Plant communities are composed of complex phenotypes that not only differ among taxonomic groups and habitats but also change over time within a species. Restoration projects (e.g. translocations and reseeding) can introduce new functional variation in plants, which further diversifies phenotypes and complicates our ability to identify locally adaptive phenotypes for future restoration. Near‐infrared spectroscopy (NIRS) offers one approach to detect the chemical phenotypes that differentiate plant species, populations, and phenological states of individual plants over time. We use sagebrush (
- Award ID(s):
- 1611616
- NSF-PAR ID:
- 10122457
- Date Published:
- Journal Name:
- APA
- ISSN:
- 1412-5242
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Artemisia spp.) as a case study to test the accuracy by which NIRS can classify variation within taxonomy and phenology of a plant that is extensively managed and restored. Our results demonstrated that NIRS can accurately classify species of sagebrush within a study site (75–96%), populations of sagebrush within a subspecies (99%), annual phenology within a population (>99%), and seasonal phenology within individual plants (>97%). Low classification accuracy by NIRS in some sites may reflect heterogeneity associated with natural hybridization, translocation of nonlocal seed sources from past restoration, or complex gene‐by‐environment interactions. Advances in our ability to detect and interpret spectral signals from plants may improve both the selection of seed sources for targeted conservation and the capacity to monitor long‐term changes in vegetation. -
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Abstract Many species show intraspecific variation in their social organization (
IVSO ), which means the composition of their social groups can change between solitary living, pair living, or living in groups. UnderstandingIVSO is important because it demonstrates species resilience to environmental change and can help us to study ultimate and proximate reasons for group living by comparing solitary and group‐living individuals in a single species. It has long been realized that the environment plays a key role in explaining the occurrence ofIVSO .IVSO is expected to have evolved in variable environments and can thus be a key adaptation to environmental change. It has previously been suggested that four different mechanisms relying on the environment exist that can lead toIVSO : environmental disrupters, genetic differentiation, developmental plasticity, and social flexibility. All four mechanisms depend on the environment such that focusing only on environmental factors alone cannot explainIVSO . Importantly, only three represent evolved mechanisms, while environmental disrupters leading to the death of important group members induce nonadaptiveIVSO . Environmental disrupters can be expected to causeIVSO even in species whereIVSO is also an adaptive response. Here, we focus on the questions of whyIVSO occurs and why it evolved. To understandIVSO at the species level, it is important to conduct continuous long‐term studies to differentiate between nonadaptive and adaptiveIVSO . We predict thatIVSO evolves in environments that vary in important ecological variables, such as rainfall, food availability, and population density.IVSO might also depend on life history factors, especially longevity.IVSO is predicted to be more common in species with a short life span and that breed only for one breeding season, being selected to respond optimally to the prevailing environmental situation. Finally, we emphasize the importance of accounting forIVSO when studying social evolution, especially in comparative studies, as not every species can be assigned to one single form of social organization. For such comparative studies, it is important to use data based on the primary literature. -
INTRODUCTION Diverse phenotypes, including large brains relative to body size, group living, and vocal learning ability, have evolved multiple times throughout mammalian history. These shared phenotypes may have arisen repeatedly by means of common mechanisms discernible through genome comparisons. RATIONALE Protein-coding sequence differences have failed to fully explain the evolution of multiple mammalian phenotypes. This suggests that these phenotypes have evolved at least in part through changes in gene expression, meaning that their differences across species may be caused by differences in genome sequence at enhancer regions that control gene expression in specific tissues and cell types. Yet the enhancers involved in phenotype evolution are largely unknown. Sequence conservation–based approaches for identifying such enhancers are limited because enhancer activity can be conserved even when the individual nucleotides within the sequence are poorly conserved. This is due to an overwhelming number of cases where nucleotides turn over at a high rate, but a similar combination of transcription factor binding sites and other sequence features can be maintained across millions of years of evolution, allowing the function of the enhancer to be conserved in a particular cell type or tissue. Experimentally measuring the function of orthologous enhancers across dozens of species is currently infeasible, but new machine learning methods make it possible to make reliable sequence-based predictions of enhancer function across species in specific tissues and cell types. RESULTS To overcome the limits of studying individual nucleotides, we developed the Tissue-Aware Conservation Inference Toolkit (TACIT). Rather than measuring the extent to which individual nucleotides are conserved across a region, TACIT uses machine learning to test whether the function of a given part of the genome is likely to be conserved. More specifically, convolutional neural networks learn the tissue- or cell type–specific regulatory code connecting genome sequence to enhancer activity using candidate enhancers identified from only a few species. This approach allows us to accurately associate differences between species in tissue or cell type–specific enhancer activity with genome sequence differences at enhancer orthologs. We then connect these predictions of enhancer function to phenotypes across hundreds of mammals in a way that accounts for species’ phylogenetic relatedness. We applied TACIT to identify candidate enhancers from motor cortex and parvalbumin neuron open chromatin data that are associated with brain size relative to body size, solitary living, and vocal learning across 222 mammals. Our results include the identification of multiple candidate enhancers associated with brain size relative to body size, several of which are located in linear or three-dimensional proximity to genes whose protein-coding mutations have been implicated in microcephaly or macrocephaly in humans. We also identified candidate enhancers associated with the evolution of solitary living near a gene implicated in separation anxiety and other enhancers associated with the evolution of vocal learning ability. We obtained distinct results for bulk motor cortex and parvalbumin neurons, demonstrating the value in applying TACIT to both bulk tissue and specific minority cell type populations. To facilitate future analyses of our results and applications of TACIT, we released predicted enhancer activity of >400,000 candidate enhancers in each of 222 mammals and their associations with the phenotypes we investigated. CONCLUSION TACIT leverages predicted enhancer activity conservation rather than nucleotide-level conservation to connect genetic sequence differences between species to phenotypes across large numbers of mammals. TACIT can be applied to any phenotype with enhancer activity data available from at least a few species in a relevant tissue or cell type and a whole-genome alignment available across dozens of species with substantial phenotypic variation. Although we developed TACIT for transcriptional enhancers, it could also be applied to genomic regions involved in other components of gene regulation, such as promoters and splicing enhancers and silencers. As the number of sequenced genomes grows, machine learning approaches such as TACIT have the potential to help make sense of how conservation of, or changes in, subtle genome patterns can help explain phenotype evolution. Tissue-Aware Conservation Inference Toolkit (TACIT) associates genetic differences between species with phenotypes. TACIT works by generating open chromatin data from a few species in a tissue related to a phenotype, using the sequences underlying open and closed chromatin regions to train a machine learning model for predicting tissue-specific open chromatin and associating open chromatin predictions across dozens of mammals with the phenotype. [Species silhouettes are from PhyloPic]more » « less
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Abstract Aim Natural selection typically results in the homogenization of reproductive traits, reducing natural variation within populations; thus, highly polymorphic species present unresolved questions regarding the mechanisms that shape and maintain gene flow given a diversity of phenotypes. We used an integrative framework to characterize phenotypic diversity and assess how evolutionary history and population genetics affect the highly polymorphic nature of a California endemic lily.
Location California, United States.
Taxon Butterfly mariposa lily,
Calochortus venustus (Liliaceae).Methods We summarized phenotypic diversity at both metapopulation and subpopulation scales to explore spatial phenotypic distributions. We sampled 174 individuals across the species range representing multiple samples for each population and each phenotype. We used restriction‐site‐associated DNA sequencing (RAD‐Seq) to detect population clusters, gene flow between phenotypes and between populations, infer haplotype networks, and reconstruct ancestral range evolution to infer historical migration and range expansion.
Results Polymorphic floral traits within the species such as petal pigmentation and distal spots are geographically structured, and inferred evolutionary history is consistent with a ring species pattern involving a complex of populations having experienced sequential change in genetic and phenotypic variation from the founding population. Populations remain interconnected yet have differentiated from each other along a bifurcating south‐to‐north range expansion, consequently indicating parallel evolution towards the white morphotype in the northern range. Thus, our phylogeographical analyses reveal morphological convergence with population genetic cohesion irrespective of phenotypic diversity.
Main conclusions Phenotypic variation in the highly polymorphic
Calochortus venustus is not due to genetic differentiation between phenotypes; rather there is genetic cohesion within six geographically defined populations, some of which maintain a high level of within‐population phenotypic diversity. Our results demonstrate that analyses of polymorphic taxa greatly benefit from disentangling phenotype from genotype at various spatial scales. We discuss results in light of ring species concepts and the need to determine the adaptive significance of the patterns we report. -
Abstract Background The teleost fish Fundulus heteroclitus inhabit estuaries heavily polluted with persistent and bioaccumulative chemicals. While embryos of parents from polluted sites are remarkably resistant to toxic sediment and develop normally, embryos of parents from relatively clean estuaries, when treated with polluted sediment extracts, are developmentally delayed, displaying deformities characteristic of pollution-induced embryotoxicity. To gain insight into parental effects on sensitive and resistant phenotypes during late organogenesis, we established sensitive, resistant, and crossed embryo families using five female and five male parents from relatively clean and predominantly PAH-polluted estuaries each, measured heart rates, and quantified individual embryo expression of 179 metabolic genes. Results Pollution-induced embryotoxicity manifested as morphological deformities, significant developmental delays, and altered cardiac physiology was evident among sensitive embryos resulting from crosses between females and males from relatively clean estuaries. Significantly different heart rates among several geographically unrelated populations of sensitive, resistant, and crossed embryo families during late organogenesis and pre-hatching suggest site-specific adaptive cardiac physiology phenotypes relative to pollution exposure. Metabolic gene expression patterns (32 genes, 17.9%, at p < 0.05; 11 genes, 6.1%, at p < 0.01) among the embryo families indicate maternal pollutant deposition in the eggs and parental effects on gene expression and metabolic alterations. Conclusion Heart rate differences among sensitive, resistant, and crossed embryos is a reliable phenotype for further explorations of adaptive mechanisms. While metabolic gene expression patterns among embryo families are suggestive of parental effects on several differentially expressed genes, a definitive adaptive signature and metabolic cost of resistant phenotypes is unclear and shows unexpected sensitive-resistant crossed embryo expression profiles. Our study highlights physiological and metabolic gene expression differences during a critical embryonic stage among pollution sensitive, resistant, and crossed embryo families, which may contribute to underlying resistance mechanisms observed in natural F. heteroclitus populations living in heavily contaminated estuaries.more » « less
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/doi:10.1037/0000011-011
