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Visualising the future of our planet – Can we do better than heat maps? Museo Nacional de Ciencias Naturales (CSIC), Spain

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Presentation on theme: "Visualising the future of our planet – Can we do better than heat maps? Museo Nacional de Ciencias Naturales (CSIC), Spain"— Presentation transcript:

1 Visualising the future of our planet – Can we do better than heat maps? Museo Nacional de Ciencias Naturales (CSIC), Spain / Joaquín Hortal Microsoft Research Ltd. Cambridge, 16-17/2012

2 Biodiversity information Quality (and quantity) of data: Wallacean Shortfall Mapping unknown species distributions Mapping ignorance

3 Imagine a magnificent and omniscient GIS for all the Earth’s living species, with the capacity to display any level of the Linnaean hierarchy on any spatial scale, for any season of the year. biodiversity and biogeography Colwell & Coddington Phil Trans Roy Soc B 1994

4 digitize available distributional information:  Natural History collections ▪ Institutional (Museums, Herbaria) ▪ Private collections gathering biodiversity information

5 digitize available distributional information:  Natural History collections ▪ Institutional (Museum, Herbaria) ▪ Private collections  Literature gathering biodiversity information

6 digitize available distributional information:  Natural History collections ▪ Institutional (Museums, Herbaria) ▪ Private collections  Literature  ad hoc surveys gathering biodiversity information

7 integrate all information on the distribution of biodiversity biodiversity databases Map of Life ; ;

8 Adequate distribution data is lacking for many of the known species and higher taxa (Lomolino 2004) Whittaker et al. Div Distr 2005; Hortal et al. Conserv Biol 2007 Wallacean shortfall

9 Adequate distribution data is lacking for many of the known species and higher taxa (Lomolino 2004) 1,131 species 1,084,971 records 960 records/species 128 records/grid cell Tenerife seed plants Whittaker et al. Div Distr 2005; Hortal et al. Conserv Biol 2007 Wallacean shortfall

10 Tenerife seed plants Records Observed Richness Whittaker et al. Div Distr 2005; Hortal et al. Conserv Biol 2007 Wallacean shortfall Adequate distribution data is lacking for many of the known species and higher taxa (Lomolino 2004)

11 taxonomic error Lozier et al J Biogeogr 2009

12 taxonomic error Lozier et al J Biogeogr 2009

13 taxonomic bias Baselga et al Biodiv Conserv 2007

14 recorder’s home range hotspots spatial bias Dennis & Thomas J Insect Conserv 2000

15 accessibility: ‘roadside bias’ spatial bias Kadmon et al Ecol Appl 2003; Hurlbert & Jetz PNAS 2007

16 butterflies scarab dung beetles bias differs between groups Hortal et al Biod Conserv 2001; Hortal et al Ecography 2004 spatial bias

17 Onthophagus fracticornis Lobo et al. Div Distr 2007 temporal bias

18 Historical survey process has been incomplete and biased:  Taxonomic bias  Temporal bias  Spatial bias quality of distributional data Pineda & Lobo J Anim Ecol 2009

19 Historical survey process has been incomplete and biased:  Taxonomic bias  Temporal bias  Spatial bias quality of distributional data Pineda & Lobo J Anim Ecol 2009 Current biodiversity picture depends on the survey process

20 Historical survey process has been incomplete and biased:  Taxonomic bias  Temporal bias  Spatial bias quality of distributional data Pineda & Lobo J Anim Ecol 2009 Current biodiversity picture depends on the survey process Current knowledge on species distribution patterns may depend on survey unevenness rather than on their actual distributions

21 fill in the gaps expert opinion predictive models mapping species distributions Carabus granulatus Copris hispanus Hortal J Biogeogr 2008; Penev et al The genus Carabus in Europe 2007; Chefaoui et al Biol Conserv 2005

22 neither the species are present everywhere within their range maps, nor all their known occurrences are within these range maps inconsistencies with atlas data Hurlbert & White Ecol Lett 2005

23 these mismatches are scale dependent inconsistencies with atlas data Hurlbert & Jetz PNAS 2007

24 probability of presence environmental gradient land classes limited knowledge on the predictors the actual responses of the species to the environment are unknown

25 data incompleteness Total All species 23 First recorded species the descriptions of the environmental responses of most species are incomplete and biased Hortal et al Oikos 2008

26 Chefaoui et al Anim Biodiv Conserv 2011 expert-drawn observed plots predictive models hybrid approach fine coarse uncertainty in predictions different techniques predict different distribution patterns whorl snail Vertigo mouninsiana southern damselfly Coenagrion mercuriale GLMGAMNNET

27 Araújo & Rahbek Science 2006; Lawler et al Global Change Biol 2006 uncertainty in future projections

28 other determinants of the distribution historical effects e.g., Lobo et al. Div Distr 2006 Chefaoui & Lobo J Wildl Man 20º7 Spanish moon moth Graellsia isabelae

29 ensemble forecasting Araújo & New Tree 2006 dealing with uncertainty?

30 maps of ignorance Boggs Proc Am Phil Soc 1949 a region is an “area of ignorance” if the total library resources of the outside world do not cover it (Boggs 1949)

31 maps of ignorance Boggs Proc Am Phil Soc 1949 a region is an “area of ignorance” if the total library resources of the outside world do not cover it (Boggs 1949)

32 accuracy of knowledge Hortal, Ladle et al in prep. K p = f ( [K 0 ·C], L t, L s ) K p = accuracy of the knowledge about a given taxon or community at area p K 0 = knowledge about such taxon or community at each area in the moment of the survey C = degree of completeness of the survey L t = loss of knowledge across time L s = loss of knowledge across space

33 – Taxonomic accuracy – Detectability (crypsis, phenology) – Adequacy of sampling method and dates – Interactions – Size of focal unit – Habitat heterogeneity – Sampling effort and success quality of initial knowledge Hortal, Ladle et al in prep.

34 Temporal decay of similarity: - Changes in taxonomy - Turnover of species (mobility, phenotypic traits) - Area of unit (small  higher turnover) - Range shifts (climate change) - Local extinctions (land use changes, biological invasions) temporal loss of knowledge Hortal, Ladle et al in prep. Magersfontein battlefield, South Africa (from Moustakas et al Front Biogeogr 2010)

35 Distance decay of similarity: - Taxon specific - Biogeographical changes - Environmental gradients - Metacommunity structure - Habitat specificity (niche width) spatial loss of knowledge Hortal, Ladle et al in prep. Magersfontein battlefield, South Africa (from Green et al Nature 2004) Species: metacommunity structure / habitat specificity (niche width) / changes in climatic scenopoetic conditions

36 1. develop tools to map ignorance - how to measure taxonomic uncertainty - how to assess uncertainty in observations - how to map the degree of reliability of species distribution models in each point of space - how to determine when distribution is being extrapolated - how… 2. attach maps of ignorance as metadata for any distributional map suggestions are welcome! looking forward

37 1. develop tools to map ignorance - how to measure taxonomic uncertainty - how to assess uncertainty in observations - how to map the degree of reliability of species distribution models in each point of space - how to determine when distribution is being extrapolated - how… 2. attach maps of ignorance as metadata for any distributional map suggestions are welcome! looking forward

38 Museo Nacional de Ciencias Naturales (CSIC), Spain ; Joaquín Hortal Richard J. Ladle Geiziane Tessarolo Jorge M. Lobo Duccio Rocchini and many others...


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