title: Network analysis of translocated Takahe populations to identify disease surveillance targets.
abstract: Social network analysis is being increasingly used in epidemiology and disease modeling in humans, domestic animals, and wildlife. We investigated this tool in describing a translocation network (area that allows movement of animals between geographically isolated locations) used for the conservation of an endangered flightless rail, the Takahe (Porphyrio hochstetteri). We collated records of Takahe translocations within New Zealand and used social network principles to describe the connectivity of the translocation network. That is, networks were constructed and analyzed using adjacency matrices with values based on the tie weights between nodes. Five annual network matrices were created using the Takahe data set, each incremental year included records of previous years. Weights of movements between connected locations were assigned by the number of Takahe moved. We calculated the number of nodes (i(total)) and the number of ties (t(total)) between the nodes. To quantify the small-world character of the networks, we compared the real networks to random graphs of the equivalent size, weighting, and node strength. Descriptive analysis of cumulative annual Takahe movement networks involved determination of node-level characteristics, including centrality descriptors of relevance to disease modeling such as weighted measures of in degree (k(i)(in)), out degree (k(i)(out)), and betweenness (B(i)). Key players were assigned according to the highest node measure of k(i)(in), k(i)(out), and B(i) per network. Networks increased in size throughout the time frame considered. The network had some degree small-world characteristics. Nodes with the highest cumulative tie weights connecting them were the captive breeding center, the Murchison Mountains and 2 offshore islands. The key player fluctuated between the captive breeding center and the Murchison Mountains. The cumulative networks identified the captive breeding center every year as the hub of the network until the final network in 2011. Likewise, the wild Murchison Mountains population was consistently the sink of the network. Other nodes, such as the offshore islands and the wildlife hospital, varied in importance over time. Common network descriptors and measures of centrality identified key locations for targeting disease surveillance. The visual representation of movements of animals in a population that this technique provides can aid decision makers when they evaluate translocation proposals or attempt to control a disease outbreak.
authors: Allan Wilson Centre for Molecular Ecology and Evolution, Massey University, Private Bag 11 222, Palmerston North, New Zealand; mEpiLab, Infectious Disease Research Centre, Institute of Veterinary, Animal and Biomedical Sciences, Massey University, Private Bag 11 222, Palmerston North, New Zealand; Wildbase, Institute of Veterinary, Animal and Biomedical Sciences, Massey University, Private Bag 11 222, Palmerston North, New Zealand. z.grange@massey.ac.nz.
authors: Zoë L
authors: ZL
authors: Grange
authors: Mary
authors: M
authors: VAN Andel
authors: Nigel P
authors: NP
authors: French
authors: Brett D
authors: BD
authors: Gartrell
mesh_terms: D000818:Animals; D001715:Bird Diseases / Q000453:epidemiology*; D001717:Birds; D003247:Conservation of Natural Resources; D004196:Disease Outbreaks / Q000662:veterinary*; D008954:Models, Biological; D009520:New Zealand; D011156:Population Density; D011159:Population Surveillance
keywords: New Zealand; Nueva Zelanda; análisis de redes sociales; disease surveillance; ecosistemas insulares; island ecosystems; pathogens; patógenos; social network analysis; supervivencia a enfermedades; translocación; translocation