Despite an increased focus on multiscale relationships and interdisciplinary integration, few macroecological studies consider the contribution of genetic-based processes to landscape-scale patterns. We tested the hypothesis that tree genetics, climate, and geography jointly drive continental-scale patterns of community structure, using genome-wide SNP data from a broadly distributed foundation tree species (Populus fremontii S. Watson) and two dependent communities (leaf-modifying arthropods and fungal endophytes) spanning southwestern North America. Four key findings emerged: (1) Tree genetic structure was a significant predictor for both communities; however, the strength of influence was both scale- and community-dependent. (2) Tree genetics was the primary driver for endophytes, explaining 17% of variation in continental-scale community structure, whereas (3) climate was the strongest predictor of arthropod structure (24%). (4) Power to detect tree genotype—community phenotype associations changed with scale of genetic organization, increasing from individuals to populations to ecotypes, emphasizing the need to consider nonstationarity (i.e., changes in the effects of factors on ecological processes across scales) when inferring macrosystem properties. Our findings highlight the role of foundation tree species as drivers of macroscale community structure and provide macrosystems ecology with a theoretical framework for linking fine- and intermediate-scale genetic processes to landscape-scale patterns. Management of genetic diversity harbored within foundation species is a critical consideration for conserving and sustaining regional biodiversity.

Methods in Brief (please refer to the associated publication for full details)

Tree Genotyping:  We collected individually geo-referenced leaf material from 453 Populus fremontii trees at 58 sampling locations spanning the southwestern US and northwestern Mexico. DNA was extracted from leaf material, and double digest restriction-associated DNA (ddRAD) libraries were prepared following a modified Peterson et al. (2012) protocol. Quality filtering and variant calling of raw sequencing data used a modified Stacks v1.3 pipeline (Catchen et al. 2013, Andrews 2018), with a minimum read depth of six and presence in at least three individuals required to call a locus. This resulted in 322 genotypes being retained, represented by 8637 loci filtered to one random SNP per locus. Genetic distance matrices (Fst) and the PHYLIP sequence alignment were generated in Stacks. Genomic data for P. fremontii are available at NCBI’s Sequence Read Archive (https://www.ncbi.nlm.nih.gov/sra), BioProject ID PRJNA868761. 

Community Data  

Leaf-modifying Arthropods:  Arthropod surveys were standardized by branch diameter (2–3 branches/tree, ~35 mm total) to account for leaf area, and survey time (15 min/tree). Arthropod species were visually identified to the lowest possible taxonomic level; any unrecognized species were collected, later identified in the lab, and added to the Northern Arizona University Insect Collection.

Twig Fungal Endophytes:  To assess twig fungal endophyte community structure, we collected 10 twigs/tree, including 3-years growth, directionally stratified around each tree’s circumference. The total DNA was extracted from twig samples, and fungal ITS2 rDNA was selectively amplified using fungal-specific primers 5.8SFun and ITS4Fun (Taylor et al. 2016). Amplification and indexing were performed following Alvarado et al. (2018). Taxonomic identities were assigned with BLAST in QIIME against the dynamic UNITE database (Kõljalg et al. 2013). OTU tables were rarefied to the lowest sample depth for the purpose of assessing alpha diversity or normalized with cumulative sum scaling for all other analyses (Paulson et al. 2013).

Environmental Data:  We identified a suite of 20 environmental predictor variables that we hypothesized are related to gene flow and connectivity among P. fremontii and its associated communities (see Table S2 of the associated publication for full details). These include latitude, longitude, average Sping wind direction vectors, hydrological, and bioclimatic variables.