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Stomatal density, stomatal length and carbon isotope composition can all provide insights into environmental controls on photosynthesis and transpiration. Stomatal measurements can be time-consuming; it is therefore wise to consider efficient sampling schemes. Knowing the variance partitioning at different measurement levels (i.e., among stands, plots, trees, leaves and within leaves) can aid in making informed decisions around where to focus sampling effort. In this study, we explored the effects of nitrogen (N), phosphorus (P) and calcium silicate (CaSiO3) addition on stomatal density, length and carbon isotope composition (δ13C) of sugar maple (Acer saccharum Marsh.) and yellow birch (Betula alleghaniensis Britton). We observed a positive but small (8%) increase in stomatal density with P addition and an increase in δ13C with N and CaSiO3 addition in sugar maple, but we did not observe effects of nutrient addition on these characteristics in yellow birch. Variability was highest within leaves and among trees for stomatal density and highest among stomata for stomatal length. To reduce variability and increase chances of detecting treatment differences in stomatal density and length, future protocols should consider pretreatment and repeated measurements of trees over time or measure more trees per plot, increase the number of leaf impressions or standardize their locations, measure more stomata per image and ensure consistent light availability.

 

Stomatal density and length were measured on leaves of sugar maple (Acer sacharrum Marsh.) and yellow birch (Betula alleghaniensis Britton.) trees in New Hampshire at the Bartlett Experimental Forest, Hubbard Brook Experimental Forest, and Jeffers Brook as part of the Multiple Elementation Limitation in Northern Hardwood Ecosystems (MELNHE) study. Leaves were collected in late July and early August in 2021 and 2022 from the tops of dominant and codominant trees using a shotgun. These measurements were made on 3 leaves from each tree. These data correspond with other foliar trait data collected from the same trees in 2021 and 2022. That EDI package is as follows: Hong, S.D., K.E. Gonzales, C.R. See, and R.D. Yanai. 2021. MELNHE: Foliar Chemistry 2008-2016 in Bartlett, Hubbard Brook, and Jeffers Brook (12 stands) ver 1. Environmental Data Initiative. https://doi.org/10.6073/pasta/b23deb8e1ccf1c1413382bf911c6be19 This data package contains the stomatal density and length derived from the raw images in a separate EDI data package: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-hbr&identifier=321 These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station. 

Stomatal density and length were measured on leaves of sugar maple (Acer sacharrum Marsh.) and yellow birch (Betula alleghaniensis Britton.) trees in New Hampshire at the Bartlett Experimental Forest, Hubbard Brook Experimental Forest, and Jeffers Brook as part of the Multiple Elementation Limitation in Northern Hardwood Ecosystems (MELNHE) study. Leaves were collected in late July and early August in 2021 and 2022 from the tops of dominant and codominant trees using a shotgun. These measurements were made on 3 leaves from each tree. These data correspond with other foliar trait data collected from the same trees in 2021 and 2022. That EDI package is as follows: Hong, S.D., K.E. Gonzales, C.R. See, and R.D. Yanai. 2021. MELNHE: Foliar Chemistry 2008-2016 in Bartlett, Hubbard Brook, and Jeffers Brook (12 stands) ver 1. Environmental Data Initiative. https://doi.org/10.6073/pasta/b23deb8e1ccf1c1413382bf911c6be19 This data package contains the raw images underlying the data reported in a separate data package on stomatal density and length: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-hbr&identifier=372 These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station. 

Trait-based analyses provide powerful tools for developing a generalizable, physiologically grounded understanding of how forest communities are responding to ongoing environmental changes. Key challenges lie in (1) selecting traits that best characterize the ecological performance of species in the community and (2) determining the degree and importance of intraspecific variability in those traits. Recent studies suggest that globally evident trait correlations (trait dimensions), such as the leaf economic spectrum, may be weak or absent at local scales. Moreover, trait-based analyses that utilize a mean value to represent a species may be misleading. Mean trait values are particularly problematic if species trait value rankings change along environmental gradients, resulting in species trait crossover. To assess how plant traits (1) covary at local spatial scales, (2) vary across the dominant environmental gradients, and (3) can be partitioned within and across taxa, we collected data on 9 traits for 13 tree species spanning the montane temperate—boreal forest ecotones of New York and northern New England. The primary dimension of the trait ordination was the leaf economic spectrum, with trait variability among species largely driven by differences between deciduous angiosperms and evergreen gymnosperms. A second dimension was related to variability in nitrogen to phosphorous levels and stem specific density. Levels of intraspecific trait variability differed considerably among traits, and was related to variation in light, climate, and tree developmental stage. However, trait rankings across species were generally conserved across these gradients and there was little evidence of species crossover. The persistence of the leaf economics spectrum in both temperate and high-elevation conifer forests suggests that ecological strategies of tree species are associated with trade-offs between resource acquisition and tolerance, and may be quantified with relatively few traits. Furthermore, the assumption that species may be represented with a single trait value may be warranted for some trait-based analyses provided traits were measured under similar light levels and climate conditions.