Climate change increases the severity of extreme climatic events that may lead to tree mortality, community shifts and loss of functions and services. Tree vulnerability to climate change is defined by their traits, which are characteristics that affect their ability to respond to environmental changes. This research aims to investigate: a) What are the patterns of tree mortality in the study area, and are there changes in mortality events distribution over the years; b) How different climatic conditions (temperature and drought) mediate tree mortality; c) Which species and traits are more vulnerable to mortality; d) How does climate change induced mortality influence the community structure, composition and function. We will conduct the study in the Permanent Plot in Caetetus Ecological Station, located in the state of São Paulo, Brazil, in a Montane Seasonal Semi-deciduous Forest. The Permanent Plot has an area of 10.24ha, subdivided into 256 20x20m subplots, of which 25 (1ha) will be randomly assigned for the survey. We will use inventory data (2003, 2005, 2010) and an additional survey (2023), to track if there are changes in mortality rates over the years. The biological data collected will be tree (diameter at breast height (DBH)> 4.8 cm) height, DBH, species, and health (dead/alive). We will compare the number of dead trees between survey years using repeated measures ANOVA. To assess if the mortality is influenced by the climate, we will collect meteorological data of nearby stations (temperature and precipitation) from the year 2000 until the present, and perform regressions between climatic conditions and tree mortality. We will collect traits information from the survey (tree height) and online databases and literature reviews (root depth, stomata regulation, leaf phenology, xylem conductivity, wood density and successional group). We will perform multivariate analysis to compare mortality between species (hierarchical cluster) and traits (MDS, LDA, PCA). Finally, we will conduct ANOVAs between survey years for community metrics (composition, structure and function), to assess the ecosystem shifts. Data analysis will be performed in R (R Core Team 2023) using p-value<0.05. The results can help guide conservation efforts in the area and nearby region and build a framework for species selection for restoration under a changing climate.
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