Mathematical and theoretical ecology: linking models with ecological processes

Codling, Edward A.; Dumbrell, Alex J.

doi:10.1098/rsfs.2012.0008

Cited by 25 publications

(18 citation statements)

References 20 publications

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“…In evolutionary biology, for example, the integration of empirical and theoretical work is essential for understanding how natural selection shapes organisms and their interactions (7)(8)(9)(10)(11)(12)(13)(14)(15)(16). Most biological research is empirical, yet empirical studies rely fundamentally on theory for generating testable predictions and interpreting observations.…”

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confidence: 99%

Heavy use of equations impedes communication among biologists

Fawcett

Higginson

2012

Proc. Natl. Acad. Sci. U.S.A.

109

105

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Most research in biology is empirical, yet empirical studies rely fundamentally on theoretical work for generating testable predictions and interpreting observations. Despite this interdependence, many empirical studies build largely on other empirical studies with little direct reference to relevant theory, suggesting a failure of communication that may hinder scientific progress. To investigate the extent of this problem, we analyzed how the use of mathematical equations affects the scientific impact of studies in ecology and evolution. The density of equations in an article has a significant negative impact on citation rates, with papers receiving 28% fewer citations overall for each additional equation per page in the main text. Long, equation-dense papers tend to be more frequently cited by other theoretical papers, but this increase is outweighed by a sharp drop in citations from nontheoretical papers (35% fewer citations for each additional equation per page in the main text). In contrast, equations presented in an accompanying appendix do not lessen a paper's impact. Our analysis suggests possible strategies for enhancing the presentation of mathematical models to facilitate progress in disciplines that rely on the tight integration of theoretical and empirical work.impact factor | mathematical formula | mathematical literacy | theoretical biology T he efficient exchange of new findings and insights between empirical and theoretical approaches is critical to a range of scientific disciplines, including nuclear physics (1), physical chemistry (2), neuroscience (3), epidemiology (4), ecology (5), and atmospheric science (6). In evolutionary biology, for example, the integration of empirical and theoretical work is essential for understanding how natural selection shapes organisms and their interactions (7-16). Most biological research is empirical, yet empirical studies rely fundamentally on theory for generating testable predictions and interpreting observations. In return, empirical data provide both tests of established theory and guidance in the development of new models.However, the importance of presenting theory in sufficient technical detail can sometimes conflict with the need to communicate the essence of a model in a clear, accessible manner. Concise and precise description of the structure of a mathematical model demands the use of equations, but such technical details might deter a broad audience of scientists doing largely empirical research. A cursory reading of the biological literature reveals that many empirical studies build largely on other empirical studies, with little direct reference to relevant theory. This observation suggests a breakdown of communication that may impede scientific progress.To explore the extent of this problem, we systematically investigated how the use of mathematical equations affects the scientific impact of studies in ecology and evolution. We examined the use of equations and obtained citation data for all papers (total n = 649; Dataset S1) published in 1998...

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confidence: 99%

Heavy use of equations impedes communication among biologists

Fawcett

Higginson

2012

Proc. Natl. Acad. Sci. U.S.A.

109

105

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“…Mathematical models have played a fundamental role in the development of both ecological theory (Caswell 1988, Codling andDumbrell 2012) and a toolbox to predict the impacts of human activities on biodiversity (Pereira et al et al 2017). Disentangling the relative impacts of patch area and isolation on biodiversity has been a driver of fragmentation research for decades (Haddad et al 2015).…”

Section: Perspectives On the Use And Integration Of Simple Biodiversimentioning

confidence: 99%

Structural uncertainty in models projecting the consequences of habitat loss and fragmentation on biodiversity

et al. 2016

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Ecological theory is essential to predict the effects of global changes such as habitat loss and fragmentation on biodiversity. Species–area relationships (SAR), metapopulation models (MEP) and species distribution models (SDM) are commonly used tools incorporating different ecological processes to explain biodiversity distribution and dynamics. Yet few studies have compared the outcomes of these disparate models and investigated their complementarity. Here we show that the processes underlying SAR (patch area), MEP (patch isolation) and SDM (environmental conditions) models can be compared with a common statistical framework. Our approach allows for species and community‐level predictions under current and future landscape scenarios, facilitates multi‐model comparison and provides the machinery for integrating multiple mechanisms into one model. We apply this framework to the distribution of eight focal vertebrate species in current and future projected landscapes where 10% of the landscape is lost to land‐use change in southwestern, Quebec, Canada. Based on a model selection approach, we found that a model including patch area was the top ranked model for four of our focal species and models including patch isolation and environmental conditions were the top ranked models for two focal species each. Community‐level predictions of models based on patch area, patch isolation and environmental conditions for both current and future landscapes showed high spatial overlap, however, patch area models always predicted a reduction of species richness per patch whereas both the patch isolation and environmental conditions models predicted an increase or decrease in species richness per patch following habitat loss and fragmentation. Our comparative tool will allow ecologists and conservation practitioners to relate structural uncertainty to key mechanisms underlying each model. Ultimately, this approach is one step in the direction of deriving robust predictions for the change and loss of biodiversity under global change, which is key for informing conservation plans.

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“…This is also a central issue in theoretical ecology [26], but evolutionary processes often take place on larger time scales which seriously impede experimental work and field observations. In their insightful review, Duputié & Massol [27] address the connection between theory and data by considering the evolution of dispersal-a fundamental question in evolutionary ecology [28].…”

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Modelling biological evolution: recent progress, current challenges and future direction

Морозов

2013

Interface Focus.

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Mathematical modelling is widely recognized as a powerful and convenient theoretical tool for investigating various aspects of biological evolution and explaining the existing genetic complexity of the real world. It is increasingly apparent that understanding the key mechanisms involved in the processes of species biodiversity, natural selection and inheritance, patterns of animal behaviour and coevolution of species in complex ecological systems is simply impossible by means of laboratory experiments and field observations alone. Mathematical models are so important because they provide wide-ranging exploration of the problem without a need for experiments with biological systems—which are usually expensive, often require long time and can be potentially dangerous. However, as the number of theoretical works on modelling biological evolution is constantly accelerating each year as different mathematical frameworks and various aspects of evolutionary problems are considered, it is often hard to avoid getting lost in such an immense flux of publications. The aim of this issue of Interface Focus is to provide a useful guide to important recent findings in some key areas in modelling biological evolution, to refine the existing challenges and to outline possible future directions. In particular, the following topics are addressed here by world-leading experts in the modelling of evolution: (i) the origins of biodiversity observed in ecosystems and communities; (ii) evolution of decision-making by animals and the optimal strategy of populations; (iii) links between evolutionary and ecological processes across different time scales; (iv) quantification of biological information in evolutionary models; and (v) linking theoretical models with empirical data. Most of the works presented here are in fact contributed papers from the international conference ‘Modelling Biological Evolution’ (MBE 2013), which took place in Leicester, UK, in May 2013 and brought together theoreticians and empirical evolutionary biologists with the main aim of creating debates and productive discussions between them. Finally, we should emphasize that the individual papers in this issue are not limited to only one of the topics mentioned above, but often lie at the interface of them.

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Mathematical and theoretical ecology: linking models with ecological processes

Cited by 25 publications

References 20 publications

Heavy use of equations impedes communication among biologists

Heavy use of equations impedes communication among biologists

Structural uncertainty in models projecting the consequences of habitat loss and fragmentation on biodiversity

Modelling biological evolution: recent progress, current challenges and future direction

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