Studying the effect of climate change in underground systems. A Novel Quality Control Workflow for Subterranean Temperature Data

Caves are some of the most understudied ecosystems on Earth. However, they are of great interest for their stable climate and for the most specialized biodiversity that caves host. Thanks to the PRIN project, Biodiversity conservation goes DEEP: integrating subterranean ecosystems into climate CHANGE agendas and biodiversity targets we can improve our understanding on subterranean environments to fill the biological and climatic knowledge gaps. To best study underground systems dynamics an interdisciplinary study is conducted merging meteorological and ecological disciplines.
Within a two years time frame, the project's workflow runs through the understanding the impacts of global change, creating models to predict the future consequences on different scenarios and implementing conservation programs to protect these ecosystems.

Seven caves in Western Alps have been selected as benchmark caves. Taxonomic and functional traits data are collected by on-site samplings and literature reviews to create a distribution and functional model of subterranean taxa.
Abiotic data are collected through monitoring cave temperatures continuously from 2020 until today and can be compared with previous data of 2012.
We created a quality control protocol for the internal cave temperature data structured in three fundamental steps: standardizing the interval between consecutive records every 6 hours; removing sudden temperature breaks using a mobile variance measure; considering the gaps to evaluate the reliability for future studies.
Analysis of cave meteorology is run to identify the thermal responses within caves compared to surface environments. Preliminary application of this QC procedure, one of the most stable caves of our dataset, has demonstrated its effectiveness in isolating relevant temporal frames and enhancing data reliability. Our first case study points out an average increase of 0.28°C from 2012 and the cave internal temperature shows a constant increase from 2020 to 2023.
The absence of a standardized approach for managing cave temperature data presents an
opportunity for innovation. We introduce a scalable and adaptable quality control protocol, designed to be generalizable across different environmental studies.
The results can be merged with ecological modelling to obtain a process-based prediction of biodiversity change in subterranean systems. Based on this model, hotspots of conservation value can be identified to create a dynamic network of protected areas that considers climate-driven shifts insubterranean ecoregions.
This protocol not only enhances data integritybut also sets a new benchmark for future research insubterranean climate analysis.