Mongoose in the rainforest: Analyzing population estimates and habitat attributes to support management in El Yunque National Forest, Puerto Rico

Small mammals are considered one of the most detrimental biological invaders in island ecosystems. The small Indian mongoose, Herpestes auropunctatus , was introduced throughout most of the Caribbean and Pacific Islands a century ago. Once established, mongooses significantly alter food webs, often becoming detrimental to natives species. Lack of published information on the effectiveness of trapping and control techniques makes it difficult for conservation managers to devise effective eradication campaigns. In El Yunque National Forest (YNF) in Puerto Rico, management plans to control mongoose populations include the removal of individuals seasonally, but populations remain stable. In this study, I focus on Puerto Rico (El Yunque National Forest) and the introduced small Indian mongoose. The objectives of my study were to: 1) compare mongoose abundances between YNF and a nearby coastal zone, 2) compare habitat conditions among the dominant forest types found in YNF and the coastal zone, 3) relate the likelihood of capturing mongooses to habitat characteristics at the forest patch scale, and 4.) quantify the influence of localized habitat features on individual trap success. I used mark-recapture in 5 different forest types in YNF and eastern coastal areas during the summer of 2012. Relative densities were 2.4 mongooses/ha for the four forest types in YNF and 12.2 mongooses/ha in the coastal forest of the NEC. At each trap I collected vegetation information (i.e., overstory canopy cover, understory cover, woody debris). Cover estimates differed among locations but I found no relationship between capture frequencies and coarse habitat characteristics at the forest patch level. I calculated distances (m) to: coastal shoreline, trails, roads (paved and unpaved), rivers, recreation areas, and also included elevation (m) for each trap location. I developed a candidate model set and estimated the likelihood of capturing a mongoose at a trap location using logistic regression. I found 4 competing models for describing the likelihood of capturing a mongoose at a trap location that included positive relationships to distances from rivers and recreational areas and canopy cover but negative relationships for distances to trails and coastal shoreline. Models revealed that vegetation features in the vicinity of traps had no influence on the likelihood of catching a mongoose. Distance metrics were the best predictors of mongoose capture probability within a trap grid. The ability to predict where to place traps and monitoring trapping outcomes are important for reducing efforts and costs and measuring progress towards the management goal. My results offer insights into the current population status of mongooses in certain areas of eastern Puerto Rico and suggest that coastal forest has the potential to serve as a source habitat for YNF. I recommend integrating different methods for mongoose detection and removal, and establishing population targets that prompt control activities.