Impacts of Environmental Heterogeneity on Moss Diversity and Distribution of Didymodon (Pottiaceae) in Tibet, China

Abstract

Tibet makes up the majority of the Qinghai-Tibet Plateau, often referred to as the roof of the world. Its complex landforms, physiognomy, and climate create a special heterogeneous environment for mosses. Each moss species inhabits its own habitat and ecological niche. This, in combination with its sensitivity to environmental change, makes moss species distribution a useful indicator of vegetation alteration and climate change. This study aimed to characterize the diversity and distribution of Didymodon (Pottiaceae) in Tibet, and model the potential distribution of its species. A total of 221 sample plots, each with a size of 10 × 10 m and located at different altitudes, were investigated across all vegetation types. Of these, the 181 plots in which Didymodon species were found were used to conduct analyses and modeling. Three noteworthy results were obtained. First, a total of 22 species of Didymodon were identified. Among these, Didymodon rigidulus var. subulatus had not previously been recorded in China, and Didymodon constrictus var. constrictus was the dominant species. Second, analysis of the relationships between species distributions and environmental factors using canonical correspondence analysis revealed that vegetation cover and altitude were the main factors affecting the distribution of Didymodon in Tibet. Third, based on the environmental factors of bioclimate, topography and vegetation, the distribution of Didymodon was predicted throughout Tibet at a spatial resolution of 1 km, using the presence-only MaxEnt model. Climatic variables were the key factors in the model. We conclude that the environment plays a significant role in moss diversity and distribution. Based on our research findings, we recommend that future studies should focus on the impacts of climate change on the distribution and conservation of Didymodon.

MERRAclim, a high-resolution global dataset of remotely sensed bioclimatic variables for ecological modelling

“…From a macro-ecological perspective, climate-richness models based on water-energy dynamics 4 have also displayed solid predictive ability to forecast responses to climate change (e.g., woody plants 5 ). These models are built with environmental variables such as temperature and specific humidity, which are also physiologically meaningful 6 , 7 , 8 , 9 , 10 , 11 , 12 in different parts of the globe 13 , 14 , 15 . The advent of GIS and the increased availability of global environmental data in recent years have favoured the proliferation of diverse kinds of SDMs intended to answer a wide range of applied ecological questions 2 (e.g., discovering biodiversity, conservation planning, health security, invasion ecology).…”

Bioclimatic modeling of moss distribution: MaxEnt interpretation for test species

“…Usually MaxEnt models deal with WorldClim climate data [1] obtained by interpolating the average monthly climate data of planet weather stations. When we examine distribution of smallsized objects confined to micro-habitats and neglecting main climate factors (mosses for example), this method applicability is not so obvious: publications about bioclimatic modeling of moss species areas are rare [4,5,6]. Total areas of most mosses are very wide: often it covers several continents and several bioclimatic zones, but ecological demands of species in different parts of areas can be different.…”

Assessing the causal relationships of ecological integrity: a re‐evaluation of Karr's iconic Index of Biotic Integrity

“…); and the determination of environmental factors affecting the distribution of mosses (Song et al. ) and bryophytes (Spitale ) as indicators of forest integrity. None of these, however, have compared the CCA approach to other methods of assessing ecological integrity.…”

“…More widely used multivariate approaches in ecology establish empirical patterns of integrity among environmental and biotic variables (Beck and Hatch 2009). Recent applications of canonical correspondence analysis (CCA) include the study of relationships among specific densities of fish, benthic indices, and environmental variables (Cai et al 2014); the examination of associations between operational taxonomic units and selected water geochemistry parameters (Zhang et al 2014); and the determination of environmental factors affecting the distribution of mosses (Song et al 2015) and bryophytes (Spitale 2015) as indicators of forest integrity. None of these, however, have compared the CCA approach to other methods of assessing ecological integrity.…”