Improving habitat and connectivity model predictions with multi-scale resource selection functions from two geographic areas
- 91 Downloads
Habitat loss and fragmentation are the most pressing threats to biodiversity, yet assessing their impacts across broad landscapes is challenging. Information on habitat suitability is sometimes available in the form of a resource selection function model developed from a different geographical area, but its applicability is unknown until tested.
We used the Mexican spotted owl as a case study to demonstrate how models developed from different geographic areas affect our predictions for habitat suitability, landscape resistance, and connectivity. We identified the most suitable habitats and core areas for dispersal and movement for the species.
We applied two multi-scale habitat selection models—a local model and a non-local model—to a broad study area in northern Arizona. We converted the models into landscape resistance surfaces and used simulations to model connectivity corridors for the species, and created composite habitat and connectivity models by averaging the local and non-local models.
While the local and the non-local models both performed well, the local model performed best in the part of the study area where it was built, but performed worse in areas that are beyond the extent of the data used to train it. The composite habitat model improved performances over both models in most cases.
With rigorous testing, multi-scale habitat selection models built on empirical data from other geographical areas can be useful. Averaging predictions of multiple models can improve performance, but the effectiveness is subject to the performance of the reference models.
KeywordsConnectivity Corridor Endangered species Fragmentation Habitat loss Habitat selection Landscape resistance Mexican spotted owl Resource selection function Scale
This project was funded by Joint Fire Sciences Project and the National Fire Plan. We thank C. Vojta, C. Aslan, and P. Fulé for their constructive comments and feedback on this project. We also thank J. Evans for his speedy response and exceptional assistance on troubleshooting and debugging spatialEco R package.
- Barrowclough GF, Groth JG, Mertz LA, Gutiérrez RJ (2006) Genetic structure of Mexican spotted owl populations in a fragmented landscape. Auk 123:1090–1102Google Scholar
- Bond ML (2016) The heat is on: spotted owls and wildfire. Reference module in earth systems and environmental sciences. Elsevier, Amsterdam. http://www.sciencedirect.com/science/article/pii/B9780124095489100144. Accessed 8 Mar 2018
- Cushman SA, Mersmann TJ, Moisen GG, McKelvey KS, Vojta CD (2013b) Chapter 5: using habitat models for habitat mapping and monitoring. In: Rowland MM, Vojta CD (eds) A technical guide for monitoring wildlife habitat. General Technical Report WO-89. USDA Forest Service, Washington, DC, pp 5.1–5.14Google Scholar
- Evans JS (2017) spatialEco. R Package version 0.0.1-7. https://CRAN.R-project.org/package=spatialEco
- Forsman ED, Anthony RG, Reid JA, Loschl PJ, Sovern SG, Taylor M, Biswell BL, Ellingson A, Meslow EC, Miller GS, Swindle KA, Thrailkill JA, Wagner FF, Seaman DE (2002) Natal and breeding dispersal of northern spotted owls. Wildl Monogr 149:1–35Google Scholar
- Ganey JL, Block WM, Dwyer JK, Strohmeyer BE, Jenness JS (1998) Dispersal movements and survival rates of juvenile Mexican spotted owls in northern Arizona. Wilson Bull 110:206–217Google Scholar
- Ganey JL, Iníguez JM, Hedwall S, Block WM, Ward JP Jr, Jonnes RS, Rawlinson TA, Kyle SC, Apprill DL (2016) Evaluating desired conditions for Mexican spotted owl nesting and roosting habitat. For Sci 62:457–462Google Scholar
- Ganey JL, Jenness JS (2013) An apparent case of long distance breeding dispersal by a Mexican spotted owl in New Mexico. Research Note RMRS-RN-53WWW. USDA Forest Service, Rocky Mountain Research Station, Fort CollinsGoogle Scholar
- Ganey JL, Ward JP Jr, Willey DW (2011) Status and ecology of Mexican spotted owls in the Upper Gila Mountains Recovery Unit, Arizona and New Mexico. General Technical Report RMRS-GTR-256WWW. USDA Forest Service, Rocky Mountain Research Station, Fort CollinsGoogle Scholar
- Gurevitch J, Hedges LV (1993) Meta-analysis: combining the results of independent experiments. In: Scheiner S, Gurevitch J (eds) Design and analysis of ecological experiments. Chapman and Hall, New York, pp 378–398Google Scholar
- Gutiérrez RJ, Seamans ME, Peery MZ (1996) Intermountain movement by Mexican spotted owls (Strix occidentalis lucida). Great Basin Nat 56:87–89Google Scholar
- Hedges LV, Olkin I (1985) Statistical methods for meta-analysis. Academic Press, OrlandoGoogle Scholar
- Keitt TH, Urban DL, Milne BT (1997) Detecting critical scales in fragmented landscapes. Conserv Ecol 1:1–17Google Scholar
- LANDFIRE (2001) Existing vegetation type layer, forest canopy cover layer, and digital elevation model layer. U.S. Department of the Interior, Geological Survey. http://landfire.cr.usgs.gov/viewer/
- Lommler M (2018) Conference presentation. In: 51st Joint Annual Meeting of the Arizona/New Mexico Chapters of the Wildlife Society and Arizona/New Mexico Chapters of the American Fisheries Society, Flagstaff, Arizona, 3 February 2018Google Scholar
- McClaran MP, Brady WW (1994) Arizona’s diverse vegetation and contributions to plant ecology. Rangelands 16(5):208–217Google Scholar
- Meiman S, Anthony R, Glenn E, Bayless T, Ellingson A, Hansen MC, Smith C (2003) Effects of commercial thinning on home-range and habitat-use patterns of a male northern spotted owl: a case study. Wildl Soc Bull 31:1254–1262Google Scholar
- Newbold T, Hudson LN, Hill SL, Contu S, Lysenko I, Senior RA, Börger L, Bennett DJ, Choimes A, Collen B, Day J, De Palma A, Díaz S, Echeverria-Londoño S, Edgar MJ, Feldman A, Garon M, Harrison ML, Alhusseini T, Ingram DJ, Itescu Y, Kattge J, Kemp V, Kirkpatrick L, Kleyer M, Correia DL, Martin CD, Meiri S, Novosolov M, Pan Y, Phillips HR, Purves DW, Robinson A, Simpson J, Tuck SL, Weiher E, White HJ, Ewers RM, Mace GM, Scharlemann JP, Purvis A (2015) Global effects of land use on local terrestrial biodiversity. Nature 520:45–50CrossRefPubMedGoogle Scholar
- Odion DC, Hanson CT, DellaSala DA, Baker WL, Bond ML (2014) Effects of fire and commercial thinning on future habitat of the northern spotted owl. Open Ecol J 7:37–51Google Scholar
- PRISM Climate Group (2014) Oregon State University. http://prism.oregonstate.edu
- 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 DV, Trombulak SC (2012) The role of landscape connectivity in planning and implementing conservation and restoration priorities. Issues Ecol 16:1–20Google Scholar
- Thomas CD, Cameron A, Green RE, Bakkenes M, Beaumont LJ, Collingham YC, Erasmus BFN, de Siqueira MF, Grainger A, Hannah L, Hughes L, Huntley B, van Jaarsveld AS, Midgley GF, Miles L, Ortega-Huerta MA, Peterson AT, Phillips OL, Williams SE (2004) Extinction risk from climate change. Nature 427:145–147CrossRefPubMedGoogle Scholar
- U.S. Department of Agriculture (2014) Final environmental impact statement for the four-forest restoration initiative, vol 1. USDA Forest Service, Southwestern Region, Coconino and Kaibab National Forests, Coconino County, ArizonaGoogle Scholar
- U.S. Department of Interior (1993) Endangered and threatened wildlife and plants; final rule to list the Mexican Spotted Owl as a threatened species. U.S. Fish and Wildlife Service. Fed Regis 58:14248–14271Google Scholar
- U.S. Department of Interior (2012) Final recovery plan for the Mexican spotted owl (Strix occidentalis lucida), first revision. U.S. Fish and Wildlife Service, AlbuquerqueGoogle Scholar
- Wan HY (2018) Habitat, connectivity, and gene flow of Mexican spotted owl in southwestern forests. Dissertation, Northern Arizona UniversityGoogle Scholar
- Willey DW, van Riper IIIC (2000) First-year movements by juvenile Mexican spotted owls in the canyonlands of Utah. J Raptor Res 34:1–7Google Scholar
- Young A, Clarke G (2000) Genetics, demography and viability of fragmented populations, vol 4. Cambridge University Press, Cambridge, pp 35–53Google Scholar