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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

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Record number 550298
Title Quantifying evolution in wild populations
Author(s) Ramakers, Jip Jacques Claudia
Source Wageningen University. Promotor(en): M.E. Visser, co-promotor(en): P. Gienapp. - Wageningen : Wageningen University - ISBN 9789463439206 - 277
Department(s) Animal Breeding and Genomics
WIAS
Publication type Dissertation, internally prepared
Publication year 2019
Keyword(s) cum laude
Abstract

The environment is changing and this is exerting selection pressures on wild populations. For example, the timing of phenological events such as reproduction and migration are driven by temperatures and climate change is leading to a differential shift in timing of phenology among trophic levels, in some cases leading to selection on consumer phenology. Individuals are often phenotypically plastic, meaning that they can change their phenotype (e.g. breeding time) in response to environmental conditions. This allows them to track the changing environment to some degree but ultimately a genetic change is necessary to safeguard populations from extinction in the long run. Many wild populations so far, however, could not be shown to be undergoing any genetic adaptation (e.g. a shift in their phenology) over time. Quantitative genetics, i.e. the study of the genetics of quantitative (polygenic) traits, is commonly used to identify the evolutionary parameters (genetic variation and selection) in wild populations to predict evolution, but for such predictions to be successful we need to understand the ecological factors underlying (constraints to) adaptation. In this thesis, I aimed to get an understanding of how populations are coping with environmental change and which ecological processes affect the rate of adaptation. I did this using a combined approach of field experiments to identify ecological constraints and long-term observations to make evolutionary predictions in the great tit (Parus major) and other vertebrate species.

In the first part of my thesis (Chapter 2), I provided a broad overview of what is known about the effects of climate change on the general biology of birds. Birds are affected by climate change in several ways: they may change (1) their geographical distribution due to a shifting ‘bioclimatic envelope’, (2) advance their timing of phenological events such as breeding and migration, (3) undergo morphological changes (e.g. in body size) as an energetic adaptation, and (4) undergo demographic changes as a direct result of changes in reproductive success or survival. There has been a strong bias in the literature on phenology and therefore we still have a lot to learn about the ecological and evolutionary consequences related to these other aspects of phenotypic change. Whether observed changes in phenology are due to plasticity or due to genetic change remains an open question.

Dutch great tits have been under increased selection for earlier laying due to increased mismatch with the caterpillar peak (the main food for their nestlings), but we see little (phenotypic) response. The lack of a response may be caused by an energetic constraint associated with breeding too early under harsh conditions, such that birds that do breed earlier may pay fitness costs. In the second part of my thesis I aimed to test whether birds were constrained to breed earlier. In Chapter 3, I used experimental food supplementation food prior to and during egg-laying to test whether females that were tricked into laying early would pay fitness costs (due to brood desertion or reduced chick-provisioning efforts) once food supplementation ceased upon the start of laying. Food supplementation was not effective at advancing laying, and any food increased, rather than decreased, fitness. Because food supplementation alone is not sufficient to test the constraints hypothesis (e.g. because it cannot distinguish whether birds are constrained by food or are just missing the essential cues to advance their breeding) we need an additional, clean manipulation of egg-laying date that does not affect the body condition of a female. In Chapter 4, I described the first results of a large-scale experiment in which great tits are genomically selected to breed early or late. Eggs produced by females from these selection lines were brought to the wild and raised by foster parents. I showed that selection lines (late vs early) did not differ in any aspect of early-life fitness (fledging success, nestling weight at fledging), but that the fitness parameters differed slightly between selection-line birds and their wild counterparts. Since only 11 birds from these fostered birds survived until breeding in 2018 (including 2 females from the early and 3 from the late selection line), we could not test whether these birds indeed bred respectively earlier or later, or whether earlier laying indeed led to higher fitness costs. I concluded that multiple years (with different environmental conditions and an increased sample size) would be needed to conclude whether birds are indeed constrained to breed earlier.

Ultimately, breeding success in great tits is largely determined by the match of the offspring needs with the caterpillar abundance. In Chapter 5, I explored the notion that to clearly understand phenological mismatch—and to determine whether birds really are mismatched—we need a thorough, temporal description of offspring needs and food availability to quantify the amount of temporal overlap between these distributions. I found that the classical way of defining mismatch, i.e. the difference in peak dates between great tit and caterpillar phenology, outperformed a more comprehensive measure that described the temporal overlap in a model explaining variation in offspring survival and selection for laying date. I concluded that a simple measure of mismatch in highly seasonal study systems is likely to be best for describing demographic processes, and that more complex measures are likely infeasible in most practical situations.

In the third part of my thesis, I deployed state-of-the-art quantitative genetic modelling approaches to unravel patterns of selection, genetic variation, and evolutionary response to selection in a reaction-norm context using long-term, pedigreed datasets of wild populations. Some methods to achieve this were explored in the preceding Intermezzo (Chapter 6 and 7). In Chapter 8, I investigated whether maternal effects as a form of transgenerational plasticity could affect the rate of adaptation in great tits. Using experimental and long-term data, I was able to show that the clutch size of a great tit is partly dependent on her body weight at fledgling and that it is negatively associated with the clutch size of her own mother. Such a negative maternal effect could constrain adaptation to a novel environment with selection for a larger of smaller clutch. We showed by simulation, however, that this negative maternal effect would likely have little impact on the rate of adaptation.

Phenological changes over time do not always match evolutionary predictions; one potential reason for this discrepancy is an unrecognised environmentally induced coupling between selection and the heritability of the trait. In Chapter 9, I investigated how general such a coupling is in wild vertebrate populations, and whether such a coupling affects the expected rate of adaptation. The expectation was that if heritability and selection are negatively associated, this constrains adaptation because little genetic variation is present under strong selection and vice versa. Making use of openly available datasets (see Chapter 7), we managed to estimate environment-specific heritability and selection in 50 traits from 16 populations of 10 species. We found that heritability and selection are only rarely associated and that this association is an unlikely explanation for apparent evolutionary stasis observed in wild populations.

Great tits respond strongly to temperature through phenotypic plasticity; this plasticity is described by a reaction norm, the linear function consisting of an elevation (the laying date in the average environment) and a slope (the sensitivity to the environment). Since different individuals have different reaction norms, selection on laying date may result in an evolutionary shift in the reaction norm. In Chapter 10, I found that individual great tits differ genetically in the elevation of the reaction norm, but not in its slope, and this reaction norm is under selection due to the advance in the caterpillar peak over time. I predicted quantitatively, however, that such evolution has been—and will be—too slow to be detected due to the high environmental variability in laying dates.

To conclude, I investigated the evolutionary potential of populations and aimed to identify ecological constraints in adaptation. I found that there is still a lot we need to learn about the ecological and evolutionary consequences of climate change beyond the few well-known study systems, including effects on demography and population viability. Experiments aimed at unravelling the fitness costs of breeding too early are inconclusive and warrant further investigation (with more samples and multiple environments). Powerful quantitative genetic tools are available to evolutionary ecologists to quantify evolutionary trajectories but these models must be based on reality to obtain reliable predictions. I have suggested in this thesis that realistic predictions could be benefited by the integration of multiple data sources (i.e. long-term observational and experimental data) and simulations. The use of open data can aid in achieving this through the answering of novel research questions at a broad taxonomic or geographic scale. Most importantly, we need a thorough understanding of the most important components of the ecosystem of our study species. Only then can we make sense of our evolutionary predictions.

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