Structural sensitivity and resilience in a predator–prey model with density-dependent mortality

Aldebert, Clement; Nérini, David; Gauduchon, Mathias; Poggiale, Jean-Christophe

doi:10.1016/j.ecocom.2016.05.004

Cited by 16 publications

(17 citation statements)

References 47 publications

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“…Furthermore, for systems exhibiting point equilibria, incorporating HOIs into model fits might afford much closer approximations to the dynamics near equilibria. At the same time, however, to the extent that HOIs improve approximations around equilibria, we can also assume that they increase the instability and structural sensitivity of system behaviour, such that small changes to population sizes or parameters are more likely to lead to qualitative shifts in system state (Adamson & Morozov 2012;Aldebert et al 2016). At this stage, a recently-developed approach for evaluating the structural stability of nonlinear population dynamics offers a promising route to the development of reliable criteria for evaluating coexistence in the presence of HOIs (Cenci & Saavedra 2018).…”

Section: Discussionmentioning

confidence: 99%

The mechanistic basis for higher-order interactions and non-additivity in competitive communities

2019

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Motivated by both analytical tractability and empirical practicality, community ecologists have long treated the species pair as the fundamental unit of study. This notwithstanding, the challenge of understanding more complex systems has repeatedly generated interest in the role of so‐called higher‐order interactions (HOIs) imposed by species beyond the focal pair. Here we argue that HOIs – defined as non‐additive effects of density on per capita growth – are best interpreted as emergent properties of phenomenological models (e.g. Lotka–Volterra competition) rather than as distinct ‘ecological processes’ in their own right. Using simulations of consumer‐resource models, we explore the mechanisms and system properties that give rise to HOIs in observational data. We demonstrate that HOIs emerge under all but the most restrictive of assumptions, and that incorporating non‐additivity into phenomenological models improves the quantitative and qualitative accuracy of model predictions. Notably, we also observe that HOIs derive primarily from mechanisms and system properties that apply equally to single‐species or pairwise systems as they do to more diverse communities. Consequently, there exists a strong mandate for further recognition of non‐additive effects in both theoretical and empirical research.

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Section: Discussionmentioning

confidence: 99%

The mechanistic basis for higher-order interactions and non-additivity in competitive communities

2019

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“…In this bistability area, the model predicts that the system will converge to one of the two alternatives depending on its initial state. The presence of alternative stable states is of particular interest to study ecosystem resilience when facing external disturbances [18,35]. This other form of the paradox of enrichment has been found in predator-prey models incorporating a more detailed description of organisms biology [19].…”

Section: Overview Of Model Predictionsmentioning

confidence: 99%

“…All these assumptions define the Rozensweig-MacArthur model, which is often refered as a predator-prey model but is generic enough to also describe a variety of consumer-resource systems. Previous studies on structural sensitivity compared a deterministic analysis of model predictions based on best-fitted functions ( [8,9,11,18,20], among others). Fitting a function to data implies uncertainty in the parameters being inferred, which means that there is additional insight to be gained by considering both parameter and model uncertainty.…”

Section: Fitting Functional Responses On Datamentioning

confidence: 99%

“…tipping points) with a change in the number of alternative stable states (subcritical Hopf bifurcation: bistabilityoscillations; and limit point of cycles bifurcation: bistabilitycoexistence). Note that if the number of alternative stable states is affected by structural sensitivity, it becomes hard to estimate ecosystem resilience under disturbances [18].…”

Section: Overview Of Model Predictionsmentioning

confidence: 99%

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Community dynamics and sensitivity to model structure: towards a probabilistic view of process-based model predictions

Aldebert

Stouffer

2018

J. R. Soc. Interface.

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ResearchCite this article: Aldebert C, Stouffer DB. 2018 Community dynamics and sensitivity to model structure: towards a probabilistic view of process-based model predictions. J. R. Soc. Interface 15: 20180741. http://dx.Statistical inference and mechanistic, process-based modelling represent two philosophically different streams of research whose primary goal is to make predictions. Here, we merge elements from both approaches to keep the theoretical power of process-based models while also considering their predictive uncertainty using Bayesian statistics. In environmental and biological sciences, the predictive uncertainty of process-based models is usually reduced to parametric uncertainty. Here, we propose a practical approach to tackle the added issue of structural sensitivity, the sensitivity of predictions to the choice between quantitatively close and biologically plausible models. In contrast to earlier studies that presented alternative predictions based on alternative models, we propose a probabilistic view of these predictions that include the uncertainty in model construction and the parametric uncertainty of each model. As a proof of concept, we apply this approach to a predator -prey system described by the classical Rosenzweig-MacArthur model, and we observe that parametric sensitivity is regularly overcome by structural sensitivity. In addition to tackling theoretical questions about model sensitivity, the proposed approach can also be extended to make probabilistic predictions based on more complex models in an operational context. Both perspectives represent important steps towards providing better model predictions in biology, and beyond.rsif.royalsocietypublishing.org J. R. Soc. Interface 15: 20180741

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“…However, the choice of a particular function among relevant ones can affect the dynamics predicted by the same model. Differences occur in predicted steady-state values, equilibrium vs. oscillating dynamics and in the system response to external disturbances (Aldebert et al, 2016). Uncertainty due to this choice of a function is coined as "structural sensitivity" (sensu Cordoleani et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Does structural sensitivity alter complexity–stability relationships?

Aldebert

Nérini

Gauduchon

et al. 2016

Ecological Complexity

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Highlights • We study food webs of varying trophic complexity (number of species, connectance) • Food web dynamics is modeled using either Holling's or Ivlev's functional response • Community persistence is mainly driven by trophic complexity • Occurrence of fluctuating dynamics is mainly driven by the functional response • Conclusions are robust to some changes in parameter values and model assumptions

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Structural sensitivity and resilience in a predator–prey model with density-dependent mortality

Cited by 16 publications

References 47 publications

The mechanistic basis for higher-order interactions and non-additivity in competitive communities

The mechanistic basis for higher-order interactions and non-additivity in competitive communities

Community dynamics and sensitivity to model structure: towards a probabilistic view of process-based model predictions

Does structural sensitivity alter complexity–stability relationships?

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