Assessing landscape functional connectivity in a forest carnivore using path selection functions
- 981 Downloads
Understanding connectivity patterns in relation to habitat fragmentation is essential to landscape management. However, connectivity is often judged from expert opinion or species occurrence patterns, with very few studies considering the actual movements of individuals. Path selection functions provide a promising tool to infer functional connectivity from animal movement data, but its practical application remains scanty.
We aimed to describe functional connectivity patterns in a forest carnivore using path-level analysis, and to explore how connectivity is affected by land cover patterns and road networks.
We radiotracked 22 common genets in a mixed forest-agricultural landscape of southern Portugal. We developed path selection functions discriminating between observed and random paths in relation to landscape variables. These functions were used together with land cover information to map conductance surfaces.
Genets moved preferentially within forest patches and close to riparian habitats. Functional connectivity declined with increasing road density, but increased with the proximity of culverts, viaducts and bridges. Functional connectivity was favoured by large forest patches, and by the presence of riparian areas providing corridors within open agricultural land. Roads reduced connectivity by dissecting forest patches, but had less effect on riparian corridors due to the presence of crossing structures.
Genet movements were jointly affected by the spatial distribution of suitable habitats, and the presence of a road network dissecting such habitats and creating obstacles in areas otherwise permeable to animal movement. Overall, the study showed the value of path-level analysis to assess functional connectivity patterns in human-modified landscapes.
KeywordsConditional logistic regression Forest carnivores Genetta genetta Landscape conductance Movement behaviour Path-level analysis Road ecology
This study was funded by the Fundação para a Ciência e Tecnologia through grant SFRH/BD/66393/2009 to FC. Logistic support was given by the Conservation Biology Unit and Institute of Mediterranean Agricultural and Environmental Sciences, both from the University of Évora. Ana Galantinho, Pedro Costa and the MOVE project team collaborated in field work; Giovanni Manghi helped in GIS processing, and José Potes (Veterinarian Hospital, University of Évora) supervised the handling of genets. We thank the careful review of the manuscript by Santiago Saura and two anonymous reviewers. Authorization for capturing, handling and tracking genets was provided by the Instituto para a Conservação da Natureza e da Biodiversidade.
- Balestrieri A, Remonti L, Ruiz-González A, Zenato M, Gazzola A, Vergara M, Dettori EE, Saino N, Capelli E, Gómez-Moliner BJ, Guidali F, Prigioni C (2015) Distribution and habitat use by pine marten Martes martes in a riparian corridor crossing intensively cultivated lowlands. Ecol Res 30:153–162CrossRefGoogle Scholar
- Barbosa AM, Brown JA, Jiménez-Valverde A, Real R (2014) modEvA: model evaluation and analysis. R package, version 1.1. http://modeva.r-forge.r-project.org/
- Barton K (2013) MuMIn: Multi-model inference. R package version 1.9.0. http://CRAN.R-project.org/package=MuMIn
- Burnham KP, Anderson DR (2002) Model selection and multimodel inference: a practical information-theoretic approach. Springer, New YorkGoogle Scholar
- Cushman SA, Compton BW, McGarigal K (2010) Habitat fragmentation effects depend on complex interactions between population size and dispersal ability: modeling influences of roads, agriculture and residential development across a range of life-history characteristics. In: Cushman SA, Huettman F (eds) Spatial complexity, informatics and wildlife conservation. Springer, Tokyo, pp 369–385CrossRefGoogle Scholar
- Fahrig L, Rytwinski T (2009) Effects of roads on animal abundance: an empirical review and synthesis. Ecol Soc 14:21Google Scholar
- Ferreras P, Rodríguez A, Palomares F, Delibes M (2010) Iberian lynx: the uncertain future of a critically endangered cat. In: Macdonald DW, Loveridge JA (eds) Biology and conservation of wild felids. Oxford University Press, Oxford, pp 507–520Google Scholar
- Guiomar N, Batista T, Fernandes JP, Souto CC (2009) Corine Land Cover Nível 5. Contribuição para a Carta de Uso do Solo em Portugal Continental. AMDE Edt. ÉvoraGoogle Scholar
- IPMA (Instituo Português do Mar e da Atmosfera) (2012) Normais climatológicas (1971–2000). http://www.ipma.pt. Accessed on 26 Apr 2012
- Legendre P, Legendre L (1998) Numerical ecology, 2nd edn. Elsevier, AmsterdamGoogle Scholar
- Lindenmayer DB, Fischer J (2006) Habitat fragmentation and landscape change: an ecological and conservation synthesis. Island Press, Washington D.C.Google Scholar
- McGarigal K, Cushman SA, Ene E (2012) FRAGSTATS v4: spatial pattern analysis program for categorical and continuous Maps. University of Massachusetts, Amherst. http://www.umass.edu/landeco/research/fragstats/fragstats.html. Accessed on 07 Apr 2015
- Quantum GIS Development Team (2014) Quantum GIS Geographic Information System. Open Source Geospatial Foundation ProjectGoogle Scholar
- R Development Core Team (2013) R: a language and environment for statistical computing. R Foundation for Statistical Computing, ViennaGoogle Scholar
- Rosalino LM, Ferreira D, Leitão I, Santos-Reis M (2011) Usage patterns of Mediterranean agro-forest habitat components by wood mice Apodemus sylvaticus. Mamm Biol 76:268–273Google Scholar
- Rudnick DA, Ryan SJ, Beier P, Cushman SA, Dieffenbach F, Epps CW, Gerber LR, Hartter J, Jenness JS, Kintsch J, Merenlender AM, Perkl RM, Preziosi V, Trombulak SC (2012) The role of landscape connectivity in planning and implementing conservation and restoration priorities. Issues Ecol 16:1–20Google Scholar
- Therneau T (2012) Coxme: mixed effects cox models. R package version 2.2–3. https://cran.r-project.org/web/packages/coxme/coxme.pdf