Grassland-shrubland transitions: Part 2

Lateral flux of water, sediment, nutrients Percolation

Schlesinger et al. (1990) “islands of fertility” or “Jornada” desertification model

Not clear how perennial grasses are lost from the ecosystem—a focus of long-term work since LTER IV

Feedback Feedback

Disruption of perennial grass cover, shrubs invade

How does connectivity impact transport vectors (wind and water) and the distribution of nutrients to impact state change?

Water flow at state boundaries

The "Scraped Site" (1991)

Mueller et al 2007

Okin et al 2001 Okin et al 2006

Do grasslands cross critical thresholds at low, but positive, levels of grass cover?

Driver-control model: grass driven extinct directly by grazing/drought

Feedback-control model: critical threshold of grass cover below which soil erosion/hydrologicalfeedbacks drive remaining grass cover extinct

Driver

Gras

s cov

er/p

rodu

ction

Bestelmeyer et al, 2013, Ecology Letters

Do changes in connectivity cause desertification?

Okin et al. 2009

Aeolian Transport – Sand Sheet and Gap Size

Okin et al 2006

C G P T0

0.1

0.2

0.3

0.4

0.5 Dry (1999-2003)Wet (2004-2012)

Vegetation type

Mea

n ho

rizon

tal s

edim

ent fl

ux

(g/c

m/d

)

M012345678

Experimental evaluation of threshold modelswith respect to aeolian transport Hypothesis 1(b) As bare gap sizes increase, a connectivity threshold level is reached that sets the stage for nonlinear increases in the spatial extent of shrub dominance owing to negative effects on grass persistence [Hypothesis 1(a)] and positive feedbacks to shrub establishment and growth. 

Split-treatment (Upwind-Downwind) experiment begun in 20043 Blocks with 4 (split) treatments and 1 control in each

1) Vegetation cover will make horizontal flux increase both upwind and downwind

2) Enhanced flux will impact soils upwind and downwind and vegetation downwind

3) Thresholds will exist where system becomes "lossy" and state change occurs downwind.

Specific expectations

(As of 2014)Upwind

Veg CoverControl 0.3825% Removal 0.2350% Removal 0.2275% Removal 0.19100% Removal 0.06

Horizontal Flux

0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.450

0.5

1

1.5

2

2.5

3

3.5

4 2005

Fractional Perennial Vegetation Cover

Horiz

onta

l Flu

x (g

cm

-1 d

-1)

0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.450

0.5

1

1.5

2

2.5

3

3.5

4 2012

Fractional Perennial Vegetation Cover

Horiz

onta

l Flu

x (g

cm

-1 d

-1)

• Horizontal flux does not scale monotonically with vegetation cover in all years.

• It's becoming increasingly common to see lower flux at the 100% removal site where cover ~6%

• Incidentally, these data have also been useful in calibrating a connectivity-based horizontal transport model

Horizontal Flux

This indicates a severe disturbance may be self-limiting, whereas a more moderate disturbance may have a comparable effect in the long term

Upwind treatment at 100% removal has eroded to the

less-erodible B horizon

With time, the flux on the treatment downwind of the 100% removal has reached the level of the the removal treatment itself

No decrease post-2007

How does enhanced flux influence vegetation?With time, the flux on the treatment downwind of the 100% removal has reached the level of the the removal treatment itself

Potential mechanisms:Abrasion? Burial/Dynamic Surface? Nutrients?

Burial or Dynamic Surface?

Upwind Downwind Upwind Downwind

Nutrients

Li et al. 2007Threshold?

Resampled Summer, 2013

• Aeolian processes have an important impact on vegetation and nutrients on both directly and indirectly impacted sites

• Evolution of the soil over the long term adds an unexpected wrinkle

• We see nonlinear decreases in grass persistence [1(a)] which supports existence of a feedback favoring shrubs due to increased (downwind) aeolian transport

• Mechanisms are uncertain (pending full proposal)

Conclusions at this point

Ongoing/Future Work• Analyze soil data for SOC & TN (changes in thresholds?

changes in spatial distribution of nutrients?)• UAV-derived aerial photo analysis of vegetation cover and

connectivity (better quantify grass + shrub cover/distribution)

LTER VI Proposal, Hypothesis 1(c): Vegetation and resource losses propagate to initiate state change dynamics in downwind /downslope locations.

Connectivity-based feedback (from Okin et al. 2009)

• Design of this experiment has been difficult:• Replication vs. size• Wind and/or water?• Connectivity or pattern?

LTER VI Proposal, Hypothesis 1(c): Vegetation and resource losses propagate to initiate state change dynamics in downwind /downslope locations.

• The plan is to conduct modeling experiments with ECOTONE-WEMO and other connectivity-based models (e.g., Stewart et al. 2014) to determine how best to design an experiment

• Considerations• Size of manipulation• Does total connectivity matter more, or how it is

arranged?• How does slope/texture/depth to B horizon matter?• Precipitation• Land Use

• The conceptual work for connecting ECOTONE and WEMO has been done, implementation and model experiments next…

ECOTONE-WEMOModel Framework