Writing mathematical ecology: A guide for authors and readers
Writing mathematical ecology: A guide for authors and readers
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DOI：
10.1002/ecs2.3701
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发表时间：
2021-08-01
期刊：
影响因子：
2.7
通讯作者：
Wisnoski，Nathan I.
中科院分区：
文献类型：
--
作者：
Shoemaker，Lauren G.;Walter，Jonathan A.;Wisnoski，Nathan I.
数学技术在生态学中有着悠久而丰富的历史，经常作为虚拟实验室来测试假设，产生新的预测，并研究潜在的生态机制。最近，新的模拟技术，在计算能力的进步，以及实现统计模型的数值方法，大大提高了我们的能力，整合经验和理论生态学。然而，数学和实证研究之间的鸿沟仍然存在，他们的读者群，并融入更广泛的文献。由于数学生态学的见解远比所采用的技术更普遍，因此在将数学进步传达给广泛的生态学家方面的局限性可以说阻碍了生态学的进步，特别是在用经验实验和数据对抗理论预测方面。在这里，我们为数学生态学的作者和读者提供了一个指南，目的是为广大的生态学家群体增加数学生态学的可及性。我们提供了一个最佳实践的列表，当写作和阅读数学生态学时，结合了生态圈这个特殊功能的例子。本指南补充了当前的科学写作指南，特别关注数学的有效沟通。
Mathematical techniques have a long and rich history in ecology, often serving as a virtual laboratory to test hypotheses, generate novel predictions, and investigate underlying ecological mechanisms. Recently, novel simulation techniques, advances in computing power, and numerical methods for implementing statistical models have significantly advanced our ability to integrate empirical and theoretical ecology. However, a divide still remains between mathematical and empirical studies, their readership, and integration into the broader literature. Because insights from mathematical ecology are far more general than the techniques employed, limitations in communicating mathematical advances to a broad spectrum of ecologists have arguably hindered ecology's progress, particularly in confronting theoretical predictions with empirical experiments and data. Here, we present a guide for both authors and readers of mathematical ecology, with the aim of increasing the accessibility of mathematical ecology for a broad group of ecologists. We provide a list of best practices when both writing and reading mathematical ecology, incorporating examples from this Special Feature ofEcosphere. This guide complements current guides for writing science, focusing specifically on effective communication of mathematics.