Biology subjects

Lind, B. M.

Publications and source records attributed to Lind, B. M..

7 recordsLinked to original sources

bioRxiv2026

Polygenic and redundant architectures of climate-adaptive traits may complicate genomic predictions of maladaptation

SignificanceUnderstanding the genetic basis of local adaptation is an important step to predict species responses to climate change. Current predictions of maladaptation to climate in trees, however, rely mostly on genotype-environment associations, which overlooks the complexity of genetic architectures underpinning climate-adaptive traits. Based on genotype-phenotype associations, we unveil key aspects of the polygenic architecture of drought tolerance and cold hardiness in two lineages of a widespread conifer. Our results suggest that predictions based solely on climate-associated alleles may mischaracterize maladaptation to future climates due to mismatches between the distribution of climate-associated alleles and alleles contributing to adaptive traits. We also show that cold hardiness and drought tolerance evolved largely independently in the two lineages, and with little pleiotropy between traits. Understanding the genetic architecture of climate-adaptive traits can help predict responses to climate change and inform management strategies that mitigate the effects of future climates. By characterizing the genetic basis of adaptation to spatially varying climate conditions, models can predict response to temporal change in climate by extrapolating from spatial patterns of variation. However, such methods are predicated upon a simple genetic basis, with a linear relationship between allele frequency, phenotype, and environment. Here, we explore the nature of the mapping between genotype, phenotype, and climate adaptation in Douglas-fir, an important North American conifer. Using two large common garden experiments combined with exome sequencing and case-control genome-wide association tests, we found that drought tolerance and cold hardiness in two Douglas-fir varieties (Pseudotsuga menziesii var. menziesii and var. glauca) are highly polygenic with little pleiotropy between the two traits. The two varieties showed little repeated evolution in the genetic basis of drought tolerance and none for cold hardiness. Allelic clines were observed along geographic and climatic gradients across 74 natural populations, but their direction of effect was often inconsistent with phenotypic clines. Our findings reveal that the genomic variation underlying climate-adaptive traits is remarkably complex, with redundancy likely playing a key role in the evolution of the divergent polygenetic architectures of abiotic stress tolerance. This suggests that predicting maladaptation to climate based solely on climate-associated alleles may misrepresent future adaptive responses. Genomic predictions and management practices should focus on individual lineages and consider the particularities of the genetic architecture of climate-adaptive traits.

evolutionary biology↗

bioRxiv2024

The limits of predicting maladaptation to future environments with genomic data

Anthropogenically driven changes in land use and climate patterns pose unprecedented challenges to species persistence. To understand the extent of these impacts, genomic offset methods have been used to forecast maladaptation of natural populations to future environmental change. However, while their use has become increasingly common, little is known regarding their predictive performance across a wide array of realistic and challenging scenarios. Here, we evaluate four offset methods (Gradient Forests, the Risk-Of-Non-Adaptedness, redundancy analysis, and LFMM2) using an extensive set of simulated datasets that vary demography, adaptive architecture, and the number and spatial patterns of adaptive environments. For each dataset, we train models using either all, adaptive, or neutral marker sets and evaluate performance using in silico common gardens by correlating known fitness with projected offset. Using over 4,850,000 of such evaluations, we find that 1) method performance is largely due to the degree of local adaptation across the metapopulation (LA{Delta}SA), 2) adaptive marker sets provide minimal performance advantages, 3) within-landscape performance is variable across gardens and declines when offset models are trained using additional non-adaptive environments, and 4) despite (1), performance declines more rapidly in novel climates for metapopulations with higher LA{Delta}SA than lower LA{Delta}SA. We discuss the implications of these results for management, assisted gene flow, and assisted migration.

genomics↗

bioRxiv2023

How useful is genomic data for predicting maladaptation to future climate?

Methods using genomic information to forecast potential population maladaptation to climate change are becoming increasingly common, yet the lack of model validation poses serious hurdles toward their incorporation into management and policy. Here, we compare the validation of maladaptation estimates derived from two methods - Gradient Forests (GFoffset) and the Risk Of Non-Adaptedness (RONA) - using exome capture pool-seq data from 35 to 39 populations across three conifer taxa: two Douglas-fir varieties and jack pine. We evaluate sensitivity of these algorithms to the source of input loci (markers selected from genotype-environment associations [GEA] or those selected at random). We validate these methods against two-year and 52-year growth and mortality measured in independent transplant experiments. Overall, we find that both methods often better predict transplant performance than climatic or geographic distances. We also find that GFoffset and RONA models are surprisingly not improved using GEA candidates. Even with promising validation results, variation in model projections to future climates makes it difficult to identify the most maladapted populations using either method. Our work advances understanding of the sensitivity and applicability of these approaches, and we discuss recommendations for their future use.

evolutionary biology↗

bioRxiv2020

Haploid, diploid, and pooled exome capture recapitulate features of biology and paralogy in two non-model tree species

Despite their suitability for studying evolution, many conifer species have large and repetitive giga-genomes (16-31Gbp) that create hurdles to producing high coverage SNP datasets that captures diversity from across the entirety of the genome. Due in part to multiple ancient whole genome duplication events, gene family expansion and subsequent evolution within Pinaceae, false diversity from the misalignment of paralog copies create further challenges in accurately and reproducibly inferring evolutionary history from sequence data. Here, we leverage the cost-saving benefits of pool-seq and exome-capture to discover SNPs in two conifer species, Douglas-fir (Pseudotsuga menziesii var. menziesii (Mirb.) Franco, Pinaceae) and jack pine (Pinus banksiana Lamb., Pinaceae). We show, using minimal baseline filtering, that allele frequencies estimated from pooled individuals show a strong positive correlation with those estimated by sequencing the same population as individuals (r > 0.948), on par with such comparisons made in model organisms. Further, we highlight the use of haploid megagametophyte tissue in identifying sites that are likely due to misaligned paralogs. Together with additional minor filtering, we show that it is possible to remove many of the loci with large frequency estimate discrepancies between individual and pooled sequencing approaches, improving the correlation further (r > 0.973). Our work addresses bioinformatic challenges in non-model organisms with large and complex genomes, highlights the use of megagametophyte tissue for the identification of paralog sites when sequencing large numbers of populations, and suggests the combination of pool-seq and exome capture to be robust for further evolutionary hypothesis testing in these systems.

genomics↗

bioRxiv2019

Evaluating genomic data for management of local adaptation in a changing climate: A lodgepole pine case study

The need for tools to cost-effectively identify adaptive variation within ecologically and economically important plant species is mounting as the detrimental effects of climate change become increasingly apparent. For crop and wild populations alike, mismatches between adaptive variation and climatic optima will reduce health, growth, survival, reproduction, and continued establishment. The ease with which land managers can quantify the relative importance of different climate factors or the spatial scale of local adaptation to climate will have direct implications for the potential of mitigating or resolving such risks. Using seed collected from 281 provenances of lodgepole pine (Pinus contorta) from across western Canada, we compare genomic data to phenotypic and climatic data to assess their effectiveness in characterizing the climatic drivers and spatial scale of local adaptation in this species. We find that genomic and climate data are nearly equivalent for describing local adaptation in seedling traits. We also find strong agreement between the climate variables associated with genomic variation and with 20-year heights from a long-term provenance trial, suggesting that genomic data may be a viable option for identifying climatic drivers of local adaptation where phenotypic data are unavailable. Genetic clines associated with cold injury occur at broad spatial scales, suggesting that standing variation of adaptive alleles for this and similar species does not require management at scales finer than are indicated by phenotypic data. This study demonstrates that genomic data are most useful when paired with phenotypic data, but can also fill some of the traditional roles of phenotypic data in management of species for which phenotypic trials are not feasible.

evolutionary biology↗

bioRxiv2018

Effect of fire and thinning on fine-scale genetic structure and gene flow in fire-suppressed populations of sugar pine (Pinus lambertiana Douglas)

Historically, frequent, low-severity fires in dry western North American forests were a major driver of ecological patterns and processes, creating resilient ecosystems dominated by widely-spaced pine species. However, a century of fire-suppression has caused overcrowding, altering forest composition to shade-tolerant species, while increasing competition and leaving trees stressed and susceptible to pathogens, insects, and high-severity fire. Exacerbating the issue, fire incidence is expected to increase with changing climate, while fire season has been observed to begin earlier and last longer than historic trends. Forest thinning and prescribed fire have been identified as important management tools to mitigate these risks. Yet little is known of how thinning, fire, or their interaction affect contemporary evolutionary processes of constituent pine species that influence fitness and play an important role in the opportunity for selection and population persistence. We assessed the impact of widely used fuel reduction treatments and prescribed fire on fine-scale gene flow on an ecologically important and historically dominant shade-intolerant pine species of the Sierra Nevada, Pinus lambertiana Dougl. Treatment prescription (no-thin-no-fire, thin-no-fire, and fire-and-thin) was found to differentially affect both fine-scale spatial and genetic structure as well as effective gene flow in this species. Specifically, the thin-no-fire prescription increases genetic structure (spatial autocorrelation of relatives) between adults and seedlings, while seed and pollen dispersal increase and decrease, respectively, as a function of increasing disturbance intensity. While these results may be specific to the stands at our study site, they indicate how assumptions relating to genetic effects based on spatial structure can be misleading. It is likely that these disequilibrated systems will continue to evolve on unknown evolutionary trajectories. The long-term impacts of management practices on reduced fitness from inbreeding depression should be continually monitored to ensure resilience to increasingly frequent and severe fire, drought, and pest stresses.

ecology↗

bioRxiv2017

The genomics of local adaptation in trees: Are we out of the woods yet?

There is substantial interest in uncovering the genetic basis of the traits underlying adaptive responses in tree species, as this information will ultimately aid conservation and industrial endeavors across populations, generations, and environments. Fundamentally, the characterization of such genetic bases is within the context of a genetic architecture, which describes the mutlidimensional relationship between genotype and phenotype through the identification of causative variants, their relative location within a genome, expression, pleiotropic effect, environmental influence, and degree of dominance, epistasis, and additivity. Here, we review theory related to polygenic local adaptation and contextualize these expectations with methods often used to uncover the genetic basis of traits important to tree conservation and industry. A broad literature survey suggests that most tree traits generally exhibit considerable heritability, that underlying quantitative genetic variation (QST) is structured more so across populations than neutral expectations (FST) in 69% of comparisons across the literature, and that single-locus associations often exhibit small estimated per-locus effects. Together, these results suggest differential selection across populations often acts on tree phenotypes underlain by polygenic architectures consisting of numerous small to moderate effect loci. Using this synthesis, we highlight the limits of using solely single-locus approaches to describe underlying genetic architectures and close by addressing hurdles and promising alternatives towards such goals, remark upon the current state of tree genomics, and identify future directions for this field. Importantly, we argue, the success of future endeavors should not be predicated on the shortcomings of past studies and will instead be dependent upon the application of theory to empiricism, standardized reporting, centralized open-access databases, and continual input and review of the communitys research.

evolutionary biology↗