Predicting the environmental suitability for onchocerciasis in Africa as an aid to elimination planning

Recent evidence suggests that, in some foci, elimination of onchocerciasis from Africa may be feasible with mass drug administration (MDA) of ivermectin. To achieve continental elimination of transmission, mapping surveys will need to be conducted across all implementation units (IUs) for which endemicity status is currently unknown. Using boosted regression tree models with optimised hyperparameter selection, we estimated environmental suitability for onchocerciasis at the 5 × 5-km resolution across Africa. In order to classify IUs that include locations that are environmentally suitable, we used receiver operating characteristic (ROC) analysis to identify an optimal threshold for suitability concordant with locations where onchocerciasis has been previously detected. This threshold value was then used to classify IUs (more suitable or less suitable) based on the location within the IU with the largest mean prediction. Mean estimates of environmental suitability suggest large areas across West and Central Africa, as well as focal areas of East Africa, are suitable for onchocerciasis transmission, consistent with the presence of current control and elimination of transmission efforts. The ROC analysis identified a mean environmental suitability index of 0·71 as a threshold to classify based on the location with the largest mean prediction within the IU. Of the IUs considered for mapping surveys, 50·2% exceed this threshold for suitability in at least one 5 × 5-km location. The formidable scale of data collection required to map onchocerciasis endemicity across the African continent presents an opportunity to use spatial data to identify areas likely to be suitable for onchocerciasis transmission. National onchocerciasis elimination programmes may wish to consider prioritising these IUs for mapping surveys as human resources, laboratory capacity, and programmatic schedules may constrain survey implementation, and possibly delaying MDA initiation in areas that would ultimately qualify.

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Estimating stock depletion level from patterns of catch history

10.31230/osf.io/5nxt7 ◽

2018 ◽

Author(s):

Shijie Zhou ◽

André E Punt ◽

Yimin Ye ◽

Nick Ellis ◽

Cathy M Dichmont ◽

...

Keyword(s):

Stock Assessment ◽

Regression Tree ◽

Threshold Value ◽

Regression Coefficients ◽

Limited Data ◽

Boosted Regression Tree ◽

Model Predictions ◽

Before And After ◽

Stock Depletion ◽

Linear Regressions

The degree to which a stock is depleted is one of the most important quantities in fisheries management because it is used to quantify the success of management and to inform management responses. However, stock depletion is extremely difficult to estimate, particularly with limited data. Using the RAM Legacy database, we developed a boosted regression tree (BRT) model to correlate depletion with a range of predictors calculated from catch data, making the model usable for many fisheries worldwide. The most important predictors were found to be catch trends obtained from linear regressions of scaled catch on time, including regression coefficients for the whole catch time series, the subseries before and after the maximum catch, and in recent years. Eight predictors explain about 80% of variation in depletion. There is a correlation of .5 between measured levels of depletion and the predictions of the BRT model. Predictions are less biased when the stock is fished down below half of the carrying capacity. The BRT model outperforms comparable existing catch‐based depletion estimators and could be used to provide priors for depletion for data‐poor stock assessment methods, or used more directly to provide estimates of the probability that depletion is below a given threshold value.

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Using boosted regression tree models to predict the diets of juvenile bull sharks in a subtropical estuary

Marine Ecology Progress Series ◽

10.3354/meps13568 ◽

2020 ◽

Author(s):

E Cottrant ◽

P Matich ◽

MR Fisher

Keyword(s):

Regression Tree ◽

Boosted Regression Tree ◽

Tree Models ◽

Subtropical Estuary

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Drivers of diversity gradients of a highly mobile marine assemblage in a mesoscale seascape

Marine Ecology Progress Series ◽

10.3354/meps13264 ◽

2020 ◽

Vol 638 ◽

pp. 149-164

Author(s):

GM Svendsen ◽

M Ocampo Reinaldo ◽

MA Romero ◽

G Williams ◽

A Magurran ◽

...

Keyword(s):

Environmental Gradients ◽

Regression Tree ◽

Demersal Fish ◽

Open Ocean ◽

Shared Resources ◽

Thermal Front ◽

Boosted Regression Tree ◽

Diversity Gradients ◽

Α Diversity ◽

Bottom Depth

With the unprecedented rate of biodiversity change in the world today, understanding how diversity gradients are maintained at mesoscales is a key challenge. Drawing on information provided by 3 comprehensive fishery surveys (conducted in different years but in the same season and with the same sampling design), we used boosted regression tree (BRT) models in order to relate spatial patterns of α-diversity in a demersal fish assemblage to environmental variables in the San Matias Gulf (Patagonia, Argentina). We found that, over a 4 yr period, persistent diversity gradients of species richness and probability of an interspecific encounter (PIE) were shaped by 3 main environmental gradients: bottom depth, connectivity with the open ocean, and proximity to a thermal front. The 2 main patterns we observed were: a monotonic increase in PIE with proximity to fronts, which had a stronger effect at greater depths; and an increase in PIE when closer to the open ocean (a ‘bay effect’ pattern). The originality of this work resides on the identification of high-resolution gradients in local, demersal assemblages driven by static and dynamic environmental gradients in a mesoscale seascape. The maintenance of environmental gradients, specifically those associated with shared resources and connectivity with an open system, may be key to understanding community stability.

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Regional Perspective of Safety Performance Functions and Their Application to Florida Intersections in Suburban Residential and Urban General Context Classification Categories

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/03611981211008447 ◽

2021 ◽

pp. 036119812110084

Author(s):

Ghalia Gamaleldin ◽

Haitham Al-Deek ◽

Adrian Sandt ◽

John McCombs ◽

Alan El-Urfali

Keyword(s):

Negative Binomial ◽

Regression Tree ◽

Influential Factors ◽

Safety Performance ◽

General Context ◽

Boosted Regression Tree ◽

Safety Performance Functions ◽

Traffic Volumes ◽

Intersection Types ◽

The Individual

Safety performance functions (SPFs) are essential tools to help agencies predict crashes and understand influential factors. Florida Department of Transportation (FDOT) has implemented a context classification system which classifies intersections into eight context categories rather than the three classifications used in the Highway Safety Manual (HSM). Using this system, regional SPFs could be developed for 32 intersection types (unsignalized and signalized 3-leg and 4-leg for each category) rather than the 10 HSM intersection types. In this paper, eight individual intersection group SPFs were developed for the C3R-Suburban Residential and C4-Urban General categories and compared with full SPFs for these categories. These comparisons illustrate the unique and regional insights that agencies can gain by developing these individual SPFs. Poisson, negative binomial, zero-inflated, and boosted regression tree models were developed for each studied group as appropriate, with the best model selected for each group based on model interpretability and five performance measures. Additionally, a linear regression model was built to predict minor roadway traffic volumes for intersections which were missing these volumes. The full C3R and C4 SPFs contained four and six significant variables, respectively, while the individual intersection group SPFs in these categories contained six and nine variables. Factors such as major median, intersection angle, and FDOT District 7 regional variable were absent from the full SPFs. By developing individual intersection group SPFs with regional factors, agencies can better understand the factors and regional differences which affect crashes in their jurisdictions and identify effective treatments.

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Spatial Modelling of Gully Erosion Using GIS and R Programing: A Comparison among Three Data Mining Algorithms

Applied Sciences ◽

10.3390/app8081369 ◽

2018 ◽

Vol 8 (8) ◽

pp. 1369 ◽

Cited By ~ 52

Author(s):

Alireza Arabameri ◽

Biswajeet Pradhan ◽

Hamid Reza Pourghasemi ◽

Khalil Rezaei ◽

Norman Kerle

Keyword(s):

Data Mining ◽

Spatial Relationship ◽

Area Under The Curve ◽

Regression Tree ◽

Drainage Density ◽

Gully Erosion ◽

Slope Aspect ◽

Topographic Wetness Index ◽

Boosted Regression Tree ◽

Area Index

Gully erosion triggers land degradation and restricts the use of land. This study assesses the spatial relationship between gully erosion (GE) and geo-environmental variables (GEVs) using Weights-of-Evidence (WoE) Bayes theory, and then applies three data mining methods—Random Forest (RF), boosted regression tree (BRT), and multivariate adaptive regression spline (MARS)—for gully erosion susceptibility mapping (GESM) in the Shahroud watershed, Iran. Gully locations were identified by extensive field surveys, and a total of 172 GE locations were mapped. Twelve gully-related GEVs: Elevation, slope degree, slope aspect, plan curvature, convergence index, topographic wetness index (TWI), lithology, land use/land cover (LU/LC), distance from rivers, distance from roads, drainage density, and NDVI were selected to model GE. The results of variables importance by RF and BRT models indicated that distance from road, elevation, and lithology had the highest effect on GE occurrence. The area under the curve (AUC) and seed cell area index (SCAI) methods were used to validate the three GE maps. The results showed that AUC for the three models varies from 0.911 to 0.927, whereas the RF model had a prediction accuracy of 0.927 as per SCAI values, when compared to the other models. The findings will be of help for planning and developing the studied region.

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A global bibliometric perspective on soil erosion modelling

10.5194/egusphere-egu2020-2919 ◽

2020 ◽

Author(s):

Nejc Bezak ◽

Keyword(s):

Conference Proceedings ◽

Soil Erosion ◽

Regression Tree ◽

Tree Model ◽

Boosted Regression Tree ◽

Field Activity ◽

Erosion Modelling ◽

Meta Information ◽

Number Of Citations ◽

Regression Tree Model

Systematic bibliometric investigations are useful to evaluate and compare the scientific impact of journal papers, book chapters and conference proceedings. Such studies allow the detection of emerging research topics, the analyses of cooperation networks, and the collection of in-depth insights into a specific research topic. In the presented work, we carried out a bibliometric study in order to obtain an in-depth knowledge on soil erosion modelling applications worldwide.As a starting point, we used the soil erosion modelling meta-analysis data collection generated by the authors of this abstract in a joint community effort. This database contains meta-information of more than 3,000 documents published between 1994 and 2018 that are indexed in the SCOPUS database. The documents were reviewed and database entries verified. The database contains various types of meta-information about the modelling studies (e.g., model used, study area, input data, calibration, etc.). The bibliometric information was also included in the database (e.g., number of citations, type of publication, Scopus category, etc.). We investigated differences among publication types and differences between papers published in journals that are part of various Scopus categories. Moreover, relationships between publication CiteScore, number of authors, and number of citations were analyzed. A boosted regression tree model was used to detect the relative impact of the selected meta-information such as erosion model used, spatial modelling scale, study period, field activity on the total number of citations. Detailed investigation of the most cited papers was also conducted. The VOSviewer software was used to analyze citations, co-citations, bibliographic coupling, and co-authorship networks of the database entries. &#160;Our bibliometric investigations demonstrated that journal publications, on average, receive more citations than book series or conference proceedings. There were differences among the erosion models used, and some specific models such as the WaTEM/SEDEM model, on average, receive more citations than other models (e.g., USLE). It should also be noted that self-citation rates in case of most frequently used models were similar. Global studies, on average, receive more citations than studies dealing with plot, regional, or national scales. According to the boosted regression tree model, model calibration, validation, or field activity do not have significant impact on the obtained publication citations. Co-citation investigation revealed some interesting patterns. Our results also indicate that papers about soil erosion modeling also attract citations from different fields and better international cooperation is needed to advance this field of research with regard to its visibility and impact on human societies.&#160;&#160;&#160;&#160;

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