Converting Ecological Currencies: Energy, Material, and Information Flows

Marleau, Justin; Peller, Tianna; Guichard, Frédéric; Gonzalez, Andrew

doi:10.1016/j.tree.2020.07.014

Cited by 17 publications

(29 citation statements)

References 64 publications

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“…This is true both of empirical research, and theoretical approaches, and we can look at three different modeling studies as examples. Marleau et al (2020) A second challenge relates to the peculiar nature of information, compared with matter and energy. Both syntactic and semiotic information need not be fixed quantities within a system, but can be created, conserved, modified and even Box 2.…”

Section: The Challenge Of Accounting For Informationmentioning

confidence: 99%

“…This is true both of empirical research, and theoretical approaches, and we can look at three different modeling studies as examples. Marleau et al (2020) model information as an ecosystem property affecting consumer density, and this property can change over time as a function of consumer and/or resource density. In contrast, O'Connor et al (2019) model the fitness of individuals in a population which have traits for information acquisition, storage, communication and use.…”

Section: The Challenge Of Accounting For Informationmentioning

confidence: 99%

“…As a field, ecology has not yet grappled explicitly with the dynamics of information in linked ecosystems. Yet, the movement of information necessarily accompanies the movement of energy and matter, and information transfer and information use by organisms both mediate and contribute to spatial feedbacks in ecological dynamics (Gil et al 2018, O'Connor et al 2019, Marleau et al 2020). Failing to account for information results in key knowledge gaps and contributes to a lack of predictability.…”

Section: Introductionmentioning

confidence: 99%

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Movement with meaning: integrating information into meta‐ecology

Little

Rizzuto

Luhring

et al. 2022

Oikos

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Section: The Challenge Of Accounting For Informationmentioning

confidence: 99%

Section: The Challenge Of Accounting For Informationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Movement with meaning: integrating information into meta‐ecology

Little

Rizzuto

Luhring

et al. 2022

Oikos

View full text Add to dashboard Cite

show abstract

“…Here, to quantify the predictability of assembly dynamics, we adopt a normalised information entropy metric (Rohr et al, 2016). Although there are many alternative uncertainty metrics (Vellend, 2016), information entropy has been useful to quantify and explain different ecological processes (Margalef, 1958; Marleau et al, 2020; O’Connor et al, 2019; Zu et al, 2020). We define the predictability of an assembly dynamics as

predictability : = 1 ‐ entropy false(uncertainties false) = 1 + \frac{\sum_{i} P false(x_{i} false) \log false(P (x_{i}) false)}{\log (2^{S} ‐ 1)},

where

x_{i}

is a species combination and

P (x_{i})

is the probability that combination

x_{i}

occurs (Figure 2A).…”

Section: Priority Effects and The Predictability Of Community Assemblymentioning

confidence: 99%

Untangling the complexity of priority effects in multispecies communities

2021

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The history of species immigration can dictate how species interact in local communities, thereby causing historical contingency in community assembly. Since immigration history is rarely known, these historical influences, or priority effects, pose a major challenge in predicting community assembly. Here, we provide a graph-based, non-parametric, theoretical framework for understanding the predictability of community assembly as affected by priority effects. To develop this framework, we first show that the diversity of possible priority effects increases super-exponentially with the number of species. We then point out that, despite this diversity, the consequences of priority effects for multispecies communities can be classified into four basic types, each of which reduces community predictability: alternative stable states, alternative transient paths, compositional cycles and the lack of escapes from compositional cycles to stable states. Using a neural network, we show that this classification of priority effects enables accurate explanation of community predictability, particularly when each species immigrates repeatedly. We also demonstrate the empirical utility of our theoretical framework by applying it to two experimentally derived assembly graphs of algal and ciliate communities. Based on these analyses, we discuss how the framework proposed here can help guide experimental investigation of the predictability of historydependent community assembly.

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“…Here, to quantify the predictability of assembly dynamics, we adopt a normalized information entropy metric (Rohr et al, 2016). Although there are many alternative uncertainty metrics (Vellend, 2016), information entropy has been useful to quantify and explain different ecological processes (O'Connor et al, 2019;Marleau et al, 2020;Zu et al, 2020;Margalef, 1973). We define the predictability of an assembly dynamics as predictability := 1 − entropy (uncertainties) = 1 + i P (x i ) log(P (…”

Section: Priority Effects and The Predictability Of Community Assemblymentioning

confidence: 99%

Untangling the complexity of priority effects in multispecies communities

Song

Fukami

Saavedra

2021

Preprint

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Priority effects arise when the history of species arrival influences local species interactions, thereby affecting the composition of ecological communities. The outcome of some priority effects may be more difficult to predict than others, but this possibility remains to be fully investigated. Here, we provide a graph-based, non-parametric, theoretical framework to understand the classification of priority effects and the predictability of multi-species communities. We show that we can classify priority effects by decomposing them into four basic dynamical sources: the number of alternative stable states, the number of alternative transient paths, the length of composition cycles, and the interaction between alternative stable states and composition cycles. Although the number of alternative stable states has received most of the attention, we show that the other three sources can contribute more to the predictability of community assembly, especially in small communities. We discuss how this theoretical framework can guide new experimental studies.

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Converting Ecological Currencies: Energy, Material, and Information Flows

Cited by 17 publications

References 64 publications

Movement with meaning: integrating information into meta‐ecology

Movement with meaning: integrating information into meta‐ecology

Untangling the complexity of priority effects in multispecies communities

Untangling the complexity of priority effects in multispecies communities

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