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Zonation analysis types and setups

Zonation analysis types and setups. Atte Moilanen, Joona Lehtomäki, Heini Kujala, Federico Montesino Pouzols, Jarno Leppänen & Laura Meller C-BIG - Conservation Biology Informatics Group Dept. of Biosciences, University of Helsinki http://cbig.it.helsinki.fi. Contents. Basic analyses

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Zonation analysis types and setups

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  1. Zonation analysis types and setups Atte Moilanen, Joona Lehtomäki, Heini Kujala, Federico Montesino Pouzols, Jarno Leppänen & Laura Meller C-BIG - Conservation Biology Informatics Group Dept. of Biosciences, University of Helsinki http://cbig.it.helsinki.fi

  2. Contents • Basic analyses • Connectivity • Uncertainty • Community/ecosystem level approaches • Advanced analyses • Data size issues • Limitations of Zonation

  3. Zonation - Basic analyses • Identification of “optimal” reserve networks • Identification of least valuable areas • Expansion of conservation area networks • Evaluation of conservation areas • Replacement cost

  4. B1. Identification of optimal reserves • The most ”traditional” function of any reserve selection method • Basic question: Where to protect?

  5. B1: Optimal areas = basic Zonation analysis These schematics are in the manual Several examples in the tutorial Look for areas with highest priority

  6. Zonation • Produces a hierarchical zoning of a landscape • looking for priority sites for conservation • aiming at species persistence

  7. Area needed to achieve 30% of sp distributions Basic output 1 • Map of landscape showing the ranking (cell removal levels)

  8. 10% top fraction Basic output 2 • Curves: performance of features (or groups) at different levels of cell removal (these curves correspond 1-1 to the rank map)

  9. B2. Identification of least valuable areas • Ecologically least valuable = where economic activity disturbs biodiversity least = impact avoidance

  10. B2: least valuable = basic Zonation analysis Look for areas with lowest priority

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

  12. ... all can be put in the same analysis… produces separation of conservation and other land uses

  13. Alternative land uses Note negative weights

  14. B4. Extending conservation area networks • Usually conservation does not start from scratch • Uses hierarchical mask layer to enforce hierarchy into ranking • When present PAs are at top rank category, highest ranked areas outside PAs identify optimal and balanced CAN expansion

  15. Extending conservation area networks Force Inclusion of PAs Tutorial example: do_rs.bat

  16. Aligning conservation priorities in Madagascar

  17. 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.

  18. Proposed extension from present 6.3% PAs • All major taxa • 2315 endemic species • Entire country at 0.7 km2 resolution • Existingreserves missed 28% of species

  19. B3. Evaluating a proposed network of sites • Comparison between ”what is” and ”what could be, ideally” • Zonation specific way: replacement cost analysis

  20. Replacement cost analysis • Situation where areas need to be forcibly included to or excluded from the final solution • Eg. evaluation of existing or proposed reserves • Forced inclusion of poor areas entails a cost to biodiversity • Broader interpretation: difference between ideal free and constrained solution

  21. B4: Evaluating a conservation area network Tutorial example: do_rs.bat (compare with and without mask)

  22. Replacement cost analysis Optimal solution Forced solution Proposed reserves

  23. Replacement cost analysis • Calculate biologically optimal solution • Force in areas that mustto be protected orforce out areas that cannot be protected • Re-optimize under constraint and calculate the difference in cost/benefit

  24. Replacement cost analysis:New Zealand revisited Leathwick et al. Conservation Letters, 2008

  25. Performance curve for ideal solution Proportion of species distribution protected COST = loss in biological value Curve for forced solution Proportion of cells removed Replacement cost analysis Leathwick et al. Conservation Letters, 2008

  26. Replacement cost analysis:New Zealand EEZ Leathwick et al. Conservation Letters, 2008

  27. 5. Connectivity • Zonation includesmanyways of accounting for connectivity

  28. Accounting for Connectivity • Qualitative /structural • Edge removal • Boundary length penalty: do_blp.bat • Feature-specific • Distribution smoothing: do_ds.bat • Boundary Quality Penalty: do_bqp.bat • Neighborhood Quality Penalty: do_nqp.bat • Interactionconnectivity • Pairwise interaction • Matrixconnectivity (many-to-oneinteraction) • Path-like connectivity; corridors • In Zonation 4 – coming soon! • Connectivity in environmental space • In Zonation 4 – coming soon!

  29. Accounting for connectivity: Distribution smoothing Original distribution Connectivity distribution Tutorial example: do_ds.bat

  30. Accounting for connectivity:Distribution smoothing No aggregation Using distribution smoothing Top 20% (color) is scrappy Top 20% well connected

  31. Accounting for connectivity: Boundary quality penalty (BQP) • Species-specific decrease in local quality due to proximity of reserve boundary • Forces connectivity only where needed. • Allows fragmentation where it does not hurt Tutorial example: do_bqp.bat

  32. focal cell large buffer small buffer Accounting for connectivity: Boundary quality penalty (BQP) Strong effect of neighboring habitat loss Small effect of neighboring habitat loss

  33. 7 species of interest Hierarchical priorities Hunter Valley, Australia Moilanen and Wintle 2006 Accounting for connectivity:Boundary Quality Penalty (BQP)

  34. Distributions Accounting for connectivity:Boundary Quality Penalty (BQP)

  35. Accounting for connectivity:Neighborhood Quality Penalty (NQP) NQP = extension of BQP to operate on planning units (e.g catchments), linked to tree structures (freshwater systems) Connectivity responses upstream and downstream Tutorial example: do_nqp.bat

  36. Directed freshwater connectivity

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

  38. Interaction connectivityconnectivity between two distributions • Between two features (Rayfield et al. 2009) • Same species; different time steps (Carrol et al. 2010) • Positive (resource-consumer) or negative

  39. Single-species prioritization

  40. Ecological interactions in Zonation, phase 1Inter- and intraspeciesconnectivitiesresult in aggregatedsolution Conservation areas for the Marten in Canada Rayfield et al. 2009. Ecological Modelling.

  41. 3. Uncertainty in inputs • Uncertainty is pervasive in ecological data, including spatial data about occurrence levels of biodiversity features Simple solution: give low weigth to uncertain data • 2 types of uncertainty analyses: • Robustness • Opportunity • Requires additional rasters: distribution of uncertainty in feature distributions

  42. Uncertainty analysis Conservation value HIGH LOW LOW HIGH Certainty of information Opportunity Robustness requirement

  43. Uncertainty analysis Tutorial example: do_uc.bat

  44. Uncertainty analysis:Distribution discounting Discounted distribution Distribution model Error or relative uncertainty surface

  45. 4. Community-level analysis Species are common biodiversity features, but many analyses can be productively done at the level of community / ecosystem / environment type Tutorial example: do_comm_similarity.bat

  46. Community-level analysis The catch: communities / ecosystems are not completely dissimilar, which should be accounted for!

  47. Combined communities and species analysis

  48. Similarity expansion

  49. Community classification and similarity expansion:Riverine communities in New Zealand WITH similarity expansion WITHOUT similarity expansion • Macroinvertebrate and fish species used for modelling community turnover (GDM) • Similarity expansion • Or, matrix connectivity accounting for similarity • Directed connectivity (NQP) Leathwick et al. Biological Conservation 2010.

  50. 5.1. Prioritization across multiple administrative regions • Different administrative regions may have different priorities • But connectivity extends across borders • Representation of species may be considered across multiple spatial scales

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