Modelling heterogeneity in the classification process in multi-species distribution models can improve predictive performance.

Species distribution models and maps from large-scale biodiversity data are necessary for conservation management. One current issue is that biodiversity data are prone to taxonomic misclassifications. Methods to account for these misclassifications in multi-species distribution models have assumed that the classification probabilities are constant throughout the study. In reality, classification probabilities are likely to vary with several covariates. Failure to account for such heterogeneity can lead to biased prediction of species distributions. Here, we present a general multi-species distribution model that accounts for heterogeneity in the classification process. The proposed model assumes a multinomial generalised linear model for the classification confusion matrix. We compare the performance of the heterogeneous classification model to that of the homogeneous classification model by assessing how well they estimate the parameters in the model and their predictive performance on hold-out samples. We applied the model to gull data from Norway, Denmark and Finland, obtained from the Global Biodiversity Information Facility. Our simulation study showed that accounting for heterogeneity in the classification process increased the precision of true species' identity predictions by 30% and accuracy and recall by 6%. Since all the models in this study accounted for misclassification of some sort, there was no significant effect of accounting for heterogeneity in the classification process on the inference about the ecological process. Applying the model framework to the gull dataset did not improve the predictive performance between the homogeneous and heterogeneous models (with parametric distributions) due to the smaller misclassified sample sizes. However, when machine learning predictive scores were used as weights to inform the species distribution models about the classification process, the precision increased by 70%. We recommend multiple multinomial regression to be used to model the variation in the classification process when the data contains relatively larger misclassified samples. Machine learning prediction scores should be used when the data contains relatively smaller misclassified samples.

Recommendations for quantitative uncertainty consideration in ecology and evolution.

Ecological and evolutionary studies are currently failing to achieve complete and consistent reporting of model-related uncertainty. We identify three key barriers - a focus on parameter-related uncertainty, obscure uncertainty metrics, and limited recognition of uncertainty propagation - which have led to gaps in uncertainty consideration. However, these gaps can be closed. We propose that uncertainty reporting in ecology and evolution can be improved through wider application of existing statistical solutions and by adopting good practice from other scientific fields. Our recommendations include greater consideration of input data and model structure uncertainties, field-specific uncertainty standards for methods and reporting, and increased uncertainty propagation through the use of hierarchical models.

Insights into the quantification and reporting of model-related uncertainty across different disciplines.

Quantifying uncertainty associated with our models is the only way we can express how much we know about any phenomenon. Incomplete consideration of model-based uncertainties can lead to overstated conclusions with real-world impacts in diverse spheres, including conservation, epidemiology, climate science, and policy. Despite these potentially damaging consequences, we still know little about how different fields quantify and report uncertainty. We introduce the "sources of uncertainty" framework, using it to conduct a systematic audit of model-related uncertainty quantification from seven scientific fields, spanning the biological, physical, and political sciences. Our interdisciplinary audit shows no field fully considers all possible sources of uncertainty, but each has its own best practices alongside shared outstanding challenges. We make ten easy-to-implement recommendations to improve the consistency, completeness, and clarity of reporting on model-related uncertainty. These recommendations serve as a guide to best practices across scientific fields and expand our toolbox for high-quality research.

Potentially Toxic Elements (PTEs) contamination and ecological risk of sediment in the upper course of the Ankobra River, Ghana.

Metals, when introduced into a high pH aquatic environment, generally sink into bottom sediments and can pose a direct and indirect threat to aquatic organisms as well as humans. The concentrations of ten heavy metals (Mn, Fe, Cd, Cu, Cr, Pb, Hg, As, Zn, and Al) in the surface sediment samples from the upper part of the Ankobra, which is an illegal mining (Galamsey) prone area, were assessed in both the dry and wet seasons. To ascertain the quality of sediments, sediment quality indices, including enrichment factor (EF), index of geo-accumulation (I-geo), and modified pollution index (MPI), were employed. A comprehensive potential ecological risk assessment was also carried out using the consensus-based sediment quality guideline (CBSQG) as well as the modified potential ecological risk index (MPERI). In general, the sediment is considered to be "slightly" to "moderately" polluted in the dry season as well as "moderately" to "heavily'' polluted in the wet season. The potential ecological risk of the metals in the sediments was within the considerable to high-risk grade for Hg, moderate to considerable risk for Cd, low to moderate risk for As, and low risk for Cu, Cr, Pb, and Zn. The order of decreasing potential ecological risk for the metals is Hg > Cd > As > Cu > Pb > Cr > Zn and the risk in the wet season was higher than that in the dry season.

Shared but Clean Household Toilets: What Makes This Possible? Evidence from Ghana and Kenya.

Shared sanitation facilities are not considered as basic sanitation owing to cleanliness and accessibility concerns. However, there is mounting evidence that some shared household toilets have a comparable level of service as private toilets. This study examined the factors that contribute to the quality of shared household toilets in low-income urban communities in Ghana and Kenya. The study design comprised household surveys and field inspections. Overall, 843 respondents were interviewed, and 838 household shared sanitation facilities were inspected. Cleanliness scores were computed from the facility inspections, while a total quality score was calculated based on 13 indicators comprising hygiene, privacy, and accessibility. Regression analyses were conducted to determine predictors of cleanliness and the overall quality of the shared sanitation facilities. More than four out of five (84%) shared toilets in Ghana (N = 404) were clean, while in Kenya (N = 434), nearly a third (32%) were clean. Flush/pour-flush toilets were six times (p < 0.01 aOR = 5.64) more likely to be clean. A functional outside door lock on a toilet facility and the presence of live-in landlords led to a threefold increase (p < 0.01 aOR = 2.71) and a twofold increase (p < 0.01 aOR = 1.92), respectively in the odds of shared sanitation cleanliness. Sanitation facilities shared by at most five households (95% CI: 6−7) were generally clean. High-quality shared toilets had live-in landlords, functional door locks, and were water-dependent. Further studies on innovative approaches to maintaining the quality of these high-quality shared toilets are needed to make them eligible for classification as basic sanitation considering the increasing reliance on the facilities.

Developing and Testing Strategies for Improving Cleanliness of Shared Sanitation in Low-Income Settlements of Kisumu, Kenya.

Sharing of sanitation is common in low-income settlements in Sub-Saharan Africa. However, shared (limited) sanitation facilities have been thought to pose health risks due to poor hygiene levels. Interventions to improve user behavior and cleanliness of shared sanitation are few, both in literature and in practice. This study details the codesign and testing of strategies to improve the cleanliness of shared sanitation facilities in low-income areas of Kisumu City in Kenya. The strategies included a cleaning plan, monitoring system, and discussions among users, and were codesigned through workshops with stakeholders and group discussions with landlords and tenants. These strategies were tested in 38 compound houses through the Trials of Improved Practices approach over a 5-month period. Field staff visited the compounds, observed the cleanliness of the shared toilets, and through discussions, encouraged users to develop a formal cleaning system and a monitoring plan. The discussions built social capital and collective action and facilitated uptake of the cleaning plan with notable improvements in cleanliness of shared toilets. The results support the acceptability of shared sanitation in low-income settlements, the importance of codesigning and coproducing solutions with users, and the need to evaluate the effects of these strategies on cleanliness of shared sanitation.

How Much Will Safe Sanitation for all Cost? Evidence from Five Cities.

Global sustainable development goals call for universal access to safely managed sanitation by 2030. Here, we demonstrate methods to estimate the financial requirements for meeting this commitment in urban settings of low-income countries. Our methods considered two financial requirements: (i) the subsidies needed to bridge the gap between the willingness-to-pay of low-income households and actual market prices of toilets and emptying services and (ii) the amounts needed to expand the municipal waste management infrastructure for unserved populations. We applied our methods in five cities- Kisumu, Malindi, Nakuru in Kenya; Kumasi in Ghana; and Rangpur in Bangladesh and compared three to five sanitation approaches in each city. We collected detailed cost data on the sanitation infrastructure, products, and services from 76 key informants across the five cities, and we surveyed a total of 2381 low-income households to estimate willingness-to-pay. We found that the total financial requirements for achieving universal sanitation in the next 10 years and their breakdown between household subsidies and municipal infrastructure varied greatly between sanitation approaches. Across our study cities, sewerage was the costliest approach (total financial requirements of 16-24 USD/person/year), followed by container-based sanitation (10-17 USD/person/year), onsite sanitation (2-14 USD/person/year), and mini-sewers connecting several toilets to communal septic tanks (3-5 USD/person/year). Further applications of our methods can guide sanitation planning in other cities.

Barriers and opportunities for cleanliness of shared sanitation facilities in low-income settlements in Kenya.

BACKGROUND: The sharing of sanitation facilities is a common practice in low-income areas in sub-Saharan Africa. However, shared sanitation is currently categorized as a limited sanitation service, and may therefore not count towards meeting the global goals. These shared facilities are often the only option available for most residents in low-income settlements, and improving their cleanliness and overall management is key to reducing open defecation and risk of disease. This study sought to investigate barriers and opportunities for improved cleanliness of shared sanitation facilities in low-income settlements of Kisumu city, Kenya. METHODS: Thirty-nine in-depth interviews and 11 focus group discussions were held with residents - mainly tenants and landlords - of a low-income settlement in Kisumu. Analysis followed a thematic approach to define the problem, specify the target behaviour and identify the changes needed. RESULTS: Sanitation facilities were mainly pit latrines, typically shared among landlords and tenants. Participants singled out behavioural (poor use of the shared toilets) and social (lack of cooperation in cleaning) challenges that led to unclean shared toilets. Available opportunities for improvement included instituting clear cleaning plans, improving communication among users, and enhanced problem-solving mechanisms between landlords and tenants. These approaches could form the basis for designing intervention strategies for improving the cleanliness of shared sanitation facilities. CONCLUSION: The results highlight the need to focus on social aspects for improvement of cleanliness in shared sanitation facilities in low-income settlements. Through a social approach, shared sanitation facilities can be managed appropriately to provide the millions of low-income residents in Kenya an opportunity to access sanitation. This study provides further evidence on approaches for improved management of shared sanitation facilities in line with the World Health Organization's (WHO) Joint Monitoring Program's (JMP) recommendation for high quality shared facilities.

Understanding the Barriers and Opportunities for Effective Management of Shared Sanitation in Low-Income Settlements-The Case of Kumasi, Ghana.

Improved sanitation for all is a daunting task for low-income countries, and shared toilets often provide an alternative to private household sanitation for most urban residents. This study sought to provide better understanding of the existing barriers and opportunities for improved management of shared sanitation. The study used focus group discussions and in-depth interviews with 70 users (landlords and tenants) of shared sanitation in Kumasi, Ghana to assess barriers and opportunities of "high-quality" shared sanitation. The commonly used toilet facilities were dry toilets-Kumasi Ventilated Improved Pit latrine and Ventilated Improved Pit latrines; and flush systems-water closet and pour flush connected to septic tanks. Between 2 and 21 households, or 4 and 84 people, shared one facility. Participants' description of "high-quality" (Ideal) shared sanitation was centred on cleanliness, user behaviour, smell, and user crowding. They also identified challenges of shared sanitation as overcrowded users, poor user behaviours, conflicts among users, and high cost associated with frequent desludging. However, opportunities for improvement included users' preference for shared toilets due to enjoyed benefits, existing facility management practices, and mutual understanding among users (tenants and landlords). Interventions and policy guidelines to influence behaviour change of shared sanitation users are proposed and are intended to be delivered by local government and users.