Modelling past and future demography of silver fir forests affected by dieback through genomic and dendroecological timelines

Citation

García-García I, Horreo JL, Méndez-Cea B, González De Andrés E, Gazol A, Colangelo M, Camarero JJ, Gallego FJ, Linares JC. Modelling past and future demography of silver fir forests affected by dieback through genomic and dendroecological timelines. Science of The Total Environment 2025;998:180329. https://doi.org/10.1016/j.scitotenv.2025.180329.

Abstract

Determining the factors leading to maladaptation is crucial for making informed conservation and management decisions, particularly in forests showing dieback and growth decline. We combined genomic and dendroecological methods to infer past and forecast future demographic trends in five silver fir (Abies alba Mill.) forests with varying levels of vigor in the species' southwestern range margin in the Pyrenees. Single nucleotide polymorphisms (SNPs) were used to infer demographic history and relate it to past major climatic changes, and to identify genome-environment associations, which were used to calculate the genomic offset under different emission scenarios, as an indicator of climate vulnerability. Basal area increment (BAI) was calculated to assess long-term growth patterns and to evaluate growth sensitivity to climate, and it was also forecasted under the same emission scenarios. Our results revealed an increasing risk of maladaptation westwards and in drier, lower elevation sites, consistent with increasing aridity, declining vigor, and reduced growth rates. These findings link demographic and growth declines to climate warming, highlighting the broad impacts of rising temperatures and declining precipitation on silver fir populations, as reflected by consistent patterns in demography, genomic offset, and growth forecasts. Nonetheless, local factors, such as elevation and specific genotypes, drive compensative and adaptive site-specific effects, buffering the negative impacts of climate change.

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Funding: This research was funded by the Spanish Ministry of Science and Innovation (projects PID2021-123675OB-C43, PID2021-123675OB-C44, TED2021-129770B-C21, TED2021-129770B-C22). AG was supported by the “Ramón y Cajal” Program of the Spanish MICINN under Grant RyC2020-030647-I, and by CSIC under grant PIE-20223AT003.

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