Forecasting insect dynamics in a changing world.

Predicting how insects will respond to stressors through time is difficult because of the diversity of insects, environments, and approaches used to monitor and model. Forecasting models take correlative/statistical, mechanistic models, and integrated forms; in some cases, temporal processes can be inferred from spatial models. Because of heterogeneity associated with broad community measurements, models are often unable to identify mechanistic explanations. Many present efforts to forecast insect dynamics are restricted to single-species models, which can offer precise predictions but limited generalizability. Trait-based approaches may offer a good compromise that limits the masking of the ranges of responses while still offering insight. Regardless of the modeling approach, the data used to parameterize a forecasting model should be carefully evaluated for temporal autocorrelation, minimum data needs, and sampling biases in the data. Forecasting models can be tested using near-term predictions and revised to improve future forecasts.

Coexistence between similar invaders: The case of two cosmopolitan exotic insects.

Biological invasions are usually examined in the context of their impacts on native species. However, few studies have examined the dynamics between invaders when multiple exotic species successfully coexist in a novel environment. Yet, long-term coexistence of now established exotic species has been observed in North American lady beetle communities. Exotic lady beetles Harmonia axyridis and Coccinella septempunctata were introduced for biological control in agricultural systems and have since become dominant species within these communities. In this study, we investigated coexistence via spatial and temporal niche partitioning among H. axyridis and C. septempunctata using a 31-year data set from southwestern Michigan, USA. We found evidence of long-term coexistence through a combination of small-scale environmental, habitat, and seasonal mechanisms. Across years, H. axyridis and C. septempunctata experienced patterns of cyclical dominance likely related to yearly variation in temperature and precipitation. Within years, populations of C. septempunctata peaked early in the growing season at 550 degree days, while H. axyridis populations grew in the season until 1250 degree days and continued to have high activity after this point. C. septempunctata was generally most abundant in herbaceous crops, whereas H. axyridis did not display strong habitat preferences. These findings suggest that within this region H. axyridis has broader habitat and abiotic environmental preferences, whereas C. septempunctata thrives under more specific ecological conditions. These ecological differences have contributed to the continued coexistence of these two invaders. Understanding the mechanisms that allow for the coexistence of dominant exotic species contributes to native biodiversity conservation management of invaded ecosystems.

Longer study length, standardized sampling techniques, and broader geographic scope leads to higher likelihood of detecting stable abundance patterns in long term black-legged tick studies.

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 ecological research and the COVID-19 anthropause: A window to understanding social-ecological disturbance.

The period of disrupted human activity caused by the COVID-19 pandemic, coined the "anthropause," altered the nature of interactions between humans and ecosystems. It is uncertain how the anthropause has changed ecosystem states, functions, and feedback to human systems through shifts in ecosystem services. Here, we used an existing disturbance framework to propose new investigation pathways for coordinated studies of distributed, long-term social-ecological research to capture effects of the anthropause. Although it is still too early to comprehensively evaluate effects due to pandemic-related delays in data availability and ecological response lags, we detail three case studies that show how long-term data can be used to document and interpret changes in air and water quality and wildlife populations and behavior coinciding with the anthropause. These early findings may guide interpretations of effects of the anthropause as it interacts with other ongoing environmental changes in the future, particularly highlighting the importance of long-term data in separating disturbance impacts from natural variation and long-term trends. Effects of this global disturbance have local to global effects on ecosystems with feedback to social systems that may be detectable at spatial scales captured by nationally to globally distributed research networks.

Phenology of Bean Leaf Beetle (Coleoptera: Chrysomelidae) in Ontario, Canada and Field Validation of a Degree-Day Model.

The phenology and voltinism of bean leaf beetle, Cerotoma trifurcata (Forster), were examined in three counties in 2010 and two counties in 2011 in Ontario soybean, Glycine max (L.) Merr., fields. Soil samples from within cages containing field-collected beetles revealed one cycle of eggs, larvae, and pupae. Observed degree-day (DD) accumulations for C. trifurcata life stage events (egg hatch, egg hatch to pupation, and oviposition to peak adult) in field experiments were compared with thermal constants determined in a temperature-dependent development laboratory experiment where C. trifurcata were reared under five constant temperatures. Observed and predicted DDs for all life stage events were nearly identical. Mean DD accumulations from first oviposition to peak adult emergence in the field studies was 589 ± 67 DD (base 10.3°C), which was nearly identical to the model prediction (581 ± 40 DD, base 10.3°C).

How long do population level field experiments need to be? Utilising data from the 40-year-old LTER network.

We utilise the wealth of data accessible through the 40-year-old Long-Term Ecological Research (LTER) network to ask if aspects of the study environment or taxa alter the duration of research necessary to detect consistent results. To do this, we use a moving-window algorithm. We limit our analysis to long-term (> 10 year) press experiments recording organismal abundance. We find that studies conducted in dynamic abiotic environments need longer periods of study to reach consistent results, as compared to those conducted in more moderated environments. Studies of plants were more often characterised by spurious results than those on animals. Nearly half of the studies we investigated required 10 years or longer to become consistent, where all significant trends agreed in direction, and four studies (of 100) required longer than 20 years. Here, we champion the importance of long-term data and bolster the value of multi-decadal experiments in understanding, explaining and predicting long-term trends.

Flower traits associated with the visitation patterns of bees.

Plant-pollinator interactions are partially driven by the expression of plant traits that signal and attract bees to the nutritional resources within flowers. Although multiple physical and chemical floral traits are known to influence the visitation patterns of bees, how distinct bee groups vary in their responses to floral traits has yet to be elucidated. In this study, we used a common garden experiment to test for morphological floral traits associated with pollen quantity at the plant species level, and examined how the visitation patterns of taxonomically and functionally distinct bee groups are related to flower trait characteristics of 39 wildflower species. We also determined how floral traits influence the structure of wild bee communities visiting plants and whether this varies among geographic localities. Our results suggest that floral area is the primary morphological floral trait related to bee visitation of several distinct bee groups, but that wild bee families and functionally distinct bee groups have unique responses to floral trait expression. The composition of the wild bee communities visiting different plants was most strongly associated with variability in floral area, flower height, and the quantity of pollen retained in flowers. Our results inform wildflower habitat management for bees by demonstrating that the visitation patterns of distinct bee taxa can be predicted by floral traits, and highlight that variability in these traits should be considered when selecting plants to support pollinators.

The Dynamic Shift Detector: An algorithm to identify changes in parameter values governing populations.

Environmental factors interact with internal rules of population regulation, sometimes perturbing systems to alternate dynamics though changes in parameter values. Yet, pinpointing when such changes occur in naturally fluctuating populations is difficult. An algorithmic approach that can identify the timing and magnitude of parameter shifts would facilitate understanding of abrupt ecological transitions with potential to inform conservation and management of species. The "Dynamic Shift Detector" is an algorithm to identify changes in parameter values governing temporal fluctuations in populations with nonlinear dynamics. The algorithm examines population time series data for the presence, location, and magnitude of parameter shifts. It uses an iterative approach to fitting subsets of time series data, then ranks the fit of break point combinations using model selection, assigning a relative weight to each break. We examined the performance of the Dynamic Shift Detector with simulations and two case studies. Under low environmental/sampling noise, the break point sets selected by the Dynamic Shift Detector contained the true simulated breaks with 70-100% accuracy. The weighting tool generally assigned breaks intentionally placed in simulated data (i.e., true breaks) with weights averaging >0.8 and those due to sampling error (i.e., erroneous breaks) with weights averaging <0.2. In our case study examining an invasion process, the algorithm identified shifts in population cycling associated with variations in resource availability. The shifts identified for the conservation case study highlight a decline process that generally coincided with changing management practices affecting the availability of hostplant resources. When interpreted in the context of species biology, the Dynamic Shift Detector algorithm can aid management decisions and identify critical time periods related to species' dynamics. In an era of rapid global change, such tools can provide key insights into the conditions under which population parameters, and their corresponding dynamics, can shift.

Temperature accounts for the biodiversity of a hyperdiverse group of insects in urban Los Angeles.

The urban heat island effect is a worldwide phenomenon that has been linked to species distributions and abundances in cities. However, effects of urban heat on biotic communities are nearly impossible to disentangle from effects of land cover in most cases because hotter urban sites also have less vegetation and more impervious surfaces than cooler sites within cities. We sampled phorid flies, one of the largest, most biologically diverse families of true flies (Insecta: Diptera: Phoridae), at 30 sites distributed within the central Los Angeles Basin, where we found that temperature and the density of urban land cover are decoupled. Abundance, richness, and community composition of phorids inside urban Los Angeles were most parsimoniously accounted for by mean air temperature in the week preceding sampling. Sites with intermediate mean temperatures had more phorid fly individuals and higher richness. Communities were more even at urban sites with lower minimum temperatures and sites located further away from natural areas, suggesting that communities separated from natural source populations may be more homogenized. Species composition was best explained by minimum temperature. Inasmuch as warmer areas within cities can predict future effects of climate change, phorid fly communities are likely to shift nonlinearly under future climates in more natural areas. Exhaustive surveys of biotic communities within cities, such as the one we describe here, can provide baselines for determining the effects of urban and global climate warming as they intensify.

Habitat Type Influences Danaus plexippus (Lepidoptera: Nymphalidae) Oviposition and Egg Survival on Asclepias syriaca (Gentianales: Apocynaceae).

As agricultural practices intensify, species once common in agricultural landscapes are declining in abundance. One such species is the monarch butterfly (Danaus plexippus L.), whose eastern North American population has decreased approximately 80% during the past 20 yr. One hypothesis explaining the monarch's decline is reduced breeding habitat via loss of common milkweed (Asclepias syriaca L.) from agricultural landscapes in the north central United States due to the adoption of herbicide-tolerant row crops. Current efforts to enhance monarch breeding habitat primarily involve restoring milkweed in perennial grasslands. However, prior surveys found fewer monarch eggs on common milkweed in grassland versus crop habitats, indicating potential preference for oviposition in row crop habitats, or alternatively, greater egg loss to predation in grasslands. We tested these alternative mechanisms by measuring oviposition and egg predation on potted A. syriaca host plants. Our study revealed that habitat context influences both monarch oviposition preference and egg predation rates and that these patterns vary by year. We found higher monarch egg predation rates during the first 24 h after exposure and that much of the predation occurs at night. Overall, we documented up to 90% egg mortality over 72 h in perennial grasslands, while predation rates in corn were lower (10-30% mortality) and more consistent between years. These findings demonstrate that weekly monarch egg surveys are too infrequent to distinguish oviposition habitat preferences from losses due to egg predation and suggest that monarch restoration efforts need to provide both attractive and safe habitats for monarch reproduction.

Disentangling data discrepancies with integrated population models.

A common challenge for studying wildlife populations occurs when different survey methods provide inconsistent or incomplete inference on the trend, dynamics, or viability of a population. A potential solution to the challenge of conflicting or piecemeal data relies on the integration of multiple data types into a unified modeling framework, such as integrated population models (IPMs). IPMs are a powerful approach for species that inhabit spatially and seasonally complex environments. We provide guidance on exploiting the capabilities of IPMs to address inferential discrepancies that stem from spatiotemporal data mismatches. We illustrate this issue with analysis of a migratory species, the American Woodcock (Scolopax minor), in which individual monitoring programs suggest differing population trends. To address this discrepancy, we synthesized several long-term data sets (1963-2015) within an IPM to estimate continental-scale population trends, and link dynamic drivers across the full annual cycle and complete extent of the woodcock's geographic range in eastern North America. Our analysis reveals the limiting portions of the life cycle by identifying time periods and regions where vital rates are lowest and most variable, as well as which demographic parameters constitute the main drivers of population change. We conclude by providing recommendations for resolving conflicting population estimates within an integrated modeling approach, and discuss how strategies (e.g., data thinning, expert opinion elicitation) from other disciplines could be incorporated into ecological analyses when attempting to combine multiple, incongruent data types.

Effect of Switchgrass Ecotype and Cultivar on Establishment, Feeding, and Development of Fall Armyworm (Lepidoptera: Noctuidae).

As interest in production of second-generation biofuels increases, dedicated biomass crops are likely to be called upon to help meet feedstock demands. Switchgrass (Panicum virgatum L.) is a North American native perennial grass that as a candidate biomass crop, combines high biomass yields with other desirable ecosystem services. At present, switchgrass is produced on limited acres in the United States and experiences relatively minor insect pest problems. However, as switchgrass undergoes breeding to increase biomass yield and quality, and is grown on more acres, insect pest pressure will probably increase. To investigate how currently available switchgrass ecotypes and cultivars may influence herbivory by generalist insect herbivores, we performed feeding trials using neonate and late-instar fall armyworm [Spodoptera frugiperda JE Smith (Lepidoptera: Noctuidae)]. No-choice feeding experiments were used to explore how switchgrass varieties influence larval establishment, consumption levels, and life-history traits in contrast to a preferred host, corn (Zea mays L.). Neonate S. frugiperda consumed greater amounts of corn than switchgrass and increased amounts of upland versus lowland ecotypes. Late-instar larvae, which do the majority of the larval feeding, exhibited lower consumption of lowland ecotypes, which led to increased development time and reduced pupal weights. The exception to these trends was the upland cultivar 'Trailblazer', which unexpectedly performed similarly to lowland cultivars. These results suggest that both switchgrass ecotype and cultivar can influence feeding damage by a common generalist herbivore. These findings can be used to help inform current switchgrass planting decisions as well as future breeding efforts.

Predicting plant attractiveness to pollinators with passive crowdsourcing.

Global concern regarding pollinator decline has intensified interest in enhancing pollinator resources in managed landscapes. These efforts frequently emphasize restoration or planting of flowering plants to provide pollen and nectar resources that are highly attractive to the desired pollinators. However, determining exactly which plant species should be used to enhance a landscape is difficult. Empirical screening of plants for such purposes is logistically daunting, but could be streamlined by crowdsourcing data to create lists of plants most probable to attract the desired pollinator taxa. People frequently photograph plants in bloom and the Internet has become a vast repository of such images. A proportion of these images also capture floral visitation by arthropods. Here, we test the hypothesis that the abundance of floral images containing identifiable pollinator and other beneficial insects is positively associated with the observed attractiveness of the same species in controlled field trials from previously published studies. We used Google Image searches to determine the correlation of pollinator visitation captured by photographs on the Internet relative to the attractiveness of the same species in common-garden field trials for 43 plant species. From the first 30 photographs, which successfully identified the plant, we recorded the number of Apis (managed honeybees), non-Apis (exclusively wild bees) and the number of bee-mimicking syrphid flies. We used these observations from search hits as well as bloom period (BP) as predictor variables in Generalized Linear Models (GLMs) for field-observed abundances of each of these groups. We found that non-Apis bees observed in controlled field trials were positively associated with observations of these taxa in Google Image searches (pseudo-R (2) of 0.668). Syrphid fly observations in the field were also associated with the frequency they were observed in images, but this relationship was weak. Apis bee observations were not associated with Internet images, but were slightly associated with BP. Our results suggest that passively crowdsourced image data can potentially be a useful screening tool to identify candidate plants for pollinator habitat restoration efforts directed at wild bee conservation. Increasing our understanding of the attractiveness of a greater diversity of plants increases the potential for more rapid and efficient research in creating pollinator-supportive landscapes.

Scaling up the diversity-resilience relationship with trait databases and remote sensing data: the recovery of productivity after wildfire.

Understanding the mechanisms underlying ecosystem resilience - why some systems have an irreversible response to disturbances while others recover - is critical for conserving biodiversity and ecosystem function in the face of global change. Despite the widespread acceptance of a positive relationship between biodiversity and resilience, empirical evidence for this relationship remains fairly limited in scope and localized in scale. Assessing resilience at the large landscape and regional scales most relevant to land management and conservation practices has been limited by the ability to measure both diversity and resilience over large spatial scales. Here, we combined tools used in large-scale studies of biodiversity (remote sensing and trait databases) with theoretical advances developed from small-scale experiments to ask whether the functional diversity within a range of woodland and forest ecosystems influences the recovery of productivity after wildfires across the four-corner region of the United States. We additionally asked how environmental variation (topography, macroclimate) across this geographic region influences such resilience, either directly or indirectly via changes in functional diversity. Using path analysis, we found that functional diversity in regeneration traits (fire tolerance, fire resistance, resprout ability) was a stronger predictor of the recovery of productivity after wildfire than the functional diversity of seed mass or species richness. Moreover, slope, elevation, and aspect either directly or indirectly influenced the recovery of productivity, likely via their effect on microclimate, while macroclimate had no direct or indirect effects. Our study provides some of the first direct empirical evidence for functional diversity increasing resilience at large spatial scales. Our approach highlights the power of combining theory based on local-scale studies with tools used in studies at large spatial scales and trait databases to understand pressing environmental issues.

Thermally moderated firefly activity is delayed by precipitation extremes.

The timing of events in the life history of temperate insects is most typically primarily cued by one of two drivers: photoperiod or temperature accumulation over the growing season. However, an insect's phenology can also be moderated by other drivers like rainfall or the phenology of its host plants. When multiple drivers of phenology interact, there is greater potential for phenological asynchronies to arise between an organism and those with which it interacts. We examined the phenological patterns of a highly seasonal group of fireflies (Photinus spp., predominantly P. pyralis) over a 12-year period (2004-2015) across 10 plant communities to determine whether interacting drivers could explain the variability observed in the adult flight activity density (i.e. mating season) of this species. We found that temperature accumulation was the primary driver of phenology, with activity peaks usually occurring at a temperature accumulation of approximately 800 degree days (base 10°C); however, our model found this peak varied by nearly 180 degree-day units among years. This variation could be explained by a quadratic relationship with the accumulation of precipitation in the growing season; in years with either high or low precipitation extremes at our study site, flight activity was delayed. More fireflies were captured in general in herbaceous plant communities with minimal soil disturbance (alfalfa and no-till field crop rotations), but only weak interactions occurred between within-season responses to climatic variables and plant community. The interaction we observed between temperature and precipitation accumulation suggests that, although climate warming has the potential to disrupt phenology of many organisms, changes to regional precipitation patterns can magnify these disruptions.