scholarly journals Characterisation of false-positive observations in botanical surveys

2017 ◽  
Author(s):  
Quentin J Groom ◽  
Sarah J. Whild
Keyword(s):  
Rare Species ◽  
False Positive ◽  
Common Knowledge ◽  
Vascular Plant ◽  
False Negative ◽  
Digital Data ◽  
Skill Levels ◽  
Final Score ◽  
Few Data ◽  
A Site

Errors in botanical surveying are a common problem. The presence of a species is easily overlooked, leading to false-absences; while misidentifications and other mistakes lead to false-positive observations. While it is common knowledge that these errors occur, there are few data that can be used to quantify and describe these errors. Here we characterise false-positive errors for a controlled set of surveys conducted as part of a field identification test of botanical skill. Surveys were conducted at sites with a verified list of vascular plant species. The candidates were asked to list all the species they could identify in a defined botanically rich area. They were told beforehand that their final score would be the sum of the correct species they listed, but false-positive errors counted against their overall grade. The number of errors varied considerably between people, some people create a high proportion of false-positive errors, but these are scattered across all skill levels. Therefore, a person’s ability to correctly identify a large number of species is not a safeguard against the generation of false-positive errors. There was no phylogenetic pattern to falsely observed species, however, rare species are more likely to be false-positive as are species from species rich genera. Raising the threshold for the acceptance of an observation reduced false-positive observations dramatically, but at the expense of more false negative errors. False-positive errors are higher in field surveying of plants than many people may appreciate. Greater stringency is required before accepting species as present at a site, particularly for rare species. Combining multiple surveys resolves the problem, but requires a considerable increase in effort to achieve the same sensitivity as a single survey. Therefore, other methods should be used to raise the threshold for the acceptance of a species. For example, digital data input systems that can verify, feedback and inform the user are likely to reduce false-positive errors significantly.

scholarly journals Characterisation of false-positive observations in botanical surveys

2017 ◽  
Author(s):  
Quentin J Groom ◽  
Sarah J. Whild
Keyword(s):  
Rare Species ◽  
False Positive ◽  
Common Knowledge ◽  
Vascular Plant ◽  
False Negative ◽  
Digital Data ◽  
Skill Levels ◽  
Final Score ◽  
Few Data ◽  
A Site

Errors in botanical surveying are a common problem. The presence of a species is easily overlooked, leading to false-absences; while misidentifications and other mistakes lead to false-positive observations. While it is common knowledge that these errors occur, there are few data that can be used to quantify and describe these errors. Here we characterise false-positive errors for a controlled set of surveys conducted as part of a field identification test of botanical skill. Surveys were conducted at sites with a verified list of vascular plant species. The candidates were asked to list all the species they could identify in a defined botanically rich area. They were told beforehand that their final score would be the sum of the correct species they listed, but false-positive errors counted against their overall grade. The number of errors varied considerably between people, some people create a high proportion of false-positive errors, but these are scattered across all skill levels. Therefore, a person’s ability to correctly identify a large number of species is not a safeguard against the generation of false-positive errors. There was no phylogenetic pattern to falsely observed species, however, rare species are more likely to be false-positive as are species from species rich genera. Raising the threshold for the acceptance of an observation reduced false-positive observations dramatically, but at the expense of more false negative errors. False-positive errors are higher in field surveying of plants than many people may appreciate. Greater stringency is required before accepting species as present at a site, particularly for rare species. Combining multiple surveys resolves the problem, but requires a considerable increase in effort to achieve the same sensitivity as a single survey. Therefore, other methods should be used to raise the threshold for the acceptance of a species. For example, digital data input systems that can verify, feedback and inform the user are likely to reduce false-positive errors significantly.

scholarly journals Characterisation of false-positive observations in botanical surveys

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e3324 ◽  
Author(s):  
Quentin J. Groom ◽  
Sarah J. Whild
Keyword(s):  
Rare Species ◽  
False Positive ◽  
Common Knowledge ◽  
Vascular Plant ◽  
False Negative ◽  
Digital Data ◽  
Skill Levels ◽  
Final Score ◽  
Few Data ◽  
A Site

Errors in botanical surveying are a common problem. The presence of a species is easily overlooked, leading to false-absences; while misidentifications and other mistakes lead to false-positive observations. While it is common knowledge that these errors occur, there are few data that can be used to quantify and describe these errors. Here we characterise false-positive errors for a controlled set of surveys conducted as part of a field identification test of botanical skill. Surveys were conducted at sites with a verified list of vascular plant species. The candidates were asked to list all the species they could identify in a defined botanically rich area. They were told beforehand that their final score would be the sum of the correct species they listed, but false-positive errors counted against their overall grade. The number of errors varied considerably between people, some people create a high proportion of false-positive errors, but these are scattered across all skill levels. Therefore, a person’s ability to correctly identify a large number of species is not a safeguard against the generation of false-positive errors. There was no phylogenetic pattern to falsely observed species; however, rare species are more likely to be false-positive as are species from species rich genera. Raising the threshold for the acceptance of an observation reduced false-positive observations dramatically, but at the expense of more false negative errors. False-positive errors are higher in field surveying of plants than many people may appreciate. Greater stringency is required before accepting species as present at a site, particularly for rare species. Combining multiple surveys resolves the problem, but requires a considerable increase in effort to achieve the same sensitivity as a single survey. Therefore, other methods should be used to raise the threshold for the acceptance of a species. For example, digital data input systems that can verify, feedback and inform the user are likely to reduce false-positive errors significantly.

scholarly journals A Framework for Estimating Human-Wildlife Conflict Probabilities Conditional on Species Occupancy

2021 ◽  
Vol 2 ◽  
Author(s):  
Rekha Warrier ◽  
Barry R. Noon ◽  
Larissa L. Bailey
Keyword(s):  
Species Distribution ◽  
False Positive ◽  
Hierarchical Modeling ◽  
Management Strategies ◽  
False Negative ◽  
Distribution Data ◽  
Modeling Framework ◽  
Bayesian Hierarchical ◽  
Tiger Conservation ◽  
A Site

Managing human-wildlife conflicts (HWCs) is an important conservation objective for the many terrestrial landscapes dominated by humans. Forecasting where future conflicts are likely to occur and assessing risks to lives and livelihoods posed by wildlife are central to informing HWC management strategies. Existing assessments of the spatial occurrence patterns of HWC are based on either understanding spatial patterns of past conflicts or patterns of species distribution. In the former case, the absence of conflicts at a site cannot be attributed to the absence of the species. In the latter case, the presence of a species may not be an accurate measure of the probability of conflict occurrence. We present a Bayesian hierarchical modeling framework that integrates conflict reporting data and species distribution data, thus allowing the estimation of the probability that conflicts with a species are reported from a site, conditional on the species being present. In doing so, our model corrects for both false-positive and false-negative conflict reporting errors. We provide study design recommendations using simulations that explore the performance of the model under a range of conflict reporting probabilities. We applied the model to data on wild boar (Sus scrofa) space use and conflicts collected from the Central Terai Landscape (CTL), an important tiger conservation landscape in India. We found that tolerance for wildlife was a predictor of the probability with which farmers report conflict with wild boars from sites not used by the species. We also discuss useful extensions of the model when conflict data are verified for potential false-positive errors and when landscapes are monitored over multiple seasons.

scholarly journals An Rshiny app for modelling environmental DNA data: accounting for false positive and false negative observation error

2020 ◽  
Author(s):  
Alex Diana ◽  
Eleni Matechou ◽  
Jim E. Griffin ◽  
Andrew S. Buxton ◽  
Richard A. Griffiths
Keyword(s):  
Statistical Method ◽  
False Positive ◽  
False Negative ◽  
Environmental Dna ◽  
Bayesian Variable Selection ◽  
Observation Error ◽  
List Type ◽  
Data Set ◽  
A Site ◽  
User Friendly

AbstractEnvironmental DNA (eDNA) surveys have become a popular tool for assessing the distribution of species. However, it is known that false positive and false negative observation error can occur at both stages of eDNA surveys, namely the field sampling stage and laboratory analysis stage.We present an RShiny app that implements the Griffin et al. (2019) statistical method, which accounts for false positive and false negative errors in both stages of eDNA surveys. Following Griffin et al. (2019), we employ a Bayesian approach and perform efficient Bayesian variable selection to identify important predictors for the probability of species presence as well as the probabilities of observation error at either stage.We demonstrate the RShiny app using a data set on great crested newts collected by Natural England in 2018 and we identify water quality, pond area, fish presence, macrophyte cover, frequency of drying as important predictors for species presence at a site.The state-of-the-art statistical method that we have implemented is the only one that has specifically been developed for the purposes of modelling false negatives and false positives in eDNA data. Our RShiny app is user-friendly, requires no prior knowledge of R and fits the models very efficiently. Therefore, it should be part of the tool-kit of any researcher or practitioner who is collecting or analysing eDNA data.

Implementation of P2P Traffic Identification System

2010 ◽  
Vol 44-47 ◽  
pp. 3318-3321
Author(s):  
Shuang Yang ◽  
Ye Du ◽  
Ru Hui Zhang
Keyword(s):  
Traffic Flow ◽  
False Positive ◽  
False Negative ◽  
Transport Layer ◽  
New Method ◽  
Identification System ◽  
Data Packets ◽  
Traffic Identification ◽  
P2p Applications ◽  
Few Data

By analyzing packets of the transport layer and the traffic flow statistic characteristics in the peer-to-peer (P2P) applications, a new P2P traffic identification system is presented. The new method in the system relies on the observation of the first few data packets of a TCP/UDP connection. It not only can identify more P2P applications, but also can identify the known and unknown P2P applications even if the data of them is encrypted. According to the results by passing a large number of tests, the system has higher identify-rate to identify the P2P applications and lower false negative and false positive. It has good effects in the actual network.

Critical Evaluation of Impedance Phlebography for the Diagnosis of Deep Vein Thrombosis

1974 ◽  
Vol 31 (02) ◽  
pp. 273-278
Author(s):  
Kenneth K Wu ◽  
John C Hoak ◽  
Robert W Barnes ◽  
Stuart L Frankel
Keyword(s):  
Deep Vein Thrombosis ◽  
False Positive ◽  
False Negative ◽  
Critical Evaluation ◽  
Original Method ◽  
Vein Thrombosis ◽  
Negative Results ◽  
False Negative Results ◽  
Deep Vein ◽  
Positive Results

SummaryIn order to evaluate its daily variability and reliability, impedance phlebography was performed daily or on alternate days on 61 patients with deep vein thrombosis, of whom 47 also had 125I-fibrinogen uptake tests and 22 had radiographic venography. The results showed that impedance phlebography was highly variable and poorly reliable. False positive results were noted in 8 limbs (18%) and false negative results in 3 limbs (7%). Despite its being simple, rapid and noninvasive, its clinical usefulness is doubtful when performed according to the original method.

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