Predicted probability of marten year-round occurrence, 2076-2095, PCM1 A2, 10 km resolution

May 25, 2012
Future (2076-2095) predicted probability of marten year-round occurrence projected under the A2 emissions scenario with the PCM1 GCM (Washington et al. 2000; Meehl et al. 2003). The projected marten distribution was created with Maxent (Phillips et al. 2006) using marten detections (N = 302, spanning 1990 – 2011) and nine predictor variables: mean annual precipitation, mean summer (July – September) precipitation, mean summer temperature amplitude, mean annual temperature maximum, mean fraction of vegetation carbon burned, mean understory index, mean vegetation carbon (g C m2), modal vegetation class, and average maximum tree LAI. Predictor variables had a grid cell size of 10 km, vegetation variables were simulated with MC1 (Lenihan et al. 2008) and projected climate variables were provided by the PRISM GROUP (Daly et al. 1994). 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

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.

Lenihan, J.M., D. Bachelet, R.P. Neilson, and R.J. Drapek. 2008. Simulated response of conterminous United States ecosystems to climate change at different levels of fire suppression, CO2 emission rate, and growth response to CO2. Global and Planetary Change 64: 16-25.

Meehl, G.A., W.M. Washington, T.M.L. Wigley, J.M. Arblaster, and A. Dai. 2003. Solar and greenhouse gas forcing and climate response in the twentieth century. J Climate 16:426–444.

Phillips, S.J., R.P. Anderson, and R.E. Schapire. 2006. Maximum entropy modeling of species geographic distributions.  Ecological Modelling 190: 231-259.

Washington, W.M., J.W. Weatherly, G.A. Meehl, A.J. Semtner, T.W.  Bettge, A.P. Craig, W.G. Stran, J. Arblaster, V.B. Wayland, R. James , and Y. Zhang. 2000. Parallel climate model (PCM) control and transient simulations. Clim Dyn 16: 755–774.
Data Provided By:
Conservation Biology Institute
Content date:
Spencer, W. and H. Rustigian-Romsos. 2012. Unpublished.
Spatial Resolution:
10000.0 (meter)
Contact Organization:
not specified
Contact Person(s):
Use Constraints:
Creative Commons LicenseThis work is licensed under a Creative Commons Attribution 3.0 License.
Layer Type:
Currently Visible Layer:
All Layer Options:
Layers in this dataset are based on combinations of the following options. You may choose from these options to select a specific layer on the map page.
Spatial Resolution:
Other Information:
Time Period:
Layer Accuracy:
Attribute Accuracy:
FGDC Standard Metadata XML
Click here to see the full FGDC XML file that was created in Data Basin for this layer.
Original Metadata XML
Click here to see the full XML file that was originally uploaded with this layer.
This dataset is visible to everyone
Dataset Type:
Layer Package
Downloaded by 1 Member
Bookmarked by 3 Groups
Included in 2 Public Galleries

About the Uploader

Conservation Biology Institute

The Conservation Biology Institute (CBI) provides scientific expertise to support the conservation and recovery of biological diversity in its natural state through applied research, education, planning, and community service.