1. 1 Bird rarity in terra-firme forest: reality or imperfect detection? Marconi Campos Cerqueira Gonçalo Ferraz, Claudeir Vargas, Christian Borges, Thiago Vernucci, Angela Midori, Marcelo Santos, Monica Ribas, Mario Conh-Haft Fotos:Erik Johnson

2. 2 Rare species play such a central role in conservation biology that the discipline has been defined as the science of scarcity and diversity “Soulé, 1986 “ Rare species play such a central role in conservation biology that the discipline has been defined as the science of scarcity and diversity “Soulé, 1986 Hypothesis: Hypothesis: - The ornithologist`s perception of commonness and rarity reflect the truth about forest patch occupancy by birds. - The ornithologist`s perception of commonness and rarity reflect the truth about forest patch occupancy by birds. Conservation implications: Conservation implications: - some common species may be mistakenly classified as rare. - some common species may be mistakenly classified as rare.

3. 3  Rarity as occupancy : - Proportion of occupied sites, or occupancy, instead of abundance. - Positive interspecific correlation between occupancy and abundance – not only obvious but well documented (Gaston and Blackburn 1999) - Locally abundant species tend to occupy more places than rare ones. -

4. 4 Fiuí fiu fiitri

5. 5 ? ? ? Detection is not perfect

6. 6 Trogons Puffbirds Jacamars Ovenbirds Woodcreepers

7. 7 Antthrushes Wrens Vireos Antwrens Antshrikes

8. 8 Study area

9. 9  2 weeks of survey: 1 st week – Ten trained researches make 3-minute point counts in ten different trails simultaneously. 660 point counts 1980 min of observation 330 km collective walk 2 nd week- playback of 16 species` songs in one more visit per point by one observer. Methodology

10. 10 Improve and measure observer skills Improve and measure observer skills  Prior training in the field  Memory training with ‘electronic flashcards’  Memory retention test -> observer score  Observer score as covariate of detection

11. 11 Analysis Parameter estimation(Occupancy and detection) Parameter estimation(Occupancy and detection) Maximum likelihood Maximum likelihood Hypothesis testing Hypothesis testing Models selection AIC Models selection AIC the model with lowest AIC value provides the most parsimonious and best approximation of information contained in the data the model with lowest AIC value provides the most parsimonious and best approximation of information contained in the data 1 1 (Burnham e Anderson 1998)

12. 12 Detection probabilities Example: Bucco tamatia ModelΔ AIC Ψ(.) p(playback+time)0 Ψ(.) p(playback+time+score)2 Ψ(.) p(playback)31.2 Ψ(.) p(playback+score)33.2 Ψ(.) p(time)37.2 Ψ(.) p(time+score)37.69 Ψ(.) p(.)51.17 Ψ(.) p(score)51.22 p(playback+time) Naive estimate = 0,25 Estimated occupancy = 0,38 ± 0.09

13. 13 Time of the day × detection (given presence)

14. Observer score × detection (given presence)

15. Playback × detection (given presence)

16. Occupancy for 20 species – final estimate

17.  So… are the birders getting it?

19. 19 Rarity team Sure they are getting it!

20. 20 Apoio

21. 21 Obrigado, thanks! marconi_cerqueira@yahoo.com.br