PURPOSE: Establishing efficient, non-destructive sampling methods for clam population assessments. DESCRIPTION: In the Gulf of St. Lawrence (GSL) Management Region, clam assessments are uncommon due to limited resources and the labour-intensive nature of sampling clam beds. Furthermore, clam assessments typically rely on destructive sampling that disturbs sediment and removes animals from their habitat. Establishing efficient, non-destructive sampling methods for clam population assessments can reduce the impact of scientific sampling on these habitats and provide for more efficient monitoring. In this study, we tested the idea that visually observing siphon holes on the sediment surface could predict the presence, number, and size of soft-shell clams across different sites in the southern GSL. Siphon holes reasonably predicted the presence, number, and size/biomass of soft-shell clams in most, but not all, sites. Thus, in many habitats in the GSL, siphon holes can be used for population assessments, providing a powerful tool to enhance Science advice to fisheries managers. Data was collected at the following sites: * Maisonnette, Parc Maisonnette, Maisonnette, New Brunswick, Canada * Kouchibouguac, Loggiecroft wharf, Kouchibouguac National Park, New Brunswick, Canada * Shemogue, Amos Point Road, Little Shemogue, New Brunswick, Canada * Powell's Cove, Powell's Point Provincial Park, Little Harbour, Nova Scotia, Canada PARAMETERS COLLECTED: - Clam abundance - Clam biomass (total sample) - Clam size (length, weight) - Siphon hole abundance - Siphon hole size - Siphon hole characterization (i.e., identification of actual clam based on shape) - Seawater temperature - Sediment grain size - Sediment organic content (%) - Sediment relative moisture content (%) NOTES ON QUALITY CONTROL: Original data entry by Jillian Hunt and/or Isabelle Brennan. Data checked and validated prior to analysis by Jeff Clements. Data further checked and validated prior to publication by Amélie Robichaud. PHYSICAL SAMPLE DETAILS: No physical samples retained. - Clam samples returned back to original habitat after measuring and weighing in the field. - Sediment core samples stored in walk-in freezer and discarded after processing and analysis. SAMPLING METHODS: i. Identifying, counting, weighing, and measuring (with calipers) clams ii. Identifying, counting, and measuring (with calipers) clam siphon holes iii. Seawater temperature monitoring via data loggers iv. Sediment grain size, organic content, and moisture content analysis USE LIMITATION: To ensure scientific integrity and appropriate use of the data, we would encourage you to contact the data custodian.

Using existing habitat datasets and natural-history traits, we created a Habitat Suitability Index (HSI) model in ArcGIS to determine the distribution of suitable habitat for harvested clams in Tillamook Bay. Existing habitat datasets were used to interpolate value estimates throughout the bay for the four input habitat variables used in the model (sediment % fines, bathymetry, salinity, and burrowing shrimp presence). Natural history traits (derived from literature) were then used to assign binary suitability values to each habitat variable for each species. The suitability sum of these variable layers then produced an overall HSI value of 0-4 (low-high). To validate this model, we used existing bivalve (presence/absence) data to calculate presence probabilities. Included in this dataset are these bivalve data, along with the habitat estimates and suitability values produced by our model. This dataset is associated with the following publication: Lewis, N., E. Fox, and T. DeWitt. Estimating the distribution of harvested estuarine bivalves with natural-history-based habitat suitability models... ESTUARINE, COASTAL AND SHELF SCIENCE. Elsevier Science Ltd, New York, NY, USA, 219: 453-472, (2019).

이 데이터는 해양수산부에서 어초 구조물(바다목장, 바다숲, 해중림 등 수산생물의 산란과 서식지 조성 등을 위해 설치한 구조물)의 설치 현황 제공을 위해 수집한 데이터입니다.본 데이터의 주요 내용은 어초의 공간정보 일련번호, 해양공간 객체번호, 어초관리일자와 어초 측정수심 정보로 구성되어 있습니다.본 데이터는 어초 설치 현황을 체계적으로 파악하여 수산자원 조성 효과 분석, 해양 생태계 복원사업 평가, 어업 조정 방안 마련, 설치 위치 및 수심 정보를 활용한 선박 행행계획 수립 지원 등의 업무에 활용될 수 있습니다.

Using existing habitat datasets and natural-history traits, we created a Habitat Suitability Index (HSI) model in ArcGIS to determine the distribution of suitable habitat for harvested clams in Yaquina Bay. Existing habitat datasets were used to interpolate value estimates throughout the bay for the four input habitat variables used in the model (sediment % fines, bathymetry, salinity, and burrowing shrimp presence). Natural history traits (derived from literature) were then used to assign binary suitability values to each habitat variable for each species. The suitability sum of these variable layers then produced an overall HSI value of 0-4 (low-high). To validate this model, we used existing bivalve (presence/absence) data to calculate presence probabilities. Included in this dataset are these bivalve data, along with the habitat estimates and suitability values produced by our model. This dataset is associated with the following publication: Lewis, N., E. Fox, and T. DeWitt. Estimating the distribution of harvested estuarine bivalves with natural-history-based habitat suitability models... ESTUARINE, COASTAL AND SHELF SCIENCE. Elsevier Science Ltd, New York, NY, USA, 219: 453-472, (2019).

Preliminary hard and soft seafloor substrate map derived from an unsupervised classification of multibeam backscatter and bathymetry derivatives at Swains Island, Territory of American Samoa, USA. The dataset was created from gridded (40 m cell size) multibeam bathymetry derivatives collected aboard R/V AHI, and NOAA ship Hi'ialakai; 2 scales of bathymetric variance and bathymetric rugosity. Backscatter data were from a 300 kHz Simrad EM300 and a 240 kHz Reson 8101 sonar, gridded at 5 m. Very limited seafloor photographs for groundtruthing are available for Swains Island and therefore no supervised classification was performed and we are unable to visually or empirically evaluate the accuracy of the unsupervised classification seafloor substrate map. However, in locations such French Frigate Shoals, NWHI and Tutuila, American Samoa, where ground truth data are available, the unsupervised classification method is a robust predictor of substrate type in similar depth ranges and seafloor environments. Since groundtruthing was not used to validate the unsupervised classification at Swains Island extreme caution should be used when examining these data to locate habitat of biological significance. The map should be used in conjunction with bathymetric derivatives such as rugosity, slope, and Bathymetric Position Index (BPI).

Preliminary hard and soft seafloor substrate map derived from an unsupervised classification of multibeam backscatter and bathymety derivatives at Ofu and Olosega Islands, Territory of American Samoa, USA . The dataset was derived using Reson 8101 backscatter data, bathymetric variance and bathymetric rugosity. The sonar frequency is 240 kHz for the Reson 8101 backscatter data, which were resampled to a 5 m grid cell size prior to the classification. Limited seafloor photographs for groundtruthing are available for Ofu and Olosega Islands and therefore no supervised classification was performed and we are unable to visually or empirically evaluate the accuracy of the unsupervised classification seafloor substrate map. However, in locations such French Frigate Shoals, NWHI and Tutuila, American Samoa, where ground truth data are available, the unsupervised classification method is a robust predictor of substrate type in similar depth ranges and seafloor environments.

Preliminary hard and soft seafloor substrate map derived from an unsupervised classification of multibeam backscatter and bathymety derivatives at Ta'u Island, Territory of American Samoa, USA . The dataset was derived using Reson 8101 backscatter data, bathymetric variance and bathymetric rugosity. The sonar frequency is 240 kHz for the Reson 8101 backscatter data, which were resampled to a 5 m grid cell size prior to the classification. Limited seafloor photographs for groundtruthing are available for Ta'u and therefore no supervised classification was performed and we are unable to visually or empirically evaluate the accuracy of the unsupervised classification seafloor substrate map. However, in locations such French Frigate Shoals, NWHI and Tutuila, American Samoa, where ground truth data are available, the unsupervised classification method is a robust predictor of substrate type in similar depth ranges and seafloor environments.

We combined taxonomically detailed surveys of macroinvertebrates and birds with targeted sampling of regionally important focal taxa including surf zone fishes, kelp and sand crabs; physical and biological metrics of the habitat; and activities of people on the beaches to develop an integrated understanding of the important ecosystem processes structuring northern California beaches. Our baseline study program consisted of the following components: Nine monthly surveys (from September 2014 through May 2015) of birds, macrophyte wrack (detached marine vegetation such as seaweeds, surfgrasses and seagrasses that are deposited on the beach), human use and physical characteristics of 12 sandy beaches and their adjacent surf zones (6 MPA and 6 reference sites); A one-time, comprehensive survey of intertidal invertebrate biodiversity during summer 2014 of the 12 focal sandy beaches; A comprehensive baseline survey of redtail surfperch (Amphistichus rhodoterus), including diet analysis, at nine sandy beaches (4 MPA, 5 reference sites) over two years; Targeted monthly surveys of sand crabs (Emerita analoga) done concurrently with redtail surfperch surveys on three long beaches for three months; and Sampling of night smelt (Spirinchus starksi) spawning aggregations from nine beaches (5 MPA, 4 reference) during spring and summer over two years.

This is a MD iMAP hosted service. Find more information at http://imap.maryland.gov. The Sensitive Areas Scallop dataset shows areas within Maryland's Coastal Bays that are significant for Scalloppopulations. The assumption is that an area's importance to Scallops is related to the density of SAV beds. The data set is based on 1999 Submerged Aquatic Vegetation (SAV) coverage dataset that was produced at the Virginia Institute of Marine Science from aerial photography flown in 1999. It is a portion of the full SAV dataset that extends back to 1971. Areas with a bed density of 1 and 2 (rank=5) were more important than those areas with a density of 3 (rank = 3).Feature Service Link:http://geodata.md.gov/imap/rest/services/Biota/MD_Shellfish/FeatureServer/7 ADDITIONAL LICENSE TERMS: The Spatial Data and the information therein (collectively "the Data") is provided "as is" without warranty of any kind either expressed implied or statutory. The user assumes the entire risk as to quality and performance of the Data. No guarantee of accuracy is granted nor is any responsibility for reliance thereon assumed. In no event shall the State of Maryland be liable for direct indirect incidental consequential or special damages of any kind. The State of Maryland does not accept liability for any damages or misrepresentation caused by inaccuracies in the Data or as a result to changes to the Data nor is there responsibility assumed to maintain the Data in any manner or form. The Data can be freely distributed as long as the metadata entry is not modified or deleted. Any data derived from the Data must acknowledge the State of Maryland in the metadata.