(3573) 0.70 (622883) 0.02 (26236) 0.56 (379679) 3.03 .34 4.three 2. three.0 two.0 two.three two.6 two.six two.0 204 0.9 (57830) 0.02 (37594) 2.43 three.chimp ID Zsexbirth yearstart yearend yeargroup hunt prob. when ID presentgroup
(3573) 0.70 (622883) 0.02 (26236) 0.56 (379679) three.03 .34 four.3 two. three.0 two.0 two.3 2.6 2.6 2.0 204 0.9 (57830) 0.02 (37594) 2.43 3.chimp ID Zsexbirth yearstart yearend yeargroup hunt prob. when ID presentgroup hunt prob. when ID Absentodds ratiopvalue 0.0002 0.00002 0.04 0.003 0.05 0.03 0.0 0.007 0.hunt participation higher ( s.e.) than mean for age Y Ya Yb Y Y N N N Nhunted first a lot more than expected Y Y N data not accessible Y N N N NKanyawaraAJMKasekelaMS AOM M975FGMFRMPX SLM M977MitumbaZS EVAM F993abIn two of 3 age classes. In later years.rstb.royalsocietypublishing.orgPhil. Trans. R. Soc. B 370:(a) .0.9 hunt participation probability 0.8 0.7 0.six 0.five 0.4 0.3 0.two 0. 0 60 5 60 25 260 35 360 male age (years) 4(b).0 0.9 0.eight 0.rstb.royalsocietypublishing.orgkill probability0.six 0.5 0.4 0.3 0.two 0. 0 60 5 60 25 260 35 360 male age (years) 4Phil. Trans. R. Soc. B 370:Figure two. Person (a) hunting and (b) killing probability at Kanyawara. Lines represent MedChemExpress GSK583 predicted values from the GLMMs described in the text, with s.e. error bars. Open triangles represent observed values for MS, solid circles for AJ.(a) (b).0 0.9 0..0 0.9 0.8 0.hunt participation probability0.7 0.6 0.five 0.4 0.3 0.2 0. 0 60 five 60 25 260 35 360 male age (years) 4kill probability0.six 0.5 0.4 0.three 0.two 0. PubMed ID:https://www.ncbi.nlm.nih.gov/pubmed/22029416 0 60 five 60 25 260 35 360 male age (years) 4Figure three. Individual (a) hunting and (b) killing probability at Kasekela. Lines represent predicted values in the GLMMs described inside the text, with s.e. error bars. Open triangles represent observed values for FG, strong circles for FR, and strong squares for AO.than the agespecific value predicted by the GLM described above. Nevertheless, FG, FR and AO did exhibit larger than anticipated hunting probabilities. As a 25yearold, FG’s hunting probability was 38 higher than the imply (figure 3a, open triangles). As a 26 to 30yearold, it was 203 higher than the mean. There are actually no information for FG as a younger male, and he died at age 29. FR (figure 3a, closed circles), who was followed for his complete life, exhibited substantially larger hunting probability at all ages, ranging from 96 to 322 larger than the mean. AO (figure 3a, closed squares) exhibited probabilities greater than the imply as a primeaged (25, 260) and older (35) male, but not as a younger male. He died at age 34. We therefore classified FR as an influence hunter for his whole life, whereas AO was an influence hunter only in his prime. We usually do not know FG’s early behaviour, but he was an effect hunter in the end of his life. Z 2.88, p 0.03), which had the highest probability of hunting (0.5). Having said that, there had been no considerable differences among hunting probabilities of to 5yearolds and males aged 25 (0.three) or 260 (0.). Males aged 35 have been drastically much less probably to hunt (0.02) than other all age classes (all p , 0.02) except 6 to 0yearolds ( p 0.30). There were only two hunts at which a male older than 36 was present; we excluded these information because the model did not converge. In sum, person hunting probability was lower for males in Mitumba than Kasekela, but males reached peak hunting prices by 5 years old. There was quite little variation in female hunting probability by age. With all the exception of 26 to 30yearolds, which had drastically reduced hunting probability than all younger and older age classes (all p , 0.03), there have been no considerable differences amongst age classes (all p . 0.05). Overall, mean female hunting probability was 0.08 (range 0.03.). Female EVA had an individual hunting probabili.