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1. Museum Genomics Reveals Temporal Genetic Stasis and Global Genetic Diversity in Arabidopsis thaliana

   https://doi.org/10.1111/mec.70081  

   Lopez, Lua; Lang, Patricia_L M; Marciniak, Stephanie; Kistler, Logan; Latorre, Sergio M; Haile, Asnake; Vasquez_Cerda, Eleanna; Gamba, Diana; Xu, Yuxing; Woods, Patrick; et al (October 2025, Molecular Ecology)

   Global patterns of population genetic variation through time offer a window into evolutionary processes that maintain diversity. Over time, lineages may expand or contract their distribution, causing turnover in population genetic composition. At individual loci, migration, drift and selection (among other processes) may affect allele frequencies. Museum specimens of widely distributed species offer a unique window into the genetics of understudied populations and changes over time. Here, we sequenced genomes of 130 herbarium specimens and 91 new field collections of Arabidopsis thaliana and combined these with published genomes. We sought a broader view of genomic diversity across the species and to test if population genomic composition is changing through time. We documented extensive and previously uncharacterised diversity in a range of populations in Africa, populations that are under threat from anthropogenic climate change. Through time, we did not find dramatic changes in genomic composition of populations. Instead, we found a pattern of genetic change every 100 years of the same magnitude seen when comparing Eurasian populations that are 185 km apart, potentially due to a combination of drift and changing selection. We found only mixed signals of polygenic adaptation at phenology and physiology QTL. We did find that genes conserved across eudicots show altered levels of directional allele frequency change, potentially due to variable purifying and background selection. Our study highlights how museum specimens can reveal new dimensions of population diversity and show how wild populations are evolving in recent history. 

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2. Spatial and temporal patterns of Pseudo-nitzschia genetic diversity in the North Pacific Ocean from the Continuous Plankton Recorder survey

   https://doi.org/10.3354/meps12711  

   Stern, R; Moore, SK; Trainer, VL; Bill, BD; Fischer, A; Batten, S (November 2018, Marine Ecology Progress Series)
3. On Reporting Durable Patterns in Temporal Proximity Graphs

   https://doi.org/10.1145/3651144  

   Agarwal, Pankaj K; Hu, Xiao; Sintos, Stavros; Yang, Jun (May 2024, Proceedings of the ACM on Management of Data)

   Finding patterns in graphs is a fundamental problem in databases and data mining. In many applications, graphs are temporal and evolve over time, so we are interested in finding durable patterns, such as triangles and paths, which persist over a long time. While there has been work on finding durable simple patterns, existing algorithms do not have provable guarantees and run in strictly super-linear time. The paper leverages the observation that many graphs arising in practice are naturally proximity graphs or can be approximated as such, where nodes are embedded as points in some high-dimensional space, and two nodes are connected by an edge if they are close to each other. We work with an implicit representation of the proximity graph, where nodes are additionally annotated by time intervals, and design near-linear-time algorithms for finding (approximately) durable patterns above a given durability threshold. We also consider an interactive setting where a client experiments with different durability thresholds in a sequence of queries; we show how to compute incremental changes to result patterns efficiently in time near-linear to the size of the changes. 

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4. Spatial and temporal patterns in Arctic mosquito abundance

   https://doi.org/10.1111/een.13198  

   DeSiervo, Melissa_H; Finger‐Higgens, Rebecca_A; Ayres, Matthew_P; Virginia, Ross_A; Culler, Lauren_E (November 2022, Ecological Entomology)

   Abstract Organisms that undergo a shift in ontogeny and habitat type often change their spatial distribution throughout their life cycle, but how this affects population dynamics remains poorly understood.We examined spatial and temporal patterns inAedes nigripesabundance, a widespread univoltine Arctic mosquito species (Diptera: Culicidae), hypothesizing that the spatial distribution of adults would be closely tied to aquatic habitat.We tracked adult densities ofA. nigripesnear Kangerlussuaq, Greenland using emergence traps, CO₂‐baited traps, and sweep‐nets.In back‐to‐back years of sampling (2017 and 2018) we found two‐fold variation in overall abundance.Adults were spatially patchy when first emerging from aquatic habitats but within a week, mean capture rates for host‐seeking adult females were similar across locations, even in places far from larval habitat.Daily variation in mosquito captures was primarily explained by weather, with virtually no mosquito activity when temperatures averaged less than 8°C or wind speeds exceeded 6 m/s. Gravid females (3% of resting adults) were spatially patchy on the landscape, but not always in the same places where most adults emerged.The spatial distribution of adults is quickly uncoupled from the spatial distribution of larvae becauseA. nigripesfemales may disperse far from their natal habitats in search of a blood‐meal and high‐quality oviposition habitat. 8. This research highlights the value of studying ecological processes that act at disparate life stages for understanding the population biology of organisms with complex life cycles. 

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5. Temporal Dependencies and Spatio-Temporal Patterns of Time Series Models

   https://doi.org/10.1609/aaai.v38i21.30396  

   Islam, Khairul (March 2024, Proceedings of the AAAI Conference on Artificial Intelligence)

   The widespread use of Artificial Intelligence (AI) has highlighted the importance of understanding AI model behavior. This understanding is crucial for practical decision-making, assessing model reliability, and ensuring trustworthiness. Interpreting time series forecasting models faces unique challenges compared to image and text data. These challenges arise from the temporal dependencies between time steps and the evolving importance of input features over time. My thesis focuses on addressing these challenges by aiming for more precise explanations of feature interactions, uncovering spatiotemporal patterns, and demonstrating the practical applicability of these interpretability techniques using real-world datasets and state-of-the-art deep learning models. 

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