Demographic responses underlying eco‐evolutionary dynamics as revealed with inverse modelling

Bruijning, Marjolein; Jongejans, Eelke; Turcotte, Martin M.

doi:10.1111/1365-2656.12966

Cited by 11 publications

(8 citation statements)

References 46 publications

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“…For example, an individual with high condition might have a high birth rate and be expected to produce more offspring than an individual with low condition. If present, trade‐offs between potential vital rates are expected to reduce among‐individual differences in condition (Bruijning et al, 2019; Dahlgren et al, 2016; Gould et al, 2018; Lemaître et al, 2015), as the benefits that individuals gain from a high value of one vital rate will be counteracted by a low value of another vital rate. Trade‐offs are not ubiquitous; however, many studies find a positive correlation between vital rates that structures the population into groups of high‐ and low‐quality individuals (e.g.…”

Section: Defining Heterogeneitymentioning

confidence: 99%

Demystifying individual heterogeneity

2021

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Among‐individual variation in vital rates, such as mortality and birth rates, exists in nearly all populations. Recent studies suggest that this individual heterogeneity produces substantial life‐history and fitness differences among individuals, which in turn scale up to influence population dynamics. However, our ability to understand the consequences of individual heterogeneity is limited by inconsistencies across conceptual frameworks in the field. Studies of individual heterogeneity remain filled with contradicting and ambiguous terminology that introduces risks of misunderstandings, conflicting models and unreliable conclusions. Here, we synthesise the existing literature into a single and comparatively straightforward framework with explicit terminology and definitions. This work introduces a distinction between potential vital rates and realised vital rates to develop a coherent framework that maps directly onto mathematical models of individual heterogeneity. We suggest the terms “fixed condition” and “dynamic condition” be used to distinguish potential vital rates that are permanent from those that can change throughout an individual's life. To illustrate, we connect the framework to quantitative genetics models and to common classes of statistical models used to infer individual heterogeneity. We also develop a population projection matrix model that provides an example of how our definitions are translated into precise quantitative terms.

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Section: Defining Heterogeneitymentioning

confidence: 99%

Demystifying individual heterogeneity

2021

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“…More recent work has investigated time series of multiple species, environmental factors, and has mechanistically modelled various ecological interactions (e.g. Adams et al, 2020; Bruijning et al, 2019; Rosenbaum et al, 2019). In these models, ecological interactions are quantified explicitly by specific parameters, rather than phenomenologically with lags.…”

Section: Introductionmentioning

confidence: 99%

“…Ecological interactions were traditionally considered linear or fixed, yet there is substantial evidence that this is not the case in nature (e.g. Bonnaffé, Legendre, et al, 2021; Bonsall et al, 2003; Bruijning et al, 2019; Gross et al, 2005; Kendall et al, 2005; Rosenbaum et al, 2019; Ushio et al, 2018). The effect of the population on itself depends on the density of individuals (e.g.…”

Section: Introductionmentioning

confidence: 99%

Fast fitting of neural ordinary differential equations by Bayesian neural gradient matching to infer ecological interactions from time‐series data

Bonnaffé

Coulson

2023

Methods Ecol Evol

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Inferring ecological interactions is hard because we often lack suitable parametric representations to portray them. Neural ordinary differential equations (NODEs) provide a way of estimating interactions non‐parametrically from time‐series data. NODEs, however, are slow to fit, and inferred interactions usually are not compared with the ground truth. We provide a fast NODE fitting method, Bayesian neural gradient matching (BNGM), which relies on interpolating time series with neural networks and fitting NODEs to the interpolated dynamics with Bayesian regularisation. We test the accuracy of the approach by inferring ecological interactions in time series generated by an ODE model with known interactions. We compare these results against three existing approaches for estimating ecological interactions, standard NODEs, ODE models and convergent cross‐mapping (CCM). We also infer interactions in experimentally replicated time series of a microcosm featuring an algae, flagellate and rotifer population, in the hare and lynx system, and the Maizuru Bay community featuring 11 species. Our BNGM approach allows us to reduce the fitting time of NODE systems to only a few seconds and provides accurate estimates of ecological interactions in the artificial system, as true ecological interactions are recovered with minimal error. Our benchmark analysis reveals that our approach is both faster and more accurate than standard NODEs and parametric ODEs, while CCM was found to be faster but less accurate. The analysis of the replicated time series reveals that only the strongest interactions are consistent across replicates, while the analysis of the Maizuru community shows the strong negative impact of the chameleon goby on most species of the community, and a potential indirect negative effect of temperature by favouring goby population growth. Overall, NODEs alleviate the need for a mechanistic understanding of interactions, and BNGM alleviates the heavy computational cost. This is a crucial step availing quick NODE fitting to larger systems, cross‐validation and uncertainty quantification, as well as more objective estimation of interactions, and complex context dependence, than parametric models.

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“…These individuals are followed in discrete time steps, typically adjusted by the generation time of the species. Indeed, time steps can vary from 12-24h as in nematode worms Caenorhabditis elegans and aphids Myzus periscae (Li, Ju, Liao, & Liao, 2014; Bruijning, Jongejans, & Turcotte, 2019), to monthly/annually periods in mammals and plants (Coulson et al, 2001; Schödelbauerová, Tremblay, & Kindlmann, 2010; Ferreira, Kajin, Cerqueira, & Vieira, 2016), all the way to 50 years in slow-growing red woods (Namkoong & Roberds, 1974). From these data, researchers estimate losses through mortality, transition probabilities among st/ages and their per-capita a/sexual contributions via reproduction (Mandujano & Escobedo-Morales, 2008; Nordstrom, Dykstra, & Wagenius, 2021; Omeyer et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

A standard protocol to report discrete stage-structured demographic information

Gascoigne

Rolph

Sankey

et al. 2023

Preprint

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Stage-based demographic methods, such as matrix population models (MPMs), are powerful tools used to address a broad range of fundamental questions in ecology, evolutionary biology, and conservation science. Accordingly, MPMs now exist for over 3,000 species worldwide. These data are being digitised as an ongoing process and periodically released into two large open-access online repositories: the COMPADRE Plant Matrix Database and the COMADRE Animal Matrix Database. During the last decade, data archiving and curation of COMPADRE and COMADRE, and subsequent comparative research, have revealed pronounced variation in how MPMs are parameterized and reported. Here, we summarise current issues related to the parameterisation and reporting of MPMs that arise most frequently and outline how they affect MPM construction, analysis, and interpretation. To quantify variation in how MPMs are reported, we present results from a survey identifying key aspects of MPMs that are frequently unreported in manuscripts. We then screen COMPADRE and COMADRE to quantify how often key pieces of information are omitted from manuscripts using MPMs. Over 80% of surveyed researchers (n=60) state a clear benefit to adopting more standardised methodologies for reporting MPMs. Furthermore, over 85% of the 300 MPMs assessed from COMPADRE and COMADRE omitted one or more elements that are key to their accurate interpretation. Based on these insights, we identify fundamental issues that can arise from MPM construction and communication and provide suggestions to improve clarity, reproducibility, and future research utilising MPMs and their required metadata. To fortify reproducibility and empower researchers to take full advantage of their demographic data, we introduce a standardized protocol to present MPMs in publications. This standard is linked to www.compadre-db.org, so that authors wishing to archive their MPMs can do so prior to submission of publications, following examples from other open-access repositories such as DRYAD, Figshare, and Zenodo. Combining and standardising MPMs parameterized from populations around the globe and across the tree of life opens up powerful research opportunities in evolutionary biology, ecology, and conservation research. However, this potential can only be fully realised by adopting standardised methods to ensure reproducibility.

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Demographic responses underlying eco‐evolutionary dynamics as revealed with inverse modelling

Cited by 11 publications

References 46 publications

Demystifying individual heterogeneity

Demystifying individual heterogeneity

Fast fitting of neural ordinary differential equations by Bayesian neural gradient matching to infer ecological interactions from time‐series data

A standard protocol to report discrete stage-structured demographic information

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