» Articles » PMID: 16794832

Hierarchical Patch Dynamics and Animal Movement Pattern

Overview
Journal Oecologia
Date 2006 Jun 24
PMID 16794832
Citations 24
Authors
Affiliations
Soon will be listed here.
Abstract

In hierarchical patch systems, small-scale patches of high density are nested within large-scale patches of low density. The organization of multiple-scale hierarchical systems makes non-random strategies for dispersal and movement particularly important. Here, we apply a new method based on first-passage time on the pathway of a foraging seabird, the Antarctic petrel (Thalassoica antarctica), to quantify its foraging pattern and the spatial dynamics of its foraging areas. Our results suggest that Antarctic petrels used a nested search strategy to track a highly dynamic hierarchical patch system where small-scale patches were congregated within patches at larger scales. The birds searched for large-scale patches by traveling fast and over long distances. Once within a large-scale patch, the birds concentrated their search to find smaller scale patches. By comparing the pathway of different birds we were able to quantify the spatial scale and turnover of their foraging areas. On the largest scale we found foraging areas with a characteristic scale of about 400 km. Nested within these areas we found foraging areas with a characteristic scale of about 100 km. The large-scale areas disappeared or moved within a time frame of weeks while the nested small-scale areas disappeared or moved within days. Antarctic krill (Euphausia superba) is the dominant food item of Antarctic petrels and we suggest that our findings reflect the spatial dynamics of krill in the area.

Citing Articles

A Layered, Hybrid Machine Learning Analytic Workflow for Mouse Risk Assessment Behavior.

Wang J, Karbasi P, Wang L, Meeks J eNeuro. 2022; 10(1).

PMID: 36564214 PMC: 9833056. DOI: 10.1523/ENEURO.0335-22.2022.


Seasonal variation and group size affect movement patterns of two pelagic dolphin species (Lagenorhynchus obscurus and Delphinus delphis).

Dans S, Luzenti E, Coscarella M, Joo R, Degrati M, Curcio N PLoS One. 2022; 17(11):e0276623.

PMID: 36350829 PMC: 9645598. DOI: 10.1371/journal.pone.0276623.


Bridging landscape ecology and urban science to respond to the rising threat of mosquito-borne diseases.

Kache P, Santos-Vega M, Stewart-Ibarra A, Cook E, Seto K, Diuk-Wasser M Nat Ecol Evol. 2022; 6(11):1601-1616.

PMID: 36303000 DOI: 10.1038/s41559-022-01876-y.


Active acoustic telemetry tracking and tri-axial accelerometers reveal fine-scale movement strategies of a non-obligate ram ventilator.

Meese E, Lowe C Mov Ecol. 2020; 8:8.

PMID: 32071719 PMC: 7011439. DOI: 10.1186/s40462-020-0191-3.


Acoustic evaluation of behavioral states predicted from GPS tracking: a case study of a marine fishing bat.

Hurme E, Gurarie E, Greif S, Herrera M L, Flores-Martinez J, Wilkinson G Mov Ecol. 2019; 7:21.

PMID: 31223482 PMC: 6567457. DOI: 10.1186/s40462-019-0163-7.


References
1.
Stommel H . Varieties of Oceanographic Experience: The ocean can be investigated as a hydrodynamical phenomenon as well as explored geographically. Science. 1963; 139(3555):572-6. DOI: 10.1126/science.139.3555.572. View

2.
Viswanathan G, Buldyrev S, Havlin S, da Luz M, Raposo E, Stanley H . Optimizing the success of random searches. Nature. 1999; 401(6756):911-4. DOI: 10.1038/44831. View

3.
Fritz H, Said S, Weimerskirch H . Scale-dependent hierarchical adjustments of movement patterns in a long-range foraging seabird. Proc Biol Sci. 2003; 270(1520):1143-8. PMC: 1691358. DOI: 10.1098/rspb.2003.2350. View

4.
Grunbaum D . Using spatially explicit models to characterize foraging performance in heterogeneous landscapes. Am Nat. 2008; 151(2):97-113. DOI: 10.1086/286105. View

5.
Kareiva P, Shigesada N . Analyzing insect movement as a correlated random walk. Oecologia. 2017; 56(2-3):234-238. DOI: 10.1007/BF00379695. View