U.S. Department of Agriculture: Animal and Plant Health Inspection Service

 

ORCID IDs

0000-0002-0823-8772

Date of this Version

2018

Citation

Resnik, J.R., W.F. Andelt, T.R. Stanley, and N.P. Snow. 2018. Island fox spatial ecology and implications for management of disease. Journal of Wildlife Management 82(6):1185-1198. doi: 10.1002/jwmg.21464

Comments

U.S. government work.

Abstract

Disease, predation, and genetic isolation resulted in 4 of 6 island fox (Urocyon littoralis) subspecies being listed as endangered in 2004. Potential for disease outbreaks continues to pose a major threat to the persistence of these isolated, endemic populations. We examined how roads influence the spatial ecology of San Clemente Island foxes (U. l. clementae), particularly in regard to spread of disease, to provide management recommendations for preventing or minimizing a disease outbreak on San Clemente Island, California, USA. Home range areas (x=0.75 km2) and core areas (x=0.19 km2) of foxes on San Clemente Island were 0.36–1.23 and 2.17 times larger, respectively, than estimates from Santa Cruz Island foxes (U. l. santacruzae). Home ranges and core areas were 78% larger and 73% larger, respectively, for foxes near roads than for foxes away from roads. Home ranges were also largest when foxes were not caring for offspring (i.e., seasons of pup-independence and breeding). We did not detect any dispersal movements, but foxes living near roads moved 33% farther in 2-hour periods than foxes not living near roads. Foxes near roads move faster, range more widely, and could more rapidly spread a pathogen throughout the island; therefore, roads might serve as transmission corridors.We recommend reducing this risk by increasing widths of vaccination firewalls (areas where vaccination is used to induce a disease-resistant or immune population of foxes), ensuring these areas deliberately intersect roads, and vaccinating a higher proportion of foxes living near roads. Disease risk models incorporating these strategies could inform the lowest risk scenarios.

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