@conference {JRN55203,
	title = {fDNA-based diet selection by Raramuri Criollo and Angus crossbreeds in the Chihuahuan Desert},
	booktitle = {Society for Range Management},
	year = {2020},
	month = {02/2020},
	address = {Denver, CO},
	abstract = {A 3-year diet study was conducted in two pastures on the Jornada Experimental Range in the northern Chihuahuan Desert, Dona Ana County, New Mexico, USA to compare diets of Raramuri Criollo and Angus x Hereford crossbreed cattle.  Fecal samples were collected directly from cattle and analyzed with both Operational Taxonimic Units (OTUs) and Exact Sequence Variants (ESVs).  ESVs performed better than OTUs at discriminating between plant taxa.  Although patterns of plant preference between the two breeds were not necessarily constant over time, Criollo diets were were found to have proportionally more Honey mesquite (Prosopis glandulosa) and proportionally less Black grama (Bouteloua eripoda) than Angus x Hereford crossbreeds.  In general, both breeds consumed proportionally more grass in the winter than in the summer.  However, the grass component for both breeds decreased over time, likely due to persistent drought which caused less grass production and availability.  Both breeds behaved the same way by replacing the grass component with forbs in the summer and with shrubs in the winter.  This study is an example of how fecal DNA analyses can illustrate how livestock alter their diets to cope with drought.},
	author = {D. K. James and Richard E. Estell and Andr{\'e}s F. Cibils and S. Spiegal and S. Nyamuryekung{\textquoteright}e and Matthew M. McIntosh and Amy L. Gonzalez and Kirsten B. Romig}
}
@conference {JRN54625,
	title = {From linear to generalized linear mixed models: A case study in repeated measures},
	booktitle = {2016 American Statistical Association (ASA) Conference on Statistical Practice},
	year = {2016},
	month = {02/2016},
	publisher = {2016 American Statistical Association (ASA)},
	organization = {2016 American Statistical Association (ASA)},
	address = {San Diego, CA},
	abstract = {<p>Compared to traditional linear mixed models, generalized linear mixed models (GLMMs) can offer better correspondence between response variables and explanatory models, yielding more efficient estimates and tests in the analysis of data from designed experiments. Using proportion data from a designed experiment with repeated measures, results from several candidate GLMMs implemented with different distributions (binomial and beta), likelihood estimation methods (integral approximation and PL), covariance structures, and bias correction methods, are compared. The results show that constructing an appropriate GLMM is not trivial, especially in the case of repeated measures. \&nbsp;The numerical estimation procedures GLMMs utilize can easily produce intractable or nonsensical results that are difficult to diagnose and rectify. \&nbsp;Many common adjustments and modeling decisions fundamentally change the model{\textquoteright}s inference space and alter appropriate interpretations of model parameters. \&nbsp;Modelers must also confront mean-variance dependency, important differences between conditional ({\textquotedblleft}G-side{\textquotedblright}) and marginal ({\textquotedblleft}R-side{\textquotedblright}) formulations of random effects, and how to implement bias correction. \&nbsp;</p>
},
	author = {D. K. James}
}
@conference {JRN54444,
	title = {Implementing AIM-based monitoring for natural resource management},
	booktitle = {68th Annual Meeting of the Society for Range Management},
	year = {2015},
	month = {02/2015},
	publisher = {Sociiety for Range Management},
	organization = {Sociiety for Range Management},
	address = {Sacramento, CA},
	abstract = {<p>Successful monitoring programs are built on clearly-defined objectives, thorough planning, and organized implementation. However, natural resource management decisions need to be made at many different organizational levels and scales {\textendash} from local to national. Developing separate monitoring programs to address each of these levels can be an inefficient and costly approach. We present the monitoring process model that is used for design and implementation of Bureau of Land Management{\textquoteright}s (BLM) Assessment, Inventory, and Monitoring (AIM) monitoring projects. The AIM program provides a framework for collecting monitoring data which can be used many times for many objectives (e.g., land health assessments, Sage-grouse [Centrocercus urophasianus] habitat monitoring, grazing management). This framework places an emphasis on designing and implementing robust monitoring programs. While coordinated monitoring across scales is not always possible, the basic AIM principles (standardized core indicators and methods, statistically-valid sample designs, effective data management, and thorough documentation of the monitoring process) facilitate the use of monitoring data to answer the original objectives and allow for merging of monitoring data between efforts to meet additional objectives. The AIM monitoring implementation framework breaks the development and implementation of monitoring down into a series of achievable, iterative and documentable steps. These steps include: developing management and monitoring objectives; defining the monitoring area, stratification approach, and reporting requirements; selecting indicators and methods; developing data management and quality assurance/control procedures; establishing monitoring locations and collecting and evaluating monitoring data; and analyzing and interpreting monitoring results. Further, we discuss the various roles and skills needed for implementing a monitoring program. We illustrate the implementation steps and roles with examples from AIM projects in northern California and southern Idaho designed to inform land health assessments and evaluations of Sage-grouse habitat. Finally, we showcase tools, databases, and best practices available to assist in implementing AIM monitoring.</p>
},
	author = {D. K. James and Sarah McCord and Emily Kachergis and Jeffrey E. Herrick}
}
