FIGURE 1 Watershed of Yuanjiang-Red River and transects A-F in the dry-hot valley.FIGURE 2 Transects designing along altitude and latitude.
2.2. Meteorological data collecting
Meteorological records, primarily annual temperature and precipitation, were collected from long-time meteorology stations in the Yuanjiang dry-hot valley at 312 m to 1758 m a.s.l . (Supplementary data: Table A). Temperature and precipitation are considered the main environmental factors correlated to fern and fern allies growth and distribution in the dry-hot valley. Linear regression is conducted based on twenty stations.
2.3. Statistical analyses
Alpha and beta biodiversity (α - and β -index) are both employed. The α -index, associating to species, is commonly represented by Shannon-Wiener index (H ), Evenness (J ), and Dominance. Theβ -index, which is based on not only species occurrence but also abundance analytically, and associated to living habitat, is represented by Bray-Curtis (Magurran, 2004; Chao & Chiu, 2016; Ricotta & Podani, 2017 ). When comparing turnover and nestednesscomponents (Baselga, 2017), it is more approachable to useBray–Curtis similarity index for analysis of abundance and incidence, following the strategy of Diserud and Ødegaard (Diserud & Ødegaard, 2007).
Additionally, there are approaches to assess the difference in plots and transects using the program EstimateS Version 9.1 (Colwell, 2019).Species_estimated , Singletons , Uniques ,ACE , ICE , and Chao2 are prevalently accepted in diverse studies, such as savanna ants of Australia (Andersen et al ., 2015), the elevational richness of Colorado Mountain (Szewczyk & McCain, 2019) and many different areas (Rajakaruna et al ., 2016; Chao et al ., 2017; Girardello, et al ., 2019), with details as follows: 1) Species_estimated indicates the expected number of species in t pooled plots. It is not a real but a calculated index. Increase of species approaches zero early or late indicating different habitats; 2) Singletons indicates the number of species with only one individual in t pooled plots. It is associated with the species rarity; 3) Uniques indicates the number of species that occurs in only one plot in t pooled plots regardless of the population size; 4) ACE is an abundance coverage-based estimator of species richness; 5) ICE is an incidence coverage-based estimator of species richness. 6) Chao2 is a species richness estimator (Chao, 1984, 1987; Rajakaruna et al ., 2016). These six indices are applied in this study, and their model inferences are tested on Chi-square statistic (χ2 ) at 0.05 significance level. Residuals are evaluated for normality using the Shapiro-Wilk test (normality was assumed when P ≥ 0.5 ) (Shapiro & Wilk, 1965). With Tukey’s method, multiple comparisons are used to test the disparity between habitats at 0.05 significant levels.
Therefore, to cope with the issues mentioned above, two kinds of species matrices are required. The first matrix is individual-based, while the second matrix is occurrence-based ignoring the number of individuals. All matrices are computed 999 times in EstimateS (version 9.10) and then the selected index is rearranged and analysed with packages vegan(Oksanen et al ., 2011), labdsv (Roberts, 2018) andlme4 (Pinheiro, 2011) in R (v.2.15.2) (R Development Core Team, 2012). Indicator value function (IndVal ) in packageindicspecies (Cáceres & Legendre, 2009) is also employed.IndVal is a random variable that takes “1” to represent a happened event and “0” for nothing. After that, multilevel pattern analysis is conducted at significance level P = 0.05 in the pooled species.
3. Results
The analytical results are from 2016 fern individuals belonging to 17 genera and 13 families (Supplementary data: Table B).
3.1. Spatial patterns of thermal and moisture in Yuanjiang dry-hot valley
Temperature and precipitation are the two main climatic factors being considered in this study. According to a 57 year (1962-2018) data profile, the temperature decreases when altitude increases (Figure 3). The maximum annual temperature was 24.5 ℃ at 401 m a.s.l . while the minimum was 16.3 ℃ at 1,635 m a.s.l . (Supplementary data: Table A). On the contrary, precipitation rose when altitude increased (Figure 3). The maximum annual precipitation was 2,159 mm at 1,263 ma.s.l . while the minimum was 738 mm at 401 m a.s.l . (Supplementary data: Table A).
Rainfall in Yuanjiang dry-hot valley depends on two factors. The first is the process of wet air sinking, pressurizing and heating-up when warm and humid airflow moves down to the valley, which weakens the uplifting of water vapor in the precipitation system and reduces water vapor condensation (Zhang et al ., 2009). Secondly, typical valley wind is generated by consistent sunshine, high temperature and rapid evaporation. Water vapor is taken to a relatively high altitude by the up-valley wind and then freezes and falls to the ground in the forms of droplets (Giovannini et al ., 2017). Therefore, the valley’s weather condition is cool and humid at the relatively high altitude, but it is reversed in the lower place. Fifty year records in the Jinsha River valley display a similar statistical profile (Zhang et al ., 2009). Dry air and high temperature prevail as typical environmental features in the dry-hot valley.
FIGURE 3 Correlation of temperature and precipitation in altitudal gradients.
3.2. Shannon-Wiener index (H) and diversity variation along mountain slope and riverine separately
Shannon-Wiener index among plots in each transect are displayed separately in Figure 4 with a general linear regression. The changes along plots can be noticed, and the habitat heterogeneity can also be identified based on these dispersing values.