A learning-based model of territory establishment

Citation
Ja. Stamps et Vv. Krishnan, A learning-based model of territory establishment, Q REV BIOL, 74(3), 1999, pp. 291-318
Citations number
205
Categorie Soggetti
Biology
Journal title
QUARTERLY REVIEW OF BIOLOGY
ISSN journal
00335770 → ACNP
Volume
74
Issue
3
Year of publication
1999
Pages
291 - 318
Database
ISI
SICI code
0033-5770(199909)74:3<291:ALMOTE>2.0.ZU;2-U
Abstract
Despite widespread interest in territorial behavior, the processes by which animals establish territories are still poorly understood. We present a ne w learning-based model of territory establishment for species in which indi viduals set up territories within large patches of spatially heterogeneous habitat. The model is based on the simple assumptions that individuals tend to return to areas in which they previously had rewarding experiences and, conversely, tend to avoid areas in which they previously engaged in costly aggressive interactions. The literature on learning and territorial establ ishment suggests that these assumptions are probably valid for many animals . individual-based, spatially-explicit simulations of settlement behavior i ncorporating the assumptions of this model generate a number of phenomena c omparable to those observed in territorial animals, including the formation of stable home ranges within large patches of uniform quality habitat, inc reases in territory size and home range exclusivity if settlers interact ag gressively with one another, greater benefits of aggressive behavior if ind ividuals settle at high density than if they settle at low density higher s uccess for residents when they compete with new corners for the same space (the "prior residency advantage"), and the avoidance by newcomers of areas used by previous residents. Although the model needs further refinement to generate some phenomena observed in territorial species, our results sugges t that the processes responsible Sor generating several basic components of territorial behavior may be simpler than is currently supposed.