Dispersive currents explain patterns of population connectivity in an ecologically and economically important fish
Abstract How to identify the drivers of population connectivity remains a fundamental question in ecology and evolution. Answering this question can be challenging in aquatic environments where dynamic lake and ocean currents coupled with high levels of dispersal and gene flow can decrease the utili...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-07-01
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| Series: | Evolutionary Applications |
| Subjects: | |
| Online Access: | https://doi.org/10.1111/eva.13567 |
| _version_ | 1797773553514315776 |
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| author | Claire E. Schraidt Amanda S. Ackiss Wesley A. Larson Mark D. Rowe Tomas O. Höök Mark R. Christie |
| author_facet | Claire E. Schraidt Amanda S. Ackiss Wesley A. Larson Mark D. Rowe Tomas O. Höök Mark R. Christie |
| author_sort | Claire E. Schraidt |
| collection | DOAJ |
| description | Abstract How to identify the drivers of population connectivity remains a fundamental question in ecology and evolution. Answering this question can be challenging in aquatic environments where dynamic lake and ocean currents coupled with high levels of dispersal and gene flow can decrease the utility of modern population genetic tools. To address this challenge, we used RAD‐Seq to genotype 959 yellow perch (Perca flavescens), a species with an ~40‐day pelagic larval duration (PLD), collected from 20 sites circumscribing Lake Michigan. We also developed a novel, integrative approach that couples detailed biophysical models with eco‐genetic agent‐based models to generate “predictive” values of genetic differentiation. By comparing predictive and empirical values of genetic differentiation, we estimated the relative contributions for known drivers of population connectivity (e.g., currents, behavior, PLD). For the main basin populations (i.e., the largest contiguous portion of the lake), we found that high gene flow led to low overall levels of genetic differentiation among populations (FST = 0.003). By far the best predictors of genetic differentiation were connectivity matrices that were derived from periods of time when there were strong and highly dispersive currents. Thus, these highly dispersive currents are driving the patterns of population connectivity in the main basin. We also found that populations from the northern and southern main basin are slightly divergent from one another, while those from Green Bay and the main basin are highly divergent (FST = 0.11). By integrating biophysical and eco‐genetic models with genome‐wide data, we illustrate that the drivers of population connectivity can be identified in high gene flow systems. |
| first_indexed | 2024-03-12T22:08:08Z |
| format | Article |
| id | doaj.art-809995a2d11c4ce0a049865f1b6ae734 |
| institution | Directory Open Access Journal |
| issn | 1752-4571 |
| language | English |
| last_indexed | 2024-03-12T22:08:08Z |
| publishDate | 2023-07-01 |
| publisher | Wiley |
| record_format | Article |
| series | Evolutionary Applications |
| spelling | doaj.art-809995a2d11c4ce0a049865f1b6ae7342023-07-24T07:47:08ZengWileyEvolutionary Applications1752-45712023-07-011671284130110.1111/eva.13567Dispersive currents explain patterns of population connectivity in an ecologically and economically important fishClaire E. Schraidt0Amanda S. Ackiss1Wesley A. Larson2Mark D. Rowe3Tomas O. Höök4Mark R. Christie5Department of Forestry and Natural Resources Purdue University West Lafayette Indiana USAWisconsin Cooperative Fishery Research Unit College of Natural Resources University of Wisconsin‐Stevens Point Stevens Point Wisconsin USANational Oceanographic and Atmospheric Administration National Marine Fisheries Service Alaska Fisheries Science Center Juneau Alaska USANOAA Great Lakes Environmental Research Laboratory Ann Arbor Michigan USADepartment of Forestry and Natural Resources Purdue University West Lafayette Indiana USADepartment of Forestry and Natural Resources Purdue University West Lafayette Indiana USAAbstract How to identify the drivers of population connectivity remains a fundamental question in ecology and evolution. Answering this question can be challenging in aquatic environments where dynamic lake and ocean currents coupled with high levels of dispersal and gene flow can decrease the utility of modern population genetic tools. To address this challenge, we used RAD‐Seq to genotype 959 yellow perch (Perca flavescens), a species with an ~40‐day pelagic larval duration (PLD), collected from 20 sites circumscribing Lake Michigan. We also developed a novel, integrative approach that couples detailed biophysical models with eco‐genetic agent‐based models to generate “predictive” values of genetic differentiation. By comparing predictive and empirical values of genetic differentiation, we estimated the relative contributions for known drivers of population connectivity (e.g., currents, behavior, PLD). For the main basin populations (i.e., the largest contiguous portion of the lake), we found that high gene flow led to low overall levels of genetic differentiation among populations (FST = 0.003). By far the best predictors of genetic differentiation were connectivity matrices that were derived from periods of time when there were strong and highly dispersive currents. Thus, these highly dispersive currents are driving the patterns of population connectivity in the main basin. We also found that populations from the northern and southern main basin are slightly divergent from one another, while those from Green Bay and the main basin are highly divergent (FST = 0.11). By integrating biophysical and eco‐genetic models with genome‐wide data, we illustrate that the drivers of population connectivity can be identified in high gene flow systems.https://doi.org/10.1111/eva.13567gene flowlarval dispersaloceanographic currentspopulation connectivityyellow perch |
| spellingShingle | Claire E. Schraidt Amanda S. Ackiss Wesley A. Larson Mark D. Rowe Tomas O. Höök Mark R. Christie Dispersive currents explain patterns of population connectivity in an ecologically and economically important fish Evolutionary Applications gene flow larval dispersal oceanographic currents population connectivity yellow perch |
| title | Dispersive currents explain patterns of population connectivity in an ecologically and economically important fish |
| title_full | Dispersive currents explain patterns of population connectivity in an ecologically and economically important fish |
| title_fullStr | Dispersive currents explain patterns of population connectivity in an ecologically and economically important fish |
| title_full_unstemmed | Dispersive currents explain patterns of population connectivity in an ecologically and economically important fish |
| title_short | Dispersive currents explain patterns of population connectivity in an ecologically and economically important fish |
| title_sort | dispersive currents explain patterns of population connectivity in an ecologically and economically important fish |
| topic | gene flow larval dispersal oceanographic currents population connectivity yellow perch |
| url | https://doi.org/10.1111/eva.13567 |
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