Browsing by Subject "betweenness"

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Open Access
Identifying Focal Wildlife Conservation Areas on Private Lands in North Carolina
(2008-04-24T19:14:16Z) Baker, Nicholas
There are over 1,200 threatened or endangered animal species in the U.S, of which 36 are located in North Carolina. To address this problem of species imperilment, all 50 states developed State Wildlife Action Plans (SWAP). As requested by Congress, each SWAP is to identify priority conservation areas in which limited resources can be directed towards. The North Carolina WAP lacks priority conservation areas. This paper identifies focal wildlife conservation areas on private lands in Moore, Hoke, Richmond, and Scotland counties for the purpose of maintaining and protecting biodiversity and assisting the NC Wildlife Resources Commission in WAP implementation. A geographic information system (GIS) was used to conduct the analysis. Three principal datasets were used in identifying focal areas: 1) North Carolina Gap Analysis Project (NCGAP) wildlife distribution models, 2) North Carolina land cover from 2001, and 3) NCGAP protected land boundaries. The focal areas were ranked individually based on three metrics: betweenness, area, and distance to protected land. Betweenness is based on the Euclidean distance between pairs of patches. A habitat patch with high betweenness is significant ecologically, because it indicates how important a particular patch is in maintaining linkages among other patches. The area of a patch is important in assessing whether a species would be able to survive a large-scale natural disturbance. Also, larger patches generally support a greater number of species or individuals. Finally, conserving patches of land that are close to protected lands increases the likelihood that species associated with the patches will continue to persist (i.e., species are more able to disperse throughout the landscape). Thirty-three potential wildlife conservation sites were identified. This information can assist conservation planners when dealing with limited funding and personnel. The approach of my analysis can be applied more broadly in order to establish habitat conservation or connectivity at a regional scale.
Open Access
Network analysis of sea turtle movements and connectivity: A tool for conservation prioritization
(Diversity and Distributions, 2022-04-01) Kot, CY; Åkesson, S; Alfaro-Shigueto, J; Amorocho Llanos, DF; Antonopoulou, M; Balazs, GH; Baverstock, WR; Blumenthal, JM; Broderick, AC; Bruno, I; Canbolat, AF; Casale, P; Cejudo, D; Coyne, MS; Curtice, C; DeLand, S; DiMatteo, A; Dodge, K; Dunn, DC; Esteban, N; Formia, A; Fuentes, MMPB; Fujioka, E; Garnier, J; Godfrey, MH; Godley, BJ; González Carman, V; Harrison, AL; Hart, CE; Hawkes, LA; Hays, GC; Hill, N; Hochscheid, S; Kaska, Y; Levy, Y; Ley-Quiñónez, CP; Lockhart, GG; López-Mendilaharsu, M; Luschi, P; Mangel, JC; Margaritoulis, D; Maxwell, SM; McClellan, CM; Metcalfe, K; Mingozzi, A; Moncada, FG; Nichols, WJ; Parker, DM; Patel, SH; Pilcher, NJ; Poulin, S; Read, AJ; Rees, AF; Robinson, DP; Robinson, NJ; Sandoval-Lugo, AG; Schofield, G; Seminoff, JA; Seney, EE; Snape, RTE; Sözbilen, D; Tomás, J; Varo-Cruz, N; Wallace, BP; Wildermann, NE; Witt, MJ; Zavala-Norzagaray, AA; Halpin, PN
Aim: Understanding the spatial ecology of animal movements is a critical element in conserving long-lived, highly mobile marine species. Analyzing networks developed from movements of six sea turtle species reveals marine connectivity and can help prioritize conservation efforts. Location: Global. Methods: We collated telemetry data from 1235 individuals and reviewed the literature to determine our dataset's representativeness. We used the telemetry data to develop spatial networks at different scales to examine areas, connections, and their geographic arrangement. We used graph theory metrics to compare networks across regions and species and to identify the role of important areas and connections. Results: Relevant literature and citations for data used in this study had very little overlap. Network analysis showed that sampling effort influenced network structure, and the arrangement of areas and connections for most networks was complex. However, important areas and connections identified by graph theory metrics can be different than areas of high data density. For the global network, marine regions in the Mediterranean had high closeness, while links with high betweenness among marine regions in the South Atlantic were critical for maintaining connectivity. Comparisons among species-specific networks showed that functional connectivity was related to movement ecology, resulting in networks composed of different areas and links. Main conclusions: Network analysis identified the structure and functional connectivity of the sea turtles in our sample at multiple scales. These network characteristics could help guide the coordination of management strategies for wide-ranging animals throughout their geographic extent. Most networks had complex structures that can contribute to greater robustness but may be more difficult to manage changes when compared to simpler forms. Area-based conservation measures would benefit sea turtle populations when directed toward areas with high closeness dominating network function. Promoting seascape connectivity of links with high betweenness would decrease network vulnerability.