THE RELATIONSHIP BETWEEN PARENTAL DEFENSE INTENSITY AND NEST SITE CHARACTERISTICS IN EURASIAN MAGPIE (PICA PICA L.) - AN ASSESSMENT WITH THE CLASSIFICATION METHODS

Kirazli C., Kilinc K. B., Yamac E.

APPLIED ECOLOGY AND ENVIRONMENTAL RESEARCH, cilt.15, sa.3, ss.1293-1308, 2017 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 15 Sayı: 3
  • Basım Tarihi: 2017
  • Doi Numarası: 10.15666/aeer/1503_12931308
  • Dergi Adı: APPLIED ECOLOGY AND ENVIRONMENTAL RESEARCH
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.1293-1308
  • Anahtar Kelimeler: nest defense, urbanization, offspring conditions, nest volume, binary logistic regression, TIT PARUS-MAJOR, FOOD SUPPLEMENTATION, LIFE-HISTORY, REPRODUCTIVE SUCCESS, BREEDING SUCCESS, BROOD REDUCTION, SEX-DIFFERENCES, OFFSPRING AGE, CLUTCH SIZE, LAYING DATE
  • Anadolu Üniversitesi Adresli: Evet

Özet

Nest defense behavior varies among species and depends on different factors. However little is known of the influence of urbanization and nest characteristics on the nest defense intensity as parental investment of the bird species. We observe the Eurasian magpie Pica pica nests to evaluate their nest defense behavior among those factors including urbanization degree of habitat, and nest characteristics such as nest volume, presence/absence of roof, offspring size and the number of offspring reduction for each breeding stages (incubation, early brooding and post brooding stage). To investigate the relationship between the nest defense behavior of Eurasian magpie and a set of those factors we used classification trees, binary logistic regression and random forest. According to our results nest defense strategies can be temporally changed primarily according to the nest volume with the auxiliary agents mostly a reduction of offspring number, which indicates parental capacities and offspring conditions could shape strongly their own nest defense strategies. Besides, we recommend classification trees, binary logistic regression and random forest modeling approaches to be considered individually or together for predictive mapping for ecosystem scientists.