How can quantitative ecology be attractive to young scientists? Balancing computer/desk work with fieldwork

Animal Conservation

Volumn 16, Issue 2, 2013, Pages 134-136

  Gimenez, O ^a   Abadi, F ^a   Barnagaud, J Y ^b   Blanc, L ^a   Buoro, M ^c   Cubaynes, S ^d   Desprez, M ^e   Gamelon, M ^a,f,g   Guilhaumon, F ^h   Lagrange, P ^a,i   Madon, B ^j   Marescot, L ^a   Papadatou, E ^k   Papaix J ^l,m   Peron G ^n,o   Servanty, S ^n,o  

^a CNRS   (France)

^b UMR 1202 BIOGECO   (France)

^c UNIVERSITY OF CALIFORNIA   (United States)

^d IMPERIAL COLLEGE LONDON   (United Kingdom)

^e MACQUARIE UNIVERSITY   (Australia)

^f UNIVERSITÉ LYON 1   (France)

^g OFFICE NATIONAL DE LA CHASSE ET DE LA FAUNE SAUVAGE   (France)

^h UNIVERSITY OF ÉVORA   (Portugal)

ⁱ UNIVERSITÉ DE SHERBROOKE   (Canada)

^j Boomerang For Earth Conservation   (France)

^k not available   (Greece)

^l INRA Institut National de la Recherche Agronomique   (France)

^m UR341 National Agronomical Research Institute (INRA)   (France)

ⁿ PATUXENT WILDLIFE RESEARCH CENTER   (United States)

^o Colorado State University ^*  (United States)

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Author keywords

[No Author keywords available]

Indexed keywords

EID: 84876459501     PISSN: 13679430     EISSN: 14691795     Source Type: Journal    
DOI: 10.1111/j.1469-1795.2012.00597.x     Document Type: Letter

References (13)

1. Clutton-Brock, T. & Sheldon, B.C. (2010). Individuals and populations: the role of long-term, individual-based studies of animals in ecology and evolutionary biology. Trends Ecol. Evol. 25, 562-573.
2. Ellison, A.M. & Dennis, B. (2010). Paths to statistical fluency for ecologists. Front. Ecol. Environ. 8, 362-370.
3. Gregory, R., Failing, L., Harstone, M., Long, G., McDaniels, T. & Ohlson, D. (2012). Structured decision making: a practical guide to environmental management choices. Chichester: John Wiley & Sons, Ltd.
4. Magurran, A.E., Baillie, S.R., Buckland, S.T., Dick, J.M., Elston, D.A., Scott, E.M., Rognvald, I.S., Somerfield, P.J. & Watt, A.D. (2010). Long-term datasets in biodiversity research and monitoring: assessing change in ecological communities through time. Trends Ecol. Evol. 25, 574-582.
5. McCarthy, M.A. & Possingham, H.P. (2007). Active adaptive management for conservation. Conserv. Biol. 21, 956-963.
6. Nichols, J.D. & Williams, B.K. (2006). Monitoring for conservation. Trends Ecol. Evol. 21, 668-673.
7. Ovaskainen, O. & Soininen, J. (2011). Making more out of sparse data: hierarchical modeling of species communities. Ecology 92, 289-295.
8. Possingham, H. (2012). How can we sell evaluating, analyzing and synthesizing to young scientists. Commentary. Anim. Conserv. 15, 229-230.
9. Restif, O., Hayman, D.T.S., Pulliam, J.R.C., Plowright, R.K., George, D.B., Luis, A.D., Cunningham, A.A., Bowen, R.A., Fooks, A.R., O'Shea, T.J., Wood, J.L.N. & Webb, C.T. (2012). Model-guided fieldwork: practical guidelines for multidisciplinary research on wildlife ecological and epidemiological dynamics. Ecol. Lett. 15, 1083-1094.
10. Runge, M.C. (2011). An introduction to adaptive management for threatened and endangered species. J. Fish Wildl. Manage. 2, 220-233.
11. Schaub, M., Gimenez, O., Sierro, A. & Arlettaz, R. (2007). Use of integrated modeling to enhance estimates of population dynamics obtained from limited data. Conserv. Biol. 21, 945-955.
12. Van Emden, H.F. (2008). Statistics for terrified biologists. Oxford: Blackwell Publishing, Ltd.
13. Walters, C.J. (1986). Adaptive management of renewable resources. New York: Macmillan Publishing Company.