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

13. 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. Arbitrary targets

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%

20. No of records No of species Units sampled Range of distrib. No of spp with 1 unit range Species richness range Birds18,2554761011-10123108-307 Mammals905122501-43272-65

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

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

23. Birds df = 3, Χ 2 = 0.677, P = 0.879 Mammals df = 3, Χ 2 = 1.226, P = 0.747

24. No of records No of species Units sampled Range of distrib. No of spp with 1 unit range Species richness range Birds18,2554761011-10123108-307 Mammals905122501-43272-65 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. 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. Other effects of sampling bias

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

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 Recording success = 0.462 Cloud cisticola Number of recorded squares = 6 Number of modelled squares = 13

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

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

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

47. Revenue '000s US$ Non-tribal areas1967 Mathenjwa409 Tembe1852 Nyawo670 Mngomezulu285 Mashabane256 Mabaso699 Siqakatha27 Zikhali-Mbila87 Myeni-Ntsinde67 Manukuza-Jobe513 Myeni-Ngwenya301 Mnqobokazi238 Qwabe-Makhasa32 Nibela59 Mpukunyoni-Mkhwanazi13 Total revenue7476

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

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.

51. Instead, it might be preferable to set individual targets based on criteria such as: Endemism Red list status or other measures of threat Life-history characteristics Original extent Non-biodiversity value (eg watersheds) Genetic diversity

52. Expert workshops Estimates of minimum viable populations Species/area curves for habitat targets 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.

53. Work developed by Desmet & Cowling (2004) has used species/area curves to estimate the amount of a landcover type that is needed to represent 90% of plant species.

54. MARXAN and clumps Another feature of MARXAN is that it can include information on patch and population size of the conservation features. MARXAN uses the term “clump” to describe these characteristics and defines clumps using the following parameters: 1) Clump distance 2) Clump size 3) Clump target

55. Clump distance This is the maximum distance between planning units, below which it is assumed that units belong to the same clump. These distances are measured from the centre of each planning unit.

58. Clump size This is the minimum size for a viable clump. The size of each clump is measured by combining the amount of the conservation feature found in each of the associated planning units.

59. 

60. A possible priority setting exercise for CWRs in Europe Use existing atlas, landcover and PA data Use clump option in MARXAN to identify different populations Set targets based on minimum number of populations

61. A possible priority setting exercise for CWRs in Europe Analyses: Identify populations in grid cells within no associated PAs Identify irreplaceable grid cells based on minimising the amount of unsuitable land found within the grid cells Carry out field work to check the presence of viable populations of CWRs in PAs in irreplaceable cells

62. Systematic conservation planning should be considered as a form of adaptive management. Outputs will change as more information is added and refined. However, there is already sufficient data to produce a useful initial product and these outputs would be the most effective at identifying where CWRs should be conserved.