Obtaining data and setting targets: methods and limitations Bob Smith

1. Obtaining data and setting targets: methods and limitations Bob Smith Durrell Institute of Conservation & Ecology

2. Problems with data quality (focussing on presence/absence data) • Suggestions on data requirements at the fine-scale • How to develop cost and threat datasets • How to set targets for species and landcover types

3. A planning system will only be useful if its results are implemented and there are several ways to increase the likelihood of this, which include: • Conduct the analysis at a relevant spatial scale • Include data on relevant conservation features • Use up-to-date information • Set justifiable representation targets • Include relevant socio-economic & political data

4. One common source of distributional data comes from atlas projects, which generally show the distribution of a range of species as presence/absence maps. These type of data are commonly analysed in the scientific literature. I will discuss the problems with using such datasets, both to identify specific limitations and illustrate broader issues.

5. This paper uses a dataset that shows the distribution of 3882 vertebrate species in 1957 1º grid squares in sub-Saharan Africa.

6. Each of these grid squares is approximately 105 km x 105 km and the presence or absence of each species in each grid square is recorded. They set a target of at least one representation for each species. The authors then used a complementarity-based algorithm to identify the 50 sites that, when combined, would represent the largest number of species. They also used the WWF ecoregion map to label each site according to the ecoregion that it falls within.

7. By failing to involve stakeholders they increase the chances of mis-naming areas or choosing unsuitable areas. This is not montane grassland so reduces credibility of output

8. Problems of scale: Implementation

9. Problems of scale: Measuring representation

10. Data quality: distribution errors

11. Data quality: sampling bias Reddy & Davalos (2003). J. of Biogeography 30, 1719-1727

13. Arbitrary targets There is no way of knowing whether protecting one representation of each species will be sufficient for their conservation or whether each population in each grid square is viable. Basing an analysis on complementarity may maximise the efficiency of the final PA system but it might not necessarily protect viable populations when using presence/absence data.

14. Beetle Butterfly Lizard Toad Tortoise Beetle Butterfly Lizard Toad Tortoise Beetle Butterfly Lizard Toad Tortoise

15. Beetle Butterfly Lizard Toad Tortoise Beetle Butterfly Lizard Toad Tortoise Beetle Butterfly Lizard Toad Tortoise

16. Beetle Butterfly Lizard Toad Tortoise Beetle Butterfly Lizard Toad Tortoise Beetle Butterfly Lizard Toad Tortoise This phenomenon may have other serious implications

18. 8 PAs 3.8% of country

19. Natural = 59.5% Degraded = 12.8% Transformed = 27.7%

21. Birds N = 101, rs = 0.09, P = 0.353 Mammals N = 50, rs = -0.05, P = 0.971

22. Birds 17 irreplaceable + 4 flexible Mammals 13 irreplaceable + 1 flexible

23. Birds df = 3, Χ2 = 0.677, P = 0.879 Mammals df = 3, Χ2 = 1.226, P = 0.747

24. The selected planning units were still not significantly less transformed than other units. One reason for this might be the number of species that were only recorded in one planning unit.

25. 67 % transformed 28 mammal species recorded Only record of Kuhl’s pipistrelle

26. Conclusions • Swaziland species distribution data were not sensitive to levels of agricultural and urban transformation. • This was partly driven by the number of species that were only recorded in one planning unit, which may have been exacerbated by under-sampling. • Species list data should ideally only be used for coarse-scale planning exercises, whereas finer scale exercises should include data that relates to ecological viability.

27. Other effects of sampling bias The recorded data came from the Southern African Bird Atlas Project (SABAP), which describes the distribution of the region’s bird species in a series of ¼ degree grid squares. The data were collected by a series of expert volunteers. Each square was visited a number of times and a list of recorded species was compiled on each occasion. Each record of each species in each grid square was then compiled and stored in a central database.

28. Point 8 – The data is affected by sampling bias Record number Species number

29. The land-cover map had an overall accuracy of 86.9 % and a resolution of 30 m. It contained 29 natural and 5 transformed land-cover types.

30. Recorded distribution Cloud cisticola Number of recorded squares = 6 Associated with woody & hygrophilous grassland

31. Recorded distribution Cloud cisticola Number of recorded squares = 6 Associated with woody & hygrophilous grassland

32. Recorded distribution Modelled distribution Cloud cisticola Number of recorded squares = 6 Number of modelled squares = 13 Recording success = 0.462

33. Distinctive species were classified on the basis of their appearance and/or song. Distinctive appearance:Plumage, bills or legs that contained red, yellow, pink or purple coloration. Bills or tails that were more than 50 % of their body length. Body length of > 80 cm Distinctive song: Described as “loud”, “characteristic”, “penetrating”, “far-carrying”, “raucous”, “strident”, “booming” or “piercing”.

34. The relationship between recording success and distinctiveness was tested and there was a significant difference between distinct and nondescript species. n = 429 t = -2.825 p = 0.005

35. Using the distinctive and complete data sets, only 7 of the 17 grid squares selected were the same. This shows that it might not always be best to include all available data.

36. This suggests that species distributions should be modelled to produce fine-scale data, rather than using raw presence/absence data. • Vegetation/geology/soil etc maps can provide much more reliable data. • Recent landcover data is also important. • Point location data reduces flexibility in the system, helping to anchor larger PAs. • MOST PLANNING EXERCISES USE WHATEVER ADEQUATE DISTRIBUTION DATA ARE AVAILABLE.

37. Vegetation types Forest types Threatened tree species Threatened vertebrate species

38. MARXAN acts to minimise the planning unit costs and these can be based on: • Area • Financial value • Human population density • Risk • Opportunity costs • Etc

39. Risk of agricultural transformation Probability of being cleared related to elevation, slope, geology and distance to agriculture

41. The spatial distribution of bark stripping

42. Game ranching profitability in Maputaland

46. Targets • 40% original extent of threatened and endemic landcover types • 20% original extent of other natural landcover types • 25% of natural landcover in each communal area Cost of each planning unit = US$10,000 – Potential profitability from game ranching

48. Setting targets Setting targets is a vital part of systematic conservation planning and the target values have a profound affect on the final conservation portfolio.

49. Setting targets Setting targets is a vital part of systematic conservation planning and the target values have a profound affect on the final conservation portfolio. Original targets were often political, eg 10% of the planet 12% of original cover

50. These have been criticised for a lack of biological relevance, with targets of 50% having been suggested. Also problematic because they assume all elements are equal.