Patterns and Drivers of Rodent Abundance across a South African Multi-Use Landscape

South Africa’s decentralized approach to conservation entails that wildlife outside formally protected areas inhabit complex multi-use landscapes, where private wildlife business (ecotourism and/or hunting) co-exist in a human-dominated landscape matrix. Under decentralized conservation, wildlife is perceived to benefit from increased amount of available habitat, however it is crucial to understand how distinct management priorities and associated landscape modifications impact noncharismatic taxa, such as small mammals. We conducted extensive ink-tracking-tunnel surveys to estimate heterogeneity in rodent distribution and investigate the effect of different environmental factors on abundance patterns of two size-based rodent groups (small- and medium-sized species), across three adjacent management contexts in NE KwaZulu-Natal, South Africa: a private ecotourism game reserve, mixed farms and traditional communal areas (consisting of small clusters of houses interspersed with grazing areas and seminatural vegetation). Our hypotheses were formulated regarding the (1) area typology, (2) vegetation structure, (3) ungulate pressure and (4) human disturbance. Using a boosted-regression-tree approach, we found considerable differences between rodent groups’ abundance and distribution, and the underlying environmental factors. The mean relative abundance of medium-sized species did not differ across the three management contexts, but small species mean relative abundance was higher in the game reserves, confirming an influence of the area typology on their abundance. Variation in rodent relative abundance was negatively correlated with human disturbance and ungulate presence. Rodent abundance seems to be influenced by environmental gradients that are directly linked to varying management priorities across land uses, meaning that these communities might not benefit uniformly by the increased amount of habitat promoted by the commercial wildlife industry.

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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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Environmental variation mediates the prevalence and co-occurrence of parasites in the common lizard, Zootoca vivipara

BMC Ecology ◽

10.1186/s12898-019-0259-3 ◽

2019 ◽

Vol 19 (1) ◽

Cited By ~ 2

Author(s):

Qiang Wu ◽

Murielle Richard ◽

Alexis Rutschmann ◽

Donald B. Miles ◽

Jean Clobert

Keyword(s):

Environmental Factors ◽

Human Disturbance ◽

Vegetation Cover ◽

Structural Equation ◽

Environmental Gradients ◽

Parasite Load ◽

Single Species ◽

Common Lizard ◽

Zootoca Vivipara ◽

The Common

Abstract Background Hosts and their parasites are under reciprocal selection, leading to coevolution. However, parasites depend not only on a host, but also on the host’s environment. In addition, a single host species is rarely infested by a single species of parasite and often supports multiple species (i.e., multi-infestation). Although the arms race between a parasite and its host has been well studied, few data are available on how environmental conditions may influence the process leading to multiple infestations. In this study, we examine whether: (1) environmental factors including altitude, temperature, vegetation cover, human disturbance, and grazing by livestock affect the prevalence of two types of ectoparasites, mites and ticks, on their host (the common lizard, Zootoca vivipara) and (2) competition is evident between mites and ticks. Results We found the probability of mite infestation increased with altitude and vegetation cover, but decreased with human disturbance and presence of livestock. In contrast, the probability of tick infestation was inversely associated with the same factors. Individuals with low body condition and males had higher mite loads. However, this pattern was not evident for tick loads. The results from a structural equation model revealed that mites and ticks indirectly and negatively affected each other’s infestation probability through an interaction involving the environmental context. We detected a direct negative association between mites and ticks only when considering estimates of parasite load. This suggests that both mites and ticks could attach to the same host, but once they start to accumulate, only one of them takes advantage. Conclusion The environment of hosts has a strong effect on infestation probabilities and parasite loads of mites and ticks. Autecological differences between mites and ticks, as indicated by their opposing patterns along environmental gradients, may explain the pattern of weak contemporary interspecific competition. Our findings emphasize the importance of including environmental factors and the natural history of each parasite species in studies of host–parasite coevolution.

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Environmental factors and river network position allow prediction of benthic community assemblies: A model of nematode metacommunities

Scientific Reports ◽

10.1038/s41598-019-51245-2 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 4

Author(s):

Birgit Gansfort ◽

Walter Traunspurger

Keyword(s):

Community Structure ◽

Environmental Factors ◽

Temporal Dynamics ◽

Regression Tree ◽

Predictive Ability ◽

River Network ◽

River Networks ◽

Aquatic Communities ◽

Boosted Regression Tree ◽

The Elbe River

Abstract The field of metacommunity studies is growing rapidly, including recent applications to river networks. Most of these studies have targeted a single river network but whether their findings are relevant to other river systems is unknown. This study investigated the influence of environmental, spatial and temporal parameters on the community structure of nematodes in the river networks of the Elbe and Rhine. We asked whether the variance in community structure was better explained by spatial variables representing the watercourse than by overland distances. After determining the patterns in the Elbe river network, we tested whether they also explained the Rhine data. The Elbe data were evaluated using a boosted regression tree analysis. The predictive ability of the model was then assessed using the Rhine data. In addition to strong temporal dynamics, environmental factors were more important than spatial factors in structuring riverine nematode communities. Community structure was more strongly influenced by watercourse than by Euclidean distances. Application of the model’s predictions to the Rhine data correlated significantly with field observations. Our model shows that the consequences of changes in environmental factors or habitat connectivity for aquatic communities across different river networks are quantifiable.

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Niche Modeling of Dengue Fever Using Remotely Sensed Environmental Factors and BRT

Online Journal of Public Health Informatics ◽

10.5210/ojphi.v10i1.8336 ◽

2018 ◽

Vol 10 (1) ◽

Author(s):

Jeffrey L. Ashby ◽

Max J. Moreno-Madriñán

Keyword(s):

Environmental Factors ◽

Dengue Fever ◽

Regression Tree ◽

Poisson Model ◽

Bernoulli Model ◽

Boosted Regression Tree ◽

Tree Analysis ◽

Regression Tree Analysis ◽

Wide Range ◽

Vector Borne

ObjectiveIn this paper we used Boosted Regression Tree analysis coupled with environmental factors gathered from satellite data, such as temperature, elevation, and precipitation, to model the niche of Dengue Fever (DF) in Colombia.IntroductionDengue Fever (DF) is a vector-borne disease of the flavivirus family carried by the Aedes aegypti mosquito, and one of the leading causes of illness and death in tropical regions of the world. Nearly 400 million people become infected each year, while roughly one-third of the world’s population live in areas of risk. Dengue fever has been endemic to Colombia since the late 1970s and is a serious health problem for the country with over 36 million people at risk. We used the Magdalena watershed of central Colombia as the site for this study due to its natural separation from other geographical regions in the country, its wide range of climatic conditions, the fact that it includes the main urban centers in Colombia, and houses 80% of the country’s population. Advances in the quality and types of remote sensing (RS) satellite imagery has made it possible to enhance or replace the field collection of environmental data such as precipitation, temperature, and land use, especially in remote areas of the world such as the mountainous areas of Colombia. We modeled the cases of DF by municipality with the environmental factors derived from the satellite data using boosted regression tree analysis. Boosted regression tree analysis (BRT), has proven useful in a wide range of studies, from predicting forest productivity to other vector-borne diseases such as Leishmaniosis, and Crimean-Congo hemorrhagic fever. Using this framework, we set out to determine what are the differences between using presence/absence and case counts of DF in this type of analysis?MethodsWe combined data on Dengue fever cases downloaded from the Instituto Nacional de Salud (INS) Programa SIVIGILA INS site with population data downloaded from the 2005 General Census administered by the National Administrative Department of Statistics (Departamento Administrativo Nacional de Estadística, DANE) and projected to 2012–2014 levels. We acquired remote sensing data from the National Aeronautics and Space Administration (NASA) data servers for each day of the study period. Imagery for each environmental variable was composited to reduce the effects of cloud cover and to match the ISO Week Date format reporting of the case data. We aggregated these weekly composite images for each variable using GIS to create annual minimum, maximum, and mean for a raster cell. These data were further aggregated to the municipality level using the GIS, again for minimum, maximum, and mean. Land use and elevation were only downloaded for one period given they change very little over time. The BRT analysis was conducted twice: once using the Bernoulli family of presence/absence and again using the Poisson family of actual case counts. In the first analysis (Bernoulli), any municipality reporting one or more cases of DF in the year was coded as having disease “presence”, while all others were coded as not having disease “absence”. The BRT model was run, using a twenty-five percent hold out of the data as a testing set, for each year. In the second analysis (Poisson), the only change to the models consisted of replacing the presence/absence data with the actual cases of reported DF within the municipality. The Poisson family was chosen in the model since the count data were highly skewed.ResultsWe calculated RMSE and Pearson r values for each of the three years. The Poisson model out-performed the Bernoulli model across all years. The RMSE values were considerably lower for the Poisson model compared to the Bernoulli model, reflecting a better model fit. The Pearson r values were higher for the Poisson model compared to the Bernoulli model, again across all three years. We created maps to compare Cases with the Poisson and the Bernoulli results. The maps shown in the figure reflect the results for 2012. The left panel represents the cases per 10,000 population per square kilometer for each municipality. The dark green color represents very low ratios of DF, while the red color reflects a higher incidence of DF. All maps used the same classification as the reported cases map for comparison, with an additional symbol (black) used for values outside the reported cases range.ConclusionsUsing actual reported case data and the Poisson function within the BRT functions created by Elith et al. and the gbm package in R, we show that the differences between using presence/absence and case counts of DF in a BRT analysis gives a clearer picture of the spatial distribution of DF. By using readily available and freely accessible data, we have shown that practitioners both within and outside of Colombia can quickly create accurate maps of annual DF incidence. The methods described here could also be extended to other regions and diseases, making it useful to a wide range of end users.

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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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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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Indirect drivers of plant diversity-productivity relationship in semiarid sandy grasslands

Biogeosciences ◽

10.5194/bg-9-1277-2012 ◽

2012 ◽

Vol 9 (4) ◽

pp. 1277-1289 ◽

Cited By ~ 19

Author(s):

X. A. Zuo ◽

J. M. H. Knops ◽

X. Y. Zhao ◽

H. L. Zhao ◽

T. H. Zhang ◽

...

Keyword(s):

Environmental Factors ◽

Plant Diversity ◽

Structural Equation ◽

Environmental Gradients ◽

Northern China ◽

Equation Modeling ◽

Vegetation Composition ◽

Wet Meadow ◽

Positive Correlation ◽

Sandy Grasslands

Abstract. Although patterns between plant diversity and ecosystem productivity have been much studied, a consistent relationship has not yet emerged. Differing patterns between plant diversity and productivity have been observed in response to spatial variability of environmental factors and vegetation composition. In this study, we measured vegetation cover, plant diversity, productivity, soil properties and site characteristics along an environmental gradient (mobile dune, semi-fixed dune, fixed dune, dry meadow, wet meadow and flood plain grasslands) of natural sandy grasslands in semiarid areas of northern China. We used multivariate analysis to examine the relationships between environmental factors, vegetation composition, plant diversity and productivity. We found a positive correlation between plant diversity and productivity. Vegetation composition aggregated by the ordination technique of non-metric multidimensional scaling had also a significantly positive correlation with plant diversity and productivity. Environmental gradients in relation to soil and topography affected the distribution patterns of vegetation composition, species diversity and productivity. However, environmental gradients were a better determinant of vegetation composition and productivity than of plant diversity. Structural equation modeling suggested that environmental factors determine vegetation composition, which in turn independently drives both plant diversity and productivity. Thus, the positive correlation between plant diversity and productivity is indirectly driven by vegetation composition, which is determined by environmental gradients in soil and topography.

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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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Increasing corvid tolerance to humans in urban ecosystems with increasing latitude

Biological Communications ◽

10.21638/spbu03.2019.404 ◽

2020 ◽

Vol 64 (4) ◽

Author(s):

Nadezhda Poddubnaya ◽

Tatyana Korotkova ◽

Polina Vanicheva

Keyword(s):

Environmental Factors ◽

Human Disturbance ◽

Urban Ecosystems ◽

Flight Initiation Distance ◽

Protection Measures ◽

Non Invasive ◽

Alert Distance ◽

Behaviour Changes ◽

Hooded Crows ◽

Flight Initiation

The rapid growth of cities causes behaviour changes in birds in response to urban environmental factors. The avian response to human disturbance has recently been studied by a non-invasive research tool as an alert distance (AD) and a flight initiation distance (FID) assessment. The tolerance of hooded crows (n = 395), jackdaws (n = 394) and rooks (n = 169) to humans was assessed by AD and FID. It was shown that the FID of all species is maximal during the summer, when the parents send clear “danger—fly away” signals to the young and the birds fly away. The AD and FID of the three species reliably correlates with the season. Rooks showed FID species-specificity in seven cities of Eastern Europe. Comparison of the attitude of birds to people in cities that have similarities in human culture showed that tolerance increases with increasing latitude in all species and is statistically significant only in the jackdaw. This should be taken into account in environmental protection measures.

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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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