Morphological Differences in Pinus strobiformis Across Latitudinal and Elevational Gradients

Abstract

The phenotype of trees is determined by the relationships and interactions among genetic and environmental influences. Understanding the patterns and processes that are responsible for phenotypic variation is facilitated by studying the relationships between phenotype and the environment among many individuals across broad ecological and climatic gradients. We used Pinus strobiformis, which has a wide latitudinal distribution, as a model species to: (a) estimate the relative importance of different environmental factors in predicting these morphological traits and (b) characterize the spatial patterns of standing phenotypic variation of cone and seed traits across the species' range. A large portion of the total variation in morphological characteristics was explained by ecological, climatic and geographical variables (54.7% collectively). The three climate, vegetation and geographical variable groups, each had similar total ability to explain morphological variation (43.4%, 43.8%, 51.5%, respectively), while the topographical variable group had somewhat lower total explanatory power (36.9%). The largest component of explained variance (33.6%) was the four-way interaction of all variable sets, suggesting that there is strong covariation in environmental, climate and geographical variables in their relationship to morphological traits of southwest white pine across its range. The regression results showed that populations in more humid and warmer climates expressed greater cone length and seed size. This may in part facilitate populations of P. strobiformis in warmer and wetter portions of its range growing in dense, shady forest stands, because larger seeds provide greater resources to germinants at the time of germination. Our models provide accurate predictions of morphological traits and important insights regarding the factors that contribute to their expression. Our results indicate that managers should be conservative during reforestation efforts to ensure match between ecotypic variation in seed source populations. However, we also note that given projected large range shift due to climate change, managers will have to balance the match between current ecotypic variation and expected range shift and changes in local adaptive optima under future climate conditions.

Keywords: climate factors; machine learning; morphological traits; multivariate canonical ordination; phenotypic variation; redundancy analysis.