Within- and Among-Observer Variation in Measurements of Animal Biometrics and their Influence on Accurate Quantification of Common Biometric-Based Condition Indices

Research using biometric data relies on consistent measurements within, and often among, observers. However, research into the relative importance of intra- and inter-observer variability is limited. More importantly, the influence of biometric variability on accurate quantification of biometric-based condition indices has not been analysed: it is unclear whether multiple errors become magnified or cancel one another out. Here, we quantify intra- and inter-specific variability in multiple biometrics, and derived condition indices, using museum bird specimens. Inter-observer variability was higher than intra-observer variability for all parameters. Measurement error (ME) varied from < 1% to > 50% for different biometrics. ME was magnified in condition estimates, reaching > 80% within-observers and > 90% among-observers. Significant differences in mean measurements were found for 17% and 67% of biometrics within-and among-observers, respectively; for condition indices, the figures were 50% and 67%, respectively. We discuss the implications of these findings for research into species' ecology, taxonomy and behaviour

Within- and Among-Observer Variation in Measurements of Animal Biometrics and their Influence on Accurate Quantification of Common Biometric-Based Condition Indices

Research using biometric data relies on consistent measurements within, and often among, observers. However, research into the relative importance of intra- and inter-observer variability is limited. More importantly, the influence of biometric variability on accurate quantification of biometric-based condition indices has not been analysed: it is unclear whether multiple errors become magnified or cancel one another out. Here, we quantify intra- and inter-specific variability in multiple biometrics, and derived condition indices, using museum bird specimens. Inter-observer variability was higher than intra-observer variability for all parameters. Measurement error (ME) varied from 50% for different biometrics. ME was magnified in condition estimates, reaching > 80% within-observers and > 90% among-observers. Significant differences in mean measurements were found for 17% and 67% of biometrics within-and among-observers, respectively; for condition indices, the figures were 50% and 67%, respectively. We discuss the implications of these findings for research into species' ecology, taxonomy and behaviour

The Citizen Science Landscape: From Volunteers to Citizen Sensors and Beyond

Within conservation and ecology, volunteer participation has always been an important component of research. Within the past two decades, this use of volunteers in research has proliferated and evolved into “citizen science.” Technologies are evolving rapidly. Mobile phone technologies and the emergence and uptake of high-speed Web-capable smart phones with GPS and data upload capabilities can allow instant collection and transmission of data. This is frequently used within everyday life particularly on social networking sites. Embedded sensors allow researchers to validate GPS and image data and are now affordable and regularly used by citizens. With the “perfect storm” of technology, data upload, and social networks, citizen science represents a powerful tool. This paper establishes the current state of citizen science within scientific literature, examines underlying themes, explores further possibilities for utilising citizen science within ecology, biodiversity, and biology, and identifies possible directions for further research. The paper highlights (1) lack of trust in the scientific community about the reliability of citizen science data, (2) the move from standardised data collection methods to data mining available datasets, and (3) the blurring of the line between citizen science and citizen sensors and the need to further explore online social networks for data collection

Use of confidence radii to visualise significant differences in principal components analysis: Application to mammal assemblages at locations with different disturbance levels

Multivariate statistical analysis is a powerful method of examining complex datasets, such as species assemblages, that does not suffer from the oversimplification prevalent in many univariate analyses. However, identifying whether data points on a multivariate plot are clustered is subjective, as there is no determination of significant differences between the points and no indication of the level of confidence in those points. The validity of drawing such conclusions may therefore be considered suspect. This paper describes a method of bootstrapping calculated principal components to estimate a confidence radius, similar to confidence intervals in univariate techniques. Plotting 3D scatterplots of the principal components, with the size of the spherical point representative of the level of confidence of the estimate, gives a clear and visual indication of significant difference between the points — where the spheres overlap there is no significant difference. We apply the technique to mammal assemblages at sites in Epping Forest (Essex, UK) that differ in the level of disturbance present and find that differences between some sites that appear large using traditional principal components analysis are actually not significantly different at the 95% confidence level, while other sites do differ significantly. Sites that differ most in anthropogenic disturbance are not significantly different in terms of assemblage structure