Predicted probability of marten year-round occurrence, 2046-2065, Hadley CM3 A2, 4 km resolution

Predicted probability of marten year-round occurrence derived from future (2046-2065) climate projections and vegetation simulations.  Projected marten distribution was created with Maxent (Phillips et al. 2006) using marten detections (N = 302, spanning 1990 – 2011) and nine predictor variables: mean winter (January – March) precipitation, mean amount of snow on the ground in March, mean understory index (fraction of grass vegetation carbon in forest),  mean fraction of total forest carbon in coarse wood carbon, average maximum tree LAI, mean fraction of vegetation carbon burned, mean forest carbon (g C m2), mean fraction of vegetation carbon in forest, 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 4km historical baseline (Daly et al. 1994). Vegetation variables were simulated with 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

References:

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., R.P. Neilson, and D.L. Phillips. 1994. A statistical topographic model for mapping climatological precipitation over mountainous terrain. Journal of Applied Meteorology 33:140–158.

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).