The importance of individual developmental variation in stage-structured population models

Ecology Letters

Volumn 17, Issue 8, 2014, Pages 1026-1038

  de Valpine, Perry ^a   Scranton, Katherine ^a,b   Knape, Jonas ^a,c   Ram, Karthik ^a   Mills, Nicholas J ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b YALE UNIVERSITY   (United States)

^c SWEDISH UNIVERSITY OF AGRICULTURAL SCIENCES   (Sweden)

Author keywords

Autocorrelated growth; Cohort model; Delay differential equation model; Individual heterogeneity; Life history theory; Matrix population model; Population growth rate; Sensitivity and elasticity analysis; Stage structured development; Stage structured phenology

Indexed keywords

ELASTICITY; GROWTH RATE; HABITAT TYPE; INDIVIDUAL VARIATION; METAPOPULATION; PHENOLOGY; POPULATION GROWTH; POPULATION THEORY; SENSITIVITY ANALYSIS;

ANIMAL; ARTHROPOD; AUTOCORRELATED GROWTH; BIOLOGICAL MODEL; COHORT MODEL; DELAY-DIFFERENTIAL EQUATION MODEL; INDIVIDUAL HETEROGENEITY; LIFE CYCLE STAGE; LIFE-HISTORY THEORY; MATRIX POPULATION MODEL; PHYSIOLOGY; POPULATION DYNAMICS; POPULATION GROWTH RATE; REVIEW; SENSITIVITY AND ELASTICITY ANALYSIS; STAGE-STRUCTURED DEVELOPMENT; STAGE-STRUCTURED PHENOLOGY; STATISTICAL MODEL;

AUTOCORRELATED GROWTH; COHORT MODEL; DELAY-DIFFERENTIAL EQUATION MODEL; INDIVIDUAL HETEROGENEITY; LIFE-HISTORY THEORY; MATRIX POPULATION MODEL; POPULATION GROWTH RATE; SENSITIVITY AND ELASTICITY ANALYSIS; STAGE-STRUCTURED DEVELOPMENT; STAGE-STRUCTURED PHENOLOGY;

ANIMALS; ARTHROPODS; LIFE CYCLE STAGES; MODELS, BIOLOGICAL; MODELS, STATISTICAL; POPULATION DYNAMICS;

EID: 84903739661     PISSN: 1461023X     EISSN: 14610248     Source Type: Journal    
DOI: 10.1111/ele.12290     Document Type: Review

References (100)

1. Acker, P., Robert, A., Bourget, R. & Colas, B. (2014). Heterogeneity of reproductive age increases the viability of semelparous populations. Funct. Ecol., 28, 458-468.
2. Ashford, J.R., Read, K.L.Q. & Vickers, G.G. (1970). A system of stochastic models applicable to studies of animal population dynamics. J. Anim. Ecol., 39, 29-50.
3. Aubry, A., Bécart, E., Davenport, J. & Emmerson, M.C. (2010). Estimation of survival rate and extinction probability for stage-structured populations with overlapping life stages. Popul. Ecol., 52, 437-450.
4. Barfield, M., Holt, R.D. & Gomulkiewicz, R. (2011). Evolution in stage-structured populations. Am. Nat., 177, 397-409.
5. Beckerman, A.P., Benton, T.G., Lapsley, C.T. & Koesters, N. (2003). Talkin' 'bout my generation: environmental variability and cohort effects. Am. Nat., 162, 754-767.
6. Bellows, T. (1986). Impact of developmental variance on behavior of models for insect populations I. Models for populations with unrestricted growth. Res. Popul. Ecol., 28, 53-62.
7. Bellows, T.S. & Birley, M.H. (1981). Estimating developmental and mortality-rates and stage recruitment from insect stage-frequency data. Res. Popul. Ecology, 23, 232-244.
8. Benton, T.G., Cameron, T.C. & Grant, A. (2004). Population responses to perturbations: predictions and responses from laboratory mite populations. J. Anim. Ecol., 73, 983-995.
9. Biek, R., Funk, W.C., Maxell, B.A. & Mills, L.S. (2002). What is missing in amphibian decline research: insights from ecological sensitivity analysis. Conserv. Biol., 16, 728-734.
10. Bierzychudek, P. (1999). Looking backwards: assessing the projections of a transition matrix model. Ecol. Appl., 9, 1278-1287.
11. Birt, A., Feldman, R.M., Cairns, D.M., Coulson, R.N., Tchakerian, M., Xi, W. et al. (2009). Stage-structured matrix models for organisms with non-geometric development times. Ecology, 90, 57-68.
12. Blackwell, B.F., Avery, M.L., Watts, B.D. & Lowney, M.S. (2007). Demographics of black vultures in North Carolina. J. Wildl. Manage., 71, 1976-1979.
13. Blythe, S.P., Nisbet, R.M. & Gurney, W.S.C. (1984). The dynamics of population-models with distributed maturation periods. Theor. Popul. Biol., 25, 289-311.
14. Bolnick, D.I., Amarasekare, P., Araujo, M.S., Buerger, R., Levine, J.M., Novak, M. et al. (2011). Why intraspecific trait variation matters in community ecology. Trends Ecol. Evol., 26, 183-192.
15. Boucher, D.H. (1997). General patterns of age-by-stage distributions. J. Ecol., 85, 235-240.
16. Braner, M. & Hairston, N.G. Jr (1989). From cohort data to life table parameters via stochastic modeling. In: Estimation and Analysis of Insect Populations, Lecture Notes in Statistics (eds McDonald, L.L., Manly, B.F.J., Lockwood, J.A., Logan, J.A.). Springer-Verlag, Berlin, pp. 81-92.
17. Brownie, C., Hines, J.E., Nichols, J.D., Pollock, K.H. & Hestbeck, J.B. (1993). Capture-Recapture studies for multiple strata including non-markovian transitions. Biometrics, 49, 1173-1187.
18. Caswell, H. (1983). Phenotypic plasticity in life-history traits - demographic effects and evolutionary consequences. Am. Zool., 23, 35-46.
19. Caswell, H. (2001). Matrix Population Models: Construction, Analysis, and Interpretation. Sinauer Associates, Sunderland, MA.
20. Caswell, H. (2012). Matrix models and sensitivity analysis of populations classified by age and stage: a vec-permutation matrix approach. Theor Ecol, 5, 403-417.
21. Chu, C. & Adler, P.B. (2014). When should plant population models include age structure? J. Ecol., 102, 531-543.
22. Cochran, M. & Ellner, S. (1992). Simple methods for calculating age-based life-history parameters for stage-structured populations. Ecol. Monogr., 62, 345-364.
23. Crone, E.E., Menges, E.S., Ellis, M.M., Bell, T., Bierzychudek, P., Ehrlén, J. et al. (2011). How do plant ecologists use matrix population models? RID H-1717-2011. Ecol. Lett., 14, 1-8.
24. Crouse, D., Crowder, L. & Caswell, H. (1987). A stage-based population-model for Loggerhead sea-turtles and implications for conservation. Ecology, 68, 1412-1423.
25. David, O., Garnier, A., Larédo, C. & Lecomte, J. (2010). Estimation of plant demographic parameters from stage-structured censuses. Biometrics, 66, 875-882.
26. De Kroon, H., van Groenendael, J. & Ehrlén, J. (2000). Elasticities: a review of methods and model limitations. Ecology, 81, 607-618.
27. De Valpine, P. (2009). Stochastic development in biologically structured population models. Ecology, 90, 2889-2901.
28. De Valpine, P. & Rosenheim, J.A. (2008). Field-scale roles of density, temperature, nitrogen, and predation on aphid population dynamics. Ecology, 89, 532-541.
29. Dennis, B., Kemp, W.P. & Beckwith, R.C. (1986). Stochastic model of insect phenology: estimation and testing. Environ. Entomol., 15, 540-546.
30. Ehrlén, J. (2000). The dynamics of plant populations: does the history of individuals matter? Ecology, 81, 1675-1684.
31. Ellner, S.P. & Rees, M. (2006). Integral projection models for species with complex demography. Am. Nat., 167, 410-428.
32. Ellner, S.P., Kendall, B.E., Wood, S.N., McCauley, E. & Briggs, C.J. (1997). Inferring mechanism from time-series data: delay-differential equations. Physica D., 110, 182-194.
33. Ennola, K., Sarvala, J. & Devai, G. (1998). Modelling zooplankton population dynamics with the extended Kalman filtering technique. Ecol. Model., 110, 135-149.
34. Ezard, T.H.G., Bullock, J.M., Dalgleish, H.J., Millon, A., Pelletier, F., Ozgul, A. et al. (2010). Matrix models for a changeable world: the importance of transient dynamics in population management. J. Appl. Ecol., 47, 515-523.
35. Fieberg, J. & Ellner, S.P. (2001). Stochastic matrix models for conservation and management: a comparative review of methods. Ecol. Lett., 4, 244-266.
36. Fox, G.A. & Gurevitch, J. (2000). Population numbers count: tools for near-term demographic analysis. Am. Nat., 156, 242-256.
37. Fujiwara, M. & Caswell, H. (2001). Demography of the endangered North Atlantic right whale. Nature, 414, 537-541.
38. Fujiwara, M., Kendall, B.E., Nisbet, R.M. & Bennett, W.A. (2005). Analysis of size trajectory data using an energetic-based growth model. Ecology, 86, 1441-1451.
39. Gilioli, G. & Pasquali, S. (2007). Use of individual-based models for population parameters estimation. Ecol. Model., 200, 109-118.
40. González-Suárez, M. & Gerber, L.R. (2008). A behaviorally explicit demographic model integrating habitat selection and population dynamics in California sea lions. Conserv. Biol., 22, 1608-1618.
41. González-Suárez, M., Le Galliard, J.-F. & Claessen, D. (2011). Population and life-history consequences of within-cohort individual variation. Am. Nat., 178, 525-537.
42. Gouno, E., Courtrai, L. & Fredette, M. (2011). Estimation from aggregate data. Comput. Stat. Data Anal., 55, 615-626.
43. Gremer, J.R., Crone, E.E. & Lesica, P. (2012). Are dormant plants hedging their bets? Demographic consequences of prolonged dormancy in variable environments. Am. Nat., 179, 315-327.
44. Gross, K., Craig, B.A. & Hutchinson, W.D. (2002). Bayesian estimation of a demographic matrix model from stage-frequency data. Ecology, 83, 3285-3298.
45. Hairston, N.G. & Twombly, S. (1985). Obtaining life table data from cohort analyses - a critique of current methods. Limnol. Oceanogr., 30, 886-893.
46. Haridas, C.V. & Tuljapurkar, S. (2007). Time, transients and elasticity. Ecol. Lett., 10, 1143-1153.
47. Hoeting, J.A., Tweedie, R.L. & Olver, C.S. (2003). Transform estimation of parameters for stage-frequency data. J. Am. Stat. Assoc., 98, 503-514.
48. Horvitz, C. & Schemske, D. (1995). Spatiotemporal variation in demographic transitions of a tropical understory herb - projection matrix analysis. Ecol. Monogr., 65, 155-192.
49. Horvitz, C.C., Tuljapurkar, S. & Pascarella, J.B. (2005). Plant-animal interactions in random environments: habitat-stage elasticity, seed predators, and hurricanes. Ecology, 86, 3312-3322.
50. Hougaard, P. (2000). Analysis of multivariate survival data. Springer, New York.
51. Hunter, C.M. & Caswell, H. (2005). The use of the vec-permutation matrix in spatial matrix population models. Ecol. Model., 188, 15-21.
52. Jellison, R., Dana, G.L. & Melack, J.M. (1995). Zooplankton cohort analysis using systems identification techniques. J. Plankton Res., 17, 2093-2115.
53. Kempton, R.A. (1979). Statistical analysis of frequency data obtained from sampling an insect population grouped by stages. In: Statistical Distributions in Ecological Work. (eds Ord, J.K., Patil, G.P., Taillie, C.). International Co-operative Publishing House, Fairland, Maryland, pp. 401-418.
54. Kendall, B.E., Fox, G.A., Fujiwara, M. & Nogeire, T.M. (2011). Demographic heterogeneity, cohort selection, and population growth. Ecology, 92, 1985-1993.
55. Kimmerer, W. & Gould, A. (2010). A Bayesian approach to estimating copepod development times from stage frequency data. Limnol. Oceanogr. Methods, 8, 118-126.
56. Klein Breteler, W.C.M., Schogt, N. & van der Meer, J. (1994). The duration of copepod life stages estimated from stage-frequency data. J. Plankton Res., 16, 1039-1057.
57. Knape, J., Jonzén, N., Sköld, M., Kikkawa, J. & McCallum, H. (2011). Individual heterogeneity and senescence in Silvereyes on Heron Island. Ecology, 92, 813-820.
58. Knape, J., Daane, K.M. & de Valpine, P. (2014). Estimation of stage duration distributions and mortality under repeated cohort censuses. Biometrics, DOI: 10.1111/biom.12138.
59. Koons, D.N., Metcalf, C.J.E. & Tuljapurkar, S. (2008). Evolution of delayed reproduction in uncertain environments: a life-history perspective. Am. Nat., 172, 797-805.
60. Lawless, J.F. (2003). Statistical models and methods for lifetime data. Wiley-Interscience, Hoboken, NJ.
61. Lewis, E.R. (1977). Linear population models with stochastic time delays. Ecology, 58, 738-749.
62. Lo, N., Smith, P. & Butler, J. (1995). Population-growth of northern anchovy and pacific sardine using stage-specific matrix models. Mar. Ecol. Prog. Ser., 127, 15-26.
63. Manly, B.F.J. (1990). Stage-structured populations: Sampling, analysis, and simulation. Chapman and Hall, New York.
64. Manly, B.F.J. (1997). A method for the estimation of parameters for natural stage-structured populations. Res. Popul. Ecol., 39, 101-111.
65. McCauley, E., Nisbet, R.M., DeRoos, A.M., Murdoch, W.W. & Gurney, W.S.C. (1996). Structured population models of herbivorous zooplankton. Ecol. Monogr., 66, 479-501.
66. Moe, S.J., Kristoffersen, A.B., Smith, R.H. & Stenseth, N.C. (2005). From patterns to processes and back: analysing density-dependent responses to an abiotic stressor by statistical and mechanistic modelling. Proc. R. Soc. B., 272, 2133-2142.
67. Morris, W.F. & Doak, D.F. (2002). Quantitative Conservation Biology: Theory and Practice of Population Viability Analysis. Sinauer Associates, Sunderland, MA.
68. Murtaugh, P.A., Emerson, S.C., McEvoy, P.B. & Higgs, K.M. (2012). The statistical analysis of insect phenology. Environ. Entomol., 41, 355-361.
69. Nelson, W.A., McCauley, E. & Wimbert, J. (2004). Capturing dynamics with the correct rates: inverse problems using semiparametric approaches. Ecology, 85, 889-903.
70. Nisbet, R.M. (1997). Delay-differential equations for structured populations. Structured-population models in marine, terrestrial, and freshwater systems. Chapman and Hall, New York, pp. 89-118.
71. Nylin, S. & Gotthard, K. (1998). Plasticity in life-history traits. Annu. Rev. Entomol., 43, 63-83.
72. Ohman, M.D. (2012). Estimation of mortality for stage-structured zooplankton populations: what is to be done? J. Mar. Syst., 93, 4-10.
73. Ohman, M.D. & Hirche, H.J. (2001). Density-dependent mortality in an oceanic copepod population. Nature, 412, 638-641.
74. Ozgul, A., Oli, M.K., Armitage, K.B., Blumstein, D.T. & Van Vuren, D.H. (2009). Influence of local demography on asymptotic and transient dynamics of a yellow-bellied marmot metapopulation. Am. Nat., 173, 517-530.
75. Pascarella, J.B. & Horvitz, C.C. (1998). Hurricane disturbance and the population dynamics of a tropical understory shrub: megamatrix elasticity analysis. Ecology, 79, 547-563.
76. Peacor, S.D., Bence, J.R. & Pfister, C.A. (2007). The effect of size-dependent growth and environmental factors on animal size variability. Theor. Popul. Biol., 71, 80-94.
77. Pfister, C.A. & Stevens, F.R. (2003). Individual variation and environmental stochasticity: implications for matrix model predictions. Ecology, 84, 496-510.
78. Plant, R.E. & Wilson, L.T. (1986). Models for age structured populations with distributed maturation rates. J. Math. Biol., 23, 247-262.
79. Pradel, R. (2005). Multievent: an extension of multistate capture-recapture models to uncertain states. Biometrics, 61, 442-447.
80. Read, K.L.Q. & Ashford, J.R. (1968). A system of models for life cycle of a biological organism. Biometrika, 55, 211-221.
81. Saether, B.E. & Bakke, O. (2000). Avian life history variation and contribution of demographic traits to the population growth rate. Ecology, 81, 642-653.
82. Schaalje, G.B. & van der Vaart, H.R. (1989). Relationships among recent models for insect population dynamics with variable rates of development. J. Math. Biol., 27, 399-428.
83. Scranton, K., Stavrinides, M., Mills, N.J. & de Valpine, P. (2013). Small-scale intraspecific life history variation in herbivorous spider mites (Tetranychus pacificus) is associated with host plant cultivar. PLoS ONE, 8, e72980.
84. Severini, M., Baumgartner, J. & Limonta, L. (2003). Parameter estimation for distributed delay based population models from laboratory data: egg hatching of Oulema duftschmidi Redthenbacher (Coleoptera, Chrysomelidae) as an example. Ecol. Model., 167, 233-246.
85. Stott, I., Townley, S. & Hodgson, D.J. (2011). A framework for studying transient dynamics of population projection matrix models. Ecol. Lett., 14, 959-970.
86. Stover, J.P., Kendall, B.E. & Fox, G.A. (2012). Demographic heterogeneity impacts density-dependent population dynamics. Theor Ecol, 5, 297-309.
87. Tenhumberg, B., Tyre, A.J. & Rebarber, R. (2009). Model complexity affects transient population dynamics following a dispersal event: a case study with pea aphids. Ecology, 90, 1878-1890.
88. Tuljapurkar, S. & Haridas, C.V. (2006). Temporal autocorrelation and stochastic population growth. Ecol. Lett., 9, 324-334.
89. Tuljapurkar, S., Steiner, U.K. & Orzack, S.H. (2009). Dynamic heterogeneity in life histories. Ecol. Lett., 12, 93-106.
90. Twombly, S., Wang, G.M. & Hobbs, N.T. (2007). Composite forces shape population dynamics of copepod crustaceans. Ecology, 88, 658-670.
91. Vandermeer, J.H. (1975). On the construction of the population projection matrix for a population grouped in unequal stages. Biometrics, 31, 239.
92. Vandermeer, J. (1978). Choosing category size in a stage projection matrix. Oecologia, 32, 79-84.
93. Viallefont, A., Besbeas, P., Morgan, B.J.T. & McCrea, R.S. (2012). Estimating survival and transition probabilities from aggregate sightings of animals. J. Ornithol., 152, S381-S391.
94. Wearing, H.J., Rohani, P., Cameron, T.C. & Sait, S.M. (2004). The dynamical consequences of developmental variability and demographic stochasticity for host-parasitoid interactions. Am. Nat., 164, 543-558.
95. Wood, S.N. (1994). Obtaining birth and mortality patterns from structured population trajectories. Ecol. Monogr., 64, 23-44.
96. Wood, S.N. (2010). Statistical inference for noisy nonlinear ecological dynamic systems. Nature, 466, 1102-1104.
97. Wood, S.N. & Nisbet, R.M. (1991). Estimation of mortality rates in stage-structured populations. Springer-Verlag, Berlin.
98. Woodward, I. (1982). Modeling Population-Growth in Stage-Grouped Organisms - a Simple Extension to the Leslie Model. Aust. J. Ecol., 7, 389-394.
99. Yamamura, K. (1998). A simple method to estimate insect mortality from field census data: a modification of the Kiritani-Nakasuji-Manly method. Res. Popul. Ecol., 40, 335-340.
100. Zuidema, P.A., Brienen, R.J.W., During, H.J. & Güneralp, B. (2009). Do persistently fast-growing juveniles contribute disproportionately to population growth? A new analysis tool for matrix models and its application to rainforest trees. Am Nat, 174, 709-719.