# Examining the Effects of Induced Plant Defenses on _Spodoptera frugiperda_ Performance
## 4 Discussion
Different induced plant defenses were equally effective at decreasing 7-day larval survival, but the higher constitutive phenolic defenses varying between cultivars were more effective at lowering larval survival before JA induction. Later life performance metrics such as development time to pupation and pupal mass were less influenced or unaffected (respectively) by plant cultivar and induced defenses. Further, individuals that survive to pupation would have similar fecundity due to the similar body sizes of individuals in the different treatment groups; extending the development rate of larvae would allow growers more time to apply treatment methods and could increase the insect pest's susceptibility to natural enemies as predicted by the slow-growth high mortality hypothesis [39]. However, induced plant defenses that act as a feeding retardant, such as in this case, would lower the efficacy of any _per os_ entomopathogens used as a microbial biological control, such as _Spodoptera frugiperda_ multiple nucleopolyhedrovirus [11; 12]. _S. frugiperda_ exhibits size susceptibility to nucleopolyhedrovirus (NPVs), meaning smaller insects are more susceptible than larger insects. Still, insect herbivores that have slower development and feeding because of plant defenses are less susceptible to NPV control measures because they consume viral particles on foliage slower, resulting in a lower treatment dose being administered per time [10; 12]. Furthermore, induced plant defenses can inactivate NPVs, interfere with their transmission inside the larva's gut, and lower pathogen efficacy/production [10; 11; 24; 40]. Given all this, it would be advisable for growers to monitor their commodities biweekly. At the same time, _S. frugiperda_ can quickly detect infestations for rapid deployment of control measures while insects are still young. Induced plant defenses could lower larval survival up to 9.6% and extend development time by \(\sim\)3 days which would alleviate pest pressure and extend the period growers have to detect _S. frugiperda_. Likewise, planting a resistant cultivar higher in phenolic compounds will greatly lower young larvae survival, with only marginal changes in _S. frugiperda_ development time that would not impact the detection window. If microbial biocontrol with NPVs is to be applied, it would be most effective on younger instars before they cause significant amounts of foliar damage that triggers an induced plant defensive response [41]. Cultivars with high constitutive plant defenses and plantings that experience high levels of damage from numerous generations of _S. frugiperda_ infestations might present a particular problem for microbial biocontrol with NPVs. Thus, an integrated pest management approach should be used to enhance the effectiveness of NPVs. Augmentative biological control specifically could be an effective additive treatment to NPV applications in the field since natural enemies either avoid sick prey or have neutral effects on pathogen spread, given the increased development time of pests allowing predators more time to forage and hunt them [42; 43]. The following are available online at [[https://www.mdpi.com/article/10.3390/app12083907/s1](https://www.mdpi.com/article/10.3390/app12083907/s1)]([https://www.mdpi.com/article/10.3390/app12083907/s1](https://www.mdpi.com/article/10.3390/app12083907/s1)), Figure S1: Posterior predictive fit for _S. frugiperda_ mortality at 7 days across different _G. max_ cultivars crossed for innate and induced jasmonic acid plant defenses, Figure S2: Posterior predictive fit estimates for _S. frugiperda_ survival at 7 days across different _G. max_ cultivars crossed for innate/induced jasmonic acid plant defenses, Figure S3: Posterior predictive fit for _S. frugiperda_ development time in days to pupation across different _G. max_ cultivars with innate/induced jasmonic acid plant defenses and sex of individual for the main effects parameterization model, Figure S4: Posterior predictive fit estimates for _S.