RMIS releases

Information on CWT salmonids throughout the Pacific region is available in an on-line coastwide database, the Regional Mark Information System (RMIS). This database (see http://www.psmfc.org/rmpc/index.html ) is maintained by the Regional Mark Processing Center (RMPC) of the Pacific States Marine Fisheries Commission (PSMFC) to facilitate exchange of CWT data between release agencies, sampling/...

Abstract

The 2017 Rwanda Malaria Indicator Survey (RMIS) is a nationwide survey with a nationally representative sample of approximately 5,041 households. The survey provides information on key malaria control indictors, such as the proportion of households having at least one bed net and at least one insecticide-treated net (ITN). It looks at the proportion under age 5 who slept under a bed net the previous night, and under an ITN, and tests for the prevalence of malaria among all household members. Among pregnant women, the survey assesses the proportion of pregnant women who slept under a bed net the previous night.

The primary objective of the 2017 RMIS project is to provide up-to-date estimates of basic demographic and health indicators related to malaria. Specifically, the 2017 RMIS collected information on household ownership of mosquito nets, care seeking behavior by adults, and treatment of fever in children. All members of sampled households were also tested for malaria infection. Knowledge of malaria was assessed among interviewed women. The information collected through the 2017 RMIS is intended to assist policy makers and program managers in evaluating and designing programs and strategies for improving the health of the country’s population.

Geographic coverage

National coverage

Analysis unit

• Household
• Woman age 15 to 49

Kind of data

Sample survey data [ssd]

Sampling procedure

The 2017 RMIS followed a two-stage sample design that would allow estimates of key indicators to be determined for the nation as a whole, for urban and rural areas, and for the five provinces. In the first stage, sample points, or clusters, were selected from the sampling frame, which consisted of enumeration areas (EAs) delineated during the 2012 Population and Housing Census. A total of 170 clusters with probability proportional to size were selected from these EAs.

In the second stage, sampling involved systematic selection of households. A household listing operation was undertaken in all selected EAs during the main data collection. Households to be included in the survey were then randomly selected from these lists. Thirty households were selected from each EA, for a total sample size of 5,100 households. Because of the approximately equal sample size for each region, the sample is not selfweighting at the national level. Results shown in this report have been weighted to account for the complex sample design. See Appendix A for additional details on the sampling procedures.

Note: See Appendix A of the final report for additional details on the sampling procedure.

Mode of data collection

Face-to-face [f2f]

Research instrument

Data was primarily collected using three questionnaires: the Household Questionnaire, the Woman’s Questionnaire, and the Biomarker Questionnaire. Core questionnaires available from the RBM-MERG were adapted to reflect the population and health issues relevant to Rwanda.

Cleaning operations

Data entry began on November 1, 2017, 2 weeks after the survey launched in the field. Data were entered by a team of eight data processing personnel recruited and trained for this task. They were assisted during these operations by two staff members who aided in questionnaire reception, data verification, and coding. Completed questionnaires were periodically brought in from the field to the MOPDD headquarters, where assigned agents checked them and coded the open-ended questions. Next, the questionnaires were sent to the data entry facility and the blood samples (blood smear slides) were sent to the lab to be read for the malaria parasites. Data were entered using CSPro, a program developed jointly by the United States Census Bureau, the ORC Macro MEASURE DHS+ program, and Serpro S.A. Processing the data concurrent with data collection allowed for regular monitoring of teams’ performance and data quality. Field check tables were regularly generated during data processing to check various data quality parameters. As a result, feedback was given on a regular basis, encouraging teams to continue quality work and to correct areas in need of improvement. Feedback was individually tailored to each team. Data entry, which included 100% double entry to minimize keying error, was completed on December 31, 2017. Data editing, was completed on January 26, 2018. Data cleaning and finalization was completed on February 9, 2018.

Response rate

A total of 5,096 households selected for the sample, 5,061 were occupied at the time of fieldwork. Among the occupied households, 5,041 were successfully interviewed, yielding a total household response rate of 99.6%. In the interviewed households, 5,088 women were identified as eligible for individual interview, and 5,022 were successfully interviewed, yielding a response rate of 98.7%.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: nonsampling errors and sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the Rwanda MIS 2017 (2017 RMIS) to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2017 RMIS is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability among all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

A sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95 percent of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2017 RMIS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulas. The computer software used to calculate sampling errors for the 2017 RMIS is a SAS program. This program used the Taylor linearization method of variance estimation for survey estimates that are means, proportions, or ratios.

Note: Detailed description of sampling error estimates is presented in APPENDIX B of the final report.

Data appraisal

Data quality tables are produced to review the quality of the data: - Household age distribution - Age distribution of eligible and interviewed women - Completeness of reporting - Births by calendar years - Household composition

Note: The tables are presented in APPENDIX C of the final report.

Abstract

The 2013 Rwanda Malaria Indicator Survey (RMIS) is a nationally representative, household-based survey that provides data on malaria indicators, which are used to assess the progress of a malaria control program. The control program is geared toward meeting Millennium Development Goals.

The objectives of the 2013 Rwanda Malaria Indicator Survey (RMIS) were to collect data on (1) ownership and utilization of treated mosquito nets and (2) knowledge of symptoms, causes, treatments, and prevention of malaria.

A related objective was to produce survey results in a timely manner and to ensure that the data were disseminated to a wide audience of potential users in government and nongovernmental organizations within and outside of Rwanda. Most survey indicators were produced separately for each of the five provinces.

Key indicators were malaria-specific and general. Malaria indicators: • Ownership of insecticide-treated mosquito nets • Usage of insecticide-treated mosquito nets among persons in the household, children under age 5, and pregnant women • Proportion of children under age 5 with recent fever who were treated with timely, appropriate antimalarial drugs • Proportions of mothers who know the symptoms, treatments, and prevention of malaria

General indicators: • Source of household drinking water; type of toilet facility • Household socioeconomic status (wealth quintile)

Geographic coverage

National coverage

Analysis unit

• Household
• Woman age 15 to 49

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Design The sample for the 2013 RMIS was designed to provide malaria indicator estimates for the country as a whole and for separate urban and rural areas. Survey estimates are also be reported for the provinces (South, West, North, and East provinces) and Kigali City.

A representative sample of 4,772 households was selected for the 2013 RMIS. The sample was selected in two stages. In the first stage, 159 villages (also known as clusters or enumeration areas) were selected with probability proportional to village size. Village size is determined by the number of households residing in the village. Then, a complete mapping and listing of all households in the selected villages was conducted. The resulting lists of households served as the sampling frame for the second stage of sample selection. Households were systematically selected from those lists for participation in the survey.

All women age 15-49 who were either permanent residents of the households or visitors present in the household on the night before the survey were eligible for interviews.

Note: Detailed description of the sample design is presented in Appendix A of the final report.

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2013 RMIS involved two questionnaires: a Household Questionnaire and a Woman’s Questionnaire for all women age 15-49 in the selected households. Both of these instruments were based on the model Demographic and Health Survey Phase III and the model Roll Back Malaria (RBM) Malaria Indicator Survey (MIS) questionnaires developed by the MEASURE DHS program, as well as on previous surveys conducted in Rwanda, including the 2007-08 Rwanda Interim DHS (RIDHS) and the 2010 Rwanda Demographic and Health Survey (RDHS). The MAL & OPD Division reviewed the draft questionnaires with potential stakeholders, including government health agencies and interested donor groups.

The Household Questionnaire was used to list all the usual members and visitors in the selected households. Some basic information was collected on the characteristics of each person listed, including age, sex, education, and relationship to the head of household. The main purpose of the Household Questionnaire was to identify women eligible for individual interview. Questions on ownership and use of mosquito nets were included in the Household Questionnaire as were questions about proxy indicators for wealth such as ownership of various durable goods, dwelling unit characteristics, and land.

The Woman’s Questionnaire was used to collect information from women age 15-49 on the following topics: • Background characteristics (age, education, media exposure, employment, religion, and so on) • Reproductive history (number of births, date of last birth, current pregnancy status, and antimalarial treatment for children under age 5 with recent fever) • Knowledge about malaria symptoms, causes, and prevention

Cleaning operations

Processing of the 2013 RMIS data began as soon as questionnaires were received from the field. Completed questionnaires were returned from the field to MAL & OPD Division headquarters, where they were entered and edited by data processing personnel who were specially trained for this task. Processing the data concurrently with data collection allowed for regular monitoring of team performance and data quality. Field check tables were regularly generated during data processing to check various data quality parameters. As a result, feedback was given on a regular basis, encouraging teams to continue their high quality work and to correct areas in need of improvement. Feedback was individually tailored to each team. Data entry, which included 100 percent double entry to minimize errors in keying and data editing, was completed on May 10, 2013. Data cleaning and finalization was completed on June 3, 2013.

Response rate

A total of 4,772 households was selected, of which 4,769 households were identified and occupied at the time of the survey. Among these households, 4,766 completed the Household Questionnaire, yielding a response rate of nearly 100 percent.

In the 4,766 households surveyed, 5,164 women age 15-49 were identified as being eligible for the individual interview. Interviews were completed with 5,135 of these women, yielding a response rate of 99.4 percent. The response rates were slightly higher in rural areas than in urban areas.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) nonsampling errors, and (2) sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2013 Rwanda Malaria Indicator Survey (2013 RMIS) to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2013 RMIS is only one of many samples that could have been selected from the same population, using the same design and identical size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling error is a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

A sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95 percent of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2013 RMIS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulas. The computer software used to calculate sampling errors for the 2013 RMIS is an SAS program. This program used the Taylor linearization method for variance estimation for survey estimates that are means or proportions.

The Taylor linearization method treats any percentage or average as a ratio estimate, r = y/x, where y represents the total sample value for variable y, and x represents the total number of cases in the group or subgroup under consideration.

Note: Detailed description of estimate of sampling error is presented in APPENDIX B of the final report.

Data appraisal

Data quality tables are produced to review the quality of the data: - Household age distribution - Age distribution of eligible and interviewed women - Completeness of reporting

Note: The tables are presented in APPENDIX C of the final report.

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Cohort reconstruction and population growth estimates for Chinook salmon (Oncorhynchus tshawytscha) on the Mokelumne River (USA) for all returning adults (Apparent population growth rate) and natural origin spawners (Actual natural population growth rate).1Age structure determined by coded wire tag recoveries of adult salmon from Mokelumne River Fish Hatchery (MRFH) caught in the freshwater sport fishery, carcass survey, or at MRFH (RMIS database). The number of survivors from each cohort is the sum of age 2, 3, 4, and 5 year olds produced in a given spawning year (cohort survival). For example, the return data in the ‘Adult Age Structure’ columns that are in the bold cells sum to the apparent cohort survival value for 1992.

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The California Central Valley steelhead (Oncorhynchus mykiss) has declined precipitously since Euro-American colonization and has been listed as threatened under the United States Endangered Species Act since 1998. Hatchery-origin fish now dominate the population and hatchery management is a key listing factor. However, scant release metric information is available. We compiled a time series of O. mykiss hatchery release data for all four Central Valley hatcheries releasing O. mykiss between 1948 and 2017. The biocomplexity of released fish has declined since the early 1980s. Individuals have been released at increasingly similar numbers, biomass, body sizes, times, and locations over time. Moreover, yearling fish have been released at larger sizes, leading to the near-exclusive release of age-1 smolts in February and March since the late 1990s and early 2000s. Pervasive reductions in release portfolios have likely occurred for other hatchery-supported Pacific salmonid stocks throughout the Pacific Rim region. In an increasingly variable environment, such reductions in intraspecific diversity could significantly affect population stability and resilience. Methods Release & Return Database Compilation

We compiled release data from 135 annual reports provided by the California Department of Fish and Wildlife (CDFW) for state-operated hatcheries. The state hatchery annual reports spanned one fiscal year from 1 July to 30 June of the following year: NFH (54 reports, 1956–57 to 2009–10), FRFH (40 reports, 1969–70 to 2009–10), and MRFH (41 reports, 1964–65 to 2007–08; no releases were reported for 1976–77, 1988–89, or 2001–02). From 1988 to 2001, release records were obtained for MRFH from the California Hatchery Scientific Review Group (CA HSRG 2012, Appendix VI) instead of annual reports because the former dataset is more detailed and complete. From 2001–2017 for the FRFH and 2003–2017 for the NFH and MRFH, an electronic dataset from CDFW's statewide inventory system was used instead of annual reports or California Hatchery Scientific Review Group data (CA HSRG 2012, Appendix VIII). Hatchery release data were considered “draft” or non-finalized from 1994–2017 for the NFH, 1992–2017 for the FRFH, and 1988–2017 for the MRFH. Release data for the CNFH from 1948 to winter 1975 were obtained from an electronic database provided by the U.S. Fish and Wildlife Service (USFWS) and data from spring 1975 to 2017 were obtained from the Regional Mark Information System (RMIS, http://www.rmpc.org/, retrieved on 21 May 2020).
The basic reporting unit for all data sources was a cohort of similarly sized fish released together in a stated release location over a specified time (hereafter referred to as a “release group”). Information about brood year (same as emergence year for winter-run O. mykiss), total number of fish planted, average or range of fish size(s)-at-release (usually expressed as fish·lb-1), release timing (from single days to months), and descriptions and (or) geographic coordinates of release locations were reported for release groups. Total numbers of returning O. mykiss trapped at state hatcheries were compiled from annual reports and the electronic database provided by CDFW.

Resulting databases: CV_Steelhead_Hatchery_Release_Database.csv

Objective 1: Temporal trends in juvenile releases Number released: We present annual release data according to the California ‘water year’ (WY; 1 October to 30 September) since this period is more relevant to the Central Valley O. mykiss life cycle than calendar years (e.g., upstream migration in fall and winter, spawning and emergence in winter, rearing in spring and summer [Williams 2006]). When the release period spanned two WYs (5.1% of total number released, 90.5% of which occurred before WY 1976), the WY possessing the larger share of the release period was assigned as the release WY. In rare instances when the release period spanned two WYs and was split equally between WYt and WYt+1 (0.2% of all releases), WYt+1 was set as the release year. A three-year moving average was applied to the annual numerical release data to highlight longer-term trends.

Data file used: CVSH_totmill.csv

Biomass released: Release group biomass was calculated as the product of total number of fish released and mean fish mass for that release group. The mean annual fish mass-at-release for a hatchery was used as the mean mass-at-release for any release group’s missing weight, length, or life history stage-at-release information (3.8% of all releases). A three year moving average was also applied to the annual biomass release data to smooth the time series data.

Data file used: CVSH_biom.csv

Release timing: We analyzed release timing at the monthly scale because release day of month was missing for 63.5% of all releases. The release period usually occurred within the same calendar month (80.3% of all releases) but occasionally a range of calendar months were reported (16.7% of all releases). In limited cases, only release year was reported (3.0% of all releases). Due to these inconsistencies, we restrict all release timing and growth rate analyses to cases when the release start and end months were the same.

Data file used: CVSH_RelTot_revised.csv

Release location: Geographic coordinates of release locations and river km distances from the releasing hatchery to the release location were obtained from Sturrock et al. (2019) (92.0% of all releases), RMIS (1.4% of all releases), or the electronic database provided by CDFW (0.7% of all releases). An additional 5.7% of release location coordinates and hatchery distances were newly determined using the methods described by Sturrock et al. (2019). Coordinates and distances are not available for 0.2% of all released fish due to insufficient descriptions of release site locations.

Data file used: CVSH_violin_hatchdist.csv

Size-at-release: Fish sizes were reported as mean mass (75.2% of all releases) or length (19.5% of total) for each release group. To facilitate dataset comparisons, length-at-release was converted to mass-at-release (and vice versa) according to the following relationship for Sacramento River O. mykiss (Hallock et al. 1961):
ln(M) = ln (8.80 ∙ 10 ―6) + 3.06 ∙ ln(FL)
Where mass is in measured grams and fork length is measured in millimeters. Note that this (M) (FL) relationship was determined for fish with FLs equal to or larger than 325 mm but predicts masses for smaller fish within 5% deviation from a similar length-weight transformation reported for California Central Coast O. mykiss (Huber 2018; 53–442 mm FL range; R2=0.99) across nearly the entire O. mykiss size range encountered in this investigation (97.0% of all hatchery fish with reported lengths smaller than 325 mm FL were larger than 53 mm FL). When size ranges were reported, the midpoint was assigned as the mean length or mass for the release group. Occasionally missing size data could be gleaned from written descriptions of the release group’s life history stage (e.g., “fry”, “fingerlings”, “yearlings”). In these cases (1.5% of all releases), the midpoint of the life stage-at-release mass range (see “Life-stage-at-release” below) was used.

Data file used: CVSH_mass_at_release_violin_data.csv

Age-at-release: All age information was estimated based on an assumed 1 February spawn date (Satterthwaite et al. 2010) and, therefore, should be considered apparent ages. Age analyses were restricted to cases when the release group beginning and end months of release are identical (80.3% of all releases). Apparent ages were estimated as the difference between release month midpoint and 1 February of the brood year.

Data file used: CVSH_age_at_rel_violin_data.csv

Life-stage-at-release: We explored both coarse- and fine-scale trends in the composition of life- stages-at-release. We first classified O. mykiss as sub-yearling (y-) or yearling or older fish (y+). We followed hatchery program guidelines (CA HSRG 2012, Appendix VIII) and assumed O. mykiss became yearlings once they grew to 71.2 g (~180 mm FL). We further classified life history stage-at-release diversity according to fish sizes and standardized nomenclature guidelines (IEP Steelhead PWT 1998). “Yolk-sac fry” were defined as fish with masses <0.3 g; “fry”: ≥0.3 to 1.4 g; “parr”: ≥1.4 g to <26.3 g; “silvery parr”: ≥26.3 g to <71.2 g; “small smolts”: ≥71.2 g to <131.6 g; “large smolts”: 131.6 g to <219.6g; “subadults”: ≥219.6 g to <954.0 g; and “adults”: ≥954.0 g. For cases when size data were missing but life-stage-at-release was described, “fed fry” were assumed to be fry, “fingerlings” were assumed to be parr, “advanced fingerlings” were assumed to be silvery parr, and “smolts” were assumed to be small smolts.Data files used: CVSH_fig8_lifehist.csv and CVSH_propyrling.csv Objective 2: Temporal variation in juvenile release metrics Life stage diversity: We characterized life stage diversity by calculating the Reciprocal Simpson’s Index (RSI; Simpson 1949) for each hatchery and all hatcheries combined per release year. The RSI measures the evenness of a community and ranges from 0 (all life stages were equally represented in every release group) to 1 (all fish were planted at the same life stage).

Data file used: CVSH_DI.csv

Interannual variation in release metrics: To investigate interannual variation in release practices, we divided the 70-year time series (1948-2017) into seven 10-year intervals and calculated the decadal coefficient of variation (CV) for six metrics associated with hatchery releases summarized CV10 annually at each hatchery and for all O. mykiss hatchery programs combined. The metrics examined included (1) total number released, (2) total biomass released, (3) mean release month, (4) mean release distance downstream of hatchery, (5)

Description: The Salmonid Enhancement Program (SEP) plays a key role in DFO's work to conserve and manage Pacific salmon stocks. The program's activities aim to rebuild vulnerable salmon stocks, provide harvest opportunities, work with First Nations and coastal communities in economic development, and improve fish habitat to sustain salmon populations.This dataset was created by determining and confirming locations of hatchery locations through the use of Regional Mark Information System (RMIS) data as well as Google Maps. Once locations were determined, supplemental information such as hatchery webpage, contact information, photo and, if applicable, partner organization, were added.Data source: https://www.pac.dfo-mpo.gc.ca/sep-pmvs/index-eng.html

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Anthropological Survey of Jaluit Atoll: Terrestrial and Underwater Reconnaissance Surveys and Oral History Recordings 1999