 34, 36, 38, 40, 42, 44 and 46 °C (nighttime temperature was constant 20 °C, Fig. 1g). We tested thirty individuals for each stage (1st, 2nd, 3rd, 4th nymph and adult) at each temperature regime. The aphids were individually settled on a wheat leaf in a pot for 1 hour prior to the experiment, allowing them to feed. We then used the transparent plastic cylinder to cover the pot to prevent the aphids to escape. Then, the survival of these aphids was checked after 24 hours. Since the survival data for all stages displayed a similar pattern across treatments, these data were pooled as source data to build the two EHT-dependent survival models with and without FSBT, respectively. We used the Gompertz model (reverse ‘S’ shape) to describe the aphid survival at day t caused by extremely high temperatures under the conditions with or without FSBT:

Survival = a * exp(-exp(-(EHTt - EHT0) / b)) (1)

where EHTt is the daily maximum air temperature at any given day t. EHT0 is the EHT causing aphid survival equal to 0, and a and b are parameters required to fit the models. The parameters EHT0, a, and b differ between the conditions with or without aphid FSBT. The EHT-survival difference between the conditions with and without FSBT can quantify the extent to which the FSBT of the aphid can increase its survival probability at any given EHT.

Global estimations of S. avenae abundance and wheat yield loss

Modelling peak density of S. avenae and wheat yield loss. Wheat yield loss due to aphid infestation is a function of aphid peak density, which depends mainly on the host plant phenology and the ambient temperatures the aphids have experienced32,35. Thus, first, we estimated aphid peak density using the data for wheat phenology and temperature. The aphid number at any given day t+1 was calculated as:

Aphid N(t+1) = Aphid N(t) * (1 + RGR(t) * Survival(EHTt)) (2)

where Aphid N(t) is the aphid number at day t, RGR(t) is the relative growth rate of the aphid population at day t. The initial aphid number (Aphid N0) was set to 1 aphid/tiller at the boot swollen of the wheat phenology (in decimal code (DC)=45 at day 0). Survival(EHTt) is the aphid survival at day t caused by extremely high temperatures under the conditions with or without FSBT. The maximum daily aphid number was chosen as the aphid peak density.

RGR preparation. We estimated the relative growth rate (RGR) of the aphid population according to Ciss et al (2014)‘s model35 based on daily mean temperature (T) and the DC of wheat growth stage:

RGR = a1 * Ln(T_Max - T) + a2 * Ln(DC_Max - DC) (3)

where T_Max is the lethal temperature for the aphid, DC_Max is the latest wheat growth stage allowing aphid feeding, DC_m is the position of the left inflexion point for the RGR response to wheat growth stage. a1, a2, b and k are parameters required to fit the model. Here we set the parameters as T_Max=30, DC_Max=92, DC_m=32.91, a1=-0.53, a2=0.53, b=-0.05, k=1.11 according to Ciss et al.35. We prepared the data for decimal code based on the growing degree-day (GDD) for the growth stages of cereal crops73:

DC = a / (1 + exp(-(GDD - GDD0) / b)) (4)

where a, b and GDD0 are parameters required to fit the model. Here we used the GDD model (USDA: Metadata for Winter/Spring Wheat Growth Stage Models)74 to calculate the degree-days of any growth stage of wheat:

GDD = Σ(T_meani - T_base) (5)

where T_base is the base temperature, which was set as 0 °C74 for spring wheat. For winter wheat, T_base was set as 0 °C before double ridge stage (0<DC<30), 3.3 °C from double ridge to heading (30≤DC<50), and 5.1 °C from heading to maturity (50≤DC≤100), respectively75. Here we used a different T_base for winter wheat to make sure the estimations of GDD were in line with the field-recorded GDD of wheat growth76. i is the day beginning at growth stage s1 and incrementing daily until the beginning of growth stage s2, T_meani is the average daily temperature, which we computed as (DT_max + DT_min) / 2. For any given location, the planting dates of winter wheat and spring wheat were derived from the literature76. The total number of days in a growing season was determined when GDD=1825 degree-days because the aphid cannot continue feeding on the wheat plant at this mature growth stage35.

Survival with and without FSBT. As ectothermic insect species with largely overlapping generations during the growing season, the aphid density at any given location and year depends mainly on how local climates (especially temperature extremes) constrain their survival and consequent population growth12,24,25. Importantly, both the intensity and frequency of temperature extremes can matter during this process12,25. Thus, here we first determined the total number of days at which the daily maximum air temperature (DT_max) reduces aphid survival during a given wheat growing season. Then we calculated the total survival of aphids with and without FSBT by multiplying the aphid survival for each day based on the data for DT_max of each site and on the EHT-survival models (Eq. 1). The estimated parameters of the two models are displayed in Fig. 1h. Note that this approach assumes that those EHT-survival models remain the same across locations.

Estimation of wheat yield loss caused by aphids. Since wheat yield loss is a linear function of the peak density of S. avenae32,77,78, we estimated the wheat relative yield loss (%) using the following model:

YL = a * Aphid + b (6)

where Aphid is the peak density (aphids/tiller) during the wheat growing season, a and b are parameters of the model. Here, the parameters were set as: a=0.29 and b=1.07 based on the extracted data derived from independent field experiments77,78 (Fig. S6, Supplementary Data 1). The use of a model for relative yield loss (%), rather than some other models for quantitative production loss (e.g., kg/acre), allows us to integrate the yield loss across the globe, regardless of wheat production variations across time and space scales.

Estimating global impacts of FSBT during 1977–2017. To understand the unique impacts of aphid FSBT on the aphid abundance and wheat yield loss globally, we calculated the difference in key traits between the two conditions (with minus without aphid FSBT) during wheat growing seasons for all locations where the aphid is present in each year throughout the period 1977–2017: (i) the difference in EHT frequency (i.e., the extreme temperature actually experienced by aphids in the two FSBT scenarios), to quantify the buffering amplitude of FSBT, (ii) the difference in aphid peak density that results from the exposure to temperature, and (iii) the difference in relative yield loss to quantify the impact of FSBT on the crop damage (Fig. 2). In the process of these estimations, we found that some unreasonable estimated values (0.154% of the total numbers of data, 44/28577) exceeded 100% and excluded the extremely rare aberrant data (0.014% of the total numbers of data, 4/28577) to avoid the negative AYL estimations.

Preparation of temperature data. To estimate the density of S. avenae at the global scale during 1977–2017, we downloaded global daily temperature data (mean, maximum and minimum) for the past 41 years (1977–2017) from Berkeley Earth (1° × 1° grid, http://berkeleyearth.org/data/). Since microclimate models cannot calculate the temperatures at the microhabitat (cm) scale for small insects such as aphids14,23, here we used a simple linear model (see Fig. S2, in Zhao et al.79) to transform macroclimate data (gridded information) into microclimate (microhabitat for aphids). This model reflects the relationship between the temperature records at the local weather station and the temperature data collected using thermosensors inserted into the wheat spikelet, the favourite microhabitat for aphids to feed. As a result, this statistical model estimated ca. 5 °C higher daily maximum temperature on average in spikelet than the local weather station records. This temperature difference is similar to that experimentally observed in other wheat fields80 and is frequently reported across various habitat types and geographic regions30,81,82.

Determination of aphid distribution. To map the S. avenae infestation globally, first, we collected the distribution data of the aphid by using its scientific name “Sitobion avenae” as a keyword in the databases of GBIF and CABI. We also collected the distribution data of wheat by using the keyword “Triticum aestivum” in these two databases due to the prevalence of this aphid species to infest wheat globally. The GBIF and CABI databases integrate information from publications written in English. However, most information for China is provided in Chinese. Therefore, for the distribution records in China, we also searched for the literature in the database of China National Knowledge Infrastructure (https://www.cnki.net/) to reduce the publication biases83,84 due to incomplete published records in Chinese. Then, we kept the aphid distribution data only for the locations that were included within the distribution maps of main wheat-producing areas for each country (according to USDA: Global Crop Production Maps by Region, https://ipad.fas.usda.gov/rssiws/al/global_cropprod.aspx). This selection process ensured that the aphid infestations on wheat are representative for each country and produced more reasonable global estimates of wheat yield loss induced by this aphid species. Then, the locations were combined according to the scales of the temperature data (1° × 1° grid) to avoid any repeated calculation. Finally, 697 locations were selected to estimate global aphid occurrence.

Furthermore, to understand the extent to which aphid FSBT will affect aphid peak density and aggravated wheat yield loss (AYL, in %) through buffering the impact of EHTs, we used linear regression models to describe the relationships between (i) the differences in EHT days and the differences in aphid peak density and (ii) between the differences in EHT days and the differences in wheat yield loss, both under the conditions with and without aphid FSBT (see Fig. S8). For example, when FSTB induces a decrease in the number of EHT days by 40 days, producing an increase of +6 in aphid peak density (because they survive better), then the yield loss worsens by +1.6% (Fig. S8). Then, to quantify how global aggravated wheat production loss caused by aphid FSBT changes with time, based on the estimated global yearly mean differences of relative yield loss (%) between the two scenarios with and without aphid BT, together with the data for global yearly wheat production (million tons) from FAO49, we calculated the yearly global aggravated wheat production loss (million tons) due to aphid thermoregulation using the following function (see Fig. 3a):

PL_FSBT = P * (YL_FSBT / 100 * YL_AP) (7)

where PL_FSBT is the aggravated global wheat production loss (million tons) caused by aphid FSBT, P is the global wheat production (million tons) obtained from FAOSTAT, YL_AP is a recently estimated wheat yield loss (%) to all wheat pests6, YL_FSBT is the global mean difference of wheat relative yield (%) between with and without aphid FSBT (see Table S4 for more details).

Then, we determined to what extent this trend of the aggravated wheat loss by FSBT of S. avenae impacted global food security. We compared the slope for the linear regression of yearly aggravated loss with the slope for the linear regression of the global wheat production (FAOSTAT) during 1977–2017, using the respective values in 1977 as the baselines. Here, the food insecurity was inferred when the global aggravated wheat loss to aphid FSBT increased faster than global wheat production, i.e., the former had a greater slope than the latter (see Fig. 3b).

Moreover, to know the differences of the aggravated yield loss between countries, we calculated the mean differences (1977–2017) of the aggravated yield loss for each country as well as for various geographical regions and for the countries with different income levels (see Fig. 4). The data for global wheat production by country were obtained from FAOSTAT. The data for development levels (income and food-deficit levels) were derived from The World Bank (https://data.worldbank.org/indicator/).

Sensitivity analysis of the aggravated wheat yield loss. Considering the complexity of the multiple models used for estimating the aggravated wheat yield loss, here we conducted sensitivity analyses for the potential factors that determine the global AYL estimations. The variables selected and their changes were shown in Table S5. Here, we determined the estimated values of AYL to artificial changes of variables in a set of models for the globe and for each geographical region (Fig. S9).

Trends in aggravated wheat yield loss under climate warming

To understand how climate warming will change the global trend in the estimations of aggravated wheat yield loss, we calculated the global mean temperatures (http://berkeleyearth.org/data/) and the global mean aggravated wheat yield loss in each year during 1977–2017. Then, to know which countries will be affected the most in the estimations of aggravated wheat yield loss caused by S. avenae FSBT under warming, especially from the perspective of global food security, first we estimated the change rate (the slope of linear regression) of yearly estimated aggravated yield loss for each country. We then showed the slopes of global mean value and the values for the top 20 countries with the largest wheat production (FAOSTAT) (Australia was not included because the aphid species S. avenae was not widespread there, according to CABI), as well as the values for countries with both low-income and food-deficit (The World Bank) (see Fig. 5).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Studies that have incorporated behavioural plasticity in pest damage and crop loss predictions are lacking. In addition, studies on the impacts of fluctuating temperatures and thermal extremes on pest insects have tended to overlook the buffering role of pests’ behavioural thermoregulation’ to extreme temperatures. Thus, this study fills important research gaps and refines estimates of future crop loss from previously underappreciated effects of thermoregulation by a wheat pest. Kudos to the authors for this research.\nThe study demonstrated that Sitobion avenae exhibited FSBT, and that Extreme High Temperatures negatively affected S. avenae. The modeling results showed that the aggregate yield loss due to aphid FSBT is greatest for underdeveloped. countries in Africa, Asia, and South America Thus, the there are major implications from the results of this study.\nThe manuscript is well-referenced and has informative figures. The current paper adequately addresses previous concerns from review, such as ones about M&Ms, which have been largely alleviated by clarifying statements and additional detail.\nA few additional minor points and corrections may still be addressed.\n1l. 23-24, etc. “...wheat yield losses globally (8.2 million tons/year, accounting for 1.1% of global production).” Your calculations were exquisitely detailed. However, if I am understanding them correctly, this estimate would be under the caveat if Sitobion avenae reached critical densities in the “697 locations [that] were selected to estimate global aphid occurrence” (ll. 678-679). That would seem to be a worst-case scenario. Is that right? If so that caveat may need to be made clear in the abstract and in the Discussion. If this is not the case, then a simple sentence clarifying this would seem appropriate.\nWhile the study presents reasonably strong evidence for behavioural thermoregulation by Sitobion avenae, and strong support for its ensuing yield impact in wheat because of behavioural plasticity, the authors need to exercise care in their writing that implies generalizing beyond this one aphid species. Of the roughly 200 uses of “aphid(s)” throughout the manuscript, there are only a few instances (roughly 30) in which “Sitobion avenae / S. avenae” should replace “aphid(s)” to avoid unwitting generalization to various aphid species, as follows:\nl. 83 global abundance of S. avenae\nl. 94 S. avenae behaviour\nl. 112 S. avenae thermoregulation\nl. 126 S. avenae performance\nl. 138 S. avenae survival\nl. 172 Global abundance of S. avenae\nl. 174 S. avenae abundance\nl. 224 due to FSBT by S. avenae\nl. 242 by FSBT in S. avenae\nl. 288 a single aphid species (S. avenae) on wheat\nl. 308 can relieve S. avenae\nl. 329 our estimations of S. avenae population growth\nl. 377 to what extent S. avenae\nl. 575. Global estimations of S. avenae abundance and wheat yield loss\nl. 576. Modeling peak density of S. avenae\nl. 629 linear function of the peak density of S. avenae\nl. 648 To estimate the density of S. avenae\nl. 662 To map the S. avenae infestation globally,\nl. 702 FSBT of S. avenae\nl. 726 S. avenae FSBT\n+ first use of “aphid” in all captions of tables and figures\nl. 99. The temperature of the soil surface and its utility as a refuge from higher temperatures depends upon whether it is exposed to solar radiation or shaded. While the arena within the cylinders was of course an artificial environment, nonetheless, was the soil fully or partially shaded by the wheat plants? If partially shaded, did the experimenters make observations as to whether aphids that had moved to the soil surface were in areas shaded by the plant canopy? Though minor, this information would be helpful for context.\nOther minor comments.\nl. 101 as high as or greater than 12 C? One or the other.\nl. 293. There are two types of yellow dwarf viruses that affect wheat and other small-grain crops—barley yellow dwarf viruses and cereal yellow dwarf viruses. Thus, it may be advisable to simply refer to them collectively as yellow dwarf viruses (YDVs). [Miller WA, Lozier Z. Yellow Dwarf Viruses of Cereals: Taxonomy and Molecular Mechanisms. Annu Rev Phytopathol. 2022 Aug 26;60:121-141. doi: 10.1146/annurev-phyto-121421-125135.]\nMinor stylistic corrections.\nl. 37. delete “to continue”\nl. 67. Change to “thereby leading to inaccurate predictions of insect density…” (“misleading” can imply an intent to deceive)\nl. 77. spp. -- no italics\nl. 87. “allows for an understanding of the importance…”\nl. 140. “when they had the possibility”\nll. 150, 427. Change to “nymphal instars” and then no need for hyphens in l. 426. Make same changes in ll. 436-438.\nl. 340 “changes of EHT can”", "reviewer_3": "The authors have addressed to the best of their abilities the criticisms I made in an earlier referees' report, and in their rebuttal have clearly stated where we disagree. Given the length of time this paper has been in review and that I acknowledge our differences are to a certain extent a matter of opinion I think the paper should be published in its current form.", "reviewer_5": "This paper combines a very interesting set of experimental work and real world data through modeling and has important implications for pest management in a changing world. The authors have addressed several concerns about the experimental design, which have bolstered their conclusions. Because I have already provided a prior strongly positive review of the paper and indicated the shortcomings of the paper were surmountable, I focus here on how the authors addressed each of the major concerns.\n1. A concern of a prior review was the creation of a thermal gradient through adding moisture to the soil. The authors have addressed this concern by adding data related to relative humidity (RH) under dry and moist soil conditions. They also conducted an additional experiment to test whether humidity interacts with temperature to affect aphid survival, and it appears not to, which is actually surprising and strengthens the results.\n2. A second concern was the whether the nature of the thermal landscape limits aphids’ abilities to thermoregulate. The description of the thermal landscape in wheatfields and the thermoregulatory behaviors was quite helpful in addressing this, and is now addressed clearly in the paper. Also, regarding the Leith et al (2024) paper: avoiding thermal extremes is a different process than thermoregulating to maximize performance, as sublethal effects of occupying suboptimal temperatures can reduce overall fitness\n3. A third concern was whether a control treatment of constant 24 degrees was appropriate. The authors argue that 5 oC daily is minimal, but 5 oC is pretty substantial for insects and there is research suggesting that EHTs under fluctuating temperatures have greater effects than under more stable conditions; thus, they authors may be overestimating the effects of EHTs on aphid survival. The authors use figure 6 to address this concern, but the data in the figure are not appropriate for doing so. From my understanding of the figure the middle row is showing performance with low thermal fluctuations whereas the bottom panel shows performance with high thermal fluctuations. The authors then suggest that the curves with low thermal fluctuations match the curves on the top row better than those on the third row. However, TPC shape cannot be derived from 3 data points, and so TPCS in rows 2 and 3 are actually not comparable to that in row 1, and in fact, both Topt and performance maximum suggested by the three data points may be significantly different in row 2 compared to row 1.\n4. Note that this 3rd concern, while not satisfactorily addressed by the authors at this time, is not substantial enough to reduce the impact of the paper, and can be addressed in two ways. First, the wording supporting the use of a constant thermal regime as a control that represents wild conditions needs to be substantially softened and the potential limitation of the data set acknowledged. Second, it would be possible to run the model with some range of TPC parameters as a comparison, which would help indicate by how much the results are over- or under-estimated.\n5. Note that figure 6 legend lacks sufficient explanation at this time. For example, is the observed vs predicted equivalent to what was observed in the experiment vs predicted if temperature was fluctuating? Also, it is confusing that the authors refer to predicted performances in each panel, but then have data points that are both observed and predicted represented by the open and closed circles.\nOverall, this paper is creative and exciting, combining real world data with experimental data to demonstrate the importance of thermoregulation in buffering aphid populations from extreme heat. Importantly, this work highlights how incorporating behavior into our understanding of how complex processes like thermal effects on herbivory impact food supplies provides unique insights into the effects of climate change on humans."}, "author_response": "Responses to reviewer comments\nReviewer #1 (Remarks to the Author)\nStudies that have incorporated behavioural plasticity in pest damage and crop loss\npredictions are lacking. In addition, studies on the impacts of fluctuating temperatures\nand thermal extremes on pest insects have tended to overlook the buffering role of\npests’ behavioural thermoregulation’ to extreme temperatures. Thus, this study fills\nimportant research gaps and refines estimates of future crop loss from previously\nunderappreciated effects of thermoregulation by a wheat pest. Kudos to the authors for\nthis research.\nThe study demonstrated that Sitobion avenae exhibited FSBT, and that Extreme High\nTemperatures negatively affected S. avenae. The modeling results showed that the\naggregate yield loss due to aphid FSBT is greatest for underdeveloped. countries in\nAfrica, Asia, and South America Thus, the there are major implications from the results\nof this study.\nThe manuscript is well-referenced and has informative figures. The current paper\nadequately addresses previous concerns from review, such as ones about M&Ms, which\nhave been largely alleviated by clarifying statements and additional detail.\nWe thank the reviewer for the insightful feedback and recognition of our work. We also\nappreciate the most constructive comments and the value the reviewer has added to\nimprove our manuscript.\nA few additional minor points and corrections may still be addressed.\n1l. 23-24, etc. “...wheat yield losses globally (8.2 million tons/year, accounting for 1.1%\nof global production).” Your calculations were exquisitely detailed. However, if I am\nunderstanding them correctly, this estimate would be under the caveat if Sitobion\navenae reached critical densities in the “697 locations [that] were selected to estimate\nglobal aphid occurrence” (ll. 678-679). That would seem to be a worst-case scenario. Is\nthat right? If so that caveat may need to be made clear in the abstract and in the\nDiscussion. If this is not the case, then a simple sentence clarifying this would seem\nappropriate.\nThis estimation is not “a worst-case scenario”, but a yield loss caused by a moderate\npest density (1 aphid/tiller at the boot swollen stage of wheat plant) without\nmanagement practices. We have added a simple sentence both in the Abstract “… (8.2\nmillion tons/year, accounting for 1.1% of global production), assuming a moderate initial\npest density without management practices.”, and in the Discussion “In the case of\nmoderate aphid density without any pest management, the resulting increase in aphid\npeak density…”.\nOur model estimations are based on the assumptions of a given initial aphid density (1\naphid/tiller) at a given plant stage (the boot swollen of the wheat phenology), and of\nthe case that there is no pest management practice during the aphid infestation. Wheat\nyield loss could be a linear function of aphid density (Duffy et al., 2017; Entwistle &\nDixon, 1987; Rossing, 1991). The higher the aphid density, the more the yield loss.\nHence, here we assumed a moderate initial aphid density at a given wheat phenology\nto indicate a more reasonableand realistic situation across the globe.\nReferences:\nDuffy, C., Fealy, R. & Fealy, R. M. An improved simulation model to describe the\ntemperature-dependent population dynamics of the grain aphid, Sitobion avenae.\nEcol. Modell. 354, 140–171 (2017).\nEntwistle, J. C. & Dixon, A. F. G. Short-term forecasting of wheat yield loss caused by the\ngrain aphid (Sitobion avenae) in summer. Ann. Appl. Biol. 111, 489–508 (1987).\nRossing, W. A. H. Simulation of damage in winter wheat caused by the grain aphid\nSitobion avenae. 3. Calculation of damage at various attainable yield levels. Neth. J.\nPlant Pathol. 97, 87–103 (1991).\nWhile the study presents reasonably strong evidence for behavioural thermoregulation\nby Sitobion avenae, and strong support for its ensuing yield impact in wheat because of\nbehavioural plasticity, the authors need to exercise care in their writing that implies\ngeneralizing beyond this one aphid species. Of the roughly 200 uses of “aphid(s)”\nthroughout the manuscript, there are only a few instances (roughly 30) in which\n“Sitobion avenae / S. avenae” should replace “aphid(s)” to avoid unwitting\ngeneralization to various aphid species, as follows:\nl. 83 global abundance of S. avenae\nl. 94 S. avenae behaviour\nl. 112 S. avenae thermoregulation\nl. 126 S. avenae performance\nl. 138 S. avenae survival\nl. 172 Global abundance of S. avenae\nl. 174 S. avenae abundance\nl. 224 due to FSBT by S. avenae\nl. 242 by FSBT in S. avenae\nl. 288 a single aphid species (S. avenae) on wheat\nl. 308 can relieve S. avenae\nl. 329 our estimations of S. avenae population growth\nl. 377 to what extent S. avenae\nl. 575. Global estimations of S. avenae abundance and wheat yield loss\nl. 576. Modeling peak density of S. avenae\nl. 629 linear function of the peak density of S. avenae\nl. 648 To estimate the density of S. avenae\nl. 662 To map the S. avenae infestation globally,\nl. 702 FSBT of S. avenae\nl. 726 S. avenae FSBT\n+ first use of “aphid” in all captions of tables and figures\nIn the revised manuscript, we have replaced “aphid(s)” by “Sitobion avenae / S. avenae”\nin the above places as well as in the first use of “aphid(s)” in all captions of tables and\nfigures, including in the supplementary information.\nl. 99. The temperature of the soil surface and its utility as a refuge from higher\ntemperatures depends upon whether it is exposed to solar radiation or shaded. While\nthe arena within the cylinders was of course an artificial environment, nonetheless, was\nthe soil fully or partially shaded by the wheat plants? If partially shaded, did the\nexperimenters make observations as to whether aphids that had moved to the soil\nsurface were in areas shaded by the plant canopy? Though minor, this information\nwould be helpful for context.\nWe agree with the reviewer that whether the aphids are exposed to solar radiation or\nshaded parts of the soil surface is critical to assign this microhabitat as a thermal\nrefugia.\nThe field data about the light distribution within wheat canopy showed that, at the\nheading stage afterwards of the wheat phenology where extreme high temperatures\nmay occur frequently, the dense wheat canopy can substantially block out the sunlight,\nleading to few areas of the soil surface exposed to sunlight (Figure R1, n-p) (Chang et\nal., 2022).\nIn our lab experiment, however, we cannot observe if the aphids move to shaded areas\nwhile at the soil surface without disturbing the trial. However, in the field, we observed\nthat when soil surface temperatures were already warm, the aphids tended to move to\nshaded areas. On the contrary, when soil surface temperatures were relatively cool, the\naphids were more willing to remain under the sunlight. Transferring from sunlight\nexposure to shaded microhabitats to avoid overheating was also found in this aphid\nspecies (Yin et al., 2018), as well as in another wheat aphid species (Rhopalosiphum\npadi) under filed conditions (Wiktelius, 1987).\nSince we cannot observe the aphid behavior to shaded areas, these explanations were\nnot given in the Results section.\n[editorial note: figure redacted]\nFigure R1: Modeling wheat canopy architecture and light distribution. (n–p) An\nillustration of light reflectance and transmittance profiles, the 3D reconstructed canopy,\nand the light distribution within the canopy. (copied from Chang et al., 2022).\nReferences:\nChang TG, Shi Z, Zhao H, Song Q, He Z, Van Rie J, Den Boer B, Galle A, Zhu XG. 3dCAP-\nWheat: An Open-Source Comprehensive Computational Framework Precisely\nQuantifies Wheat Foliar, Nonfoliar, and Canopy Photosynthesis. Plant Phenomics,\n9758148 (2022).\nWiktelius, S. Distribution of Rhopalosiphum padi (Homoptera: Aphididae) on spring\nbarley plants. Ann. Appl. Biol. 110, 1–7 (1987).\nYin WD, Hoffmann AA, Gu XB, Ma CS. Behavioral thermoregulation in a small herbivore\navoids direct UVB damage. J. Insect Physiol. 107, 276–283 (2018).\nOther minor comments.\nl. 101 as high as or greater than 12 C? One or the other.\nIt should be “greater than 12 oC”, according to the recorded differences in microclimate\ntemperature data between air and soil surface. This has been clarified in the revised\nmanuscript.\nl. 293. There are two types of yellow dwarf viruses that affect wheat and other small-\ngrain crops—barley yellow dwarf viruses and cereal yellow dwarf viruses. Thus, it may\nbe advisable to simply refer to them collectively as yellow dwarf viruses (YDVs). [Miller\nWA, Lozier Z. Yellow Dwarf Viruses of Cereals: Taxonomy and Molecular Mechanisms.\nAnnu Rev Phytopathol. 2022 Aug 26;60:121-141. doi: 10.1146/annurev-phyto-121421-\n125135.]\nWe have referred to these two types of viruses as “yellow dwarf viruses (YDVs)”.\nMinor stylistic corrections.\nl. 37. delete “to continue”\nWe have deleted “to continue”.\nl. 67. Change to “thereby leading to inaccurate predictions of insect density…”\n(“misleading” can imply an intent to deceive)\nThis was changed according to the reviewer.\nl. 77. spp. -- no italics\nThis has been corrected.\nl. 87. “allows for an understanding of the importance…”\nWe have rephrased this sentence as “allows for an understanding of the importance…\",\nbased on the reviewer comment.\nl. 140. “when they had the possibility”\n“when they have the possibility” has been corrected to “when they had the possibility”.\nll. 150, 427. Change to “nymphal instars” and then no need for hyphens in l. 426. Make\nsame changes in ll. 436-438.\nWe have changed these according to the reviewer’s comments.\nl. 340 “changes of EHT can”\nThis has been corrected.\nReviewer #3 (Remarks to the Author)\nThe authors have addressed to the best of their abilities the criticisms I made in an\nearlier referees' report, and in their rebuttal have clearly stated where we disagree.\nGiven the length of time this paper has been in review and that I acknowledge our\ndifferences are to a certain extent a matter of opinion I think the paper should be\npublished in its current form.\nWe thank the reviewer for his appreciation of our revisions.\nReviewer #5 (Remarks to the Author)\nThis paper combines a very interesting set of experimental work and real world data\nthrough modeling and has important implications for pest management in a changing\nworld. The authors have addressed several concerns about the experimental design,\nwhich have bolstered their conclusions. Because I have already provided a prior strongly\npositive review of the paper and indicated the shortcomings of the paper were\nsurmountable, I focus here on how the authors addressed each of the major concerns.\n1. A concern of a prior review was the creation of a thermal gradient through adding\nmoisture to the soil. The authors have addressed this concern by adding data related to\nrelative humidity (RH) under dry and moist soil conditions. They also conducted an\nadditional experiment to test whether humidity interacts with temperature to affect\naphid survival, and it appears not to, which is actually surprising and strengthens the\nresults.\n2. A second concern was the whether the nature of the thermal landscape limits aphids’\nabilities to thermoregulate. The description of the thermal landscape in wheatfields and\nthe thermoregulatory behaviors was quite helpful in addressing this, and is now\naddressed clearly in the paper. Also, regarding the Leith et al (2024) paper: avoiding\nthermal extremes is a different process than thermoregulating to maximize\nperformance, as sublethal effects of occupying suboptimal temperatures can reduce\noverall fitness\nWe sincerely appreciate the reviewer’s comments about our revisions.\n3. A third concern was whether a control treatment of constant 24 degrees was\nappropriate. The authors argue that 5 oC daily is minimal, but 5 oC is pretty substantial\nfor insects and there is research suggesting that EHTs under fluctuating temperatures\nhave greater effects than under more stable conditions; thus, they authors may be\noverestimating the effects of EHTs on aphid survival. The authors use figure 6 to\naddress this concern, but the data in the figure are not appropriate for doing so. From\nmy understanding of the figure the middle row is showing performance with low\nthermal fluctuations whereas the bottom panel shows performance with high thermal\nfluctuations. The authors then suggest that the curves with low thermal fluctuations\nmatch the curves on the top row better than those on the third row. However, TPC\nshape cannot be derived from 3 data points, and so TPCS in rows 2 and 3 are actually\nnot comparable to that in row 1, and in fact, both Topt and performance maximum\nsuggested by the three data points may be significantly different in row 2 compared to\nrow 1.\n4. Note that this 3rd concern, while not satisfactorily addressed by the authors at this\ntime, is not substantial enough to reduce the impact of the paper, and can be\naddressed in two ways. First, the wording supporting the use of a constant thermal\nregime as a control that represents wild conditions needs to be substantially softened\nand the potential limitation of the data set acknowledged. Second, it would be possible\nto run the model with some range of TPC parameters as a comparison, which would\nhelp indicate by how much the results are over- or under-estimated.\n5. Note that figure 6 legend lacks sufficient explanation at this time. For example, is the\nobserved vs predicted equivalent to what was observed in the experiment vs predicted\nif temperature was fluctuating? Also, it is confusing that the authors refer to predicted\nperformances in each panel, but then have data points that are both observed and\npredicted represented by the open and closed circles.\nAll the above three comments (3-5) are mainly about “whether a control treatment of\nconstant 24 degrees was appropriate” in our experiments, and the reviewer thought we\nmay be overestimating the effects of EHTs on aphid performance as well as the model\nestimations of yield losses. Here we address these concerns together.\nFirst, we are sorry for the confusion caused by lack of details in our description in the\nlegend for Figure 6 (see below). We thought the reviewer might read the detailed\nmethods from the original paper (Wang & Ma 2022). Now we try our best to describe\nit clearly:\nIn Figure 6, top panels (A-1, B-1, C-1, D-1): thermal performance curves (95% CI) of\ndifferent traits derived from an experiment for S. avenae at 9 constant temperatures\n(11 °C, 14 °C, 16 °C, 20 °C, 22 °C, 25 °C, 28 °C, 30 °C, and 32 °C).\nMiddle panels (A-2, B-2, C-2, D-2): the same thermal performance curves as top panels\n(A-1, B-1, C-1, D-1); closed circles are experimental values under fluctuating\ntemperatures with mild amplitude (22 ± 5 °C, 25 ± 5 °C, 28 ± 5 °C); open circles\nare predicted values at fluctuating temperatures (22 ± 5 °C, 25 ± 5 °C, 28 ± 5 °C)\nby Dowd et al.’s method (2015).\nBottom panels (A-3, B-3, C-3, D-3): the same thermal performance curves as top\npanels (A-1, B-1, C-1, D-1); closed circles are experimental values under fluctuating\ntemperatures with large amplitude (22 ± 8 °C, 25 ± 8 °C, 28 ± 8 °C); open circles\nare predicted values at fluctuating temperatures (22 ± 8 °C, 25 ± 8 °C, 28 ± 8 °C)\nby Dowd et al.’s method (2015).\nThus, figure 6 indicates that ±5°C have not caused a great divergence from the\nconstant regimes. We hope that our explanations answered the reviewer’s concern.\nMeanwhile, we compared our results from the constant temperature of 24 oC\n(Experiment 2 of the present study) with that obtained from a fluctuating condition at\nthe same mean temperature (Ma et al., 2015). Our results at constant 24 oC were very\nsimilar to those at the fluctuating thermal environment (see Table R1).\nTable R1. Performances derived from constant (from the present study) and corresponding\nfluctuating thermal conditions (Ma et al.2015) with the same mean temperature of 24 oC.\nConstant Fluctuating\n(Experiment 2 of the present study) (Ma et al., 2015)\nDevelopment rate (1/day) 0.13 0.11\nLifetime fecundity (per aphid) 10.3 10.0\nAdult longevity (days) 10.6 9.3\nIntrinsic rate of increase (r ) 0.16 0.17\nm\nImportantly, all data on aphid survival under extreme high temperatures (Experiment 4\nof the present study), used in the models, were collected from fluctuating thermal\nconditions, rather than from constant temperatures. Thus, the results obtained from\nExperiment 2 of the present study do not affect the model predictions.\nTo sum up, using a constant temperature as a control is reasonable, and this\napproach does not necessarily overestimate the model predictions.\nReferences:\nDowd, W. W., King, F. A., & Denny, M. W. Thermal variation, thermal extremes and the\nphysiological performance of individuals. J. Exp. Biol. 218, 1956–1967 (2015).\nMa, G., Hoffmann, A. & Ma, C. S. Daily temperature extremes play an important role in\npredicting thermal effects. J. Exp. Biol. 218, 2289–2296 (2015).\nWang XJ, Ma CS. 2022. Challenge generality of prediction based on Jensen’s inequality:\nModerate and large temperature fluctuations can lead to opposite performance\ndeviation at high mean temperature. Entomologia Generalis, 42: 681–689.\nOverall, this paper is creative and exciting, combining real world data with experimental\ndata to demonstrate the importance of thermoregulation in buffering aphid\npopulations from extreme heat. Importantly, this work highlights how incorporating\nbehavior into our understanding of how complex processes like thermal effects on\nherbivory impact food supplies provides unique insights into the effects of climate\nchange on humans.\nWe deeply appreciate the reviewer’s endorsement of our work’s innovative approach\nbridging experiments and field data."}]

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