Are Soil Characteristics Correlated with Piñon Pine Mortality in New Mexico?

 

Nope!  But read on to find out how I figured that out…

 

                                                                                                                                                                                    Photo: Robert Parmenter

 

Piñon-juniper woodlands are widespread in New Mexico, but the recent drought has resulted in severe piñon mortality in some areas of the state.

 

 

In this project I used the State Soil Geographic Database (STATSGO) to examine features of the soil which might be related to piñon death. 

I used USDA Forest Service data from 2003 as my dependent variable. 

In ArcGIS, I clipped the STATSGO coverage of New Mexico to the extent of the 2003 USFS survey and to the area where piñon mortality was documented.

 

 

I assessed four soil characteristics to see if they correlated with piñon mortality. 

Click on the links below to learn more about each one and see the maps I generated.

 

 

Surface Texture

Available Water Capacity

Permeability

Slope

 

 

None of my variables differed between the areas where piñons died and areas where they didn’t, as the table below shows. 

This is an interesting case of visual assessments being somewhat misleading; the maps linked to the variables above sometimes seem to show a pattern.

 

 

 

Piñon Mortality

No Piñon Mortality

 

Mean

Standard Deviation

Mean

Standard Deviation

Surface Texture Proxy (0-40)

15.6

5.9

15.8

5.9

Available Water Content

(inches per inch)

1.9

0.7

1.8

0.8

Permeability

(inches per hour)

35.7

36.1

37.0

43.5

Slope

(degrees)

21.2

13.8

21.2

15.4

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Why didn’t I find an effect of soil type?

·        Poor soil data resolution – SSURGO data would give more resolution, but isn’t available for all of New Mexico yet

·        Mortality data was only from one year – multiple years might have shown mortality intensification

·        Mortality data was listed as “dead trees per acre” but in reality there were only four categories of data and I input it as a binary variable (presence/absence) – more continuous mortality data might allow regression analysis

·        There really isn’t any effect!

o       Differences in mortality could be due to precipitation patterns, bark beetle population dynamics, aspect, or genetic variability among piñon pine populations

 

 

You can find out more about my:

Methods

Data sources

Related Research