1932
0
  1. Home
  2. A-Z Publications
  3. Annual Review of Ecology, Evolution, and Systematics
  4. Volume 48, 2017
  5. Article

Annual Review of Ecology, Evolution, and Systematics

Volume 48, 2017

Process-Based Models of Phenology for Plants and Animals

Abstract

Phenology is a key aspect of plant and animal life strategies that determines the ability to capture seasonally variable resources. It defines the season and duration of growth and reproduction and paces ecological interactions and ecosystem functions. Phenology models have become a key component of models in agronomy, forestry, ecology, and biogeosciences. Plant and animal process-based phenology models have taken different paths that have so far not crossed. Yet, they share many features because plant and animal annual cycles also share many characteristics, from their stepwise progression, including a resting period, to their dependence on similar environmental factors. We review the strengths and shortcomings of these models and the divergences in modeling approaches for plants and animals, which are mostly due to specificities of the questions they tackle. Finally, we discuss the most promising avenues and the challenges phenology modeling needs to address in the upcoming years.

Keyword(s): annual cycle, diapause, dormancy, environmental control, extratropical species, parameter estimation, response functions
Loading

Article metrics loading...

/content/journals/10.1146/annurev-ecolsys-110316-022706
2017-11-02
2024-04-25
Loading full text...

Full text loading...

/deliver/fulltext/ecolsys/48/1/annurev-ecolsys-110316-022706.html?itemId=/content/journals/10.1146/annurev-ecolsys-110316-022706&mimeType=html&fmt=ahah

Literature Cited

  1. Alberto FJ, Aitken SN, Alia R, Gonzalez-Martinez SC, Hänninen H. et al. 2013. Potential for evolutionary responses to climate change evidence from tree populations. Glob. Change Biol. 19:61645–61 [Google Scholar]
  2. Alderman PD, Stanfill B. 2017. Quantifying model-structure- and parameter-driven uncertainties in spring wheat phenology prediction with Bayesian analysis. Eur. J. Agron. 88:1–9 [Google Scholar]
  3. Anderson JJ, Gugarie E, Bracis C, Burke BJ, Laidre KL. 2013. Modeling climate change impacts on phenology and population dynamics of migratory marine species. Ecol. Model. 264:83–97 [Google Scholar]
  4. Asbury DA, Angiletta MJ Jr.. 2010. Thermodynamic effects on the evolution of performance curves. Am. Nat. 176:E40–49 [Google Scholar]
  5. Basler D. 2016. Evaluating phenological models for the prediction of leaf-out dates in six temperate tree species across central Europe. Agric. For. Meteorol. 217:10–21 [Google Scholar]
  6. Benech-Arnold RL, Sanchez RA, Forcella F, Kruk BC, Ghersa CM. 2000. Environmental control of dormancy in weed seed banks in soil. Field Crops Res 67:10–122 [Google Scholar]
  7. Bentz BJ, Logan LA, Vandygriff JC. 2001. Latitudinal variation in Dendroctonus ponderosae (Coleoptera: Scolytidae) development time and adult size. Can. Entomol. 133:375–87 [Google Scholar]
  8. Bratzel F, Turck F. 2015. Molecular memories in the regulation of seasonal flowering: from competence to cessation. Genome Biol 16:1192 [Google Scholar]
  9. Brown JH, Gillooly JF, Allen AP, Savage VM, West GB. 2004. Toward a metabolic theory of ecology. Ecology 85:1771–89 [Google Scholar]
  10. Bullied WJ, Bullock PR, Flerchinger GN, Van Acker RC. 2014. Process-based modeling of temperature and water profiles in the seedling recruitment zone: Part II. Seedling emergence timing. Agric. For. Meteorol. 188:104–20 [Google Scholar]
  11. Burghardt LT, Metcalf CJE, Wilczek AM, Schmitt J, Donohue K. 2015. Modeling the influence of genetic and environmental variation on the expression of plant life cycles across landscapes. Am. Nat. 185:2212–27 [Google Scholar]
  12. Caffarra A, Donnelly A, Chuine I. 2011a. Modelling the timing of Betula pubescens budburst. II. Integrating complex effects of photoperiod into process-based models. Clim. Res. 46:2159–70 [Google Scholar]
  13. Caffarra A, Donnelly A, Chuine I, Jones MB. 2011b. Modelling the timing of Betula pubescens budburst. I. Temperature and photoperiod: a conceptual model. Clim. Res. 46:2147–57 [Google Scholar]
  14. Candy SG. 1991. Modeling insect phenology using ordinal regression and continuation models. Environ. Entomol. 20:190–95 [Google Scholar]
  15. Charrier G, Ngao J, Saudreau M, Améglio T. 2015. Effects of environmental factors and management practices on microclimate, winter physiology, and frost resistance in trees. Front. Plant Sci. 6:April1–18 [Google Scholar]
  16. Chatfield C. 1988. Problem Solving: A Statistician's Guide London: Chapman & Hall
  17. Chew YH, Smith RW, Jones HJ, Seaton DD, Grima R. 2014. Mathematical models light up plant signaling. Plant Cell 26:5–20 [Google Scholar]
  18. Chuine I. 2010. Why does phenology drive species distribution?. Philos. Trans. R. Soc. B 365:3149–60 [Google Scholar]
  19. Chuine I, Beaubien E. 2001. Phenology is a major determinant of temperate tree range. Ecol. Lett. 4:5500–10 [Google Scholar]
  20. Chuine I, Bonhomme M, Legave J-M, Garcia de Cortazar-Atauri I, Charrier G. et al. 2016. Can phenological models predict tree phenology accurately in the future? The unrevealed hurdle of endodormancy break. Glob. Change Biol. 22:103444–60 [Google Scholar]
  21. Chuine I, Cour P, Rousseau DD. 1999. Selecting models to predict the timing of flowering of temperate trees: implications for tree phenology modelling. Plant Cell Environ 22:11–13 [Google Scholar]
  22. Chuine I, Garcia de Cortazar-Atauri I, Kramer K, Hänninen H. 2013. Plant development models. Phenology: An Integrative Environmental Science MD Schwarz 275–93 Dordrecht, Neth.: Springer. , 2nd ed.. [Google Scholar]
  23. Colinet H, Sinclair BJ, Vernon P, Renault D. 2015. Insects in fluctuating thermal environments. Annu. Rev. Entomol. 60:123–40 [Google Scholar]
  24. Considine MJ, Considine JA. 2016. On the language and physiology of dormancy and quiescence in plants. J. Exp. Bot. 67:113189–203 [Google Scholar]
  25. Cooke JEK, Eriksson ME, Junttila O. 2012. The dynamic nature of bud dormancy in trees: environmental control and molecular mechanisms. Plant Cell Environ 35:101707–28 [Google Scholar]
  26. Cooke JEK, Weih M. 2005. Nitrogen storage and seasonal nitrogen cycling in Populus: bridging molecular physiology and ecophysiology. New Phytol 167:19–30 [Google Scholar]
  27. Dahlin KM, Fisher RA, Lawrence PJ. 2015. Environmental drivers of drought deciduous phenology in the Community Land Model. Biogeosciences 12:5061–74 [Google Scholar]
  28. de Jong G, van der Have TM. 2009. Temperature dependence of development rate, growth rate and size: from biophysics to adaptation. Phenotypic Plasticity of Insects: Mechanisms and Consequences DW Whitman, TN Ananthakrishnan 523–88 Enfield, NH: Science [Google Scholar]
  29. de Reaumur RAF. 1735. Observations du thermomètre, faites à Paris pendant l'année 1735, comparées avec celles qui ont été faites sous la ligne, à l'isle de France, à Alger et quelques unes de nos isles de l'Amérique. Mem. Paris Acad. Sci. 1735545 ff.
  30. DeAngelis DL, Grimm V. 2014. Individual-based models in ecology after four decades. F1000Prime Rep 6:39 [Google Scholar]
  31. Delisle J, Royer L, Bernier-Cardou M, Bauce E, Labrecque A. 2009. The combined effect of photoperiod and temperature on egg dormancy in an island and mainland population of the hemlock looper, Lambdina fiscellaria.. Entomol. Exp. Appl. 133:232–43 [Google Scholar]
  32. Delpierre N, Dufrêne E, Soudani K, Ulrich E, Cecchini S. et al. 2009a. Modelling interannual and spatial variability of leaf senescence for three deciduous tree species in France. Agric. For. Meteorol. 149:6938–48 [Google Scholar]
  33. Delpierre N, Soudani K, Francois C, Kostner B, Pontailler JY. et al. 2009b. Exceptional carbon uptake in European forests during the warm spring of 2007: a data-model analysis. Glob. Change Biol. 15:61455–74 [Google Scholar]
  34. Delpierre N, Vitasse Y, Chuine I, Guillemot J, Bazot S. et al. 2016. Temperate and boreal forest tree phenology: from organ-scale processes to terrestrial ecosystem models. Ann. For. Sci. 73:5–25 [Google Scholar]
  35. Deutsch CA, Tewksbury JJ, Huey RB, Sheldon KS, Ghalambor CK. et al. 2008. Impacts of climate warming on terrestrial ectotherms across latitude. PNAS 105:6668–72 [Google Scholar]
  36. Donohue K, Burghardt LT, Runcie D, Bradford KJ, Schmitt J. 2015. Applying developmental threshold models to evolutionary ecology. Trends Ecol. Evol. 30:266–77 [Google Scholar]
  37. Duman JG. 2001. Antifreeze and ice nucleator proteins in terrestrial arthropods. Annu. Rev. Physiol. 63:327–57 [Google Scholar]
  38. Duputié A, Rutschmann A, Ronce O, Chuine I. 2015. Consequences of phenological plasticity for range width and niche breadth of temperate tree species under current and future climates. Glob. Change Biol. 21:3062–75 [Google Scholar]
  39. Fantinou AA, Perdikis DC, Zota KF. 2004. Reproductive responses to photoperiod and temperature by diapausing and non-diapausing populations of Sesamia nonagrioides Lef. (Lepidoptera–Noctuidae). Physiol. Entomol. 29:169–75 [Google Scholar]
  40. Forcella F, Benech Arnold RL, Sanchez R, Ghersa CM. 2000. Modeling seedling emergence. Field Crops Res 67:2123–39 [Google Scholar]
  41. Forkel M, Carvalhais N, Schaphoff S, Bloh W, Migliavacca M. et al. 2014. Identifying environmental controls on vegetation greenness phenology through model–data integration. Biogeosciences 11:7025–50 [Google Scholar]
  42. Fracheboud Y, Luquez V, Björkén L, Sjödin A, Tuominen H, Jansson S. 2009. The control of autumn senescence in European aspen. Plant Physiol 149:41982–91 [Google Scholar]
  43. Fu YH, Campioli M, Van Oijen M, Deckmyn G, Janssens IA. 2012. Bayesian comparison of six different temperature-based budburst models for four temperate tree species. Ecol. Model. 230:92–100 [Google Scholar]
  44. Gallinat AS, Primack RB, Wagner DL. 2015. Autumn, the neglected season in climate change research. Trends Ecol. Evol. 30:3169–76 [Google Scholar]
  45. Godfray HCJ, Hassel MP, Holt RD. 1994. The population dynamics consequences of phenological asynchrony between parasitoids and their hosts. J. Anim. Ecol. 63:1–10 [Google Scholar]
  46. Gray DR, Ravlin FW, Braine JA. 2001. Diapause in the gypsy moth: a model of inhibition and development. J. Insect Physiol. 47:173–84 [Google Scholar]
  47. Hand SC, Denlinger DL, Podrabsky JE, Roy R. 2016. Mechanisms of animal diapause: recent developments from nematodes, crustaceans, insects, and fish. Am. J. Physiol. Regul. Integr. Comp. Physiol. 310:R1193–211 [Google Scholar]
  48. Hänninen H. 1990. Modelling bud dormancy release in trees from cool and temperate regions. Acta For. Fenn. 213:159–65 [Google Scholar]
  49. Hänninen H, Kramer K. 2007. A framework for modelling the annual cycle of trees in boreal and temperate regions. Silva Fenn 41:1167–205 [Google Scholar]
  50. Hikosaka K, Ishikawa K, Borjigidai A, Muller O, Onoda Y. 2006. Temperature acclimation of photosynthesis: mechanisms involved in the changes in temperature dependence of photosynthetic rate. J. Exp. Bot. 57:2291–302 [Google Scholar]
  51. Hodek I. 1996. Diapause development, diapause termination and the end of diapause. Eur. J. Entomol. 93:475–87 [Google Scholar]
  52. Hogg EH, Price DT, Black TA. 2000. Postulated feedbacks of deciduous forest phenology on seasonal climate patterns in the western Canadian interior. J. Clim. 13:4229–43 [Google Scholar]
  53. Jaworski T, Hilszczanski J. 2013. The effect of temperature and humidity changes on insect development and their impact on forest ecosystems in the context of climate change. For. Res. Pap. 74:345–55 [Google Scholar]
  54. Jia P, Lu L, Chen X, Chen J, Guo J. et al. 2016. A climate-driven mechanistic population model of Aedes albopictus with diapause. Parasites Vectors 9:175–90 [Google Scholar]
  55. Johansson J, Kristensen NP, Nilsson J-Å, Jonz N. 2015. The eco-evolutionary consequences of interspecific phenological asynchrony—a theoretical perspective. Oikos 124:102–12 [Google Scholar]
  56. Kang DS, Cotton MA, Denlinger DL, Sim C. 2016. Comparative transcriptomics reveals key gene expression differences between diapausing and nondiapausing adults of Culex pipiens. PLOS ONE 11:e0154892 [Google Scholar]
  57. Kingsolver JG. 2009. The well-temperatured biologist. Am. Nat. 174:755–68 [Google Scholar]
  58. Knies JI, Kingsolver JG. 2010. Erroneous Arrhenius: Modified Arrhenius model best explains the temperature dependence of ectotherm fitness. Am. Nat. 176:227–33 [Google Scholar]
  59. Koenig WD, Liebhold AM. 2013. Avian predation pressure as a potential driver of periodical cicada cycle length. Am. Nat. 181:145–49 [Google Scholar]
  60. Kogan M. 1998. Integrated pest management: historical perspective and contemporary developments. Annu. Rev. Entomol. 43:243–70 [Google Scholar]
  61. Kozlowski TT, Pallardy SG. 2002. Acclimation and adaptive responses of woody plants to environmental stresses. Bot. Rev. 68:2270–334 [Google Scholar]
  62. Kramer K. 1994. Selecting a model to predict the onset of growth of Fagus sylvatica. J. Appl. Ecol. 31:172–81 [Google Scholar]
  63. Kudoh H. 2016. Molecular phenology in plants: innatura systems biology for the comprehensive understanding of seasonal responses under natural environments. New Phytol 210:2399–412 [Google Scholar]
  64. Lang GA, Early JD, Martin GC, Darnell RL. 1987. Endo-, para-, and ecodormancy: physiological terminology and classification for dormancy research. HortScience 22:371–77 [Google Scholar]
  65. Lobo JM. 2016. The use of occurrence data to predict the effects of climate change on insects. Curr. Opin. Insect Sci. 17:62–68 [Google Scholar]
  66. Logan JA. 1988. Toward an expert system for development of pest simulation models. Environ. Entomol. 17:359–76 [Google Scholar]
  67. Logan JA, Régnière J, Powell JA. 2003. Assessing the impacts of global warming on forest pest dynamics. Front. Ecol. Environ. 1:130–37 [Google Scholar]
  68. Maino JL, Kong JD, Hoffman AA, Barton MG, Kearny MR. 2016. Mechanistic models for predicting insect responses to climate change. Curr. Opin. Insect Sci. 17:81–86 [Google Scholar]
  69. Marçais B, Desprez-Loustau ML. 2014. European oak powdery mildew: impact on trees, effects of environmental factors, and potential effects of climate change. Ann. For. Sci. 71:633–42 [Google Scholar]
  70. Meuti ME, Denlinger DL. 2013. Evolutionary link between circadian clocks and photoperiodic diapause in insects. Integr. Comp. Biol. 53:131–43 [Google Scholar]
  71. Midmore EK, McCartan SA, Jinks RL, Cahalan CM. 2015. Using thermal time models to predict germination of five provenances of silver birch (Betula pendula Roth) in southern England. Silva Fenn 49:21266 [Google Scholar]
  72. Misson L, Degueldre D, Collin C, Rodriguez R, Rocheteau A. et al. 2011. Phenological responses to extreme droughts in a Mediterranean forest. Glob. Change Biol. 17:21036–48 [Google Scholar]
  73. Moustakas A, Evans ER. 2013. Integrating evolution into ecological modelling: accommodating phenotypic changes in agent-based models. PLOS ONE 8:e71125 [Google Scholar]
  74. Murillo-Rincón AP, Kolter NA, Laurila A, Orizaola G. 2017. Intraspecific priority effects modify compensatory responses to changes in hatching phenology in an amphibian. J. Anim. Ecol. 86:128–35 [Google Scholar]
  75. Mutke S, Ordo JG, Liment JC, Il LG. 2003. Shoot growth and phenology modelling of grafted Stone pine (Pinus pinea L.) in inner Spain. Ann. For. Sci. 60:527–37 [Google Scholar]
  76. Nagano AJ, Sato Y, Mihara M, Antonio BA, Motoyama R, Itoh H. 2012. Theory deciphering and prediction of transcriptome dynamics under fluctuating field conditions. Cell 151:61358–69 [Google Scholar]
  77. Niehaus AC, Angiletta MJ, Sears MW, Franklin CE, Wilson RS. 2012. Predicting the physiological performance of ectotherms in fluctuating thermal environments. J. Exp. Biol. 215:694–701 [Google Scholar]
  78. Nissanka SP, Karunaratne AS, Perera R, Weerakoon WMW, Thorburn PJ, Wallach D. 2015. Calibration of the phenology sub-model of APSIM-Oryza: going beyond goodness of fit. Environ. Model. Softw. 70:128–37 [Google Scholar]
  79. O'Brien C, Bradshaw WE, Holzapfel CM. 2011. Testing for causality in covarying traits: genes and latitude in a molecular world. Mol. Ecol. 20:2471–76 [Google Scholar]
  80. Olsson C, Bolmgren K, Lindström J, Maria A. 2013. Performance of tree phenology models along a bioclimatic gradient in Sweden. Ecol. Model. 266:103–17 [Google Scholar]
  81. Palacio S, Hester AJ, Maestro M, Millard P. 2013. Simulated browsing affects leaf shedding phenology and litter quality of oak and birch saplings. Tree Physiol 33:4438–45 [Google Scholar]
  82. Parton WJ, Logan JA. 1981. A model for diurnal variation in soil and air temperature. Agric. Meteorol. 23:205–16 [Google Scholar]
  83. Petterle A, Karlberg A, Bhalerao RP. 2013. Daylength mediated control of seasonal growth patterns in perennial trees. Curr. Opin. Plant Biol. 16:3301–6 [Google Scholar]
  84. Pörtner HO, Bennett AF, Bozinovic F, Clarke A, Lardies MA. et al. 2006. Trade-offs in thermal adaptation: the need for a molecular to ecological integration. Physiol. Biochem. Zool. 79:295–313 [Google Scholar]
  85. Powell JA, Logan JA. 2005. Insect seasonality: circle map analysis of temperature-driven life cycles. Theor. Popul. Biol. 67:161–79 [Google Scholar]
  86. Press WH, Flannery BP, Teukolsky SA, Vetterling WT. 1989. Numerical Recipes in Pascal Cambridge, UK: Cambridge Univ. Press
  87. Pureswaran DS, De Grandpré L, Paré D, Taylor A, Barrette M. et al. 2015. Climate-induced changes in host tree–insect phenology may drive ecological state-shift in boreal forests. Ecology 96:1480–91 [Google Scholar]
  88. Redaubo F, Le Rouzic A, Dupas S, Silvain J-F, Harry M, Dangles O. 2013. SimAdapt: an individual-based genetic model for simulating landscape management impacts on populations. Methods Ecol. Evol. 4:595–600 [Google Scholar]
  89. Régnière J, Bentz BJ, Powell JA, St-Amant R. 2015. Individual-based modeling: mountain pine beetle seasonal biology in response to climate. Simulation Modeling of Forest Landscape Disturbances AH Perera, BR Sturtevant, LJ Buse 135–64 Dordrecht, Neth.: Springer [Google Scholar]
  90. Régnière J, Nealis V, Porter K. 2009. Climate suitability and management of the gypsy moth invasion into Canada. Biol. Invasions 11:135–48 [Google Scholar]
  91. Régnière J, Powell JA. 2013. Animal life cycle models (Poikilotherms). Phenology: An Integrative Environmental Science MD Schwartz 295–315 Dordrecht, Neth.: Springer. , 2nd ed.. [Google Scholar]
  92. Régnière J, Powell J, Bentz B, Nealis V. 2012a. Effects of temperature on development, survival and reproduction of insects: experimental design, data analysis and modeling. J. Insect Physiol. 58:634–47 [Google Scholar]
  93. Régnière J, St-Amant R, Duval P. 2012b. Predicting insect distributions under climate change physiological responses: spruce budworm as an example. Biol. Invasions 14:1571–86 [Google Scholar]
  94. Richardson AD, Anderson RS, Arain MA, Barr AG, Bohrer G. et al. 2012. Terrestrial biosphere models need better representation of vegetation phenology: results from the North American Carbon Program Site Synthesis. Glob. Change Biol. 18:2566–84 [Google Scholar]
  95. Richardson AD, Keenan TF, Migliavacca M, Ryu Y, Sonnentag O, Toomey M. 2013. Climate change, phenology, and phenological control of vegetation feedbacks to the climate system. Agric. For. Meteorol. 169:156–73 [Google Scholar]
  96. Ridsdill-Smith J, Pavri C, De Boer E, Kriticos D. 2005. Predictions of summer diapause in the redlegged earth mite, Halotydeus destructor (Acari: Pethaleidea), in Australia. J. Insect Physiol. 51:717–26 [Google Scholar]
  97. Román-Paoli E, Welch S, Vanderlip R. 2000. Comparing genetic coefficient estimation methods using the CERES-Maize model. Agric. Syst. 65:129–41 [Google Scholar]
  98. Rossi S, Isabel N. 2016. Bud break responds more strongly to daytime than nighttime temperature under asymmetric experimental warming. Glob. Change Biol. 23:446–54 [Google Scholar]
  99. Sarvas R. 1972. Investigations on the annual cycle of development on forest trees active period. Commun. Inst. For. Fenn. 76:3110 [Google Scholar]
  100. Satake A, Kawagoe T, Saburi Y, Chiba Y, Sakurai G, Kudoh H. 2013. Forecasting flowering phenology under climate warming by modelling the regulatory dynamics of flowering-time genes. Nat. Commun. 4:2303 [Google Scholar]
  101. Saunders DS. 2014. Insect photoperiodism: effects of temperature on the induction of insect diapause and diverse roles for the circadian system in the photoperiodic response. Entomol. Sci. 17:25–40 [Google Scholar]
  102. Schaber J, Badeck FW. 2003. Physiology-based phenology models for forest tree species in Germany. Int. J. Biometeorol. 47:4193–201 [Google Scholar]
  103. Schoolfield RM, Sharpe PJH, Magnuson CE. 1981. Non-linear regression of biological temperature-dependent rate models based on absolute reaction-rate theory. J. Theor. Biol. 88:719–31 [Google Scholar]
  104. Schramski JR, Dell AI, Grady JM, Sibly RM, Brown JH. 2015. Metabolic theory predicts whole-ecosystem properties. PNAS 112:2617–22 [Google Scholar]
  105. Seebacher F, White CR, Franklin CE. 2015. Physiological plasticity increases resilience of ectothermic animals to climate change. Nat. Clim. Change 5:61–66 [Google Scholar]
  106. Seehausen ML, Régnière J, Martel V, Smith SM. 2017. Developmental and reproductive responses of the spruce budworm (Lepidoptera: Tortricidae) parasitoid Tranosema rostrale (Hymenoptera: Ichneumonidae) to temperature. J. Insect Physiol. 98:38–46 [Google Scholar]
  107. Sidaway-Lee K, Josse EM, Brown A, Gan Y, Halliday KJ. et al. 2010. SPATULA links daytime temperature and plant growth rate. Curr. Biol. 20:161493–97 [Google Scholar]
  108. Singh RK, Svystun T, AlDahmash B, Jönsson AM, Bhalerao RP. 2016. Photoperiod- and temperature-mediated control of phenology in trees—a molecular perspective. New Phytol 213:511–24 [Google Scholar]
  109. Soltani A, Hammer GL, Torabi B, Robertson MJ, Zeinali E. 2006. Modeling chickpea growth and development: phenological development. Field Crops Res 99:11–13 [Google Scholar]
  110. Sorensen JG, Kritensen TN, Overgaard J. 2016. Evolutionary and ecological patterns of thermal acclimation capacity in Drosophila: Is it important for keeping up with climate change. Curr. Opin. Insect Sci. 17:98–104 [Google Scholar]
  111. Speybroeck N, Lindsey PJ, Billiouw M, Madder M, Lindsey JK, Berkvens DL. 2006. Modeling diapause termination of Rhipicephalus appendiculatus using statistical tools to detect sudden behavioral changes and time dependencies. Environ. Ecol. Stat. 13:69–87 [Google Scholar]
  112. Sporleder M, Tonnang HEZ, Carhuapoma P, Gonzales JC, Juarez H, Kroschel J. 2013. Insect Life Cycle Modeling (ILCYM) software—a new tool for regional and global insect pest risk assessments under current and future climate change scenarios. Potential Invasive Pests of Agricultural Crops JE Peña 412–64 Boston: CABI [Google Scholar]
  113. Stillman JH. 2003. Acclimation capacity underlies susceptibility to climate change. Science 301:65 [Google Scholar]
  114. Stoks R, De Block M, Van de Meutter F, Johansson F. 2005. Predation cost of rapid growth: behavioral coupling and physiological decoupling. J. Anim. Ecol. 74:708–15 [Google Scholar]
  115. Stone M. 1977. An asymptotic equivalence of choice of model by cross-validation and Akaike's Criterion. J. R. Stat. Soc. B 39:44–47 [Google Scholar]
  116. Takahashi K, Koike S. 2014. Altitudinal differences in bud burst and onset and cessation of cambial activity of four subalpine tree species. Landsc. Ecol. Eng. 10:2349–54 [Google Scholar]
  117. Thorsen SM, Höglind M. 2010. Modelling cold hardening and dehardening in timothy. Sensitivity analysis and Bayesian model comparison. Agric. For. Meteorol. 150:121529–42 [Google Scholar]
  118. Tran K, Ylioja T, Billings R, Régnière J, Ayres MP. 2007. Impact of minimum winter temperatures on the population dynamics of Dendroctonus frontalis. Ecol. Appl. 17:882–99 [Google Scholar]
  119. van der Schoot C, Rinne PLH. 2011. Dormancy cycling at the shoot apical meristem: Transitioning between self-organization and self-arrest. Plant Sci 180:120–31 [Google Scholar]
  120. Visser ME, Holleman LJ. 2001. Warmer springs disrupt the synchrony of oak and winter moth phenology. Proc. Biol. Sci. 268:1464289–94 [Google Scholar]
  121. Wallach D, Nissanka SP, Karunaratne AS, Weerakoon WMW, Thorburn PJ. et al. 2017. Accounting for both parameter and model structure uncertainty in crop model predictions of phenology: a case study on rice. Eur. J. Agron. 88:53–62 [Google Scholar]
  122. Walters RG. 2005. Towards an understanding of photosynthetic acclimation. J. Exp. Bot. 56:411435–47 [Google Scholar]
  123. Warren J, Topping C. 2001. Trait evolution in an individual-based model of herbaceous vegetation. Evol. Ecol. 15:15–35 [Google Scholar]
  124. Weinberger JH. 1950. Chilling requirements of peach varieties. Proc. Am. Soc. Hort. Sci. 56:122–28 [Google Scholar]
  125. Wilson KB, Baldocchi D. 2000. Seasonal and interannual variability of energy fluxes over a broadleaved temperate deciduous forest in North America. Agric. For. Meteorol. 100:1–18 [Google Scholar]
  126. Xiongbing T, Wang J, Hao K, Whitmann DW, Fan Y. et al. 2015. Transcriptomic and proteomic analysis of pre-diapause and non-diapause eggs of migratory locust, Locusta migratoria L. (Orthoptera: Acridoidaea). Mol. Biol. Evol. 33:2391–95 [Google Scholar]
  127. Yamanaka T, Melson WA, Uchimura K, Bjornstad ON. 2012. Generation separation in simple structured life cycles: models and 48 years of field data on a tea tortrix moth. Am. Nat. 179:95–109 [Google Scholar]
  128. Yang X, Tang J, Mustard JF. 2014. Beyond leaf color: comparing camera-based phenological metrics with leaf biochemical, biophysical, and spectral properties throughout the growing season of a temperate deciduous forest. J. Geophys. Res. Biogeosciences 119:181–91 [Google Scholar]
  129. Zohner CM, Benito BM, Svenning J-C, Renner SS. 2016. Day length unlikely to constrain climate-driven shifts in leaf-out times of northern woody plants. Nat. Clim. Change 6:1120–23 [Google Scholar]
/content/journals/10.1146/annurev-ecolsys-110316-022706
Loading
Process-Based Models of Phenology for Plants and Animals
Annual Review of Ecology, Evolution, and Systematics 48, 159 (2017); https://doi.org/10.1146/annurev-ecolsys-110316-022706
/content/journals/10.1146/annurev-ecolsys-110316-022706
/content/journals/10.1146/annurev-ecolsys-110316-022706
Loading

Data & Media loading...

Supplemental Material

Supplementary Data

  • Download all Supplemental Material as a single PDF. Includes Sections 1-3, Figures 1-3, and References.

  • Article Type: Review Article

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error