A new method to estimate branch biomass from terrestrial laser scanning data by bridging tree structure models.

BACKGROUND AND AIMS: Branch biomass and other attributes are important for estimating the carbon budget of forest stands and characterizing crown structure. As destructive measuring is time-consuming and labour-intensive, terrestrial laser scanning (TLS) as a solution has been used to estimate branch biomass quickly and non-destructively. However, branch information extraction from TLS data alone is challenging due to occlusion and other defects, especially for estimating individual branch attributes in coniferous trees. METHODS: This study presents a method, entitled TSMtls, to estimate individual branch biomass non-destructively and accurately by combining tree structure models and TLS data. The TSMtls method constructs the stem-taper curve from TLS data, then uses tree structure models to determine the number, basal area and biomass of individual branches at whorl level. We estimated the tree structural model parameters from 122 destructively measured Scots pine (Pinus sylvestris) trees and tested the method on six Scots pine trees that were first TLS-scanned and later destructively measured. Additionally, we estimated the branch biomass using other TLS-based approaches for comparison. KEY RESULTS: Tree-level branch biomass estimates derived from TSMtls showed the best agreement with the destructive measurements [coefficient of variation of root mean square error (CV-RMSE) = 9.66 % and concordance correlation coefficient (CCC) = 0.99], outperforming the other TLS-based approaches (CV-RMSE 12.97-57.45 % and CCC 0.43-0.98 ). Whorl-level individual branch attributes estimates produced from TSMtls showed more accurate results than those produced from TLS data directly. CONCLUSIONS: The results showed that the TSMtls method proposed in this study holds promise for extension to more species and larger areas.

[Comparison of global sensitivity analysis techniques based on a process-based model CROBAS.] / 基于过程模型CROBASçš„å ¨å±€çµæ•åº¦åˆ†æžæ–¹æ³•æ¯”è¾ƒ.

Constructions of process or mechanistic models are limited by physiological parameters, due to difficulty in direct and precise measurement. Global sensitivity analysis could evaluate the response of model outputs to changes in physiological parameters, and provide information for improving model structure, data collection, and parameter calibration. Based on a process model CROBAS, 10 parameters related to tree structure of Pinus armandii were selected to compare three widely used global sensitivity analysis methods (the Morris screening method, the variance-based Sobol indices, and the Extended Fourier Amplitude Sensitivity Test (EFAST)), with the objective function formulated by the Nash-Sutcliffe Efficiency (NSE) of tree height and biomass. The results showed that the sensitivity order of parameters slightly varied across different methods, which considerably changed with different objective functions. Both the Morris method and the EFAST method outperformed the Sobol method in terms of time consuming and convergence efficiency. All outputs were sensitive to the maximum rate of canopy photosynthesis per unit area, the specific leaf area, and the extinction coefficient. The light interception of tree canopy played a key role in the simulation of tree growth with CROBAS, suggesting that the module of photosynthetic carbon fixation took priority over any other modules for data collection and model validation during module calibration and tree growth simulation for CROBAS. The calculation and validation of foliage biomass module were crucial when applying carbon balance theory to biomass simulations. In conclusion, for the sensitivity analysis of a complex process-based model, the Morris method was suitable for qualitative studies, while the EFAST method was recommended for quantitative studies.

Weaker Light Response, Lower Stomatal Conductance and Structural Changes in Old Boreal Conifers Implied by a Bayesian Hierarchical Model.

Age-related effects on whole-tree hydraulics are one of the key challenges to better predicting the production and growth of old-growth forests. Previous models have described the optimal state of stomatal behaviour, and field studies have implied on age/size-induced trends in tree ecophysiology related to hydraulics. On these bases, we built a Bayesian hierarchical model to link sap flow density and drivers of transpiration directly. The model included parameters with physiological meanings and accounted for variations in leaf-sapwood area ratio and the time lag between sap flow and transpiration. The model well-simulated the daily pattern of sap flow density and the variation between tree age groups. The results of parameterization show that (1) the usually higher stomatal conductance in young than old trees during mid-summer was mainly because the sap flow of young trees were more activated at low to medium light intensity, and (2) leaf-sapwood area ratio linearly decreased while time lag linearly increased with increasing tree height. Uncertainty partitioning and cross-validation, respectively, indicated a reliable and fairly robust parameter estimation. The model performance may be further improved by higher data quality and more process-based expressions of the internal dynamics of trees.

Estimating canopy gross primary production by combining phloem stable isotopes with canopy and mesophyll conductances.

Gross primary production (GPP) is a key component of the forest carbon cycle. However, our knowledge of GPP at the stand scale remains uncertain, because estimates derived from eddy covariance (EC) rely on semi-empirical modelling and the assumptions of the EC technique are sometimes not fully met. We propose using the sap flux/isotope method as an alternative way to estimate canopy GPP, termed GPPiso/SF , at the stand scale and at daily resolution. It is based on canopy conductance inferred from sap flux and intrinsic water-use efficiency estimated from the stable carbon isotope composition of phloem contents. The GPPiso/SF estimate was further corrected for seasonal variations in photosynthetic capacity and mesophyll conductance. We compared our estimate of GPPiso/SF to the GPP derived from PRELES, a model parameterized with EC data. The comparisons were performed in a highly instrumented, boreal Scots pine forest in northern Sweden, including a nitrogen fertilized and a reference plot. The resulting annual and daily GPPiso/SF estimates agreed well with PRELES, in the fertilized plot and the reference plot. We discuss the GPPiso/SF method as an alternative which can be widely applied without terrain restrictions, where the assumptions of EC are not met.

Extending the range of applicability of the semi-empirical ecosystem flux model PRELES for varying forest types and climate.

Applications of ecosystem flux models on large geographical scales are often limited by model complexity and data availability. Here we calibrated and evaluated a semi-empirical ecosystem flux model, PREdict Light-use efficiency, Evapotranspiration and Soil water (PRELES), for various forest types and climate conditions, based on eddy covariance data from 55 sites. A Bayesian approach was adopted for model calibration and uncertainty quantification. We applied the site-specific calibrations and multisite calibrations to nine plant functional types (PFTs) to obtain the site-specific and PFT-specific parameter vectors for PRELES. A systematically designed cross-validation was implemented to evaluate calibration strategies and the risks in extrapolation. The combination of plant physiological traits and climate patterns generated significant variation in vegetation responses and model parameters across but not within PFTs, implying that applying the model without PFT-specific parameters is risky. But within PFT, the multisite calibrations performed as accurately as the site-specific calibrations in predicting gross primary production (GPP) and evapotranspiration (ET). Moreover, the variations among sites within one PFT could be effectively simulated by simply adjusting the parameter of potential light-use efficiency (LUE), implying significant convergence of simulated vegetation processes within PFT. The hierarchical modelling of PRELES provides a compromise between satellite-driven LUE and physiologically oriented approaches for extrapolating the geographical variation of ecosystem productivity. Although measurement errors of eddy covariance and remotely sensed data propagated a substantial proportion of uncertainty or potential biases, the results illustrated that PRELES could reliably capture daily variations of GPP and ET for contrasting forest types on large geographical scales if PFT-specific parameterizations were applied.

[Simulation and uncertainty analysis of natural regeneration for pine-oak forests.] / æ¾æ Žæž—å¤©ç„¶æ›´æ–°æ¨¡æ‹Ÿä¸Žä¸ç¡®å®šæ€§åˆ†æž.

The complexity and uncertainty of forest regeneration is crucial for predicting forest ecosystem dynamics. A natural regeneration model of pine-oak forests in Qinling Mountains was constructed with competition, climate and topography factors using Bayesian statistics and global sensitivity analysis (GSA). The alternative models were based on Poisson, negative binomial (NB), zero-inflated Poisson (ZIP), and zero-inflated negative binomial (ZINB) models. According to the uncertainty of model parameter transfer, the analysis results were quantified, and the dominant factors of small probability events affecting forest regeneration were explained. The results showed that the ZINB model was the best one in the simulation of Pinus tabuliformis and Quercus aliena var. acuteserrata. Stand basal area, light interception, slope location and minimum temperature during growing season were the most critical factors affecting natural regeneration of P. tabuliformis, while stand basal area, cosine of aspect interacted with the natural logarithm of elevation, annual mean temperature, and precipitation of the warmest quarter were the most critical factors for Q. aliena var. acuteserrata. The contributions of various factors to the predictive uncertainty were: competition factor (25%) < climate factor (29%) < topography factor (46%) for the simulation of P. tabuliformis regeneration, and climate factor (12%) < competition factor (24%) < topography factor (64%) for the simulation of Q. aliena var. acuteserrata regeneration. The natural regeneration quantity of P. tabuliformis was positively correlated with mean annual temperature and minimum precipitation during growing season, and negatively correlated with the mean temperature in the driest quarter. The natural regeneration quantity of Q. aliena var. acuteserrata was positively correlated with mean annual temperature, minimum precipitation during growing season, precipitation of the warmest quarter, and negatively correlated with mean temperature of the driest quarter. The ZINB model based on Bayesian methods could effectively quantify the major factors driving forest regeneration and interpret the uncertainty propagated from parameters, which was useful for predicting forest regeneration.

Comparative analysis of the mitochondrial genomes of Colletotrichum gloeosporioides sensu lato: insights into the evolution of a fungal species complex interacting with diverse plants.

BACKGROUND: The fungal species complex Colletotrichum gloeosporioides sensu lato contains over 20 plant-interacting species. These species exhibit different life styles (e.g., endophytes, foliar and fruit pathogens) and show considerable variation in host and tissue adaptation strategies. Accurate species delimitation in C. gloeosporioides s.l. is very challenging due to nascent lineage boundaries and phenotypic plasticity, which strongly impedes studies of the complex's host-interaction biology. In this study, we first sequenced and compared nine mitogenomes belonging to four C. gloeosporioides s.l. species lineages (C. gloeosporioides, C. fructicola, C. aenigma, and C. siamense s.l.), and evaluated the usefulness of mitogenome sequence in complementing prevailing nuclear markers for species delimitation. RESULTS: The C. gloeosporioides s.l. mitogenomes ranged between 52,671 and 58,666 bp in size, and each contained an identical set of genes transcribed in the same direction. Compared with previously reported Colletotrichum mitogenomes, these mitogenomes were uniquely featured by: (1) significantly larger genome size due to richer intron content and longer intergenic region; (2) striking GC content elevation at the intergenic region; and (3) considerable intron content variation among different species lineages. Compared with nuclear DNA markers commonly used in phylogeny, the mitogenome nucleotide diversity was extremely low, yet the mitogenome alignment contained the highest number of parsimony informative sites, which allowed the generation of a high-resolution phylogeny recognizing all taxonomic lineages, including ones belonging to the very nascent C. siamense s.l. complex. The tree topology was highly congruent with the phylogeny based on nuclear marker concatenation except for lineages within C. siamense s.l. Further comparative phylogenetic analysis indicated that lineage-specific rapid divergence of GS and SOD2 markers confounded concatenation-based species relationship inference. CONCLUSIONS: This study sheds light on the evolution of C. gloeosporioides s.l. mitogenomes and demonstrates that mitogenome sequence can complement prevailing nuclear markers in improving species delimitation accuracy. The mitogenome sequences reported will be valuable resources for further genetic studies with C. gloeosporioides s.l. and other Colletotrichum species.