U.S. Department of the Interior

U.S. Geological Survey

Improving Paleoecology Studies for Future 

Predictions—Role of Spatial and Temporal 

Scales for Understanding Ecology of the Arid 

and Semiarid Landscape of the Southwest

Open-File Report 2014–1029

Prepared for the Southwest Climate Summit 2012

By David M. Miller, Gene-Hua Crystal Ng, and Katherine Maher
U.S. Department of the Interior

SALLY JEWELL, Secretary

U.S. Geological Survey

Suzette M. Kimball, Acting Director

U.S. Geological Survey, Reston, Virginia: 2014

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Suggested citation:

Miller, D.M., Ng, G-H.C., and Maher, K., 2014, Improving paleoecology studies for future predictions— 

Role of spatial and temporal scales for understanding ecology of the arid and semiarid landscape of the Southwest: 

U.S. Geological Survey Open-File Report 2014–1029, 25 p., http://dx.doi.org/10.3133/ofr20141029. 

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ISSN 2331-1258 (online)
3 

By David M. Miller,1 Gene-Hua Crystal Ng,1

and Katherine Maher2 

1U.S. Geological Survey.

2Stanford University School of Earth Sciences.

Improving Paleoecology Studies for Future 

Predictions—Role of Spatial and Temporal 

Scales for Understanding Ecology of the Arid 

and Semiarid Landscape of the Southwest
4 

Paleoecology (or Ecological Biogeography)

• Past distribution of species or communities (Swetnam and others, 

1999):

• Describes changes in concert with environmental changes

• Illustrates lags (for example, Pinyon Pine (Pinus spp.) invasion following 

climate change)

• Informs on tolerance limits of species

• Describes resilience of species in the face of change

• Paleoecology describes real systems—uses a broad suite of 

environmental variables, which may provide a more comprehensive view 

than will individual measures of climate.

•Changes over the past several thousand years happened in the presence 

of landscape manipulations by humans—adds relevance but increases 

difficulty of interpretation.
5 

Desert Tortoise: An Example

• The desert tortoise (Gopherus agassizii) has lived in the Southwest for 

several million years. This persistence is potentially meaningful.

• It has adapted through stable warm periods and several patterns of ice 

ages and interstadials.

• Imagine if we developed regional datasets for:

• Its habitat range through time, as well as

• climate variables

• plant community ranges

• soil conditions

• Over the time of tortoise survival, this would be very useful for 

evaluating its resilience to change and making predictions for the 

future of this listed species.

• Developing this kind of paleoecology data is much more difficult than 

the already difficult job of establishing paleoclimate, yet powerful for 

interpreting the climate data.
6 

What paleo-records are needed?

• Understanding past climate-driven changes?

• climate proxies

• Resolving species sensitivity to and resilience against change?

• biogeographical data

• Understanding past ecosystem function and changes?

• environmental data

How do we apply this knowledge for predictions?

• What information is most urgently needed for ecosystem forecasts?

• Are there kinds of monitoring we need to start now so that we will have ground 

truth in the near future?

These are major questions. Answering them in part relies on careful thought 

about the spatial and temporal scales of data needed.
7 

Spatial Resolution: Mojave Desert Piedmonts

At face value, a uniform “plant-scape” of isolated shrubs stretching off into the distance?
8 

Spatial Resolution: Mojave Desert Piedmont Reality—Soils 

Control Plant Distribution

Colors on the fan are caused by desert varnish, which illustrates 

that soils form intricate patterns; soils control plants.

From Miller and others, 2009
9 

Spatial Resolution: Mojave Desert piedmont reality—Streams 

structure vegetation!

Stream channels viewed from a balloon camera.
10

Spatial Resolution: 

Mojave Desert 

Piedmont Reality—

Streams are Bordered 

by Denser Plant Growth

Lower piedmont, channels 

shallowly incised

Upper piedmont, channels 

deeply incised (note rocky banks)

In both cases, channels are 

lined with many small gray 

shrubs and fewer large green 

shrubs. 
11

Stream Channel Effects (from Schwinning and others, 2011) 

Peak Plant Cover

Larrea Tridentata (creosote bush)

1.2 – 1.6 meters (m) from wash edge

Ambrosia Dumosa (white bursage)

0.5 – 1.0 m from wash

Cover 

Shaded bands indicate 

standard error.
12

Stream Channel Effects: Watering Experiments in July 

(Advanced Stress)

• Sustained increase in water availability >75 days

• Visual “greening” for >6 months

• No change >3 meters (m) from channel

before

after

Creosote bush leaves.

(Data from Newlander and others, 2009; Sandquist and others, 2011; Bedford and others, 2011)

Larrea Tridentata

Ambrosia Dumosa 
13

How much of the piedmont is influenced by runoff?

What is the result of increased or decreased discharge? Altered 

frequency?

How will monsoon changes affect stream flow?

Stream Channels: 

The Maps

Blue: Active stream channels

Yellow: Inactive fan surfaces
14

Temporal Resolution: Mojave Desert Piedmonts—Drought 

Mortality 1998–2002

• Drought mortality is plant-function specific 

• Shows strongest correlation with 5-year aridity (cool season) 

(From McAuliffe and Hamerlynck, 2010)
15

Temporal Resolution: Mojave Desert Piedmonts—Recruitment 

Long-term plot studies, Nevada Test Site (From Webb and others, 2003). 

Periods of recruitment and drought strongly affect plant cover and species composition. 
16

Temporal Resolution: Mojave Desert Model—Vegetation 

Response to Climate 

Data assimilation modeling: (Ng and others, unpublished data) 

• Numerical ecohydrological model (hourly time step) combined with 

monitoring data

• Reveals prolonged impact of climate on creosote bush over a range of time 

scales

1-yr ave

3-yr moving ave

(simulated) 

(model input) 

Greater than 1-year (yr) rainfall totals appear to drive trends in creosote bush 

leaf area index (LAI). ave, average; mm/s, millimeters per second.
17

Temporal Resolution: Mojave Desert Model—Seasonal Effects

Bimodal rainfall

(intense, short summer 

rains, steadier winter rains)

3-yr summer

3-yr winter

(simulated) 

(model input) 

Summer: May-October Winter: November-April 

Winter rains seem more important than summer rains for creosote. yr, year; mm/s, 

millimeters per second.

high elev.

low elev.
18

Temporal Resolution: Mojave Desert Model—Plant Persistence

Lag (months)

Auto-corr of 

leaf-area-index (LAI)

Auto-corr rain

X-corr of rain, LAI

Correlation 

Correlation (corr) calculated with rainfall input data and calibrated leaf area index 

(LAI) simulations

 Vegetation trends persist 

~5–6 years

 Vegetation dynamics lag 

rainfall ~1 year

DIVERSE relevant time scales in climate and vegetation:

2–3+ year winter rains drive vegetation (creosote) dynamics. 

Vegetation trends lag rains by a year and then persist for as much as 5 years.
19

Temporal Resolution: Mojave Desert Piedmonts

Extreme events are very important:

• Subhourly (to predict rainfall runoff in stream channels)

• Seasonal (heat wave, drought)

• Multiseasonal (drought, winter wet, freezing roles in recruitment)

We depend on datasets that honor the extreme values. 
20

Temporal and Spatial Scales

Paleoecology records: Various kinds of records 

offer varying scales of 

temporal and spatial data.

century 

decade 

year 

season 

sub-hour 

hour 

day 

1 m 100 m 1 km 10 km 100 km 1,000 km 

other lake

cave

midden 

stream 

soils

climate 

models 

tree-ring

varved 

lake 

m, meters; km, kilometers
21

Temporal and Spatial Scales

Needs for ecology do not match much of the data available. 

Paleo-records that offer temporal resolution come from 

unusual places (caves, lakes, springs, forests)!

century 

decade 

year 

season 

sub-hour 

hour 

day 

1 m 100 m 1 km 10 km 100 km 1,000 km 

other lake

cave

midden 

stream 

soils

climate 

models 

tree-ring

varved 

lake 

ecological 

systems 

m, meters; km, kilometers
22

High Spatial Resolution: Can We 

Get Smart About Soils in Typical 

Lowland Settings?

mm/yr, millimeters per year. From Oster and others, 2012.

An Example: Microbeam Technology to Explore Uranium 

(U) Isotopes in Soil Opal

Uranium-234/238 (U234/238) ratio is a measure of residence time of 

soil water and shows climatic correlations.

Modern soil-water U isotopes, Fish Lake Valley, Nevada, 

correspond to modern rainfall.

Paleo soil-water U isotopes from Arizona (AZ), Nevada (NV), and Wyoming 

(WY) over the past glacial cycle compared to Oxygen (O) isotopes and 

Greenland ice sheet (North Greenland Ice Core Project, NGRIP) data. LGM, 

Last Glacial Maximum; SHRIMP-RG, Sensitive High Resolution MicroprobeReverse Geometry; MSWD, mean square weighted deviation; ka, kilo-annum.
23

High Spatial Resolution: Can We 

Get Smart About Soils in Typical 

Lowland Settings?

Can we use phytoliths in soil to create dated highresolution proxies for soil properties and plant 

communities? 

What other informative objects persist in soil? 

Secondary Ion Mass 

Spectrometry (SIMS) 

analyses USGS/Stanford

SHRIMP-RG:

50 m spatial resolution

Newark Valley, Nevada Pedogenic CaCO3 and SiO2(am)

Soil profile Banded soil opal (~1 mm wide)

mm, millimeter; CaCO3, calcium carbonate; 

SiO2(am), silicon dioxide (amorphous).

Paleo soil-water U isotopes from Arizona (AZ), Nevada (NV), and Wyoming 

(WY) over the past glacial cycle compared to Oxygen (O) isotopes and 

Greenland ice sheet (North Greenland Ice Core Project, NGRIP) data. LGM, 

Last Glacial Maximum; SHRIMP-RG, Sensitive High Resolution MicroprobeReverse Geometry; MSWD, mean square weighted deviation; ka, kilo-annum.
24

Solutions for The Resolution Gap

• Use mechanistic and assimilation 

models to generalize spatially and 

temporally without(?) loss of content

• Use climate models and climate 

dynamics to identify proxies for high 

temporal resolution events in lower 

resolution data

• Improve paleoecologic and 

paleoclimate data for targeted 

species, time periods, and processes

• Use assimilation models to simulate 

landscape processes, validate with 

paleoecologic data, and then forward 

model the future
25

References Cited

Bedford, D.R., Macias, M., Miller, D.M., Newlander, A., Perkins, K., Sandquist, D.R., and Schwinning, S., 2011, Ecohydrological function and 

disturbance of desert ephemeral stream channels [abs.]: American Geophysical Union Fall Meeting, abstract B24C-07.

McAuliffe, J.R., and Hamerlynck, E.P., 2010, Pernnial plant mortality in the Sonoran and Mojave deserts in response to severe, multi-year drought: 

Journal of Arid Environments, v. 74, p. 885–896.

Miller, D.M.,. Bedford, D.R, Hughson, D.L., McDonald, E.V., Robinson, S.E., and Schmidt, K.M., 2009, Mapping Mojave Desert ecosystem properties 

with surficial geology, in Webb, R.H., Fenstermaker, L.F., Heaton, J.S., Hughson, D.L., McDonald, E.V., Miller, D.M., eds., The Mojave Desert—

Ecosystem processes and sustainability: University of Nevada Press, p. 225–251.

Oster, J.L., Ibarra, D.E., Harris, C.R., Maher, K., 2012. Influence of eolian deposition and rainfall on the U-isotopic composition of soil water and soil 

minerals: Geochimca et Cosmochimica Acta, v. 88, p. 146–166.

Newlander, A.R., Bedford, D. R., Miller, D.M., and Sandquist, D.R., 2009, Physiological responses of Mojave Desert shrubs to simulated summer wash 

flow—Preliminary results: Mojave National Preserve Science Newsletter, no. 2, p. 12–16. (Also available at 

http://www.nps.gov/moja/naturescience/upload/200911science.pdf.)

Sharp, W.D., Ludwig, K.R., Chadwick, O.A., Amundson, R., and Glaser, L.L., 2003. Dating fluvial terraces by Th-230/U on pedogenic carbonate, Wind 

River Basin, Wyoming: Quaternary Research, v. 59, p. 139–150.

Sandquist, D.R., Bedford, D.R., Macias, M., Miller, D.M., Newlander, A.R., and Schwinning, S., 2011, Small wash functions and effects of their 

disturbance on vegetation of a Mojave Desert bajada [abs.]: American Geophysical Union Fall Meeting, abstract B21G–0348.

Schwinning, S., Sandquist, D.R., Miller, D.M., Bedford, D.R., Phillips, S.L., and Belnap, J., 2011, The influence of stream channels on distributions of 

Larrea tridentata and Ambrosia dumosa in the Mojave Desert, CA, USA—Patterns, mechanisms and effects of stream redistribution: Ecohydrology: v. 4, 

p. 12–25.

Swetnam, T.W., Allen, C.D., and Betancourt, J.L., 1999, Applied historical ecology: Using the past to manage for the future: Ecological Applications, v. 

9, p. 1189–1206.

Wagner, J.D.M., Cole, J.E., Beck, J.W., Patchett, P.J., Henderson, G.M., Barnett, H.R., 2010, Moisture variability in the southwestern United States 

linked to abrupt glacial climate change: Nature Geoscience, v. 3, p. 110–113.

Webb, R.H., Murov, M.B., Esque, T.C., Boyer, D.E., DeFalco, L.A., Haines, D.F., Oldershaw, D., Scoles, S.J., Thomas, K.A., Blainey, J.B., and Medica, 

P.A., 2003, Perennial vegetation data from permanent plots on the Nevada Test Site, Nye County, Nevada: U.S. Geological Survey Open-File Report 

03–336, 257 p.

Winograd, I.J., Landwehr, J.M., Coplen, T.B., Sharp, W.D., Riggs, A.C., Ludwig, K.R., and Kolesar, P.T., 2006, Devils Hole, Nevada, δ18O record 

extended to the mid-Holocene: Quaternary Research, v. 66, p. 202–212.