Effects of Land Use Change on Juvenile Fishes and Brown Shrimp Abundance in North Carolina’s Estuarine Nursery Habitats Fisheries Resource Grant Final Report Grant EP-06-08 2004-1773-41 Feb 5, 2010 Submitted by Joseph J. Luczkovich 1,2 Gregory F. R. Meyer 1,3 Mark M. Brinson 1 Terry T. West 1 Jason Hassell 4 East Carolina University Greenville, North Carolina 27858 1 2 3 4 Department of Biology Institute for Coastal Science and Policy Coastal Resources Management PhD Program Hassell Seafood, 321 Hassell Avenue, Washington, NC 27889 Table of Contents List of Figures................................................................................................................................................................3 List of Tables .................................................................................................................................................................5 Acknowledgements .......................................................................................................................................................6 Executive Summary .......................................................................................................................................................7 List of abbreviations .................................................................................................................................................... 13 Introduction ................................................................................................................................................................. 15 LIFE HISTORY FOR THE SELECTED SPECIES .............................................................................................................. 18 Spot (Leiostomus xanthurus).............................................................................................................................. 18 Southern Flounder (Paralichthys lethostigma) .................................................................................................. 19 Atlantic croaker (Micropogonias undulatus) ..................................................................................................... 20 Atlantic menhaden (Brevoortia tyrannus).......................................................................................................... 20 Pinfish (Lagodon rhomboides) ........................................................................................................................... 21 Brown Shrimp (Farfantepenaeus aztectus) ........................................................................................................ 21 OBJECTIVES ............................................................................................................................................................. 22 Methods ....................................................................................................................................................................... 23 LAND USE CLASSIFICATION .................................................................................................................................... 25 Landsat Imagery Data Sources........................................................................................................................... 25 Sources for GIS data layers ................................................................................................................................ 28 Land Use Classification ..................................................................................................................................... 34 Classification Accuracy Assessment .................................................................................................................. 38 Land Use Land Cover Change Detection ........................................................................................................... 44 Data Collection Methods.................................................................................................................................... 49 Statistical Analysis of Change in Program 120 Catches by Station ................................................................... 51 Results ......................................................................................................................................................................... 55 LAND USE CHANGE PATTERNS 1980-2000 .............................................................................................................. 55 CHANGES IN CATCHES OF TARGET SPECIES (1980-2004) ........................................................................................ 64 Spot .................................................................................................................................................................... 65 Southern flounder ............................................................................................................................................... 69 Atlantic croaker .................................................................................................................................................. 73 Atlantic menhaden ............................................................................................................................................. 77 Pinfish ................................................................................................................................................................ 81 Brown shrimp ..................................................................................................................................................... 85 LONG-TERM CHANGES IN SPECIES BY NCDMF STATIONS ....................................................................................... 89 ABIOTIC FACTORS AND ABUNDANCE ....................................................................................................................... 91 MULTIVARIATE CART ANALYSES OF LAND USE CHANGE ON TARGET SPECIES ........................................................ 97 Spot CART analysis ........................................................................................................................................... 98 Southern flounder CART analysis ................................................................................................................... 100 Atlantic croaker CART analysis ...................................................................................................................... 102 Atlantic menhaden CART analysis .................................................................................................................. 104 Pinfish CART analysis ..................................................................................................................................... 106 Brown shrimp CART analysis ......................................................................................................................... 108 Summary of the CART results ......................................................................................................................... 110 Discussion.................................................................................................................................................................. 111 Conclusion ................................................................................................................................................................. 115 References ................................................................................................................................................................. 116 2 List of Figures Figure 1.The overall scheme used to relate land use and juvenile fish and shrimp catch statistics……………….... 24 Figure 2. Pamlico River NCDMF stations and stream networks displayed over the 14-digit USGS HU watershed boundaries.................................................................................................................................................................... 31 Figure 3. Pamlico River NCDMF stations and the stream networks plotted over a USGS topographic quadrangle. . 32 Figure 4. Pamlico River NCDMF stations, stream network, USGS 14-digit HU watersheds, and new catchment boundaries.................................................................................................................................................................... 32 Figure 5. Location of the 71 NCDMF core stations . .................................................................................................. 33 Figure 6. Three Landsat image scenes (a) for the North Carolina coastal area are combined into a single mosaic image (b)...................................................................................................................................................................... 35 Figure 7. Classified land use from May 2000 Landsat imagery showing the 71 NCDMF stations. ............................ 37 Figure 8. Random point assignment ............................................................................................................................ 40 Figure 9. Schematic representation of the process used to relate land use change to changes in species catch between 1980, 1990, and 2000. ................................................................................................................................................. 53 Figure 10. Change in percent forested and wetland area compared to agriculture and developed area....................... 57 Figure 11. The mean percentage of each catchment surrounding the 71 NC DMF stations in each of the years examined (1980, 1990, 2000) . .................................................................................................................................... 59 Figure 12. Spot, Leiostomus xanthurus. The geometric mean catch per trawl from 1978 through 2004. ................. 66 Figure 13. Spot, Leiostomus xanthurus. Long-term change in catch .......................................................................... 67 Figure 14. Spot, Leiostomus xanthurus. Change in trawl catch z-score (1980-2004) at each NCDMF station as a function of percentage change in land use (1980-2000) within each catchment. ........................................................ 68 Figure 15. Southern flounder, Paralichthys lethostigma. The geometric mean of catch per trawl from 1978 through 2004.. ........................................................................................................................................................................... 70 Figure 16. Southern flounder, Paralichthys lethostigma. Long-term change in catch. ............................................... 71 Figure 17. Southern flounder, Paralichthys lethostigma. Change in trawl catch z-score (1980-2004) at each NCDMF station as a function of percentage change in land use (1980-2000) within each catchment........................ 72 Figure 18. Atlantic croaker, Micropogonias undulatus. The geometric mean catch per trawl from 1978 through 2004. ............................................................................................................................................................................ 74 Figure 19. Atlantic croaker, Micropogonias undulatus. Long-term change in catch. ............................................... 75 Figure 20. Atlantic croaker, Micropogonias undulatus. Change in trawl catch Z-score (1980-2004) at each NCDMF station as a function of percentage change in land use (1980-2000) within each catchment........................ 76 Figure 21. Atlantic menhaden, Brevoortia tyrannus. The geometric mean catch per trawl from 1978 through 2004. . ..................................................................................................................................................................................... 78 3 Figure 22. Atlantic menhaden, Brevoortia tyrannus. Long-term change in catch. ...................................................... 79 Figure 23. Atlantic menhaden, Brevoortia tyrannus. Change in trawl catch Z-score (1980-2004) at each NCDMF station as a function of percentage change in land use (1980-2000) within each catchment. ...................................... 80 Figure 24. Pinfish, Lagodon rhomboides. The geometric mean of catch per trawl from 1978 through 2004.. ........ 82 Figure 25. Pinfish, Lagodon rhomboides. Long-term change in catch ........................................................................ 83 Figure 26. Pinfish, Lagodon rhomboides. Change in trawl catch Z-score (1980-2004) at each NCDMF station as a function of percentage change in land use (1980-2000) within each catchment. ........................................................ 84 Figure 27. Brown shrimp, Farfantepenaeus aztecus. The geometric mean of catch per trawl from 1978 through 2004. ............................................................................................................................................................................ 86 Figure 28. Brown shrimp, Farfantepenaeus aztecus. Long-term change in catch. ..................................................... 87 Figure 29. Brown shrimp, Farfantepenaeus aztecus. Change in trawl catch Z-score (1980-2004) at each NCDMF station as a function of percentage change in land use (1980-2000) within each catchment. ...................................... 88 Figure 30. The average catch of spot at stations with different salinities for 1980-84, 1990-94, and 2000-2004. ..... 93 Figure 31. The average catch of southern flounder at stations with different salinities for 1980-84, 1990-94, and 2000-2004. ................................................................................................................................................................... 94 Figure 32. The average catch of pinfish at stations with different salinities for 1980-84, 1990-94, and 2000-2004. .. 95 Figure 33. The average catch of brown shrimp at stations with different salinities for 1980-84, 1990-94, and 20002004. ............................................................................................................................................................................ 96 Figure 34. A Classification and Regression Tree (CART) analysis of the standardized z-score of change in catch of spot between 1980 and 2000. ..................................................................................................................................... 99 Figure 35. A Classification and Regression Tree (CART) analysis of the standardized z-score of change in catch of Southern flounder between 1980 and 2000. ............................................................................................................ 101 Figure 36. A Classification and Regression Tree (CART) analysis of the standardized Z-score of change in catch of Atlantic croaker between 1980 and 2000. . ............................................................................................................... 103 Figure 37. A Classification and Regression Tree (CART) analysis of the standardized Z-score of change in catch of Atlantic menhaden between 1980 and 2000. ............................................................................................................. 105 Figure 38. A Classification and Regression Tree (CART) analysis of the standardized z-score of change in catch of pinfish between 1980 and 2000. .............................................................................................................................. 107 Figure 39. A Classification and Regression Tree (CART) analysis of the standardized z-score of change in catch of Brown shrimp between 1980 and 2000. .................................................................................................................... 109 4 List of Tables Table 1. North Carolina’s commercial fishery for 2007 including, catch, value, and rank in catch for the seven selected estuarine-dependent species. .......................................................................................................................... 17 Table 2. Spectral and ground pixel resolution of the Multi-Spectral Scanner on Landsat 2 ....................................... 26 Table 3. Spectral and ground pixel resolution of sensors on the Thematic Mapper used on Landsat 5 and the Enhanced Thematic Mapper used on Landsat 7 . ........................................................................................................ 27 Table 4. Satellite imagery, specific dates, and data sources. ....................................................................................... 28 Table 5. Sample field sheet showing site number, Easting, Northing and 1980, 1990, and 2000 land use . . ............. 41 Table 6. Error matrix showing the ground reference data versus the image classification for land use types in coastal watersheds. .................................................................................................................................................................. 42 Table 7. Producer’s accuracy matrix for the image classification. .............................................................................. 43 Table 8. User’s accuracy matrix for the image classification ...................................................................................... 43 Table 9. Areal estimates of land use change from 1980 - 2000. .................................................................................. 45 Table 10. The number of hectares of forest and wetland area converted to developed and agriculture land uses between 1980 and 2000. . ........................................................................................................................................... 49 Table 11. Sampling effort (number of stations visited each year and month) in the NC DMF Program 120.............. 50 Table 12. Summary statistics of changes in land use types between 1980 and 2000.. ................................................ 58 Table 13. Number of catchments showing dominant types of land use between 1980 and 2000. ............................... 59 Table 14. Loss of forested land by county ................................................................................................................... 62 Table 15. Gain of agricultural land by county. ........................................................................................................... 62 Table 16. Gain of developed land by county ............................................................................................................... 63 Table 17 Catchment Areas of change in catch per species in the 71 stations .............................................................. 90 Table 18. Mean catches of each of the target species and significant factors affecting abundance at all stations identified in the CART analysis .................................................................................................................................. 92 Table 19. Summary of partitions of the CART tree for change in catch at 71 NCDMF stations for the six target species. ...................................................................................................................................................................... 110 5 Acknowledgements We thank the many individuals who contributed their time and effort in the field or office, collecting or analyzing data, or sharing the knowledge of juvenile fish recruitment to North Carolina’s estuarine waters. We thank especially Katy West and Sean McKenna, biologists at the North Carolina Division of Marine Fisheries (NCDMF), Pamlico District Office for their insight into program 120 as well as their efforts in data quality control. NCDMF’s field technicians: Greg Judy, Lele Judy, and Mike Pulley were helpful in demonstrating field procedures used in data collection. Jason Hassell and Leland Tetterton were helpful in sharing their observations and knowledge about juvenile fish in the Pamlico Sound. Kevin Hart, Biologist at the NCDMF and Chris Mascio, former East Carolina University student, provided valuable office and field assistance. 6 Executive Summary This is a study report on the relationship between land use change and recruitment of a selected set of six commercially and ecologically important species of finfish and shrimp in North Carolina sounds and coastal waters. The selected species were: Atlantic croaker (Micropogonias undulatus), spot (Leiostomus xanthurus), pinfish (Lagodon rhomboides), brown shrimp (Farfantepenaeus aztectus), Atlantic menhaden (Brevoortia tyrannus), and southern flounder (Paralichthys lethostigma). This report follows a recent study (Luczkovich et al. 2007) that analyzed land use changes effects on blue crab (Callinectes sapidus), and therefore uses similar methodologies to determine the potential relationships between watershed land use characteristics and aquatic life. Estuarine-dependent fish and crustacean species rank as among the most important of North Carolina’s fisheries, producing over $44 million and 50 million pounds in commercial harvest annually. Commercial harvests are dependent on estuarine nursery habitats (shallow tidal creeks and submerged aquatic vegetation areas) for the juvenile fish and crustaceans to avoid predators, find food, and have access to high quality habitat conditions that promote good growth. However, coastal development and agriculture have been identified as land uses that may degrade the nursery function of estuaries, because runoff of water from such developed landscapes can change the water quality and food availability of estuarine nursery habitats. The goal of this report is to examine historical data collected by the NC Division of Marine Fisheries’ juvenile fish trawl survey to see if the change in the abundances of the estuarine-dependent species listed above were associated with land use change within catchments adjoining North Carolina estuarine nursery areas over a period from 1980-2004. As in Luczkovich et al. (2007), land use changes were determined by classifying and performing 7 change detection on satellite images of the coastal areas of North Carolina (images were obtained from USGS Landsat program in 1980, 1990, and 2000). The Landsat visible wavelength bands were classified into one of the following land use classes using a combination of unsupervised and supervised classification routines: forested lands, wetlands, developed land, and agricultural lands. Between 1980 and 2000, deforestation constituted the single greatest land use alteration, with an average catchment loss of 28.7 ± 8.2 % (mean ± 1 standard deviation). No net wetland loss occurred when averaged across all catchments; in fact there was a slight increase in wetland acreage. Agricultural land showed a substantial increase, averaging 22.8 ± 12.7% per catchment. Developed land showed no significant net change when averaged across all catchments. Most of the land use change consisted of the conversion of forest to agriculture. Deforested land was also a source of local gains in developed land, and occasionally to local increases in wetland coverage. Loss of forest, and the conversion to agriculture was widely distributed among the catchments within Beaufort, Carteret, Hyde, Onslow, and Pamlico counties. The only significant (>10%) increases in developed land via deforestation occurred in catchments in Onslow County. Overall, catchment basins became dominated by agricultural land, suggesting that conversion of land from forest to agriculture would lead to water quality degradation, potentially reducing the abundance of juvenile fishes and invertebrates in catchments with large changes in land use. Abundance data on juvenile fish and crustacean were obtained from the North Carolina Division of Marine Fisheries Program 120 data set (juvenile fish and invertebrate nursery survey) and encompassed 71 sampling stations. Although these data have been collected since 1971 at some stations and during the months of May through October each year until 1989, we limited the analysis to data collected in May and June since 1980 because these months have been sampled consistently over the period for which we also had land use data. Data were 8 analyzed for temporal trends and spatial patterns of abundance during the same period as the land use changes by computing 5-year averages during 1980-1984 and 2000-2004. By examining time series plots for each station and computing a change in catch statistic using the difference between the average catches in the early years (1980-1984) and in the later years (2000-2004), we were able to examine long-term changes in the abundance of the selected species and map them along with land use changes derived from the satellite images. Data were smoothed using a time-series analysis approach in which a LOWESS (locally weighted) curve fit was applied to the species data for each station and used to gauge trends in abundance over time. Time series plots were generated for each station (shown in the Appendix) and a geometric mean plot for all stations combined. There was an overall increase in abundance of spot (Leiostomus xanthurus), southern flounder (Paralichthys lethostigma), and pinfish (Lagodon rhomboides) during the study period, with peak abundances occurring after 1996. The increase in pinfish was dramatic (increasing from a geometric mean of 1.2 fish/trawl in 1980 to 36.3 fish/trawl in 2001), suggesting that these increases may be associated with shifts in the food web of Pamlico Sound or coast-wide climate change. Pinfish are sub-tropical fish that are normally abundant in warm water in estuaries of the southeastern USA; North Carolina is near their northern range limit, but they appear to be spreading further north. Brown shrimp (Farfantepenaeis aztectus), Atlantic menhaden (Brevoortia tyrannus) and Atlantic croaker (Micropogonias undulatus) did not appear to change when averaged over all stations. Large differences in species abundances were noted for the 71 NCDMF Program 120 stations. Some stations had consistently high or low catches of individual species year after year. The Pamlico River stations, in particular, are important nurseries for southern flounder, with the 9 largest catches recorded there year after year. Some stations showed increases and other showed declines; most stations (66 or more) showed no significant change (i.e., catches were with 2 standard deviations of the long-term mean) over time. Some of the stations showing significant declines for Atlantic croaker were in Pamlico (Aurora PAR11), Neuse (Oriental F3N) and Hyde County (PUR5). Stations with significant declines for Atlantic menhaden were in Swan Quarter Bay (SQB3) and the Neuse River at Oriental (F3N). Spot showed significant declines at Bath Creek on the Pamlico River (PAR 7) and Atlantic in Carteret County (CC11). Land use changes, other anthropogenic variables (the number of NPDES sites and human population density in each catchment in 2000) and abiotic factors (mean bottom temperature, mean bottom salinity and distance to nearest inlet for each NCDMF trawling station) were used as predictors of change in catch of the six species 1980-2004 using a multivariate classification and regression tree (CART) statistical program. The CART analysis indicated a significant decline in catch occurred between1980-2004 at stations that had > 21 % change (for southern flounder) and > 54 % (for Atlantic croaker) in land use in the surrounding catchment (conversion of forested land use to agriculture and developed land use). Additionally, the CART suggested that high densities of people in surrounding watershed catchments were associated with declines in spot and pinfish catches. Some abiotic factors were associated with change in catches of the species in the CART analyses. The CART analyses suggested that change in catch was also influenced by bottom salinity for some species, with brown shrimp and southern flounder increasing in abundance in salinities > 14 ppt. In the CART analyses, increased bottom temperature was associated with increases in Atlantic menhaden and pinfish, and to a lesser extent, brown shrimp and Atlantic croaker. Distance to an inlet influenced the change in catch of spot, brown shrimp, and to a lesser extent, Atlantic menhaden and Atlantic croaker. Finally, 10 depth of water at the collection site significantly influenced the change in catch of southern flounder and brown shrimp. We conclude that there was not a major land use change effect on juvenile estuarine fish populations that we studied in North Carolina between 1980 and 2004, with some notable exceptions. Southern flounder and Atlantic croaker, based on the multivariate CART analyses, declined where excessive land use change (i.e., > –21- 54% of the catchment converted from forested land to other uses between 1980-2004) occurred. In addition, pinfish increased dramatically in abundance between 1980 and 2004, but only where human population density was low in a catchment. We note that these three species (and possibly spot, which declined as the percentage of land use devoted to agriculture increased, although this species did not change significantly in the CART analysis) are all dependent on benthic food resources. These species may be declining when land use change is extensive because agricultural runoff may cause reduced benthic food availability during the summer months. While benthic organisms in the deep portions of the Neuse River, Pamlico River and Pamlico Sound are influenced by summertime hypoxia and decline in summer, the relatively shallow stations sampled in NCDMF Program 120 with extensive land use change may exhibit increased areas of hypoxia in the early summer. Hypoxia or other stressors (agricultural runoff with pesticides and herbicides) may be impacting in the nursery areas in early summer samples (May and June) examined in this study, and could be reflected in these declines of benthos-consuming fishes. In contrast, Atlantic menhaden, the one planktivorous species considered in our analysis, did not show any observable trends in abundance as land use changed in the surrounding watershed. To protect critical nursery habitats for the selected species, management steps should be taken to conserve land use, limit agricultural run-off, and limit development in areas adjoining 11 the productive nursery areas surrounding the Pamlico and other North Carolina Sounds. Continued monitoring of the stations surveyed in the Program 120 is recommended, but this survey should be extended into the late summer months (July – Oct), when water temperatures are highest and hypoxia is likely to reach the maximum extent. Late summer catches can be compared to catches made during the same months in early years (pre-1989) of the Program 120. Land use conservation and prevention of agricultural run-off entering the estuarine areas might be encouraged through limiting agriculture and development along creek banks that drain directly into the estuarine areas, greater protection of forested land as well as wetlands, and reduction in sources of non-point water pollution. 12 List of abbreviations AOI Area of Interest B-IBI Benthic Index of Biotic Integrity BASINPRO A group of tools and layers for watershed visualization and analysis, developed by the North Carolina Center for Geographic Information Analysis. BSQ Band Sequential (Format). CART Classification and Regression Tree C-CAP Coastal Change Analysis Program CW Carapace Width DOQQ Digital Ortho Quarter Quad EROS Earth Resources Observation Systems ESRI Environmental Systems Research Institute ETM+ Enhanced Thematic Mapper Plus GCLF Global Land Cover Facility GIS Geographic Information Analysis GLCF Global Land Cover Facility GPS Global Positioning System HU Hydrologic Unit LOWESS Locally Weighted Smooth Surface MSS Multi Spectral Scanner NC CGIA North Carolina Center for Geographic Information Analysis NC DMF North Carolina Division of Marine Fisheries NOAA National Oceanic and Atmospheric Administration 13 NPDES National Pollution Discharge Elimination System NRCS Natural Resources Conservation Service PSU Practical Salinity Unit PRE Proportional Reduction in Error TM Thematic Mapper UMD University of Maryland, College Park USDA US Department of Agriculture USGS United States Geological Survey WAAS Wide Area Augmentation System WBD Watershed Boundary Dataset 14 Introduction Estuarine and coastal areas contain some of the nation’s most densely populated and rapidly growing areas (Beach 2002, Crosset 2004, Crawford 2007). As population density increases, so does the potential for degradation of the natural environment by human activities (Cairns and Pratt 1992). In the year 2000, New Hanover County, North Carolina, had the highest population density (803 persons / mi2) among the 20 CAMA counties in North Carolina, and since the 1980’s, coastal counties have exceeded the statewide average growth rate by 3.6% (Street et al. 2005). This population growth affects land use practices, which in turn can influence aquatic species habitat quality by contributing excessive sediments, nutrients, and pollutants through storm water and groundwater flows into adjacent water bodies. Accordingly, a number of shellfish closures have resulted in the loss of harvests throughout the Pamlico Sound region during the past several years (Street et al. 2005). Alteration of watersheds by human activities like farming and coastal development could threaten the functioning of the extensive system of North Carolina’s estuarine nursery areas. Runoff from farmland, confined animal facilities, forests, wetlands and urban areas differ greatly in the content of sediments, nutrients and pesticides (Kennish 2002). Farmland runoff and industrial farming activities can promote phytoplankton growth (Boynton et al. 1985, Malone et al. 1996), reduce the coverage of submerged aquatic vegetation (SAV) habitats due to competition for light with phytoplankton (Livingston 2001), and decrease the abundance of certain groups of fishes dependent on such SAV (Heck and Orth 1980), thus altering estuarine food webs. Weisberg et al. (1997) and Bilkovic et al. (2006) demonstrated that low estuarine 15 macro-benthos community indices were associated with developed land uses in watersheds along Chesapeake Bay. The Benthic Index of Biotic Integrity, or B-IBI, which ranges from 1 (stressed) to 5 (unstressed), is a multimetric index that combines the abundance of various invertebrate species (annelids, bivalves, and crustaceans) into a single metric. The lowest B-IBI values were associated with commonly hypoxic aquatic habitats located near developed watersheds. Estuarine benthic communities near areas with a forested watershed, by contrast, had higher BIBI values than those with developed watersheds. Bilkovic et al. (2006) concluded that if as little as 10-12 % of the watershed area was deforested and developed, then the estuarine benthic community would exhibit stress from hypoxia and land nutrient inputs. In our investigation of the relationship between land use and nursery area function in North Carolina, we focused on changes in the abundances of six estuarine-dependent species of juvenile finfish and an invertebrate species. The species were: spot (Leiostomus xanthurus), southern flounder (Paralichthys lethostigma), Atlantic croaker (Micropogonias undulatus), Atlantic menhaden (Brevoortia tyrannus), pinfish (Lagodon rhomboides), and brown shrimp (Farfantepenaeus aztectus). These species are common in the shallow estuaries in late spring and early summer as juveniles, settling in the Pamlico Sound and coastal waters of North Carolina after a planktonic larval dispersal stage. Once in the estuary, their feeding habits vary. Several are benthic feeders (Atlantic croaker, spot, pinfish, brown shrimp), one is planktivore (Atlantic menhaden), and one is a piscivore (southern flounder). We had planned to analyze patterns for bay anchovies (Anchoa mitchilli); however, data gaps in the NCDMF Program 120 database precluded an analysis of that species. 16 This report follows a recent study (Luczkovich et al. 2007) that analyzed land use changes effects on the abundance of the blue crab (Callinectes sapidus), and therefore uses similar methodologies. Estuarine-dependent fish and crustacean species rank as among the most important of North Carolina’s fisheries, producing over $44 million and 50 million pounds in commercial harvest annually (Table 1). Commercial harvests are dependent on the estuarine nursery habitats (shallow tidal creeks and submerged aquatic vegetation areas) utilized by juvenile fishes and crustaceans to avoid predators, find food and maintain water quality conditions that promote good growth. However, coastal development and agriculture have been identified as land uses that may threaten the nursery function of estuaries, because runoff of water from such developed landscapes can change the water quality and food availability in estuarine nursery habitats. Table 1. North Carolina’s commercial fishery for 2007 including, catch, value, and rank in catch for the seven selected estuarine-dependent species (source: www.ncdmf.net, last accessed February 23, 2009). Common name Scientific name Catch (lbs) Value ($) Blue crab Callinectes sapidus 21,420,719 $21,429,574 Penaeid shrimps Farfantepenaeus aztecus, 9,548,371 $17,930,804 (brown, pink and F. duorarum, white)1 Litopenaeus setiferus Atlantic croaker Micropogonias undulatus 7,301,295 $ 2,726,029 Southern flounders Paralichthys lethostigma 2,078,020 $ 4,958,657 Atlantic menhaden Brevoortia tyrannus 1,134,167 $ 139,178 Spot Leiostomus xanthurus 878,989 $ 612,608 Pinfish Lagodon rhomboides 73,238 $ 14,899 Totals 50,088,807 $44,212,810 1 These penaeid shrimp species are pooled by NCDMF in commercial catches 17 Rank (lbs) Rank ($) 1 2 1 2 3 7 10 12 51 5 4 37 26 65 Life History for the selected species Based on the amount and completeness of information available for the first year of life of Middle Atlantic Bight fish species, Able and Fahay (1998) synthesized information of economically important species including six of the seven species studied. Able and Fahay (1998) refer to estuarine-dependant species as transient species since they spend only a portion of their lives in the estuaries. Estuarine-dependant species characteristically spawn in the ocean, but where, when and how the larvae or juveniles return to the estuaries vary greatly according to species. The small size of recently settled fish may make them experience a high mortality rate due to predation or inhospitable abiotic factors such as extremely low temperatures. Spot (Leiostomus xanthurus) Spot are distributed between Massachusetts Bay to Campeche Bay, Mexico, and are most abundant between the Chesapeake Bay and the North Carolina coast (Able and Fahay 1998). The spot population is mostly euryhaline; their larvae and juveniles are more abundant in the Middle Atlantic Bight estuaries between Cape Hatteras and the Hudson River. Spawning takes place in the continental shelf from winter to spring and is more intense in warm waters in the outer shelf and south of Cape Hatteras (Able and Fahay 1998). Larval development takes place on the continental shelf before larvae enter estuaries and then migrate to lower salinity, colder water of the upper estuary. Using field observations and laboratory experiments, Rakocinski et al. (2006) found that temperature and salinity are the two most important abiotic factors affecting juvenile spot migration. 18 Southern Flounder (Paralichthys lethostigma) The geographic range of Southern flounder extends from Virginia to northern Mexico. They are found in rivers, estuaries and coastal waters. The Southern flounder is part of the Bothidae family (left eye flounders). They are closely related and morphologically similar to summer flounder and Gulf flounder. The three species are found in North Carolina and along the most of the Atlantic coast including the Gulf of Mexico waters (Able and Fahay 1998). Flounder spend much of their life on the bottom, where camouflage helps reduce detection by prey and predators. According to Watterson and Monaghan (2001), adult southern flounder migrate out of the rivers and estuaries in the late fall to spawn offshore in the warmer waters of the Gulf Stream between November and February. However, juvenile flounder are believed to overwinter in the low salinity waters of the rivers and bays for the first two years of their life rather than migrating offshore (Powell and Schwartz 1977). After the spawning period, adult flounders return to the estuaries, coastal waters and rivers through the inlets (NCDMF 2005). Newly hatched larvae are transported back to estuaries and coastal waters by oceans currents (Powell and Henley 1995). It is believed that these developing larval flounder remain in the offshore waters for 30 to 60 days before getting carried through the inlets into the estuaries during night time flood tides (Warlen and Burke 1990, Burke et al. 1991, Burke et al. 1998). After metamorphosis, the juvenile flounder settle on tidal flats towards the head of the estuaries and move upstream to lower salinity habitats (Burke et al. 1991, Guidon and Miller (1995). Powell and Schwartz (1977) have found that benthic substrate and salinity are the two most important factors governing the distribution of southern and summer flounders; with southern being more 19 abundant in areas of low salinity and clayey silt or organic rich mud bottoms while summer flounder are most abundant in areas of moderate to high salinities and sandy bottom. Atlantic croaker (Micropogonias undulatus) The Atlantic croaker is distributed from Massachusetts to the Gulf of Mexico (Able and Fahay 1999). It is less common north of New Jersey but abundant farther south where it is a bottom feeding fish of coastal waters and estuaries. During winter, adults move offshore and head south. Spawning takes place in the Middle Atlantic Bight starting in September and peaks in October and ending in December. Pelagic larvae enter the estuaries via ocean inlets and ultimately end up in low salinity nursery areas. The Atlantic croaker’s important habitats include the continental shelf for larvae and low salinity habitat such as tidal creeks and tributaries of major bay systems for the early settlement stages. Atlantic menhaden (Brevoortia tyrannus) According to Able and Fahay (1998), Murky et al. (1997), and Epifanio and Carvine (2001), menhaden occurs mainly along the Atlantic coast of North America, from Nova Scotia to Florida. They migrate north during spring and return to the south during the fall where they spend the winter, mostly south of Cape Hatteras, North Carolina. It has been determined that there is limited spawning during the spring northward migration that extends as far north as Cape Cod and that limited spawning also occurs during the summer. Spawning increases remarkably during the fall southern migration. In Delaware estuaries, larvae are typically 10 to 20 mm total length when they ingress from December through May. Larvae are pelagic and ride ocean currents into estuaries where they transform into juveniles at about a length of 30 to 38 mm. In New Jersey and North Carolina estuaries, Able and Fahay (1998) and Warlen (1994) observed 20 that periods of low catch are often associated with low water temperatures that are usually followed by higher temperatures and great number of larvae from the South Atlantic. After arrival in the estuary, juveniles move upstream in low salinity waters and in areas of maximum phytoplankton (Friedland et al. 1996). They stay in estuarine habitat until temperatures start dropping in September/October. Pinfish (Lagodon rhomboides) Pinfish spawn offshore and their larvae are transported shoreward where they migrate into estuaries for continued development (Forward et al.1998). Adams et al. (2004) states that juvenile pinfish, an abundant and trophically important species, are dependent on seagrass beds because seagrass and associated habitats (macroalgae) provide not only shelter from predation, but also a fertile food source. In a larval fish migration study in the Newport River estuary, North Carolina, Warlen and Burke (1990) found that pinfish larvae constituted about 13% of the immigrating larvae. The early season peak is in December, which is followed by a very low recruitment from mid-January to mid-February. The main period of recruitment occurred from late February to early April. Brown Shrimp (Farfantepenaeus aztectus) The range of brown shrimp extends from Massachusetts to Florida and the Gulf of Mexico and extends south to the Yucatan Peninsula. They are most abundant in the Gulf of Mexico. Adult brown shrimp spawn in deep ocean waters, and like most estuarine dependent fish and invertebrates, their larvae are transported to estuaries and coastal waters by wind and ocean currents. Brown shrimp larvae pass through several developmental stages before becoming post-larvae and juveniles. Once post-larval shrimp enter the estuaries, growth is swift and 21 dependent on salinity and temperature (NCDMF 2006). Feeding occurs mostly at night; however, they have also been observed feeding in turbid water during the day. Shrimp are omnivorous; they feed on sediment, detritus, algae, and benthic organisms. According to Wenner and Beatty (1993), wind driven currents transport shrimp larvae to the upper reaches of estuaries beginning in February with peaks occurring in mid-March through mid-April. Adult and sub adult shrimp seek higher and more stable salinities because of a decrease in the ability for osmoregulation. Blanton et al. (2002) found that water temperature, tidal phase and wind direction greatly influence the spatial and temporal variation in densities of postlarval white and brown shrimp within two inlets in South Carolina and Georgia. Shrimp feed primarily on sediment, detritus, algae and benthic organisms. Feeding occurs mostly at night, although some daytime feeding will occur in turbid water. Growth and production of penaeids in estuaries are related to temperature, salinity, and presence of vegetation (Rulifson 1981, Wenner and Beatty 1993). Field observations and laboratory experiments indicate that brown shrimp seem to prefer marshes and shallow water habitat with sea-grass beds that provide food, substrate and protection for the young (Zimmerman et al. 1984, Rulifson 1981). Objectives We first review the results of the land use classification and change analysis, based on Landsat satellite imagery from 1980, 1990, and 2000, essentially identical to the analysis reported in Luczkovich et al. (2007). Next, we analyze the data of the NC DMF Program 120 juvenile fish trawl survey from 1980-2004 with respect to the six species (spot, southern flounder, Atlantic croaker, Atlantic menhaden, pinfish and brown shrimp). We examine their 22 long-term change in catch by plotting the time series of geometric means for each species in catches for all stations. We also examine the change in average abundance of the target species in three 5-year periods (1980-84, 1990-94, and 2000-04) with respect to abiotic factors.. Next, we compute a change in catch statistic for each station by averaging the catches in trawls taken at each station in May and June early in time series (1980-1984), at a point near the start of our land-use analysis, and subtract them from the averages of trawl catches taken in May and June 2000-2004 at the same stations, near the end of the land-use analysis. Using normalized z-score of this difference and a multivariate analysis (CART or Classification and Regression Trees), we analyze the change in catch statistic with respect to anthropogenic (land use change, human population density, number of pollution discharge points) and abiotic (temperature, salinity, depth, distance to nearest inlet) factors. Methods To examine the long-term trends in land use and catches of selected species of juvenile estuarine dependant fish and invertebrates, we used a combined approach of (a) satellite remote sensing, (b) land use classification of a multi-date satellite imagery, (c) change analysis of the land-use classes within defined catchment areas; and analysis of trawl catch data from NCDMF nursery areas, and (d) analysis of trawl catch data (NCDMF Program 120). The imagery data sets were classified into Anderson level 2 land use categories (Anderson et al. 1976) and analyzed using ERDAS Imagine, an image processing software from Leica Geosystems and ARGIS 9.2, a GIS software by Environmental Science Research Institute (ESRI, Redlands, California). The resultant land use data was merged with summary statistics of the NCDMF Program 120 catch 23 data to allow a comparison of changes in land use and catches in each of the 71 associated sampling stations. The overall approach is shown in a conceptual framework shown in Figure 1. Figure 1.The overall scheme used to relate land use and juvenile fish and shrimp catch statistics. Adapted from Mohsen A. Environmental land use Change detection and assessment using with multi-temporal satellite imagery (visited 12/15/2009) http://www.gisdevelopment.net/application/environment/overview/frpf0004.htm 24 Land Use Classification Landsat Imagery Data Sources Land use information was derived by classification of satellite imagery of the North Carolina coast: 1980 Landsat 2 Multispectral Scanner (MSS), 1990 Landsat 5 Thematic Mapper (TM), and 2000 Landsat 7 Enhanced Thematic Mapper (ETM+). Table 2 shows the spectral and spatial resolution of the Landsat MSS data, which are the oldest and thus the best in terms of the time series that matches the start of our NCDMF data. However, the MSS data have lower spectral resolution (only 4 spectral bands) and lower ground pixel resolution (57m x 79m pixels) than the Landsat TM and ETM+ data (Table 3) which has 8 spectral bands and 30m x 30m ground-pixel resolution. Band 4 in MSS (visible green) is similar to Band 2 in TM and ETM+; Band 5 in MSS (visible red) is similar to Band 3 in TM and ETM+; Band 6 (near infrared) in MSS is similar to Band 4 in TM and ETM+; Band 7 (also near infrared) in MSS is similar to Bands 4 and 5 in TM and ETM+. These differences in spectral and ground pixel resolution presented us with problems in comparison of the early Landsat MSS and later Landsat TM and ETM+ data. We chose to project the MSS, TM, ETM+ data in the same geo-referenced data frame in ERDAS Imagine using the smaller ground pixel resolution of the later Landsat TM and ETM+ satellite data by resampling the earlier MSS data at 30m X 30 m resolution to obtain a correct registration of the earlier imagery. 25 Table 2. Spectral and ground pixel resolution of the Multi-Spectral Scanner (MSS) on Landsat 2 which was operational from January 22, 1975 - February 25, 1982. (Source : US Geological Survey: http://landsat.usgs.gov/about_landsat2.php, last visited 12/15/2009). Sensor Description Wavelength (µm) Band 4 Visible Green 0.5 - 0.6 Band 5 Visible Red 0.6 - 0.7 Band 6 Near Infrared 0.7 - 0.8 Band 7 Near Infrared 0.8 - 1.1 26 Common use Emphasizes sedimentladen water and delineates areas of shallow water. Emphasizes cultural features. Emphasizes vegetation boundary between land and water. Penetrates atmospheric haze best, emphasizes vegetation, bou ndary between land and water, and landforms. Ground resolution (m ) 57 x 79 m 57 x 79 m 57 x 79 m 57 x 79 m Table 3. Spectral and ground pixel resolution of sensors on the Thematic Mapper (TM) used on Landsat 5 (March 1, 1984 – present) and the Enhanced Thematic Mapper (ETM+ ) used on Landsat 7 (April 15, 1999 - present). (Source: US Geological Survey: http://landsat.usgs.gov/about_landsat5.php, last visited 12/15/2009) Electromagnetic Spectrum Wavelength (µm) Band 1 Visible blue-green, reflected 0.450 - 0.515 Sediments in water 30 Band 2 Visible green, reflected 0.525 - 0.605 Vegetation 30 Band 3 Visible red, reflected 0.630 - 0.690 Vegetation 30 Band 4 Near infrared, reflected 0.75 - 0.90 Vegetation, Chlorophyll 30 Band 5 Mid infrared, reflected 1.55 - 1.75 Moisture in plants/soils, clouds vs snow, mineral content 30 Band 6* Thermal infrared, reflected 10.4 -12.5 Geothermal activity, moisture 120, 60 2.09 - 2.35 Moisture in vegetation /soils, rock formations 30 0.52 - 0.90 High resolution used to enhance details in other bands 15 Sensor # Band 7 Band 8** Shortwave infrared, reflected Panchromatic, visible light, reflected *The thermal Band 6 has a 120-m resolution for TM and 60-m resolution for ETM **Landsat TM does not have Band 8. 27 Resolution (m) Common use + Table 4. Satellite imagery, specific dates, and data sources. [Sources: Earth Resources Observation Systems (EROS) data center at The USGS; The Global Land Cover Facility (GLCF) at the University of Maryland (UMD), College Park.] Dataset Date Source 1980 Landsat MSS p14r035 p14r036 p15r036 1990 Landsat TM p14r035 p14r036 p15r036 + 2000 Landsat ETM 08/02/1980 08/02/1980 08/03/1980 USGS USGS USGS 06/12/1988 09/06/1990 05/08/1990 UMD UMD UMD p14r035 p14r036 p15r036 09/23/1999 09/23/1999 05/11/2000 USGS USGS USGS Sources for GIS data layers Physiographic and topographic features of landscapes associated with primary nursery areas were derived from The North Carolina Center for Geographic Information Analysis (CGIA), which serves as a hub for Geographic information Systems (GIS) data used and or produced by State agencies. We used GIS data layers contained in BASINPRO 8.0, a statewide data compilation produced by CGIA. Among the layers of interest, we used hydrography, watershed boundaries, confined animal operations, and water bodies. We also used data layers available to the public at The North Carolina Department of Transportation. These include detailed county roads and 1998 Digital Orthographic Quarter Quadrangle (DOQQ) photos, both of which were used as reference to locate some satellite imagery features. 28 After the basic datasets were acquired and archived, the first task was to bring together all the GIS data layers that we judged useful in computing landscape variables. We built a multilayer GIS database in ARCGIS 9.2 (ESRI Inc.) that brought together many watershed descriptive layers. These included hydrography, watershed boundaries, confined animal operations, National Pollution Discharge Elimination System (NPDES), water bodies, and detailed county road layers. We added the NCDMF sampling stations, watershed boundaries, shorelines, parks and preserves, inlets, and streams to the GIS database. 1998 DOQQ photos were used as references to locate several satellite imagery features that are not easily identifiable on a 30 meter resolution. These variables were merged within the NRCS watershed boundaries and were used to relate to catches with respect to stations and years. Because these NRCS watersheds were much larger than the extent of the drainage area impacting tidal creeks where NCDMF took samples, we delineated our own catchment boundaries for the analysis. These are sub-watershed or catchments, each of which encompasses all the streams and ditches draining to a nursery area as denoted by the location of specific NCDMF sampling stations. Catchment boundaries were determined using the Federal Standards for delineation of hydrologic unit boundaries (USGS and USDA, 2009) that establishes standards and guidelines for creating and delineating hydrologic unit boundaries, modifying existing hydrologic units, and establishing a national Watershed Boundary Dataset (WBD). Additional guidelines for coastal watersheds (Ferguson and Mew 2000) were also followed. Catchments were delineated in 3 distinct steps using ArcGIS 9.2 for display and analysis: (1) Overview of the NRCS 14-digit hydrologic unit boundaries. We displayed major stream networks (from NC Geographic Information Analysis data) and NCDMF sampling 29 stations over the NRCS 14-digit watershed boundaries (Hydrologic Units or HU’s) to generate a general view of the watershed area as displayed in Figure 2. (2) Topographic overlays: We displayed the 14-digit HU’s over the USGS topographic quadrangles to note the topographic setting of the watershed with respect to sampling station (Figure 3). (3) Taking into account the stream network and topographic setting in steps 1 and 2, we performed on-screen digitizing for the catchments draining to each of the NCDMF sampling stations. Figure 4 shows sample catchments in the lower Pamlico. Catchments boundaries are displayed in yellow while the USGS watershed lines are in grey. The resulting catchment layer is then used to compute land-use parameters related to the NCDMF sampling stations. Depending on the location of a given NCDMF sampling station, catchment size can be smaller or equal to the source watershed. Using this technique, we found that the entire 105 NCDMF Program 120’s core sampling stations were located within just 99 catchment areas because some catchments contained more than one sampling station (Figure 5). Figure 5 also shows the final catchments and NCDMF locations for the entire coast. Note however, that we limited our analysis to the 71 stations out of 105 that had been sampled consistently from 1980 to 2004; the remaining 34 stations (yellow stations on the map) were established more recently than 1980. Images were clipped to exactly the study area with a 300 feet buffer. A buffer zone was added to the catchment boundary to account for possible spatial inaccuracies in layer boundaries. Each clipped image was visually inspected and classified using ERDAS Imagine 9.0 (remote sensing software from Leica Geosystems GIS & Mapping, LLC) to denote type of land use. 30 Figure 2. Pamlico River NCDMF stations and stream networks displayed over the 14-digit USGS HU watershed boundaries (grey lines). 31 Figure 3. Pamlico River NCDMF stations and the stream networks plotted over a USGS topographic quadrangle. Figure 4. Pamlico River NCDMF stations, stream network, USGS 14-digit HU watersheds (grey lines), and new catchment boundaries (yellow lines) displayed on the USGS watershed layer. Catchment boundaries were drawn within each USGS watershed area. 32 Figure 5. Location of the 71 NCDMF core stations (red circles) and, the other NCDMF core stations not analyzed (yellow circles). 33 Land Use Classification Several techniques can be used for land use land cover classification. The most common techniques are supervised classification, unsupervised classification, or a combination of both. Supervised classification of land use in a remote sensing image is done interactively while the analyst is looking at a display of an image, delimiting areas of similar land use, either by having visited the areas on the ground or by using other imagery of higher resolution to determine land use. In contrast, unsupervised classification of an image is done using a computer algorithm that simply recodes each pixel in the image into classes of land use based on the spectral signatures in the digital data (Lillesand and Kiefer 1994). Given that the images we have chosen vary in resolution (1980 Landsat has 60-m resolution while 1990 and 2000 have a 30-m resolution), we used a combination of both supervised and unsupervised to improve the quality of the resulting thematic map. Landsat satellite imagery was processed using ERDAS IMAGINE 9.0 software (Leica Geosystems, Inc. 2003). The dataset obtained from USGS in generic binary format (BSQ) was imported into .img format, which is the main format for ERDAS IMAGINE software. This import procedure brings each scene’s images in separate wavelength spectral bands. For Landsat MSS, we used all four spectral bands for classification (Table 1), while for Landsat TM and ETM+ we used six of the seven and 8 bands, respectively, for analysis (bands 1, 2, 3, 4, 5, and 7; Table 2). Band 6 or the thermal band, has a much coarser resolution than the other bands and was not used. After the bands were imported for each imagery type, they were stacked and placed together as a mosaic in a geo-referenced grid (Figure 6b). The geo-referenced imagery was then imported to ArcGIS 9.2. Areas of interest were extracted from a mosaic of the images 34 using an ArcGIS layer of the catchment areas surrounding the 71 NCDMF stations as shown in Figure (6c). Figure 6. Three Landsat image scenes (a) for the North Carolina coastal area are combined into a single mosaic image (b). The coastal watersheds that were studied are outlined in red, and NCDMF program 120 sampling stations are shown in blue (c). 35 Images were clipped to the study area with a three mile buffer. Each clipped image was visually inspected / analyzed before classification in ERDAS Imagine 9.0 to generate land use categories. Image classification was conducted using a combination of unsupervised and supervised classification scheme (Jensen 2007). Unsupervised classification was first used because its unbiased mathematical algorithm can help generate any desired number of land use categories simply by analyzing images’ spectral signatures. We followed methods outlined by the NOAA Coastal Services Center guidelines (Dobson et al. 1995) and NOAA C-CAP (2002) to create 21 classes of level 2 Anderson land use classification categories (Anderson et al. 1976). The unsupervised classification was supplemented with a supervised classification, whereby an analyst inspected the land-use classes automatically generated by the ERDAS software and reclassified them using ancillary and ground-truth data to reduce possible classification errors. The ERDAS “RECLASS” algorithm was then trained to identify the pixels in all the imagery with similar characteristics based on the ground-truth data. By setting class identifier values for these pixels, and using the trained reclassification algorithm to assign the same class value to each similar pixel in the original data set, each resulting class corresponds to a land-use pattern that was originally identified in the ground-truth (Lillesand and Keifer 1994). This process helped cluster the 21 land-use classes (Anderson Level 2) into 5 coarse categories (Anderson Level 1, Anderson et al. 1976) for ease of comparison of large scale maps. We collapsed all wetlands subcategories into “wetlands” (Anderson classes 10 through 15), all forested subcategories to “forested” (Anderson classes 6 through 9), all development subcategories to “developed” (Anderson classes 2 and 3), and all agricultural subcategories to “agriculture” (Anderson classes 4 and 5); the “water” class remaining the same. The reconstituted and 36 renumbered classes are: 1 = Water, 2 = Developed,3 = Forested, 4 = Wetland, 6 = Agriculture. A map showing these classifications based on the 2000 imagery is shown in Figure 7. Figure 7. Classified land use from May 2000 Landsat imagery showing the 71 NCDMF stations sampled consistently since 1980 and the catchment boundaries. 37 Classification Accuracy Assessment After the 1980, 1990, and 2000 Landsat images (ETM+, TM and MSS) were classified, an accuracy assessment protocol was developed. This consisted of conducting field verifications on a set of sample points randomly distributed over the study area and determining the frequency of congruence with the corresponding satellite imagery. Ground-truthing was conducted in the summer of 2006 using a combination of aerial photographs, USGS’s Digital Ortho Quadrangles (DOQQ), and site visits. Accuracy assessment also requires that an adequate number of points be sampled for each map category in order to yield valid statistical analyses. To determine how many sample points are required for a given size of area of interest, researchers have used equations based on the binomial distribution or normal approximation, which performs well in determining the overall mapping accuracy. Congalton and Green (1999) suggested that a multinomial approximation tends to provide a good balance between statistical validity and field practicality. Also, the number of samples for each category can be adjusted based on the relative importance of that category within the objectives of the mapping project or by the inherent variability within one of the categories. Categories with less variability such as water or even-aged forest plantations can be sampled at a lower density, while more variable categories such as uneven-aged forests and agriculture should be sampled at higher densities to capture the wide spectrum of those classes. The procedure for generating the appropriate sample size using a multinomial distribution, originally presented by Tortora (1978) and described by Congalton and Green (1999), can be summarized by the following equation 38 where n = sample size, i = 1,…k are the number of land use classes, пi is the proportion of the population in the ith category, B is a constant determined from a Chi square table with 1 degree of freedom, and bi is the desired precision (for example, if 95% confidence level is desired, bi= 0.05). In our study, we computed that we needed to visit at least 239 points to achieve a 95% confidence interval, based on the proportion of the “wetlands” category (π = 0.10 for wetlands, bi= 0.05, B(1,0.01) = 6.63). Similar calculations were made for the other classes. In choosing ground-truth points, we used both simple random sampling and stratified random sampling. In simple random sampling, points are randomly chosen within land use strata and each sample unit in the study area has an equal chance of being selected. This has an advantage over stratified random sampling because it does not assume prior knowledge of the study area. One problem with simple random sampling is that it tends to under-sample rare map categories. However, one can increase the number of sample points to compensate for rarity. To balance statistical validity and practical application, we used a simple random sampling scheme early in the project and then complemented it with stratified random sampling later because the “developed” land use category had been initially under-sampled. Thus, on later trips, we visited additional random ground-truth points within the “developed” category. The random point generator function of Excel was utilized to generate a random distribution of sampling points. At first, we started with 104 points spanning between (xmin, ymin) and (xmax, ymax) coordinates, which are respectively the lower left and upper right coordinates of our study area (Figure 8 b). This is relatively a high number of points, but it is done in this manner knowing that many of the points would be eliminated for being outside the study area or 39 would be inaccessible because they are located on a water body, at gated properties, or on inaccessible private property. After elimination of unusable points, we only retained 256 points as our final number of sample sites to visit (Figure 8b). (a) (b) Figure 8. Random point assignment. (a) 104 random points overprinted on the image; (b) 256 random points that intersected with catchment areas and were accessible to ground-truthing are highlighted in red; random points outside of catchments were removed The coordinates from these 256 points were transferred to a Global Positioning System (GPS)-GARMIN GPSmap 76S (Garmin International Inc. 2005). We used the “send to GPS” function of ExpertGPS software (TopoGrafix Inc.) to transfer waypoints/random sample sites from ARCGIS to GARMIN. This GPS receiver has an accuracy of ± 3 meters using the Wide Area Augmentation System (WAAS) capability. Table 5 is an example field sheet. 40 Table 5. Sample field sheet showing site number, Easting, Northing and 1980, 1990, 2000 land use and comments. The easting is the projected distance of the position from the central meridian, while the northing is the projected distance of the point from the Equator. Site # GIS ID EASTING NORTHING Land use 1980 Land use 1990 Landuse 2000 COMMENT 1 46 335835 3931190 2 131 347702 3894787 3 290 361023 3868910 4 292 337602 3926415 5 323 231927 3781800 6 366 339385 3901330 7 509 409064 3926463 8 511 337485 3861975 9 528 284808 3844072 10 603 390878 3921448 11 632 365832 3855203 12 663 278146 3846343 W W water body west of Croatan Sound 13 667 417821 3944121 F F F spoke to owner, forest about 50 years old 14 683 426151 3941618 15 685 347407 3880977 Ag Ag O.G.F cotton fields 16 686 330086 3932700 17 741 290618 3836513 F 18 750 382527 3920965 Ag 19 757 189615 3768903 20 845 225759 3800104 Site unaccessible 21 871 361594 3869576 Site unaccessible 22 879 351025 3922017 23 943 352472 3919829 Ag Agriculture - currently soy field, behind a small church 24 958 184467 3763836 Ag Agriculture 25 1009 446714 3969988 F 26 1073 348465 3895600 F was forested-recently cut about 2 years old Forested close to Pamlico beach, between Waders and Dave Moore pt. 27 1096 340044 3924262 28 1169 363832 3863034 Ag South of Lake Mattamuskeet 29 1187 445719 3900300 Ag Young forest,less than 5 years old, 2000 clear cut? 30 1194 275346 3825469 31 1277 443823 3966758 F F pine forest dying off, near DMF station No access No access same as 51/A40 Ag young mixed forest, possible transition from pine to hardwood, unknown age off Jackson Swamp Rd/Jackson Hungting club across No access at Beaufort/Pamlico county line 41 Land classified using ground-truthing data was compared to that classified in the office, and based on satellite data using a confusion matrix (Congalton and Green 1999). A confusion matrix contains information about actual and predicted classifications done by a classification scheme (Goodchild 1994). Performance of such systems is commonly evaluated using the data in the matrix. Observations made in the field were tabulated and compared to maps made in the laboratory. Table 6 shows an error matrix displaying the number of sample units assigned to a particular category in one computer based classification relative to the field based reference data, assumed to be correct. Table 6. Error matrix showing the ground reference data versus the image classification for land use types in coastal watersheds. Ground Reference Image Classification Developed Developed 11 Forest 2 Wetland Forest Wetland Agriculture Total 1 12 63 1 1 67 1 13 1 15 48 54 56 148 Agriculture 5 1 Total 18 65 14 Using this matrix, we computed producer’s, user’s and overall accuracy for the classified land use maps. Producer’s accuracy or commission error tells how often the map correctly predicts known features. It is calculated by dividing the number of correct pixels for a class by the actual number of ground truth pixels for that class (Congalton and Green 1991, Jensen 2007). User’s accuracy or omission error shows how often the map successfully leads the user to 42 unknown features. It is a measure of the reliability of an output map generated from a classification scheme, and a statistic that can tell the map user what percentage of a class corresponds to the ground-truthed class. User's accuracy is calculated by dividing the number of correct pixels for a class by the total pixels assigned to that class (Congalton and Green 1998, Jensen 2007). The overall accuracy is computed by dividing the total correct (i.e., the sum of the major diagonal) by the total number of pixels in the error matrix (Congalton 1991). The computed overall classification accuracy was 91% (Table 6), the producer’s accuracy was 84% (Table 7), while user accuracy was 90% (Table 8). Table 7. Producer’s accuracy matrix for the image classification. Producer’s Accuracy Developed 11/18 Forest 63/65 Wetland 13/14 Agriculture 48/56 Overall producer’s accuracy 135/148 Percentage 61 97 93 86 84 Table 8. User’s accuracy matrix for the image classification User Accuracy Developed Forest Wetland Agriculture Overall user’s accuracy Percentage 11/12 63/67 13/15 48/54 92 94 87 89 90 43 Land Use Land Cover Change Detection Change analysis was performed on the 1980, 1990, and 2000 satellite imagery. This was done by aligning the images and matching pixel to pixel, the five classified categories (developed, forest, wetland, agriculture, water). Change detection usually involves two steps: (1) comparison between land cover maps which are independently produced using selected statistical algorithms and (2) change enhancement by editing the resulting layer into readily interpretable “from to” classes. Change detection was performed using the ERDAS post classification image differencing algorithm. In this algorithm, if two pixels have the same class of wetland (class 4) in 1980 and 2000, the “from to” class is 44 and this is coded as “no change” in the final image. A gray pixel is shown for this change class. If, however, the pixel changed from forest (class 3) in 1980 to developed (class 2) land use in 2000, a non-zero change was detected, so a “from to” class value of 32 was assigned to that pixel. By examining all pixels in this way, changes of all classes were detected and mapped as an image of the watersheds. For each time series (1980, 1990, 2000) classified areas of interest (AOI) were made into a mosaic using the histogram stretch and minimum value options. The catchments layer was overlaid on thematic maps and zonal statistics generated using the spatial analysis module of ARCGIS 9.2 to generate land uses within each catchment area. The zonal statistics function was used to calculate statistics on values of an image (raster) within the zones defined in another dataset that can be either raster or vector. We used “Tabulate Area,” an algorithm that cross-tabulates areas between two datasets (ESRI, ARCGIS 9.2, 2006). For this study, we used the catchment's boundary layer as a zone layer to extract land use changes from the changed map the whole study area by catchment. Table 9 shows the resulting land use change areas tabulated for each catchment. Land changes are summarized in Table 10. 44 Table 9. Areal estimates of land use change from 1980 - 2000. Shown for each catchment are the identification numbers (ID#), NCDMF station codes (Station), longitude and latitude of the stations, county name, USGS topographic quadrangle name, total area, water area, and land area of catchment (in ha), change in land use between 1980 and 2000 for the following change classes (in ha): forested land to developed land; forested land to agricultural land; wetland to developed land; wetland to agricultural land; agricultural land to developed land, total area of change (in ha), and % land changed. Catch ment ID Station Long. Lat 4 CFR11 -77.922 33.98 5 CFR4 -77.929 34.12 5 CFR5 -77.95 34.13 6 CFR1 -77.968 34.25 8 CFR2 -77.947 9 VC1 10 County NEW HANOVER NEW HANOVER NEW HANOVER USGS Quadrangle Kure Beach Total Area (ha) Water Area (ha) Land Area (ha) Forest to Develo ped (ha) Forest to Agriculture (ha) Wetland to Develop ed (ha) Wetland to Agriculture (ha) Agriculture to Develop ed (ha) Total Area of Change (ha) % of Land Use Change 6410.41 2913.39 3497.02 254.88 270.8 36.06 51.58 29.48 642.81 18.38 Carolina 3801.6 945.99 2855.61 301.75 311.99 37.12 48 30.22 729.08 25.53 Wilmington 3801.6 945.99 2855.61 301.75 311.99 37.12 48 30.22 729.08 25.53 Castle Hayne 738.24 9.72 728.52 6.01 33.55 1.71 5.04 5.77 Castle Hayne 7170.9 169.65 7001.25 334.32 869.27 19.66 17.3 379.24 52.07 1619.7 9 7.15 34.27 BRUNSWICK NEW HANOVER 23.14 -77.606 34.43 PENDER Holly Ridge 1662.99 27.72 1635.27 180.48 422.78 1.71 4.47 17.95 627.38 38.37 SSO1 -77.475 34.48 ONSLOW Spicer Bay 1300.56 438.48 862.08 48.25 50.2 33.79 27.54 10.88 170.65 19.8 12 SSI1 -77.424 34.52 ONSLOW Sneads Ferry 1662.44 1.98 1660.46 164.07 396.62 1.3 3.57 23.47 589.04 35.47 13 NR10 -77.398 34.59 ONSLOW Sneads Ferry 315.24 78.66 236.58 8.29 24.04 0.57 1.14 0.08 34.11 14.42 14 NR13 -77.43 34.63 ONSLOW Jacksonville 1105.91 2.88 1103.03 48.98 378.83 0 3.09 4.47 39.47 15 NR6 -77.331 34.64 ONSLOW Camp Lejeune 3395.14 39.24 3355.9 163.59 1211.55 2.84 15.03 3.17 435.37 1396.1 8 16 NR2 -77.426 34.74 ONSLOW Jacksonville 1900.74 114.03 1786.71 198.6 205.74 2.44 6.01 101.61 514.4 28.79 17 NR1 -77.436 34.76 ONSLOW Jacksonville 2324.75 23.22 2301.53 306.3 249.04 11.78 3.17 209.07 33.86 18 NR4 -77.383 34.72 ONSLOW Jacksonville 5316.28 283.5 5032.78 748.49 502.3 7.47 6.09 493.36 779.35 1757.7 1 21 CC3 -76.751 34.75 CARTERET Newport 2952.6 182.16 2770.44 101.77 299.96 2.03 45.73 81.79 531.29 19.18 22 CC5 -76.623 34.82 CARTERET Williston 2401.67 18.09 2383.58 29.81 986.8 2.44 64.09 5.36 1088.5 45.67 23 CC7 -76.458 34.83 CARTERET Davis 1456.17 101.07 1355.1 4.71 235.55 2.68 6.82 0.57 250.34 18.47 25 CC6 -76.481 34.81 CARTERET Davis 3275.94 343.35 2932.59 10.15 304.68 2.03 8.53 3.09 328.47 11.2 45 41.6 34.93 Table 9 (continued). Areal estimates of land use change from 1980 – 2000. Catchment ID Station Longitude Latitude County USGS Quadrangle Total Area (ha) Water Area (ha) Land Area (ha) Forest to Developed (ha) Forest to Agriculture (ha) Wetland to Developed (ha) Wetland to Agriculture (ha) Agriculture to Developed (ha) Total Area of Change (ha) % Land Use Change 26 H2 -76.762 34.87 CRAVEN Newport 6199.66 352.35 5847.31 225.07 760.59 8.29 28.1 80.01 1102.06 18.85 27 CC10 -76.359 34.92 CARTERET Atlantic 2985.57 932.31 2053.26 157.74 50.12 13 9.26 0.81 230.92 11.25 28 J2 -76.464 34.94 CARTERET Long Bay 1461.94 272.61 1189.33 16.33 864.15 0.16 14.62 0.41 895.67 75.31 29 G19 -76.637 34.94 CARTERET Merrimon 1461.94 72 1389.94 11.13 371.28 0.32 3.49 0.49 386.71 27.82 30 J10 -76.406 34.94 CARTERET Long Bay 2690.79 426.51 2264.28 64.9 64.17 0.65 5.04 0 134.75 5.95 31 CC11 -76.289 34.95 CARTERET Atlantic 754.29 24.93 729.36 2.19 17.87 0.16 1.54 0 21.77 2.98 33 G3 -76.507 34.98 CARTERET South River 4823.01 809.01 4014 10.88 557.85 2.68 12.1 0 583.52 14.54 35 F3N -76.666 35.04 PAMLICO Oriental 786.96 149.49 637.47 11.94 90.32 1.62 0.41 18.44 122.73 19.25 36 F12 -76.724 35.04 PAMLICO Oriental 2188.41 12.69 2175.72 8.77 283.39 0 1.06 6.09 299.31 13.76 37 F1 -76.645 35.06 PAMLICO Oriental 942.51 176.85 765.66 5.69 174.8 0 0.57 0.73 181.78 23.74 38 E10 -76.658 35.08 PAMLICO Oriental 1292.01 38.61 1253.4 6.25 393.21 0.16 0.49 1.14 401.25 32.01 39 E15 -76.586 35.11 PAMLICO Broad Cre 582.45 100.35 482.1 0.41 60.51 0.08 0.89 0 61.89 12.84 40 D5 -76.594 35.14 PAMLICO Jones Bay 1029.51 86.31 943.2 0.16 142.63 0 0.08 0 142.87 15.15 41 CS13 -76.667 35.15 PAMLICO Vandemere 340.75 6.21 334.54 1.87 79.03 0.08 1.3 0.57 82.85 24.77 42 CS2 -76.636 35.15 PAMLICO Vandemere 1045.15 81.9 963.25 12.51 248.14 0 0.08 1.79 262.52 27.25 43 D8 -76.569 35.16 PAMLICO Jones Bay 988.44 279.45 708.99 0 0.81 0 0 0 0.81 0.11 44 CN1 -76.675 35.18 PAMLICO Vandemere 894.33 31.95 862.38 7.55 310.77 0 12.27 6.09 336.68 39.04 45 CN6 -76.558 35.2 PAMLICO Jones Bay 286.57 35.11 251.46 0 6.82 0 0.08 0 6.9 2.75 46 CN14 -76.714 35.15 PAMLICO Vandemere 2315.59 47.34 2268.25 10.64 792.92 1.95 50.36 5.69 861.55 37.98 47 B43 -76.572 35.22 PAMLICO Jones Bay 560.04 69.3 490.74 0.49 16.33 0 0.08 0 16.89 3.44 49 CN3 -76.621 35.2 PAMLICO Jones Bay 1668.33 261.81 1406.52 5.2 532.67 0 3.74 0.89 542.5 38.57 50 B40 -76.591 35.24 PAMLICO Jones Bay 1165.89 197.19 968.7 10.07 122.41 2.76 9.67 1.62 146.53 15.13 51 B20 -76.502 35.26 PAMLICO Lowland 883.68 281.16 602.52 0 45.24 0 14.95 0 60.19 9.99 46 Table 9 (continued). Areal estimates of land use change from 1980 – 2000. Catchment ID Station Longitud e Latitud e County USGS Quadrangle Total Area (ha) Water Area (ha) Land Area (ha) Forest to Develop - ed (ha) Forest to Agricul -ture (ha) Wetlan d to Developed (ha) Wetlan d to Agriculture (ha) Agricul -ture to Developed (ha) Total Area Change (ha) % Land Change 53 A12 -76.601 35.3 PAMLICO Lowland 1316.1 66.51 1249.59 30.22 121.27 2.84 8.61 4.87 167.81 13.43 54 A58 -76.511 35.3 Lowland 475.63 235.8 239.83 0 1.46 0 34.44 0 35.9 14.97 55 PAR11 -76.783 35.31 PAMLICO BEAUFOR T 9988.57 37.08 9951.49 183.41 3745.69 0.49 79.76 45.24 4054.59 40.74 56 B10 -76.501 35.32 103.36 38.3 65.06 0 1.46 0 6.5 0 7.96 12.24 58 A2 -76.637 35.32 South Creek 1011.13 64.35 946.78 33.79 162.61 0 15.68 2.76 214.84 22.69 59 PAR16 -76.644 35.34 South Creek 215.57 7.47 208.1 0 20.06 0 7.47 0 27.54 13.23 60 PAR13 -76.685 35.33 South Creek 794.09 21.42 772.67 27.7 152.05 0.97 11.78 28.67 221.18 28.62 63 PAR7 -76.817 35.38 PAMLICO BEAUFOR T BEAUFOR T BEAUFOR T BEAUFOR T 1967.35 23.22 1944.13 39.48 329.85 0 9.99 0.08 379.4 19.52 65 AB1 -76.508 35.41 HYDE Pamlico B 739.74 130.23 609.51 0.16 33.55 0 64.66 0 98.36 16.14 66 OC1 -76.13 35.36 Bluff Point 2453.53 190.53 2263 0 44.59 0.16 0 0 44.76 1.98 67 PUR3 -76.596 35.4 HYDE BEAUFOR T Pamlico B 662.75 32.13 630.62 0.49 118.59 0.32 23.23 0 142.63 22.62 68 RB3 -76.434 35.43 HYDE 455.27 61.02 394.25 0 64.82 0 12.91 0 77.73 19.72 69 SQB3 -76.312 35.39 1805.09 34.11 1770.98 2.92 815.5 0 4.14 0 822.57 46.45 70 PAR9 -76.761 35.43 HYDE BEAUFOR T Scranton Swanquarte r 838.03 26.37 811.66 10.97 305.41 0.57 18.03 0.16 335.13 41.29 72 PUR5 -76.528 35.45 HYDE Pamlico B 1480.57 95.04 1385.53 0.08 450.96 0 38.26 0 489.3 35.32 73 RB1 -76.431 35.44 HYDE 494.04 16.92 477.12 0 164.56 0 8.2 0 172.77 36.21 74 SQB1 -76.357 35.41 HYDE 1811.78 59.76 1752.02 0.49 200.95 0 20.23 0 221.66 12.65 76 JB1 -76.255 35.39 HYDE Scranton Swanquarte r Swanquarte r 5445.91 100.44 5345.47 1.79 380.86 0 27.54 0 410.19 7.67 77 WB3 -76.065 35.41 HYDE Middletown 1996.05 77.4 1918.65 69.93 239.9 0 21.12 2.36 333.31 17.37 79 WB1 -76.064 35.43 HYDE Middletown 3331.92 205.83 3126.09 39.96 429.76 0 4.63 0.89 475.25 15.2 Aurora Lowland Bath Bath 47 Table 9 (continued). Areal estimates of land use change from 1980 – 2000. Catchment ID Station Longitude Latitude County USGS Quadrangle 81 FC3 -76.008 35.47 HYDE Middletown 83 FC1 -75.986 35.51 HYDE 84 PAR8 -76.818 35.45 86 LSR3 -75.903 87 LSR5 88 89 Total Area (ha) Water Area (ha) Land Area (ha) Forest to Developed (ha) Forest to Agriculture (ha) Wetland to Developed (ha) Wetland to Agriculture (ha) Agriculture to Developed (ha) Total Area Change (ha) % Land Change 866.19 38.61 827.58 18.93 125.31 0 60.68 0.16 205.08 24.78 Engelhard 2462.95 287.1 2175.85 0 391.75 0 6.99 0 398.73 18.33 BEAUFORT Bath 9897.73 348.84 9548.89 38.26 3779.4 1.87 48.41 5.28 3873.21 40.56 35.6 HYDE Engelhard 2605.55 22.59 2582.96 0 0 0 0 0 0 0 -75.818 35.6 DARE Long Shoal 2082.72 471.51 1611.21 0.16 0.08 0 0.24 0.02 0.5 0.03 LSR1 -75.864 35.62 DARE Long Shoal 3979.51 317.43 3662.08 0.16 3.66 0 3.82 0.1 7.74 0.21 SPB1 -75.771 35.7 DARE Engelhard 4119.5 812.34 3307.16 0.16 15.43 0 15.6 0.47 31.66 0.96 48 Table 10. The number of hectares of forest and wetland area converted to developed and agriculture land uses between 1980 and 2000. These land use change categories were used in later multivariate analyses of land use change. Land Use Changes Forest to Developed Forest to Agriculture Wetland to Developed Wetland to Agriculture Agriculture to Developed 1980 - 1990 1990 - 2000 (hectares) (hectares) 5,341 3,145 33,047 21,408 887 444 1,357 3,685 2,067 4,481 NC DMF Program 120 Trawl Data Data Collection Methods The North Carolina Division of Marine Fisheries has conducted juvenile fish and invertebrate trawl surveys (“Program 120”) since 1972 to assess population trends in North Carolina nursery areas. The NCDMF stations were initially selected on the basis of depth, salinity, character of the bottom sediments, and the abundance of commercially important estuarine-dependent fish and invertebrates. Monthly surveys were carried out March through November each from 1972 until 1987; subsequent data collections were restricted to sampling in May and June because of budget limitations. Consequently, we only analyzed the May and June data for the entire 25-year period. Table 11 lists the 71 stations of focus, which were sampled in May and June from 1978 to 2004. 49 Table 11. Sampling effort (number of stations visited each year and month) in the NC DMF Program 120 data set. Year Total number Stations sampled Number of Stations sampled in May Number of Stations sampled in June 1978 87 32 55 1979 136 67 69 1980 141 70 71 1981 146 73 73 1982 154 77 77 1983 183 91 92 1984 186 94 92 1985 195 97 98 1986 204 104 100 1987 206 105 101 1988 209 104 105 1989 206 103 103 1990 206 102 104 1991 207 104 103 1992 208 105 103 1993 204 101 103 1994 202 99 103 1995 204 102 102 1996 205 103 102 1997 206 103 103 1998 208 104 104 1999 206 103 103 2000 206 103 103 2001 208 104 104 2002 208 104 104 2003 208 105 103 2004 208 104 104 50 Stations were sampled using an otter trawl with a 10.5-foot-headrope and 3.2 mm mesh cod-end that was towed for 1 minute (~ 75 m distance) in the middle of a sampling day. Sampling of the 71 stations was not done on the same day in each month, because of NCDMF manpower limitations. Although sampling days were spread throughout the months of May and June across regions, all stations within a region (~10stations/region) were sampled on the same day. Thus, it took approximately 1 week to ten days of sampling each month to sample all stations. The NCDMF field crew recorded the total numbers of each of the selected species collected per trawl. In addition, surface and bottom water temperature and salinity were measured at each station using water quality meters, thermometers, and refractometers. Depth was measured using a measurement stick or weight and line at the time of trawling. We used the average of depths measured over 25 years. Dissolved oxygen was not recorded over the entire sequence of sampling (only after 1987), so was not considered in the analysis here. Statistical Analysis of Change in Program 120 Catches by Station We plotted the 25-year time series of the catches of juvenile fishes and shrimp averaged over all the stations (using a geometric mean) to examine the overall temporal trends in abundance and assess how physical factors might have affected catches. Each of the selected species abundances in trawls at each station were correlated with surface and bottom temperature (◦C), surface and bottom salinity (psu, practical salinity unit or parts per thousand), and bottom depth (m). We created time series plots of mean (May and June trawls averaged) of each species’ abundance for all 71 stations from 1980 through 2004. Because of the extreme variability inherent in these trawl data, we used a geometric mean to compute the averages by years, which 51 is a log-transformed mean number per trawl after adding 1 to each data point (which makes all zeros = 1, but after log-transformation, they are 0 for computing the mean), then backtransforming using the exponential to get geometric mean number/trawl. This method of averaging smoothed the data, and a LOWESS line (a locally weighted curve fit to the data, with a smoother span f = 0.667, which is the proportion of the data used to make the smoothed fit at each year) was superimposed over each plot to further identify the long-term trends over all these stations. To analyze for trends in abundance of a species at a station or within a county, we used a time series plot of the May and June catches per trawl with DWLS (Distance Weighted Least Squares with a tension value = 0.2) line plots (SYSTAT 12.0). Distance weighted least squares smoothing is similar to a moving average; it fits a line through a set of points by least-squares method, but it weights the points near the mean being computed more than the points farther away. Thus, the 25-year time-series line for each station and species is allowed to flex at any given year on the series to fit the data. The amount of flex is controlled by the tension parameter. This method produces a true, locally weighted curve running through the points (SYSTAT 12.0 user manual) using an algorithm attributed to McLain (1974). Associated with the time series plots, are maps of each catchment in which we examine the spatial and temporal patterns in the trawl catch data as a function of land use. We averaged catches from each station into three 5-year periods (1980-1984, 1990-1994, and 2000-2004) connected with each of the Landsat images(1980, 1990, and 2000). Intervening years were omitted. This allowed us to use the classified Landsat imagery and catch data together in the classification and regression tree analysis. In this way, the trawl catches of selected fishes and brown shrimp and the associated land use change in each catchment can be visualized and integrated. Figure 9 offers a schematic display of how these variables are related. 52 Figure 9. Schematic representation of the process used to relate land use change to changes in species catch between 1980, 1990, and 2000. For each of the species, we computed for every station a z-score of the normalized change in mean catch during May and June using the following formulae: ( x( 2000−2004 ) - x(1980−1984 ) ) for station s and z-score of change in catch for station s = (∆ s ) − x (∆ s ) σ ( ∆s ) where Δs is the change in catch at each station during the interval 1980-2004, x(1980−1984 ) is the average catch at each station during the 5-year period 1980-1984 at the start of the time series considered in this analysis, x( 2000−2004 ) is the 5-year average catch at each station at the end of the 53 time series period, and σ ( ∆ s ) is the standard deviation of the change in catch at each station. These Z-scores of change in catch were computed for each of the target species in this report. A z-score indicates how many standard deviations an observation is above or below the mean change in catch for all stations. It allows comparison of observations of trawl catches with different ranges, i.e., it normalizes the changes in catch for comparison across species and with land-use. A z-score can be negative, positive, or zero. If z is negative, the corresponding Δs value is below the mean of all stations. If z is positive, the corresponding Δs value is above the mean of all stations. And if z = 0, the corresponding Δs value is equal to the mean of all stations. For example, a z-score of 1.5 means that the Δs is 1.5 standard deviations above the mean. A negative z-scores of 1.5 means that the species in question has declined by 1.5 standard deviations below the long-term mean change in catch for that species at that station. The z-score approach allowed us to assess, using a normalized index of catches for each station, whether or not a station had exhibited a significant increase or decrease of each species over the 25-year period between 1980 and 2004. A z-score ≤ -1.9 represents a statistically significant decline (two standard deviations below the mean) for that station. A z-score between 1.96 and 1.96 represents no statistically significant change (within two standard deviations of the overall mean) for that station. Finally, a z-score ≥ 1.96 represents a statistically significant increase (two standard deviations above the mean) for that station. These symbols were plotted on maps of land use change in the position where the sampling station is located in the catchment (see Appendix). We used the Classification and Regression Tree (CART) analysis (De’Ath and Fabricus 2000, King et al. 2005) in SYSTAT 11.0 to determine the most significant factors that affected the 25-year average species abundances during 1980-2004 at each station (dependent variable). 54 For the CART analysis, the response variable was the z-score of catch change at each station (see section above for computation), where the difference in average abundance at each station between trawls taken in 1980-1984 and 2000-2004 was converted to a z-score (relative to the differences in catch observed at all stations) to correct for heteroscedasticity and non-normality of the data. The land use change predictor variables for the CART analysis were the total percentage of land use change in each catchment; that is, the sum of (1) forested land changed to developed land, (2) forested land that changed to agriculture, (3) agricultural land that changed to development use, (4) wetland that changed to development use, and (5) wetland to developed, all divided by the catchment land areas (see Table 9, last column). Additional landscape parameters considered as predictor variables included (a) distance of each station to closest inlet in km, (b) the number of National Pollution Discharge Elimination System (NPDES) point sources (including animal feeding operations) within the 14-digit watershed containing the catchment area, (c) human population within the census track containing the catchment area during the 2000 US Census , (d) average station depth in m, (e) 24-year average bottom temperature in °C at time of capture, and (f) 24-year average bottom salinity in psu measured at time of capture. Results Land Use Change Patterns 1980-2000 Forested areas along the coast declined between 1980 and 2000; much of this land was converted to agriculture. The percent of catchment area surrounding the NC DMF Program 120 stations represented by forest declined between 1980 and 2000, from a mean of 71.8 ± 2.4% (standard error of the mean or SEM) in 1980, to 55.3 ± 2.4% in 1990, and to 43.1 ± 2.3% in 2000 55 (Figure 10). These values are similar when forested land use classes were summed across all catchments in the study, then divided by total catchment area (Table 12). Over the same time period, areal percentage of agriculture within these watersheds increased from 6.2 ± 0.8% in 1980 to 18.2 ± 2.0 % in 1990, and to 28.4 ± 2.0 % in 2000 (Figure 10). Thus the incidence of forest as a dominant land form (defined as covering at least 55% of the land) decreased by nearly half (from 57 catchments in 1980 to 28 catchments in 2000) over the course of these two decades. Fifty-eight catchments showed a net loss of at least 10% of forest between 1980 and 2000; the average loss was 28.7% ± 8.2. The total amount of wetlands expressed comparatively little net change in size, and actually showed a small increase in average percent cover of 4.5 ± 1.8 (Figure 10). Overall, there were no significant changes in percentage developed land (urban) or wetlands in the catchments, although increases percentage of catchments covered by wetlands and urban development within certain watersheds were evident (Figure 10 and Table 12). 56 100 Wetland Forest Agriculture Urban 90 Percent land use 80 70 60 50 40 30 20 10 0 1975 1980 1985 1990 1995 2000 2005 Year Figure 10. Change in percent forested and wetland area compared to agriculture and developed area. 57 Table 12. Summary statistics of changes in land use types between 1980 and 2000. Values were computed by summing total areas of catchments and land use class totals for all watersheds surrounding the NC DMF stations. Land use type 1980 1990 2000 Total Total Total area area area Percent Percent (ha) (ha) Percent (ha) Agriculture 10335 7.5 31743.1 23.3 42621.2 31.6 Wetland 15146 11.0 18667.7 13.7 20913.1 15.5 Forest 104510 76.1 76698.5 56.3 61560.5 45.6 Developed 7371 5.4 9122.2 6.7 9834.1 7.3 Percent change Percent Percent Percent change change change 1980 to 1990 to 1980 to 2000 2000 1990 15.8 8.3 24.1 2.7 1.8 4.5 -19.8 -10.7 -30.5 1.3 0.6 1.9 Combining forest and wetland areas to serve as a proxy for “unaltered” land, and combining agriculture and developed land area as a proxy for “altered land”, revealed that unaltered land declined as a percentage of the catchment areas, while “altered” land increased (Table 13). In 1980, forest and wetland covered 89.7 ± 1.6 % (SEM) of the total area of the 67 catchments; this percentage declined to 76.1 ± 2.4 % by 1990 and to 66.0 % ± 2.3 % by 2000. In the same time period, agriculture and developed areas increased from 10.3 ± 1.6 % to 23.9 ± 2.4 % in 1990, and to 34.0 ± 2.3 % in 2000. Sixty-five of the 67 catchments were dominated forest and wetland in 1980 (Table 13). By 1990, forest and wetland cover was dominant in only 53 of the 67 catchments. In 2000, dominance of combined forest and wetland cover declined to 43 catchments. 58 Table 13. Number of catchments showing dominant types of land use between 1980 and 2000. F+W = combined forest and wetland acreage, a proxy for “unaltered” land; A+D = combined agriculture and developed, a proxy for “altered” land. A dominant land use type is here defined as a type that comprises 55% or more of the catchment. Watersheds Watersheds Percent Percent dominated dominated by A+D by F+W F+W A+D 87.1 12.9 65 2 70.0 30.0 53 14 61.1 38.9 43 34 Year 1980 1990 2000 Reductions in the dominance of the combined forested and wetland categories were primarily due to the loss of forested land (Figure 11). 100 Forest and Wetland Agriculture and urban Percent land use 80 60 40 20 0 1975 1980 1985 1990 1995 2000 2005 Year Figure 11. The mean percentage of each catchment surrounding the 71 NC DMF stations that was covered by “unaltered” land (forest and wetland) and “altered” land (agriculture and development) in each of the years examined (1980, 1990, 2000) for land use on Landsat imagery. Error bars are 1 SEM. 59 Number of Watersheds 67 54 40 27 13 0 1980 1985 1990 1995 2000 Year Watersheds dominated by F+W Watersheds dominated by A+D Figure 12. Change in number of catchments dominated by “unaltered” land (forest and wetland) versus the number of watershed dominated by “altered” (agriculture and developed) land. Land used for agriculture showed the largest increases during the study period (Table 12). Agriculture was a minor land category in 1980, but had become the dominant land category in 10 of the 67 catchments by 2000. The average increase in land used for agriculture between 1980 and 2000 was 22.1% ± 6.5. Increases in developed land were localized; thus at the level of all of the catchments, developed land cover increased by an average of only 1.5% ± 5.9. Nearly all of the gain in agricultural land was a consequence of conversion of forested land (Table 12). Deforestation was also the primary source of new developed land. Some wetlands were also converted to agriculture; six catchments showed a conversion of 10-20% of original wetland area to agricultural use. Loss of forested land was generally widely distributed among the counties containing the nursery area catchments. Thus Beaufort, Hyde, Onslow, and Pamlico counties each contained catchments that spanned the entire range of deforestation, from less than 10% to over 50% loss 60 (Table 14). These counties are also the best represented in the catchments sampled by NC DMF, with each of them containing at least 8 stations. Beaufort County had catchments that exhibited a wide range of increases in agriculture, although considerable gains were also evident in Hyde, Onslow and Pamlico counties (Table 15). In contrast, the most extensive accumulation of developed lands (> 20% of land area) was confined to 4 catchments in Onslow County (Table 16). 61 Table 14. Loss of forested land by county. The numbers within the table represent the number of catchments within each county showing a particular percentage range of land lost or gained. The numbers in parentheses indicate the total number of catchment per county. County (catchment #) > 50 % Beaufort (8) 2 Brunswick (1) Carteret (10) Craven (1) 1 Dare (3) Hanover (3) 1 Hyde (15) 1 Onslow (8) 1 Pamlico (17) 2 Pender (1) Totals 50-41 % 2 3 1 1 4 1 8 40-31% 1 3 1 1 2 1 4 - 12 30-21% 1 1 1 1 3 1 - 13 20-11% 1 2 1 7 2 3 - 8 < 10% 1 1 1 1 3 3 - 16 Total 8 1 10 1 3 3 15 8 17 1 10 67 Table 15. Gain of agricultural land by county. The numbers within the table represent the number of catchments within each county showing a particular percentage range of land lost or gained County (cathment #) > 50% Beaufort (8) Brunswick (1) Carteret (10) Craven (1) Dare (3) Hanover (3) Hyde (15) Onslow (8) Pamlico (17) Pender (1) 50-41% 1 40-31% 3 30-21% 1 4 1 2 3 12 62 1 1 2 < 10% Total 1 1 5 1 1 1 4 6 2 1 3 4 2 1 3 5 5 17 4 13 18 1 67 2 1 1 1 1 >20-11% 16 8 1 10 1 3 3 15 8 Table 16. Gain of developed land by county. The numbers within the table represent the number of catchments within each county showing a particular percentage range of land lost or gained County Beaufort (8) Brunswick (1) Carteret (10) Craven (1) Dare (3) Hanover (3) Hyde (15) Onslow (8) Pamlico (17) Pender (1) > 50% 50-41% 40-31% 30-21% 20-11% 1 1 63 < 10% Total 8 8 1 1 10 10 1 1 3 3 3 3 15 15 3 4 8 17 17 1 1 3 63 67 Changes in Catches of Target Species (1980-2004) We evaluated long-term trends in the NC DMF Program 120 trawls at 71 stations for selected species over the period 1980-2004. Catches were recorded as number of individual fish or shrimp per trawl during May and June each year at each station. We plotted the overall time series for each species, computing a geometric mean (computed over all 71 stations and from trawls in both months) for each year and fitting a LOWESS curve to the data. Next we examined a z-score of change in catch (the difference between the 1980-1984 average and the 2000-2004 average catch), which was computed for each station and can be interpreted as the change in normalized abundance (relative to all stations) in the trawls for each species (in units of standard deviation). This normalized index allows for the comparison of relative changes at stations and species with very different baseline abundances. Thus, a z-score of +1.0 indicates and increase in catch by 1 standard deviation beyond the mean for a particular species and station between 1980-1984 and 2000-2004. The z-scores for each species were first plotted to examine the relative variation in catch among stations within each of the North Carolina coastal counties (some counties have many stations, some only one station), and thereby relate trends in catch to pattern in land use. Subsequently, z-scores for each species were plotted against the percentage of land use that had changed in the catchments surrounding each station (between 1980 and 2000), with a LOWESS smoothing function applied to the data. These plots allow the reader to make a visual inspection of the change in catch in NCDMF program 120 data for each species as a function of land use change over the same 24-year period. An appendix is provided at the end of this report that contains detailed maps of catches of each species at each NCDMF station, and the surrounding catchment land use classifications that were measured and used in the analysis. We provide notes in this section on the maps, indicting 64 which stations showed changes in catches. Changes in catch are represented by color codes where red circles = decline, green circles = no change, and pink circles = increase. Red circles indicate that catch means declined z-score values greater than 1.96; pink circles indicate that mean catch increased by a z-score greater than 1.96. Green circles indicate that changes in catch remained within the upper (positive) and lower (negative) z-score boundaries; accordingly, we interpret this to mean that no significant change in catch occurred. Spot The catch for spot was highest overall, with a geometric mean catch of 114.7 fish/trawl Over all stations, spot increased in mean abundance from a geometric mean of 60.7 per trawl in 1980 to178.4 per trawl in 2004, but this increase was not significant (Figure 12, correlation r = 0.25, p = 0.23). There were significant declines of spot in Carteret and Dare counties (Figure 13). Finally, there was a decline in catch z-score as land use change increased (Figure 14), although a correlation analysis suggested this decline was not significant (r = -0.08, p = 0.51). 65 Figure 12. Spot, Leiostomus xanthurus. The geometric mean catch per trawl (May and June averages from the 71 NC DMF stations) from 1978 through 2004. The curve is a LOWESS fit (f=0.67). The correlation between year and geometric mean catch was not significant (r = 0.25, p = 0.23). 66 Figure 13. Spot, Leiostomus xanthurus. Long-term change in catch (reported as a z-score of change in trawl catches between 1980-2004; see Methods for computation of this index) for each NCDMF station in different coastal counties. A z-score of 0 indicates no change; a negative zscore indicates a decline in the catch, and a positive z-score indicates an increase in catch between 1980 and 2004. 67 Figure 14. Spot, Leiostomus xanthurus. Change in trawl catch z-score (1980-2004) at each NCDMF station as a function of percentage change in land use (1980-2000) within each catchment. Notes on the station maps in Appendix: Out of 71 stations, three have had an increase (A2, A12, and B10) in catch of juvenile spot, while two (CC11 and PAR7) have had a decline (Appendix Map 1). Of the two stations showing declines, CC11 is located on the southwest prong of Lewis Creek in Carteret County, and drains into Core Sound. The watershed around station CC11 has had a negligible amount of land use change (~3%) during the study period, probably because it is mostly covered by estuarine and riverine marsh, and therefore is not suitable to agriculture or 68 urban development. Station PAR7 is found on Porter Creek in Beaufort County, downstream from the Potash Corporation mining site. Several large farming operations are located in its wider watershed (Map 4). During the course of the study, the watershed upstream has lost around 33% of its forested area to farming and mining. Stations A2, A12 and B10, which showed increases, are located in the lower Pamlico River area. They have had differing amount of forest and wetland loss, which are respectively ~21%, 2.3% and ~0%, in the 20 years covered by imagery. Southern flounder The average catch for Southern flounder was among the lowest of the species examined, with a geometric mean = 3.7 per trawl, but their abundance has increased significantly from a geometric mean of 2.8 fish per trawl 1980 to 4.2 per trawl in 2004 (Figure 15, correlation between year and geometric mean catch, r = 0.44, p=0.03). Increases in z-score were most noticeable in Pamlico, Hyde, Dare, Craven, and Brunswick counties (Figure 16). The z-score declined as percentage of land use change increased (Figure 17), which is a significant linear decrease (linear regression: southern flounder z-score in catch change = -1.47 * (% land use change) + 0.41, p=0.03). 69 Figure 15. Southern flounder, Paralichthys lethostigma. The geometric mean catch per trawl (May and June averages from the 71 NC DMF stations) from 1978 through 2004. The curve is a LOWESS fit (f=0.67). 70 Figure 16. Southern flounder, Paralichthys lethostigma. Long-term change in catch (reported as a z-score of change in trawl catches between 1980-2004; see Methods for computation of this index) for each NCDMF station in different coastal counties. A z-score of 0 indicates no change; a negative z-score indicates a decline in the catch, and a positive z-score indicates an increase in catch between 1980 and 2004. 71 Figure 17. Southern flounder, Paralichthys lethostigma. Change in trawl catch z-score (19802004) at each NCDMF station as a function of percentage change in land use (1980-2000) within each catchment. Line is a Lowess fit. Notes on the station maps in Appendix: A map of the changes in southern flounder catches between 1980 and 2004 using standardized scores (z-scores) for the entire coastal region is shown in Appendix Map 10. Appendix Maps 11 through 18 show time series plots at each station of changes in southern flounder’s catch 1980-2004. Some stations have very high average catches (FC3 and LSR1, Map 11; A2, A58, B10, B20, B40, B43 on Map13) and some 72 have low catches (SPB1, Map 11; CN1, CN14, CN3, CS13 and F1, Map 14; all stations on the Carteret County region map, Map 16; the New river stations, Map 17; and the Cape Fear River stations, Map 18). Some stations show an increase (AB1, SB3, Map 12 ; B20, B43, Map 13; D8, Map 14; E10, E15,F3N, and J2, Map 15) and some display a slight decline in catch of juvenile southern flounder ( F1, Map 15; NR13, Map 17). The number of juvenile flounder caught in the 71 NCDMF stations between 1980 and 2004 remained unchanged at 68 stations, and significantly increased at three stations. These are CFR1, Cape Fear station 1, located on the lower reaches of the Cape Fear River (Map 18); station H2, Club foot creek in Craven County (Map 15), and D8 located on Dipping creek, tributary of Long Creek in Pamlico County (Map 14). These stations have had respectively 14%, ~26% and ~0% land conversions from forest/wetland to agriculture urban areas. The stations with increasing catch of juvenile southern flounder have no clear common denominator, suggesting that multiple factors might be at play to cause the observed results. Atlantic croaker Atlantic croaker catches exhibited large fluctuations in abundance, with a low of 6.1 fish/trawl in 1991, highs of 58.4 fish/trawl in 1983 and an overall geometric mean abundance of 21.6 fish/trawl. Hyde and Dare County stations exhibited declines, while New Hanover and Brunswick County stations exhibited increases (Figure 19). There was a decline in the z-score of change in catch for Atlantic croaker with increased percentage of land use change (Figure 20), although this was not a significant effect in a linear regression analysis (P = 0.493) 73 Figure 18. Atlantic croaker, Micropogonias undulatus. The geometric mean catch per trawl (May and June averages from the 71 NC DMF stations) from 1978 through 2004. The curve is a LOWESS fit (f=0.67). 74 Figure 19. Atlantic croaker, Micropogonias undulatus. Long-term change in catch (reported as a Z-score of change in trawl catches between 1980-2004; see Methods for computation of this index) for each NCDMF station in different coastal counties. A Z-score of 0 indicates no change; a negative Z-score indicates a decline in the catch, and a positive Z-score indicates an increase in catch between 1980 and 2004. 75 Figure 20. Atlantic croaker, Micropogonias undulatus. Change in trawl catch Z-score (19802004) at each NCDMF station as a function of percentage change in land use (1980-2000) within each catchment. Notes on the station maps in Appendix: Appendix Map 19 shows Atlantic croaker catches at 71 stations and land use changes in adjacent catchments. Two of the stations registered an increase while three have had a decline. Stations A2, located on Betty Creek, tributary of Spring Creek in Pamlico County and PAR16, Cypress Branch, secondary tributary of the East Prong Creek 76 showed an increase in juvenile Atlantic croaker catch between 1980 and 2004 (Map 22). Stations F1, PAR11 and PUR 5 have experienced a decline. Station F1 is found in the upper reaches of Orchard creek in Pamlico County and is a tributary of the Neuse River (Map 23). F1 catchment has had about 21.5% of its forested land developed. Station PAR11 is found on South Creek in Beaufort County, downstream from a heavy agricultural watershed, where about 51% of the forested land has been converted to agriculture (Map 22); PUR5 is located on Warner Creek, a tributary of Fortescue Creek in Hyde County and which drains to the Pamlico River (Map 21). The upstream watershed has known a forest land conversion of about 38%. Atlantic menhaden The catches of Atlantic menhaden declined significantly over all stations from a geometric mean of 8.6 fish/trawl during 1980 to 3.2 fish/trawl in 2002 with an overall geometric mean of 7.2 individuals per trawl (Figure 21, correlation between year and geometric mean catch r = -0.39, p = 0.05). However, the Z-scores of average change in catch at the various counties’ stations was remarkably consistent, with few long-term changes noted (Figure 22). There was no relationship between the Z-score and the percentage of land use change within the catchments (linear regression, P = 0.55; Figure 23). 77 Figure 21. Atlantic menhaden, Brevoortia tyrannus. The geometric mean catch per trawl (May and June averages from the 71 NC DMF stations) from 1978 through 2004. The curve is a LOWESS fit (f=.67). 78 Figure 22. Atlantic menhaden, Brevoortia tyrannus. Long-term change in catch (reported as a Zscore of change in trawl catches between 1980-2004; see Methods for computation of this index) for each NCDMF station in different coastal counties. A Z-score of 0 indicates no change; a negative Z-score indicates a decline in the catch, and a positive Z-score indicates an increase in catch between 1980 and 2004. 79 Figure 23. Atlantic menhaden, Brevoortia tyrannus. Change in trawl catch Z-score (1980-2004) at each NCDMF station as a function of percentage change in land use (1980-2000) within each catchment. Notes on Maps in the Appendix: Appendix Map 28 shows Atlantic menhaden catches at 71 stations on a color-coded map to show long-term changes. Sixty nine stations showed no significant change and two registered a decline. The stations that show a decline in menhaden 80 catch are F3N, located on Pierce Creek in Oriental, NC (Map 33) and SQB3, on Oyster Creek off Swanquarter Bay (Map 29). Pinfish The catches of pinfish increased significantly during 1980 through 2004, with a geometric mean of 1.2 fish per trawl in 1980, peaking at 36.3 per trawl during 2001 (Figure 24, correlation between year and geometric mean catch r = 0.65, p = 0.0002). Their dramatic increase suggests that some external factor (land-based nutrient inputs, climate changes leading to increased survival, increases in food availability, or the release from predation by a decline in a predator in the food web) is causing them to increase at most stations. The differences in mean catch between 1980-84 and 2000-2004 showed mostly positive values at the 71 stations. The Z-scores of change in catch was variable among counties, but increased more in Pamlico, Onslow, and Hyde counties (Figure 25). There was no decline in the Z-score of change in catch of pinfish as the percentage of land use change increased (Figure 26, correlation between Z-score and land use change r = -0.07, p = 0.59). 81 Figure 24. Pinfish, Lagodon rhomboides. The geometric mean catch per trawl (May and June averages from the 71 NC DMF stations) from 1978 through 2004. The curve is a LOWESS fit (f=0.67). 82 Figure 25. Pinfish, Lagodon rhomboides. Long-term change in catch (reported as a Z-score of change in trawl catches between 1980-2004; see Methods for computation of this index) for each NCDMF station in different coastal counties. A Z-score of 0 indicates no change; a negative Zscore indicates a decline in the catch, and a positive Z-score indicates an increase in catch between 1980 and 2004. 83 Figure 26. Pinfish, Lagodon rhomboides. Change in trawl catch Z-score (1980-2004) at each NCDMF station as a function of percentage change in land use (1980-2000) within each catchment. Notes on the maps in the Appendix: Appendix Map 37 displays the overall picture of changes in pinfish catch at 71 stations, color-coded to show long-term changes. Sixty eight stations showed 84 no significant change while three showed a significant increase. The stations that showed an increase in pinfish were CFR1 (lower Cape Fear, Map 45), H2 (Clubfoot Creek, tributary of the lower Neuse River, and close to Cherry Point Air Force Base, Map 42), and D8 (Long Creek, tributary Bonner Bay, which drains in Bay River, Map 40), an area that has been extensively drained. Although three stations out of 71 are not a significant number, we find that they are all associated with increased land use change. Brown shrimp Finally, brown shrimp were relatively abundant (geometric mean = 16.7 individuals/trawl) during the study period, and increased from 20.3 individuals/trawl during 1980 to 37.0 individuals/trawl in 2002 (there was an extreme peak of 47.8 individuals/trawl in 1985 and 5.1 individuals/trawl an extreme low in 1998), however, this increase was not statistically significant (Figure 27, correlation between year and geometric mean catch r = 0.09, p = 0.65). There were long-term increases in the Z-score change in catch in Pamlico, Dare, Hyde and Craven counties (Figure 28). However, there was no significant association between Z-score of change in catch and percent land use change (linear regression, P = 0.77; Figure 29). 85 Figure 27. Brown shrimp, Farfantepenaeus aztecus. The geometric mean catch per trawl (May and June averages from the 71 NC DMF stations) from 1978 through 2004. The curve is a LOWESS fit (f=0.67). 86 Figure 28. Brown shrimp, Farfantepenaeus aztecus. Long-term change in catch (reported as a Zscore of change in trawl catches between 1980-2004; see Methods for computation of this index) for each NCDMF station in different coastal counties. A Z-score of 0 indicates no change; a negative Z-score indicates a decline in the catch, and a positive Z-score indicates an increase in catch between 1980 and 2004. 87 Figure 29. Brown shrimp, Farfantepenaeus aztecus. Change in trawl catch Z-score (1980-2004) at each NCDMF station as a function of percentage change in land use (1980-2000) within each catchment. Notes on maps in Appendix: Appendix Map 46 displays a map of overall changes in brown shrimp catch along the North Carolina coast. Three of the stations showed a significant increase, one station showed a decrease. The three that increased are FC3 (Middletown Creek, Map 47), WB1 (Douglas Bay, Map 47) and WB3 (Wysocking Bay, Map 47), all in Hyde County. Overall Brown shrimp catch declined slightly at station CC6 (Jarrett Bay) off Core Sound (Map 52). 88 Long-term changes in species by NCDMF stations Most species did not show any long-term declines or increases in abundance in the May and June trawls taken at individual NCDMF stations 1980-2004. Table 17 provides a summary list of stations where the target species declined or increased. Using a Z-score of the change in abundance at each station (equivalent to a confidence interval of 95%; Z-scores were computed for each station of less 1.96, or greater than -1.96, see Z-score analysis in methods section), we find that all species have remained unchanged at > 90% of the 71 stations (Table 17). Five species have shown increases at a few stations: southern flounder (3 stations), Atlantic croaker (2 stations), spot (3 stations), pinfish (5 stations) and brown shrimp (3 stations). At a few stations, Atlantic croaker (3 stations), spot (2 stations), Atlantic menhaden (3 stations) and brown shrimp (1 station) have gotten less abundant over time. Interestingly, there seems to be a concentration of the stations showing these declines in the lower Pamlico River for spot and croaker. Note that some stations would be expected to show increases or declines by chance alone (5 % of the time, or 3-4 stations), so these results are unsurprising. 89 Table 17 Catchment Areas of change in catch per species in the 71 stations Species Southern flounder, Paralichtys lethostigma Atlantic croaker, Micropogonias undulatus Spot, Leiostomous xanthurus Pinfish, Lagodon rhomboides Atlantic menhaden, Brevoortia tyrannus Brown shrimp, Farfantepenaeus aztecus Number of stations without a significant change (percentage) Number of stations with catch increase (Station IDs) Number of stations with catch decline (Station IDs) 68 (96%) 3 (D8,H2,CFR1) 0 66 (93%) 2 (PAR13,A2) both stations with increase are located 3 (PAR11,F3N,PUR5) in Lower Pamlico 66 (93%) 3 (A2,A12,B10) 2 (PAR7, CC11) 66 (93%) 5 (CFR1,H2,D8) 0 69 (97%) 0 2 (SQB3,F3N) 67 (94%) 3 (FC3,WB1,WB3) 1(CC6) 90 Notes Decline at Potash Corp., Cedar Island Abiotic factors and abundance Abiotic factors (bottom temperature and salinity measured by NC DMF personnel at the time of the trawl collection) were associated with changes in abundance of the target species. Note that dissolved oxygen was not measured by NCDFM until 1989, and no attempt was made to include those data in analyses reported here. Salinity and temperature affected the change in abundance of several species, but effects were apparent only after multivariate analysis. Table 18 provides a summary of the geometric mean catches per trawl and the significant abiotic factors influencing the change in abundance at each station as analyzed using a Classification and Regression Tree (CART) for each species. The complete results of the CART analyses will be discussed in the next section. Bottom temperature was a significant factor associated with the change in abundance of Atlantic croaker, Atlantic menhaden, and pinfish. Atlantic croaker increased more between 19802005 at stations where temperature was > 25 °C, and similarly, Atlantic menhaden increased more where temperatures were greater > 24 °C. Pinfish increased more between 1980 and 2004 at stations where temperature was < 25 °C or over 26 °C, depending on other factors (see CART results in the next section). Overall, the most important abiotic factor for spot, flounder, and brown shrimp was salinity (Table 18). Peak catches of juvenile spot in all years were associated with low salinity stations (< 15 ppt, Figure 30), and peak catches of southern flounder occurred at stations with intermediate salinity (10-15 ppt, Figure 31). Pinfish (Figure 32) peak catches were bimodal in 2000-04 (when they had increased to significant levels though the study area; no trends were apparent in earlier years), with peaks both at low salinity (oligohaline, 5-10 ppt) and mesohaline 91 (20-25 ppt) stations. Brown shrimp abundance was highest at stations with high salinity in 1980-84, 1990-94, and 2000-04 (> 25 ppt, Figure 33). The greatest increases of southern flounder and brown shrimp occurred in shallow depth, and this was another significant abiotic factor for these species in the CART analysis (Table 18). Table 18. Mean catches (number per trawl) of each of the target species and significant factors affecting abundance at all stations identified in the Classification and Regression Tree (CART) analysis, discussed in the next section. SPECIES Spot Southern flounder Atlantic croaker Atlantic menhaden Pinfish Brown shrimp Geometric mean catch per trawl Bottom temperature Bottom salinity Depth 114.7 x 3.7 21.6 7.2 7.3 16.7 x x x x 92 x x x 300 400 2000-04 1990-94 1980-84 0 100 200 Number/trawl 500 Spot 0 5 10 15 20 25 30 35 Salinity Figure 30. The average catch of spot at stations with different salinities; LOWESS curves (f=0.67) for 1980-84, 1990-94, and 2000-2004. 93 10 2000-04 1990-94 1980-84 0 5 Number/trawl 15 Southern flounder 0 5 10 15 20 25 30 35 Salinity Figure 31. The average catch of southern flounder at stations with different salinities; LOWESS curves (f=0.67) for 1980-84, 1990-94, and 2000-2004. 94 80 2000-04 1990-94 60 1980-84 0 20 40 Number/trawl 100 Pinfish 5 10 15 20 25 30 Salinity Figure 32. The average catch of pinfish at stations with different salinities; LOWESS curves (f=0.67) for 1980-84, 1990-94, and 2000-2004. 95 200 150 2000-04 1990-94 1980-84 0 50 100 Number/trawl 250 Brown shrimp 0 5 10 15 20 25 30 35 Salinity Figure 33. The average catch of brown shrimp at stations with different salinities; LOWESS curves (f=0.67) for 1980-84, 1990-94, and 2000-2004. 96 Multivariate CART analyses of land use change on target species We used a multivariate approach called Classification and Regression Trees (CART) to test our hypothesis that land use changes, along with abiotic factors or other anthropogenic variables, were associated with changes in the long-term abundance of the juvenile stages of the target species. In CART, the Z-scores of change in catch at each of station is partitioned into two groups in a hierarchical way, so that the greatest amount of variation occurs between groups, and the least amount within groups. Each predictor variable, including the percentage of land use change within each catchment around the 71 NCDMF stations, is used to split the response variable data though a recursive, iterative procedure to assess variation in Z-score of catch change at each step. The predictor variable that explains the greatest amount of variation (has the greatest PRE or Proportional Reduction in Error) is used to create the first partition of the stations into two groups. The variable with next greatest PRE creates the second partition of each of those groups, and so on. A threshold for dividing the groups on the basis of that variable is also reported with each split. Splitting continues until the groups are too small to be split (PRE is not significant). We assessed the influence of the following predictor variables on Z-score of change in catch (1980-2004) for each station: (1) percentage of land use that changed between 1980-2000 within the surrounding catchment (PLU_CHAN), (2) bottom depth in m (DEPTH_M), (3) bottom temperature in °C (BTEMP), (4) bottom salinity in ppt (BSALIN), (5) the distance to the nearest inlet in km (DIS_INLE), (6) number of National Pollution Discharge Elimination System discharge points in the 14 digit hydrologic unit area (NPDES), and (7) the human population (people/km2) in the census tract containing the station’s catchment in 2000 (POP_2000). 97 Spot CART analysis Spot showed positive catch change Z-scores (mean = 0.5 SD units) at 29 stations that were greater than 42 km from the nearest inlet (first partition). At those stations, even greater Zscores (mean = 2 SD units) occurred at five stations where the human population density was greater than 883 persons/km2 (second partition; Figure 34). A third partition was also significant within the 24 stations where the human population did not exceed that density: if the human population was less than 883 persons/km2, then positive catch Z-scores were observed at 18 stations located in less than 64 km from an inlet. 98 Figure 34. A Classification and Regression Tree (CART) analysis of the standardized z-score of change in catch of spot between 1980 and 2000. Predictor variables included in CART are depth in meters (DEPTH_M), distance to nearest inlet in km (DIS_INLE), bottom salinity (BSALIN), bottom temperature (BTEMP), number of NPDES sites in the watershed (NPDES), total human population in year 2000 (POP_2000) and overall change in land use as a percentage of catchment area (PLU_CHAN). 99 Southern flounder CART analysis For the southern flounder, the first CART partition involved 50 stations where the bottom salinity was < 14.0 ppt; at these stations, there were positive Z-scores of catch change (mean = 0.3 SD units, Figure 35). In the second partition of these 50 stations, there were 20 stations that had positive Z-scores (mean = 0.8 SD units) if the percentage land use change was less than 21.3 %; if the land use change exceeded that threshold, the Z-scores became slightly negative (mean = 0.1 SD units). Within the group of 30 stations that had land use change exceeding 21%, if station depths < 0.9 m (N= 6 stations), then the Z-score was once again positive (mean = 0.8 SD units, Figure 31). In our study, CART shows juvenile southern flounder catch increasing in lower salinity water (<14 ppt) and low percentage of land use change (< 21%). 100 Figure 35. A Classification and Regression Tree (CART) analysis of the standardized z-score of change in catch of Southern flounder between 1980 and 2000. Predictor variables included in CART are depth in meters (DEPTH_M), distance to nearest inlet in km (DIS_INLE), bottom salinity (BSALIN), bottom temperature (BTEMP), number of National Pollution Discharge Elimination Sites (NPDES) in the watershed, total human population in the year 2000 (POP_2000) and overall change in land use as a percentage of catchment area (PLU_CHAN). 101 Atlantic croaker CART analysis The first partition of the CART indicated that Atlantic croaker’s catches increased slightly (mean = 0.1 SD units) at the 65 stations that had percentage land use change less than 54%; if there were greater change in land use, the Z-score of catch change became negative (mean = -1.1 SD units at 6 stations, Figure 36) . The second partition of the 65 stations indicated that the Z-score of change in catch increased at 21 stations (mean = 0.6 SD units) where the distance to an inlet was greater than 47 km; at the 44 stations that were closer than 47 km to an inlet, catches declined slightly (Z-score of catch change mean = -0.1 SD units) . The third partition suggested that croaker increased more (mean = 0.9 SD units) at 14 stations (of 21 stations > 47 km from an inlet) that were less than 62 km from an inlet (Figure 32). Another partition suggested that increased Z-scores occurred at 22 stations closer than 23 km from an inlet, among the 44 stations closer than 47 km to an inlet. Finally, of the 14 stations between 47 and 62 km from an inlet, seven stations that had bottom temperatures greater than 25 °C and also had high Z-scores of change in catch (mean = 1.7 SD units). Thus, for Atlantic croaker, the CART analysis suggests than land use changes exceeding 54% are associated with declines in catches (low Z-scores), but that stations between 47 and 62 km from inlets with temperatures greater than 25 °C had increasing catches (greatest Z-scores) between 1980 and 2004. 102 Figure 36. A Classification and Regression Tree (CART) analysis of the standardized Z-score of change in catch of Atlantic croaker between 1980 and 2000. Predictor variables included in CART are depth in meters (DEPTH_M), distance to nearest inlet in km (DIS_INLE), bottom salinity (BSALIN), bottom temperature (BTEMP), number of NPDES sites in the watershed (NPDES), total human population in year 2000 (POP_2000) and overall change in land use as a percentage of catchment area (PLU_CHAN). 103 Atlantic menhaden CART analysis The first partition of the CART indicated that Atlantic menhaden’s catches increased slightly (mean = 0.1 SD units) at the 66 stations that had bottom water temperatures > 24 °C (Figure 37). The second partition of the 66 stations indicated that the Z-score of change in catch increased at 20 stations (mean = 0.4 SD units) where the distance to an inlet was less than 24 km; at the 46 stations that were greater than 47 km to an inlet, catches declined slightly (Z-score of catch change mean = -0.1 SD units) . Percentage land use change was not a significant predictor variable in the CART analysis for menhaden. 104 Figure 37. A Classification and Regression Tree (CART) analysis of the standardized Z-score of change in catch of Atlantic menhaden between 1980 and 2000. Predictor variables included in CART are depth in meters (DEPTH_M), distance to nearest inlet in km (DIS_INLE), bottom salinity (BSALIN), bottom temperature(BTEMP), number of NPDES sites in the watershed (NPDES), total human population in year 2000 (POP_2000) and overall change in land use as a percentage of catchment area (PLU_CHAN). 105 Pinfish CART analysis The first partition of the CART indicated that pinfish’s catches increased (mean = 0.43 zscore units) at the 27 stations that had temperature < 24.8 °C (Figure 38). If temperatures were greater than this, the Z-score of catch change became negative (mean = -0.28 Z-score units at 44 stations). The second partition of these 27 stations indicated that the Z-score of change in catch increased at 7 stations (mean = 1.5 Z-score units) where the population in 2000 was less than 2 persons/km2 and the temperature was less than 24.8 °C; if population exceeded this threshold, catches remained close to the 1980-1984 level (catch change mean = 0.09 Z-score units at 20 stations). The third partition suggested that if the 2000 US Census population exceeded 2 people per km2, pinfish increased (mean = 1.08 Z-score units) at 5 stations with temperatures > 24. 7 °C (Figure 38). Thus, the CART analysis shows that pinfish increases 1980-2004 were associated with stations with temperatures < 24.8 °C and low human populations in the surrounding watershed during that period, or at temperatures > 24.7 C if the human populations were higher than 2 persons/km2. 106 Figure 38. A Classification and Regression Tree (CART) analysis of the standardized z-score of change in catch of pinfish between 1980 and 2000. Predictor variables included in CART are depth in meters (DEPTH_M), distance to nearest inlet in km (DIS_INLE), bottom salinity (BSALIN), bottom temperature(BTEMP), number of NPDES sites in the watershed, total human population in year 2000 (POP_2000) and overall change in land use as a percentage of catchment area (PLU_CHAN). 107 Brown shrimp CART analysis The first partition of the CART indicated that brown shrimp catches increased (mean = 0.3 SD units) at the 50 stations that were > 21 km to the nearest inlet (Figure 39). If a station were closer to an inlet than this, the z-score of catch change became negative (mean = -0.7 SD units at 21 stations, Figure 39). In addition, a second partition within this group of 21 stations close to inlets suggested that there were relatively more shrimp (but still a declining number, mean z-score= -0.3 SD) at 16 stations where the water temperature was not greater than 25 °C at the time of capture. The second partition of the 50 stations that were distant from inlets indicated that the z-score of change in catch increased at 6 stations (mean = 1.7 SD units) where the bottom water salinity >14 ppt; if the salinity was less than this threshold, catches declined (zscore of catch change mean = 0.1 SD units at 44 stations). The third partition within the 44 stations with salinity < 14 ppt suggested that brown shrimp increased (mean = 0.6 SD units) at 14 stations that had depths less than 0.9 m. The percentage land use change in the surrounding watershed during 1980-2000 was not a significant factor for brown shrimp catches. Thus, brown shrimp increases were associated with great distances from inlets, shallow water, and bottom salinity greater than 14 ppt. 108 Brown shrimp, Farfantepenaeus aztectus Decision Tree MEAN = 0 SD = 1 N = 71 DIS_INLE < 20.91 MEAN = -0.7 SD = 1.1 N = 21 MEAN = 0.3 SD = 0.8 N = 50 PLU_8090 < 0.51 MEAN = -1 SD = 1.1 N = 16 BSALIN < 13.60 MEAN = 0.1 SD = 0.9 N=5 MEAN = 0.1 SD = 0.5 N = 44 BTEMP < 25.01 MEAN = -1.7 MEAN = -0.6 SD = 0.3 SD = 1.6 N= 6 N = 10 MEAN = 1.7 SD = 1.1 N=6 DEPTH < 0.96 MEAN = -0.1 SD = 0.3 MEAN = 0.6 SD = 0.5 N = 31 N = 13 Figure 39. A Classification and Regression Tree (CART) analysis of the standardized z-score of change in catch of Brown shrimp between 1980 and 2000. Predictor variables included in CART are depth in meters (DEPTH_M), distance to nearest inlet in km (DIS_INLE), bottom salinity (BSALIN), bottom temperature (BTEMP), number of NPDES sites in the watershed (NPDES), total human population in year 2000 (POP_2000) and overall change in land use as a percentage of catchment area (PLU_CHAN). 109 Summary of the CART results According to CART results (Table 19), distance from an inlet, bottom salinity, bottom water temperature, percent land use change within a catchment surrounding the station, and station depth are the factors that most influence these species. Table 19. Summary of partitions of the CART tree for change in catch at 71 NCDMF stations for the six target species (1= first CART tree split, based on the highest amount of variation explained in z-score of change in catch 1989-2004, 2 = second CART tree split, etc.). Species Spot Southern flounder Atlantic croaker Atlantic menhaden Pinfish Brown shrimp Bottom temperature ( oC ) Bottom salinity (ppt) NPDES (# permits) Distance from an inlet Depth (km) (m) 1,3 1 2 Popula- % Land use tion in change 2000 2 3 4 1 1,3 2 2 1 2,3 2 2 1 3 The number of NPDES permits in the watershed were not associated with changes in catch for any species. Each species responded in somewhat different ways to the changes in land use and other factors in the study period (1980-2004). It appears that southern flounder, Atlantic croaker, and pinfish are the species significantly affected by an increased amount of land use change in the catchment surrounding certain stations, which appeared to cause a decline in abundance relative to other stations. Spot, Atlantic menhaden and brown shrimp did not show an association with increased percentage of land use change in the surrounding watershed and declining catches in the DMF program 120 trawls. 110 Discussion Land use changed significantly between 1980 and 2000 in North Carolina coastal areas near nursery area monitoring stations surveyed by the North Carolina Division of Marine Fisheries (NCDMF Program 120), with most of the change being forested land converted to agriculture, and to a lesser extent, suburban and urban development. In contrast, the abundance of the juvenile fishes and brown shrimp in the estuarine areas immediately adjacent to the changing land area stayed largely unchanged at more than 90% of the stations surveyed. Where changes in abundance happened, however, they were associated with change in land use or human population density. Declines in abundance of Atlantic croaker and southern flounder were associated with increased land use associated with agriculture. Pinfish increased significantly between 1980 and 2004 in watersheds where population density (based in the 2000 Census data) was low. Spot, brown shrimp, and Atlantic menhaden did not show any changes associated with change in land use in the surrounding watersheds. Spot actually showed increased abundance in watersheds where the US Census was > 883 persons/km2 in 2000. This lack of agreement (significant land use changes and the lack of significant fish and shrimp changes) may be due to four possible factors: 1) the change in land use or human population did not exceed a threshold that would cause a detectable change in the water quality, ecosystem quality and function or estuarine species and food webs (Holland et al. 2004); 2) the change in juvenile fish abundance in estuarine areas was not detectable by the NCDMF Program 120 sampling because of inadequate statistical power of the methods used to detect change (1 trawl per station per month in May and June, which means that no replication was available for statistical testing); 3) the impact of land use changes occurred at times other than the NCDMF sampling period of May and June ( greater stress to aquatic organisms would be expected in July 111 through September when hypoxia associated with high water temperatures and benthic respiration would be more likely (Christian et al. 2009)); or 4) there may be many more factors at play such as varying substrate type (West et al.2000), ocean currents, as well as presence or absence of aquatic vegetation as reported in Ross and Epperly (1985), or changes related to global warming and sea level rise. We tentatively conclude that that there was not a major land use effect on juvenile estuarine fish populations in North Carolina between 1980 and 2004 (cause 1), with some notable exceptions. The exceptions are southern flounder and Atlantic croaker, based on the multivariate CART analyses, which suggest that where excessive land use change (i.e., > 20% of the catchment converted from forested land to other uses between 1980-2004) occurred, estuarine juvenile stages of southern flounder and Atlantic croaker declined. In addition, pinfish increased only where human population density was low in a catchment. We note that these three species (and possibly spot, which declined as the percentage of land use devoted to agriculture increased, although this was change not significant in a linear regression) are all dependent on benthic food resources. These species may be stressed by reduced benthic food availability that persists at certain stations due to summertime hypoxia. In contrast, Atlantic menhaden, the one planktivorous species considered in our analysis, did not show any observable trends in abundance as land use changed in the surrounding watershed. Given the lack of statistical power to such detect long-term changes in estuarine species (Cause 2 above) and the lack of later summer sampling in Program 120 (Cause 3), our analyses may have under-estimated the influence of land use change in North Carolina estuaries. We note that in other estuarine systems [which were focused on blue crabs and mollusks in King et al. (2005), macrobenthic taxa in Holland et al. 2004, and a benthic community Index of Biological 112 Integrity in Bilkovic et al. (2006) in Chesapeake Bay], land use changes have been associated with degradation of benthic estuarine conditions. Runoff from agricultural fields has been demonstrated to increase phytoplankton growth, depress SAV, degrade benthic habitats, and lowering dissolved oxygen in receiving estuarine waters. We expected to see similar effects here in North Carolina coastal areas, especially for Atlantic croaker and Southern flounder, which depend directly or indirectly on benthos. The levels of change in previous studies in Chesapeake Bay suggested that developed (urban) land use would have to exceed 10% of the land area, and North Carolina coastal areas never reached such high levels (except in Onslow County, which had consistently low catches and z-scores of catch change in our analyses). We conclude that our analyses, because it focused on fishes and invertebrates that are mobile, highly variable spatially and temporally, and operating at a higher trophic level than organisms measured in these other studies, may have been insensitive to the level of land use changes that North Carolina experienced between 1980-2004. It is possible that all three causes contributed to this insensitivity. In order to address the concern raised with possible cause 2 above, we recommend that a power analysis of the existing Program 120 data sets (i.e., what amount of change in abundance could be detected, given the past sampling effort and variability in the Program 120 data?). Our initial analysis indicates that changes in land use must be large (> 20%) to show a detectable influence on the Program 120 dataset, and that a 10% change in land use may not register a strong signal, given the extreme variability in the juvenile stages of fishes and invertebrates. Increased sampling each month at selected stations in the future (more trawls, perhaps 7-10 per station, not simply a single trawl) may improve the statistical power to detect small changes in fish and invertebrate abundance. 113 To address the third possible cause of the insensitivity, and to improve the monitoring of coastal areas in the future, NCDMF should consider adding additional trawling surveys in the late summer (July through September) at selected stations. This increased sampling effort should be applied to selected watershed areas, but not at all 105 Program 120 stations, or even at the subset of 71 stations considered in this study. The rationale for increased temporal sampling of nursery areas is that the stress induced by summer temperatures, due to climate warming , and increased urbanization of the coast, due to current development trends that emphasize waterfront residences along the estuarine shoreline, will continue. A good pre-1990 late-summer baseline Program 120 dataset for comparison with the increased sampling exists for all 71 stations examined here. Thus, in the future, late summer trawling surveys at selected Program 120 stations can be compared to data taken in July – October in 1980-1989 (and data which we did not use in this analysis, because we did not have post-1990 late summer data to compare it with). This sampling may provide additional insight into cause 3 above (late summer stress due to increase benthic community respiration) as a possible consequence of land use change. An additional benefit of this approach would be an opportunity to monitor growth and survival of these species after the peak in recruitment in May and June in watersheds with differing amounts of land use. We would also recommend monitoring of benthic community respiration and ecosystem function (benthic and planktonic communities, similar to work done in the Chesapeake Bay noted earlier) at these times and at different stations with varying levels of landuse alteration. 114 Conclusion Analysis of long-term land use change in coastal watersheds revealed an increase in the amount of land devoted to agricultural use and a decline of forested areas between 1980 and 2000. There were negligible changes in wetlands and a slight increase in developed land for urban and suburban residential uses. The long-term analyses of the Program 120 data indicated that 90% of the stations were unaffected by land use change in the surrounding watershed, and the abundance of Atlantic menhaden, and brown shrimp did not change significantly between 1980 and 2004. Atlantic croaker exhibited a long-term decline in abundance. At some stations, spot, Southern flounder, and pinfish have increased, and a coastwide increase was noted for these three species (based on geometric means and smoothed data plots). Most stations did not show an increase in these species, however, and a few showed declines that can be attributed to land use changes or human population increases. It appears that climate-related or other unexplained factors may be causing an increase of these three species coast-wide. Stations with large scale land use modifications (> 20% area changed, mostly from conversion of forest to agriculture) in the surrounding watershed were associated with long-term declines (negative z-scores of catch change) of some species (Atlantic croaker and southern flounder). Pinfish, while increasing, did not increase if the human population was dense in the surrounding watershed. 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