Data compilation on the biological response to ocean acidification: environmental and experimental context of data sets and related literature

The exponential growth of studies on the biological response to ocean acidification over the last few decades has generated a large amount of data. To facilitate data comparison, a data compilation hosted at the data publisher PANGAEA was initiated in 2008 and is updated on a regular basis (doi:10.1594/PANGAEA.149999). By January 2015, a total of 581 data sets (over 4 000 000 data points) from 539 papers had been archived. Here we present the developments of this data compilation five years since its first description by Nisumaa et al. (2010). Most of study sites from which data archived are still in the Northern Hemisphere and the number of archived data from studies from the Southern Hemisphere and polar oceans are still relatively low. Data from 60 studies that investigated the response of a mix of organisms or natural communities were all added after 2010, indicating a welcomed shift from the study of individual organisms to communities and ecosystems. The initial imbalance of considerably more data archived on calcification and primary production than on other processes has improved. There is also a clear tendency towards more data archived from multifactorial studies after 2010. For easier and more effective access to ocean acidification data, the ocean acidification community is strongly encouraged to contribute to the data archiving effort, and help develop standard vocabularies describing the variables and define best practices for archiving ocean acidification data.

(Table 1, Page 1046-1047) Chemical composition of iron-manganese concretions from the Indian Ocean recovered by the R/V Vityaz and the R/V Ob

The data given in this and previous communications is insufficient to assess the quantitative role of these supplementary sources in the Indian Ocean, but they do not rule out their local significance. Elucidation of this problem requires further data on the characteristics of the composition and structure of nodules in various different metallogenic regions of the ocean floor. A study of the distribution of ore elements in nodules both depthwise and over the area of the floor together with compilation of the first schematic maps based on the results of analyses of samples from 54 stations) enables us to give a more precise empirical relation between the Mn, Fe, Ni, Cu, and Co contents in Indian Ocean nodules, the manganese ratio and the values of the oxidation potential, which vary regularly with depth. This in turn also enables us to confirm that formation of nodules completes the prolonged process of deposition of ore components from ocean waters, and the complex physico-chemical transformations of sediments in the bottom layer. Microprobe investigation of ore rinds revealed the nonuniform distribution of a num¬ber of elements within them, owing to the capacity of particles of hydrated oxides of manganese and iron to adsorb various elements. High concentration of individual elements is correlated with local sectors of the ore rinds, in which the presence of todorokite, in particular, has been noted. The appearance of this mineral apparently requires elevated Ca, Mg, Na, and K concentrations, because the stable crystalline phase of this specific mineral form of the psilomelane group may be formed when these cations are incorporated into a lattice of the delta-MnO2 type.

Data to Decision in a Dynamic Ocean: Robust Species Distribution Models and Spatial Decision Frameworks

Human use of the oceans is increasingly in conflict with conservation of endangered species. Methods for managing the spatial and temporal placement of industries such as military, fishing, transportation and offshore energy, have historically been post hoc; i.e. the time and place of human activity is often already determined before assessment of environmental impacts. In this dissertation, I build robust species distribution models in two case study areas, US Atlantic (Best et al. 2012) and British Columbia (Best et al. 2015), predicting presence and abundance respectively, from scientific surveys. These models are then applied to novel decision frameworks for preemptively suggesting optimal placement of human activities in space and time to minimize ecological impacts: siting for offshore wind energy development, and routing ships to minimize risk of striking whales. Both decision frameworks relate the tradeoff between conservation risk and industry profit with synchronized variable and map views as online spatial decision support systems.

For siting offshore wind energy development (OWED) in the U.S. Atlantic (chapter 4), bird density maps are combined across species with weights of OWED sensitivity to collision and displacement and 10 km2 sites are compared against OWED profitability based on average annual wind speed at 90m hub heights and distance to transmission grid. A spatial decision support system enables toggling between the map and tradeoff plot views by site. A selected site can be inspected for sensitivity to a cetaceans throughout the year, so as to capture months of the year which minimize episodic impacts of pre-operational activities such as seismic airgun surveying and pile driving.

Routing ships to avoid whale strikes (chapter 5) can be similarly viewed as a tradeoff, but is a different problem spatially. A cumulative cost surface is generated from density surface maps and conservation status of cetaceans, before applying as a resistance surface to calculate least-cost routes between start and end locations, i.e. ports and entrance locations to study areas. Varying a multiplier to the cost surface enables calculation of multiple routes with different costs to conservation of cetaceans versus cost to transportation industry, measured as distance. Similar to the siting chapter, a spatial decisions support system enables toggling between the map and tradeoff plot view of proposed routes. The user can also input arbitrary start and end locations to calculate the tradeoff on the fly.

Essential to the input of these decision frameworks are distributions of the species. The two preceding chapters comprise species distribution models from two case study areas, U.S. Atlantic (chapter 2) and British Columbia (chapter 3), predicting presence and density, respectively. Although density is preferred to estimate potential biological removal, per Marine Mammal Protection Act requirements in the U.S., all the necessary parameters, especially distance and angle of observation, are less readily available across publicly mined datasets.

In the case of predicting cetacean presence in the U.S. Atlantic (chapter 2), I extracted datasets from the online OBIS-SEAMAP geo-database, and integrated scientific surveys conducted by ship (n=36) and aircraft (n=16), weighting a Generalized Additive Model by minutes surveyed within space-time grid cells to harmonize effort between the two survey platforms. For each of 16 cetacean species guilds, I predicted the probability of occurrence from static environmental variables (water depth, distance to shore, distance to continental shelf break) and time-varying conditions (monthly sea-surface temperature). To generate maps of presence vs. absence, Receiver Operator Characteristic (ROC) curves were used to define the optimal threshold that minimizes false positive and false negative error rates. I integrated model outputs, including tables (species in guilds, input surveys) and plots (fit of environmental variables, ROC curve), into an online spatial decision support system, allowing for easy navigation of models by taxon, region, season, and data provider.

For predicting cetacean density within the inner waters of British Columbia (chapter 3), I calculated density from systematic, line-transect marine mammal surveys over multiple years and seasons (summer 2004, 2005, 2008, and spring/autumn 2007) conducted by Raincoast Conservation Foundation. Abundance estimates were calculated using two different methods: Conventional Distance Sampling (CDS) and Density Surface Modelling (DSM). CDS generates a single density estimate for each stratum, whereas DSM explicitly models spatial variation and offers potential for greater precision by incorporating environmental predictors. Although DSM yields a more relevant product for the purposes of marine spatial planning, CDS has proven to be useful in cases where there are fewer observations available for seasonal and inter-annual comparison, particularly for the scarcely observed elephant seal. Abundance estimates are provided on a stratum-specific basis. Steller sea lions and harbour seals are further differentiated by ‘hauled out’ and ‘in water’. This analysis updates previous estimates (Williams & Thomas 2007) by including additional years of effort, providing greater spatial precision with the DSM method over CDS, novel reporting for spring and autumn seasons (rather than summer alone), and providing new abundance estimates for Steller sea lion and northern elephant seal. In addition to providing a baseline of marine mammal abundance and distribution, against which future changes can be compared, this information offers the opportunity to assess the risks posed to marine mammals by existing and emerging threats, such as fisheries bycatch, ship strikes, and increased oil spill and ocean noise issues associated with increases of container ship and oil tanker traffic in British Columbia’s continental shelf waters.

Starting with marine animal observations at specific coordinates and times, I combine these data with environmental data, often satellite derived, to produce seascape predictions generalizable in space and time. These habitat-based models enable prediction of encounter rates and, in the case of density surface models, abundance that can then be applied to management scenarios. Specific human activities, OWED and shipping, are then compared within a tradeoff decision support framework, enabling interchangeable map and tradeoff plot views. These products make complex processes transparent for gaming conservation, industry and stakeholders towards optimal marine spatial management, fundamental to the tenets of marine spatial planning, ecosystem-based management and dynamic ocean management.

Biomarker distributions in surface sediments from the Central Arctic Ocean and adjacent marginal seas

Records of the spatial and temporal variability of Arctic Ocean sea ice are of significance for understanding the causes of the dramatic decrease in Arctic sea-ice cover of recent years. In this context, the newly developed sea-ice proxy IP25, a mono-unsaturated highly branched isoprenoid alkene with 25 carbon atoms biosynthesized specifically by sea-ice associated diatoms and only found in Arctic and sub-Arctic marine sediments, has been used to reconstruct the recent spatial sea-ice distribution. The phytoplankton biomarkers 24S-brassicasterol and dinosterol were determined alongside IP25 to distinguish ice-free or permanent ice conditions, and to estimate the sea-ice conditions semi-quantitatively by means of the phytoplankton-IP25 index (PIP25). Within our study, for the first time a comprehensive data set of these biomarkers was produced using fresh and deep-frozen surface sediment samples from the Central Arctic Ocean proper (>80°N latitude) characterised by a permanent ice cover today and recently obtained surface sediment samples from the Chukchi Plateau/Basin partly covered by perennial sea ice. In addition, published and new data from other Arctic and sub-Arctic regions were added to generate overview distribution maps of IP25 and phytoplankton biomarkers across major parts of the modern Arctic Ocean. These comprehensive biomarker data indicate perennial sea-ice cover in the Central Arctic, ice-free conditions in the Barents Sea and variable sea-ice situations in other marginal seas. The low but more than zero values of biomarkers in the Central Arctic supported the low in-situ productivity there. The PIP25 index values reflect modern sea-ice conditions better than IP25 alone and show a positive correlation with spring/summer sea ice. When calculating and interpreting PIP25 index as a (semi-quantitative) proxy for reconstructions of present and past Arctic sea-ice conditions from different Arctic/sub-Arctic areas, information of the source of phytoplankton biomarkers and the possible presence of allochthonous biomarkers is needed, and the records of the individual biomarkers always should be considered as well.

Near real time surface ocean velocity, Tyrrhenian Sea and Ligurian Sea in September 2016

The data set consists of maps of total velocity of the surface current in the North-Western Tyrrhenian Sea and Ligurian Sea averaged over a time interval of 1 hour around the cardinal hour. Surface ocean velocities estimated by HF Radar are representative of the upper 0.3-2.5 meters of the ocean. Total velocities are derived using least square fit that maps radial velocities measured from individual sites onto a cartesian grid. The final product is a map of the horizontal components of the ocean currents on a regular grid in the area of overlap of two or more radar stations.

Near real time surface ocean velocity in the Bay of Biscay - Basque Country from Mai to September 2016

The data set consists of maps of total velocity of the surface current in the Southeastern Bay of Biscay averaged over a time interval of 1 hour around the cardinal hour. Surface ocean velocities estimated by this HF Radar(4.65 MHz) are representative of the upper 2-3 meters of the ocean. The main objective of near real time processing is to produce the best product from available data at the time of processing. Total velocities are derived using least square fit that maps radial velocities measured from individual sites onto a cartesian grid. The final product is a map of the horizontal components of the ocean currents on a regular grid in the area of overlap of two or more radar stations.

Map of Nassau Heights in the village of Newtown, Queens County, L.I. : formerly the Brettonnierre Farm, now the Caldwall Farm : about 4 miles from Peck Slip Ferry in Williamsburgh /

Cadastral survey map of proposed streets and lots, showing dimensions, lot numbers, and existing buildings.

Map of the village of Jamaica, Queens County, N.Y. : showing every lot and building.

Cadastral map showing lot lines, property-owners' names, and planned streets.

Map of the village of Youngstown in Niagara County : being lots nos. 1 & 2 of the New York state reserved lands along the E. side of the Niagara River /

Cadastral map showing streets, lots, and lot numbers.

New Jersey; county subdivisions, townships and places. 1970.

Scale ca. 1:250,000.

On the effect of Arabian Sea and Indian Ocean sea surface temperature anomaly on the monsoon rainfall of south-east of Iran

Southeast region of the country has hot and dry weather which causes to happen heavy rainfall in short time period of warm seasons and to occur river flooding. These precipitations are influenced by monsoon system of India ocean. In these thesis, It was tried to evaluate the relation between thermal anomaly of sea surface in India ocean and Arab sea which effects on southeast monsoon precipitations of Iran, For evaluation of this happening in southeast, data were collected from 7 synoptic observation stations of Bandar Abbas, Minab, Kerman , Bam, Chabahar, Iranshahr, Zahedan and 17 rain gauge stations during June to September of each year from 1980 to 2010. Rainy days were determine and then some information about synoptic circulation models, maps of average pressure of sea surface, geopotential height of 700hP surface, geopotential height of 500hP surface, temperature of 850 hPa surface, humidity of 700 hPa surface, vertical velocity of 700 hPa surface, vertical velocity of 500 hP and humidity of 2 meters height for 6 systems were extracted from NCEP/NCAR website for evaluation. By evaluation of these systems it was determined that the monsoon low pressure system tab brings needed humidity of these precipitations to this region from India ocean and Arab sea with a vast circulation. It is seen that warm air pool locates on Iran and cold air pool locates on west of India at 800 hPa surface. In a rainy day this warm air transfers to high latitudes and influences the temperature trough of southeast cold air pool of the country. In the middle surfaces of 700 and 500 hPa, the connection between low height system above India and low height system above the higher latitudes causes the low height system above India to be strength and developed. By evaluation of humidity at 2 meters height and 700 hPa surface we observe that humidity Increases in the southeast region. With penetrating of the low height system of India above the 700 and 500 hPa surfaces of southeast of Iran, the value of negative omega (Rising vertical velocity) is increased. In the second pace, it was shown the evaluation of how the correlation between sea surface temperature anomaly in India Ocean and Arab sea influences southeast monsoon precipitation of Iran. For this purpose the data of water surface temperature anomaly of Arab sea and India ocean, the data of precipitation anomaly of 7 synoptic stations , mentioned above, and correlation coefficient among the data of precipitation anomaly and water surface temperature anomaly of Arab Sea, east and west of India ocean were calculated. In conclusion it was shown that the maximum correlation coefficient of precipitation anomaly had belonged to India Ocean in June and no meaningful correlation was resulted in July among precipitation anomaly and sea surface temperature anomaly for three regions, which were evaluated.

Land use and zoning map, Jackson Brook watershed, Morris County, New Jersey /

Shows "1988 land use."

General soil map, Jackson Brook watershed, Morris County, New Jersey /

"January 1988."

General soil map, Burlington County, New Jersey /

"June 1970."

Acidic substrata potential map : [Burlington County, New Jersey] /

"This map estimates the potential of encountering a sulfide bearing geologic substratum beneath the soil."