Paper List
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Ill-Conditioning in Dictionary-Based Dynamic-Equation Learning: A Systems Biology Case Study
This paper addresses the critical challenge of numerical ill-conditioning and multicollinearity in library-based sparse regression methods (e.g., SIND...
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Hybrid eTFCE–GRF: Exact Cluster-Size Retrieval with Analytical pp-Values for Voxel-Based Morphometry
This paper addresses the computational bottleneck in voxel-based neuroimaging analysis by providing a method that delivers exact cluster-size retrieva...
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abx_amr_simulator: A simulation environment for antibiotic prescribing policy optimization under antimicrobial resistance
This paper addresses the critical challenge of quantitatively evaluating antibiotic prescribing policies under realistic uncertainty and partial obser...
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PesTwin: a biology-informed Digital Twin for enabling precision farming
This paper addresses the critical bottleneck in precision agriculture: the inability to accurately forecast pest outbreaks in real-time, leading to su...
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Equivariant Asynchronous Diffusion: An Adaptive Denoising Schedule for Accelerated Molecular Conformation Generation
This paper addresses the core challenge of generating physically plausible 3D molecular structures by bridging the gap between autoregressive methods ...
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Omics Data Discovery Agents
This paper addresses the core challenge of making published omics data computationally reusable by automating the extraction, quantification, and inte...
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Single-cell directional sensing at ultra-low chemoattractant concentrations from extreme first-passage events
This work addresses the core challenge of how a cell can rapidly and accurately determine the direction of a chemoattractant source when the signal is...
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SDSR: A Spectral Divide-and-Conquer Approach for Species Tree Reconstruction
This paper addresses the computational bottleneck in reconstructing species trees from thousands of species and multiple genes by introducing a scalab...
可变食性范围模型中向泛化主义的缓慢演化
Department of Mathematical Sciences, University of Bath, Claverton Down, Bath, BA2 7AY, United Kingdom
30秒速读
IN SHORT: 通过证明是种群统计噪声(而非确定性动力学)驱动了模式形成和泛化食性的演化,解决了间接竞争下物种形成的悖论。
核心创新
- Methodology Develops a continuous-space resource-consumer model with explicit resource dynamics and evolvable dietary range, extending beyond fixed-preference Lotka-Volterra frameworks.
- Theory Demonstrates that deterministic analysis predicts homogeneous steady states (no species), but stochastic simulations with demographic noise induce persistent pattern formation interpreted as species.
- Methodology Uses Fourier analysis of linearized dynamics to predict the dominant perturbation modes (e.g., number of species) from the power spectrum, linking analytical predictions to simulation outcomes.
主要结论
- 对于固定食性范围(w=0.2),傅里叶分析预测在 kL/2π=5 处存在主导模式,这对应于在随机模拟中观察到的5个等间距物种的形成(图2,3A)。
- 在可演化食性范围模型中,动力学发生在两个时间尺度上:快速协同演化到准稳态流形,随后缓慢弛豫向均匀态。种群统计噪声阻止了这种弛豫,维持了模式。
- 泛化食性(宽w)在长时间尺度上随机演化,因为与由相同资源生产率支持的、种群规模较小的专化集群相比,它们更不易受到灭绝风险的影响。
摘要: 共享栖息地的物种会协同演化以利用可用资源,因为消费受到消费者与资源之间竞争和负反馈回路的调节。给定物种的食性范围决定了其可获取的资源,从而决定了与之竞争的其他物种。狭窄的食性范围以过度依赖少量资源为代价避免竞争;相反,广泛的食性范围提供了更多替代选择,但也增加了与其他物种竞争的机会。在此,我们研究了生态位形成数学模型中食性范围的演化。我们发现了高度路径依赖的协同演化动力学,其特征是长寿命的准稳态。最终,随机效应驱动了泛化食性的演化,正如我们在分析和模拟中所揭示的。