DOI: 10.5061/dryad.ksn02v7ck
title: Data from: Monitoring the growth and habitat shifts of epiphyllous liverworts in subtropical forests of China

description: 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.
description: # **Monitoring the growth and habitat shifts of epiphyllous liverworts in
 subtropical forests of China**
 [https://doi.org/10.5061/dryad.ksn02v7ck](https://doi.org/10.5061/dryad.p2ngf1w1p) 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.       ##