Population- and family-level variation of brittlebush (Encelia farinosa , Asteraceae) pubescence: its relation to drought and implications for selection in variable environments

2003 ◽  
Vol 90 (10) ◽  
pp. 1481-1486 ◽  
Author(s):  
Darren R. Sandquist ◽  
James R. Ehleringer
2017 ◽  
Vol 10 (1) ◽  
Author(s):  
Gerard Terradas ◽  
Scott L. Allen ◽  
Stephen F. Chenoweth ◽  
Elizabeth A. McGraw

2021 ◽  
Vol 288 (1948) ◽  
Author(s):  
Phoebe L. Reuben ◽  
Justin C. Touchon

In response to environmental stressors, organisms often demonstrate flexible responses in morphology, life history or behaviour. However, it is currently unclear if such plastic responses are coordinated or operate independently of one another. In vertebrates, this may partly result from studies examining population- or species-level mean responses, as opposed to finer grained analyses of individuals or families. We measured predator-specific morphological and coloration plasticity in 42 families of tadpoles of the treefrog Dendropsophus ebraccatus and behavioural plasticity from 18 of these families, allowing us to examine the correlation between three predator-induced plastic responses. For all three plastic responses, tadpoles showed strong opposing responses to each of two predators, providing the appearance of covariation in plasticity. However, the examination of individual families revealed a strong correlation between morphological and coloration plasticity, but no correlations between either morphology or colour and behavioural plasticity. Thus, our analysis shows that some aspects of the plastic phenotype develop together while others function independently. This highlights the importance of examining individual- and family-level variation for understanding the adaptive significance of developmental plasticity, which is crucial for a holistic appreciation of phenotypic plasticity and its importance in ecology and evolution.


2019 ◽  
Vol 55 (2) ◽  
pp. 380-392 ◽  
Author(s):  
Christopher J. T. Mabin ◽  
Craig R. Johnson ◽  
Jeffrey T. Wright

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Virginia M. Chu ◽  
Maria Anice Mureb Sallum ◽  
Timothy E. Moore ◽  
Kevin J. Emerson ◽  
Carl D. Schlichting ◽  
...  

Author(s):  
Jovo Lojanica ◽  

All management standards have requirements for different aspects of improvements on the personal level, family level, company level, in business and life. What is about national level and country level? Is it possible for today’s generations to learn history of nations and of civilizations? If it is — ok, let’s apply it on actual time and people to have less problems and difficulties — especially if is actual in field of risk management. Majority of people are occupied by today’s problems. They don’t consider past and future challenges. People from each country strive for better quality, better and cleaner environment, higher safety etc. historically and today. But could we remember: How did Genghis Khan conquer many regions and how was he defeated? How did Mayas and Aztecs die out? How were Native Americans in North America drastically reduced in numbers? How did the Roman Imperium vanish? How was the Ottoman Imperium established and how it vanished? How many people were killed in the wars in XX century, etc? In all these catastrophic changes risks were not considered in an adequate way. Requirements of risk management — Principles and guidelines — ISO 31000:2009 are very consultative. They could be used on country level, national level, regional level, continental and intercontinental level.


Author(s):  
Amal Alzain ◽  
Suhaib Alameen ◽  
Rani Elmaki ◽  
Mohamed E. M. Gar-Elnabi

This study concern to characterize the brain tissues to ischemic stroke, gray matter, white matter and CSF using texture analysisto extract classification features from CT images. The First Order Statistic techniques included sevenfeatures. To find the gray level variation in CT images it complements the FOS features extracted from CT images withgray level in pixels and estimate the variation of thesubpatterns. analyzing the image with Interactive Data Language IDL software to measure the grey level of images. The results show that the Gray Level variation and   features give classification accuracy of ischemic stroke 97.6%, gray matter95.2%, white matter 97.3% and the CSF classification accuracy 98.0%. The overall classification accuracy of brain tissues 97.0%.These relationships are stored in a Texture Dictionary that can be later used to automatically annotate new CT images with the appropriate brain tissues names.


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