OCS Study 

•

MMS 2003-075 

Species-level identification of infaunal 

samples and the relationship between 

taxonomic aggregation and the 

Before-After/Control-Impact Paired 

Series assessment design 

Final Technical Summary 

Final Study Report 

U.S. Department of the Interior 

Minerals Management Service

Pacific OCS Region 
DRAFT Final Technical Summary – Holbrook et al 

2 
Species-level identification of infaunal 

samples and the relationship between 

taxonomic aggregation and the 

Before-After/Control-Impact Paired 

Series assessment design 

Final Technical Summary 

Final Study Report 

Authors 

S.J. Holbrook 

M.H. Carr 

C.W. Osenberg 

Principal Investigators 

Prepared under MMS Cooperative 

Agreement No. 14-35-0001-30761 

by 

Southern California Educational Initiative 

Marine Science Institute 

University of California 

Santa Barbara, CA 93106-6150 

U.S. Department of the Interior 

Minerals Management Service Camarillo

Pacific OCS Region November 2003
DRAFT Final Technical Summary – Holbrook et al 

4 

Disclaimer 

This report has been reviewed by the Pacific Outer Continental Shelf Region, Minerals Management 

Service, U.S. Department of the Interior and approved for publication. The opinions, findings, 

conclusions, or recommendations in this report are those of the author, and do not necessarily reflect 

the views and policies of the Minerals Management Service. Mention of trade names or commercial 

products does not constitute an endorsement or recommendation for use. This report has not been 

edited for conformity with Minerals Management Service editorial standards. 

Availability of Report

Extra copies of the report may be obtained from: 

U.S. Dept. of the Interior 

Minerals Management Service 

Pacific OCS Region 

770 Paseo Camarillo 

Camarillo, CA 93010 

Phone: 805-389-7621 

A PDF file of this report is available at: 

http://www.coastalresearchcenter.ucsb.edu/SCEI/ 

Suggested Citation 

The suggested citation for this report is: 

Holbrook, S.J., Carr, M.H., and Osenburg C.W. Species-level identification of infaunal samples and 

the relationship between taxonomic aggregation and the Before-After/Control-Impact Paired Series 

assessment design. MMS OCS Study 2003-075. Coastal Research Center, Marine Science Institute, 

University of California, Santa Barbara, California. MMS Cooperative Agreement Number 14-35-

0001-30761. 34 pages. 
Table of Contents 

FINAL TECHNICAL SUMMARY....................................................................... 1 

FINAL STUDY REPORT...................................................................................... 11 

Motivation........................................................................................................... 11 

 Previous Studies of Taxonomic Aggregation ..................................................... 11

 The BACIPS Design ........................................................................................... 13

 Expected Effects of Taxonomic Aggregation..................................................... 15

 Taxonomic Aggregation and the Portfolio Analogy........................................... 16 

 Species-level Identification Using Existing Samples ......................................... 17 

 Produced Water....................................................................................... 17 

 Power Plant ............................................................................................. 18 

 Approach Taken...................................................................................... 19 

 The Databases ......................................................................................... 24 

 Issues that can be addressed with the new databases.............................. 25 

 Literature Cited ................................................................................................... 27 

 Appendix A: Carpenteria Infaunal Density Cores ............................................. 30 

 Appendix B: Gaviota Density Cores.................................................................. 31

 Appendix C: Gaviota Emergence Traps ............................................................ 32 

 Appendix D: Gaviota Re-Entry Traps................................................................ 33 

 Appendix E: Taxonomic List............................................................................. 34 

List of Tables and Figures 

Table 1. (Technical Summary) Summary of Sampling Results.............................. 4 

Table 1. Sources of Variance ................................................................................... 12 

Table 2. Summary of Samples for Species-level Identification............................... 19 

Figure 1. Patterns of spatial and temporal variation ................................................ 14 

Figure 2. Responses of two species to different sources of variation ...................... 15 

Figure 3. Total and mean abundance of Tellina carpenteri..................................... 20 

Figure 4. Example comparison of the size structure of Tellina carpenteri.............. 22 

Figure 4. (continued) Panel B ................................................................................. 23 
DRAFT Final Technical Summary – Holbrook et al 

ii 
Species-level Identification of Infaunal Samples 

 1 

 

FINAL TECHNICAL SUMMARY 

STUDY TITLE: Detecting Ecological Impacts: Effects of Taxonomic Aggregation in 

the Before-After/Control-Impact Paired Series Design 

REPORT TITLE: Species-level identification of infaunal samples and the relationship 

between taxonomic aggregation and the Before-After/Control-Impact Paired Series 

assessment design 

CONTRACT NUMBER: 14-35-0001-30761 

SPONSORING OCS REGION: Pacific

APPLICABLE PLANNING AREA(S): Southern California 

FISCAL YEAR(S) OF PROJECT FUNDING: FY 96, FY 97, FY 98 

COMPLETION DATE OF REPORT: November 2003 

COST(S): FY 96: $211,616; FY 97: $69,476; FY 98: $61,540; FY 99: no cost 

CUMULATIVE PROJECT COST: $342,633 

PROJECT MANAGER: Russell J. Schmitt 

AFFILIATION: University of California, Santa Barbara 

ADDRESS: Coastal Research Center, Marine Science Institute, University of California, 

Santa Barbara, CA 93106-6150 

PRINCIPAL INVESTIGATORS: 1

S.J. Holbrook, 2M.H. Carr, and 3C.W. Osenberg

ADDRESSES: 1

Department of Ecology, Evolution, and Marine Biology, University of 

California, Santa Barbara, CA 93106-9610; 2

Department of Ecology and Evolution, Long 

Marine Laboratory, 100 Shaffer Road, University of California, Santa Cruz, CA 95060; 

3

Department of Zoology, PO Box 118525, 223 Bartram Hall, University of Florida, 

Gainesville, FL 32611-8525

KEY WORDS: Santa Barbara Channel; produced water; infauna, taxonomy; BACIPS; 

assessment design; statistical power 

BACKGROUND: Traditional studies of environmental impacts are notoriously 

expensive, due in part, to the cost of species-level identification in systems that are 

extremely speciose (e.g., as exemplified by infaunal marine communities). However, less 

expensive studies using higher taxonomic levels of identification (e.g., family or class) are 

typically chided as inadequate. Such criticism is often unsubstantiated: e.g., Carney 

(1996) concluded that studies of the effects of taxonomic aggregation are critically needed 
Final Technical Summary – Holbrook et al 

2 

before we can best design future assessments. Importantly, any critical evaluation must 

occur in the context of an appropriate design. For example, previous attempts to address 

this issue (e.g., Ferraro and Cole 1992) have been based on simple, Control-Impact 

designs that confound spatial variation driven by other processes with the effects of the 

anthropogenic activity being studied (Osenberg and Schmitt 1996). Instead, we require an 

evaluation of taxonomic aggregation in the context of a sampling design that can reliably 

separate a putative impact from other sources of temporal and spatial variability. 

Therefore, effects of taxonomic aggregation should be explored in the context of the 

Before-After/Control-Impact Paired Series assessment design (Stewart-Oaten et al. 1986; 

Schmitt and Osenberg 1996). 

OBJECTIVES: Our main objective was to obtain species-level identification of 

organisms that were archived in previously collected MMS/UC SCEI samples (a portion 

of these samples formed the basis of the previous analyses of produced water by Osenberg 

et al. 1992a,b, 1994, 1996). We also aimed to obtain data on size-frequency distributions, 

which could prove to be a more sensitive indicator of environmental impacts (e.g., 

Osenberg et al. 1996). Finally, we updated the taxonomic designations for another 

comprehensive dataset collected for a study of the effects of a nuclear power plant so that 

all datasets used an identical taxonomic scheme and could therefore be analyzed similarly. 

We selected these previous studies because they offered the opportunities to examine 

taxonomic aggregation in the context of BACIPS assessment designs. Finally, we had 

hoped to conduct analyses of these data, but the species-level identification took 

significantly more time and resources than anticipated (further highlighting a limitation of 

species level data). As a result, we are making the resulting data available to the scientific 

community via the internet (e.g., on the web page for the UCSB led Long Term 

Ecological Research program) to facilitate subsequent analyses (e.g., to quantify the effect 

of taxonomic pooling on the error variance in BACIPS designs and thus the power of 

assessment studies). While the species level identifications were being completed, we 

also continued our application of BACIPS to new environmental contexts, including the 

design of MPAs and the study of artificial reefs. 

DESCRIPTION: Scientifically based policy decisions are often difficult to make given 

the uncertainty associated with the documentation of environmental impacts. This 

uncertainty has at least two key sources: (i) the extreme expense of doing field 

assessments (which typically require species-level identification, which therefore limits 

the number of studies that can be adequately funded), and (ii) the application of 

inappropriate assessment designs. Species-level identification is costly in time and money 

and requires the work of a few highly trained specialists. Such detailed taxonomic 

resolution may not be necessary. Indeed high statistical power might be achieved even 

with studies using crude levels of taxonomic resolution. Unfortunately, most studies of 

the effects of taxonomic resolution have relied on ineffective assessment designs. In 

contrast to these commonly used designs, the Before-After/Control-Impact Paired Series 

(BACIPS) design is more likely to be able to separate effects of a perturbation from other 

sources of spatial and temporal variation in environmental parameters (Stewart-Oaten et 

al. 1986, Osenberg and Schmitt 1996). However, to date, there has been little study of the 

effects of taxonomic aggregation (i.e., the effect of identifying organisms to differ levels 

of taxonomic resolution) within the context of BACIPS assessment designs. 
Species-level Identification of Infaunal Samples 

 3 

 

In a BACIPS design, the efficacy of taxonomic aggregation will depend on the pattern of 

temporal and spatial variation in density as well as the covariance (in space and time) of 

species that are pooled into higher units and their relative abundance. It has been argued 

that pooling could reduce error variance and thus increase power of a statistical test of an 

impact (e.g., Carney 1996). This could occur if species negatively covary in their 

responses to natural processes that drive spatial and temporal variation. However, this 

assertion ignores the sensitivity of different species to the impact. If species within a 

pooled taxonomic group exhibit opposite responses to the impact (e.g., some increase in 

abundance while other decrease), as suggested by the assumption about their response to 

natural processes, then pooling is likely to inhibit the detection of impacts by reducing the 

"strength of the signal". The overall effect of taxonomic pooling (or resolution) can only 

be assessed by simultaneous consideration of species' responses to natural variation and to 

the putative impact. 

It is vital that these more sophisticated analyses based on a BACIPS assessment design be 

conducted. The results not only have important implications for the management of 

produced water discharge, but may also fundamentally change the way in which field 

assessments are conducted. In these times of limited finances, it is vital that scientifically 

defensible decisions be made with regard to the allocation of funds among competing 

needs. Enormous expenditures on species-level identification might be a poor use of 

funds (if it adds little to the power of an assessment); on the other hand, using crude levels 

of taxonomic resolution might yield a large number of equivocal studies (if higher 

resolution is necessary to document the impacts). Answers to these issues require a 

comparison of the effect of taxonomic resolution in the context of a BACIPS design. This 

requires species-level identification of organisms sampled in a BACIPS design, and 

subsequent exploration of the effects of taxonomic aggregation on the statistical 

inferences derived from the BACIPS design. 

STUDY RESULTS: Our project produced detailed databases that includes high level 

taxonomic resolution on over 250,000 organisms collected in four different sampling 

schemes as part of previous MMS-sponsored studies: 1) infaunal cores collected at 20 

different sites representing a gradient of distances from a produced water discharge in 

Carpenteria, California; 2) infaunal cores collected at three study sites near and far from a 

proposed produced water outfall near Gaviota, California; 3) emergence traps deployed at 

these same three sites near Gaviota, and used to sample infauna as they emerged from 

bottom sediments during the night; and 4) re-entry traps used to sample infauna as they 

returned to the sediments at night. Between 54 and 86% of all organisms were identified 

to the species level, depending on the study (Table 1). Overall, only 12.1% of organisms 

could not be identified to at least the genus level. 
Final Technical Summary – Holbrook et al 

4 

Table 1. Summary of sampling results in which organisms in each of four different studies were identified 

to the lowest possible taxonomic level (i.e., to Phylum, Class, Order, Family, Genus, or Species). 

Site: CARPENTERIA GAVIOTA GAVIOTA GAVIOTA TOTAL

Sample type: Infaunal 

cores 

Infaunal 

cores 

Emergence 

traps 

Reentry 

traps 

total number of 

organisms 

identified: 

124,101 104,317 16,010 6,287 250,715

Percent identified 

to Genus or 

Species: 

93.3% 84.7% 66.4% 87.7% 87.9% 

Percent identified 

to Species: 

86.2% 69.8% 54.4% 77.4% 77.1% 

In addition to the creation of the species-level databases, we also trained students, 

disseminated the results of the MMS/SCEI program to other scientists, and extended the 

BACIPS design to coastal resource management, in particular the assessment of marine 

reserves and artificial reefs. We presented the application of the BACIPS sampling design 

and analytical framework, which is the focus of our MMS project, at numerous scientific 

venues. Many of these talks discussed the application of BACIPS in evaluating the 

effectiveness of marine reserves and artificial reef programs for management of coastal 

resources. The theoretical framework developed in the context of artificial reefs also has 

relevance to the recent discussion of the "rigs-to-reef" programs, which is of considerable 

interest to MMS. 

SIGNIFICANT CONCLUSIONS: The BACIPS design continues to be underutilized in 

environmental assessments. For example, we have found that no studies of artificial reefs 

and marine protected areas have used a properly executed BACIPS and thus are unable to 

unequivocally separate effects of these activities from other sources of variation. The 

same continues to hold for the study of taxonomic aggregation, where conclusions have 

been based on incomplete designs, such as the Control-Impact and the Before-After 

designs. To this end, we have 1) extended the application of the BACIPS assessment 

design to new contexts; and 2) completed an extensive database giving species-level 

identification for previously collected MMS samples. Over 250,000 organisms were 

individually identified and counted to generate four different databases that provide 

spatial, temporal, and spatiotemporal variation in densities of infauna. These databases 

are being made available to the public in the hope that they will facilitate future analysis 

of the effects of taxonomic aggregation in the context of BACIPS assessment designs. 

STUDY PRODUCTS: 

1) Talks at national and international meetings that directly benefited from MMS/SCEI 

support (e.g., and highlighted SCEI projects or the BACIPS assessment design): 
Species-level Identification of Infaunal Samples 

 5 

 

Carr, M.H. Evaluating the effectiveness of marine reserves. California and the World 

Ocean ’02 Conference, Santa Barbara, CA, October 2002. 

Carr, M.H. Science and Marine Protected Areas. Federal Marine Protected Areas 

Education Workshop, Morro Bay, CA, September, 2002. 

Carr, M.H. Science and Marine Reserves. United States Commission on Marine 

Policy – Monterey, CA, April, 2002. 

Carr, M.H. The Ecological Basis of the Design and Evaluation of Marine Reserves. 

Fisherman’s Forum on Marine Protected Areas, sponsored by the Pacific 

Marine Conservation Council – Portland, OR, January, 2002. 

Osenberg, C.W., C.M. St. Mary, and B. Bolker. 2002. Assessing the efficacy of 

ecosystem management and marine reserves: the need for new approaches. 

The Fourth Mote International Symposium in Fisheries Ecology: Confronting 

Tradeoffs in the Ecosystem Approach to Fisheries Management, Sarasota, 

Florida, November 2002. 

Osenberg, C.W. and C.M. St. Mary. 2002. Marine reserves: a tentative and 

cautionary evaluation of a powerful tool. Annual meeting of the Florida 

Chapter of the American Fisheries Society. Brooksville, FL. February 2002 

(invited). 

Osenberg, C.W. 2002. Marine protected areas: a critique of current assessment 

approaches. International Workshop: Restoring and Sustaining Diversity of 

Tropical Pacific Coral Reef Fish. Mo'orea, French Polynesia. April 2002 

(invited). 

Carr, M.H. and C. Syms. Evaluating the Effectiveness of MPAs: The Whys and 

Hows. Invited Plenary talk for the Fisheries, Oceanography and Society 

Symposium Series presented by the Ocean Life Institute of the Woods Hole 

Oceanographic Institute. Woods Hole, Mass. August, 2001. 

Carr, M.H. and C. Syms. Natural Science and Marine Protected Areas: Moving 

Forward. Invited Plenary talk for the Pacific Marine Protected Areas Science 

and Coordination Workshop convened by the NOAA Marine Protected Areas 

Science Institute – Monterey, CA, August, 2001. 

Carr, M.H. and C. Syms. Setting Conservation Targets in a Changing World: 

Evaluating Effectiveness of MPAs. Building Linkages for Marine Protected 

Areas in North America II: A Workshop of the North American Marine 

Protected Areas Network. NOAA Marine Protected Areas Science Institute – 

Monterey, May, 2001. 

Carr, M.H. Science and the Design and Evaluation of Marine Reserves. Invited talk to 

the Alliance for Communities of Sustainable Fisheries – Monterey, CA, 

November, 2001. 

Wilson, J.A., C.W. Osenberg, C.M. St. Mary, C.A. Watson, and W.J. Lindberg. 2001. 

Artificial reefs, the attraction-production issue, and density-dependence in 

coral reef fishes. Larry McEdward Memorial Symposium. Gainesville, FL. 

December 2001. (invited) 

Wilson, J.A., C.W. Osenberg, C.M. St. Mary, C.A. Watson, and W.J. Lindberg. 

Artificial reefs, the attraction-production issue, and density-dependence in 

coral reef fishes. Annual meeting of the American Society of Ichthyologists 

and Herpetologists, La Paz, Mexico, June 2000. 

Carr, M.H., P. Raimondi, C. Syms. 1999. Effectiveness of Marine Reserve: 

Approaches to Evaluation and Need for Adaptive Management. Invited 
Final Technical Summary – Holbrook et al 

6 

Symposium talk at the 80th Annual Meeting of the Western Society of 

Naturalists. Monterey, CA, December, 1999. 

Carr, M.H. Sustainable Ecosystems, Fisheries and Marine Protected Areas” Invited 

Speaker, National Research Council (NRC), Ocean Studies Board. Committee 

on the Evaluation, Design and Monitoring of Marine Reserves and Protected 

Areas in the United States. Monterey, CA, May, 1999. 

Osenberg, C.W., C.M. St. Mary, J.A. Wilson, and W.J. Lindberg. A quantitative 

framework to evaluate the attraction-production controversy, with application 

to marine ornamental fisheries. Seventh International Conference on Artificial 

Reefs and Related Aquatic Habitats. Sanremo, Italy, October 1999. 

Osenberg, C.W., C.M. St. Mary, W.J. Lindberg, and T.K. Frazer. Marine reserves: 

implications of stage-structured fish populations and density dependence. 

Annual meeting of the Ecological Society of America, Spokane, Washington, 

August 1999. 

Osenberg, C.W., C.M. St. Mary, W.J. Lindberg, and T.K. Frazer. The application of 

ecological models to the design of marine reserves: what are the effects of 

population stage-structure and density dependence? Florida Ecological and 

Evolutionary Symposium, Achbold Biological Station, Lake Placid, FL, April 

1999. (invited by graduate student organizers) 

Wilson, J.A., C.W. Osenberg, C.M. St. Mary, C.A. Watson, and W.J. Lindberg. 

Artificial reefs, the attraction-production issue, and density-dependence in 

marine ornamental fishes. First International Conference on Marine 

Ornamentals. Kailua - Kona, Hawaii, November 1999. 

Lindberg, W.J., C.M. St. Mary, T.K. Frazer, and C.W. Osenberg. Marine reserves, 

complex life histories, and the meaning of essential fish habitat: the importance 

of closing the life cycle. The Second Mote International Symposium in 

Fisheries Ecology: Essential Fish Habitat and Marine Reserves, Sarasota, 

Florida, November 1998. 

Osenberg, C.W., R.J. Schmitt, S.J. Holbrook, C.M. St. Mary, and T.W.-M. Fan. 

Effects of produced water on mussel growth and production: application of the 

BACIPS design. 27th Benthological Ecology Meetings, Melbourne, Florida, 

March 1998. 

2) Invited seminars that included data or approaches developed in MMS/SCEI projects. 

Carr, M.H. 

• Monterey Institute of International Studies, Monterey, CA, March 

2003. 

Holbrook, S.J. 

• (previously reported in Reed and Holbrook Final Report). 

Osenberg, C.W: 

• Bodega Marine Laboratory, Bodega Bay, CA, Jan 2002 

• Department of Biology, University of California, Santa Cruz, March 

2002. 

• Department of Zoology, University of Florida, September 2002. 
Species-level Identification of Infaunal Samples 

 7 

 

• Ecology & Evolution Seminar Series, University of California, Davis, 

CA, Jan 2002 

• National Marine Fisheries Service (Northwest Fisheries Science 

Center), Seattle, WA, May 2002 

• Department of Zoology, University of Washington, Seattle, March 

2001 (Invited as the first W.T. Edmondson Memorial lecturer). 

• Department of Ecology and Environmental Science, Umeå University, 

Sweden, March 2000. 

• Department of Zoology, University of Florida, September 1998. 

• Department of Wildlife Ecology and Conservation, University of 

Florida, March 1997. 

3) Invited participation in workshops that benefited from MMS/SCEI projects (e.g., the 

application of BACIPS to environmental assessments). 

Annual workshop of the UC Toxic Substances Research and Teaching Program 

(Coastal Toxicology Component). Bodega Marine Laboratory, September, 

2002. (Holbrook, Osenberg).

Restoring and Sustaining Diversity of Tropical Pacific Coral Reef Fish. Mo'orea, 

French Polynesia. April 2002 (Holbrook, Osenberg).

Springs Coast Technical Advisory Meeting, Southwest Florida Water Management 

District. Crystal River, FL. January 2002. (Osenberg)

Steinhatchee Reef Ecosystem Workshop. Steinhatchee, FL, November, 2002. 

(Osenberg) 

Annual symposium of the UC Toxic Substances Research and Teaching Program. 

Tahoe, California. April, 2001 (Holbrook, Osenberg).

Development of an integrated research plan for analyzing the viability of a marine 

reserve network in the Bahamas. Lee Stocking Island, Bahamas. December 

2000 (Osenberg).

Annual workshop of the UC Toxic Substances Research and Teaching Program 

(Coastal Toxicology Component). Bodega Marine Laboratory, September, 

1999. (Holbrook, Osenberg).

Southwest Florida Water Management District assembly concerning Ecological effects 

of nutrient loading along the Gulf coast of Florida. Crystal River, Florida, 

May 1998. (Osenberg) 

Florida Big Bend coastal research workshop: toward a scientific basis for ecosystem 

management. Sponsored by Florida Sea Grant, UF Department of Fisheries 

and Aquatic Sciences, USGS (Florida Caribbean Science Center), and 

Suwannee River Water Management District. Steinhatchee, Florida, May 

1997. (Osenberg) 

4) Publications and technical reports for research that directly benefited from 

MMS/SCEI support (e.g., and highlighted SCEI projects or the environmental 

assessment designs): 

Carr, M.H., M.V. McGinnis, G.E. Forrester, J. Harding and P.T. Raimondi 

Consequences of Alternative Decommissioning Options To Reef Fish 

Assemblages and Implications for Decommissioning Policy. MMS OCS 
Final Technical Summary – Holbrook et al 

8 

Study 2003-0XX. Coastal Research Center, Marine Science Institute, 

University of California, Santa Barbara, California. MMS Cooperative 

Agreement Number 14-35-0001-30758. 140 pages. 

Osenberg, C.W., C.M. St. Mary, J.A. Wilson, and W.J. Lindberg. 2002. A 

quantitative framework to evaluate the attraction-production controversy, with 

application to marine ornamental fisheries. ICES Journal of Marine Science 

59S:212-219. 

Syms, C. and M. H. Carr. 2001. Marine protected areas: evaluating MPA 

effectiveness in an uncertain world. (30 pp.) Available at Marinet: North 

American Commission for Environmental Cooperation site: 

http://www.orchestrabycrossdraw.com/30/ 

Posting.cfm?2B07183C303E151A01097F505D546A

Wilson, J.A., C.W. Osenberg, C.M. St. Mary, C.A. Watson, and W.J. Lindberg. 2001. 

Artificial reefs, the attraction-production issue, and density dependence in 

marine ornamental fishes. Aquarium Sciences and Conservation 3:95-105. 

Osenberg, C.W., O. Sarnelle, S.D. Cooper, and R.D. Holt. 1999. Resolving 

ecological questions through meta-analysis: goals, metrics and models. 

Ecology 80:1105-1117. 

5) New Funding Stimulated by MMS support: 

Florida Sea Grant Program: "Pilot studies to assess the use of artificial reefs in marine 

ornamental fisheries" (C.W. Osenberg, C. St. Mary). $6,000. [This was seed 

money to initiate the above project, based on the application of BACIPS to 

artificial reefs] 

National Sea Grant Program: "Fisheries habitat: a field assessment of the effects of 

artificial reefs and its role in fisheries management" (C.W. Osenberg, C. St. 

Mary and B. Bolker). $294,088. [This is a novel application of BACIPS to 

artificial reefs] 

6) Other Significant Accomplishments: 

SCEI projects and the BACIPS design are highlighted in lectures in several courses 

taught at UCSB, UCSC and UF. 

UCSB: 

 Fall 1998 EEMB 595P Graduate seminar in Coastal Toxicology 

 Fall 1999 EEMB 595P Graduate seminar in Coastal Toxicology 

 Fall 2000 EEMB 595P Graduate seminar in Coastal Toxicology 

 Fall 2001 EEMB 595P Graduate seminar in Coastal Toxicology 

UCSC: 

 Spring 1999 Biol 80-M Conservation in the Sea 

 Spring 2000 Biol 80-M Conservation in the Sea 

 Spring 2001 Biol 80-M Conservation in the Sea 

 Spring 2002 Biol 80-M Conservation in the Sea 

UF: 
Species-level Identification of Infaunal Samples 

 9 

 

 Spring 1998 Zoo 6927/5205 Quantitative Methods and Ecological 

 Inference 

 Spring 1998 Zoo 6927/4884 Topics in Quantitative Methods 

 Fall 1999 PCB 4044C General Ecology 

 Fall 2000 Zoo 6927 Integrative Principles I 

 Spring 2001 PCB 4044C General Ecology 

 Fall 2001 Zoo 6927 Integrative Principles I 

 Fall 2002 Zoo 6927/0503 Quantitative Methods and Ecological 

 Inference 

 Fall 2002 Zoo 6927 Integrative Principles 

 Spring 2003 PCB 4044C General Ecology 
Final Technical Summary – Holbrook et al 

10 
Species-level Identification of Infaunal Samples 

 11 

 

FINAL STUDY REPORT 

Motivation 

Traditional studies of environmental impacts are notoriously expensive, due in part, to the 

cost of species-level identification in systems that are extremely speciose (e.g., as 

exemplified by infaunal marine communities). However, less expensive studies using 

higher taxonomic levels of identification (e.g., family or class) are typically chided as 

inadequate. Such criticism is often unsubstantiated and in a recent review, Carney (1996) 

has concluded that studies of the effects of taxonomic aggregation are critically needed 

before we can best design future assessments. Importantly, any critical evaluation must 

occur in the context of an appropriate design. For example, previous attempts to address 

this issue (e.g., Ferraro and Cole 1992) have been based on simple designs that are 

unsuitable for the detection of environmental impacts (Osenberg and Schmitt 1996). 

Instead, we require an evaluation of taxonomic aggregation in the context of a sampling 

design that can reliably separate a putative impact from other sources of temporal and 

spatial variability. Therefore, we will evaluate the effects of taxonomic aggregation using 

the Before-After-Control-Impact Paired Series (BACIPS) assessment design (StewartOaten et al. 1986). 

 

In a BACIPS design, the efficacy of taxonomic aggregation will depend on the pattern of 

temporal and spatial variation in density as well as the covariance (in space and time) of 

species that are pooled into higher units and their relative abundance. It has been argued 

that pooling could reduce error variance and thus increase power of a statistical test of an 

impact (e.g., Carney 1996). This could occur if species negatively covary in their 

responses to natural processes that drive spatial and temporal variation. However, this 

assertion ignores the sensitivity of different species to the impact. If species within a 

pooled taxonomic group exhibit opposite responses to the impact (e.g., some increase in 

abundance while other decrease), as suggested by the assumption about their response to 

natural processes, then pooling is likely to inhibit the detection of impacts by reducing the 

"strength of the signal". The overall effect of taxonomic pooling (or resolution) can only 

be assessed by simultaneous consideration of species' responses to natural variation and to 

the putative impact. 

Previous studies of taxonomic aggregation 

The ability of a study to detect impacts of anthropogenic activities is often evaluated in the 

context of statistical power (e.g., Ferraro and Cole 1990, Osenberg et al. 1994). The 

power of any test of an environmental impact is simultaneously constrained by (i) the 

variability of the data, (ii) the magnitude of the putative impact and (iii) the number of 

independent sampling events. However, the meaning of these components will vary 

depending on the assessment design being applied. The effect of taxonomic aggregation 

will also depend on the assessment design, because it will depend on the patterns of 

spatial and temporal variation in the densities of units that comprise the higher taxonomic 

levels and how the chosen assessment design uses different sources of variation. 
Final Study Report – Holbrook et al 

12 

Table 1. Sources of variance considered in some previous studies of taxonomic aggregation of 

macrobenthos data. When explicit statistical models were not stated, they were inferred by the form of data 

presentation. When multiple types of comparisons were conducted in a single paper, they are distinguished 

by a) and b). 'Error' indicates the variance component used as the error term, and 'Effect' indicates the 

source used to estimate of the size of the effect. 

Effects of taxonomic aggregation on the detection of anthropogenic impacts on the 

macrobenthos has been explored in a number of previous studies (Ferraro and Cole 1990, 

1992, 1995, Herman and Heipp 1988, James et al. 1995, Marchant et al. 1995, Warwick 

1988a, b, Wright et al. 1995). However, these studies have focused almost exclusively on 

spatial variation (Table 1: see also Frost et al. 1992 for an example of temporal variation). 

Thus, these studies examine the ability to detect the difference in infaunal density between 

two sites amid within site spatial variability in density. These studies have typically 

shown that aggregation has little effect on the patterns revealed at the species level. There 

are several problems with the general approach that has been applied in many of these 

studies: (1) it assumes that the anthropogenic activity is the only factor producing 

between-site differences in density; (2) as a result, it assumes that the within site spatial 

variation is the only source of error that obscures the detection of the impact (i.e., that the 

within site variation can be used as an error term in a test of the difference between two 

sites); (3) it often relies on qualitative assessment of the resulting patterns, or lacks 

quantitative estimates of the magnitude of variation and the magnitude of the impact; and 

(4) most of the studies have focused on community measures (such as species diversity), 

which may have little value in impact assessment (Carney 1996) 

 

Source Sample 

(within 

site or 

among 

subsites 

within 

regions) 

Among 

sites 

(Control, 

Impact) 

Time 

(within 

Periods) 

Period 

(Before, 

After) 

Site x 

Time 

Site x 

Period 

Previous Analyses: 

 Ferraro and Cole 

(1990) b) Error 

a) Effect 

b) Effect 

a) Error 

 Ferraro and Cole 

(1992) 

 Error Effect 

 Ferraro and Cole 

(1995) 

 Error Effect 

 Heip et al. (1988) Error Effect 

 Herman and Heip 

(1988) 

 Error Effect 

 James et al. (1995) Error Effect 

 Marchant et al. (1995) Error Effect 

 Warwick (1988a) 

b) Error b) Effect 

a) Error a) Effect 

 Warwick (1988b) Error Effect 

 Wright et al. (1995) Error Effect 

 

Analyses Based on 

BACIPS Design (no 

published studies) 

 

 Error Effect 
Species-level Identification of Infaunal Samples 

 13 

 

The first two problems are directly related to the application of a "Control-Impact" 

assessment design (i.e., comparison of a Control and Impact site after an intervention has 

occurred). The Control-Impact design is predicated on the tenuous assumption that there 

are no other sources of large-scale spatial variation: i.e., that the difference between a 

Control site and Impact site is due only to the effects of the impact. Such an assumption is 

unlikely to be satisfied (Underwood 1991, Osenberg and Schmitt 1996); no two sites 

(even two that are very "similar" and unaffected by an activity) will have identical 

densities. Likewise, application of a Before-After design (sampling at an Impact site 

Before and After an intervention), can confound some sources of temporal variation with 

the impact (Osenberg and Schmitt 1996, Stewart-Oaten 1996a,b). In contrast, the 

BACIPS design explicitly accounts for many sources of spatial and temporal variation, 

and uses different sources of variation to isolate the effect of an anthropogenic activity 

from background variation (Table 1). Unfortunately, there are no existing studies that 

examine the effect of taxonomic aggregation on the ability to detect environmental 

impacts using the BACIPS design. This information vacuum is particularly troublesome 

because the BACIPS design is the most powerful field assessment tool available, and its 

application is becoming more widespread (Schmitt and Osenberg 1996). Yet, we have 

virtually no information to guide the selection of taxonomic resolution in BACIPS studies. 

The BACIPS design 

In the basic BACIPS design, a Control and an Impact site (or multiple sites) are sampled 

simultaneously several times Before and After the perturbation. The metric of interest is 

the difference (hereafter referred to as "delta", ∆, or D for its estimate) in density (or other 

suitable variable) between the Control and Impact sites as estimated on each sampling 

date (e.g., DP,i = NI,P,i - NC,P,i, where NI,P,i and NC,P,i are estimates of the parameter at the 

Control and Impact sites on the i th date of Period P: i.e., Before or After). The average 

delta in the Before period (DP•) is an estimate of the spatial variation between the two 

sites (∆B),which provides an estimate of the expected delta that should exist in the After 

period in the absence of an environmental impact. The difference between the average 

Before and After deltas (DB•-DA•) provides a measure of the magnitude of the 

environmental impact. Confidence in this estimate is determined by the variance in deltas 

(among sampling dates within a period, s

2

D), as well as the number of sampling dates (i.e., 

replicates) in each of the Before and After periods (nB + nA = n). 

 

For the purposes of this study, we follow the convention of Osenberg et al. (1994) and 

define 

 Effect Size: E = DB• - DA• (1) 

 Variability: s

2

P = [Σ(DP,i - DP•)

2

] / (nP - 1) (2) 

In calculations of statistical power (Cohen 1977, Osenberg et al. 1994), the effect size 

should be standardized by dividing by the standard deviation of deltas (which can be 

averaged between the two periods, to yield sD): 

 Standardized Effect Size = Es = | DB• - DA• | / (2sD) (3) 
Final Study Report – Holbrook et al 

14 

We double the standard deviation in the denominator of Equation 3 based on the 

assumption that the resulting test will be two-tailed (Gill 1978). 

Figure 1. Patterns of spatial and temporal variation in population densities that lead to high and low 

variation in deltas. Simulated data (top panels) are from two pairs of sites. In both panels temporal 

variation in density (at a site) and the average difference between the sites are similar. The panels differ in 

the degree to which the estimated densities at the paired sites track one another through time. On the left, 

poor tracking (i.e., low coherence: Magnuson et al. 1990) leads to a low correlation between densities at the 

two sites (r=-0.25), while on the right, good tracking (i.e., high coherence) leads to a stronger correlation in 

densities (r=0.98). The bottom graphs show the resulting differences in density (deltas). Low temporal 

coherence leads to high variability in deltas and thus low statistical power, while high coherence leads to 

low variability and higher power. From Osenberg et al. (1996). 

Thus, the two main sources of variation in a BACIPS design are quite different from those 

used in other designs (e.g., Table 1). First, the estimate of an effect is derived from the 

Period x Location term, which indicates how much the density at the Impact site (relative

to the Control site) changes from the Before to After periods (i.e., DB• - DA•). Second, the 

error component measures how much the difference between the densities at the Control 

and Impact sites vary when there is no change in the intervention. Other designs focus on 

the within site variation in density (Control-Impact design) or temporal variation in 

density (Before-After design). By taking the differences in density, the BACIPS design 

accounts for the effects of many sources of spatial and temporal variation, and thus does 

not confound them with effects of the intervention (Stewart-Oaten et al. 1986, StewartOaten 1996a,b). To highlight these distinctions, we have illustrated how variability of 

deltas (s

2

D) can be altered without any change in the temporal or spatial variability of a 

parameter (e.g., density), or in the amount of within-site sampling error (Figure 1). The 

critical feature in determining the variability of deltas is the extent to which estimates of 

parameters at the two sites track one another though time; Magnuson et al. (1990) refer to 

this as temporal coherence. 
Species-level Identification of Infaunal Samples 

 15 

 

Expected Effects of Taxonomic Aggregation 

In a BACIPS design, the efficacy of taxonomic aggregation will depend on the covariance 

(in space and time) of the deltas (differences in density) of the species that are pooled into 

higher units. It will also depend upon the relative abundances of the species because the 

pattern of a higher unit will be most influenced by the most abundant of the lower units. It 

has been argued that pooling could reduce error variance and thus increase power of a 

statistical test of an impact (e.g., Carney 1996). This could occur if species negatively 

covary in their responses to natural processes that drive spatial and temporal variation 

(i.e., compare Figures 2a and 2b). However, this assertion ignores the sensitivity of 

different species to the impact. If species within a pooled taxonomic group exhibit 

opposite responses to the impact (e.g., some increase in abundance while other decreased), 

then pooling would reduce the probability of detecting the impact by reducing the 

"strength of the signal" (Figure 2c vs. 2d). Thus, the effect of taxonomic aggregation on 

power will depend on how species (within a higher taxonomic level) respond to "natural" 

sources of variation (i.e., sources other than the activity being studied) vs. the impact 

itself: e.g., concordant responses to one source need not dictate concordant responses to 

the other. Because the processes driving these changes can be very different, there seem 

to be few reasons to expect, a priori, aggregation to either increase (e.g., through 

combination of 2a and 2d) or decrease (2b and 2c) power of a statistical test. The overall 

effect of taxonomic pooling (or resolution) can only be assessed by simultaneous 

consideration of different species' responses to "natural" variation and to the putative 

impact. This cannot be assessed by existing studies of taxonomic aggregation because 

they have not considered the appropriate sources of variation. 

Figure 2. Schematic illustrating responses of two species to different sources of variation: background 

variation, which comprises the error term, and the impact, which comprises the signal. Concordant 

responses to the impact (d), and discordant responses to background variation (a) facilitate detection of 

impacts using aggregated taxonomic units (because the error term is reduced while the signal is enhanced). 

Similarly, discordant responses to the impact (c) and concordant responses to background variation (b) 

decrease power by reducing the signal:noise ratio. 
Final Study Report – Holbrook et al 

16 

Taxonomic Aggregation and the Portfolio Analogy 

Doak et al. (1998) and Tilman et al. (1998) examined the effects of species aggregation on 

temporal variance. Tilman's approach emphasized the portfolio theory of investment 

diversification. Although both papers were most interested in the relationship between 

species diversity and stability, the approach has direct relevance to the effects of 

taxonomic aggregation in BACIPS designs. Thus, we briefly review this body of work, 

extend it, and discuss its relevance to BACIPS. 

Both models, as well as our own, make the following assumptions. First, that the 

abundance of each species in a community is random and independent, with all 

covariances equal to zero. Further the models assume that all members of the community 

are equally abundant, on average (i.e., m1=m2=...=mk). In the Doak et al. model and the 

Tilman et al. model they further assume that the total community biomass is M. Thus, for 

a community of k species, mi=M/k. At this point, Tilman et al. argue that a general 

relationship between the mean, mi, and variance, V(mi), can be modeled using 

V(mi) = cmi

z

, where z represents the scaling between the mean and variance of a species. 

Tilman et al.’s also showed that the effects of statistical averaging (as represented by the 

degree to which the coefficient of variation of the community, CV(M), depends on k) are 

dependent on the values of z. Importantly, Doak et al. considered only the case where z=2 

and found no relationship between CV and the number of species that were combined, 

whereas Tilman et al. argued that 1 < z < 1.5 might be more appropriate. Indeed, with 

z=1, k has no effect on variability. 

And that, with what they argue are more realistic values of z, CV(M) is independent of k. 

While we agree that the magnitude of statistical averaging will depend on z, the models 

considered by Doak et al. and Tilman et al. both rely on yet another simplifying 

assumption which, as we will show, affects their results. Specifically, both models 

assume the total biomass of the community, M, is independent of biodiversity. Thus, a 

monoculture will have the same mean biomass as a community consisting of several 

species. This is certainly not true for our problem that deals with aggregation and it is 

probably also not true for communities studied in the context of biodiversity-stability 

relationships. Therefore, we have generalized their models to include the relationship 

between species numbers and community biomass, such that M=ka

 and therefore, 

mi = M/k = ka-1. In this generalized formulation, a represents the scaling of community 

biomass with species numbers. For taxonomic aggregation, we are most interested in the 

case where a=1. 

Given this generalized formulation of the model, we can now evaluate the circumstances 

under which the CV(M) is dependent or independent of k, and to what degree. In the 

general case, V(mi) = cmi

z

 = cka-1. Thus, 

a k

i

M = ∑ mi = μk =1 and ∑ ∑ − +

=

= = = ( 1) 1

1

( ) ( ) z z z a k

i

V M V mi c m c k i μ . 

The coefficient of variation of the community is therefore, 
Species-level Identification of Infaunal Samples 

 17 

 

CV

V M

M

ck

uk

c k M

za z

a

z z a a

( )

( ) () ( ) ( )

== =

− + − −+−

1

2 1 1

1

2 1

2 2

1

2

1

1

2 μ

μ . 

This is a messy formulation, but in essence we can now define the conditions under which 

CV(M) is dependent on k and to what degree by evaluating the exponent of k, namely 

E=z(a-1)/2 + 1⁄2 - a. The effect of diversity on stability depends on the parameters a and z, 

and this more general formulation includes the special cases examined by Doak et al. and 

Tilman et al. 

Doak et al. presented the specific case of z = 2. In this case, E = 1⁄2., and is independent of 

a. Thus, CV(M) ∝ k

-0.5, and variability of an aggregated assemblage declines as the 

number of species in the assemblage increases. Tilman et al. discussed the case of a=0 

and z = 1, and later varied z, 0 ≤ z ≤ 1. In the first instance, E = 1, and thus, CV(M) is 

independent of the number of species, k. However, if z < 1, then E > 0 and thus variability 

increases as k increases. For example, if z = 0.5, then CV(M) ∝ k

+0.5, and thus, the 

averaging phenomenon (caused by aggregation) will result in greater levels of variation in 

more species rich communities. 

In the majority of (a,z) space, E < 0 or E > 0. Generally speaking, values of E < -0.5 can 

be expected within two of four quadrants of (a,z) space, when the quadrants are defined by 

the lines a = 1 and z = 2. Positive values of E are associated with the other two quadrants. 

For our case, being interested in aggregation in a BACIPS context, diversity is not varied. 

Instead, we have a particular community and we are choosing to aggregate the data in 

different ways. This simplifies to the case where a=1 (and assuming all species are 

equally represented – an assumption needed for mathematical convenience, but which can 

be relaxed via simulation). In this case, CV(M) ∝ k

-0.5, indicating that the CV will decline 

with aggregation: aggregation will lead to a decline in this measure of temporal variance. 

What effect it will have on other measures of variability (spatial or spatiotemporal) is still 

unknown. 

Species-level identification using existing samples 

Produced water 

Several previous studies funded through the MMS/UC SCEI provided samples of infaunal 

density (e.g., projects by Osenberg et al., Schmitt and Osenberg, Carr et al.) at sites that 

have been (or were expected to be) subjected to produced water discharge near 

Carpenteria and Gaviota, California. These studies have yielded important findings about 

the impacts of produced water and the design of assessment studies (Osenberg et al. 

1992a,b, 1994, Stewart-Oaten et al. 1992, Krause et al. 1992, Raimondi and Schmitt 1992, 

Krause 1994, Schmitt and Osenberg 1996). However, the studies (due to financial 

constraints) were limited to identifying organisms to crude taxonomic levels (e.g., Family 

at best, and often Class, or Order and sometimes even Phylum). At Gaviota, California, 

sampling at three study sites yielded 6 years (1989-1994) of infaunal samples collected in 

the absence of produced water (these comprise a "Before" sampling period at a proposed 

produced water outfall). These samples are important because they provide sufficient 
Final Study Report – Holbrook et al 

18 

sampling intensity to quantify natural spatial and temporal patterns of variation in density 

(e.g., 6 years, 4-12 surveys per year, from 3 sites situated .25 - 2 km from one another -- a 

common spatial scale for Control-Impact studies). In conjunction with the sampling of 

infaunal density, emergence rates of demersal zooplankton were also quantified using reentry traps and emergence traps placed on the sediments over a 24-hour period. 

Environmental characteristics (e.g., sexton flux, sediment organic matter, grain size, 

temperature) were also recorded. A subset of the Gaviota data also were previously used 

to conduct a power analysis using the BACIPS design and infaunal densities of crude 

taxonomic groups (e.g., typically to the level of Order or Family) (Osenberg et al. 1994). 

These analyses suggested that information on the behavior, size and performance of 

individuals might yield the more powerful tests of impacts than assessments based on 

density (see also Carney 1987). 

At Carpenteria, California, sampling was conducted at 20 sites (distributed across a 2 km 

transect) over a 5-year period (1990-1994) that straddled a "discharge" period and a "shutdown" period; this facility, which had discharged produced water since before 1978, went 

out of operation in 1992. Thus, this study provides sufficient sampling intensity to 

quantify shifts in the spatial patterns between the "discharge" and "shut-down" periods 

using the Before-After/Control-Impact Paired Series Assessment (BACIPS) design 

(Stewart-Oaten et al. 1986, Schmitt and Osenberg 1996). The Carpenteria data allow us to 

estimate the magnitude of the effect of produced water, but provide relatively few data to 

assess patterns of spatial and temporal variance. 

Thus, the Gaviota and Carpenteria studies complement one another quite nicely -- one 

provides a long time series in the absence of a perturbation (to quantify background 

spatial and temporal variation) and the other provides an estimate of the response of taxa 

to produced water discharge (albeit with a more limited time series). Before this study, 

however, the benthic samples had only been identified to broad taxonomic levels, upon 

which previous analyses (e.g., Osenberg et al. 1992, 1994, 1996) were based. During this 

project, organisms in these samples were identified to much lower taxonomic levels, 

making it possible to explore the effects of taxonomic aggregation. 

Power plant 

The Marine Review Committee (MRC) conducted an intensive investigation of the effects 

of the discharge of cooling water from the San Onofre Nuclear Generating Station, 

SONGS (Murdoch et al. 1989). The MRC used a BACIPS design for many of their 

assessments (including benthos), and their data are available. The data for infauna span a 

period of 7 years, (3 years before operations, ~1 year during an interim operating period, 

and 3 years during operation), yielding a total of 59 surveys from 12 different sites. All 

benthic infaunal samples were identified to the species level (with a few exceptions). To 

make these data comparable to the Carpenteria and Gaviota datasets, we required that the 

species coding from these samples be standardized with the coding generated as part of 

the species-level identification performed on the Carpenteria and Gaviota samples. This 

was one of the goals of our project. 
Species-level Identification of Infaunal Samples 

 19 

 

Approach taken 

Samples were selected from the archived Gaviota and Carpenteria studies (Table 2) to 

provide a representative spatial and temporal coverage. All samples that were further 

analyzed were collected between 1990 and 1995 and collectively contained over 250,000 

individual organisms and 800 taxonomic groups. 

Table 2. Summary of samples for which species level identification was obtained. 

To obtain taxonomic identifications at the finest resolution possible (usually to species), 

we arranged a sub-contract between the University of California and Lovell Taxonomic 

Consultants. Gaviota and Carpenteria samples had been previously identified to crude 

taxonomic levels which facilitated transport of the different samples to taxonomists who 

specialized in different groups. Larry Lovell identified the polychaetes, Doug Deiner 

identified the crustaceans, and John Ljubenkov identified "other taxa" (an aggregate of a 

diverse set of taxa, such as sipunculids, chordates, and chaetognaths). These three 

taxonomists have extensive expertise with southern California marine invertebrates and 

were also involved in the species-level identification of the MRC samples. Two 

technicians at UCSB (Kristin Zabaronick and Bryn Evans) identified the molluscs 

following training by Paul Scott and Henry Chaney at the Santa Barbara Museum of 

Natural History. Paul Scott and Henry Chaney assembled a voucher collection of all 

mollusk species, and Lovell et al. completed one for Polychaetes, Crustaceans and Others. 

Problematic species identifications were double checked with other taxonomic experts 

that are also members of the Southern California Association of Marine Invertebrate 

Taxonomists (SCAMIT). 

 

For Gaviota, species-level identification was obtained for 19 surveys (5 years, 3-4 

surveys/year, 3 sites), consisting of infaunal samples, re-entry trap samples, and 

emergence trap samples (for all animals retained on a 0.5 mm sieve). The emergence and 

Site: CARPENTERIA GAVIOTA GAVIOTA GAVIOTA

Sample type: Infaunal 

cores 

Infaunal 

cores 

Emergence 

traps 

Reentry 

traps 

Earliest date February 

1990 

January

1990 

January 

1990 

January 

1990 

Latest date June 

1995 

October

1994 

October 

1994 

October 

1994 

Number of 

dates 

9 19 19 19 

Number of 

sites 

20 3 3 3 

Number of 

replicates per 

date and site 

3 2 or 3 2 3 

Total number 

of organisms 

124,099 104,307 16,010 6,285 
Final Study Report – Holbrook et al 

20 

re-entry traps (Alldredge and King 1980; Stretch 1983) provide estimates of migration 

rates of infauna. For the Carpenteria study, a total of 9 surveys were enumerated (3 

surveys conducted during discharge and 6 surveys conducted after discharge was 

terminated; 20 sites/survey, which vary in their proximity to the diffusers [range: 3 - 1000 

m upcoast and downcoast]). 

Figure 3. Total and mean abundance of Tellina carpenteri across sample dates during discharge of 

produced water and after its cessation. Currents tend to flow from east to west, leading to a possible 

westward displacement of any impact (relative to the location of the diffuser: i.e., at "0"). 

0

50

100

150

200

250

1000 500 250 100 50 25 10 0-5 0 0-5 10 25 50 100 250 500 1000

Distance from Diffuser (meters)

During

After

East West

Total (During) and Mean (After) Abundance

0

50

100

150

200

250

1000 500 250 100 50 25 10 0-5 0 0-5 10 25 50 100 250 500 1000

Distance from Diffuser (meters)

During

After

East West

Total (During) and Mean (After) Abundance

Each organism was assigned a species code that indicated its identification (to the lowest 

possible taxonomic level). The same species codes that were established for the MRC 

studies were used for these samples. The codes were designed in the following manner. 

Each species code is an eight character alphanumeric designation. The first letter 

representing the group designation (P=Polychaetes, C=Crustaceans, M=Mollusc, 

O=Others). For the Polychaetes and Others, the next three letters represent the Family to 

which the species belongs. The four digit code that follows the family abbreviation that 

designates different groups (e.g., species) within that family. An example: Acmira 

catherinae is encoded as PPAR0001: the first 'P' is for Polychaete, 'PAR' is for the Family 

Paraonidae, and 0001 is an arbritrary numeric codes that indicates the genus and species 

(i.e., Acmira catherinae). For Crustaceans, the three-letter abbreviation reflects the Order, 

not the Family, to which the species belongs. For example: Photis sp. is encoded as 

'CAMP0160', where 'C' indicates 'Crustacean', 'AMP' indicates the Order Amphipoda, and 

'0160' is the unique numeric code within Amphipoda for Photis sp. The Molluscs’ letter 

codes indicate Class (MGAS= Gastropoda, MPEL= Pelecypoda (or Bivalva)). All codes 

ending in 0000, are for individuals in the appropropriate higher taxonomic level (e.g., 

Family for Crustacea or Class for Mollusca) that could not be identified beyond the 

Family level. For example: 'ONEM0000' indicates Nemerteans that could not be further 

identified. Species occurring at Gaviota and Carpenteria that were not originally present 
Species-level Identification of Infaunal Samples 

 21 

 

in the MRC list were given temporary codes for the purpose of data entry and were then 

reassigned permanent codes by Dr. Lovell according to the system mentioned above. 

77.1% of all individuals could be identified to species; only 12.1% could not be identified 

to at least the genus level. Species codes and counts for each sample were recorded on 

datasheets and transferred to UCSB where data were entered into electronic spreadsheets 

indicating the species code, the count within each sample (or subsample), the date, the 

site, and the person who identified the organisms. 

 

The taxonomists also went through the MRC list of species and updated them based on 

taxonomic information available in 1997 (since some taxonomic revisions had occurred 

since the MRC studies of the mid-1970's). This "Taxon List" therefore contains all 

species codes used in the MRC and Carpenteria/Gaviota studies. The Taxon List was later 

expanded to include the Phylum, Class, Order, Family, Genus, Species, and when 

appropriate Subspecies. 

The electronic databases and Taxon List were proofed and finalized by technicians at UF. 

The species-level identifications, data entry, and data proofing all took more time than 

originally budgeted. Data files are now complete and will be made available to the 

scientific community via the UCSB LTER web site (http://sbc.lternet.edu). 
Final Study Report – Holbrook et al 

22 

Figure 4. Example comparison of the size structure (size frequency distributions) of Tellina carpenteri at 

six distances to the west and east sides of the discharge (a) during produce water discharge, and b) after 

cessation of discharge at the Carpenteria study site. Sampling dates were 10 and 11 February 1990 during 

discharge and 13 and 14 January 1994 post-discharge. Discharge ceased in July 1992. 

10m

50m

100m

500m

1000m

0-5m

10m

50m

100m

500m

1000m

0-5m

West Discharge East Discharge

Frequency (number of individuals)

Size (mm maximum shell diameter)

a.)

10

30

50

70

1.0 2.0 3.0 4.0 5.0

10

30

50

70

1.0 2.0 3.0 4.0 5.0

10

30

50

70

1.0 2.0 3.0 4.0 5.0

10

30

50

70

1.0 2.0 3.0 4.0 5.0

10

30

50

70

1.0 2.0 3.0 4.0 5.0

10

30

50

70

1.0 2.0 3.0 4.0 5.0

10

30

50

70

1.0 2.0 3.0 4.0 5.0

10

30

50

70

1.0 2.0 3.0 4.0 5.0

10

30

50

70

1.0 2.0 3.0 4.0 5.0

10

30

50

70

1.0 2.0 3.0 4.0 5.0

10

30

50

70

1.0 2.0 3.0 4.0 5.0

10

30

50

70

1.0 2.0 3.0 4.0 5.0

10m

50m

100m

500m

1000m

0-5m

10m

50m

100m

500m

1000m

0-5m

West Discharge East Discharge

Frequency (number of individuals)

Size (mm maximum shell diameter)

a.)

10

30

50

70

1.0 2.0 3.0 4.0 5.0

10

30

50

70

1.0 2.0 3.0 4.0 5.0

10

30

50

70

1.0 2.0 3.0 4.0 5.0

10

30

50

70

1.0 2.0 3.0 4.0 5.0

10

30

50

70

1.0 2.0 3.0 4.0 5.0

10

30

50

70

1.0 2.0 3.0 4.0 5.0

10

30

50

70

1.0 2.0 3.0 4.0 5.0

10

30

50

70

1.0 2.0 3.0 4.0 5.0

10

30

50

70

1.0 2.0 3.0 4.0 5.0

10

30

50

70

1.0 2.0 3.0 4.0 5.0

10

30

50

70

1.0 2.0 3.0 4.0 5.0

10

30

50

70

1.0 2.0 3.0 4.0 5.0

10

30

50

70

1.0 2.0 3.0 4.0 5.0

10

30

50

70

1.0 2.0 3.0 4.0 5.0

10

30

50

70

1.0 2.0 3.0 4.0 5.0

10

30

50

70

1.0 2.0 3.0 4.0 5.0

10

30

50

70

1.0 2.0 3.0 4.0 5.0

10

30

50

70

1.0 2.0 3.0 4.0 5.0

10

30

50

70

1.0 2.0 3.0 4.0 5.0

10

30

50

70

1.0 2.0 3.0 4.0 5.0

10

30

50

70

1.0 2.0 3.0 4.0 5.0
Species-level Identification of Infaunal Samples 

 23 

 

Figure 4. (continued: Panel b). 

West Post-discharge

10m

50m

100m

500m

1000m

0-5m

10m

50m

100m

500m

1000m

0-5m

10

30

50

70

1.0 2.0 3.0 4.0 5.0

East Post-discharge

Frequency (number of individuals)

Size (mm maximum shell diameter)

b.)

10

30

50

70

1.0 2.0 3.0 4.0 5.0

10

30

50

70

1.0 2.0 3.0 4.0 5.0

10

30

50

70

1.0 2.0 3.0 4.0 5.0

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30

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1.0 2.0 3.0 4.0 5.0

10

30

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1.0 2.0 3.0 4.0 5.0

10

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1.0 2.0 3.0 4.0 5.0

10

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1.0 2.0 3.0 4.0 5.0

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1.0 2.0 3.0 4.0 5.0

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1.0 2.0 3.0 4.0 5.0

West Post-discharge

10m

50m

100m

500m

1000m

0-5m

10m

50m

100m

500m

1000m

0-5m

10

30

50

70

1.0 2.0 3.0 4.0 5.0

10

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1.0 2.0 3.0 4.0 5.0

East Post-discharge

Frequency (number of individuals)

Size (mm maximum shell diameter)

b.)

10

30

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70

1.0 2.0 3.0 4.0 5.0

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1.0 2.0 3.0 4.0 5.0

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1.0 2.0 3.0 4.0 5.0

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1.0 2.0 3.0 4.0 5.0

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1.0 2.0 3.0 4.0 5.0

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1.0 2.0 3.0 4.0 5.0

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1.0 2.0 3.0 4.0 5.0

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1.0 2.0 3.0 4.0 5.0

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1.0 2.0 3.0 4.0 5.0

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1.0 2.0 3.0 4.0 5.0

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1.0 2.0 3.0 4.0 5.0

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1.0 2.0 3.0 4.0 5.0

As each sample was opened and identified by the taxonomists, certain species were 

removed and packaged for possible analysis of body size by students working at UCSC or 

other interested investigators. The selected species from Gaviota consisted of 

Amphideutopus oculatus, Campylaspis hartae, Levinsenia gracilis, Foxiphalus 

obtusidens, Nephtys cornuta, Parvilucina tenisculpta, Rochefortia tumida, Rudiderma 

rostratum, Spiophanes missionensis, and Tellinidae (including a mix of species, but 

primarily Tellina carpenteri; other species included T. bodogensis, T. modesta, T. 

nucloides, T. idea, Macoma carlottensis, M. yoldiformis); the selected species from 

Carpenteria were Apoprionospio pygmea, Diastylopsis tenuis, Levinsenia gracilis, 

Nephtys cornuta, Parasterope hulingsi, Spiophanes missionensis and Tellinidae (see 

above). 
Final Study Report – Holbrook et al 

24 

Samples separate out for size-frequency analysis were then shipped to Carr's lab, where 

we used an image analysis system to obtain images and sizes of individuals. The system 

consisted of a Leica ‘StereoZoom 6 Photo’ dissecting scope with a Pulnix TM-&CN black 

and white video camera attached to a Macintosh PowerComputing computer with frame 

grabber and Scion Image (version 1.62) software. This system allowed video images to 

be captured and saved as JPG image files. Scion Image software allows image databases 

to be exported as Microsoft Excel files. For each image, one to several individual 

organisms (depending on size) were placed together with a ruler in a petri dish of 95% 

ethanol. Once the image was captured, the ruler was calibrated for each image and each 

bivalve was measured digitally along the longest axis of the valve. Length measurements 

are stored for each individual in the image. Because the entire process from setup to 

measurements was time consuming, we first captured images from many samples and 

subsequently recorded their lengths. 

 

We focused the initial processing and analysis of size-structure on bivalve mollusks from 

biocore samples because of their abundance and ease of measuring individual size relative 

to more complex body forms. Macoma yoldiformis, Parvilucina spp., Rictaxis spp., 

Rochefortia spp., Tellinaidae, Telliina spp., and Tellina Carpenteri were targeted for sizefrequency estimation and analysis based upon their abundance and occurrence across most 

sample sites at Gaviota and Carpenteria. Images were collected for all of these taxa at 

both the Gaviota and Carpenteria study sites except for Parvilucina spp., Rictaxis spp., 

Rochefortia spp., for which images were collected for Gaviota only. Thousands of images 

have been recorded, and hundreds of measurements have been made. A summary file of 

the images and measurements available will be provided as databases (next section). 

 

Initial comparisons of size structure were limited to Tellina carpenteri, because of its 

relatively higher abundance across sample sites and dates. During the period of produced 

water discharge, the abundance of T. carpenteri was greater near the diffuser array and 

tended to decline as distance from the array increased (Figure 3). Following the cessation 

of produced water discharge, the marked abundance pattern was greatly reduced and 

dissipated with time (Figure 3). Spatial and temporal changes in size-structure were less 

clear, but there was a tendency for larger individuals to be present at sites closer to the 

discharge (Figure 4a). Following the cessation of produced water discharge, this pattern 

was somewhat reduced if not reversed (Figure 4b). 

The Databases 

Four databases were generated from the species-level data on infaunal densities; one for 

the Carpenteria infaunal density cores, and three for the Gaviota studies (one each for the 

infaunal density cores, emergence traps, and re-entry traps). Information on each database 

is given in Appendices A-E. 

Two databases were generated from the size-structure data. One database is a collection 

of digital images of bivalves that were recorded in the process of measuring individuals. 

The second database contains the size measurements recorded from the digital images. 

 

As noted above, these databases will be available to the public via the UCSB LTER 

website and upon request. We especially encourage other scientists to make use of these 
Species-level Identification of Infaunal Samples 

 25 

 

data in their analyses and future publications. Requests for density data should be sent to 

Dr. Craig Osenberg (osenberg@zoology.ufl.edu), and requests for size-frequency data 

should be sent to Dr. Mark Carr (carr@biology.ucsc.edu). We request that we be 

consulted after data acquisition and prior to publication so that we can ensure that the 

sampling and identification protocols are appropriately interpreted. 

 

Samples are currently stored in the labs of Sally Holbrook (UCSB) and Mark Carr 

(UCSC), but we are attempting to house them more permanently at the LA County 

Museum. 

Issues that can be addressed with the new databases 

Carpenteria: BACIPS analysis 

Using species-level identifications, the Carpenteria database can be used to conduct a 

BACIPS analysis of the environmental effects of produced water. The analysis could 

follow the general approach laid out in Osenberg et al. (1992a), who analyzed the data 

from the first "discharge" survey. Analyses could be based on log-transformed data (to 

better satisfy assumptions of additivity: Stewart-Oaten et al. 1986, Osenberg et al. 1994). 

Under this scenario, estimates of effect size would provide a common measure of 

proportionate change that should remove concerns about measurement scale as lower units 

are combined into larger groups. 

Carpenteria: Effect of taxonomic aggregation on estimates of the strength of the impact 

The effect of taxonomic resolution (e.g., identification to Species, Genus, Family, Order, 

or Class) can be evaluated by aggregating the species-level data into different taxonomic 

levels and repeating the BACIPS analysis. 

Carpenteria and Gaviota: Effect of taxonomic aggregation on background variability 

The Carpenteria and Gaviota data can be used to estimate background variability 

(Equation 2), which constitutes the error term in the BACIPS design. The Gaviota data 

set is valuable here because of its relatively long time frame (5 years of data at the species 

level) without an intervention. For a given sampling effort (number of surveys) these 

results would yield estimates of the power to detect a particular, proportionate change in 

density. These results can then be examined as the level of taxonomic aggregation is 

varied. 

Individual-based metrics 

Data for species-specific migration rates (the Gaviota study), and body size (the Gaviota 

and Carpenteria studies) can be used in analogous ways as the density estimates to look at 

the effects of taxonomic aggregation in BACIPS designs. The data can also be used to 

test the suggestion (Osenberg et al. 1994, and Carney 1987) that metrics more closely tied 

to individual behavior and performance might provide more powerful tests of 

environmental impacts. 

Comparison with the MRC's study of SONGS 

The MRC's study of infaunal density responses to cooling water discharge from SONGS 

provides one of the most complete BACIPS studies, consisting of 59 surveys of 12 sites 

(which vary from 0.7 to 9.4 km from the discharge) distributed over 7 years, (3 years 
Final Study Report – Holbrook et al 

26 

before operations, ~1 year during an interim operating period, and 3 years during 

operation). The data are available to the public and can be directly compared to the results 

obtained using the Carpenteria and Gaviota data. 

Other forms of aggregation 

Much of the interest in taxonomic aggregation lies in the belief that ecologically similar 

species will be affected similarly by a perturbation, and that ecologically similar species 

are more likely to be closely related. This expectation corresponds to that illustrated in 

Figure 2d. [However, similar species might also display concordant responses to other 

sources of variation (Figure 2a) and thus reduce power.] If the correlation between 

phylogeny and ecology is low, then a more powerful way to aggregate data would be at 

the level of functional groups rather than taxonomic units (although this would usually 

require identification to the species level and then pooling into higher functional units). It 

has also been argued that random aggregation retains much of the pattern revealed at the 

species level (Herman and Heip 1988). These issues can be explored by attempting 

different aggregation schemes, including that based on functional groups (although there 

is a relative dearth of detailed ecological information about many infaunal invertebrates) 

and random assignment. Such an approach can also be useful in isolating the causes for 

the observed effects of taxonomic aggregation. The pattern of covariance in density 

among species belonging to the same group (taxonomic or functional) can be quantified. 

The most critical source of this covariance is their response in relative abundance (i.e., 

D's) through time in response to background variation and in response to the perturbation 

(Figure 2). Different results among different taxa (e.g., molluscs vs. crustaceans vs. 

polychaetes) might help isolate features that distinguish the effects of aggregation among 

these groups (e.g., if aggregation leads to reduced power for molluscs but increased power 

for polychaetes, is it because polychaetes show a more concordant response to the impact, 

or a more discordant response to other sources of variation). 
Species-level Identification of Infaunal Samples 

 27 

 

Literature Cited 

Alldredge, A.L. and J.M. King. 1980. Effects of moonlight on the temporal migration 

patterns of demersal zooplankton. Journal of Experimental Marine Biology 

Ecology 44:133-156. 

Carney, R.S. 1987. A review of study designs for the detection of long-term 

environmental effects of offshore petroleum activities. Pages 651-696 in D.F. 

Boesch and N.N. Rabalais (eds.) Long-term effects of offshore oil and gas 

development. Elsevier Applied Science, New York. 

Carney, R.S. 1996. On the adequacy and improvement of marine benthic pre-impact 

surveys: examples from the Gulf of Mexico Outer Continental Shelf. Pages xx-xx 

in R.J. Schmitt and C.W. Osenberg (eds.) Detecting ecological impacts:Concepts 

and applications in coastal habitats. Academic Press. 

Cohen, J. 1977. Statistical power analysis for the behavioral sciences. Academic Press, 

New York. 

Doak, D.F., D. Bigger, E.K. Harding, M.A. Marvier, R.E. O’Malley and D. Thomson. 

1998. The statistical inevitability of stability-diversity relationships in community 

ecology. Am. Nat. 151:264-276. 

Ferraro, S.P. and F.A. Cole. 1990. Taxonomic level and sample size sufficient for 

assessing pollution impacts on the Southern California Bight macrobenthos. 

Marine Ecology Progress Series 67:251-262. 

Ferraro, S.P. and F.A. Cole. 1992. Taxonomic level sufficient for assessing a moderate 

impact on macrobenthic communities in Puget Sound, Washington, USA. 

Canadian Journal of Fisheries and Aquatic Sciences 49:1184-1188. 

Ferraro, S.P. and F.A. Cole. 1995. Taxonomic level sufficient for assessing pollution 

impacts on the Southern California Bight macrobenthos -- revisited. 

Environmental Toxicology and Chemistry, 14:1031-1040. 

Frost, T.M., S.R. Carpenter, and T.K. Kratz. 1992. Choosing ecological indicators: 

effects of taxonomic aggregation on sensitivity to stress and natural variability. 

Pages 215-227 in D.H. McKenzie, D.E. Hyatt, and V.J. McDonald (eds.) 

Ecological Indicators. Elsevier Applied Science. 

Gill, J.L. 1978. Design and analysis of experiments in the animal and medical sciences. 

Vols. 1-3. Iowa State University Press, Ames, Iowa. 

Heip, C., R.M. Warwick, M.R. Carr, P.M.J. Herman, R. Huys, N. Smol, and K. Van 

Holsbeke. 1988. Analyses of community attributes of the benthic meiofauna of 

Frierfjord/Langesundfjord. Marine Ecology Progress Series 46:171-180. 

Herman, P.M.J. and C. Heip. 1988. On the use of meiofauna in ecological monitoring: 

who needs taxonomy? Marine Pollution Bulletin 19:665-668. 

James, R.J., M.P. Lincoln Smith, P.G. Fairweather. 1995. Sieve mech-size and 

taxonomic resolution needed to describe natural spatial variation on marine 

macrofauna. Marine Ecology Progress Series 118:187-198. 

Krause, P.R. 1994. Effects of an oil production effluent on gametogenesis and gamete 

performance in the purple sea urchin (Strongylocentrotus purpuratus Stimpson). 

Environmental Toxicology and Chemistry 13:1153-1161. 
Final Study Report – Holbrook et al 

28 

Krause, P.R., C.W. Osenberg, R.J. Schmitt. 1992. Effects of produced water on early life 

stages of a sea urchin: gender-specific responses and delayed expression. Pages 

431-444 in J.P. Ray and F.R. Englehardt (eds.), Produced water: 

technological/environmental issues and solutions, Plenum Publishing Corp., New 

York. 

Marchant, R. L.A. Barmuta, B.C. Chessman. 1995. Influence of sample quantification 

and taxonomic resolution on the ordination of macroinvertebrate communities 

from running waters in Victoria, Australia. Marine and Freshwater Research 

46:501-506. 

Magnuson, J.J., B.J. Benson, and T.K. Kratz. 1990. Temporal coherence in the 

limnology of a suite of lakes in Wisconsin, U.S.A. Freshwater Biology 23:145-

159. 

Murdoch, W.W., R.C. Fay and B.J. Mechalas. 1989. Final report of the Marine Review 

Committee to the California Coastal Commission. Marine Review Committee, 

Inc. 

Osenberg, C.W., S.J. Holbrook and R.J. Schmitt. 1992a. Implications for the design of 

environmental assessment studies. Pages 75-90 In P.M. Grifman and S.E. Yoder 

(Eds.) Perspectives on the Marine Environment. USC Sea Grant, Los Angeles, 

California. 

Osenberg, C.W. and R.J. Schmitt. 1996. Detecting ecological impacts caused by human 

activities. Pages 3-16 in R.J. Schmitt and C.W. Osenberg (eds.) Detecting 

ecological impacts:Concepts and applications in coastal habitats. Academic 

Press. 

Osenberg, C.W., R.J. Schmitt, S.J. Holbrook, and D. Canestro. 1992a. Spatial scale of 

ecological effects associated with an open coast discharge of produced water. 

Pages 387-402 in J.P. Ray and F.R. Englehardt, editors. Produced water: 

technological/environmental issues and solutions. Plenum Press., New York. 

Osenberg, C.W., R.J. Schmitt, S.J. Holbrook, K. E. Abu-Saba, and A. R. Flegal. 1994. 

Detection of environmental impacts: natural variability, effect size, and power 

analysis. Ecological Applications 4:16-30. 

Osenberg, C.W., R.J. Schmitt, S.J. Holbrook, K. E. Abu-Saba, and A. R. Flegal. 1996. 

Detection of environmental impacts: natural variability, effect size, and power 

analysis. Pages 83-108 in R.J. Schmitt and C.W. Osenberg (eds.) Detecting 

ecological impacts: Concepts and applications in coastal habitats. Academic 

Press, San Diego. 

Raimondi, P.T., and R.J. Schmitt. 1992. Effects of produced water on settlement of 

larvae: field tests using red abalone. Pages 415-430 in J.P. Ray and F.R. 

Englehardt, editors. Produced water: technological/environmental issues and 

solutions. Plenum Press., New York. 

Schmitt, R.J. and C.W. Osenberg (editors and contributing authors). 1996. Detecting 

ecological impacts: concepts and applications in coastal habitats. Academic 

Press. 

Stewart-Oaten, A. 1996a. Goals in environmental monitoring. Pages 17-27 in R.J. 

Schmitt and C.W. Osenberg (eds.) Detecting ecological impacts:Concepts and 

applications in coastal habitats. Academic Press. 

Stewart-Oaten, A. 1996b. Problems in the analysis of environmental monitoring data. 

Pages 109-131 in R.J. Schmitt and C.W. Osenberg (eds.) Detecting ecological 

impacts:Concepts and applications in coastal habitats. Academic Press. 
Species-level Identification of Infaunal Samples 

 29 

 

Stewart-Oaten, A., J.R. Bence and C.W. Osenberg. 1992. Detecting effects of 

unreplicated perturbations: no simple solution. Ecology 73:1396-1404. 

Stewart-Oaten, A., W. W. Murdoch, and K. R. Parker. 1986. Environmental impact 

assessment: "pseudoreplication" in time? Ecology 67:929-940. 

Stretch, J.J. 1983. Habitat selection and vertical migration of sand-dwelling demersal 

gammarid amphipods. Ph.D. dissertation, University of California, Santa Barbara, 

California. 

Tilman, D., D.L. Lehman and C.E. Bristow. 1998. Diversity-stability relationships: 

statistical inevitability or ecological consequence. Am. Nat. 151:277-282. 

Warwick, R.M. 1988a. The level of taxonomic discrimination required to detect 

pollution effects on marine behthic communities. Marine Pollution Bulletin 

19:259-268. 

Warwick, R.M. 1988b. Analysis of community attributes of the macrobenthos of 

Frierfjord/Langesundfjord at taxonomic levels higher than species. Marine 

Ecology Progress Series 46:167-170. 

Wright, I.A., B.C. Chessman, P.G. Fairweather, L.J. Benson. 1995. Measuring the 

impact of sewage effluent on the macroinvertebrate community of an upland 

stream: the effect of different levels of taxonomic resolution and quantification. 

Australian Journal of Ecology 20:142-149. 

Underwood, A. J. 1991. Beyond BACI: experimental designs for detecting human 

environmental impacts on temporal variations in natural populations. Australian 

Journal of Marine and Freshwater Research 42:569-587. 
Final Study Report – Holbrook et al 

30 

Appendix A: Carpenteria Infaunal Density Cores 

File Contents: Carpenteria Infaunal Density Cores (11-Feb-90 to 20-Jun-95) 

File Name: Carbio_t.xls (Excel 2000) or Carbio_t.sas7bdat (SAS v 8.02) 

Variable Name Description Units Options for data field

SPCODE Species code - Numeric & Character 

 (e.g., CAMP0034) 

PHYLUM Taxonomic 

CLASS classification Character Many... 

ORDER 

FAMILY 

GENUS 

SPECIES 

SUBSPECIES 

STAGE Life stage Character ' ' (adult or not noted), 

 LARVAE, JUVENILE, 

ZOEA, ARMS (used for Astropecten

that were broken during 

collection) 

DATE Date na DD-MM-YY 

 (e.g., 11-Feb-90) 

TRANS Transect na E (East) 

 direction W (West) 

DISTANCE Distance Meters Distance W (2, 3, 5, 10, 25, 

 50, 100, 250, 500, 1000) 

 Distance E (5, 7, 10, 15, 30, 

 55, 100, 250, 500, 1000) 

REP Replicate - 1 or 2 

SIEVE Sieve Size mm 2.0, 1.0, or 0.5 

NUMBER Number of No./sample Numeric (>0) 

 species per 

 sample 

IDBY Identified by - LL (Larry Lovell) 

 DD (Doug Deiner) 

 JL (John Ljubenkov) 

 KZ (Kristin Zabaronick) 

 BE (Bryn Evans) 
Species-level Identification of Infaunal Samples 

 31 

 

Appendix B: Gaviota Infaunal Density Cores 

File Contents: Gaviota Infaunal Density Cores (04-Jan-90 to 26-Oct-94) 

File Name: gavbio_t.xls (Excel 2000) or gavbio_t.sas7bdat (SAS 8.02) 

Variable Name Description Units Options for data field

SPCODE Species code - Numeric & Character 

 (e.g., CAMP0034) 

PHYLUM Taxonomic 

CLASS classification Character Many... 

ORDER 

FAMILY 

GENUS 

SPECIES 

SUBSPECIES 

STAGE Life stage Character ' ' (adult or not noted), 

 LARVAE, JUVENILE, 

ZOEA, ARMS (used for Astropecten

that were broken during 

collection) 

DATE Date - DD-MM-YY 

 (e.g., 04-Jan-90) 

SITE Sample site - NI (Near Impact or Outfall) 

 FI (Far Impact or Upcoast) 

 C (Control Site) 

REP Replicate - 1 or 2 

SIEVE Sieve Size mm 2.0, 1.0, or 0.5 

NUMBER Number of No./sample Numeric (>0) 

 species per 

 sample 

IDBY Identified by - LL (Larry Lovell) 

 DD (Doug Deiner) 

 JL (John Ljubenkov) 

 KZ (Kristin Zabaronick) 

 BE (Bryn Evans) 
Final Study Report – Holbrook et al 

32 

Appendix C: Gaviota Emergence Traps 

File Contents: Gaviota Emergence Traps (05-Jan-90 to 26-Oct-94) 

File Name: gavet_t.xls (Excel 2000) or gavet_t.sas7bdat (SAS 8.02) 

Variable Name Description Units Options for data field

SPCODE Species code - Numeric & Character 

 (e.g., CAMP0034) 

PHYLUM Taxonomic 

CLASS classification Character Many... 

ORDER 

FAMILY 

GENUS 

SPECIES 

SUBSPECIES 

STAGE Life stage Character ' ' (adult or not noted), 

 LARVAE, JUVENILE, 

ZOEA, ARMS (used for Astropecten

that were broken during 

collection) 

DATE Date - DD-MM-YY 

 (e.g., 05-Jan-90) 

SITE Sample site - NI (Near Impact or Outfall) 

 FI (Far Impact or Upcoast) 

 C (Control Site) 

REP Replicate - 1 or 2 

SIEVE Sieve Size mm 2.0, 1.0, or 0.5 

NUMBER Number of No./sample Numeric (>0) 

 species per 

 sample 

IDBY Identified by - LL (Larry Lovell) 

 DD (Doug Deiner) 

 JL (John Ljubenkov) 

 KZ (Kristin Zabaronick) 

 BE (Bryn Evans) 
Species-level Identification of Infaunal Samples 

 33 

 

Appendix D: Gaviota Re-Entry Traps 

File Contents: Gaviota Re-Entry Traps (30-Apr-90 to 26-Oct-94) 

File Name: gavrt_t.xls (Excel 2000) or gavrt_t.sas7bdat (SAS 8.02) 

Variable Name Description Units Options for data field

SPCODE Species code - Numeric & Character 

 (e.g., CAMP0034) 

PHYLUM Taxonomic 

CLASS classification Character Many... 

ORDER 

FAMILY 

GENUS 

SPECIES 

SUBSPECIES 

STAGE Life stage Character ' ' (adult or not noted), 

 LARVAE, JUVENILE, 

ZOEA, ARMS (used for Astropecten

that were broken during 

collection) 

DATE Date - DD-MM-YY 

 (e.g., 30-Apr-90) 

MUDSITE Collection site - NI (Near Impact or Outfall) 

 for sediments FI (Far Impact or Upcoast) 

 C (Control Site) 

TRAPSITE Site at which re- - NI (Near Impact or Outfall) 

 entry trap was FI (Far Impact or Upcoast) 

 deployed C (Control Site) 

NUMBER Number of No./sample Numeric (>0) 

 species per 

 sample 

IDBY Identified by - LL (Larry Lovell) 

 DD (Doug Deiner) 

 JL (John Ljubenkov) 

 KZ (Kristin Zabaronick) 

 BE (Bryn Evans) 
Final Study Report – Holbrook et al 

34 

Appendix E: Taxonomic List 

File Contents: List of Species Codes and Taxonomic Affinities

File Name: taxonlst.xls (Excel 2000) or taxonlst.sas7bdat (SAS 8.02) 

Variable Name Description Units Options for data field

SPCODE Species code - Numeric & Character 

 (e.g., CAMP0034) 

PHYLUM Taxonomic 

CLASS classification Character Many... 

ORDER 

FAMILY 

GENUS 

SPECIES 

SUBSPECIES 

STAGE Life stage Character ' ' (adult or not noted), 

 LARVAE, JUVENILE, 

ZOEA, ARMS (used for Astropecten

that were broken during 

collection) 
Species-level Identification of Infaunal Samples 

 35 

 

The Department of the Interior Mission 

As the Nation's principal conservation agency, the Department of the Interior has responsibility for most 

of our nationally owned public lands and natural resources. This includes fostering sound use of our 

land and water resources; protecting our fish, wildlife, and biological diversity; preserving the 

environmental and cultural values of our national parks and historical places; and providing for the 

enjoyment of life through outdoor recreation. The Department assesses our energy and mineral 

resources and works to ensure that their development is in the best interests of all our people by 

encouraging stewardship and citizen participation in their care. The Department also has a major 

responsibility for American Indian reservation communities and for people who live in island territories 

under U.S. administration. 

The Minerals Management Service Mission 

As a bureau of the Department of the Interior, the Minerals Management Service's (MMS) primary 

responsibilities are to manage the mineral resources located on the Nation's Outer Continental Shelf 

(OCS), collect revenue from the Federal OCS and onshore Federal and Indian lands, and distribute 

those revenues. 

Moreover, in working to meet its responsibilities, the Offshore Minerals Management Program

administers the OCS competitive leasing program and oversees the safe and environmentally sound 

exploration and production of our Nation's offshore natural gas, oil and other mineral resources. The 

MMS Royalty Management Program meets its responsibilities by ensuring the efficient, timely and 

accurate collection and disbursement of revenue from mineral leasing and production due to Indian 

tribes and allottees, States and the U.S. Treasury. 

The MMS strives to fulfill its responsibilities through the general guiding principles of: (1) being 

responsive to the public's concerns and interests by maintaining a dialogue with all potentially affected 

parties and (2) carrying out its programs with an emphasis on working to enhance the quality of life for 

all Americans by lending MMS assistance and expertise to economic development and environmental 

protection.