The Effects of Climate Change on the Phenological Interactions of Plants and Pollinators

Symposium title: Interdisciplinary Canary: Linkages between Ecology and Sustainable Decision Making in a Dynamic Environment

*1) Background/Question/Methods*
The responses of pollinators to climate change could include changes in phenology of migratory pollinators and in the routes or destinations for their migration, changes in the phenology and distribution of non-migratory species, and changes in the host plants they visit for nectar and pollen. Plants face similar challenges with regard to changes in their distributions, their reproductive phenology, and interactions with both co-flowering species and pollinators (competition, facilitation, etc.). Unless pollinators and their host plants are responding similarly to changing environmental cues that affect their phenology, their historical patterns of interaction, both mutualistic and competitive, are likely to change. Long-term data are essential to investigating which if any of these potential outcomes are occurring. A 36-year record of abundance and phenology of flowering of 90+ wildflower species, surveys of the altitudinal distribution of bumble bees in the 1970s and the past few years, and data from a long-term Malaise trap sampling program, all near the Rocky Mountain Biological Laboratory (West Elk mountains, Colorado) are used for this investigation. 

*2) Results/Conclusions*
Although the flowering phenology of all species examined to date is affected by a single environmental event, disappearance of the winter snowpack (range 22 April -19 June since 1975), either their responses to that single cue are not uniform, or different species respond to additional cues in addition to snowmelt (e.g., growing degree days). Thus the community of co-flowering species varies temporally and quantitatively among years; differential sensitivity to frost damage is an example of an environmental variable that generates the quantitative variation among years, and is in turn affected by date of snowmelt. Arrival dates of migratory Broad-tailed Hummingbirds are significantly correlated with the amount of snow remaining on 30 April, and with the day of first flowering of Erythronium grandiflorum (glacier lily), the first flower that they visit at this site in the spring. Altitudinal distributions of at least some bumble bee species, and of the flowers they feed on, are also changing, with one bee species occurring about 600m higher than it did 30 years ago and one wildflower (Mertensia cilata) disappearing from lower altitudes where it was historically common. As these communities of plants and pollinators respond to environmental changes with changes in phenology and distribution, new interactions will be created and old ones will be lost

Appropriate, accessible and appealing probabilistic graphical models

Appropriate - Many multivariate probabilistic models either use independent distributions or dependent Gaussian distributions. Yet, many real-world datasets contain count-valued or non-negative skewed data, e.g. bag-of-words text data and biological sequencing data. Thus, we develop novel probabilistic graphical models for use on count-valued and non-negative data including Poisson graphical models and multinomial graphical models. We develop one generalization that allows for triple-wise or k-wise graphical models going beyond the normal pairwise formulation. Furthermore, we also explore Gaussian-copula graphical models and derive closed-form solutions for the conditional distributions and marginal distributions (both before and after conditioning). Finally, we derive mixture and admixture, or topic model, generalizations of these graphical models to introduce more power and interpretability. Accessible - Previous multivariate models, especially related to text data, often have complex dependencies without a closed form and require complex inference algorithms that have limited theoretical justification. For example, hierarchical Bayesian models often require marginalizing over many latent variables. We show that our novel graphical models (even the k-wise interaction models) have simple and intuitive estimation procedures based on node-wise regressions that likely have similar theoretical guarantees as previous work in graphical models. For the copula-based graphical models, we show that simple approximations could still provide useful models; these copula models also come with closed-form conditional and marginal distributions, which make them amenable to exploratory inspection and manipulation. The parameters of these models are easy to interpret and thus may be accessible to a wide audience. Appealing - High-level visualization and interpretation of graphical models with even 100 variables has often been difficult even for a graphical model expert---despite visualization being one of the original motivators for graphical models. This difficulty is likely due to the lack of collaboration between graphical model experts and visualization experts. To begin bridging this gap, we develop a novel "what if?" interaction that manipulates and leverages the probabilistic power of graphical models. Our approach defines: the probabilistic mechanism via conditional probability; the query language to map text input to a conditional probability query; and the formal underlying probabilistic model. We then propose to visualize these query-specific probabilistic graphical models by combining the intuitiveness of force-directed layouts with the beauty and readability of word clouds, which pack many words into valuable screen space while ensuring words do not overlap via pixel-level collision detection. Although both the force-directed layout and the pixel-level packing problems are challenging in their own right, we approximate both simultaneously via adaptive simulated annealing starting from careful initialization. For visualizing mixture distributions, we also design a meaningful mapping from the properties of the mixture distribution to a color in the perceptually uniform CIELUV color space. Finally, we demonstrate our approach via illustrative visualizations of several real-world datasets.Computer Science

Cooperative Distribution Alignment via JSD Upper Bound

Unsupervised distribution alignment estimates a transformation that maps two or more source distributions to a shared aligned distribution given only samples from each distribution. This task has many applications including generative modeling, unsupervised domain adaptation, and socially aware learning. Most prior works use adversarial learning (i.e., min-max optimization), which can be challenging to optimize and evaluate. A few recent works explore non-adversarial flow-based (i.e., invertible) approaches, but they lack a unified perspective and are limited in efficiently aligning multiple distributions. Therefore, we propose to unify and generalize previous flow-based approaches under a single non-adversarial framework, which we prove is equivalent to minimizing an upper bound on the Jensen-Shannon Divergence (JSD). Importantly, our problem reduces to a min-min, i.e., cooperative, problem and can provide a natural evaluation metric for unsupervised distribution alignment. We show empirical results on both simulated and real-world datasets to demonstrate the benefits of our approach. Code is available at https://github.com/inouye-lab/alignment-upper-bound.Comment: Accepted for publication in Advances in Neural Information Processing Systems 36 (NeurIPS 2022

Known allosteric proteins have central roles in genetic disease

Allostery is a form of protein regulation, where ligands that bind sites located apart from the active site can modify the activity of the protein. The molecular mechanisms of allostery have been extensively studied, because allosteric sites are less conserved than active sites, and drugs targeting them are more specific than drugs binding the active sites. Here we quantify the importance of allostery in genetic disease. We show that 1) known allosteric proteins are central in disease networks, and contribute to genetic disease and comorbidities much more than non-allosteric proteins, in many major disease types like hematopoietic diseases, cardiovascular diseases, cancers, diabetes, or diseases of the central nervous system. 2) variants from cancer genome-wide association studies are enriched near allosteric proteins, indicating their importance to polygenic traits; and 3) the importance of allosteric proteins in disease is due, at least partly, to their central positions in protein-protein interaction networks, and probably not due to their dynamical properties