Portland State University PDXScholar Dissertations and Theses Dissertations and Theses 4-25-1995 Phytoplankton in Mt. St. Helens Lakes, Washington Cynthia Fay Baker Portland State University Follow this and additional works at: https://pdxscholar.library.pdx.edu/open_access_etds Part of the Biology Commons Let us know how access to this document benefits ou.y Recommended Citation Baker, Cynthia Fay, "Phytoplankton in Mt. St. Helens Lakes, Washington" (1995). Dissertations and Theses. Paper 5017. https://doi.org/10.15760/etd.6893 This Thesis is brought to you for free and open access. It has been accepted for inclusion in Dissertations and Theses by an authorized administrator of PDXScholar. Please contact us if we can make this document more accessible: [email protected]. THESIS APPROVAL The abstract and thesis of Cynthia Fay Baker for the Master of Science in Biology were presented April 25, 1995, and accepted by the thesis committee and the department. COMMITTEE APPROVALS: Richard Petersen, Chair Mark Sytsma Bob Fountain, Representative of the Office of Graduate Studies DEPARTMENT APPROVAL: Leonard Simpson, Chair, H101ogy ************************************************************************ ACCEPTED FOR PORTLAND STATE UNIVERSITY BY THE LIBRARY on .2?' tfv k /99-1- ABSTRACT An abstract of the thesis of Cynthia Fay Baker for the Master of Science in Biology presented April 25, 1995. Title: Phytoplankton in Mount St. Helens Lakes, Washington Phytoplankton communities in fifteen lakes in the Mt. St. Helens area were surveyed to assess the abundance and species present. Eleven of the lakes were inside the blast zone of the 1980 eruption and four were located outside the blast zone as a comparison. The hypothesis is that lakes will cluster together based on the algal species present and that some algae will be correlated with certain environmental conditions. A cluster analysis was performed to determine if the lakes would group together based on algal abundance. There did not appear to be any distinct clustering among the study lakes, but this analysis did help to sort out some similarities of algal species present between lakes. It demonstrated that the lakes outside the blast zone were not functional as control lakes because they were very different from the blast-zone lakes. They had different assemblages of algae and their origin was so different from the blast-zone lakes that there was little overlap between them. The factor analysis was applied to determine the relationships between environmental variables and phytoplankton. The hypothesis is that certain algae are associated with each other and with identifiable environmental factors. Factor analysis should detect these patterns. The factors represent some condition in the environment but the analysis would be virtually meaningless unless these conditions can be recognized and the factors named. From the factor analysis alone, I could not name the factors but 2 returned to the task after the canonical correlation analysis was performed. The canonical correlation analysis gave some clues to identify the environmental conditions that exert control on these algae. The most useful statistical technique used in this study was the canonical correlation analysis. This analysis is a useful tool in community ecology studies where species-environment relationships can be inferred from community composition and environmental data. The environmental data used was nutrient and light attenuation present at the time the phytoplankton samples were taken. From this analysis I summarized a list of algae and with what environmental conditions that they are associated. Trophic state categories were assigned to the lakes from a trophic state index based on phytoplankton biovolume. t ) PHYTOPLANKTON IN MOUNT ST. HELENS LAKES, WASHINGTON by CYNTlilA FAYaAKER (j A thesis submitted in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE m BIOLOGY Portland State University 1995 ACKNOWLEDGEMENTS Many people contributed to this work for whom I would like to show my appreciation by mentioning. Dr. Richard Petersen, my graduate advisor, gave me the opportunity to do this work. Many students of his participated in collecting the samples. I am very grateful to Jim Sweet for helping me learn to identify the phytoplankton and for all of his reliable advice throughout this project. Dr. Bob Fountain and Damon Shepherd from the statistic counseling service provided the statistical analysis, advice on the interpretation of their analysis, and were patient with me as I tried to understand the techniques that they used. Nutrient data used in the canonical correlation analysis and my discussion of the ordination diagrams was provided by Kurt Carpenter. The map of the study area is due largely to Dr. Joe Porasky who helped my learn how to make the map and assisted with its editing. I thank my committee members: Dr. Richard Petersen, Dr. Mark Sytsma, Dr. John Reuter, and Dr. Bob Fountain for their advice and ideas. I would like to thank my family for their encouragement to continue my education, especially the unwavering support of my husband, Thomas. ii TABLE OF CONTENTS ACKNOWLEDGEMENTS LIST OF TABLES IV LIST OF FIGURES v INTRODUCTION 1 Study Lakes 2 METHODS 6 Sampling procedure 6 Laboratory methods 7 Identification and counting 7 Data analysis 8 RESULTS AND DISCUSSION 13 Patterns found clustering by biovolume 13 Patterns found clustering by density 16 Comparison of dominant phytoplankton 18 Comparison with other studies 24 The factor analysis 28 The canonical correlation analysis 31 Trophic state index among lake samples 62 Quality assurance 63 CONCLUSION 70 111 LITERATURE CITED 72 APPENDICES 74 A PHYTOPLANKTON DATA SHEETS 74 B T AXONO~C LIST OF PHYTOPLANKTON 240 C QUALITY ASSURANCE DATA SHEETS 257 IV LIST OF TABLES Table 1. Brief description of the study lakes 4 Table 2. Samples in which less than 100 algal units were counted 8 Table 3. Abundance of Cyclotella Com ta in Coldwater, Fawn, and 15 Hanaford lakes by average percent biovolume Table 4. Comparison of Chlamydomonas-like sp. and G/enodinium 15 sp. in Castle and Blue lakes by average percent biovolume Table 5. Comparison of Chromulina sp. and Rhodomonas minuta 17 in Merrill and Hanaford lakes by average percent biovolume Table 6. Abundance of Planktosphaeria ge/atinosa found in 17 Coldwater Lake by average percent biovolume Table 7. Comparison of phytoplankton in lakes analyzed by 20 Aquatic Analysts and Portland State University Table 8. Comparison of phytoplankton in Castle and Coldwater 23 lakes analyzed by Aquatic Analysts and Portland State University Table 9. Comparison of studies of lakes in the Mt. St. Helens area 26 after the 1980 eruption Table 10. Diatoms that loaded into the first four factor groups and 29 their degree of correlation with that factor group Table 11. Significance of canonical variable pairs 32 Table 12. Trophic state categories of lakes 63 v LIST OF FIGURES Figure 1. Lakes in the Mt. St. Helens Study Area 3 Figure 2. Boot Lake canonical variables VI vs. V2 36 Figure 3. Castle Lake - samples from 6/26/93 canonical variables 37 VI VS. V2 Figure 4. Castle Lake - samples from 9/17/93 canonical variables 38 VI vs. V2 Figure 5. Coldwater Lake canonical variables V 1 vs. V2 40 Figure 6. Fawn Lake canonical variables VI vs. V2 4I Figure 7. Hanaford Lake canonical variables V2 vs. V3 43 Figure 8. June Lake canonical variables VI vs. V2 44 Figure 9. McBride Lake canonical variables VI vs. V2 46 Figure IO. Merrill Lake - samples from 4/47/93 canonical variables 47 VI vs V2 Figure I I . Merrill Lake - September I 993 samples canonical 48 variables VI vs. V2 Figure I2. Merrill Lake - October I993 samples canonical 50 variables VI vs. V2 Figure I 3. Meta Lake - samples from June and August I 993 51 canonical variables VI vs. V2 Figure 14. Meta Lake - samples from October I 993 canonical 52 variables Vl vs. V3 Figure 15. Panhandle Lake canonical variables VI vs. V2 54 Figure I6. Ryan Lake canonical variables VI vs. V2 56 Figure 17. St. Helens Lake canonical variables VI vs. V2 57 lA I Introduction Mount St. Helens has drawn interest from the scientific community since its eruption on May 18, 1980 as it has provided an opportunity to study an environment which has undergone a large-scale natural disturbance. Lakes inside the blast zone were chosen as study lakes and compared with lakes outside the blast zone. This paper covers the phytoplankton portion of the study. It is a mensurative experiment. The phytoplankton communities were surveyed and the relationships between the algae in these assemblages and the chemical and physical environment are described in this work. The hypothesis is that some algae will be correlated with certain environmental conditions. Samples (208 total, see Appendix A) from 15 lakes were collected during 1992 and 1993 on several sampling trips. Phytoplankton i.n the samples were identified and counted. A cluster analysis, factor analysis, and a canonical correlation analysis was performed with the help of the Statistic Counseling Service at Portland State University. The cluster analysis is a procedure used to form groups of similar objects. The purpose of this cluster analysis is to determine if lakes are similar based on which algae were present in the samples and the quantity that were present. In a similar study, Allen and Koonce ( 1973) used an agglomerative cluster analysis to identify the functional place of algal species in a lake community. They identified persistent species with slow growth rates or rarer species with rapid growth rates. Norusis (1993) defines the factor analysis as a statistical technique used to identify a small number of factors that can be used to represent relationships among sets of many interrelated variables; groups of variables constitute the factors. In this thesis, many algal 2 species were identified in the lakes and it was the goal to fit them into a much smaller number of factors.