Cryptogam biomass estimation using taxonomic and life form models for accurate assessment

Abstract

Abstract Accurate estimation of cryptogam biomass, encompassing bryophytes and lichens, is crucial for understanding their ecological significance. This estimation is conducted based on the strong correlations between mass and volume of cryptogams. However, mass–volume correlations vary among cryptogams because of their morphological differences. This problem can be solved using models that consider life forms that classify cryptogams based on morphological similarities. In this study, we investigated whether life form models improve cryptogam biomass estimation accuracy. The cryptogam mass–volume correlation of each life form was estimated using Bayesian linear models. The coefficients and intercepts of linear models differed between life forms, which was attributed to the morphological characteristics of each life form. Therefore, life form models can improve the accuracy of estimation models by incorporating morphological differences. However, taxonomic models that consider only the taxonomic difference (bryophytes vs lichens) demonstrated better overall estimation than the life form models, probably because of the ability of taxonomic models to capture systematic differences between bryophytes and lichens. Furthermore, these models may mitigate estimation errors related to morphological variations that cannot be adequately represented by life form types. Based on these results, we propose the appropriate use of estimation models.

Keywords

• Bryophyte
• Lichen
• Morphology
• Taxonomy
• Nutrient cycling
• Forest ecosystem