TY - JOUR
T1 - The social nestwork
T2 - Tree structure determines nest placement in Kenyan weaverbird colonies
AU - Echeverry-Galvis, Maria Angela
AU - Peterson, Jennifer K.
AU - Sulo-Caceres, Rajmonda
N1 - Funding Information:
The work was part of a collaborative effort between Princeton University and the University of Illinois at Chicago during the Computational Population Biology Course in the spring of 2010. We thank the staff at Mpala Research Centre, Laikipia, Kenya; Professors D. I. Rubenstein, I. Couzin (Princeton University), and T. Berger-Wolf (University of Illinois at Chicago); Kenya Ministry of Education, Science and Technology (research permit MOST 31/001/29C 80Vol.11 to D. I. Rubenstein). We also thank two anonymous reviews for comments on an earlier version.
PY - 2014/2/13
Y1 - 2014/2/13
N2 - Group living is a life history strategy employed by many organisms. This strategy is often difficult to study because the exact boundaries of a group can be unclear. Weaverbirds present an ideal model for the study of group living, because their colonies occupy a space with discrete boundaries: a single tree. We examined one aspect of group living. nest placement, in three Kenyan weaverbird species: the Black-capped Weaver (Pseudonigrita cabanisi), Grey-capped Weaver (P. arnaudi) and White-browed Sparrow Weaver (Ploceropasser mahali). We asked which environmental, biological, and/or abiotic factors influenced their nest arrangement and location in a given tree. We used machine learning to analyze measurements taken from 16 trees and 516 nests outside the breeding season at the Mpala Research Station in Laikipia Kenya, along with climate data for the area. We found that tree architecture, number of nests per tree, and nest-specific characteristics were the main variables driving nest placement. Our results suggest that different Kenyan weaverbird species have similar priorities driving the selection of where a nest is placed within a given tree. Our work illustrates the advantage of using machine learning techniques to investigate biological questions.
AB - Group living is a life history strategy employed by many organisms. This strategy is often difficult to study because the exact boundaries of a group can be unclear. Weaverbirds present an ideal model for the study of group living, because their colonies occupy a space with discrete boundaries: a single tree. We examined one aspect of group living. nest placement, in three Kenyan weaverbird species: the Black-capped Weaver (Pseudonigrita cabanisi), Grey-capped Weaver (P. arnaudi) and White-browed Sparrow Weaver (Ploceropasser mahali). We asked which environmental, biological, and/or abiotic factors influenced their nest arrangement and location in a given tree. We used machine learning to analyze measurements taken from 16 trees and 516 nests outside the breeding season at the Mpala Research Station in Laikipia Kenya, along with climate data for the area. We found that tree architecture, number of nests per tree, and nest-specific characteristics were the main variables driving nest placement. Our results suggest that different Kenyan weaverbird species have similar priorities driving the selection of where a nest is placed within a given tree. Our work illustrates the advantage of using machine learning techniques to investigate biological questions.
UR - https://www.scopus.com/pages/publications/84895812985
U2 - 10.1371/journal.pone.0088761
DO - 10.1371/journal.pone.0088761
M3 - Article
C2 - 24551157
AN - SCOPUS:84895812985
SN - 1932-6203
VL - 9
JO - PLoS ONE
JF - PLoS ONE
IS - 2
M1 - e88761
ER -