Historical data show that recent climate change has caused advances in seasonal timing (phenology) in many animals and plants, particularly in temperate and higher latitude regions. The population and fitness consequences of these phenological shifts for insects and other ectotherms have been heterogeneous: warming can increase development rates and the number of generations per year (increasing fitness), but can also lead to seasonal mismatches between animals and their resources and increase exposure to environmental variability (decreasing fitness). Insect populations exhibit local adaptation in their developmental responses to temperature, including lower developmental thresholds and the thermal requirements to complete development, but climate change can potentially disrupt seasonal timing of juvenile and adult stages and alter population fitness. We investigate these issues using a global dataset describing how insect developmental responds to temperature via two traits: lower temperature thresholds for development (T0) and the cumulative degree-days required to complete development (G). As suggested by previous analyses, T0 decreases and G increases with increasing (absolute) latitude; however, these traits and the relationship between G and latitude varies significantly among taxonomic orders. The mean number of generations per year (a metric of fitness) increases with both decreasing T0 and G, but the effects of these traits on fitness vary strongly with latitude, with stronger selection on both traits at higher (absolute) latitudes. We then use the traits to predict developmental timing and temperatures for multiple generations within seasons and across years (1970-2010). Seasonality drives developmental temperatures to peak mid-season and for generation lengths to decline across seasons, particularly in temperate regions. We predict that climate warming has advanced phenology and increased the number of generations, particularly at high latitudes. The magnitude of increases in developmental temperature varies little across latitude. Increases in the number of seasonal generations have been greatest for populations experiencing the greatest phenological advancements and warming. Shifts in developmental rate and timing due to climate change will have complex implications for selection and fitness in seasonal environments.

Transferring knowledge between species is key for many biological applications, but is complicated by divergent and convergent evolution. Many current approaches for this problem leverage sequence and interaction network data to transfer knowledge across species, exemplified by network alignment methods. While these techniques do well, they are limited in scope, creating metrics to address one specific problem or task. We take a different approach by creating an environment where multiple knowledge transfer tasks can be performed using the same protein representations. Specifically, our kernel-based method, MUNK, integrates sequence and network structure to create functional protein representations , embedding proteins from different species in the same vector space. First we show proteins in different species that are close in MUNK-space are functionally similar. Next, we use these representations to share knowledge of synthetic lethal interactions between species . Importantly, we find that the results using MUNK-representations are at least as accurate as existing algorithms for these tasks. Finally, we generalize the notion of a phenolog (‘orthologous phenotype’) to use functionally similar proteins (i.e. those with similar representations). We demonstrate the utility of this broadened notion by using it to identify known phenologs and novel non-obvious ones supported by current research.

Despite the pressing need for accurate forecasts of ecological and evolutionary responses to environmental change, commonly used modeling approaches exhibit mixed performance because they omit many important aspects of how organisms respond to spatially and temporally variable environments. Integrating models based on organismal phenotypes at the physiological, performance, and fitness levels can improve model performance. We summarize current limitations of environmental data and models and discuss potential remedies. The paper reviews emerging techniques for sensing environments at fine spatial and temporal scales, accounting for environmental extremes, and capturing how organisms experience the environment. Intertidal mussel data illustrate biologically important aspects of environmental variability. We then discuss key challenges in translating environmental conditions into organismal performance including accounting for the varied timescales of physiological processes, for responses to environmental fluctuations including the onset of stress and other thresholds, and for how environmental sensitivities vary across lifecycles. We call for the creation of phenotypic databases to parameterize forecasting models and advocate for improved sharing of model code and data for model testing. We conclude with challenges in organismal biology that must be solved to improve forecasts over the next decade.

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Abstract.A key challenge to transferring knowledge between species is that different species have fundamentally different genetic architectures. Initial computational approaches to transfer knowledge across species have relied on measures of heredity such as genetic homology, but these approaches suffer from limitations. First, only a small subset of genes have homologs, limiting the amount of knowledge that can be transferred, and second, genes change or repurpose functions, complicating the transfer of knowledge. Many approaches address this problem by expanding the notion of homology by leveraging high-throughput genomic and proteomic measurements, such as through network alignment.In this work, we take a new approach to transferring knowledge across species by expanding the notion of homology through explicit measures of functional similarity between proteins in different species. Specifically, our kernel-based method, Handl (Homology Assessment across Networks using Diffusion and Landmarks), integrates sequence and network structure to create a functional embedding in which proteins from different species are embedded in the same vector space. We show that inner products in this space and the vectors themselves capture functional similarity across species, and are useful for a variety of functional tasks. We perform the first whole-genome method for predicting phenologs, generating many that were previously identified, but also predicting new phenologs supported from the biological literature. We also demonstrate the Handl embedding captures pairwise gene function, in that gene pairs with synthetic lethal interactions are significantly separated in Handl space, and the direction of separation is conserved across species. Software for the Handl algorithm is available at http://bit.ly/lrgr-handl.