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Mapping trees species in geographical and environmental space: a comparison of vegetation and habitat suitability maps

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Mapping trees species in geographical and environmental space: a comparison of vegetation and habitat suitability maps

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Mapping trees species in geographical and environmental space: a comparison of vegetation and habitat suitability maps

  1. 1. Transforming Lives and Landscapes with Trees Mapping tree species in geographical and environmental space: a comparison of vegetation and habitat suitability maps Roeland Kindt, Ian Dawson, Jens-Peter B Lillesø, Ramni Jamnadass, Lars Graudal
  2. 2. Transforming Lives and Landscapes with Trees www.vegetationmap4africa.org www.worldagroforestry.org/products/switchboard The right tree for the right place for forestry, agroforestry and restoration planning
  3. 3. Transforming Lives and Landscapes with Trees vegetationmap4africa van Breugel et al. 2015. Environmental Gap Analysis to Prioritize Conservation Efforts in Eastern Africa http://ecoregions2017.appspot.com/ Potential Natural Vegetation (PNV) and conservation planning Dinerstein et al. 2017. An Ecoregion-Based Approach to Protecting Half the Terrestrial Realm
  4. 4. Transforming Lives and Landscapes with Trees Potential Natural Vegetation (PNV) and tree genetics Dawson et al. 2017. A meta-analysis of molecular marker genetic datasets for eastern Africa trees supports the utility of potential natural vegetation maps for planning climate-smart restoration initiatives “Our analysis indicated that site suitability and stability values from PNV-based ecological niche modelling involving current and past climate scenarios were positively related to population genetic diversity values revealed by molecular markers, supporting the value of PNV maps for the practical planning of restoration activities accounting for anthropogenic climate change.”
  5. 5. Transforming Lives and Landscapes with Trees Seed zonation (synecological maps) • Potential natural vegetation or ecoregion maps have been used to develop tree seed zonation maps (eg O’Neill et al. 2017 and references therein; Castellanos-Acuña et al. 2018) • “The largest, cheapest and fastest gains in most forest tree improvement programs can be made by ensuring the use of the proper species and seed sources within the species” • Various tree seed zonation maps developed by the Danida Forest Seed Centre (eg Tree Planting Zones in Nepal - an ecological approach; see Lillesø et al. 2005) • Hypothesis that vegetation boundaries reflect differences in adaptation of tree populations (genotype X environment interactions)
  6. 6. Transforming Lives and Landscapes with Trees baseline 2050s, RCP4.5 Ensemble suitability modelling with BiodiversityR • Ensemble modelling based on 25 algorithms (maximum entropy, boosted regression trees, random forests, artificial neural networks, support vector machines, (stepwise) GLM, (stepwise) GAM, BIOCLIM, DOMAIN, …) • Unique features such as tuning of ensemble weights, ‘environmental thinning’ of presence observations, transformation of suitability values, count suitability maps… • Baseline and future (2050s) modelling underway for various sets of species • 150+ useful tree species selected by PATSPO Ethiopia restoration project • 126 vegetable species important in Africa (in collaboration with World Vegetable Centre) • 100 species identified by the African Orphan Crops Consortium • 23 priority species for Gambia GCF • BiodiversityR has already been installed > 100,000 times in RStudio • Graphical User Interface and manuals Kindt 2018
  7. 7. Transforming Lives and Landscapes with Trees Explanatory variables (AFRICLIM / ENVIREM) moisture index (P/PET) (VIF: 3.44) Topographic wetness index (Variance Inflation Factor: 3.72) Mean diurnal range (VIF: 3.14) Topographic roughness index (VIF: 3.09) Maximum temp. of warmest month (VIF: 2.81) Minimum temp. of coldest month (VIF: 2.52) Precipitation of driest month (VIF: 1.55) 2/3 of bioclimatic variables 1 recommended by Booth 2016 to estimate potential (future) ranges of tree species 1 Growing Degree Days was strongly correlated with MI and BIO6 and therefore excluded
  8. 8. Transforming Lives and Landscapes with Trees Adansonia digitata baseline suitability Weighted average suitability Count of algorithms predicting presencePlants of the World online Presence observations More details in parallel session L2 – Agroforestry and Adaptation to Climate Change
  9. 9. Transforming Lives and Landscapes with Trees Juniperus procera baseline suitability Weighted average suitability Count of algorithms predicting presencePlants of the World online Presence observations More details in parallel session L2 – Agroforestry and Adaptation to Climate Change
  10. 10. Transforming Lives and Landscapes with Trees Adansonia digitata vegetation zones Distribution in geographical space (10 random samples per vegetation zone) Distribution in environmental space (redundancy analysis, P < 0.0001) Distribution in environmental space (Moisture index, Minimum Temperature Coldest Month) Vegetation codes as in www.vegetationmap4africa.org Constrained ordination methods: see Tree Diversity Analysis Manual - free download
  11. 11. Transforming Lives and Landscapes with Trees Adansonia digitata vegetation vs. clusters Distribution in geographical space (10 random samples per vegetation zone) Distribution in geographical space (K-means cluster analysis with K = number of vegetation zones) Distribution in environmental space (Moisture index, Minimum Temperature Coldest Month)
  12. 12. Transforming Lives and Landscapes with Trees Juniperus procera vegetation zones Distribution in geographical space (10 random samples per vegetation zone) Vegetation codes as in www.vegetationmap4africa.org Constrained ordination methods: see Tree Diversity Analysis Manual - free download Distribution in environmental space (redundancy analysis, P < 0.0001) Distribution in environmental space (Moisture index, Minimum Temperature Coldest Month)
  13. 13. Transforming Lives and Landscapes with Trees Juniperus procera vegetation vs. clusters Distribution in geographical space (10 random samples per vegetation zone) Distribution in geographical space (K-means cluster analysis with K = number of vegetation zones) Distribution in environmental space (Moisture index, Minimum Temperature Coldest Month)
  14. 14. Transforming Lives and Landscapes with Trees How much variation can be explained? Species Number of random points per vegetation type 10 (5 repetitions) 20 (5 repetitions) 50 (5 repetitions) 100 Vegetation K-means Vegetation K-means Vegetation K-means Veg. K-means Hagenia abyssinica 65.9 (59.6 - 71.1) 69.5 (58.6 - 75.8) 67.0 (65.8 - 68.4) 66.3 (61.1 - 72.3) 65.8 (63.9 - 68.3) 66.4 (61.2 - 71.8) 64.5 60.5 Balanites aegyptiaca 64.2 (57.7 - 68.2) 79.3 (74.3 - 84.9) 61.8 (57.5 - 64.3) 80.4 (79.4 - 82.2) 61.3 (59.5 - 63.5) 78.4 (71.5 - 81.8) 61.0 70.5 Tamarindus indica 53.1 (43.3 - 60.4) 75.7 (72.8 - 79.5) 57.1 (53.1 - 60.1) 68.7 (58.6 - 79.5) 53.1 (49.4 - 56.8) 73.1 (71.7 - 74.1) 51.4 69.6 Adansonia digitata 56.8 (53.4 - 59.9) 63.9 (57.0 - 72.5) 51.7 (48.9 - 56.7) 65.1 (60.2 - 70.7) 47.9 (46.8 - 49.6) 64.9 (62.5 - 69.2) 48.0 59.0 Juniperus procera 51.4 (41.7 - 59.7) 40.8 (25.1 – 58.0) * 48.8 (42.5 - 55.9) 39.7 (32.3 - 45.1) 46.6 (45.9 - 47.1) 47.4 (31.5 - 61.9) 45.9 46.1 Faidherbia albida 52.2 (45.1 - 55.6) 52.2 (38.6 – 62.0) 45.0 (43.7 - 48.2) 37.8 (32.5 - 48.6) 44.5 (41.3 – 49.0) 44.7 (35.0 - 49.7) 43.4 45.3 Cordia africana 46.8 (42.7 - 54.1) 68.6 (64.2 - 73.8) 44.4 (41.4 – 48.0) 69.3 (66.4 - 72.1) 41.3 (39.0 - 42.8) 64.2 (62.3 - 67.1) 40.7 64.3 Ziziphus jujuba 22.4 (18.2 - 25.4) * 30.5 (20.7 - 43.9) * 21.8 (17.0 - 27.8) 33.1 (25.7 - 47.7) 19.6 (17.7 - 21.7) 25.5 (18.5 - 31.5) 18.7 27.9 Afrocarpus falcatus 22.3 (15.5 - 27.6) * 44.4 (34.5 - 52.5) 18.9 (16.5 - 21.3) 42.2 (36.3 - 48.5) 16.0 (14.1 - 17.7) 37.5 (29.2 - 46.1) 16.0 42.4 A comparison between percentage of variation (‘inertia’) that can explained by vegetation type as explanatory variable or K-means cluster allocation as explanatory variable * Some cases where P was above Bonferroni threshold value of 0.000173 (0.05 / (9 x 16 x 2))
  15. 15. Transforming Lives and Landscapes with Trees • Statistical support that borders of the vegetationmap4africa correspond to differentiation (niches) in environmental space • However, for individual species, there may be groupings that distinguish bioclimatic variation better • Using the precautionary principle, it is good practice for selection of planting materials to match the vegetation types of sources and planting sites • The PNV concept for seed zonation is a hypothesis that can be tested via breeding seed orchard, common garden or ecological genomics research Summary and conclusions
  16. 16. Transforming Lives and Landscapes with Trees World Agroforestry (ICRAF), United Nations Avenue, Gigiri, P.O Box 30677-00100, Nairobi, Kenya Phone: +254 20 722 4000 Fax: +254 20 722 4001 Email: icraf@CGIAR.org Website: www.worldagroforestry.org Thank you! Roeland KINDT R.Kindt@CGIAR.org Google Scholar

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