Compatibility of synthetic and biological pesticides with a biocontrol agent Phytoseiulus longipes (Acari: Phytoseiidae).

Phytoseiulus longipes is a predatory mite of Tetranychus evansi, which is an invasive pest in Africa and elsewhere. The introduction of this predator in Africa has considerable potential, but little is known about the compatibility of P. longipes with commonly used pesticides. Here, we examined lethal and sublethal effects of two pyrethroids (cypermethrin and deltamethrin), two organophosphates (dimethoate and chlorpyrifos), one nicotinoid (imidacloprid), two acaricides (propargite and abamectin), two naturally derived pesticides (oxymatrine and azadirachtin), and one entomopathogenic fungal-based formulation (Hirsutella thompsonii) on P. longipes eggs and adults. The pesticides were sprayed at their maximum recommended concentrations. Topical exposures to azadirachtin, imidacloprid, propargite, abamectin, oxymatrine, and H. thompsonii significantly reduced the net reproductive rate (R0), intrinsic rate of increase (r) and finite rate of increase (λ)of P. longipes. Pesticide lethal and sublethal effects on the predator were summarized in a reduction coefficient (Ex) for the classification based on IOBC toxicity categories. Results revealed that Azadirachtin and H. thompsonii were slightly harmful effects to adults. Imidacloprid, propargite, abamectin, and oxymatrine were moderately harmful to both eggs and adults. Residual persistence bioassays revealed that 4-day-old residue of azadirachtin had no harmful effect on the predator. Abamectin, oxymatrine, and H. thompsonii became harmless to it 10 days post-spraying, and propargite and imidacloprid were considered harmless after 20 days. Cypermethrin, deltamethrin, dimethoate, and chlorpyrifos were highly harmful to both eggs and adults, persistence remaining high even after 31 days of application. These findings provide valuable insights into decision-making when considering P. longipes for use in IPM programs.

Calibration to maximize temporal radiometric repeatability of airborne hyperspectral imaging data.

Many studies provide insight into calibration of airborne remote sensing data but very few specifically address the issue of temporal radiometric repeatability. In this study, we acquired airborne hyperspectral optical sensing data from experimental objects (white Teflon and colored panels) during 52 flight missions on three separate days. Data sets were subjected to four radiometric calibration methods: no radiometric calibration (radiance data), empirical line method calibration based on white calibration boards (ELM calibration), and two atmospheric radiative transfer model calibrations: 1) radiometric calibration with irradiance data acquired with a drone-mounted down-welling sensor (ARTM), and 2) modeled sun parameters and weather variables in combination with irradiance data from drone-mounted down-welling sensor (ARTM+). Spectral bands from 900-970 nm were found to be associated with disproportionally lower temporal radiometric repeatability than spectral bands from 416-900 nm. ELM calibration was found to be highly sensitive to time of flight missions (which is directly linked to sun parameters and weather conditions). Both ARTM calibrations outperformed ELM calibration, especially ARTM2+. Importantly, ARTM+ calibration markedly attenuated loss of radiometric repeatability in spectral bands beyond 900 nm and therefore improved possible contributions of these spectral bands to classification functions. We conclude that a minimum of 5% radiometric error (radiometric repeatability<95%), and probably considerably more error, should be expected when airborne remote sensing data are acquired at multiple time points across days. Consequently, objects being classified should be in classes that are at least 5% different in terms of average optical traits for classification functions to perform with high degree of accuracy and consistency. This study provides strong support for the claim that airborne remote sensing studies should include repeated data acquisitions from same objects at multiple time points. Such temporal replication is essential for classification functions to capture variation and stochastic noise caused by imaging equipment, and abiotic and environmental variables.

Modeling and validation of oviposition by a polyphagous insect pest as a function of temperature and host plant species.

Modeling oviposition as a function of female insect age, temperature, and host plant suitability may provide valuable insight into insect population growth of polyphagous insect pests at a landscape level. In this study, we quantified oviposition by beet leafhoppers, Circulifer (= Neoaliturus) tenellus (Baker) (Hemiptera: Cicadellidae), on four common non-agricultural host plant species [Erodium cicutarium (L.) L'Hér. (Geraniaceae), Kochia scoparia (L.) Schrader (Amaranthaceae), Plantago ovata Forsskál (Plantaginaceae), and Salsola tragus L. (Amaranthaceae)] at two constant temperature conditions. Additionally, temperature-based oviposition models for each host plant species were validated, under semi-field and greenhouse conditions. We found that K. scoparia was the most suitable host plant, and optimal temperature for oviposition was estimated to be 30.6°C. Accordingly, beet leafhoppers appear to be well-adapted to high-temperature conditions, so increasing temperatures due to climate change may favor population growth in non-agricultural areas. Maximum total fecundity (Rm) was used as an indicator of relative suitability of host plants. S. tragus has been considered an important non-agricultural host plant, however, we found that S. tragus and E. cicutarium have lower Rm compared to K. scoparia and P. ovata. The combination of detailed experimental oviposition bioassays, modeling, and model validation is considered widely relevant and applicable to host plant assessments and modeling of population dynamics of other polyphagous insect pests.

Data-driven vermiculite distribution modelling for UAV-based precision pest management.

In recent decades, unmanned aerial vehicles (UAVs) have gained considerable popularity in the agricultural sector, in which UAV-based actuation is used to spray pesticides and release biological control agents. A key challenge in such UAV-based actuation is to account for wind speed and UAV flight parameters to maximize precision-delivery of pesticides and biological control agents. This paper describes a data-driven framework to predict density distribution patterns of vermiculite dispensed from a hovering UAV as a function of UAV's movement state, wind condition, and dispenser setting. The model, derived by our proposed learning algorithm, is able to accurately predict the vermiculite distribution pattern evaluated in terms of both training and test data. Our framework and algorithm can be easily translated to other precision pest management problems with different UAVs and dispensers and for difference pesticides and crops. Moreover, our model, due to its simple analytical form, can be incorporated into the design of a controller that can optimize autonomous UAV delivery of desired amount of predatory mites to multiple target locations.

Experimental data manipulations to assess performance of hyperspectral classification models of crop seeds and other objects.

BACKGROUND: Optical sensing solutions are being developed and adopted to classify a wide range of biological objects, including crop seeds. Performance assessment of optical classification models remains both a priority and a challenge. METHODS: As training data, we acquired hyperspectral imaging data from 3646 individual tomato seeds (germination yes/no) from two tomato varieties. We performed three experimental data manipulations: (1) Object assignment error: effect of individual object in the training data being assigned to the wrong class. (2) Spectral repeatability: effect of introducing known ranges (0-10%) of stochastic noise to individual reflectance values. (3) Size of training data set: effect of reducing numbers of observations in training data. Effects of each of these experimental data manipulations were characterized and quantified based on classifications with two functions [linear discriminant analysis (LDA) and support vector machine (SVM)]. RESULTS: For both classification functions, accuracy decreased linearly in response to introduction of object assignment error and to experimental reduction of spectral repeatability. We also demonstrated that experimental reduction of training data by 20% had negligible effect on classification accuracy. LDA and SVM classification algorithms were applied to independent validation seed samples. LDA-based classifications predicted seed germination with RMSE = 10.56 (variety 1) and 26.15 (variety 2), and SVM-based classifications predicted seed germination with RMSE = 10.44 (variety 1) and 12.58 (variety 2). CONCLUSION: We believe this study represents the first, in which optical seed classification included both a thorough performance evaluation of two separate classification functions based on experimental data manipulations, and application of classification models to validation seed samples not included in training data. Proposed experimental data manipulations are discussed in broader contexts and general relevance, and they are suggested as methods for in-depth performance assessments of optical classification models.

Insect vector manipulation by a plant virus and simulation modeling of its potential impact on crop infection.

There is widespread evidence of plant viruses manipulating behavior of their insect vectors as a strategy to maximize infection of plants. Often, plant viruses and their insect vectors have multiple potential host plant species, and these may not overlap entirely. Moreover, insect vectors may not prefer plant species to which plant viruses are well-adapted. In such cases, can plant viruses manipulate their insect vectors to preferentially feed and oviposit on plant species, which are suitable for viral propagation but less suitable for themselves? To address this question, we conducted dual- and no-choice feeding studies (number and duration of probing events) and oviposition studies with non-viruliferous and viruliferous [carrying beet curly top virus (BCTV)] beet leafhoppers [Circulifer tenellus (Baker)] on three plant species: barley (Hordeum vulgare L.), ribwort plantain (Plantago lanceolata L.), and tomato (Solanum lycopersicum L.). Barley is not a host of BCTV, whereas ribwort plantain and tomato are susceptible to BCTV infection and develop a symptomless infection and severe curly top symptoms, respectively. Ribwort plantain plants can be used to maintain beet leafhopper colonies for multiple generations (suitable), whereas tomato plants cannot be used to maintain beet leafhopper colonies (unsuitable). Based on dual- and no-choice experiments, we demonstrated that BCTV appears to manipulate probing preference and behavior by beet leafhoppers, whereas there was no significant difference in oviposition preference. Simulation modeling predicted that BCTV infection rates would to be higher in tomato fields with barley compared with ribwort plantain as a trap crop. Simulation model results supported the hypothesis that manipulation of probing preference and behavior may increase BCTV infection in tomato fields. Results presented were based on the BCTV-beet leafhopper pathosystem, but the approach taken (combination of experimental studies with complementary simulation modeling) is widely applicable and relevant to other insect-vectored plant pathogen systems involving multiple plant species.

An Interactive Teaching Tool Describing Resistance Evolution and Basic Economics of Insecticide-Based Pest Management.

Effective teaching of complex concepts relies heavily on the ability to establish relevance of topics and to engage students in a constructive dialogue. To connect students with abstract concepts and basic theory, instructors foster and facilitate an engaging teaching environment. Population modeling is a cornerstone in applied entomology. However, it is also a topic and skill set that requires both basic mathematical and biological knowledge, and it may be perceived by students as being abstract and exceedingly theoretical. As a way to introduce entomology students at both that undergraduate and graduate levels to hands-on experience with population modeling, a well-established and widely used deterministic genetic population model is presented as an interactive teaching tool. Moreover, the general model describes three genotypes (SS = homozygous susceptible, SR = heterozygous, and RR = homozygous resistant) during 30 discrete and univoltine generations under a shared population density dependence (carrying capacity). Based on user inputs for each genotype (survival, fitness cost, reproductive rate, emigration, and immigration) and an initial resistance allele frequency, model outputs related to resistance evolution are produced. User inputs related to insecticide-based pest management (pest density action threshold, crop damage rate, insecticide treatment costs, and profit potential) can also be introduced to examine and interpret the basic economic effects of different insect pest management scenarios. The proposed model of resistance evolution and basic economics of pest management relies on a large number of important simplifications, so it may only have limited ability to predict the outcomes of real-world (commercial) scenarios. However, as a teaching tool and to introduce students to a well-known and widely used genetic population model structure, the interactive teaching tool is believed to have considerable utility and relevance.

Early infestations by arthropod pests induce unique changes in plant compositional traits and leaf reflectance.

BACKGROUND: With steadily growing interest in the use of remote-sensing technologies to detect and diagnose pest infestations in crops, it is important to investigate and characterize possible associations between crop leaf reflectance and unique pest-induced changes in plant compositional traits. Accordingly, we compiled plant compositional traits from chrysanthemum and gerbera plants in four treatments: non-infested, or infested with mites, thrips or whiteflies, and we acquired hyperspectral leaf reflectance data from the same plants over time (0-14 days). RESULTS: Plant compositional traits changed significantly in response to arthropod infestations, and individual chrysanthemum and gerbera plants were classified with 78% and 80% accuracy, respectively. Based on leaf reflectance, individual plants from the four treatments were classified with moderate accuracy levels of 76% (gerbera) and 73% (chrysanthemum) but with a clear distinction between non-infested and infested plants. Accurate and consistent diagnosis of biotic stressors was not achieved. CONCLUSION: To our knowledge, this is the first study in which infestations by multiple economically important arthropod pests are directly compared and associated with leaf reflectance responses and changes in plant compositional traits. It is important to highlight that imposed stress levels were low, period of infestation was short, and hyperspectral remote-sensing data were acquired at four time points with analyses based on large data sets (3826 leaf reflectance profiles for chrysanthemum and 4041 for gerbera). This study provides novel insight into crop responses to different biotic stressors and into possible associations between plant compositional traits and hyperspectral leaf reflectance data acquired from crop leaves.

Root-associated entomopathogenic fungi manipulate host plants to attract herbivorous insects.

Root-associated entomopathogenic fungi (R-AEF) indirectly influence herbivorous insect performance. However, host plant-R-AEF interactions and R-AEF as biological control agents have been studied independently and without much attention to the potential synergy between these functional traits. In this study, we evaluated behavioral responses of cabbage root flies [Delia radicum L. (Diptera: Anthomyiidae)] to a host plant (white cabbage cabbage Brassica oleracea var. capitata f. alba cv. Castello L.) with and without the R-AEF Metarhizium brunneum (Petch). We performed experiments on leaf reflectance, phytohormonal composition and host plant location behavior (behavioral processes that contribute to locating and selecting an adequate host plant in the environment). Compared to control host plants, R-AEF inoculation caused, on one hand, a decrease in reflectance of host plant leaves in the near-infrared portion of the radiometric spectrum and, on the other, an increase in the production of jasmonic, (+)-7-iso-jasmonoyl-L-isoleucine and salicylic acid in certain parts of the host plant. Under both greenhouse and field settings, landing and oviposition by cabbage root fly females were positively affected by R-AEF inoculation of host plants. The fungal-induced change in leaf reflectance may have altered visual cues used by the cabbage root flies in their host plant selection. This is the first study providing evidence for the hypothesis that R-AEF manipulate the suitability of their host plant to attract herbivorous insects.

Hyperspectral remote sensing to detect leafminer-induced stress in bok choy and spinach according to fertilizer regime and timing.

BACKGROUND: Detection and diagnosis of emerging arthropod outbreaks in horticultural glasshouse crops, such as bok choy and spinach, is both important and challenging. A major challenge is to accurately detect and diagnose arthropod outbreaks in growing crops (changes in canopy size, structure, and composition), and when crops are grown under three fertilization regimes. Day-time remote sensing inside glasshouses is highly sensitive to inconsistent lighting, spectral scattering, and shadows casted by glasshouse structures. To avoid these issues, a unique feature of this study was that hyperspectral remote sensing data were acquired after sunset with an active light source. As part of this study, we describe a comprehensive approach to performance assessment of classification algorithms based on hyperspectral remote sensing data. RESULTS: Based on average hyperspectral remote sensing profiles from individual crop plants, none of the 31 individual spectral bands showed consistent significant response to leafminer infestation and non-significant response to fertilizer regime. Multi-band classification algorithms were subjected to a comprehensive performance assessment to quantify risks of model over-fitting and low repeatability of classification algorithms. The performance assessment of classification algorithms addresses the important 'bias-variance trade-off'. Truly independent validation (training and validation data sets being separated over time) revealed that leafminer infestation could be detected with >99% accuracy in both bok choy and spinach. CONCLUSION: We conclude that detailed hyperspectral profiles (not single spectral bands) can accurately detect and diagnose leafminer infestation over time and across fertilizer regimes. Hyperspectral remote sensing data acquisition at night with an active light source has the potential to enable arthropod infestations in glasshouse-grown crops, such as, bok choy and spinach. In addition, we conclude that effective use and deployment of hyperspectral remote sensing requires thorough performance assessments of classification algorithms, and we propose an analytical performance method to address this important matter. © 2020 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Proximal Remote Sensing to Non-destructively Detect and Diagnose Physiological Responses by Host Insect Larvae to Parasitism.

As part of identifying and characterizing physiological responses and adaptations by insects, it is paramount to develop non-destructive techniques to monitor individual insects over time. Such techniques can be used to optimize the timing of when in-depth (i.e., destructive sampling of insect tissue) physiological or molecular analyses should be deployed. In this article, we present evidence that hyperspectral proximal remote sensing can be used effectively in studies of host responses to parasitism. We present time series body reflectance data acquired from individual soybean loopers (Chrysodeixis includens) without parasitism (control) or parasitized by one of two species of parasitic wasps with markedly different life histories: Microplitis demolitor, a solitary larval koinobiont endoparasitoid and Copidosoma floridanum, a polyembryonic (gregarious) egg-larval koinobiont endoparasitoid. Despite considerable temporal variation in reflectance data 1-9 days post-parasitism, the two parasitoids caused uniquely different host body reflectance responses. Based on reflectance data acquired 3-5 days post-parasitism, all three treatments (control larvae, and those parasitized by either M. demolitor or C. floridanum) could be classified with >85 accuracy. We suggest that hyperspectral proximal imaging technologies represent an important frontier in insect physiology, as they are non-invasive and can be used to account for important time scale factors, such as: minutes of exposure or acclimation to abiotic factors, circadian rhythms, and seasonal effects. Although this study is based on data from a host-parasitoid system, results may be of broad relevance to insect physiologists. Described approaches provide a non-invasive and rapid method that can provide insights into when to destructively sample tissue for more detailed mechanistic studies of physiological responses to stressors and environmental conditions.

Penetration and scattering-Two optical phenomena to consider when applying proximal remote sensing technologies to object classifications.

Proximal remote sensing is being used across a very wide range of research fields and by scientists, who are often without deep theoretical knowledge optical physics; the author of this article falls squarely in that category! This article highlights two optical phenomena, which may greatly influence the quality and robustness of proximal remote sensing: penetration and scattering. Penetration implies that acquired reflectance signals are associated with both physical and chemical properties of target objects from both the surface and internal tissues/structures. Scattering implies that reflectance signals acquired from one point or object are influenced by scattered radiometric energy from neighboring points or objects. Based on a series of laboratory experiments, penetration and scattering were discussed in the context of "robustness" (repeatability) of hyperspectral reflectance data. High robustness implies that it is possible to control imaging conditions and therefore: 1) obtain very similar radiometric signals from inert objects (objects that do not change) over time, and 2) be able to consistently distinguish objects that are otherwise highly similar in appearance (size, shape, and color) and in terms of biochemical composition. It was demonstrated that robustness of hyperspectral reflectance data (40 spectral bands from 385 to 1024 nm) were significantly influenced by penetration and scattering of radiometric energy. In addition, it was demonstrated that the influence of penetration and scattering varied across the examined spectrum. Characterization of how optical phenomena may affect the robustness of reflectance data is important when using proximal remote sensing technologies as tools used to classify engineering and biological objects.

Hyperspectral imaging to characterize plant-plant communication in response to insect herbivory.

BACKGROUND: In studies of plant stress signaling, a major challenge is the lack of non-invasive methods to detect physiological plant responses and to characterize plant-plant communication over time and space. RESULTS: We acquired time series of phytocompound and hyperspectral imaging data from maize plants from the following treatments: (1) individual non-infested plants, (2) individual plants experimentally subjected to herbivory by green belly stink bug (no visible symptoms of insect herbivory), (3) one plant subjected to insect herbivory and one control plant in a separate pot but inside the same cage, and (4) one plant subjected to insect herbivory and one control plant together in the same pot. Individual phytocompounds (except indole-3acetic acid) or spectral bands were not reliable indicators of neither insect herbivory nor plant-plant communication. However, using a linear discrimination classification method based on combinations of either phytocompounds or spectral bands, we found clear evidence of maize plant responses. CONCLUSIONS: We have provided initial evidence of how hyperspectral imaging may be considered a powerful non-invasive method to increase our current understanding of both direct plant responses to biotic stressors but also to the multiple ways plant communities are able to communicate. We are unaware of any published studies, in which comprehensive phytocompound data have been shown to correlate with leaf reflectance. In addition, we are unaware of published studies, in which plant-plant communication was studied based on leaf reflectance.

Avoidance of parasitized host by female wasps of Aphidius gifuensis (Hymenoptera: Braconidae): The role of natal rearing effects and host availability?

Natal rearing experience of animals may affect their behaviors, such as habitat selection and oviposition decision. As part of the overall fitness of insect parasitoids, successful host discrimination (distinguishing parasitized hosts from unparasitized hosts) is of paramount importance. In this study we examined whether and how parasitoids' natal rearing experience would affect their host discrimination ability according to host availability. We established separate colonies of Aphidius gifuensis Ashmead by continual rearing on two hosts, Sitobion avenae F. and Myzus persicae (Suzler), and quantified self superparasitism and self superparasitism versus parasitism ratio for the four combinations of parasitoid colonies and host species (S. aveane and M. persicae) at four host densities (30, 50, 100 or 150 per plant). Results showed that self superparasitism of M. persicae by A. gifuensis reared on S. avenae was significantly higher than by those reared on M. persicae, no matter whether the host densities were 30, 50, 100 or 150. Aphidius gifuensis reared on M. persicae significantly superparasitized more S. avenae than those reared on S. aveane only when host density was 30. Self superparasitism versus parasitism ratio of A. gifuensis from both colonies was always lower on natal hosts than on new hosts, and the difference was more pronounced as the host density decreased. These results suggested that natal rearing effects is important on host discrimination and oviposition decision of the parasitoid A. gifuensis. These effects promoted the parasitoid's host adaptation and made them confer greater fitness.

Using proximal remote sensing in non-invasive phenotyping of invertebrates.

Proximal imaging remote sensing technologies are used to phenotype and to characterize organisms based on specific external body reflectance features. These imaging technologies are gaining interest and becoming more widely used and applied in ecological, systematic, evolutionary, and physiological studies of plants and also of animals. However, important factors may impact the quality and consistency of body reflectance features and therefore the ability to use these technologies as part of non-invasive phenotyping and characterization of organisms. We acquired hyperspectral body reflectance profiles from three insect species, and we examined how preparation procedures and preservation time affected the ability to detect reflectance responses to gender, origin, and age. Different portions of the radiometric spectrum varied markedly in their sensitivity to preparation procedures and preservation time. Based on studies of three insect species, we successfully identified specific radiometric regions, in which phenotypic traits become significantly more pronounced based on either: 1) gentle cleaning of museum specimens with distilled water, or 2) killing and preserving insect specimens in 70% ethanol. Standardization of killing and preservation procedures will greatly increase the ability to use proximal imaging remote sensing technologies as part of phenotyping and also when used in ecological and evolutionary studies of invertebrates.

Use of banker plant system for sustainable management of the most important insect pest in rice fields in China.

To meet the World's food demand, there is a growing need for sustainable pest management practices. This study describes the results from complementary laboratory and field studies of a "banker plant system" for sustainable management of the rice brown planthopper (BPH) (Nilaparvata lugens Stål) - the economically most important rice pest in Asian rice growing areas. The banker plant system consisted of planting a grass species, Leersia sayanuka, adjacent to rice fields. L. sayanuka is the host plant of a planthopper, Nilaparvata muiri. An egg parasitoid, Anagrus nilaparvatae, parasitizes eggs of both BPH and N. muiri, and its establishment and persistence are improved through plantings of L. sayanuka and thereby attraction of N. muiri. Laboratory results showed that BPH was unable to complete its life cycle on L. sayanuka, and N. muiri could not complete its life cycle on rice. Thus, planting L. sayanuka did not increase the risk of planthopper damage to rice fields. Field studies showed that BPH densities were significantly lower in rice fields with banker plant system compared to control rice fields without banker plant system.

Hyperspectral Technologies for Assessing Seed Germination and Trifloxysulfuron-methyl Response in Amaranthus palmeri (Palmer Amaranth).

Weed infestations in agricultural systems constitute a serious challenge to agricultural sustainability and food security worldwide. Amaranthus palmeri S. Watson (Palmer amaranth) is one of the most noxious weeds causing significant yield reductions in various crops. The ability to estimate seed viability and herbicide susceptibility is a key factor in the development of a long-term management strategy, particularly since the misuse of herbicides is driving the evolution of herbicide response in various weed species. The limitations of most herbicide response studies are that they are conducted retrospectively and that they use in vitro destructive methods. Development of a non-destructive method for the prediction of herbicide response could vastly improve the efficacy of herbicide applications and potentially delay the evolution of herbicide resistance. Here, we propose a toolbox based on hyperspectral technologies and data analyses aimed to predict A. palmeri seed germination and response to the herbicide trifloxysulfuron-methyl. Complementary measurement of leaf physiological parameters, namely, photosynthetic rate, stomatal conductence and photosystem II efficiency, was performed to support the spectral analysis. Plant response to the herbicide was compared to image analysis estimates using mean gray value and area fraction variables. Hyperspectral reflectance profiles were used to determine seed germination and to classify herbicide response through examination of plant leaves. Using hyperspectral data, we have successfully distinguished between germinating and non-germinating seeds, hyperspectral classification of seeds showed accuracy of 81.9 and 76.4%, respectively. Sensitive and resistant plants were identified with high degrees of accuracy (88.5 and 90.9%, respectively) from leaf hyperspectral reflectance profiles acquired prior to herbicide application. A correlation between leaf physiological parameters and herbicide response (sensitivity/resistance) was also demonstrated. We demonstrated that hyperspectral reflectance analyses can provide reliable information about seed germination and levels of susceptibility in A. palmeri. The use of reflectance-based analyses can help to better understand the invasiveness of A. palmeri, and thus facilitate the development of targeted control methods. It also has enormous potential for impacting environmental management in that it can be used to prevent ineffective herbicide applications. It also has potential for use in mapping tempo-spatial population dynamics in agro-ecological landscapes.

Reflectance-based determination of age and species of blowfly puparia.

Forensic entomology is primarily concerned with the estimation of time since death and involves determination of the age of immature insects colonising decomposing remains. Accurate age determination of puparia is usually accomplished by dissection, which means destructive sampling of evidence. As part of improving abilities to correctly identify species and developmental age, it is highly desirable to have available non-destructive methods. In this study, we acquired external hyperspectral imaging (HSI) data (77 spectral bands, 389-892 nm) from the dorsal and ventral sides of individual puparia of two species of blowfly (Diptera: Calliphoridae), Calliphora dubia Macquart 1855 and Chrysomya rufifacies Macquart 1842. Puparia were dissected to determine the presence/absence of eight internal morphological development characteristics (legs, wings, labella, abdominal segments, antennae, thoracic bristles, orbital/facial bristles and eye colour and arista). Based on linear discriminant analysis and independent validation of HSI data, reflectance features from puparia could be used to successfully (1) distinguish the two species (classification accuracy = 92.5 %), (2) differentiate dorsal and ventral sides of puparia (classification accuracy C. dubia = 81.5 %; Ch. rufifacies = 89.2 %) and (3) predict the presence of these morphological characteristics and therefore the developmental stage of puparia (average classification accuracy using dorsal imaging: C. dubia = 90.3 %; Ch. rufifacies = 94.0 %). The analytical approach presented here provides proof of concept for a direct puparial age relationship (i.e. days since the onset of pupation) between external puparial reflectance features and internal morphological development. Furthermore, this approach establishes the potential for further refinement by using a non-invasive technique to determine the age and developmental stage of blowflies of forensic importance.

Does Drought Increase the Risk of Insects Developing Behavioral Resistance to Systemic Insecticides?

Increases in severity and frequency of drought periods, average global temperatures, and more erratic fluctuations in rainfall patterns due to climate change are predicted to have a dramatic impact on agricultural production systems. Insect pest populations in agricultural and horticultural systems are also expected to be impacted, both in terms of their spatial and temporal distributions and in their status as pest species. In this opinion-based article, we discuss how indirect effects of drought may adversely affect the performance of systemic insecticides and also lead to increased risk of insect pests developing behavioral insecticide resistance. We hypothesize that more pronounced drought will decrease uptake and increase the magnitude of nonuniform translocation of systemic insecticides within treated crop plants, and that may have two concurrent consequences: 1) reduced pesticide performance, and 2) increased likelihood of insect pests evolving behavioral insecticide resistance. Under this scenario, pests that can sense and avoid acquisition of lethal dosages of systemic insecticides within crop plants will have a selective advantage. This may lead to selection for insect behavioral avoidance, so that insects predominantly feed and oviposit on portions of crop plants with low concentration of systemic insecticide. Limited research has been published on the effect of environmental variables, including drought, on pesticide performance, but we present and discuss studies that support the hypothesis described above. In addition, we wish to highlight the importance of studying the many ways environmental factors can affect, directly and indirectly, both the performance of insecticides and the risk of target insect pests developing resistance.

Spatially-Optimized Sequential Sampling Plan for Cabbage Aphids Brevicoryne brassicae L. (Hemiptera: Aphididae) in Canola Fields.

The cabbage aphid is a significant pest worldwide in brassica crops, including canola. This pest has shown considerable ability to develop resistance to insecticides, so these should only be applied on a "when and where needed" basis. Thus, optimized sampling plans to accurately assess cabbage aphid densities are critically important to determine the potential need for pesticide applications. In this study, we developed a spatially optimized binomial sequential sampling plan for cabbage aphids in canola fields. Based on five sampled canola fields, sampling plans were developed using 0.1, 0.2, and 0.3 proportions of plants infested as action thresholds. Average sample numbers required to make a decision ranged from 10 to 25 plants. Decreasing acceptable error from 10 to 5% was not considered practically feasible, as it substantially increased the number of samples required to reach a decision. We determined the relationship between the proportions of canola plants infested and cabbage aphid densities per plant, and proposed a spatially optimized sequential sampling plan for cabbage aphids in canola fields, in which spatial features (i.e., edge effects) and optimization of sampling effort (i.e., sequential sampling) are combined. Two forms of stratification were performed to reduce spatial variability caused by edge effects and large field sizes. Spatially optimized sampling, starting at the edge of fields, reduced spatial variability and therefore increased the accuracy of infested plant density estimates. The proposed spatially optimized sampling plan may be used to spatially target insecticide applications, resulting in cost savings, insecticide resistance mitigation, conservation of natural enemies, and reduced environmental impact.