Habitat suitability based landscape optimization vs. expert rules in agricultural landscapes

1. Habitat suitability based landscape optimization vs. expert rules in agricultural landscapes Lutz Tischendorf in association with O2 Planning + Design Inc. Elutis Modelling & Consulting Inc. January 26, 2007

2. Outline – Scope and Context • NAESI • Study Area • General Approach • Landscape Optimization • Case Study • Results • Conclusions Elutis Modelling & Consulting Inc. January 26, 2007

3. NAESI National Agricultural Environmental Standards Initiative • Develop standards to reduce negative effects of Agriculture on: • Air, Water, Soil, Biodiversity • Identify quantitative targets for evaluating performance of standards Elutis Modelling & Consulting Inc. January 26, 2007

4. NAESI - Biodiversity • Tasks: • Develop Habitat based Biodiversity Standards • Identify Habitat based Biodiversity Targets Elutis Modelling & Consulting Inc. January 26, 2007

5. Study Area Elutis Modelling & Consulting Inc. January 26, 2007

6. Study Area Elutis Modelling & Consulting Inc. January 26, 2007

7. Study Area Elutis Modelling & Consulting Inc. January 26, 2007

8. Study Area Elutis Modelling & Consulting Inc. January 26, 2007

9. General Approach Surrogate Species Selection Landcover Data HSI Models Habitat based Biodiversity Standards + Target Customized Software Landscape Optimization Objective Constraints Elutis Modelling & Consulting Inc. January 26, 2007

10. Landscape Optimization Expert input, Scenario: - transition rules - constraints accept landcover conversions and continue Change Objective: (e.g. measurable biodiversity target) Analyze maximum number of runs or generations completed? Stop yes no did target value improve? Typical Feedback Loop of Heuristic Approaches yes no reject landcover conversions and use previous landscape again Elutis Modelling & Consulting Inc. January 26, 2007

11. Landscape Optimization Elutis Modelling & Consulting Inc. January 26, 2007

12. Case Study • Objective: • Identify a potential biodiversity target as the best possible landscape • configuration under consideration of: • conflicting species’ habitat requirements and • socio-economic constraints • 2 Approaches: • Expert Rules • Landscape Optimization using Genetic Algorithms Elutis Modelling & Consulting Inc. January 26, 2007

13. Case Study What would/could this landscape look like, if it would provide optimal habitat suitability for 4 selected surrogate species with 50% less cropland? Elutis Modelling & Consulting Inc. January 26, 2007

14. Case Study – Expert Rules • Retain large patches (>10 ha) within the landscape, and any portions of large patches (>800 ha) • Retain corridors and stepping stones across the landscape, Maintain inter-patch distances and stepping stone sizes appropriate to dispersing species • Build concentric management buffer zones around wetlands so that 10 m surrounding each wetland is unmanaged cover • Create large patches of natural vegetation and untilled cover. In this case, the large patches chosen were areas of wetlands complexes (defined as wetlands within 200 m of each other) that would benefit from the conversion of the intervening arable agricultural land to permanent cover and natural grasslands • Protect quarter-sections with >25% badlands and saline flats • Riparian buffers of 60 m on all streams • Riparian corridors of 100 m on >3rd order rivers • Riparian corridors of at least 150 m along significant landscape connections • Buffer grassland patches to leave adjacent areas within 200 m in open vegetation (no tree or shrub plantings) Elutis Modelling & Consulting Inc. January 26, 2007

15. Case Study – Expert Rules Current Landscape Elutis Modelling & Consulting Inc. January 26, 2007

16. Case Study – Expert Rules Expert Landscape Elutis Modelling & Consulting Inc. January 26, 2007

17. Case Study – Landscape Optimization • Data Preparations: • intersect current landscape with Quarter Sections • create 120 m buffer polygons around water and wetland • exclude areas with high irrigation potential • Transition Rules: • A cropland polygon could be transformed into grassland, pasture or hay/forage • A transformed grassland polygon could be changed back into cropland, pasture or hay/forage • A pasture polygon could be transformed into grassland, cropland or hay/forage • A hay/forage polygon could be transformed into grassland, pasture or cropland • A cropland buffer polygon around water or wetland could be transformed into shrub. • A pasture buffer polygon around water or wetland could be transformed into shrub. • A hay/forage buffer polygon around water or wetland could be transformed into shrub. • A transformed shrub buffer polygon could be changed back to cropland buffer, pasture buffer, hay/forage buffer or grassland. • Set Socio-economic Constraints: • keep 150,000 ha (22%) cropland • do not convert cropland in areas with highest irrigation potential Elutis Modelling & Consulting Inc. January 26, 2007

18. Case Study – Landscape Optimization • Chose Surrogate Species: Loggerhead Shrike Swift Fox Grey Copper • 3 taxonomic groups • home ranges 6 ha – 1080 ha Northern Pintail Elutis Modelling & Consulting Inc. January 26, 2007

19. Case Study – Landscape Optimization • Habitat Suitability Models: • Grey Copper (6ha home range, wetland + hay/forage) • Loggerhead Shrike (64 ha home range, shrub + grassland + no slope) • Northern Pintail (650 ha home range, grassland + wetland) • Swift Fox (1080 ha home range, grassland on even ground) Elutis Modelling & Consulting Inc. January 26, 2007

20. Case Study - Landscape Optimization 1. Data Preparation 2. Transition Rules accept landcover conversions and continue Change 3. Constraints 4. Calculate HSI for 4 surrogate species Analyze maximum number of runs or generations completed? Stop yes no did target value improve? Genetic Algorithm yes no reject landcover conversions and use previous landscape again Elutis Modelling & Consulting Inc. January 26, 2007

21. Case Study – Results • Quantitative Measures: • Average Habitat Suitability Index • Habitat amount Qualitative Results: • Habitat Suitability Maps • Generalized Landcover Map – Optimized Landscape Elutis Modelling & Consulting Inc. January 26, 2007

22. Case Study – Landscape Optimization Current Landscape Elutis Modelling & Consulting Inc. January 26, 2007

23. Case Study – Landscape Optimization Optimized Landscape Elutis Modelling & Consulting Inc. January 26, 2007

24. Case Study – Landscape Optimization Optimized Landscape Elutis Modelling & Consulting Inc. January 26, 2007

25. Case Study – Results Habitat Suitability Improvement during Optimization Elutis Modelling & Consulting Inc. January 26, 2007

26. Case Study – Results Habitat Suitability Improvement after Optimization Elutis Modelling & Consulting Inc. January 26, 2007

27. Case Study – Results Habitat Amount Increase after Optimization Elutis Modelling & Consulting Inc. January 26, 2007

28. Case Study – Results Habitat Suitability Maps – Grey Copper (6ha home range) current landscape optimized landscape Elutis Modelling & Consulting Inc. January 26, 2007

29. Case Study – Results Habitat Suitability Maps – Loggerhead Shrike (64 ha home range) current landscape optimized landscape Elutis Modelling & Consulting Inc. January 26, 2007

30. Case Study – Results Habitat Suitability Maps – Northern Pintail (650 ha home range) current landscape optimized landscape Elutis Modelling & Consulting Inc. January 26, 2007

31. Case Study – Results Habitat Suitability Maps – Swift Fox (1080 ha home range) current landscape optimized landscape Elutis Modelling & Consulting Inc. January 26, 2007

32. Case Study – Results: Expert vs. Optimization expert landscape optimized landscape Elutis Modelling & Consulting Inc. January 26, 2007

33. Case Study – Results Habitat Suitability Improvement in Expert and Optimized Landscape Elutis Modelling & Consulting Inc. January 26, 2007

34. Case Study – Results Habitat Amount Increase in Expert and Optimized Landscape Elutis Modelling & Consulting Inc. January 26, 2007

35. Conclusions • Initial Tasks: Develop Habitat based Biodiversity Standards • maintain landscape heterogeneity at different spatial scales • habitat is not just “green stuff” • consider all of one or multiple species’ relevant landcover types when assessing habitat based biodiversity Identify Habitat based Biodiversity Targets • optimal landscape represents a potential reference condition, benchmark or biodiversity target under consideration of: • conflicting habitat requirements • habitat evaluation at different spatial scales • socio-economic constraints Elutis Modelling & Consulting Inc. January 26, 2007

36. Conclusions • landscape heterogeneity at different spatial scales is important • habitat for a species is usually composed of more than just one natural landcover type • species operate at different spatial scales, which must be considered in biodiversity standards • landscape optimization has the potential to reveal optimal landscape composition and configuration for best possible suitability and amount of habitat for multiple surrogate target • landscape optimization does not reveal whether habitat amount and configuration is “enough” or “optimal” for population viability • effects of corridors, stepping stones and connectivity in general on population viability can only be explored by optimizing a landscape for population viability rather than habitat suitability • habitat suitability patterns in optimal landscapes can enhance expert rules • landscape optimization produced a landscape scenario with results superior to those obtained from expert rules in enhancing existing and creating new habitat for a set of target species Elutis Modelling & Consulting Inc. January 26, 2007

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38. Selection Protocol: • represent most important ecosystem/cover types • respond to important agricultural stressors • are primarily area- and dispersal-limited • are habitat specialists and generalists • have different home range scales • have different life-cycle lengths • represent keystone species • represent terrestrial/aquatic interface • represent a sufficiently large suite of taxonomic groups Elutis Modelling & Consulting January 26, 2007