---
_id: '11640'
abstract:
- lang: eng
text: Spatially explicit population genetic models have long been developed, yet
have rarely been used to test hypotheses about the spatial distribution of genetic
diversity or the genetic divergence between populations. Here, we use spatially
explicit coalescence simulations to explore the properties of the island and the
two-dimensional stepping stone models under a wide range of scenarios with spatio-temporal
variation in deme size. We avoid the simulation of genetic data, using the fact
that under the studied models, summary statistics of genetic diversity and divergence
can be approximated from coalescence times. We perform the simulations using gridCoal,
a flexible spatial wrapper for the software msprime (Kelleher et al., 2016, Theoretical
Population Biology, 95, 13) developed herein. In gridCoal, deme sizes can change
arbitrarily across space and time, as well as migration rates between individual
demes. We identify different factors that can cause a deviation from theoretical
expectations, such as the simulation time in comparison to the effective deme
size and the spatio-temporal autocorrelation across the grid. Our results highlight
that FST, a measure of the strength of population structure, principally depends
on recent demography, which makes it robust to temporal variation in deme size.
In contrast, the amount of genetic diversity is dependent on the distant past
when Ne is large, therefore longer run times are needed to estimate Ne than FST.
Finally, we illustrate the use of gridCoal on a real-world example, the range
expansion of silver fir (Abies alba Mill.) since the last glacial maximum, using
different degrees of spatio-temporal variation in deme size.
acknowledgement: ES was supported by an IST studentship provided by IST Austria. BT
was funded by the European Union's Horizon 2020 research and innovation programme
under the Marie Sklodowska-Curie Independent Fellowship (704172, RACE). This project
received further funding awarded to KC from the Swiss National Science Foundation
(SNSF CRSK-3_190288) and the Swiss Federal Research Institute WSL. We thank Nick
Barton for many invaluable discussions and his comments on the thesis chapter and
this manuscript. We thank Peter Ralph and Jerome Kelleher for useful discussions
and Bisschop Gertjan for comments on this manuscript. We thank Fortunat Joos for
providing us with the raw data from the LPX-Bern model for silver fir, and Willy
Tinner for helpful insights about the demographic history of silver fir. We also
thank the editor Alana Alexander for useful comments and advice on the manuscript.
Open access funding provided by Eidgenossische Technische Hochschule Zurich.
article_processing_charge: Yes (via OA deal)
article_type: original
author:
- first_name: Eniko
full_name: Szep, Eniko
id: 485BB5A4-F248-11E8-B48F-1D18A9856A87
last_name: Szep
- first_name: Barbora
full_name: Trubenova, Barbora
id: 42302D54-F248-11E8-B48F-1D18A9856A87
last_name: Trubenova
orcid: 0000-0002-6873-2967
- first_name: Katalin
full_name: Csilléry, Katalin
last_name: Csilléry
citation:
ama: Szep E, Trubenova B, Csilléry K. Using gridCoal to assess whether standard
population genetic theory holds in the presence of spatio-temporal heterogeneity
in population size. Molecular Ecology Resources. 2022;22(8):2941-2955.
doi:10.1111/1755-0998.13676
apa: Szep, E., Trubenova, B., & Csilléry, K. (2022). Using gridCoal to assess
whether standard population genetic theory holds in the presence of spatio-temporal
heterogeneity in population size. Molecular Ecology Resources. Wiley. https://doi.org/10.1111/1755-0998.13676
chicago: Szep, Eniko, Barbora Trubenova, and Katalin Csilléry. “Using GridCoal to
Assess Whether Standard Population Genetic Theory Holds in the Presence of Spatio-Temporal
Heterogeneity in Population Size.” Molecular Ecology Resources. Wiley,
2022. https://doi.org/10.1111/1755-0998.13676.
ieee: E. Szep, B. Trubenova, and K. Csilléry, “Using gridCoal to assess whether
standard population genetic theory holds in the presence of spatio-temporal heterogeneity
in population size,” Molecular Ecology Resources, vol. 22, no. 8. Wiley,
pp. 2941–2955, 2022.
ista: Szep E, Trubenova B, Csilléry K. 2022. Using gridCoal to assess whether standard
population genetic theory holds in the presence of spatio-temporal heterogeneity
in population size. Molecular Ecology Resources. 22(8), 2941–2955.
mla: Szep, Eniko, et al. “Using GridCoal to Assess Whether Standard Population Genetic
Theory Holds in the Presence of Spatio-Temporal Heterogeneity in Population Size.”
Molecular Ecology Resources, vol. 22, no. 8, Wiley, 2022, pp. 2941–55,
doi:10.1111/1755-0998.13676.
short: E. Szep, B. Trubenova, K. Csilléry, Molecular Ecology Resources 22 (2022)
2941–2955.
date_created: 2022-07-24T22:01:43Z
date_published: 2022-11-01T00:00:00Z
date_updated: 2023-08-03T12:11:01Z
day: '01'
ddc:
- '570'
department:
- _id: NiBa
doi: 10.1111/1755-0998.13676
ec_funded: 1
external_id:
isi:
- '000825873600001'
file:
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checksum: 3102e203e77b884bffffdbe8e548da88
content_type: application/pdf
creator: dernst
date_created: 2023-02-02T08:11:23Z
date_updated: 2023-02-02T08:11:23Z
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file_name: 2022_MolecularEcologyRes_Szep.pdf
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language:
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month: '11'
oa: 1
oa_version: Published Version
page: 2941-2955
project:
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call_identifier: H2020
grant_number: '704172'
name: Rate of Adaptation in Changing Environment
publication: Molecular Ecology Resources
publication_identifier:
eissn:
- 1755-0998
issn:
- 1755-098X
publication_status: published
publisher: Wiley
quality_controlled: '1'
scopus_import: '1'
status: public
title: Using gridCoal to assess whether standard population genetic theory holds in
the presence of spatio-temporal heterogeneity in population size
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year: '2022'
...
---
_id: '13062'
abstract:
- lang: eng
text: 'This paper analyzes the conditions for local adaptation in a metapopulation
with infinitely many islands under a model of hard selection, where population
size depends on local fitness. Each island belongs to one of two distinct ecological
niches or habitats. Fitness is influenced by an additive trait which is under
habitat-dependent directional selection. Our analysis is based on the diffusion
approximation and accounts for both genetic drift and demographic stochasticity.
By neglecting linkage disequilibria, it yields the joint distribution of allele
frequencies and population size on each island. We find that under hard selection,
the conditions for local adaptation in a rare habitat are more restrictive for
more polygenic traits: even moderate migration load per locus at very many loci
is sufficient for population sizes to decline. This further reduces the efficacy
of selection at individual loci due to increased drift and because smaller populations
are more prone to swamping due to migration, causing a positive feedback between
increasing maladaptation and declining population sizes. Our analysis also highlights
the importance of demographic stochasticity, which exacerbates the decline in
numbers of maladapted populations, leading to population collapse in the rare
habitat at significantly lower migration than predicted by deterministic arguments.'
article_processing_charge: No
author:
- first_name: Eniko
full_name: Szep, Eniko
id: 485BB5A4-F248-11E8-B48F-1D18A9856A87
last_name: Szep
- first_name: Himani
full_name: Sachdeva, Himani
id: 42377A0A-F248-11E8-B48F-1D18A9856A87
last_name: Sachdeva
- first_name: Nicholas H
full_name: Barton, Nicholas H
id: 4880FE40-F248-11E8-B48F-1D18A9856A87
last_name: Barton
orcid: 0000-0002-8548-5240
citation:
ama: 'Szep E, Sachdeva H, Barton NH. Supplementary code for: Polygenic local adaptation
in metapopulations: A stochastic eco-evolutionary model. 2021. doi:10.5061/DRYAD.8GTHT76P1'
apa: 'Szep, E., Sachdeva, H., & Barton, N. H. (2021). Supplementary code for:
Polygenic local adaptation in metapopulations: A stochastic eco-evolutionary model.
Dryad. https://doi.org/10.5061/DRYAD.8GTHT76P1'
chicago: 'Szep, Eniko, Himani Sachdeva, and Nicholas H Barton. “Supplementary Code
for: Polygenic Local Adaptation in Metapopulations: A Stochastic Eco-Evolutionary
Model.” Dryad, 2021. https://doi.org/10.5061/DRYAD.8GTHT76P1.'
ieee: 'E. Szep, H. Sachdeva, and N. H. Barton, “Supplementary code for: Polygenic
local adaptation in metapopulations: A stochastic eco-evolutionary model.” Dryad,
2021.'
ista: 'Szep E, Sachdeva H, Barton NH. 2021. Supplementary code for: Polygenic local
adaptation in metapopulations: A stochastic eco-evolutionary model, Dryad, 10.5061/DRYAD.8GTHT76P1.'
mla: 'Szep, Eniko, et al. Supplementary Code for: Polygenic Local Adaptation
in Metapopulations: A Stochastic Eco-Evolutionary Model. Dryad, 2021, doi:10.5061/DRYAD.8GTHT76P1.'
short: E. Szep, H. Sachdeva, N.H. Barton, (2021).
date_created: 2023-05-23T16:17:02Z
date_published: 2021-03-02T00:00:00Z
date_updated: 2023-09-05T15:44:05Z
day: '02'
ddc:
- '570'
department:
- _id: NiBa
doi: 10.5061/DRYAD.8GTHT76P1
main_file_link:
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url: https://doi.org/10.5061/dryad.8gtht76p1
month: '03'
oa: 1
oa_version: Published Version
publisher: Dryad
related_material:
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title: 'Supplementary code for: Polygenic local adaptation in metapopulations: A stochastic
eco-evolutionary model'
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type: research_data_reference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2021'
...
---
_id: '9252'
abstract:
- lang: eng
text: 'This paper analyses the conditions for local adaptation in a metapopulation
with infinitely many islands under a model of hard selection, where population
size depends on local fitness. Each island belongs to one of two distinct ecological
niches or habitats. Fitness is influenced by an additive trait which is under
habitat‐dependent directional selection. Our analysis is based on the diffusion
approximation and accounts for both genetic drift and demographic stochasticity.
By neglecting linkage disequilibria, it yields the joint distribution of allele
frequencies and population size on each island. We find that under hard selection,
the conditions for local adaptation in a rare habitat are more restrictive for
more polygenic traits: even moderate migration load per locus at very many loci
is sufficient for population sizes to decline. This further reduces the efficacy
of selection at individual loci due to increased drift and because smaller populations
are more prone to swamping due to migration, causing a positive feedback between
increasing maladaptation and declining population sizes. Our analysis also highlights
the importance of demographic stochasticity, which exacerbates the decline in
numbers of maladapted populations, leading to population collapse in the rare
habitat at significantly lower migration than predicted by deterministic arguments.'
acknowledgement: We thank the reviewers for their helpful comments, and also our colleagues,
for illuminating discussions over the long gestation of this paper.
article_processing_charge: Yes (via OA deal)
article_type: original
author:
- first_name: Eniko
full_name: Szep, Eniko
id: 485BB5A4-F248-11E8-B48F-1D18A9856A87
last_name: Szep
- first_name: Himani
full_name: Sachdeva, Himani
id: 42377A0A-F248-11E8-B48F-1D18A9856A87
last_name: Sachdeva
- first_name: Nicholas H
full_name: Barton, Nicholas H
id: 4880FE40-F248-11E8-B48F-1D18A9856A87
last_name: Barton
orcid: 0000-0002-8548-5240
citation:
ama: 'Szep E, Sachdeva H, Barton NH. Polygenic local adaptation in metapopulations:
A stochastic eco‐evolutionary model. Evolution. 2021;75(5):1030-1045. doi:10.1111/evo.14210'
apa: 'Szep, E., Sachdeva, H., & Barton, N. H. (2021). Polygenic local adaptation
in metapopulations: A stochastic eco‐evolutionary model. Evolution. Wiley.
https://doi.org/10.1111/evo.14210'
chicago: 'Szep, Eniko, Himani Sachdeva, and Nicholas H Barton. “Polygenic Local
Adaptation in Metapopulations: A Stochastic Eco‐evolutionary Model.” Evolution.
Wiley, 2021. https://doi.org/10.1111/evo.14210.'
ieee: 'E. Szep, H. Sachdeva, and N. H. Barton, “Polygenic local adaptation in metapopulations:
A stochastic eco‐evolutionary model,” Evolution, vol. 75, no. 5. Wiley,
pp. 1030–1045, 2021.'
ista: 'Szep E, Sachdeva H, Barton NH. 2021. Polygenic local adaptation in metapopulations:
A stochastic eco‐evolutionary model. Evolution. 75(5), 1030–1045.'
mla: 'Szep, Eniko, et al. “Polygenic Local Adaptation in Metapopulations: A Stochastic
Eco‐evolutionary Model.” Evolution, vol. 75, no. 5, Wiley, 2021, pp. 1030–45,
doi:10.1111/evo.14210.'
short: E. Szep, H. Sachdeva, N.H. Barton, Evolution 75 (2021) 1030–1045.
date_created: 2021-03-20T08:22:10Z
date_published: 2021-05-01T00:00:00Z
date_updated: 2023-09-05T15:44:06Z
day: '01'
ddc:
- '570'
department:
- _id: NiBa
doi: 10.1111/evo.14210
external_id:
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checksum: b90fb5767d623602046fed03725e16ca
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relation: main_file
success: 1
file_date_updated: 2021-08-11T13:39:19Z
has_accepted_license: '1'
intvolume: ' 75'
isi: 1
issue: '5'
keyword:
- Genetics
- Ecology
- Evolution
- Behavior and Systematics
- General Agricultural and Biological Sciences
language:
- iso: eng
license: https://creativecommons.org/licenses/by-nc-nd/4.0/
month: '05'
oa: 1
oa_version: Published Version
page: 1030-1045
publication: Evolution
publication_identifier:
eissn:
- 1558-5646
issn:
- 0014-3820
publication_status: published
publisher: Wiley
quality_controlled: '1'
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title: 'Polygenic local adaptation in metapopulations: A stochastic eco‐evolutionary
model'
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short: CC BY-NC-ND (4.0)
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user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
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...
---
_id: '10535'
abstract:
- lang: eng
text: Realistic models of biological processes typically involve interacting components
on multiple scales, driven by changing environment and inherent stochasticity.
Such models are often analytically and numerically intractable. We revisit a dynamic
maximum entropy method that combines a static maximum entropy with a quasi-stationary
approximation. This allows us to reduce stochastic non-equilibrium dynamics expressed
by the Fokker-Planck equation to a simpler low-dimensional deterministic dynamics,
without the need to track microscopic details. Although the method has been previously
applied to a few (rather complicated) applications in population genetics, our
main goal here is to explain and to better understand how the method works. We
demonstrate the usefulness of the method for two widely studied stochastic problems,
highlighting its accuracy in capturing important macroscopic quantities even in
rapidly changing non-stationary conditions. For the Ornstein-Uhlenbeck process,
the method recovers the exact dynamics whilst for a stochastic island model with
migration from other habitats, the approximation retains high macroscopic accuracy
under a wide range of scenarios in a dynamic environment.
acknowledged_ssus:
- _id: ScienComp
acknowledgement: "Computational resources for the study were provided by the Institute
of Science and Technology, Austria.\r\nKB received funding from the Scientific Grant
Agency of the Slovak Republic under the Grants Nos. 1/0755/19 and 1/0521/20."
article_number: e1009661
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Katarína
full_name: Bod'ová, Katarína
id: 2BA24EA0-F248-11E8-B48F-1D18A9856A87
last_name: Bod'ová
orcid: 0000-0002-7214-0171
- first_name: Eniko
full_name: Szep, Eniko
id: 485BB5A4-F248-11E8-B48F-1D18A9856A87
last_name: Szep
- first_name: Nicholas H
full_name: Barton, Nicholas H
id: 4880FE40-F248-11E8-B48F-1D18A9856A87
last_name: Barton
orcid: 0000-0002-8548-5240
citation:
ama: Bodova K, Szep E, Barton NH. Dynamic maximum entropy provides accurate approximation
of structured population dynamics. PLoS Computational Biology. 2021;17(12).
doi:10.1371/journal.pcbi.1009661
apa: Bodova, K., Szep, E., & Barton, N. H. (2021). Dynamic maximum entropy provides
accurate approximation of structured population dynamics. PLoS Computational
Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1009661
chicago: Bodova, Katarina, Eniko Szep, and Nicholas H Barton. “Dynamic Maximum Entropy
Provides Accurate Approximation of Structured Population Dynamics.” PLoS Computational
Biology. Public Library of Science, 2021. https://doi.org/10.1371/journal.pcbi.1009661.
ieee: K. Bodova, E. Szep, and N. H. Barton, “Dynamic maximum entropy provides accurate
approximation of structured population dynamics,” PLoS Computational Biology,
vol. 17, no. 12. Public Library of Science, 2021.
ista: Bodova K, Szep E, Barton NH. 2021. Dynamic maximum entropy provides accurate
approximation of structured population dynamics. PLoS Computational Biology. 17(12),
e1009661.
mla: Bodova, Katarina, et al. “Dynamic Maximum Entropy Provides Accurate Approximation
of Structured Population Dynamics.” PLoS Computational Biology, vol. 17,
no. 12, e1009661, Public Library of Science, 2021, doi:10.1371/journal.pcbi.1009661.
short: K. Bodova, E. Szep, N.H. Barton, PLoS Computational Biology 17 (2021).
date_created: 2021-12-12T23:01:27Z
date_published: 2021-12-01T00:00:00Z
date_updated: 2022-08-01T10:48:04Z
day: '01'
ddc:
- '570'
department:
- _id: NiBa
- _id: GaTk
doi: 10.1371/journal.pcbi.1009661
external_id:
arxiv:
- '2102.03669'
pmid:
- '34851948'
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checksum: dcd185d4f7e0acee25edf1d6537f447e
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publication: PLoS Computational Biology
publication_identifier:
eissn:
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issn:
- 1553-734X
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publisher: Public Library of Science
quality_controlled: '1'
scopus_import: '1'
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title: Dynamic maximum entropy provides accurate approximation of structured population
dynamics
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type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 17
year: '2021'
...
---
_id: '8574'
abstract:
- lang: eng
text: "This thesis concerns itself with the interactions of evolutionary and ecological
forces and the consequences on genetic diversity and the ultimate survival of
populations. It is important to understand what signals processes \r\nleave on
the genome and what we can infer from such data, which is usually abundant but
noisy. Furthermore, understanding how and when populations adapt or go extinct
is important for practical purposes, such as the genetic management of populations,
as well as for theoretical questions, since local adaptation can be the first
step toward speciation. \r\nIn Chapter 2, we introduce the method of maximum entropy
to approximate the demographic changes of a population in a simple setting, namely
the logistic growth model with immigration. We show that this method is not only
a powerful \r\ntool in physics but can be gainfully applied in an ecological framework.
We investigate how well it approximates the real \r\nbehavior of the system, and
find that is does so, even in unexpected situations. Finally, we illustrate how
it can model changing environments.\r\nIn Chapter 3, we analyze the co-evolution
of allele frequencies and population sizes in an infinite island model.\r\nWe
give conditions under which polygenic adaptation to a rare habitat is possible.
The model we use is based on the diffusion approximation, considers eco-evolutionary
feedback mechanisms (hard selection), and treats both \r\ndrift and environmental
fluctuations explicitly. We also look at limiting scenarios, for which we derive
analytical expressions. \r\nIn Chapter 4, we present a coalescent based simulation
tool to obtain patterns of diversity in a spatially explicit subdivided population,
in which the demographic history of each subpopulation can be specified. We compare
\r\nthe results to existing predictions, and explore the relative importance of
time and space under a variety of spatial arrangements and demographic histories,
such as expansion and extinction. \r\nIn the last chapter, we give a brief outlook
to further research. "
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Eniko
full_name: Szep, Eniko
id: 485BB5A4-F248-11E8-B48F-1D18A9856A87
last_name: Szep
citation:
ama: Szep E. Local adaptation in metapopulations. 2020. doi:10.15479/AT:ISTA:8574
apa: Szep, E. (2020). Local adaptation in metapopulations. Institute of Science
and Technology Austria. https://doi.org/10.15479/AT:ISTA:8574
chicago: Szep, Eniko. “Local Adaptation in Metapopulations.” Institute of Science
and Technology Austria, 2020. https://doi.org/10.15479/AT:ISTA:8574.
ieee: E. Szep, “Local adaptation in metapopulations,” Institute of Science and Technology
Austria, 2020.
ista: Szep E. 2020. Local adaptation in metapopulations. Institute of Science and
Technology Austria.
mla: Szep, Eniko. Local Adaptation in Metapopulations. Institute of Science
and Technology Austria, 2020, doi:10.15479/AT:ISTA:8574.
short: E. Szep, Local Adaptation in Metapopulations, Institute of Science and Technology
Austria, 2020.
date_created: 2020-09-28T07:33:38Z
date_published: 2020-09-20T00:00:00Z
date_updated: 2023-09-07T13:11:39Z
day: '20'
ddc:
- '570'
degree_awarded: PhD
department:
- _id: NiBa
doi: 10.15479/AT:ISTA:8574
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page: '158'
publication_identifier:
eissn:
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publication_status: published
publisher: Institute of Science and Technology Austria
status: public
supervisor:
- first_name: Nicholas H
full_name: Barton, Nicholas H
id: 4880FE40-F248-11E8-B48F-1D18A9856A87
last_name: Barton
orcid: 0000-0002-8548-5240
title: Local adaptation in metapopulations
type: dissertation
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
year: '2020'
...