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11th INTECOL Congress, Ecology: Into the next 100 years , London 2013

INDIRECT EFFECTS OF CLIMATE CHANGE ON MEDITERRANEAN BIODIVERSITY: THE ROLE OF LAND USE CHANGES AND FIRE. Lluís Brotons & Miquel de Cáceres. 11th INTECOL Congress, Ecology: Into the next 100 years , London 2013. Talk outline.

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11th INTECOL Congress, Ecology: Into the next 100 years , London 2013

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  1. INDIRECT EFFECTS OF CLIMATE CHANGE ON MEDITERRANEAN BIODIVERSITY: THE ROLE OF LAND USE CHANGES AND FIRE Lluís Brotons & Miquel de Cáceres 11th INTECOL Congress, Ecology: Into the next 100 years, London 2013

  2. Talk outline • Species distribution models (SDMs) and biodiversity projections to future environmental conditions… and why they fail (sometimes…) • Challenges of building biodiversity projections. Including multiple environmental pressures and matching models to ecological processes. • Biodiversity projections in Mediterranean dynamic landscapes. Fire regimes and the indirect effects of climate change on trees and birds.

  3. Projecting distribution dynamics • Objective:anticipate potential impacts of global change on biodiversity distribution patterns. • Temporal and spatial projections SDMs have had an enormous influencein global change science. • They have allowed predictions of large scale biodiversity changes using simple and widely available data!!!! • Climate change and invasive species impact at large spatial scales influential applications…

  4. Global change and biodiversity. Gil-Tena et al. 2009. Global ChangeBiology 15:474-485.

  5. What’s the problem… and the challenge • SDMs summarise, through simple statistical relationships, very complex,interacting ecological processes. • When spatial dynamics are to be predicted SDMs may perform poorly because they are unlikely to adequately capture relevant processes at all spatial scales. • Challenge: how to adequately integrate ecological and environmental processes relevant to distribution changes?

  6. Projecting distribution dynamics External dynamics Climate scenarios Static

  7. Projecting distribution dynamics External dynamics Climate scenarios Habitat filtering Imposed Static

  8. Projecting distribution dynamics External dynamics Climate scenarios Habitat filtering Imposed Population dynamics Dispersal Intrinsic Static Internal dynamics

  9. Projecting distribution dynamics External dynamics Climate scenarios Habitat filtering Imposed Species interactions Population dynamics Dispersal Intrinsic Static Internal dynamics

  10. Projecting distribution dynamics External dynamics Climate scenarios Phenotypic plasticity Habitat filtering Imposed Species interactions Population dynamics Dispersal Physiology Evolution Intrinsic Static Internal dynamics

  11. Disturbance regimes Climate change Land use changes/management Invasive species Biodiversity Global change and biodiversity.

  12. Mediterranean landscapes

  13. Novel, changing fire regimes

  14. Land abandonment Fire Climate change Invasive species Biodiversity (trees, birds) Global change and biodiversity.

  15. Relationships pressures - biodiversity

  16. Relationships pressures - biodiversity

  17. Relationships pressures - biodiversity

  18. Relationships pressures - biodiversity

  19. Landscape dynamics model (100 m, 1 year step) Otras especies Matorral Prados Cultivos Agua Suelo urbano Suelo desnudo Pinus halepensis P. nigra P. pinea P. sylvestris Quercus suber Otros Q. Q. ilex Otras coníferas Fire submodel Vegetation submodel Landscape and tree modelling Post-fire regeneration Fire - Tree dominance - Orientation - Bioclimatic region. • - Fire regime • - Ignition probability • Fire spread model • Elevation Succession Brotons et al. (2013) PLoS ONE 8(5): e62392

  20. Landscape and tree modelling

  21. Bird responses to dynamic landscapes. • We know from previous studies that fire impacthas an important effect on species distribution via habitat filtering. • New habitat constrained by dispersal limitations. Vallecillo, S. et al. 2009. Ecological Applications. Brotons, et al. 2008. Journal of Applied Ecology.

  22. Hybrid metamodelling. Combining dynamic landscape models and species distribution models LANDSCAPE DYNAMICS Dynamic spatial inputs Static spatial inputs DYNAMIC LANDSCAPE LAYERS (dominant tree species) Landscape model Perturbation and vegetation processes Species potential distribution Habitat model DYNAMIC SPECIES DISTRIBUTION RESPONSES (Bird occupancy) Non-spatial parameters Species habitat coefficients Dynamic, process based model (dispersal) Dispersal parameters DYNAMIC SPECIES DISTRIBUTION MODEL

  23. Changes in fire regime. Fire supression treatments Brotons et al. (2013) PLoS ONE 8(5): e62392

  24. Changes in forest type. De Cáceres & Brotons. (2013) J. Biog.

  25. Changes in forest type. De Cáceres & Brotons. (2013) J. Biog.

  26. Changes in forest type. De Cáceres & Brotons. (2013) J. Biog.

  27. Changes in bird communities. v v v v v De Cáceres & Brotons. (2013) J. Biog.

  28. How to move from here… • Better matching objectives, ecological processes and key environmental pressures and integration when buiding biodiversity scenarios. • Keep models simple and look for a way around to implement your processes externally. • Identify key internal processes (feedbacks) relevant to your context and link them to your external processes via meta-modelling. • Keep in mind data availability when designing predictive studies.

  29. How to move from here… • In dynamiclandscapessuch as theMediterranean, indirectclimatechangeeffects can havemajorimpactviachanges in disturbanceregimes. • Initial condition and lags in past environmental pressures (land abandonment) important in determining future changes. • Scenarios of future biodiversity change should account for major drivers of environmental change (including key indirect pathways and relevant feedbacks) at regional scales. • This is a major challenge!!!

  30. THANK YOU • FOR YOUR ATTENTION • Collaborators: • Mariee Josée Fortin • Andrew Fall • Núria Aquilué • Núria Roura- Pascual • Javier Retana • Lluís Coll

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