scholarly journals Null model analyses of temporal patterns of bird assemblages and their foraging guilds revealed the predominance of positive and random associations

2019 ◽  
Vol 9 (15) ◽  
pp. 8541-8554
Author(s):  
Martin Korňan ◽  
Marek Svitok ◽  
Anton Krištín
Keyword(s):  
Null Model ◽  
Temporal Patterns ◽  
Bird Assemblages ◽  
Foraging Guilds

Temporal Patterns of Wetland-Associated Bird Assemblages in Altered Wetlands in Turkey

2020 ◽  
Vol 67 (4) ◽  
pp. 316
Author(s):  
Akif Keten ◽  
Erdinc Sarcan ◽  
James T. Anderson
Keyword(s):  
Temporal Patterns ◽  
Bird Assemblages

scholarly journals Null models for community dynamics: Beware of the cyclic shift algorithm

2019 ◽  
Author(s):  
Michael Kalyuzhny
Keyword(s):  
Time Series ◽  
Community Dynamics ◽  
Null Model ◽  
Temporal Patterns ◽  
Null Models ◽  
Type I ◽  
Cyclic Shift ◽  
Initial State ◽  
Temporal Autocorrelation ◽  
Compositional Changes

AbstractAimTemporal patterns of community dynamics are drawing increasing interest due to their potential to shed light on assembly processes and anthropogenic effects. However, interpreting such patterns considerably benefits from comparing observed dynamics to the reference of a null model. For that aim, the cyclic shift permutations algorithm, which generates randomized null communities based on empirically observed time series, has recently been proposed. The use of this algorithm, which shifts each species time series randomly in time, has been justified by the claim that it preserves the temporal autocorrelation of single species. Hence it has been used to test the significance of various community patterns, in particular excessive compositional changes, biodiversity trends and community stability.InnovationHere we critically study the properties of the cyclic shift algorithm for the first time. We show that, unlike previously suggested, this algorithm does not preserve temporal autocorrelation due to the need to “wrap” the time series and assign the last observations to the first years. Moreover, this algorithm scrambles the initial state of the community, making any dynamics that results from deviations from equilibrium seem excessive. We exemplify that these two issues lead to a highly elevated type I error rate in tests for excessive compositional changes and richness trends.ConclusionsCaution is needed when using the cyclic shift permutation algorithm and interpreting results obtained using it. Interpretation is further complicated because the algorithm removes all correlations between species. We suggest guidelines for using this method and discuss several possible alternative approaches. More research is needed on the best practices for using null models for temporal patterns.

A Cautionary Note on Incremental Fit Indices Reported by LISREL

Methodology ◽  
2005 ◽  
Vol 1 (2) ◽  
pp. 81-85 ◽  
Author(s):  
Stefan C. Schmukle ◽  
Jochen Hardt
Keyword(s):  
Factor Analysis ◽  
Maximum Likelihood ◽  
Structural Equation ◽  
Structural Equation Models ◽  
Null Model ◽  
Likelihood Estimation ◽  
Parameter Estimates ◽  
Fit Indices ◽  
Cautionary Note ◽  
Confirmatory Factor

Abstract. Incremental fit indices (IFIs) are regularly used when assessing the fit of structural equation models. IFIs are based on the comparison of the fit of a target model with that of a null model. For maximum-likelihood estimation, IFIs are usually computed by using the χ2 statistics of the maximum-likelihood fitting function (ML-χ2). However, LISREL recently changed the computation of IFIs. Since version 8.52, IFIs reported by LISREL are based on the χ2 statistics of the reweighted least squares fitting function (RLS-χ2). Although both functions lead to the same maximum-likelihood parameter estimates, the two χ2 statistics reach different values. Because these differences are especially large for null models, IFIs are affected in particular. Consequently, RLS-χ2 based IFIs in combination with conventional cut-off values explored for ML-χ2 based IFIs may lead to a wrong acceptance of models. We demonstrate this point by a confirmatory factor analysis in a sample of 2449 subjects.

Temporal Patterns for Pitch and Loudness

1978 ◽  
Vol 23 (11) ◽  
pp. 856-857
Author(s):  
W. LAWRENCE GULICK
Keyword(s):  
Temporal Patterns

Temporal patterns of flow in the workplace

2013 ◽  
Author(s):  
J. Navarro ◽  
L. Ceja ◽  
J. Poppelbaum ◽  
D. Gomes
Keyword(s):  
Temporal Patterns

SPATIO-TEMPORAL PATTERNS OF WORKFORCE IN INDIA DURING 1981-2011

2019 ◽  
Vol 38 (2) ◽  
pp. 239-254
Author(s):  
M.B. SINGH ◽  
◽  
NITIN KUMAR MISHRA ◽  
Keyword(s):  
Temporal Patterns ◽  
Spatio Temporal

Counter culture: Causes, extent and solutions of systematic bias in the analysis of behavioural counts

2019 ◽  
Author(s):  
Joel L Pick ◽  
Nyil Khwaja ◽  
Michael A. Spence ◽  
Malika Ihle ◽  
Shinichi Nakagawa
Keyword(s):  
Recent Literature ◽  
Null Model ◽  
Mechanistic Explanation ◽  
Effect Sizes ◽  
Systematic Bias ◽  
Intra Class Correlation ◽  
Counter Culture ◽  
Provisioning Rate ◽  
Parental Provisioning ◽  
Observation Period

We often quantify a behaviour by counting the number of times it occurs within a specific, short observation period. Measuring behaviour in such a way is typically unavoidable but induces error. This error acts to systematically reduce effect sizes, including metrics of particular interest to behavioural and evolutionary ecologists such as R2, repeatability (intra-class correlation, ICC) and heritability. Through introducing a null model, the Poisson process, for modelling the frequency of behaviour, we give a mechanistic explanation of how this problem arises and demonstrate how it makes comparisons between studies and species problematic, because the magnitude of the error depends on how frequently the behaviour has been observed (e.g. as a function of the observation period) as well as how biologically variable the behaviour is. Importantly, the degree of error is predictable and so can be corrected for. Using the example of parental provisioning rate in birds, we assess the applicability of our null model for modelling the frequency of behaviour. We then review recent literature and demonstrate that the error is rarely accounted for in current analyses. We highlight the problems that arise from this and provide solutions. We further discuss the biological implications of deviations from our null model, and highlight the new avenues of research that they may provide. Adopting our recommendations into analyses of behavioural counts will improve the accuracy of estimated effect sizes and allow meaningful comparisons to be made between studies.

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