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Urban greenspaces, including green roofs and ground-level urban habitats provide habitat for insect communities in cities. However, beneficial insect communities likely differ between human-managed habitats because of varying provision of resources and connectivity in these greenspaces. This study examined the insect communities in four extensive green roofs and three non-adjacent, similarly structured, managed ground-level habitats. We detected a high degree of overlap in insect taxa but found moderate differences in overall insect community composition between the green roof and ground-level habitats. While there was no difference in Shannon diversity between green roofs and ground-level habitats, the ground-level habitat had greater insect taxa richness. Green roof and ground-level habitats supported pollinators and natural enemies, while ground-level had greater mean pollinator and natural enemy richness and Shannon diversity. Green roofs intentionally designed for biodiversity using native plants for habitat did not differ from those designed for stormwater management and energy reduction using non-native plants in insect community metrics used in this study. These findings suggest that urban greenspaces continue to provide valuable habitat while connectivity and structure play a role in shaping urban insect communities. 

Abstract Understanding causes of insect population declines is essential for the development of successful conservation plans, but data limitations restrict assessment across spatial and temporal scales. Museum records represent a source of historical data that can be leveraged to investigate temporal trends in insect communities. Native lady beetle decline has been attributed to competition with established alien species and landscape change, but the relative importance of these drivers is difficult to measure with short‐term field‐based studies. We assessed distribution patterns for native lady beetles over 12 decades using museum records, and evaluated the relative importance of alien species and landscape change as factors contributing to changes in communities. We compiled occurrence records for 28 lady beetle species collected in Ohio, USA, from 1900 to 2018. Taxonomic beta‐diversity was used to evaluate changes in lady beetle community composition over time. To evaluate the relative influence of temporal, spatial, landscape, and community factors on the captures of native species, we constructed negative binomial generalized additive models. We report evidence of declines in captures for several native species. Importantly, the timing, severity, and drivers of these documented declines were species‐specific. Land cover change was associated with declines in captures, particularly forCoccinella novemnotatawhich declined prior to the arrival of alien species. Following the establishment and spread of alien lady beetles, processes of species loss/gain and turnover shifted communities toward the dominance of a few alien species beginning in the 1980s. Because factors associated with declines in captures were highly species‐specific, this emphasizes that mechanisms driving population losses cannot be generalized even among closely related native species. These findings also indicate the importance of museum holdings and the analysis of species‐level data when studying temporal trends in insect populations. 

Odonates (dragonflies and damselflies) have become popular study organisms for insect-based climate studies, due to the taxon’s strong sensitivity to environmental conditions, and an enthusiastic following by community scientists due to their charismatic appearance and size. Where formal records of this taxon can be limited, public efforts have provided nearly 1,500,000 open-sourced odonate records through online databases, making real-time spatio-temporal monitoring more feasible. While these databases can be extensive, concerns regarding these public endeavors have arisen from a variety of sources: records may be biased by human factors (ex: density, technological access) which may cause erroneous interpretations. Indeed, records of odonates in the east-central US documented in the popular database iNaturalist bear striking patterns corresponding to political boundaries and other human activities. We conducted a ‘ground-truthing’ study using a structured sampling method to examine these patterns in an area where community science reports indicated variable abundance, richness, and diversity which appeared to be linked to observation biases. Our observations were largely consistent with patterns recorded by community scientists, suggesting these databases were indeed capturing representative biological trends and raising further questions about environmental drivers in the observed data gaps. 

Background Understanding how study design and monitoring strategies shape inference within, and synthesis across, studies is critical across biological disciplines. Many biological and field studies are short term and limited in scope. Monitoring studies are critical for informing public health about potential vectors of concern, such as Ixodes scapularis (black-legged ticks). Black-legged ticks are a taxon of ecological and human health concern due to their status as primary vectors of Borrelia burgdorferi , the bacteria that transmits Lyme disease. However, variation in black-legged tick monitoring, and gaps in data, are currently considered major barriers to understanding population trends and in turn, predicting Lyme disease risk. To understand how variable methodology in black-legged tick studies may influence which population patterns researchers find, we conducted a data synthesis experiment. Materials and Methods We searched for publicly available black-legged tick abundance dataset that had at least 9 years of data, using keywords about ticks in internet search engines, literature databases, data repositories and public health websites. Our analysis included 289 datasets from seven surveys from locations in the US, ranging in length from 9 to 24 years. We used a moving window analysis, a non-random resampling approach, to investigate the temporal stability of black-legged tick population trajectories across the US. We then used t-tests to assess differences in stability time across different study parameters. Results All of our sampled datasets required 4 or more years to reach stability. We also found several study factors can have an impact on the likelihood of a study reaching stability and of data leading to misleading results if the study does not reach stability. Specifically, datasets collected via dragging reached stability significantly faster than data collected via opportunistic sampling. Datasets that sampled larva reached stability significantly later than those that sampled adults or nymphs. Additionally, datasets collected at the broadest spatial scale (county) reached stability fastest. Conclusion We used 289 datasets from seven long term black-legged tick studies to conduct a non-random data resampling experiment, revealing that sampling design does shape inferences in black-legged tick population trajectories and how many years it takes to find stable patterns. Specifically, our results show the importance of study length, sampling technique, life stage, and geographic scope in understanding black-legged tick populations, in the absence of standardized surveillance methods. Current public health efforts based on existing black-legged tick datasets must take monitoring study parameters into account, to better understand if and how to use monitoring data to inform decisioning. We also advocate that potential future forecasting initiatives consider these parameters when projecting future black-legged tick population trends. 

Long-term monitoring programs are a fundamental part of both understanding ecological systems and informing management decisions. However, there are many constraints which might prevent monitoring programs from being designed to consider statistical power, site selection, or the full costs and benefits of monitoring. Key considerations can be incorporated into the optimal design of a management program with simulations and experiments. Here, we advocate for the expanded use of a third approach: non-random resampling of previously-collected data. This approach conducts experiments with available data to understand the consequences of different monitoring approaches. We first illustrate non-random resampling in determining the optimal length and frequency of monitoring programs to assess species trends. We then apply the approach to a pair of additional case studies, from fisheries and agriculture. Non-random resampling of previously-collected data is underutilized, but has the potential to improve monitoring programs.