Climate warming is expected to cause the poleward and upward elevational expansion of temperate plant species, but non‐climatic factors such as soils could constrain this range expansion. However, the extent to which edaphic constraints on range expansion have an abiotic (e.g. soil chemistry) or biotic (e.g. micro‐organisms) origin remains undetermined. We conducted greenhouse experiments to test if the survival and growth of a major North American temperate tree species, Acer saccharum (sugar maple), is independently or jointly constrained by abiotic and biotic properties of field‐collected soils from within and beyond the species' elevational range. Abiotic factors, particularly low base cation concentrations, were major constraints to seedling establishment in boreal forest soils (beyond the range edge), but insufficient arbuscular mycorrhizal fungal inoculum (biotic factor) also strongly reduced seedling performance in these soils. Synthesis. Our results suggest that forecasting future changes in forest composition under climate warming requires consideration of soil properties as well as the mycorrhizal status of tree species.
Soil phosphorus (P) availability in lowland tropical rainforests influences the distribution and growth of tropical tree species. Determining the P-acquisition strategies of tropical tree species could therefore yield insight into patterns of tree b-diversity across edaphic gradients. In particular, the synthesis of root phosphatases is likely to be of significance given that organic P represents a large pool of potentially available P in tropical forest soils. It has also been suggested that a high root phosphatase activity in putative nitrogen (N)-fixing legumes might explain their high abundance in lowland neotropical forests under low P supply. Here, we measured phosphomonoesterase (PME) activity on the first three root orders of cooccurring tropical tree species differing in their N-fixation capacity, growing on soils of contrasting P availability in Panama. Our results show that root PME activity was higher on average in P-poor than in P-rich soils, but that local variation in PME activity among cooccurring species within a site was larger than that explained by differences in soil P across sites. Legumes expressed higher PME activity than nonlegumes, but nodulated legumes (i.e., actively fixing nitrogen) did not differ from legumes without nodules, indicating that PME activity is unrelated to N fixation. Finally, PME activity declined with increasing root order, but the magnitude of the decline varied markedly among species, highlighting the importance of classifying fine roots into functional groups prior to measuring root traits. Our results support the hypothesis that low-P promotes a high root PME activity, although the high local variation in this trait among co-occurring species points toward a high functional diversity in P-acquisition strategies within an individual community.
Summary Plant ecologists use functional traits to describe how plants respond to and influence their environment. Reflectance spectroscopy can provide rapid, non‐destructive estimates of leaf traits, but it remains unclear whether general trait‐spectra models can yield accurate estimates across functional groups and ecosystems. We measured leaf spectra and 22 structural and chemical traits for nearly 2000 samples from 103 species. These samples span a large share of known trait variation and represent several functional groups and ecosystems, mainly in eastern Canada. We used partial least‐squares regression (PLSR) to build empirical models for estimating traits from spectra. Within the dataset, our PLSR models predicted traits such as leaf mass per area (LMA) and leaf dry matter content (LDMC) with high accuracy (R2 > 0.85; %RMSE < 10). Models for most chemical traits, including pigments, carbon fractions, and major nutrients, showed intermediate accuracy (R2 = 0.55–0.85; %RMSE = 12.7–19.1). Micronutrients such as Cu and Fe showed the poorest accuracy. In validation on external datasets, models for traits such as LMA and LDMC performed relatively well, while carbon fractions showed steep declines in accuracy. We provide models that produce fast, reliable estimates of several functional traits from leaf spectra. Our results reinforce the potential uses of spectroscopy in monitoring plant function around the world.
Plant ecologists use functional traits to describe how plants respond to and influence their environment. Reflectance spectroscopy can provide rapid, non-destructive estimates of leaf traits, but it remains unclear whether general trait-spectra models can yield accurate estimates across functional groups and ecosystems. We measured leaf spectra and 22 structural and chemical traits for nearly 2000 samples from 104 species. These samples span a large share of known trait variation and represent several functional groups and ecosystems. We used partial least-squares regression (PLSR) to build empirical models for estimating traits from spectra. Within the dataset, our PLSR models predicted traits like leaf mass per area (LMA) and leaf dry matter content (LDMC) with high accuracy (R2 > 0.85; %RMSE < 10). Models for most chemical traits, including pigments, carbon fractions, and major nutrients, showed intermediate accuracy (R2 = 0.55-0.85; %RMSE = 12.7-19.1). Micronutrients such as Cu and Fe showed the poorest accuracy. In validation on external datasets, models for traits like LMA and LDMC performed relatively well, while carbon fractions showed steep declines in accuracy. We provide models that produce fast, reliable estimates of several widely used functional traits from leaf reflectance spectra. Our results reinforce the potential uses of spectroscopy in monitoring plant function around the world.
In bilaterian organisms, structures can be symmetrical or asymmetrical, the latter being subdivided into three forms: antisymmetry, directional asymmetry, and fluctuating asymmetry (Endler, 1986; Lahti et al., 2009). Fluctuating asymmetry (FA) consists of random deviations from perfect bilateral symmetry on a population of organism (Graham et al., 2010). This variation around the perfect symmetrical distribution represents a measure of development noise or developmental instability (Rott, 2003). As both sides of a bilateral trait develop under the control of the same genome, the developmental phenotypic target of a population should be perfect symmetry (De Coster et al., 2013), but developmental noise results in deviations from perfect bilateral symmetry or an increase in FA
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