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ArticleTitle Mapping Biological Resources in the Coastal Zone: An Evaluation of Methods in a Pioneering Study from Norway

Article Sub-Title

Article CopyRight Royal Swedish Academy of Sciences

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Journal Name AMBIO

Corresponding Author Family Name Knutsen

Particle

Given Name Halvor

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Division Department of Coastal Zone, Institute of Marine Research Organization Flødevigen Marine Research Station

Address His, 4817, Norway

Email [email protected]

Author Family Name Knutsen

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Given Name Jan Atle

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Author Family Name Rinde

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Given Name Eli

Suffix Division

Organization Norwegian Institute for Water Research Address Gaustadalléen 21, Oslo, 0349, Norway

Author Family Name Christie

Particle

Given Name Hartvig

Suffix Division

Organization Norwegian Institute for Water Research Address Gaustadalléen 21, Oslo, 0349, Norway

Author Family Name Bodvin

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Given Name Torjan

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Author Family Name Dahl

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Given Name Einar

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Received 21 March 2009

Revised 23 November 2009

Accepted 25 November 2009

Abstract For many years, the planning and management of terrestrial areas has been supported by a detailed knowledge of the distribution of habitats and their associated species. However, the detailed mapping of biological resources in extent coastal areas, such as the Norwegian coastal zone, is unrealistic due to its enormous coastline. Here, we present a useful and feasible approach and a set of simple, cost-effective methods which are suitable for providing a broad-scale overview of marine habitats and fish resources. This approach was developed in conjunction with a pioneer study conducted along the southern coast of the Skagerrak, where we combined knowledge gathered from local fishermen with scientific knowledge of important species and nature types to establish a coastal sea mapping program. GIS modeling tools were used in both the mapping program and to integrate local and scientific knowledge into digital maps made available to local area management. This multi-faceted approach, which combines local knowledge and scientific methods, provides valuable information with respect to marine biodiversity, and has been used extensively by local

environmental management.

Keywords (separated by '-') Coastal zone management - Fish resources - Habitat mapping - Stakeholders - Norwegian Skagerrak coast Footnote Information

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R E V I E W P A P E R 1

2

Mapping Biological Resources in the Coastal Zone: An Evaluation

3

of Methods in a Pioneering Study from Norway

4 Jan Atle Knutsen, Halvor Knutsen, Eli Rinde, 5 Hartvig Christie, Torjan Bodvin, Einar Dahl

6 Received: 21 March 2009 / Revised: 23 November 2009 / Accepted: 25 November 2009

7 Abstract For many years, the planning and management 8 of terrestrial areas has been supported by a detailed 9 knowledge of the distribution of habitats and their associ- 10 ated species. However, the detailed mapping of biological 11 resources in extent coastal areas, such as the Norwegian 12 coastal zone, is unrealistic due to its enormous coastline.

13 Here, we present a useful and feasible approach and a set of 14 simple, cost-effective methods which are suitable for pro- 15 viding a broad-scale overview of marine habitats and fish 16 resources. This approach was developed in conjunction 17 with a pioneer study conducted along the southern coast of 18 the Skagerrak, where we combined knowledge gathered 19 from local fishermen with scientific knowledge of impor- 20 tant species and nature types to establish a coastal sea 21 mapping program. GIS modeling tools were used in both 22 the mapping program and to integrate local and scientific 23 knowledge into digital maps made available to local area 24 management. This multi-faceted approach, which com- 25 bines local knowledge and scientific methods, provides 26 valuable information with respect to marine biodiversity, 27 and has been used extensively by local environmental 28 management.

29

30 Keywords Coastal zone management

31 Fish resourcesHabitat mappingStakeholders 32 Norwegian Skagerrak coast

33 INTRODUCTION

34 Biological resources in the coastal zone are under extensive 35 pressure worldwide. Over the past 20 years, there has been 36 a fundamental change in our understanding of the human 37 impact to the coastal marine environment. Previously, the 38 public focus has primarily been on pollution, although

today, habitat destruction, climate change, invasive spe- 39 cies, and overfishing must also be taken into account to 40 maintain a high biodiversity in coastal seas. 41

Over the past few decades, the high demand for coastal 42 resources has led to extensive use of the coastal zone, and 43 resulted in irreversible damage and loss of important bio- 44 logical resources in coastal areas throughout Europe. 45 Commercial fisheries have ceased to operate due to pollution 46 in many coastal areas (Bakke et al.2006; Næss et al.2002; 47 Dahl et al.2008), the anthropogenic impact has degraded 48 coastal marine habitats and ecosystems (Phil et al. 2006; 49 Baden et al.2003), and invasive species have re-organized 50 the biodiversity of the shallow coastal waters (Carlton 51 1996). Overharvesting has contributed to a well documented 52 collapse in some coastal fish populations (e.g., Atlantic cod; 53 Sveda¨ng and Bardon2003; Myers et al.1996). 54

For years, terrestrial areas have been managed based 55 upon a detailed knowledge and comprehensive data on the 56 distribution of habitats and their associated species 57 (Wundram and Loeffler 2008). Much of this information 58 is visualized on maps easily available to local, regional, 59 and national management. By contrast, only maps show- 60 ing bathymetric features and on rare occasions, physical, 61 and chemical oceanographic data, exist for undersea 62 areas. Until recently, little focus has been addressed with 63 regard to the identification and mapping of marine bio- 64 logical resources in the coastal zone, with a particular 65 scarcity of such information in temperate areas. Recent 66 studies have either been concentrated on a single habitat 67 type, e.g., Sta˚l and Pihl (2008) performing a quantitative 68 investigation for the utilization of shallow areas for fish- 69 ing along a specific part of the western Swedish coast, or 70 on one particular resource or habitat, e.g., Sta˚l et al. 71 (2007) studying the distribution and quality of plaice 72 nursery grounds. 73

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Article No. : 23 h LE h TYPESET

MS Code : 09A690 h4CP h4DISK

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DOI 10.1007/s13280-010-0023-6

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74 The coastal zone of the Skagerrak is the most populated 75 part of the Norwegian coast. During the last few decades, 76 there has been an expanded use of the coastal zone in 77 Norwegian waters, and development has been carried out 78 with no consideration of the biological assets, with irre- 79 versible consequences for biological diversity (Dahl et al.

80 2008; Knutsen et al. 2003). It is important to establish a 81 management procedure for the coastal zone that will ensure 82 a continuation of the remaining biological diversity and 83 productivity. Obviously, a detailed knowledge of marine 84 habitats and biological resources, and marine species and 85 their ecological relationships is needed. Even so, such 86 mapping is highly demanding and at present not possible to 87 accomplish for extent coastal areas, such as the Norwegian 88 coastal zone. The aim of this article is to present a useful 89 and feasible approach together with a set of simple, cost- 90 effective methods suitable for providing a broad-scale 91 overview of marine habitats and fish resources. The 92 approach and methods were developed in a pioneering 93 study on the Norwegian Skagerrak coast. We combined 94 local and scientific knowledge to establish a relevant and 95 cost-effective sea mapping program, and discussed how the

acquired information may be used by local environmental 96 management. 97

MATERIALS AND METHODS 98 Study Area 99

This pioneering study was carried out in the 100 Tvedestrandfjord on the Skagerrak coast of Norway in 101 2002. The fjord system is 8 km long, and the water cir- 102 culation is reduced due to several sills among three well- 103 defined water basins (Fig.1). Hydrographical and chemical 104 studies have been conducted along the Skagerrak coast 105 since 1928 (Johannessen and Dahl 1996). Studies of the 106 water masses of the Tvedestrandfjord have revealed severe 107 a deoxygenating of the water below sill levels in the basins 108 (Knutsen et al. 2003; Johannessen and Dahl 1996; Dahl 109 et al. 1987). Kroglund et al. (1998) studied the seaweed 110 vegetation at different localities in the fjord and found that 111 opportunistic green and brown algae, which tolerate high 112 levels of nutrients, dominated the shoreline. 113

Fig. 1 Study area showing: egg stations (filled circle) from the Tvedestrandsfjord to off coast, gill net locations (solid line), and spawning areas for cod (circled times) where three different criteria were fulfilled:

amore than two local fishermen marked an area as an important spawning ground independent of each other,bspawning cod were captured, andccod eggs were identified

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114 Available GIS Layers

115 The Norwegian Mapping Authority provided digital bathy- 116 metric maps of the marine areas of Tvedestrandfjord, with a 117 resolution of 25925 m². In order to identify areas with an 118 appropriate slope, we established a slope layer from the 119 bathymetric model as well as used a wave exposure model 120 (Isæus2004), which was later applied for the entire Nor- 121 wegian coast in the national mapping and monitoring pro- 122 gram on biological diversity (Longva 2006). These maps 123 were used for interviews, for planning transects with scuba 124 diving and video recording, and for the GIS-analyses.

125 Selected Benthic Habitats and Fish Resources

126 A national guide describing key habitats and fish resources 127 in the coastal zone, published by the Directorate of Nature 128 Management (DN) in 2001, constituted the basis for 129 selecting which habitats/resources to map. The list was 130 expanded to include nursery areas for fish and seagrass 131 beds (later included in the revised version of the DNs list;

132 DN 2007). The following benthic habitats were mapped:

133 seagrass beds (Zostera marinaandRuppia maritima), soft 134 bottom areas (mud flats), and Laminaria hyperboreakelp 135 forests.Registered occurrences of the various habitats were 136 labeled on maps according to the proposal from the DN 137 (2001) and the Norwegian mapping standard known as 138 SOSI. SOSI is the Norwegian standard for the exchange of 139 geographical data, and includes codes for different subject

areas, such as biodiversity and fishery science. The system 140 is closely related to international standards developed by 141 ISO/TC211. The aim with respect to biodiversity is to 142 describe biological data registered through different map- 143 ping projects under management, to create a standard for 144 the sampling and documentation of species and nature type 145 distribution, as well as area use. The code list for marine 146 nature types at present is shown in Table 1. 147

Interviews 148

Mapping of spawning, nursery areas, and marine habitats 149 were based on interviews of 12 local fishermen with a 150 thorough knowledge of local areas within the study area. 151 The interviews were performed independently and con- 152 ducted according to pre-made forms. Information was 153 recorded by the individual fishermen on available maps 154 directly (e.g., locations of different nature types, fishing 155 areas, spawning areas, and other relevant information). 156

GIS Modeling of the Distribution of Seagrass Beds, 157 Mud Flats, and Kelp Forests 158

Based on the bathymetric and wave exposure maps, transects 159 were selected and mapped in the field to assess the upper and 160 lower values for the distribution of seagrass beds, mud flats 161 (soft bottom areas), and kelp forests along the depth and 162 wave exposure gradients using the methods described above. 163 Based on these values and on some general criteria for 164

Table 1 Code list of marine nature types in SOSI from the website of the Norwegian mapping authority, 14 September 2009, draft of English version of SOSI standard version 4 for biodiversity (http://www.

statkart.no/sosi/)

Nr Code name Definition/Description Code

13 Code list/BdNatureTypeMarine Prioritized nature types collected through municipal surveys of important biodiversity areas in accordance with DN Manual 19-2001, surveying marine biodiversity.

13.1 Large kelp forest areas I01

13.2 Strong tidal currents I02

13.3 Fjords with low oxygen content I03

13.4 Deep fjords I04

13.5 Round fjord with narrow inlet I05

13.6 Litoral basins I06

13.7 Ice-marginal deposits I07

13.8 Soft bottom areas in the beach zone I08

13.9 Corals I09

13.10 Loosely bedded calcareous algae deposits I10

13.11 Eelgrass community I11

13.12 Shell sand I12

13.13 Oyster populations I13

13.14 Major scallop populations I14

13.15 Other important populations I15

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165 occurrences across the gradient’s slope and wave exposure 166 based on the long-term field experience of scientists 167 (Table1), a GIS overlay analysis was applied to identify the 168 probable distribution of these benthic habitats.

169 Methods to Identify and Verify Marine Habitats

170 The following three methods were used to identify marine 171 habitats in the field:

172 (a) In late autumn during nights with calm weather and 173 clear seas (excellent visibility), a small boat with strong 174 lights was used to survey shallow areas (\10 m depth) 175 to verify the existence of seagrass beds suggested by 176 GIS analysis and interviews. Seagrass beds and mud 177 flats are easily recognized by this method. In addition, 178 hydroscopes were used during the daytime to identify 179 bottom substrates and vegetation types.

180 (b) Scuba diving was used in the summer to identify the 181 distribution of marine habitats to a depth of approx- 182 imately 20 m. For kelp a forest, 5–10 transects were 183 designed to evenly span potential distribution areas 184 identified by local fishermen and GIS analysis. For 185 seagrass beds, scuba diving was used to verify the 186 distribution and lower depth limits of exposed, 187 moderately exposed and sheltered areas at 10 loca- 188 tions of the different exposure classes.

189 (c) In summer, an underwater video camera (Dacon Sub 190 Sea) was launched from the Research Vessel (RV) 191 ‘‘G. M. Dannevig’’. The camera focused vertically on 192 the bottom and was connected by cable to a monitor on 193 board the ship. In combination with a GPS-based OLEX 194 system (http://www.olex.no/index_e.html), benthic 195 habitats (including kelp forests, sand, or rock-dominated 196 substrates, and ‘‘degree of hardness of the substrate’’) 197 were plotted directly onto digital maps. This method was 198 especially useful in areas not suitable for scuba diving 199 due to strong currents (e.g., in shallow offshore areas).

200 The study area was visited twice and the criteria for the 201 distribution of kelp forests and seagrass beds were 202 improved during the last visit (cf. Table2).

203204 Field Verification of Spawning and Nursery Areas

for Fish 205

In order to verify the information obtained through inter- 206 views concerning spawning areas, we performed: (a) egg 207 sampling and (b) test fishing with traditional fish nets 208 during the spawning period from February to April. It was 209 decided to specifically focus on fish species of commercial 210 interest (cod and pollack). Areas were only assigned as 211 active spawning areas if all the following criteria were 212 fulfilled: (a) the spawning grounds were identified inde- 213 pendently by more than two fishermen during the inter- 214 views, (b) fish eggs were included in the samples, and (c) 215 spawning fish were captured in the same area. Analo- 216 gously, nursery areas for fish species of commercial 217 interest were assigned as active if identified independently 218 by more than two fishermen. Nursery areas operate through 219 a combination of several factors, such as density, growth, 220 survival of juveniles, and movement to adult habitats, and 221 are crucial for the survival of newly settled larvae (Beck 222 et al.2001). 223

Egg Sampling 224

Vertical tows with a plankton net (WP2-diameter: 60 cm, 225 mesh width: 500 lm, filtering approximately 8.4 m³ of 226 water each haul) were performed from the RV ‘‘G. M. 227 Dannevig’’ in a transect from the inner section of the fjord 228 to the exposed areas outside the fjord mouth. This design 229 covers areas identified as both spawning areas by local 230 fishermen and areas not identified as spawning areas (i.e., 231 control areas). The hauls were performed during three 232 temporal occasions (weeks 8, 11, and 14) to ensure hitting 233 the spawning period for cod. We tested for the difference in 234 the average number of eggs between these areas in week 14 235 (locations 21–30 vs. locations 31 and 32 in Fig. 1) using a 236 t-test. The boat was kept in roughly the same position 237 during the tow by the use of GPS and a nearby reference 238 landmark, and the tows were taken from a depth of 30 m. 239 The net was raised at a speed of 0.5 m s^-1(Barnes1949) 240 to avoid turbulence in the opening, and the eggs were 241 Table 2 Criteria used in the GIS analysis for the different habitat types

Depth (m) Corrected depth Slope (angle) Corrected slope Exposure Habitat

0–2 \10° \3° Partly exposed

to protected

Soft bottom areas-mudflats

1–7 \25° \7° Slightly exposed

to protected

Seagrass beds 3–15 3–15 in mod exp 3–20

in exposed

[25° Independent

of slope

Exposed Kelp forest (Laminaria hyperboreacommunities

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242 counted and identified according to standing literature 243 (Hiemstra1962; Hoek and Ehrenbaum1911; Russel1976).

244 Test Fishing

245 Based on the designated spawning areas obtained from the 246 interviews, test fishing was executed by use of a stan- 247 dardized series of 30 fishnets (24.095.5 m, 80 mm bar 248 mesh) within the spawning period for cod and pollack 249 (February–April). On each sampling occasion, the nets 250 were set at the precise locations marked by the fishermen, 251 and we fished for about 18 h. The fish were frozen and later 252 weighed (Win g), measured (total lengthLTin mm), sexed, 253 and classified as either juvenile, maturing (about to spawn 254 within the season of capture), or spawned. Fish age was 255 estimated from otholiths (Fotland et al.2008).

256 Distribution of Maps to Local Management

257 The resulting maps for the likely distribution of the studied 258 habitats (kelp forests, seagrass beds, mud flats, and spawn- 259 ing and nursery areas for fish), based on combining the 260 results from the interviews and field sampling, were digi- 261 talized and transformed to the standard SOSI–map system.

262 The resulting digital maps (SOSI-files) were included in the 263 official map system of Tvedestrand municipality, and made 264 available to the public through a web-based solution (www.

265 tvedestrand.kommune.no).

266 RESULTS

267 The Tvedestrandsfjord contains a unique variety of bio- 268 logical assets in the coastal zone. Consequently, a diversity 269 of marine habitats and a number of spawning and nursery 270 areas for fish were identified during the study.

271 Seagrass Beds

272 Both sets of criteria for this habitat overestimated the dis- 273 tribution of seagrass beds in the Tvedestrandfjord. During 274 the second field survey, we observed that the slope limit 275 seemed to decrease with exposure, from roughly 10° or 276 higher at the sheltered areas to approximately 5° at the 277 most exposed seagrass localities, though seagrass beds 278 were not found at the most exposed sites. Figure2shows 279 the modeled seagrass localities based on the improved 280 criteria after the second survey (using 7°for all areas as a 281 compromise) compared to the exact seagrass localities 282 observed by visual inspection from the boat at night. As 283 seen in Fig.2, the model has a limited fit, and partially 284 overestimated the seagrass distribution. The improved 285 criteria was later applied and tested within the national

program for the mapping and monitoring of biodiversity, 286 and was found to have a fit of 78.2% for the modeled area 287 within the Skagerrak region (Longva 2006). Still, the 288 specificity and sensitivity of the model was rather low (19 289 and 46.7%, respectively). The specificity expresses the 290 number of the modeledseagrass areas that were found to 291 actually contain seagrass, whereas the sensitivity expresses 292 the number ofobservedseagrass areas that also were pre- 293 dicted to have seagrass. 294

Mud Flats 295

The GIS models gave very precise estimates ([90% area 296 overlap based on interviews of local fishermen) for the 297 distribution of mud flats (soft bottom areas). Mud flats were 298 localized close to seagrass beds and are important nursery 299 areas for fish (Fig.3). 300

Kelp Forest 301

Inspections by scuba diving and video registration showed 302 that the depth range for the offshore L. hyperborea kelp 303 forest increased with exposure from about 15 m near shore 304 to about 20 m at the most exposed sites. We also observed 305 large areas of kelp forest on completely plain substrates in 306 these areas. Our first suggestion of using a slope of 25°as 307 an indication of rocky substrate with a kelp forest was not 308 successful (Table1). Figure4 shows the difference in the 309 modeled kelp forest area using the first applied criteria set 310 based on depth and slope (Table1) compared to the 311 improved criteria based on a field survey in the subtidal 312 area of exposed areas, including wave exposure in the 313 criteria sets. The first set of criteria implies a large 314 underestimation of the kelp forest (over 90%) in the 315 Fig. 2 Modeled seagrass localities based on the improved criteria (yellow areas) compared to exact distribution of seagrass observed by visual inspection from boat at night (green areas). For localization, seeblack frameat Furøya in Fig.1

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316 municipality by not including the large, plain area offshore, 317 with a well-developed kelp forest down to a depth of 318 approximately 20 m. The improved model criteria were

later applied and tested in the national program for the 319 mapping and monitoring of biodiversity, and was found to 320 have a fit of 81.7% for the modeled area within the 321 Fig. 3 Distribution of mud flats

in Tvedestrandsfjord analyzed by GIS-analyses (brown areas), and seagrass localities (green areas) registered by boat surveys

Fig. 4 Modeled kelp forest based on the improved criteria compared to old criteria.Red areasvisualize kelp forest after old criteria, whereasbeige areas visualize kelp forest due to new criteria

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322 Skagerrak region (Longva 2006). The specificity of the 323 model was 61% and the sensitivity 77.3%.

324 Spawning and Nursery Areas for Fish

325 In total, 317 cod (Gadus morhua L.) and 97 pollack 326 (Pollachius pollachius L.) were captured within the 327 spawning areas pointed out by the local fishermen. More 328 than 70% of the cod and 100% of the pollack in these areas 329 were classified as mature (Fotland et al. 2008); although 330 pollack was only caught in the inner parts of the fjord, and 331 are not included in Table3. By comparison, only 22% of 332 cod captured in the control areas outside the spawning 333 grounds were spawning fish (Table3). The average num- 334 ber of cod eggs within the spawning areas was much higher 335 (a mean of 49.7 in the spawning areas vs. a mean of 7 in the 336 control areas), and significantly larger than in the control 337 areas (t=3.18; P\0.0055). A stratified egg sampling 338 found that the density of cod egg was highest in the inner 339 fjords close to the spawning areas (Table3). Figure1 340 shows the spawning areas in the Tvedestrandsfjord based 341 upon the criteria described in the ‘‘Materials and Methods’’

342 section. A number of nursery areas were identified and 343 designated as being active based on independent informa- 344 tion obtained from more than two fishermen.

345 DISCUSSION

346 During this pioneering study, we have developed an 347 approach and set of methods suitable for mapping marine 348 habitats and fish resources on a scale appropriate for 349 coastal zone management. For several of the habitats (e.g., 350 spawning areas, nursery areas, and seagrass habitats), we 351 recommend a multi-faceted approach that combines the

gathering of local knowledge from fishermen and verifi- 352 cation through scientific field sampling methods. Spawning 353 grounds were identified by a combination of interviews, 354 egg sampling, and test fishing. The field verification shows 355 that experienced-based information from the interviewing 356 of fishermen is highly reliable, as field sampling only 357 marginally adjusted the areas reported by the fishermen. 358 The distribution of kelp forests were well-predicted 359 through the use of GIS models based on criteria established 360 by field sampling across some of the most important 361 environmental gradients for this species (i.e., depth and 362 wave exposure; Bekkby et al.2009). However, the seagrass 363 models have a limited fit and partially overestimate the 364 actual distribution of the habitat (Fig.2), and can therefore 365 only be used as a tool for the planning of field mapping in 366 combination with information from local eel fishermen. For 367 this habitat, we recommend a detailed mapping based on 368 visual inspections from boats in the areas which are either 369 reported to have seagrass beds based on the interviews, or 370 based on the model are suitable for containing large areas 371 with this type of habitat. We found that the most important 372 nursery areas overlap to a large degree with the seagrass 373 distribution (Jackson et al. 2001). However, kelp forests 374 and mudflats are also important habitats for juvenile coastal 375 fish species. 376

For mudflats, the GIS analysis gave a good indication of 377 the geographic distribution, and the same approach is used 378 in the national mapping program for marine biodiversity. 379 However, the depth model developed and used in the 380 national program includes elevation data for land as well as 381 bathymetric data for the sea area, thus providing a better 382 model for the terrain structure in the land-sea boundary 383 than the one used in the pioneer study. In the national 384 program, the mud flats are identified as the area between 385

?1 m (land) and-2 m (sea) with slopes\3°. This mud flat 386

Table 3 Number of cod eggs observed at different localities by VP II hauls (0–30 m) during spawning period in winter, weeks 8, 11, and 14, and CPUE of fish caught close to spawning areas (thegray dotted areas close to localities 21–30) in the Tvedestrandfjord and off coast (control localities 31–32) during winter

Data show number of fish nets (n), CPUE, percentage (%) of spawning cod (Hoek and Ehrenbaum1911)

Localities Cod egg densities Gill net sampling

Week 8 Week 11 Week 14 n(nets) CPUE % spaw.

cod

21 7 49 149

22 4 43 90 16 1.4 90

23 0 100 40

24 4 13 37

25 6 18 46 56 1.5 79.4

26 3 9 49

27 3 5 16

28 4 0 49 48 3.1 77.4

29 0 8 17

30 3 12 4

31 5 8 (week 13) 24 1.25 33.9

32 7 6 (week 13) 25 1.3 37.4

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387 model is used as a foundation for identifying mud flats 388 through verification from aerial photographs or field sam- 389 pling. Hence, the pioneer study led to the development and 390 use of a simple method now employed for mapping mud 391 flats along the entire Norwegian coastal zone.

392 The Tvedestrand area holds a unique variety of bio- 393 logical assets in the coastal zone, and is well-suited as a 394 model/system for testing out various methods. Below, we 395 discuss our results in more detail and indicate how coastal 396 management may implement these methods and data in 397 their future plans.

398 Collecting Local Knowledge Through Interviews 399 and Field Verification

400 Local fishermen have prominent knowledge of marine 401 habitats and fish resources in both the Tvedestrandfjord and 402 offshore areas. The information gathered from the inter- 403 views about the marine habitats was verified using different 404 methods (manual inspection from the boat during nights, 405 scuba diving, underwater video camera, and GIS-analyses).

406 In all the cases, there was a high degree of agreement 407 among the results from the field sampling and the infor- 408 mation obtained from the interviews of the local fishermen.

409 The classification of marine habitats based on depth, 410 exposure, and slope was a new approach when this pioneer 411 study was performed, and therefore needs further refine- 412 ment. Nevertheless, the verification through visual inspec- 413 tions and diving demonstrates that this approach is useful 414 and accurate for some of the marine habitats. Through 415 repeated surveys planned to cover the distribution of the 416 various habitats across the important physical gradients 417 depth and wave exposure, the criteria for distribution were 418 improved. The field observations showed that the distribu- 419 tion of seagrass beds was more random within its ‘‘funda- 420 mental niche’’ compared to the kelp forest. The distribution 421 of kelp was far more predictable, which made the criteria for 422 this habitat easier to define. The pioneer study showed that 423 planning field sampling based on GIS-analyses and infor- 424 mation from local fishermen are useful in identifying and 425 delimiting important marine habitats, such as seagrass beds, 426 mud flats, and kelp forests.

427 Spawning Areas

428 Local fishermen designated several spawning areas for fish 429 in the Tvedestrandsfjord, which were later tested by means 430 of scientific methods (gill net fishing, egg sampling, and 431 echo sounding; Fig.1; Table3). Gill net sampling clearly 432 identified that adult cod caught in the nominated areas 433 inside the fjord were spawners (77–90%), whereas offshore 434 cod were less likely to spawn (33–37% in offshore habitats;

435 Fig.1; Table3). We also applied echo sounding to identify

clusters of spawning fish in different sections of the fjord. 436 Echo sounding turned out to be difficult as the bottom 437 bathymetry was very rough within the narrow fjord and 438 shadows prevented a meaningful interpretation of the data. 439 Egg sampling demonstrated that a significantly higher 440 density of pelagic eggs inside the fjord and decreasing 441 levels further offshore (Table3). This pattern has recently 442 been demonstrated in a number of fjords along the Nor- 443 wegian coast (Knutsen et al. 2007). The authors suggest 444 that fjord sills play a significant role in keeping the eggs 445 within the fjords, and are a retention mechanism that 446 probably aids in maintaining the genetic structure among 447 cod populations in the fjords along the coastal areas of 448 Norway (Knutsen et al.2003,2004). Egg sampling is cost- 449 effective in that it covers large areas for short periods, and 450 allows for the identification of several species at the same 451 time, although this method also has some weaknesses. 452 Even though the density of the pelagic eggs will normally 453 point out the spawning sites, i.e., the density is highest in 454 the vicinity of the spawning grounds and the egg stages 455 here are premature, the egg distribution may also vary 456 considerably on a temporal scale (Espeland et al. 2006, 457 2007). This temporal effect could be due to variable cur- 458 rents forcing the dispersal of eggs and larvae throughout 459 the spawning basin, or adult fish which move around and 460 use more than one specific spawning site. A multi-faceted 461 approach would therefore minimize these sources of errors. 462 Combining information from the interviews of local fish- 463 ermen with a stratified grid-based egg sampling regime and 464 good topographic maps would thereby be sufficient in most 465 cases for identifying the specific spawning sites for fish at 466 inshore sections of the coastal sea. 467

The identification of local spawning sites in fjords is 468 also clearly supported by recent telemetry studies (Espe- 469 land et al. 2006; Bergstad et al. 2008). Interestingly, a 470 recent study found that the local cod populations have a 471 difference in age and size at maturity, and in survival and 472 growth rates, indicating locally evolved life histories on an 473 unexpectedly small spatial scale (Olsen et al.2004,2008). 474 Nursery Areas 475

A number of nursery areas for fish were pointed out by the 476 local fishermen in the fjords and offshore areas, with many 477 of them coincident with habitats identified by the GIS- 478 analyses or by manual surveys of habitats from boats. 479 There was a convincing overlap among the fishermen who 480 gave information to the project, and all were very precise 481 about the nursery areas of fish. In general, the nursery areas 482 designated were shallow coastal waters habitats, such as 483 mud flats, eel grass beds, or kelp forests. 484

It is a challenge for all marine fish to place reproductive 485 propagules into an environment where they are likely to 486 AMBIO

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487 hatch and settle into an appropriate habitat. The strategy of 488 placing eggs in protected water masses deep inside fjords 489 closely situated to nursery grounds may enhance the repro- 490 ductive output of the fish. Obviously, fish from local 491 spawning areas are dependent on accessible nursery grounds, 492 as the quality and quantity of recruitment habitats may be a 493 limiting factor for fish populations (Gotceitas et al.1997).

494 The importance of vegetation beds, especially Zostera 495 marinaand other seagrasses as an epibentic fish habitat, has 496 been demonstrated for a wide variety of marine fishes 497 [Gotceitas et al.1997; Cote et al.2001; see also review in 498 Orth et al. (1984)]. Like seagrasses throughout the world, the 499 eelgrass (Zostera marinaL.) populations in Nordic waters 500 are under great pressure (Baden et al. 2003), and human- 501 induced disturbances and climate change are among the main 502 factors threatening this habitat (Short and Willie-Eschever- 503 ria2000). The great loss of seagrass along the Swedish part of 504 the Skagerrak coast within areas with the highest nutrient 505 loads (Baden et al.2003) gives a serious warning signal, and 506 increases the importance of both knowing the distribution of 507 seagrass beds and achieving a careful management of such 508 ecologically important areas.

509 Important nursery areas for fish can be identified rather 510 precisely by combining ecological information from the 511 fishermen with the results from GIS-analyses and field 512 sampling, which provides maps of the potential distribution 513 of nursery habitats, such as seagrass, mud flats, and kelp 514 forests.

515 CONCLUSION

516 The interviews with local fishermen provided knowledge 517 acquired from centuries of catching experience in the 518 coastal zone. Using their ecological ‘‘know how’’ as 519 background information combined with scientific approa- 520 ches and methods, we were able to design a sea mapping 521 program for several habitats and fish resources along the 522 coast.

523 In most cases, a multi-faceted approach was found to be 524 the desirable strategy when testing different methods by 525 combining a set of modern scientific approaches with the 526 ecological information given by the fishermen. Marine 527 habitat and resource mapping is a powerful approach and an 528 essential prerequisite for developing an ecosystem-based 529 and sustainable management of the coastal zone. Today, this 530 field has been raised to a very high level of importance, both 531 in national waters and international areas of interest (Coagen 532 et al.2009). This holistic approach is highly needed to meet 533 the challenges, as biological resources in the coastal zone are 534 under extensive pressure worldwide.

535 However, in the context of conservation and manage- 536 ment issues, it is also important to distribute the marine

habitat data and information to public management, so that 537 biological resources are taken into account by the relevant 538 stakeholders. 539

Acknowledgments This study was financed by the Tvedestrand 540 municipality, the County Governor of Aust Agder, and the County 541 Authority of Aust Agder. We thank Svein Erik Enersen, Kate Ener- 542 sen, Øystein Paulsen, and Jan Henrik Simonsen for their assistance in 543 lab and fieldwork. We also thank Asbjørn Aass and Henry Knutsen 544 for their assistance with interviewing local fishermen, all the local 545 marine surveillance personnel (especially Jim Gu¨ttrup), and all the 546 local fishermen who shared their information with us. 547

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695 AUTHOR BIOGRAPHIES 696

Jan Atle Knutsen is researcher at the Institute of Marine Research. 697 His main focus is Marine Protected areas and habitat mapping. 698 Address:Department of Coastal Zone, Institute of Marine Research, 699 Flødevigen Marine Research Station, 4817 His, Norway. 700

e-mail: [email protected] 701

Halvor Knutsen (&) is a researcher at the Institute of Marine 702 Research. His focus is on population biology/genetics and habitat 703 mapping. 704

Address:Department of Coastal Zone, Institute of Marine Research, 705 Flødevigen Marine Research Station, 4817 His, Norway. 706

Eli Rinde is researcher at the Norwegian Institute for Water Research. 708 She holds a PhD in kelp forest ecology and has 20 years of experience 709 with studies of coastal ecosystems. Main focus recent years have been 710 on habitat mapping and modelling. 711

Address:Norwegian Institute for Water Research, Gaustadalle´en 21, 712 0349 Oslo, Norway. 713

Hartvig Christie is researcher at the Norwegian Institute for Water 715 Research. He has more than 25 years experience with ecological 716 studies, included effects of perturbations and habitat manipulations, 717 of benthic coastal ecosystems. He has long experience in field work 718 (included diving) as well as experience with field experiments and 719 experiments in mesocosms and aquaria. 720

Address:Norwegian Institute for Water Research, Gaustadalle´en 21, 721 0349 Oslo, Norway. 722

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724 Torjan Bodvin is researcher at the Institute of Marine Research. His 725 main focus is habitat mapping and classification.

726 _Address:Department of Coastal Zone, Institute of Marine Research, 727 Flødevigen Marine Research Station, 4817 His, Norway.

728 e-mail: [email protected]

729 _{Einar Dahl} is head of ‘‘The coastal zone ecology programme’’ at the 730 Institute of Marine Research in Norway and has a broad insight into 731 coastal ecology in general. His basic expertise is on phytoplankton

ecology. He has long experience from monitoring of phytoplankton in 732 coastal waters including the physical and chemical conditions 733 important to their occurrence. 734

Address:Department of Coastal Zone, Institute of Marine Research, 735 Flødevigen Marine Research Station, 4817 His, Norway. 736

738 AMBIO

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