Equipe : Sureau

Porteur de projet :   COCHARD Hervé

Lien du projet : https://portal.nifa.usda.gov/web/crisprojectpages/1030214-partnership-pinephys-predicting-ideotypes-novel-and-extant-from-physiology-hyperspectral-reflectance-and-snp-genotypes.html

Context

Climate change is leading to more frequent, and more extreme compound hotter-drought events globally. In the southeastern US, these events have led to climate-induced growth declines and have even caused recent mortality in loblolly pine--the most economically important timber species in the region. Future climate projections indicate significant potential risk to growth and survival potentially costing billions in lost value to southeastern forestland owners and industry. Yet the climate-resilience for both growth (to sustain value) and survival (to prevent massive economic losses) remains largely unknown both in adults and juveniles. During hotter-drought stress, a complex suite of traits determines when plants will stop growing, with important physiological indicators and thresholds acting as mechanisms of reduced plant growth and survival.Our overall goal is to improve the climate-resilience of loblolly pine plantations while preserving production by identifying climate-ready genotypes for deployment today, and climate-resilient "ideotypes" (ideal trait combinations) to inform breeding efforts in the most economically important forest tree species in the US. Our objectives are to determine genetically-based growth and survival trait combinations that confer climate resilience in loblolly pine, and to discover ideal climate-resilient growth and survival trait combinations across developmental stages of the species.Our study directly addresses goals and priorities of the Physiology of Agricultural Plants as we focus on how to improve productivity and performance, including survival, under future hotter-and-drier climates associated with climate change. We will reveal mechanisms of plant response to abiotic stress, by fusing physiological, genomic, and spectral methods with mechanistic models.

Objectives

Our overall goal is to improve the climate-resilience of loblolly pine plantations while preserving production by identifying climate-ready genotypes for deployment today, and climate-resilient "ideotypes" (ideal trait combinations) to inform breeding efforts in the most economically important forest tree in the US. We will achieve our overall goal by leveraging our project team's diverse expertise, and integrating ecophysiological, spectral, genetic, and mechanistic modeling approaches. To achieve our overarching goal, we propose two aims:Aim 1: Determine genetic basis of growth and survival trait combinations that confer climate resilience in loblolly pine.Task 1.1: Identify range-wide physiological trait distributions for climate-resilient growth and survival. We will measure a suite of important physiological growth and survival traits, including the response of carbon assimilation, stomatal conductance, and water use efficiency to elevated temperature, xylem vulnerability, bark and leaf residual conductance, thermal tolerance, and pressure-volume traits (e.g., turgor loss point, capacitance). We will do so in a common garden of replicated, SNP-genotyped clones (ADEPT2), whose parent material was collected at 315 sites across the entire climatic, geographic, and genetic range of P. taeda, Figure 2. We hypothesize that genotypes from cooler and wetter sites will have a greater xylem vulnerability, lower minimum conductance, and lower thermal tolerance; we expect the opposite from trees of hotter and drier western provenance.Task 1.2: Characterize the genetic architecture and candidate genes/alleles of physiological traits for climate-resilience. We will conduct quantitative genetic analyses of all traits to estimate genetic control and trait-trait genetic correlations to inform breeding efforts. We will conduct genetic association analysis to identify SNPs in candidate genes that are significantly associated with individual traits. With the candidate genes we will build network models to explain genetic associations and identify genotypes suited for deployment in hotter and drier environments.Task 1.3: Develop hyperspectral trait prediction models for physiological traits in loblolly pine and their variation. With loblolly pine growing on over 19,000,000,000 hectares of land, measurement of all possible trait values across the species, or even in a small percentage of individuals, using a physiological approach is impossible. While sampling the genetically diverse common garden of loblolly pine for survival traits in Task 1.1, above, we will also collect plant hyperspectral reflectance data at two scales (point-source of foliage and branch-level images, and develop models that predict our measured physiological traits from reflected light.We will use these models to identify wavelengths that consistently predict growth- and survival-related traits as a first step towards drone- and satellite-based (SBG, CHIME) landscape-level trait prediction. Finally, we will use both physiological traits (from task 1.1) and spectrally-predicted traits (from task 1.3) to model which extant genotypes may be most successful under future climate scenarios (see task 2.2, below). We hypothesize that growth and survival traits can be predicted from spectral reflectance, given previous work on remote sensing prediction of traits and future perspectives.Aim 2: Discover ideal climate-resilient growth and survival trait combinations across developmental stages of loblolly pine. Task 2.1: Quantify growth and survival trait variations across developmental stages. Because growth and survival strategies change as trees mature, the most successful combination of traits likely changes as trees develop from seedlings to adults. Following the field sampling of mature trees in the ADEPT2 common garden (age = 11 y), we will obtain cloned plants from industry partner ArborGen (see letter of support from lead scientist Dr. Patrick Cumbie, Key Personnel on this project) from five provenances: Virginia, Georgia, Alabama, Florida, and Texas, to sample growth and survival traits on somatic seedlings (1 year old emblings) and saplings (2 years old). We will submit them to drought stress ranging from mild (stomatal closure) to lethal doses (LD50) as determined in our prior work with loblolly pine [25], and identify traits and their combinations that lead to prolonged growth and survival time during climate stress, at each life stage. Leaf, branch, and canopy spectral reflectance will also be collected under stress and used as the mechanistic backbone to predict growth and survival time (e.g., time to mortality) from spectral traits. At the end of each experiment, we will destructively sample all plants for an allometry analysis [41], weighing the fresh and dry mass of taproots, fine roots, the main stem, branches, and foliage separately. We hypothesize that earlier life stages will be more vulnerable to climate-induced growth declines and mortality, with lower temperature optima of carbon assimilation and water use efficiency, lower thermal tolerance, and less drought resistance (higher P50s, TLPs, gres).Task 2.2: Mechanistic model of "ideotypes" to identify existing climate-resilient trait combinations, and propose new ones. Using a process-based model, SurEau, developed by members of our research team, we will mechanistically model growth, mortality risk, and time-to-death for loblolly pine based on quantified seedling and sapling traits measured in Task 2.1. During greenhouse drought studies, we will validate model predictions in seedlings and saplings on the UF Plant Array, a physiological phenotyping platform. Once validated, we will use SurEau to build highly climate-resilient "ideotypes" with optimal trait combinations for seedlings, saplings, and adult trees. We will then identify those trait combinations that maximize performance across life stages and that have a genetic association (based on Task 1.2). Furthermore, we will also parameterize the model with spectrally-predicted trait values (from Task 2.1) to determine if mechanistic models can be trained and used to accurately forecast tree growth and survival time with our spectra from Task 1.3, rather than time-intensive physiological trait data. In planted forest production systems like loblolly pine, the emphasis is often on maximizing production. Management silvicultural practices most common in this system include site preparation, stocking densities, fertilization, and chemical or mechanical thinning. These practices, especially appropriate stocking densities and stand thinning, may already be improving the climate resilience of existing loblolly pine plantations. However, incomplete understanding of the climate resilience of the species, especially for early developmental stages, means that loblolly pine might experience widespread forest mortality events (see Fig. 2F-H, below) like those observed across global forests in recent decades.

Partners

University of Florida

Funding source

AFRI COMPETITIVE GRANT