GeoServer Web Map Tile Service Predefined map tiles generated from vector data, raster data, and imagery. OGC WMTS 1.0.0 NONE NONE GeoServer EMODnet Biology team Oostende 8400 Belgium bio@emodnet.eu KVP KVP KVP alpha_diversity 10.291228707624114 54.81334288967846 24.0605432179078 65.98905153837853 alpha_diversity image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:4326:0 0 0 1 1 EPSG:4326:1 0 0 2 2 EPSG:4326:2 0 0 4 4 EPSG:4326:3 1 1 8 9 EPSG:4326:4 2 3 16 18 EPSG:4326:5 4 6 33 36 EPSG:4326:6 8 12 67 72 EPSG:4326:7 17 25 135 145 EPSG:4326:8 34 50 270 290 EPSG:4326:9 68 100 541 580 EPSG:4326:10 136 200 1082 1160 EPSG:4326:11 273 400 2165 2321 EPSG:4326:12 546 800 4330 4643 EPSG:4326:13 1092 1601 8660 9287 EPSG:4326:14 2185 3202 17320 18574 EPSG:4326:15 4371 6405 34641 37148 EPSG:4326:16 8742 12811 69282 74296 EPSG:4326:17 17484 25622 138565 148592 EPSG:4326:18 34968 51244 277131 297184 EPSG:4326:19 69936 102488 554263 594369 EPSG:4326:20 139873 204977 1108526 1188738 EPSG:4326:21 279747 409954 2217053 2377477 EPSG:900913 EPSG:900913:0 0 0 0 0 EPSG:900913:1 0 0 1 1 EPSG:900913:2 1 1 2 2 EPSG:900913:3 2 2 4 4 EPSG:900913:4 4 5 8 9 EPSG:900913:5 8 10 16 18 EPSG:900913:6 16 20 33 36 EPSG:900913:7 32 40 67 72 EPSG:900913:8 64 81 135 145 EPSG:900913:9 129 162 270 290 EPSG:900913:10 259 324 541 580 EPSG:900913:11 519 649 1082 1160 EPSG:900913:12 1038 1299 2165 2321 EPSG:900913:13 2077 2598 4330 4643 EPSG:900913:14 4155 5197 8660 9287 EPSG:900913:15 8310 10394 17320 18574 EPSG:900913:16 16620 20788 34641 37148 EPSG:900913:17 33241 41576 69282 74296 EPSG:900913:18 66483 83152 138565 148592 EPSG:900913:19 132967 166304 277131 297184 EPSG:900913:20 265935 332608 554263 594369 EPSG:900913:21 531870 665216 1108526 1188738 EPSG:900913:22 1063741 1330432 2217053 2377477 EPSG:900913:23 2127483 2660864 4434107 4754955 EPSG:900913:24 4254967 5321728 8868214 9509910 EPSG:900913:25 8509934 10643456 17736428 19019821 EPSG:900913:26 17019869 21286913 35472856 38039642 EPSG:900913:27 34039739 42573827 70945712 76079284 EPSG:900913:28 68079478 85147655 141891424 152158569 EPSG:900913:29 136158957 170295310 283782848 304317138 EPSG:900913:30 272317914 340590620 567565697 608634277 Habitat suitability maps for harbour porpoises in the North Sea-Baltic Sea transition zone from 1997 to 2022 The dataset includes 6 layers showing modelled habitat suitability for harbour porpoises (Phocoena phocoena) across the North Sea–Baltic Sea transition zone over a 25-year period (1997–2004; 2005-2012; 2013-2022) during winter (Nov–Apr) and summer (May–Oct). Habitat suitability was estimated using the maximum entropy (MaxEnt) algorithm, based on satellite tag data of 111 individuals and a range of dynamic and static environmental conditions as predictor variables. Habitat suitability models and maps were developed by researchers of the Department of Ecoscience, Aarhus University to quantify potential spatiotemporal changes in the overlap between suitable habitats and an existing network of 62 MPAs dedicated to harbour porpoise protection. The habitat suitability rasters can inform effective design of forthcoming MPAs under the EU Biodiversity Strategy for 2030. Harbour porpoise habitat suitability layers cover a seascape area of 70,145 km2. Habitat suitability layers are available for the periods: 1997–2004; 2005-2012; 2013-2022 and for both winter (Nov–Apr) and summer (May–Oct). All layers are projected using the EEA reference grid (ETRS89-LAEA Europe, EPSG:3035), have a 1x1 km spatial resolution (cell size) and a spatial extent of 4275000, 4545000, 3415000, 4105000 (xmin, xmax, ymin, ymax). Habitat suitability values (probabilities) range between 1 and 0 with values closer to 1 indicating habitat being highly suitable for harbour porpoises while values closer to 0 indicate habitat that is poorly suitable for harbour porpoises. Location data underlying the habitat suitability models were collected from 111 harbour porpoises tracked over the period 1997–2022 as part of a long-term satellite telemetry monitoring program in Denmark. Location and meta data are freely available at Zenodo (https://doi.org/10.5281/zenodo.13968991).  Harbour porpoise habitat suitability layers were produced as part of the projects: GES4SEAS (grant agreement no. 101059877) and OBAMA-NEXT (101091642)) 9.094689276840526 53.71659362409777 14.080289276840528 60.12646362409777 habitat_suitability_porpoise image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:4326:0 0 0 1 1 EPSG:4326:1 0 0 2 2 EPSG:4326:2 0 0 4 4 EPSG:4326:3 1 1 8 8 EPSG:4326:4 2 3 16 17 EPSG:4326:5 5 6 33 34 EPSG:4326:6 10 12 67 69 EPSG:4326:7 21 25 134 138 EPSG:4326:8 42 51 268 276 EPSG:4326:9 84 103 537 552 EPSG:4326:10 169 206 1075 1104 EPSG:4326:11 339 412 2151 2208 EPSG:4326:12 679 825 4302 4416 EPSG:4326:13 1359 1651 8605 8832 EPSG:4326:14 2719 3302 17211 17665 EPSG:4326:15 5438 6605 34423 35331 EPSG:4326:16 10876 13210 68847 70662 EPSG:4326:17 21753 26420 137694 141324 EPSG:4326:18 43506 52841 275389 282649 EPSG:4326:19 87012 105683 550778 565299 EPSG:4326:20 174025 211366 1101556 1130599 EPSG:4326:21 348051 422732 2203112 2261199 EPSG:900913 EPSG:900913:0 0 0 0 0 EPSG:900913:1 0 0 1 1 EPSG:900913:2 1 1 2 2 EPSG:900913:3 2 2 4 4 EPSG:900913:4 4 5 8 8 EPSG:900913:5 9 10 16 17 EPSG:900913:6 18 20 33 34 EPSG:900913:7 37 41 67 69 EPSG:900913:8 74 82 134 138 EPSG:900913:9 148 165 268 276 EPSG:900913:10 296 330 537 552 EPSG:900913:11 593 660 1075 1104 EPSG:900913:12 1186 1320 2151 2208 EPSG:900913:13 2373 2641 4302 4416 EPSG:900913:14 4746 5282 8605 8832 EPSG:900913:15 9492 10564 17211 17665 EPSG:900913:16 18985 21129 34423 35331 EPSG:900913:17 37970 42259 68847 70662 EPSG:900913:18 75941 84519 137694 141324 EPSG:900913:19 151883 169039 275389 282649 EPSG:900913:20 303767 338078 550778 565299 EPSG:900913:21 607535 676156 1101556 1130599 EPSG:900913:22 1215071 1352312 2203112 2261199 EPSG:900913:23 2430143 2704625 4406225 4522398 EPSG:900913:24 4860287 5409250 8812451 9044797 EPSG:900913:25 9720575 10818500 17624902 18089594 EPSG:900913:26 19441150 21637000 35249804 36179188 EPSG:900913:27 38882301 43274001 70499609 72358376 EPSG:900913:28 77764602 86548003 140999219 144716752 EPSG:900913:29 155529204 173096007 281998439 289433505 EPSG:900913:30 311058408 346192014 563996879 578867010 Seabed habitat and macrobenthos occurrences in European seas This product is built from two existing EMODnet data products. It uses the EUSeaMap product created by EMODNet Seabed Habitats and the Presence/absence data of macrozoobenthos in the European Seas product created by EMODnet Biology to obtain lists of all macrobenthic species occurring in all EUNIS seabed habitat types. For the purposes of this product the seabed habitat map is summarised on a grid at approx. 500m resolution, with each grid cell assigned to the dominant EUNIS code present within it. Summaries of benthic diversity are created for each grid cell, as well as more synthetic summaries by EUNIS category. The full matched benthos community data was written to file in the derived data folder as benthos_full_matched in parquet format with gzip compression. -35.97180141999996 23.680472180000038 43.00059858000003 81.54087218000004 eusm_benthos_n_surveys image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:4326:0 0 0 0 1 EPSG:4326:1 0 0 1 2 EPSG:4326:2 0 1 3 4 EPSG:4326:3 0 2 6 9 EPSG:4326:4 0 5 12 19 EPSG:4326:5 1 11 25 39 EPSG:4326:6 3 23 51 79 EPSG:4326:7 6 47 102 158 EPSG:4326:8 12 94 204 317 EPSG:4326:9 24 188 409 634 EPSG:4326:10 48 377 819 1268 EPSG:4326:11 96 754 1638 2537 EPSG:4326:12 192 1509 3277 5074 EPSG:4326:13 384 3018 6554 10149 EPSG:4326:14 769 6036 13109 20298 EPSG:4326:15 1539 12073 26219 40596 EPSG:4326:16 3079 24146 52439 81192 EPSG:4326:17 6159 48292 104878 162384 EPSG:4326:18 12319 96584 209756 324768 EPSG:4326:19 24638 193169 419512 649536 EPSG:4326:20 49277 386339 839025 1299072 EPSG:4326:21 98555 772678 1678050 2598145 EPSG:900913 EPSG:900913:0 0 0 0 0 EPSG:900913:1 0 0 0 1 EPSG:900913:2 0 1 1 2 EPSG:900913:3 0 3 3 4 EPSG:900913:4 1 6 6 9 EPSG:900913:5 2 13 12 19 EPSG:900913:6 5 27 25 39 EPSG:900913:7 10 55 51 79 EPSG:900913:8 21 110 102 158 EPSG:900913:9 43 221 204 317 EPSG:900913:10 87 442 409 634 EPSG:900913:11 175 885 819 1268 EPSG:900913:12 350 1770 1638 2537 EPSG:900913:13 700 3541 3277 5074 EPSG:900913:14 1401 7082 6554 10149 EPSG:900913:15 2802 14164 13109 20298 EPSG:900913:16 5604 28328 26219 40596 EPSG:900913:17 11208 56657 52439 81192 EPSG:900913:18 22416 113315 104878 162384 EPSG:900913:19 44832 226630 209756 324768 EPSG:900913:20 89665 453261 419512 649536 EPSG:900913:21 179330 906523 839025 1299072 EPSG:900913:22 358660 1813046 1678050 2598145 EPSG:900913:23 717320 3626093 3356100 5196290 EPSG:900913:24 1434641 7252186 6712200 10392581 EPSG:900913:25 2869283 14504373 13424401 20785162 EPSG:900913:26 5738566 29008747 26848802 41570324 EPSG:900913:27 11477132 58017495 53697604 83140649 EPSG:900913:28 22954265 116034990 107395208 166281298 EPSG:900913:29 45908530 232069981 214790417 332562596 EPSG:900913:30 91817060 464139963 429580835 665125192 Phytoplankton alpha diversity in the greater Baltic Sea area The number of marine seaweeds outside their natural boundaries has increased in the last decades generating impacts on biodiversity and economy. This makes the development of management tools necessary, where species distribution models (SDMs) play a crucial role. SDMs can help in the early detection of invasions and predict the extent of the potential spread. However, modelling non-native marine species distributions is still challenging in terms of model building, evaluation and selection. This product aims to predict the European distribution of four widespread introduced seaweed species selecting the best model building process. 10.291228707624114 54.81334288967846 24.0605432179078 65.98905153837853 alpha_diversity_richness image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:4326:0 0 0 1 1 EPSG:4326:1 0 0 2 2 EPSG:4326:2 0 0 4 4 EPSG:4326:3 1 1 8 9 EPSG:4326:4 2 3 16 18 EPSG:4326:5 4 6 33 36 EPSG:4326:6 8 12 67 72 EPSG:4326:7 17 25 135 145 EPSG:4326:8 34 50 270 290 EPSG:4326:9 68 100 541 580 EPSG:4326:10 136 200 1082 1160 EPSG:4326:11 273 400 2165 2321 EPSG:4326:12 546 800 4330 4643 EPSG:4326:13 1092 1601 8660 9287 EPSG:4326:14 2185 3202 17320 18574 EPSG:4326:15 4371 6405 34641 37148 EPSG:4326:16 8742 12811 69282 74296 EPSG:4326:17 17484 25622 138565 148592 EPSG:4326:18 34968 51244 277131 297184 EPSG:4326:19 69936 102488 554263 594369 EPSG:4326:20 139873 204977 1108526 1188738 EPSG:4326:21 279747 409954 2217053 2377477 EPSG:900913 EPSG:900913:0 0 0 0 0 EPSG:900913:1 0 0 1 1 EPSG:900913:2 1 1 2 2 EPSG:900913:3 2 2 4 4 EPSG:900913:4 4 5 8 9 EPSG:900913:5 8 10 16 18 EPSG:900913:6 16 20 33 36 EPSG:900913:7 32 40 67 72 EPSG:900913:8 64 81 135 145 EPSG:900913:9 129 162 270 290 EPSG:900913:10 259 324 541 580 EPSG:900913:11 519 649 1082 1160 EPSG:900913:12 1038 1299 2165 2321 EPSG:900913:13 2077 2598 4330 4643 EPSG:900913:14 4155 5197 8660 9287 EPSG:900913:15 8310 10394 17320 18574 EPSG:900913:16 16620 20788 34641 37148 EPSG:900913:17 33241 41576 69282 74296 EPSG:900913:18 66483 83152 138565 148592 EPSG:900913:19 132967 166304 277131 297184 EPSG:900913:20 265935 332608 554263 594369 EPSG:900913:21 531870 665216 1108526 1188738 EPSG:900913:22 1063741 1330432 2217053 2377477 EPSG:900913:23 2127483 2660864 4434107 4754955 EPSG:900913:24 4254967 5321728 8868214 9509910 EPSG:900913:25 8509934 10643456 17736428 19019821 EPSG:900913:26 17019869 21286913 35472856 38039642 EPSG:900913:27 34039739 42573827 70945712 76079284 EPSG:900913:28 68079478 85147655 141891424 152158569 EPSG:900913:29 136158957 170295310 283782848 304317138 EPSG:900913:30 272317914 340590620 567565697 608634277 Probability maps for different benthos species in the North Sea Probability of occurrence of different macroinvertebrate benthic species in the North Sea. This product was created using DIVAnd, an interpolation method that takes into account several environmental variables and physical coastlines. -16.05 44.95 9.05 66.05000000000001 probability image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:4326:0 0 0 0 1 EPSG:4326:1 0 0 1 2 EPSG:4326:2 0 1 3 4 EPSG:4326:3 1 2 7 8 EPSG:4326:4 2 4 14 16 EPSG:4326:5 4 8 29 33 EPSG:4326:6 8 16 58 67 EPSG:4326:7 17 32 116 134 EPSG:4326:8 34 64 233 268 EPSG:4326:9 68 128 466 537 EPSG:4326:10 136 256 932 1075 EPSG:4326:11 272 512 1865 2150 EPSG:4326:12 544 1025 3730 4301 EPSG:4326:13 1089 2050 7461 8603 EPSG:4326:14 2179 4100 14923 17207 EPSG:4326:15 4359 8201 29846 34415 EPSG:4326:16 8719 16402 59692 68831 EPSG:4326:17 17439 32804 119384 137662 EPSG:4326:18 34879 65608 238769 275324 EPSG:4326:19 69759 131217 477538 550648 EPSG:4326:20 139518 262435 955077 1101296 EPSG:4326:21 279037 524870 1910155 2202592 EPSG:900913 EPSG:900913:0 0 0 0 0 EPSG:900913:1 0 0 0 1 EPSG:900913:2 1 1 1 2 EPSG:900913:3 2 2 3 4 EPSG:900913:4 4 5 7 8 EPSG:900913:5 8 11 14 16 EPSG:900913:6 16 23 29 33 EPSG:900913:7 32 46 58 67 EPSG:900913:8 64 92 116 134 EPSG:900913:9 129 184 233 268 EPSG:900913:10 259 368 466 537 EPSG:900913:11 518 737 932 1075 EPSG:900913:12 1037 1474 1865 2150 EPSG:900913:13 2074 2948 3730 4301 EPSG:900913:14 4148 5896 7461 8603 EPSG:900913:15 8296 11793 14923 17207 EPSG:900913:16 16593 23587 29846 34415 EPSG:900913:17 33187 47175 59692 68831 EPSG:900913:18 66374 94351 119384 137662 EPSG:900913:19 132749 188702 238769 275324 EPSG:900913:20 265498 377404 477538 550648 EPSG:900913:21 530997 754809 955077 1101296 EPSG:900913:22 1061994 1509619 1910155 2202592 EPSG:900913:23 2123989 3019239 3820311 4405184 EPSG:900913:24 4247978 6038478 7640623 8810368 EPSG:900913:25 8495957 12076957 15281247 17620737 EPSG:900913:26 16991914 24153914 30562495 35241474 EPSG:900913:27 33983829 48307828 61124990 70482948 EPSG:900913:28 67967658 96615657 122249980 140965897 EPSG:900913:29 135935317 193231314 244499961 281931794 EPSG:900913:30 271870634 386462628 488999922 563863588 Habitat suitability maps for harbour porpoises in the North Sea-Baltic Sea transition zone from 1997 to 2022 The dataset includes 6 layers showing modelled habitat suitability for harbour porpoises (Phocoena phocoena) across the North Sea–Baltic Sea transition zone over a 25-year period (1997–2004; 2005-2012; 2013-2022) during winter (Nov–Apr) and summer (May–Oct). Habitat suitability was estimated using the maximum entropy (MaxEnt) algorithm, based on satellite tag data of 111 individuals and a range of dynamic and static environmental conditions as predictor variables. Habitat suitability models and maps were developed by researchers of the Department of Ecoscience, Aarhus University to quantify potential spatiotemporal changes in the overlap between suitable habitats and an existing network of 62 MPAs dedicated to harbour porpoise protection. The habitat suitability rasters can inform effective design of forthcoming MPAs under the EU Biodiversity Strategy for 2030. 9.094689276840526 53.71659362409777 14.080289276840528 60.12646362409777 porpoise_habitat_suitability_Baltic image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:4326:0 0 0 1 1 EPSG:4326:1 0 0 2 2 EPSG:4326:2 0 0 4 4 EPSG:4326:3 1 1 8 8 EPSG:4326:4 2 3 16 17 EPSG:4326:5 5 6 33 34 EPSG:4326:6 10 12 67 69 EPSG:4326:7 21 25 134 138 EPSG:4326:8 42 51 268 276 EPSG:4326:9 84 103 537 552 EPSG:4326:10 169 206 1075 1104 EPSG:4326:11 339 412 2151 2208 EPSG:4326:12 679 825 4302 4416 EPSG:4326:13 1359 1651 8605 8832 EPSG:4326:14 2719 3302 17211 17665 EPSG:4326:15 5438 6605 34423 35331 EPSG:4326:16 10876 13210 68847 70662 EPSG:4326:17 21753 26420 137694 141324 EPSG:4326:18 43506 52841 275389 282649 EPSG:4326:19 87012 105683 550778 565299 EPSG:4326:20 174025 211366 1101556 1130599 EPSG:4326:21 348051 422732 2203112 2261199 EPSG:900913 EPSG:900913:0 0 0 0 0 EPSG:900913:1 0 0 1 1 EPSG:900913:2 1 1 2 2 EPSG:900913:3 2 2 4 4 EPSG:900913:4 4 5 8 8 EPSG:900913:5 9 10 16 17 EPSG:900913:6 18 20 33 34 EPSG:900913:7 37 41 67 69 EPSG:900913:8 74 82 134 138 EPSG:900913:9 148 165 268 276 EPSG:900913:10 296 330 537 552 EPSG:900913:11 593 660 1075 1104 EPSG:900913:12 1186 1320 2151 2208 EPSG:900913:13 2373 2641 4302 4416 EPSG:900913:14 4746 5282 8605 8832 EPSG:900913:15 9492 10564 17211 17665 EPSG:900913:16 18985 21129 34423 35331 EPSG:900913:17 37970 42259 68847 70662 EPSG:900913:18 75941 84519 137694 141324 EPSG:900913:19 151883 169039 275389 282649 EPSG:900913:20 303767 338078 550778 565299 EPSG:900913:21 607535 676156 1101556 1130599 EPSG:900913:22 1215071 1352312 2203112 2261199 EPSG:900913:23 2430143 2704625 4406225 4522398 EPSG:900913:24 4860287 5409250 8812451 9044797 EPSG:900913:25 9720575 10818500 17624902 18089594 EPSG:900913:26 19441150 21637000 35249804 36179188 EPSG:900913:27 38882301 43274001 70499609 72358376 EPSG:900913:28 77764602 86548003 140999219 144716752 EPSG:900913:29 155529204 173096007 281998439 289433505 EPSG:900913:30 311058408 346192014 563996879 578867010 Biomass-Weighted Fish Functional Feeding Traits for the Northeast Atlantic (1997-2020) This product enables users to assess spatial and temporal change in fish functional feeding traits across the northeast Atlantic shelf seas. Values are based on non-metric multidimensional axis scores, weighted by biomass observed in otter trawl surveys. This product is the result of collating and analyzing fish stomach contents data from the North Atlantic and Arctic Oceans to classify fish into feeding guilds. The feeding guilds identified include planktivores, benthivores, bentho-piscivores, and piscivores. Non-metric multidimensional scaling (NMDS) analysis was used to provide a more nuanced understanding of different predator feeding traits in relation to others. The NMDS analysis presents axis scores that offer a bounded data perspective, as opposed to categorical, of different predator feeding traits. This analysis is part of the process to classify feeding guilds based on predator stomach contents data. See figure S4 in https://essd.copernicus.org/preprints/essd-2024-102/essd-2024-102-supplement.pdf for a pictorial depiction. Uses attributes recommended by http://cfconventions.org -16.0 36.0 13.0 61.5 axis_score image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:4326:0 0 0 0 1 EPSG:4326:1 0 0 1 2 EPSG:4326:2 0 1 3 4 EPSG:4326:3 1 2 7 8 EPSG:4326:4 2 4 14 17 EPSG:4326:5 5 9 29 34 EPSG:4326:6 10 19 58 68 EPSG:4326:7 20 38 116 137 EPSG:4326:8 40 76 233 274 EPSG:4326:9 81 153 466 548 EPSG:4326:10 162 307 932 1097 EPSG:4326:11 324 614 1865 2195 EPSG:4326:12 648 1228 3731 4391 EPSG:4326:13 1297 2457 7463 8783 EPSG:4326:14 2594 4915 14927 17567 EPSG:4326:15 5188 9830 29855 35134 EPSG:4326:16 10376 19660 59710 70269 EPSG:4326:17 20753 39321 119421 140538 EPSG:4326:18 41506 78643 238842 281076 EPSG:4326:19 83012 157286 477684 562153 EPSG:4326:20 166024 314572 955369 1124306 EPSG:4326:21 332049 629145 1910738 2248612 EPSG:900913 EPSG:900913:0 0 0 0 0 EPSG:900913:1 0 0 0 1 EPSG:900913:2 1 1 1 2 EPSG:900913:3 2 3 3 4 EPSG:900913:4 4 6 7 8 EPSG:900913:5 9 12 14 17 EPSG:900913:6 18 25 29 34 EPSG:900913:7 36 50 58 68 EPSG:900913:8 72 100 116 137 EPSG:900913:9 144 201 233 274 EPSG:900913:10 288 402 466 548 EPSG:900913:11 577 804 932 1097 EPSG:900913:12 1154 1608 1865 2195 EPSG:900913:13 2309 3216 3731 4391 EPSG:900913:14 4618 6433 7463 8783 EPSG:900913:15 9236 12867 14927 17567 EPSG:900913:16 18472 25735 29855 35134 EPSG:900913:17 36945 51470 59710 70269 EPSG:900913:18 73890 102940 119421 140538 EPSG:900913:19 147781 205880 238842 281076 EPSG:900913:20 295562 411760 477684 562153 EPSG:900913:21 591125 823521 955369 1124306 EPSG:900913:22 1182250 1647043 1910738 2248612 EPSG:900913:23 2364500 3294086 3821476 4497225 EPSG:900913:24 4729001 6588173 7642953 8994451 EPSG:900913:25 9458002 13176346 15285907 17988903 EPSG:900913:26 18916005 26352692 30571815 35977807 EPSG:900913:27 37832011 52705385 61143631 71955615 EPSG:900913:28 75664022 105410771 122287263 143911230 EPSG:900913:29 151328044 210821543 244574526 287822461 EPSG:900913:30 302656088 421643086 489149053 575644922 Global predicted thermal distributions for intertidal macroalgae Ocean warming is expected to drive quick shifts in marine species distributions modifying coastal communities. Previous research has shown how the southern Bay of Biscay is particularly affected and the canopy-forming subtidal and intertidal macroalgal assemblages are expected to be replaced by turf-forming Mediterranean-like communities with warmer affinities by the end of the century. The effect of temperature on macroalgal communities could be assessed using thermal metrics that can target temperature effects alone. An example is the Community Temperature Index (CTI) which is an abundance-weighted mean of the species’ optimal temperature. It has been previously used to understand the thermal preferences of different organisms such us birds, fish or invertebrates. -180.0 -90.0 180.0 90.0 macroalgae_habitat_suitability image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Fraction of mixoplankton (photo-phagotrophic) species in the Greater North Sea and Celtic Seas Mixoplankton (sensu Flynn et al., 2019) is a newly introduced term indicating plankton that is capabable of both photosynthesis and phagotrophy. More details are found in Flynn et al., (2019). The potential trophic state can be seen as an inherent characteristic of plankton species. A literature and expert-knowledge study has provided the classification in either phototrophy or mixotrophy which is submitted as traits data to WoRMS. This analysis makes use of this classification to estimate the spatial and temporal distribution of the fraction of mixoplanktonic species in the Greater North Sea including the Celtic Seas. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 766327. -16.0 46.0 9.0 66.0 fraction_mixoplankton image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:4326:0 0 0 0 1 EPSG:4326:1 0 0 1 2 EPSG:4326:2 0 0 3 4 EPSG:4326:3 1 1 7 8 EPSG:4326:4 2 3 14 16 EPSG:4326:5 4 7 29 33 EPSG:4326:6 8 15 58 67 EPSG:4326:7 17 31 116 134 EPSG:4326:8 34 62 233 268 EPSG:4326:9 68 125 466 537 EPSG:4326:10 136 250 932 1075 EPSG:4326:11 273 500 1865 2150 EPSG:4326:12 546 1001 3731 4300 EPSG:4326:13 1092 2002 7463 8601 EPSG:4326:14 2184 4004 14927 17203 EPSG:4326:15 4369 8009 29855 34406 EPSG:4326:16 8738 16019 59710 68812 EPSG:4326:17 17476 32039 119421 137625 EPSG:4326:18 34952 64079 238842 275251 EPSG:4326:19 69905 128159 477684 550502 EPSG:4326:20 139810 256318 955369 1101004 EPSG:4326:21 279620 512637 1910738 2202009 EPSG:900913 EPSG:900913:0 0 0 0 0 EPSG:900913:1 0 0 0 1 EPSG:900913:2 1 1 1 2 EPSG:900913:3 2 2 3 4 EPSG:900913:4 4 5 7 8 EPSG:900913:5 8 11 14 16 EPSG:900913:6 16 22 29 33 EPSG:900913:7 32 45 58 67 EPSG:900913:8 64 91 116 134 EPSG:900913:9 129 182 233 268 EPSG:900913:10 259 364 466 537 EPSG:900913:11 519 728 932 1075 EPSG:900913:12 1038 1457 1865 2150 EPSG:900913:13 2077 2914 3731 4300 EPSG:900913:14 4154 5828 7463 8601 EPSG:900913:15 8308 11657 14927 17203 EPSG:900913:16 16616 23315 29855 34406 EPSG:900913:17 33232 46630 59710 68812 EPSG:900913:18 66464 93260 119421 137625 EPSG:900913:19 132928 186521 238842 275251 EPSG:900913:20 265857 373043 477684 550502 EPSG:900913:21 531714 746086 955369 1101004 EPSG:900913:22 1063428 1492173 1910738 2202009 EPSG:900913:23 2126856 2984346 3821476 4404019 EPSG:900913:24 4253713 5968692 7642953 8808038 EPSG:900913:25 8507426 11937384 15285907 17616076 EPSG:900913:26 17014852 23874768 30571815 35232153 EPSG:900913:27 34029705 47749537 61143631 70464307 EPSG:900913:28 68059411 95499074 122287263 140928614 EPSG:900913:29 136118823 190998149 244574526 281857228 EPSG:900913:30 272237646 381996298 489149053 563714457 Presence/Absence maps of phytoplankton in the Greater North Sea The project aims to produce comprehensive data product of the occurence and absence of (phyto)plankton species. As a basis, data from EMODnet Biology are used. The selection of relevant datasets is optimized in order to find all planktonic species, and exclude all species that are not planktonic. The occurences from EMODnet Biology were complemenented with absence data assuming fixed species lists within each dataset and year. The products are presented as maps of the distribution of the 100 most common species of (phyto)plankton in the Greater North Sea. This product then is also used for interpolated maps, using the DIVA software. -16.0 46.0 9.0 66.0 probability_occurrence_phyto_northsea image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:4326:0 0 0 0 1 EPSG:4326:1 0 0 1 2 EPSG:4326:2 0 0 3 4 EPSG:4326:3 1 1 7 8 EPSG:4326:4 2 3 14 16 EPSG:4326:5 4 7 29 33 EPSG:4326:6 8 15 58 67 EPSG:4326:7 17 31 116 134 EPSG:4326:8 34 62 233 268 EPSG:4326:9 68 125 466 537 EPSG:4326:10 136 250 932 1075 EPSG:4326:11 273 500 1865 2150 EPSG:4326:12 546 1001 3731 4300 EPSG:4326:13 1092 2002 7463 8601 EPSG:4326:14 2184 4004 14927 17203 EPSG:4326:15 4369 8009 29855 34406 EPSG:4326:16 8738 16019 59710 68812 EPSG:4326:17 17476 32039 119421 137625 EPSG:4326:18 34952 64079 238842 275251 EPSG:4326:19 69905 128159 477684 550502 EPSG:4326:20 139810 256318 955369 1101004 EPSG:4326:21 279620 512637 1910738 2202009 EPSG:900913 EPSG:900913:0 0 0 0 0 EPSG:900913:1 0 0 0 1 EPSG:900913:2 1 1 1 2 EPSG:900913:3 2 2 3 4 EPSG:900913:4 4 5 7 8 EPSG:900913:5 8 11 14 16 EPSG:900913:6 16 22 29 33 EPSG:900913:7 32 45 58 67 EPSG:900913:8 64 91 116 134 EPSG:900913:9 129 182 233 268 EPSG:900913:10 259 364 466 537 EPSG:900913:11 519 728 932 1075 EPSG:900913:12 1038 1457 1865 2150 EPSG:900913:13 2077 2914 3731 4300 EPSG:900913:14 4154 5828 7463 8601 EPSG:900913:15 8308 11657 14927 17203 EPSG:900913:16 16616 23315 29855 34406 EPSG:900913:17 33232 46630 59710 68812 EPSG:900913:18 66464 93260 119421 137625 EPSG:900913:19 132928 186521 238842 275251 EPSG:900913:20 265857 373043 477684 550502 EPSG:900913:21 531714 746086 955369 1101004 EPSG:900913:22 1063428 1492173 1910738 2202009 EPSG:900913:23 2126856 2984346 3821476 4404019 EPSG:900913:24 4253713 5968692 7642953 8808038 EPSG:900913:25 8507426 11937384 15285907 17616076 EPSG:900913:26 17014852 23874768 30571815 35232153 EPSG:900913:27 34029705 47749537 61143631 70464307 EPSG:900913:28 68059411 95499074 122287263 140928614 EPSG:900913:29 136118823 190998149 244574526 281857228 EPSG:900913:30 272237646 381996298 489149053 563714457 Modelled projections of habitat for commercial fish around North-western Europe under climate change, 2020 to 2060 Environmental Niche Model (ENM) outputs for 49 commercial fish species under climate change until the decade of 2060 around northwestern Europe. A model ensemble of 5 ENMs was used (MaxEnt, Generalised Linear Models, Support Vector Machine, Random Forest and BIOCLIM ), and projections were made under three different emission scenarios: A1B, RCP4.5 and RCP 8.5. The data shows model agreement (normalised to 1) for presence/absence decadal projections from 2020 to 2060. Additionally we provide data on model performance, with the Area Under the Curve (AUC) scores of the Receiver Operator Characteristic (ROC) curve for each of the 5 ENMs trained for each combination of fish species and emission scenario. Only ENMs with an AUC score of at least 0.7 were considered. The data is based on the output of an ensemble of 5 Environmental Niche Modeling techniques: Maximum Entropy (MaxEnt), Generalised Linear Model (GLM), Support Vector Machine (SVM), Random Forest and BIOCLIM. Each model was trained to capture the environmental requirements of individual fish species, using the 20 year-average in the period 1997-2016 of the environmental variables: near-bed sea temperature, sea surface temperature, near-bed salinity, sea surface salinity, the difference in near-bed and surface salinity and temperature (an approximation of stratification), and depth. Once the models were trained, they were used to study future habitat suitability for the 49 commercially-important fish species in 20-year averages centered in 2020, 2030, 2040, 2050 and 2060. To produce maps of the suitable habitat, the model projections were converted to binary presence/absence data using the thresholds that optimized the True Skill Statistics (TSS) of each model. The data shows the model agreement, normalised to 1 ("1" denotes all ENMs agree an area is suitable, "0" denotes all ENMs agree an area is not suitable). The number of ENMs considered for each combination of fish species and emission scenario depends on model performance. A ENM model was considered if its Area Under the Curve (AUC) score of the Receiver Operator Characteristic (ROC) curve was at least 0.7. Model performance is listed in the file "Model_performance_AUC.xlsx". Model input data: Climate change projection data from Met Office and Plymouth Marine Laboratory (PML) and species presence and abundance records from online databases (Ocean Biodiversity Information System (OBIS), Global Biodiversity Information System (GBIF) and Marine Scotland (Moriarty, M., Greenstreet, S.P.R. and Rasmussen, J. (2017) Derivation of Groundfish Survey Monitoring and Assessment Data Product for the Northeast Atlantic Area. Scottish Marine and Freshwater Science, 8, 240pp. DOI: 10.7489/1984-1). Models were trained using the R and Maxent softwares. Study region: northeast Atlantic shelf. Latitudes between -17° and 9.25°; longitudes between 44° and 65°. These datasets were produced for the study in the article "Climate change projections of commercial fish distribution and suitable habitat around northwestern Europe", by Bryony L. Townhill, Elena Couce, Jonathan Tinker, Susan Kay, John K. Pinnegar, in Fish and Fisheries (2023; http://doi.org/10.1111/faf.12773). -17.1666574557696 43.8333484883211 9.166677367145837 64.83334967621568 mod_hab_comm_fish_prob_occurrence image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:4326:0 0 0 0 1 EPSG:4326:1 0 0 1 2 EPSG:4326:2 0 1 3 4 EPSG:4326:3 1 2 7 8 EPSG:4326:4 2 4 14 16 EPSG:4326:5 4 8 28 33 EPSG:4326:6 8 16 57 67 EPSG:4326:7 17 32 115 134 EPSG:4326:8 35 65 231 269 EPSG:4326:9 71 131 463 538 EPSG:4326:10 143 262 926 1076 EPSG:4326:11 286 525 1852 2152 EPSG:4326:12 572 1050 3705 4304 EPSG:4326:13 1145 2101 7410 8609 EPSG:4326:14 2290 4202 14821 17218 EPSG:4326:15 4581 8404 29642 34436 EPSG:4326:16 9162 16808 59285 68873 EPSG:4326:17 18325 33617 118571 137746 EPSG:4326:18 36651 67235 237143 275493 EPSG:4326:19 73303 134470 474286 550987 EPSG:4326:20 146606 268940 948572 1101975 EPSG:4326:21 293212 537880 1897145 2203951 EPSG:900913 EPSG:900913:0 0 0 0 0 EPSG:900913:1 0 0 0 1 EPSG:900913:2 1 1 1 2 EPSG:900913:3 2 2 3 4 EPSG:900913:4 4 5 7 8 EPSG:900913:5 8 11 14 16 EPSG:900913:6 16 23 28 33 EPSG:900913:7 33 46 57 67 EPSG:900913:8 66 93 115 134 EPSG:900913:9 133 186 231 269 EPSG:900913:10 267 373 463 538 EPSG:900913:11 535 746 926 1076 EPSG:900913:12 1070 1492 1852 2152 EPSG:900913:13 2140 2984 3705 4304 EPSG:900913:14 4281 5968 7410 8609 EPSG:900913:15 8563 11936 14821 17218 EPSG:900913:16 17126 23872 29642 34436 EPSG:900913:17 34253 47744 59285 68873 EPSG:900913:18 68506 95489 118571 137746 EPSG:900913:19 137013 190978 237143 275493 EPSG:900913:20 274026 381956 474286 550987 EPSG:900913:21 548053 763913 948572 1101975 EPSG:900913:22 1096107 1527827 1897145 2203951 EPSG:900913:23 2192214 3055654 3794291 4407903 EPSG:900913:24 4384428 6111308 7588583 8815806 EPSG:900913:25 8768857 12222616 15177167 17631612 EPSG:900913:26 17537714 24445232 30354335 35263224 EPSG:900913:27 35075429 48890464 60708670 70526449 EPSG:900913:28 70150859 97780929 121417340 141052898 EPSG:900913:29 140301719 195561859 242834680 282105796 EPSG:900913:30 280603439 391123719 485669361 564211592 Density of Loggerhead Turtles in the Mediterranean Sea Loggerhead turtles (Caretta caretta) are a globally vulnerable species of marine turtle. The Mediterranean Sea subpopulation, which ranges throughout the entire Mediterranean basin, is listed as least concern by the International Union for the Conservation of Nature (IUCN), but experiences significant threats in the region including bycatch in fisheries, climate change, coastal development, and marine pollution. Broad-scale patterns of distribution and abundance can provide regional managers a tool to effectively conserve and manage this species at basin and sub-basin scales. In 2020, a spatial density model estimating loggerhead density, abundance, and distribution across the Mediterranean Sea was generated as a long-term annual average. The model was adjusted for availability bias using dive data from loggerhead turtles tagged with time depth recorders from multiple regions within the Mediterranean Sea. Geographic extrapolation in areas near surveys was undertaken with caution. Mean abundance for the long-term average model was estimated as 1,201,845 (CV 0.22). This estimate represents the first basin-wide estimate of abundance for this species in the Mediterranean not based on demographic models. This work was funded by the U.S. Navy to assist with assessing risk to protected marine species resulting from military readiness activities. The data are an annual estimated density of loggerhead turtles, expressed as the number of individuals per square kilometer, within the Mediterranean Sea. The study area was gridded into 5x5 kilometer blocks, the resolution at which density modeling was performed. The study area encompasses all major subregions of the Mediterranean Sea, except the Sea of Marmara, which loggerhead turtles do not frequent. The DENSITY attribute contains the estimated density (individuals per square kilometer) and the UNCERTAINTY attribute contains the estimated coefficient of variation (CV) for the density estimate. The density estimates represent the mean throughout year, averaged for the period 2003-2018. For most of the study area, density was estimated using a habitat-based density model that correlated local abundance observed on systematic line transect surveys with environmental conditions observed at that same location and time. For unsurveyed areas and times, density was estimated by extrapolation. Density was not estimated for nearshore locations and inshore waters as they were often not covered by surveys or lacked reliable remoted sensed covariate coverage. See Sparks and DiMatteo (2020) for more information about the modeling process. The metadata documentation has more information regarding individual attributes. The shoreline and other geographic features were delinated by NAVFAC from a database provided by the National Geospatial-Intelligence Agency (NGA). These data have been re-projected from the original published data presented in DiMatteo et al. 2024, in order to meet EMODnet technical requirements. Small spatial mismatches and value discrepancies between the original model and the version available here may exist but have not been formally assessed. -75.50927310292121 39.82705136375554 -13.835957466013047 73.53996152495834 abundance_loggerheads image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:4326:0 0 0 0 0 EPSG:4326:1 0 0 1 1 EPSG:4326:2 0 1 2 3 EPSG:4326:3 0 2 4 7 EPSG:4326:4 1 4 9 14 EPSG:4326:5 2 8 18 29 EPSG:4326:6 5 17 37 59 EPSG:4326:7 11 35 74 118 EPSG:4326:8 23 71 148 236 EPSG:4326:9 46 142 297 472 EPSG:4326:10 93 285 594 945 EPSG:4326:11 187 570 1188 1890 EPSG:4326:12 374 1141 2377 3781 EPSG:4326:13 749 2283 4755 7562 EPSG:4326:14 1498 4566 9510 15124 EPSG:4326:15 2996 9133 19021 30249 EPSG:4326:16 5992 18267 38043 60498 EPSG:4326:17 11985 36534 76087 120996 EPSG:4326:18 23971 73069 152175 241993 EPSG:4326:19 47943 146139 304351 483987 EPSG:4326:20 95886 292278 608702 967975 EPSG:4326:21 191773 584557 1217405 1935951 EPSG:900913 EPSG:900913:0 0 0 0 0 EPSG:900913:1 0 0 0 0 EPSG:900913:2 0 1 1 1 EPSG:900913:3 1 3 2 3 EPSG:900913:4 3 6 4 7 EPSG:900913:5 6 12 9 14 EPSG:900913:6 12 24 18 29 EPSG:900913:7 24 48 37 59 EPSG:900913:8 49 97 74 118 EPSG:900913:9 98 194 148 236 EPSG:900913:10 196 388 297 472 EPSG:900913:11 393 776 594 945 EPSG:900913:12 787 1553 1188 1890 EPSG:900913:13 1575 3106 2377 3781 EPSG:900913:14 3150 6212 4755 7562 EPSG:900913:15 6300 12425 9510 15124 EPSG:900913:16 12600 24851 19021 30249 EPSG:900913:17 25201 49703 38043 60498 EPSG:900913:18 50402 99406 76087 120996 EPSG:900913:19 100804 198812 152175 241993 EPSG:900913:20 201609 397625 304351 483987 EPSG:900913:21 403218 795251 608702 967975 EPSG:900913:22 806436 1590503 1217405 1935951 EPSG:900913:23 1612873 3181006 2434810 3871902 EPSG:900913:24 3225746 6362012 4869620 7743805 EPSG:900913:25 6451493 12724025 9739241 15487611 EPSG:900913:26 12902987 25448050 19478483 30975222 EPSG:900913:27 25805975 50896100 38956966 61950445 EPSG:900913:28 51611951 101792201 77913933 123900890 EPSG:900913:29 103223903 203584402 155827866 247801780 EPSG:900913:30 206447806 407168805 311655732 495603561 Density of Loggerhead Turtles in the Mediterranean Sea -180.0 -85.06 180.0 85.06 loggerhead_mediterranean image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:4326:0 0 0 0 1 EPSG:4326:1 0 1 0 3 EPSG:4326:2 0 3 0 7 EPSG:4326:3 0 7 0 15 EPSG:4326:4 0 15 0 31 EPSG:4326:5 0 31 0 63 EPSG:4326:6 1 62 0 127 EPSG:4326:7 3 124 0 255 EPSG:4326:8 7 248 0 511 EPSG:4326:9 14 497 0 1023 EPSG:4326:10 28 995 0 2047 EPSG:4326:11 56 1991 0 4095 EPSG:4326:12 112 3983 0 8191 EPSG:4326:13 224 7967 0 16383 EPSG:4326:14 449 15934 0 32767 EPSG:4326:15 899 31868 0 65535 EPSG:4326:16 1798 63737 0 131071 EPSG:4326:17 3597 127474 0 262143 EPSG:4326:18 7194 254949 0 524287 EPSG:4326:19 14388 509899 0 1048575 EPSG:4326:20 28777 1019798 0 2097151 EPSG:4326:21 57555 2039596 0 4194303 EPSG:900913 Seabed habitat and macrobenthos occurrences in European seas This product is built from two existing EMODnet data products. It uses the EUSeaMap product created by EMODNet Seabed Habitats and the Presence/absence data of macrozoobenthos in the European Seas product created by EMODnet Biology to obtain lists of all macrobenthic species occurring in all EUNIS seabed habitat types. For the purposes of this product the seabed habitat map is summarised on a grid at approx. 500m resolution, with each grid cell assigned to the dominant EUNIS code present within it. Summaries of benthic diversity are created for each grid cell, as well as more synthetic summaries by EUNIS category. The full matched benthos community data was written to file in the derived data folder as benthos_full_matched in parquet format with gzip compression. -35.97180141999996 23.680472180000038 43.00059858000003 81.54087218000004 eusm_benthos_n_species image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:4326:0 0 0 0 1 EPSG:4326:1 0 0 1 2 EPSG:4326:2 0 1 3 4 EPSG:4326:3 0 2 6 9 EPSG:4326:4 0 5 12 19 EPSG:4326:5 1 11 25 39 EPSG:4326:6 3 23 51 79 EPSG:4326:7 6 47 102 158 EPSG:4326:8 12 94 204 317 EPSG:4326:9 24 188 409 634 EPSG:4326:10 48 377 819 1268 EPSG:4326:11 96 754 1638 2537 EPSG:4326:12 192 1509 3277 5074 EPSG:4326:13 384 3018 6554 10149 EPSG:4326:14 769 6036 13109 20298 EPSG:4326:15 1539 12073 26219 40596 EPSG:4326:16 3079 24146 52439 81192 EPSG:4326:17 6159 48292 104878 162384 EPSG:4326:18 12319 96584 209756 324768 EPSG:4326:19 24638 193169 419512 649536 EPSG:4326:20 49277 386339 839025 1299072 EPSG:4326:21 98555 772678 1678050 2598145 EPSG:900913 EPSG:900913:0 0 0 0 0 EPSG:900913:1 0 0 0 1 EPSG:900913:2 0 1 1 2 EPSG:900913:3 0 3 3 4 EPSG:900913:4 1 6 6 9 EPSG:900913:5 2 13 12 19 EPSG:900913:6 5 27 25 39 EPSG:900913:7 10 55 51 79 EPSG:900913:8 21 110 102 158 EPSG:900913:9 43 221 204 317 EPSG:900913:10 87 442 409 634 EPSG:900913:11 175 885 819 1268 EPSG:900913:12 350 1770 1638 2537 EPSG:900913:13 700 3541 3277 5074 EPSG:900913:14 1401 7082 6554 10149 EPSG:900913:15 2802 14164 13109 20298 EPSG:900913:16 5604 28328 26219 40596 EPSG:900913:17 11208 56657 52439 81192 EPSG:900913:18 22416 113315 104878 162384 EPSG:900913:19 44832 226630 209756 324768 EPSG:900913:20 89665 453261 419512 649536 EPSG:900913:21 179330 906523 839025 1299072 EPSG:900913:22 358660 1813046 1678050 2598145 EPSG:900913:23 717320 3626093 3356100 5196290 EPSG:900913:24 1434641 7252186 6712200 10392581 EPSG:900913:25 2869283 14504373 13424401 20785162 EPSG:900913:26 5738566 29008747 26848802 41570324 EPSG:900913:27 11477132 58017495 53697604 83140649 EPSG:900913:28 22954265 116034990 107395208 166281298 EPSG:900913:29 45908530 232069981 214790417 332562596 EPSG:900913:30 91817060 464139963 429580835 665125192 Summary presence/absence maps of macro-endobenthos in the greater North Sea, based on nearly 100,000 samples from 65 assembled monitoring data sets The large databases of EMODNET Biology only store confirmed presences of species. However, when mapping species distribution, it is also important where the species did not occur: there is at least as much information in absences as in presences. Inferring absences from presence-only databases is difficult and always involves some guesswork. In this product we have used as much meta-information as possible to guide us in inferring absences. -16.0 46.000000000000014 8.999999999999993 66.0 probability_occurrence_benthos_northsea image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:4326:0 0 0 0 1 EPSG:4326:1 0 0 1 2 EPSG:4326:2 0 0 3 4 EPSG:4326:3 1 1 7 8 EPSG:4326:4 2 3 14 16 EPSG:4326:5 4 7 29 33 EPSG:4326:6 8 15 58 67 EPSG:4326:7 17 31 116 134 EPSG:4326:8 34 62 233 268 EPSG:4326:9 68 125 466 537 EPSG:4326:10 136 250 932 1075 EPSG:4326:11 273 500 1865 2150 EPSG:4326:12 546 1001 3731 4300 EPSG:4326:13 1092 2002 7463 8601 EPSG:4326:14 2184 4004 14927 17203 EPSG:4326:15 4369 8009 29855 34406 EPSG:4326:16 8738 16019 59710 68812 EPSG:4326:17 17476 32039 119421 137625 EPSG:4326:18 34952 64079 238842 275251 EPSG:4326:19 69905 128159 477684 550502 EPSG:4326:20 139810 256318 955369 1101004 EPSG:4326:21 279620 512637 1910738 2202009 EPSG:900913 EPSG:900913:0 0 0 0 0 EPSG:900913:1 0 0 0 1 EPSG:900913:2 1 1 1 2 EPSG:900913:3 2 2 3 4 EPSG:900913:4 4 5 7 8 EPSG:900913:5 8 11 14 16 EPSG:900913:6 16 22 29 33 EPSG:900913:7 32 45 58 67 EPSG:900913:8 64 91 116 134 EPSG:900913:9 129 182 233 268 EPSG:900913:10 259 364 466 537 EPSG:900913:11 519 728 932 1075 EPSG:900913:12 1038 1457 1865 2150 EPSG:900913:13 2077 2914 3731 4300 EPSG:900913:14 4154 5828 7463 8601 EPSG:900913:15 8308 11657 14927 17203 EPSG:900913:16 16616 23315 29855 34406 EPSG:900913:17 33232 46630 59710 68812 EPSG:900913:18 66464 93260 119421 137625 EPSG:900913:19 132928 186521 238842 275251 EPSG:900913:20 265857 373043 477684 550502 EPSG:900913:21 531714 746086 955369 1101004 EPSG:900913:22 1063428 1492173 1910738 2202009 EPSG:900913:23 2126856 2984346 3821476 4404019 EPSG:900913:24 4253713 5968692 7642953 8808038 EPSG:900913:25 8507426 11937384 15285907 17616076 EPSG:900913:26 17014852 23874768 30571815 35232153 EPSG:900913:27 34029705 47749537 61143631 70464307 EPSG:900913:28 68059411 95499074 122287263 140928614 EPSG:900913:29 136118823 190998149 244574526 281857228 EPSG:900913:30 272237646 381996298 489149053 563714457 Habitat suitability of reef-froming species in the North Sea Due to fishing and other human activities, reef forming species have almost completely disappeared over roughly the past century. They are important structures that accomodate juvenile fish and other small organisms. For protection of areas where such reefs could possibly be reintroduced, it is important to define areas that are suitable habitats. This product aims to classify areas in the North Sea based on current occurence in combination with environmental variables that are particularly suitable for these organisms. -2.84806123504988 50.8754356582586 9.05746876495012 57.60983565825861 habitat_suitability image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:4326:0 0 0 0 1 EPSG:4326:1 0 0 1 2 EPSG:4326:2 0 0 3 4 EPSG:4326:3 1 1 7 8 EPSG:4326:4 2 3 15 16 EPSG:4326:5 5 6 31 33 EPSG:4326:6 11 13 62 67 EPSG:4326:7 23 27 125 134 EPSG:4326:8 46 55 251 268 EPSG:4326:9 92 111 503 537 EPSG:4326:10 184 222 1007 1075 EPSG:4326:11 368 445 2015 2151 EPSG:4326:12 737 890 4031 4302 EPSG:4326:13 1474 1780 8062 8604 EPSG:4326:14 2948 3561 16124 17208 EPSG:4326:15 5896 7122 32249 34416 EPSG:4326:16 11792 14244 64499 68833 EPSG:4326:17 23585 28489 128998 137667 EPSG:4326:18 47171 56979 257996 275334 EPSG:4326:19 94343 113958 515992 550669 EPSG:4326:20 188686 227917 1031984 1101339 EPSG:4326:21 377372 455834 2063969 2202679 EPSG:900913 EPSG:900913:0 0 0 0 0 EPSG:900913:1 0 0 0 1 EPSG:900913:2 1 1 1 2 EPSG:900913:3 2 2 3 4 EPSG:900913:4 4 5 7 8 EPSG:900913:5 9 10 15 16 EPSG:900913:6 19 21 31 33 EPSG:900913:7 38 42 62 67 EPSG:900913:8 77 85 125 134 EPSG:900913:9 155 171 251 268 EPSG:900913:10 310 343 503 537 EPSG:900913:11 621 686 1007 1075 EPSG:900913:12 1242 1373 2015 2151 EPSG:900913:13 2484 2746 4031 4302 EPSG:900913:14 4968 5493 8062 8604 EPSG:900913:15 9936 10987 16124 17208 EPSG:900913:16 19872 21975 32249 34416 EPSG:900913:17 39744 43951 64499 68833 EPSG:900913:18 79488 87903 128998 137667 EPSG:900913:19 158976 175807 257996 275334 EPSG:900913:20 317953 351615 515992 550669 EPSG:900913:21 635906 703230 1031984 1101339 EPSG:900913:22 1271813 1406461 2063969 2202679 EPSG:900913:23 2543627 2812923 4127939 4405358 EPSG:900913:24 5087255 5625846 8255878 8810716 EPSG:900913:25 10174511 11251693 16511757 17621433 EPSG:900913:26 20349022 22503387 33023514 35242866 EPSG:900913:27 40698044 45006774 66047029 70485733 EPSG:900913:28 81396089 90013548 132094059 140971466 EPSG:900913:29 162792179 180027097 264188119 281942932 EPSG:900913:30 325584358 360054195 528376238 563885864 3D habitat suitability maps of the 30 main commercial fish species from the Atlantic Ocean 3-D habitat suitability maps (HSM) or probability of occurrence maps, built using Shape-Constrained Generalized Additive Models (SC-GAMs) for the 30 main commercial species of the Atlantic region. Predictor variables for each species were selected from: sea water temperature, salinity, nitrate, net primary productivity, distance to seafloor, distance to coast, and relative position to mixed layer depth. Each species HSM contains 47 maps, one per depth level from 0 to 1000 m. Probability values of each map range from 0 (unsuitable habitat) to 1 (optimal habitat). For depth levels below the 0.99 quantile of the depth values found on the species occurrence data, NA values were assigned. Maps have been masked to species native range regions. See Valle et al. (2024) in Ecological Modelling 490:110632 (https://doi.org/10.1016/j.ecolmodel.2024.110632), for more details. -98.0 -83.0 68.5 90.0 occurrence_probability_fish image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:4326:0 0 0 0 1 EPSG:4326:1 0 1 0 2 EPSG:4326:2 0 3 1 5 EPSG:4326:3 0 7 3 11 EPSG:4326:4 0 15 7 22 EPSG:4326:5 0 30 14 44 EPSG:4326:6 0 61 29 88 EPSG:4326:7 0 123 58 176 EPSG:4326:8 0 246 116 353 EPSG:4326:9 0 492 233 706 EPSG:4326:10 0 984 466 1413 EPSG:4326:11 0 1968 932 2827 EPSG:4326:12 0 3936 1865 5654 EPSG:4326:13 0 7873 3731 11309 EPSG:4326:14 0 15746 7463 22619 EPSG:4326:15 0 31493 14927 45238 EPSG:4326:16 0 62987 29855 90476 EPSG:4326:17 0 125974 59710 180952 EPSG:4326:18 0 251949 119421 361904 EPSG:4326:19 0 503899 238842 723808 EPSG:4326:20 0 1007798 477684 1447617 EPSG:4326:21 0 2015596 955369 2895234 EPSG:900913 EPSG:900913:0 0 0 0 0 EPSG:900913:1 0 1 0 1 EPSG:900913:2 0 3 0 2 EPSG:900913:3 0 7 1 5 EPSG:900913:4 0 15 3 11 EPSG:900913:5 0 30 7 22 EPSG:900913:6 0 60 14 44 EPSG:900913:7 0 120 29 88 EPSG:900913:8 0 241 58 176 EPSG:900913:9 0 483 116 353 EPSG:900913:10 0 967 233 706 EPSG:900913:11 0 1934 466 1413 EPSG:900913:12 0 3869 932 2827 EPSG:900913:13 0 7739 1865 5654 EPSG:900913:14 0 15478 3731 11309 EPSG:900913:15 0 30956 7463 22619 EPSG:900913:16 0 61912 14927 45238 EPSG:900913:17 0 123825 29855 90476 EPSG:900913:18 0 247651 59710 180952 EPSG:900913:19 0 495302 119421 361904 EPSG:900913:20 0 990604 238842 723808 EPSG:900913:21 0 1981208 477684 1447617 EPSG:900913:22 0 3962416 955369 2895234 EPSG:900913:23 0 7924833 1910738 5790469 EPSG:900913:24 0 15849667 3821476 11580939 EPSG:900913:25 0 31699334 7642953 23161878 EPSG:900913:26 0 63398668 15285907 46323757 EPSG:900913:27 0 126797336 30571815 92647515 EPSG:900913:28 0 253594673 61143631 185295030 EPSG:900913:29 0 507189346 122287263 370590060 EPSG:900913:30 0 1014378692 244574526 741180120 Modelled projections of habitat for fish species feeding guilds around North-western Europe under climate change, 2010 to 2095 These data are Bayesian Additive Regression Tree model annual predictions for habitat suitability of marine fish species across a range of body sizes and belonging to different feeding guilds from 2010 to 2095 in 5 year intervals in the northeast Atlantic shelf seas. Feeding guilds were allocated based on classifications following Thompson et al. (2020). The purpose of this study was to predict how climate change could affect the species richness of consumers lower in the food web (planktivores), compared to those intermediate (benthivores) and higher up (piscivores). In brief, a collation of trophic interactions spanning the northeast Atlantic shelf seas has been applied to define feeding guilds by grouping fish species size classes (we use the taxonomic level of Gobiidae and Ammodytes for taxa that are not consistently identified to species) that have prey taxa in common, and whose prey differentiate them from other predator guilds based on cluster analysis. Taxon-specific size categories were defined as: <3 cm as larvae; small juvenile fish between 3 cm and half of length at maturity; juvenile-medium fish from half of length at maturity to length at maturity; medium fish from length at maturity to half-maximum length; and all remaining larger fish as large. Taxon-specific length at maturity and maximum length (i.e., asymptotic length at infinity) were estimated using the R package Fishlife (Thorson et al., 2017). Environmental projections were derived from the coupled marine ecosystem models POLCOMS/NEMO-ERSEM. We focus on two emission scenarios (representative concentration pathways, RCPs) developed for the Intergovernmental Panel on Climate Change (IPCC)’s fifth phase of the Coupled Model Intercomparison Project (CMIP5): RCP4.5, the “stabilization scenario”, characterised by medium emissions and high mitigations, and the “no mitigation policy” scenario RCP8.5, derived from high fossil fuel emission and low mitigations. We use projections of temperature, salinity, pH, nitrate, phosphate, dissolved oxygen, current velocity, chlorophyll, gross primary production, non-living organic carbon, zooplankton carbon concentration and secondary carbon production by zooplankton in our predictive habitat models. For temperature, salinity, current velocity, dissolved oxygen, and pH both surface and bottom mean annual averages were considered, and in the case of temperature, also the difference between bottom and surface values, to account for stratification. For chlorophyll, gross primary production, non-living organic carbon, phosphate, nitrate, zooplankton carbon concentration and secondary carbon production by zooplankton the total across the water column was used, rather than surface or bottom values. We also include depth (from the General Bathymetric Chart of the Oceans GEBCO; www.gebco.net, at 15 second resolution), distance to coast and substrate composition (median grain size and percentages of mud, sand and gravel from Wilson et al., 2018) to capture key spatial gradients that affect habitat suitability for fish. All environmental data was processed onto a 10 km by 10 km grid, and because an annual mean of, e.g., temperature, does not capture the environmental variability that ultimately determines the thresholds within which biota must survive; for temperature, salinity, pH, oxygen and current speed we also include the standard deviation of the 12 monthly means in each year, for all locations within a radius of 75 km of each grid cell, in order to provide a measure of spatio-temporal heterogeneity. For the environmental variables where surface and bottom values were extracted, we use sea surface values to model habitat suitability for planktivores, which are largely pelagic species, and seabed values for the benthivores and piscivores which are largely demersal species. Pairwise Pearson correlation coefficients were computed for the set of environmental variables used to model the habitat of planktivores and of non-planktivores separately, to assess multicollinearity. Variables were removed if they correlated with another >0.7. Additionally we provide data on model performance and presence/absence thresholds, in the file "Model performance and threshold.xlsx". Model performance is measured via the area under the curve (AUC) of the Receiver Operating Characteristic (ROC) plot, the AUC of the Precision-Recall (PR) plot (He & Garcia, 2009), and the Miller slope (Miller et al., 1991). We assess model performance in both space and time. Spatial performance was analysed via 8-fold block cross validation using the R library “blockCV” (Valavi et al., 2019). Temporal performance of the models was assessed by training a new model that excluded the last 5 years of survey data (2015 to 2019), using the resulting model to predict to those 5 years, a “novel time period” for that model. These data were produced for the study in the article "Climate change affects the distribution of diversity across marine food webs", by Murray S. A. Thompson, Elena Couce, Michaela Schratzberger and Christopher P. Lynam, in Global Change Biology (in press). -19.94240986660436 41.33388978486105 21.34871875890968 64.99715255196696 mod_habi_feeding_guilds_prob_occurrence image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:4326:0 0 0 0 1 EPSG:4326:1 0 0 1 2 EPSG:4326:2 0 1 3 4 EPSG:4326:3 1 2 7 8 EPSG:4326:4 2 4 14 17 EPSG:4326:5 4 8 28 35 EPSG:4326:6 8 17 56 71 EPSG:4326:7 17 34 113 143 EPSG:4326:8 35 69 227 286 EPSG:4326:9 71 138 455 572 EPSG:4326:10 142 276 910 1145 EPSG:4326:11 284 553 1821 2290 EPSG:4326:12 568 1107 3642 4581 EPSG:4326:13 1137 2214 7284 9163 EPSG:4326:14 2275 4429 14568 18327 EPSG:4326:15 4551 8859 29137 36654 EPSG:4326:16 9103 17718 58275 73308 EPSG:4326:17 18206 35437 116550 146617 EPSG:4326:18 36413 70875 233100 293235 EPSG:4326:19 72826 141750 466201 586470 EPSG:4326:20 145652 283500 932403 1172941 EPSG:4326:21 291304 567001 1864806 2345882 EPSG:900913 EPSG:900913:0 0 0 0 0 EPSG:900913:1 0 0 0 1 EPSG:900913:2 1 1 1 2 EPSG:900913:3 2 2 3 4 EPSG:900913:4 4 5 7 8 EPSG:900913:5 8 11 14 17 EPSG:900913:6 16 23 28 35 EPSG:900913:7 33 47 56 71 EPSG:900913:8 66 95 113 143 EPSG:900913:9 133 191 227 286 EPSG:900913:10 266 382 455 572 EPSG:900913:11 533 765 910 1145 EPSG:900913:12 1066 1530 1821 2290 EPSG:900913:13 2132 3061 3642 4581 EPSG:900913:14 4264 6122 7284 9163 EPSG:900913:15 8528 12245 14568 18327 EPSG:900913:16 17056 24490 29137 36654 EPSG:900913:17 34112 48980 58275 73308 EPSG:900913:18 68225 97961 116550 146617 EPSG:900913:19 136450 195923 233100 293235 EPSG:900913:20 272901 391846 466201 586470 EPSG:900913:21 545802 783693 932403 1172941 EPSG:900913:22 1091605 1567386 1864806 2345882 EPSG:900913:23 2183211 3134772 3729612 4691765 EPSG:900913:24 4366422 6269544 7459224 9383530 EPSG:900913:25 8732845 12539089 14918448 18767060 EPSG:900913:26 17465690 25078178 29836897 37534121 EPSG:900913:27 34931381 50156357 59673794 75068243 EPSG:900913:28 69862763 100312714 119347589 150136486 EPSG:900913:29 139725527 200625428 238695178 300272972 EPSG:900913:30 279451055 401250857 477390357 600545945 Abundance maps of Sea Birds in the European Seas The data product consists of a netCDF file containing the gridded field obtained by the interpolation of the observations of more than 400 sea bird species. The field is computed for 5 time periods: 1970-1979, 1980-1989, 1990-1999, 2000-2009 and 2010-2019. Along with the gridded field, an error field is also provided. It allows user to only consider the regions where the analysis can be trusted, i.e. where the error field is lower than a given threshold (0.25 for example). -180.0 -90.0 180.0 90.0 seabirds_gridded_count image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Density of Loggerhead Turtles in the Mediterranean Sea Loggerhead turtles (Caretta caretta) are a globally vulnerable species of marine turtle. The Mediterranean Sea subpopulation, which ranges throughout the entire Mediterranean basin, is listed as least concern by the International Union for the Conservation of Nature (IUCN), but experiences significant threats in the region including bycatch in fisheries, climate change, coastal development, and marine pollution. Broad-scale patterns of distribution and abundance can provide regional managers a tool to effectively conserve and manage this species at basin and sub-basin scales. In 2020, a spatial density model estimating loggerhead density, abundance, and distribution across the Mediterranean Sea was generated as a long-term annual average. The model was adjusted for availability bias using dive data from loggerhead turtles tagged with time depth recorders from multiple regions within the Mediterranean Sea. Geographic extrapolation in areas near surveys was undertaken with caution. Mean abundance for the long-term average model was estimated as 1,201,845 (CV 0.22). This estimate represents the first basin-wide estimate of abundance for this species in the Mediterranean not based on demographic models. This work was funded by the U.S. Navy to assist with assessing risk to protected marine species resulting from military readiness activities. The data are an annual estimated density of loggerhead turtles, expressed as the number of individuals per square kilometer, within the Mediterranean Sea. The study area was gridded into 5x5 kilometer blocks, the resolution at which density modeling was performed. The study area encompasses all major subregions of the Mediterranean Sea, except the Sea of Marmara, which loggerhead turtles do not frequent. The DENSITY attribute contains the estimated density (individuals per square kilometer) and the UNCERTAINTY attribute contains the estimated coefficient of variation (CV) for the density estimate. The density estimates represent the mean throughout year, averaged for the period 2003-2018. For most of the study area, density was estimated using a habitat-based density model that correlated local abundance observed on systematic line transect surveys with environmental conditions observed at that same location and time. For unsurveyed areas and times, density was estimated by extrapolation. Density was not estimated for nearshore locations and inshore waters as they were often not covered by surveys or lacked reliable remoted sensed covariate coverage. See Sparks and DiMatteo (2020) for more information about the modeling process. The metadata documentation has more information regarding individual attributes. The shoreline and other geographic features were delinated by NAVFAC from a database provided by the National Geospatial-Intelligence Agency (NGA). These data have been re-projected from the original published data presented in DiMatteo et al. 2024, in order to meet EMODnet technical requirements. Small spatial mismatches and value discrepancies between the original model and the version available here may exist but have not been formally assessed. -8.21644052494895 27.464931067437558 38.9894722234046 45.7809284038443 coefficient_variation_loggerheads image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:4326:0 0 0 0 1 EPSG:4326:1 0 0 1 2 EPSG:4326:2 0 1 3 4 EPSG:4326:3 1 2 7 9 EPSG:4326:4 3 5 15 19 EPSG:4326:5 7 11 30 38 EPSG:4326:6 15 22 61 77 EPSG:4326:7 31 44 122 155 EPSG:4326:8 62 88 244 311 EPSG:4326:9 125 177 488 622 EPSG:4326:10 251 355 977 1245 EPSG:4326:11 503 711 1954 2491 EPSG:4326:12 1006 1423 3909 4983 EPSG:4326:13 2012 2846 7818 9966 EPSG:4326:14 4024 5692 15636 19932 EPSG:4326:15 8049 11384 31272 39865 EPSG:4326:16 16099 22768 62544 79731 EPSG:4326:17 32199 45536 125088 159463 EPSG:4326:18 64398 91073 250177 318926 EPSG:4326:19 128797 182146 500355 637853 EPSG:4326:20 257594 364293 1000711 1275706 EPSG:4326:21 515189 728586 2001423 2551412 EPSG:900913 EPSG:900913:0 0 0 0 0 EPSG:900913:1 0 0 0 1 EPSG:900913:2 1 1 1 2 EPSG:900913:3 2 3 3 4 EPSG:900913:4 5 6 7 9 EPSG:900913:5 11 13 15 19 EPSG:900913:6 22 26 30 38 EPSG:900913:7 45 53 61 77 EPSG:900913:8 91 107 122 155 EPSG:900913:9 182 215 244 311 EPSG:900913:10 365 430 488 622 EPSG:900913:11 730 861 977 1245 EPSG:900913:12 1460 1722 1954 2491 EPSG:900913:13 2921 3445 3909 4983 EPSG:900913:14 5843 6891 7818 9966 EPSG:900913:15 11686 13782 15636 19932 EPSG:900913:16 23372 27564 31272 39865 EPSG:900913:17 46745 55129 62544 79731 EPSG:900913:18 93490 110259 125088 159463 EPSG:900913:19 186980 220519 250177 318926 EPSG:900913:20 373960 441038 500355 637853 EPSG:900913:21 747920 882076 1000711 1275706 EPSG:900913:22 1495840 1764153 2001423 2551412 EPSG:900913:23 2991680 3528306 4002847 5102824 EPSG:900913:24 5983360 7056612 8005694 10205649 EPSG:900913:25 11966720 14113225 16011388 20411298 EPSG:900913:26 23933441 28226451 32022776 40822596 EPSG:900913:27 47866882 56452902 64045552 81645192 EPSG:900913:28 95733764 112905805 128091105 163290385 EPSG:900913:29 191467529 225811610 256182211 326580771 EPSG:900913:30 382935059 451623220 512364423 653161542 Data product numerical abundance of benthic macroinvertebrates in North Sea and Baltic Sea The data product on benthic living modes (Beauchard, 2018), was based on an extensive compilation of data on benthic abundance and biomass. However, this dataset was only present as a data file, without the underlying scripts to reproduce the result. With the present data product, we correct this procedural gap. This dataset differs in details from the file underlying the data product on living modes. Datasets were selected that were sufficiently similar in methods for sampling (either boxcore or grab), sampled surface (in the order of 0.1 square meter, although the exact value is variable - it can be found back in the data files) and sieves (1 mm and 0.5 mm sieves were included). For all datasets, abundance was either used directly from the given abundance in the dataset, or calculated from the given counts and area sampled. -16.0 40.0 36.0 74.0 abundance_benthos_North_Baltic_Seas image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:4326:0 0 0 0 1 EPSG:4326:1 0 0 1 2 EPSG:4326:2 0 1 3 4 EPSG:4326:3 0 2 7 9 EPSG:4326:4 1 4 14 19 EPSG:4326:5 2 8 29 38 EPSG:4326:6 5 17 58 76 EPSG:4326:7 11 35 116 153 EPSG:4326:8 22 71 233 307 EPSG:4326:9 45 142 466 614 EPSG:4326:10 91 284 932 1228 EPSG:4326:11 182 568 1865 2457 EPSG:4326:12 364 1137 3731 4915 EPSG:4326:13 728 2275 7463 9830 EPSG:4326:14 1456 4551 14927 19660 EPSG:4326:15 2912 9102 29855 39321 EPSG:4326:16 5825 18204 59710 78643 EPSG:4326:17 11650 36408 119421 157286 EPSG:4326:18 23301 72817 238842 314572 EPSG:4326:19 46603 145635 477684 629145 EPSG:4326:20 93206 291271 955369 1258291 EPSG:4326:21 186413 582542 1910738 2516582 EPSG:900913 EPSG:900913:0 0 0 0 0 EPSG:900913:1 0 0 0 1 EPSG:900913:2 0 1 1 2 EPSG:900913:3 1 3 3 4 EPSG:900913:4 3 6 7 9 EPSG:900913:5 6 12 14 19 EPSG:900913:6 12 24 29 38 EPSG:900913:7 24 48 58 76 EPSG:900913:8 48 96 116 153 EPSG:900913:9 96 193 233 307 EPSG:900913:10 192 387 466 614 EPSG:900913:11 384 775 932 1228 EPSG:900913:12 768 1550 1865 2457 EPSG:900913:13 1537 3101 3731 4915 EPSG:900913:14 3075 6202 7463 9830 EPSG:900913:15 6150 12405 14927 19660 EPSG:900913:16 12300 24810 29855 39321 EPSG:900913:17 24601 49621 59710 78643 EPSG:900913:18 49203 99242 119421 157286 EPSG:900913:19 98407 198484 238842 314572 EPSG:900913:20 196814 396969 477684 629145 EPSG:900913:21 393629 793938 955369 1258291 EPSG:900913:22 787258 1587876 1910738 2516582 EPSG:900913:23 1574517 3175752 3821476 5033164 EPSG:900913:24 3149034 6351504 7642953 10066329 EPSG:900913:25 6298068 12703008 15285907 20132659 EPSG:900913:26 12596137 25406017 30571815 40265318 EPSG:900913:27 25192274 50812034 61143631 80530636 EPSG:900913:28 50384548 101624068 122287263 161061273 EPSG:900913:29 100769096 203248137 244574526 322122547 EPSG:900913:30 201538192 406496275 489149053 644245094 Presence/Absence maps of phytoplankton in the Greater Baltic Sea The project aims to produce comprehensive data product of the occurrence and absence of (phyto)plankton species. As a basis, data from EMODnet Biology are used. The selection of relevant datasets is optimized in order to find all planktonic species, and exclude all species that are not planktonic. The occurrences from EMODnet Biology were complemented with absence data assuming fixed species lists within each dataset and year. The products are presented as maps of the distribution of the 20 most common species of (phyto)plankton in the Greater Baltic Sea. 8.999999999999996 51.999999999999964 37.000000000000036 68.00000000000003 probability_occurrence_phyto_baltic image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:4326:0 0 0 1 1 EPSG:4326:1 0 0 2 2 EPSG:4326:2 0 0 4 4 EPSG:4326:3 0 1 8 9 EPSG:4326:4 1 3 16 19 EPSG:4326:5 3 6 33 38 EPSG:4326:6 7 13 67 77 EPSG:4326:7 15 27 134 154 EPSG:4326:8 31 54 268 308 EPSG:4326:9 62 108 537 617 EPSG:4326:10 125 216 1075 1234 EPSG:4326:11 250 432 2150 2468 EPSG:4326:12 500 864 4300 4937 EPSG:4326:13 1001 1729 8601 9875 EPSG:4326:14 2002 3458 17203 19751 EPSG:4326:15 4004 6917 34406 39503 EPSG:4326:16 8009 13835 68812 79007 EPSG:4326:17 16019 27670 137625 158014 EPSG:4326:18 32039 55341 275251 316029 EPSG:4326:19 64079 110683 550502 632058 EPSG:4326:20 128159 221366 1101004 1264116 EPSG:4326:21 256318 442732 2202009 2528233 EPSG:900913 EPSG:900913:0 0 0 0 0 EPSG:900913:1 0 0 1 1 EPSG:900913:2 0 1 2 2 EPSG:900913:3 1 2 4 4 EPSG:900913:4 3 5 8 9 EPSG:900913:5 7 10 16 19 EPSG:900913:6 15 21 33 38 EPSG:900913:7 30 42 67 77 EPSG:900913:8 61 84 134 154 EPSG:900913:9 122 169 268 308 EPSG:900913:10 245 338 537 617 EPSG:900913:11 490 676 1075 1234 EPSG:900913:12 980 1352 2150 2468 EPSG:900913:13 1960 2705 4300 4937 EPSG:900913:14 3920 5411 8601 9875 EPSG:900913:15 7841 10823 17203 19751 EPSG:900913:16 15683 21647 34406 39503 EPSG:900913:17 31367 43295 68812 79007 EPSG:900913:18 62734 86590 137625 158014 EPSG:900913:19 125469 173180 275251 316029 EPSG:900913:20 250938 346360 550502 632058 EPSG:900913:21 501877 692720 1101004 1264116 EPSG:900913:22 1003755 1385441 2202009 2528233 EPSG:900913:23 2007511 2770883 4404019 5056466 EPSG:900913:24 4015022 5541767 8808038 10112932 EPSG:900913:25 8030044 11083535 17616076 20225865 EPSG:900913:26 16060088 22167070 35232153 40451731 EPSG:900913:27 32120176 44334141 70464307 80903463 EPSG:900913:28 64240352 88668282 140928614 161806927 EPSG:900913:29 128480705 177336565 281857228 323613855 EPSG:900913:30 256961410 354673131 563714457 647227710 Global predicted thermal distributions for intertidal macroalgae Ocean warming is expected to drive quick shifts in marine species distributions modifying coastal communities. Previous research has shown how the southern Bay of Biscay is particularly affected and the canopy-forming subtidal and intertidal macroalgal assemblages are expected to be replaced by turf-forming Mediterranean-like communities with warmer affinities by the end of the century. The effect of temperature on macroalgal communities could be assessed using thermal metrics that can target temperature effects alone. An example is the Community Temperature Index (CTI) which is an abundance-weighted mean of the species’ optimal temperature. It has been previously used to understand the thermal preferences of different organisms such us birds, fish or invertebrates. -180.0 -90.0 180.0 90.0 macroalgae_presence_absence image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:900913 Spatio-Temporal Interpolation of Zooplankton Alpha Diversity in the Greater Baltic Sea Region Biodiversity loss due to human activities is an increasing threat for marine ecosystems and the services we obtain from them. As biodiversity is directly related to the resilience of ecosystems to temporary disturbance, biodiversity monitoring is a vital task for areas subjected to conservation goals. Environmental factors often control the community composition and biodiversity of marine plankton, such as the pronounced salinity gradient in the Baltic Sea (e.g. Hu et al. 2016). Time series data of biodiversity can therefore provide an indication of changes in community composition due to environmental stressors, such as climate change or eutrophication. As many biodiversity estimates are biased by sampling effort, caution must be taken when interpreting alpha diversity from microscopy counts. By rarefaction and evenness estimation, these biases can be reduced, but not ignored. This data product provides a spatial-temporal interpolation of zooplankton alpha diversity in the greater Baltic Sea area (including Skagerrak and Kattegat). Using observed Shannon diversity indices from environmental monitoring data, the product employs the DIVAnd (Data-Interpolating Variational Analysis in n-Dimensions) to create a three-dimensional interpolation across longitude, latitude, and seasonal time dimensions. The output includes interpolated diversity values, an associated error map, and metadata on the spatial-temporal grid resolution. It is tailored for ecological and oceanographic studies, aiding in the visualization and analysis of diversity trends over time and space. 9.396464646464647 53.44266055045872 30.103535353535353 66.05733944954129 zooplankton_baltic_shannon image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:4326:0 0 0 1 1 EPSG:4326:1 0 0 2 2 EPSG:4326:2 0 0 4 4 EPSG:4326:3 1 1 8 9 EPSG:4326:4 2 3 16 18 EPSG:4326:5 4 6 33 37 EPSG:4326:6 8 12 67 74 EPSG:4326:7 17 25 134 149 EPSG:4326:8 34 51 269 298 EPSG:4326:9 68 103 538 597 EPSG:4326:10 136 207 1077 1195 EPSG:4326:11 272 415 2154 2390 EPSG:4326:12 544 831 4309 4781 EPSG:4326:13 1089 1663 8619 9562 EPSG:4326:14 2179 3327 17239 19124 EPSG:4326:15 4358 6655 34478 38248 EPSG:4326:16 8717 13310 68957 76496 EPSG:4326:17 17434 26620 137914 152992 EPSG:4326:18 34869 53240 275828 305985 EPSG:4326:19 69738 106480 551657 611970 EPSG:4326:20 139476 212961 1103314 1223941 EPSG:4326:21 278952 425923 2206628 2447883 EPSG:900913 EPSG:900913:0 0 0 0 0 EPSG:900913:1 0 0 1 1 EPSG:900913:2 1 1 2 2 EPSG:900913:3 2 2 4 4 EPSG:900913:4 4 5 8 9 EPSG:900913:5 8 10 16 18 EPSG:900913:6 16 20 33 37 EPSG:900913:7 32 41 67 74 EPSG:900913:8 64 82 134 149 EPSG:900913:9 129 165 269 298 EPSG:900913:10 259 331 538 597 EPSG:900913:11 518 662 1077 1195 EPSG:900913:12 1036 1325 2154 2390 EPSG:900913:13 2073 2651 4309 4781 EPSG:900913:14 4147 5303 8619 9562 EPSG:900913:15 8295 10606 17239 19124 EPSG:900913:16 16590 21213 34478 38248 EPSG:900913:17 33180 42427 68957 76496 EPSG:900913:18 66361 84855 137914 152992 EPSG:900913:19 132722 169711 275828 305985 EPSG:900913:20 265445 339422 551657 611970 EPSG:900913:21 530891 678844 1103314 1223941 EPSG:900913:22 1061783 1357688 2206628 2447883 EPSG:900913:23 2123567 2715376 4413257 4895767 EPSG:900913:24 4247135 5430752 8826514 9791534 EPSG:900913:25 8494271 10861505 17653029 19583068 EPSG:900913:26 16988543 21723011 35306059 39166137 EPSG:900913:27 33977087 43446023 70612119 78332275 EPSG:900913:28 67954175 86892046 141224239 156664550 EPSG:900913:29 135908350 173784093 282448479 313329101 EPSG:900913:30 271816700 347568187 564896959 626658203 Phytoplankton alpha diversity in the greater Baltic Sea area The number of marine seaweeds outside their natural boundaries has increased in the last decades generating impacts on biodiversity and economy. This makes the development of management tools necessary, where species distribution models (SDMs) play a crucial role. SDMs can help in the early detection of invasions and predict the extent of the potential spread. However, modelling non-native marine species distributions is still challenging in terms of model building, evaluation and selection. This product aims to predict the European distribution of four widespread introduced seaweed species selecting the best model building process. 10.291228707624114 54.81334288967846 24.0605432179078 65.98905153837853 alpha_diversity_shannon image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:4326:0 0 0 1 1 EPSG:4326:1 0 0 2 2 EPSG:4326:2 0 0 4 4 EPSG:4326:3 1 1 8 9 EPSG:4326:4 2 3 16 18 EPSG:4326:5 4 6 33 36 EPSG:4326:6 8 12 67 72 EPSG:4326:7 17 25 135 145 EPSG:4326:8 34 50 270 290 EPSG:4326:9 68 100 541 580 EPSG:4326:10 136 200 1082 1160 EPSG:4326:11 273 400 2165 2321 EPSG:4326:12 546 800 4330 4643 EPSG:4326:13 1092 1601 8660 9287 EPSG:4326:14 2185 3202 17320 18574 EPSG:4326:15 4371 6405 34641 37148 EPSG:4326:16 8742 12811 69282 74296 EPSG:4326:17 17484 25622 138565 148592 EPSG:4326:18 34968 51244 277131 297184 EPSG:4326:19 69936 102488 554263 594369 EPSG:4326:20 139873 204977 1108526 1188738 EPSG:4326:21 279747 409954 2217053 2377477 EPSG:900913 EPSG:900913:0 0 0 0 0 EPSG:900913:1 0 0 1 1 EPSG:900913:2 1 1 2 2 EPSG:900913:3 2 2 4 4 EPSG:900913:4 4 5 8 9 EPSG:900913:5 8 10 16 18 EPSG:900913:6 16 20 33 36 EPSG:900913:7 32 40 67 72 EPSG:900913:8 64 81 135 145 EPSG:900913:9 129 162 270 290 EPSG:900913:10 259 324 541 580 EPSG:900913:11 519 649 1082 1160 EPSG:900913:12 1038 1299 2165 2321 EPSG:900913:13 2077 2598 4330 4643 EPSG:900913:14 4155 5197 8660 9287 EPSG:900913:15 8310 10394 17320 18574 EPSG:900913:16 16620 20788 34641 37148 EPSG:900913:17 33241 41576 69282 74296 EPSG:900913:18 66483 83152 138565 148592 EPSG:900913:19 132967 166304 277131 297184 EPSG:900913:20 265935 332608 554263 594369 EPSG:900913:21 531870 665216 1108526 1188738 EPSG:900913:22 1063741 1330432 2217053 2377477 EPSG:900913:23 2127483 2660864 4434107 4754955 EPSG:900913:24 4254967 5321728 8868214 9509910 EPSG:900913:25 8509934 10643456 17736428 19019821 EPSG:900913:26 17019869 21286913 35472856 38039642 EPSG:900913:27 34039739 42573827 70945712 76079284 EPSG:900913:28 68079478 85147655 141891424 152158569 EPSG:900913:29 136158957 170295310 283782848 304317138 EPSG:900913:30 272317914 340590620 567565697 608634277 Abundance maps of Sea Birds in the European Seas The data product consists of a netCDF file containing the gridded field obtained by the interpolation of the observations of more than 400 sea bird species. The field is computed for 5 time periods: 1970-1979, 1980-1989, 1990-1999, 2000-2009 and 2010-2019. Along with the gridded field, an error field is also provided. It allows user to only consider the regions where the analysis can be trusted, i.e. where the error field is lower than a given threshold (0.25 for example). -55.25 13.75 21.25 72.25 seabirds_gridded_count_error image/png image/jpeg text/plain application/vnd.ogc.gml text/xml application/vnd.ogc.gml/3.1.1 text/xml text/html application/json EPSG:4326 EPSG:4326:0 0 0 0 1 EPSG:4326:1 0 0 1 2 EPSG:4326:2 0 1 2 4 EPSG:4326:3 0 3 5 8 EPSG:4326:4 1 6 11 17 EPSG:4326:5 3 13 22 35 EPSG:4326:6 6 27 44 71 EPSG:4326:7 12 54 88 143 EPSG:4326:8 25 108 177 286 EPSG:4326:9 50 216 354 572 EPSG:4326:10 100 433 709 1144 EPSG:4326:11 201 867 1419 2289 EPSG:4326:12 403 1735 2838 4579 EPSG:4326:13 807 3470 5677 9159 EPSG:4326:14 1615 6940 11355 18318 EPSG:4326:15 3231 13880 22710 36636 EPSG:4326:16 6462 27761 45420 73272 EPSG:4326:17 12925 55523 90840 146545 EPSG:4326:18 25850 111047 181680 293091 EPSG:4326:19 51700 222094 363360 586183 EPSG:4326:20 103401 444188 726721 1172366 EPSG:4326:21 206802 888376 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