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Density dependence in animal populations: effects of biological predictors and methodological biases


Density dependence in animal populations: effects of biological predictors and methodological biases

Matte, Jean-Michel (2020) Density dependence in animal populations: effects of biological predictors and methodological biases. PhD thesis, Concordia University.

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The importance of density in the regulation of animal populations is well established, but the mechanisms by which it operates are still equivocal. More specifically, the extent to which density influences individual fitness remains uncertain, and the variability in responses to density across gradients of environmental conditions, and between distinct populations, species, taxonomic classes, and experimental designs has received limited empirical investigation. Using a combination of field experiments with salmonids and meta-analyses at a broader scale, my thesis investigates the relationship between density, somatic growth, and survival, and the extent by which these relationships can be related to biological predictors and methodological biases. In chapter 1, the mechanisms of density dependence are investigated by manipulating the density (range: 0.3 - 7 fish/m^2) of young-of-the-year brook trout (Salvelinus fontinalis) in three genetically distinct populations (θST = 0.13-0.30) during three consecutive summers in sections of streams in Cape Race, Newfoundland. I found that populations exhibited population-specific patterns of density dependence that were consistent across years, which were partially related to environmental conditions. In chapter 2, the mechanisms of competition that cause density dependence in these populations were investigated. To do so, I quantified the consumption and depletion of invertebrate prey communities across brook trout densities in the same experiment as chapter 1. My results demonstrated that strong density dependence can occur without prey depletion or reductions in consumption, suggesting that alternative mechanisms can be important. In chapter 3, a meta-analysis was conducted to quantify the relative importance of biological predictors and methodological biases on the patterns of density dependence in salmonids. This meta-analysis demonstrated that methodological biases (experimental design, density gradient) were better predictors of the shape and strength of density dependence across salmonids than biological predictors (food abundance, predators, habitat, species). However, salmonids differ from other animals in several key ways (e.g. territoriality, life history, habitat, etc.), and whether relationships derived from salmonids can be applied to other animals is uncertain. Therefore, in chapter 4, I conducted a similar meta-analysis at a broader scale across all animals, to quantify the prevalence of the same biological predictors and methodological biases on density dependence, and to quantify potential differences across taxonomic classes. Patterns of density dependence across animals varied according to both biological (taxonomic groups, food abundance, age) and methodological biases (density gradient). However, these relationships were different than those present in salmonids, suggesting that important variation occurs at multiple taxonomic levels. Overall, my thesis demonstrates that the patterns of density dependence can vary according to multiple factors simultaneously (environment, populations, taxonomic classes, methodology). These findings have important implications for the management of wild populations and our understanding of density dependence. More specifically, they demonstrate that the outcome of density dependence is highly context-dependent, and that care should be exercised both for research and the management of endangered populations.

Divisions:Concordia University > Faculty of Arts and Science > Biology
Item Type:Thesis (PhD)
Authors:Matte, Jean-Michel
Institution:Concordia University
Degree Name:Ph. D.
Date:August 2020
Thesis Supervisor(s):Grant, James and Fraser, Dylan
ID Code:987272
Deposited On:25 Nov 2020 15:54
Last Modified:25 Nov 2020 15:54
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