Howe, G. T. | Search bioRxiv

bioRxiv2025

Zone Matcher: A climate-based web application for deployment and assisted migration of forest trees

Populations of forest trees are generally adapted to the climates they inhabit. The farther trees are moved from their local climates, the more long-term growth and survival tend to decrease. Current tree deployment and assisted migration rely on climate distance thresholds (CDTs), which are climatic distances beyond which tree performance is considered unacceptable. Fixed zone systems, which have been used to guide deployment of native or orchard seedlots for more than 50 years, usually consist of contiguous geographic areas (zone units) divided into elevational bands (zones). In contrast, focal zone systems allow seed transfer among fixed zones that have similar climates. By using recent historical climates and future climate projections, focal zones can be used for current tree deployment or assisted migration. We developed a focal-zone system for the Pacific Northwest region of North America. First, we worked with stakeholders to select the base zones for the system. These consisted of geographic zone sets from Washington, Oregon, California, and Idaho/western Montana, and ecological zone sets from the U.S. and British Columbia. Second, by analyzing climate variation across the region, we developed a normalized Euclidean climate distance function consisting of nine climate variables from ClimateNA. Third, we inferred CDTs from analyses of climate variation within the base zones and from provenance tests. Fourth, we compared seed deployment areas using the fixed zone versus focal zone system, with and without assisted migration. Finally, we developed the Zone Matcher web application which implements our focal zone system. Across the region, we identified climate matches among 4,393 partially overlapping zones covering approximately 252 M ha. The unique area covered by these zones was about 167 M ha. Compared to fixed zones, our focal zone system increased the deployment area about 17-to 35-fold for the ecological zones and 70-to 300-fold for the geographic zones. This expands seed deployment options, allows more seedlots to be considered for a planting site, facilitates assisted migration, and simplifies sharing of seedlots among organizations. In addition to climate, seed transfer should also consider factors such as plantation soils, microtopography, and projections of competing vegetation, insects, diseases, and fire.

ecology↗

bioRxiv2024

Population structure limits inferences from genomic prediction and genome-wide association studies in a forest tree

There is overwhelming evidence that forest trees are locally adapted to climate. Thus, genecological models based on population phenotypes have been used to measure local adaptation, assess risks of genetic maladaptation to climate, and guide assisted migration. However, instead of phenotypes, there is increasing interest in using genomic data for gene resource management. We used whole-genome resequencing and a replicated common- garden experiment to understand the genetic architecture of adaptive traits in black cottonwood. We studied the potential of using genome-wide association studies (GWAS) and genomic prediction to detect causal loci, identify climate-adapted phenotypes, and practice assisted migration. We analyzed hierarchical population structure by partitioning phenotypic and genomic (SNP) variation among 840 genotypes collected from 91 stands along 16 rivers. Most phenotypic variation (60-81%) occurred at the population level and was strongly associated with climate. Population phenotypes were predicted well using genomic data (e.g., predictive ability r > 0.9) but almost as well using climate or geography (r > 0.8). In contrast, genomic prediction within populations was poor (r < 0.2). Similarly, we identified many GWAS associations among populations, but most appeared to be spurious based on pooled within-population analyses. Hierarchical partitioning of linkage disequilibrium and haplotype sharing suggested that within-population genomic prediction and GWAS were poor because allele frequencies of causal loci and linked markers differed among populations. Our results highlight the difficulty of using GWAS to identify causal loci when there is population structure, and the limitations of using genomic information alone to guide assisted migration.

genomics↗