National population mapping from sparse survey data: A hierarchical Bayesian modeling framework to account for uncertainty.

Population estimates are critical for government services, development projects, and public health campaigns. Such data are typically obtained through a national population and housing census. However, population estimates can quickly become inaccurate in localized areas, particularly where migration or displacement has occurred. Some conflict-affected and resource-poor countries have not conducted a census in over 10 y. We developed a hierarchical Bayesian model to estimate population numbers in small areas based on enumeration data from sample areas and nationwide information about administrative boundaries, building locations, settlement types, and other factors related to population density. We demonstrated this model by estimating population sizes in every 10- m grid cell in Nigeria with national coverage. These gridded population estimates and areal population totals derived from them are accompanied by estimates of uncertainty based on Bayesian posterior probabilities. The model had an overall error rate of 67 people per hectare (mean of absolute residuals) or 43% (using scaled residuals) for predictions in out-of-sample survey areas (approximately 3 ha each), with increased precision expected for aggregated population totals in larger areas. This statistical approach represents a significant step toward estimating populations at high resolution with national coverage in the absence of a complete and recent census, while also providing reliable estimates of uncertainty to support informed decision making.

A review of geospatial methods for population estimation and their use in constructing reproductive, maternal, newborn, child and adolescent health service indicators.

BACKGROUND: Household survey data are frequently used to measure reproductive, maternal, newborn, child and adolescent health (RMNCAH) service utilisation in low and middle income countries. However, these surveys are typically only undertaken every 5 years and tend to be representative of larger geographical administrative units. Investments in district health management information systems (DHMIS) have increased the capability of countries to collect continuous information on the provision of RMNCAH services at health facilities. However, reliable and recent data on population distributions and demographics at subnational levels necessary to construct RMNCAH coverage indicators are often missing. One solution is to use spatially disaggregated gridded datasets containing modelled estimates of population counts. Here, we provide an overview of various approaches to the production of gridded demographic datasets and outline their potential and their limitations. Further, we show how gridded population estimates can be used as alternative denominators to produce RMNCAH coverage metrics in combination with data from DHMIS, using childhood vaccination as examples. METHODS: We constructed indicators on the percentage of children one year old for diphtheria, pertussis and tetanus vaccine dose 3 (DTP3) and measles vaccine dose (MCV1) in Zambia and Nigeria at district levels. For the numerators, information on vaccines doses was obtained from each country's respective DHMIS. For the denominators, the number of children was obtained from 3 different sources including national population projections and aggregated gridded estimates derived using top-down and bottom-up geospatial methods. RESULTS: In Zambia, vaccination estimates utilising the bottom-up approach to population estimation substantially reduced the number of districts with > 100% coverage of DTP3 and MCV1 compared to estimates using population projection and the top-down method. In Nigeria, results were mixed with bottom-up estimates having a higher number of districts > 100% and estimates using population projections performing better particularly in the South. CONCLUSIONS: Gridded demographic data utilising traditional and novel data sources obtained from remote sensing offer new potential in the absence of up to date census information in the estimation of RMNCAH indicators. However, the usefulness of gridded demographic data is dependent on several factors including the availability and detail of input data.

Application of multiple-population viability analysis to evaluate species recovery alternatives.

Population viability analysis (PVA) is a powerful conservation tool, but it remains impractical for many species, particularly species with multiple, broadly distributed populations for which collecting suitable data can be challenging. A recently developed method of multiple-population viability analysis (MPVA), however, addresses many limitations of traditional PVA. We built on previous development of MPVA for Lahontan cutthroat trout (LCT) (Oncorhynchus clarkii henshawi), a species listed under the U.S. Endangered Species Act, that is distributed broadly across habitat fragments in the Great Basin (U.S.A.). We simulated potential management scenarios and assessed their effects on population sizes and extinction risks in 211 streams, where LCT exist or may be reintroduced. Conservation populations (those managed for recovery) tended to have lower extinction risks than nonconservation populations (mean = 19.8% vs. 52.7%), but not always. Active management or reprioritization may be warranted in some cases. Eliminating non-native trout had a strong positive effect on overall carrying capacities for LCT populations but often did not translate into lower extinction risks unless simulations also reduced associated stochasticity (to the mean for populations without non-native trout). Sixty fish or 5-10 fish/km was the minimum reintroduction number and density, respectively, that provided near-maximum reintroduction success. This modeling framework provided crucial insights and empirical justification for conservation planning and specific adaptive management actions for this threatened species. More broadly, MPVA is applicable to a wide range of species exhibiting geographic rarity and limited availability of abundance data and greatly extends the potential use of empirical PVA for conservation assessment and planning.

Aplicación de un Análisis de Viabilidad Multi-Poblacional para Evaluar Alternativas de Recuperación de Especies Resumen El análisis de viabilidad poblacional (AVP) es una herramienta poderosa de conservación, que desafortunadamente sigue siendo impráctica para muchas especies, en particular para aquellas con poblaciones múltiples distribuidas ampliamente, para las cuales puede ser un reto la recolección de datos apropiados. Sin embargo, un método recientemente desarrollado de análisis de viabilidad multi-poblacional (AVMP) aborda muchas de las limitaciones de los AVP tradicionales. Partimos del desarrollo previo de un AVMP para la trucha degollada lahontana (LCT, en inglés) (Oncorhynchus clarkii henshawi), una especie enlistada bajo el Acta de Especies en Peligro de los Estados Unidos, la cual está distribuida ampliamente a lo largo de los fragmentos de hábitat que se encuentran en la Gran Cuenca (E.U.A.). Simulamos los escenarios potenciales de manejo y evaluamos sus efectos sobre el tamaño de las poblaciones y los riesgos de extinción en 211 arroyos en donde existe la LCT o en donde podría ser reintroducida. Las poblaciones de conservación (aquellas manejadas para su recuperación) tuvieron una tendencia hacia un riesgo de extinción más bajo que las poblaciones sin conservación (media = 19.8% vs. 52.7%), pero no en todos los casos. El manejo activo o la repriorización podrían ser justificadas en algunos casos. La eliminación de las truchas no nativas tuvo un fuerte efecto positivo generalizado sobre las capacidades de carga de las poblaciones de LCT, aunque frecuentemente esto no se transformó en un riesgo de extinción más bajo a menos que las simulaciones también redujeran la estocasticidad asociada (para la media de las poblaciones sin truchas no nativas). Para proporcionar un éxito de reintroducción cercano al máximo, el número mínimo de reintroducción debió ser de 60 peces o una densidad de 5-10 peces/km. Este marco de trabajo para el modelo proporcionó una percepción muy importante y una justificación empírica para la planeación de la conservación y para las acciones de manejo adaptativo para esta especie amenazada. En términos más generales, el AVMP puede aplicarse a una gama amplia de especies que exhiban una rareza geográfica y una disponibilidad limitada de datos de abundancia, además de que expande enormemente el uso potencial de AVP empíricos para la evaluación y planeación de la conservación.

Hierarchical multi-population viability analysis.

Population viability analysis (PVA) uses concepts from theoretical ecology to provide a powerful tool for quantitative estimates of population dynamics and extinction risks. However, conventional statistical PVA requires long-term data from every population of interest, whereas many species of concern exist in multiple isolated populations that are only monitored occasionally. We present a hierarchical multi-population viability analysis model that increases inference power from sparse data by sharing information among populations to assess extinction risks while accounting for incomplete detection and sampling biases with explicit observation and sampling sub-models. We present a case study in which we customized this model for historical population monitoring data (1985-2015) from federally threatened Lahontan cutthroat trout populations in the Great Basin, USA. Data were counts of fish captured during backpack electrofishing surveys from locations associated with 155 isolated populations. Some surveys (25%) included multi-pass removal sampling, which provided valuable information about capture efficiency. GIS and remote sensing were used to estimate August stream temperatures, peak flows, and riparian vegetation condition in each population each year. Field data were used to derive an annual index of nonnative trout densities. Results indicated that population growth rates were higher in colder streams and that nonnative trout reduced carrying capacities of native trout. Extinction risks increased with more environmental stochasticity and were also related to population extent, water temperatures, and nonnative densities. We developed a graphical user interface to interact with the fitted model results and to simulate future habitat scenarios and management actions to assess their influence on extinction risks in each population. Hierarchical multi-population viability analysis bridges the gap between site-level field observations and population-level processes, making effective use of existing datasets to support management decisions with robust estimates of population dynamics, extinction risks, and uncertainties.

How accurate are WorldPop-Global-Unconstrained gridded population data at the cell-level?: A simulation analysis in urban Namibia.

Disaggregated population counts are needed to calculate health, economic, and development indicators in Low- and Middle-Income Countries (LMICs), especially in settings of rapid urbanisation. Censuses are often outdated and inaccurate in LMIC settings, and rarely disaggregated at fine geographic scale. Modelled gridded population datasets derived from census data have become widely used by development researchers and practitioners; however, accuracy in these datasets are evaluated at the spatial scale of model input data which is generally courser than the neighbourhood or cell-level scale of many applications. We simulate a realistic synthetic 2016 population in Khomas, Namibia, a majority urban region, and introduce several realistic levels of outdatedness (over 15 years) and inaccuracy in slum, non-slum, and rural areas. We aggregate the synthetic populations by census and administrative boundaries (to mimic census data), resulting in 32 gridded population datasets that are typical of LMIC settings using the WorldPop-Global-Unconstrained gridded population approach. We evaluate the cell-level accuracy of these gridded population datasets using the original synthetic population as a reference. In our simulation, we found large cell-level errors, particularly in slum cells. These were driven by the averaging of population densities in large areal units before model training. Age, accuracy, and aggregation of the input data also played a role in these errors. We suggest incorporating finer-scale training data into gridded population models generally, and WorldPop-Global-Unconstrained in particular (e.g., from routine household surveys or slum community population counts), and use of new building footprint datasets as a covariate to improve cell-level accuracy (as done in some new WorldPop-Global-Constrained datasets). It is important to measure accuracy of gridded population datasets at spatial scales more consistent with how the data are being applied, especially if they are to be used for monitoring key development indicators at neighbourhood scales within cities.

High-resolution population estimation using household survey data and building footprints.

The national census is an essential data source to support decision-making in many areas of public interest. However, this data may become outdated during the intercensal period, which can stretch up to several decades. In this study, we develop a Bayesian hierarchical model leveraging recent household surveys and building footprints to produce up-to-date population estimates. We estimate population totals and age and sex breakdowns with associated uncertainty measures within grid cells of approximately 100 m in five provinces of the Democratic Republic of the Congo, a country where the last census was completed in 1984. The model exhibits a very good fit, with an R2 value of 0.79 for out-of-sample predictions of population totals at the microcensus-cluster level and 1.00 for age and sex proportions at the province level. This work confirms the benefits of combining household surveys and building footprints for high-resolution population estimation in countries with outdated censuses.

Flow alteration-ecology relationships in Ozark Highland streams: Consequences for fish, crayfish and macroinvertebrate assemblages.

We examined flow alteration-ecology relationships in benthic macroinvertebrate, fish, and crayfish assemblages in Ozark Highland streams, USA, over two years with contrasting environmental conditions, a drought year (2012) and a flood year (2013). We hypothesized that: 1) there would be temporal variation in flow alteration-ecology relationships between the two years, 2) flow alteration-ecology relationships would be stronger during the drought year vs the flood year, and 3) fish assemblages would show the strongest relationships with flow alteration. We used a quantitative richest-targeted habitat (RTH) method and a qualitative multi-habitat (QMH) method to collect macroinvertebrates at 16 USGS gaged sites during both years. We used backpack electrofishing to sample fish and crayfish at 17 sites in 2012 and 11 sites in 2013. We used redundancy analysis to relate biological response metrics, including richness, diversity, density, and community-based metrics, to flow alteration. We found temporal variation in flow alteration-ecology relationships for all taxa, and that relationships differed greatly between assemblages. We found relationships were stronger for macroinvertebrates during the drought year but not for other assemblages, and that fish assemblage relationships were not stronger than the invertebrate taxa. Magnitude of average flow, frequency of high flow, magnitude of high flow, and duration of high flow were the most important categories of flow alteration metrics across taxa. Alteration of high and average flows was more important than alteration of low flows. Of 32 important flow alteration metrics across years and assemblages, 19 were significantly altered relative to expected values. Ecological responses differed substantially between drought and flood years, and this is likely to be exacerbated with predicted climate change scenarios. Differences in flow alteration-ecology relationships among taxonomic groups and temporal variation in relationships illustrate that a complex suite of variables should be considered for effective conservation of stream communities related to flow alteration.