Universal reaction norms for the sustainable cultivation of hybrid poplar clones under climate change in Italy

Abstract

The cultivation of hybrid poplar clones is increasing worldwide. Hundreds of hectares of plantations now occur across Europe and other continents such as North America, using tested clones and novel genotypes. Research effort aims are to develop fast growing disease-and pest-resistant clones to improve production quality and quantity. In this study the phenotypic plasticity of poplar clones was tested across environmental and temporal gradients. The growth performance of 49 hybrid poplar clones recorded between 1980 and 2021 was analysed using a mixed-effects model with climatic data as a predictor variable. Clones were aggregated into two groups according to their breeding protocol (i.e., standard clone, and improved material) and their growth modelled for future climate scenarios of RCPs 2.6 and 8.5 using a downscaled version of the variants 01 and 21 of UKCP18 climate projections dataset for three 30year normal period time-slices: 2030s, 2040s, 2050s. The fitted growth models showed highly significant results, explaining more than 85% of the variance, with a mean relative absolute error of approximately 2%. Improved material showed more resistance to warmer and drier climates and less sensitivity to the changing climate. While no unique pattern was found when comparing growth performances, new improved clones were more productive than older clones (e.g., 'I-214') with an additional benefit of resistance to rust and pests. Spatial predictions confirmed the Po valley as the most important geographic area for poplar cultivation in Italy, but zones in Central and Southern Italy show potential. However, the Po Valley is also where poplars are predicted to be suitable in the next decades with large uncertainties. The analysis identified the need for more research on the topic of poplar breeding. For example, models using the most extreme (warm and dry) climate projection, variant 01 of RCP8.5 of the UKCP18, exceeded the historic climate threshold, and predictions used model extrapolation, with associated statistical uncertainty. Therefore, predictions should be considered with care and more research effort is required to test clones over wider environmental conditions.

ClimateDT: A Global Scale-Free Dynamic Downscaling Portal for Historic and Future Climate Data

“…The use of tailored downscaled data and climatological indices (more than 80 in ClimateDT) can allow researchers to develop an ecological modeling procedure faster and easier. One of the main outputs of ecological models such as Species Distribution Models [72], Joint Species Distribution Models [73,74], reaction norms [28,30], response functions [71,[75][76][77], and transfer models [27,78] is the prediction of the possible changes in the spatial distribution and/or the performance of living organisms under changing climatic regimes. An additional feature is to detect provenances that may show better adaptation to the future climate predicted in a specific area [79][80][81].…”

“…Many different algorithms have been developed, linking the environmental variability and physiographic parameters, such as elevation or distance from the sea, applied to "delta" approaches [23] and statistical procedures [18,21,[24][25][26]. Powerful workstations and tuned algorithms are readily accessible for research groups, often used in a statistical environment to predict the potential impact of a warming (and drying) climate on animals or plants [27,28]. In this context, the past climate is used to analyze the species' responses to climate fluctuations [29,30], while the future scenarios are applied to forecast the possible impacts.…”

On the Inclusion of Adaptive Potential in Species Distribution Models: Towards a Genomic-Informed Approach to Forest Management and Conservation

“…In parallel to trait-based models, transfer functions and reaction norms were developed to identify deployment zones and to inform the usage of seeds for afforestation in a changing climate [31][32][33][34]. In agreement with trait-based models, the climatic divergence between provenances and test sites was the main driver of the model, along with some geographic variables, such as latitude.…”

Responses to Drought Stress in Poplar: What Do We Know and What Can We Learn?

“…The largest cultivated areas are found in Canada and China, followed by Europe where France and Italy are the main producers. In Italy, plantations are in the Po’ valley cover about 43,400 hectares [ 9 ]. Furthermore, poplar is a model organism for tree physiology and genomics thanks to its small genome size, fast growth rate and ease of transformation [ 10 ].…”