Predicting trajectories of temperate forest understorey vegetation responses to global change

Wen, Bingbin; Blondeel, Haben; Baeten, Lander; Perring, Michael P. ORCID: https://orcid.org/0000-0001-8553-4893; Depauw, Leen; Maes, Sybryn L.; De Keersmaeker, Luc; Van Calster, Hans; Wulf, Monika; Naaf, Tobias; Kirby, Keith; Bernhardt-Römermann, Markus; Dirnböck, Thomas; Máliš, František; Kopecký, Martin; Vild, Ondřej; Macek, Martin; Hédl, Radim; Chudomelová, Markéta; Lenoir, Jonathan; Brunet, Jörg; Nagel, Thomas A.; Verheyen, Kris; Landuyt, Dries. 2024 Predicting trajectories of temperate forest understorey vegetation responses to global change. Forest Ecology and Management, 566, 122091. 13, pp. https://doi.org/10.1016/j.foreco.2024.122091

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Abstract/Summary

Predicting forest understorey community responses to global change and forest management is vital given the importance of the understorey for biodiversity conservation and forest functioning. Though substantial effort has gone into disentangling the impact of global change on understorey communities, scarcity of information on site-specific environmental drivers across large temporal-spatial scales has limited our ability to predict global change effects at specific forest sites. In this study, using vegetation resurvey and soil data from 1363 plots across temperate Europe, we applied a machine learning approach (gradient boosting regression, GBR) to model and predict site-specific responses of four understorey properties to global change. We applied our final GBR models at 8 forest sites in Austria to validate the model performance, predict understorey trajectories, and evaluate the effect of alternative scenarios for future nitrogen(N) deposition, climate change and forest management on the projected trajectories. Our results showed that the R² value of the four final GBR models on the independent testing dataset ranged between 0.611 and 0.723 and the most important environmental drivers in predicting the trajectory of understorey properties at specific forest sites were soil pH, soil total carbon-to-nitrogen ratio, overstorey shade-casting ability and regional-scale mean annual precipitation. The out-of-sample R2 value of the four final GBR models on the Austrian data ranged between 0.224 and 0.561. The forecasted trajectories for the Austrian forest sites showed that site-specific understorey responses to near-future climate warming were expected to be weak. Under N deposition decreases, the proportion of woody species was predicted to increase, while species richness and total vegetation cover were predicted to decrease. Furthermore, under a closed canopy, the understorey community was predicted to shift towards more woody species and more forest specialists, albeit with reduced species richness and vegetation cover. Given expected warming and declining N pollution pressures, our presented GBR models allow the prediction of trajectories of understorey vegetation responses to global change and management interventions at specific forest sites. Such projections could aid forest management in addressing challenges posed by global change.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1016/j.foreco.2024.122091
UKCEH and CEH Sections/Science Areas:	Soils and Land Use (Science Area 2017-)
ISSN:	0378-1127
Additional Keywords:	forestREplot, forest understorey, climate change, soil pH, machine learning, site-scale
NORA Subject Terms:	Ecology and Environment Agriculture and Soil Science Computer Science
Date made live:	20 Jun 2024 08:06 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/537600

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