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A major obstacle to selecting the most appropriate crops and closing the yield gap in many areas of the world is a lack of site-specific soil information. Accurate information on soil properties is critical for identifying soil limitations and the management practices needed to improve crop yields. However, acquiring accurate soil information is often difficult due to the high spatial and temporal variability of soil properties at fine scales and the cost and inaccessibility of laboratory-based soil analyses. With recent advancements in predictive soil mapping, there is a growing expectation that soil map predictions can provide much of the information needed to inform soil management. Yet, it is unclear how accurate current soil map predictions are at scales relevant to management. The main objective of this study was to address this issue by evaluating the site-specific accuracy of regional-to-global soil maps, using Ghana as a test case. Four web-based soil maps of Ghana were evaluated using a dataset of 6514 soil profile descriptions collected on smallholder farms using the LandPKS mobile application. Results from this study revealed that publicly available soil maps in Ghana lack the needed accuracy (i.e., correct identification of soil limitations) to reliably inform soil management decisions at the 1–2 ha scale common to smallholders. Standard measures of map accuracy for soil texture class and rock fragment class predictions showed that all soil maps had similar performance in estimating the correct property class. Overall soil texture class accuracies ranged from 8 %–14 % but could be as high as 38 %–64 % after accounting for uncertainty in the evaluation dataset. Soil rock fragment class accuracies ranged from 26 %–29 %. However, despite these similar overall accuracies, there were substantial differences in soil property predictions among the four maps, highlighting that soil map errors are not uniform between maps. To better understand the functional implications of these soil property differences, we used a modified version of the FAO Global Agro-Ecological Zone (GAEZ) soil suitability modeling framework to derive soil suitability ratings for each soil data source. Using a low-input, rain-fed, maize production scenario, we evaluated the functional accuracy of map-based soil property estimates. This analysis showed that soil map data significantly overestimated crop suitability for over 65 % of study sites, potentially leading to ineffective agronomic investments by farmers, including cash-constrained smallholders.

A major obstacle to selecting the most appropriate crops and closing the yield gap in many areas of the world is a lack of site-specific soil information. Accurate information on soil properties is critical for identifying soil limitations and the management practices needed to improve crop yields. However, acquiring accurate soil information is often difficult due to the high spatial and temporal variability of soil properties at fine scales and the cost and inaccessibility of laboratory-based soil analyses. With recent advancements in predictive soil mapping, there is a growing expectation that soil map predictions can provide much of the information needed to inform soil management. Yet, it is unclear how accurate current soil map predictions are at scales relevant to management. The main objective of this study was to address this issue by evaluating the site-specific accuracy of regional-to-global soil maps, using Ghana as a test case. Four web-based soil maps of Ghana were evaluated using a dataset of 6,514 soil profile descriptions collected on smallholder farms using the LandPKS mobile application. Results from this study revealed that publicly available soil maps in Ghana lack the needed accuracy (i.e., correct identification of soil limitations) to reliably inform soil management decisions at the 1–2 ha scale common to smallholders. Standard measures of map accuracy for soil texture class and rock fragment class showed that all soil maps had similar performance in estimating the correct property class, with overall accuracies ranging from 8–39 % for soil texture classes and 26–33 % for soil rock fragment classes. Furthermore, there were substantial differences in soil property predictions among the four maps, highlighting that soil map errors are not uniform between maps despite their similar overall accuracies. To better understand the functional implications of these soil property differences, we used a modified version of the FAO Global Agro-Ecological Zone (GAEZ) soil suitability modelling framework to derive soil suitability ratings for each soil data source. Using a low-input, rain-fed, maize production scenario, we evaluated the functional accuracy of map-based soil property estimates. This analysis showed that soil map data significantly overestimated crop suitability for over 65 % of study sites, potentially leading to ineffective agronomic investments by farmers, including cash-constrained smallholders.

Increasingly, satellite-derived rainfall data is used for climate research and action in Africa. In this study, we use six years of rain gauge data from 596 stations operated by the Trans-African Hydro-Meteorological Observatory (TAHMO) to validate three gauge-calibrated satellite rainfall products – CHIRPS, TAMSAT and GSMaP_wGauge – and one satellite-only rainfall product – GSMaP. Validations are stratified to evaluate performance across the continent and in East Africa, Southern Africa, and West Africa at daily, pentadal, and monthly timescales. For daily mean rainfall over Africa, CHIRPS has the highest bias at 15.5 % (0.5 mm) whereas GSMaP_wGauge has the lowest bias at 0.02 mm (0.7 %). We find higher daily rainfall event detection scores in the GSMaP products than in CHIRPS or TAMSAT. Generally, for every two rainfall events predicted by CHIRPS and TAMSAT, the GSMaP products predict three or more events. The highest mean monthly biases are produced by CHIRPS in East Africa (29 %; 26.3 mm wet bias), TAMSAT in Southern Africa (13 %; 10.4 mm dry bias) and GSMaP in West Africa (23 %; 19.6 mm wet bias). Considerable biases in seasonal rainfall are observed in all sub-regions for every satellite product. There is an increase of 0.6–1.3 mm in satellite rainfall RMSE for a 1 km increase in elevation revealing the influence of elevation on rainfall estimation by satellite models. Overall, satellite-derived rainfall products have notable errors, while GSMaP products produce comparable or better results at multiple timescales relative to CHIRPS and TAMSAT.

One of the major causes of poverty globally is land degradation and poor natural resource conservation, leading to reduced agricultural productivity. This degradation is often caused by a mismatch between land use and land potential, specifically using marginal lands for agriculture. For over 50 years the Land Capability Classification (LCC) system has been used globally for land evaluation to support soil and natural resource conservation. The LCC system classifies the land into eight classes; however, its use is currently limited by two factors: the lack of digital platforms for data input, storage, and management, and an insufficient technical capacity in many regions necessary to generate the required inputs. This paper describes the development of a system to facilitate rapid, flexible, and transparent determinations of LCC by non-soil scientists using a newly developed function of the Land-Potential Knowledge System (LandPKS) mobile app. Inputs include soil texture and rock fragment volume by depth, slope, and site observations of soil limiting factors. A standardized system for evaluating inputs and calculated indicators was developed based on US and international implementations of LCC. The system was evaluated using USDA Natural Resources Conservation Service soil survey data in eight US counties. Results show that the standardized system predictions were within one class for 73.8% of the 1,312 soils tested, despite a high level of variability in how LCC was determined within the US database. The LandPKS LCC system was further tested in Tanzania and Ethiopia to examine site-specific applications, usability, and usefulness of the system for national land use planning efforts. It was concluded that the LandPKS app automates a globally applied system (LCC) for supporting natural resource conservation and sustainable land management and can serve as a foundation for crop-specific land suitability evaluations. More generally, improved land evaluation efforts can contribute to better soil and natural resource conservation, more sustainable agricultural systems, and increased food security.

Soil health is one of the key factors in determining the sustainability of global agricultural systems and the stability of natural ecosystems. Microbial decomposition activity plays an important role in soil health; and gaining spatiotemporal insights into this attribute is critical for understanding soil function as well as for managing soils to ensure agricultural supply, stem biodiversity loss, and mitigate climate change. Here, a novel in situ electronic soil decomposition sensor that relies on the degradation of a printed conductive composite trace utilizing the biopolymer poly(3-hydroxybutyrate-co-3-hydroxyvalerate) as a binder is presented. This material responds selectively to microbially active environments with a continuously varying resistive signal that can be readily instrumented with low-cost electronics to enable wide spatial distribution. In soil, a correlation between sensor response and intensity of microbial decomposition activity is observed and quantified by comparison with respiration rates over 14 days, showing that devices respond predictably to both static conditions and perturbations in general decomposition activity.

The response hierarchy of “Avoid > reduce > reverse” is increasingly acknowledged as the best strategy for prioritizing actions designed to address land degradation at hectare to national scales. This hierarchy is based on the assumption that the economic return on investment (ROI) will usually be higher for actions that help avoid degradation than for those required to restore already degraded land. While a useful first step, the hierarchy fails to account for how differences in land potential, defined as its potential to sustainably generate ecosystem services, may affect the ROI of actions at each level of the response hierarchy. The objective of this paper is to present a strategy for improving ROI at the landscape scale and above by systematically applying a more holistic understanding of land potential to the identification and prioritization of land investments. This objective is addressed in three sections. The first section explains how the potential short- and long-term resistance and resilience of the land can be used together with its potential productivity to prioritize actions designed to avoid, reduce and reverse degradation. In the second section we explain how this prioritization can be further optimized based on an understanding of degradation risk as indicated by the land’s potential to generate relatively high short-term profits under management systems that are likely to increase degradation, or result in degradation of restored land. This potential, and land managers’ perception of it, depend on a wide variety of factors including markets, infrastructure, and access to technology. Together these first two sections provide a framework for increasing ROI, while reducing the risk of failure at hectare to national scales. In the final section we briefly describe the Land-Potential Knowledge System (LandPKS), a modular mobile app that makes it possible for virtually anyone with a smartphone to make the land potential determinations necessary to apply the framework described in the first two sections.

Crop models are increasingly used to evaluate crop yields at regional/global scales. These applications require the integration and processing of very large data sets in order to explore the implications of land management options across spatially heterogeneous scales. These modeling involve the combination of large spatially explicit data sets for climate, biophysical and crop management variables as well as significant computational capacity for regional/global scale simulations. As a result, the application of crop models at regional/global scales is challenging due to the requirements for input data, calibration, validation and simulation setups appropriate for thousands to millions of spatial points. Not surprisingly, the implementation of these models across large areas using fine-scale grids can be limited by computational time requirements. To reduce the large computational load of an agroecosystem simulation process for regional and global scales, we developed an E PIC P arallel C omputing F ramework (EPCF) to facilitate regional/global gridded crop modeling. The EPCF can make full use of the CPU resources of the workstation through parallel processing. For future users, only a few lines of additional code modification are needed to convert the single process code to parallel computing code. Parallel processing in one machine makes it easy to handle the whole system without the overhead and expertise required for a distributed system. EPCF is a system that provides not only the ease of development but also cost-efficiency.

Smallholder agriculture is a major source of income and food for developing nations. With more frequent drought and increasing scarcity of arable land, more accurate land-use planning tools are needed to allocate land resources to support regional agricultural activity. To address this need, we created Land Capability Classification (LCC) system maps using data from two digital soil maps, which were compared with measurements from 1305 field sites in the Dosso region of Niger. Based on these, we developed 250 m gridded maps of LCC values across the region. Across the region, land is severely limited for agricultural use because of low available water-holding capacity (AWC) that limits dry season agricultural potential, especially without irrigation, and requires more frequent irrigation where supplemental water is available. If the AWC limitation is removed in the LCC algorithm (i.e., simulating the use of sufficient irrigation or a much higher and more evenly distributed rainfall), the dominant limitations become less severe and more spatially varied. Finally, we used additional soil fertility data from the field samples to illustrate the value of collecting contemporary data for dynamic soil properties that are critical for crop production, including soil organic carbon, phosphorus and nitrogen.

Understanding carbon (C) dynamics from ecosystem to global scales remains a challenge. Although expansion of global carbon dioxide (CO

The United States is a world leader in the production of maize and other crops and the agricultural success of the country is directly linked to the intensive use of fertilizers and irrigation. However, even in advanced agricultural systems, soils can become degraded over time due to factors such as soil organic matter (SOM) loss and erosion. Here, we use a series of scenario‐based model analyses to show that about one‐third of current annual US. N fertilizer use in maize agriculture is used to compensate for the long‐term loss of soil fertility through erosion and organic matter loss. This leads to over a half billion dollars per year in extra fertilizer supply costs to US farmers. These results highlight the potential to reduce both the input costs and environmental impacts of agriculture through the restoration of SOM in agricultural soils.

Smallholder agriculture is a major source of income and food for developing nations. With more frequent drought and increasing scarcity of arable land, land use planning can be used to better allocate land resources to support regional agricultural activity. To support this objective, we used the Land Capability Classification (LCC) system to map the basic limitations to agricultural use of land. The LCC is a stepwise hierarchical land assessment system that can be used to understand factors that limit land use potential. We carried out our assessment in the Dosso region of Niger. Using two public soil data sets, Food and Agriculture Organization Harmonized World Soil Database and International Soil Reference and Information Center (ISRIC) SoilGrids, and a modified version of the LCC, we developed 250 m gridded maps of LCC values across the region. To validate the LCC maps, we interpolated soil physical data from 1308 field sites in the Dosso region and created LCC maps based on these interpolated data. We find that across the region, land is very severely limited for agricultural use by available water-holding capacity (AWC) which limits dry season agricultural potential, especially without irrigation, and requires more frequent irrigation where supplemental water is available. If the AWC limitation is removed in the LCC algorithm (i.e. simulating the use of sufficient irrigation or a much higher and more evenly distributed rainfall than is received by the Dosso region), the dominant regional limitations become less severe and more spatially varied.

In this poster we will outline a new ambitious cross-disciplinary project focused on detecting soil degradation and restoration through a novel multi-functional soil sensing platform that combines conventional and newly created sensors and a machine learning framework. Our work aims to advance our understanding of dynamic soil processes that operate at different temporal/spatial scales. Through the creation of an innovative new approach to capturing and analyzing high frequency data from in-situ sensors, this project will predict the rate and direction of soil system functions for sites undergoing degradation or restoration. To do this, we will build and train a new mechanistically-informed machine learning system to turn high frequency data on multiple soil functions, such as water infiltration, CO2 production, and surface soil movement, into predictions of longer term changes in soil health including the status of microbial processes, soil organic matter (SOM) content, and other properties and processes. Such an approach could be transformative: a system that will allow short-term sensor data to be used to evaluate longer term soil transformations in key ecosystem functions. We will start our work with a suite of off-the-shelf sensors observing multiple soil functions that can be installed quickly. These data will allow us to rapidly initiate development and training of a novel mechanistically informed machine learning framework. In parallel we will develop two new soil health sensors focused on in-situ real time measurement of decomposition rates and transformation of soil color that reflects the accumulation or loss of SOM. We will then link these new sensors with a suite of conventional sensors in a novel data collection and networking system coupled to the Swarm satellite network to create a low cost sensor array that can be deployed in remote areas and used to support studies of soil degradation or progress toward restoration worldwide.

Mobile phone use is increasing in Sub-Saharan Africa, spurring a growing focus on mobile phones as tools to increase agricultural yields and incomes on smallholder farms. However, the research to date on this topic is mixed, with studies finding both positive and neutral associations between phones and yields. In this paper we examine perceptions about the impacts of mobile phones on agricultural productivity, and the relationships between mobile phone use and agricultural yield. We do so by fitting multilevel statistical models to data from farmer-phone owners (n = 179) in 4 rural communities in Tanzania, controlling for site and demographic factors. Results show a positive association between mobile phone use for agricultural activities and reported maize yields. Further, many farmers report that mobile phone use increases agricultural profits (67% of respondents) and decreases the costs (50%) and time investments (47%) of farming. Our findings suggest that there are opportunities to target policy interventions at increasing phone use for agricultural activities in ways that facilitate access to timely, actionable information to support farmer decision making.

Forests around the world are undergoing rapid changes due to changing climate and increasing physiological stress, but forest response to climate at the ecosystem scale can be highly variable due to the mixed responses of different trees across heterogeneous landscapes. To determine the response of ecosystems in the Rocky Mountains to climate stress, we investigated the response of subalpine fir (Abies lasiocarpa) and Engelmann spruce (Picea engelmannii), two widely distributed subalpine forest species of Rocky Mountains, to climate warming across a region characterized by gradients of elevation, aspect and soil type. We investigated the growth trend of individual trees through time, determined the climate variables most important for driving growth and quantified the interactions between climate and topography that influence long-term growth trends and potential ecological changes across the study region. Growth trends of these two species are similar through the first part of the century, but diverge during the last several decades. Since 1975, subalpine fir growth decreased through time, while Engelmann spruce growth increased. We find that aspect and warm summer temperatures are the most important factors determining growth in subalpine fir, and subalpine fir growth declines are greatest on east- and south-facing aspects. In contrast, Engelmann spruce growth is uniformly unresponsive to climate. In addition to highlighting the importance of species-level differences in growth response to climate, our results also identify interactions between climate and local physiography as controls on long-term growth trends and suggest that the local landscape physiography can mediate climate-related stress in forested ecosystems. This work advances our understanding of how forest stress is mitigated by landscape factors at the ecosystem scale, and how interactions of species, landscape and climate will control future ecosystem composition and forest growth dynamics.

Mineral dusts can have profound effects on climate, clouds, ecosystem processes, and human health. Because regional dust emission and deposition in western North America are not well understood, measurements of total suspended particulate (TSP) from 2011 to 2013 were made along a 500-km transect of five remote sites in Utah and Colorado, USA. The TSP concentrations in μg m−3 adjusted to a 24-h period were relatively high at the two westernmost, dryland sites at Canyonlands National Park (mean = 135) and at Mesa Verde National Park (mean = 99), as well as at the easternmost site on the Great Plains (mean = 143). The TSP concentrations at the two intervening montane sites were less, with more loading on the western slope of the Rocky Mountains (Telluride, mean = 68) closest to the desert sites compared with the site on the eastern slope (Niwot Ridge, mean = 58). Dust concentrations were commonly highest during late winter-late spring, when Pacific frontal storms are the dominant causes of regional wind. Low concentrations ( 10), as revealed by relatively low average daily concentrations of fine (

Federal lands in the United States have been identified as important areas where forests could be managed to enhance carbon storage and help mitigate climate change. However, there has been little work examining the context for decision making for carbon in a multiple-use public land environment, and how science can support decision making. This case study of the San Juan National Forest and the Bureau of Land Management Tres Rios Field Office in southwestern Colorado examines whether land managers in these offices have adequate tools, information, and management flexibility to practice effective carbon stewardship. To understand how carbon was distributed on the management landscape we added a newly developed carbon map for the SJNF–TRFO area based on Landsat TM texture information (Kelsey and Neff in Remote Sens 6:6407–6422. doi: 10.3390/rs6076407 , 2014). We estimate that only about 22 % of the aboveground carbon in the SJNF–TRFO is in areas designated for active management, whereas about 38 % is in areas with limited management opportunities, and 29 % is in areas where natural processes should dominate. To project the effects of forest management actions on carbon storage, staff of the SJNF are expected to use the Forest Vegetation Simulator (FVS) and extensions. While identifying FVS as the best tool generally available for this purpose, the users and developers we interviewed highlighted the limitations of applying an empirically based model over long time horizons. Future research to improve information on carbon storage should focus on locations and types of vegetation where carbon management is feasible and aligns with other management priorities.

We investigated several key limiting factors that control alpine tundra productivity by developing an ecosystem biogeochemistry model. The model simulates the coupled cycling of carbon (C), nitrogen (N), and phosphorus (P) and their interactions with gross primary production (GPP). It was parameterized with field observations from an alpine dry meadow ecosystem using a global optimization strategy to estimate the unknown parameters. The model, along with the estimated parameters, was first validated against independent data and then used to examine the environmental controls over plant productivity. Our results show that air temperature is the strongest limiting factor to GPP in the early growing season, N availability becomes important during the middle portion of the growing season, and soil moisture is the strongest limiting factors by late in the growing season. Overall, the controls over GPP during the growing season, from strongest to weakest, are soil moisture content, air temperature, N availability, and P availability. This simulation provides testable predictions of the shifting nature of physical and nutrient limitations on plant growth. The model also indicates that changing environmental conditions in the alpine will likely lead to changes in productivity. For example, warming eliminates the control of P availability on GPP and makes N availability surpass air temperature to become the second strongest limiting factor. In contrast, an increase in atmospheric nutrient deposition eliminates the control of N availability and enhances the importance of P availability. These analyses provide a quantitative and conceptual framework that can be used to test predictions and refine ecological analyses at this long-term ecological research site.

The South Korean government has recently focused on environmental protection efforts to improve water quality which has been degraded by nonpoint sources of water pollution from runoff. In order to take care of environmental issues, many physically-based models have been used. However, the physically-based models take a large amount of work to carry out site simulations, and there is a need to find faster and more efficient approaches. For an alternative approach for sediment management using the physically-based models, the machine learning-based models were used for estimating sediment trapping efficiency of vegetative filter strips. The seven nonlinear regression algorithms of machine learning models (e.g., decision tree, multilayer perceptron, k-nearest neighbors, support vector machine, random forest, AdaBoost and gradient boosting) were applied to select the model which best estimates the sediment trapping efficiency of vegetative filter strips. The sediment trapping efficiencies calculated by the machine learning models showed similar results as those of vegetative filter strip modeling system (VFSMOD-W) model. As a result of the accuracy evaluation among the seven machine learning models, the multilayer perceptron model-derived the best fit with VFSMOD-W model. It is expected that the sediment trapping efficiency of the vegetative filter strips in various cases in agricultural fields in South Korea can be predicted easier, faster and accurately by the machine learning models developed in this study. Machine learning models can be used to evaluate sediment trapping efficiency without complicated physically-based model design and high computational cost. Therefore, decision makers can maximize the quality of their outputs by minimizing their efforts in the decision-making process.

High elevation alpine ecosystems—the ‘water towers of the world’—provide water for human populations around the globe. Active geomorphic features such as glaciers and permafrost leave alpine ecosystems susceptible to changes in climate which could also lead to changing biogeochemistry and water quality. Here, we synthesize recent changes in high-elevation stream chemistry from multiple sites that demonstrate a consistent and widespread pattern of increasing sulfate and base cation concentrations or fluxes. This trend has occurred over the past 30 years and is consistent across multiple sites in the Rocky Mountains of the United States, western Canada, the European Alps, the Icelandic Shield, and the Himalayas in Asia. To better understand these recent changes and to examine the potential causes of increased sulfur and base cation concentrations in surface waters, we present a synthesis of global records as well as a high resolution 33 year record of atmospheric deposition and river export data from a long-term ecological research site in Colorado, USA. We evaluate which factors may be driving global shifts in stream chemistry including atmospheric deposition trends and broad climatic patterns. Our analysis suggests that recent changes in climate may be stimulating changes to hydrology and/or geomorphic processes, which in turn lead to accelerated weathering of bedrock. This cascade of effects has broad implications for the chemistry and quality of important surface water resources.

Anthropogenic activities have significantly altered atmospheric chemistry and changed the global mobility of key macronutrients. Here we show that contemporary global patterns in nitrogen (N) and phosphorus (P) emissions drive large hemispheric variation in precipitation chemistry. These global patterns of nutrient emission and deposition (N:P) are in turn closely reflected in the water chemistry of naturally oligotrophic lakes (r2 = 0.81, p

The atmospheric transport and deposition of aerosols has the potential to influence the chemistry and biology of oligotrophic alpine lakes. In recent decades, dust and nitrogen emissions to alpine ecosystems have increased across large areas of the western U.S., including Wyoming. Here, we use sediment geochemistry and 87Sr/86Sr and 143Nd/144Nd isotopes to examine historical dust deposition rates to alpine lakes in the southwestern region of the Wind River Range, Wyoming. We evaluate the biological response using diatom fossil assemblages and sediment pigment concentrations. Sediment core analyses indicated that prior to a recent rise in dust flux, phosphorus concentrations and species composition were similar to those found in other alpine lakes in the region. Concomitant with a ∼50 fold increase in dust flux to the sediments circa 1940, sediment proxies revealed a two- to threefold increase in normalized sediment phosphorus content, an increase in the diatom-inferred total dissolved phosphorus concentration from ∼ 4 to 9-12 μg L−1, a tenfold increase in diatom production, and a relative increase in cyanobacteria abundance. The increase in dust influx during the 20th century appears to be due in part to human factors and demonstrates the potential for dust and other atmospheric pollutants to significantly alter remote aquatic ecosystems.

Savannas cover 60% of the African continent and play an important role in the global carbon (C) emissions from fire and land use. To better characterize the biophysical controls over soil respiration in these settings, half-hourly observations of volumetric soil–water content, temperature, and the concentration of carbon dioxide (CO 2 ) at different soil depths were continually measured from 2005 to 2007 under trees (“sub-canopy”) and between trees (“inter-canopy”) in a savanna vegetation near Skukuza, Kruger National Park, South Africa. The measured soil climate and CO 2 concentration data were assimilated into a process-based model that estimates the CO 2 production and flux with coupled dynamics of dissolved organic C (DOC) and microbial biomass C. Our results show that temporal and spatial variations in CO 2 flux were strongly influenced by precipitation and vegetation cover, with two times greater CO 2 flux in the sub-canopy plots (∼2421 g CO 2 m −2 yr −1 ) than in the inter-canopy plots (∼1290 g CO 2 m −2 yr −1 ). Precipitation influenced soil respiration by changing soil temperature and moisture; however, our modeling analysis suggests that the pulsed response of soil respiration to precipitation events (known as “Birch effect”) is a key control on soil fluxes at this site. At this site, “Birch effect” contributed to approximately 50% and 65% of heterotrophic respiration or 20% and 39% of soil respiration in the sub-canopy and inter-canopy plots, respectively. These results suggest that pulsed response of respiration to precipitation events is an important component of the C cycle of savannas and should be considered in both measurement and modeling studies of carbon exchange in similar ecosystems.

Aeolian dust can influence nutrient availability, soil fertility, plant interactions, and water-holding capacity in both source and downwind environments. A network of 85 passive collectors for aeolian sediment spanning numerous plant communities, soil types, and land-use histories covering approximately 4000 square kilometers across southeastern Utah was used to sample horizontal emissions of aeolian sediment. The sample archive dates to 2004 and is currently the largest known record of field-scale dust emissions for the southwestern United States. Sediment flux peaked during the spring months in all plant communities (mean: 38.1 g m −2 d −1 ), related to higher, sustained wind speeds that begin in the early spring. Dust flux was lowest during the winter period (mean: 5 g m −2 d −1 ) when surface wind speeds are typically low. Sites dominated by blackbrush and sagebrush shrubs had higher sediment flux (mean: 19.4 g m −2 d −1 ) compared to grasslands (mean: 11.2 g m −2 d −1 ), saltbush shrublands (mean: 10.3 g m −2 d −1 ), and woodlands (mean: 8.1 g m −2 d −1 ). Contrary to other studies on dust emissions, antecedent precipitation during one, two, and three seasons prior to sample collection did not significantly influence emission rates. Physical site-scale factors controlling dust emissions were complex and varied from one vegetation type to another.

Peatlands store vast amounts of soil carbon and are significant sources of greenhouse gases, including carbon dioxide (CO2) and methane (CH4) emissions. The traditional approach in biogeochemical model simulations of peatland emissions is to simply divide the soil domain into an aerobic zone above and an anaerobic zone below the water table (WT) and then calculate CO2 and CH4 emissions based on the assumed properties of these two discrete zones. However, there are major potential drawbacks associated with the traditional WT-based approach, because aerobic or anaerobic environments are ultimately determined by oxygen (O2) concentration rather than water content directly. Variations in O2 content above and below the WT can be large and thus may play an important role in partitioning of carbon fluxes between CO2 and CH4. In this paper, we propose an oxygen-based approach, which simulates the vertical and radial components of O2 movement and consumption through the soil aerobic and anaerobic environments. We then use both our oxygen-based and the traditional WT-based approaches to simulate CO2 and CH4 emissions from an Alaskan fen peatland. The results of model calibration and validation suggest that our physically realistic approach (i.e., oxygen-based approach) cause less biases on the simulated flux of CO2 and CH4. The results of model simulations also suggest that the traditional WT-based approach might substantially under-estimate CH4 emissions and over-estimate CO2 emissions from the fen due to the presence of anaerobic zones in unsaturated soil. Our oxygen-based approach can be easily incorporated into existing ecosystem or earth system models but will require additional validation with more extensive field observations to be implemented within biogeochemical models to improve simulations of soil C fluxes at regional or global scale.

Alpine lakes receive a large fraction of their nutrients from atmospheric sources and are consequently sensitive to variations in both the amount and chemistry of atmospheric deposition. In this study we explored the spatial changes in lake water chemistry and biology along a gradient of dust deposition in the Wind River Range, Wyoming. Regional differences were explored using the variation in bulk deposition, lake water, sediment, and bedrock geochemistry and catchment characteristics. Dust deposition rates in the Southwestern region averaged 3.34 g m -2 year -1 , approximately three times higher than deposition rates in the North- western region (average 1.06 g m -2 year -1 ). Dust-P deposition rates ranged from 87 l gPm 2 day -1 in the Northwestern region to 276 l gP m 2 day -1 in the Southwestern region. Subalpine and alpine lakes in the Southwestern region had greater total phosphorus (TP) concentrations (5-13 l gL -1 ) and greater sediment phosphorus (SP) concentrations (2-5 mg g -1 ) than similar lakes elsewhere in the region (1-8 l gL -1 TP, 0.5-2 mg g -1 SP). Lake phosphorus concentrations were related to dissolved organic carbon (DOC) across vegetation gradients, but related to the percent of bare rock, catchment area to lake area, and catchment steepness across dust deposition gradients. Modern phytoplankton and zooplankton biomasses were two orders of magnitude greater in the Southwest than in the Northwest, and alpine lakes in the Southwest had a unique diatom species assemblage with relatively higher concentrations of Asterionella formosa, Pseudostauros

Atmospheric aerosols have been shown to be an important input of organic carbon and nutrients to alpine watersheds and influence biogeochemical processes in these remote settings. For many remote, high elevation watersheds, direct evidence of the sources of water soluble organic aerosols and their chemical and optical characteristics is lacking. Here, we show that the concentration of water soluble organic carbon (WSOC) in the total suspended particulate (TSP) load at a high elevation site in the Colorado Rocky Mountains was strongly correlated with UV absorbance at 254 nm (Abs254, r = 0.88 p r = 0.95 p 90% of OC on average. According to source apportionment analysis, biomass burning had the highest contribution (50.3%) to average WSOC concentration; SOA formation and motor vehicle emissions dominated the contribution to WSOC in the summer. The source apportionment and backward trajectory analysis results supported the notion that both wildfire and Colorado Front Range pollution sources contribute to the summertime OC peaks observed in wet deposition at high elevation sites in the Colorado Rocky Mountains. These findings have important implications for water quality in remote, high-elevation, mountain catchments considered to be our pristine reference sites.

Atmospheric deposition is an important source of nutrients to many ecosystems, but is of particular importance to plant nutrition in areas where nutrients are scarce. Nutrient containing aerosols enter the atmosphere through industrial and agricultural activities, wildfires, and the production of terrigenous and marine aerosols. In this study, we collected bulk rain precipitation along the Atlantic coast of South Africa in a coastal “strandveld” vegetation region. This region is relatively remote from significant anthropogenic influences and is downwind of a highly productive and stormy portion of the Atlantic. Samples were collected over 12 months at sites along a 17 km downwind transect from the shoreline and analyzed for N, P, Na, Ca, Mg and K. Annual total N and total P fluxes of 4.8 kg ha−1 yr−1 and 0.16 kg ha−1 yr−1 are low compared to global averages. In contrast, fluxes of Na were 88.7 kg ha−1 yr−1, 16.2 kg ha−1 yr−1 for Ca, 12.1 kg ha−1 yr−1 for Mg and 5.2 kg ha−1 yr−1 for K; rates that are higher than most other measurements elsewhere in the world. Dissolved organic N represented ca. 71% of the N flux while 43% of the P flux was in the form of soluble reactive P (SRP). These results combined with the high fluxes of Na and Mg strongly suggest that marine aerosols are important contributors to nutrient deposition at this site.

Received 27 June 2013; revised 26 August 2013; accepted 18 September 2013; published 10 October 2013. [1] Mineral aerosols are produced during the erosion of soils by wind and are a common source of particles (dust) in arid and semiarid regions. The size of these particles varies widely from less than 2 μm to larger particles that can exceed 50 μm in diameter. In this study, we present two continuous records of total suspended particle (TSP) concentrations at sites in Mesa Verde and Canyonlands National Parks in Colorado and Utah, USA, respectively, and compare those values to measurements of fine and coarse particle concentrations made from nearby samplers. Average annual concentrations of TSP at Mesa Verde were 90 μ gm � 3 in 2011 and at Canyonlands were 171 μ gm � 3 in 2009, 113 μ gm � 3 in 2010, and 134 μ gm � 3 in 2011. In comparison, annual concentrations of fine (diameter of 2.5 μm and below) and coarse (2.5–10 μm diameter) particles at these sites were below 10 μ gm � 3 in all years. The high concentrations of TSP appear to be the result of regional dust storms with elevated concentrations of particles greater than 10 μm in diameter. These conditions regularly occur from spring through fall with 2 week mean TSP periodically in excess of 200 μ gm � 3 . Measurement of particles on filters indicates that the median particle size varies between approximately 10 μm in winter and 40 μm during the spring. These persistently elevated concentrations of large particles indicate that regional dust emission as dust storms and events are important determinants of air quality in this region.

When dust inputs are large or have persisted for long periods of time, the signature of dust additions are often apparent in soils. The of dust will be greatest where the geochemical composition of dust is distinct from local sources of soil parent material. In this study the influence of dust accretion on soil geochemistry is quantified for two different soils from the San Juan Mountains of southwestern Colorado, USA. At both study sites, dust is enriched in several trace elements relative to local rock, especially Cd, Cu, Pb, and Zn. Mass-balance calculations that do not explicitly account for dust inputs indicate the accumulation of some elements in soil beyond what can be explained by weathering of local rock. Most observed elemental enrichments are explained by accounting for the long-term accretion of dust, based on modern isotopic and geochemical estimates. One notable exception is Pb, which based on mass-balance calculations and isotopic measurements may have an additional source at one of the study sites. These results suggest that dust is a major factor influencing the development of soil in these settings and is also an important control of soil weathering fluxes. After accounting for dust inputs in mass-balance calculations, Si weathering fluxes from San Juan Mountain soils are within the range observed for other temperate systems. Comparing dust inputs with mass-balanced based flux estimates suggests dust could account for as much as 50–80% of total long-term chemical weathering fluxes. These results support the notion that dust inputs may sustain chemical weathering fluxes even in relatively young continental settings. Given the widespread input of far-traveled dust, the weathering of dust is likely and important and underappreciated aspect of the global weathering engine.

Massive investments in climate change mitigation and adaptation are projected during coming decades. Many of these investments will seek to modify how land is managed. The return on both types of investments can be increased through an understanding of land potential: the potential of the land to support primary production and ecosystem services, and its resilience. A Land‐Potential Knowledge System (LandPKS) is being developed and implemented to provide individual users with point‐based estimates of land potential based on the integration of simple, geo‐tagged user inputs with cloud‐based information and knowledge. This system will rely on mobile phones for knowledge and information exchange, and use cloud computing to integrate, interpret, and access relevant knowledge and information, including local knowledge about land with similar potential. The system will initially provide management options based on long‐term land potential, which depends on climate, topography, and relatively static soil properties, such as soil texture, depth, and mineralogy. Future modules will provide more specific management information based on the status of relatively dynamic soil properties such as organic matter and nutrient content, and of weather. The paper includes a discussion of how this system can be used to help distinguish between meteorological and edaphic drought.

Carbon dynamics of high-latitude regions are an important and highly uncertain component of global carbon budgets, and efforts to constrain estimates of soil-atmosphere carbon exchange in these regions are contingent on accurate representations of spatial and temporal variability in carbon fluxes. This study explores spatial and temporal variability in soilatmosphere carbon dynamics at both fine and coarse spatial scales in a high-elevation, permafrost-dominated boreal black spruce forest. We evaluate the importance of landscape-level investigations of soil-atmosphere carbon dynamics by characterizing seasonal trends in soil-atmosphere carbon exchange, describing soil temperature-moisture-respiration relations, and quantifying temporal and spatial variability at two spatial scales: the plot scale (0–5 m) and the landscape scale (500–1000 m). Plot-scale spatial variability (average variation on a given measurement day) in soil CO2 efflux ranged from a coefficient of variation (CV) of 0.25 to 0.69,...

This study analyzes mid-21st century projections of daily surface air minimum (Tmin) and maximum (Tmax) temperatures, by season and elevation, over the southern range of the Colorado Rocky Mountains. The projections are from four regional climate models (RCMs) that are part of the North American Regional Climate Change Assessment Program (NARCCAP). All four RCMs project 2°C or higher increases in Tmin and Tmax for all seasons. However, there are much greater (>3°C) increases in Tmax during summer at higher elevations and in Tmin during winter at lower elevations. Tmax increases during summer are associated with drying conditions. The models simulate large reductions in latent heat fluxes and increases in sensible heat fluxes that are, in part, caused by decreases in precipitation and soil moisture. Tmin increases during winter are found to be associated with decreases in surface snow cover, and increases in soil moisture and atmospheric water vapor. The increased moistening of the soil and atmosphere facilitates a greater diurnal retention of the daytime solar energy in the land surface and amplifies the longwave heating of the land surface at night. We hypothesize that the presence of significant surface moisture fluxes can modify the effects of snow-albedo feedback and results in greater wintertime warming at night than during the day.

The deposition of dust has recently increased significantly over some regions of the western US. Here we explore how changes in dust deposition have affected the biogeochemistry of two alpine watersheds in Colorado, US. We first reconstruct recent changes in the mass accumulation rate of sediments and then we use isotopic measurements in conjunction with a Bayesian mixing model to infer that approximately 95% of the inorganic fraction of lake sediments is derived from dust. Elemental analyses of modern dust indicate that dust is enriched in Ca, Cr, Cu, Mg, Ni, and in one watershed, Fe and P relative to bedrock. The increase in dust deposition combined with its enrichment in certain elements has altered the biogeochemisty of these systems. Both lakes showed an increase in primary productivity as evidenced by a decrease in carbon isotopic discrimination; however, the cause of increased primary productivity varies due to differences in watershed characteristic. The lake in the larger watershed experienced greater atmospheric N loading and less P loading from the bedrock leading to a greater N:P flux ratio. In contrast, the lake in the smaller watershed experienced less atmospheric N loading and greater P loading from the bedrock, leading to a reduced N:P flux ratio. As a result, primary productivity was more constrained by N availability in the smaller watershed. N-limited primary productivity in the smaller watershed was partly ameliorated by an increase in nitrogen fixation as indicated by reduced nitrogen isotopic values in more contemporary sediments. This study illustrates that alpine watersheds are excellent integrators of changes in atmospheric deposition, but that the biogeochemical response of these watersheds may be mediated by their physical (i.e. watershed area) and chemical (i.e. underlying geology) properties.

We examined chemical changes from leaf tissue to soil organic matter (SOM) to determine the persistence of plant chemistry into soil aggregate fractions. We characterized a slow (Dicranopteris linearis) and fast-decomposing species (Cheirodendron trigynum) and surface (O), and subsurface (A-horizon) SOM beneath each species using pyrolysis-gas chromatography/mass spectrometry (py-GC/MS), with and without derivatization. The live tissues of Dicranopteris had greater lignin content whereas Cheirodendron had a greater lipid, N-bearing, and polysaccharide component. Despite this difference in leaf chemistry, SOM chemistry was similar between soil aggregate fractions, but different between horizons. The O-horizon contained primarily lignin and polysaccharide biomarkers whereas the A-horizon contained polysaccharide, aromatic, and N-derived compounds, indicating considerable microbial processing of plant litter. The soils beneath Cheirodendron inherited a greater lipid signal composed of cutin and suberin biomarkers whereas the soils beneath Dicranopteris contained greater aromatic biomarker content, possibly derived from plant lignins. The soils beneath both species were more similar to root polysaccharides, lipids, and lignins than aboveground tissue. This study indicates that although plant-derived OM is processed vigorously, species-specific biomarkers and compound class differences persist into these soils and that differences in plant chemical properties may influence soil development even after considerable reworking of plant litter by microorganisms.

Black spruce (Picea mariana (Mill.) B.S.P.) forest stands range from well drained to poorly drained, typically contain large amounts of soil organic carbon (SOC), and are often underlain by permafrost. To better understand the role of soil drainage class in carbon dioxide (CO2) exchange and decomposition, we measured soil respiration and net CO2 fluxes, litter decomposition and litterfall rates, and SOC stocks above permafrost in three Alaska black spruce forest stands characterized as well drained (WD), moderately drained (MD), and poorly drained (PD). Soil respiration and net CO2 fluxes were not significantly different among sites, although the relation between soil respiration rate and temperature varied with site (Q10: WD > MD > PD). Annual estimated soil respiration, litter decomposition, and groundcover photosynthesis were greatest at PD. These results suggest that soil temperature and moisture conditions in shallow organic horizon soils at PD were more favorable for decomposition compared with the better drained sites. SOC stocks, however, increase from WD to MD to PD such that surface decomposition and C storage are diametric. Greater groundcover vegetation productivity, protection of deep SOC by permafrost and anoxic conditions, and differences in fire return interval and (or) severity at PD counteract the relatively high near-surface decomposition rates, resulting in high net C accumulation.

Wind erosion and associated dust emissions play a fundamental role in many ecological processes and provide important biogeochemical connectivity at scales ranging from individual plants up to the entire globe. Yet, most ecological studies do not explicitly consider dust-driven processes, perhaps because most relevant research on aeolian (wind-driven) processes has been presented in a geosciences rather than an ecological context. To bridge this disciplinary gap, we provide a general overview of the ecological importance of dust, examine complex interactions between wind erosion and ecosystem dynamics from the scale of plants and surrounding space to regional and global scales, and highlight specific examples of how disturbance affects these interactions and their consequences. It is likely that changes in climate and intensification of land use will lead to increased dust production from many drylands. To address these issues, environmental scientists, land managers, and policy makers need to consider wind erosion and dust emissions more explicitly in resource management decisions.

Recent large-scale dieback of pinon–juniper (P–J) woodlands and forests across the western US occurred as a result of multi-year drought and subsequent insect and disease outbreaks. P–J vegetation is spatially extensive, thus large-scale mortality events such as the one that has occurred over the past several years could significantly alter regional carbon (C) budgets. Our objective was to use a remote sensing technique coupled with field-based data to estimate changes in aboveground live C stocks across a 4100 km 2 region of Colorado caused by P–J tree mortality. We hypothesized that dieback would amplify the phenological dynamics of P–J vegetation, and these variations would be related to drought-induced losses of live P–J aboveground biomass (AGB) that are discernible using time-series remote sensing vegetation data. Here, we assess live P–J AGB loss using dry season fractional photosynthetic vegetation cover (PV) derived from multi-year Landsat images. Our results showed a strong linear positive relationship between the maximum decline in PV and field-measured losses of live P–J AGB during the period 2000–05 ( r 2 = 0.64, p = 0.002). These results were then used to map AGB losses throughout the study region. Mean live aboveground C loss (± sd) was 10.0 (± 3.4) Mg C ha − 1 . Total aboveground live P–J C loss was 4.6 Tg C, which was approximately 39 times higher than the concurrent C loss attributed to wildfire and management treatments within or near to the national forests of the study region. Our results suggest that spatially extensive mortality events such as the one observed in P–J woodlands across the western US in the past decade may significantly alter the ecosystem C balance for decades to come. Remote sensing techniques to monitor changes in aboveground C stocks, such as the one developed in our study, may support regional and global C monitoring in the future.

The movement of dissolved organic carbon (DOC) throughboreal ecosystems has drawn increased attention because of its poten-tial impact on the feedback of OC stocks to global environmentalchange in this region. Few models of boreal DOC exist. Herewe presenta one-dimensional modelwith simultaneous production, decomposition,sorption/desorption, and transport of DOC to describe the behavior ofDOC in the OC layers above the mineral soils. The field-observed con-centration profiles of DOC in two moderately well-drained black spruceforest sites (one with permafrost and one without permafrost), coupledwith hourly measured soil temperature and moisture, were used to in-versely estimate the unknown parameters associated with the sorption/desorption kinetics using a global optimization strategy. The model,along with the estimated parameters, reasonably reproduces the con-centration profiles of DOC and highlights some important potentialcontrols over DOC production and cycling in boreal settings. Thevaluesof estimated parameters suggest that humic OC has a larger potentialproduction capacity for DOC than fine OC, and most of the DOCproduced from fine OC was associated with instantaneous sorption/desorption whereas most of the DOC produced from humic OC wasassociated with time-dependent sorption/desorption. The simulatedDOC efflux at the bottom of soil OC layers was highly dependent onthe component and structure of the OC layers. The DOC efflux wascontrolled by advection at the site with no humic OC and moist con-ditions and controlled by diffusion at the sitewith the presence of humicOC and dry conditions.Key words: Black spruce, boreal, carbon, advection, diffusion,inversion.(Soil Science 2010;175: 223Y232)

We investigated several forms of phosphorus (P) in dryland soils to examine the chemical and textural controls on P stabilization on a diverse set of substrates. We examined three P fractions including labile, moderately labile, and occluded as determined by a modified Hedley fractionation technique. The P fractions were compared to texture measurements and total elemental concentrations determined by inductively coupled plasma-atomic emission spectroscopy (ICP-AES). Labile P related to the absence of materials involved in P sorption. Moderately labile P was most strongly associated with high total Al & Fe content that we interpret to represent oxides and 1:1 clay minerals. The occluded P fraction was strongly associated with low total Al & Fe environments and interpreted to represent 2:1 clay minerals where ligand exchange tightly sequesters P. The results indicate that the controls on P fraction distribution are initially closely tied to the chemical and physical properties of the bedrock units that contribute to soil formation. Further, these results suggest that the progression of stabilized P forms in dryland areas differs from the progression observed in mesic environments. Soil development in dryland settings, such as the formation of pedogenic carbonates, may lead to differing controls on P availability and the proportional size of the moderately labile fraction.

Over the last century there has been marked expansion and infilling of pinyon ( Pinus spp.)–juniper ( Juniperus spp.) woodlands into grassland and shrubland ecosystems across the western United States. Although range expansions in pinyon-juniper populations have been documented with changing climate throughout the Holocene, over the last century, local scale impacts such as livestock grazing, changes in fire regimes, and increasing atmospheric CO 2 concentrations are thought to be more recent drivers of pinyon-juniper woodland distribution. Our objective was to examine the role of historical livestock grazing relative to past climate in regulating pinyon ( Pinus edulis Engelm.) recruitment and growth over the last century in a persistent pinyon-juniper woodland. We compared pinyon dynamics on a remote mesa site, minimally grazed by domestic livestock, to a nearby historically grazed site in southeastern Utah. The presence of a significant number of old trees (> 200 yr) at both sites suggests that these populations were well established prior to the 20th century. No differences in pinyon density or basal area were observed between grazed and ungrazed sites. Stand age structure data showed that pinyon recruitment across these sites was highly synchronous, with a large recruitment event occurring during the early 1900s; 17% of the pinyons on both sites dated to the 1920s. Climatic conditions during this decade were consistently cool and wet—conditions known to support enhanced recruitment and growth in pinyon pines. Pinyon growth was also strongly synchronous across sites ( r = 0.96). Pinyon growth was significantly correlated with winter/spring precipitation and negatively correlated with June temperature. Taken together, our results suggest that past climate may be more important in structuring pinyon populations than historical land use in these persistent pinyon-juniper woodlands. Given future climate projections of increasing temperature and more extended drought periods, regeneration of pinyon populations following the recent regional-scale dieback may be slow. Moreover, prolonged drought combined with potentially slow regeneration times for pinyon under future climate scenarios could result in substantial declines in pinyon populations across the region, a result that land managers should consider when planning for future restoration treatments in persistent pinyon-juniper woodlands.

Contemporary soil organic matter (SOM) models have been successful at simulating decomposition across a range of spatial and temporal scales using first-order kinetics to represent the decomposition process; however, recent work suggests the simplicity of the first-order representation of decomposition is not adequate to capture the microbially-driven dynamics of SOM decomposition over short timescales. For example, the response of soils to drying-rewetting events may best be explained by microbial and/or exoenzyme controls on decomposition. To test if adding these microbial mechanisms improves the ability of SOM models to simulate the response of soils to short-term environmental changes, we developed four different SOM decomposition models with varying mechanistic complexity and compared their ability to simulate soil respiration from a pulsed drying-rewetting laboratory-based experiment. Specifically, we tested the ability of the models to capture the timing and magnitude of soil CO2 efflux in response to rewetting or constant moisture conditions. The results of the comparison suggest that the inclusion of exoenzyme and microbial controls on decomposition can improve the ability to simulate pulsed rewetting dynamics; however, less mechanistic first-order models prevail under steady-state moisture conditions. These modeling results may have implications for understanding the long-term response of soil carbon stocks in response to local and regional climate change.

The deposition of aeolian, or windblown, dust is widely recognized as an important physical and chemical flux to ecosystems. Dust deposition adds exogenous mineral and organic material to terrestrial surfaces and can be important for the biogeochemical cycling of nutrients. There have been many studies that characterize the physical and chemical composition of dust. However, few studies have synthesized these observations in order to examine patterns geochemical fluxes. We have compiled observations of dust deposition rates, particle size distributions (PSD), mineralogy and bulk elemental and organic chemistry. The rates of dust deposition observed across the globe vary from almost 0 to greater than 450 g m− 2 yr− 1. Sites receiving dust deposition can be partitioned into broad categories based on there distance from dust source regions. When compared to global dust models our results suggest some models may underestimate dust deposition rates at the regional and local scales. The distance from the source region that dust is deposited also influences the particle size distributions, mineralogy, and chemical composition of dust; however, more consistent dust sampling and geochemical analyses are needed to better constrain these spatial patterns. On average, the concentrations of most major elements (Si, Al, Fe, Mg, Ca, K) in aeolian dust tend to be similar (± 20%) to the composition of the upper continental crust (UCC), but there is substantial variability from sample to sample. In contrast, some elements tend to be depleted (Na) or enriched (Ti) in dust, likely as a result of soil weathering processes prior to dust emissions. Trace elements, especially heavy metals, are consistently enriched in dust relative to the UCC. Ecologically important nutrients, such as N and P, are also present in dust deposition. The geochemical flux attributable to dust deposition can be substantial in ecosystems located proximal to dust source regions. We calculate estimates of elemental flux rates based on the average chemical composition of aeolian dust and varying rates of deposition. These estimated flux rates are useful as a rough gauge of the degree to which dust deposition may influence biogeochemical cycling in terrestrial ecosystems and should be utilized to better constrain deposition estimates of global dust models.

Regional, high-resolution mapping of vegetation cover and biomass is central to understanding changes to the terrestrial carbon (C) cycle, especially in the context of C management. The third most extensive vegetation type in the United States is pinyon-juniper (P-J) woodland, yet the spatial patterns of tree cover and aboveground biomass (AGB) of P-J systems are poorly quantified. We developed a synoptic remote-sensing approach to scale up pinyon and juniper projected cover (hereafter ''cover'') and AGB field observations from plot to regional levels using fractional photosynthetic vegetation (PV) cover derived from airborne imaging spectroscopy and Landsat satellite data. Our results demonstrated strong correlations (P , 0.001) between field cover and airborne PV estimates (r 2 ¼ 0.92), and between airborne and satellite PV estimates (r 2 ¼ 0.61). Field data also indicated that P-J AGB can be estimated from canopy cover using a unified allometric equation (r 2 ¼ 0.69; P , 0.001). Using these multiscale cover-AGB relationships, we developed high-resolution, regional maps of P-J cover and AGB for the western Colorado Plateau. The P-J cover was 27.4% 6 9.9% (mean 6 SD), and the mean aboveground woody C converted from AGB was 5.2 6 2.0 Mg C/ha. Combining our data with the southwest Regional Gap Analysis Program vegetation map, we estimated that total contemporary woody C storage for P-J systems throughout the Colorado Plateau (113 600 km 2 ) is 59.0 6 22.7 Tg C. Our results show how multiple remote-sensing observations can be used to map cover and C stocks at high resolution in drylands, and they highlight the role of P-J ecosystems in the North American C budget.

We investigated the role of rock-derived mineral nutrient availability on the nutrient dynamics of overlying forest communities (Populus tremuloides and Picea engelmanni-Abies lasiocarpa v. arizonica) across three parent materials (andesite, limestone, and sandstone) in the southern Rocky Mountains of Colorado. Broad geochemical differences were observed between bedrock materials; however, bulk soil chemistries were remarkably similar between the three different sites. In contrast, soil nutrient pools were considerably different, particularly for P, Ca, and Mg concentrations. Despite variations in nutrient stocks and nutrient availability in soils, we observed relatively inflexible foliar concentrations and foliar stoichiometries for both deciduous and coniferous species. Foliar nutrient resorption (P and K) in the deciduous species followed patterns of nutrient content across substrate types, with higher resorption corresponding to lower bedrock concentrations. Work presented here indicates a complex plant response to available soil nutrients, wherein plant nutrient use compensates for variations in supply gradients and results in the maintenance of a narrow range in foliar stoichiometry.