Exploring Climate Niches of Ponderosa Pine (Pinus ponderosa Douglas ex Lawson) Haplotypes in the Western United States: Implications for Evolutionary History and Conservation

Abstract

Ponderosa pine (Pinus ponderosa Douglas ex Lawson) occupies montane environments throughout western North America, where it is both an ecologically and economically important tree species. A recent study using mitochondrial DNA analysis demonstrated substantial genetic variation among ponderosa pine populations in the western U.S., identifying 10 haplotypes with unique evolutionary lineages that generally correspond spatially with distributions of the Pacific (P. p. var. ponderosa) and Rocky Mountain (P. p. var. scopulorum) varieties. To elucidate the role of climate in shaping the phylogeographic history of ponderosa pine, we used nonparametric multiplicative regression to develop predictive climate niche models for two varieties and 10 haplotypes and to hindcast potential distribution of the varieties during the last glacial maximum (LGM), ~22,000 yr BP. Our climate niche models performed well for the varieties, but haplotype models were constrained in some cases by small datasets and unmeasured microclimate influences. The models suggest strong relationships between genetic lineages and climate. Particularly evident was the role of seasonal precipitation balance in most models, with winter- and summer-dominated precipitation regimes strongly associated with P. p. vars. ponderosa and scopulorum, respectively. Indeed, where present-day climate niches overlap between the varieties, introgression of two haplotypes also occurs along a steep clinal divide in western Montana. Reconstructed climate niches for the LGM suggest potentially suitable climate existed for the Pacific variety in the California Floristic province, the Great Basin, and Arizona highlands, while suitable climate for the Rocky Mountain variety may have existed across the southwestern interior highlands. These findings underscore potentially unique phylogeographic origins of modern ponderosa pine evolutionary lineages, including potential adaptations to Pleistocene climates associated with discrete temporary glacial refugia. Our predictive climate niche models may inform strategies for further genetic research (e.g., sampling design) and conservation that promotes haplotype compatibility with projected changes in future climate.

Characterizing Spatial Neighborhoods of Refugia Following Large Fires in Northern New Mexico USA

“…We concur with recommended conservation measures considering the potential impacts of climate change that include maintenance of the greatest possible number of local refugia as habitat networks [25]. In this way, the potential contribution of refugia to regional diversity and landscape resilience under different future scenarios could be maximized [80,90]. …”

Wildfires and climate change push low-elevation forests across a critical climate threshold for tree regeneration

“…This mechanism of mortality is especially important in disturbed areas where there is no remaining canopy cover to ameliorate high maximum temperatures near the soil surface (41,42). Intraspecific differences in climatic tolerances are also likely to exist (43)(44)(45). For example, across the study area, ponderosa pine recruitment was more strongly related to site-specific VPD anomalies than raw VPD values, suggesting that ecotypic variation or local adaptation among populations in part influences how annual climate impacts germination and survival (36).…”

Incorporating microclimate into species distribution models

“…For example, strong correlations have been observed between changes in the frequency of plant species over time and the in-situ temperature of their preferred microhabitat on mountain summits in Switzerland (Kulonen et al 2018). Sometimes, topoclimatic variables derived directly from DEMs (like elevation, solar radiation or cold-air pooling) are also used independently in SDMs, thus using an indirect topoclimatic derivative instead of actually downscaled climate to improve the spatial resolution of SDMs (Roslin et al 2009, Maclean et al 2015, Shinneman et al 2016, Patsiou et al 2017.…”