Temperature-Dependent Functional Response of Harmonia Axyridis Pallas (Coleoptera: Coccinellidae) Preying on Acyrthosiphon Pisum (Harris) (Hemiptera: Aphididae) Nymphs


 Functional response models are often used to understand the foraging interactions and determine the suitable biocontrol agents. We determined the functional response of Harmonia axyridis to nymph Acyrthosiphon pisum at different but constant temperatures (between 15 and 35 °C) and prey densities. Logistic regression and Roger’s random predator models were employed to determine the type and parameters of functional response. Harmonia axyridis larvae and adults exhibited Type II functional responses to different densities of A. pisum. Warming increased both the predation activity and host aphid control mortality. The 4th instar and female H. axyridis consumed the most aphids. Warming contributed markedly in accelerating the predator action. For fourth instar larvae and female H. axyridis adult, the successful attack rates were 0.234 ± 0.014 h−1 and 0.247 ± 0.015 h−1; the handling times were 0.132 ± 0.005 h and 0.156 ± 0.004 h; and the estimated maximum predation rates were 181.28 ± 14.54 and 153.85 ± 4.06, respectively. These findings accentuate the high performance of 4th instar and female H. axyridis and the role of temperature in their efficiency. Further studies exploring intraguild predation and mutual interference will be required to conclude the biocontrol potential of H. axyridis to A. pisum.


Introduction
The concept of integrated pest management strategies based on biocontrol agents has received increasing prominence worldwide 1 . It has been implemented with tremendous success to both under elds and greenhouses [2][3][4] , in the particular context of reducing the large-scale use of pesticides [5][6][7][8] . While the adoption of biological control is desirable, the successful implementation depends upon the comprehensive understanding of predator-prey interactions, owing to their fundamental role towards ecosystem functionality and food web stability 9 . Several methods can be applied to quantifying these interactions 10,11 , functional response 12 , numerical response 13 , kill rate 14 , and consumption rate 15 are typically employed when foraging interactions have to formalize 16 . Functional response describes how predation rate changes with resource density 17 . Three types of functional responses can be present, with consumption rate being linear up to a constant plateau (Type I), hyperbolic (Type II), or sigmoid (Type III) 18 , depending upon the parameter of functional response, i.e., the enemy attack rate, and prey handling time. The attack rate de nes the steepness of the increase in predation with the increase of prey density, and handling time helps rate the satiation threshold 19 . Many sources of variation are known to modulate outcomes of these functional response parameters 20,21 .
Temperature is a chief driver of biological systems through the temperature-dependent nature of biological rates (e.g., metabolic rates) 24 . Effects of temperature on biological rates are likely to be realized up to species level thus expected to in uence the population growth rates and carrying capacities 25 as well as ecosystem functions. Temperature can regulate the prey-predator interactions by altering their responses to metamorphosis and population dynamics 22 . Warming is shown to increase the food intake of predators 23 . In order to consume large prey biomass, a predator should become quickly adept to search/handle its prey, so that it may spend time on consumption events rather than on prey searching/handling attempts, therefore a change of predatory behavior or functional response may be expected under warming 26,27 . For instance, the handling time decreased, consumption rate increased, and the type of functional response changed from Type II to Type III for Podisus maculiventris (Say) and Podisus nigrispinus (Dallas) (Hemiptera: Pentatomidae) preying on Spodoptera exigua (Hübner) (Lepidoptera: Noctuidae) when the temperature increased from 18 ºC to 27 ºC 28 . Similarly, functional response for Euborellia annulipes (Dermaptera: Anisolabididae) preying on larvae Plutella xylostella (Lepidoptera: Plutellidae) changed from Type III at low temperature (i.e.,18 ºC) to Type II at higher temperature (i.e., 25 ºC and 32 ºC) 29 . A changing functional response with warming may destabilize some predator-prey interactions, it may favor prey depletion by increasing predator action 29 . Therefore, a large body of literature is directed towards understanding the consequence of warming on food webs and stability 30,31 .
The pea aphid, Acyrthosiphon pisum (Harris) (Hemiptera: Aphididae), originally a Palearctic species 32 , has now become a pest of global concern for pulse and legume producers 33 . It has a broad host range, infesting grass pea (Lathryus sativus L.), faba bean (Vicia faba L.), pea (Pisum sativum L.), alfalfa (Medicago sativa L.), chickpea (Cicer arietinum L.), lentil (Lens culinaris Medik.), and lupin (Lupinus albus L.) (Fabales: Fabaceae) 34 . The aphid in icts injury either directly, i.e., by removing sap from succulent phloem tissues or via injecting phytotoxic saliva 35 , or indirectly, i.e., by vectoring multiple plant viruses (e.g., the cucumber mosaic virus, the pea enation mosaic virus, the bean leaf roll virus, and the beet yellow virus) 36,37 or by producing honeydew, subsequently inviting sooty-mould that disturbs plant photosynthetic and respirational functions 38 . Its prolonged infestation could lead to plant stunting, deformation, and discoloration, ultimately reducing crop yields by 35.7% 39 . The broad host range, complex life cycle, and quick adaptiveness to new environments make it di cult to control this aphid.
Moreover, this aphid has been reported for the development of insecticide resistance 40 .
Many aphidophagous ladybird beetles (Coleoptera: Coccinellidae) are known to be exploited for conservative or augmentative biocontrol programs of several economically important aphids in diverse crops, outdoor and in greenhouses, allowing aphid suppression to well below economically damaging levels 41 . Harmonia axyridis Pallas (Coleoptera: Coccinellidae) is a generalist predator, geographically wide-spread 42 , and extensively employed as a biocontrol agent of soft-bodied insects, including aphids in a diversity of crops 43,44 . Various biological aspects of H. axyridis of importance for its predatory potential (e.g. phenological characteristics, life table parameters, and functional response (generally with a Type II response), have been investigated with respect to the temperature and other concerning factors [45][46][47][48][49] to many crop pests [50][51][52] , however, information on its predatory behavior and functional response to A. pisum almost lacking 53 .
Here, we report the functional response of H. axyridis to A. pisum under various thermal conditions. We expect that, based on its close association with predator growth and development, the temperature change will accordingly modify consumption. Further, we aim to assess whether thermal conditions and aphid density affect the functional response of larvae and adult H. axyridis.
Logistic regression between the initial aphid densities offered to larvae and adult H. axyridis and the proportion of aphid consumed (Na/No) showed all signi cantly negative values of the linear coe cients P 1 ; exhibiting a Type II functional response across all growth stages and temperatures tested ( Table 1).
The declining consumption with increasing aphid densities (Fig. 5) also con rmed a Type II functional response. The monotonically declining proportion of consumption with increased aphid densities, led to further con rmation of Type II functional responses (Fig. 6). Clearly, a more linear trend for the proportion of prey eaten with increasing aphid density was noted for 4th instar (Fig. 6d) and female H. axyridis (Fig. 6f) at higher (i.e., 30 and 35°C) than lower temperatures. Estimates of functional response parameters, determined through Rogers random predator model, revealed that the H. axyridis exhibited the highest attack rate (Fig. 7a), the shortest handling time (Fig. 7b), and the maximum predation (Fig. 7c) at higher temperatures, and typically at later growth stages. The outcomes of all three parameters were generally low at low thermal conditions (15 and 20°C) but started to improve with warming, with the best results at 30 and 35°C. The outcomes of all three parameters were much low for 1st, 2nd, and 3rd instar when compared with the 4th instar and adult H. axyridis. Female H. axyridis performed much better than male H. axyridis, especially in terms of handling time and maximum predation rate (Fig. 7).

Discussion
Exploring the temperature in uence on predator-prey interactions 16 can be highly insightful towards conservative or augmentative biocontrol programs 54 . As insects are ectotherms, their development and biology are responsive towards temperature 55 , so are their behavioral trophic interactions 16,56 . Theories concerning temperature change and its in uence on ecosystem functions, including foraging interactions 57 , are gaining increasing prominence since the past decade 30,31 . The current research is the rst report exploring functional response of H. axyridis to A. pisum at different growth stages and temperatures. The temperature ranges we have tested are existent across a range of temperate or subtropical regions. We determined increasing host aphid mortality with warming, meaning warming can lead towards prey depletion. A low-temperature threshold (i.e., 20 ºC) is reported the best for aphid growth and development, whereas, higher temperatures > 30 ºC become unfavorable for aphid fertility and development, and discourage its population buildup 93 . We showed a Type II functional response by all stages of H. axyridis, within the tested host aphid density ranges and thermal conditions, with maximum predation by the 4th instar and female H. axyridis between 25 and 35°C. Warming is shown to accelerate insect metamorphosis. Accelerated growth/development enhances metabolic rate and energy gain requirements 50,58 , which the predator meets by consuming large meals 59  Functional response models are employed to quantifying the consumption relationship between a predator and its resource 12 , and a typical model illustrating these relations is the Type II, explaining a curvilinear increase in predation with respect to increasing prey density, changing to asymptotic at high prey densities, and thenceforth hanging around constant by reason of satiation 12 . A Type II response is often a characteristic of those predators that provide e cient control at smaller resource 61 , though often associated with unstable predator-prey population dynamics 17,62,63 , owing to the decreasing risk of predation with resource abundance. This means a negative density-dependent mortality, which can unstable population dynamics 64 . Many sources of variations are shown to have regulatory roles towards functional response, including prey/predator biology, switching, preference, host distribution, patch allocation time of predator, and intra/interspeci c competitions 65-67 .
In our ndings, despite its positive role towards consumption, no change in H. axyridis functional response type (i.e., Type II) was observed towards thermal changes. A changing functional response type with thermal changes has been reported for many predators 29  The attack rate and handling time are important parameters, describing functional response magnitude.
The attack rate (also called the space clearance rate or attack e ciency) describes the ability a predator possesses to catch its prey in a given time frame, and handling time describes the time lost from searching per host consumed 80 . A high attack rate means that the predator is adept at quickly removing hosts from the volumes or areas it is searching, and low handling time means how quickly a predator traps, hunts, and digests prey 80 . In our ndings, the parameter estimates showed greater variations across predator growth stages, with frequently better estimates at later growth stages and higher temperatures. Maximum daily predation rates were temperature-dependent, especially their increase, and the handling time of H. axyridis presented an exponential decrease for all growth stages at the lowest thermal conditions, meaning predators will spend less investment on foraging, convert that to resting, for example, and decrease predation event 81,82 . Conversely, the greater number of prey consumed owing to the warming implies a decrease in handling time with a resultant increase in time to approach other prey at alleviated temperature 83 . The increased metabolic rates under warming were associated with greater energy demands, which should expectedly maximize food intake and foraging activity. Determining these parameters with respect to phenological stage con rmed poor response by the rst three instars as reported earlier 72,84 , whereas better by nal instar and adult H. axyridis (especially female when compared with male) 85,86 suggesting H. axyridis better biocontrol e ciency at later stages, plausibly owing to better searching e ciencies and agilities. The 4th instar requires large meals to attain the required weight for pupation 87 and adult predators have to prepare for reproduction 69 and other functions related to egg maturation or fertilization 88 .
Though employed commonly to understand the predator-prey interactions related to food consumption and food web stability 89,90 , the functional response models are usually applied under controlled conditions and do not thoroughly represent the eld circumstances, hence fail to count for complex and diverse ecological interactions. For example, small-scale setups (such as Petri dish) may not mimic the actual eld conditions 91 , and a predator with abundant prey resource, as under controlled conditions, will never going to experience the challenges of emigration or cannibalism 92 , which could occur under poor resource availability. Despite these limitations, the functional response models allow us to understand complex foraging interactions and forecast ecosystem stability. We determined the strong e ciency of H. axyridis towards A. pisum control, but the predation activity depended upon the growth stage and thermal conditions imposed. The 4th instar and female H. axyridis emerged as the best performing biocontrol candidates, with best e ciencies under warming conditions. Another striking effect of warming was determined on prey mortality that increased with warming. This implies that warming may lead towards aphid depletion and consequently trigger intraguild predation or other antagonistic interactions among predator populations 94 . This allows us to suggest that H. axyridis can be used at low temperatures (between 20 and 25 ºC), but the risk of intraguild predation can be expected at higher temperatures.
Provision with alternative prey resources could be a feasible way of supporting this generalist predator without changing functional response type; however, further research is needed to synthesize careful conclusions. Thus, we recommend further studies to evaluate current ndings with the inclusion of the other contributable factors, such as intraguild predation, alternative prey resource, mutual interference, pesticides, etc., so that the actual predation behaviour of H. axyridis to A. pisum may be approached and an e cient biological control may be developed and implemented.
The seeds were sown in 30 × 25 cm diameter regular pots lled with (3:1) soil: manure. The seedlings were maintained under greenhouse conditions of 12-26°C, 45-55 % RH, and 16:8 h (Light: Dark) photoperiod, and subsequently used for rearing and conducting functional response assays. The plant materials used were obtained with prior permission, and the present study is in compliance with relevant guidelines and legislation.
For establishing A. pisum culture, the initial populations of aphid collected from unsprayed alfalfa elds were subsequently brought to the laboratory and reared on broad bean plants inside net cages (20 × 10 × 30 cm height). The stock culture of H. axyridis was developed from a pre-established laboratory colony, already available in the same laboratory. The predator was reared on A. pisum infested bean plants (7-8 leaves) inside net cages (60 × 42 × 30 cm height) for three consecutive generations at laboratory conditions of 24 ± 1°C, 65 ± 5 % RH and 16:8 h (Light: Dark) photoperiod. Bean plants were checked daily for predator eggs. Egg batches when found were carefully removed, placed on tissue paper in Petri dishes (9 cm), and transferred to a computer-operated growth chamber, maintained at settings of 25 ± 1°C, 65 ± 5 % RH and 16:8 h (Light: Dark) photoperiod. The post-emergence larvae were separated and reared in Petri dishes containing aphid as their diet, refreshed daily. The whole culture was maintained at the Department of Plant Protection, Huazhong Agricultural University, China.
The experimental arena consisted of clear Petri dishes (9 cm diameter), with a micromesh screen over the top for ventilation and bottom covered with clean cucumber leaf disk. The desiccation of cucumber leaf disc was prevented by adding 1% agar solution 95 . The assays were performed with H. axyridis larvae (i.e., 1st instar, 2nd instar, 3rd instar, 4th instar) and adults (male, female) at constant temperatures (i.e., 15, 20, 25, 30, 35°C). The homogeneity of predator age was maintained within each tested growth stage. The rst instar larvae were separated one by one shortly after hatching to avoid sibling cannibalism.
Hatchlings were reared in Petri dishes (9 cm diameter) until maturity on 4th instar nymphs ( Prior to analysis, the mortality data were tested for normality and homogeneity of error variance (i.e., homoscedasticity) by using Shapiro-Wilk and Levene tests, and Y = √ x + 1 transformed to improve compliance with these assumptions. All means and standard errors in text and gures are calculated with untransformed data.
Aphid consumption by H. axyridis for temperature, growth stage, density, and their two-way and three-way interactions were analyzed by using Generalized Linear Models (GLM) in SPSS (version 21). Kolmogorov-Smirnov test con rmed non-normal distributions of data (P > 0.05), and due to over-dispersion, the data were tted with negative binomial distribution and a log link function, and factors and interaction effects were analyzed by using the Wald Chi-Square test for a con dence level (CI) of 95%. If needed, the multiple follow up tests were run to analyze the temperature and growth stage effects, separately, at each aphid density, and the signi cance for each test was adjusted by following Bonferroni correction to avoid Type 1 error.
Analysis of the functional response was done in two different phases 17 : rst phase involved the determination of type and estimation of the parameters of the functional response curve. It is compulsory to nd the type of functional response for calculating the functional response parameters using a proper model. The type was determined by applying logistic regression of the proportion of prey eaten as a function of initial prey density offered. A polynomial logistic regression equation assuming a binomial distribution of data to de ne the type of functional response 17 (Eq. 1) was tted as under: Where N a and N o indicate the number of prey consumed and the initial prey density offered, respectively, and is the proportion of prey consumed. The P o , P 1 , P 2 , and P 3 are the regression parameters representing intercept or constant, linear, quadratic, and cubic coe cients, respectively. The coe cients were calculated by using the maximum likelihood method. The values of the linear and quadratic coe cients indicate the nature of functional response either it is Type II or Type III. When the value of a linear parameter is negative, the functional response is Type II, and if it is positive with a negative quadratic coe cient, then response is of Type III. The Type II response shows that the proportion of prey consumption decreases as the prey density increases, and a Type III response represents that the proportion of prey consumed increases until an in ection point and then decreases 17 . Once the functional response type was determined, the second phase started where functional response parameters were determined. For which, data were tted to Rogers' type II random predator equation, with the help of nonlinear least square regression, and determined and analyzed the parameters of functional response. As the prey was not changed or replaced during the entire experiment, the random predator equation was determined to be more appropriate for such a dataset 98 . The attack rate (a) and handling time (Th) were calculated by using the random predator model as under: (Eq. 2).
Where, a is the attack rate, T h is the handling time, T is time available for predator during the experiment.
Here, "glm" function was used to t the logistic regression, and the parameters (attack rate a and handling time T h of functional response were estimated by using FRAIR (Functional Response Analysis in R, version 4.0.0) 99 in the R statistical environment 100 . The maximum predation rate is the ratio between T/Th 101 and estimates the maximum amount of prey that a predator can consume in a given time frame.