<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Throop, HL</style></author><author><style face="normal" font="default" size="100%">Archer, S. R.</style></author><author><style face="normal" font="default" size="100%">Monger, H. C.</style></author><author><style face="normal" font="default" size="100%">Waltman, S.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">When bulk density methods matter: implications for estimating soil organic carbon pools in coarse soils</style></title></titles><keywords><keyword><style  face="normal" font="default" size="100%">JRN</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">bibliography/12-001.pdf</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">77</style></volume><pages><style face="normal" font="default" size="100%">66-71</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Resolving uncertainty in the carbon cycle is paramount to refining climate predictions. Soil organic carbon (SOC) is a major component of terrestrial C pools, and accuracy of SOC estimates are only as good as the measurements and assumptions used to obtain them. Dryland soils account for a substantial portion of global SOC, but the pool dynamics are highly uncertain. One crucial component of accurate estimates of SOC on an areal basis is bulk density (&amp;rho;b), the mass of soil per unit volume. Here, we review methods used for calculating &amp;rho;b and assess their prevalence. We show how treatment of coarse fragments (particles &amp;gt;2 mm diameter) influences &amp;rho;b values and discuss the implications for SOC estimates in drylands. In four dryland examples, methods that varied in their treatment of coarse fragments led to substantial (up to 26%) differences in &amp;rho;b. Calculated SOC pools responded proportionally, with SOC differing by up to 518 g C m&amp;minus;2. We suggest a revised method for accounting for coarse fractions in &amp;rho;b calculations. A large portion of the world&amp;rsquo;s soils, particularly in drylands, are fine enough to allow &amp;rho;b determination with cores, but contain coarse fragments that substantially impact SOC mass estimates if not explicitly considered.&lt;/p&gt;</style></abstract><accession-num><style face="normal" font="default" size="100%">LTER.2012-89963</style></accession-num></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Browning, D.M.</style></author><author><style face="normal" font="default" size="100%">Archer, S. R.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Protection from livestock fails to deter shrub proliferation in a desert landscape with a history of heavy grazing</style></title></titles><keywords><keyword><style  face="normal" font="default" size="100%">JRN</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">bibliography/11-014.pdf</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">21</style></volume><pages><style face="normal" font="default" size="100%">1629-1642</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Desertification is often characterized by the replacement of mesophytic grasses with xerophytic shrubs. Livestock grazing is considered a key driver of shrub encroachment, although most evidence is anecdotal or confounded by other factors. Mapping of velvet mesquite (Prosopis velutina) shrubs in and out of exclosures in 1932, 1948, and 2006 in semiarid grasslands of southeastern Arizona, USA, afforded the opportunity to quantify livestock grazing effects on mesquite proliferation over 74 years in the absence of fire to test the widespread assumption that livestock grazing promotes shrub proliferation. In 1932, shrub cover, density, and aboveground biomass were compared on grazed (12%, 173 plants/ha, 4182 kg/ha) and newly protected areas (8%, 203 plants/ha, 3119 kg/ha). By 1948, cover on both areas increased to 18%; yet, density on the protected area increased 300% (to 620 plants/ha), nearly twice that of the grazed area (325 plants/ha). From 1932 to 1948, differences in recruitment of new plants and growth of existing plants were reflected in biomass, which was higher on the protected area (415 plants/ha, 8788 kg/ha) relative to the grazed area (155 plants/ha, 7085 kg/ha), although mortality was equally low (0.06%). In 2006, 42 years after an herbicide application reset mesquite cover to 10% on both areas, aboveground mesquite mass was comparable on both areas (4700 kg/ha), but cover and density on the protected area (22%, 960 plants/ha) exceeded that on the grazed area (15%, 433 plants/ha). Mesquite mass in 2006 was substantially below 1948 levels, so continued accrual is likely. That shrub recovery from herbicides on a biomass basis was much less than recovery on a cover basis suggests that remotely sensed biomass estimates should integrate land management history. Contrary to widely held assumptions, protection from livestock since 1932 not only failed to deter woody-plant proliferation, but actually promoted it relative to grazed areas. Results suggest (1) that thresholds for grassland resistance to shrub encroachment had been crossed by the 1930s, and (2) fire management rather than grazing management may be key to maintaining grassland physiognomy in this bioclimatic region.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">5</style></issue><accession-num><style face="normal" font="default" size="100%">LTER.2011-90000</style></accession-num></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bestelmeyer, BT</style></author><author><style face="normal" font="default" size="100%">Goolsby, D.P.</style></author><author><style face="normal" font="default" size="100%">Archer, S. R.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Spatial perspectives in state-and-transition models: A missing link to land management?</style></title></titles><keywords><keyword><style  face="normal" font="default" size="100%">JRN</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">bibliography/11-007.pdf</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">48</style></volume><pages><style face="normal" font="default" size="100%">746-757</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Conceptual models of alternative states and thresholds are based largely on observations of ecosystem processes at a few points in space. Because the distribution of alternative states in spatially-structured ecosystems is the result of variations in pattern-process interactions at different scales, we argue that data and models need to explicitly consider ecosystem processes in the context of the spatial pattern of drivers, spatial dependence in responses to drivers, and the spatial structure of feedbacks. Here, we review the literature on spatial patterns and processes in the context of state transitions (or regime shifts) and draw upon examples from semiarid ecosystems to illustrate these linkages. We suggest three spatial perspectives be used to expand conceptual state-and-transition (S&amp;amp;T) models to landscape systems. First, S&amp;amp;T models should represent how spatial patterns of natural and anthropogenic drivers interact to initiate transitions. This would account for often observed situations wherein contrasting states occur along gradients of disturbance intensity or in different management units. Second, spatial dependence in response to drivers and triggers should be represented. This would help account for observed differences in ecological resilience tied to inherent spatial variation in environmental variables (e.g., soil and landform attributes) that mediate driver effects and lead to variation in the likelihood of state transitions. Third, the nature of feedbacks, how they propagate over time and space, and how they are conditioned by patterns of spatial dependence and drivers should be represented. At fine scales, changes in the nature and intensity of feedbacks may be associated with predictable changes in patch structure (e.g., fragmentation or coalescence processes foreshadowing thresholds). At coarser scales, feedbacks may be associated with the spatial extent and arrangement of land cover states, broad-scale resource redistribution (e.g., wind/water erosion/deposition) or land surface/climate interactions. S&amp;amp;T conceptual models with a spatial component would make use of readily-obtainable patch-scale data to predict how the likelihood of transitions might vary across landscapes and to forecast changes leading to degradation or recovery. Such predictions would be valuable in the design of monitoring schemes aimed at adjusting management to avert crossing degradation thresholds and to take advantage of environmental conditions required to restore desired states.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue><accession-num><style face="normal" font="default" size="100%">LTER.2011-90014</style></accession-num></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Browning, D.M.</style></author><author><style face="normal" font="default" size="100%">Archer, S. R.</style></author><author><style face="normal" font="default" size="100%">Byrne, A.T.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Field validation of 1930s aerial photography: What are we missing?</style></title></titles><keywords><keyword><style  face="normal" font="default" size="100%">JRN</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">bibliography/09-019.pdf</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">73</style></volume><pages><style face="normal" font="default" size="100%">844-853</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Aerial photography from the 1930s serves as the earliest synoptic depiction of vegetation cover.  We generated a spatially explicit database of shrub (Prosopis velutina) stand structure within two 1.8 ha field plots established in 1932 to address two questions: (1) What are the detection limits of panchromatic 1936 aerial photography?, and (2) How do these influence P. velutina biomass estimates?  Shrub polygons were manually digitized on 1936 imagery and linked to 1932 field measurements of P. velutina canopy area.  Aboveground 1932 P. velutina biomass was estimated using a site-specific allometric relationship for field-measured canopy area. Shrub canopy detection limits on the 1936 imagery were comparable to those reported for contemporary imagery.  Based on a conservative shrub size detection threshold of 3.8 m2, 5.8% of P. velutina biomass was missed. Spatial resolution (0.6 vs. 1.0 m) did not influence detection limits, but the overall accuracy of shrub cover estimates was greater on 1.0 m images.   Presence of the sub-shrub Isocoma tenuisecta may also have significantly influenced estimates of P. velutina canopy area.  These analyses illustrate the importance of standardizing aerial photo interpretation protocols, accounting for uncertainty estimating shrub biomass, and caution species-specific interpretations for historic aerial photography.</style></abstract><accession-num><style face="normal" font="default" size="100%">LTER.2009-90130</style></accession-num></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ryan, M. G.</style></author><author><style face="normal" font="default" size="100%">Oren, R.</style></author><author><style face="normal" font="default" size="100%">Randerson, J.</style></author><author><style face="normal" font="default" size="100%">Schlesinger, W.</style></author><author><style face="normal" font="default" size="100%">Archer, S. R.</style></author><author><style face="normal" font="default" size="100%">Birdsey, R.</style></author><author><style face="normal" font="default" size="100%">Dahm, C.</style></author><author><style face="normal" font="default" size="100%">Heath, L.</style></author><author><style face="normal" font="default" size="100%">Hicke, J.</style></author><author><style face="normal" font="default" size="100%">Hollinger, D.</style></author><author><style face="normal" font="default" size="100%">Huxman, T.</style></author><author><style face="normal" font="default" size="100%">Okin, G.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Schimel, David</style></author></secondary-authors><tertiary-authors><author><style face="normal" font="default" size="100%">Walsh, Margaret</style></author></tertiary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Land resources: forests and arid lands</style></title><secondary-title><style face="normal" font="default" size="100%">The effects of climate change on agriculture, land resources, water resources, and biodiversity in the United States.</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Report by the U.S. Climate Change Science Program and the Subcommittee on Global Change Research</style></tertiary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">JRN</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.climatescience.gov/Library/sap/sap4-3/final-report/default.htm</style></url></web-urls></urls><pub-location><style face="normal" font="default" size="100%">Washington, DC., USA</style></pub-location><pages><style face="normal" font="default" size="100%">362 pp.</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This synthesis and assessment report builds on an extensive scientific literature and series of recent assessments of the historical and potential impacts of climate change and climate variability on managed and unmanaged ecosystems and their constituent biota and processes. It identifies changes in resource conditions that are now being observed and examines whether these changes can be attributed in whole or part to climate change. It also highlights changes in resource conditions that recent scientific studies suggest are most likely to occur in response to climate change, and when and where to look for these changes. As outlined in the Climate Change Science Program (CCSP) Synthesis and Assessment Product 4.3 (SAP 4.3) prospectus, this chapter will specifically address climate-related issues in forests and arid lands.In this chapter the focus is on the near-term future. In some cases, key results are reported out to 100 years to provide a larger context but the emphasis is on next 25-50 years. This nearer-term focus is chosen for two reasons. First, for many natural resources, planning and management activities already address these time scales through development of long-lived infrastructure, forest rotations, and other significant investments. Second, climate projections are relatively certain over the next few decades. Emission scenarios for the next few decades do not diverge from each other significantly because of the “inertia</style></abstract><accession-num><style face="normal" font="default" size="100%">LTER.2008-90331</style></accession-num></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A.K. Knapp</style></author><author><style face="normal" font="default" size="100%">Pendall, E.</style></author><author><style face="normal" font="default" size="100%">Cleary, M. B.</style></author><author><style face="normal" font="default" size="100%">Briggs, J.M.</style></author><author><style face="normal" font="default" size="100%">Collins, Scott L.</style></author><author><style face="normal" font="default" size="100%">Archer, S. R.</style></author><author><style face="normal" font="default" size="100%">Bret-Harte, M. S.</style></author><author><style face="normal" font="default" size="100%">Ewers, B. E.</style></author><author><style face="normal" font="default" size="100%">Peters, D.P.</style></author><author><style face="normal" font="default" size="100%">Young, D. R.</style></author><author><style face="normal" font="default" size="100%">Shaver, G. R.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Shrub encroachment in North American grasslands: shifts in growth form dominance rapidly alters control of ecosystem carbon inputs</style></title><secondary-title><style face="normal" font="default" size="100%">Global Change Biology</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">KNZ</style></keyword><keyword><style  face="normal" font="default" size="100%">VCR</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">&lt;Go to ISI&gt;://000252929900014</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">14</style></volume><pages><style face="normal" font="default" size="100%">615-623</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Shrub encroachment into grass-dominated biomes is occurring globally due to a variety of anthropogenic activities, but the consequences for carbon (C) inputs, storage and cycling remain unclear. We studied eight North American graminoid-dominated ecosystems invaded by shrubs, from arctic tundra to Atlantic coastal dunes, to quantify patterns and controls of C inputs via aboveground net primary production (ANPP). Across a fourfold range in mean annual precipitation (MAP), a key regulator of ecosystem C input at the continental scale, shrub invasion decreased ANPP in xeric sites, but dramatically increased ANPP (&gt; 1000 g m(-2)) at high MAP, where shrub patches maintained extraordinarily high leaf area. Concurrently, the relationship between MAP and ANPP shifted from being nonlinear in grasslands to linear in shrublands. Thus, relatively abrupt (&lt; 50 years) shifts in growth form dominance, without changes in resource quantity, can fundamentally alter continental-scale pattern of C inputs and their control by MAP in ways that exceed the direct effects of climate change alone.</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue><accession-num><style face="normal" font="default" size="100%">LTER.2008-82485</style></accession-num></record></records></xml>