<b>Simulated climate change impacts health, growth, photosynthesis and reproduction of high-elevation epiphytic lichens</b>

<b>Description</b><br>This dataset provides the underpinning data for the following findings:<br>Epiphytic lichens are expected to be especially threatened by accelerated climate change experienced at high elevations. All lichens are sensitive to atmospheric conditions. Treelines constrain the ability of epiphytes to migrate upwards to follow suitable conditions. Thus, acclimation to changing conditions will be a key determinant of their survival. To simulate lichens response to climate change, we conducted a translocation field experiment in three regions of the trans-Himalayas. Translocation of thalli to lower elevations simulated future increased temperatures, reduced relative humidity and increased vapour pressure deficit. We hypothesised that this would decrease initial establishment, growth rates, reproduction, photosynthetic pigments and photosynthesis, but that lichen might alternatively acclimate or shift life history strategies.<br>The study species comprised three fruticose (<i>Dolichousnea longissima, Sulcaria sulcata and Ramalina conduplicans</i>) and six foliose species (<i>Hypogymnia flavida, Lobaria retigera, Lobaria pindarensis, Dendriscosticta hookeri, Hypotrachyna cirrhata and Parmotrema reticulatum</i>) with regional medicinal or culinary uses. Persistence of <i>D. longissima</i> is also crucial for the endangered, endemic, lichenivorous monkey: <i>Rhinopithecus bieti</i>.<br>We found some support for each hypothesis, but high interspecific, intraspecific and regional variability. Recipient host-tree associations and bark pH impacted lichen, but were less important than microclimate variables. Increased winter temperatures were most influential at the highest-elevation region, whereas reduced relative humidity was most important at mid-elevations. For foliose species, increased vapour pressure deficit caused thallus damage and reduced photosynthetic pigments. Some species shifted temperature optimum for photosynthesis and all could increase net photosynthetic rates with raised CO₂ concentrations. Only <i>S. sulcata</i> switched life history strategies to begin apothecia production in younger thalli, but <i>D. hookeri</i> switched to greater investment in growth, while delaying apothecia production. The prime beneficiary of simulated climate change was <i>D. longissima</i>, which had faster growth, healthier thalli and increased photosynthetic pigments. The worst outcomes were for <i>H. flavida</i>, which will probably undergo range contraction. Survival of other species will depend on the rate of temperature increases with climatic change. Conservation of intact mature forest habitat in high elevation regions will be crucial for all these epiphytic lichens.<br>We provide the following unique files:1. Explanation of specimen names2. Herbarium numbers for translocated lichen specimens3. Microclimate data for Yunnan China 2018 -20204. KEY to damage &amp; growth &amp; chlorophyll spreadsheets<br>Then nine files (named by lichen species) of photosynthesis records and nine files of damage, growth and chlorophyll data.<br><b>Steps to reproduce</b>Methods are provided in detail in the associated publication. Background details, keys and explanations are provided in these 3 Excel files - these are not intended to be used directly in data analyses, but will aid the interpretation of the data:1. Worthy et al. 2024 Explanation of specimen names.xlsx2. Worthy et al. 2024 Herbarium numbers for translocated lichen specimens.xlsx3. Worthy et al. 2024 KEY to damage &amp; growth &amp; chlorophyll spreadsheets.xlsxThe specimens included in 2. have already been deposited in the KUN herbarium.<br>Microclimate data recorded at each of the 18 study sites is provided in a single dataset. This is formatted ready to be imported into 'R' statistical software.4. Worthy et al. 2024 Microclimate data for Yunnan China 2018 -2020.xls<br>The other 18 files each include spreadsheets which have been formatted ready to be imported into 'R' statistical software. These data would be sufficient to replicate the data analyses reported in the associated manuscript. In files with multiple pages, some are the raw data, others show calculations, including the Excel formulas which were used. Other pages are the final dataset as used in the statistical analysis. In such cases, the page of the spreadsheet labelled '4R' is the page that has been formatted ready to be uploaded into 'R' software for statistical analysis. Any additional page labelled '4stepwise' has been changed to the specific format required for use in stepwise linear regression, using the ‘leaps’ package, using repeated k-fold cross-validation, training the model using the ‘leapSeq’ method.