Root morphophysiology changes during the habitat transition from soil to canopy of the aroid vine Rhodospatha oblongata.

BACKGROUND AND AIMS: The aroid vine Rhodospatha oblongata is characterized by a habitat change from terrestrial to canopy, relying on aerial roots at maturity to obtain water and nutrients from the forest soil. We hypothesize that morphophysiological acclimation occurs in roots as they grow under atmospheric conditions. These changes would guarantee the whole-plant survival of aroid vines in the new and potentially stressful habitat of the canopy. METHODS: Terrestrial and aerial roots were compared on a morphophysiological basis. Root anatomy, water balance, water absorption capacity via fluorescent tracer, and photochemical activity via chlorophyll fluorescence were measured. KEY RESULTS: While thin fasciculate roots occur on terrestrial crawling individuals, two clearly distinct aerial roots (anchor and feeder) are produced on canopy individuals, which both adhere to the host trunk. The colour of both aerial roots changes during development from red and brownish to striped and green at maturity. Colour changes are induced by the replacement of epidermis, exodermis and outer cortex by an inner layer of lignified cork on the root region exposed to the atmosphere. In the root region that is in contact with the host, covering substitutions do not occur and both exodermis and lignified cork, along with several epidermal hairs, appear. Water retention capacity was higher in green roots than in other root types. Rehydration capacity via water absorption by hairs of aerial roots was confirmed by fluorescence. Chlorophyll fluorescence data indicated low levels of photosynthetic capacity in aerial roots. CONCLUSIONS: Plants should evolve strategies to survive stress situations. The transition from soil to canopy imposes abiotic changes and potentially stressful situations on R. oblongata. We conclude that the morphophysiological changes observed represent an important strategy that permits the maintenance of aroid roots and the survival of R. oblongata in the canopy.

When does stratification of a subtropical soil spectral library improve predictions of soil organic carbon content?

More accurate models for the prediction of soil organic carbon (SOC) by visible-near-infrared (Vis-NIR) spectroscopy remains a challenging task, especially when the soil spectral libraries (SSL) is composed of soils with a high pedological variation. One proposition to increase the models accuracy is to reduce the SSL variance, which can be achieved by stratifying the library into sub-libraries. Thus, the main objective of this study was to evaluate whether the stratification of a SSL by environmental, pedological and Vis-NIR spectral criteria results in greater accuracy of spectroscopic models than to general models for prediction of SOC content. The performance of the models was evaluated considering the variance of soil components and sample number. In addition, we tested the effect of two spectral preprocessing techniques and two multivariate calibration methods on spectroscopic modeling. For these purposes, a SSL composed of 2471 samples from Southern Brazil was stratified based on i) physiographic region; ii) land-use/land-cover; iii) soil texture, and iv) spectral class. Two spectral processing techniques: Savitzky-Golay - 1st derivative (SGD) and continuum removed reflectance (CRR) and two multivariate methods (partial least squares regression - PLSR and Cubist) were used to fit the models. The best performances for the global and local models were achieved with the CRR spectral processing associated with the Cubist method. The stratification of the SSL in more homogeneous sample groups by environmental criteria (physiographic regions and land-use/land-cover) improved the accuracy of SOC predictions compared to pedological (soil texture) and Vis-NIR spectral (spectral classes) criteria. The reduction in the number of samples negatively affected the performance of models for sub-libraries with high pedological and spectral variation. Stratification criteria were proposed in a flowchart to assist in decision making in future studies. Our findings suggest the importance of sample balance across environmental, pedological and spectral strata, in order to optimize SOC predictions.

Soil phosphorus landscape models for precision soil conservation.

Phosphorus (P) enrichment in soils has been documented in the Santa Fe River watershed (SFRW, 3585 km) in north-central Florida. Yet the environmental factors that control P distribution in soils across the landscape, with potential contribution to water quality impairment, are not well understood. The main goal of this study was to develop soil-landscape P models to support a "precision soil conservation" approach combining fine-scale (i.e., site-specific) and coarse-scale (i.e., watershed-extent) assessment of soil P. The specific objectives were to: (i) identify those environmental properties that impart the most control on the spatial distribution of soil Mehlich-1 extracted P (MP) in the SFRW; (ii) model the spatial patterns of soil MP using geostatistical methods; and (iii) assess model quality using independent validation samples. Soil MP data at 137 sites were fused with spatially explicit environmental covariates to develop soil MP prediction models using univariate (lognormal kriging, LNK) and multivariate methods (regression kriging, RK, and cokriging, CK). Incorporation of exhaustive environmental data into multivariate models (RK and CK) improved the prediction of soil MP in the SFRW compared with the univariate model (LNK), which relies solely on soil measurements. Among all tested environmental covariates, land use and vegetation related properties (topsoil) and geologic data (subsoil) showed the largest predictive power to build inferential models for soil MP. Findings from this study contribute to a better understanding of spatially explicit interactions between soil P and other environmental variables, facilitating improved land resource management while minimizing adverse risks to the environment.

Spectroscopic models of soil organic carbon in Florida, USA.

Soil organic carbon (SOC) is an indicator of ecosystem quality and plays a major role in the biogeochemical cycles of major nutrients and water. Shortcomings exist to estimate SOC across large regions using rapid and cheap soil sensing approaches. Our objective was to estimate SOC in 7120 mineral and organic soil horizons in Florida using visible/near-infrared diffuse reflectance spectroscopy (VNIRS) calibrated by committee trees and partial least squares regression (PLSR). The derived VNIRS models were validated using independent datasets and explained up to 71 and 38% of the variance of SOC in mineral and organic horizons, respectively. We stratified the mineral horizons into seven soil orders and derived PLSR models for each order, which explained from 32% (Histosols) to 75% (Ultisols) of the variance of SOC concentration in validation mode. Estimates of SOC from all models were highly scattered along the regression lines, especially for high SOC values, and the slopes of the regression lines were generally <1 because VNIRS models tended to underestimate high SOC values and overestimate low SOC. Despite the great scatter of estimates in the prediction plots, VNIRS models had reasonable explanatory power for mineral horizons, given the heterogeneity of soils and environmental conditions in Florida, and have potential for the rapid assessment of SOC, with implications for regional SOC assessments, modeling, and monitoring. However, VNIRS models for organic horizons were hampered by small sample size and had very limited explanatory power.