The capacity to respond to environmental challenges ultimately relies on phenotypic variation which manifests from complex interactions of genetic and non-genetic mechanisms through development. While we know something about genetic variation and structure of many species of conservation importance, we know very little about the non-genetic contributions to variation. Rhizophora mangle is a foundation species that occurs in coastal estuarine habitats throughout the neotropics where it provides critical ecosystem functions, and is potentially threatened by climate change. Several studies have documented landscape level patterns of genetic variation in this species, but we know virtually nothing about the inheritance of non-genetic variation. To assess one type of non-genetic variation, we examined the patterns of DNA sequence and DNA methylation in maternal plants and offspring from natural populations of R. mangle from the Gulf Coast of Florida. We used a reduced representation bisulfite sequencing approach (epi-genotyping by sequencing or epiGBS) to address the following questions: a) What are the levels of genetic and epigenetic diversity in natural populations of R. mangle? b) How are genetic and epigenetic variation structured within and among populations? c) How faithfully is epigenetic variation inherited? We found low genetic diversity but high epigenetic diversity from natural populations of maternal plants in the field and that a large portion (up to ~25%) of epigenetic differences among offspring grown in common garden was explained by maternal family. Therefore, epigenetic variation could be an important source of response to challenging environments in the genetically depauperate populations of this foundation species.
The capacity to respond to environmental challenges ultimately relies on phenotypic variation which manifests from complex interactions of genetic and nongenetic mechanisms through development. While we know something about genetic variation and structure of many species of conservation importance, we know very little about the nongenetic contributions to variation. Rhizophora mangle is a foundation species that occurs in coastal estuarine habitats throughout the neotropics where it provides critical ecosystem functions and is potentially threatened by anthropogenic environmental changes. Several studies have documented landscape‐level patterns of genetic variation in this species, but we know virtually nothing about the inheritance of nongenetic variation. To assess one type of nongenetic variation, we examined the patterns of DNA sequence and DNA methylation in maternal plants and offspring from natural populations of R. mangle from the Gulf Coast of Florida. We used a reduced representation bisulfite sequencing approach (epi‐genotyping by sequencing; epiGBS) to address the following questions: (a) What are the levels of genetic and epigenetic diversity in natural populations of R. mangle? (b) How are genetic and epigenetic variation structured within and among populations? (c) How faithfully is epigenetic variation inherited? We found low genetic diversity but high epigenetic diversity from natural populations of maternal plants in the field. In addition, a large portion (up to ~25%) of epigenetic differences among offspring grown in common garden was explained by maternal family. Therefore, epigenetic variation could be an important source of response to challenging environments in the genetically depauperate populations of this foundation species.
Vital for many marine and terrestrial species, and several other environmental services, such as carbon sink areas, the mangrove ecosystem is highly threatened due to the proximity of large urban centers and climate change. The forced fragmentation of this ecosystem affects the genetic diversity distribution among natural populations. Moreover, while restoration efforts have increased, few studies have analyzed how recently-planted areas impact the original mangrove genetic diversity. We analyzed the genetic diversity of two mangroves species (Laguncularia racemosa and Avicennia schaueriana) in three areas in Brazil, using inter-simple sequence repeat (ISSR) markers. Using the local approach, we identified the genetic diversity pool of a restored area compared to nearby areas, including the remnant plants inside the restored area, one well-conserved population at the shore of Guanabara Bay, and one impacted population in Araçá Bay. The results for L. racemosa showed that the introduced population has lost genetic diversity by drift, but remnant plants with high genetic diversity or incoming propagules could help improve overall genetic diversity. Avicennia schaueriana showed similar genetic diversity, indicating an efficient gene flow. The principal component analysis showing different connections between both species indicate differences in gene flow and dispersal efficiencies, highlighting the needed for further studies. Our results emphasize that genetic diversity knowledge and monitoring associated with restoration actions can help avoid bottlenecks and other pitfalls, especially for the mangrove ecosystem.
Current climate change species response models usually not include evolution. We integrated remote sensing with population genomics to improve phenotypic response prediction to drought stress in the key forest tree European beech (Fagus sylvatica L.). We used whole-genome sequencing of pooled DNA from natural stands along an ecological gradient from humid-cold to warm-dry climate. We phenotyped stands for leaf area index (LAI) and moisture stress index (MSI) for the period 2016-2022. We predicted this data with matching meteorological data and a newly developed genomic population prediction score in a Generalised Linear Model. Model selection showed that addition of genomic prediction decisively increased the explanatory power. We then predicted the response of beech to future climate change under evolutionary adaptation scenarios. A moderate climate change scenario would allow persistence of adapted beech forests, but not worst-case scenarios. Our approach can thus guide mitigation measures, such as allowing natural selection or proactive evolutionary management.
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