Designing permanent sample plots by using a spatially hierarchical matrix population model

Journal of the Royal Statistical Society. Series C: Applied Statistics

Volumn 58, Issue 3, 2009, Pages 345-367

  Chagneau, P ^a   Mortier, F ^a   Picard, N ^a  

^a CIRAD   (France)

Author keywords

Asymptotic properties; Matrix model; Permanent sample plot; Point process; Population dynamic model; Usher model

Indexed keywords

EID: 65949124689     PISSN: 00359254     EISSN: 14679876     Source Type: Journal    
DOI: 10.1111/j.1467-9876.2008.00657.x     Document Type: Article

References (45)

1. African Timber Organization-International Tropical Timber Organization (2003) ATO/ITTO principles, criteria and indicators for the sustainable management of African natural tropical forests. Policy Development Series 14. International Tropical Timber Organization, Yokohama.
2. Alder, D. Synnott, T. J. (1992) Permanent sample plot techniques for mixed tropical forest. Tropical Forestry Paper 25. Oxford Forestry Institute, University of Oxford, Oxford.
3. Alvarez-Buylla, E. R., García-Barrios, R., Lara-Moreno, C. Martínez-Ramos, M. (1996) Demographic and genetic models in conservation biology: applications and perpectives for tropical rain forest tree species. A. Rev. Ecol. Syst., 27, 387 421.
4. Alvarez-Buylla, E. R. Slatkin, M. (1991) Finding confidence limits on population growth rates. Trends Ecol. Evoln, 6, 221 224.
5. Alvarez-Buylla, E. R. Slatkin, M. (1993) Finding confidence limits on population growth rates: Monte Carlo test of a simple analytic method. Oikos, 68, 273 282.
6. Alvarez-Buylla, E. R. Slatkin, M. (1994) Finding confidence limits on population growth rates: three real examples revised. Ecology, 75, 255 260.
7. Bakker, J. P., Olff, H., Willems, J. H. Zobel, M. (1996) Why do we need permanent plots in the study of long-term vegetation dynamics? J. Vegtn Sci., 7, 147 155.
8. Banerjee, S., Carlin, B. P. Gelfand, A. E. (2004) Hierarchical Modeling and Analysis for Spatial Data. Boca Raton : Chapman and Hall-CRC.
9. Bartoli, N. Del Moral, P. (2001) Simulation & Algorithmes Stochastiques. Toulouse : Cépaduès.
10. Caswell, H. (2001) Matrix Population Models: Construction, Analysis and Interpretation, 2nd edn. Sunderland : Sinauer.
11. Cochran, W. G. (1977) Sampling Techniques, 3rd edn. New York : Wiley.
12. Cressie, N. (1993) Statistics for Spatial Data, 2nd edn. New York : Wiley.
13. Cuevas, A. Romo, J. (1995) On the estimation of the influence curve. Can. J. Statist., 23, 1 9.
14. Dallmeier, F. (1992) Long-term Monitoring of Biological Diversity in Tropical Forest Areas: Methods for Establishment and Inventory of Permanent Plots. Paris : United Nations Educational, Scientific and Cultural Organisation.
15. DeAngelis, D. L Gross, L. J eds 1992) Individual-based Models and Approaches in Ecology - Populations, Communities and Ecosystems. New York : Chapman and Hall.
16. Demyanov, V., Wood, S. N. Kedwards, T. J. (2006) Improving ecological impact assessment by statistical data synthesis using process-based models. Appl. Statist., 55, 41 62.
17. Durrieu de Madron, L., Forni, E., Karsenty, A., Loffeier, E. Pierre, J. M. (1998) Le Projet d'Aménagement Pilote Intégré de Dimako, Cameroun, 1992-1996. Montpellier : Cirad-Forêt.
18. Favrichon, V. (1994) Classification des espèces arborées en groupes fonctionnels en vue de la réalisation d'un modèle de dynamique de peuplement en forêt guyanaise. Rev. Ecol., 49, 379 403.
19. Fieberg, J. Ellner, S. P. (2001) Stochastic matrix models for conservation and management: a comparative review of methods. Ecol. Lett., 4, 244 266.
20. Flores, O., Gourlet-Fleury, S. Picard, N. (2006) Local disturbance, forest structure and dispersal effects on saplings distributions of light-demanding and shade-tolerant species in a French Guianian forest. Acta Œcol., 29, 141 154.
21. Forget, P. M., Mercier, F. Collinet, F. (1999) Spatial patterns of two rodent-dispersed rain forest trees Carapa procera (Meliaceae) and Vouacapoua americana (Caesalpiniaceae) at Paracou, French Guiana. J. Trop. Ecol., 15, 301 313.
22. Gourlet-Fleury, S., Guehl, J. M Laroussinie, O eds 2004) Ecology and Management of a Neotropical Rainforest: Lessons Drawn from Paracou, a Long-term Experimental Research Site in French Guiana. Paris : Elsevier.
23. Hampel, F. R. (1974) The influence curve and its role in robust estimation. J. Am. Statist. Ass., 69, 383 393.
24. Houllier, F., Lebreton, J. D. Pontier, D. (1989) Sampling properties of the asymptotic behavior of age- or stage-grouped population models. Math. Biosci., 95, 161 177.
25. Kaye, T. N. Pyke, D. A. (2003) The effect of stochastic technique on estimates of population viability from transition matrix models. Ecology, 84, 1464 1476.
26. Kirkpatrick, S. (1984) Optimization by simulated annealing: quantitative studies. J. Statist. Phys., 34, 975 986.
27. Kruskal, J. B. (1956) On the shortest spanning tree of graph and the traveling salesman problem. Proc. Am. Math. Soc., 7, 48 50.
28. Lefkovitch, L. P. (1965) The study of population growth in organisms grouped by stages. Biometrics, 21, 1 18.
29. Leite, A. M. C. Lleras, E. (1993) Areas prioritárias na amazônia para conservação dos recursos genéticos de espécies florestais nativas: fase preliminar. Acta Bot. Bras., 7, 61 94.
30. Leslie, P. H. (1945) In the use of matrices in certain population mathematics. Biometrika, 33, 183 212.
31. Magnus, J. R. Neudecker, H. (1999) Matrix Differential Calculus with Applications in Statistics and Econometrics, 2nd edn. Chichester : Wiley.
32. Møller, J. Waagepetersen, R. P. (2004) Statistical Inference and Simulation for Spatial Point Processes. Boca Raton : Chapman and Hall-CRC.
33. R Development Core Team (2005) R: a Language and Environment for Statistical Computing. Vienna : R Foundation for Statistical Computing.
34. Rogers-Bennett, L. Rogers, D. W. (2006) A semi-empirical growth estimation method for matrix models of endangered species. Ecol. Modellng, 195, 237 246.
35. Saltelli, A., Chan, K. Scott, E. M. (2000) Sensitivity Analysis. Chichester : Wiley.
36. Schlather, M. (1999) Introduction to positive definite functions and to unconditional simulation of random fields. Technical Report ST-99-10. Lancaster University, Lancaster.
37. Stoyan, D. Stoyan, H. (1994) Fractals, Random Shapes and Point Fields. Chichester : Wiley.
38. Taylor, B. L. (1995) The reliability of using population viability analysis for risk classification of species. Conservn Biol., 9, 551 558.
39. Traissac, S 2003) Dynamique spatiale de Vouacapoua americana (Aublet), arbre de forêt tropicale humide à répartition agrégée. PhD Thesis. Université Claude Bernard-Lyon 1, Lyon.
40. Usher, M. B. (1966) A matrix approach to the management of renewable resources, with special reference to the selection forests. J. Appl. Ecol., 3, 355 367.
41. Usher, M. B. (1969) A matrix model for forest management. Biometrics, 25, 309 315.
42. Vanclay, J. K. (1992) Permanent plots for multiple objectives: defining goals and resolving conflicts. In Remote Sensing and Permanent Plot Techniques for World Forest Monitoring (eds H. G. Lund, R. Päivinen S. Thammincha pp. 157 163. Pattaya : International Union of Forest Research Organizations.
43. Wikle, C. K. (2003) Hierarchical Bayesian models for predicting the spread of ecological processes. Ecology, 84, 1382 1394.
44. Zeide, B. (1980) Plot size optimization. For. Sci., 26, 251 257.
45. Zetlaoui, M., Picard, N. Bar-Hen, A. (2006) Asymptotic distribution of stage-grouped population models. Math. Biosci., 200, 76 89.