Predicted probability of marten year-round occurrence, 2076-2095, Hadley CM3 A2, 800 m resolution
Dates
Original Data Basin Creation Date
2012-06-15 17:19:05
Original Data Basin Modified Date
2012-06-15 17:35:51
Summary
Predicted probability of marten year-round occurrence derived from future (2076-2095) climate projections and vegetation simulations. Projected marten distribution was created with Maxent (Phillips et al. 2006) using marten detections (N = 102, spanning 1993 – 2011) and eight predictor variables: mean potential evapotranspiration, mean annual precipitation, mean fraction of vegetation carbon burned, mean forest carbon (g C m2), mean fraction of vegetation carbon in forest, understory index (fraction of grass vegetation carbon in forest), average maximum tree LAI, and modal vegetation class. Future climate drivers were generated using statistical downscaling (simple delta method) of general circulation model projections, in this case [...]
Summary
Predicted probability of marten year-round occurrence derived from future (2076-2095) climate projections and vegetation simulations. Projected marten distribution was created with Maxent (Phillips et al. 2006) using marten detections (N = 102, spanning 1993 – 2011) and eight predictor variables: mean potential evapotranspiration, mean annual precipitation, mean fraction of vegetation carbon burned, mean forest carbon (g C m2), mean fraction of vegetation carbon in forest, understory index (fraction of grass vegetation carbon in forest), average maximum tree LAI, and modal vegetation class.
Future climate drivers were generated using statistical downscaling (simple delta method) of general circulation model projections, in this case Hadley CM3 (Johns et al. 2003) under the A2 emission scenario (Naki?enovi? et al. 2000). The deltas (differences for temperatures and ratios for precipitation) were used to modify PRISM 800m historical baseline (Daly et al. 2008).
Vegetation variables were simulated with the MC1 dynamic global vegetation model (Bachelet et al. 2001). This marten distribution projection was generated as part of a pilot project to apply and evaluate the Yale Framework (Yale Science Panel for Integrating Climate Adaptation and Landscape Conservation Planning).
Grid Value Predicted Probability of Occurrence
1 0 – 0.2
2 0.2 – 0.4
3 0.4 – 0.6
4 0.6 – 0.8
5 0.8 – 1.0
Bachelet D., R.P. Neilson, J.M. Lenihan, and R.J. Drapek. 2001. Climate change effects on vegetation distribution and carbon budget in the U.S. Ecosystems 4:164-185.
Daly, C., M. Halbleib, J.I. Smith, W.P. Gibson, M.K. Doggett, G.H. Taylor, J. Curtis, and P.A. Pasteris. 2008. Physiographically-sensitive mapping of temperature and precipitation across the conterminous United States. International Journal of Climatology 28: 2031-2064.
Johns, T.C., J.M. Gregory, W.J. Ingram, C.E. Johnson, A. Jones, J.A. Lowe, J.F.B. Mitchell, D.L. Roberts, D.M.H. Sexton, D.S. Stevenson, S.F.B. Tett, and M.J. Woodage. 2003. Anthropogenic climate change for 1860 to 2100 simulated with the HadCM3 model under updated emissions scenarios. ClimDyn 20: 583-612.
Naki?enovi?, N. and R. Swart, Eds. 2000. Emissions Scenarios: A Special Report of Working Group III of the Intergovernmental Panel on Climate Change. Cambridge Univ. Press, Cambridge, U. K.
Phillips, S.J., R.P. Anderson, and R.E. Schapire. 2006. Maximum entropy modeling of species geographic distributions. Ecological Modelling 190: 231-259.
Note: The MC1 model is described in data basin (http://databasin.org/climate-center/features/mc1-dynamic-global-vegetation-model).
