1. 1 Atte Moilanen, Joona Lehtomäki, Heini Kujala, Federico M. Pouzols, Jarno Leppänen, Laura Meller & Victoria Veach C-BIG - Biodiversity Conservation Informatics Group Dept. of Biosciences, University of Helsinki http://cbig.it.helsinki.fi Conservation resource allocation and the Zonation framework

2. 2 1. Introduction to conservation resource allocation 2. Zonation Illustrative example Operational principle and features More examples Introduction: contents 1h+

3. 3 Conservation Resource allocation 01

4. 4 To identify different (spatial) allocations of conservation resources (actions) best possible long-term conservation outcome (population sizes, persistence) Limited resources prioritization Spatial allocation, various forms of land use: protection, management, restoration, offsetting, competing uses What are the consequences and interactions between different (possibly complementary) actions Objective

5. 5 Often: species Many others: Habitat types and properties (e.g. suitability) Communities Ecological processes Ecosystem services Vegetation classes Functional traits Genetic information Socio-cultural factors Surrogates, pervasive: complete information usually missing Biodiversity features

6. 6 Fundamental quantities of spatial population biology: 1. Area: the available habitat (spatial amount) 2. Quality: resource density (e.g. micro-climate) 3. Aggregation: spatial (network) structure of the habitat Area and quality determine the carrying capacity Aggregation affects the local dynamics and occupancy 3 key dimensions

7. 7 3 key dimensions: Quality Area Aggregation

8. Fundamental quantities Fundamental quantities of spatial population biology

9. 9 Spatial distributions and local occurrence levels of biodiversity features (species, communities etc...) Connectivity and minimum population size requirements Habitat loss and degradation, landscape change Climate change Availability of conservation resources Socio-political constraints Pervasive uncertainties about biological facts and economic realities, sparse data Conservation prioritization: Relevant factors

10. 10 CRA is not the only part of the puzzle – Social Dimension! Knight et al. Cons Biol. 2006.

11. 11 More about Spatial Conservation Prioritization + Recent review: Kukkala, A. & A. Moilanen. 2013. The core concepts of spatial prioritization in systematic conservation planning. Biological Reviews, 88: 443-464.

12. 12 The Zonation framework and software 02

13. 13 Illustrative example: evaluation of the proposed benthic protection areas of New Zealand Leathwick et al. 2008. Novel methods for the design and evaluation of marine protected areas in offshore waters. Conservation Letters, 1: 91-102.

14. 14 Aim: evaluate proposed New Zealand’s Benthic Protected Areas (BPAs)

15. 15 Marine protection areas of New Zealand: Data 1.59 million 1 km 2 grid cells 100 demersal fish species Habitat models based on 21000 experimental trawls ~20 environmental variables Locations of commercial trawls = cost data

16. 16 Basic Zonation output 1 Map of priority rank Cell rank 0 - 50% 50 - 75% 75 - 90% 90 - 100% (= 10% best)

17. 17 Basic output 2: representation of features with different ranks Endemic weighted higher With equal weights 10% of total area Proportion of feature distribution protected Rank (proportion of landscape not under conservation action)

18. 18 Proportion of species distribution protected Proportion of cells removed Replacement cost analysis for proposed reserve areas LOSS = COST Performance curve for ”ideal” solution Curve for forced solution Rank (proportion of landscape not under conservation action) Proportion of feature distribution protected

19. 19 Influence of cost Fishing opportunity cost [%] Conservation benefit, % of all Proposed BPAs Zonation, Full cost Zonation, Ideal free solution

20. 20 Zonation operational principle and features

21. 21 Zonation Produces a hierarchical zoning of a landscape looking for priority sites for conservation indirectly aiming at species persistence using large grids

22. 22 Zonation Persistence by considering: Habitat quantity, quality and connectivity For multiple biodiversity features simultaneously (species, communities, ecoregions, functional traits, etc.) Can optimize: Return on investment (ROI) Targets

23. 23 Basic input: Spatial distributions of biodiversity features as static patterns in raster maps: Presence Abundance Probability Many more optional inputs: uncertainty, PAs, interactions, etc. Produces 2 main outputs: Spatial priority ranking for conservation (map) Performance curves (x-y plots) Zonation is not about: GIS processing PVAs, dynamic models, etc. Zonation: inputs and outputs

24. 24 Zonation: inputs and outputs Input data GIS Experts Ecological knowledge FeaturesWeightsCostsConnectivity Higher/lower priority areas for conservation Performance/ potential for proctection Data collection Data preparation Data analysisInference/ Decision

25. 25 A general Zonation workflow Basic output 1 Basic output 2 Lehtomäki, J., Moilanen, A., 2013. Methods and workflow for spatial conservation prioritization using Zonation. Env. Model. & Sof. 47, 128-137.

26. 26 Basic output 1 Landscape map showing the ranking

27. 27 Basic output 2 Performance: curves of representation of features (or groups) at different rank levels 10% top fraction Rank (proportion of landscape not protected) Proportion of feature distribution protected

28. 28 Additional basic output (3): Post-processing analyses For example: Comparison of different solutions Connected sets of sites with similar species compositions can be connected into management landscapes Tutorial example: do_ppa.bat

29. 29 Zonation - Basic analyses 1. Identification of optimal reserve areas 2. Identification of least valuable areas 3. Evaluation of conservation areas 4. Expansion of conservation areas

30. 30 Major Zonation Features Species/feature weighting Species-specific connectivity Handles uncertainty and costs Combined species and community level prioritization Balancing alternative land uses Landscape condition and retention analysis Prioritization across multiple administrative regions Direct link: GIS  distribution modeling  Zonation

31. 31 Improved detection of errors in setups Manage and monitor multiple analyses Post-process and explore output Explore transformed layers used in computations Explore all output curves interactively Import/export publication-quality maps Simple interface for comparing/merging maps New Graphical User Interface, much improved for Zv3.1

32. 32 Zonation strategy summarized Minimize loss of weighted range-size rarity = Maximize retention of weighted range-size normalized (rarity corrected) feature richness

33. 33 in other words Zonation produces a complementarity- based balanced priority ranking through the landscape.

34. 34 Zonation Meta-algorithm 1. Start from full landscape 2. Determine cell that has least marginal value and remove it 3. Update occurrence levels of features (in the remaining landscape) 4. Repeat (2 and 3) until no cells remain

35. 35 0.020.050.075 0.025 0.115 0.16 0.10.2 0.255 0.05100.0765 0.0255 0.1174 0.1632 0.1020.2041 0.2602 0.05230.0785 0.1204 0.1675 0.10470.2094 0.2670 0.0828 0.1270 0.1767 0.11040.2209 0.2817 0.1385 0.1927 0.12040.2409 0.3072 0.1575 0.2191 0.2739 0.3493 0.2601 0.3252 0.4146 0.4395 0.56041.0 41015 5 23 32 2040 51 Absolute value Normalized values & Removal sequence Cell removal

36. 36

37. 37 1 1111 1 11 11 1 11 1111 1111 1111 1111 111 111 1 11 1 1 1 1111 1111 11 111 111 11 1 1 1 11 0.042 11 111 1111 11111 111 11 11 11 1111 1 0.036 0.025

38. 38 0.042 0.0360.078 0.036 0.078 0.036 0.078 0.036 0.025 0.103 0.067 0.061 0.025 0.061

39. 39 1 1111 1111 11 111 111 11 1 1 1 11 0.042 0.036 0.025 1 1111 1 11 11 1 11 1111 1111 1111 1111 111 11 1 11 1 1 0.026

40. 40 = definition of marginal loss in conservation value = different rules implement different conceptions of conservation value, how is it aggregated across space, time and features? Cell removal rule

41. 41 Determines how marginal loss is aggregated when a cell is lost Four alternatives Core-area Zonation (CAZ) Additive benefit function (ABF) Targeting benefit function (TBF) Generalized benefit function (GBF) These alternatives Have different aims Value representation differently Cell removal rule

42. 42 Cell removal rules Core-area Zonation Cell value is the maximum biological value within the cell, across all features/species Cell with the smallest (max) value will be removed Additive benefit function Cell value is the sum of value across species within the cell Cell with the smallest sum value will be removed

43. 43 Cell removal rules 0.60 0.05 0.10 0.30 0.05 0.15 0.25 0.50 0.600.30 0.150.50 0.650.40 0.200.75 Core-area Zonation Additive benefit function 0.6316 0.0588 0.1053 0.3529 0.2632 0.5882 0.63160.3529 0.5882 0.69040.4582 0.8514 0.7059 0.0909 0.2941 0.9091 0.7059 0.9091 0.7968 1.2032 1.0 2.0 Species 2 Species 1

44. 44 Zonation: Cell removal principles “More rare” “More important” “less prop. remains”

45. 45 over cells i over spp j weight of sp j proportion of remaining distribution of sp j in cell i in remaining landscape S cost of site i Core-Area Zonation (CAZ) emphasizes the most valuable feature in the cell CAZ valuation of site i

46. 46 ABF uses a power function, which has a smooth shape, and can replicate, for example, the species-area curve loss of representation => loss of value GBF has a more flexible shape (incl. sigmoids) Cell removal rules: Additive benefit function & Generalized BF Sum over species-specific loss ΔVj; free trade between spp; implicitly emphasizes locations with many species (richness)

47. 47 Cell removal rules: Finnish breeding birds – CAZ vs. ABF Additive benefit function Core-area Zonation somewhat emphasizes rarity somewhat emphasizes richness

48. 48 Other cell removal rules Target-based planning Below target: 0 value Above target: power function Generalized benefit functions

49. 49 What can be done using Zonation? Some Zonation study summaries

50. 50 Aligning conservation priorities in Madagascar

51. 51 Plan of extension of Madagascar protected areas to 10% Most extensive example of conservation prioritization at the time + Extensive surrogacy analysis Kremen, Cameron, Moilanen, Phillips, Thomas et al. 2008 Science 320: 222-226.

52. 52 Bird habitat restoration Victoria, Australia Multiple time steps Maturation of restored habitat Suitability for birds Connectivity Thomson et al. 2009. Ecol. Appl.

53. 53 Urban analysis around Melbourne Extending reserves Guiding placement of green areas Gordon et al. 2009. Landscape & Urban Planning

54. 54 Ecological interactions in Zonation, phase 1 Inter- and intraspecies connectivities Conservation for the Marten in Canada Rayfield et al. 2009. Ecological Modelling

55. 55 Core-area Zonation Freshwater planning accounting for hydrological connectivity of catchments Rivers in New Zealand Moilanen, Leathwick & Elith. Freshwater Biology 2008. Leathwick et al. Biological Conservation 2010. + condition + connectivity

56. 56 Balancing between competing land-uses biodiversity (+) agri (-) urban (-) carbon (+)

57. 57... all can be put in the same analysis Moilanen, A., B.J. Anderson, F. Eigenbrod, A. Heinemeyer, D. B. Roy, S. Gillings, P. R. Armsworth, K. J. Gaston, and C.D. Thomas. 2011. Balancing alternative land uses in conservation prioritization. Ecological Applications, 21: 1419-1426.

58. 58 Administrative units analysis Admin. areas have different priorities Balancing national & global priorities Local, global or compromise analyses Striking edge artifacts! Need for ”Collaboration in conservation” Moilanen, A., and Arponen A. 2011b. Administrative regions in conservation: balancing local priorities with regional to global preferences in spatial planning. Biological Conservation, 144: 1719-1725. Moilanen, A., Anderson, B.J., Arponen, A., Pouzols, F.M., and C.D. Thomas. 2012. Edge artefacts and lost performance in national versus continental conservation priority areas. Diversity and Distributions, 19: 171-183.

59. 59 Administrative units analysis Western hemisphere mammals, birds and amphibians

60. 60 Largest landscape (at the time...): Arponen, A., J. Lehtomäki, J. Leppänen, E. Tomppo, and A. Moilanen. 2012. Effects of connectivity and spatial resolution of analyses on conservation prioritization across large extents. Conservation Biology, 26: 294–304. Spatial planning and connectivity in Finnish forests Entire country up to 1ha resolution Up to 28 million grid cells with data

61. The Academy of Finland, EU FP7 SCALES, the European Research Council ERC, Finnish Ministry of Environment; the Finnish Natural Heritage Services Univ. York:Chris Thomas, Aldina Franco, Regan Early Barbara Anderson Univ. Melbourne: Mark Burgman, Brendan Wintle, Jane Elith Finnish Environment Institute Risto Heikkinen, Raimo Heikkilä NIWA & DOC, New-ZealandJohn Leathwick Berkeley/Princeton Alison Cameron, Claire Kremen Israel Univ. Techn. Yakov Ben-Haim Royal Melbourne Univ. Techn. Sarah Bekessy, Ascelin Gordon CSIRO, AustraliaSimon Ferrier Univ. Queensland, AustraliaHugh Possingham, Kerrie Wilson Klamath conservationCarlos Carroll Special thanks to