Data from: Monitoring the growth and habitat shifts of epiphyllous liverworts in subtropical forests of China
Data files
Nov 06, 2025 version files 44.20 KB
Abstract
Changes in the distribution range of species may be induced by climate change, but they may also be attributed to insufficient collection. Epiphyllous liverworts, known for their poikilohydric lifestyle, are more susceptible to climatic variations compared to other higher plants. However, due to their diminutive size, the issue of imperfect detection is more likely in epiphyllous liverworts than in other higher plants. Our observations from the permanent monitoring plots indicate a rapid expansion in the distribution range of epiphyllous liverworts under the influence of climate change. After analyzing temperature and humidity data across an altitude gradient, we have identified the specific microclimate conditions necessary for the growth of epiphyllous liverworts. The results from altitude transplantation experiments emphasize the significance of considering dispersal limitations when modeling the species distribution of epiphyllous liverworts for accurate predictive outcomes.
https://doi.org/10.5061/dryad.ksn02v7ck
The data include the monitoring photos of epiphyllous liverworts and forest air microclimate in Tianmushan National Nature Reserve.
Description of the data and file structure
We conducted several years of monitoring of the distribution of epiphyllous liverworts on Mt. Tianmu (2018–2022). The file ‘Number_of_leaves_that_host_epiphyllous_liverworts_in_the_transplant_experiment.xlsx’ shows changes in the number of leaves of the transplanted host plants over time. The file ‘The_interpolation_analysis_of_mean_annual_temperature_and_precipitation_from_1900_to_2020_in_Mt._Tianmu.xlsx’ presents how the mean annual temperature and precipitation changed in Mt. Tianmu from 1900 to 2020 by using interpolation analysis. Moreover, we provided the Microenvironment_factors_of_37_plots_in_Mt._Tianmu.xlsx, long: longitude; lat: latitude; Elevation; airtemp_bio1: Mean annual temperature; airtemp_bio2: Mean diurnal range of monthly temperature (mean of max temp - min temp); airtemp_bio3: Isothermality (bio2/bio7) (* 100); airtemp_bio4: Temperature seasonality (standard deviation *100); airtemp_bio5: Max temperature of warmest month; airtemp_bio6: Min temperature of coldest month; airtemp_bio7: Temperature annual range (bio5-bio6); airtemp_bio10: Mean temperature of warmest quarter/growing season (Jun., Jul. & Aug.); airtemp_bio11: Mean temperature of coldest quarter (Dec., Jan. & Feb.); airMois_bio1: Mean annual moisture; airMois_bio2: Mean diurnal range of monthly moisture (mean of max temp - min temp); airMois_bio3: Isothermality (bio2/bio7) (* 100); airMois_bio4: moisture seasonality (standard deviation *100); airMois_bio5: Max moisture of warmest month; airMois_bio6: Min moisture of coldest month; airMois_bio7: moisture annual range (bio5-bio6); airMois_bio10: Mean moisture of warmest quarter/growing season (Jun., Jul. & Aug.); airMois_bio11: Mean moisture of coldest quarter (Dec., Jan. & Feb.); n/a: data are missing due to datalog corruption.