Periodic Reporting for period 1 - SAL-MOVE (Migration timing genotype as a predictor of salmon vulnerability to environmental change)
Período documentado: 2022-04-01 hasta 2024-03-31
As the environment changes, species are increasingly suffering mismatches between seasonally timed movements and conditions encountered. How and whether they can adjust their timing to overcome this mismatch depends on how it is shaped by genetics. The long-distance ocean and river migrations of Atlantic salmon (Salmo salar) are an example of such movements. Salmon spend the first part of their life in freshwater, then migrate out to the ocean where they feed and grow before returning to their home river to breed. To successfully complete this life cycle they need to match timing of their juvenile migration out to the ocean and their adult migration back to the river with conditions that enable their survival and reproductive success. By combining genetics, climate change projections and machine-learning, SAL-MOVE aims to predict how Atlantic salmon populations will be globally impacted by human-induced changes through their migration.
Results from this project can help manage and conserve populations of Atlantic salmon that are declining across their range, ensuring the sustainability of fisheries that support indigenous communities and local economies through tourism and recreational fishing. The societal importance also extends to addressing complex environmental challenges through international collaboration, contributing to the preservation of biodiversity in a rapidly changing environment.
The three inter-related objectives of SAL-MOVE are:
O1: To identify the environmental correlates of Atlantic salmon run timing throughout the species’ range
O2: To characterise the genomic architecture of run timing throughout the species’ range
O3: To combine the outcomes of O1 and O2 in a modelling framework to predict the migration-mediated vulnerability of Atlantic salmon populations to future environmental change
Results from this project can help manage and conserve populations of Atlantic salmon that are declining across their range, ensuring the sustainability of fisheries that support indigenous communities and local economies through tourism and recreational fishing. The societal importance also extends to addressing complex environmental challenges through international collaboration, contributing to the preservation of biodiversity in a rapidly changing environment.
The three inter-related objectives of SAL-MOVE are:
O1: To identify the environmental correlates of Atlantic salmon run timing throughout the species’ range
O2: To characterise the genomic architecture of run timing throughout the species’ range
O3: To combine the outcomes of O1 and O2 in a modelling framework to predict the migration-mediated vulnerability of Atlantic salmon populations to future environmental change
O1: Identifying the environmental correlates of Atlantic salmon run timing throughout the species’ range
SAL-MOVE has created a large network of Atlantic salmon researchers and biologists from 8 countries: Norway, Canada, Iceland, France, Finland, Ireland and the UK. This collaboration has shared scientific resources, including genetic samples and data on migration timing for young salmon (smolts) and adults.
Our results have shown that some adult Atlantic salmon populations in North America have started migrating earlier over the past 28 years (1993-2021). Worryingly, the population that migrates the earliest is now at record low numbers.
Environmental data has been obtained for each trapping location from the publicly available WorldClim database. We are using this information to create models to predict how vulnerable different salmon populations are to future climate changes (see O3).
O2: Characterising the genomic architecture of run timing throughout the species’ range
Our datasets on the times of returning adult salmon migration span decades, but we did not have genetic samples for most of these salmon. To overcome this problem, we used measures that characterised the return time of whole populations and then related these to population-level genetic differences. We did this for 11 Atlantic salmon populations in North America, using a tool (a ‘SNP array’) that examines over 60,000 genetic markers across the whole genome.
We find that the timing of salmon returns is related to many different genes. Some of these genes are already known to be linked to adult return migration timing in European Atlantic salmon, indicating that there are shared genetic factors affecting run timing across the species’ distribution. Additionally, our study suggests that interactions between European and North American salmon populations around the end of the last Ice Age may have played a role in shaping genetic traits related to migration.
We followed up this study by relating the return time of individual adult salmon to their genetic makeup. We used a form of whole genome sequencing known as lc-WGS which enabled us to examine genetic variation in more detail than the SNP array. We examined salmon from 7 North American populations.
Using this more detailed approach, we found a new region of the genome that was closely linked to salmon migration timing, near a gene also linked to the timing of long-distance migrations in birds.
We are also examining how genetics influences the timing of a young salmon’s ‘smolt migration’ from the river out to the ocean. Via our collaborators, we have obtained samples from Atlantic salmon smolts across 7 countries and 10 rivers, along with the dates at which each of these smolts left the river, and we are genotyping >1,500 of these samples with a SNP array.
O3: combining the outcomes of O1 and O2 in a modelling framework to predict the migration-mediated vulnerability of Atlantic salmon populations to future environmental change
Once we identify the genetic variation that is associated with the timing of a salmons’ migration, and understand how this timing is linked to the salmons’ environment, we use machine-learning models to predict how climate change may threaten a population by causing its timing and environment to become mis-matched. We use these models to forecast how much a populations’ genetic makeup might need to change in response to future climate conditions. We call the difference between the current and required future genetic variation "genomic offset", and it is a measure of how vulnerable a population is to climate change.
By applying this approach to North American Atlantic salmon populations, we found that temperature variation throughout the day, and temperature during the wettest part of the year, were the climate variables most strongly correlated with migration timing-linked genetic variation. Genomic offsets indicated that the populations that migrate earliest and populations that are the most northerly are the most vulnerable to climate change.
Before we can study how climate change might affect the migration timing of young salmon, we first need to gather genetic data. This will be our focus for the rest of the project.
SAL-MOVE has created a large network of Atlantic salmon researchers and biologists from 8 countries: Norway, Canada, Iceland, France, Finland, Ireland and the UK. This collaboration has shared scientific resources, including genetic samples and data on migration timing for young salmon (smolts) and adults.
Our results have shown that some adult Atlantic salmon populations in North America have started migrating earlier over the past 28 years (1993-2021). Worryingly, the population that migrates the earliest is now at record low numbers.
Environmental data has been obtained for each trapping location from the publicly available WorldClim database. We are using this information to create models to predict how vulnerable different salmon populations are to future climate changes (see O3).
O2: Characterising the genomic architecture of run timing throughout the species’ range
Our datasets on the times of returning adult salmon migration span decades, but we did not have genetic samples for most of these salmon. To overcome this problem, we used measures that characterised the return time of whole populations and then related these to population-level genetic differences. We did this for 11 Atlantic salmon populations in North America, using a tool (a ‘SNP array’) that examines over 60,000 genetic markers across the whole genome.
We find that the timing of salmon returns is related to many different genes. Some of these genes are already known to be linked to adult return migration timing in European Atlantic salmon, indicating that there are shared genetic factors affecting run timing across the species’ distribution. Additionally, our study suggests that interactions between European and North American salmon populations around the end of the last Ice Age may have played a role in shaping genetic traits related to migration.
We followed up this study by relating the return time of individual adult salmon to their genetic makeup. We used a form of whole genome sequencing known as lc-WGS which enabled us to examine genetic variation in more detail than the SNP array. We examined salmon from 7 North American populations.
Using this more detailed approach, we found a new region of the genome that was closely linked to salmon migration timing, near a gene also linked to the timing of long-distance migrations in birds.
We are also examining how genetics influences the timing of a young salmon’s ‘smolt migration’ from the river out to the ocean. Via our collaborators, we have obtained samples from Atlantic salmon smolts across 7 countries and 10 rivers, along with the dates at which each of these smolts left the river, and we are genotyping >1,500 of these samples with a SNP array.
O3: combining the outcomes of O1 and O2 in a modelling framework to predict the migration-mediated vulnerability of Atlantic salmon populations to future environmental change
Once we identify the genetic variation that is associated with the timing of a salmons’ migration, and understand how this timing is linked to the salmons’ environment, we use machine-learning models to predict how climate change may threaten a population by causing its timing and environment to become mis-matched. We use these models to forecast how much a populations’ genetic makeup might need to change in response to future climate conditions. We call the difference between the current and required future genetic variation "genomic offset", and it is a measure of how vulnerable a population is to climate change.
By applying this approach to North American Atlantic salmon populations, we found that temperature variation throughout the day, and temperature during the wettest part of the year, were the climate variables most strongly correlated with migration timing-linked genetic variation. Genomic offsets indicated that the populations that migrate earliest and populations that are the most northerly are the most vulnerable to climate change.
Before we can study how climate change might affect the migration timing of young salmon, we first need to gather genetic data. This will be our focus for the rest of the project.
This study is the first to uncover the genetic connections to the timing of the adult Atlantic salmon migration in North America. By using advanced genetic techniques, we have pinpointed to key regions of the genome. We find that one of these genes is also linked to migration timing in birds, an unexpected result that improves our understanding of long-distance migration in vertebrates as a whole. As we extend our work to understand genetics behind the timing of juvenile salmon migrations out to sea, we expect to gain similarly important insights.
The new genetic knowledge generated by this project enables us to predict how different salmon populations might react to climate change. We found that salmon populations that migrate early, as well as populations in the northern regions, are most at risk. This information can guide conservation efforts, helping us prioritise resources to protect the most vulnerable populations.
Our findings could significantly impact conservation and fisheries management, benefiting local economies and helping us understand how climate change affects migratory patterns. Overall, our research has broad implications for understanding and addressing the challenges of climate change. By fostering interdisciplinary research and innovative ideas, we aim to develop effective strategies for adapting to a changing environment.
The new genetic knowledge generated by this project enables us to predict how different salmon populations might react to climate change. We found that salmon populations that migrate early, as well as populations in the northern regions, are most at risk. This information can guide conservation efforts, helping us prioritise resources to protect the most vulnerable populations.
Our findings could significantly impact conservation and fisheries management, benefiting local economies and helping us understand how climate change affects migratory patterns. Overall, our research has broad implications for understanding and addressing the challenges of climate change. By fostering interdisciplinary research and innovative ideas, we aim to develop effective strategies for adapting to a changing environment.