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Mangroves are critical nursery habitats for fish and invertebrates, providing livelihoods for many coastal communities. Despite their importance, there is currently no estimate of the number of fishers engaged in mangrove associated fisheries, nor of the fishing intensity associated with mangroves at a global scale. We address these gaps by developing a global model of mangrove associated fisher numbers and mangrove fishing intensity. To develop the model, we undertook a three-round Delphi process with mangrove fisheries experts to identify the key drivers of mangrove fishing intensity. We then developed a conceptual model of intensity of mangrove fishing using those factors identified both as being important and for which appropriate global data could be found or developed. These factors were non-urban population, distance to market, distance to mangroves and other fishing grounds, and storm events. By projecting this conceptual model using geospatial datasets, we were able to estimate the number and distribution of mangrove associated fishers and the intensity of fishing in mangroves. We estimate there are 4.1 million mangrove associated fishers globally, with the highest number of mangrove fishers found in Indonesia, India, Bangladesh, Myanmar, and Brazil. Mangrove fishing intensity was greatest throughout Asia, and to a lesser extent West and Central Africa, and Central and South America., SCOPUS: ar.j, info:eu-repo/semantics/published

Funder: The German Federal Ministry for the Environment, Nature Conservation and Nuclear Safety (BMU), Funder: RCUK NERC Newton, Funder: Kyoto, Funder: Australian Research Council Discovery Early Career Research Award, Funder: Arcadia Fund; doi: http://dx.doi.org/10.13039/100012088, Mangrove forests provide many ecosystem services but are among the world’s most threatened ecosystems. Mangroves vary substantially according to their geomorphic and sedimentary setting; while several conceptual frameworks describe these settings, their spatial distribution has not been quantified. Here, we present a new global mangrove biophysical typology and show that, based on their 2016 extent, 40.5% (54,972 km2) of mangrove systems were deltaic, 27.5% (37,411 km2) were estuarine and 21.0% (28,493 km2) were open coast, with lagoonal mangroves the least abundant (11.0%, 14,993 km2). Mangroves were also classified based on their sedimentary setting, with carbonate mangroves being less abundant than terrigenous, representing just 9.6% of global coverage. Our typology provides a basis for future research to incorporate geomorphic and sedimentary setting in analyses. We present two examples of such applications. Firstly, based on change in extent between 1996 and 2016, we show while all types exhibited considerable declines in area, losses of lagoonal mangroves (− 6.9%) were nearly twice that of other types. Secondly, we quantify differences in aboveground biomass between mangroves of different types, with it being significantly lower in lagoonal mangroves. Overall, our biophysical typology provides a baseline for assessing restoration potential and for quantifying mangrove ecosystem service provision.

The growing demand for spatially detailed data to advance the Sustainable Development Goals agenda of ‘leaving no one behind’ has resulted in a shift in focus from aggregate national and province-based metrics to small areas and high-resolution grids in the health and development arena. Vaccination coverage is customarily measured through aggregate-level statistics, which mask fine-scale heterogeneities and ‘coldspots’ of low coverage. This paper develops a methodology for high-resolution mapping of vaccination coverage using areal data in settings where point-referenced survey data are inaccessible. The proposed methodology is a binomial spatial regression model with a logit link and a combination of covariate data and random effects modelling two levels of spatial autocorrelation in the linear predictor. The principal aspect of the model is the melding of the misaligned areal data and the prediction grid points using the regression component and each of the conditional autoregressive and the Gaussian spatial process random effects. The Bayesian model is fitted using the INLA-SPDE approach. We demonstrate the predictive ability of the model using simulated data sets. The results obtained indicate a good predictive performance by the model, with correlations of between 0.66 and 0.98 obtained at the grid level between true and predicted values. The methodology is applied to predicting the coverage of measles and diphtheria-tetanus-pertussis vaccinations at 5 × 5 km2in Afghanistan and Pakistan using subnational Demographic and Health Surveys data. The predicted maps are used to highlight vaccination coldspots and assess progress towards coverage targets to facilitate the implementation of more geographically precise interventions. The proposed methodology can be readily applied to wider disaggregation problems in related contexts, including mapping other health and development indicators.

Highlights • Geostatistical models showing strong predictive performance are used to produce maps of measles vaccination coverage at 1 × 1 km resolution. • Remoteness, measured as travel time to nearest major settlement, was consistently a key predictor of coverage. • The maps reveal heterogeneities and ‘coldspots’ of low vaccination coverage that are missed using large area summaries. • Aggregated estimates of coverage that do not account for local heterogeneities potentially over-estimate the numbers of children vaccinated by over 10%. • Relating to the WHO GVAP targets of 80% coverage, the integration of high resolution coverage and population maps shows the districts that have attained the threshold in the study countries., Background The expansion of childhood vaccination programs in low and middle income countries has been a substantial public health success story. Indicators of the performance of intervention programmes such as coverage levels and numbers covered are typically measured through national statistics or at the scale of large regions due to survey design, administrative convenience or operational limitations. These mask heterogeneities and ‘coldspots’ of low coverage that may allow diseases to persist, even if overall coverage is high. Hence, to decrease inequities and accelerate progress towards disease elimination goals, fine-scale variation in coverage should be better characterized. Methods Using measles as an example, cluster-level Demographic and Health Surveys (DHS) data were used to map vaccination coverage at 1 km spatial resolution in Cambodia, Mozambique and Nigeria for varying age-group categories of children under five years, using Bayesian geostatistical techniques built on a suite of publicly available geospatial covariates and implemented via Markov Chain Monte Carlo (MCMC) methods. Results Measles vaccination coverage was found to be strongly predicted by just 4–5 covariates in geostatistical models, with remoteness consistently selected as a key variable. The output 1 × 1 km maps revealed significant heterogeneities within the three countries that were not captured using province-level summaries. Integration with population data showed that at the time of the surveys, few districts attained the 80% coverage, that is one component of the WHO Global Vaccine Action Plan 2020 targets. Conclusion The elimination of vaccine-preventable diseases requires a strong evidence base to guide strategies and inform efficient use of limited resources. The approaches outlined here provide a route to moving beyond large area summaries of vaccination coverage that mask epidemiologically-important heterogeneities to detailed maps that capture subnational vulnerabilities. The output datasets are built on open data and methods, and in flexible format that can be aggregated to more operationally-relevant administrative unit levels.