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1. Destabilizing evolutionary and eco-evolutionary feedbacks drive empirical eco-evolutionary cycles

   https://doi.org/10.1098/rspb.2019.2298  

   Cortez, Michael H.; Patel, Swati; Schreiber, Sebastian J. (January 2020, Proceedings of the Royal Society B: Biological Sciences)

   null (Ed.)

   We develop a method to identify how ecological, evolutionary, and eco-evolutionary feedbacks influence system stability. We apply our method to nine empirically parametrized eco-evolutionary models of exploiter–victim systems from the literature and identify which particular feedbacks cause some systems to converge to a steady state or to exhibit sustained oscillations. We find that ecological feedbacks involving the interactions between all species and evolutionary and eco-evolutionary feedbacks involving only the interactions between exploiter species (predators or pathogens) are typically stabilizing. In contrast, evolutionary and eco-evolutionary feedbacks involving the interactions between victim species (prey or hosts) are destabilizing more often than not. We also find that while eco-evolutionary feedbacks rarely altered system stability from what would be predicted from just ecological and evolutionary feedbacks, eco-evolutionary feedbacks have the potential to alter system stability at faster or slower speeds of evolution. As the number of empirical studies demonstrating eco-evolutionary feedbacks increases, we can continue to apply these methods to determine whether the patterns we observe are common in other empirical communities. 

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2. Eco-PiNN: A Physics-informed Neural Network for Eco-toll Estimation

   https://doi.org/10.1137/1.9781611977653.ch94  

   Li, Yan; Yang, Mingzhou; Eagon, Matthew; Farhadloo, Majid; Xie, Yiqun; Northrop, William; Shekhar, Shashi (January 2023, Proceedings of the SIAM International Conference on Data Mining)

   The eco-toll estimation problem quantifies the expected environmental cost (e.g., energy consumption, exhaust emissions) for a vehicle to travel along a path. This problem is important for societal applications such as eco-routing, which aims to find paths with the lowest exhaust emissions or energy need. The challenges of this problem are threefold: (1) the dependence of a vehicle's eco-toll on its physical parameters; (2) the lack of access to data with eco-toll information; and (3) the influence of contextual information (i.e. the connections of adjacent segments in the path) on the eco-toll of road segments. Prior work on eco-toll estimation has mostly relied on pure data-driven approaches and has high estimation errors given the limited training data. To address these limitations, we propose a novel Eco-toll estimation Physics-informed Neural Network framework (Eco-PiNN) using three novel ideas, namely, (1) a physics-informed decoder that integrates the physical laws governing vehicle dynamics into the network, (2) an attention-based contextual information encoder, and (3) a physics-informed regularization to reduce overfitting. Experiments on real-world heavy-duty truck data show that the proposed method can greatly improve the accuracy of eco-toll estimation compared with state-of-the-art methods. *The full version of the paper can be accessed at https://arxiv.org/abs/2301.05739 

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3. Eco-evolutionary significance of “loners”

   https://doi.org/10.1371/journal.pbio.3000642  

   Rossine, Fernando W.; Martinez-Garcia, Ricardo; Sgro, Allyson E.; Gregor, Thomas; Tarnita, Corina E.; Parent, Carole A. (March 2020, PLOS Biology)
4. Socio‐eco‐evolutionary dynamics in cities

   https://doi.org/10.1111/eva.13065  

   Des Roches, Simone; Brans, Kristien I.; Lambert, Max R.; Rivkin, L. Ruth; Savage, Amy Marie; Schell, Christopher J.; Correa, Cristian; De Meester, Luc; Diamond, Sarah E.; Grimm, Nancy B.; et al (July 2020, Evolutionary Applications)

   null (Ed.)
5. Eco-evolutionary effects of keystone genes

   https://doi.org/10.1126/science.abo3575  

   Nosil, Patrik; Gompert, Zach (April 2022, Science)

   There is increasing evidence that genetic evolution can occur rapidly enough to affect the ecological dynamics of populations and communities ( 1 – 3 ). To better predict the future of ecosystems, it is necessary to understand how evolutionary changes within species influence and interact with ecological changes through processes known as “eco-evolutionary dynamics” ( 4 ). On page 70 of this issue, Barbour et al. ( 5 ) demonstrate that a gene affecting a plant’s resistance to herbivory also influences the persistence of the food web through the gene’s effect on plant growth (see the figure). Subsequent studies of natural selection in the wild can help explain how variations of such “keystone genes” can be maintained ( 6 , 7 ). The maintenance of genetic variation in keystone genes is required for eco-evolutionary dynamics to be perpetual rather than transient. 

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