This readme.txt file was generated on 2022-08-11 by Angelique D. Dahlberg Recommended citation for the data: Dahlberg, Angelique, D.; Waller, Diane, L.; Hammond, David; Lund, Keegan; Phelps, Nicholas, B. D.. (2022). Open water dreissenid mussel control projects: lessons learned from a retrospective analysis. Retrieved from the Data Repository for the University of Minnesota, https://doi.org/10.13020/azjm-w270. ------------------- GENERAL INFORMATION ------------------- 1. Title of Dataset Data for Open water dreissenid mussel control projects: lessons learned from a retrospective analysis 2. Author Information Principal Investigator Contact Information Name: Angelique D. Dahlberg Institution: Minnesota Aquatic Invasive Species Research Center, University of Minnesota Address: 135 Skok Hall, 2003 Upper Buford Circle, St Paul, MN 55108, USA Email: edge0023@umn.edu ORCID: 0000-0001-9965-3017 Associate or Co-investigator Contact Information Name: Diane L. Waller Institution: U. S. Geological Survey, Upper Midwest Environmental Sciences Center Address: 2630 Fanta Reed Road, La Crosse, WI 54603, USA Email: dwaller@usgs.gov Associate or Co-investigator Contact Information Name: David Hammond Institution: Earth Science Laboratories, Inc. Address: 903 N 47th Street, Suite 105, Rogers AR, AR 72712, USA Email: dhammond@earthsciencelabs.com Associate or Co-investigator Contact Information Name: Keegan Lund Institution: Minnesota Department of Natural Resources Address: 500 Lafayette Road, St. Paul, MN, USA Email: keegan.lund@state.mn.us Associate or Co-investigator Contact Information Name: Nicholas B. D. Phelps Institution: Minnesota Aquatic Invasive Species Research Center, University of Minnesota Address: 135 Skok Hall, 2003 Upper Buford Circle, St Paul, MN 55108, USA Email: phelp083@umn.edu ORCID: 0000-0003-3116-860X 3. Date published or finalized for release: 4. Date of data collection (single date, range, approximate date) Datasets collected from partners between 2021-04-01-2022-04-01. Datasets include data from 2004-2021. 5. Geographic location of data collection (where was data collected?): Partner data collection occurred in Base Lake, Offutt Air Force Base (Sarpy County, Nebraska); Billmeyer Quarry (Lancaster County, Pennsylvania); Bone Lake (Washington County, Minnesota); Christmas Lake (Hennepin County, Minnesota); Crosley Lake (Kosciusko County, Indiana); Deep Quarry Lake (DuPage County, Illinois); Lake Erie (Monroe County, Michigan); Highland Lake (Lake County, Illinois); Lake Independence (Hennepin County, Minnesota); Lake Irene (Douglas County, Minnesota); Lake Marion (Dakota County, Minnesota); Good Harbor Bay, Lake Michigan (Leelanau County, Michigan); Millbrook Quarry (Prince William County, Virginia); Robinson’s Bay, Lake Minnetonka (Hennepin County, Minnesota); St. Alban’s Bay, Lake Minnetonka (Hennepin County, Minnesota); Lake Minnewashta (Carver County, Minnesota); Lake Ossawinamakee (Crow Wing County, Minnesota); Richland Chambers Reservoir (Freestone County, Texas); Rose Lake (Otter Tail County, Minnesota); Round Lake (Emmet County, Michigan); Ruth Lake (Crow Wing County, Minnesota); Valley Lo Lake (Cook County, Illinois); Lake Winnipeg (Manitoba). Data were compiled for this analysis in St. Paul, MN. 6. Information about funding sources that supported the collection of the data: Funding for this study was provided by the Minnesota Environment and Natural Resources Trust Fund as recommended by the Minnesota Aquatic Invasive Species Research Center (MAISRC) and the Legislative-Citizen Commission on Minnesota Resources (LCCMR), and the State of Minnesota. Funding for the MAISRC Zebra Mussel Research Fellowship that supported AD was provided by the Fletcher Family Foundation, Pelican Lakes Association of Crow Wing County, and Bay Lake Improvement Association. 7. Overview of the data (abstract): Dreissenid mussels are one of the most problematic aquatic invasive species (AIS) in North America, causing significant ecological and economic impacts in waterbodies where established. To date, dreissenid mussel control efforts in open water have included physical, biological, and chemical methods. The feasibility of successfully managing or even eradicating dreissenid mussels in lakes is relatively undocumented and unstudied in freshwater management literature. Additionally, control efforts are sometimes stymied by perceptions that the impacts to nontarget species will be unacceptable. The published literature evaluating both these two aspects is limited. Here, we present information on 33 open water dreissenid mussel control projects in 23 lakes across North America. Projects were categorized as rapid response eradication (n=16), established population eradication (n=8), suppression (n=3), or research (n=6). Since 2004, there has been a slight increase in the number of control projects conducted annually (r=0.27, p=0.21). The size of the treated areas relative to the total surface area of the lake has not changed for rapid response eradication projects (rτ=-0.10, p=0.61), and has decreased for established population eradication projects (r=-0.96, p=0.0001) as those projects have shifted from entire surface area treatment to entire shoreline treatment. The number of pre-treatment survey methods per project has not changed for either rapid response or established population eradication projects (r=0.10, p=0.60, and rτ =-0.60, p=0.14, respectively). Rapid response and established population eradication projects were successful at short-term eradication of dreissenid mussels within the treated area; however, long-term success was variable and depended on the potential for reintroduction of mussels from outside the treated area. The most likely predictor of control project success was treating larger surface areas and accounting for veliger and adult life stages. We reviewed data from past dreissenid mussel control projects and identified patterns and knowledge gaps to help inform adaptive management strategies. The three key lessons learned include 1) pre- and post-treatment survey methods should be designed to meet management objectives, 2) defining the treatment area is critical to meeting management objectives, and 3) control projects are missing an opportunity to collect data that can inform safe and effective adaptive management. -------------------------- SHARING/ACCESS INFORMATION -------------------------- 1. Licenses/restrictions placed on the data: NA 2. Links to publications that cite or use the data: 3. Was data derived from another source? Y If yes, list source(s): Base Lake: URS Group, Inc. Final Summary Report: Zebra Mussel Eradication Project. 2009. Allison Zach (personal communication) Program Coordinator Nebraska Invasive Species Program azach3@unl.edu Billmeyer Quarry: Hammond, D., and G. Ferris. 2019. “Low doses of Earthtec QZ ionic copper used in effort to eradicate quagga mussels from an entire Pennsylvania lake.” Management of Biological Invasions 10 (3): 500–516. https://doi.org/10.3391/mbi.2019.10.3.07 David Hammond (personal communication) VP of Applications Development Earth Science Labs, Inc. dhammond@earthsciencelabs.com Bone Lake: Keegan Lund (personal communication) Aquatic Biologist, Invasive Species Program Minnesota Department of Natural Reosources keegan.lund@state.mn.us Christmas Lake: Lund, K., K. Bloodsworth Cattoor, E. Fieldseth, J. Sweet, and M. A. McCartney. 2018. “Zebra mussel (Dreissena polymorpha) eradication efforts in Christmas Lake, Minnesota.” Lake and Reservoir Management 34 (1): 7–20. https://doi.org/10.1080/10402381.2017.1360417 Keegan Lund (personal communication) Aquatic Biologist, Invasive Species Program Minnesota Department of Natural Reosources keegan.lund@state.mn.us Crosley Lake: David Hammond (personal communication) VP of Applications Development Earth Science Labs, Inc. dhammond@earthsciencelabs.com Deep Quarry Lake: Greg Whitledge (personal communication) Professor, Center for Fisheries, Aquaculture, and Aquatic Sciences School of Biological Sciences Southern Illinois University gwhit@siu.edu Whitledge, G. W., M. M. Weber, J. DeMartini, J. Oldenburg, D. Roberts, C. Link, S. M. Rackl, N. P. Rude, A. J. Yung, L. R. Bock, and D. C. Oliver. 2015. "An evaluation Zequanox efficacy and application strategies for targeted control of zebra mussels in shallow-water habitats in lakes." Management of Biological Invasions 6(1): 71-82. http://dx.doi.org/10.3391/mbi.2015.6.1.06 Lake Erie: Weber, M. M. Zequanox Application Technique Pilot Study on Lake Erie. Davis, CA, USA: Marrone Bio Innovations, Inc., 2015. Highland Lake: David Hammond (personal communication) VP of Applications Development Earth Science Labs, Inc. dhammond@earthsciencelabs.com John Sonnenberg (personal communication) Chairman of the Lake Management Committee Highland Lakes Property Owners Association carmentaeducation@gmail.com Lake Independence: Keegan Lund (personal communication) Aquatic Biologist, Invasive Species Program Minnesota Department of Natural Reosources keegan.lund@state.mn.us Lake Irene: Keegan Lund (personal communication) Aquatic Biologist, Invasive Species Program Minnesota Department of Natural Reosources keegan.lund@state.mn.us Lake Marion: Keegan Lund (personal communication) Aquatic Biologist, Invasive Species Program Minnesota Department of Natural Reosources keegan.lund@state.mn.us Barbour, M. T., J. K. Wise, and J. A. Luoma. A bioassay assessment of a zebra mussel (Dreissena polymorpha) eradication treatment. Reston, VA, USA: U.S. Geological Survey, 2018. https://doi.org/10.3133/ofr20181138 Good Harbor Bay, Lake Michigan: LimnoTech. Good Harbor Bay dreissenid mussel control demonstration project discharge summary report. Ann Arbor, MI, USA, 2020. Millbrook Quarry: Fernald, R. T., and B. T. Watson. 2014. “Eradication of zebra mussels (Dreissena polymorpha) from Millbrook Quarry, Virginia: rapid response in the real world.” In Quagga and Zebra Mussels: Biology Impacts and Control, 195–213. Boca Raton, FL: CRC Press LLC. Virginia Department of Game and Inland Fisheries. U.S. Fish and Wildlife Service Final Environmental Assessment - Millbrook Quarry Zebra Mussel and Quagga Mussel Eradication. Richmond, VA, USA: 2005. Robinson’s Bay, Lake Minnetonka: Luoma, J. A., and T. Severson. Efficacy of Spray-Dried Pseudomonas fluorescens, Strain CL145A (Zequanox®), for Controlling Zebra Mussels (Dreissena polymorpha) within Lake Minnetonka, MN Enclosures. La Crosse, WI, USA: U.S. Geological Survey, 2016. St. Alban’s Bay, Lake Minnetonka: Barbour, M. T., A. D Dahlberg, J. A. Luoma, T. J. Severson, J. K. Wise, B. Bennie, D. Hammond, and D. Waller. n.d. “Evaluation of low-dose copper molluscicide to suppress zebra mussel (Dreissena polymorpha) veligers and settlement in a natural waterbody.” Lake Minnewashta: Keegan Lund (personal communication) Aquatic Biologist, Invasive Species Program Minnesota Department of Natural Reosources keegan.lund@state.mn.us Lake Ossawinamakee: Gary Montz (personal communication) Research Scientist 2, Division of Ecological and Water Resources Minnesota Department of Natural Reosources gary.montz@state.mn.us Richland Chambers Reservoir: David Hammond (personal communication) VP of Applications Development Earth Science Labs, Inc. dhammond@earthsciencelabs.com Rose Lake: Keegan Lund (personal communication) Aquatic Biologist, Invasive Species Program Minnesota Department of Natural Reosources keegan.lund@state.mn.us Round Lake: James Luoma (personal communication) Fisheries Biologist - Great Lakes Fishery Commission Technical Assistance Program Lead U.S. Geological Survey jluoma@usgs.gov Luoma, J. A., D. L. Waller, T. J. Severson, M. T. Barbour, J. K. Wise, E. G. Lord, L. A. Bartsch, and M. R. Bartsch. Assessment of uncontained Zequanox applications for zebra mussel control in a Midwestern lake. Reston, VA, USA: U.S. Geological Survey, 2019. https://doi.org /10.3133/ofr20191126 Ruth Lake: Keegan Lund (personal communication) Aquatic Biologist, Invasive Species Program Minnesota Department of Natural Reosources keegan.lund@state.mn.us Valley Lo Lake: David Hammond (personal communication) VP of Applications Development Earth Science Labs, Inc. dhammond@earthsciencelabs.com Lake Winnipeg: Laureen Janusz (personal communication) Fisheries Science and Fish Culture Section Wildlife, Fisheries and Resource Enforcement Branch Agriculture and Resource Development Province of Manitoba Laureen.Janusz@gov.mb.ca 4. Terms of Use: Data Repository for the U of Minnesota (DRUM) By using these files, users agree to the Terms of Use. https://conservancy.umn.edu/pages/drum/policies/#terms-of-use --------------------- DATA & FILE OVERVIEW --------------------- 1. File List A. Filename: Projects_database Short description: Access database containing all available data from dreissenid mussel control projects. B. Filename: Projects_analysis Short description: R code for statistical analysis used in manuscript. C. Filename: Projects_narratives Short description: Word file containing all available data, in narrative form, from dreissenid mussel control projects. Data were originally communicated in in folders containing spreadsheets, photos, maps, scanned handwritten notes, and other file types. We compiled everything in table format, but because each project was unique, that table did not capture the complexity of these projects. To better convey the many details of each project, we summarized projects in a narrative to accompany the table. ***The following files are CSVs of tables within the Access database (File A) and captured in the "Projects_database_CSV.zip" file: D. Filename: lu_contacts Short description: CSV file containing project contacts. E. Filename: lu_observation_details Short description: CSV file containing the categories of observations made during projects and their corresponding abbreviations. F. Filename: lu_observation_types Short description: CSV file containing the specific types of observations (within categories) made during projects and their corresponding abbreviations. G. Filename: lu_products Short description: CSV file containing the different products used to control dreissenid mussels along with their active ingredients. H. Filename: lu_species Short description: CSV file containing the dreissenid mussel species common and scientific names. I. Filename: lu_states Short description: CSV file containing US state names and abbreviations. Manitoba, Canada, was added to the file to accomodate the one Canadian project. J. Filename: lu_treatment_depths Short description: CSV file containing the different depths at which dreissenid mussel treatment could occur. K. Filename: lu_treatment_goals Short description: CSV file containing the list of all project goals and their descriptions. L. Filename: lu_treatment_phases Short description: CSV file containing the list of treatment phases (pre-, during, post-) and their definitions. M. Filename: tbl_lakes Short description: CSV file containing the list of lakes and information pertaining to each lake. N. Filename: tbl_posttreatment_obs Short description: CSV file containing details on which projects had which type(s) of post-treatment surveys performed. O. Filename: tbl_pretreatment_obs Short description: CSV file containing details on which projects had which type(s) of pre-treatment surveys performed. P. Filename: tbl_treatment Short description: CSV file containing the list of all projects and information pertaining to each project. Q. Filename: tbl_treatment_obs Short description: CSV file containing details on which projects had which type(s) of observations were made during the project. 2. Relationship between files: Files A and C contain all available data from dreissenid mussel control projects. The information in each file is similar, but does not entirely overlap (some data can only be communicated effectively in one or the other file type). File B contains analysis of data from File A. The coding in File B requires connecting to File A. Files D through Q are CSV flat files of the data stored in File A. Queries are not included, but all data are available to recreate queries or develop new queries. Queries are retained in File A. -------------------------- METHODOLOGICAL INFORMATION -------------------------- 1. Description of methods used for collection/generation of data: We collected information on all open-water dreissenid mussel control projects that have occurred in North America through direct contact to natural resource professionals as well as an exhaustive review of published literature. We contacted resource managers and researchers within the invasive species community, including The Invasive Mussel Collaborative listserv (https://invasivemusselcollaborative.net), staff from control product vendors (i.e., Earth Science Labs, Marrone Bio Innovations, and ASI Group Ltd.), and staff within agencies who are known to have conducted treatments (i.e., Minnesota Department of Natural Resources (MN DNR), US Geological Survey (USGS)), and others identified by the initial contacts. The final list was shared with the Invasive Mussel Collaborative to confirm all control projects were identified. We requested all available information on the control project from the project manager, including summary reports, raw data, personal communication, and maps. Available data were organized in a narrative format (File C) and Microsoft Access database (File A) and categorized into pre-treatment, treatment, or post-treatment activities 2. Methods for processing the data: 3. Instrument- or software-specific information needed to interpret the data: Microsoft Access (File A) and software to read R code (File B) and text (File C). 4. Standards and calibration information, if appropriate: NA 5. Environmental/experimental conditions: NA 6. Describe any quality-assurance procedures performed on the data: Simplified data were shared with the Invasive Mussel Collaborative for general corrections (e.g., ultimate infestation status of lakes, treatment years, etc.). For control projects with a primary project manager, data were provided to those managers for direct review. 7. People involved with sample collection, processing, analysis and/or submission: Angelique Dahlberg, Diane Waller, David Hammond, Keegan Lund, Nicholas Phelps ------------------------------------------------ DATA-SPECIFIC INFORMATION FOR: Projects_database ------------------------------------------------ Metadata are stored within the database. ------------------------------------------------ DATA-SPECIFIC INFORMATION FOR: Projects_analysis ------------------------------------------------ 1. Number of variables: Data are derived from Projects_database; there are no unique data in this code. 2. Number of cases/rows: NA 3. Missing data codes: NA 4. Variable List: NA -------------------------------------------------- DATA-SPECIFIC INFORMATION FOR: Projects_narratives -------------------------------------------------- 1. Number of variables: Data are narrative descriptions of case studies. There are 33 projects in 23 lakes described. 2. Number of cases/rows: NA 3. Missing data codes: NA 4. Variable List: NA ------------------------------------------ DATA-SPECIFIC INFORMATION FOR: lu_contacts ------------------------------------------ 1. Number of variables: 7 2. Number of cases/rows: NA 3. Missing data codes: missing data are left as blanks 4. Variable List: Contact_id (Autonumber): unique identifier Company or agency (short text): The company or agency associated with a project Abbreviation (short text): An abbreviation for the company or agency name Website (short text): The website with additional information about the company or agency Contact (short text): An individual contact within the company or agency who can provide additional detail Phone (short text): The phone number for the individual contact Email (short text): The email address for the individual contact ----------------------------------------------------- DATA-SPECIFIC INFORMATION FOR: lu_observation_details ----------------------------------------------------- 1. Number of variables: 3 2. Number of cases/rows: 51 3. Missing data codes: missing data are left as blanks 4. Variable List: observation_detail_id (AutoNumber): unique identifier Observation (short text): Observation full name Abbreviation (short text): Observation abbreviation --------------------------------------------------- DATA-SPECIFIC INFORMATION FOR: lu_observation_types --------------------------------------------------- 1. Number of variables: 3 2. Number of cases/rows: 4 3. Missing data codes: missing data are left as blanks 4. Variable List: observation_type_id (AutoNumber): unique identifier data_type (short text): The category of data; includes environmental observations, non-target observations, survey methods, and treatment practices description (short text): A full description of data categories ------------------------------------------ DATA-SPECIFIC INFORMATION FOR: lu_products ------------------------------------------ 1. Number of variables: 3 2. Number of cases/rows: 5 3. Missing data codes: missing data are left as blanks 4. Variable List: product_id (AutoNumber): unique identifier Treat_product (short text): dreissenid mussel control product name Active Ingredient (short text): active ingredient of the control product ----------------------------------------- DATA-SPECIFIC INFORMATION FOR: lu_species ----------------------------------------- 1. Number of variables: 3 2. Number of cases/rows: 2 3. Missing data codes: missing data are left as blanks 4. Variable List: species_id (AutoNumber): unique identifier Common name (short text): organism common name Scientific name (short text): organism scientific name (Genus species) ---------------------------------------- DATA-SPECIFIC INFORMATION FOR: lu_states ---------------------------------------- 1. Number of variables: 3 2. Number of cases/rows: 55 3. Missing data codes: missing data are left as blanks 4. Variable List: state_id (AutoNumber): unique identifier Code (short text): state/province abbreviation State/Province (short text): state/province full name -------------------------------------------------- DATA-SPECIFIC INFORMATION FOR: lu_treatment_depths -------------------------------------------------- 1. Number of variables: 2 2. Number of cases/rows: 4 3. Missing data codes: missing data are left as blanks 4. Variable List: treatment_depth_id (AutoNumber): unique identifier Treat_depth (short text): Depth category at which treatment occurred ------------------------------------------------- DATA-SPECIFIC INFORMATION FOR: lu_treatment_goals ------------------------------------------------- 1. Number of variables: 3 2. Number of cases/rows: 4 3. Missing data codes: missing data are left as blanks 4. Variable List: Treatment_goal_id (AutoNumber): unique identifier Treatment_goal (short text): treatment project goal Definition (short text): goal description and detail -------------------------------------------------- DATA-SPECIFIC INFORMATION FOR: lu_treatment_phases -------------------------------------------------- 1. Number of variables: 3 2. Number of cases/rows: 3 3. Missing data codes: missing data are left as blanks 4. Variable List: phase_id (AutoNumber): unique identifier phase (short text): Temporal phase related to treatment description (short text): Definition of that phase ---------------------------------------- DATA-SPECIFIC INFORMATION FOR: tbl_lakes ---------------------------------------- 1. Number of variables: 14 2. Number of cases/rows: 24 3. Missing data codes: missing data are left as blanks 4. Variable List: LakeID (AutoNumber): unique identifier Lake_name (Short Text): The lake name state_id (Number): The state in which the given lake is found Area (Short Text): The area (acres) of a given lake contact_id (Number): The primary contact for data related to a given lake species_id (Number): The species of dreissenid mussel found in a given lake Year_confirmed (Number): The year dreissenid mussels were confirmed in a given lake no_treatments (Number): The number of treatments conducted in a given lake lat (Number): Latitude long (Number): Longitude Eradication_WithinST (Short Text): Eradication of dreissenid mussels within the treated area in the short-term Eradication_WithinLT (Short Text): Eradication of dreissenid mussels within the treated area in the long-term Eradication_OutsideST (Short Text): Eradication of dreissenid mussels outside of the treated area in the short-term Eradication_OutsideLT (Short Text): Eradication of dreissenid mussels outside of the treated area in the long-term ---------------------------------------------------- DATA-SPECIFIC INFORMATION FOR: tbl_posttreatment_obs ---------------------------------------------------- 1. Number of variables: 4 2. Number of cases/rows: 78 3. Missing data codes: missing data are left as blanks 4. Variable List: posttreatment_obs_id (AutoNumber): unique identifier treatment_ID (Number): The project (identified by a number) observation_type_id (Number): The type of observation associated with that project observation_detail_id (Number): Details about the observation made (specifics) --------------------------------------------------- DATA-SPECIFIC INFORMATION FOR: tbl_pretreatment_obs --------------------------------------------------- 1. Number of variables: 4 2. Number of cases/rows: 71 3. Missing data codes: missing data are left as blanks 4. Variable List: pretreatment_obs_id (AutoNumber): unique identifier treatment_ID (Number): The project (identified by a number) observation_type_id (Number): The type of observation associated with that project observation_detail_id (Number): Details about the observation made (specifics) -------------------------------------------- DATA-SPECIFIC INFORMATION FOR: tbl_treatment -------------------------------------------- 1. Number of variables: 31 2. Number of cases/rows: 33 3. Missing data codes: missing data are left as blanks 4. Variable List: treatment_ID (AutoNumber): unique identifier lakeid (Number): Lake Year_treated (Number): Treatment year PreTreat_SurveyHours (Number): The number of hours spent conducting pre-treatment surveys PreTreat_OtherSurvey (Short Text): Any other details about pre-treatment surveys PreTreat_AdultMussels (Short Text): Presence/absence of adult dreissenid mussels prior to treatment PreTreat_Veligers (Short Text): Presence/absence of veligers prior to treatment product_id (Number): The molluscicide used in treatment Treat_StartDate (Date/Time): The start date for a treatment Treat_EndDate (Date/Time): The end date for a treatment Treat_Precision (Short Text): How accurate the start and end dates are (day/month/year) Treat_Length (Number): The length of the treatment Treat_Dose (Number): The target concentration for treatment Treat_DoseMax (Number): The max concentration observed during treatment Treat_DoseMin (Number): The min concentration observed during treatment Treat_DoseAvg (Number): The average concentration observed during treatment Treat_BumpTreats (Short Text): The frequency of any bump treatments conducted during treatment Treat_notes (Short Text): Notes on the treatment treatment_depth_id (Number): Treatment application depth Treat_Barrier (Short Text): Use of a barrier during treatment Treatment_goal_id (Number): The goal of a treatment Treat_Area (Number): The area treated NonTarget_Notes (Long Text): Any notes on nontarget organism impacts NonTarget_OtherImpacts (Short Text): Any notes on other nontarget organism impacts PostTreat_SurveyHours (Short Text): The number of hours spent conducting post-treatment surveys PostTreat_OtherSurvey (Short Text): Any other details about post-treatment surveys PostTreat_Notes (Short Text): Notes on post-treatment observations PostTreat_AdultMussels (Short Text): Presence/absence of adult dreissenid mussels following treatment PostTreat_Veligers (Short Text): Presence/absence of veligers following treatment PostTreat_eDNA (Short Text): Presence/absence of dreissenid eDNA following treatment PostTreat_LifeStageNotes (Short Text): Notes on dreissenid lifestages observed following treatment ------------------------------------------------ DATA-SPECIFIC INFORMATION FOR: tbl_treatment_obs ------------------------------------------------ 1. Number of variables: 4 2. Number of cases/rows: 189 3. Missing data codes: missing data are left as blanks 4. Variable List: treatment_obs_id (AutoNumber): unique identifier treatment_ID (Number): The project (identified by a number) observation_type_id (Number): The type of observation associated with that project observation_detail_id (Number): Details about the observation made (specifics)