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Insect herbivory can be an important selective pressure and contribute substantially to local plant richness. As herbivory is the result of numerous ecological and evolutionary processes, such as complex insect population dynamics and evolution of plant antiherbivore defenses, it has been difficult to predict variation in herbivory across meaningful spatial scales. In the present work, we characterize patterns of herbivory on plants in a species‐rich and abundant tropical genus (Piper) across forests spanning 44° of latitude in the Neotropics. We modeled the effects of geography, climate, resource availability, andPiperspecies richness on the median, dispersion, and skew of generalist and specialist herbivory. By examining these multiple components of the distribution of herbivory, we were able to determine factors that increase biologically meaningful herbivory at the upper ends of the distribution (indicated by skew and dispersion). We observed a roughly twofold increase in median herbivory in humid relative to seasonal forests, which aligns with the hypothesis that precipitation seasonality plays a critical role in shaping interaction diversity within tropical ecosystems. Site level variables such as latitude, seasonality, and maximumPiperrichness explained the positive skew in herbivory at the local scale (plot level) better for assemblages ofPipercongeners than for a single species. Predictors that varied between local communities, such as resource availability and diversity, best explained the distribution of herbivory within sites, dampening broad patterns across latitude and climate and demonstrating why generalizations about gradients in herbivory have been elusive. The estimated population means, dispersion, and skew of herbivory responded differently to abiotic and biotic factors, illustrating the need for careful studies to explore distributions of herbivory and their effects on forest diversity.

 

The pressures of global change acting on wild plants and animals include exposure to environmental toxins, the introduction of non-native species, and climate change. Relatively few studies have been reported in which these three main classes of stressors have been examined simultaneously, allowing for the possibility of synergistic effects in an experimental context. In this study, we exposed caterpillars of the Melissa blue butterfly ( Lycaeides melissa ) to three concentrations of chlorantraniliprole, under three experimental climates, on a diet of a native or a non-native host plant throughout larval development in a fully factorial experiment. We find that high pesticide exposure and a non-native diet exhibit strong negative effects on caterpillars, resulting in 62% and 42% reduction in survival, respectively, while interactive effects tend to be weaker, ranging from 15% to 22% reduction in survival. Interactive effects have been shown to be strong in other contexts, but do not appear to be universal; however, our study shows that the cumulative effects of stressors acting in isolation (additively) are sufficiently strong to severely reduce survival and by extension population persistence in the wild. 

One of the defining features of the Anthropocene is eroding ecosystem services, decreases in biodiversity, and overall reductions in the abundance of once-common organisms, including many insects that play innumerable roles in natural communities and agricultural systems that support human society. It is now clear that the preservation of insects cannot rely solely on the legal protection of natural areas far removed from the densest areas of human habitation. Instead, a critical challenge moving forward is to intelligently manage areas that include intensively farmed landscapes, such as the Central Valley of California. Here we attempt to meet this challenge with a tool for modeling landscape connectivity for insects (with pollinators in particular in mind) that builds on available information including lethality of pesticides and expert opinion on insect movement. Despite the massive fragmentation of the Central Valley, we find that connectivity is possible, especially utilizing the restoration or improvement of agricultural margins, which (in their summed area) exceed natural areas. Our modeling approach is flexible and can be used to address a wide range of questions regarding both changes in land cover as well as changes in pesticide application rates. Finally, we highlight key steps that could be taken moving forward and the great many knowledge gaps that could be addressed in the field to improve future iterations of our modeling approach. 

Research on plant-pollinator interactions requires a diversity of perspectives and approaches, and documenting changing pollinator-plant interactions due to declining insect diversity and climate change is especially challenging. Natural history collections are increasingly important for such research and can provide ecological information across broad spatial and temporal scales. Here, we describe novel approaches that integrate museum specimens from insect and plant collections with field observations to quantify pollen networks over large spatial and temporal gradients. We present methodological strategies for evaluating insect-pollen network parameters based on pollen collected from museum insect specimens. These methods provide insight into spatial and temporal variation in pollen-insect interactions and complement other approaches to studying pollination, such as pollinator observation networks and flower enclosure experiments. We present example data from butterfly pollen networks over the past century in the Great Basin Desert and Sierra Nevada Mountains, United States. Complementary to these approaches, we describe rapid pollen identification methods that can increase speed and accuracy of taxonomic determinations, using pollen grains collected from herbarium specimens. As an example, we describe a convolutional neural network (CNN) to automate identification of pollen. We extracted images of pollen grains from 21 common species from herbarium specimens at the University of Nevada Reno (RENO). The CNN model achieved exceptional accuracy of identification, with a correct classification rate of 98.8%. These and similar approaches can transform the way we estimate pollination network parameters and greatly change inferences from existing networks, which have exploded over the past few decades. These techniques also allow us to address critical ecological questions related to mutualistic networks, community ecology, and conservation biology. Museum collections remain a bountiful source of data for biodiversity science and understanding global change. 

Plant–insect interactions are common and important in basic and applied biology. Trait and genetic variation can affect the outcome and evolution of these interactions, but the relative contributions of plant and insect genetic variation and how these interact remain unclear and are rarely subject to assessment in the same experimental context. Here, we address this knowledge gap using a recent host-range expansion onto alfalfa by the Melissa blue butterfly. Common garden rearing experiments and genomic data show that caterpillar performance depends on plant and insect genetic variation, with insect genetics contributing to performance earlier in development and plant genetics later. Our models of performance based on caterpillar genetics retained predictive power when applied to a second common garden. Much of the plant genetic effect could be explained by heritable variation in plant phytochemicals, especially saponins, peptides, and phosphatidyl cholines, providing a possible mechanistic understanding of variation in the species interaction. We find evidence of polygenic, mostly additive effects within and between species, with consistent effects of plant genotype on growth and development across multiple butterfly species. Our results inform theories of plant–insect coevolution and the evolution of diet breadth in herbivorous insects and other host-specific parasites. 

Climate change is contributing to declines of insects through rising temperatures, altered precipitation patterns, and an increasing frequency of extreme events. The impacts of both gradual and sudden shifts in weather patterns are realized directly on insect physiology and indirectly through impacts on other trophic levels. Here, we investigated direct effects of seasonal weather on butterfly occurrences and indirect effects mediated by plant productivity using a temporally intensive butterfly monitoring dataset, in combination with high‐resolution climate data and a remotely sensed indicator of plant primary productivity. Specifically, we used Bayesian hierarchical path analysis to quantify relationships between weather and weather‐driven plant productivity on the occurrence of 94 butterfly species from three localities distributed across an elevational gradient. We found that snow pack exerted a strong direct positive effect on butterfly occurrence and that low snow pack was the primary driver of reductions during drought. Additionally, we found that plant primary productivity had a consistently negative effect on butterfly occurrence. These results highlight mechanisms of weather‐driven declines in insect populations and the nuances of climate change effects involving snow melt, which have implications for ecological theories linking topographic complexity to ecological resilience in montane systems.

 

Infections by maternally inherited bacterial endosymbionts, especially Wolbachia, are common in insects and other invertebrates but infection dynamics across species ranges are largely under studied. Specifically, we lack a broad understanding of the origin of Wolbachia infections in novel hosts, and the historical and geographical dynamics of infections that are critical for identifying the factors governing their spread. We used Genotype-by-Sequencing data from previous population genomics studies for range-wide surveys of Wolbachia presence and genetic diversity in North American butterflies of the genus Lycaeides. As few as one sequence read identified by assembly to a Wolbachia reference genome provided high accuracy in detecting infections in host butterflies as determined by confirmatory PCR tests, and maximum accuracy was achieved with a threshold of only 5 sequence reads per host individual. Using this threshold, we detected Wolbachia in all but 2 of the 107 sampling localities spanning the continent, with infection frequencies within populations ranging from 0% to 100% of individuals, but with most localities having high infection frequencies (mean = 91% infection rate). Three major lineages of Wolbachia were identified as separate strains that appear to represent 3 separate invasions of Lycaeides butterflies by Wolbachia. Overall, we found extensive evidence for acquisition of Wolbachia through interspecific transfer between host lineages. Strain wLycC was confined to a single butterfly taxon, hybrid lineages derived from it, and closely adjacent populations in other taxa. While the other 2 strains were detected throughout the rest of the continent, strain wLycB almost always co-occurred with wLycA. Our demographic modeling suggests wLycB is a recent invasion. Within strain wLycA, the 2 most frequent haplotypes are confined almost exclusively to separate butterfly taxa with haplotype A1 observed largely in Lycaeides melissa and haplotype A2 observed most often in Lycaeides idas localities, consistent with either cladogenic mode of infection acquisition from a common ancestor or by hybridization and accompanying mutation. More than 1 major Wolbachia strain was observed in 15 localities. These results demonstrate the utility of using resequencing data from hosts to quantify Wolbachia genetic variation and infection frequency and provide evidence of multiple colonizations of novel hosts through hybridization between butterfly lineages and complex dynamics between Wolbachia strains.

 

Identifying patterns of pathogen infection in natural systems is crucial to understanding mechanisms of host–pathogen interactions. In this study, we explored how Junonia coenia densovirus (JcDV) infection varies over space and time in populations of the Melissa blue butterfly (Lycaeides melissa: Lycaenidae) using two different host plants. Collections ofL. melissaadults from multiple populations and years, along with host plant tissue and community samples of arthropods found on host plants, were screened to determine JcDV prevalence and load. Additionally, we sampled at multiple time points within a singleL. melissaflight season to investigate intra‐annual variation in infection patterns.

We found population‐specific variation in viral prevalence ofL. melissaacross collection years, with historical samples potentially having higher viral prevalence than contemporary samples, although host plant diet was not informative for these patterns. Patterns of infection across multiple generations within a flight season showed that late‐season samples had a higher proportion of JcDV‐positive individuals, suggesting an accumulation of virus over the season. Sequence data from a segment of the JcDV capsid gene showed a lack of viral genetic diversity betweenL. melissacollected from different localities, and little to no viral particles were found in the surrounding environment.

Our discovery of temporal variation in infection suggests that multiple sampling efforts must be made when describing pathogen prevalence in multivoltine hosts. Our findings represent an important first step towards further exploration of the ecological factors mediating disease prevalence and host‐specific variability of infection in wild insect populations.