Monarch butterfly and milkweed models the western U.S.

Today Matt Forister and I presented the results of our habitat modeling work for monarch butterflies and milkweeds for the western U.S. I've been involved in this research now since January 2017 and am very excited about having some preliminary maps and models ready. The work is an extension of the 2015 habitat modeling effort by Madeline Steele while she was at the U.S. Fish & Wildlife Service. Our work is a collaborative effort by University of Nevada Reno, the Xerces Society, the U.S. Fish & Wildlife Service, and others. I'm excited to get to work with such an iconic species, and I hope that through conservation and societal awareness of pesticides we can halt the decline of one of the most beautiful insect species in the world.

New version of Maxent software and paper out

It looks like Phillips and company has a new open-source version of Maxent out as well as an accompanying paper - http://biodiversityinformatics.amnh.org/open_source/maxent/

Sarah Barga wins 2nd place in GSA poster contest

It is always great news when students win awards!  Recently Sarah Barga, an EECB Ph.D. student advised by Beth Leger, won 2nd place in UNR's Graduate Student Association student poster contest for her poster entitled "Creating maps of potential habitat for Great Basin forbs using herbarium data". I'm proud to say that I helped her get started with the Maxent modeling, but really, Sarah has done a tremendous amount of interesting work and is currently working on finalizing two manuscripts at the same time.  The maps on the right are straight off of her poster. The different colors correspond with the amount of overlap for different top models. You can view her poster by clicking HERE and her research by visiting her website HERE .

New paper - Functionally relevant climate variables for arid lands: using climatic water deficit modeling to predict distribution of desert shrub vegetation

I'm pleased to announce that my paper "Functionally relevant climate variables for arid lands: using climatic water deficit modeling to predict distribution of desert shrub vegetation" got accepted in the Journal of Biogeography. The paper compares two different suites of climatic variables used for modeling the distributions (climatic envelopes, niches, habitat suitability) of eighteen shrub species in the Great Basin. The first approach uses the so-called bioclimatic variables commonly used today in species distribution modeling. The latter approach uses the climatic water deficit which uses a Thornthwaite water balance model to estimate potential and actual evapotranspiration and climatic water deficit (PET minus AET). We expanded upon the work of others by deriving new variables that more fully describe the  shape of the water balance climograph. Our study found that for these shrub species there was similar model performance using the two suites of variables. However, we believe that the climatic water deficit approach may prove to be more effective in studies that predict range shifts under climate change.

The paper compliments the Climatic Water Deficit Toolbox for ArcGIS which can be found HERE. Thank you co-authors Peter Weisberg, Jeanne Chambers, and Camie Dencker as well as those who provided critical reviews.

The paper can be downloaded HERE.

Updates to the Prepare Rasters for Maxent Tool - upscaling rasters

Maxent software (http://www.cs.princeton.edu/~schapire/maxent) is frequently used for presence-only species distribution modeling. Maxent requires, however, that input ASCII raster files be aligned with one another and have the same spatial extent. This tool pre-processes raster data in preparation for Maxent modeling to ensure that all rasters have the same extent, same cell size, and aren't missing data. You can download the tool HERE.

There is a new tool that can be used to upscale data. Upscaling is useful when simple resampling is insufficient. For example, if you have a 30 meter classified vegetation map simply using the majority resample to resample to 1000 meter cell size would be insufficient because it would just tell you about the majority vegetation type present. Better would be to count the number of pixels of the vegetation type of interest within the 1000 meter cells and calculate the proportion of that vegetation type. This also solves the problem of having to treat the data as categorical in Maxent. Vegetation type would have to be treated as a categorical variable whereas proportion of X vegetation type is a continuous variable.

This example illustrates the idea of upscaling.

In the image above we have a forest/non-forest GIS layer overlaid on a coarse-resolution grid. The need for the coarse resolution grid is due to uncertainty in the location of the occurrence points and the resolution of other datasets that will be used in Maxent (e.g. the MODIS satellite products have a cell size of 250 - 1000 meters depending upon the product). The image below shows tree cover at the 1000 meter scale derived from running the Upscale Rasters for Maxent Tool. An important pre-processing step is deciding which vegetation types should be included in the model and which should be omitted. Included vegetation types should be reclassified as 1 prior to running the tool and omitted vegetation types should be reclassified as 0.