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Taking Stock: From Pattern to Proces...

Rademaker, Mark.

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Taking Stock: From Pattern to Process in Modelling the Population Dynamics of Marine Communities.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Taking Stock: From Pattern to Process in Modelling the Population Dynamics of Marine Communities./
Author:	Rademaker, Mark.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2024,
Description:	205 p.
Notes:	Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
Contained By:	Dissertations Abstracts International85-12B.
Subject:	Ecology. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31472673
ISBN:	9798382829098

Taking Stock: From Pattern to Process in Modelling the Population Dynamics of Marine Communities.
Rademaker, Mark.

Taking Stock: From Pattern to Process in Modelling the Population Dynamics of Marine Communities. - Ann Arbor : ProQuest Dissertations & Theses, 2024 - 205 p.

Source: Dissertations Abstracts International, Volume: 85-12, Section: B.

Thesis (Ph.D.)--Wageningen University and Research, 2024.

Human-induced changes in the global environment have led to major changes in the structuring of marine communities. At the same time, the amount of information available to study the responses of marine communities to global change has increased by orders of magnitude through the advent of big data. These developments have reignited a century old scientific debate on how to best understand what is driving the observed fluctuations of marine populations; a debate between scientific inference based on pattern versus process. This PhD synthesis integrates the findings from five studies, representing a gradient of pattern-to-process based approaches, and different marine systems, in an attempt to answer the main research question: How to best move forward to the goal of understanding how and why marine populations fluctuate over time in an era of big data and global change?The thesis starts at the pattern side of the gradient, with a focus on the ecological insights that can be gained from fully data-driven, machine-learning approaches. In chapter 2, I developed and used a deep-learning species distribution model (DL-SDM) to explore the community-wide structuring of interspecific interactions between benthic invertebrates in a 2400 km2 intertidal ecosystem. The dataset consisted of > 30 000 (a)biotic samples collected between 2008-2020 as part of the Synoptic Intertidal Benthic Survey (SIBES). Our data-driven approach showed that populations of benthic invertebrates in the Wadden Sea form a network in which each individual species engages in relatively few strong interactions embedded in a larger network of weak interactions. We could use these outcomes and combine them with more formal methods of statistical inference to show interaction network structure affirms existing stability-connectivity theories. We could further statistically link the number of interspecific interactions of a species to certain functional traits. However, the biological interpretation of these links remains open. Rather than posing a catch-all solution for ecological inference, the data-driven DL-SDM provides a baseline mapping tool and starting point for more targeted experiments to elucidate underlying mechanisms.The outcomes of chapters 2-6 illustrate the difference in inferential capacity between pattern, hybrid and process-based approaches in understanding what is driving observed population responses in marine communities. On the pattern side of the gradient, big data enables the identification of many new correlative interdependencies in large and high-dimensional datasets. However, based on the outcomes of this thesis I argue that the role of data-driven work should not start to outweigh that of formal theory when generating inferences on underlying mechanisms. An overreliance on data-driven 'discovery' will lead to a skewed understanding of the functioning of marine communities and their responses to global change; with outcomes that can be unrobust and difficult to validate. In chapter 5 and the global synthesis of this study I discuss a roadmap to reconcile the different approaches across the pattern-to-process gradient. As part of this perspective, I argue that most insight is gained when these different approaches are combined within single research projects. To achieve this, better collaboration is required between research groups with different specializations along the pattern-to-process-gradient. Hopefully, this might also lessen the current 'schism' between empirical and theoretical approaches in marine population ecology. I argue that such an approach will lead to a better understanding of how and why marine populations fluctuate in an era of big data and global change.

ISBN: 9798382829098Subjects--Topical Terms:

516476
Ecology.

Taking Stock: From Pattern to Process in Modelling the Population Dynamics of Marine Communities.
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500 $a Advisor: Murk, Tinka;Peck, Myron;van Leeuwen, Anieke;Smallegange, Im.
502 $a Thesis (Ph.D.)--Wageningen University and Research, 2024.
520 $a Human-induced changes in the global environment have led to major changes in the structuring of marine communities. At the same time, the amount of information available to study the responses of marine communities to global change has increased by orders of magnitude through the advent of big data. These developments have reignited a century old scientific debate on how to best understand what is driving the observed fluctuations of marine populations; a debate between scientific inference based on pattern versus process. This PhD synthesis integrates the findings from five studies, representing a gradient of pattern-to-process based approaches, and different marine systems, in an attempt to answer the main research question: How to best move forward to the goal of understanding how and why marine populations fluctuate over time in an era of big data and global change?The thesis starts at the pattern side of the gradient, with a focus on the ecological insights that can be gained from fully data-driven, machine-learning approaches. In chapter 2, I developed and used a deep-learning species distribution model (DL-SDM) to explore the community-wide structuring of interspecific interactions between benthic invertebrates in a 2400 km2 intertidal ecosystem. The dataset consisted of > 30 000 (a)biotic samples collected between 2008-2020 as part of the Synoptic Intertidal Benthic Survey (SIBES). Our data-driven approach showed that populations of benthic invertebrates in the Wadden Sea form a network in which each individual species engages in relatively few strong interactions embedded in a larger network of weak interactions. We could use these outcomes and combine them with more formal methods of statistical inference to show interaction network structure affirms existing stability-connectivity theories. We could further statistically link the number of interspecific interactions of a species to certain functional traits. However, the biological interpretation of these links remains open. Rather than posing a catch-all solution for ecological inference, the data-driven DL-SDM provides a baseline mapping tool and starting point for more targeted experiments to elucidate underlying mechanisms.The outcomes of chapters 2-6 illustrate the difference in inferential capacity between pattern, hybrid and process-based approaches in understanding what is driving observed population responses in marine communities. On the pattern side of the gradient, big data enables the identification of many new correlative interdependencies in large and high-dimensional datasets. However, based on the outcomes of this thesis I argue that the role of data-driven work should not start to outweigh that of formal theory when generating inferences on underlying mechanisms. An overreliance on data-driven 'discovery' will lead to a skewed understanding of the functioning of marine communities and their responses to global change; with outcomes that can be unrobust and difficult to validate. In chapter 5 and the global synthesis of this study I discuss a roadmap to reconcile the different approaches across the pattern-to-process gradient. As part of this perspective, I argue that most insight is gained when these different approaches are combined within single research projects. To achieve this, better collaboration is required between research groups with different specializations along the pattern-to-process-gradient. Hopefully, this might also lessen the current 'schism' between empirical and theoretical approaches in marine population ecology. I argue that such an approach will lead to a better understanding of how and why marine populations fluctuate in an era of big data and global change.
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