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UID:pretalx-soil-health-now-2026-FSMT7Z@pretalx.earthmonitor.org
DTSTART;TZID=Europe/Athens:20261208T160000
DTEND;TZID=Europe/Athens:20261208T161500
DESCRIPTION:Soil biodiversity plays a central role in regulating ecosystem 
 functioning and maintaining soil health through its influence on nutrient 
 cycling\, carbon dynamics\, soil structure formation and water regulation.
  At the same time\, environmental change and land-use intensification alte
 r soil physicochemical conditions and vegetation dynamics\, with cascading
  consequences for belowground communities and ecosystem functioning. Despi
 te increasing recognition of the importance of soil biodiversity within Eu
 ropean soil health initiatives\, identifying scalable and operational indi
 cators capable of capturing soil ecological responses across contrasting p
 edoclimatic regions remains a major challenge.\nUsing harmonized datasets 
 generated within the SOB4ES project\, we integrated soil biodiversity indi
 cators\, soil physicochemical properties\, land-use information and Earth 
 Observation-derived climatic\, vegetation and topographic variables across
  European sites. The analyses employed machine-learning approaches and exp
 lainable artificial intelligence methods to investigate large-scale relati
 onships among soil biodiversity\, environmental gradients and ecosystem se
 rvice-related functions under different climatic and land-use contexts.\nA
 cross multiple organism groups\, diversity-based metrics generally showed 
 stronger and more consistent relationships with environmental gradients th
 an abundance- or density-based indicators\, supporting their relevance for
  large-scale soil health assessment frameworks. Soil pH\, soil organic car
 bon\, moisture conditions and soil texture parameters emerged as dominant 
 cross-taxa predictors\, while climatic and vegetation-related variables de
 rived from Earth Observation data explained a substantial proportion of sp
 atial variation in belowground biodiversity patterns. However\, the influe
 nce of environmental predictors frequently differed among land-use types\,
  indicating that biodiversity responses are strongly context-dependent and
  shaped by interactions among soil conditions\, vegetation structure and m
 anagement intensity. \n\nOverall\, the integration of harmonized biodivers
 ity observations\, remote-sensing data and machine-learning approaches dem
 onstrated strong potential for supporting spatially explicit soil health m
 onitoring and the identification of robust biological indicators under glo
 bal change and land-use pressures across Europe.\n\nAcknowledgments: The w
 ork and all the authors were supported by the Horizon Europe project SOB4E
 S under Grant Agreement No. 101112831. We acknowledge all participating in
 vestigators from the SOB4ES consortium who contributed to the existing sam
 ple collection and the field sampling for the generation of the spatial da
 tabase used in the current analysis. Partners from KNAW\, UVIGO\, NUID UCD
 \, UNICT\, KU Leuven\, CU\, ARO\, IBB\, UL\, UoC\, SLU\, EFWSL\, Airfield\
 , MFO\, and INRAe provided these contributions.
DTSTAMP:20260825T193229Z
LOCATION:Amphitheater II
SUMMARY:Using earth observation and machine learning to identify environmen
 tal drivers and land-use pressures of soil biodiversity across European la
 ndscapes - Maria Marily Christou\, Snezhana Mourouzidou
URL:https://pretalx.earthmonitor.org/soil-health-now-2026/talk/FSMT7Z/
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