Journal Paper Digests

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Journal Paper Digests 2020 #21

  • Beyond ecosystem modeling: A roadmap to community cyberinfrastructure for ecological data‐model integration
  • Monitoring of wheat crops using the backscattering coefficient and the interferometric coherence derived from Sentinel-1 in semi-arid areas
  • Synergistic use of SMAP and OCO-2 data in assessing the responses of ecosystem productivity to the 2018 U.S. drought
  • lidR: An R package for analysis of Airborne Laser Scanning (ALS) data
  • Proximally sensed digital data library to predict topsoil clay across multiple sugarcane fields of Australia: Applicability of local and universal support vector machine
  • Hypotheses, machine learning and soil mapping
  • Predicting soil properties in 3D: Should depth be a covariate?
  • Paper chromatography: An inconsistent tool for assessing soil health
  • UAV‐based training for fully fuzzy classification of Sentinel‐2 fluvial scenes
  • Integrating portable X-ray fluorescence (pXRF) measurement uncertainty for accurate soil contamination mapping
  • A Bayesian approach toward the use of qualitative information to inform on-farm decision making: The example of soil compaction
  • Errors induced by spectral measurement positions and instrument noise in soil organic carbon prediction using vis-NIR on intact soil
  • Combination of soil texture with Nix color sensor can improve soil organic carbon prediction
  • Numerical soil classification supports soil identification by citizen scientists using limited, simple soil observations
  • Beyond x,y,z(t); Navigating New Landscapes of Science in the Science of Landscapes
  • From pools to flow: The PROMISE framework for new insights on soil carbon cycling in a changing world
  • Reference state and benchmark concepts for better biodiversity conservation in contemporary ecosystems
  • COSORE: A community database for continuous soil respiration and other soil‐atmosphere greenhouse gas flux data

Beyond ecosystem modeling: A roadmap to community cyberinfrastructure for ecological data‐model integration

In an era of rapid global change, our ability to understand and predict Earth’s natural systems is lagging behind our ability to monitor and measure changes in the biosphere. Bottlenecks to informing models with observations have reduced our capacity to fully exploit the growing volume and variety of available data. Here, we take a critical look at the information infrastructure that connects ecosystem modeling and measurement efforts, and propose a roadmap to community cyberinfrastructure development that can reduce the divisions between empirical research and modeling and accelerate the pace of discovery. A new era of data‐model integration requires investment in accessible, scalable, and transparent tools that integrate the expertise of the whole community, including both modelers and empiricists. This roadmap focuses on five key opportunities for community tools: the underlying foundations of community cyberinfrastructure; data ingest; calibration of models to data; model‐data benchmarking; and data assimilation and ecological forecasting. This community‐driven approach is a key to meeting the pressing needs of science and society in the 21st century.

Monitoring of wheat crops using the backscattering coefficient and the interferometric coherence derived from Sentinel-1 in semi-arid areas

Radar data at C-band has shown great potential for the monitoring of soil and canopy hydric conditions of wheat crops. In this study, the C-band Sentinel-1 time series including the backscattering coefficients σ0 at VV and VH polarization, the polarization ratio (PR) and the interferometric coherence ρ are first analyzed with the support of experimental data gathered on three plots of irrigated winter wheat located in the Haouz plain in the center of Morocco covering five growing seasons. The results showed that ρ and PR are tightly related to the canopy development. ρ is also sensitive to soil preparation. By contrast, σ0 was found to be widely linked to changes in surface soil moisture (SSM) during the first growth stages when Leaf Area Index remains moderate (<1.5 m2/m2). In addition, drastic changes in the crop geometry associated to heading had a strong impact on the C-band σ0, in particular for VH polarization. The coupled water cloud and Oh models (WCM) were then calibrated and validated on the study sites. The comparison between the predicted and observed σ0 yielded a root mean square error (RMSE) values ranging from 1.50 dB to 2.02 dB for VV and between 1.74 dB to 2.52 dB for VH with significant differences occurring in the second part of the season after heading. Finally, new approaches based on the inversion of the WCM for SSM retrieval over wheat fields were proposed using Sentinel-1 radar data only. To this objective, the dry above-ground biomass (AGB) and the vegetation water content (VWC) were retrieved from the interferometric coherence and the PR. The relationships were then used as the vegetation descriptor in the WCM. The best retrieval results were obtained using the relationship between ρVV and the AGB (R and RMSE of 0.82, 0.05 m3/m3 respectively and no bias). The new retrieval approaches were then applied to a large database covering a rainfed field in Morocco and 18 plots of rainfed and irrigated wheat of the Kairouan plain (Tunisia) and compared to other classical techniques of SSM retrieval including simple linear relationships between SSM and σ0. The method based on the WCM and the ρVV-AGB relationships also provided with slightly better results than the others on the validation database (r = 0.75, RMSE = 0.06 m3/m3 and bias = 0.01 m3/m3 over the 18 plots of Tunisia) but the simple linear relationships performed also reasonably well (r = 0.62, RMSE = 0.07, bias = −0.01 in Tunisia for instance). This study opens perspectives for high resolution soil moisture mapping from Sentinel-1 data over south Mediterranean wheat crops and in fine, for irrigation scheduling and retrieval through the assimilation of these new products in an evapotranspiration model.

Synergistic use of SMAP and OCO-2 data in assessing the responses of ecosystem productivity to the 2018 U.S. drought

Soil moisture and gross primary productivity (GPP) estimates from the Soil Moisture Active Passive (SMAP) and solar-induced chlorophyll fluorescence (SIF) from the Orbiting Carbon Observatory-2 (OCO-2) provide new opportunities for understanding the relationship between soil moisture and terrestrial photosynthesis over large regions. Here we explored the potential of the synergistic use of SMAP and OCO-2 based data for monitoring the responses of ecosystem productivity to drought. We used complementary observational information on root-zone soil moisture and GPP (9 km) from SMAP and fine-resolution SIF (0.05°; GOSIF) derived from OCO-2 SIF soundings. We compared the spatial pattern and temporal evolution of anomalies of these variables over the conterminous U.S. during the 2018 drought, and examined to what extent they could characterize the drought-induced variations of flux tower GPP and crop yield data. Our results showed that SMAP GPP and GOSIF, both freely available online, could well capture the spatial extent and dynamics of the impacts of drought indicated by the U.S. Drought Monitor maps and the SMAP root-zone soil moisture deficit. Over the U.S. Southwest, monthly anomalies of soil moisture showed significant positive correlations with those of SMAP GPP (R2 = 0.44, p < 0.001) and GOSIF (R2 = 0.76, p < 0.001), demonstrating strong water availability constraints on plant productivity across dryland ecosystems. We further found that SMAP GPP and GOSIF captured the impact of drought on tower GPP and crop yield. Our results suggest that synergistic use of SMAP and OCO-2 data products can reveal the drought evolution and its impact on ecosystem productivity and carbon uptake at multiple spatial and temporal scales, and demonstrate the value of SMAP and OCO-2 for studying ecosystem function, carbon cycling, and climate change.

lidR: An R package for analysis of Airborne Laser Scanning (ALS) data

Airborne laser scanning (ALS) is a remote sensing technology known for its applicability in natural resources management. By quantifying the three-dimensional structure of vegetation and underlying terrain using laser technology, ALS has been used extensively for enhancing geospatial knowledge in the fields of forestry and ecology. Structural descriptions of vegetation provide a means of estimating a range of ecologically pertinent attributes, such as height, volume, and above-ground biomass. The efficient processing of large, often technically complex datasets requires dedicated algorithms and software. The continued promise of ALS as a tool for improving ecological understanding is often dependent on user-created tools, methods, and approaches. Due to the proliferation of ALS among academic, governmental, and private-sector communities, paired with requirements to address a growing demand for open and accessible data, the ALS community is recognising the importance of free and open-source software (FOSS) and the importance of user-defined workflows. Herein, we describe the philosophy behind the development of the lidR package. Implemented in the R environment with a C/C++ backend, lidR is free, open-source and cross-platform software created to enable simple and creative processing workflows for forestry and ecology communities using ALS data. We review current algorithms used by the research community, and in doing so raise awareness of current successes and challenges associated with parameterisation and common implementation approaches. Through a detailed description of the package, we address the key considerations and the design philosophy that enables users to implement user-defined tools. We also discuss algorithm choices that make the package representative of the ‘state-of-the-art’ and we highlight some internal limitations through examples of processing time discrepancies. We conclude that the development of applications like lidR are of fundamental importance for developing transparent, flexible and open ALS tools to ensure not only reproducible workflows, but also to offer researchers the creative space required for the progress and development of the discipline.

Proximally sensed digital data library to predict topsoil clay across multiple sugarcane fields of Australia: Applicability of local and universal support vector machine

Knowledge about topsoil (0–0.3 m) clay is required to maintain sugarcane profitability in Queensland, Australia. However, laboratory analysis to get this knowledge is tedious and time consuming. To add value to limited clay data, a digital soil map (DSM) can be created by using digital data and mathematical models. At the field level, site-specific linear regression (LR) models are often used along with gamma-ray (γ-ray) spectrometry and electromagnetic induction data (i.e. soil apparent electrical conductivity – ECa). But these LR might not perform well in site-independent calibrations across multiple sites. In this regard, support vector machine (SVM) might be useful. In this research, we first aimed to determine, using a stepwise SVM and calibration dataset, the optimal digital data (i.e. individual or combined) to develop local (for each individual site) and universal (for combined sites) SVM models. Using optimal digital data, our second aim was to predict clay for validation datasets by using local SVM in site-specific approach and universal SVM in site-independent, holdout and spiking approaches. Using these approaches, DSM of predicted clay and associated uncertainty were generated for a representative study site (i.e., Mossman). The third aim was to determine the suitable number of spiking samples and by varying the size of both spiking set and calibration model. Approaches were compared using prediction agreement (Lin’s concordance) and accuracy (ratio of performance to deviation – RPD). We concluded from stepwise SVM that combining digital data resulted in better accuracy (RPD = 2.17) than individual γ-ray (1.79) or ECa (1.49) data. In terms of independent validation, the results of Mossman reflected the general rank order of different approaches with site-specific (3.03) excellent, spiking (1.89) very good, site-independent (1.84) good and holdout (1.18) poor predictions. In case of DSM and uncertainty maps, under-predictions were problematic at field edges and where digital data changed as a function of soil type and or proximity to a prior stream channel. Considering the suitable number of spiking samples, size of spiking set showed linear relationship with improvement in predictions while that of calibration model showed no influence. These results suggest the usefulness of proximally sensed data and universal SVM to effectively predict clay across multiple sites.

Hypotheses, machine learning and soil mapping

Hypotheses are of major importance in scientific research. In current applications of machine learning algorithms for soil mapping the hypotheses being tested or developed are often ambiguous or undefined. Mapping soil properties or classes, however, does not tell much about the dynamics and processes that underly soil genesis and evolution. When the interest in the soil map is for applications in a context different than soil science, such as for policy making or baseline production of quantitative soil information, the interpretation should be made in light of this application. If otherwise, we recommend soil scientists to provide hypotheses to accompany their research. The hypothesis is formulated at the beginning of the research and, in some cases, motivates data collection. Here we argue that when applying data-driven techniques such as machine learning, developing hypotheses can be a useful end point of the research. The spatial pattern predicted by the machine learning model and the correlation found among the covariates are an opportunity to develop hypotheses which are likely to require additional analyses and datasets to be tested. Systematically providing scientific hypotheses in digital soil mapping studies will enable the soil science community to build on previous work, and to increase the credibility of data-driven algorithms as a means to accelerate discovery on soil processes.

Predicting soil properties in 3D: Should depth be a covariate?

Soil is a three-dimensional volume with property variability in all three dimensions. In Digital Soil Mapping (DSM), the variation of soil properties down a profile is usually harmonised by the use of the equal-area spline depth function approach. Soil observations at various depth intervals are harmonised to pre-determined depth intervals. To create maps of soil at the defined depth intervals, 2.5D model produces maps of individual depth intervals separately. Those maps can be reconstructed to produce a continuous depth function for each predicted location. More recently, several studies propose that soil property at any depth can be mapped using a model incorporating depth along with spatial covariates as predictor variables, creating a ‘3D’ model. The aim of this study is to evaluate the proposition that soil properties can be predicted at any depth. This study compares the 2.5D model and 3D model in two areas. The first test is on a 1500 km2 area in Edgeroi, New South Wales (NSW), Australia, mapping soil organic carbon (SOC, %), carbon storage (kg m−2), pH (H2O), clay content (%), and cation exchange capacity (CEC, mg/kg) based on depth-interval observations. The second study area in the Lower Hunter Valley has SOC observations at every 2 cm increment from a 210 km2 area. 2.5D and 3D models were tested in both study areas using four machine learning techniques: Cubist regression tree, Quantile Regression Forest (QRF), Artificial Neural Network (ANN), and 3D Generalised Additive Model (GAM). Results show that, in terms of R2 and RMSE, 2.5D and 3D models using different machine learning models produce comparable results on the validation of depth interval observations. The 3D tree-based models produce “stepped” prediction of properties with depth. Results on the Hunter Valley area with point observations show that the 3D model cannot replicate field point observations. 3D soil mapping on point depth observation has lower accuracy and larger uncertainty compared to the 2.5D model. For future DSM studies, 3D soil mapping with depth as a covariate requires caution with respect to the prediction method and the requirements of the results.

Paper chromatography: An inconsistent tool for assessing soil health

Although paper chromatography is being promoted as a cost-effective tool for rapid assessment of soil health, few studies have explored the quantitative relationship between chromatogram features and soil health variables, and no studies have investigated the association between chromatogram features and microbial diversity as determined using DNA sequences. We assessed 343 soil samples from varying land uses in southwestern Australia to investigate the relationship between total organic carbon, microbial activity and diversity, salinity and pH levels and 12 chromatogram features. Spearman’s correlations and variance partitioning were used to detect relationships. Although total organic carbon and microbial activity displayed the strongest correlations with chromatogram features, they were not associated with greater development of radial features and median zone radius as expected. This was in contrast to what has been previously reported, implying context dependent responses of chromatogram features to gradients in soil variables. Microbial community structure was found to explain changes in chromatogram features better than the measured soil variables. These results indicate that further studies are necessary before paper chromatography is embraced as a tool for soil health assessment, and raises questions regarding the use of chromatography by community groups as a tool to measure soil health.

UAV‐based training for fully fuzzy classification of Sentinel‐2 fluvial scenes

An estimated 76% of global stream area is occupied by channels with widths above 30 m. Sentinel‐2 imagery with resolutions of 10 m could supply information about the composition of river corridors at national and global scales. Fuzzy classification models that infer sub‐pixel composition could further be used to compensate for small channel widths imaged at 10 m of spatial resolution. A major challenge to this approach is the acquisition of suitable training data useable in machine learning models that can predict land‐cover type information from image radiance values. In this contribution, we present a method which combines unmanned aerial vehicles (UAVs) and Sentinel‐2 imagery in order to develop a fuzzy classification approach capable of large‐scale investigations. Our approach uses hyperspatial UAV imagery in order to derive high‐resolution class information that can be used to train fuzzy classification models for Sentinel‐2 data where all bands are super‐resolved to a spatial resolution of 10 m. We use a multi‐temporal UAV dataset covering an area of 5.25 km2. Using a novel convolutional neural network (CNN) classifier, we predict sub‐pixel membership for Sentinel‐2 pixels in the fluvial corridor as divided into classes of water, vegetation and dry sediment. Our CNN model can predict fuzzy class memberships with median errors from −5% to +3% and mean absolute errors from 10% to 20%. We also show that our CNN fuzzy predictor can be used to predict crisp classes with accuracies from 95.5% to 99.9%. Finally, we use an example to show how a fuzzy CNN model trained with localized UAV data can be applied to longer channel reaches and detect new vegetation growth. We therefore argue that the novel use of UAVs as field validation tools for freely available satellite data can bridge the scale gap between local and regional fluvial studies.

Integrating portable X-ray fluorescence (pXRF) measurement uncertainty for accurate soil contamination mapping

significant reduction in the costs associated with contamination assessments can be achieved if traditional soil sampling for contaminated-site characterization is complemented by real-time sampling using proximal soil sensors. Real-time sampling using a portable X-ray fluorescence (pXRF) device is a cheap and fast sampling method to provide more data and reduce the time needed to map soil contamination. The main disadvantage of using pXRF is the degree of uncertainty of these in situ measurements due to the technology’s indirect nature, and its sensitivity to soil heterogeneity and soil moisture content. This study evaluates the potential of using both pXRF and traditional soil sampling measurements to accurately map soil contamination due to the presence of heavy metals. The approach proposed uses geostatistical sequential simulation with local probability distributions to characterize and integrate pXRF uncertainty at each sampling location. The resulting maps agree with the contamination map obtained using traditional laboratory data only, in terms of mapping accuracy and extent of contaminated areas. This study shows that with few collocated pXRF and laboratory analytical data it is possible to identify contaminated areas accurately, thus providing a cost-effective solution to work with pXRF data directly.

A Bayesian approach toward the use of qualitative information to inform on-farm decision making: The example of soil compaction

As the development of predictive tools to aid decision making in agriculture increases, it must be acknowledged that the ability to incorporate management decisions as input data is limited. The data is either not recorded, or highly inadequate in terms of the volume, variety or relevance. Some circumstances are further hampered by the lack of benchmark data for comparison, and soil compaction is an example of this. The premise of this work was to take a probability based approach to decision making, utilising both qualitative and quantitative data to provide a probability distribution of risk against decisions made, in the context of grains production systems as an example of an agricultural enterprise. A Bayesian Belief Network (BBN) was constructed for soil compaction risk, as an exemplar. The BBN conditional probability tables for nodes were populated via a combination of biophysical model output (namely SoilFlex for soil stress distribution, and the APSIM package for soil-water and crop parameters) and expert opinion. Input nodes were parameterised with measured soil data, and the risk of soil compaction, given the soil stress at the wheel of a particular vehicle, was provided as the output. Potential effect on yield was subsequently calculated on the basis of percent change in soil bulk density, which was determined using literature based information (expert opinion). The tool broadly estimated yield impacts due to various agricultural traffic scenarios, providing means to highlight the financial consequences of failing to adopt controlled traffic farming management for a particular agricultural enterprise. Of significance, the BBN approach was determined useful for data limiting environments where empirical models struggle, thus providing a pragmatic and novel approach to on farm decision making incorporating management nuance.

Errors induced by spectral measurement positions and instrument noise in soil organic carbon prediction using vis-NIR on intact soil

Soil spectroscopy potentially would become a routine method for the measurement of soil properties in the field on soil cores. However, many factors could lead to errors in the measurement of spectra from intact soil cores. So far, errors induced by spectral measurement positions and instrument noise have rarely been quantified. The present study evaluated these errors based on 160 intact ring core samples collected from 20 profiles in a forest, southwest China. Each ring sample was scanned using a visible near-infrared (vis-NIR) spectrometer at nine evenly distributed positions at both ends of the ring to characterize within-sample variability. In addition, 10 scans were made at each position to characterize instrument error. A position spectrum was then calculated by averaging the 10 scans at each position, while a sample spectrum was calculated by averaging all 180 scans of each sample. The samples were then tested for soil organic carbon (SOC) content using the wet oxidation method. Based on the sample spectra and SOC content, a partial least squares regression (PLSR) model was calibrated. The modeling error was empirically calculated. Each scan, position spectrum, and sample spectrum was then fed into the calibrated PLSR model to predict SOC content, and the prediction results were evaluated for errors induced by instrument noise and different spectral measurement positions. Results showed that the error induced by different spectral measurement positions was about 75% of the modeling error, while the error induced by instrument noise was small and negligible. These three errors in terms of standard error of prediction (SEP) accounted for 18.6%, 23.8% and 1.46% of the average measured SOC content of the samples, respectively. Besides, the error due to different soil core sampling positions was also considerable. In terms of SEP, this error accounted for 12.5% of the average measured SOC content. These quantified error budgets would help to account for uncertainty in the measurement of soil properties using spectroscopy in the field.

Combination of soil texture with Nix color sensor can improve soil organic carbon prediction

The optimum ecosystem functioning is reliant on soil organic carbon (SOC) content which is traditionally measured in the laboratory via a cumbersome wet-chemistry method. This preliminary research tested whether a combination of Nix color sensor and portable X-ray fluorescence (PXRF) spectrometer data with soil texture data can improve soil SOC prediction accuracy relative to using them independently. A total of 371 samples representing diverse soil texture and SOC content were collected from three different ecoregions of eastern India: coastal saline zone, red and lateritic zone, and Gangetic alluvial zone. All dried, ground, and sieved samples were scanned via Nix and PXRF and random forest (RF) regression was used to predict soil SOC with different combinations of data. Soils were grouped into nine textural classes while soil SOC content exhibited substantial variability (0.08–2.26%). Comparing soil SOC with texture (sand + silt + clay), satisfactory prediction accuracy was observed (validation R2 = 0.70). Combining Nix extracted color parameters with texture substantially improved the model performance, producing the validation determination coefficient of 0.81. In contrast, PXRF-Rb, as a proxy of soil clay content was unable to achieve satisfactory prediction performance (R2 = 0.24), indicating the heterogeneity in soil mineralogical composition. The RF variable importance plot using Nix alone identified redness (a) and yellowness to blueness (b) as influential predictors, manifesting the impact of red color from Fe and Al-oxides and their significant negative correlation with soil SOC (r = −0.62 and −0.57 for a* and b*, respectively). These color parameters were again identified by the RF variable importance plot of (Texture + Nix)-model, implying that the SOC prediction improvement may be linked with the Nix sensor’s capability of extracting useful information in the visible range. Summarily, a combination of Nix color variables and texture data was adept at predicting soil SOC in lieu of traditional laboratory analysis. The robustness of the (Texture + Nix)-based SOC prediction model can be augmented by incorporating more soil samples representing all 12 soil textural classes and variable SOC content, showing all possible soil colors in the pedogenic environment.

Numerical soil classification supports soil identification by citizen scientists using limited, simple soil observations

Accurately identifying the soil map unit component at a specific point‐location within a landscape is critical for implementing sustainable soil management. Recent developments in smartphone‐based technologies for characterizing soil profiles, coupled with improved numerical soil classification algorithms, have made it more accessible for non‐soil scientists to sample, characterize, and classify soil profiles. The main objective of this study was to evaluate an operational soil classification framework for identifying the soil component at a sampling‐location based on the numerical similarity of soil property values between the sampled soil profile and the soil components mapped in that area. To evaluate this soil identification framework, we used a subset of the U.S. National Cooperative Soil Survey Soil Characterization Database (NCSS–SCD) as our soil profile test dataset and the U.S. Soil Survey Geographic (SSURGO) database as our reference dataset using profile data of soil components in the area surrounding each test profile. Numerical similarity was tested using soil property data representing different degrees of generalization, both in terms of generalizing depth‐wise variability (i.e., depth‐support) and generalizing across feature space (i.e., soil properties). Three soil property groups (i.e., Novice, Expert, Expert‐Plus) representing different levels of detail and three types of depth‐support (i.e., genetic horizon, depth intervals, and depth functions) were evaluated. Using a simple set of soil property inputs (i.e., Novice: soil texture class, rock fragment volume class, and soil color) resulted in nearly as high identification accuracy (46–53%) as that achieved with an Expert (48–57%) dataset that included more precise determinations (percent sand, silt, clay, and rock fragment volume), and virtually no further improvement with the addition of pH and organic matter in the Expert‐Plus dataset (53–60%). This study also showed minimal effect from the type of depth‐support used to represent depth‐wise variability. Furthermore, we evaluated several measures of soil functional similarity (i.e., ecological sites, land capability, taxonomic distance) which resulted in management relevant accuracies ranging from 65–89%. These findings support the utility of simple soil observations sampled at fixed depths for soil identification.

Beyond x,y,z(t); Navigating New Landscapes of Science in the Science of Landscapes

At the start of its centennial year, AGU’s surface process community revisited G. K. Gilbert’s legacy of landscape description and experimental models of surface processes, as well as his embrace of critique and pragmatism in the practice of landscape science. In the 100 years, since Gilbert and especially since the dawn of the 21st century, we have seen an intensified focus on the acquisition of more and more earth observation data and the numerical modeling of landscapes, alongside widespread use of deterministic and predictive practices to find solutions to the social, economic, and environmental challenges of today. What have we gained and lost in this pursuit? Here we lay out some of the challenges for the discipline in an increasingly data‐rich and complex world in which earth science is also being called to reorient itself towards more societally relevant roles. We ask the community to ponder the following: Is the discipline serving our scientific and societal goals, or is there a need for the science of landscapes to adopt new frameworks of thinking and to question the deterministic approaches that have dominated our discipline to date, in order to attend to the needs of living in the Anthropocene?

From pools to flow: The PROMISE framework for new insights on soil carbon cycling in a changing world

Soils represent the largest terrestrial reservoir of organic carbon, and the balance between soil organic carbon (SOC) formation and loss will drive powerful carbon‐climate feedbacks over the coming century. To date, efforts to predict SOC dynamics have rested on pool‐based models, which assume classes of SOC with internally homogenous physicochemical properties. However, emerging evidence suggests that soil carbon turnover is not dominantly controlled by the chemistry of carbon inputs, but rather by restrictions on microbial access to organic matter in the spatially heterogeneous soil environment. The dynamic processes that control the physicochemical protection of carbon translate poorly to pool‐based SOC models; as a result, we are challenged to mechanistically predict how environmental change will impact movement of carbon between soils and the atmosphere. Here, we propose a novel conceptual framework to explore controls on belowground carbon cycling: Probabilistic Representation of Organic Matter Interactions within the Soil Environment (PROMISE). In contrast to traditional model frameworks, PROMISE does not attempt to define carbon pools united by common thermodynamic or functional attributes. Rather, the PROMISE concept considers how SOC cycling rates are governed by the stochastic processes that influence the proximity between microbial decomposers and organic matter, with emphasis on their physical location in the soil matrix. We illustrate the applications of this framework with a new biogeochemical simulation model that traces the fate of individual carbon atoms as they interact with their environment, undergoing biochemical transformations and moving through the soil pore space. We also discuss how the PROMISE framework reshapes dialogue around issues related to SOC management in a changing world. We intend the PROMISE framework to spur the development of new hypotheses, analytical tools, and model structures across disciplines that will illuminate mechanistic controls on the flow of carbon between plant, soil, and atmospheric pools.

Reference state and benchmark concepts for better biodiversity conservation in contemporary ecosystems

Measuring the status and trends of biodiversity is critical for making informed decisions about the conservation, management or restoration of species, habitats and ecosystems. Defining the reference state against which status and change are measured is essential. Typically, reference states describe historical conditions, yet historical conditions are challenging to quantify, may be difficult to falsify, and may no longer be an attainable target in a contemporary ecosystem. We have constructed a conceptual framework to help inform thinking and discussion around the philosophical underpinnings of reference states and guide their application. We characterize currently recognized historical reference states and describe them as Pre‐Human, Indigenous Cultural, Pre‐Intensification and Hybrid‐Historical. We extend the conceptual framework to include contemporary reference states as an alternative theoretical perspective. The contemporary reference state framework is a major conceptual shift that focuses on current ecological patterns and identifies areas with higher biodiversity values relative to other locations within the same ecosystem, regardless of the disturbance history. We acknowledge that past processes play an essential role in driving contemporary patterns of diversity. The specific context for which we design the contemporary conceptual frame is underpinned by an overarching goal—to maximize biodiversity conservation and restoration outcomes in existing ecosystems. The contemporary reference state framework can account for the inherent differences in the diversity of biodiversity values (e.g. native species richness, habitat complexity) across spatial scales, communities and ecosystems. In contrast to historical reference states, contemporary references states are measurable and falsifiable. This ‘road map of reference states’ offers perspective needed to define and assess the status and trends in biodiversity and habitats. We demonstrate the contemporary reference state concept with an example from south‐eastern Australia. Our framework provides a tractable way for policy‐makers and practitioners to navigate biodiversity assessments to maximize conservation and restoration outcomes in contemporary ecosystems.

COSORE: A community database for continuous soil respiration and other soil‐atmosphere greenhouse gas flux data

Globally, soils store two to three times as much carbon as currently resides in the atmosphere, and it is critical to understand how soil greenhouse gas (GHG) emissions and uptake will respond to ongoing climate change. In particular, the soil‐to‐atmosphere CO2 flux, commonly though imprecisely termed soil respiration (RS), is one of the largest carbon fluxes in the Earth system. An increasing number of high‐frequency RS measurements (typically, from an automated system with hourly sampling) have been made over the last two decades; an increasing number of methane measurements are being made with such systems as well. Such high frequency data are an invaluable resource for understanding GHG fluxes, but lack a central database or repository. Here we describe the lightweight, open‐source COSORE (COntinuous SOil REspiration) database and software, that focuses on automated, continuous and long‐term GHG flux datasets, and is intended to serve as a community resource for earth sciences, climate change syntheses and model evaluation. Contributed datasets are mapped to a single, consistent standard, with metadata on contributors, geographic location, measurement conditions and ancillary data. The design emphasizes the importance of reproducibility, scientific transparency and open access to data. While being oriented towards continuously measured RS, the database design accommodates other soil‐atmosphere measurements (e.g. ecosystem respiration, chamber‐measured net ecosystem exchange, methane fluxes) as well as experimental treatments (heterotrophic only, etc.). We give brief examples of the types of analyses possible using this new community resource and describe its accompanying R software package.

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