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As a general rule, plants defend against herbivores with multiple traits. The defense synergy hypothesis posits that some traits are more effective when co-expressed with others compared to their independent efficacy. However, this hypothesis has rarely been tested outside of phytochemical mixtures, and seldom under field conditions. We tested for synergies between multiple defense traits of common milkweed (Asclepias syriaca) by assaying the performance of two specialist chewing herbivores on plants in natural populations. We employed regression and a novel application of random forests to identify synergies and antagonisms between defense traits. We found the first direct empirical evidence for two previously hypothesized defense synergies in milkweed (latex by secondary metabolites, latex by trichomes) and identified numerous other potential synergies and antagonisms. Our strongest evidence for a defense synergy was between leaf mass per area and low nitrogen content; given that these "leaf economic" traits typically covary in milkweed, a defense synergy could reinforce their co-expression. We report that each of the plant defense traits showed context-dependent effects on herbivores, and increased trait expression could well be beneficial to herbivores for some ranges of observed expression. The novel methods and findings presented here complement more mechanistic approaches to the study of plant defense diversity and provide some of the best evidence to date that multiple classes of plant defense synergize in their impact on insects. Plant defense synergies against highly specialized herbivores, as shown here, are consistent with ongoing reciprocal evolution between these antagonists.

The US Endangered Species Act aims to recover threatened species and preserve their ecosystems, often through habitat management and restoration. In the face of climate change and phenological shifts, however, habitat management can seem futile. One of the most conspicuous effects of climate change is that many species are shifting in phenology, i.e., the timing of life history events. These shifts are assumed to have population-level effects on at-risk species, although it is less clear whether these effects are likely to be positive or negative. Here, we use a 27-year-long data set of Fender’s blue (Icaricia icarioides fenderi), an endangered butterfly, to investigate long-term changes in abundance and phenology at nine sites near Eugene, Oregon, USA. For Fender’s blue, day of peak flight activity consistently advanced at all sites from 1993 to 2019. At the same time, Fender’s blue populations increased at some sites and abundance was not changing at others. There was no association of population growth and advancement of peak flight activity. This suggests that although phenological shifts may be a “fingerprint” of climate change, they may not always be a cause for concern. Implications for insect conservation Lessons from Fender’s blue butterfly are likely applicable to conservation of other at-risk butterflies. Despite a rapidly changing climate, at least some rare species can be conserved and recovered with appropriate habitat management.

Allee effects are processes that become disrupted at low population sizes, causing further declines to eventual extinction. Identifying Allee effects in natural populations is important for both ecological theory and conservation efforts but challenging because populations typically only experience them briefly before collapsing. In 2018, the Western monarch butterfly population fell below the threshold that scientists predicted would trigger Allee effects. Here we show that since 2018, the Western monarch population has expanded more slowly during spring and summer breeding and filled a smaller breeding range. Other aspects of the monarch life cycle (winter survival, fecundity, morphology) have not declined after the crash. However, winter survival is much lower and wing sizes of overwintering monarchs are larger now than in the 20th century, suggesting higher mortality and selection for increased dispersal in recent decades. Delayed arrival to parts of the Western monarch breeding range is a novel Allee effect, which could lead to temporal mismatches between monarchs and their and host plants.

Estimating population abundance is central to population ecology. With increasing concern over declining insect populations, estimating trends in abundance has become even more urgent. At the same time, there is an emerging in interest in quantifying phenological patterns, in part because phenological shifts are one of the most conspicuous signs of climate change. Existing techniques to fit activity curves (and thus both abundance and phenology) to repeated transect counts of insects (a common form of data for these taxa) frequently fail for sparse data, and often require advanced knowledge of statistical computing. These limitations prevent us from understanding both population trends and phenological shifts, especially in the at-risk species for which this understanding is most vital. Here we present a method to fit repeated transect count data with Gaussian curves using linear models, and show how robust abundance and phenological metrics can be obtained using standard regression tools. We then apply this method to eight years of Baltimore checkerspot data using generalized linear models (GLMs). This case study illustrates the ability of our method to fit even years with only a few non-zero survey counts, and identifies a significant negative relationship between population size and annual variation in thermal environment (in growing degree days). We believe our new method provides a key tool to unlock previously-unusable sparse data sets, and may provide a useful middle ground between ad hoc metrics of abundance and phenology and custom-coded mechanistic models.

An important part of landscape ecology is determining how the arrangement (aggregation or fragmentation) of patches in space influences the population dynamics of foraging organisms. One hypothesis in agricultural ecology is that fine-grain spatial heterogeneity in cropping (many small agricultural fields) should provide better pest control than coarse-grain heterogeneity (few large agricultural fields); this hypothesis has been proposed as an explanation for the increased pest abundance associated with agricultural intensification. However, empirical studies have found mixed support for this hypothesis, and some, surprisingly, demonstrate a strong decrease in pest abundance with increased crop aggregation. We developed a spatially explicit simulation model of pest movement across an agricultural landscape to uncover basic processes that could reduce pest abundance in landscapes with fewer, larger fields. This model focuses on herbivore movement and does not include predation effects or other biological interactions. We found that field aggregation in the model led to severely reduced pest densities and further discovered that this relationship was due to an increased distance between fields and a decreased “target area” in more aggregated landscapes. The features that create a negative relationship between aggregation and pest densities rely on crop rotation and limited dispersal capabilities of the pests. These findings help to explain seemingly counter-intuitive empirical studies and provide an expectation for when field aggregation may reduce pest populations in agro-ecosystems.

Summary In observational demographic data, the number of measured factors that could potentially drive demography (such as daily weather records between two censuses) can easily exceed the number of independent observations. Thus, identifying the important drivers requires alternatives to standard model selection and variable selection methods. Spline methods that estimate smooth functions over continuous domains (such as space or time) have the potential to resolve high-dimensional problems in ecological systems. We consider two examples that are important for many plant populations: competition with neighbours that vary in size and distance from the focal individual and climate variables during a window of time before a response (growth, survival, etc.) is measured. For competition covariates, we use a simulation study based on empirical data to show that a monotone spline estimate of competition kernels via approximate AIC returns very accurate estimates. We then apply the method to long-term, mapped quadrat data on the four dominant species in an Idaho (US) sagebrush steppe community. For climate predictors and their temporal lags, we use simulated data sets to compare functional smoothing methods with competing linear (LASSO) or machine learning (random forests) methods. Given sufficient data, functional smoothing methods outperformed the other two methods. Functional smoothing methods can advance data-driven population modelling by providing alternatives to specifying competition kernels a priori and to arbitrarily aggregating continuous environmental covariates. However, there are important open questions related to modelling of nonlinear climate responses and size × climate interactions.