The bijection from data to parameter space with the standard DEB model quantifies the supply–demand spectrum

Lika, Konstadia; Augustine, Starrlight; Pecquerie, Laure; Kooijman, S.A.L.M.

doi:10.1016/j.jtbi.2014.03.025

Cited by 62 publications

(42 citation statements)

References 21 publications

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“…Free ranging animals owe their mobility in large part to a better homeostatic regulation [28, 29], which in turn simplifies their energy budgets. Accordingly, in describing the full life-cycle of loggerhead turtles, we used the least complex DEB formulation called the standard DEB model [24, 25, 26].…”

Section: Methodsmentioning

confidence: 99%

Inferring physiological energetics of loggerhead turtle (Caretta caretta) from existing data using a general metabolic theory

Marn

Kooijman

Jusup

et al. 2016

Preprint

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Loggerhead turtle is an endangered sea turtle species with a migratory lifestyle and worldwide distribution, experiencing markedly different habitats throughout its lifetime. Environmental conditions, especially food availability and temperature, constrain the acquisition and the use of available energy, thus affecting physiological processes such as growth, maturation, and reproduction. These physiological processes at the population level determine survival, fecundity, and ultimately the population growth rate-a key indicator of the success of conservation efforts. As a first step towards the comprehensive understanding of how environment shapes the physiology and the life cycle of a loggerhead turtle, we constructed a full life cycle model based on the principles of energy acquisition and utilization embedded in the Dynamic Energy Budget (DEB) theory. We adapted the standard DEB model using data from published and unpublished sources to obtain parameter estimates and model predictions that could be compared with data. The outcome was a successful mathematical description of ontogeny and life history traits of the loggerhead turtle. Some deviations between the model and the data existed (such as an earlier age at sexual maturity and faster growth of the * nina.marn@gmail.com * * mjusup@gmail.com Preprint submitted to Marine Environmental Research August 22, 2016peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/070987 doi: bioRxiv preprint first posted online Aug. 23, 2016; post-hatchlings), yet probable causes for these deviations were found informative and discussed in great detail. Physiological traits such as the capacity to withstand starvation, trade-offs between reproduction and growth, and changes in the energy budget throughout the ontogeny were inferred from the model. The results offer new insights into physiology and ecology of loggerhead turtle with the potential to lead to novel approaches in conservation of this endangered species.

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Section: Methodsmentioning

confidence: 99%

Inferring physiological energetics of loggerhead turtle (Caretta caretta) from existing data using a general metabolic theory

Marn

Kooijman

Jusup

et al. 2016

Preprint

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“…Existing AOPs generally converge on single biological endpoints as if independent (e.g., growth or reproduction) and thus ignore the trade-offs implied by resource limitation, e.g., through the competition for energy among physiological processes. This is in contrast to demand-type organisms, such as mammals and birds, in which growth and reproduction schedules are preprogrammed, and it is up to the organism to find appropriate resources to meet the demand (Lika et al 2014;Jager 2016). In supply-type organisms, which are the majority of species, physiological processes such as growth and reproduction are heavily dependent on environmental conditions, such as food availability and temperature.…”

Section: Adverse Outcome Pathways and Ecotoxicologymentioning

confidence: 99%

“…Therefore, for these organisms, if an AOP converged on feeding rates, maintenance, or growth, the energetic tradeoffs between physiological processes would become more important (Jager 2016). This is in contrast to demand-type organisms, such as mammals and birds, in which growth and reproduction schedules are preprogrammed, and it is up to the organism to find appropriate resources to meet the demand (Lika et al 2014;Jager 2016). Furthermore, important feedbacks exist at each level of organization.…”

Section: Adverse Outcome Pathways and Ecotoxicologymentioning

confidence: 99%

Incorporating Suborganismal Processes into Dynamic Energy Budget Models for Ecological Risk Assessment

Murphy

Nisbet

Antczak

et al. 2018

Integr Envir Assess & Manag

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A working group at the National Institute for Mathematical and Biological Synthesis (NIMBioS) explored the feasibility of integrating 2 complementary approaches relevant to ecological risk assessment. Adverse outcome pathway (AOP) models provide "bottom-up" mechanisms to predict specific toxicological effects that could affect an individual's ability to grow, reproduce, and/or survive from a molecular initiating event. Dynamic energy budget (DEB) models offer a "top-down" approach that reverse engineers stressor effects on growth, reproduction, and/or survival into modular characterizations related to the acquisition and processing of energy resources. Thus, AOP models quantify linkages between measurable molecular, cellular, or organ-level events, but they do not offer an explicit route to integratively characterize stressor effects at higher levels of organization. While DEB models provide the inherent basis to link effects on individuals to those at the population and ecosystem levels, their use of abstract variables obscures mechanistic connections to suborganismal biology. To take advantage of both approaches, we developed a conceptual model to link DEB and AOP models by interpreting AOP key events as measures of damage-inducing processes affecting DEB variables and rates. We report on the type and structure of data that are generated for AOP models that may also be useful for DEB models. We also report on case studies under development that merge information collected for AOPs with DEB models and highlight some of the challenges. Finally, we discuss how the linkage of these 2 approaches can improve ecological risk assessment, with possibilities for progress in predicting population responses to toxicant exposures within realistic environments. Integr Environ Assess Manag 2018;14:615-624. © 2018 SETAC.

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“…informative prior based on theoretical lower bound (Lika et al, 2014) This choice of likelihood offered a straightforward way to parameterize the likelihood, given that zero-variate data are often reported as means and standard deviations, while the truncation ensures that only positive values are allowable. The full likelihood of error at time t is therefore as follows…”

Section: Data Model and Likelihoodmentioning

confidence: 99%

Two case studies detailing Bayesian parameter inference for dynamic energy budget models

Boersch‐Supan

Johnson

2018

Preprint

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1Mechanistic representations of individual life-history trajectories are powerful tools for the pre- while also making use of prior information to constrain the parameter space. We outline a Bayesian 8 inference approach for energy budget models and provide two case studies -based on a simplified 9 DEBkiss model, and the standard DEB model -detailing the implementation of such inference pro- 10cedures using the open-source software package deBInfer. We demonstrate how DEB and DEBkiss 11 parameters can be estimated in a Bayesian framework, but our results also highlight the difficulty 12 of identifying DEB model parameters which serves as a reminder that fitting these models requires 13 statistical caution.

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The bijection from data to parameter space with the standard DEB model quantifies the supply–demand spectrum

Cited by 62 publications

References 21 publications

Inferring physiological energetics of loggerhead turtle (Caretta caretta) from existing data using a general metabolic theory

Inferring physiological energetics of loggerhead turtle (Caretta caretta) from existing data using a general metabolic theory

Incorporating Suborganismal Processes into Dynamic Energy Budget Models for Ecological Risk Assessment

Two case studies detailing Bayesian parameter inference for dynamic energy budget models

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