Conference: Advances in Mathematical Ecology

该奖项将于2023年6月2日至3日在匹兹堡大学举办研讨会（https：//www.pittics.edu/eco23）。 该研讨会将为初级研究人员提供机会，包括研究生和本科生，博士后，早期职业教师，以及经验丰富的数学家和生物学家，学习数学生态学进步的创新研究实践。特别是，研讨会将是一个跨学科的活动，汇集了数学和生态学的研究人员，讨论生态因素的影响，如环境条件的变化对物种的适应，迁移和共存。这些主题将通过基于实验数据，建模方法以及理论和计算数学的讲座和海报演示来解决。 因此，研讨会将促进数学各领域之间的联系，并促进数学与生态学、自然资源管理、进化生物学和计算机科学等其他领域之间的思想交流。讲习班包括扩大代表性不足群体的参与和促进数学科学的公平和包容的活动。具体而言，参与者的选择是为了促进参与者之间的公平和多样性，包括来自地方和全国机构的不同职业阶段的研究人员和受训人员。经发言者同意，会议发言的幻灯片和录音将张贴在会议网页上，以更广泛地传播成果和影响。本次研讨会的主题是“数学生态学的进展”，旨在促进数学、生态学、自然资源管理、进化生物学和计算机科学等领域的研究人员之间的研究活动和跨学科合作。虽然生态模型的数学分析往往忽略了环境的变化，以帮助实现分析结果，实验数据表明，生态上重要的性状可以迅速演变，以应对环境变化/变异。 这可能会影响物种的适应性，持久性，迁移和免疫系统，并强调需要将环境波动纳入预测物种将如何应对这些环境因素的努力。事实上，了解物种如何应对不断变化的环境是物种及其生态系统可持续管理的基础。该研讨会将突出环境变化的影响，从实验和分析数据，生态和生态进化模型中的环境波动的实施技术，以及研究其对物种如何随时间演变的影响的方法来衡量。讲习班的具体议题将包括：一）在时空生态模型中实施环境波动的方法，并比较季节性强迫模型参数和包括概率参数的模型。ii）生态学中数据驱动模型的潜力和局限性，以及它们与理论模型的一致性。㈢实验观察和模型预测的环境变化对物种扩散、物种间相互作用以及物种如何应对疾病的影响。这些主题涉及数学建模、生态学、进化、非线性动力系统、随机动力学、非线性偏微分方程、变分法和统计学等领域的联系。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

This award will support a workshop on June 2-3, 2023, at the University of Pittsburgh (https://www.mathematics.pitt.edu/eco23). The workshop will provide an opportunity for junior researchers, including graduate and undergraduate students, postdocs, early career faculty, as well as experienced mathematicians and biologists to learn innovative research practices on advances in mathematical ecology. Particularly, the workshop will be an interdisciplinary event that brings together researchers from Mathematics and Ecology to discuss the effects of ecological factors such as changes in environmental conditions on species adaptation, migration, and coexistence. These topics will be addressed via talks and poster presentations based on experimental data, modeling approaches, and theoretical and computational mathematics. As such, the workshop will promote connections across areas of mathematics and facilitate the exchange of ideas between mathematics and other areas such as ecology, natural resource management, evolutionary biology, and computer science. The workshop includes activities for broadening participation of the underrepresented groups and promoting equity and inclusion in the mathematical sciences. Specifically, participants are selected to promote equity and diversity across participants and include researchers and trainees from a diverse pool of career stages, both from local and nationwide institutions. With the speaker's consent, slides and recordings from conference talks will be posted on the conference webpage to achieve an even broader dissemination of results and impact. All participants will be encouraged to submit their work to the Journal of Biological Systems, an open-access platform, which agreed to feature a special issue dedicated to this workshop.The workshop “Advances in Mathematical Ecology” aims to increase the research activities and interdisciplinary collaborations between researchers from mathematics, ecology, natural resource management, evolutionary biology, and computer science. Although the mathematical analysis of ecological models often neglects environmental variability to aid in the attainment of analytical results, experimental data suggests that ecologically important traits can evolve rapidly in response to environmental changes/variability. This may affect species fitness, persistence, migration, and immune systems and highlights the need to incorporate environmental fluctuations into efforts to predict how species will respond to such environmental factors. Indeed, understanding how species respond to changing environments is fundamental for the sustainable management of species and their ecosystems. The workshop will highlight the impact of environmental changes as measured from experimental and analyzed data, techniques for the implementation of environmental fluctuations in ecological and eco-evolutionary models, and methods to study the effects thereof on how species evolve over time. The specific workshop topics will include: i) Methods of implementing environmental fluctuations in spatio-temporal ecological models with a comparison of seasonal forced model parameters and models including probabilistic parameters. ii) The potential and limitations of data-driven models in ecology, and their alignment with theoretical models. iii) Experimentally observed and model-predicted effects of environmental changes on the dispersal of species, on the interaction across species, and on how species respond to diseases. These topics involve links across the areas of Mathematical Modeling, Ecology, Evolution, Nonlinear Dynamical Systems, Stochastic Dynamics, Nonlinear Partial Differential Equations, Calculus of Variations, and Statistics.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.