Sexual reproduction is critical to the resilience of seagrass beds impacted by habitat degradation or environmental changes, as robust seed banks allow new shoots to establish each year. Reproductive strategies of seagrass beds range on a continuum from strictly annual to perennial, driven by local environmental conditions. We examined the reproductive dynamics of Zostera marina beds at six sites on the Atlantic coast of Canada to characterize how life history strategies are shaped by the surrounding environment. Sites were categorized as wave protected and wave exposed, where protected sites were warm, shallow, with little water movement and muddy sediments, and exposed sites were either shallow or deep, with cooler water and sandy sediments. While mixed life history strategies were evident at all sites, protected eelgrass beds exhibited both the highest and lowest sexual reproductive effort relative to exposed beds. These beds regularly experienced thermal stress, with higher temperature range and extended warm water events relative to exposed beds. The development of reproductive shoots were similar across sites with comparable Growing Degree-days at the beginning and end of anthesis, but the First Flowering Date was earlier at the protected warmer sites relative to exposed sites. With different reproductive shoot density among sites, seed production, seed retention, and seedling recruitment also varied strongly. Only one site, located in a warm, shallow and protected lagoon, contained a mixed life history population with a high reproductive effort (33.7%), strong seed bank, and high seedling establishment. However, a primarily perennial population with the lowest reproductive effort (0.5%) was identified at the warmest site, suggesting that conditions here could not support high sexual reproduction. Robustness of seed banks was strongly linked to reproductive shoot density, although the role of seed retention, germination and seedling survival require further investigation. Our study provides insights into one key aspect of seagrass resilience, and suggests that resilience assessments should include reproductive shoot density to inform their management and conservation.Cite this data: Vercaemer B. and Wong M. Reproductive ecology of Zostera marina L. (eelgrass) across varying environmental conditions. Published: May 2022. Coastal Ecosystems Science Division, Fisheries and Oceans Canada, Dartmouth, N.S. https://open.canada.ca/data/en/dataset/56cfea6f-aeca-47ed-94ab-c519d9e63c91

The purpose of this study was to characterize the kelp bed at Batture-aux-Alouettes, a preferred food source for the green sea urchin (Strongylocentrotus droebachiensis). The green urchin is fished commercially in Quebec and the fishing effort is concentrated on the Batture-aux-Alouettes near Tadoussac, at the mouth of the Saguenay Fjord. The study was conducted in two separate phases in 2018 and 2019. The main objective of this study was to determine the abundance and biomass of the kelp bed at Batture-aux-Alouettes. The first phase, using a stratified random sampling design, was conducted from August 21th to August 24th, 2018. Sampling of two 50 x 50 cm quadrats, separated by a distance of approximately 30 m, was conducted at eleven sites during twelve dives in the eastern section of the Batture-aux-Alouettes to collect kelp for biomass estimation and macroalgal species richness assessment. In the second phase, a total of 429 stations were first sampled between July 15 and 18, 2019 with a camera system dropped in two 50 x 50 cm quadrats. The presence or absence of kelp, percent macroalgal cover, and substrate type were assessed for each photo. As a result of this underwater photographic analysis, 129 of these stations were identified as having a presence of kelp and 88 of these stations had a presence of other algal species. To ensure equal representation of the different depth strata, the stations with kelp were divided into three depth categories: shallow (-1.7 m to 0 m), medium (0 m to 2 m) and deep (2 m to 5 m). Dives were conducted from August 13 to 15, 2019, at ten of these stations using a stratified random sampling design, taking care to ensure a balanced spatial distribution as well as an equal distribution of the different depth strata (four in the shallow, three in the medium, and two in the deep). Sampling of the 50 x 50 cm dive quadrat took place at three different distances spaced 5 m apart from a transect, i.e. at the 3 m (_3m), 8 m (_8m) and 13 m (_13m) mark. If there was little or no kelp in the quadrat, the quadrat sampling could be repeated for up to four quadrats per distance for a total area of 1 m². Two additional quadrats were conducted (_x) at two stations. Biomass assessment was also done via "cookie cutter" sampling (_CC). Divers took the same 50 x 50 cm quadrat and placed it on a selected (i.e., non-random) plot with 100% kelp cover.The three files provided (DarwinCore format) are complementary and are linked by the "eventID" key. The "event_information" file includes generic information about the event, such as date and location. The "additional_information_event_and_occurrence" file includes sample size, protocol and sampling effort. The "taxon_occurrence" file includes the taxonomy of the species observed, identified to the species or lowest possible taxonomic level. To obtain the abundance and biomass assessment of the kelp bed at Batture-aux-Alouettes, contact Rénald Belley (renald.belley@dfo-mpo.gc.ca).For quality control, the organisms were identified in the field fallowing the guide: Chabot, Robert et Anne Rossignol. 2003. Algues et faune du littoral du Saint-Laurent maritime : Guide d'identification. Institut des Sciences de la mer de Rimouski, Rimouski; Pêches et Océans Canada (Institut Maurice-Lamontagne), Mont-Joli. 113 pages. The taxonomy was checked against the World Register of Marine Species (WoRMS) to match recognized standards and using the R obistools and worrms libraries. The WoRMS match was placed in the "scientificNameID" field of the occurrence file. All sample locations were spatially validated. This project was funded by DFO Coastal Environmental Baseline Program under Canada’s Oceans Protection Plan. This initiative aims to acquire environmental baseline data contributing to the characterization of important coastal areas and to support evidence-based assessments and management decisions for preserving marine ecosystems.

Historical finds of Operophtera brumata

Historical finds of Gilpinia hercyniae

This data set includes the locations of all known seabird colonies along the coast of British Columbia, and provides a compilation of the population estimates of seabirds breeding at those colonies since 1980, and historical estimates prior to 1980 for some colonies. It does not include an estimate of the numbers of juvenile birds or non-breeders in the population.The rationale for developing this inventory was the recognized need for a product that could assist with: coastal zone and conservation area planning; emergency response to environmental emergencies and identifying areas of potential interactions between seabirds and anthropogenic activities. In addition, the data used to develop the document provides a baseline to compare with future seabird population estimates in order to measure the impacts of shifts in composition, abundance and/or distribution of prey, and climatic and oceanographic changes. The database is not a substitute for on-site surveys usually required for environmental assessment.Here we present data on the breeding colony population estimates of the 17 species of seabirds (including two storm petrels, three cormorants, one gull, and eight alcids) and one shorebird (Black Oystercatcher Haematopus bachmani) that breed on the coast of British Columbia. Over 5.5 million colonial birds are currently estimated to nest at 649 sites (Rodway et al. 2024). Five species (Cassin's Auklets Ptychoramphus aleuticus, Fork-tailed Storm-petrels (Hydrobates fucatus), Rhinoceros Auklets Cerorhinca monocerata, Ancient Murrelets Synthliboramphus antiquus, and Leach's Storm-petrels (Hydrobates leucorhous), comprise the vast majority of that population, although Black Oystercatchers (Haematopus bachmani), Pigeon Guillemots (Cepphus columba), and Glaucous-winged Gulls (Larus glaucescens), nest at the most sites. Marbled Murrelets (Brachyramphus marmoratus), which nest on the mossy limbs of mature and old-growth trees within the coastal forests, are not included in this database, due to their dispersed nesting habit.The population estimates presented in this database are compiled from the results of several surveys. Many of the seabird breeding colonies in British Columbia have been known for more than 50 years, but because of the remoteness of the sites, visits to them have been rare. The majority of the data are the results of comprehensive inventories of colonial nesting seabirds along the British Columbia coastline conducted between 1974 and 1977 by the British Columbia Provincial Museum (BCPM; now Royal British Columbia Museum ) and between 1980 and 1989 by the Canadian Wildlife Service of Environment and Climate Change Canada (CWS). The goal of the BCPM surveys was to explore the entire BC coast to identify seabird nesting sites. Reliable, quantitative data were gathered on the breeding populations of all surface-nesting seabird species and rough estimates of population sizes were made for burrow-nesting species. The goal of the CWS survey program was to establish baseline estimates of breeding populations of burrow-nesting species in BC using standardized survey techniques to allow future comparisons and monitoring of those populations. During the 1980s, repeat surveys of surface-nesting species were also conducted by CWS in most regions of the BC coast. A few colonies on small remote islands were not visited during those surveys. Therefore, for some colonies the most current population estimates are from the first complete survey of the BC coastline, carried out by the Royal British Columbia Museum in the mid 1970’s.Since 1989, surveys have been conducted by CWS on some alcid, cormorant and gull colonies along the BC coast, and results have been included in the dataset (data entry ongoing). As well as data from CWS surveys, we have attempted to obtain recent data from all other sources including Parks Canada, Transport Canada, the BC Ministry of Forests, Lands, Natural Resource Operations and Rural Development, the City of Vancouver, the Bamfield Marine Station, and the Laskeek Bay Conservation Society. Since 2000, inventories of nesting BlackOystercatchers have been conducted in some regions of the coast by Parks Canada and partners (Gulf Islands National Park Reserve, Pacific Rim National Park Reserve, and Gwaii Haanas National Park Reserve) and results have been included in the dataset (data entry ongoing). A long time series of nesting Black Oystercatcher data collected by Laskeek Bay Conservation Society in the Laskeek Bay area of the East Coast of Moresby Island has also been included in this dataset.

This dataset contains two geotiff layers. The first layer (1) represents the coastwide distribution of Dungeness crab as predicted from a geostatistical model. The model predicts the mean coastwide probability of Dungeness crab detection using trap sampling gear. The second layer (2) represent the uncertainty in those predictions. Detailed descriptions of these data products can be found in Nephin et al. (2023) and the code used to produce them can be found at https://gitlab.com/dfo-msea/dungeness-sdm/.The objectives of this work was to model the habitat of Dungeness crab (_Metacarcinus magister_), a data-limited coastal marine species, to evaluate the efficacy of data integration when making predictions to geographic areas larger than the area covered by any one data source. In British Columbia, Dungeness crab are sampled regionally and sporadically with a variety of sampling gears and survey protocols, making them an ideal case study to investigate whether the integration of disparate surveys can improve habitat predictions. To that aim, we assemble data from dive, trawl, and baited-trap surveys to generate six candidate generalized linear mixed-effect models with spatial random fields. This dataset contains the mean (1) and difference (2) between the Survey-effect and Gear-effect model predictions.

An annual trawl survey is conducted in Southwestern Nova Scotia and the Bay of Fundy to assess the lobster stocks in the area. The survey is conducted with the Northeast Fisheries Science Center Ecosystem Survey Trawl (NEST), a small mesh trawl with a cod end liner, which ensures the capture of various sizes of lobster. The dimensions and location of the trawl are monitored and recorded throughout the tow using an electronic trawl mensuration system. In addition, water temperature and depth are also monitored. The target tow length is 1 kilometer which is tracked using an Olex marine charting system. Vessel crew, DFO science staff and a contracted at sea observer work together to perform required tasks and collect all relevant data. Catch from each tow is separated by species, weighed and counted. Length frequency data is collected on select groundfish and crab species and detailed morphometric data is collected on each lobster.PARAMETERS COLLECTED:Set information and profile - includes set date, time depth and locationCatch summary - weight and number caught of each speciesLength Freqeuncies - completed for up to 100 fish for selected speciesLobster Morphological Data - detailed data collected on each lobsterBottom TemperatureTrawl MetricsNOTES ON QUALITY CONTROL:Data is visually verified and double keypunched. On loading to Oracle, data is run through rigorous automated checks to verify data accuracy and integrity.SAMPLING METHODS:The target tow length is 1 kilometer which is tracked using an Olex marine charting system. Vessel crew, DFO science staff and a contracted at sea observer work together to perform required tasks and collect all relevant data. Catch from each tow is separated by species, weighed and counted. Length frequency data is collected on select groundfish and crab species, detailed morphometric data is collected on each lobster. Bottom temperature and trawl metrics are collected for each tow.CITATION LIST:Denton, Cheryl M.. 2020. Maritimes Region Inshore Lobster Trawl Survey Technical Description. Canadian technical reports of fisheries and aquatic sciences (DFO) 3376.

Historical finds of Pristiphora erichsonii

PURPOSE:Understanding and predicting species range shifts is crucial for conservation amid global warming. This study analyzes life-history traits of four seal species (ringed (Pusa hispida Schreber, 1775), bearded (Erignathus barbatus Pallas, 1811), harp (Pagophilus groenlandicus Erxleben, 1777), and harbour (Phoca vitulina Linnaeus, 1758) seals) in the Canadian Arctic using data from Inuit subsistence harvests. Bearded seals are largest, followed by harp seals, harbour seals, and ringed seals. Seasonal blubber depth patterns show minimal variation in bearded seals, whereas harbour and ringed seals accumulate fat in open-water seasons and use it during ice-covered seasons. Endemic Arctic seals (ringed and bearded) exhibit greater longevity and determinate body growth, reaching maximum size by 5 years, while harbour and harp seals grow indeterminately, physically maturing around 10-15 years. Age of maturation varies, with ringed and harbour seals being more sensitive to environmental fluctuations. Most bearded seals reproduce successfully each year, while ringed seals exhibit more variability in their annual reproductive success. Analysis of isoprenoid lipids in liver tissue indicates that ringed and bearded seals rely on ice-algal production, whereas harp and harbour seals depend on open-water phytoplankton production. Bearded seals appear more specialized and potentially face less competition, while harp seals may adapt better to changing habitats. Despite expected range shifts to higher latitudes, all species exhibit tradeoffs, complicating predictions for the evolving Arctic environment. DESCRIPTION:This dataset contains the data reported in Steven H. Ferguson, Jeff W. Higdon, Brent G. Young, Stephen D. Petersen, Cody G. Carlyle, Ellen V. Lea, Caroline C. Sauvé, Doreen Kohlbach, Aaron T. Fisk, Gregory W. Thiemann, Katie R. N. Florko, Derek C. G. Muir, Charmain D. Hamilton, Magali Houde, Enooyaq Sudlovenick, and David J. Yurkowski. 2024. A comparative analysis of life-history features and adaptive strategies of Arctic and subarctic seal species - who will win the climate change challenge? Canadian Journal of Zoology 2024-0093.R1The data set includes species, location, harvest date, sex, age, standard length, girth, fat depth, teste size, parity status, pregnancy status, corpora lutea (n), corpus albicans (n), follicles (n). This dataset includes raw, unfiltered, and unprocessed historical data provided by harvesters that have not been screened for outliers. Individual users should screen the data for their specific use.Cite these data as:Steven H. Ferguson, Jeff W. Higdon, Brent G. Young, Stephen D. Petersen, Cody G. Carlyle, Ellen V. Lea, Caroline C. Sauvé, Doreen Kohlbach, Aaron T. Fisk, Gregory W. Thiemann, Katie R. N. Florko, Derek C. G. Muir, Charmain D. Hamilton, Magali Houde, Enooyaq Sudlovenick, and David J. Yurkowski. 2024. Arctic and Aquatic Research Division, Fisheries and Oceans Canada, Winnipeg, MB. https://open.canada.ca/data/en/dataset/ea9ff038-8b16-11ef-8cce-55cc7f028297

All island polygons. Islands may overlap as there are islands within islands (e.g., a lake on an island contains an island). GNIS_NAME_1 contains the most atomic name for the island. For example, there are 3797 "Haida Gwaii" islands. If the island has not been named as part of a more specific group or with an individual name, "Haida Gwaii" is the GNIS_NAME_1 value. GNIS_NAME_2 and GNIS_NAME_3 values are null. If the island has a more specific name, "Haida Gwaii" moves to GNIS_NAME_2, and the more atomic name, such as "Moresby Island" is the GNIS_NAME_1. If the island has an individual name, belongs to a group, and is part of Haida Gwaii, the same logic of naming from most to least specific applies. For example, GNIS_NAME_1 = "George Island", GNIS_NAME_2 = "Copper Islands", GNIS_NAME_3 = "Haida Gwaii".