Yb3+/Er3+ codoped Ba5Gd8Zn4O21 up-conversion (UC) phosphors with tunable emission were synthesized using a facile sol–gel method. UC spectra are composed of green emission from 2H11/2/4S3/2 → 4I15/2 transitions and red emission from 4F9/2 → 4I15/2 transition of Er3+ ion with the excitation of 980 nm laser diodes. Modulation of emitting color from green to red could be achieved by adjusting dopant concentrations or pulse width of 980 nm laser. The mechanism of the former strategy was figured out through analyzing visible and near-infrared (NIR) down-conversion emission spectra together with the corresponding green level (4S3/2) lifetimes under excitation of 490 nm light, and the latter method was explained by the non-steady-state up-converison process. Temperature detection range was expanded to low temperature region by utilizing red-emitting stark levels of Er3+ ion as thermally coupled levels. Thermal sensing performances based on green-emitting levels (2H11/2/4S3/2) and red emitting stark levels (4F9/2(1)/ 4F9/2(2)) of Er3+ ion were estimated and the maximum sensitivity are 0.0032 K–1 at 490 and 0.0029 K–1 at 200 K in our experimental range, respectively. Moreover, the effects of UC emission color from different dopant concentrations and pulse widths of lasers on sensor sensitivity were also investigated in detail. Results imply that the present phosphor Ba5Gd8Zn4O21:Er3+/Yb3+ exhibits high and stable sensitivity in a wide temperature detection scope, which makes it an excellent candidate for an optical thermometer.

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Community assembly processes is the primary focus of community ecology. Using phylogenetic-based and functional trait-based methods jointly to explore these processes along environmental gradients are useful ways to explain the change of assembly mechanisms under changing world. Our study combined these methods to test assembly processes in wide range gradients of elevation and other habitat environmental factors. We collected our data at 40 plots in Taibai Mountain, China, with more than 2,300 m altitude difference in study area and then measured traits and environmental factors. Variance partitioning was used to distinguish the main environment factors leading to phylogeny and traits change among 40 plots. Principal component analysis (PCA) was applied to colligate other environment factors. Community assembly patterns along environmental gradients based on phylogenetic and functional methods were studied for exploring assembly mechanisms. Phylogenetic signal was calculated for each community along environmental gradients in order to detect the variation of trait performance on phylogeny. Elevation showed a better explanatory power than other environment factors for phylogenetic and most traits' variance.Phylogenetic and several functional structure clustered at high elevation while some conserved traits overdispersed. Convergent tendency which might be caused by filtering or competition along elevation was detected based on functional traits. Leaf dry matter content (LDMC) and leaf nitrogen content along PCA 1 axis showed conflicting patterns comparing to patterns showed on elevation. LDMC exhibited the strongest phylogenetic signal. Only the phylogenetic signal of maximum plant height showed explicable change along environmental gradients. Synthesis. Elevation is the best environment factors for predicting phylogeny and traits change. Plant's phylogenetic and some functional structures show environmental filtering in alpine region while it shows different assembly processes in middle-and low-altitude region by different trait/phylogeny. The results highlight deterministic processes dominate community assembly in large-scale environmental gradients. Performance of phylogeny and traits along gradients may be independent with each other. The novel method for calculating functional

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