4/8/2023 0 Comments Ecotone vs continuum![]() Conclusion: Climate signals in the pattern of tree growth responses and recruitment are site-specific. Linear relationships were observed between beta diversities (ßSD and ßA) and disturbance gradient. Furthermore, in differently disturbed Quercus semecarpifolia forest at Phulchoki and Ghorepani, alpha and gamma diversity show a unimodal response to disturbance gradient. The low species turnover and minor differences in alpha diversity could be attributed to human influence. Detrended Correspondence Analysis revealed low species turnover and a continuum in species composition across the forest border ecotone. ![]() In a set of vertical transects sampled across the forest line, a positive correlation between canopy and temperature gradients was found. Number of trees and saplings in the dry area is higher compared to the mesic area suggesting that tree establishment rate is higher in the dry area giving a higher potential for treeline advance. Climate and land-use are both important factors for treeline structuring processes. The current treelines in both areas have remained stationary over the decades. Reduced growth at the treeline was related to the high winter snow fall and delayed onset of growing season. At the mesic locality, growth at lower altitudes (forest line) showed signals of drought limitation, whereas at higher altitudes, decreased growth was associated with an early onset of the monsoon. Main results: At the dry locality, tree growth at the forest line responded positively to warm summers and after cold winters: possibly a response to early onset of growing season. Gamma diversity was estimated as total species number present in the landscape. Beta diversity was based on gradient length estimated by Detrended Correspondense Analysis (DCA). Alpha diversity was estimated as average species richness per plot. Vascular plant species richness and environmental variables were recorded in each plot (10 m × 10 m). Alpha, beta and gamma diversity were studied across a forest border from a forested to open landscape in a subalpine-alpine region (paper III) and an anthropogenic disturbance gradient (paper IV). Seedlings, saplings and trees were sampled in each transect for age analysis. Transects were laid out between the forest line and tree species line, crossing the treeline. Trees from various ecological elevations (forest line, treeline and krummholz line) were cored, annual growth was measured and site chronology was developed for analysing climate-growth relationships. Methods: I used dendroclimatological techniques to examine spatial and temporal changes in tree growth responses (paper I) and recruitment patterns (paper II) to climatic variability across a dry Pinus wallichiana and a mesic Abies spectabilis treeline ecotone. Use this information, in addition to the purpose of your analysis to decide what is best for your situation.Aims: To describe and evaluate patterns of vegetation response to ongoing environmental changes across climate-limited (alpine treeline ecotone) and humanmodified (temperate Himalayan oak forests) ecosystems in Nepal, central Himalaya. When you treat a predictor as a categorical variable, a distinct response value is fit to each level of the variable without regard to the order of the predictor levels. Treating a predictor as a continuous variable implies that a simple linear or polynomial function can adequately describe the relationship between the response and the predictor. If the discrete variable has many levels, then it may be best to treat it as a continuous variable. If you have a discrete variable and you want to include it in a Regression or ANOVA model, you can decide whether to treat it as a continuous predictor (covariate) or categorical predictor (factor). For example, the length of a part or the date and time a payment is received. A continuous variable can be numeric or date/time. Continuous variable Continuous variables are numeric variables that have an infinite number of values between any two values. For example, the number of customer complaints or the number of flaws or defects. Discrete variable Discrete variables are numeric variables that have a countable number of values between any two values. For example, categorical predictors include gender, material type, and payment method. Categorical data might not have a logical order. Categorical variable Categorical variables contain a finite number of categories or distinct groups. Quantitative variables can be classified as discrete or continuous.
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