Geology topics

Research about Rwanda

Source-linked reports with geographic coverage including Rwanda.

7 recordsLinked to original sources

USGS2021

Optimal allocation of law enforcement patrol effort to mitigate poaching activities

Poaching is a global problem causing the decline of species worldwide. Optimizing the efficiency of ranger patrols to deter poaching activity at the lowest possible cost is crucial for protecting species with limited resources. We applied decision analysis and spatial optimization algorithms to allocate efforts of ranger patrols throughout a national park. Our objective was to mitigate poaching activity at or below management risk targets for the lowest monetary cost. We examined this trade‐off by constructing a Pareto efficiency frontier using integer linear programming. We used data from a ranger‐based monitoring program in Nyungwe National Park, Rwanda. Our measure of poaching risk is based on dynamic occupancy models that account for imperfect detection of poaching activities. We found that in order to achieve a 5% reduction in poaching risk, 622 ranger patrol events (each corresponding to patrolling 1‐km 2 sites) were needed within a year at a cost of US$49,760. In order to attain a 60% reduction in poaching risk, 15,560 patrol events were needed at a cost of US \$ 1,244,800. We evaluated the trade‐off between patrol cost and poaching risk based on our model by constructing a Pareto efficiency frontier and park managers found the solution for a 50% risk reduction to be a practical trade‐off based on funding constraints (comparable to recent years) and the diminishing returns between risk mitigation and cost. This expected reduction in risk required 8,558 patrol events per year at a cost of US \$ 684,640. Our results suggest that optimal solutions could increase efficiency compared to the actual effort allocations from 2006 to 2016 in Nyungwe National Park (e.g., risk reductions of ~30% under recent budgets compared to ~50% reduction in risk under the optimal strategy). The modeling framework in this study took into account imperfect detection of poaching risk as well as the directional and conditional nature of ranger patrol events given the spatial adjacency relationships of neighboring sites and access points. Our analyses can help to improve the efficiency of ranger patrols, and the modeling framework can be broadly applied to other spatial conservation planning problems with conditional, multilevel, site selection.

Nyungwe National Park↗

USGS2021

What is the effect of poaching activity on wildlife species?

Poaching is a pervasive threat to wildlife, yet quantifying the direct effect of poaching on wildlife is rarely possible because both wildlife and threat data are infrequently collected concurrently. In this study, we used poaching data collected through the Management Information System (MIST) and wildlife camera trap data collected by the Tropical Ecology Assessment and Monitoring (TEAM) network from 2014 to 2017 in Volcanoes National Park, Rwanda. We implemented co-occurrence multi-season occupancy models that accounted for imperfect detection to investigate the effect of poaching on initial occupancy, colonization, and extinction of 5 mammal species. Specifically, we focused on 2 species of conservation concern (mountain gorilla ( Gorilla beringei beringei ) and golden money ( Cercopithecus mitis kandti )), and 3 species targeted by poachers (black-fronted duiker ( Cephalophus nigrifrons ), bushbuck ( Tragelaphus scriptus ), and African buffalo ( Syncerus caffer )). We found that the probability of local extinction was highest in sites with poaching activity for golden monkey and bushbuck. In addition, the probability of initial occupancy for golden monkey was highest in sites without poaching activity. We only found weak evidence of effects of poaching on parameters governing the occupancy dynamics of the other species. All species showed evidence of poaching presence affecting the probability of detection of the wildlife species. This is the first study to our knowledge to combine direct threat observations from ranger-based monitoring data with camera trap wildlife observations to quantify the effect of poaching on wildlife. Given the widespread collection of ranger-based monitoring and camera trap data, our approach is broadly applicable to numerous protected areas and has the potential to significantly improve conservation management. Specifically, the relationship between poaching activity and wildlife population dynamics (this paper) can be combined with information on the relationship between ranger patrols and poaching activity (Moore et al. 2017) to develop models useful for making wise decisions about ranger patrol deployment.

Volcanoes National Park↗

USGS2020

Economic, land use, and ecosystem services impacts of Rwanda's Green Growth Strategy: An application of the IEEM+ESM platform

We develop and link the Integrated Economic-Environmental Modeling (IEEM) Platform to ecosystem services modeling (ESM). The IEEM+ESM Platform is an innovative decision-making framework for exploring complex public policy goals and elucidating synergies and trade-offs between alternative policy portfolios. The IEEM+ESM approach is powerful in its ability to shed light on (i) change in land use and ecosystem services driven by public policy and the supply and demand responses of businesses and households; and (ii) impacts on standard economic indicators of concern to Ministries of Finance such as gross domestic product and employment, as well as changes in wealth and ecosystem services. The IEEM+ESM approach is being adopted rapidly and by the end of 2020, IEEM+ESM Platforms will be implemented for about 25 countries. To demonstrate the insights generated by the IEEM+ESM approach, we apply it to the analysis of alternative green growth strategies in Rwanda, a country that has made strong progress in reducing poverty and enhancing economic growth in the last 15 years. The case of Rwanda is particularly compelling as it faces intense pressure on its natural capital base and ecosystem services, already with the highest population density in Africa, which is projected to double by 2050. In applying IEEM+ESM and comparing the outcomes of Rwanda’s green growth policies, increasing fertilization of agricultural crops shows the largest economic gains but also trade-offs in environmental quality reflected through higher nutrient export and reduced water quality. Combining crop fertilization with forest plantations better balances critical ecosystem services and their role in underpinning economic development as Rwanda progresses toward its target of middle-income status by 2035. This application to Rwanda’s green growth strategy demonstrates the value-added of the IEEM+ESM approach in generating results that speak to both economic outcomes and impacts on market and non-market ecosystem services.

Science of the Total Environment↗

USGS2019

Toward ecosystem accounts for Rwanda: Tracking 25 years of change in potential supply and flows of ecosystem services

1. Rwanda, a small but rapidly developing central African nation, has undertaken development of natural capital accounts to better inform its economic development through the World Bank’s Wealth Accounting and Valuation of Ecosystem Services (WAVES) Partnership. In this paper, we develop ecosystem service (ES) models to quantify the physical supply components of ecosystem accounts in Rwanda from 1990 to 2015. 2. We applied the InVEST carbon storage, sediment delivery ratio, and annual and seasonal water yield models to map changes in potential ES supply nationwide. We also quantified flows of sediment and water to 96 hydroelectric dam, irrigation dam, and water treatment plant sites. 3. Over a 25-year period, we found declines in all ES, which were most strongly driven by conversion of forests to cropland. Declines were most pronounced from 1990 to 2000 and 2010 to 2015; ES were relatively stable from 2000 to 2010. From 2010 to 2015, over 42% of Rwanda’s water-use sites (representing 17% of the nation’s hydroelectric generation capacity and 69% of its water treatment capacity) had increases in sediment export and quick flow greater than the national average. 4. Our results quantify nationwide ES trends, their implications for key water-dependent industries, and the importance of protected areas in safeguarding ES potential supply and flows in Rwanda. They also provide data that can be integrated with existing land, water, and economic accounts for Rwanda, as well as a baseline to inform development strategies that better link economic and environmental goals.

People and Nature↗

USGS2019

Factors affecting species richness and distribution spatially and temporally within a protected area using multi-season occupancy models

Exploring trends in species richness and the distribution of individual species over time as well as the factors affecting these trends informs conservation priorities in protecting species and ecosystems as a whole. We used data from 41 park-wide line transect surveys in 2009 and 2014 and multi-season occupancy models with multi-species data to explore trends in species richness and distribution of individual species and factors affecting these trends in Nyungwe National Park (NNP), Rwanda. Mammalian species richness and the distributional range of 5 of the 7 species increased between 2009 and 2014 in NNP. The probability of colonization of a species into a new area in 2014, where it was not present in 2009, was highest in sites with a lower probability of poaching activity, close to tourist trails, and at lower elevations. The probability of colonization with no poaching activity was about 50% but dropped to about 10% with a 100% chance of poaching activity. Duiker species had the largest increase in distribution during the study, while there was a decrease in the distribution of the eastern chimpanzee and blue monkey. Our results suggest that increased patrols should be implemented in areas of the park with low species richness and areas with a low probability of occurrence for species of conservation concern to combat poaching activity and thus increase the probability of a species moving into a new area. Our use of a single multi-season model for multiple species explicitly accounts for imperfect detection and species-specific identities, while allowing for inferences to be made about rarely detected species by sharing covariates with common species. These results can be used to improve conservation planning in NNP for species management and ranger patrol protocols and our modelling framework is broadly applicable to any protected area with presence/absence species field data.

Nyungwe Forest National Park↗

USGS2018

The sensitivity of ecosystem service models to choices of input data and spatial resolution

Although ecosystem service (ES) modeling has progressed rapidly in the last 10–15 years, comparative studies on data and model selection effects have become more common only recently. Such studies have drawn mixed conclusions about whether different data and model choices yield divergent results. In this study, we compared the results of different models to address these questions at national, provincial, and subwatershed scales in Rwanda. We compared results for carbon, water, and sediment as modeled using InVEST and WaSSI using (1) land cover data at 30 and 300 m resolution and (2) three different input land cover datasets. WaSSI and simpler InVEST models (carbon storage and annual water yield) were relatively insensitive to the choice of spatial resolution, but more complex InVEST models (seasonal water yield and sediment regulation) produced large differences when applied at differing resolution. Six out of nine ES metrics (InVEST annual and seasonal water yield and WaSSI) gave similar predictions for at least two different input land cover datasets. Despite differences in mean values when using different data sources and resolution, we found significant and highly correlated results when using Spearman's rank correlation, indicating consistent spatial patterns of high and low values. Our results confirm and extend conclusions of past studies, showing that in certain cases (e.g., simpler models and national-scale analyses), results can be robust to data and modeling choices. For more complex models, those with different output metrics, and subnational to site-based analyses in heterogeneous environments, data and model choices may strongly influence study findings.

Applied Geography↗

USGS2017

Are ranger patrols effective in reducing poaching-related threats within protected areas?

Poaching is one of the greatest threats to wildlife conservation world-wide. However, the spatial and temporal patterns of poaching activities within protected areas, and the effectiveness of ranger patrols and ranger posts in mitigating these threats, are relatively unknown. We used 10 years (2006–2015) of ranger-based monitoring data and dynamic multi-season occupancy models to quantify poaching-related threats, to examine factors influencing the spatio-temporal dynamics of these threats and to test the efficiency of management actions to combat poaching in Nyungwe National Park (NNP), Rwanda. The probability of occurrence of poaching-related threats was highest at lower elevations (1,801–2,200 m), especially in areas that were close to roads and tourist trails; conversely, occurrence probability was lowest at high elevation sites (2,601–3,000 m), and near the park boundary and ranger posts. The number of ranger patrols substantially increased the probability that poaching-related threats disappear at a site if threats were originally present (i.e. probability of extinction of threats). Without ranger visits, the annual probability of extinction of poaching-related threats was an estimated 7%; this probability would increase to 20% and 57% with 20 and 50 ranger visits per year, respectively. Our results suggest that poaching-related threats can be effectively reduced in NNP by adding ranger posts in areas where they do not currently exist, and by increasing the number of patrols to sites where the probability of poaching activities is high. Synthesis and applications . Our application of dynamic occupancy models to predict the probability of presence of poaching-related threats is novel, and explicitly considers imperfect detection of illegal activities. Based on the modelled relationships, we identify areas that are most vulnerable to poaching, and offer insights regarding how ranger patrols can be optimally deployed to reduce poaching-related threats and other illegal activites, while taking into account potential sampling biases. We show that poaching can be effectively reduced by increasing ranger patrols to areas under high risk of poaching activities, and by adding ranger patrols near these sites. These findings are broadly applicable to national parks and protected areas experiencing a high degree of poaching and other illegal activities.

Nyungwe National Park↗