Spatial selection and inheritance: applying evolutionary concepts to population dynamics in heterogeneous space.
Organisms in highly suitable sites generally produce more offspring, and offspring can inherit this suitability by not dispersing far. This combination of spatial selection and spatial inheritance acts to bias the distribution of organisms toward suitable sites and thereby increase mean fitness (i.e., per capita population increase). Thus, population growth rates in heterogeneous space change over time by a process conceptually analogous to evolution by natural selection, opening avenues for theoretical cross-pollination between evolutionary biology and ecology. We operationally define spatial inheritance and spatial selective differential and then combine these two factors in a modification of the breeder's equation, derived from simple models of population growth in heterogeneous space. The modified breeder's equation yields a conservative criterion for persistence in hostile environments estimable from field measurements. We apply this framework for understanding gypsy moth population persistence amidst abundant predators and find that the predictions of the modified breeder's equation match initial changes in population growth rate in independent simulation output. The analogy between spatial dynamics and natural selection conceptually links ecology and evolution, provides a spatially implicit framework for modeling spatial population dynamics, and represents an important null model for studying habitat selection.
Schauber
Eric M
EM
Cooperative Wildlife Research Laboratory and Department of Zoology, Southern Illinois University, Carbondale, Illinois 62901, USA. schauber@siu.edu
Goodwin
Brett J
BJ
Jones
Clive G
CG
Ostfeld
Richard S
RS
D005075:Biological Evolution; D017753:Ecosystem; D005828:Genetics, Population; D063245:Plant Development; D018521:Plant Physiological Phenomena; D010944:Plants / Q000235:genetics; D011157:Population Dynamics; D011158:Population Growth; D012641:Selection, Genetic; D013029:Space-Time Clustering