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Species richness in tropical forests is correlated with other dimensions of diversity, including the diversity of plant–herbivore interactions and the phytochemical diversity that influences those interactions. Understanding the complexity of plant chemistry and the importance of phytochemical diversity for plant–insect interactions and overall forest richness has been enhanced significantly by the application of metabolomics to natural systems. The present work used proton nuclear magnetic resonance spectroscopy (1H‐NMR) profiling of crude leaf extracts to study phytochemical similarity and diversity among Piper plants growing naturally in the Atlantic Rainforest of Brazil. Spectral profile similarity and chemical diversity were quantified to examine the relationship between metrics of phytochemical diversity, specialist and generalist herbivory, and understory plant richness. Herbivory increased with understory species richness, while generalist herbivory increased and specialist herbivory decreased with the diversity of Piper leaf material available. Specialist herbivory increased when conspecific host plants were more spectroscopically dissimilar. Spectral similarity was lower among individuals of common species, and they were also more spectrally diverse, indicating phytochemical diversity is beneficial to plants. Canopy openness and soil nutrients also influenced chemistry and herbivory. The complex relationships uncovered in this study add information to our growing understanding of the importance of phytochemical diversity for plant–insect interactions and tropical plant species richness. [ABSTRACT FROM AUTHOR]

Information‐theoretic approaches to model selection, such as Akaike's information criterion (AIC) and cross validation, provide a rigorous framework to select among candidate hypotheses in ecology, yet the persistent concern of overfitting undermines the interpretation of inferred processes. A common misconception is that overfitting is due to the choice of criterion or model score, despite research demonstrating that selection uncertainty associated with score estimation is the predominant influence. Here we introduce a novel selection rule that identifies a parsimonious model by directly accounting for estimation uncertainty, while still retaining an information‐theoretic interpretation. The new rule, which is a modification of the existing one‐standard‐error rule, mitigates overfitting and reduces the likelihood that spurious effects will be included in the selected model, thereby improving its inferential properties. We present the rule and illustrative examples in the context of maximum‐likelihood estimation and Kullback‐Leibler discrepancy, although the rule is applicable in a more general setting, including Bayesian model selection and other types of discrepancy. [ABSTRACT FROM AUTHOR]

Host species that can independently maintain a pathogen in a host community and contribute to infection in other species are important targets for disease management. However, the potential of host species to maintain a pathogen is not fixed over time, and an important challenge is understanding how within‐ and across‐season variability in host maintenance potential affects pathogen persistence over longer time scales relevant for disease management (e.g., years). Here, we sought to understand the causes and consequences of seasonal infection dynamics in leopard frogs (Rana sphenocephala and Rana pipiens) infected with the fungal pathogen Batrachochytrium dendrobatidis (Bd). We addressed three questions broadly applicable to seasonal host–parasite systems. First, to what degree are observed seasonal patterns in infection driven by temperature‐dependent infection processes compared to seasonal host demographic processes? Second, how does seasonal variation in maintenance potential affect long‐term pathogen persistence in multi‐host communities? Third, does high deterministic maintenance potential relate to the long‐term stochastic persistence of pathogens in host populations with seasonal infection dynamics? To answer these questions, we used field data collected over 3 years on >1400 amphibians across four geographic locations, laboratory and mesocosm experiments, and a novel mathematical model. We found that the mechanisms that drive seasonal prevalence were different from those driving seasonal infection intensity. Seasonal variation in Bd prevalence was driven primarily by changes in host contact rates associated with breeding migrations to and from aquatic habitat. In contrast, seasonal changes in infection intensity were driven by temperature‐induced changes in Bd growth rate. Using our model, we found that the maintenance potential of leopard frogs varied significantly throughout the year and that seasonal troughs in infection prevalence made it unlikely that leopard frogs were responsible for long‐term Bd persistence in these seasonal amphibian communities, highlighting the importance of alternative pathogen reservoirs for Bd persistence. Our results have broad implications for management in seasonal host–pathogen systems, showing that seasonal changes in host and pathogen vital rates, rather than the depletion of susceptible hosts, can lead to troughs in pathogen prevalence and stochastic pathogen extirpation. [ABSTRACT FROM AUTHOR]

Wisconsin's plant communities are responding to shifting disturbance regimes, habitat fragmentation, aerial nitrogen deposition, exotic species invasions, ungulate herbivory, and successional processes. To better understand how plant functional traits mediate species' responses to changing environmental conditions, we collected a large set of functional trait data for vascular plant species occupying Wisconsin forests and grasslands. We used standard protocols to make 76,213 measurements of 34 quantitative traits. These data provide rich information on genome size, physical leaf traits (length, width, circularity, thickness, dry matter content, specific leaf area, etc.), chemical leaf traits (carbon, nitrogen, phosphorus, potassium, calcium, magnesium, ash), life history traits (vegetative and flower heights, seed mass), and traits affecting plant palatability (leaf fiber, fat, and lignin). These trait values derive from replicate measurements on 12+ individuals of each species from multiple sites and 45+ individuals for a selected subset of species. Measurements typically reflect values for individuals although some chemical traits involved composite samples from several individuals at the same site. We also qualitatively characterized each species by plant family, woodiness, functional group, and Raunkiaer lifeform. These data allow us to characterize trait dimensionality, differentiation, and covariation among temperate plant species (e.g., leaf and stem economic syndromes). We can also characterize species' responses to environmental gradients and drivers of ecological change. With survey and resurvey data available from >400 sites in Wisconsin, we can analyze variation in community trait distributions and diversity over time and space. These data therefore allow us to assess how trait divergence vs. convergence affects community assembly and how traits may be related to half‐century shifts in the distribution and abundance of these species. The data set can be used for non‐commercial purposes. The data set is licensed as follows: CC‐By Attribution 4.0 International. We request users cite both the OSF data set and this Ecology data paper publication. [ABSTRACT FROM AUTHOR]