Biodiversity and Conservation

, Volume 23, Issue 11, pp 2761–2779 | Cite as

Assessing multi-taxa sensitivity to the human footprint, habitat fragmentation and loss by exploring alternative scenarios of dispersal ability and population size: a simulation approach

  • Brian K. HandEmail author
  • Sam A. Cushman
  • Erin L. Landguth
  • John Lucotch
Original Paper


Quantifying the effects of landscape change on population connectivity is compounded by uncertainties about population size and distribution and a limited understanding of dispersal ability for most species. In addition, the effects of anthropogenic landscape change and sensitivity to regional climatic conditions interact to strongly affect habitat fragmentation and loss. To further develop conservation theory and to understand the interplay between all of these factors, we simulated habitat fragmentation and loss across the Western United States for several hypothetical species associated with four biome types, and a range of habitat requirements and dispersal abilities. We found dispersal ability and population size of the focal species to be equally sensitive to habitat extent, while dispersal ability is more sensitive to habitat fragmentation. There were also strong critical threshold effects where habitat connectivity decreased disproportionately to decreases in life-history traits making these species near these thresholds more sensitive to changes in habitat loss and fragmentation. Overall, grassland and forest associated species are also most at risk from habitat loss and fragmentation driven by human related land-use. These two largest biome types were most sensitive at large contiguous patch sizes which is often considered most important for metapopulation viability and biodiversity conservation. Hypothetical simulation studies such as this can be of great value to scientists in further conceptualizing and developing conservation theory, and evaluating spatially-explicit scenarios of habitat connectivity. Our results are available for download in a web-based interactive mapping prototype useful for accessing the results of this study.


Connectivity modeling Least-cost paths Resistant kernels UNICOR 



BKH was supported by a National Science Foundation Grant (DGE-0504628). This work was supported in part by funds provided by the Rocky Mountain Research Station, Forest Service, U.S. Department of Agriculture.


  1. Allendorf FW, Luikart G, Aitken SN (2013) Conservation and the genetics of populations, 2nd edn. Wiley-Blackwell, LondonGoogle Scholar
  2. Bowne DR, Bowers MA (2004) Interpatch movements in spatially structured populations: a literature review. Landsc Ecol 19:1–20. doi: 10.1023/B:LAND.0000018357.45262.b9 CrossRefGoogle Scholar
  3. Brook BW, Sodhi NS, Bradshaw CJA (2008) Synergies among extinction drivers under global change. Trends Ecol Evol 23:453–460. doi: 10.1016/j.tree.2008.03.011 PubMedCrossRefGoogle Scholar
  4. Compton BW, McGarigal K, Cushman SA, Gamble LR (2007) A resistant-kernel model of connectivity for amphibians that breed in vernal pools. Conserv Biol 21:788–799. doi: 10.1111/j.1523-1739.2007.00674.x PubMedCrossRefGoogle Scholar
  5. Crooks KR, Sanjayan A (2006) Connectivity conservation. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  6. Cushman SA, Landguth EL (2012) Multi-taxa population connectivity in the Northern Rocky Mountains. Ecol Modell 231:101–112. doi: 10.1016/j.ecolmodel.2012.02.011 CrossRefGoogle Scholar
  7. Cushman SA, McGarigal K (2004) Hierarchical analysis of forest bird species-environment relationships in the Oregon coast range. Ecol Appl 14:1090–1105. doi: 10.1890/03-5131 CrossRefGoogle Scholar
  8. 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, Huettmann F (eds) Spatial complexity, informatics, and wildlife conservation. Springer, New York, pp 369–387CrossRefGoogle Scholar
  9. Cushman SA, Shirk AJ, Landguth EL (2013) Landscape genetics and limiting factors. Conserv Genet 14:263–274. doi: 10.1007/s10592-012-0396-0 CrossRefGoogle Scholar
  10. Debinski DM, Holt RD (2000) A survey and overview of habitat fragmentation experiments. Conserv Biol 14:342–355. doi: 10.1046/j.1523-1739.2000.98081.x CrossRefGoogle Scholar
  11. Dixo M, Metzger JP, Morgante JS, Zamudio KR (2009) Habitat fragmentation reduces genetic diversity and connectivity among toad populations in the Brazilian Atlantic Coastal Forest. Biol Conserv 142:1560–1569. doi: 10.1016/j.biocon.2008.11.016 CrossRefGoogle Scholar
  12. Dyer SJ, Neill JPO, Wasel SM, Boutin S (2002) Quantifying barrier effects of roads and seismic lines on movements of female woodland caribou in northeastern Alberta. Can J Zool 80:839–845. doi: 10.1139/Z02-060 CrossRefGoogle Scholar
  13. ESRI (2011) ArcGIS desktop: release 10. Environmental Systems Research Institute, Redlands, CAGoogle Scholar
  14. Fahrig L (2001) How much habitat is enough? Biol Conserv 100:65–74. doi: 10.1016/S0006-3207(00)00208-1 CrossRefGoogle Scholar
  15. Fahrig L (2003) Effects of habitat fragmentation on biodiversity. Annu Rev Ecol Evol Syst 34:487–515. doi: 10.1146/annurev.ecolsys.34.011802.132419 CrossRefGoogle Scholar
  16. Fahrig L, Pedlar JH, Pope SE et al (1995) Effect of road traffic on amphibian density. Biol Conserv 73:177–182CrossRefGoogle Scholar
  17. Flather CH, Bevers M (2002) Patchy reaction–diffusion and population abundance: the relative importance of habitat amount and arrangement. Am Nat 159:40–56. doi: 10.1086/324120 PubMedCrossRefGoogle Scholar
  18. Frankham R, Ballou JD, Briscoe DA (2002) Introduction to conservation genetics. Cambridge University Press, New YorkCrossRefGoogle Scholar
  19. Franklin JF (1993) Preserving biodiversity: species, ecosystems, or landscapes? Ecol Appl 3:202–205. doi: 10.2307/1941820 CrossRefGoogle Scholar
  20. Freemark KE, Merriam HG (1986) Importance of area and habitat heterogeneity to bird assemblages in temperate forest fragments. Biol Conserv 36:115–141. doi: 10.1016/0006-3207(86)90002-9 CrossRefGoogle Scholar
  21. Hijmans RJ, van Etten J (2013) raster: geographic data analysis and modelingGoogle Scholar
  22. Homer C, Dewitz J, Fry J et al (2007) Completion of the 2001 National Land Cover Database for the conterminous United States. Photogramm Eng Remote Sensing 73:337–341Google Scholar
  23. Keitt TH, Urban DL, Milne BT (1997) Detecting critical scales in fragmented landscapes. Conserv Ecol 1:4Google Scholar
  24. Landguth EL, Hand BK, Glassy J et al (2012) UNICOR: a species connectivity and corridor network simulator. Ecography (Cop) 35:9–14. doi: 10.1111/j.1600-0587.2011.07149.x CrossRefGoogle Scholar
  25. McGarigal K, Cushman SA (2002) Comparative evaluation of experimental approaches to the study of habitat fragmentation effects. Ecol Appl 12:335–345. doi: 10.1890/1051-0761 CrossRefGoogle Scholar
  26. McGarigal K, Cushman SA, Ene E (2013) FRAGSTATS v4: spatial pattern analysis program for categorical and continuous maps. Computer software program produced by the authors at the University of Massachusetts, AmherstGoogle Scholar
  27. Neilson RP (1993) Vegetation redistribution: a possible biosphere source of CO2 during climatic change. Water Air Soil Pollut 70:659–673CrossRefGoogle Scholar
  28. Neilson RP (1995) A model for predicting continental-scale vegetation distribution and water balance. Ecol Appl 5:362–385. doi: 10.2307/1942028 CrossRefGoogle Scholar
  29. Neilson RP, Marks D (1994) A global perspective of regional vegetation and hydrologic sensitivities from climatic change. J Veg Sci 5:715–730. doi: 10.2307/3235885 CrossRefGoogle Scholar
  30. R Core Team (2013) R: a language and environment for statistical computing. R Foundation for Statistical Computing, ViennaGoogle Scholar
  31. Robbins CS, Dawson DK, Dowell BA (1989) Habitat area requirements of breeding forest birds of the middle Atlantic states. Wildl Monogr 103:1–34Google Scholar
  32. Roberge J-M, Angelstam P (2004) Usefulness of the umbrella species concept as a conservation tool. Conserv Biol 18:76–85. doi: 10.1111/j.1523-1739.2004.00450.x CrossRefGoogle Scholar
  33. Sawyer SC, Epps CW, Brashares JS (2011) Placing linkages among fragmented habitats: do least-cost models reflect how animals use landscapes? J Appl Ecol 48:668–678. doi: 10.1111/j.1365-2664.2011.01970.x CrossRefGoogle Scholar
  34. Soulé M, Mackey B, Recher H et al (2006) The role of connectivity in Australian conservation. In: Crooks KR, Sanjayan M (eds) Connectivity conservation. Cambridge University Press, Cambridge, pp 649–675CrossRefGoogle Scholar
  35. Spear SF, Balkenhol N, Fortin M-J et al (2010) Use of resistance surfaces for landscape genetic studies: considerations for parameterization and analysis. Mol Ecol 19:3576–3591. doi: 10.1111/j.1365-294X.2010.04657.x PubMedCrossRefGoogle Scholar
  36. Vallan D (2000) Influence of forest fragmentation on amphibian diversity in the nature reserve of Ambohitantely, highland Madagascar. Biol Conserv 96:31–43. doi: 10.1016/S0006-3207(00)00041-0 CrossRefGoogle Scholar
  37. With KA, King AW (1999) Extinction thresholds for species in fractal landscapes. Conserv Biol 13:314–326. doi: 10.1046/j.1523-1739.1999.013002314.x CrossRefGoogle Scholar
  38. Zeller KA, McGarigal K, Whiteley AR (2012) Estimating landscape resistance to movement: a review. Landsc Ecol 27:777–797. doi: 10.1007/s10980-012-9737-0 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Brian K. Hand
    • 1
    Email author
  • Sam A. Cushman
    • 3
  • Erin L. Landguth
    • 2
  • John Lucotch
    • 2
  1. 1.Fish and Wildlife Genomics Group, Flathead Lake Biological StationUniversity of MontanaPolsonUSA
  2. 2.Division of Biological SciencesUniversity of MontanaMissoulaUSA
  3. 3.Rocky Mountain Research StationU.S. Forest ServiceFlagstaffUSA

Personalised recommendations