Data and code from: Quantifying feedback among traits in coevolutionary models

Phenotypic traits rarely evolve in isolation. Instead, multiple traits typically interact to influence fitness, resulting in complex coevolutionary dynamics. Such dynamics can be predicted using mathematical frameworks such as adaptive dynamics and quantitative genetics. Selection gradients play a crucial role in these frameworks, describing the direction and strength of selection and thus predicting evolutionary trajectories and potential endpoints. Current theory focuses mainly on analysing how traits change in response to selection, which changes over time as traits evolve. However, the extent to which changes in each trait contribute to changes in the selection environment remains unquantified, leaving much of our understanding of trait coevolution reliant on verbal reasoning. To advance a more comprehensive and quantitative understanding of coevolutionary dynamics, we develop a general framework that examines how trait changes feed back to influence the selection environment. This ..., , # Data and code from: Quantifying feedback among traits in coevolutionary models

Dataset DOI: [10.5061/dryad.8w9ghx419](https://doi.org/10.5061/dryad.8w9ghx419)

## Description of the data and file structure

This repository contains Mathematica scripts used to perform the feedback analysis in Examples 1 and 2 of this study, along with the data generated by running the code.

**Example 1**: We investigate the evolution of a single trait: offspring size (e.g., the size of eggs at laying or offspring at birth in species with no further parental care), building on the framework established by Smith and Fretwell (1974). The larger the offspring, the higher its probability of survival, but the fewer offspring the parents will produce.

**Example 2**: We next demonstrate the application of our feedback analysis to a model with two coevolving traits: the anisogamy model of Lehtonen and Kokko (2011).

### Files and variables

#### File: Supplementary_Materials_Mathematica_scripts.nb

**Descrip...,

表型性状（Phenotypic traits）极少孤立演化。相反，多数性状通常会相互作用以影响适合度，进而形成复杂的共演化动力学。这类动力学可通过适应性动力学（adaptive dynamics）与数量遗传学（quantitative genetics）等数学框架进行预测。选择梯度在这些框架中发挥关键作用，它描述了选择的方向与强度，借此可预测演化轨迹与潜在的演化终点。
当前理论主要聚焦于分析性状如何响应选择而发生变化，而选择本身会随性状演化随时间推移发生改变。然而，各性状的变化对选择环境变化的贡献程度仍未被量化，导致我们对性状共演化的多数理解仍依赖于文字推理。为推动对共演化动力学形成更全面且定量的认知，我们构建了一个通用框架，用以探究性状变化如何反馈作用于选择环境。本研究的数据与代码源自：《量化共演化模型中性状间的反馈作用》（Quantifying feedback among traits in coevolutionary models）。

数据集DOI：[10.5061/dryad.8w9ghx419](https://doi.org/10.5061/dryad.8w9ghx419)

### 数据与文件结构说明
本仓库包含用于开展本研究示例1与示例2中反馈分析的Mathematica脚本，以及运行代码所生成的数据集。

**示例1**：我们基于Smith与Fretwell（1974）构建的分析框架，探究单一性状——后代体型（例如，无后续亲代抚育的物种中，产卵时的卵大小或出生时的幼崽体型）的演化规律。后代体型越大，其存活概率越高，但亲代所能产生的后代数量越少。

**示例2**：我们接下来将反馈分析方法应用于包含两个共演化性状的模型：Lehtonen与Kokko（2011）提出的异形配子模型。

#### 文件与变量
##### 文件：Supplementary_Materials_Mathematica_scripts.nb
**描述……