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ABSTRACT Ecological differences among species, particularly dispersal capacity and life history strategies, influence population response to environmental changes. Genetic simulations now allow us to directly incorporate this variation into models of past demographic changes. However, the impact of life history strategies in demographic inference has been far less explored relative to that of dispersal capacity. Here, we utilise individual‐based simulations of a non‐Wright‐Fisher population to ask whether differences in life history traits (the average age of first reproduction of individuals, the average adult mortality and the average number of mates per reproductive season) lead to consistent and predictable differences in the summary statistics of genetic diversity commonly used for simulation‐based parameter estimation and demographic inference. Using a Random Forest model, we also estimate three population parameters (variance in reproductive success, generation time and effective population size) from genome‐wide SNP variation for two bird species known to have distinct life history strategies. The results demonstrate that life history variation leads to predictable differences in patterns of genetic diversity: higher values of life history traits, representing extreme polygamy, long adult longevity and later onset of reproduction, are associated with higher variance in reproductive success, longer generation times, smaller effective population sizes and overall lower genetic diversity. Parameter estimates from empirical datasets also agree with the general expectation that polygamic species with later onset of reproduction and long adult longevity exhibit higher variance in reproductive success, longer generation times and smaller effective population sizes. Since the signal of life history differences is observed in the genetic summary statistics, we argue that simulation‐ and model‐based multi‐species demographic inference will gain from the incorporation of life history information.
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Species' attributes predict the relative magnitude of ecological and genetic recovery following mass mortality
Theoretically, species' characteristics should allow estimation of dispersal potential and, in turn, explain levels of population genetic differentiation. However, a mismatch between traits and genetic patterns is often reported for marine species, and interpreted as evidence that life-history traits do not influence dispersal. Here, we couple ecological and genomic methods to test the hypothesis that species with attributes favouring greater dispersal potential—e.g., longer pelagic duration, higher fecundity and larger population size—have greater realized dispersal overall. We used a natural experiment created by a large-scale and multispecies mortality event which created a “clean slate” on which to study recruitment dynamics, thus simplifying a usually complex problem. We surveyed four species of differing dispersal potential to quantify the abundance and distribution of recruits and to genetically assign these recruits to probable parental sources. Species with higher dispersal potential recolonized a broader extent of the impacted range, did so more quickly and recovered more genetic diversity than species with lower dispersal potential. Moreover, populations of taxa with higher dispersal potential exhibited more immigration (71%–92% of recruits) than taxa with lower dispersal potential (17%–44% of recruits). By linking ecological with genomic perspectives, we demonstrate that a suite of interacting life-history and demographic attributes do influence species' realized dispersal and genetic neighbourhoods. To better understand species' resilience and recovery in this time of global change, integrative eco-evolutionary approaches are needed to more rigorously evaluate the effect of dispersal-linked attributes on realized dispersal and population genetic differentiation.
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- PAR ID:
- 10378640
- Date Published:
- Journal Name:
- Molecular Ecology
- ISSN:
- 0962-1083
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1111/mec.16707
