Born 17 June 1949 in London (Ont.) Canada.
Married 1972, divorced 1995, remarried 1997
Children born in 1978, 1981, 1983, 1999 & 2002
Nationality: Dutch and Canadian citizenship
Primary school in Amsterdam and secondary schools in Amsterdam and Hilversum, HBS-B exam at the 'Nieuwe Lyceum' in Hilversum in 1967.
Biology-study at the 'Rijksuniversiteit Utrecht' with:
1972 kandidaatsexam B-1 and
1976 doctoraalexam: Major (12 months) in theoretical biology (bioinformatics) (Prof Dr A. Lindenmayer / Dr P. Hogeweg), Minor (6 months) in population genetics (Prof Dr W. Scharloo / Dr G. de Jong) & Minor (6 months) in Marine ecology (Prof Dr G. Persoone, R.U. Gent).
1980 Doctor of science (cum laude) Rijksuniversiteit Utrecht (Prof Dr W. Scharloo) Thesis: "On the genetical ecology of the Great Tit, L." The doctoral research was carried out in a cooperative project of the "vakgroep Populatie en Evolutiebiologie R.U. Utrecht" and the "Instituut voor Oecologisch Onderzoek", in Heteren.
1986 Habilitation in Zoology, University of Basel.
- 1994-2004 Professor of Animal Population Ecology at Utrecht University.
- 1991-present Netherlands Institute of Ecology (Centre for Terrestrial Ecology) from 1991-2002 Head of the department "Animal Population Biology"
- 1984 - 1991 Assistent (Professor) at the "Zoologisches Institut" of the University of Basel.
- 1986 - 1994 "Privatdozent" in Zoology at the University of Basel.
- 1983 - 1984 Theoretical biologist at the "Tjeukemeer Laboratorium" of the "Limnologisch Instituut" of the K.N.A.W.
- 1982 - 1983 Scientist in the department of Systems-analysis of the "Sociaal en Cultureel Planbureau" in Rijswijk (NL).
- 1980 - 1981 Research Fellowship paid by the Netherlands Organisation for the Advancement of Pure Research (Z.W.O.) in the department of Ecology and Evolution, State University of New York at Stony Brook, N.Y., USA.
- 1978 - 1980 Scientist BION (Foundation for Fundamental Biological Research).
- 1976 - 1977 Scientist dept. of Population and Evolutionary Biology R.U. Utrecht.
- 1973 - 1976 Assistent Theoretical Biology dept R.U. Utrecht.
- 1971 - 1973 Assistent genetics lab-course, R.U. Utrecht.
MOST CITED PUBLICATIONS
van Noordwijk, A.J. & G. de Jong, 1986. Acquisition and allocation of resources: their influence on variation in life history tactics. Am. Nat. 128 : 137 - 142.
van Noordwijk, A.J., R.H. McCleery & C.M. Perrins 1995. Selection for the synchronisation of great tit (Parus major) breeding with caterpillar growth, using temperature as a predictor. J. Anim Ecol. 64: 451-458.
van Noordwijk, A.J., J.H. van Balen & W. Scharloo 1980. Heritability of ecologically important traits in the Great Tit. Ardea 68: 193-203.
Boag, P.T. & A.J. van Noordwijk 1987. Quantitative genetics in wild bird populations. pp 45 - 78 in F. Cooke & P.A. Buckley (eds) Avian Genetics, Academic Press 1987.
Graveland, J., van der Wal, R., van Balen, J.H. & A.J. van Noordwijk 1994. Poor reproduction in forest passerines from decline of snail abundance on acidified soils. Nature 368: 226-228.
Dingemanse, N.J., Both, C., van Noordwijk, A. J. , Rutten, A.L. & Drent, P.J. 2003. Natal dispersal and personalities in great tits (Parus major ).Proc R Soc Lond B 270: 741-747
Dingemanse, N. J., Both, C., Drent, P. J., van Oers, K. & van Noordwijk, A. J. 2002. Repeatability and heritability of exploratory behaviour in great tits from the wild. Animal Behaviour, 64: 929-938
Drent,P.J., van Oers,K. & van Noordwijk,A.J. 2003 Realised heritability of personalities in the great tit (Parus major) Proc. R. Soc. Lond. B . 270: 45-51
Visser, M.E., A.J. van Noordwijk, J. M. Tinbergen & C. M. Lessells 1998. Warmer springs lead to mis-timed reproduction in Great Tits (Parus major). Proc. R. Soc. Lond. B. 265: 1867-1870.
> 100 times:
van Noordwijk, A.J., J.H. van Balen & W. Scharloo 1981. Genetic variation in the timing of reproduction in the Great Tit Oecologia (Berl.) 49: 158 - 166.
van Noordwijk, A.J. & W. Scharloo. 1981. Inbreeding in an island population of the great tit. Evolution 35 : 674 ‑ 688
van Noordwijk, A.J., J.H. van Balen & W. Scharloo 1981. Genetic and environmental variation in clutch size of the great tit (Parus major) Neth. J. Zool. 31: 342 ‑ 372.
de Jong, G. & A.J. van Noordwijk 1992. Acquisition and allocation of resources: Genetic (Co)variances, selection and life‑histories. American Naturalist: 139: 749-770.
van Oers K, Drent P, de Goede P, van Noordwijk AJ 2004 Realized heritability and repeatability of risk-taking behaviour in relation to avian personalities. P Roy Soc Lond B Bio 271: 65-73.
Postma, E; Van Noordwijk, AJ 2005 Gene Flow Maintains A Large Genetic Difference In Clutch Size At A Small Spatial Scale. NATURE 433 (7021): 65-68
My interest lies in evolutionary processes and particularly in the border area between genetics and ecology. In this border area there are two main themes: the maintenance of genetic variation for life history traits and the spatial structure of populations. These themes are linked, because the exchange of individuals between areas with different selection regimes is a potent mechanism to maintain genetic variation.
My personal motivation for this research programme is that we know far too little about the processes involved to make any sort of prediction which species (or populations) have the ability to adapt to which changes in environmental conditions and at what maximum rates. Such knowledge is crucial if we want to assess long term effects of human environmental impact. At the moment, we do not even know what one should study if one wants to make predictions for particular cases.
I have largely worked with birds, because these organisms allow the combination of many techniques. In birds, it is possible to follow individuals throughout their life in natural environments, to know who are the parents of whom in natural populations, to study their movements over small and large distances and large data-sets with such information exist. It is also possible, although not easy, to hold and breed them in captivity in order to study physiological processes in detail under controlled standardised conditions, or to make selection lines. At the moment we are at the verge of a new development where selection lines enable the detection of the gene loci involved with the help of molecular techniques.
My main interest and my main strength are in making cross-connections between different techniques and different fields. In making these combinations, I have developed a strong methodological interest, particularly in the field of data analysis.
I am proud of the fact that my work has found its way into textbooks on evolution, ecology, behaviour and quantitative genetics and it is my ambition to add the solution to several problems to that list, either by myself or through my colleagues and students.
Life history evolution
Population structure: inbreeding and dispersal
Kluijver (1951) explicitly stated that the Great Tit (Parus major) is an ideal study species for bridging the gap between the individual and the population level. To study populations as the sum of what happens to all its individual members, brings the variation among individuals and the importance of the interactions to the foreground. On top of that, the spatial structure of populations comes out as an important element.
The spatial structure of popualtions is largely determined by dispersal, the (net) displacement of individuals between their site of birth and their site of reporduction. However, even with large amounts of data on individuals where we know the site of birth and the site of reproduction drawing conclusions about dispersal is not so easy because of a number of methodological problems. The observations that can be made are quite different depending on where an individual was born (e.g. in the centre or at the periphery of the study area.
A first objective is therefore to develop methods for describing and then filtering out the limitations on our observations. Several methods are currently under development.
The knowledge of pedigrees has allowed us to study the effects of inbreeding and mate choice with respect to relatedness, in particular in the island populations on Vlieland and more analyses are under way.
When the same genes (or a single genotype) produces different phenotypes we call this phenotypic plasticity. This phenomenon becomes particularly interesting when the plasticity is adaptive, that is when the different phenotypes increase the fitness in the environment where those phenotypes are produced. When we understand the main environmental factors determining the phenotypic plasticity of certain traits, we can study the reaction norms, which describe the phenotype for a specific genotype as a function of the environment.
Whereas reaction norms can be measured directly in clonal organisms by raising the same clones in different environments, we can only deduce reaction norms indirectly in obligately sexual organisms. Nevertheless, the reaction norm approach allows us to consider genetic variation and structured environmental variation in a single framework. Since natural environments are always structured and since animals can often choose to some extent where the live, we need a theoretical framework such as the reaction norm approach to put al the different elements in relation to each other.
This is a relatively young area and my contributions are mainly in the further development of conceptual models, although the possibilities for application are always explicitly present.
How to spot type I errors or the insignificance of low P values.
The human eye is deceptively good at detecting patterns. Over the last few months, I have encountered several cases where scientists reported nominally highly significant results in a part of their data. In none of these cases was it clear that splitting the data was part of a design made before the data analysis started. Whereas the use of Bonferroni corrections of significance levels for the number of tests made is becoming a standard practice, the problem of reporting nominal probabilities in cases where these are inappropriate is persistent. Statistical testing of hypotheses that are generated from the same data is never allowed. There are several recent publications emphasising the same point in various forms and in various disciplines of science and social science (e.g. search the web with the keyword insignificance). If one would allow hypotheses to be tested on the data from which they are generated, there is no way of accounting for the implicit process of selecting promising avenues in describing the data. It is of course a sensible strategy to try and find patterns in datasets. It is of course likely that stronger patterns are better candidates for further research, but nominal probability values are only appropriate in separate independent datasets where one tests the hypotheses generated from the first dataset.
The whole idea of significance testing rests on specifying the probability that the reported or a more extreme result would have been found if the null hypothesis, no relation or no difference, is true. When we accept a probability of 5 % as a critical border, we mistakenly accept that in one case in twenty we reject the null hypothesis of no relation or no difference. This raises the question of how to recognise cases where these so called type I errors play a role. Every scientist who makes more than twenty statistical tests in a lifetime, will be confronted with presumed results that are in fact type I errors. In the first year that I collected field data I found a relation between body size of female birds and their onset of egg-laying. I found a number of publications reporting the same pattern, but only later found out that there were as many publications reporting the opposite pattern. I was helped by the comments of a senior colleague who tempered my excitement by remarking: "if you show me the same pattern in next years data, I will start thinking about it." By now it is clear that the pattern I observed is nominally significant in about one case in twenty. The question is how I could have known or suspected myself or in other words could I have spotted that I was dealing with a type I error? First of all, I was in the position that I did not have to rush into print or at least try to do so. Unfortunately, it seems that fewer and fewer people can afford to do so. Second, I should have realised that a causal explanation for the opposite pattern was only slightly less plausible. Third, under my hypothetical explanation, I should have found a similar relation in males which was absent. Fourth, I should have realised that I was probably not the first one to look at this relation. Thus, internal consistency and consistency with what is known are important tools in spotting type I errors.
Rare events do occur, I did once win a car in a sweepstake. Type I errors will occur by definition in one case out of twenty. Not identifying type I errors will lead to many people losing a lot of effort in trying to repeat or build on the results. Genuine type I errors will occur, and will lead to a situation where only one repetition in twenty being successful. It should at all costs be avoided that these are augmented by a large number of extra cases where nominal P-values are inappropriate because the hypothesis tested was derived from the same data. In particular splitting data along an independent class variable or at some value of the independent variable can only be used to generate hypotheses that should be tested on independent data.
Arie J. van Noordwijk