Electronic Atlas for All Marine Species Rainer Froese

Electronic Atlas for All Marine Species Rainer Froese IFM-GEOMAR rfroese@ifm-geomar. de 1

Rainer’s Project Ambitious goals: Create an online Atlas with standardized maps for eventually all marine species – Maps for 100, 000 species – 3 -D maps (with depth) – Before-After maps – Seasonal maps – Predictive maps 2

Rainer’s Project Assumptions: • Algorithms and partners available (Bi. OSC, Why. Where, Lifemapper) • Enough point data available (OBIS) • Suitable ‘future ocean’ data available Reality check: • Life. Mapper stopped mapping, GARP-maps not reproducible, can not be edited • Not enough points in OBIS 3

Point Data in OBIS • 13. 7 million records for 80, 000 species • Only ~ 22, 000 species with > 10 points (11/2007) 4

Rainer’s Project Assumptions: • Algorithms and partners available (Bi. OSC, Why. Where, Lifemapper) • Enough point data available (OBIS) • Suitable ‘future ocean’ data available Reality check: • Life. Mapper stopped mapping, GARP-maps not reproducible, can not be edited • OBIS has points (>10) for only 22, 000 species • ‘Future ocean’ data problematic (modeling of primary production unsatisfactory) • So? ? ? 5

Actions • More money: INCOFISH WP Biomapping (0. 5 M$, Sven Kullander, Jonathan Ready) • More support: Fish. Base (Kathy Reyes), Sealife. Base (M. D. L. Palomares), CSIRO (Tony Rees), SAUP (Kristin Kaschner, Reg Watson), Hexacorals (Daphne Fautin) • Three workshops to develop, test and implement a suitable algorithm 6

Environmental envelope type modeling approach Physical • bathymetry • sea temperature • salinity • ice concentration Species-specific environmental envelope Relative probability of occurrence PMax Biological • primary production Predictor Min Preferred max Max 7

Getting Minimum Information Grey triggerfish Balistes capriscus Gmelin, 1789 § Depth range § Pelagic (No) § FAO areas § Bounding box coordinates § occurrence points (minimum of 10) OBIS – Ocean Biogeographic Information System (www. iobis, org) GBIF – Global Biodiversity Information Facility (www. gbif. org) Species and online point databases are primary sources of key minimum input data. 8

Selecting “Good Point Data” • Occurrence points within bounding box or known FAO areas Bounding box or FAO area limits serve as independent verification of the validity of occurrence records. 9

Extracting Environmental Parameters Global grid of 259, 200 half degree cells Good cells are used to derive the range of environmental parameters within the species’ native range. 10

Building Environmental Envelopes • depth from the literature • range minima and maxima are derived from “good cells” • preferred min = 10 th percentile; preferred max = 90 th percentile The environmental envelopes describe tolerances of a species with respect to each environmental parameter. 11

Predicting Probability of Occurrence Relative probability of occurrence PMax Predictor Min Preferred max Max Pc = Pbathymetryc x Ptemperaturec x Psalinityc x PPPc x PIce. Concc The overall probability of species occurrence for a given cell is the product of the probabilities under the different environmental parameters. 12

Plotting Species Aqua. Maps Predictions document large-scale and long-term presence of a species. They cannot be assumed to 13 precisely represent local occurrence of a species on a specific day of a specific year.

Actions • More money: INCOFISH WP Biomapping (0. 5 M$, Sven Kullander, Johnathan Ready) • More support: Fish. Base (Kathy Reyes), Sealife. Base (M. D. L. Palomares), CSIRO (Tony Rees), SAUP (Kristin Kaschner), Hexacorals (Daphne Fautin) • Three workshops to develop, test and implement a suitable algorithm • More data: ZIN collection, Gazetteer (Nina Bogutskaya) 14

St. Petersburg Fish Collection 30, 000 records with 15, 000 coordinates from Russia 15

Gazetteer of Type Localities 7, 000 coordinates from W. Eschmeyer’s Catalogue of fishes 20, 000 coordinates added by ZIN 16

Actions • More money: INCOFISH WP Biomapping (0. 5 M$, Sven Kullander, Johnathan Ready) • More support: Fish. Base (Kathy Reyes), Sealife. Base (M. D. L. Palomares), CSIRO (Tony Rees), SAUP (Kristin Kaschner), Hexacorals (Daphne Fautin) • Three workshops to develop, test and implement a suitable algorithm • More data: ZIN collection, Gazetteer (Nina Bogutskaya) • Design map interface, start mapping Internet Example 17

www. aquamaps. org 18

Whale Shark 19

Latimeria chalumnae 20

Blackfin Spiderfish (deep sea) 21

Porbeagle (Lamna nasus) 22

Actions • More money: INCOFISH WP Biomapping (0. 5 M$, Sven Kullander, Johnathan Ready) • More support: Fish. Base (Kathy Reyes), Sealife. Base (M. D. L. Palomares), CSIRO (Tony Rees), SAUP (Kristin Kaschner), Hexacorals (Daphne Fautin) • Three workshops to develop, test and implement a suitable algorithm • More data: ZIN collection, Gazetteer (Nina Bogutskaya) • Design map interface Internet Example • Start mass-production of maps • Do cross-species maps 23

Species Richness of 7, 237 Species (of 230, 000) 24

25

Species Richness of Sharks & Rays Including 437 maps of altogether 968 sharks & rays (45%) 26

Species Richness Merlucciidae 27

Mean Trophic Level of Merlucciidae 28

Atlantic Deep Lobster 29

Seasonal distribution of Mola mola 30

Before – After Map 31

Modelled effects of climate change from 2000 to 2050 on the suitable habitat for Bogue (Boops boops) Move north! 32 Predictive Map by Jonathan Ready Foto R. Freitas

Next Steps • Involve OBIS (Co. ML), GBIF, Eo. L – 10 -12 December 2007 4 th Mapping workshop in Kiel, on mass-production of maps • Find more funding – European Species. Base initiative • Write 10, 000 Maps paper in 2007 • Go freshwater – NRM Stockholm • Go terrestrial – SMNS Stuttgart 33

Thanks to the Pew Fellows Program for making this possible 34