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1. Radiolysis generates a complex organosynthetic chemical network

   https://doi.org/10.1038/s41598-021-81293-6  

   Adam, Zachary R.; Fahrenbach, Albert C.; Jacobson, Sofia M.; Kacar, Betul; Zubarev, Dmitry Yu. (December 2021, Scientific Reports)

   null (Ed.)

   Abstract The architectural features of cellular life and its ecologies at larger scales are built upon foundational networks of reactions between molecules that avoid a collapse to equilibrium. The search for life’s origins is, in some respects, a search for biotic network attributes in abiotic chemical systems. Radiation chemistry has long been employed to model prebiotic reaction networks, and here we report network-level analyses carried out on a compiled database of radiolysis reactions, acquired by the scientific community over decades of research. The resulting network shows robust connections between abundant geochemical reservoirs and the production of carboxylic acids, amino acids, and ribonucleotide precursors—the chemistry of which is predominantly dependent on radicals. Moreover, the network exhibits the following measurable attributes associated with biological systems: (1) the species connectivity histogram exhibits a heterogeneous (heavy-tailed) distribution, (2) overlapping families of closed-loop cycles, and (3) a hierarchical arrangement of chemical species with a bottom-heavy energy-size spectrum. The latter attribute is implicated with stability and entropy production in complex systems, notably in ecology where it is known as a trophic pyramid. Radiolysis is implicated as a driver of abiotic chemical organization and could provide insights about the complex and perhaps radical-dependent mechanisms associated with life’s origins. 

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2. Using Plant Invasions to Compare Occurrence‐ and Abundance‐Based Calculations of Biotic Homogenisation: Are Results Complementary or Contradictory?

   https://doi.org/10.1111/geb.70022  

   Buonaiuto, D M; Barnett, David; Blumenthal, Dana M; Nebhut, Andrea N; Pearse, Ian S; Sofaer, Helen R; Sorte, Cascade_J B; Corbin, Jeffrey D; Early, Regan; Garbowski, Magda; et al (March 2025, Global Ecology and Biogeography)

   ABSTRACT AimBeta diversity quantifies the similarity of ecological assemblages. Its increase, known as biotic homogenisation, can be a consequence of biological invasions. However, species occurrence (presence/absence) and abundance‐based analyses can produce contradictory assessments of the magnitude and direction of changes in beta diversity. Previous work indicates these contradictions should be less frequent in nature than in theory, but a growing number of empirical studies report discrepancies between occurrence‐ and abundance‐based approaches. Understanding if these discrepancies represent a few isolated cases or are systematic across a diversity of ecosystems would allow us to better understand the general patterns, mechanisms and impacts of biotic homogenisation. LocationUnited States. Time Period1963–2020. Major Taxa StudiedVascular plants. MethodsWe used a dataset of more than 70,000 vegetation survey plots to assess differences in biotic homogenisation with and without invasion using both occurrence‐ and abundance‐based metrics of beta diversity. We estimated taxonomic biotic homogenisation by comparing beta diversity of invaded and uninvaded plots with both classes of metrics and investigated the characteristics of the non‐native species pool that influenced the likelihood that these metrics disagree. ResultsIn 78% of plot comparisons, occurrence‐ and abundance‐based calculations agreed in direction, and the two metrics were generally well correlated. Our empirical results are consistent with previous theory. Discrepancies between the metrics were more likely when the same non‐native species was at high cover at both plots compared for beta diversity, and when these plots were spatially distant. Main ConclusionsIn about 20% of cases, our calculations revealed differences in direction (homogenisation vs. differentiation) when comparing occurrence‐ and abundance‐based metrics, indicating that the metrics are not interchangeable, especially when distances between plots are high and invader diversity is low. When data permit, combining the two approaches can offer insights into the role of invasions and extirpations in driving biotic homogenisation/differentiation. 

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3. Forecasting Gas Usage for Big Buildings Using Generalized Additive Models and Deep Learning

   https://doi.org/10.1109/SMARTCOMP.2018.00092  

   Pathak, Nilavra; Ba, Amadou; Ploennigs, Joern; Roy, Nirmalya (June 2018, 2018 IEEE International Conference on Smart Computing (SMARTCOMP))

   Time series behavior of gas consumption is highly irregular, non-stationary, and volatile due to its dependency on the weather, users' habits and lifestyle. This complicates the modeling and forecasting of gas consumption with most of the existing time series modeling techniques, specifically when missing values and outliers are present. To demonstrate and overcome these problems, we investigate two approaches to model the gas consumption, namely Generalized Additive Models (GAM) and Long Short-Term Memory (LSTM). We perform our evaluations on two building datasets from two different continents. We present each selected feature's influence, the tuning parameters, and the characteristics of the gas consumption on their forecasting abilities. We compare the performances of GAM and LSTM with other state-of-the-art forecasting approaches. We show that LSTM outperforms GAM and other existing approaches, however, GAM provides better interpretable results for building management systems (BMS). 

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4. Reconstruction of plant–pollinator networks from observational data

   https://doi.org/10.1038/s41467-021-24149-x  

   Young, Jean-Gabriel; Valdovinos, Fernanda S.; Newman, M. E. (December 2021, Nature Communications)

   null (Ed.)

   Abstract Empirical measurements of ecological networks such as food webs and mutualistic networks are often rich in structure but also noisy and error-prone, particularly for rare species for which observations are sparse. Focusing on the case of plant–pollinator networks, we here describe a Bayesian statistical technique that allows us to make accurate estimates of network structure and ecological metrics from such noisy observational data. Our method yields not only estimates of these quantities, but also estimates of their statistical errors, paving the way for principled statistical analyses of ecological variables and outcomes. We demonstrate the use of the method with an application to previously published data on plant–pollinator networks in the Seychelles archipelago and Kosciusko National Park, calculating estimates of network structure, network nestedness, and other characteristics. 

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5. Combined volunteer and ecological network observations show broad‐scale temperature‐sensitivity patterns for deciduous plant flowering and leaf‐out times across the eastern USA

   https://doi.org/10.1111/1365-2745.70165  

   Gallinat, Amanda S; Schwartz, Mark D; Donnelly, Alison; Li, Xiaolu; Crimmins, Theresa M (September 2025, Journal of Ecology)

   Many plants are responding to increases in spring temperatures by advancing their leaf‐out and flowering times in temperate regions around the world. The magnitudes of species' sensitivities to temperature vary widely, and patterns within that variation can illuminate underlying phenological drivers related to species' life histories and local‐scale adaptations.The USA National Phenology Network (USA‐NPN) and the National Ecological Observatory Network (NEON) are two rapidly growing, taxonomically and geographically extensive phenology data resources in the USA that offer opportunities to explore emergent properties of spring phenology. Using observations of leaf‐out and flowering in temperate deciduous plant species from USA‐NPN (2009–2024) and NEON (2014–2022), we estimated species‐level flowering (n = 164) and leaf‐out (n = 136) sensitivities to temperatures of the preceding months, obtained through PRISM. We used the results to assess differences in sensitivities between the two datasets and among life history traits (e.g. introduced or native status, seasonal timing and growth habit) and to explore latitudinal patterns in sensitivity among and within species. We found significant relationships between temperature and leaf‐out phenology (2009–2024 for 109 (80%) species, ranging from −7.4 to −1.3 days/°C, and between temperature and flowering phenology for 140 (85%) species, ranging from −8.0 to −1.1 days/°C. Plant sensitivities were highly consistent among the USA‐NPN and NEON datasets, suggesting these datasets can be reasonably combined to expand the coverage of publicly available phenological data across the USA. Introduced species showed stronger sensitivity to temperature than native species for both leaf‐out (−0.8 days/°C difference) and flowering (−0.7 days/°C difference). The strongest (i.e. most negative) leaf‐out sensitivities to temperature were associated with earlier leaf‐out dates and strong flowering sensitivities. Latitudinal analyses within and across species indicate that flowering and leaf‐out sensitivities are both stronger at lower latitudes. Synthesis. Phenological ‘big data’ encompassing over 100 species across the eastern USA shows that leaf‐out and flowering occur earlier with warmer temperatures and that native species and individuals at high latitudes tend to have weaker temperature sensitivities than introduced species and more southern plants; these findings suggest adaptations within and across species to avoid leafing out and flowering under harsh environmental conditions. 

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