Practical Guide to Measuring Wetland Carbon Pools and Fluxes.

Wetlands cover a small portion of the world, but have disproportionate influence on global carbon (C) sequestration, carbon dioxide and methane emissions, and aquatic C fluxes. However, the underlying biogeochemical processes that affect wetland C pools and fluxes are complex and dynamic, making measurements of wetland C challenging. Over decades of research, many observational, experimental, and analytical approaches have been developed to understand and quantify pools and fluxes of wetland C. Sampling approaches range in their representation of wetland C from short to long timeframes and local to landscape spatial scales. This review summarizes common and cutting-edge methodological approaches for quantifying wetland C pools and fluxes. We first define each of the major C pools and fluxes and provide rationale for their importance to wetland C dynamics. For each approach, we clarify what component of wetland C is measured and its spatial and temporal representativeness and constraints. We describe practical considerations for each approach, such as where and when an approach is typically used, who can conduct the measurements (expertise, training requirements), and how approaches are conducted, including considerations on equipment complexity and costs. Finally, we review key covariates and ancillary measurements that enhance the interpretation of findings and facilitate model development. The protocols that we describe to measure soil, water, vegetation, and gases are also relevant for related disciplines such as ecology. Improved quality and consistency of data collection and reporting across studies will help reduce global uncertainties and develop management strategies to use wetlands as nature-based climate solutions. Supplementary Information: The online version contains supplementary material available at 10.1007/s13157-023-01722-2.

Predicting Soil Properties and Interpreting Vis-NIR Models from across Continental United States.

The United States NRCS has a soil database that has data collected from across the country over the last several decades. This also includes soil spectral scans. This data is available, but it may not be used to its full potential. For this study, pedon, horizon and spectral data was extracted from the database for samples collected from 2011 to 2015. Only sites that had been fully described and horizons that had been analyzed for the full suite of desired properties were used. This resulted in over 14,000 samples that were used for modeling and eight soil properties: soil organic carbon (SOC); total nitrogen (TN); total sulfur (TS); clay; sand; exchangeable calcium (Caex); cation exchange capacity (CEC); and pH. Four chemometric methods were employed for soil property prediction: partial least squares (PLSR); Random Forest (RF); Cubist; and multivariable adaptive regression splines (MARS). The dataset was sufficiently large that only random subsetting was used to create calibration (70%) and validation (30%) sets. SOC, TN, and TS had the strongest prediction results, with an R2 value of over 0.9. Caex, CEC and pH were predicted moderately well. Clay and sand models had slightly lower performance. Of the four methods, Cubist produced the strongest models, while PLSR produced the weakest. This may be due to the complex relationships between soil properties and spectra that PLSR could not capture. The only drawback of Cubist is the difficult interpretability of variable importance. Future research should include the use of environmental variables to improve prediction results. Future work may also avoid the use of PLSR when dealing with large datasets that cover large areas and have high degrees of variability.

Evaluating the effect of remote sensing image spatial resolution on soil exchangeable potassium prediction models in smallholder farm settings.

Major end users of Digital Soil Mapping (DSM) such as policy makers and agricultural extension workers are faced with choosing the appropriate remote sensing data. The objective of this research is to analyze the spatial resolution effects of different remote sensing images on soil prediction models in two smallholder farms in Southern India called Kothapally (Telangana State), and Masuti (Karnataka State), and provide empirical guidelines to choose the appropriate remote sensing images in DSM. Bayesian kriging (BK) was utilized to characterize the spatial pattern of exchangeable potassium (Kex) in the topsoil (0-15 cm) at different spatial resolutions by incorporating spectral indices from Landsat 8 (30 m), RapidEye (5 m), and WorldView-2/GeoEye-1/Pleiades-1A images (2 m). Some spectral indices such as band reflectances, band ratios, Crust Index and Atmospherically Resistant Vegetation Index from multiple images showed relatively strong correlations with soil Kex in two study areas. The research also suggested that fine spatial resolution WorldView-2/GeoEye-1/Pleiades-1A-based and RapidEye-based soil prediction models would not necessarily have higher prediction performance than coarse spatial resolution Landsat 8-based soil prediction models. The end users of DSM in smallholder farm settings need select the appropriate spectral indices and consider different factors such as the spatial resolution, band width, spectral resolution, temporal frequency, cost, and processing time of different remote sensing images. Overall, remote sensing-based Digital Soil Mapping has potential to be promoted to smallholder farm settings all over the world and help smallholder farmers implement sustainable and field-specific soil nutrient management scheme.

Spatial downscaling of soil prediction models based on weighted generalized additive models in smallholder farm settings.

Digital soil mapping (DSM) is gaining momentum as a technique to help smallholder farmers secure soil security and food security in developing regions. However, communications of the digital soil mapping information between diverse audiences become problematic due to the inconsistent scale of DSM information. Spatial downscaling can make use of accessible soil information at relatively coarse spatial resolution to provide valuable soil information at relatively fine spatial resolution. The objective of this research was to disaggregate the coarse spatial resolution soil exchangeable potassium (Kex) and soil total nitrogen (TN) base map into fine spatial resolution soil downscaled map using weighted generalized additive models (GAMs) in two smallholder villages in South India. By incorporating fine spatial resolution spectral indices in the downscaling process, the soil downscaled maps not only conserve the spatial information of coarse spatial resolution soil maps but also depict the spatial details of soil properties at fine spatial resolution. The results of this study demonstrated difference between the fine spatial resolution downscaled maps and fine spatial resolution base maps is smaller than the difference between coarse spatial resolution base maps and fine spatial resolution base maps. The appropriate and economical strategy to promote the DSM technique in smallholder farms is to develop the relatively coarse spatial resolution soil prediction maps or utilize available coarse spatial resolution soil maps at the regional scale and to disaggregate these maps to the fine spatial resolution downscaled soil maps at farm scale.

Assessment of Carbon Stocks in the Topsoil Using Random Forest and Remote Sensing Images.

Wetland soils are able to exhibit both consumption and production of greenhouse gases, and they play an important role in the regulation of the global carbon (C) cycle. Still, it is challenging to accurately evaluate the actual amount of C stored in wetlands. The incorporation of remote sensing data into digital soil models has great potential to assess C stocks in wetland soils. Our objectives were (i) to develop C stock prediction models utilizing remote sensing images and environmental ancillary data, (ii) to identify the prime environmental predictor variables that explain the spatial distribution of soil C, and (iii) to assess the amount of C stored in the top 20-cm soils of a prominent nutrient-enriched wetland. We collected a total of 108 soil cores at two soil depths (0-10 cm and 10-20 cm) in the Water Conservation Area 2A, FL. We developed random forest models to predict soil C stocks using field observation data, environmental ancillary data, and spectral data derived from remote sensing images, including Satellite Pour l'Observation de la Terre (spatial resolution: 10 m), Landsat Enhanced Thematic Mapper Plus (30 m), and Moderate Resolution Imaging Spectroradiometer (250 m). The random forest models showed high performance to predict C stocks, and variable importance revealed that hydrology was the major environmental factor explaining the spatial distribution of soil C stocks in Water Conservation Area 2A. Our results showed that this area stores about 4.2 Tg (4.2 Mt) of C in the top 20-cm soils.

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.

Holistic aboveground ecological productivity efficiency modeling using data envelopment analysis in the southeastern U.S.

Aboveground net primary productivity (ANPP) of an ecosystem is among the most important metrics of valued ecosystem services. Measuring the efficiency scores of ecological production (ESEP) based on ANPP using relevant variables is valuable for identifying inefficient sites. The efficiency scores computed by the Data Envelopment Analysis (DEA) may be influenced by the number of input variables incorporated into the models and two DEA settings-orientations and returns-to-scales (RTSs). Therefore, the objectives were threefold to: (1) identify soil-environmental variables relevant to ANPP, (2) assess the sensitivity of ESEP to the number of input variables and DEA settings, and (3) assess local management relations with ESEP. The ANPP rates were calculated for pine forests in the southeastern U.S. where 10 management types were used. This was followed by an all-relevant variable selection technique based on 696 variables that cover biotic, pedogenic, climatic, geological, and topographical factors. Five minimal-optimal variable selection techniques were further applied to create five parsimonious sets that contain a different number of variables used as DEA inputs. After setting ANPP as the output variable, two DEA orientations (input/output) and six RTS were applied to compute ESEP. The variable selection methods succeeded in objectively identifying the major factors relevant to ANPP variation. The site index showed the highest correlation with ANPP (r = 0.39), while various precipitation factors were negatively correlated (r = - 0.15~ - 0.29, p < 0.01). Parsimonious ESEP models observed a decrease in score variances as the number of input variables increased. Various RTS produced similar scores across orientations. Of the DEA settings, an output orientation with decreasing RTS produced the most progressive ESEP with large variation. Results also suggested that macro- and micronutrient fertilization is the best combination of management strategies to achieve high ESEP. This holistic benchmark approach can be applied to other ecological functions in diverse regions.

Spatial distributions and eco-partitioning of soil biogeochemical properties in the Everglades National Park.

Large-scale ecosystem restoration efforts, such as those in the Florida Everglades, can be long-term and resource intensive. To gauge success, restoration efforts must have a means to evaluate positive or negative results of instituted activities. Edaphic properties across the Everglades landscape have been determined to be a valuable metric for such evaluation, and as such, a baseline condition from which to make future comparisons and track ecosystem response is necessary. The objectives of this work were to document this baseline condition in the southern most hydrologic unit of the Everglades, Everglades National Park (ENP), and to determine if significant eco-partitioning of soil attributes exists that would suggest the need to focus monitoring efforts in particular eco-types within the ENP landscape. A total of 342 sites were sampled via soil coring and parameters such as total phosphorus (TP), total nitrogen (TN), total carbon (TC), total calcium, total magnesium, and bulk density were measured at three depth increments in the soil profile (floc, 0-10 cm, and 10-20 cm). Geostatistical analysis and GIS applications were employed to interpolate site-specific biogeochemical properties of soils across the entire extent of the ENP. Spatial patterns and eco-type comparisons suggest TC and TN to be highest in Shark River Slough (SRS) and the mangrove interface (MI), following trends of greatest organic soil accumulation. However, TP patterns suggest greatest storages in MI, SRS, and western marl and wet prairies. Eco-partitioning of soil constituents suggest local drivers of geology and hydrology are significant in determining potential areas to focus monitoring for future change detection.

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.

Modeling of phosphorus loads in sugarcane in a low-relief landscape using ontology-based simulation.

Water flow and P dynamics in a low-relief landscape manipulated by extensive canal and ditch drainage systems were modeled utilizing an ontology-based simulation model. In the model, soil water flux and processes between three soil inorganic P pools (labile, active, and stable) and organic P are represented as database objects. And user-defined relationships among objects are used to automatically generate computer code (Java) for running the simulation of discharge and P loads. Our objectives were to develop ontology-based descriptions of soil P dynamics within sugarcane- (Saccharum officinarum L.) grown farm basins of the Everglades Agricultural Area (EAA) and to calibrate and validate such processes with water quality monitoring data collected at one farm basin (1244 ha). In the calibration phase (water year [WY] 99-00), observed discharge totaled 11,114 m3 ha(-1) and dissolved P 0.23 kg P ha(-1); and in the validation phase (WY 02-03), discharge was 10,397 m3 ha(-1) and dissolved P 0.11 kg P ha(-). During WY 99-00 the root mean square error (RMSE) for monthly discharge was 188 m3 ha(-1) and for monthly dissolved P 0.0077 kg P ha(-1); whereas during WY 02-03 the RMSE for monthly discharge was 195 m3 ha(-1) and monthly dissolved P 0.0022 kg P ha(-1). These results were confirmed by Nash-Sutcliffe Coefficient of 0.69 (calibration) and 0.81 (validation) comparing measured and simulated P loads. The good model performance suggests that our model has promise to simulate P dynamics, which may be useful as a management tool to reduce P loads in other similar low-relief areas.

Integrative environmental modeling of soil carbon fractions based on a new latent variable model approach.

Soil-environmental correlation has been extensively studied as a cost-effective method for regional-scale soil attribute modeling. However, the limitations of commonly used statistical methods in soil-factorial modeling entail multicollinearity in bigdata soil-factorial prediction data and mixed type of soil-environmental variables (categorical and continuous). Both of these shortcomings were addressed resulting in a new soil-factorial modeling approach. The objective of this study was to develop a novel statistical technique for factorial modeling of topsoil soil total (TC), organic (SOC), recalcitrant (RC), moderately-available (MC), and hot-water extractable carbon (HC) in Florida. This article introduced a two-step regression technique (2Step-R) combining linear regressions (i.e., Ridge Regression-RR and Bayesian Linear Regression) and latent variable models (i.e., Partial Least Squares Regression-PLSR and Sparse Bayesian Infinite Factor-SBIF) for the integration of mixed type soil-environmental datasets. Results of this research showed the new technique capabilities to derive acceptable models for TC, SOC, RC, and MC predictions (R2 > 0.65; residual prediction deviation, RPD > 1.6), but fair for HC prediction (R2 ≤ 0.60; RPD ≤ 1.6). This novel method improved TC, SOC, and MC prediction accuracies compared with standard PLSR and RR methods. In conclusion, the new modeling approach that incorporates categorical along with continuous soil-environmental predictor variables in latent variable models has profound potential to improve soil attribute predictions in other regions.

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.

Accounting for two-billion tons of stabilized soil carbon.

The pedosphere is the largest terrestrial reservoir of organic carbon, yet soil-carbon variability and its representation in Earth system models is a large source of uncertainty for carbon-cycle science and climate projections. Much of this uncertainty is attributed to local and regional-scale variability, and predicting this variation can be challenging if variable selection is based solely on a priori assumptions due to the scale-dependent nature of environmental determinants. Data mining can optimize predictive modeling by allowing machine-learning algorithms to learn from and discover complex patterns in large datasets that may have otherwise gone unnoticed, thus increasing the potential for knowledge discovery. In this analysis, we identify important, regional-scale determinants for top- and subsoil-carbon stabilization in production forestland across the southeastern US. Specifically, we apply recursive feature elimination to a large suite of socio-environmental data to strategically select a parsimonious, yet highly predictive covariate set. This is achieved by recursively considering smaller and smaller covariate sets-or features-by first training the estimator on the full set to obtain feature importance. The least important features are pruned, and the procedure is recursively repeated until a desired number of covariates is identified. We show that although carbon ranges from 0.3 to 8.2 kg m-2 in the topsoil (0 to 20 cm), and from 0.4 to 17.6 kg m-2 in the subsoil (20 to 100 cm), this variability is predictably distributed with precipitation, soil moisture, nitrogen and sand content, gamma ray emissions, mean annual minimum temperature, and elevation. From our spatial predictions, we estimate that 2.6 Pg of soil carbon is currently stabilized in the upper 100 cm of production forestland, which covers 34.7 million ha in the southeastern US.

Long-term water quality trends after implementing best management practices in South Florida.

A mandatory best management practices (BMP) program was implemented in the Everglades Agricultural Area (EAA) farms basin-wide in 1995 as required by the Everglades Forever Act to reduce P loads in drainage water reaching the Everglades ecosystem. All farms in the EAA basin implement similar BMPs, and basin wide P load reductions have exceeded the 25% reduction required by law; however, differences remain in water quality between subbasins. Our objective was to determine long-term trends in P loads in discharge water in the EAA after implementing BMPs for 7 to10 yr and to explore reasons for differences in the performance of the subbasins. Two monitoring datasets were used, one from 10 research farms and the second from the EAA basin inflow and outflow locations. Mann-Kendall trend analysis was used to determine the degree of change in water quality trends. A decreasing trend in P loads was observed in general on sugarcane (Saccharum officinarum L.) farms, while mixed crop farms showed either decreasing or insignificant trends. The insignificant trends are probably related to management practices of mixed crop systems. Decreasing trends in P loads were observed in the outflow of the EAA basin, S5A, and S8 subbasins from 1992 to 2002. Inflow water from Lake Okeechobee had increasing P concentration from 1992 to 2006 with the highest trend in the east side of the lake. This analysis indicated there may be other factors impacting the success of BMPs in individual farms including cropping rotations and flooding of organic soils. Elevated P concentrations in Lake Okeechobee water used for irrigation may pose a future risk to degrade water quality on farms in the EAA, especially in the S5A subbasin.

Spatial relationships between nitrogen status and pitch canker disease in slash pine planted adjacent to a poultry operation.

Pitch canker disease (Fusarium circinatum Nirenberg & O'Donnell) causes serious shoot dieback, reduced growth and mortality in pines found in the southern and western USA, and has been linked to nutrient imbalances. Poultry houses with forced-air ventilation systems produce nitrogen (N) emissions. This study analyzed spatial correlations between pitch canker disease and foliar, forest floor, soil, and throughfall N in a slash pine (Pinus elliottii var. elliottii Engelm.) plantation adjacent to a poultry operation in north Florida, USA. Tissue and throughfall N concentrations were highest near the poultry houses and remained elevated for 400 m. Disease incidence ranged from 57-71% near the poultry houses and was spatially correlated with N levels. Similarly, stem mortality ranged from 41-53% in the most heavily impacted area, and declined to 0-9% at distances greater than 400 m. These results suggest that nutritional processes exacerbate changes in disease susceptibility and expression in slash pine.

Modeling Soil Organic Carbon at Regional Scale by Combining Multi-Spectral Images with Laboratory Spectra.

There is a great challenge in combining soil proximal spectra and remote sensing spectra to improve the accuracy of soil organic carbon (SOC) models. This is primarily because mixing of spectral data from different sources and technologies to improve soil models is still in its infancy. The first objective of this study was to integrate information of SOC derived from visible near-infrared reflectance (Vis-NIR) spectra in the laboratory with remote sensing (RS) images to improve predictions of topsoil SOC in the Skjern river catchment, Denmark. The second objective was to improve SOC prediction results by separately modeling uplands and wetlands. A total of 328 topsoil samples were collected and analyzed for SOC. Satellite Pour l'Observation de la Terre (SPOT5), Landsat Data Continuity Mission (Landsat 8) images, laboratory Vis-NIR and other ancillary environmental data including terrain parameters and soil maps were compiled to predict topsoil SOC using Cubist regression and Bayesian kriging. The results showed that the model developed from RS data, ancillary environmental data and laboratory spectral data yielded a lower root mean square error (RMSE) (2.8%) and higher R2 (0.59) than the model developed from only RS data and ancillary environmental data (RMSE: 3.6%, R2: 0.46). Plant-available water (PAW) was the most important predictor for all the models because of its close relationship with soil organic matter content. Moreover, vegetation indices, such as the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), were very important predictors in SOC spatial models. Furthermore, the 'upland model' was able to more accurately predict SOC compared with the 'upland & wetland model'. However, the separately calibrated 'upland and wetland model' did not improve the prediction accuracy for wetland sites, since it was not possible to adequately discriminate the vegetation in the RS summer images. We conclude that laboratory Vis-NIR spectroscopy adds critical information that significantly improves the prediction accuracy of SOC compared to using RS data alone. We recommend the incorporation of laboratory spectra with RS data and other environmental data to improve soil spatial modeling and digital soil mapping (DSM).

Interaction effects of climate and land use/land cover change on soil organic carbon sequestration.

Historically, Florida soils stored the largest amount of soil organic carbon (SOC) among the conterminous U.S. states (2.26 Pg). This region experienced rapid land use/land cover (LULC) shifts and climate change in the past decades. The effects of these changes on SOC sequestration are unknown. The objectives of this study were to 1) investigate the change in SOC stocks in Florida to determine if soils have acted as a net sink or net source for carbon (C) over the past four decades and 2) identify the concomitant effects of LULC, LULC change, and climate on the SOC change. A total of 1080 sites were sampled in the topsoil (0-20 cm) between 2008 and 2009 representing the current SOC stocks, 194 of which were selected to collocate with historical sites (n = 1251) from the Florida Soil Characterization Database (1965-1996) for direct comparison. Results show that SOC stocks significantly differed among LULC classes--sugarcane and wetland contained the highest SOC, followed by improved pasture, urban, mesic upland forest, rangeland, and pineland while crop, citrus and xeric upland forest remained the lowest. The surface 20 cm soils acted as a net sink for C with the median SOC significantly increasing from 2.69 to 3.40 kg m(-2) over the past decades. The SOC sequestration rate was LULC dependent and controlled by climate factors interacting with LULC. Higher temperature tended to accelerate SOC accumulation, while higher precipitation reduced the SOC sequestration rate. Land use/land cover change observed over the past four decades also favored the C sequestration in soils due to the increase in the C-rich wetland area by ~140% and decrease in the C-poor agricultural area by ~20%. Soils are likely to provide a substantial soil C sink considering the climate and LULC projections for this region.

Spatiotemporal modeling of soil organic carbon stocks across a subtropical region.

Given the significance and complex nature of soil organic carbon in the context of the global carbon cycle, the need exists for more accurate and economically feasible means of soil organic carbon analysis and its underlying spatial variation at regional scale. The overarching goal of this study was to assess both the spatial and temporal variability of soil organic carbon within a subtropical region of Florida, USA. Specifically, the objectives were to: i) quantify regional soil organic carbon stocks for historical and current conditions and ii) determine whether the soils have acted as a net sink or a net source for atmospheric carbon-dioxide over an approximate 40 year time period. To achieve these objectives, geostatistical interpolation models were used in conjunction with "historical" and "current" datasets to predict soil organic carbon stocks for the upper 20 cm soil profile of the study area. Soil organic carbon estimates derived from the models ranged from 102 to 108 Tg for historical conditions and 211 to 320 Tg for current conditions, indicating that soils in the study area have acted as a net sink for atmospheric carbon over the last 40 years. A paired resampling of historical sites supported the geostatistical estimates, and resulted in an average increase of 0.8 g carbon m(-2) yr(-1) across all collocated samples. Accurately assessing the spatial and temporal state of soil organic carbon at regional scale is critical to further our understanding of global carbon stocks and provide a baseline so that the effects sustainable land use policy can be evaluated.

Examining the spatial distribution of flower thrips in southern highbush blueberries by utilizing geostatistical methods.

Flower thrips (Frankliniella spp.) are one of the key pests of southern highbush blueberries (Vaccinium corymbosum L. x V. darrowii Camp), a high-value crop in Florida. Thrips' feeding and oviposition injury to flowers can result in fruit scarring that renders the fruit unmarketable. Flower thrips often form areas of high population, termed "hot spots", in blueberry plantings. The objective of this study was to model thrips spatial distribution patterns with geostatistical techniques. Semivariogram models were used to determine optimum trap spacing and two commonly used interpolation methods, inverse distance weighting (IDW) and ordinary kriging (OK), were compared for their ability to model thrips spatial patterns. The experimental design consisted of a grid of 100 white sticky traps spaced at 15.24-m and 7.61-m intervals in 2008 and 2009, respectively. Thirty additional traps were placed randomly throughout the sampling area to collect information on distances shorter than the grid spacing. The semivariogram analysis indicated that, in most cases, spacing traps at least 28.8 m apart would result in spatially independent samples. Also, the 7.61-m grid spacing captured more of the thrips spatial variability than the 15.24-m grid spacing. IDW and OK produced maps with similar accuracy in both years, which indicates that thrips spatial distribution patterns, including "hot spots," can be modeled using either interpolation method. Future studies can use this information to determine if the formation of "hot spots" can be predicted using flower density, temperature, and other environmental factors. If so, this development would allow growers to spot treat the "hot spots" rather than their entire field.

Recent changes in soil total phosphorus in the Everglades: Water Conservation Area 3.

We assessed recent changes in the distribution of soil total phosphorus (TP) in Water Conservation Area 3 (WCA-3) of the Everglades. Soil cores were collected in 1992 and 2003 at 176 sites. To reflect hydrologic boundaries within the system, WCA-3 was divided into three zones (3AN, 3AS, and 3B). Total P was mapped on both a mass (TPm) and a volumetric basis (TPv) to determine if spatial distributions varied depending on the choice of units. Interpolated maps for both years showed that the highest levels of TPm were located in 3AN and in boundary areas of all zones that received surface water inputs of P from canals. Increases in TPm were greatest in central 3AN in an area adjacent to the Miami Canal that received inputs from a water control structure. Interpolated maps for TPv illustrated that a hotspot present in 1992 had disappeared by 2003. The highest levels of TPv in 2003 were located in northwestern 3AN, a region of WCA-3 that has been chronically overdrained and burned in 1999. From 1992 to 2003, increases in TPm were observed for 53% of the area of WCA-3, while only 16% of WCA-3 exhibited increases in TPv. In 1992, approximately 21% of WCA-3 had TPm concentrations in the 0-10 cm layer >500 mg kg(-1), indicating P enrichment beyond historic levels. Eleven years later, 30% of the area of WCA-3 had TPm >500 mg kg(-1). This indicated that during this period, the area of WCA-3 with enriched TPm concentrations increased about one % year(-1).