Progress in Mathematical Ecology

Petrovskii, Sergei

doi:10.3390/math6090167

Cited by 1 publication

(2 citation statements)

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“…As implied in this list, we take a broad view of what constitutes mathematical ecology, considering it to include theoretical and system-specific models as well as statistical and computational tools, particularly those that are not yet standard tools of ecology. Mathematical ecology, thus broadly defined, continues to push our field forward by connecting theoretical and methodological developments to ecological applications (Elith et al 2011) and represents a virtual laboratory where we can test hypotheses, generate novel predictions, and investigate mechanisms underpinning observed patterns (Petrovskii 2018).…”

Section: Introductionmentioning

confidence: 99%

“…Mathematical ecology, thus broadly defined, continues to push our field forward by connecting theoretical and methodological developments to ecological applications (Elith et al. 2011) and represents a virtual laboratory where we can test hypotheses, generate novel predictions, and investigate mechanisms underpinning observed patterns (Petrovskii 2018).…”

Section: Introductionmentioning

confidence: 99%

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Writing mathematical ecology: A guide for authors and readers

et al. 2021

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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 of Ecosphere. This guide complements current guides for writing science, focusing specifically on effective communication of mathematics.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%