Modelling and reconstructing tree ring growth index with climate variables through artificial intelligence and statistical methods

Salehnia, Narges; Ahn, Jinho

doi:10.1016/j.ecolind.2021.108496

Cited by 14 publications

(2 citation statements)

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“…Classical approaches consider the relation between climate elements (e.g., temperature or precipitation) and a tree-ring proxy, for example, tree-ring width (TRW) or maximum latewood density (MXD), by scaling or building linear regression models (Briffa et al, 1992;Cook et al, 2019;Cook & Kairiukstis, 1990;Esper et al, 2005Esper et al, , 2012Gurskaya et al, 2012;Lara et al, 2020;Li et al, 2012;Wilson & Luckman, 2003). ML algorithms are tested as transfer functions for this relationship by training artificial neural networks, random forests, or boosted regression trees (Gu et al, 2019;Jevšenak et al, 2018;Jevšenak & Skudnik, 2021;Salehnia & Ahn, 2022).…”

Section: Introductionmentioning

confidence: 99%

“…The ability of computers to learn on the basis of existing data (machine learning [ML]) bears great potential to improve various scientific fields including bio‐ and geoscience (Jordan & Mitchell, 2015; Keitt & Abelson, 2021). In tree‐ring research, ML has recently been applied for modeling stem diameter growth and vessel lumen or for climate reconstruction purposes (Bodesheim et al, 2022; Jevšenak & Skudnik, 2021; Ou et al, 2019; Salehnia & Ahn, 2022). In the (paleo)climatological context, tree‐rings are an essential source to reconstruct past climate fluctuations beyond the instrumental period.…”

Section: Introductionmentioning

confidence: 99%

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Using machine learning on tree‐ring data to determine the geographical provenance of historical construction timbers

et al. 2023

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Dendroclimatology offers the unique opportunity to reconstruct past climate at annual resolution and wood from historical buildings can be used to extend such information back in time up to several millennia. However, the varying and often unclear origin of timbers affects the climate sensitivity of individual tree‐ring samples. Here, we compare tree‐ring width and density of 143 living larch (Larix decidua Mill.) trees at seven sites along an elevational transect from 1400 to 2200 m asl and 99 historical tree‐ring series to parametrize state‐of‐the‐art classification models for the European Alps. To achieve geographical provenance of the historical series, nine different supervised machine learning algorithms are trained and tested in their capability to solve our classification problem. Based on this assessment, we consider a tree‐ring density‐based and a tree‐ring width‐based dataset for model building. For each of these datasets, a general not species‐related model and a larch‐specific model including the cyclic larch budmoth influence are built. From the nine tested machine learning algorithms, Extreme Gradient Boosting showed the best performance. The density‐based models outperform the ring‐width models with the larch‐specific density model reaching the highest skill (f1 score = 0.8). The performance metrics reveal that the larch‐specific density model also performs best within individual sites and particularly in sites above 2000 m asl, which show the highest temperature sensitivities. The application of the specific density model for larch allows the historical series to be assigned with high confidence to a particular elevation within the valley. The procedure can be applied to other provenance studies using multiple tree growth characteristics. The novel approach of building machine learning models based on tree‐ring density features allows to omit a common period between reference and historical data for finding the provenance of relict wood and will therefore help to improve millennium‐length climate reconstructions.

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