Characterising Heatwave Responses and Climate Driver Impacts Using Multicollinearity-Controlled Generalised Linear Mixed Models in Urban and Forest Trees (2018-2023).

Climate change is intensifying stressors on tree ecosystems, creating an urgent need to understand how climate variables influence vegetation health. This study investigated the influence of leaf temperature, wind speed, surface pressure, and moisture levels on tree health in a deciduous forest at Aspley Heath and an urban area in Milton Keynes, UK, using the normalised difference vegetation index (NDVI) as a vegetation health indicator. Satellite data from 2018 to 2023 revealed multiple heatwave events, with leaf temperatures surpassing the critical thresholds of 38 °C and 42 °C, as determined through controlled experiments. Generalised linear mixed models (GLMMs) demonstrated strong interactions between climate variables and vegetation health. Increased leaf temperature was positively associated with NDVI in both ecosystems, though urban trees showed a weaker response (β = 0.32, 95% CI: 0.16-0.48, p < 0.001) compared to forest trees (β = 0.45, 95% CI: 0.35-0.55, p < 0.001), suggesting urban heat island effects limit thermal benefits. Since all predictors were standardised, these coefficients indicate that a 3.82 °C increase in leaf temperature corresponds to a 0.32 NDVI increase in urban areas and a 0.45 increase in forests, reflecting meaningful ecological responses to moderate warming. Wind speed had a consistently negative impact on NDVI, with stronger effects in urban areas Graphical Abstract: The graphical abstract provides a concise visual summary of the study, illustrating how climate variables impact tree health in contrasting urban and forest environments. Using satellite-derived NDVI data and Generalised Linear Mixed Models (GLMMs), the abstract displays the standardised effect sizes (β-values) for key leaf temperature, wind speed, surface pressure, and moisture availability predictors. Each climate variable is clearly labelled alongside its corresponding β coefficient to improve interpretability. Leaf temperature positively influenced vegetation health in both ecosystems, with a stronger effect in forests (β = 0.45) than in urban areas (β = 0.32). Similarly, moisture availability showed a more substantial effect in forests (β = 0.39) compared to urban areas (β = 0.30). Wind speed negatively affected tree health, with a more pronounced impact in urban environments (β = - 0.21) than in forests (β = - 0.17). Surface pressure showed a smaller but positive influence in both settings (urban: β = 0.17; forest: β = 0.14). A colour gradient background from cool blue-green on the forest side to warm orange-red on the urban side, as well as the blue and red thermometers respectively, represents the cooler environment in the forest compared to the warmer urban area. A thermometer icon with the warming planet next to the heatwave graph and spatial maps visually highlights the extreme heat events.