Pest population dynamics are related to a continental overwintering gradient.

Overwintering success is an important determinant of arthropod populations that must be considered as climate change continues to influence the spatiotemporal population dynamics of agricultural pests. Using a long-term monitoring database and biologically relevant overwintering zones, we modeled the annual and seasonal population dynamics of a common pest, Helicoverpa zea (Boddie), based on three overwintering suitability zones throughout North America using four decades of soil temperatures: the southern range (able to persist through winter), transitional zone (uncertain overwintering survivorship), and northern limits (unable to survive winter). Our model indicates H. zea population dynamics are hierarchically structured with continental-level effects that are partitioned into three geographic zones. Seasonal populations were initially detected in the southern range, where they experienced multiple large population peaks. All three zones experienced a final peak between late July (southern range) and mid-August to mid-September (transitional zone and northern limits). The southern range expanded by 3% since 1981 and is projected to increase by twofold by 2099 but the areas of other zones are expected to decrease in the future. These changes suggest larger populations may persist at higher latitudes in the future due to reduced low-temperature lethal events during winter. Because H. zea is a highly migratory pest, predicting when populations accumulate in one region can inform synchronous or lagged population development in other regions. We show the value of combining long-term datasets, remotely sensed data, and laboratory findings to inform forecasting of insect pests.

Factors affecting nurse practitioners' integration of evidence into practice.

BACKGROUND: Evidence-based clinical practice guidelines bridge the gap between clinical practice and research, improve patient outcomes, promote consistency of care, and enhance quality of care. However, guideline adherence varies widely among individual providers and organizations. PURPOSE: To identify factors that facilitate or impede nurse practitioners' integration of guideline recommendations into practice. METHODS: Every nurse practitioner in Alabama was invited to complete an online 45-question survey evaluating beliefs and attitudes regarding evidence-based guidelines, facilitators and barriers to implementation, and utilization of information resources in patient care. RESULTS: The five most commonly identified barriers to evidence-based guideline implementation in participants' current work settings are patients with multiple comorbidities, time constraints, pressure from patients to provide nonrecommended care, insufficient staffing, and inadequate financial resources. The five most commonly identified facilitators in participants' current work settings are easy access to guidelines, support from leadership, free access to guidelines, in-person education regarding a guideline, and clinical decision support software programs. Participants expressed a desire for free and easy access to evidence-based practice (EBP) guidelines and clinical decision support programs, as well as education regarding guidelines and opportunities to discuss evidence with colleagues. IMPLICATIONS FOR PRACTICE: The barriers and facilitators of guideline implementation that were identified in this study should be useful in the development and refinement of future studies and interventions to enhance guideline implementation among individuals and organizations.

Tumor BRCA Testing in High Grade Serous Carcinoma: Mutation Rates and Optimal Tissue Requirements.

BACKGROUND: Approximately 25% of women diagnosed with tubo-ovarian high-grade serous carcinoma have germline deleterious mutations in BRCA1 or BRCA2, characteristic of hereditary breast and ovarian cancer syndrome, while somatic mutations have been detected in 3-7%. We set out to determine the BRCA mutation rates and optimal tissue requirements for tumor BRCA testing in patients diagnosed with tubo-ovarian high-grade serous carcinoma. METHODS: Sequencing was performed using a multiplexed polymerase chain reaction-based approach on 291 tissue samples, with a minimum sequencing depth of 500X and an allele frequency of >5%. RESULTS: There were 253 surgical samples (87%), 35 biopsies (12%) and 3 cytology cell blocks (1%). The initial failure rate was 9% (25/291), including 9 cases (3%) with insufficient tumor, and 16 (6%) with non-amplifiable DNA. Sequencing was successful in 78% (228/291) and deemed indeterminate due to failed exons or variants below the limit of detection in 13% (38/291). Repeat testing was successful in 67% (28/42) of retested samples, with an overall success rate of 86% (251/291). Clinically significant (pathogenic, likely pathogenic) variants were identified in 17% (48/276) of complete and indeterminate cases. Successful sequencing was dependent on sample type, tumor cellularity and size (p ≤ 0.001) but not on neoadjuvant chemotherapy or age of blocks (p > 0.05). CONCLUSIONS: Our study shows a 17% tumor BRCA mutation rate, with an overall success rate of 86%. Biopsy and cytology samples and post-chemotherapy specimens can be used for tumor BRCA testing, and optimal tumors measure ≥5 mm in size with at least 20% cellularity.

Implementation of a Diagnostic-Concentration Bioassay for Detection of Susceptibility to Pyrethroids in Soybean Aphid (Hemiptera: Aphididae).

Soybean aphid, Aphis glycines Matsumura, remains the most economically damaging arthropod pest of soybean in the midwestern United States and southern Canada. Foliar applications of a limited number of insecticide modes of action have been the primary management tactic, and pyrethroid resistance was documented recently with full concentration-response leaf-dip and glass-vial bioassays. Full concentration-response bioassays can be cumbersome, and a more efficient assessment tool was needed. In this study, we implemented a diagnostic-concentration glass-vial bioassay using bifenthrin and λ-cyhalothrin. Bioassays were conducted with field-collected soybean aphid populations to assess the geographic extent and severity of resistance to pyrethroids. In 2017, 10 of 18 and 11 of 21 field populations tested with bifenthrin and λ-cyhalothrin, respectively, had mean proportion mortalities less than the susceptible laboratory population. In 2018, 17 of 23 and 13 of 23 field populations tested with bifenthrin and λ-cyhalothrin, respectively, had mean proportion mortalities less than the susceptible laboratory population. Populations collected after reported field failures of a pyrethroid insecticide generally had mean proportion mortalities less than the susceptible laboratory population. In both years, there was a strong correlation between chemistries, which suggests cross-resistance between these insecticides. The diagnostic-concentration glass-vial bioassays reported here will provide the foundation for an insecticide resistance monitoring program with the ability to determine practical levels and geographic extent of insecticide resistance.

Variable Isotopic Compositions of Host Plant Populations Preclude Assessment of Aphid Overwintering Sites.

Soybean aphid (Aphis glycines Matsumura) is a pest of soybean in the northern Midwest whose migratory patterns have been difficult to quantify. Improved knowledge of soybean aphid overwintering sites could facilitate the development of control efforts with exponential impacts on aphid densities on a regional scale. In this preliminary study, we explored the utility of variation in stable isotopes of carbon and nitrogen to distinguish soybean aphid overwintering origins. We compared variation in bulk 13C and 15N content in buckthorn (Rhamnus cathartica L.) and soybean aphids in Wisconsin, among known overwintering locations in the northern Midwest. Specifically, we looked for associations between buckthorn and environmental variables that could aid in identifying overwintering habitats. We detected significant evidence of correlation between the bulk 13C and 15N signals of soybean aphids and buckthorn, despite high variability in stable isotope composition within and among buckthorn plants. Further, the 15N signal in buckthorn varied predictably with soil composition. However, lack of sufficient differentiation of geographic areas along axes of isotopic and environmental variation appears to preclude the use of carbon and nitrogen isotopic signals as effective predictors of likely aphid overwintering sites. These preliminary data suggest the need for future work that can further account for variability in 13C and 15N within/among buckthorn plants, and that explores the utility of other stable isotopes in assessing likely aphid overwintering sites.

From pest data to abundance-based risk maps combining eco-physiological knowledge, weather, and habitat variability.

Noxious species, i.e., crop pest or invasive alien species, are major threats to both natural and managed ecosystems. Invasive pests are of special importance, and knowledge about their distribution and abundance is fundamental to minimize economic losses and prioritize management activities. Occurrence models are a common tool used to identify suitable zones and map priority areas (i.e., risk maps) for noxious species management, although they provide a simplified description of species dynamics (i.e., no indication on species density). An alternative is to use abundance models, but translating abundance data into risk maps is often challenging. Here, we describe a general framework for generating abundance-based risk maps using multi-year pest data. We used an extensive data set of 3968 records collected between 2003 and 2013 in Wisconsin during annual surveys of soybean aphid (SBA), an exotic invasive pest in this region. By using an integrative approach, we modelled SBA responses to weather, seasonal, and habitat variability using generalized additive models (GAMs). Our models showed good to excellent performance in predicting SBA occurrence and abundance (TSS = 0.70, AUC = 0.92; R2  = 0.63). We found that temperature, precipitation, and growing degree days were the main drivers of SBA trends. In addition, a significant positive relationship between SBA abundance and the availability of overwintering habitats was observed. Our models showed aphid populations were also sensitive to thresholds associated with high and low temperatures, likely related to physiological tolerances of the insects. Finally, the resulting aphid predictions were integrated using a spatial prioritization algorithm ("Zonation") to produce an abundance-based risk map for the state of Wisconsin that emphasized the spatiotemporal consistency and magnitude of past infestation patterns. This abundance-based risk map can provide information on potential foci of pest outbreaks where scouting efforts and prophylactic measures should be concentrated. The approach we took is general, relatively simple, and can be applied to other species, habitats and geographical areas for which species abundance data and biotic and abiotic data are available.

Explicit modeling of abiotic and landscape factors reveals precipitation and forests associated with aphid abundance.

Increases in natural or noncrop habitat surrounding agricultural fields have been shown to be correlated with declines in insect crop pests. However, these patterns are highly variable across studies suggesting other important factors, such as abiotic drivers, which are rarely included in landscape models, may also contribute to variability in insect population abundance. The objective of this study was to explicitly account for the contribution of temperature and precipitation, in addition to landscape composition, on the abundance of a widespread insect crop pest, the soybean aphid (Aphis glycines Matsumura), in Wisconsin soybean fields. We hypothesized that higher soybean aphid abundance would be associated with higher heat accumulation (e.g., growing degree days) and increasing noncrop habitat in the surrounding landscape, due to the presence of the overwintering primary hosts of soybean aphid. To evaluate these hypotheses, we used an ecoinformatics approach that relied on a large dataset collected across Wisconsin over a 9-year period (2003-2011), for an average of 235 sites per year (n = 2,110 fields total). We determined surrounding landscape composition (1.5-km radius) using publicly available satellite-derived land cover imagery and interpolated daily temperature and precipitation information from the National Weather Service COOP weather station network. We constructed linear mixed models for soybean aphid abundance based on abiotic and landscape explanatory variables and applied model averaging for prediction using an information theoretic framework. Over this broad spatial and temporal extent in Wisconsin, we found that variation in growing season precipitation was positively related to soybean aphid abundance, while higher precipitation during the nongrowing season had a negative effect on aphid populations. Additionally, we found that aphid populations were higher in areas with proportionally more forest but were lower in areas where minor crops, such as small grains, were more prevalent. Thus, our findings support our hypothesis that including abiotic drivers increases our understanding of crop pest abundance and distribution. Moreover, by explicitly modeling abiotic factors, we may be able to explore how variable climate in tandem with land cover patterns may affect current and future insect populations, with potentially critical implications for crop yields and agricultural food webs.

Putting the brakes on a cycle: bottom-up effects damp cycle amplitude.

Pest population density oscillations have a profound effect on agroecosystem functioning, particularly when pests cycle with epidemic persistence. Here, we ask whether landscape-level manipulations can be used to restrict the cycle amplitude of the European corn borer moth [Ostrinia nubilalis (Hübner)], an economically important maize pest. We analysed time series from Minnesota (1963-2009) and Wisconsin (1964-2009) to quantify the extent of regime change in the US Corn Belt where rates of transgenic Bt maize adoption varied. The introduction of Bt maize explained cycle damping when the adoption of the crop was high (Minnesota); oscillations were damped but continued to persist when Bt maize was used less intensely (Wisconsin). We conclude that host plant quality is key to understanding both epidemic persistence and the success of intervention strategies. In particular, the dichotomy in maize management between states is thought to limit the spatial autocorrelation of O. nubilalis.