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Evaluating the Ability of the California Rapid Assessment Method (CRAM) to Capture Seasonal

Variability within the Kachituli Oxbow Wetland Restoration Site

By Leticia Morris

Environmental Studies Department

Environmental Policy Thesis

California State University, Sacramento

December 14, 2014

In thanks to Dr. Michelle Stevens for sharing her expertise in CRAM and providing me

invaluable feedback during this learning process.

Thanks also to Dr. Foran for providing much needed feedback during these revision stages

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ABSTRACT

The California Rapid Assessment Method (CRAM) is a tool utilized for the assessment of

wetland restoration sites across California. Developed in collaboration of a broad range of state,

local and non-profit agencies, the CRAM is a level 2 component of the national EPA

monitoring framework guided by regulatory permitting and Water Quality Monitoring. To test

the CRAM’s applicability and ability to detect seasonal variability within wetland restoration

sites, the Kachituli Oxbow restoration site was chosen for this case study. The Kachituli

Restoration site was implemented in 1991 and has maintained success without outside water

inputs even through severe drought conditions experienced in Northern California. Given these

water constraints, the first CRAM was performed during the spring of 2014 and the second was

compared to the subsequent CRAM performed during the summer of 2014. The prediction was

that in light of drought conditions, the CRAM would be able to detect changes occurring at the

sight from season to season. Our findings suggest that because the overall CRAM scores

remained the same, either the CRAM may not be suitable for this application or that the changes

on-site were not sizeable to warrant sizeable changes in CRAM scores. However, because the

individual attribute scores did show fluxes, it can be inferred that the CRAM does still detect

seasonal variation. Ultimately, although the CRAM may not be applicable for detecting these

changes at a finer level than vegetative sampling, the CRAM did indicate that its scores across

seasons are consistent for their intended use as assessments of overall ecological functions.

Future studies at Kachituli may contribute data for long-term correlations. Long-term data may

be helpful in providing resources for continued management of the Kachituli Restoration sight.

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Table of Contents

Abstract……………...…………………………………………………………………………………………………2

1 INTRODUCTION.……………………………………………………………..…………………………….4

1.1 Objectives…………………….…………………………………………………….…………………….4

2 SITE DESCRIPTION…………………………………….…………………………………….................4

2.1 Project Background………....…………………………………….………………...4

2.2 Climate and Hydrology……………………..………………………………………..6

2.3 Soils…………………………………………………………………………………...7

2.4 Historic Land-Use and Cultural Resources……………………………………..……8

2.5 Vegetation Design…………………………..…………..…………….…………….9

3 METHODS…….…………………………………………………………………………………………………10

3.1 California Rapid Assessment Method (CRAM)……….………..…………………………...…….10

4 RESULTS……………………………………………………………………………………………………….13

4.1 Soil Survey 2014………….....… ..………...………..……..…..………………………13

4.2 Spring 2014 CRAM Scores ..…………………..………………….……….………….15

4.3 Summer 2014 CRAM Scores ..………...…………..………………………………….16

5 DISCUSSION………………………………...………………………………………..………………………18

6 REFERENCES……………..………………………………………………………………………………….33

1.0 INTRODUCTION

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1.1 OBJECTIVES

This assessment investigates the site conditions of the Kachituli Oxbow Mitigation

Project in Yolo County, California twenty years after its initial successful implementation.

Although this year marks the third year of an extreme drought experienced throughout

California, remarkably, 2014 has been the first year that the Kachituli Oxbow has gone dry.

Given these extreme drought conditions, it is predicted that other signs intra-annual variability

will be detected on site. Thereby, this study utilizes the California Rapid Assessment Method

(CRAM) to compare data collected during the spring 2014 growing season and the summer 2014

dry season. The goals of this comparison are to: characterize the range of variability that occurs

between seasons and assess the applicability of the CRAM as a tool for detecting seasonal

changes at the Kachituli Oxbow. It was hypothesized that: 1) the CRAM will capture seasonal

variability between spring and summer and 2) the CRAM (Attribute 4: Biotic Structure) will be

less accurate in detecting variability in vegetation communities than independent qualitative

vegetation analysis. This analysis may add to existing data utilized to maintain the success of

Kachituli’s long-term restoration.

2.0 SITE DESCRIPTION

2.1 PROJECT BACKGROUND

The Kachituli Oxbow Restoration Project is located east of the Sacramento International

Airport in Yolo County. It is located in close proximity to the Sacramento River nearest the

intersection of Road 22 and Interstate 5 This project was initiated in 1991 as mitigation for

habitat loss proposed at a nearby site. Although funded by the Lighthouse Marina and Riverbend

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Development, this nearby project was never actually completed. Still the Kachituli Restoration

project was implemented (D. Kelley, Kelley and Associates, personal comm).

One of the main components of the Kachituli project was to construct an oxbow, or lake

that forms as meandering flows become disconnected from a river. Thereby, this restoration

project did not re-create an oxbow lake on an area that was previously connected to the river, but

rather, the project construction took place on land maintained for tomato crops. The planning of

the project consisted of restoration of over 110 acres of agricultural fields to woodland and

riparian habitat. The focus of the construction of these woodlands was directed toward the

preservation of habitats for the Valley Elderberry Longhorn Beetle (Desmocerus californicus

dimorphus) and the Swainson’s Hawk (Buteo swainsoni) (Green and Associates Kelley and

Associates, Thasos 1990). The Kachituli oxbow habitat restoration was the largest of its kind in

the Western United States (Hey and Philippi, 1999). Over twenty years later, as earlier

assessments have taken place, this restoration site is still functioning as a woodland and riparian

habitat corridor without outside irrigation (McGuirk, 2014).

U.S. Fish and Wildlife (USFWS) and the Army Corps approved the Lighthouse Marina

and Riverbend development to undertake the Kachituli restoration project on this site in Yolo

County because its site conditions were surveyed and found to be suitable for the restoration of

an oxbow. The 110 acres of agricultural area designated for this restoration project were

originally known as Amen Ranch. In total, Amen Ranch consisted of over 280 acres and so the

acreage that was neither serviced for the habitat creation by the Kachituli Restoration designers

nor the Army Corps was turned over to the USFWS to serve as additional habitat for waterfowl

in the Yolo Basin (Green and Kelley Associates, 1990; Hey and Phillipi, 99).

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2.2 CLIMATE AND HYDROLOGY

The Kachituli restoration project resides adjacent to the levee side of the Sacramento

River basin watershed. Regional climate is characterized as Mediterranean with annual

precipitation estimated at nearly 18 inches (USCD, 2012). However, several reports have

characterized current precipitation patterns have reached a 20-year low as this is one of the many

regions within California that is experiencing a severe drought (Jackson et al, 2012).

Specific to monthly precipitation data for the site location in Woodland, the U.S Climate

Data (2014) estimates that the average rainfall of the winter months of December, January and

February has dropped from 2.86 inches in 2013 to 1.71 inches averaged for those same months in

2014. As for the average rainfall for the spring months of March, April and May, there has been

a drop form 1.24 inches in 2013 to 0.77 inches in 2014 (USCD, 2014). Furthermore, although

temperatures during summer months have typically been around 93-96 degrees Fahrenheit

(SRWP, 2014); long-term climate data indicated an upward trend in average temperatures in both

summer and winter months. This trend of decreasing precipitation and increased temperatures in

the northern California region is expected to continue as drought conditions persist in a region

experiencing the impacts of climate change (Jackson et al, 2012).

Kachituli resides alongside the region of the Sacramento River that begins at Shasta Lake

and drains further south into the Sacramento Delta before it reaches one of its main tributaries,

the American River (Ibid). However, although Kachituli rests within the boundary of Yolo

County that borders the Sacramento River, its hydrologic connection to this river has been

separated for nearly a century. The site rests on the floodplains of the Sacramento River; thereby,

because of its proximity to the levee, the Kachituli site would become inundated in the event of

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an extreme flood. Thus, Kachituli is not directly subject to surface water conditions of the river;

rather, its primary water source is ground-water (Green and Kelley, 1990; Hey and Phillipi, 99).

2.3 SOIL SURVEY PRIOR TO RESTORATION PROJECT IMPLEMENTATION

Kachituli is composed of fertile alluvial soils that were deposited several thousand years

ago (Cultural Resources Unlimited, 1990). According to a 1972 site survey performed by the

Soil Conservation Service, the soil profiles were delineated as Sycamore Silty Clay Loam,

Sycamore Drained, Sacramento Clay, Sacramento Drained and Valdez soils (figure 1) (Green

and Kelley, 1990). Both Sycamore Silty Clay Loam and Sycamore Drained soils are

characteristic types that hold water well, and therefore tend not to drain quickly. Both of these

soils are types that you would find on alluvial fans, or landscapes onto which courser sediment

accumulates as streams flow. Similar to these clays, both types of Sacramento soils also have a

high water retention capacity, except these soils are usually found beneath the Sycamore clays.

Likewise, the Valdez soils tend to drain slowly; this soil series is also composed of more sandy

materials than the others identified within the Kachituli study area (Hey and Phillipi, 99).

Figure 1: Kachituli Soil Profile

(Hey and Philippi, 1999)

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2.4 HISTORIC LAND USE AND CULTURAL RESOURCES

Before contact with Spanish and European conquistadors, the Kachituli study area was

once home to several indigenous peoples whose tending of the land dates back several hundreds

of generations. The greater Yolo County surrounding the site was tended by the Patwin, a tribe

who belong to the southern band of the Wintun Nation. The stewards of Kachituli, however,

were tended by the Nisenan Maidu (Cultural Resources Unlimited, 1990).

The Nisenan Maidu were known to be harvesters of many rhizomatous plants and several

types of acorns, nuts and berries. Historically, multitudes of plants and shrubs were respectfully

harvested in native communities within the Central Valley. Culturally important species include:

willow (Salix exigua, Salix laevigata, Salix lasiolepis, Salix lucida, Salix lucida), narrow-leaf

milkweed (Asclepias fascicularis), Indian hemp (Apocynum cannabinum), deer grass

(Muhlenbergia rigens), and Santa Barbara sedge (Carex barbarae), which were used for several

purposes, ranging from food and fiber to materials for basket-weaving (Stevens, 2004).

Although the Nisenan Maidu held claim to the western outlet of the Sacramento River,

collectively, the Patwin and the Nisenan Maidu shared access to the Sacramento River to harvest

Salmon and other shellfish. Also in the region of Kachituli, elk, deer and several other animals

were hunted for the sustenance of the community. In honor of theses historic tenders of this

study area, the designers of this mitigation project named the site “Kachituli” which was the

name of another Patwin community farther out in Woodland (Cultural Resources Unlimited,

1990).

Beginning in the mid 1960’s, the site was known as Amen Ranch and was recorded to

have been a hops farm; more presently, the site was managed as a tomato farm (Hey and Phillipi,

99). Before the site was sold in 1989 to Lighthouse Marina and Riverbend Development for

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mitigation purposes, it was considered prime agricultural land. There was significant protest

against this project from local landowners who weren’t in favor of taking these prime farmlands

out of agricultural cultivation (Cultural Resources Unlimited, 1990).

2.5 VEGETATION DESIGN

Based on the series of soil, geological and hydrological conditions of the site, the 1991

Kachituli restoration project designers determined that the oxbow would be an optimal feature

for the site; These assessments determined that the water table was accessible enough to support

healthy wetland and riparian plant communities. Specifically, the plans called for the

establishment of riparian plantings including: an Oak woodland (Quercus lobata), an Elderberry

(Sambucus nigra) savannah, a Cottonwood forest (Populus Fremontii), a Willow thicket, and

wetland plants (Hey and Phillipi, 1999). The complete breakdown of all of the species (Figure 1)

corresponds to each of these vegetation communities.

Table 1: Vegetation Community Design (Green and Kelly, 1990; Mcguirk, 2014)

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3.0 METHODS

3.1 CALIFORNIA RAPID ASSESSMENT METHOD

The California Rapid Assessment Method (CRAM) was used to assess the landscape,

hydrologic, physical and biotic condition of the site. The CRAM is part of a broader assessment

method, known as the national Wetland and Riparian Area Monitoring Program (WRAMP) and

is used to characterize the performance of wetlands across the nation. These types of assessments

are tools that aid in the monitoring of regulatory programs, including mitigation requirements

initiated as per the Porter-Cologne Water Quality Control Act as well as the US Clean Water act

sections 404 and 401 (CWMW, 2010). As a wetland status evaluation tool, the WRAMP model

breaks down this assessment process into three distinct categories (Table 1) (CWMW, 2013).

In documenting the adaptation of the larger Rapid Assessment Method (RAM) for

implementation in California, Sutula et al. (2006) indicates that the CRAM incorporates

methodologies from several other states including the Washington state Wetland Rating System,

the Ohio Rapid Assessment Method (ORAM), as well as RAMs from Montana and

Pennsylvania. So that the CRAM can be utilized on a statewide monitoring basis, developers of

the CRAM also draw from some of the conceptual assessment approaches enacted by the CA

Fish and Wildlife Service as well as from the methods of the State Water Resources Control

Board. Several additional interagency collaborations have taken place in selecting the biological

metrics specific to California’s wetland demographic, like for example, the inclusion of seasonal

depressions or vernal pool metrics that may not have otherwise been captured in the metrics

specified in the assessments of other states (Sutula et al, 2006).

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Table 2: Components of the WRAMP Model

Level 1

Level 2

Level 3

Map- bases inventories and landscape profiles (CAR)

Rapid Assessment of overall condition (CRAM)

Intensive assessment of selected aspects of condition, stress, or function

(CWRCB, 2011)

Thus, the CRAM falls within the level 2 category (CWRCB, 2011). According to the

State Water Resources Control Board (2011), the CRAM can be characterized as an “expert

diagnostic tool…requiring expertise comparable to jurisdictional delineation.” Thereby, this

assessment method relies heavily on professional judgment in the field. Each of the three model

components (level 1, 2, and 3) of these assessments are used in generating condition summaries

which are presented to resource agencies at the state-level (Stein et al, 2009).

One of the goals of these rapid assessment methods is to characterize the health and

ecological condition of a site as a management tool. Because addressing ecological site

conditions require complex training, scientific understanding, ecologically sensitive protocols

and management participation, CRAM has been utilized as a cost-effective and timely approach

to such an, often, underfunded process. Stein et al. (2009, p. 248) confers that “the resultant state

[of a particular wetland] can be evaluated based on a core set of visible field metrics.” In this

way, the CRAM relies on “observable field indicators as surrogates for direct measures of

condition” (Stein et al, 2009, p. 247). Notwithstanding, independent level 3 quantitative data is

collected to validate evaluations that have been determined utilizing level 2 methods such as the

CRAM.

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Specific to level 2 data, the CRAM utilizes four attribute categories used to assess

ecological conditions: 1) landscape context, 2) hydrology, 3) physical structure and 4) biotic

structure (Stein et al, 2009). The actual scoring for CRAM is generated from the scaled

computation of each of the four attributes: buffer and landscape context, hydrology, physical

structure and biotic structure (figure 2). Within each of these four attributes, there are sub-

metrics that are assigned according to

how closely the site condition is

associated with the “A, B, C or D”

rating. In this rating rubric, “A” equals

the highest score of 12 and “D” equals

the lowest score of 3 etc. This scoring

method totals each value within each

attribute into a raw score and then a final

attribute score. Finally, from all of these

respective scores, an overall AA score is

computed form the average of each of

these attributes (CWMW, 2013).

Prior to the Kachituli site visit, the GPS location was established and a map was

generated to define the boundary of the assessment area, the AA). Using the map, the buffer area

was also generated according to a 500m-study area determined from the north, south, east and

west directions from the AA (CWMW, 2013).

Then, at the Kachituli site, the CRAM was utilized first in the spring, May 3, 2014 to

determine the conditions in the depressional wetland, and then again in the late summer, August

Figure 2: CRAM Attributes and Metrics (CWMW, 2008)

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18, 2014. Both samplings have taken place during one of the most extreme drought years

experienced in Northern California. Summer of 2014 was the first time in 20 years that the

oxbow has gone dry (D. Kelley, K&AES, Inc., personal communication). During both

assessments, the CRAM field sheet was tallied on site; whereas, the overall AA score was

computed afterward off-site. Likewise, plant specimens that could not be identified on site were

collected and eventually keyed out off-site using the Jepson Manual. Plant specimens that could

not be identified to species were categorized as unknown but were identified to their genus.

4.0 RESULTS

4.1 SOIL SURVEY 2014

Although pre-project soils classified the Kachituli site as consisting mainly of Sycamore

Silty Clay Loam, Sycamore Drained, Sacramento Clay, Sacramento Drained and Valdez soils

(Kelley and Green, 1990; Hey and Philippi, 1999). Prior to restoration on site, the Sycamore

Silty and Sycamore Drained soils were found on the top layer throughout the area (Kelley and

Green, 1990), but because the topmost layers of these soils were excavated from the Oxbow area

and then piled onto the adjacent upland area nearest the entrance of the site, the soil profiles

were, thereby, disturbed. Even given this disturbance caused over 20 years ago, it unlikely that

soils beneath the surface would have changed given that these alluvial soils may have

accumulated and developed on the project site over a span of several thousands of years. (A.,

Margenot, UC Davis, personal comm).

However, since no soil cores or reclassification had been done on Kachituli post-project

implementation (D, Kelley, Kelley and Associates, personal comm.), soil cores were taken at the

site to 1) explore this relationship between the soil profiles mapped pre-project and the profiles

of 2014 and 2) estimate potential for carbon sequestration at the sight.

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Figure 3: Kachituli 2014 Soil Profile

(Margenot, 2014)

The soil profile was surveyed at the eastern most edge of the Oxbow at the slope of the

upland area (figure 3). Because the soils beneath the topmost layer were exposed to natural

weathering during the excavation, these soils were compacted by the weight of the machinery

used to remove the souls. Thereby these soils were more likely to have a decreased level of plant

activity in the roots (Ballantin eet al, 2011). In this way, the removal of the cabon-rich topsoil

that was placed on the perimeter of the oxbow allowed for more Soil organic carbon (SOM) to

accumulate on this area, which was sampled (A. Margenot, U.C. Davis, personal comm.). This

restoration process which compacts the soil was expected to slow the ability for nutrient cycling,

but these processes are generally considered to continue years later after the initial restoration

(Ballantine et al, 2011).

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This helps to explain why the 2014 soil profile showed evidence of more organic carbon

in the A horizon of the soil , and this SOM was much farther down in depth than was expected

of the soils mapped prior to restoration sampled. Thereby because these soils were affected by

human-caused process of restoration, these layers were less able to develop and so the carbon

layer is more significant. Thereby, the redoximorphic features that you would usually find in the

initial breakdown of the soils mapped, were more isolated in the soil profile found in 2014 (A.

Margenot, U.C. Davis, personal comm). The success of this restoration site is evidenced by the

succession of plant communities supported by the reconstructed Oxbow decades after the initial

restoration (McGuirk, 2014). The healthy, carbon-rich soils may have provided increased

nutrient support to the habitat patches. Further exploration and analysis of the carbon storage

capacity in wetland soils such as these top layers with high SOM may add to the growing

discussion of wetland restoration to contribute to carbon sequestration (DOI, 2011).

4.2 FALL 2014 CRAM

The attribute scores of the McGuirk et al (2014) CRAM completed at the Kachituli

Oxbow during the spring of 2014 captured the scores of attributes1, 2, 3, and 4 as 56, 100, 75,

and 66 percent respectively (Table 2). Thus the final overall AA score for the site was 74%.

McGuirk et al (2014) indicated that this 74% was “well above average” given the range of other

CRAM scores administered throughout other restoration sites within the Bay/Delta region of

California. Furthermore, McGuirk (2014) found that most of the overall CRAM scores assigned

to project sites in the region fell between 55% and 85% while none received overall scores of

higher than a 95%. Thus, the overall score of 75% characterizes the site’s ecological condition as

“good” in that it is sustaining habitats for a variety of plant and animal communities, compared

to other sites in the Bay/Delta region, especially given that it receives no outside irrigation.

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Specific to the individual attribute metrics, there were eight patch types for the sub-metric of

“structural patch richness” and that there were four plant community layers and 10 co-dominants

that were identified for the “plant community” sub-metrics (McGuirk, 2014). The calculation and

computation data for this assessment are attached (Table 1). Likewise, the complete California

Wetlands Monitoring Workgroup guide to the scoring method for each of these attributes is also

provided (See Appendix).

4.3 SUMMER 2014 CRAM

The results of the summer 2014 CRAM attained the following scores for attributes 1,2, 3

and 4: 68, 100, 62.5 and 67 percent respectively (Table 2). The scoring of these calculations were

performed with regard to the same protocols as the Fall 2014 CRAM. The overall average of the

above scores of the AA for this CRAM was 74.37%. Because this overall score was the same as

the previous CRAM scores for the spring, the assessment from McGuirk (2014) was supported in

these results.

The CRAM index considers scores between 63-81 as “good” while scores between 82-

100 are considered to be sites whose ecological condition or overall health is “excellent”

(SWRCB, 2014). Thereby, the overall ecological condition assessed during the summer of 2014

was characterized as “good,” as is indicated by relatively high average of each of the metrics

observed on site, especially given that the site receives no outside irrigation and relies

completely on its below –ground surface connection and precipitation. Even given this

hydrologic condition, observations supported evidence of several structural patches which show

the potential for a variety of plant and animal communities.

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Table 3: Scoring Sheet sorted by Attribute and Sub-metric for both Spring 2014 and Summer 2014

Attribute & Sub-metrics

A=12; B=9; C=6; D=3

Spring 2014 CRAM

(McGuirk, 2014)

Summer 2014 CRAM

BUFFER AND LANDSCAPE

Aquatic Area Abundance 3 6

Percent of AA With Buffer 12 12

Average Buffer Width 12 12

Buffer Condition 9 9

TOTAL SCORE 13.39/24*100=56% 16.39/24*100=68%

HYDROLOGY

Water Source 12 12

Hydro-period 12 12

Hydrologic Connectivity 12 12

TOTAL SCORE 36/36*100=100% 36/36*100=100%

PHYSICAL STRUCTURE

Structural Patch Richness 9 6

Topographic Complexity 9 9

TOTAL SCORE 18/24*100=75% 15/24*100=62.5%

BIOTIC STRUCTURE (Plant Community Composition = A-C)

A: Number of Plant Layers 12 12

B: Number of Co-dominant Species

12 12

C: Percent Invasion 3 3

Plant Community Composition Metric ( Average of A-C)

9 9

Horizontal Interspersion

9 9

Vertical Biotic Structure

9 6

TOTAL SCORE 24/36*100= 66.7% 24/36*100=66.7%

OVERALL SCORE 74% 74%

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5.0 DISCUSSION

The results from the spring 2014 CRAM and the summer 2014 CRAM indicated that

overall scores remained the same: 74%. Thus, the overall CRAM scores did not measure sizeable

changes within Kachituli’s site conditions from the growing season compared to its dry season.

In exploration of these results, three possible explanations arose as the most likely:

1.) the CRAM scores did not reflect a change because there truly were no changes

evident in the site conditions between the growing season and the driest season.

2.) the CRAM scores did not reflect a change because although there were seasonal

changes at the site, these changes were not sizeable enough to warrant changes in CRAM scores.

3.) the CRAM scores did not reflect changes because the CRAM was not an evaluation

tool sensitive enough to detect such inter-seasonal changes even in an extreme drought year.

ATTRIBUTE 1:Landscape and Buffer

Because the structure of the overall AA score is generated from averaging the scores of

the attributes (CWMW, 2009), the scoring of the individual attributes of buffer/landscape

context, hydrology, physical structure and biotic structure was assessed to determine whether

their sub-metrics had also remained the same.

Our data revealed an inconsistency within the results of buffer and land context attribute

that may have derived from sampling error, thus detracting from the anticipated effect of a

decreased the physical structure attribute. Specifically, the AA scored higher during the summer

season (“C”) versus the spring (“D”). This AA metric measures “the percentage of the transect

that passes through an aquatic feature of any kind” (CWRCB, 2011). Because the depressional

area of Kachituli has not had any new stressor identified in the checklist, it was safe to interpret

that the site area had not lost or gained acreage, but rather, the boundary of the AA had changed

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during the second CRAM; this was verified by the differing aerial photographs generated for

each separate CRAM in spring and in the summer.

Thereby, the changes measured in this attribute seem to have been due to varying

sampling regions within the depressional wetland site, since the transect lines had been

established at different locations during both sampling opportunities in the spring versus the

summer. The procedures for delineating the AA require mapping prior to sampling at the site so

that once in the field, they can be refined. Upon comparison to the data from spring 2014, it then

appeared that 1) the transect lines were drawn from separate starting locations and 2) project

boundaries were established differently. Specific to the spring 2014 CRAM, the adjacent Army

Corps site was noted as “south of site 0-15m.”

Thus, it seems possible that the spring 2014 data may have considered the Corp site to be

outside of the AA. This may have been what generated a key distinction in the scores for the AA.

If we were to assume that this was true and adjust the first metric of attribute one to retrofit the

boundary of the AA in the summer 2014 CRAM to align with that of the spring, then the overall

score for Summer 2014 would change form 74 % to 71%. Although this three percent reduction

would still result in the overall characterization of the site’s ecological condition as “good”

relative to others in the region, this reduction would leave more room to interpret that either the

intra-seasonal changes observed at Kachituli were sizeable enough to warrant a change in the

CRAM score or that CRAM is a tool sensitive enough to detect seasonal changes.

However, because one of the goals of this study was also to “assess the applicability of

the CRAM as a tool,” one observation is that the application of the tool may not be standardized

well enough for teams to be able to replicate the sampling procedures in the same location. This

may indicate that both increased level of expertise and practice is needed to carry out the CRAM

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(Stein et al, 2009; Klimas, 2006), as well as the need for more clarification in the user’s manual

regarding the assessment of both the larger AA area and the project area. Therefore, if both

samples were able to continue with this assumption that multiple boundaries could have been

established for this attribute at different sampling areas, and both could result in varying attribute

scores, then it is possible that the methodology for this attribute may require more refinement.

Furthermore, editing the AA boundary post-sampling would bias the second sampling by

attempting to match the prior season, and the goal of this study was to examine the results of

summer 2014 independently of the results of the fall 2014 so that a comparison could be made

after both samples had been completed, thereby introducing the least amount of bias as possible.

In light of the potential for inconsistency, boundary establishment can be refined in future

research to rectify these results. Fennessey et al. (2004) reviewed a sample of rapid assessment

methods utilized across the nation, having noted that defining the AA is critical because of how

it sets the foundation for how the data is collected. Establishing the AA requires consistency in

mapping and sample design (Fennessey et al, 2004). If sampling error was not the explanation

for this result, the second and third explanation might still have been likely.

ATTRIBUTE 2: Hydrology

The results indicated that the hydrology attribute and each of the sub-metrics of water-

source, hydro-period and hydrologic connectivity were indeed the same from spring to late

summer, which supports rationale number one that there had not been observable changes to

Kachituli’s water source. This is consistent with the methodology that assigns an “A” (12 points)

to depressional wetlands whose water source is from natural processes like groundwater and

precipitation (CWMW, 2013). Given that water sources are often characterized prior to site visits

based on background research on the site, the hydrology was expected to remain the same even

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though it was possible that the hydrologic connectivity underground could have changed.

However, to the extent of our visual observations, there were no indications during sampling that

the oxbow was not receiving water diversions to sustain the pond nor was runoff from storm

drains or irrigation detected on-site.

Correspondingly, these water source metrics are key in assessing the capability of the

area to sustain both plant and animal habitats, especially during dry seasons (CWMW, 2013).

Based on these observations, this was the only attribute that received both an overall score of

100% and the same scores for its sub-metrics for both the spring and summer. This is consistent

with the results of the Stein et al. (2009) CRAM validation study which determined that

compared to the other three attributes, the hydrology attribute had the lowest percent error rates

between teams of practitioners who assign scores.

However, what did remain uncertain in the context of determining seasonal variation

during this drought year was the sub-metric of hydroperiod. The CRAM manual rating for this

sub-metric states that depressional wetlands receive an “A” when the “hydroperiod of the AA is

characterized by mostly natural patterns of filling or inundation and drying or drawdown”

(CWMW, 2013, p. 19). Because evidence of “stress and mortality of hydrophytes” and

“encroachment of terrestrial vegetation” was observed, we were able to characterize that

Kachituli was experiencing a reduced duration of its hydroperiod and thereby assign an “A”

score. What was unclear, however, was whether this drawdown fit into the natural drawdown

patterns of the site was significantly induced by severe drought conditions. The hydro-period

metric captures inundation during average or natural patterns (CWMW, 2013) but decreased

regional rainfall indicates that 2014 was not an average year. Therefore, both because no

previous CRAM data exists for this site, and because the severity of this current drought is

22

unprecedented, characterizing the variations within the sub-metric hydro-period during this

atypical period may skew the assessment in favor of “A” when conditions may suggest lower

scores when/if drought pressures result in variations that eventually become “natural.” This may

have direct restoration management implications, in that stakeholders have to be able to identify

which site conditions are changing because natural, expected patterns and therefore do not

require allocation of funding, and which site features are in need of more resource investment to

maintain optimal habitat communities. Thus, in order to account for seasonal variability more

clearly, perhaps parameters could be established within these assessments that specify how long

and by how much hydro-period patterns have to deviate from the “norm” in order to be of

concern.

ATTRIBUTE 3:Physical Structure

Based on germination, flowering and dormancy patterns among differing plant

communities such as annuals and perennials, we expected to observe changes within the physical

structure attribute (Mitsch & Gosselink, 2007). CRAM scores did, indeed, reflect changes within

the raw attribute score of this attribute. The first of these changes were measured within

the “structural patch richness” sub-metric. During the spring sampling period, nine patch types

were identified as follows: organic debris across depressional wetland, animal mounds/burrows,

parallel high water marks, algal mats, large woody debris, non-vegetated flats or bare ground,

open water, soil cracks, and standing snags (CWMW, 2013).

Whereas, in the summer, six patch types were identified: standing snags, animal mounds,

parallel water marks, large woody debris, bare ground and soil cracks (Ibid). Because data

gathered from this physical structure attribute is used to infer relationships between the

landforms and the wildlife that inhabits the area, these observable habitat patches are key

23

components of the CRAM (Sutula et al, 2006). For instance, the presence of animal mounds may

infer the existence of mammals that occupy Kachituli’s depressional wetland. Although not

directly listed as one of the “structural patch types, ” scat, possibly from a larger animal (i.e.

coyote or bear) were observed within the AA (figure 4). In this way, because direct sampling

of these animals is not performed with the CRAM, such observations serve as indicators from

which the potential capacity of the Kachituli to provide

habitat for a variety of animals can be inferred (Stein et

al , 2009).

The presence of standing snags can be a habitat

feature utilized by the endangered species Swainson’s

Hawk (Buteo swainsonii) (Green and Kelley, 1990). In

this way, this patch type sub-metric act as a surrogate

for habitat since the species itself is not directly

measured (Sutula, et al, 2006; Stein et al, 2009; Fennesey et al, 2004).

Although it was expected that we the CRAM would also measure changes within the sub-

metric of topographic complexity, the CRAM score did not capture variations from spring to

summer. Still, the seasonal changes measured by the CRAM in the “structural patch richness”

sub-metric were sizeable enough to reduce the overall score of this attribute. Specifically, these

changes resulted in a reduction in the score for that attribute by six points resulting in the

reduction in the final attribute score from 75% to 62.5 % or “C to D.” The physical structure

attribute represents expected capacity of the site to provide habitat for and support of various

plant, animal, bird and amphibian populations (SWRCB, 2014). Thereby, our observations

resulting in a decreased score in this attribute corresponds to an expected reduction in the

Figure 4: Inferred Habitat Use by Mammals through CRAM Metric-Positive effect on CRAM Score

24

capacity of the site to maintain a diversity of habitat features during the summer as compared

with the spring (SWRCB, 2014). This is consistent with what was observed in the spring

sampling: the oxbow was ponded (figure 4) which captured both open water and algal mats.

Whereas, during the summer sampling, the oxbow had gone dry and therefore the algal mats

were absent (figure 5). In a “normal year” the Oxbow would remain ponded and so open water

and algal mats could be expected to provide habitats or food sources for amphibians, migratory

birds as well as other obligate wetland species.

Figure 5: Kachituli Oxbow Fall 2014 (McGuirk, 2014)

25

Figure 6: Kachituli Oxbow Summer 2014 (Stevens, 2014)

Given this importance of habitat to restoration condition, it was anticipated that such a

decrease in this attribute’s score would have resulted in a decrease in the overall AA scores from

spring to summer. However, it seemed that because the scores of attribute one (buffer and

landscape context) also changed, there seemed to be a cancelling-out effect between these two

attributes, thereby nullifying individual effect upon the overall CRAM scores as a whole. This

phenomenon was also observed by a Corps regulatory review that suggested that because the

overall score is aggregated, it is often unclear which scores are actually changing within the

overall score (Klimas, 2008). Therefore, it may not have been that the decrease in patch types

26

was not sizeable enough to warrant a change but rather that other attribute fluxes masked this

attribute’s significance.

ATTRIBUTE 4: Biotic Structure

Attribute four, biotic structure, also received the same score in the summer as was issued

for the spring. However, when reviewing the sub-metric indicators, it becomes evident that

although the scores assigned were the same from season to season, the number of co-dominant

species observed did change from season to season, which was consistent with our ocular

estimates and photo records. This observation was, thereby, inconsistent with explanation

number one, that there really was no change. The presence of co-dominant species falls within

the plant community sub-metric of attribute 4 (CWMW, 2009). Specifically, during the spring,

10 species were identified, 4 of which were invasive; during the summer 12 species were

identified, 5 of which were invasive. Thus, the percent invasion increased from 40% in the spring

to 42% in the summer, specifically, a sizeable increase in the invasive weed horseweed, (Conyza

canadensis (figure 7). As is well documented, the presence of invasive species has been a critical

concern in adaptive management strategies (Moore et al, 2011; Golet et al., 2013). Perhaps, then,

if it was true that invasive species were indicators of stress on the site and that the summer site

did inventory more invasives than during the spring, it may have been that this 2% increase in

invasives was not sizeable enough to warrant a lower attribute score, which supported

explanation number two.

27

Figure 7: Presence of Invasive Species- Negative effect on CRAM Score

However, the results of the “biotic structure” attribute were not fully explained by

rationale number two. This inconsistency was shared in the Stein et al (2009) study that

correlated statewide CRAM scores with comparable, independent, intensive data, also referred to

as level three data. Specific to the “biotic structure” attribute, this study detected a twenty-five

percent margin of error within scores performed months apart from each other (Stein et al, 2009).

This study further suggested that “seasonal differences in plant communities may contribute to

variability in this attribute” (Stein et al, 2009, p. 258), which was the expectation of the summer

CRAM performed at Kachituli. Plants used in restoration of this Sacramento Valley region

typically exhibit characteristic responses to increased duration of sunlight, decreased amount of

precipitation, reduced access to shade and hotter temperatures (Moore et al, 2011). That the raw

attribute score for attribute four (biotic structure) remained the same even throughout the driest

28

season is an inconsistency that may have been supported by the third explanation. Perhaps there

were sizeable differences in the site condition that the CRAM was unable to detect. In this

interpretation, the CRAM is too course of a filter to detect inter-seasonal variation even in

extreme conditions (Stevens, 2014).

Another inconsistency within the data indicated that there were four species of willows in

the very tall layer in the summer but only 3 species of willows in the spring. This is inconsistent

with the characteristics of the “very tall” layer, in that it is more likely to observe a species

moving from the “short layer” to “medium layer” within a season, especially if the species is a

grass privy to more vigorous growth during its growing season. It is less likely, however, to

observe an absence of a species in the “very tall” layer from one season to the next. Or, perhaps

the particular species may have been considered plentiful enough to be considered a dominant in

the strata layer in one sampling and yet not in the next. Assuming that both sampling areas were

representative of the Oxbow, this would mean that this species would have had to suffer a loss so

significant that less than ten percent still remained. Because the stressor checklist surveyed

within the CRAM had not indicated that any drastic stressors occurred within the area, we

predict that this result may have been due to sampling error through the misidentification of

plants.

To recalibrate the inconsistency presented in this attribute, the co-dominant species and

percent invasion were recalculated both with all four willows and with three. The result was that

either way, the presence of + one species had no effect on the alpha- numeric score of sub-

metrics A or B. This is the case because the scores of “12” are assigned when there are at least 4

plant layers and when the number of co-dominant species is > 9. In both CRAM seasons, +

species maintained these thresholds. As for percent invasion (sub-metric C), in order to have

29

produced a variation in the score, the percentage would have had to be reduced by over 10 % to

be re-scored as a “C” instead of the “D” that was captured in both systems.

Still, as noted within the methodology, in order for the CRAM to be an effective indicator

of site conditions, it is optimally executed by professionals in the field (Sutula et al 2006;

Klimas, 2008). Nonetheless, as attested by Stein et al. (2009), misidentification of species is

common even within a broad spectrum of expertise levels, and so it is likely that the willow was

mis-identified, especially given that species of Willows are often identified when their catkins

are present, as was not the case during samplings. Moreover, because inconsistent plant

identification is recorded even in situations outside of academic training, expertise in wetland

identification has been noted as a substantial area for improvement in the validation phase (Stein

et al, 2009). However, Sutula et al (2006) suggested that there are tradeoffs to necessitating high

level of botanical expertise: the more skill that is needed to perform these assessments, the more

accurate your scores are likely to be. But this comes at the expense of time and money. Thereby,

with higher costs and sizably more time invested in assessments, the scope to which the CRAM

can be applied becomes smaller as well (Sutula et al, 2006).

Even accounting for these tradeoffs in expertise, studies suggest that CRAM scores have

been statistically reliable in relaying the overall condition of mitigation sites (Stein et al, 2009,

Sutula et al, 2006). Since measure to troubleshoot errors have been attempted, it seems that

although the scoring of the CRAM has many complex metrics where potential for errors can

manifest, it still seems that skills-set may have been less of a factor than rationales 2 and 3.

The Need for Qualitative Data Collection in Future Studies

As several authors suggest, these indicators, or metrics, relate to how ecosystems

function and therefore need to be validated using the components of the 1, 2 and 3, tier

30

framework (Stein et al, 2009; Sutula et al, 2006; CWMW, 2008; CWMW, 2011). In light of this,

Stein et al (2009) assert that these assessments have to be compared to independent results in

order to maintain credibility. This distinction affirms the need for the CRAM results of this

paper to be compared with an independent, consistent and repeatable method for qualitative

sampling in addition to the CRAM for each season. This is not to suggest that the implications of

a CRAM score poorly indicate a habitat's function, but rather that its scores should not be

performed as the single method in the field. Intensive L3 data collection like vegetation

sampling, bird surveying or benthic macro-invertebrate surveying is necessary validate the

assessment of the CRAM on a state-wide scale (Stein et al, 2009). Therefore to the CRAM's

credit, and therefore, legitimacy, this concern is emphatically addressed as an integral component

of the CRAM methodology, specifically within the "validation" process which attempts to rectify

these qualms between the CRAMs and independent sampling methods (CWMW, 2009; Fennessy

et al, 2004; Stein et al, 2009; CCWG, 2013). However, in agreement with many other studies,

Stein et al (2009) provide a caveat to this need for comparison to independent data, noting that

similar, direct-in-the- field data is often unavailable, especially for metrics like invertebrate or

avian populations where direct "collections" are far from optimal.

Specific to the extent of this study, collecting qualitative vegetation data within Kachituli

would be the preferred L3 intensive data needed for this site, especially since surveying for

Valley Elderberry Longhorn Beetle (Desmocerus californicus dimorphus) would have been

restricted to the riparian areas South of the Oxbow where the CRAM was performed. Vegetation

data was collected during this study, but was insufficient for the needs of this comparison. Since

qualitative data was not collected during the spring 2014 sampling but was collected during the

summer sampling, characterizations could be made from comparing the summer vegetative data

31

to the summer CRAM, but these comparisons could not be then taken together and compared to

the spring 2014 data to characterize seasonal variability because the goal of this study was to

characterize the inter-seasonal variability. Using the vegetative data to validate one season

without having vegetation data available to validate the other would not adequately address the

research goals of this study. Therefore in future studies, one approach in validating the results of

the CRAM as a tool for assessing this site would be to collect qualitative (L3) data for both

seasons of interest so that the changes in plant community composition and abundance in one

season can be directly compared to the other in the same way that the CRAM scores have

already been compared from one season to the other in this study. For example, the L3 data may

show how well the percent invasion metric within the biotic structure attribute (attribute 3)

assessed within the CRAM compares to the plant community composition sampling. Future

studies have the potential to shed light on several of the inconsistencies detected within the

CRAM as a tool for detecting the inter-seasonal variability of site conditions.

What was consistent within these CRAM scores was an observable metric within each of

the four attributes. This highlights the biotic-focused intent of scoring for each attribute within

the CRAM. Specifically, the hydrology attribute is scored according to its capacity to provide

habitat for animals and plants (Stein et al, 2009). As McGuirk et al (2014) concluded, the spring

2014 results of Kachituli’s CRAM score characterized it as successful, faring above average in

the hydrology and biotic attribute. Because our overall scores for the summer remained the same,

the conclusions of McGuirk et al (2014) can be inferred that the site conditions at Kachituli have

successfully maintained landscapes that support abundant habitats for flora and fauna. However,

further studies are needed to characterize the initial objectives of this study.

32

It is still unclear whether the site is still successful because seasonal changes were not

sizeable enough to reduce these scores or if the CRAM is not a tool sensitive enough to utilize

for this purpose. Consistent “average” scores from year to year are important for measuring

mitigation or restoration success since they serve as a tool for management and landowners to

characterize the continuing needs of a project (Stein et al, 2009). The expectation of this study

was that the CRAM assessment of this mitigation project’s ecological condition during a drought

year would result in a deviation from the “average” CRAM score. Because the scores did not

change overall, it is still unclear whether this consistency signifies that the drought has not

significantly and negatively impaired the Kachituli Oxbow or whether the CRAM is simply not

sensitive enough to detect the changes that may be occurring as the drought continues. Perhaps

with continued sampling and utilization of the CRAM at regular intervals, a better

characterization of the seasonal changes will be encountered.

33

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