@article {JRN55233,
	title = {Numerical soil classification supports soil identification by citizen scientists using limited, simple soil observations},
	journal = {Soil Science Society of America Journal},
	year = {2020},
	month = {09/2020},
	pages = {1-18},
	abstract = {Accurately identifying the soil map unit component at a specific point-location or position 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 point-location based on its numerical similarity to the soil components mapped in that area. To implement this testing framework, we used a subset of the United States National Cooperative Soil Survey Soil Characterization Database (NCSS{\textendash}SCD) as our soil profile point dataset and the U.S. Soil Survey Geographic (SSURGO) database for querying profile data of mapped soil classes in the area surrounding each point. Numerical similarity was tested using soil property data representing different degrees of generalization, both in terms of geographic space (i.e., depth-wise variably) and feature space (e.g., texture class vs. clay percentage). Three soil property groups (i.e., Novice, Expert, Expert-Plus) representing different levels of experience required for their measurement 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 classification 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\%). A measure of management relevant accuracy was assessed using correlated Ecological Sites (ESDs) and Land Capability Classifications (LCC) which followed similar trends for the Novice (ESD: 78-82\%; LCC: 70-76\%), Expert (ESD: 79-84\%; LCC: 71-77\%), and Expert-Plus (ESD: 83-85\%; LCC: 74-79\%) datasets. This study also showed minimal effect from the type of depth-support used to represent depth-wise variability.},
	keywords = {characterizing soil profiles, landscape, numerical, smartphone-based technologies, soil classification algorithms, soil map unit component, specific point-location, sustainable soil management},
	doi = {https://doi.org/10.1002/saj2.20119},
	url = {files/bibliography/20-030.pdf},
	author = {Jonathan Maynard and S. W. Salley and D. Beaudette and Jeffrey E. Herrick}
}
@conference {50252,
	title = {Assessing the importance of landscape attributes for livestock distribution within a complex desert environment},
	booktitle = {Ecological Society of America Abstracts},
	year = {2008},
	month = {August 4-8, 2008},
	pages = {54-22},
	address = {Milwaukee, WI},
	abstract = {Predictive models of grazing distribution are used to explore livestock {\textendash} landscape interactions. However, conventional models based on local foraging rules or regression analyses have limited predictive value at larger scales commonly of interest to land managers. We developed a landscape scale model that matches the spatial scale of decision factors with the predicted distribution pattern.  The model was used to infer how animals integrate landscape factors. Predictive models of grazing suitability were generated for different seasons (Spring and Fall) and cattle breeds (Angus and Criollo) in a 2,425 ha pasture on the Jornada Experimental Range located in the northern Chihuahuan Desert. Models generate raster maps of grazing suitability using an adapted version of the Multi-Criteria Evaluation method from decision theory. Four factors: vegetation, distance from water, shrub density, and elevation were represented by 10 m grid maps. Independent GPS collars grazing positions data acquired at 5-minute intervals were used to calculate decision functions, which describe animal preference for different landscape attributes, and to evaluate how well the model fit the data. Models with three predictors produced the best fit of the data. Distance to water was the most important factor in all models. Animals showed higher preferences for areas within 4 to 6 km of permanent water depend upon breeds. Other factors were included with lower relative importance. Animal preferences were greater for areas with low shrub density and for low elevation sites where Pleuraphis mutica, Schleropogon brevifolius, and Sporobulus spp. dominate heavier texture soils. Suitable areas delimited by the models realistically represent the observed spatial pattern of grazing. Between 70 to 90 \% of grazing positions occurred in cells classified by the model as suitable. Conversely, the number of suitable cells was 5 to 8 times larger than the observed grazed cells. These differences may have occurred because grazing positions correspond to a very low stocking rate (6 cows in a 2,425 ha pasture); under a realistic situation the number of grazed cells is expected to largely increase. According to the models the pasture is characterized by a single or few large grazing patches which includes about 80\% of all suitable cells for grazing. This modeling approach can contribute to an understanding of how large animals perceive landscape heterogeneity.},
	keywords = {abstrace, landscape, landscape attributes, livestock},
	author = {H. Raul Peinetti and E.L. Fredrickson and Debra C. Peters and Roacho-Estrada, J.O. and Andrea S. Laliberte}
}
@article {50439,
	title = {Introduction to special issue - Landscape linkages and cross scale interactions in arid and semiarid ecosystems},
	journal = {Journal of Arid Environments},
	volume = {65},
	year = {2006},
	month = {April 2006},
	pages = {193-195},
	abstract = {The conversion of grasslands to shrublands in arid and semiarid ecosystems is a serious global problem. Although a great deal of research has been conducted on these conversions, we lack a complete understanding of the processes underlying the transitions. More importantly, our ability to predict when and where desertification will occur and our ability to manage landscapes to prevent degradation and restore degraded landscapes is limited. Research being conducted at the Jornada Experimental Range (JER) is addressing these problems in a new, synthetic way. Since 1981, scientists affiliated with the Jornada Basin Long-Term Ecological Research site in collaboration with scientists from the JER and New Mexico State University generated a number of new ideas and concepts regarding the processes governing desertification that changed our thinking about how these systems operate. Our focus now emphasizes cross-scale spatial and temporal heterogeneity in vegetation patterns and dynamics that have been difficult to explain using traditional approaches. The realization that our current knowledge often cannot explain the existing variability among land units led to the development of a conceptual model by JER scientists that considers nonlinear dynamics and connections among land units across scales. This cross-scale landscape ecology approach will further our understanding of critical processes occurring in arid regions globally and will provide insight to help identify mechanisms to address the pressing issue of desertification.},
	keywords = {arid, cross scale, desertification, ecosystems, invasion, landscape, linkages, semiarid, shrub},
	doi = {10.1016/j.jaridenv.2005.04.001},
	url = {/files/bibliography/06-006.pdf},
	author = {Richard E. Estell and E.L. Fredrickson and Debra C. Peters}
}
@article {50456,
	title = {Monitoring ecological processes for restoration projects},
	journal = {Journal of Nature Conservation},
	volume = {14},
	number = {3-4},
	year = {2006},
	month = {September 2006},
	pages = {161-171},
	abstract = {Restoration of ecological processes is key to restoring the capacity of ecosystems to support social, economic, cultural and aesthetic values. The sustainability of the restored system also depends on processes associated with carbon, nutrient and hydrologic cycles, yet most restoration monitoring is limited to plant community composition. Our research has shown that short-term plant composition monitoring is a necessary but insufficient predictor of long-term restoration success. Long-term (up to 75 years) studies in the western United States show that short-term monitoring of plant community composition alone incorrectly predicted the failure of treatments that were ultimately successful, and the success of treatments that ultimately failed. We propose that vegetation composition monitoring be combined with one or more ecological process indicators reflecting changes in three fundamental ecosystem attributes on which restoration success depends: soil and site stability, hydrologic function and biotic integrity. These simple, rapid, plot-level indicators reflect changes in resource redistribution and vegetation structure. We include a case study involving restoration of mixed grass prairie on mineland in the west-central United States.},
	keywords = {ecosystem services, erosion, indicators, landscape, monitoring, restoration, runoff, spatial pattern, spatial variability},
	url = {/files/bibliography/06-032.pdf},
	author = {Jeffrey E. Herrick and G.E. Schuman and A. Rango}
}
@conference {50689,
	title = {Broad-scale landscape, soil, and wind influences on fine-scale dynamics},
	booktitle = {Sixth Symposium on the Natural Resources of the Chihuahuan Desert Region},
	year = {2004},
	month = {October 15, 2004},
	pages = {35},
	address = {Alpine, TX},
	abstract = {Desertification and other ecogeomorphic changes in the Chihuahuan Desert are connected to broad-scale landscape, soil, and wind drivers via fine-scale processes. Landscapes function as a driver by their lithic composition and topographic configuration. These impact water redistribution and wind movement across the landscape that influence soil patterns at the landscape scale. Strong winds in the Jornada Basin, for example, have decapitated sandy soils, formed erosional streets, pushed sand grains up the piedmont slope and over ridges into intermountain valleys where they have accumulated as thick, eolian deposits overlying bedrock. Fine-scale processes are influenced by these broad-scale drivers in several ways. Wind erosion, for instance, removes organic matter and coarsens soil texture, thereby lowering water-holding capacity, cation exchange capacity, and nutrient availability. Because such fine-scale processes exert important controls on plants, vegetation patterns at the landscape scale reflect patterns of the broad-scale drivers. Consequently, some ecogeomorphic conditions, such as black grama on sandy, eolian upland soils, are more prone to desertification than other conditions, like tobosa grass on silty, alluvial soils in run-in positions.},
	keywords = {broad-scale, Chihuahuan Desert, desertification, ecogeomorphic, fine-scale dynamics, landscape, soil, wind},
	author = {Curtis H. Monger and D. E. Gillette and Brandon T Bestelmeyer}
}
@article {50707,
	title = {Selection of models of invasive species dynamics},
	journal = {Weed Technology},
	volume = {18},
	year = {2004},
	month = {August 1, 2004},
	pages = {1236-1239},
	abstract = {<p>Mathematical and process-based simulation models are powerful tools for synthesizing information about invasive species. However, there are a number of different types of models, ranging from simple to complex that can be selected for any given application. In this article, a model classification framework of three types of models is applied to studies of invasive species that allows the objective selection of a model type on the basis of its ability to capture key processes and dynamics, yet minimize the errors in prediction. Model selection is illustrated using a series of increasingly complex models.</p>
},
	keywords = {biotic, dynamics, environmental, invasive, landscape, Nonspatial models, spatially explicit models, spatially implicit models.},
	doi = {10.1614/0890-037X(2004)018[1236:SOMOIS]2.0.CO;2},
	url = {files/bibliography/JRN00420.pdf},
	author = {Debra C. Peters}
}
@proceedings {51063,
	title = {The impact of the structure and composition of shrub-coppice dune landscapes on MASTER reflectance anisotropy},
	volume = {IAHS Publication No. 267},
	year = {2000},
	note = {Proceedings of a symposium held at Santa Fe, NM, April 2000},
	month = {2001},
	pages = {162-167},
	publisher = {Remote Sensing and Hydrology},
	address = {Santa Fe, NM },
	abstract = {This study assess the effects of physical structure and composition of shrub-coppice dune landscapes on anisotropy in the NASA MODIS-ASTER Airborne Simulator (MASTER) solar channels, and investigates the viability of simulating multi-angular data sets using off-nadir airborne imaging radiometry from a single overpass by means of data segmentation.},
	keywords = {anisotropy, dunes, landscape, mesquite, multiple view angle, New Mexico, structure},
	url = {/files/bibliography/01-080.pdf},
	author = {M. Chopping and T. J. Schmugge and A. Rango and Jerry Ritchie and William P. Kustas}
}
