Posts Tagged ‘SDMs’

The decimation of Madagascar’s rainforest habitat

Monday, December 23rd, 2019

It is honestly with sadness that I announce our new publication on the fate of Madagscar’s rainforest habitat in Nature Climate Change. Modeling deforestation assuming the lowest rate of deforestation across the period 2000-2014, I could only get the rainforest to last to the 2070s… and the highest rate of loss occurred in 2018, outside the time period over which I had data. The slight hope is that protected areas are deforested at a slower rate, and if were to (unrealistically) assume no new deforestation in these areas, then some rainforest habitat would remain.

Morelli*, T.L., Smith*, A.B., Mancini, A.N., Balko, E. A., Borgenson, C., Dolch, R., Farris, Z., Federman, S., Golden, C.D., Holmes, S., Irwin, M., Jacobs, R.L., Johnson, S., King, T., Lehman, S., Louis, E.E. Jr., Murphy, A., Randriahaingo, H.N.T., Lucien, Randriannarimanana, H.L.L., Ratsimbazafy, J., Razafindratsima, O.H., and Baden, A.L. 2019. The fate of Madagascar’s rainforest habitat. Nature Climate Change 10:89-96. * Equal contribution. (article | “behind the paper” | Washington Post | National Geographic | The Conversation | ScienceDaily)

How well do species distribution models measure variable importance?

Friday, July 26th, 2019

One of the most common applications of SDMs is to identify important variables and measure their relative effect. Despite hundreds of papers assessing the predictive power of SDMs, there are none assessing their inferential power. Maria Santos and Adam recently completed the first such analysis!

Permute-after-calibration test of variable importance. The OMNI model perfectly recreates the species’ range so serves as a benchmark for the other models.

Smith, A.B. and Santos, M.J. Testing the ability of species distribution models to infer variable importance. bioRxiv doi: 10.1101/715904