Technical Memorandum #10 Merced River Ranch Revegetation

Merced River Corridor Restoration Plan Phase IV: Dredger Tailings Reach

Technical Memorandum #10

Merced River Ranch

Revegetation Experiment

Prepared for CALFED ERP

Sacramento, California Recipient Agreement No. ERP-02-P12-D

Prepared by Stillwater Sciences

2855 Telegraph Avenue, Suite 400 Berkeley, California 94705

January 2007January 2007January 2007January 2007

For more information or copies of this Technical Memorandum, please contact:

Stillwater Sciences

2855 Telegraph Avenue, Suite 400

Berkeley, CA 94705

stillwatersci.com

(510) 848-8098

Suggested citation: Stillwater Sciences. 2006. Merced River Ranch revegetation

experiment. Prepared by Stillwater Sciences, Berkeley, California, for CALFED,

Sacramento, California.

Table of Contents

i Merced River Ranch Revegetation Experiment

Table of Contents

1 INTRODUCTION ............................................................................... 1

1.1 Study Area ........................................................................................... 1

1.2 Restoration Planning .......................................................................... 3

1.3 Site Revegetation ................................................................................. 4

1.4 Experiment Goals and Approach ..................................................... 5

2 METHODS .......................................................................................... 7

2.1 Experimental Design .......................................................................... 7

2.2 Data Collection .................................................................................. 10 2.2.1 Site Conditions ........................................................................ 10 2.2.2 Survival ................................................................................... 11 2.2.3 Growth .................................................................................... 12 2.2.4 Water Potential ....................................................................... 12 2.2.5 Weed Percent Cover ................................................................ 13

2.3 Statistical Analyses ........................................................................... 13 2.3.1 Initial Size Analysis ................................................................ 13 2.3.2 Growth Analysis ..................................................................... 14 2.3.3 Water Potential Analysis of Variance ..................................... 15 2.3.4 Survival and Hazard Analysis ................................................ 16 2.3.5 Cox Proportional Hazard Model ............................................. 16

3 RESULTS ........................................................................................... 21

3.1 Site Conditions .................................................................................. 21 3.1.1 Soil Texture and Nutrients ..................................................... 21 3.1.2 Depth to Groundwater, River Stage, and Pond Stage ............ 22 3.1.3 Temperature ............................................................................ 23

3.2 Plant Size and Growth ..................................................................... 24 3.2.1 Initial Size at Planting ............................................................ 24 3.2.2 Plant Growth Timing.............................................................. 26 3.2.3 Patterns in Plant Growth between Treatment Groups ........... 26 3.2.4 ANCOVA Models of 3-Year Diameter Increment Growth .... 27

3.3 Water Potential .................................................................................. 29

Table of Contents

ii Merced River Ranch Revegetation Experiment

3.4 Plant Survival .................................................................................... 32 3.4.1 Survival and Hazard Patterns ................................................ 32 3.4.2 First-Year Survival (2004) ...................................................... 33 3.4.3 Second-Year Survival (2005) .................................................. 36 3.4.4 Third-Year Survival (2006) .................................................... 39

3.5 Weed Percent Cover ......................................................................... 40

4 DISCUSSION.................................................................................... 43

4.1 Treatment/Non-treatment Effects and Revegetation

Recommendations ............................................................................ 43 4.1.1 Initial Size ............................................................................... 43 4.1.2 Block and Relative Elevation above Groundwater .................. 44 4.1.3 Irrigation ................................................................................. 45 4.1.4 Weed Reduction ...................................................................... 46 4.1.5 Soil Amendments .................................................................... 47

4.2 Species Responses ............................................................................. 48 4.2.1 Acer negundo .......................................................................... 48 4.2.2 Fraxinus latifolia ..................................................................... 48 4.2.3 Populus fremontii .................................................................... 49 4.2.4 Quercus lobata ........................................................................ 49

5 REFERENCES .................................................................................... 51

6 FIGURES ........................................................................................... 55

APPENDIX A SOIL ANALYSIS REPORTS ............................................................... A-1

APPENDIX B INITIAL CONDITIONS ANOVA RESULTS AND PAIRWISE

COMPARISONS ................................................................................... B-1

APPENDIX C EXPERIMENTAL SCHEDULE ........................................................... C-1

LIST OF TABLES

Table 2-1. Revegetation experiment hypotheses, treatments, and treatment levels............... 8 Table 2-2. As-built experimental plot elevations. .................................................................... 9 Table 3-1. Soil analytes at each experimental block. ............................................................. 22 Table 3-2. Average, minimum, and maximum groundwater, river stage, and pond stage

elevations (m NGVD). .......................................................................................... 22 Table 3-3. Monthly average temperatures at the MRR (°C). ................................................. 24 Table 3-4. Initial seedling height, diameter and number of leaves at planting time (means

±1SE) by plot (block and relative elevation). ....................................................... 25 Table 3-5. End of year height and diameter (mean±1 SE) for all species. ............................. 27

Table of Contents

iii Merced River Ranch Revegetation Experiment

Table 3-6. Top five candidate ANCOVA models of factor influences on diameter growth

increment. ............................................................................................................. 28 Table 3-7. Parameter estimates for the best ANCOVA models for each species. ................. 29 Table 3-8. Average water potentials (MPa) (±1SE) ................................................................ 31 Table 3-9. Water potential sample sizes. ............................................................................... 31 Table 3-10. ANOVA models for pre-dawn and afternoon water potential. ......................... 32 Table 3-11. End-of-year survival by species and irrigation treatment for all three years. ... 33 Table 3-12. Pearson Correlation Matrix for the three explanatory variables: initial height,

diameter, and number of leaves. .......................................................................... 34 Table 3-13. Year 1 top five candidate Cox models for each species. ..................................... 34 Table 3-14. Parameter estimates, hazard ratio (HR), and HR confidence limits for the best

Year 1 Cox survival model for each species. ....................................................... 35 Table 3-15. Year 2 top five candidate Cox models for each species. ..................................... 37 Table 3-16. Parameter estimates, hazard ratio (HR), and HR confidence limits for the best

Year 2 Cox survival model for each species. ....................................................... 38 Table 3-17. Year 3 top five candidate Cox models for each species. ..................................... 39 Table 3-18. Parameter estimates, hazard ratio (HR), and HR confidence limits for the best

Year 3 Cox survival model for each species. ....................................................... 40 Table 3-19. Percent of non-weed reduction plants within each weed cover class. ............... 41 Table 3-20. Weed species identified in experimental plots. .................................................. 42

LIST OF FIGURES

Figure 1. Merced River watershed and project location.

Figure 2. Typical conditions of the Merced River Ranch resulting from historical dredging

operations.

Figure 3. Locations of experiment plots, groundwater monitoring wells, staff gauges, and access

roads at the Merced River Ranch.

Figure 4. Experimental block design.

Figure 5. Photographs of experimental design, subjects, and treatments.

Figure 6. 2004 groundwater elevations, river stage and swale pond stage.

Figure 7. 2005 groundwater elevations, river stage and swale pond stage.

Figure 8. 2006 groundwater elevations and river stage.

Figure 9. Temperatures at the control area during 2004.



Figure 12. Notched boxplots illustrating the distributions of initial seedling condition (height,

basal diameter and no. of leaves) at planting for each species in Block 1 and Block 2

high, mid, and low elevation plots.

Figure 13. Year 1 leaf-out timing for seedlings of each species.

Figure 14. Year 1 and Year 2 relative seasonal growth timing for all species.

Figure 15. Seedling height growth by irrigation treatment.

Figure 16. Seedling basal diameter growth by irrigation treatment.

Figure 17. Seedling height growth by distance to groundwater.

Figure 18. Seedling basal diameter growth by distance to groundwater.

Figure 19. Final seedling growth by irrigation level.

Figure 20. Final seedling growth by elevation level.

Figure 21. Xylem water potential values for irrigation treatment groups in Year 2 (2005).

Table of Contents

iv Merced River Ranch Revegetation Experiment

Figure 22. Xylem water potential values for high and low treatment groups in Year 2 (2005).

Figure 23. Final cohort survival by irrigation treatment.

Figure 24. Final hazard rate by irrigation treatment.

Figure 25. Final hazard rate by distance to groundwater.

Figure 26. Year 2 cohort survival by irrigation treatment.

Figure 27. Year 2 cohort survival by distance to groundwater.

Figure 28. Year 3 cohort survival by irrigation treatment.

Figure 29. Year 3 cohort survival by distance to groundwater.

Introduction

1 Merced River Ranch Revegetation Experiment

1 INTRODUCTION

The Merced River Ranch revegetation experiment has been undertaken as a

component of the Merced River Corridor Restoration Plan - Phase IV Project

(CALFED ERP-02-P12-D), which is intended to evaluate strategies for channel and

floodplain restoration within the context of the contemporary flow regime. The

Phase IV Project focuses on restoration planning activities on the Merced River

Ranch (MRR), located at the uppermost end of the Merced River Dredger Tailings

Reach (Figure 1). The Dredger Tailings Reach (DTR) has been severely impacted by

historic gold dredger mining and alteration of the natural hydrograph by upstream

dams. The reach is also the primary spawning area in the Merced River for fall-run

Chinook salmon (Oncorhynchus tshawytscha), an important management species for

the California Department of Fish and Game (CDFG), and potentially steelhead (O.

mykiss), which is listed as threatened under the Federal Endangered Species Act.

This technical memorandum reports the results of the three-year MRR revegetation

experiment and develops revegetation recommendations for inclusion in

restoration planning documents prepared for the Phase IV Project.

1.11.11.11.1 Study AreaStudy AreaStudy AreaStudy Area

The Merced River is a tributary to the San Joaquin River in the southern portion of

California’s Central Valley (Figure 1a). The river, which drains an approximately

3,305-km2 (1,276-mi2) watershed, originates in Yosemite National Park and flows

southwest through the Sierra Nevada range before joining the San Joaquin River

140 km (87 mi) south of the City of Sacramento. Elevations in the watershed range

from 3,960 m (13,000 ft) at its crest to 15 m (49 ft) at the confluence with the San

Joaquin River. The DTR of the Merced River extends from Crocker-Huffman Dam

(river mile [RM] 52) to approximately 1.9 km (1.2 mi) downstream of the Snelling

Road Bridge (RM 45.2), a reach of approximately 11.6 km (7.2 mi) (Figure 1b and c).

The 129 ha (318 ac) MRR is located in the upstream portion of the DTR (RM 51 to

50) and was purchased by California Department of Fish and Game (CDFG) in 1998

as a source of coarse sediment for future river restoration projects and as a

floodplain restoration site.

The hydrology of the Merced River has been altered by water supply requirements

and flood control operations, which together have reduced flood frequency,

Introduction


reduced peak flow magnitude, altered seasonal flow patterns, and reduced the

temporal variability of flows. These changes in hydrologic conditions have altered

the frequency, duration, and magnitude of floodplain inundation, and reduced the

frequency of sediment transport and bed mobilization, but, in conjunction with a

lack of sediment supply, have caused bed scour and armoring in the remaining

flood events (Stillwater Sciences 2001).

Since 1926, sediment supply from the upper 81 percent of the watershed has been

intercepted at the original Exchequer Dam and then the New Exchequer Dam. This

interception has eliminated the vast majority of the river’s historical sediment

supply, thus depriving the river of a basic element necessary to maintain

geomorphic equilibrium.

In addition to the effects of flow regulation and loss of sediment supply from the

upper watershed, this reach has been extensively modified by gold dredging. In

the early-to-mid twentieth century, gold dredges excavated the river channel,

floodplain, and valley floor. The dredges had earthmoving capacities of 1.4–3.4

million cubic yards/year and excavated the channel and floodplain deposits to

bedrock, usually at a depth of 20–36 feet (Clark 1998). After recovering the gold,

the dredgers redeposited the remaining tailings in long rows, often roughly parallel

to the river channel, on the floodplain (Figure 2). Although they were originally

thought to consist of fine sand and gravel overlain by cobbles and boulders

(Goldman 1964) extending to the original dredging depths, recent surveys indicate

that the tailings piles exhibit little stratification (URS 2004b). As a result of gold

dredging, the channel has been depleted of coarse sediment, the adjacent

floodplain has been raised and covered with dredger tailings piles, and soil and

fine sediment have been washed downstream. An estimated 3.22 million cubic

yards (2.46 million m3) of dredger tailings currently cover approximately 305 acres

(1,236,000 m2) of the riparian corridor of the DTR (URS 2004b).

Sparse, weedy herbaceous vegetation consisting largely of non-native grasses and

forbs dominates the large expanse of tailing surfaces and floodplain area of the

MRR. Native riparian vegetation is typically restricted to narrow bands adjacent to

the river, measuring 33 m (100 ft) or less in width on each bank of the river, and

linear patches confined to swales within the dredger tailings (Whitlow and Bahre

1984, Stillwater Sciences 2002). The dominant vegetation along the narrow river

banks is a mix of individual or small patches of valley oak (Quercus lobata) and

mixed willow (Salix spp.), with cottonwood forest, grassland, riparian scrub, and

off-channel marsh habitat generally located farther away from the river (Stillwater

Sciences 2002). In some areas, dredging operations left behind low-lying swales

between tailing piles. Several of these swales, subsequently referred to as swale

ponds, are connected to a perennial or seasonal groundwater supply and support a

variety of wetland vegetation types (primarily freshwater emergent marsh,

Introduction


seasonal wetland, open water/ponds, mixed willow, and cottonwood forest). Most

of the smaller, linear patches of riparian scrub and forest in the swale ponds are

dominated by narrow-leaf willow (Salix exigua) with edible fig (Ficus carica),

California wild grape (Vitas californica), and Himalaya blackberry (Rubus discolor) as

common associated species (Stillwater Sciences 2001, URS 2006a). The deepest,

wettest swale ponds support cattail (Typha spp.) marsh habitat and/or perennial

ponds. These swale ponds support floating plants, such as various duckweeds

(Lemnaceae) and water fern (Azolla filiculoides). The introduced water hyacinth

(Eichhornia crassipes) also occurs in some swale ponds. Many of the swale ponds

also contain beds of submergent macrophytes. Marsh pennywort (Hydrocotyle

ranunculoides) forms dense beds in some shallower swale ponds (Stillwater Sciences

2001).

1.21.21.21.2 Restoration PlanningRestoration PlanningRestoration PlanningRestoration Planning

The Phase IV Project, and therefore MRR revegetation experiment, stem from the

larger Merced River Corridor Restoration Plan (MRCRP). Funded by the CALFED

Ecosystem Restoration Program, the intent of the MRCRP was to provide a

technically sound, publicly supported, and implementable plan to improve

ecological function in the Merced River corridor from Crocker-Huffman Dam (RM

52) to the confluence with the San Joaquin River (RM 0). Crocker-Huffman Dam is

the downstream-most dam on the Merced River and the upstream limit of

anadromous fish access. The MRCRP (Stillwater Sciences 2002) identifies

restoration objectives and provides management recommendations based on

current scientific understanding of the Merced River with input from the Merced

River Stakeholders (MRS), Merced River Technical Advisory Committee (MRTAC),

and the broader public. Since a broad spectrum of interests, represented by the

MRS, MRTAC, and public, provided input to the restoration objectives, they

address not only geomorphic and ecological restoration in the river, but also the

concerns of local citizens, landowners, and other stakeholders.

To guide reach-scale restoration efforts and address various anthropogenic impacts

to the DTR, the MRCRP identified the following objectives for the DTR (Stillwater

Sciences 2002):

• balance sediment supply and transport capacity to allow the accumulation

and retention of channel bed material suitable for spawning and to prevent

riparian vegetation encroachment;

• restore floodplain functions to improve the establishment of riparian

vegetation and the quality of riparian habitat;

• increase in-channel habitat complexity to improve aquatic habitat for native

aquatic species; and

• scale low-flow and bankfull channel geometry to current flow conditions.

Introduction


The Phase IV Project begins to address the MRCRP objectives through the design of

pilot floodplain and channel restoration experiments. The Phase IV project

includes conducting: 1) DTR- and MRR-scale studies of current conditions to

provide the basis for and to inform the design of restoration actions (Stillwater

Sciences 2004a, b and c; URS 2004a and b; Stillwater Sciences 2005, 2006; URS

2006a); and 2) experiments to test actions that will initiate the restoration of natural

ecosystem function at the MRR to the extent feasible. The project will provide

transferable scientific information to reduce uncertainty in future restoration

design on the Merced and potentially in other rivers in the Central Valley. For

example, removal of the tailings from the floodplain has the potential to yield

multiple restoration opportunities and ecosystem benefits, but the detailed impacts

of such restoration activities are largely unknown. The Phase IV Project

experiments, of which the revegetation experiment is one, are designed to increase

the collective scientific understanding of the potential for dredger tailings removal

and re-use (e.g., as material to use as fill during channel reconstruction or for gravel

augmentation), and is intended to improve restoration effectiveness and reduce

project uncertainty when implementing restoration actions in the future.

1.31.31.31.3 Site RevegetationSite RevegetationSite RevegetationSite Revegetation

Revegetation will be an essential component of floodplain restoration at the MRR

to ameliorate the factors currently limiting vegetation and habitat quality. The

impacted hydrology, sediment supply, and floodplain conditions of the DTR

strongly affect riparian vegetation and habitat extent, floristic and structural

composition, and health in the following ways:

1) Replacement of native riparian vegetation by dredger tailings. Most of the natural

riparian vegetation in the reach was removed and replaced by piles of dredger

tailings. Throughout the DTR riparian vegetation is currently limited to narrow

bands along the river channel and fragmented patches in low-lying areas

among the dredger tailings piles (Whitlow and Bahre 1984, Stillwater Sciences

2001).

2) Altered flood regime. Recruitment of new plants is hindered by reduced flood

magnitude and alteration of flood timing as a result of flow regulation. Limited

floodplain inundation and the shift of peak flows from spring to winter has

resulted in: a) inadequate wetting of appropriate recruitment sites during the

spring seed release period and, b) flow recession rates too steep to allow

seedlings to develop adequate root systems to ensure survival and vigorous

growth in the first growing season (Stillwater Sciences 2001, Stella 2005).

3) Reduced sediment supply. Recruitment of new plants is hindered by reduced

sediment supply as a result of a dam which is located upstream of the reach.

The reduction in sediment supply has reduced the deposition of fine sediment

Introduction


on the floodplain during flood events, thus reducing the creation of suitable

substrates for seedling germination (Stillwater Sciences 2001).

4) Degraded floodplain substrates. The river channel in the dredger tailings reach is

confined by piles of dredger tailings which have replaced the natural floodplain

soils. The cobble dredger tailing piles contain very little soil (Whitlow and

Bahre 1984, URS 2004a), provide a poor growing substrate for vegetation, and

likely retain very little water moisture.

5) Increased floodplain elevation. The piles of dredger tailings have increased the

floodplain elevation along the river, further limiting inundation of the

floodplain by flood flows (URS 2004b). In addition, it is believed that the

dredger mining process and water diversion have severely altered groundwater

patterns at the site. As a result, very little water is available to growing plants

beyond the immediate channel margin.

Even under restored floodplain conditions, natural recruitment of pioneer riparian

plant species is not expected to significantly contribute to the development of a

self-sustaining, diverse riparian corridor (Stillwater Sciences 2001, Stella 2005). The

morphological changes to the river channel and floodplain that result from

restoration are not expected to lead to sufficient process change to create

recruitment-friendly conditions. While restored floodplain conditions will improve

natural recruitment potential over existing conditions by increasing the frequency

of floodplain inundation, active revegetation of restored floodplains will be

necessary to recreate a riparian corridor that provides multiple ecosystem benefits.

Active revegetation will also be needed on the large areas of the floodplain that are

too far from the river to experience flooding under the regulated flow regime.

Future revegetation efforts will, therefore, need to be extensive and will include

improving substrate conditions, planting propagules of local origin (seed, cuttings,

and seedlings), irrigating and maintaining planted areas during initial

establishment, and fostering and monitoring natural recruitment.

1.41.41.41.4 Experiment Goals and ApproachExperiment Goals and ApproachExperiment Goals and ApproachExperiment Goals and Approach

The results of other riparian revegetation can be used to some extent to inform and

guide revegetation planning at the MRR, but riparian revegetation efforts in the

Central Valley, particularly on floodplains covered or formerly covered in dredger

tailings, have had mixed results (AMFSTP 2002). Part of the problem is the lack of

formal monitoring data that has been collected on past revegetation efforts. While

increasing numbers of studies and revegetation efforts are being designed and

implemented to increase understanding of issues affecting riparian revegetation

and incorporate more formalized monitoring (e.g., CDWR and CDFG 2003a and b,

Stella et al. 2003, AMFSTP 2004, CDWR 2004, Kiparsky 2005, Souza Environmental

Solution et al. 2005, Stella 2005), few projects have the time or funding to conduct

Introduction


revegetation in an experimental setting where multiple factors are tested and

monitored.

Because of the large extent of planned revegetation efforts (Stillwater Sciences

2005) and uncertainty in how site conditions will affect revegetation performance,

the pilot riparian revegetation experiment was included in the Phase IV project.

The experiment is also a response to the recommendations of the Merced River

Adaptive Management Forum to improve the linkages between scientific input and

project design, conduct active experiments with revegetation design when the

opportunity exists, and increase the amount of transferable information generated

from the Merced River (AMFSTP 2002 and 2004).

The goals and objectives of the revegetation experiment were developed with the

long-term intention of improving revegetation effort effectiveness. The overarching

goals of the experiment are to: 1) increase scientific understanding of factors

limiting the success of riparian revegetation on restored floodplains, and 2) provide

transferable scientific information that will reduce the scientific uncertainty in

future revegetation projects.

The objectives of the revegetation experiment are to:

• assess the influence of different design parameters to determine the most

effective and efficient revegetation techniques on floodplains within the MRR

once the tailing piles have been removed;

• develop vegetation-related recommendations for the restoration design of the

MRR; and

• assist in the adaptive management of the Phase IV and other restoration

projects on the Merced River and other Central Valley rivers by informing the

revegetation of floodplains currently covered in tailing piles and by refining

hypotheses that could be tested during or through future revegetation projects.

This experiment tests the effects of initial size, depth to groundwater, irrigation

duration, and weed reduction on the survival, growth, and water potential of four

native riparian tree species. The experimental areas were designed to provide the

substrate textures and range of floodplain elevations likely to occur once the tailing

piles have been removed for restoration purposes. The range of experimental

treatments considered was refined following conversations with several

revegetation practitioners in the Central Valley and a review of the literature; final

treatments were selected to inform several of the primary uncertainties in

revegetation success. The final design was a balance between the range of

treatments, the number of statistically required replicates, and logistical and cost

constraints. The experiment was initiated in April 2004 and concluded in October

2006.

Methods


2 METHODS

2.12.12.12.1 Experimental DesignExperimental DesignExperimental DesignExperimental Design

The MRR revegetation experiment tests the effects of groundwater depth, irrigation

duration, and weed reduction treatments on water stress, survival, and growth of

Fremont cottonwood (Populus fremontii), box elder (Acer negundo), Oregon ash

(Fraxinus latifolia), and valley oak (Quercus lobata). These species are dominant or

co-dominant components of Central Valley mixed riparian forests. They exhibit

different life history traits and occur within a predictable range of geomorphic

recruitment positions on river banks and floodplains (Stillwater Sciences 2001,

Stillwater Sciences 2003, Stella et al. 2003, Vaghti and Greco in press, Greco et al. in

review). At the MRR, these species have been found to occur naturally at

elevations between 86 and 91 m (282 and 299 ft) (KSN, unpublished data),

primarily at relative elevations of approximately 0.61–4.57 m (2–15 ft) above

summer baseflow water surface elevation and presumed groundwater levels

(Stillwater Sciences 2001, Stella et al. 2003). Throughout this report, these four

species are abbreviated using the first two letters of their genus and species name:

Acer negundo = ACNE; Fraxinus latifolia = FRLA; Populus fremontii = POFR; Quercus

lobata = QULO. Hypotheses, experimental treatments, and treatment levels are

listed in Table 2-1.

Methods


Table Table Table Table 2222----1111.... Revegetation experiment hypotheses, treatments, and treRevegetation experiment hypotheses, treatments, and treRevegetation experiment hypotheses, treatments, and treRevegetation experiment hypotheses, treatments, and treatment levels.atment levels.atment levels.atment levels.

Hypothesis Experimental

treatments Treatment levels

Merced River Ranch floodplains restored to

functional elevations will provide shorter distances to

groundwater, resulting in increased establishment

and survival of revegetated plants.

Floodplain

elevation

Floodplain plots at:

1. Low,

2. middle, or

3. high relative

elevations

Controlling weeds in the immediate vicinity of

plantings increases plant survival and growth

because of reduced competition from herbaceous

plants.

Weed

reduction

Weed reduction:

1. applied

2. not applied

Irrigating seedlings and cuttings after planting will

increase survival and growth because of reduced

moisture stress. Plants will require irrigation for at

least one year to become established. Plants irrigated

for greater than one year will demonstrate increased

survival over plants irrigated for only one year.

Irrigation

Drip irrigation during

the growing season for:

1. one,

2. two, or

3. three years

The hypotheses and factors tested in the experiment were developed in response to

the establishment needs of pioneer riparian tree species and designed to answer

some of the primary current unknowns in floodplain revegetation specific to

dredge tailing areas (AMFSTP 2002). Experimental treatments were refined based

on the planting plans, experiences, and results of other Central Valley revegetation

efforts on restored floodplain surfaces (J. Bair, pers. comm.; D. Boucher, pers. comm.;

CDWR and CDFG 2003a and b; CDWR 2004; K. Dulik, pers. comm.; W. Moise, pers.

comm.; J. Souza, pers. comm.; Souza Environmental Solution et al. 2005).

Two experimental areas (Block 1 and Block 2) were graded on the MRR in areas

that were representative of overall site conditions but that did not require the

disturbance of wetland habitat or high-quality riparian vegetation (Figure 3). Two

experimental block areas were used in an attempt to account for intra-site

variability in uncontrolled physical environmental factors. A groundwater

monitoring well was installed at each block location just prior to plot excavation

(Figure 3). Low, middle, and high relative elevation treatment plots were

excavated at each block in April 2004 in relation to the groundwater elevation at

that time, and were designed to be 1, 2, and 4 m above groundwater, respectively

(Figures 3 and 4). These elevations were selected to replicate the range of

floodplain relative elevations likely to occur once the tailing piles have been

removed for restoration purposes (URS 2006b). Following plot excavation and

planting (which required the use of heavy equipment), and the results of

groundwater monitoring (see Section 3.1.2), final relative elevations (at plant bases)

above groundwater varied somewhat from the initial design. Table 2-2 reports the

final, as-built elevation at each experimental plot, and includes the abbreviation

Methods


convention for each relative elevation plot that is used throughout this report.

Monitoring of the two groundwater wells has revealed that groundwater levels

remain relatively stable throughout the year (see Section 3.1.2).

Table Table Table Table 2222----2222. . . . AsAsAsAs----built built built built experimental plot elevationexperimental plot elevationexperimental plot elevationexperimental plot elevations.s.s.s.

Experimental PlotExperimental PlotExperimental PlotExperimental Plot (Plot (Plot (Plot (Plot Abbreviation)Abbreviation)Abbreviation)Abbreviation)

ElevationElevationElevationElevation (NGVD)(NGVD)(NGVD)(NGVD) Depth toDepth toDepth toDepth to GroundwaterGroundwaterGroundwaterGroundwater****

m (ft)m (ft)m (ft)m (ft) m (ft)m (ft)m (ft)m (ft)

Block 1 Low (B1L) 87.54 (287.2) 0.6 (1.97)

Middle (B1M) 89.16 (292.5) 2.2 (7.22)

High (B1H) 90.54 (297.1) 3.6 (11.81)

Block 2 Low (B2L) 88.52 (290.4) 0.7 (2.30)

Middle (B2M) 89.69 (294.3) 1.9 (6.23)

High (B2H) 91.80 (301.2) 4.0 (13.12)

*based on average (2004 and 2005) groundwater elevation at each block (see

Table 3-2)

Each relative elevation plot contained 10 replicates of each species/irrigation/weed

reduction treatment combination, for a subtotal of 240 plants per elevation plot. A

total of 1,440 individual plants were planted using container stock or cuttings (see

below) and monitored for the experiment. Sixty of each species were planted

randomly on 2 m-centers in each relative elevation treatment plot (Figure 5a). This

spacing was selected to prevent interactions and/or competition between the root

systems of the plants for the duration of the experiment while minimizing the size

of the experimental plots, which needed to be excavated using heavy equipment. A

backhoe was required to dig the planting holes, which were approximately 0.61–

0.92 m (2–3 ft) deep, because of the large substrate size. Approximately 0.01 m3

(0.35 ft3) of commercial source topsoil was added to every planting hole to improve

the existing, extremely poor soil conditions (see Section 3.1.1). Fremont

cottonwood was planted as 0.61–0.92 m (2–3 ft) long cuttings while box elder,

Oregon ash, and valley oak were planted as approximately 1 year-old container

stock (Figure 5b–d). Cottonwood cuttings were collected at the MRR by River

Partners; valley oak acorns were collected near Oakdale, CA and grown by the

USDA Forest Service in Davis, CA; Oregon ash and box elder seeds were collected

on the Tuolumne River and grown by Circuit Rider Productions in Windsor, CA.

Cuttings and container stock were placed into planting holes and backfilled with

the added topsoil and tailings. Protective plastic mesh sleeves were placed around

each plant upon planting to reduce potential impacts from herbivory (Figure 5).

Because of the coarse substrate, high summer temperatures at the site, and a late

initial planting time, all plants were irrigated during the first year of the

experiment. A drip irrigation system, using water from the Merced River, supplied

7.5 L (2 gal) of water per hour to all plants during each irrigation session. The

irrigation system was run 4 to 6 hours per day, 3 days per week during the growing

Methods


season (April through October). Frequent watering was required due to the very

low water retention of the tailings substrate. Before the second year of the

experiment, irrigation treatments were re-assigned randomly to all surviving trees,

stratified by species. Irrigation was shut-off (i.e., drip emitters were plugged) to

one-third of the surviving trees (stratified by treatment), while the other two-thirds

were assigned a second year of irrigation. A further reassignment of irrigation

treatment was applied randomly to 50% of the remaining plants prior to the start of

the 2006 growing season to determine which surviving trees were given a third

year of irrigation.

Weed reduction treatments were also randomly assigned to all species. Plants with

weed reduction had a 1 m2 black fabric weed control mat installed at planting and

manual weed removal during the growing season within a 1 m2 area around the

plant (Figure 5a, b, and c). Plants with no weed reduction had no weed control mat

or manual weed removal; weeds were allowed to grow to the extent that they did

not invade weed-reduction-treatment plants.

2.22.22.22.2 Data Data Data Data CollectionCollectionCollectionCollection

Project monitoring was conducted primarily during the plant growing season

(April through October) of 2004, 2005 and 2006. A variety of physical site

conditions were monitored, including substrate texture and nutrients, groundwater

elevation, swale pond and river stage, and ambient air temperature. Plant survival,

growth, and vigor were monitored as the primary response variables to the

experimental treatments. Xylem water potential was monitored to quantify plant

responses to relative elevation and irrigation treatments. Weed percent cover was

monitored to evaluate the effects of the weed reduction treatment. Specific

monitoring methods are described in the following subsections.

2.2.1 Site Conditions

Soil texture and nutrients were measured to evaluate their potential to limit

revegetation success and to inform future revegetation efforts. Substrate texture

was measured at the MRR during an earlier Phase IV project study (URS 2004b).

Soil samples were collected at each experimental block prior to planting and sent to

a lab for analysis of total nitrogen, phosphorous, and sulphur, as well as soil

minerals such as potassium, calcium, magnesium, copper, and zinc.

Depth to groundwater, river stage, and swale pond stage were monitored during

the growing season at the MRR to document river/groundwater interactions that

may influence the planning and performance of revegetation efforts. Depth to

groundwater was monitored weekly at the two monitoring wells on the MRR

(Figure 3) from April 12–November 12, 2004 and March 31–November 25, 2005, and

Methods


monitored continuously from April 28–November 3, 2006. In 2004 and 2005 a

water level meter (Solinst Mini 101) was used to measure the distance to

groundwater from the top of each monitoring well. The height of the well from

was subtracted from the measured depth during data processing. In 2006, Solinst

Gold Water-level dataloggers were installed in each well to continuously record

groundwater level. This data was corrected for the depth of the well and for

barometric pressure. The stage of the Merced River at the MRR was monitored

continuously with a water level logger (Global Water WL16) (Figure 3) from April

21, 2004 through December 31, 2005. In 2006, due to an equipment malfunction, a

stage-discharge rating curve was developed to estimate stage from flows measured

at the Merced ID Crocker-Huffman gauge. A staff gauge was installed in one of the

swale ponds at the MRR (Figure 3) and monitored weekly from April 12–November

12, 2004 and March 31–November 25, 2005. During data processing, all

groundwater and stage data was adjusted to National Geodetic Vertical Datum of

1929 (NGVD29) using elevation data collected with survey-grade GPS equipment at

each piece of monitoring equipment (i.e., the end of the pressure transducer, the

bottom of the staff gauge, and the top and bottom of each groundwater monitoring

well).

Temperature was monitored at relative elevation treatment plots and in one control

location to: 1) document seasonal temperature conditions at the MRR; 2) evaluate

differences between experimental areas; and 3) evaluate the effects on temperature

on plant survival and growth. Outdoor temperature data loggers (Onset HOBO

Pro RH/Temp) that continuously record data were installed in the middle of each

experimental block and in one control area at the beginning of the experiment on

April 12, 2004. These three data loggers were removed on August 18, 2004, as

instrument drift was suspected, in order to calibrate them with each other and with

four additional data loggers. On April 12, 2005, all seven data loggers were

deployed (one control and one in each relative elevation treatment plot) and

recorded data through November 3, 2006.

Several permanent photo monitoring stations were established at each relative

elevation plot at the initiation of the experiment in order to document conditions

and changes at the experimental plots. A digital photograph was taken at each

photo monitoring station during growth and vigor monitoring efforts.

2.2.2 Survival

Plant survival was monitored weekly during the growing season in 2004 and 2005,

and twice a month in 2006. Each plant was inspected and recorded as Alive, Dead,

or Stressed on field datasheets. Plants listed as Dead were tested by scratching

through the bark at the base to reveal any living tissue and flagged if verified as

dead. The irrigation system was inspected and maintained during the survival

monitoring efforts. All Stressed plants were categorized as Alive prior to data

Methods


analysis. Where plants were erroneously recorded as Dead and later found to be

Alive, weekly monitoring results were compared with monthly growth and vigor

monitoring results (which were more detailed monitoring efforts; see Section 2.2.3)

and corrected. Approximately 40 such corrections were made to the data (out of

1,440 plants) and were primarily necessitated by data collection errors (e.g.,

survival status was recorded for the adjacent plant) or because the above-ground

portions of some plants died back completely, only to resprout later in the season

from the roots or lowest portion of the trunk.

2.2.3 Growth

Plant growth and vigor were monitored monthly during the first growing season of

the experiment (2004), every other month during 2005, and twice during 2006. The

height and basal diameter of each living plant was measured to quantify growth.

The height of the longest living stem (from the ground surface to stem apex) was

measured using meter sticks and recorded in centimeters. Where crown die-back

occurred, we measured height to the top of the live crown. Basal diameter was

measured at the base of the primary stem/trunk above the root mound using a

caliper and recorded in millimeters. During the first year of the experiment, the

number of leaves on each plant was counted as an additional measure of growth.

This practice was discontinued in Year 2 as there were too many leaves on

surviving trees to complete the monitoring on schedule.

A qualitative assessment of each plant’s vigor was also made during these

monitoring efforts. The following vigor codes were used: (0) dead; (1) extreme

stress or damage; (2) appears stressed or diseased; (3) stable, healthy; (4) active

growth, robust. These vigor codes were useful to field technicians in describing

plant conditions, but were not used in any subsequent data analyses.

2.2.4 Water Potential

The water status of a plant can be evaluated by measuring its xylem water potential

(Boyer 1967, Boyer 1995). Xylem water potential was measured on a randomly

selected subset of surviving trees in B1L and B1H on June 1 and September 18, 2005

using a pressure chamber instrument (PMS Instruments Model 670). Both

monitoring efforts consisted of pre-dawn and afternoon sampling events. Pre-dawn

sampling occurred between 3 and 6 AM; afternoon sampling of the same plants

occurred the same day between 1 and 4 PM. At each sampling event, a leaf or

small terminal branch was cut from each sampled tree, placed immediately in a

moist plastic bag, and stored in a cooler until it was placed in the pressure

chamber. The chamber was slowly pressurized until water was visually detected at

the cut surface. Pressure was recorded in megapascals (MPa) and is reported as a

negative value to capture the water potential of the plant being sampled rather

than the pressure in the chamber (Boyer 1967).

Methods


2.2.5 Weed Percent Cover

The effects of the weed reduction treatment were evaluated by monitoring weed

percent cover at each plant once each year during the height of the growing season.

Aerial percent cover of all weed (i.e., non-planted) species was visually estimated

within a 1 m2 plot around each plant. A modified Braun-Blanquet (1965) cover

class system was used, and includes the following classes: 0=0%, 1=1–5%; 2=6–10%;

3=11–25%; 4=26–50%; 5=51–75%; 6=76–100%. Plants in the weed reduction

treatment had a cover class of 0. Species composition within each plot was

recorded during the 2005 percent cover monitoring to document the most prolific

weed species and the presence of non-native versus native weed species. To

evaluate potential temperature effects of the weed reduction mats, substrate

temperatures were intermittently recorded at the base of weed reduction and non-

weed reduction treatment plants using a handheld infrared thermometer.

2.32.32.32.3 Statistical AnalysesStatistical AnalysesStatistical AnalysesStatistical Analyses

All data were entered into a project database (Access 2003, Microsoft) and checked

for errors. The database was used to conduct primary queries of the data and

calculate summary statistics. All other statistical tests were conducted in S-Plus

(Version 6.1, Insightful Corp., Seattle, WA).

2.3.1 Initial Size Analysis

In order to evaluate the results of final size and growth rate calculations (see

below), we first analyzed whether seedlings and cuttings varied significantly in

initial size (height, basal diameter and number of leaves upon planting) by the

location where they were planted. The basic unit of analysis was the 6 planting

plots (2 blocks x 3 relative elevation levels). Relative elevation, block, weed

reduction and irrigation were not tested as separate factors since the planting

effectively occurred before any treatments were experienced by the plants. Each

species was analyzed separately since seedling morphologies varied at planting

(e.g., POFR were planted as cuttings). We used analysis of variance (ANOVA)

models to test whether seedlings differed in initial height, basal diameter, and

number of leaves by plot. For tests in which initial size or leaf number varied by

plot at a p<0.05 significance level, we conducted post-hoc pairwise comparisons to

identify which plot(s) had extreme distributions. Group means were compared

using simultaneous 95% confidence intervals calculated using the Tukey method

(Zar 1999).

Methods


2.3.2 Growth Analysis

Growth over the duration of the experiment was plotted from seasonal monitoring data.

We generated plots of cumulative height and basal diameter over the three years, and

number of leaves for the first growing season. Data plotted were only from plants that

were alive at each survey date. We also plotted relative growth of the three size metrics

(height, basal diameter, number of leaves) for each species to evaluate the seasonal

timing of biomass allocation. The change in each metric over the growing season was

normalized relative to its maximum value (resulting in units of proportion change over

time) and all three metrics plotted on a common time axis.

We evaluated treatment influences on growth using analysis of covariance (ANCOVA)

models. Each species was tested individually because of gross differences in growth

rates and tree morphology. The dependent variable chosen for the ANCOVA models

was basal diameter increment from initial planting to November 2006, the end of the

third growing season. We chose this as the best growth measurement for several

reasons. Increment was chosen rather than final values to account for differences in

seedling size at planting. Basal diameter increment was a good representative growth

measure because it was correlated with height increment for each species (Pearson

correlations for ACNE=0.76, FRLA=0.73, POFR=0.89 and QULO=0.50). Unlike height,

however, basal diameter was not a problematic growth metric for any species. Height

values decreased over the experiment for many ACNE stems because of crown dieback,

and initial height, which is subtracted from final values to calculate increment, was

meaningless for POFR because this species was planted as cuttings.

Independent variables in the ANCOVA growth models included the treatment variables

of interest, which were elevation, weed control, and number of years irrigated. In

addition to these, we included two environmental covariates that may influence growth:

(1) planting block; and (2) initial basal diameter at planting, which we hypothesized may

have had some additional growth influence, even after its correlation with final basal

diameter was eliminated in the increment calculation.

For model development and selection we adopted Akaike’s Information Criteria

(AIC) as detailed by Burnham and Anderson (1998). Initially we specified models

with all combination of treatment variables of interest (main effects plus

interactions for elevation, weed control and number of years irrigated); this

resulted in 23 candidate models per species. To this candidate set we compared

another model that represented the best fit when the environmental covariates

‘block’ and initial basal diameter were included. Models were compared using

their AIC values, which maximizes the likelihood using Kullback-Leibler distance

and penalizes overly-complex models (Burnham and Anderson 1998).

AIC is calculated as:

Methods


KdatalAIC 2))|ˆ(log(2 +−= θ (2)

where l( θ̂ |data) is the maximized likelihood of the model given the estimated

parameters, and K is the number of parameters including the intercept. Smaller

values indicate a relatively better model.

In comparing AIC values between models, the absolute AIC value does not matter

so much as the differences and relative weights between them. A model’s AIC

difference value is calculated as its AIC value minus the lowest AIC value of the

candidate models:

minAICAICAIC idiff −= (3)

From these difference values we calculated each model’s Akaike weight (wi) and

evidence ratio (ER). The Akaike weight represents an approximate probability that

a candidate model is the best of all those being compared (Burnham and Anderson

1998). The Akaike weight for model i, wi , is calculated:

∑=

−

−

=n

p

AIC

AIC

ipdiff

idiff

e

ew

1

)5.0(

)5.0(

(4)

Since wi sums to unity, each weight can be thought of in terms of percentages (e.g.,

wi =0.40 suggests that model i is 40% likely to be the best model of the given

models). The evidence ratio is a relative measure of model performance and is

calculated as the ratio of the Akaike weight for each model to the best model’s

weight. As a rule of thumb, an ER of one model should be at least twice that of the

next best model to consider it substantially better (Burnham and Anderson 1998).

When comparing top candidate models, evidence ratios do not depend on the

number of models considered, as do Akaike weights. Akaike weights and evidence

ratios were computed for the entire candidate set of treatment models (n=23 for

each species), plus the best overall model from the step-wise AIC selection process.

The top five models for each species were tabulated and compared.

2.3.3 Water Potential Analysis of Variance

Pre-dawn and afternoon water potentials were summarized (means and standard

errors) by species, time of year, plot, and irrigation treatment. We used ANOVA to

analyze whether pre-dawn and afternoon water potential values were significantly

affected by species, time of year, relative elevation from groundwater, and/or

irrigation treatment. We did not include time of day (pre-dawn vs. afternoon) as a

covariant in the analysis as a strong difference was expected regardless of species,

Methods


time of year, or treatment level. We conducted post-hoc pair-wise contrasts of

simultaneous 95% confidence intervals using the Tukey method to see which

covariants had the strongest influences on the ANOVA significance levels.

2.3.4 Survival and Hazard Analysis

We analyzed influences on plant survival from the experimental treatment levels,

testing each species independently because of obvious differences in survival

patterns. Seedling survival was analyzed separately in each of the three years

because of the imposition of a new irrigation treatment in both 2005 and 2006,

which resulted in 3 irrigation levels for the experiment: 1, 2, or 3 years. For each

growing season, survivorship over time was calculated for each treatment group

using Kaplan-Meier non-parametric estimations to account for censored

observations (Machin et al. 2006). From the survival curves we plotted the

empirical hazard rate in order to evaluate changes in mortality risk over time for

different species and treatments. The hazard function describes the temporal

change in the instantaneous death rate experienced by individuals in a sample per

unit of time; it is expressed in units of deaths individual-at-risk-1 time-1 (Zens and

Peart 2003). We generated empirical hazard plots using a cubic-B spline first

derivative (predict.smooth.spline function in S-Plus) fit to the inverse of the

Kaplan-Meier survival function (T. Therneau, pers. comm.). The empirical hazard

plots were used to evaluate how the baseline hazard rates (i.e., the force of

mortality) varied over time and between species and experimental units (Burnham

and Rexstad 1993, Dunlap et al. 1994, Pletcher and Curtsinger 2000, Tableman et al.

2004).

2.3.5 Cox Proportional Hazard Model

We analyzed differences in seedling survival between treatment levels in each year

of the experiment using a Cox proportional hazard model. The Cox model, which

is commonly used in the health sciences (Vittinghoff et al. 2005, Machin et al. 2006),

is a flexible regression model for assessing the effects of multiple predictors on

time-to-event data (time-to-death or machine failure, for example). The model is

non-parametric with respect to the distribution of survival times, a feature that

makes the Cox model very flexible for representing complex mortality patterns. A

primary assumption in the Cox model is that the ratio of the hazard rate, or

instantaneous risk of death, between groups does not change with time. This

means that though the number of individuals in each group that die at any time

may vary, the death totals are proportional between groups at all times. This is a

reasonable assumption if mortality is affected by a treatment factor but is also

influenced by environmental factors that vary in intensity over time. In the Cox

model, the linear predictors are linked through log transformation of the hazard

ratio:

Methods


log[HR(x)] = log [h(t|x)/h0(t)] = β1x1 + β2x2 + . . . + βpxp (5)

where h(t|x) is the hazard at time t (in units of deaths individual-at-risk-1 time-1) for

an observation with covariate value x, and h0(t) is the baseline hazard function,

defined as the hazard at time t for observations with all predictors equal to zero

(Vittinghoff et al. 2005). The baseline hazard rate h0(t) can be adjusted for

environmental covariates by means of categorical or continuous terms. Like a

logistic regression model, the Cox model is a log-linear model in which the linear

predictors act multiplicatively on the independent variable, in this case the hazard

ratio. Unlike other linear models, the Cox model does not require a parametric

form for the baseline hazard rate.

A key feature of the Cox model is its ability to separate, via the linear predictors,

treatment effects, or factors of interest under the experimenters’ control, from

environmental covariates, which are of lesser interest and may vary naturally

(Vittinghoff et al. 2005). In the case of this experiment, environmental covariates

include initial size and planting block. Initial plant size is a potentially important

factor influencing survival, and we incorporated it into the Cox models for each

species as a continuous covariate. As is discussed in Section 3.2.1, plants were not

distributed evenly among plots during the planting process with respect to initial

height, basal diameter, and number of leaves. As a result, initial size may be a

confounding influence on treatment effects if not explicitly included into the

survival models. The influence of initial size on survival is of restoration interest

as well because of its utility in selecting appropriate planting stock to maximize

seedling success, especially in the first year. For the Cox models predicting

survival during the first growing season (2004), we included the three seedling

metrics available at planting: initial height (cm), basal diameter (mm), and number

of leaves. Height and leaves were not used for POFR models because this species

was planted as cuttings. Before model development, data distributions of these

three initial size variables were plotted and a Pearson product-moment correlation

matrix calculated. For the Cox models predicting survival during the second year

(2005), we included covariates for plant height and basal diameter at the end of the

first year; likewise we included plant height and basal diameter at the end of the

second year for the Year 3 models (2006). These size factors were included to

account for potentially confounding effects from both initial conditions and

differential survival and growth in previous years.

The block variable, which refers to the two replicate planting areas in the

experiment, is analogous to a random variable in mixed linear models in which

some variation is assumed to be caused by the variable but its effect is not of

clinical or ecological interest (Underwood 1997). Cox models handle this kind of

variable via a stratified model, in which the baseline hazard rate is allowed to vary

independently for particular groups, but the other linear predictors have an

Methods


equally-proportional effect on the stratified groups. This is a useful feature to

avoid making unwarranted assumptions of proportional hazards for the

stratification variable that could bias the treatment effect estimates. However,

stratified Cox models cannot estimate parameter values for the stratification

variable; therefore this feature is appropriate for variables for which the specific

effect size is not of interest. In this experiment, planting block was included as a

stratified variable for each species’ Cox model.

For each species we developed best explanatory Cox models of plant survival using

an AIC model selection process similar to the growth models described above. As

with the growth models, we initially specified a candidate set of 23 models of

interest, one for each combination of treatment variables and their interactions.

Block was included as a stratification variable. We also generated a ‘best’ model

that resulted from an AIC-optimization of all possible variables, including the three

initial size variables of height, basal diameter and number of leaves at planting. As

with the growth models, we compared the candidate models for each species using

their AIC values, Akaike weights and evidence ratios.

For the best model of each species (those with the highest Akaike weights), we

interpreted the effects of each explanatory variable via the hazard ratio.

Proceeding from equation (5), the hazard ratio for a model predictor is the

exponentiated coefficient estimate for that predictor. For a binary predictor (e.g.,

weed control), the resulting hazard ratio indicates the proportionally greater (for

HR>1) or lesser (for HR<1) mortality risk of one treatment versus another. For

continuous variables such as initial size, the HR indicates the proportional effect on

mortality risk of a one-unit increase in the predictor variable (e.g., an incremental

height increase of 1 cm).

We used the confidence limits for the hazard ratio and the change-in-estimate

method to evaluate the ecological importance of each variable in the final Cox

survival models. The AIC-based model selection method is somewhat liberal as to

parameter inclusion compared to frequentist-based methods (Burnham and

Anderson 2000); therefore it is especially important to evaluate the magnitude and

ecological importance of any particular variable. In a Cox model, the confidence

limits of the hazard ratio are well-suited for this process; these are calculated by

exponentiating the confidence limits of the parameter estimate (Vittinghoff et al.

2005). A hazard ratio equal to 1 for a particular variable (i.e., a parameter estimate =

e) indicates no difference in mortality risk between groups. Therefore, if the hazard

ratio is close to unity and/or the 95% confidence limits of the hazard ratio bracket 1,

one may conclude that a factor has little effect on survival.

The change-in-estimate method is another way to assess the ecological importance

of factors included in the final Cox survival models (Machin et al. 2006). This

Methods


strategy compares an estimate of the hazard ratio from a full model with

environmental covariates to that from a simpler model with only the design factors

of interest (i.e., treatment variables). If the ratio of the two estimates is greater than

C or smaller than 1/C, the change is considered practically or clinically important

and the extra variables are retained in the final Cox model. The constant C reflects

the researcher’s judgment of what constitutes an acceptable level of confoundment

that must be adjusted for; in practice, it is often set at C=1.1 and 1/C = 0.9, or 10% of

a hazard ratio estimate (Maldonado and Greenland 1993, Machin et al. 2006). For

each of the best survival models determined using the AIC-based selection process,

we evaluated the coefficient estimates compared to simpler models to determine

which coefficients were not practically important for plant survival, using a C value

of 1.1.

Results


3 RESULTS

3.13.13.13.1 Site ConditionsSite ConditionsSite ConditionsSite Conditions

3.1.1 Soil Texture and Nutrients

A study of tailing pile texture and volume at the MRR found the tailing piles to be

a heterogeneous mix of cobbles and boulders in a matrix of gravel, sand and silt

(URS 2004b). Minimal stratigraphic differentiation was observed in the tailing

piles, with the exception of a shallow(0.2 m [0.7 ft]) surface layer of large cobles

and boulders (URS 2004b). The depth of the study test pits (between 3 and 8 m [10

and 26 ft] deep) and the condition of the excavated revegetation experimental plots

suggest that restored floodplains, upon which revegetation will be conducted, will

have approximately the same texture as the tailing piles, with the exception of the

coarse surface layer, which will be removed during restoration.

There may be a sufficient volume of fine material recovered during the floodplain

restoration process to improve soil conditions somewhat before revegetation

begins. From the 2004 study results, it was estimated that approximately 5.5% of

the tailings is composed of material less than 2 mm (texturally designated as

medium sand, fine sand, silt and clay). Conceptual restoration plans for the MRR

call for the removal of, on average, 4.6 m (15 ft) of tailings from the floodplain

portion of the site (Stillwater Sciences 2005). Sorting this volume of tailings could

potentially recover enough fine material to cover the restored MRR floodplain with

0.2 to 0.3 m (9 to 10 in) of sand, silt, and clay (G. Strnad, pers. comm.). While this

fine sediment could improve substrate conditions prior to revegetation, it is also

the size material that is most likely to be contaminated with mercury (Stillwater

Sciences 2004). For this reason, material being considered for re-use at the MRR

will need to be batch-tested for mercury (Stillwater Sciences 2004). Only batches of

fine material found to be below or within the range of natural background mercury

levels (50–80 ng/g) for California’s Central Valley (Bouse et al. 1996) should be used

in appropriate areas of the floodplain not prone to frequent river inundation or on

higher terrace surfaces above the 100-year floodplain.

Soil analyses of both experimental blocks indicated that soils were highly disturbed

(low micronutrient and zinc levels), but not necessarily to a level likely to critically

Results


limit plant establishment (M. Buttress, pers. comm.). Table 3-1 summarizes the

results of soil analyses for Block 1 and Block 2.

Table Table Table Table 3333----1111. . . . Soil analytesSoil analytesSoil analytesSoil analytes at each expeat each expeat each expeat each experimental block.rimental block.rimental block.rimental block.

AnalyteAnalyteAnalyteAnalyte Block 1Block 1Block 1Block 1 Block 2Block 2Block 2Block 2

Organic matter (%) 2.6 0.7

Nitrogen (ppm) 5 3

Phosphorus – weak bray (ppm) 13 7

Potassium (% cation saturation) 2.3 1.8

Magnesium (% cation saturation) 35.0 32.6

Calcium (% cation saturation) 56.0 64.5

Sodium (% cation saturation) 0.7 1.1

Zinc (ppm) 0.5 0.3

Boron (ppm) 0.3 0.2

pH 6.6 7.0

Soils at Block 1 had coarser texture and higher levels of most nutrients than Block

2, but in general the ranges of nutrient levels were similar at both blocks (Appendix

A). Soil at Block 1 was considered to have low to medium organic matter while soil

at Block 2 had very low organic matter (M. Buttress, pers. comm.). Both blocks had

very low nitrogen, phosphorus, sodium, and zinc, low potassium; and high

magnesium levels (see Appendix A).

3.1.2 Depth to Groundwater, River Stage, and Pond Stage

Groundwater elevations at the two monitoring wells, river stage, and swale pond

stage in 2004, 2005 and 2006 are presented in Figures 6–8 and summarized in Table

3-2.

Table Table Table Table 3333----2222. . . . Average, minimum, and maximum groAverage, minimum, and maximum groAverage, minimum, and maximum groAverage, minimum, and maximum groundwater, river stage, and undwater, river stage, and undwater, river stage, and undwater, river stage, and swale swale swale swale pond pond pond pond stage stage stage stage elevationselevationselevationselevations over the experiment monitoring periodover the experiment monitoring periodover the experiment monitoring periodover the experiment monitoring period (m (m (m (m NGVD)NGVD)NGVD)NGVD)....

LocationLocationLocationLocation

2004200420042004 2005200520052005 2006200620062006

AverageAverageAverageAverage (ft NGVD)(ft NGVD)(ft NGVD)(ft NGVD)

RangeRangeRangeRange (ft NGVD)(ft NGVD)(ft NGVD)(ft NGVD)





River Stage 84.4

(276.8) 84.2–85.0

(276.2–278.8) 84.7

(277.8) 84.3–85.9

(276.3–281.6) 85.0

(278.6) 84.4–85.6

(276.6–280.7)

Groundwater at Block 1

86.9 (285.0)

86.7–87.1 (284.5–285.7)

87.0 (285.5)

87.0–87.1 (285.3–285.7)

87.1 (285.6)

87.0–87.2 (285.3–285.8)

Groundwater at Block 2

87.7 (287.8)

87.6 –87.9 (287.5–288.5)

87.9 (288.3)

87.9–88.0 (288.2–288.9)

88.0 (288.4)

87.9–88.1 (288.3–288.7)

Swale Pond Stage

87.9 (288.2)

87.7–88.0 (287.8–288.8)

87.9 (288.4)

87.5—88.1 (287.2–289.1)

N/A N/A

Results


River stage was approximately 84.4 m (276.6 ft) during summer and fall baseflows

in both 2004 and 2005, and 84.6 m (277.5 ft) in 2006 (Figures 6–8). Short increases in

river stage were seen in May and October 2004 (to 85.0 m and 84.6 m, respectively)

and corresponded with scheduled flow releases from upstream dams designed to

improve salmon outmigration and emigration conditions. Sustained high stages

from March to May 2005, ranging from 85.9 m (281.6 ft) to 84.9 m (278.5 ft), and

April to July 2006, ranging from 85.6 m to 85.2 m (280.7 ft to 279.3 ft), were results

of flow releases due to higher-than-average winter rain and snow fall.

Groundwater elevations, and therefore depth to groundwater (see Table 2-2),

fluctuated very little over the monitoring period (Figures 6–8). Over three years,

from their lowest to highest recorded levels, groundwater varied no more than 0.4

m (1.3 ft) and 0.4 m (1.2 ft) at Block 1 and Block 2, respectively. The groundwater

table showed little response or relationship to river flow conditions and appears to

be perched 2.5 to 3.4 m (8.0 to 11.0 ft) above summer and fall baseflows (Figures 6–

8).

The monitored swale pond was inundated year-round, as were all of the larger

ponds at the site (Z. Diggory, pers. obs.). Pond stage levels remained relatively

stable over the monitoring period, fluctuating no more than 0.3 m (1.0 ft) in 2004

and 0.6 m (2.0 ft) in 2005 (pond stage was not consistently monitored in 2006), and

showed no response to river flow conditions (Figure 6–8).

3.1.3 Temperature

Temperature was originally monitored, in part, to evaluate potential differences in

temperature between experimental plots. In 2004, B1M was an average of 0.3 °C

warmer than the control area and 0.8 °C warmer than B2M. B2M was an average of

0.5 °C warmer than the control area. In 2005, B1M was 0.9 °C warmer than the

control area. There were no consistent differences between other monitored areas

in 2005. Despite B1M being consistently warmer than other monitored areas, in

general, daily average temperatures were not remarkably different between areas.

For example, on July 16, 2005, one of the hottest days that year, the daily average

temperature ranged from 32.9 to 33.8 °C between areas, a difference of 0.9 °C. The

typical calibration error value specified for the data loggers by the manufacturer is

± 0.2 °C.

Because of the small differences between experimental plots, only temperature

from the control area is presented and discussed below. Daily average, minimum,

and maximum temperatures from 2004–2006 are presented in Figures 9–11.

Monthly average temperatures are summarized in Table 3-3.

Results


Table Table Table Table 3333----3333. . . . Monthly average temperatures at the MRRMonthly average temperatures at the MRRMonthly average temperatures at the MRRMonthly average temperatures at the MRR ((((±1SE±1SE±1SE±1SE) during the experiment ) during the experiment ) during the experiment ) during the experiment ((((°C°C°C°C))))....

2004200420042004 2005200520052005 2006200620062006

JanJanJanJan – – 8.4 (±0.3)

FebFebFebFeb – – 10.0 (±0.6)

MarMarMarMar – – 9.7 (±0.4)

AprAprAprApr 18.6 (±1.0) 15.3 (±0.4) 14.7 (±0.5)

MaMaMaMayyyy 20.6 (±0.4) 19.8 (±0.6) 21.3 (±0.6)

JunJunJunJun 24.3 (±0.4) 22.6 (±0.5) 26.2 (±0.6)

JulJulJulJul 27.2 (±0.3) 29.1 (±0.4) 29.6 (±0.6)

AugAugAugAug 25.6 (±0.6) 27.1 (±0.4) 25.4 (±0.3)

SeptSeptSeptSept – 21.5 (±0.4) 22.4 (±0.5)

OctOctOctOct – 17.2 (±0.4) 15.9 (±0.4)

NovNovNovNov – 11.9 (±0.4 –

DecDecDecDec – 9.3 (±0.6) –

During the experimental monitoring periods, monthly average temperatures were

lowest in January (8.4 ±0.3 °C in 2006) and highest in July (28.6±0.7 °C). At the

control area, daily temperatures ranged from 3.3 °C (on April 18) to 42.5 °C (on

August 11) in 2004; from -3.9 °C (on November 27) to 42.9 °C (on July 14) in 2005;

and from -3.4 °C (on February 16) to 45.9 °C (on July 23) in 2006.

3.23.23.23.2 Plant Size and GrowthPlant Size and GrowthPlant Size and GrowthPlant Size and Growth

3.2.1 Initial Size at Planting

The initial height, basal diameter and number of leaves of each species at the time

of planting varied substantially by planting plot; these differences are summarized

in Table 3-4. Boxplots of initial size means and distributions are presented in

Figure 12.

Results


Table Table Table Table 3333----4444. . . . Initial seedling height, Initial seedling height, Initial seedling height, Initial seedling height, basal basal basal basal diameter and number of leaves at planting time (means diameter and number of leaves at planting time (means diameter and number of leaves at planting time (means diameter and number of leaves at planting time (means ±1SE) ±1SE) ±1SE) ±1SE) by plot (block by plot (block by plot (block by plot (block and and and and relative elevationrelative elevationrelative elevationrelative elevation))))....

VVVVariableariableariableariable SSSSpeciespeciespeciespecies B1HB1HB1HB1H B1MB1MB1MB1M B1LB1LB1LB1L B2HB2HB2HB2H B2MB2MB2MB2M B2LB2LB2LB2L

Height (cm)

ACNE 26.7 (±1.5) 27.5 (±1.5) 31.2 (±1.5) 28.1 (±1.4) 24.9 (±1.3) 28.2 (±1.3)

FRLA 6.3 (±0.3) 4.2 (±0.3) 5.4 (±0.4) 6.4 (±0.4) 5.3 (±0.4) 6.1 (±0.3)

POFR 47.9 (±1.1) 45.1 (±1.1) 46.1 (±1.2) 48.5 (±1.1) 45.6 (±0.8) 44.7 (±1.1)

QULO 42 (±1.4) 45 (±1.2) 42.3 (±1.1) 48.9 (±1.3) 44.9 (±1.3) 45.1 (±1.3)

Basal Diameter (mm)

ACNE 3.1 (±0.1) 3.1 (±0.1) 3.3 (±0.1) 3.1 (±0.1) 2.9 (±0.1) 3.1 (±0.1)

FRLA 2.4 (±0.1) 2.3 (±0.1) 2.1 (±0) 2.4 (±0.1) 2.2 (±0.1) 2.2 (±0.1)

POFR 14.4 (±0.5) 12.8 (±0.4) 14.8 (±0.4) 17 (±0.6) 7.7 (±0.2) 13.2 (±0.4)

QULO 4.6 (±0.2) 5.6 (±0.2) 5.7 (±0.2) 4.8 (±0.1) 5 (±0.1) 4.1 (±0.1)

Number of Leaves

ACNE 5.2 (±0.2) 5.7 (±0.3) 5.6 (±0.2) 4.2 (±0.3) 4.2 (±0.2) 3.9 (±0.3)

FRLA 3.8 (±0.3) 3.2 (±0.3) 3 (±0.4) 2 (±0.2) 1.1 (±0.1) 2.4 (±0.2)

POFR 0 (±0) 0 (±0) 0 (±0) 0 (±0) 0 (±0) 0 (±0)

QULO 28.1 (±1.6) 29.2 (±1.6) 25.1 (±1.4) 39.5 (±1.7) 35 (±1.9) 32.1 (±1.5)

Analysis of variance tests indicate that for most species and variables (initial

height, basal diameter and number of leaves), there were significant differences in

mean values by plot at an α=0.05 level (see Appendix B). This may be a function of

large differences in planting stock between plots, or of large sample sizes (n=60),

which would result in sufficient resolution to detect very small differences among

plots.

Of the significantly different factors analyzed, and follow-up pairwise

comparisons, the strongest differences in initial conditions were:

1. POFR cutting basal diameter differed by plot (F5,345=56.21, p=<0.001). Follow up

pairwise comparisons indicated that cuttings in B2M had smaller basal

diameters (by approximately 6 to 10 mm) than in other plots (Figure 12g and

Appendix B).

2. FRLA seedling leaf number differed by plot (F5,345=14.57, p=<0.001); seedlings in

B2M had fewer leaves (by approximately 1 to 3 leaves) than in other plots

(Figure 12j and Appendix B).

3. ACNE seedling leaf number differed by plot (F5,345=10.38, p=<0.001); seedlings in

B1L had more leaves (by approximately 1 to 2 leaves) than in other plots (Figure

12i and Appendix B).

4. QULO seedling height and leaf number differed by plot (F5,345=3.92, p=0.002 and

F5,345=10.49, p=<0.001); seedlings in B2H were taller (by approximately 5 to 8 cm)

and had more leaves (by approximately 10 to 15 leaves) than in other plots

(Figures 12d, 12e and Appendix B).

The smaller basal diameters of POFR cuttings in plot B2M (7.7±0.2 mm) relative to

other plots (Table 3-4 and Figure 12), in particular, is notable. In this plot, FRLA

seedlings had the lowest mean number of leaves at planting (1.1±0.1) as well. B2M

was the last experimental plot to be planted and it is likely that, despite attempts to

Results


randomize cuttings and container stock, field crews were left with the smallest cuttings

and less than ideal FRLA container stock at the end of the planting effort.

3.2.2 Plant Growth Timing

All species began to grow new leaves approximately ten weeks after planting (Figure 13;

see Appendix C for week dates). Leaf-out for POFR began sooner than the other species,

and POFR trees had double the mean number of leaves at the end of the first growing

season compared to QULO, and four times that of ACNE and FRLA. Because the four

species have different leaf morphologies (e.g., ACNE and FRLA have compound leaves),

raw leaf numbers do not give good indications of leaf area differences between species.

In contrast to the other species, POFR continued to accumulate leaves at a rapid rate

throughout the first growing season. QULO leaf accumulation slowed abruptly after the

18th week (mid-August; see Appendix C), whereas the other three species continued

accumulating leaves through late October. As discussed in Section 2.2.5 (Methods), the

number of leaves was not tracked in subsequent years.

Relative timing of leaf-out, height growth and basal diameter growth varied by species,

but three species showed consistent timing patterns between the first and second years

(Figure 14). Early in the growing season, most tree species allocate biomass first to

height, then to basal diameter increment (Oliver and Larson 1996). This pattern is

believed to be an adaptation to competition for light as plants race to the top of the

canopy following bud break. Height growth preceded basal diameter increment for

ACNE, FRLA and POFR in the first growing season (2004), and for ACNE and FRLA in

the second. Height and basal diameter growth progressed apace for QULO in both

years and height growth trailed basal diameter growth for POFR in the second year.

Leaf-out trailed height growth in the first year for all species except FRLA, for which

both processes occurred simultaneously.

3.2.3 Patterns in Plant Growth between Treatment Groups

Over the course of the experiment, growth was greatest for POFR, followed by ACNE,

with slower growth rates for FRLA and QULO (Table 3-5). In the first year, POFR

seedlings had the greatest increase in height, followed by ACNE, QULO, and FRLA. In

the second year, ACNE growth was greater than that of the other species. After 3 years,

mean height for POFR was greatest. 38% of ACNE alive at the end of the experiment

experienced crown dieback (Stillwater Sciences, unpublished data), resulting in lower live

crown heights compared to Year 2. Basal diameter growth over the course of the

experiment was greatest for POFR, followed by ACNE, QULO, and FRLA.

Results


Table Table Table Table 3333----5555. . . . EndEndEndEnd----ofofofof----year height and year height and year height and year height and basal basal basal basal diameter (mean±1 SE) for all species.diameter (mean±1 SE) for all species.diameter (mean±1 SE) for all species.diameter (mean±1 SE) for all species.

Growth MeasureGrowth MeasureGrowth MeasureGrowth Measure YearYearYearYear ACNEACNEACNEACNE FRLAFRLAFRLAFRLA POFRPOFRPOFRPOFR QULOQULOQULOQULO

Height (cm)

Year 1 87.0±1.5 46.8±1.8 125.2±3.4 85.7±2.0

Year 2 193.2±3.5 95.3±3.2 183.4±5.5 113.3±2.

Year 3 177.0±8.0 118.1±3.7 226.2±9.1 120.2±2.6

Basal Diameter (mm)

Year 1 14.0±0.4 6.2±0.2 18.7±0.4 9.0±0.1

Year 2 24.5±0.5 13.2±0.4 29.4±0.9 14.3±0.2

Year 3 29.1±1.1 17.7±0.6 42.0±2.1 18.7±0.5

Growth patterns did not vary systematically by treatment factors, but some factors were

important for individual species. Figures 15 and 16 show height and basal diameter

growth over the 3-year experiment by irrigation treatments, and Figures 17 and 18 show

height and basal diameter growth by elevation level. No positive irrigation effect (i.e.,

higher growth with increased irrigation duration) is apparent until the third year, and

only then for ACNE basal diameter. Differences in growth between elevation levels are

more apparent (Figures 17 and 18). In general, growth is greater for stems on lower

surfaces compared to upper surfaces. However, in many cases the differences are not

proportional between the treatments, and the middle levels had either higher or lower

growth than the other two levels. Differences in growth between weed control

treatments were not shown because they were minimal.

In contrast to the treatment effects, the largest growth influence was due to planting

plot, as evidenced in large differences between combinations of block and elevation level

(with no consistent elevation trend). When growth patterns are compared by treatment

and planting block, differences are larger due to block than to either elevation or

irrigation. Figure 19 shows final plant size (2006) by irrigation and block, and Figure 20

shows final plant size by elevation and block. The only consistently positive effect of

increased irrigation duration occurred for QULO, and the only increasingly negative

effect of depth to groundwater was for FRLA.

3.2.4 ANCOVA Models of 3-Year Basal Diameter Increment Growth

The ANCOVA basal diameter growth models confirmed that planting location had the

greatest influence on growth throughout the experiment. Table 3-6 shows the top five

candidate models for each species. Elevation and/or block were the most common

factors in the top model for all species. Elevation was the best explanatory factor for

FRLA, and block was best for POFR. For ACNE, block, elevation, and initial basal

diameter were important, with an interaction between the latter two variables. For

QULO, all treatment factors were included in the best model, with initial basal diameter

and interactions between block and elevation, and weed control and irrigation as well.

For QULO especially, absolute values of growth differences were not great for any factor

(Figures 15–20).

Results


Table Table Table Table 3333----6666. . . . Top five candidate ANCOVA models of factor influences on Top five candidate ANCOVA models of factor influences on Top five candidate ANCOVA models of factor influences on Top five candidate ANCOVA models of factor influences on basal diameterbasal diameterbasal diameterbasal diameter growth igrowth igrowth igrowth increment.ncrement.ncrement.ncrement.

model deviance K AIC delta weights ER parameters

ACNEACNEACNEACNE

1 1462.02 5 1472.36 0.00 1.00 1.00 (Intercept)+elev+iDiam+block+elev:iDiam

2 1483.80 4 1492.03 19.67 0.00 0.00 (Intercept)+elev+weed+elev:weed

3 1483.68 5 1494.02 21.66 0.00 0.00 (Intercept)+elev+weed+irrigate+elev:weed

4 1490.00 2 1494.07 21.70 0.00 0.00 (Intercept)+elev

5 1490.24 2 1494.31 21.95 0.00 0.00 (Intercept)+irrigate

FRLAFRLAFRLAFRLA

1 1165.49 2 1169.56 0.00 0.20 1.00 (Intercept)+elev

2 1164.29 3 1170.43 0.87 0.13 0.65 (Intercept)+elev+weed

3 1162.84 4 1171.08 1.52 0.09 0.47 (Intercept)+elev+weed+elev:weed

4 1165.48 3 1171.63 2.07 0.07 0.36 (Intercept)+elev+irrigate

5 1167.66 2 1171.73 2.17 0.07 0.34 (Intercept)+weed

POFRPOFRPOFRPOFR

1 1274.29 2 1278.38 0.00 0.33 1.00 (Intercept)+block

2 1276.25 2 1280.34 1.96 0.12 0.38 (Intercept)+elev


4 1277.26 2 1281.34 2.96 0.07 0.23 (Intercept)+weed

5 1275.73 3 1281.91 3.53 0.06 0.17 (Intercept)+elev+weed

QULOQULOQULOQULO

1 1811.18 8 1827.70 0.00 1.00 1.00 (Intercept)+elev+weed+irrigate+block+iDiam+block:elev+weed:irrigate

2 1872.11 4 1880.26 52.56 0.00 0.00 (Intercept)+weed+irrigate+weed:irrigate

3 1871.40 5 1881.61 53.92 0.00 0.00 (Intercept)+elev+weed+irrigate+weed:irrigate


5 1878.92 2 1882.96 55.27 0.00 0.00 (Intercept)+elev

The best model for ACNE and QULO were much better than any other model (Table

3-6). Parameter estimates for the coefficients of each species’ best model are shown in

Table 3-7. For the top models Akaike weights and evidence ratios were close to unity

for both species, indicating an overwhelmingly better fits than other candidate models

(Table 3-6). For FRLA, the top model including only the elevation term was 20% likely

given the candidate set (Akaike weight), and about 35% better than the next best model

(evidence ratio). Using the rule of thumb of a two-time ER, the top three FRLA models

are reasonable approximations. Elevation is the most common factor in all these models;

the elevation parameter estimate in the best model is negative, indicating that as depth

to groundwater increased, growth decreased (Table 3-7, Figure 20). For POFR, the best

model containing only the block variable was 33% likely given the candidate set and

67% better than the next best model, which had elevation as the sole explanatory factor.

Because of interaction terms, interpretation of the trend in the continuous variables (i.e.,

the sign of the parameter estimates for elevation, irrigation duration, and initial basal

diameter) is problematic. For QULO, the estimate for initial basal diameter is negative,

indicating that growth was less for larger plants (Table 3-7). This is counter to our

initial expectations. However, the biological importance of this relationship is suspect,

Results


and the standard error of the slope estimate is large, approximately half of the parameter

value.

Table Table Table Table 3333----7777. . . . Parameter estimates for the best ANCOVA models for each species.Parameter estimates for the best ANCOVA models for each species.Parameter estimates for the best ANCOVA models for each species.Parameter estimates for the best ANCOVA models for each species.

SpeciesSpeciesSpeciesSpecies ParParParParameterameterameterameter ValueValueValueValue SESESESE

ACNE

(Intercept) 8.86 11.31

elev 13.31 4.73

iDiam 1.59 3.07

block 6.16 2.23

elev:iDiam -3.90 1.45

FRLA (Intercept) 17.18 1.09

elev -0.83 0.44

POFR (Intercept) 38.65 6.50

block -7.49 4.05

QULO

(Intercept) 1.68 3.05

elev 3.89 0.90

weed -2.51 0.92

irrigate 0.53 0.42

block 8.83 1.37

iDiam -0.54 0.25

block:elev -2.62 0.54

weed:irrigate 1.24 0.42

3.33.33.33.3 Water PotentialWater PotentialWater PotentialWater Potential

Pressures in plant xylem can vary markedly during the day. When plants are not

water stressed, pressures are generally close to atmospheric levels (standard

atmospheric pressure is 0.1 MPa) before sun-up when transpiration is not

occurring. After sun-up, transpiration begins and the pressure falls in the xylem,

frequently reaching tensions of -1 to -2 MPa (Boyer 1995). Plants have a range of

different physiological mechanisms to contend with water stress, so xylem

pressures also vary greatly depending on species. With the exception of QULO, the

species in the experiment are not considered drought tolerant, so water potentials

less than approx. -1.9 are indicative of water stressed plants.

Water potentials and sample sizes of plants in B1L and B1H are summarized in

Table 3-8 and Table 3-9. Figures 21 and 22 also present the results of the water

potential monitoring by treatment. In interpreting the water potential monitoring

results it is important to note that in June 2005 the irrigation treatment was in the

initial stages of taking effect and no mortality had yet occurred in 2005, so the

sampled plants represent a smaller percentage of the total plants alive in Block 1.

By September 2005 high mortality had occurred in Block 1 as a result of

discontinued irrigation (see Section 3.4.2). The plants sampled in September 2005

Results


were thus a much larger percentage of the total plants alive, indicating that they

were the heartier, better established individuals or, in the case of un-irrigated

FRLA, the only individuals left alive to sample (Table 3-9).

Pre-dawn water potential values were influenced by species, time of year, relative

elevation above groundwater, and month-relative elevation above groundwater

interactions (Table 3-10). Afternoon water potential values were significantly

influenced by species only (Table 3-10). Post-hoc pair-wise comparisons of

simultaneous 95% confidence intervals indicate that, with both pre-dawn and

afternoon values, POFR was the species driving the significant differences. In

general, POFR had higher water potentials (i.e., better water status) than the other

species. This difference and high pre-dawn values regardless of relative elevation

or irrigation treatment level suggest that POFR plantings (at least those that

survived to September 2005) had the deepest rooting system and had reached a

reliable groundwater source.

While irrigation and relative elevation treatment did not have significant effects on

water potential values, results were suggestive of an effect. ACNE and FRLA

demonstrated the predicted directional response to relative elevation and irrigation

treatment (i.e., higher potentials/less water stress in the low relative elevation plot

and when irrigated than in the high relative elevation plot and when not irrigated)

in both June and September. QULO demonstrated no response to relative elevation

or irrigation in June and the predicted responses in September (Figures 21 and 22).

Results


Table Table Table Table 3333----8888. . . . Average water potentials (MPa) (Average water potentials (MPa) (Average water potentials (MPa) (Average water potentials (MPa) (±1SE)±1SE)±1SE)±1SE)....

LocationLocationLocationLocation Sampling TimeSampling TimeSampling TimeSampling Time

ACNEACNEACNEACNE FRLAFRLAFRLAFRLA POFRPOFRPOFRPOFR QULOQULOQULOQULO

Not IrrigatedNot IrrigatedNot IrrigatedNot Irrigated IrrigatedIrrigatedIrrigatedIrrigated Not IrrigatedNot IrrigatedNot IrrigatedNot Irrigated IrrigatedIrrigatedIrrigatedIrrigated Not IrrigatedNot IrrigatedNot IrrigatedNot Irrigated IrrigatedIrrigatedIrrigatedIrrigated Not IrrigatedNot IrrigatedNot IrrigatedNot Irrigated IrrigatedIrrigatedIrrigatedIrrigated

B1L June pre-dawn -0.55 (±0.14) -0.5 (±0.07) -0.55 (±0.22) -0.4 (±0.06) -0.41 (±0.05) -0.37 (±0.03) -0.69 (±0.02) -0.55 (±0.17)

afternoon -1.97 (±0.09) -1.59 (±0.18) -1.93 (±0.17) -1.93 (±0.19) -1.71 (±0.05) -1.54 (±0.08) -2.34 (±0.06) -2.15 (±0.21)

B1H June pre-dawn -0.73 (±0.2) -0.73 (±0.03) -1.02 (±0) -0.62 (±0.04) -0.54 (±0.04) -0.41 (±0.05) -0.58 (±0.08) -0.84 (±0.04)

afternoon -1.83 (±0.09) -1.89 (±0.16) -2.47 (±0) -2.05 (±0.1) -1.5 (±0.13) -1.42 (±0.06) -2.15 (±0.08) -2.33 (±0.19)

B1L Sept pre-dawn -1.1 (±0.29) -1.01 (±0.1) -1.03 (±0.41) -1.37 (±0.2) -0.79 (±0.09) -0.72 (±0.1) -1.12 (±1.1) -1.21 (±0.2)

afternoon -2.17 (±0.47) -2.25 (±0.23) -2.86 (±0) -1.93 (±0.27) -1.81 (±0.26) -1.76 (±0.11) -2.84 (±0.76) -2.63 (±0.35)

B1H Sept pre-dawn -1.14 (±0) -0.93 (±0.14) N/A -1.08 (±0.13) -0.7 (±0.15) -0.66 (±0.1) -3.22 (±0.06) -1.22 (±0.11)

afternoon -2.6 (±0.17) -1.94 (±0.2) -3.79 (±0.17) -2.25 (±0.41) -1.28 (±0.2) -1.33 (±0.13) -3.57 (±0.26) -3.02 (±0.22)

Table Table Table Table 3333----9999. . . . Water potential sampWater potential sampWater potential sampWater potential sample sizes.le sizes.le sizes.le sizes.

LocationLocationLocationLocation Sampling TimeSampling TimeSampling TimeSampling Time

ACNEACNEACNEACNE FRLAFRLAFRLAFRLA POFRPOFRPOFRPOFR QULOQULOQULOQULO

Not IrrigatedNot IrrigatedNot IrrigatedNot Irrigated IrrigatedIrrigatedIrrigatedIrrigated Not IrrigatedNot IrrigatedNot IrrigatedNot Irrigated IrrigatedIrrigatedIrrigatedIrrigated Not IrrigatedNot IrrigatedNot IrrigatedNot Irrigated IrrigatedIrrigatedIrrigatedIrrigated Not IrrigatedNot IrrigatedNot IrrigatedNot Irrigated IrrigatedIrrigatedIrrigatedIrrigated

B1L June pre-dawn 5 5 4 5 5 5 5 5

afternoon 5 5 5 5 5 5 5 5

B1H June pre-dawn 3 5 1 5 5 5 3 5

afternoon 4 5 1 5 5 5 3 5

B1L Sept pre-dawn 5 5 2 5 4 5 3 5

afternoon 5 5 1 5 4 5 6 5

B1H Sept pre-dawn 2 7 0 5 4 7 5 5

afternoon 2 7 2 5 4 7 5 5

Results


Table Table Table Table 3333----10101010. . . . ANOVA models for preANOVA models for preANOVA models for preANOVA models for pre----dawn and afternoon watedawn and afternoon watedawn and afternoon watedawn and afternoon water potential.r potential.r potential.r potential.

Model FactorModel FactorModel FactorModel Factor DDDDegrees of egrees of egrees of egrees of ffffreedomreedomreedomreedom

SSSSum of um of um of um of squaressquaressquaressquares MMMMean squareean squareean squareean square FFFF----RatioRatioRatioRatio Pr(F)Pr(F)Pr(F)Pr(F)

PrePrePrePre----DawnDawnDawnDawn

Species 3 0.615121 0.201707 16.3696 <0.001

Month 1 1.266324 1.266324 102.7689 <0.001

Elevation 1 0.060560 0.060560 4.9148 0.028

Month:Elevation 1 0.083874 0.083874 6.8068 0.010

Residuals 133 1.638832 0.012322

AfternoonAfternoonAfternoonAfternoon

Species 3 0.318675 0.106225 12.27087 <0.001

Residuals 142 1.229250 0.008657

3.43.43.43.4 Plant SurvivalPlant SurvivalPlant SurvivalPlant Survival

3.4.1 Survival and Hazard Patterns

Plant mortality over the course of the experiment varied substantially between

species and was strongly influenced by both the treatment variables and the

environmental covariates for initial size. In general, mortality was influenced most

strongly by initial planting size in the first year, by irrigation treatment in the

second year, and by relative elevation in the third year. It is likely that this

temporal change in the primary drivers of mortality is the result of the interaction

of the treatment regimes with the plants’ growth patterns and changing

vulnerability profiles over time. Figure 23 shows plant mortality over the course of

the experiment for the different irrigation groups, and Table 3-11 summarizes

survival at the end of each growing season for those groups. Figure 24 shows the

empirical instantaneous hazard rate (deaths individuals-at-risk-1 time-1) among

irrigation treatments, and Figure 25 shows the hazard rate among elevation

treatments. The most consistent contrast in hazard rate in the second year was

between the two irrigation treatments (Figure 24). In the third year (2006), the

elevation group hazard plots show a higher mortality rate for ACNE in the high

plots, and for POFR and QULO in the high and medium elevation plots (Table

3-11).

Results


Table Table Table Table 3333----11111111. . . . EndEndEndEnd----ofofofof----year survival year survival year survival year survival ((((±1SE)±1SE)±1SE)±1SE) by species and irrigation treatment for all three years.by species and irrigation treatment for all three years.by species and irrigation treatment for all three years.by species and irrigation treatment for all three years.****

Survival PeriodSurvival PeriodSurvival PeriodSurvival Period TreatmentTreatmentTreatmentTreatment ACNEACNEACNEACNE FRLAFRLAFRLAFRLA POFRPOFRPOFRPOFR QULOQULOQULOQULO

Year 1 (Apr—Oct 2004)

All irrigated 1 year 0.96±0.01 0.78±0.03 0.58±0.04 0.96±0.01


Irrigated 1 year 0.65±0.07 0.44±0.12 0.77±0.07 0.83±0.04

Irrigated 2 years 0.96±0.01 0.86±0.03 0.93±0.02 0.99±0.01


Irrigated 1 year 0.67±0.08 0.92±0.05 0.69±0.09 0.93±0.03

Irrigated 2 years 0.45±0.1 0.77±0.06 0.76±0.07 0.78±0.05

Irrigated 3 years 0.69±0.06 0.84±0.05 0.78±0.07 0.92±0.03

Final Survival (Apr 2004—Oct 2006)

Irrigated 1 year 0.42 0.32 0.31 0.74

Irrigated 2 years 0.41 0.52 0.41 0.74

Irrigated 3 years 0.64 0.56 0.42 0.87

* Values are Kaplan-Meier survival estimates. Year 1 values represent proportion of original cohort alive at the

end of the first growing season (October 2004). Year 2 values represent proportion alive at the end of the second

growing season (October 2005) of all seedlings that survived the first year. Year 3 values represent proportion

alive at the end of the third growing season (October 2006) of all seedlings that survived the second year. Final

survival represents the survival of the original cohort over the three years.

3.4.2 First-Year Survival (2004)

Survival in the first year was greatest for QULO and ACNE (approx. 96%) and

averaged 58% for POFR and 78% for FRLA (Table 3-11; Figure 23). For ACNE,

FRLA, and QULO, the mortality risk was highest at approximately week 6 (late

May 2004; see Appendix C for week dates); the POFR hazard rate peaked a month

later in late June (Figure 24).

Among all blocks and relative elevation levels, survival was relatively uniform for

ACNE and QULO, but ranged greatly for POFR and FRLA. B2M had the lowest

survival for both POFR (13%) and FRLA (53%). The hazard rate was highest for

POFR in B2M from the beginning of the experiment and peaked in week 11 (week

beginning July 1, 2004). The hazard rate was highest for FRLA in B2H and B2M. In

most cases the largest proportion of seedling mortality occurred by week 15 (week

beginning July 29, 2004); most plants alive at that time survived through the end of

the season.

In several instances, high mortality among specific plots was correlated with

systematic differences in planting stock size. For example, the POFR plants in B2M

suffered much higher mortality than other plots; these cuttings also had much

smaller mean initial basal diameter than those in other plots (Figure 12). For this

reason, the Cox proportional hazard models were necessary to isolate the treatment

effects from confounding environmental variables. For the first-year Cox models,

we included all three initial factors (except for POFR, for which only basal diameter

was appropriate). The Pearson correlation matrix for the three explanatory

variables showed low to moderate correlation among factors; no value was >0.5

(Table 3-12). Because multicolinearity of all factors was fairly low and the relative

Results


strength of each factor on survival was unknown, all factors were considered as

covariates in the survival models.

Table Table Table Table 3333----12121212. . . . Pearson Correlation Matrix for the three explanatory variables: Pearson Correlation Matrix for the three explanatory variables: Pearson Correlation Matrix for the three explanatory variables: Pearson Correlation Matrix for the three explanatory variables: initial height, initial height, initial height, initial height, basal basal basal basal diameter, and number of leaves.diameter, and number of leaves.diameter, and number of leaves.diameter, and number of leaves.

SpeciesSpeciesSpeciesSpecies FactorFactorFactorFactor Basal Basal Basal Basal DiameterDiameterDiameterDiameter No. of LeavesNo. of LeavesNo. of LeavesNo. of Leaves

ACNE (n=359)

height 0.44 -0.02

basal diameter – 0.04

FRLA (n=359)

height 0.24 0.07


POFR (n=360)

height 0.24 -0.08

basal diameter – -0.03

QULO (n=360)

height 0.27 0.37


Table 3-13 lists the top five candidate Cox models for each species using the AIC-

based selection criteria. For all species, the best model that included initial size

covariates were overwhelmingly better at explaining the data than the candidate

set of treatment models. The better fit is evident in the Akaike weights (wi), which

approximated 1 for the best models for ACNE, FRLA and POFR, indicating that

they are ~100% likely given the full candidate set. The best QULO model was over

twice as likely as the next best model, as indicated by the evidence ratios.

Table Table Table Table 3333----13131313. . . . Year 1 top five candidate Cox models for each species.Year 1 top five candidate Cox models for each species.Year 1 top five candidate Cox models for each species.Year 1 top five candidate Cox models for each species.****

ModelModelModelModel DevianceDevianceDevianceDevianceaaaa KKKKaaaa AICAICAICAIC aaaa DeltaDeltaDeltaDeltaaaaa wwww i i i i aaaa ERERERERaaaa ParametersParametersParametersParameters

ACNEACNEACNEACNE

1111 115.39115.39115.39115.39 1111 117.39117.39117.39117.39 0.000.000.000.00 1.001.001.001.00 1.0001.0001.0001.000 iDiamiDiamiDiamiDiambbbb

2 134.58 1 136.58 19.19 0.00 0.000 elev

3 134.53 1 136.53 19.14 0.00 0.000 weed

4 134.50 2 138.50 21.11 0.00 0.000 elev+weed

5 134.46 3 140.46 23.07 0.00 0.000 elev+weed+elev:weed

FRLAFRLAFRLAFRLA

1111 753.39753.39753.39753.39 5555 763.39763.39763.39763.39 0.000.000.000.00 1.001.001.001.00 1.0001.0001.0001.000 elev+iDiamelev+iDiamelev+iDiamelev+iDiambbbb+iLvs+iLvs+iLvs+iLvsbbbb+iHt+iHt+iHt+iHtbbbb++++ strata(block):elevstrata(block):elevstrata(block):elevstrata(block):elev bbbb

2 796.78 1 798.78 35.39 0.00 0.000 elev

3 798.76 1 800.76 37.37 0.00 0.000 weed

4 796.72 2 800.72 37.34 0.00 0.000 elev+weed


POFRPOFRPOFRPOFR

1111 1389.851389.851389.851389.85 3333 1395.851395.851395.851395.85 0.000.000.000.00 0.850.850.850.85 1.0001.0001.0001.000 elev+iDiamelev+iDiamelev+iDiamelev+iDiambbbb+strata(block):iD+strata(block):iD+strata(block):iD+strata(block):iDiamiamiamiambbbb

2 1488.18 1 1490.18 94.34 0.00 0.000 elev

3 1488.14 1 1490.14 94.30 0.00 0.000 weed

4 1487.55 2 1491.55 95.70 0.00 0.000 elev+weed

Results




QULOQULOQULOQULO

1111 142.17142.17142.17142.17 1111 144.17144.17144.17144.17 0.000.000.000.00 0.440.440.440.44 1.0001.0001.0001.000 weedweedweedweed

2 142.17 1 144.17 0.00 0.44 1.000 weed

3 143.96 1 145.96 1.78 0.18 0.410 elev

4 141.21 2 145.21 1.03 0.26 0.596 elev+weed


* The best model for each species is indicated in bold font. a Deviance is the residual model deviance; K is the number of model parameters estimated; AIC is Akaike

Information Criteria; Delta is the AIC difference; wi is Akaike weights; and ER is the evidence ratio. See text for

explanation of these values. b Initial size measures: iDiam=initial basal diameter; iHt=initial height; iLvs=initial number of leaves. Strata(block)

is the inclusion of the stratified block variable in the Cox model.

Table 3-14 shows the parameter values, hazard ratios, and HR confidence limits for

all parameters included in the best model for each species. For each estimated

model parameter, the corresponding hazard ratio represents the proportional

difference in mortality for two seedlings that differed in treatment groups for a

binary variable or a one-unit difference in increment for a continuous variable.

Strong effects are indicated by hazard ratios much less or much greater than one,

and the precision of the effect size is noted by the confidence intervals. Intervals

that contain one indicate indeterminate or no effect on mortality by a variable.

Table Table Table Table 3333----14141414. . . . Parameter estimates, hazard ratio (HR), and HR confidence limits for the best Year 1 Cox survival modelParameter estimates, hazard ratio (HR), and HR confidence limits for the best Year 1 Cox survival modelParameter estimates, hazard ratio (HR), and HR confidence limits for the best Year 1 Cox survival modelParameter estimates, hazard ratio (HR), and HR confidence limits for the best Year 1 Cox survival model for each species.for each species.for each species.for each species.****

SpeciesSpeciesSpeciesSpecies ParameterParameterParameterParameter CoefficientCoefficientCoefficientCoefficient EstimateEstimateEstimateEstimate Coefficient SECoefficient SECoefficient SECoefficient SE Hazard RatioHazard RatioHazard RatioHazard Ratio

Lower 95% Lower 95% Lower 95% Lower 95% CLCLCLCL

Upper 95% Upper 95% Upper 95% Upper 95% CLCLCLCL

ACNEACNEACNEACNE iDiamiDiamiDiamiDiam ----1.501.501.501.50 0.520.520.520.52 0.220.220.220.22 0.080.080.080.08 0.620.620.620.62

FRLAFRLAFRLAFRLA

elev 0.10 0.10 1.11 0.91 1.36

iDiam -0.51 0.32 0.60 0.32 1.13

iLvsiLvsiLvsiLvs ----0.280.280.280.28 0.080.080.080.08 0.760.760.760.76 0.650.650.650.65 0.890.890.890.89

iHtiHtiHtiHt ----0.130.130.130.13 0.050.050.050.05 0.880.880.880.88 0.800.800.800.80 0.970.970.970.97

strata(block):elev 0.20 0.10 1.23 1.01 1.50

POFRPOFRPOFRPOFR

elevelevelevelev 0.170.170.170.17 0.070.070.070.07 1.181.181.181.18 1.021.021.021.02 1.361.361.361.36

iDiamiDiamiDiamiDiam ----0.220.220.220.22 0.030.030.030.03 0.810.810.810.81 0.760.760.760.76 0.850.850.850.85

strata(block):iDiam -0.06 0.03 0.95 0.90 1.00

QULOQULOQULOQULO weed -0.47 0.30 0.63 0.35 1.12

* Parameters that have a substantial effect on plant mortality are indicated in bold font.

The best Cox survival models for the 2004 growing season are dominated by initial

size parameters, indicating that this boundary condition was the most important

factor controlling mortality risk during the first year. For ACNE, a 1 mm increment

in basal diameter corresponds to a hazard ratio of 0.22. This means that a seedling

of basal diameter 2 mm will be 4-5 times as likely to die in the first year as one with

Results


a basal diameter of 3 mm. Though the survival probability will still be relatively

high (>80%) for the smaller plants, a five-fold increase in the risk of mortality may

translate into substantial losses in a horticultural restoration scenario, depending

on the size distribution of planting stock. For POFR, initial basal diameter was also

the

most important factor in the first year, with a hazard ratio of 0.81. Though this

represents only a 19% difference in mortality between two seedlings with a 1 mm

basal diameter differential, the POFR cuttings varied greatly in basal diameter and

suffered large losses the first year among the smaller size classes (Figure 23). The

best POFR model also included elevation, with a hazard ratio of 1.18, indicating

that plants increase their mortality risk 18% for every additional meter they are

planted above the water table. An interaction term between the stratified block

variable and initial basal diameter is also included in the model but does not have a

large effect on mortality (HR=0.95).

For FRLA seedlings, an incremental increase in stem height of 1 cm results in a 12%

lower probability of mortality, whereas an individual with one more leaf than

another seedling will have a 24% lower mortality risk in the first year (recall that

FRLA seedlings initially had only 1 to 4 leaves upon planting). Other factors in

that model were not important. For QULO, weed control was retained as the sole

predictor in the final Cox model, with a hazard ratio of 0.63 indicating a 47% lower

mortality risk in the weed control group over controls. However, the confidence

limits for this variable contain 1, indicating that treatment difference are not

detectable at the 0.05 probability level. Therefore for this species no treatment

factor or environmental covariate was a good predictor of mortality in the first

year.

3.4.3 Second-Year Survival (2005)

Seedling survival in Year 2 was analyzed separately from the first year because of

the imposition of a new treatment factor. Beginning in the early spring, the drip

irrigation system was removed from a third of the plants that had survived the first

year in order to test the effects of continued irrigation on plant survival and

growth. This factor proved to be the largest determinant of mortality in the second

year; in fact unirrigated FRLA survival dropped below 50% (Figure 26). In contrast,

differences in planting elevation and weed control had little effect (Figure 27). For

the plants that were irrigated for two years, survival in 2005 ranged among species

86–99%; survival within this group was higher than in the unirrigated group by

31% for ACNE, 42% for FRLA, and 16% for both POFR and QULO (Table 14).

Hazard plots indicate that mortality peaked between weeks 65–70 (July 14, 2005–

August 18, 2005), and the greatest differences were between irrigated and

unirrigated groups (Table 3-11, Figures 24 and 25). Unlike in Year 1, mortality in

Year 2 was greater in the second half of the growing season and did not subside

following the death of vulnerable plants. Mortality was also higher for all species

Results


in Block 1, the experimental block closest to the river, especially for seedlings that

were not irrigated.

For all species the best Year 2 Cox survival models contained terms for plant size,

and these models were overwhelmingly better than next best models from the

treatment candidate set (Table 3-15). Irrigation was a factor in all the species’ best

models and had the strongest influence of any parameter for FRLA and POFR

(Table 3-15, Figure 26). The mortality risk for FRLA plants irrigated both years was

nearly half (49%) of that for those not irrigated a second year. For POFR plants, the

decrease in mortality risk for plants irrigated both years was 44% of that for plants

not irrigated a second year. For ACNE, the factors with substantial effect on

mortality risk were interactions between block, elevation, irrigation and weed

control, making straightforward interpretation difficult. For QULO, the only

parameter with non-overlapping confidence limits was first-year height. A 1-cm

height increment confers an 8% decrease in mortality risk; this can be a

considerable margin considering the variation in growth between plants (Figures

15 and 17).



ACNEACNEACNEACNE

1111 385.48385.48385.48385.48 8888 401.91401.91401.91401.91 0.000.000.000.00 1.001.001.001.00 1111.00.00.00.00 elev+weed+irr+ht1elev+weed+irr+ht1elev+weed+irr+ht1elev+weed+irr+ht1bbbb+strata(block):elev++strata(block):elev++strata(block):elev++strata(block):elev+ elev:irr+elev:weed+weed:ht1elev:irr+elev:weed+weed:ht1elev:irr+elev:weed+weed:ht1elev:irr+elev:weed+weed:ht1bbbb

2 420.78 4 428.90 26.99 0.00 0.00 elev+weed+irr+elev:irr

3 417.33 6 429.57 27.66 0.00 0.00 elev+weed+irr+elev:weed+elev:irr+ elev:weed:irr

4 415.71 7 430.04 28.13 0.00 0.00 elev+weed+irr+elev:weed+weed:irr+ elevirrweedN+elevirrweedY

5 415.71 7 430.04 28.13 0.00 0.00 elev+weed+irr+elev:weed+elev:irr+ weed:irr+elev:weed:irr

FRLAFRLAFRLAFRLA

1111 604.32604.32604.32604.32 5555 614.54614.54614.54614.54 0.000.000.000.00 1.001.001.001.00 1.001.001.001.00 ht1ht1ht1ht1bbbb+irr+diam1+irr+diam1+irr+diam1+irr+diam1bbbb+irr:ht1+irr:ht1+irr:ht1+irr:ht1bbbb+strata(block):ht1+strata(block):ht1+strata(block):ht1+strata(block):ht1bbbb

2 688.92 1 690.93 76.39 0.00 0.00 irr

3 688.13 2 692.18 77.64 0.00 0.00 elev+irr

4 688.82 2 692.86 78.32 0.00 0.00 weed+irr

5 686.97 3 693.05 78.52 0.00 0.00 elev+irr+elev:irr

POFRPOFRPOFRPOFR

1111 191.11191.11191.11191.11 7777 205.67205.67205.67205.67 0.000.000.000.00 1.001.001.001.00 1.001.001.001.00 elev+weed+ht1elev+weed+ht1elev+weed+ht1elev+weed+ht1bbbb+irr+diam1+irr+diam1+irr+diam1+irr+diam1bbbb+elev:weed++elev:weed++elev:weed++elev:weed+ ht1:diam1ht1:diam1ht1:diam1ht1:diam1bbbb

2 217.52 4 225.71 20.04 0.00 0.00 elev+weed+irr+elev:weed

3 223.84 1 225.85 20.18 0.00 0.00 irr

4 211.97 7 226.53 20.86 0.00 0.00 elev+weed+irr+elev:weed+weed:irr+ elevirrweedN+elevirrweedY

5 211.97 7 226.53 20.86 0.00 0.00 elev+weed+irr+elev:weed+elev:irr+weed:irr+ elev:weed:irr

Results



QULOQULOQULOQULO

1111 166.73166.73166.73166.73 5555 176.91176.91176.91176.91 0.000.000.000.00 1.001.001.001.00 1.001.001.001.00 elev+irr+ht1elev+irr+ht1elev+irr+ht1elev+irr+ht1bbbb+elev:irr+elev:ht1+elev:irr+elev:ht1+elev:irr+elev:ht1+elev:irr+elev:ht1bbbb

2 191.99 1 194.01 17.10 0.00 0.00 irr

3 188.53 3 194.60 17.69 0.00 0.00 elev+irr+elev:irr

4 191.67 2 195.70 18.80 0.00 0.00 weed+irr

5 191.78 2 195.82 18.91 0.00 0.00 elev+irr

* The best model for each species is indicated in bold font.

a Deviance is the residual model deviance; K is the number of model parameters estimated; AIC is Akaike

Information Criteria; Delta is the AIC difference; wi is Akaike weights; and ER is the evidence ratio. See text

for explanation of these values. b Size after the first year: diam1= Year 1 final basal diameter; ht1=Year 1 final height. Strata(block) is the

inclusion of the stratified block variable in the Cox model.

Table Table Table Table 3333----16161616. . . . Parameter estimates, hazard ratio (HR), and HR confidence limits for the best Year 2 Cox survival model Parameter estimates, hazard ratio (HR), and HR confidence limits for the best Year 2 Cox survival model Parameter estimates, hazard ratio (HR), and HR confidence limits for the best Year 2 Cox survival model Parameter estimates, hazard ratio (HR), and HR confidence limits for the best Year 2 Cox survival model for each species.for each species.for each species.for each species.****

SpeciesSpeciesSpeciesSpecies ParameterParameterParameterParameter CoefficientCoefficientCoefficientCoefficient EstimateEstimateEstimateEstimate Coefficient SECoefficient SECoefficient SECoefficient SE

Hazard Hazard Hazard Hazard RatioRatioRatioRatio

LoLoLoLower 95% wer 95% wer 95% wer 95% CLCLCLCL


ACNE

elev -0.08 0.26 0.93 0.56 1.55

weed -0.52 0.48 0.60 0.23 1.53

irr -0.46 0.31 0.63 0.35 1.15

ht1 -0.03 0.01 0.98 0.96 0.99

strata(block):elev 0.49 0.16 1.64 1.19 2.25

elev:irr -0.54 0.22 0.59 0.38 0.90

elev:weed -0.31 0.12 0.73 0.58 0.93

weed:ht1 0.01 0.01 1.01 1.00 1.03

FRLA

ht1 -0.04 0.01 0.96 0.94 0.98

irr -0.72 0.21 0.49 0.32 0.75

diam1 -0.07 0.08 0.94 0.80 1.09

irr:ht1 -0.01 0.01 0.99 0.97 1.00

strata(block):ht1 0.01 0.01 1.01 1.00 1.02

POFR

elev 0.36 0.22 1.43 0.93 2.21

weed 0.52 0.39 1.68 0.78 3.61

ht1 0.00 0.02 1.00 0.95 1.05

irr -0.82 0.21 0.44 0.29 0.67

diam1 0.28 0.14 1.32 1.00 1.74

elev:weed -0.38 0.18 0.68 0.48 0.97

ht1:diam1 0.00 0.00 1.00 1.00 1.00

QULO

elev -0.78 0.55 0.46 0.16 1.35

irr -0.25 0.52 0.78 0.28 2.19

ht1 -0.08 0.03 0.92 0.86 0.99

elev:irr -0.62 0.32 0.54 0.29 1.01

elev:ht1 0.01 0.01 1.01 0.99 1.03


Results


3.4.4 Third-Year Survival (2006)

Despite the imposition of a third irrigation treatment in 2006, the strongest

influence on plant mortality in the third growing season was relative elevation.

Whereas mortality for the three irrigation groups was not greatly differentiated in

the third year (Table 3-11, Figure 24 and 28), plants in the lowest elevation plots

generally had lower mortality than those in higher plots (Figures 25 and 29).

For all species the best 2006 Cox survival models contained terms for plant size,

and these models were overwhelmingly better than next best models from the

treatment candidate set (Table 3-17). Elevation was a factor in all species’ best

models, and a 1-meter increase in planting elevation resulted in an increased

mortality risk of 124% for ACNE, 67% for POFR, and 68% for QULO (Table 3-18).

For FRLA, the most influential factor was an interaction between block and

elevation, indicating that mortality risk was correlated with planting location, but

the elevation effect was not consistent between the planting blocks. The number of

years the plants were irrigated had species-specific influences on mortality: no

effect for FRLA and POFR; an 87% greater risk of mortality for plants irrigated 2

years versus 1 or 3 for QULO; and an indeterminate effect for ACNE because of

interactions with basal diameter and weed control treatment. Plant basal diameter

after the second year was an important factor influencing ACNE mortality,

conferring an 11% survival benefit for every 1-mm basal diameter increment.



ACNEACNEACNEACNE

1111 962.02962.02962.02962.02 9999 980.64980.64980.64980.64 0.000.000.000.00 1.001.001.001.00 1.001.001.001.00 elev+weed+diam2elev+weed+diam2elev+weed+diam2elev+weed+diam2bbbb+irr1+irr2+diam2+irr1+irr2+diam2+irr1+irr2+diam2+irr1+irr2+diam2bbbbirr1+irr1+irr1+irr1+ diam2diam2diam2diam2bbbbirr2+weedirr1+weedirr2irr2+weedirr1+weedirr2irr2+weedirr1+weedirr2irr2+weedirr1+weedirr2

2 1075.30 6 1087.59 106.95 0.00 0.00 elev+weed+irr1+irr2+weedirr1+weedirr2

3 1085.47 3 1091.55 110.91 0.00 0.00 elev+irr1+irr2

4 1073.79 9 1092.42 111.78 0.00 0.00 elev+weed+irr1+irr2+elev:weed+ elevirr1+elevirr2+weedirr1elev+ weedirr2elev

5 1082.58 5 1092.78 112.14 0.00 0.00 elev+irr1+irr2+elevirr1+elevirr2

FRLAFRLAFRLAFRLA

1111 287.41287.41287.41287.41 3333 293.53293.53293.53293.53 0.000.000.000.00 1.001.001.001.00 1.001.001.001.00 eleeleeleelev+ht2v+ht2v+ht2v+ht2bbbb+strata(block)+strata(block)+strata(block)+strata(block) bbbb:elev:elev:elev:elev

2 305.86 5 316.17 22.64 0.00 0.00 weed+irr1+irr2+weedirr1+weedirr2

3 312.43 2 316.49 22.97 0.00 0.00 irr1+irr2

4 305.70 6 318.13 24.60 0.00 0.00 elev+weed+irr1+irr2+weedirr1+weedirr2

5 294.82 11 318.19 24.67 0.00 0.00 elev+weed+irr1+irr2+elev:weed+weedirr1+ weedirr2+elevirr1weedN+elevirr2weedN+ elevirr1weedY+elevirr2weedY

POFRPOFRPOFRPOFR

1111 370.47370.47370.47370.47 3333 376.60376.60376.60376.60 0.000.000.000.00 1.001.001.001.00 1.001.001.001.00 elev+ht2elev+ht2elev+ht2elev+ht2bbbb+strata(block)+strata(block)+strata(block)+strata(block) bbbb:ht2:ht2:ht2:ht2bbbb

2 399.27 1 401.29 24.69 0.00 0.00 elev

3 397.57 2 401.63 25.04 0.00 0.00 elev+weed

Results




5 397.98 3 404.11 27.51 0.00 0.00 elev+irr1+irr2

QULOQULOQULOQULO

1111 376.99376.99376.99376.99 5555 387.18387.18387.18387.18 0.000.000.000.00 0.980.980.980.98 1.001.001.001.00 elev+ht2elev+ht2elev+ht2elev+ht2bbbb+irr1+irr2+diam2+irr1+irr2+diam2+irr1+irr2+diam2+irr1+irr2+diam2bbbb

2 391.06 3 397.14 9.96 0.01 0.01 elev+irr1+irr2

3 390.97 4 399.10 11.92 0.00 0.00 elev+weed+irr1+irr2

4 389.47 5 399.66 12.48 0.00 0.00 elev+irr1+irr2+elevirr1+elevirr2

5 389.81 5 400.00 12.82 0.00 0.00 elev+weed+irr1+irr2+elev:weed

* The best model for each species is indicated in bold font.

a Deviance is the residual model deviance; K is the number of model parameters estimated; AIC is Akaike

Information Criteria; Delta is the AIC difference; wi is Akaike weights; and ER is the evidence ratio. See text for

explanation of these values. b Size after the second year: diam2= Year 2 final basal diameter; ht2=Year 2 final height. Strata(block) is the

inclusion of the stratified block variable in the Cox model.

Table Table Table Table 3333----18181818. . . . Parameter estimates, hazard ratio (HR), and HR confideParameter estimates, hazard ratio (HR), and HR confideParameter estimates, hazard ratio (HR), and HR confideParameter estimates, hazard ratio (HR), and HR confidence limits for the best Year 3 Cox survival model nce limits for the best Year 3 Cox survival model nce limits for the best Year 3 Cox survival model nce limits for the best Year 3 Cox survival model for each species.for each species.for each species.for each species.****

SpeciesSpeciesSpeciesSpecies ParameterParameterParameterParameter CoefficientCoefficientCoefficientCoefficient EstimateEstimateEstimateEstimate Coefficient SECoefficient SECoefficient SECoefficient SE

Hazard Hazard Hazard Hazard RatioRatioRatioRatio

Lower 95% Lower 95% Lower 95% Lower 95% CLCLCLCL


ACNEACNEACNEACNE


weed 0.10 0.11 1.11 0.89 1.38

diam2diam2diam2diam2 ----0.120.120.120.12 0.020.020.020.02 0.890.890.890.89 0.0.0.0.86868686 0.920.920.920.92

irr1 0.46 0.41 1.59 0.71 3.55

irr2irr2irr2irr2 0.580.580.580.58 0.250.250.250.25 1.791.791.791.79 1.091.091.091.09 2.932.932.932.93

diam2irr1 0.00 0.02 1.00 0.96 1.03

diam2irr2 -0.04 0.01 0.96 0.94 0.99

weedirr1 -0.07 0.15 0.93 0.70 1.25

weedirr2weedirr2weedirr2weedirr2 ----0.200.200.200.20 0.080.080.080.08 0.820.820.820.82 0.700.700.700.70 0.950.950.950.95

FRLAFRLAFRLAFRLA

elev -0.04 0.13 0.96 0.75 1.24

ht2 -0.02 0.00 0.98 0.97 0.99

strata(block):elevstrata(block):elevstrata(block):elevstrata(block):elev 0.290.290.290.29 0.130.130.130.13 1.341.341.341.34 1.041.041.041.04 1.731.731.731.73

POFRPOFRPOFRPOFR


ht2 -0.02 0.01 0.98 0.97 0.99

strata(block):ht2 -0.01 0.01 0.99 0.98 1.00

QULOQULOQULOQULO


ht2 -0.01 0.01 0.99 0.98 1.00

irr1irr1irr1irr1 0.620.620.620.62 0.220.220.220.22 1.871.871.871.87 1.221.221.221.22 2.862.862.862.86

irr2 -0.22 0.13 0.80 0.62 1.04

diam2 -0.09 0.05 0.91 0.82 1.02


3.53.53.53.5 Weed Percent CoverWeed Percent CoverWeed Percent CoverWeed Percent Cover

Weed percent cover monitoring revealed an increase in weeds between 2004 and

2005 where no weed reduction was provided. Table 3-19 summarizes the percent

Results


cover of weeds (i.e., vegetation not specifically planted for the experiment) that

established around experiment plants that did not receive the weed reduction

treatment. All plants with weed reduction had a weed cover class of zero, so they

are not included in Table 3-19 or any further discussion.

Table Table Table Table 3333----19191919. . . . Percent of plants Percent of plants Percent of plants Percent of plants withinwithinwithinwithin each weed each weed each weed each weed percent percent percent percent cover cover cover cover categorycategorycategorycategory....****

PlotPlotPlotPlot YearYearYearYear

WWWWeed eed eed eed Percent CPercent CPercent CPercent Cover over over over Category**Category**Category**Category**

0%0%0%0% 1111----5%5%5%5% 6666----10%10%10%10% 11111111----25%25%25%25% 26262626----50%50%50%50% 51515151----75%75%75%75% 76767676----100%100%100%100%

B1L

2004 94 3 0 3 0 0 0

2005 15 17 22 18 15 8 5

2006 3 0 7 28 31 25 7

B1M

2004 87 10 2 2 0 0 0

2005 24 13 23 23 12 4 1

2006 6 8 16 31 22 16 3

B1H

2004 87 11 0 3 0 0 0

2005 28 22 18 20 7 5 1

2006 11 6 17 25 27 13 2

B2L

2004 83 8 2 2 3 2 1

2005 17 12 21 18 21 8 4

2006 7 5 7 16 35 25 6

B2M

2004 98 1 2 0 0 0 0

2005 56 16 17 8 3 1 0

2006 50 12 24 11 3 1 0

B2H

2004 97 1 1 2 0 0 0

2005 26 25 28 11 8 2 0

2006 31 3 26 24 10 7 0 * Only those experiment plants with out the weed reduction treatment are included in percent calculations.

** A modified Braun-Blanquet (1965) cover class system was used, and included the following classes: (0) 0%, (1) 1–5%; (2)

6–10%; (3) 11–25%; (4) 26–50%; (5) 51–75%; (6) 76–100%.

In 2004, the percent cover of weeds during the peak of the growing season was

generally quite low. In all plots, the majority (> 83%) of plants had no weeds (Table

3-19). B2L had the highest percentage of plants with weeds (17% of plants had at

least some weed cover). With the exception of B2L, no plots had any plants with

weed cover greater than 11–25%. The relatively low cover of weeds in 2004 is likely

a result of the harsh climatic conditions at the site and the late planting schedule

(see discussion in Section 4.1.4). In 2005, the majority of non-weed reduction plants

in all plots, with the exception of B2M, had at least some weed cover. The highest

amount of weeds occurred at B1L and B2L, which also had the greatest amount of

nearby existing vegetation (Z. Diggory, pers. obs.). In 2006, most plants had weed

cover of 26–75%. Plants in B1L and B2L continued to have the highest amount of

weeds. Conversely, B2H and B2M, the two plots farthest from any existing

vegetation, had the lowest amount of weeds. There were no notable differences in

substrate temperature as a result of the weed reduction mats (Stillwater Sciences,

unpublished data).

Results


Field technicians identified the most prevalent weeds to species or, if required

characters for identification were not present, to genus. These species, and whether

or not they are native to California, are listed in Table 3-20.

Table Table Table Table 3333----20202020. . . . Weed species identified in experimental plots.Weed species identified in experimental plots.Weed species identified in experimental plots.Weed species identified in experimental plots.****

Latin NameLatin NameLatin NameLatin Name Common NameCommon NameCommon NameCommon Name Native?Native?Native?Native?

Amsinckia menzeisii Rancher's fireweed Y

Avena fatua Wild oat N

Brassica nigra Black mustard N

Bromus hordeaceus Soft chess N

Bromus madritensis Foxtail chess N

Calandrinia ciliata Red maids Y

Cichorium intybus Chicory N

Conium maculatum Poison hemlock N

Cynodon dactylon Bermuda grass N

Cyperus squarrosus Bearded flatsedge Y

Datura wrightii Sacred datura Y

Eragrostis pectinacea Lovegrass Y

Eremocarpus setigerus Turkey mullein Y

Erodium cicutarium Storksbill, Filaree N

Ficus carica Edible fig N

Galium parisiense Wall bedstraw N

Hypochaeris glabra Smooth cat's-ear N

Lotus purshianus Spanish clover Y

Medicago polymorpha California burclover N

Phacelia cicutaria Phacelia Y

Populus fremontii Fremont cottonwood Y

Raphanus raphanistrum Wild radish N

Rubus discolor Himalayan blackberry N

Rumex sp. Dock Y&N

Salix sp. Willow Y

Senecio vulgaris Common groundsel N

Silybum marianum Milk thistle N

Sonchus asper ssp. asper Prickly sow thistle N

Sorghum halepense Johnsongrass N

Vicia sativa Vetch N

* In this case, a weed was defined as any plant not specifically planted

for the experiment.

Discussion


4 DISCUSSION

4.14.14.14.1 Treatment/Treatment/Treatment/Treatment/NonNonNonNon----treatment Effectstreatment Effectstreatment Effectstreatment Effects and and and and Revegetation Revegetation Revegetation Revegetation RecommendationsRecommendationsRecommendationsRecommendations

4.1.1 Initial Size

The initial size of cuttings and container stock had a strong effect on first year

survival and growth of most species (Section 3.2.1 and 3.4.2). ACNE and POFR

survival in Year 1 were positively correlated with initial basal diameter, with a 78%

(ACNE) and 19% (POFR) lower mortality risk with every 1-mm basal diameter

increment in planting stock (Table 3-14). FRLA seedlings in the first year were

highly sensitive to initial height and number of leaves, with a 12% lower mortality

risk with every 1 cm increase in height and a 24% lower mortality risk with one

additional leaf (Table 3-14). QULO seedling mortality was not sensitive to initial

size in the first year, but in the second year, relative mortality risk decreased

approximately 8% with every 1-cm increment in height at the beginning of the

growing season (Table 3-16). Though size was a factor in Year 3 survival models

(Table 3-18), the factor had a negligible influence on mortality (i.e., the hazard ratio

was ~1). While it may seem intuitive that plant size would influence survival,

explicitly modeling the effect via the Cox proportional hazard model allows for

quantification of both the predicted survival rate for other restoration projects, and

the range of planting stock size that would result in the lowest mortality (see

Section 4.3.1).

To ensure that adequate survival rates are achieved, we recommend that all

cuttings and container stock used in MRR revegetation efforts meet the size

thresholds indicated in the logistic regression survival models. ACNE container

stock should have basal diameters ≥2.5 mm (0.1 in), which should result in 95%

survival in the first year. To facilitate achieving a target of 80% survival in the first

year, FRLA container stock should be greater than 12.0 cm (4.7 in) tall. POFR

cuttings should have basal diameters >15.0 mm (>0.6 in); 80% survival is predicted

for cuttings of this size. No size recommendations resulted from QULO survival

models, but this experiment established that >90% survival can be achieved in the

first year for seedlings with size distributions of 45.0 (±1.0) cm height and 5.0 (±0.3)

mm basal diameter. Where these size thresholds are not met for particular species,

cuttings or container stock should be rejected and/or grown out in the nursery until

they reach adequate sizes.

Discussion


4.1.2 Block and Relative Elevation above Groundwater

Elevation above groundwater and, to a lesser extent, experimental block were

found to be important influences on survival in the third year, but not in the

previous two years. For ACNE, POFR and QULO, plant mortality risk in the third

year was 67–124% greater for every meter increment increase in elevation above

groundwater. In the first two years, there were block/elevation combinations that

were more, or less, conducive to plant survival than others, but no systematic

relationship with elevation. From the Cox survival models, these effects appear to

be due more to differences in initial size in the first year, and irrigation treatment

in the second year.

Similarly, negative growth effects were associated with elevation, particularly for

FRLA and for POFR in Block 1 (Figure 20). However, FRLA growth reduction with

elevation was modest, approximately 0.8 mm reduced for every 1 m gain in

elevation above groundwater, and block, not elevation, was the most important

factor in the POFR basal diameter growth model (Table 3-7). Interestingly, height

growth effects due to elevation appear to be stronger in the first year; lower

elevation plants are taller at the end of the first year for all species (Figure 17).

However, in the second and third years, surviving plants no longer show the same

systematic stratification by relative elevation. Because mortality over the course of

the experiment gradually reduces the sample size and potentially skews the

distributions with regards to growth, it is difficult to isolate the growth treatment

effects.

The significant effect of relative elevation on survival in the third year may be a

result of plant roots having finally reached permanent groundwater at the lower

elevation treatment plots. If this is the case, we could expect that on lower

floodplain surfaces (<2 m above groundwater) two years of irrigation may be

sufficient to establish all species. On higher surfaces (>2 m), three or more years

may be necessary to establish ACNE and POFR. It appears that POFR is better than

ACNE at establishing at higher floodplain elevations (Figure 29), perhaps through

a combination of higher root growth (allowing more plants at the 2 m elevation

level to access groundwater) or through better drought tolerance. ACNE crowns

experienced high rates of dieback (Figure 17), which is both an indicator of drought

stress and a functional adaptation that decreases transpiring area.

Based on these results, and with the goals of minimizing the need for irrigation and

promoting long-term survival of revegetated plants, we recommend that

floodplains at the MRR be restored to elevations less than or not much greater than

2 m above groundwater. “Drowning” of riparian trees from exceedingly high

groundwater levels is not expected to limit plant survival on the restored

floodplain. Stella (2005) showed no ill-effects of saturated soils on the growth and

Discussion


survival of POFR and willow species for up to 60 days. Revegetation monitoring of

the lower Clear Creek Floodway Rehabilitation Project found higher survival of

plantings where groundwater depths were shallow and concluded that shallow

depths to groundwater were even more critical in areas with coarse substrate than

with alluvial soils (Souza Environmental Solutions et al. 2005). There are, of course,

other factors that will need to be considered in determining appropriate restored

floodplain elevations, and they will be evaluated in MRR restoration planning

documents (e.g., Stillwater Sciences 2005).

4.1.3 Irrigation

The lack of a significant relative elevation effect in the first two years may be the

result of the overwhelming effect of the irrigation treatment on plant survival. This

factor was a strong determinant of survival in the second year, when irrigation was

stopped for one group of plants (Table 3-16, Figures 23, 24 and 26). For plants with

ongoing irrigation in Year 2, end-of-season survival was high, ranging among

species from 86–99% (Table 3-11). For those plants that had irrigation shut off in

Year 2, survival ranged from 44–83% (Table 3-11). In Year 2, the difference in

ACNE and QULO survival rates between irrigation treatment levels (Table 3-11)

represents an important threshold since many permits and/or performance criteria

require restoration projects to achieve and maintain 80% survival of plantings.

Irrigation did not have a significant effect on growth or water potential values

(Tables 3-7 and 3-11). The influence of irrigation on survival suggests that

irrigation provides sufficient benefits to plants (such as accelerated root growth

and/or adequate water supply) that they are able to overcome, or are no longer

adversely affected by, greater distances to groundwater. By the third year,

however, irrigation treatment had a minor effect on survival except for ACNE

(Table 3-11 and Table 3-17, Figure 28).

Final survival among the irrigation groups indicates the benefit of irrigating at least

two years for FRLA and POFR, one year for QULO, and at least three years for

ACNE to achieve the greatest marginal value of survival. There were small

differences in survival rates for FRLA and POFR irrigated 2 versus 3 years. QULO

irrigated for only one growing season still retained 74% survival at the end of the

third year (Table 3-11). For ACNE, plants watered three years had a 23% greater

survival rate than those watered two years; therefore for this species a longer

irrigation plan is warranted.

The strong effect of irrigation on Year 2 survival but not on growth or Year 3

survival (Figures 15, 16 and 28) suggests that supplying irrigation is critical to the

successful establishment of re-vegetated plants over a range of relative elevations,

but not necessarily to their longer-term development. In this case, irrigation may

be necessary to achieve the survival rates required by restoration project

Discussion


environmental compliance documents and permits, but may not be as useful in

meeting growth and/or canopy density objectives or requirements.

Based on these results, we recommend that at least two years of irrigation be

provided to all species regardless of floodplain elevation. On higher surfaces (>2

m), three or more years of irrigation may be necessary, particularly to establish

ACNE and POFR. Where irrigation is to be provided at the MRR, we recommend

the use of a drip irrigation system. The drip irrigation system used for the

experiment was inexpensive, easy to install, and required minimal maintenance.

We also believe that the use of drip irrigation, rather than overhead sprinklers or

flood irrigation, limited the establishment of weed species in the experimental

areas. The MRR property has riparian water rights associated with it, so water can

be pumped from the Merced River during the irrigation season at no cost, so long

as a USFWS-approved fish screen is installed at the pump intact. During the

experiment the irrigation system was run by two gas-powered pumps. While this

was sufficient for the experiment, the risk of theft and/or vandalism of the pumps

is high and the small size of the fuel tanks constrain how long the pumps can run.

For these reasons, we recommend that housing for the irrigation pumps, valves,

and filters be constructed to protect the system from vandalism and theft. We also

recommend that, if feasible, electricity be provided to the MRR in order to power

the irrigation pumps and an automatic timer for the irrigation system.

4.1.4 Weed Reduction

Weed reduction did not emerge as a significant predictor of growth or mortality,

except as a minor influence on QULO growth (Table 3-7). The lack of effect on

survival is somewhat surprising, since weeds have been reported as having severe

negative impacts on other revegetation projects in the Central Valley, but may be

explained by the harsh conditions at the MRR and/or the late start of the

experiment. In comparison with most floodplains, the MRR does not support much

herbaceous vegetation. The coarse substrate and extreme summer temperatures

appear to restrict the establishment of perennial herbaceous vegetation, although

annual species do establish after the winter and spring rains. It could be that the

harsh site conditions inhibit weed establishment to the extent that weeds cannot

out-compete planted vegetation, particularly container stock which is usually one

to two years old at outplanting. Weeds may have been further restricted in the

experimental areas due to the late start date of the experiment. The experiment was

started in April 2004, several months later than planned due to permit schedules.

As a result, the newly excavated experimental areas were not exposed to winter

and spring rainfall that could have supported the establishment of greater amounts

of weeds. This explanation is supported qualitatively by the observation that the

vast majority of weeds in Year 1 occurred immediately adjacent to irrigation

emitters. Our experimental results suggest that, at the MRR, plants installed as

Discussion


cuttings or as at least one-year old seedlings are large enough to escape many of

the competitive impacts of herbaceous weeds.

Information about the potential impact of weeds on future revegetation efforts at

the MRR has also come from the observations of field technicians. These

observations provide insight into potential vectors for non-native invasive weed

introduction and suggest actions that may be taken to minimize the negative

impacts of weeds on future revegetation efforts. For example, where potting soil

(which was placed in each planting hole at the start of the experiment) was

delivered at the site, a thick cover of weeds established quickly. This suggests that

the potting soil may have been contaminated with weed seed or that the improved

substrate conditions dramatically facilitated weed establishment. Therefore, we

recommend that any soil amendment brought to the site, such as topsoil or wood

chips, should be certified as sterilized and/or weed-free. Since this is difficult to

document, we recommend that, when feasible, organic material from on-site, such

as salvaged sand and wood chips from trees grubbed during restoration

implementation, be used for soil amendments rather than imported material.

Material from on-site may well contain weed seed, but its use would prevent the

introduction of new weed species and potentially harmful bacteria or fungi.

Weed monitoring indicated that cottonwood and willow seedlings are frequent

“weeds” in the experimental areas (Table 3-20), further suggesting that improved

conditions provided by soil amendments and irrigation will facilitate the

recruitment of both native and non-native species. Weed control mats or some

other weed control activity may be required in areas that are expected to support a

vigorous cover of weeds, such as wet spots, areas with sand substrates, or where

existing vegetation is nearby.

Several noxious weed species were observed along the experiment access roads,

indicating that vehicles traveling into the site are likely responsible for their

introduction. To minimize the introduction of non-native invasive weed species to

the MRR, we recommend that vehicular access to the site be restricted to the

greatest extent possible. Vehicles that must enter the site for restoration and/or

revegetation purposes should be required to spray down or brush off their tires

beforehand.

4.1.5 Soil Amendments

Soil analyses indicate that floodplain substrates could be improved with the

addition of organic matter (Appendix A). We recommend that organic matter

produced during floodplain restoration activities, such as wood chips from tress

and shrubs that are grubbed prior to tailing excavation, be salvaged and applied to

areas where revegetation is planned. Organic matter from on-site is preferable to

compost from outside the restoration area, as foreign compost could be

Discussion


contaminated with weed seed (see Section 4.1.4) and harmful bacteria or fungus (G.

Strnad, pers. comm.). Seeding newly restored areas with native herbaceous species

will also increase the organic content of floodplain substrates and facilitate soil

development.

Soil nutrient analyses suggest that current levels of nitrogen, phosphorus,

potassium, zinc and boron are so low at the site that they could inhibit the growth

of plantings or naturally recruited plants (M. Buttress, pers. comm.). Additions of

nitrogen, phosphate (P2O5), potash (K2O), zinc, and boron (which should be added

with caution) should increase nutrient levels and improve soil fertility on the

restored floodplains (Appendix A). In Block 1, adding gypsum to restored areas

should increase calcium levels and counteract the negative effects of high

magnesium levels, such as poor drainage and reduced potassium availability. In

Block 2, the addition of sulfur should improve plant vigor. Appendix A provides

guidelines for nutrient additions and rates of application. Nutrient applications

must be conducted with care to ensure that amendments do not affect groundwater

or Merced River water quality. In addition, the need for fertilizer should be

balanced with the expected increase in weeds resulting from improved soil nutrient

conditions.

4.24.24.24.2 Species ResponsesSpecies ResponsesSpecies ResponsesSpecies Responses

4.2.1 Acer negundo

ACNE container stock was considered to be in good condition at planting.

In general, ACNE demonstrated some of the highest survival and growth rates in

the experiment, although some plants experienced dramatic crown die-back in Year

3. Physiologically, ACNE benefited from additional irrigation, particularly at

higher relative elevations, but likely reached groundwater in lower relative

elevation plots. In general, survival, growth, and water potential data from this

experiment indicate that, with three years of irrigation and/or short distances to

groundwater, ACNE demonstrates good survival within the first three years

following revegetation. Under harsher conditions (e.g., drought, extreme

temperatures, or greater distances to groundwater), ACNE trees may experience

dramatic annual dieback. While they appear to re-sprout from the base once

conditions improve (e.g., temperatures decrease or water supply increases), annual

ACNE die-back may limit a revegetation project’s potential to meet canopy or

vegetative cover objectives.

4.2.2 Fraxinus latifolia

FRLA container stock was in poor condition at planting, and this strongly affected

first year survival rates. Once plants were established, or were given at least two

years of irrigation, survival rates improved markedly. FRLA water potential values

Discussion


demonstrated strong and predictable responses to irrigation and relative elevation

treatments. FRLA showed the greatest pre-dawn/afternoon difference in water

potential. This result, in combination with the high mortality of non-irrigated

individuals, suggests that FRLA is not efficient at controlling water loss. In

general, the results from this experiment indicate that, if outplanted in good

condition (i.e., at least 12 cm tall) and provided with at least two years of irrigation

and short distances to groundwater, FRLA demonstrates good survival within the

first three years following revegetation.

4.2.3 Populus fremontii

POFR cuttings were in variable condition at planting. High mortality of cuttings

less than 15 mm indicates that initial deficiencies in basal diameter can have large

effects on POFR survival, and subsequently on restoration success and cost for this

species. POFR water potentials were always the highest and showed little difference

between treatments. This suggests that POFR (at least those that survived to August

2005) had the deepest rooting system of the four species and had reached a reliable

groundwater source regardless of plot elevation at the time of sampling. In

general, the results from this experiment indicate that, if outplanted in good

condition (i.e., at least 15 mm in basal diameter) and provided with at least two

years of irrigation and/or short distances to groundwater, POFR demonstrates good

survival within the first three years following revegetation. In addition, the

dramatic growth demonstrated by many POFR plants during the experiment

suggests that this species can be important during revegetation to provide canopy

and vegetative cover quickly.

4.2.4 Quercus lobata

QULO container stock was in good condition at planting. QULO consistently had

the highest survival rates and slowest growth of all the species in the experiment.

Low pre-dawn water potential values for QULO suggest that, at the time of

sampling, the species was not particularly deep-rooted (i.e., it had not reached

groundwater). This species is expected to eventually grow a very deep tap root,

but in the meantime it is able to tolerate the most water stress of the four species.

In general, the results from this experiment indicate that, if outplanted in good

condition and provided with at least one year of irrigation, QULO demonstrates

excellent survival within the first three years following revegetation. The

consistently high survival and drought tolerance of this species suggest that QULO

will be critical to future revegetation to provide gradual but long-term

improvements in canopy and vegetative cover.

References


5 REFERENCES

AMFSTP (Adaptive Management Forum Scientific and Technical Panel). 2002.

Merced River adaptive management forum report. USFWS Anadromous Fish

Restoration Program and CALFED Bay-Delta Program, Sacramento, CA.

AMFSTP (Adaptive Management Forum Scientific and Technical Panel). 2004.

Final report: adaptive management forum for large-scale channel and riverine

habitat restoration projects. USFWS Anadromous Fish Restoration Program and

CALFED Bay-Delta Program, Sacramento, CA.

Bouse, R.M., M.D. Hornberger, S.N. Luoma. 1996. Sr and Nd compositions and

trace element concentrations in San Francisco Bay cores to distinguish sediment

deposited from hydraulic gold mining and mercury mining (abstract). EOS,

Transactions of the American Geophysical Union 77:201.

Boyer, J.S. 1967. Leaf water potentials measured with a pressure chamber. Plant

Physiology 42:133-137.

Boyer, J.S. 1995. Measuring the water status of plants and soils. Academic Press,

Inc., San Diego, CA.

Braun-Blanquet, J. 1965. Plant sociology: the study of plant communities. Hafner,

London.

Burnham, K.P. and D.R. Anderson. 1998. Model selection and inference: a practical

information-theoretic approach. Springer, New York.

Burnham, K.P. and E.A. Rexstad. 1993. Modeling heterogeneity in survival rates of

banded waterfowl. Biometrics 49:1194-1208.

Clark, W.B. 1998. Gold districts of California. Bulletin No. 193. California

Division of Mines and Geology, Sacramento, California.199 pp.

References


CDWR (California Department of Water Resources). 2004. Merced River Salmon

Habitat Enhancement Project, Ratzlaff Reach: vegetation sampling transect data.

CDWR, Fresno, CA.

CDWR (California Department of Water Resources) and CDFG (California

Department of Fish and Game). 2003a. Revised revegetation plan: Merced River

Salmon Habitat Enhancement Project, Robinson Reach. CDWR, Fresno, CA.

CDWR (California Department of Water Resources) and CDFG (California

Department of Fish and Game). 2003b. Monitoring plan: Merced River Salmon

Habitat Enhancement Project, Robinson Reach. CDWR, Fresno, CA.

Dunlap, J.M., P.E. Heilman, and R.F. Stettler. 1994. Genetic variation and

productivity of Populus trichocarpa and its hybrids. Two-year survival and growth

of native black cottonwood clones from four river valleys in Washington. Canadian

Journal of Forest Research-Revue 24:1539-1549.

Goldman, H. B. 1964. Sand and gravel in California: an inventory of deposits.

Part B - Central California. Bulletin No. 180-B. California Department of Mines

and Geology, Sacramento.

Greco, S.E., E.H. Girvetz, E.W. Larsen, J.P. Mann, and C. Lowney. In review. A

method to model a relative elevation topographic surface of a large alluvial river

floodplain and riparian ecological applications.

Kiparsky, M. (ed.). 2005. Getting results: integrating science and management to

achieve system-level responses. A summary for managers and scientists of the 3rd

biennial CALFED Science Conference, October 4-6, 2004, Sacramento, CA.

CALFED Science Program, Sacramento, CA.

Machin, D., Y.B. Cheung and M.K.B Parmar. 2006. Survival analysis: a practical

approach. 2nd edition. John Wiley and Sons, Inc., West Sussex, England.

Maldonado, G. and S. Greenland. 1993. Simulation study of confounder-selection

strategies. American Journal of Epidemiology 138(11): 923-936.

Oliver, C.D. and B.L. Larson. 1996. Forest stand dynamics: update edition. John

Wiley and Sons, Inc., New York. 520 p.

Pletcher, S.D. and J.W. Curtsinger. 2000. The influence of environmentally induced

heterogeneity on age-specific genetic variance for mortality rates. Genetical

Research 75:321-329.

References


Souza Environmental Solutions, Terrestrial Connections, and N.C. Schwertman.

2005. 2004 riparian revegetation monitoring report for the lower Clear Creek

floodway rehabilitation project. Prepared for Western Shasta Resource

Conservation District, Anderson, CA.

Stella, J.C., J.Vick, and B.K. Orr. 2003. Riparian vegetation dynamics on the Merced

River. Proceedings of the Riparian Habitat and Floodplains Conference. March 12-

14, 2001. Sacramento, California.

Stella JC. 2005. A field-calibrated model of pioneer riparian tree recruitment for the

San Joaquin Basin, CA. University of California, Berkeley, Berkeley, CA.

Stillwater Sciences. 2001. Merced River Corridor Restoration Plan Baseline Studies

Volume II: Geomorphic and riparian vegetation investigations. Prepared by

Stillwater Sciences, Berkeley, California for CALFED, Sacramento, California.

Stillwater Sciences. 2002. Merced River Corridor Restoration Plan. Prepared by

Stillwater Sciences, Berkeley, California, for CALFED, Sacramento, California.

Stillwater Sciences. 2004a. Channel and floodplain surveys of the Merced River

Dredger Tailings Reach. Stillwater Sciences, Berkeley, CA.

Stillwater Sciences. 2004b. Sediment transport model of the Merced River Dredger

Tailings Reach. Stillwater Sciences, Berkeley, CA.

Stillwater Sciences. 2004c. Mercury assessment of the Merced River Ranch.

Stillwater Sciences, Berkeley, CA.

Stillwater Sciences. 2005. Conceptual restoration design for the Merced River

Ranch, Vol. I: conceptual design report. Stillwater Sciences, Berkeley, CA.

Stillwater Sciences. 2006. Baseline monitoring of the Merced River Dredger Tailings

Reach. Stillwater Sciences, Berkeley, CA.

Tableman, M., J.S. Kim and S. Portnoy. 2004. Survival Analysis Using S: Analysis of

Time-to-Event Data. In Texts in Statistical Science. Chapman & Hall/CRC, Boca

Raton. 260 p.

Underwood, A.J. 1997. Experiments in ecology: their logical design and

interpretation using analysis of variance. Cambridge University Press, Cambridge,

UK.

References


URS Corporation. 2004a. Hydraulic model of the Merced River Dredger Tailings

Reach. Prepared for Stillwater Sciences, Berkeley, CA.

URS Corporation. 2004b. Volume and texture analysis of the Merced River dredger

tailings. Prepared for Stillwater Sciences, Berkeley, CA.

URS Corporation. 2006a. Final jurisdictional wetland delineation for the Merced

River Ranch. Prepared for Stillwater Sciences, Berkeley, CA.

URS Corporation. 2006b. Merced River Ranch floodplain restoration project near

Snelling, Merced County, California: 75% design drawings and specifications.

Prepared for Stillwater Sciences, Berkeley, CA.

Vaghti, M.G. and S.E. Greco. In press. Riparian vegetation of the Great Valley. In

Barbour, M, T. Keeler-Wolf, and J. Major (editors). Terrestrial Vegetation of

California, 3rd Edition

Vittinghoff, E., D.V. Glidden, S.C. Shiboski, C.E. McCulloch. 2005. Regression

methods in biostatistics: linear, logistic, survival, and repeated measures models

(Statistics for Biology and Health). Springer Science and Business Media, New

York, NY.

Whitlow, T.H. and C.J. Bahre. 1984. Plant succession on Merced River dredge

spoils. Pages 68-74 in R.E. Warner and K.M. Hendrix, editors. California riparian

systems: ecology, conservation, and productive management. University of

California Press, Berkeley, CA.

Zar, J.H. 1999. Biostatistical analysis, 4th edition. Prentice Hall, Upper Saddle

River, New Jersey.

Zens, M.S. and D.R. Peart. 2003. Dealing with death data: individual hazards,

mortality and bias. Trends in Ecology & Evolution 18:366-373.

Figures


6 FIGURES

Appendix A

A-1 Riparian Revegetation Experiment for the Merced River Ranch

A p p e n d i x A

SOIL ANALYSIS REPORTS

Appendix B

B-1 Riparian Revegetation Experiment for the Merced River Ranch

A p p e n d i x B

INITIAL CONDITIONS ANOVA RESULTS AND

PAIRWISE COMPARISONS

Variable

Model

Error

Source

Degrees of

Freedom

Sum of

Squares

Mean

Square F-ratio Pr(F)

ACNE height plot 5 1301.66 260.33 2.20 0.054

residuals 354 41824.29 118.15

ACNE basal

diameter plot 5 5.29 1.06 2.04 0.072

residuals 353 182.69 0.52

ACNE leaf number plot 5 196.03 39.21 10.38 <0.001

residuals 354 1337.35 3.78

FRLA height plot 5 195.12 39.02 6.17 <0.001

residuals 354 2240.30 6.33

FRLA basal

diameter plot 5 4.22 0.84 4.04 0.001

residuals 353 73.64 0.21

FRLA leaf number plot 5 262.31 52.46 14.57 <0.001

residuals 354 1274.28 3.60

POFR height plot 5 698.33 139.67 2.00 0.078

residuals 354 24753.85 69.93

POFR basal

diameter plot 5 2933.51 586.70 56.21 <0.001

residuals 354 3694.75 10.44

POFR leaf number plot 5 0.06 0.01 1.00 0.418

residuals 354 3.93 0.01

QULO height plot 5 1867.30 373.46 3.92 0.002

residuals 354 33711.89 95.23

QULO basal

diameter plot 5 107.36 21.47 11.91 <0.001

residuals 354 638.42 1.80

QULO leaf number plot 5 8092.36 1618.47 10.49 <0.001

residuals 354 54623.62 154.30

Appendix B

B-2 Merced River Ranch Revegetation Experiment

(

(

(

(

(

(

(

(

(

(

(

(

(

(

(

)

)

)

)

)

)

)

)

)

)

)

)

)

)

)

B1H-B1L

B1H-B1M

B1H-B2H

B1H-B2L

B1H-B2M

B1L-B1M

B1L-B2H

B1L-B2L

B1L-B2M

B1M-B2H

B1M-B2L

B1M-B2M

B2H-B2L

B2H-B2M

B2L-B2M

-12 -8 -4 0 4 8 12

simultaneous 95 % confidence limits, Tukey method

response variable: init.ht

(

(

(

(

(

(

(

(

(

(

(

(

(

(

(

)

)

)

)

)

)

)

)

)

)

)

)

)

)

)

B1H-B1L

B1H-B1M

B1H-B2H

B1H-B2L

B1H-B2M

B1L-B1M

B1L-B2H

B1L-B2L

B1L-B2M

B1M-B2H

B1M-B2L

B1M-B2M

B2H-B2L

B2H-B2M

B2L-B2M

-0.6 -0.3 0.0 0.3 0.6


response variable: init.diam

(

(

(

(

(

(

(

(

(

(

(

(

(

(

(

)

)

)

)

)

)

)

)

)

)

)

)

)

)

)

B1H-B1L

B1H-B1M

B1H-B2H

B1H-B2L

B1H-B2M

B1L-B1M

B1L-B2H

B1L-B2L

B1L-B2M

B1M-B2H

B1M-B2L

B1M-B2M

B2H-B2L

B2H-B2M

B2L-B2M

-2.0 -1.0 0.0 1.0 2.0 3.0


response variable: init.lvs

ACNE Initial Size, 95% Conf Intervals by Plot

(

(

(

(

(

(

(

(

(

(

(

(

(

(

(

)

)

)

)

)

)

)

)

)

)

)

)

)

)

)

B1H-B1L

B1H-B1M

B1H-B2H

B1H-B2L

B1H-B2M

B1L-B1M

B1L-B2H

B1L-B2L

B1L-B2M

B1M-B2H

B1M-B2L

B1M-B2M

B2H-B2L

B2H-B2M

B2L-B2M

-3.5 -2.0 -0.5 1.0 2.5



(

(

(

(

(

(

(

(

(

(

(

(

(

(

(

)

)

)

)

)

)

)

)

)

)

)

)

)

)

)

B1H-B1L

B1H-B1M

B1H-B2H

B1H-B2L

B1H-B2M

B1L-B1M

B1L-B2H

B1L-B2L

B1L-B2M

B1M-B2H

B1M-B2L

B1M-B2M

B2H-B2L

B2H-B2M

B2L-B2M

-0.6 -0.3 0.0 0.2 0.4 0.6



(

(

(

(

(

(

(

(

(

(

(

(

(

(

(

)

)

)

)

)

)

)

)

)

)

)

)

)

)

)

B1H-B1L

B1H-B1M

B1H-B2H

B1H-B2L

B1H-B2M

B1L-B1M

B1L-B2H

B1L-B2L

B1L-B2M

B1M-B2H

B1M-B2L

B1M-B2M

B2H-B2L

B2H-B2M

B2L-B2M

-1.5 0.0 1.0 2.0 3.0 4.0



FRLA Initial Size, 95% Conf Intervals by Plot

Appendix B

B-3 Riparian Revegetation Experiment for the Merced River Ranch

(

(

(

(

(

(

(

(

(

(

(

(

(

(

(

)

)

)

)

)

)

)

)

)

)

)

)

)

)

)

B1H-B1L

B1H-B1M

B1H-B2H

B1H-B2L

B1H-B2M

B1L-B1M

B1L-B2H

B1L-B2L

B1L-B2M

B1M-B2H

B1M-B2L

B1M-B2M

B2H-B2L

B2H-B2M

B2L-B2M

-8 -4 0 2 4 6 8 10



(

(

(

(

(

(

(

(

(

(

(

(

(

(

(

)

)

)

)

)

)

)

)

)

)

)

)

)

)

)

B1H-B1L

B1H-B1M

B1H-B2H

B1H-B2L

B1H-B2M

B1L-B1M

B1L-B2H

B1L-B2L

B1L-B2M

B1M-B2H

B1M-B2L

B1M-B2M

B2H-B2L

B2H-B2M

B2L-B2M

-6 -2 0 2 4 6 8 10



(

(

(

(

(

(

(

(

(

(

(

(

(

(

(

)

)

)

)

)

)

)

)

)

)

)

)

)

)

)

B1H-B1L

B1H-B1M

B1H-B2H

B1H-B2L

B1H-B2M

B1L-B1M

B1L-B2H

B1L-B2L

B1L-B2M

B1M-B2H

B1M-B2L

B1M-B2M

B2H-B2L

B2H-B2M

B2L-B2M

-0.10 -0.04 0.0 0.04 0.08



POFR Initial Size, 95% Conf Intervals by Plot

(

(

(

(

(

(

(

(

(

(

(

(

(

(

(

)

)

)

)

)

)

)

)

)

)

)

)

)

)

)

B1H-B1L

B1H-B1M

B1H-B2H

B1H-B2L

B1H-B2M

B1L-B1M

B1L-B2H

B1L-B2L

B1L-B2M

B1M-B2H

B1M-B2L

B1M-B2M

B2H-B2L

B2H-B2M

B2L-B2M

-14 -10 -6 -2 2 4 6 8



(

(

(

(

(

(

(

(

(

(

(

(

(

(

(

)

)

)

)

)

)

)

)

)

)

)

)

)

)

)

B1H-B1L

B1H-B1M

B1H-B2H

B1H-B2L

B1H-B2M

B1L-B1M

B1L-B2H

B1L-B2L

B1L-B2M

B1M-B2H

B1M-B2L

B1M-B2M

B2H-B2L

B2H-B2M

B2L-B2M

-2.0 -1.0 0.0 1.0 2.0



(

(

(

(

(

(

(

(

(

(

(

(

(

(

(

)

)

)

)

)

)

)

)

)

)

)

)

)

)

)

B1H-B1L

B1H-B1M

B1H-B2H

B1H-B2L

B1H-B2M

B1L-B1M

B1L-B2H

B1L-B2L

B1L-B2M

B1M-B2H

B1M-B2L

B1M-B2M

B2H-B2L

B2H-B2M

B2L-B2M

-25 -15 -5 0 5 10 15



QULO Initial Size, 95% Conf Intervals by Plot

Appendix C

C-1 Riparian Revegetation Experiment for the Merced River Ranch

A p p e n d i x C

EXPERIMENTAL SCHEDULE

Week

Beginning

Experiment

Week

Week

Beginning

Experiment

Week

Week

Beginning

Experiment

Week

Week

Beginning

Experiment

Week

4/15/2004 0 12/9/2004 34 8/4/2005 68 3/30/2006 102

4/22/2004 1 12/16/2004 35 8/11/2005 69 4/6/2006 103

4/29/2004 2 12/23/2004 36 8/18/2005 70 4/13/2006 104

5/6/2004 3 12/30/2004 37 8/25/2005 71 4/20/2006 105

5/13/2004 4 1/6/2005 38 9/1/2005 72 4/27/2006 106

5/20/2004 5 1/13/2005 39 9/8/2005 73 5/4/2006 107

5/27/2004 6 1/20/2005 40 9/15/2005 74 5/11/2006 108

6/3/2004 7 1/27/2005 41 9/22/2005 75 5/18/2006 109

6/10/2004 8 2/3/2005 42 9/29/2005 76 5/25/2006 110

6/17/2004 9 2/10/2005 43 10/6/2005 77 6/1/2006 111

6/24/2004 10 2/17/2005 44 10/13/2005 78 6/8/2006 112

7/1/2004 11 2/24/2005 45 10/20/2005 79 6/15/2006 113

7/8/2004 12 3/3/2005 46 10/27/2005 80 6/22/2006 114

7/15/2004 13 3/10/2005 47 11/3/2005 81 6/29/2006 115

7/22/2004 14 3/17/2005 48 11/10/2005 82 7/6/2006 116

7/29/2004 15 3/24/2005 49 11/17/2005 83 7/13/2006 117

8/5/2004 16 3/31/2005 50 11/24/2005 84 7/20/2006 118

8/12/2004 17 4/7/2005 51 12/1/2005 85 7/27/2006 119

8/19/2004 18 4/14/2005 52 12/8/2005 86 8/3/2006 120

8/26/2004 19 4/21/2005 53 12/15/2005 87 8/10/2006 121

9/2/2004 20 4/28/2005 54 12/22/2005 88 8/17/2006 122

9/9/2004 21 5/5/2005 55 12/29/2005 89 8/24/2006 123

9/16/2004 22 5/12/2005 56 1/5/2006 90 8/31/2006 124

9/23/2004 23 5/19/2005 57 1/12/2006 91 9/7/2006 125

9/30/2004 24 5/26/2005 58 1/19/2006 92 9/14/2006 126

10/7/2004 25 6/2/2005 59 1/26/2006 93 9/21/2006 127

10/14/2004 26 6/9/2005 60 2/2/2006 94 9/28/2006 128

10/21/2004 27 6/16/2005 61 2/9/2006 95 10/5/2006 129

10/28/2004 28 6/23/2005 62 2/16/2006 96 10/12/2006 130

11/4/2004 29 6/30/2005 63 2/23/2006 97 10/19/2006 131

11/11/2004 30 7/7/2005 64 3/2/2006 98 10/26/2006 132

11/18/2004 31 7/14/2005 65 3/9/2006 99 11/2/2006 133

11/25/2004 32 7/21/2005 66 3/16/2006 100

12/2/2004 33 7/28/2005 67 3/23/2006 101

Documents

Technical Memorandum #10 Merced River Ranch Revegetation