El-Galaly, Tarec Christoffer3; Pedersen, Martin B.4; Gormsen, Lars Christian5; Juul Mylam, Karen11; Gang, Anne Ortved6; Madsen, Jakob6; Iyer, Victor Vishwanath7; Hendel, Helle Westergren8; Loft, Annika9; Nielsen, Anne Lerberg12; Brown, Peter De Nully10; Hutchings, Martin8; d´Amore, Francesco4
1 Haematology, Department of Clinical Research, Det Sundhedsvidenskabelige Fakultet, SDU2 Clinical Physiology and Nuclear Medicine, Department of Clinical Research, Det Sundhedsvidenskabelige Fakultet, SDU3 Institut for Klinisk Medicin - Hæmatologisk Afd. R, THG4 Aarhus Universitetshospital5 Institut for Klinisk Medicin - Klinisk fysiologi og nuclearmedicin, SKS6 Aalborg Universitetshospital7 Klinik Diagnostik8 Institut for Klinisk Medicin9 Rigshospitalet10 Bachelor- og kandidatuddannelser11 Haematology, Department of Clinical Research, Det Sundhedsvidenskabelige Fakultet, SDU12 Clinical Physiology and Nuclear Medicine, Department of Clinical Research, Det Sundhedsvidenskabelige Fakultet, SDU
Integral Projection Models (IPMs) use information on how an individual's state influences its vital rates - survival, growth and reproduction - to make population projections. IPMs are constructed from regression models predicting vital rates from state variables (e.g., size or age) and covariates (e.g., environment). By combining regressions of vital rates, an IPM provides mechanistic insight into emergent ecological patterns such as population dynamics, species geographic distributions, or life history strategies. Here, we review important resources for building IPMs and provide a comprehensive guide, with extensive R code, for their construction. IPMs can be applied to any stage-structured population; here we illustrate IPMs for a series of plant life histories of increasing complexity and biological realism, highlighting the utility of various regression methods for capturing biological patterns. We also present case studies illustrating how IPMs can be used to predict species’ geographic distributions and life history strategies. IPMs can represent a wide range of life histories at any desired level of biological detail. Much of the strength of IPMs lies in the strength of regression models. Many subtleties arise when scaling from vital rate regressions to population-level patterns, so we provide a set of diagnostics and guidelines to ensure that models are biologically plausible. Moreover, IPMs can exploit a large existing suite of analytical tools developed for Matrix Projection Models.
Blood, 2013, Vol 122, Issue 21, p. 99-110
demography; elasticity; life history; matrix projection model; population growth rate; population projection model; sensitivity; stage structure; vital rates