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Chapter 2 - The Intertidal Zone of the North-East Atlantic Region
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- By Stephen J. Hawkins, Kathryn E. Pack, Louise B. Firth, Nova Mieszkowska, Ally J. Evans, Gustavo M. Martins, Per Åberg, Leoni C. Adams, Francisco Arenas, Diana M. Boaventura, Katrin Bohn, C. Debora G. Borges, João J. Castro, Ross A. Coleman, Tasman P. Crowe, Teresa Cruz, Mark S. Davies, Graham Epstein, João Faria, João G. Ferreira, Natalie J. Frost, John N. Griffin, ME Hanley, Roger J. H. Herbert, Kieran Hyder, Mark P. Johnson, Fernando P. Lima, Patricia Masterson-Algar, Pippa J. Moore, Paula S. Moschella, Gillian M. Notman, Federica G. Pannacciulli, Pedro A. Ribeiro, Antonio M. Santos, Ana C. F. Silva, Martin W. Skov, Heather Sugden, Maria Vale, Kringpaka Wangkulangkul, Edward J. G. Wort, Richard C. Thompson, Richard G. Hartnoll, Michael T. Burrows, Stuart R. Jenkins
- Edited by Stephen J. Hawkins, Marine Biological Association of the United Kingdom, Plymouth, Katrin Bohn, Louise B. Firth, University of Plymouth, Gray A. Williams, The University of Hong Kong
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- Book:
- Interactions in the Marine Benthos
- Published online:
- 07 September 2019
- Print publication:
- 29 August 2019, pp 7-46
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Summary
The rocky shores of the north-east Atlantic have been long studied. Our focus is from Gibraltar to Norway plus the Azores and Iceland. Phylogeographic processes shape biogeographic patterns of biodiversity. Long-term and broadscale studies have shown the responses of biota to past climate fluctuations and more recent anthropogenic climate change. Inter- and intra-specific species interactions along sharp local environmental gradients shape distributions and community structure and hence ecosystem functioning. Shifts in domination by fucoids in shelter to barnacles/mussels in exposure are mediated by grazing by patellid limpets. Further south fucoids become increasingly rare, with species disappearing or restricted to estuarine refuges, caused by greater desiccation and grazing pressure. Mesoscale processes influence bottom-up nutrient forcing and larval supply, hence affecting species abundance and distribution, and can be proximate factors setting range edges (e.g., the English Channel, the Iberian Peninsula). Impacts of invasive non-native species are reviewed. Knowledge gaps such as the work on rockpools and host–parasite dynamics are also outlined.
Contributors
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- By Sherese Ali, Danielle Molinari Andrade, Elinor Ben-Menachem, Weerawadee Chandranipapongse, Pamela Crawford, Anne Davis, Carin Dove, Nancy Foldvary-Schaefer, Elizabeth E. Gerard, Cristina Y. Go, Cynthia L. Harden, Dini Hui, Shinya Ito, Jaromir Janousek, Nathalie Jette, Pavel Klein, A. Gabriela Lizama, Kristi A. McIntosh, Georgia Montouris, Brian J. Murray, Ori Nevo, Eugene Ng, Alison M. Pack, Sima Indubhai Patel, Page B. Pennell, Kalliopi A. Petropoulou, Mark Quigg, Alessandra Scaparrotta, Marianna Sebastiani, Patricia Osborne Shafer, O. Carter Snead, Diane T. Sundstrom, Alberto Verrotti, Carla Verrotti, Jonathan H. Waters, Fatima Zahir
- Edited by Esther Bui, Autumn M. Klein
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- Book:
- Women with Epilepsy
- Published online:
- 05 August 2014
- Print publication:
- 31 July 2014, pp vii-x
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Aspects of robot repeatability
- B. W. Mooring, T. J. Pack
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- Article
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One of the most important specifications associated with a robot manipulator is repeatability. Unfortunately, there are no standard procedures for determining or specifying the repeatability of a robot. This situation has resulted in a good deal of confusion when attempting to use a published repeatability specification to determine if a robot will meet the requirements of an application.
The purpose of this paper, therefore, is to propose a systematic procedure for the determination and specification of robot repeatability. To accomplish this, three aspects of repeatability analysis are examined. First, a method of collecting data to study repeatability is discussed. A data acquisition system is described as well as an associated test procedure. Next, a procedure for utilizing the acquired data to develop a repeatability specification for a robot is proposed. This procedure results in a specification of the robot's ability to orient objects as well as the classical specification for positioning repeatability. A methodical approach to determining where in the workspace the repeatability test should be made is also addressed. Finally, an approach is presented whereby the data collected during the repeatability test may be used to determine which joints contribute most to the variations in robot repeatability.
10 - Collaboration between university and industry
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- By B. J. T. Morgan, Canterbury University, P. M. North, Canterbury University, S. E. Pack, Canterbury University
- Edited by D. J. Hand, B. S. Everitt
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- Book:
- The Statistical Consultant in Action
- Published online:
- 05 June 2012
- Print publication:
- 05 March 1987, pp 134-152
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Summary
Introduction
In this paper we describe two of the several different ways in which statisticians in a university can act as consultants for industry. In both cases the consulting is effectively carried out by more than one statistician, but there the similarity ends. We hope that these examples will provide a flavour of the activities of an applied statistics department and an applied statistics research unit working together within a university. A number of problems are considered, including: the analysis of dominant lethal assay data; the analysis of quantal assay data incorporating time to response; the analysis of pain data relating to episiotomy; the analysis of aggression in mentally handicapped patients.
The analysis of dominant lethal assay data
Tables 1 and 2 present two sets of data from the paper by Haseman and Soares (1976). In each case, for over 500 litters of mice, the number of dead fetuses was recorded. Tables 1 and 2 are control groups from dominant lethal assays (taken from Haseman and Soares, 1976). In this experiment a drug's ability to cause damage to reproductive genetic material, sufficient to kill the fertilised egg or developing embryo, is tested by dosing a male mouse (typically) and mating it to one or more females. A significant increase in fetal deaths is indicative of a mutagenic effect.
As in many areas of statistics, typically two questions arise:
Can we describe such sets of data in a relatively simple manner?
How might we make comparisons between such data sets?