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Regressions Fit for Purpose: Models of Locust Phase State Must Not Conflate Morphology With Behavior

Overview of attention for article published in Frontiers in Behavioral Neuroscience, July 2018
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Title
Regressions Fit for Purpose: Models of Locust Phase State Must Not Conflate Morphology With Behavior
Published in
Frontiers in Behavioral Neuroscience, July 2018
DOI 10.3389/fnbeh.2018.00137
Pubmed ID
Authors

Swidbert R. Ott

Abstract

Phenotypic plasticity often entails coordinated changes in multiple traits. The effects of two alternative environments on multiple phenotypic traits can be analyzed by multivariable binary logistic regression (LR). Locusts are grasshopper species (family Acrididae) with a capacity to transform between two distinct integrated phenotypes or "phases" in response to changes in population density: a solitarious phase, which occurs when densities are low, and a gregarious phase, which arises as a consequence of crowding and can form very large and economically damaging swarms. The two phases differ in behavior, physiology and morphology. A large body of work on the mechanistic basis of behavioral phase transitions has relied on LR models to estimate the probability of behavioral gregariousness from multiple behavioral variables. Mart́ın-Blázquez and Bakkali (2017; [10.1111/eea.12564]10.1111/eea.12564) have recently proposed standardized LR models for estimating an overall "gregariousness level" from a combination of behavioral and, unusually, morphometric variables. Here I develop a detailed argument to demonstrate that the premise of such an overall "gregariousness level" is fundamentally flawed, since locust phase transformations entail a decoupling of behavior and morphology. LR models that combine phenotypic traits with markedly different response times to environmental change are of very limited value for analyses of phase change in locusts, and of environmentally induced phenotypic transitions in general. I furthermore show why behavioral variables should not be adjusted by measures of body size that themselves differ between the two phases. I discuss the models fitted by Mart́ın-Blázquez and Bakkali (2017) to highlight potential pitfalls in statistical methodology that must be avoided when analysing associations between complex phenotypes and alternative environments. Finally, I reject the idea that "standardized models" provide a valid shortcut to estimating phase state across different developmental stages, strains or species. The points addressed here are pertinent to any research on transitions between complex phenotypes and behavioral syndromes.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 14 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 4 29%
Student > Ph. D. Student 3 21%
Student > Doctoral Student 2 14%
Student > Master 2 14%
Student > Bachelor 1 7%
Other 0 0%
Unknown 2 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 6 43%
Biochemistry, Genetics and Molecular Biology 2 14%
Neuroscience 1 7%
Unknown 5 36%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 07 September 2021.
All research outputs
#15,010,626
of 23,090,520 outputs
Outputs from Frontiers in Behavioral Neuroscience
#2,060
of 3,212 outputs
Outputs of similar age
#198,252
of 329,795 outputs
Outputs of similar age from Frontiers in Behavioral Neuroscience
#63
of 83 outputs
Altmetric has tracked 23,090,520 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 3,212 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.3. This one is in the 30th percentile – i.e., 30% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 329,795 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.
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