Next Generation Sonoran Desert Researchers (N-Gen) is a growing community of researchers dedicated to the study of society, culture, geography, and ecology in the Sonoran Desert. It was formed to facilitate communication and collaboration among early-career researchers in the region, as well as to support connections and build bridges between this new group and later-career researchers.
In April 2012 N-Gen hosted an inaugural summit in Tucson, Arizona. Participants came from Mexico and the United States, representing 16 different disciplines and 37 different institutions, including nongovernmental organizations, academia, indigenous communities, and government agencies. The overarching goal of the summit was to establish lasting connections and develop a network of collaborators from a group that was previously largely unconnected.
Before N-Gen there was no organized network for Sonoran Desert researchers. Further, our understanding of the networks connecting those working in the region was limited. With few exceptions (see Marcos-Iga’s 2004 master’s thesis looking at networks of conservation organizations in the Colorado River Delta and Laird-Benner and Ingram’s 2011 study of network “weavers” in the U.S.-Mexico border region of Arizona and Sonora), little is known about how researchers in the Sonoran Desert are connected.
Here we use social network analysis and cluster analysis to examine the patterns of connections between N-Gen summit participants (for a more detailed explanation of social network analysis and how it relates to natural resource management, see Bodin et al. 2006; Carlsson and [End Page 187] Sandström 2008; Bodin and Prell 2011). Past studies of networks of those working on conservation issues in the U.S.-Mexico border region have shown that activities such as the N-Gen summit can positively impact the environment in the long term (Laird-Benner and Ingram 2011). In this research we show that the creation of this new international network engenders multidisciplinary linkages and collaboration and fills an important gap in the Sonoran Desert region.
In the weeks following the summit we conducted an online survey of all participants to better understand how the summit impacted professional connections. We gathered information about primary and secondary disciplines of work or research interests, geographic region of study, and ways in which respondents were connected to other summit attendees. The relationships of interest were: worked together (shared information/knowledge, published together, collaborated on projects, or conducted fieldwork together), was a mentor of the respondent, or met for the first time at the N-Gen summit. We got information about primary and secondary disciplines and geographic region of study for summit attendees who did not respond to the survey by mining information from the member directory on the N-Gen website (http://nextgensd.com/researchers/). This allowed us to construct more complete matrices for our three different relations even though some attendees did not respond to the survey.
Social Network Analysis
We built symmetrical matrices of 87 N-Gen summit participants for three social networks: work together,1 mentor, and met for first time at N-Gen summit. We used UCINET (Borgatti et al. 2002) to calculate network density and degree centrality. Network density is the proportion of actual ties to the proportion of possible ties. It can help understand how quickly network members share information with each other (Wasserman and Faust 1994; Hanneman and Riddle 2005; Prell 2012). Degree centrality is a count of the number of ties of each network member [End Page 188] and is a measure of power. Network members who are well connected (i.e., have a high degree centrality), and particularly those who have connections that are not duplicated by others in the network, are in advantageous positions. They may serve as brokers between otherwise unconnected network members, helping to transfer information and resources (Wasserman and Faust 1994; Hanneman and Riddle 2005; Prell 2012), serving as mentors, or connecting those who work in different disciplines or different geographic regions. Finally, we created an attribute data set with individual-level characteristics for each network member (e.g., discipline, geographic region...