How urban green spaces support pollinators A. Rivera 1 , J. Chen 2 1 Department of Biology 2 Center for Urban Ecology Introduction Urban green spaces can provide food and nesting habitat for pollinators in otherwise heavily developed neighborhoods. The value of these spaces depends on the plants available and the timing of their flowering. Garden managers often choose ornamental species for appearance, while native species may offer different resources to local insects. Evidence collected across several gardens can help managers make planting decisions that account for seasonal change. We examined how the composition of small urban plots related to observed visits during one growing season. The study also considered whether differences in flower availability and weather might help explain variation between gardens and survey dates. Objectives • Compare visits to native and ornamental plantings across a network of urban gardens. • Assess whether flower abundance and survey date help explain differences between plots. • Identify practical directions for a longer study of pollinator habitat. Methods We selected twelve community gardens that contained both native and ornamental flowering plots. Observers visited each site on three mornings during the growing season and recorded insects entering flowers during ten-minute intervals. At each plot they counted open blooms, noted cloud cover and measured air temperature; observers practiced the same identification and counting protocol before the first survey to improve consistency across sites and repeated survey days. Visits were grouped into bees, butterflies and other flower visitors using a field identification guide. We compared visit rates by planting type while accounting for site and survey date. The design describes visitation rather than nesting success or pollinator population size. Figure 1. Survey plots were visited on three mornings. Results Native plots had more recorded visits during peak bloom. The difference was visible at most sites, although the size of the difference varied among gardens. Bee visits represented the largest share of observations, while butterfly visits were less frequent and more variable. Plots with more open flowers tended to receive more visits regardless of planting type. Because the gardens were not randomly assigned, local management and nearby habitat could also have influenced the pattern. Figure 2. Native plots received more visits at peak bloom. Figure 3. Visit rates varied across the garden sites. Conclusions • Include native flowering species with staggered bloom periods in urban gardens. • Maintain nesting habitat and limit pesticide use alongside planting changes. • Pair visitor counts with nesting observations before drawing conclusions about population health. • Repeat surveys across years and neighborhoods to assess weather and surrounding land use. • Work with garden managers to monitor bloom periods and adapt planting plans to local conditions. Figure 4. Summary of habitat measures for urban gardens. References Rivera A, Chen J. Floral resources in small urban gardens. Fictional Urban Ecology. 2024;10:1–8. Patel N, Ellis M. Counting insect visitors in city plots. Fictional Field Methods. 2023;8:44–52. Kim S, Park L. Seasonal bloom diversity in managed gardens. Fictional Habitat Studies. 2022;6:13–21. Acknowledgments We thank the garden volunteers and field survey team for their contributions. Illustrative example; data and references are fictional.
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Biology research poster example View full size Use this example Improving follow-up after outpatient visits L. Park 1 , M. Ellis 2 1 Department of Medicine 2 Health Services Research Unit Introduction Follow-up appointments allow clinicians and patients to review treatment plans after an outpatient visit. When a follow-up does not occur, unresolved questions and medication concerns can remain unaddressed. Clinics use a variety of reminders, but workflows differ in their timing, language and responsibility for sending them. A simple process that fits existing staff routines may be easier to sustain than a more complex intervention. Patients may also face transportation, scheduling and communication barriers that a reminder alone cannot resolve; an evaluation should therefore describe the clinic context and distinguish a missed visit from a visit that was successfully rescheduled. We evaluated whether a standard reminder could support attendance across several clinics. Objectives • Compare attendance within the recommended follow-up window before and after a reminder workflow. • Describe variation by clinic and appointment type. • Identify implementation questions for a future prospective evaluation. • Consider the needs of patients who prefer different languages or communication channels and access needs. Methods We assembled a de-identified dataset of scheduled follow-up appointments from four outpatient clinics. The comparison included a baseline period and a later period after staff introduced a standard reminder message. Reminders were sent several days before the scheduled visit using the contact preference recorded by the clinic. We summarized attendance within the recommended window and calculated results separately for each clinic. Appointment type and the time between booking and the visit were examined as descriptive factors, and rescheduled visits were retained when they occurred within the recommended follow-up window for each individual patient. The evaluation was observational, and no patient-level causal effect was assumed. Figure 1. The workflow compares two appointment periods. Results Attendance was higher during the reminder period, with differences across the participating clinics. The largest apparent improvement occurred for appointments booked further in advance. Some patients rescheduled before the original appointment, so attendance within the full follow-up window was more informative than attendance on a single date. Clinic staffing and appointment availability also changed during the comparison periods. These concurrent changes limit interpretation of the observed difference and motivate a controlled follow-up study. Figure 2. Attendance increased in the reminder period. Figure 3. Changes differed across participating clinics. Conclusions • A consistent reminder workflow may be a practical starting point for supporting continuity of care. • Clinics should offer communication choices and review the timing of messages with patients. • A prospective comparison should account for access, rescheduling and changes in clinic capacity. • Implementation measures should include staff workload and patient preferences, not attendance alone. References Park L, Ellis M. Reminder workflows in outpatient care. Fictional Clinical Care. 2024;12:14–22. Brown T, Chen J. Patterns of scheduled follow-up. Fictional Health Services. 2023;8:30–38. Rivera A, Kim S. Measuring access after outpatient visits. Fictional Care Methods. 2022;5:91–99. Acknowledgments We thank the clinic teams and administrative staff who contributed to this study. Illustrative example; data and references are fictional.
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Clinical research poster example View full size Use this example Efficient classification of environmental sounds N. Patel 1 , S. Kim 1 Department of Computer Science Introduction Environmental sound classification can help researchers monitor changing conditions without manually reviewing every recording. Field devices often operate with limited memory, energy and network access, making large models difficult to deploy. Compact architectures offer a tradeoff between predictive performance and computational cost. Recordings may contain traffic, weather and overlapping sources, and changes in microphone placement can affect performance at new sites. Comparing models across independently collected recordings helps identify where a compact system remains reliable and where additional evaluation is needed. An effective monitoring pipeline must distinguish target sounds from background activity while preserving enough detail to compare sites and survey periods; these requirements can change with microphone placement, recording duration, traffic intensity and seasonal weather, as well as differences in the microphones used by field teams. Objectives • Compare a compact classifier with a larger reference model on held-out recordings. • Measure trainable parameters and category-level performance across recording locations. • Examine overlapping sources and variation across repeated training runs. • Identify the latency, memory and energy measurements needed before field deployment, including the cost of loading the model and preprocessing each recording. Methods We divided labeled audio clips into training, validation and held-out test sets by recording location. Each clip was converted to a log-mel spectrogram using the same preprocessing pipeline for both models. The compact classifier used fewer convolutional channels than the reference architecture. We trained each model with several random seeds and selected settings using the validation set. Test accuracy and category-level errors were summarized alongside the number of trainable parameters; clips from the same recording session were kept in the same split to reduce leakage. Preprocessing settings were fixed before the final test evaluation, and we inspected category-level errors using a confusion matrix; both models used the same held-out clips for the descriptive comparison, with identical clip duration and feature normalization to support a fair evaluation across the selected sound categories. Figure 1. Audio clips follow a shared preprocessing pipeline. Results The compact model used fewer parameters with similar overall accuracy. Errors were more common in clips with overlapping sound sources. Performance varied across categories, particularly where background noise masked a target sound. Parameter counts alone do not establish device latency or energy use. Figure 2. The compact model uses fewer parameters. Figure 3. Performance varies by sound category. Conclusions • Compact models can reduce storage requirements for acoustic monitoring. • Report category-level results and uncertainty alongside overall accuracy. • Measure latency, memory and energy on the intended hardware before deployment. • Test recordings from new environments and devices to assess robustness to background noise. Figure 4. Summary of accuracy and resource measurements. References Patel N, Kim S. Compact models for acoustic classification. Fictional Computing Studies. 2024;15:1–10. Chen R, Park L. Evaluation splits for sound monitoring. Fictional Signal Research. 2023;7:20–29. Ellis M, Brown T. Reporting resources for field classifiers. Fictional Machine Learning Methods. 2022;4:55–64. Acknowledgments We thank the research computing team for infrastructure support. Illustrative example; data and references are fictional.
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Computer science poster example View full size Use this example Native plants and pollinator visits A. Rivera 1 , J. Chen 2 1 Department of Biology 2 Center for Urban Ecology Native flowers attracted more pollinator visits in urban gardens. Illustration: Visits by planting type at peak bloom. Introduction Urban gardens can provide flowering resources in landscapes dominated by buildings and pavement. Gardeners choose plants for many reasons, including seasonal color, maintenance and availability. Local climate, water availability and the surrounding landscape can also influence the insects observed at a site; different flowering times mean a single survey may miss seasonal changes. Native and ornamental plants may differ in the resources they offer visiting insects. We compared visits to flowering plots at multiple sites during one growing season to identify patterns that merit further study. Objectives • Compare the number of insect visits to native and ornamental flowering plots. • Examine how open flower counts and survey dates relate to visits. • Describe the limits of interpreting short observations as habitat quality. • Identify practical questions for gardeners and a longer field study across seasons and neighborhoods and share findings locally. Methods We selected twelve gardens with both planting types and surveyed each garden on three mornings. Trained observers counted visits during standard ten-minute intervals at each plot. They also recorded open blooms, temperature and cloud cover at the time of the survey, and used the same identification guide and counting rules at each site. Visits were counted only when an insect entered a flower, using the same rule for all plots; sites were observed in a rotating order to reduce differences associated with time of day. Insects were grouped into broad categories using a field guide, and rates were summarized by site and date. The comparison was observational, so management differences between gardens were not controlled experimentally. Figure 1. Each garden was surveyed on three mornings. Results Native plots attracted more recorded visits at peak bloom. Bee visits made up the largest share of the observations, but the difference between planting types varied by site. Plots with more open flowers tended to receive more visits in both groups. Visit rates varied across sites and survey days, suggesting that local conditions and the timing of flowering may affect the observed difference between planting types. Longer monitoring would be needed to assess nesting and survival. Figure 2. Native flowers received more peak-season visits. Figure 3. Visit rates differed among garden sites. Conclusions • Consider diverse native plantings when designing small gardens for pollinator habitat. • Include species with staggered flowering periods to support resources over the season. • Combine visit counts with observations of nesting and survival in future studies. • Compare additional sites and years before making general claims about urban habitat. • Record maintenance and nearby habitat when interpreting differences among gardens. References Rivera A, Chen J. Flowering resources in city gardens. Fictional Urban Ecology. 2024;10:1–8. Patel N, Ellis M. Field surveys of urban insects. Fictional Field Methods. 2023;8:44–52. Kim S, Park L. Bloom timing in small habitats. Fictional Habitat Studies. 2022;6:13–21. Acknowledgments We thank the field team and garden volunteers for their contributions. Illustrative example; data and references are fictional.
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