@article {50730,
	title = {Pairwise species associations in the perennial vegetation of the northern Chihuahuan Desert},
	journal = {The Southwestern Naturalist},
	volume = {49},
	number = {1},
	year = {2004},
	month = {March 1, 2004},
	pages = {1-10},
	abstract = {We analyzed pairwise species associations based on presence/absence data from transects of perennial vegetation at 129 sites located in the northern Chihuahuan Desert.  The sites were chosen to represent the range of commonly encountered desert vegetation types from grassland to shrubland.  Twenty-four significant associations were found, all of which were positive (i.e., the species pairs co-occurred more frequently than would be expected by chance).  Some of the species associations identified by our analysis have been noted in the literature previously but others have not and warrant further investigation.  Given the nature and scale of the data used in our analysis, the associations we uncovered most likely represent species with similarities in ecological requirements or tolerances but might or might not indicate strong interaction at the scale of individual plants.},
	keywords = {Chihuahuan Desert, pairwise species, perennial vegetation},
	url = {/files/bibliography/04-077.pdf},
	author = {S.J. Turner and A. R. Johnson and Walter G. Whitford}
}
@conference {51307,
	title = {Classification, ecological risk, and decision: assessing desertification in south-central New Mexico},
	booktitle = {International Symposium and Workshop Combating Desertification: Connecting Science with Community Action},
	year = {1997},
	month = {May 12-16, 1997},
	pages = {Abstract 44},
	address = {Tucson, AZ},
	abstract = {<p>Classification of remotely sensed and ground-based data is at the heart of landscape monitoring, calculating ecological risk, and decision making. The simplest classification may be a binary decision: the land is either degraded or it is not. This research investigates several problems that arise because of the subjective nature of that final decision and suggests a process for minimizing the least-desirable errors. We use a combination of field vegetation surveys, classified remotely sensed AVHRR images, and error analysis to answer questions about the impact of classification error on management decisions. We use our data to estimate (1) the probability that the classification of an arbitrary pixel is incorrect, (2) the rates of different types of mis-classification (Type I vs. Type II statistical errors), and (3) the implication of such errors for management and risk analysis. Although our study focuses on south-central New Mexico (USA), we expect the approach will be broadly applicable and have implications for other arid regions.</p>
},
	author = {S.J. Turner and A. R. Johnson and A.G. de Soyza and Justin Van Zee and M.D. Eve and Walter G. Whitford}
}
