Use of environmental DNA (eDNA) in streams to detect feral swine (Sus scrofa)

Invasive feral swine can damage ecosystems, disrupt plant and animal populations, and transmit diseases. Monitoring of feral swine populations requires expensive and labor-intensive techniques such as aerial surveys, field surveys for sign, trail cameras, and verifying landowner reports. Environmental DNA (eDNA) provides an alternative method for locating feral swine. To aid in detection of this harmful invasive species, a novel assay was developed incorporating molecular methods. From August 2017 to April 2018, water samples and stream data were collected along 400 m transects in two different stream types where swine DNA was artificially introduced to investigate potential factors affecting detection. A generalized linear model (family binomial) was used to characterize environmental conditions affecting swine DNA detection; detection was the dependent variable and stream measurements included stream type, distance downstream, water temperature, velocity, turbidity, discharge, and pH as independent variables. Parameters from the generalized linear model were deemed significant if 95% confidence intervals did not overlap 0. Detection probability for swine DNA negatively related to water temperature (β = − 0.21, 95% CI [−0.35 to −0.09]), with the highest detection probability (0.80) at 0 °C and lowest detection probability (0.05) at 17.9 °C water temperature. Results indicate that sampling for swine eDNA in free-flowing stream systems should occur at lower water temperatures to maximize detection probability. This study provides a foundation for further development of field and sampling techniques for utilizing eDNA as a viable alternative to monitoring a terrestrial invasive species in northern regions of the United States.

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Secondary Succession under invasive species (Pteridium aquilinum) conditions in a seasonal dry tropical forest in southeastern Mexico

PeerJ ◽

10.7717/peerj.6974 ◽

2019 ◽

Vol 7 ◽

pp. e6974

Author(s):

Alberto Jean Baptiste ◽

Pedro A. Macario ◽

Gerald A. Islebe ◽

Benedicto Vargas-Larreta ◽

Luciano Pool ◽

...

Keyword(s):

Invasive Species ◽

Species Richness ◽

Linear Model ◽

Generalized Linear Model ◽

Secondary Succession ◽

Dry Forest ◽

Pteridium Aquilinum ◽

Soil Conditions ◽

Site Conditions ◽

Importance Value

The role of invasive species in ecosystem functioning represents one of the main challenges in ecology. Pteridium aquilinum is a successful cosmopolitan invasive species with negative effects on the ecological mechanisms that allow secondary succession. In this study, we evaluated the influence of P. aquilinumon secondary succession under different disturbances in a seasonal dry forest of the Yucatán Peninsula. We determined species richness, composition and the relative importance value in four sampling units. Fabaceae followed by Asteraceae, Meliaceae, Rubiaceae, Sapindaceae and Verbenaceae were the most species rich families. A dissimilarity analysis determined significant differences in beta diversity between sampling units. With a generalized linear model we found that species richness was best explained by site conditions, followed by calcium and soil organic matter. Also, the generalized linear model showed that abundance resulted in a strong correlation with site conditions and soil characteristics. Specific soil conditions related to phosphoro and calcium were also detected as beneficiary to the successional processes. Our results suggest that applying fire restriction and periodic cutting of the bracken fern, this can increase a higher diversity of species.

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Generalized Linear Model for Automobile Fatality Rate Prediction in R

Open Source Software for Statistical Analysis of Big Data - Advances in Computer and Electrical Engineering ◽

10.4018/978-1-7998-2768-9.ch005 ◽

2020 ◽

pp. 137-161

Author(s):

Gao Niu ◽

Alan Olinsky

Keyword(s):

Linear Model ◽

Generalized Linear Model ◽

Descriptive Analysis ◽

Real Data ◽

The United States ◽

Contributing Factors ◽

Statistical Software ◽

Validation Metrics ◽

Fatal Crash ◽

Accident Data

This chapter demonstrates the descriptive and statistical modeling function in R. The automobile fatal accident data of the United States is extracted from the Fatality Analysis Reporting System (FARS). The model will be used to understand significant contributing factors of automobile accident death when a fatal crash happens. First, descriptive analysis is performed by basic R functions and packages. Then, generalized linear model (GLM) with logit link function is explored and constructed. Finally, multiple validation metrics are introduced and calculated to ensure the reasonability and accuracy of the predictions. The focus of this chapter is to demonstrate the power and flexibility of the most popular Open Source Statistical Software (OSSS) through a real data analysis.

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