WCPA-SSC Joint Task Force on Biodiversity and Protected Areas

1. WCPA-SSC Joint Task Force on Biodiversity and Protected Areas Stephen Woodley and Thomas Brooks Co-Chairs, Joint Task Force on Biodiversity and Protected Areas Luigi Boitani (University of Rome) Nigel Dudley (Equilibrium) Gustavo Fonseca (GEF) Jaime Garcia-Moreno (Wetlands International) Marc Hockings (Uni. Queensland) Jon Hutton (UNEP-WCMC) Kathy MacKinnon (World Bank) Paul Matiku (NatureKenya) Kent Redford (WCS) Yvonne Sadovy (University of Hong Kong) Yoshihisa Shirayama (Kyoto University) Jane Smart (IUCN) Ali Stattersfield (BirdLife International) Sue Stolton (Equilibrium) Topiltzin Contreras MacBeath (Universidad Autónoma del estado de Morelos) Justina Ray (WCS) Ian Craigie, James Cook University Supported by a full-time IUCN staff position – Annabelle Cuttlod and Diego Juffe PhD students – Megan Barnes, Jonas Geldman and Luke Harrison

2. Which of these variables influences the ability of a protected area to conserve biodiversity? • Size of the Protected Area • IUCN Protected Area Category – I thru VI • GDP of the country • Human Development Index of the Country • Corruption Index • Size of the biodiversity – big vs small wildlife • External land use around the protected area • Proximity of human populations • Management Effectiveness

3. Global Study: Dependant Variable – Biological OutcomesChange in biodiversity - species (Habitats) • Population trend data information on species (and communities) • All presence /absence data on species and communities • Relative abundance in and out of PAs • Focus on biodiversity values identified in the management plan

4. Potential drivers of protected area outcomes

5. What others have found

6. Ecosystem level analyses- 80% increased or stable Bruner et al. (2001) Science

7. The change in percent coral cover from 2004 to 2005inside and outside of MPAs (Selig and Bruno, 2010)

8. African Protected Area Population Index Source: Dr. Ian Craigie

9. Biodiversity Outcomes • Large mammal population declines in African PAs? Craigie et al. 2010

10. WF Lauranceet al. Nature 2012 – Tropical Protected Areas • Drivers of Decreasing Health • Changes outside reserves (declining forest cover, increasing logging and increasing fires outside reserves; • Changes within reserves (particularly declining forest cover, increasing hunting, increasing logging and harvests of non-timber forest products. • Drivers of Increasing Health • An important predictor of reserve health was improving reserve management.

11. Effects of improving on-the-ground protection on a relative index of reserve health. WF Lauranceet al. Nature000, 1-5 (2012) doi:10.1038/nature11318

12. Percentages of reserves that are worsening versus improving for key disturbance-sensitive guilds, contrasted between ‘suffering’ and ‘succeeding’ reserves (which are distinguished by having lower (<–0.25) versus higher (≥–0.25) values for the reserve-health index, respectively). WF Lauranceet al. Nature000, 1-5 (2012) doi:10.1038/nature11318

13. Comparison of ecological changes inside versus outside protected areas, for selected environmental drivers. WF Lauranceet al. Nature000, 1-5 (2012) doi:10.1038/nature11318

14. Are PAs in the right place?PA coverage is poor for species… Rodrigues et al. (2004) Nature

15. extinction risk in and outside pasButchart el al 2012 A IBAs + + WDPA Red List AZEs

16. Reducing the rate of loss Butchart et al 2012

17. Biological Outcomes and Protected Areas – Task Force Source: Living Planet Report 2012 (2012) WWF.

18. Independent (predictor) variables- Case Studies- More detail than the global study

19. Systematic Review • Only case studies no large scale analysis • 40 studies linked input and outcomes • 73% found positive effects • 65% from mammals • 45% from Africa • 57 studies • 53 used NDVI type satellite products • All but two from tropical forest • 82% showed reduced deforestation inside boundaries Jonas Geldman, University of Copenhagen – PhD Candidate

20. Drivers of effectiveness Drivers and interventions identified to have a positiveor negative effect on protected areas ability to protect habitat integrity Jonas Geldman, University of Copenhagen – PhD Candidate

21. Global Study of Biodiversity Outcomes of Protected Areas • Collected population time-series data • 3391 time series – calculated a slope for each with a log link model • 914 protected areas • 1368 species • Created 50 predictor variables for each time series • Satellite data for specific protected areas • Global data sets by country and protected area • WDPA

22. Some caveats……. • May change our mind about the conclusions • Early in the analysis • Not published

23. Available Data are Biased to Birds and Mammals Figure. Distribution of published literature with trend data by class.

24. Available Data are Biased to Africa and Europe – mammals in Africa and Birds in Europe Distribution of published literature with trend data by geographic region. Europe Africa Asia Australia North America Latin America

25. Not Biased – Distribution of Species by Red List Category 120O 60O O Near Threatened CRITICALLY ENDANGERED Least Concern Vulnerable Data Deficient Endangered

26. Time Series Show a Wide Range of Outcomes Frequency Distribution of Calculated Slopes Started with 3500 time series data sets, but only 1620 were useable

27. 1620 population time series • 496 terrestrial vertebrate species • 378 protected areas globally Global Protected Area Population Index from 1970 -2010 using all population time series certainly in a terrestrial protected area Amin, Loh & Collen, ZSL & WWF re: LPI data and code

28. On Average there are Differences in the Success rates of the Global Regions

29. IUCN Protected Area Category No significance Frequency Distribution of Protected Areas In Each IUCN Category Number of Protected Areas in Sample IUCN Category

30. Size of Protected Areas No significance • Counter intuitive • Effect may be swamped by many things • Large protected areas in some regions that are paper parks

31. External - Roads, People, Urbanization No significance • Population, Roads, Urban Travel Time all highly correlated • But bigger parks are farther from people and roads, which caused some model instability when they were both included

32. Social-economic variables Significant • Strong predictive value • Human Development Index (HDI) • Corruption Index • GPD (Gross Domestic Product) • All highly correlated • May relate to management effectiveness

33. Body Size of Biodiversity Significant • Larger bodied biodiversity are doing better • Driven by African mammals • Perhaps a bias toward stewardship or measurement of large mammals

34. Mid-year of Trend Significant • Populations are doing better later in the time series, or more recently • Perhaps indicates better global management

35. What Can we Conclude • Understanding protected areas outcomes is highly complex • Population time series are generally not available from protected areas – hard to measure biodiversity outcomes • Many lines of evidence support the notions that protected areas work when they are well managed • Classic notions of size and fragmentation are not well tested by existing data • Protected area success appears very contextual to place

36. Where to from here • Analysis of Task Force data is ongoing • Search for additional data is ongoing • Setting up a global data centre in partnership with the Living Planet Index and Protected Planet.net • Work with BIOPAMA project • Need to monitor inside and outside

37. A plea for data • It we are going to make the case that we need protected area, we need to know why and when they work • The Joint Task force on Biodiversity and Protected Areas needs your data • All contributions recognized • Stephen.Woodley@iucn.org • M043 Biodiversity, protected areas and Key Biodiversity Areas