Abundance and biomass of mesozooplankton from the Romanian littoral during DANUBS_2001 in spring and autumn 2001

The Danubs 2001 dataset contains zooplankton data collected in March, June, September and October 2001 in 11 station allong 5 transect in front of the Romanian littoral. Zooplankton sampling was undertaken at 11 stations where samples were collected using a Juday closing net in the 0-10, 10-25, and 25-50m layer (depending also on the water masses). The dataset includes samples analysed for mesozooplankton species composition and abundance. Sampling volume was estimated by multiplying the mouth area with the wire length. Taxon-specific mesozooplankton abundance was count under microscope. Total abundance is the sum of the counted individuals. Total biomass Fodder, Rotifera , Ctenophora and Noctiluca was estimated using a tabel with wet weight for each species an stage.

Abundance of mesozooplankton from the S-IT4 cruise in the Western Mediterranean Sea in March and April 2008

The "SESAME_IT4_ZooAbundance_0-50-100m_SZN" dataset contains data of mesozooplankton species composition and abundance (ind./m**3) from samples collected in the Western Mediterranean in the early spring of 2008 (20 March-5 April) during the SESAME-WP2 cruise IT4. Samples were collected by vertical tows with a closing WP2 net (56 cm diameter, 200 µm mesh size) in the following depth layers: 100-200 m, 50-100 m, 0-50 m. Sampling was always performed in light hours. A flowmeter was applied to the mouth of the net, however, due to its malfunctioning, the volume of filtered seawater was calculated by multiplying the the area by the height of the sampled layer from winch readings. After collection, each sample was split in two halves (1/2) after careful mixing with graduated beakers. Half sample was immediately fixed and preserved in a formaldehyde-seawater solution (4% final concentration) for species composition and abundance. The other half sample was kept fresh for biomass measurements (data already submitted to SESAME database in different files). Here, only the zooplankton abundance of samples in the upper layers 0-50 m and 50-100 m are presented. The abundance data of the samples in the layer 50-100 m will be submitted later in a separate file. The volume of filtered seawater was estimated by multiplying the the area by the height of the sampled layer from winch readings. Identification and counts of specimens were performed on aliquots (1/20-1/5) of the fixed sample or on the total sample (half of the original sample) by using a graduate large-bore pipette. Copepods were identified to the species level and separated into females, males and juveniles (copepodites). All other taxa were identified at the species level when possible, or at higher taxonomic levels. Taxonomic identification was done according to the most relevant and updated taxonomic literature. Total mesozooplankton abundance was computed as sum of all specific abundances determined as explained above.

Abundance and biomass of zooplankton from the Romanian littoral collected in May, July and September 1998

The SHELF 1998 dataset contains zooplankton data collected in May, July and September 19978 allong 5 transect in front of the Romanian littoral. Zooplankton sampling was undertaken using a Juday closing net in the 0-10, 10-25, and 25-50m layer (depending also on the water masses). The dataset includes samples analysed for mesozooplankton species composition and abundance. Sampling volume was estimated by multiplying the mouth area with the wire length. Taxon-specific mesozooplankton abundance was count under microscope. Total abundance is the sum of the counted individuals. Total biomass Fodder, Rotifera , Ctenophora and Noctiluca was estimated using a tabel with wet weight for each species an stage.

Abundance and biomass of zooplankton collected monthly from January 1979 to december 1979 in front of Constanta city, Black Sea

The Est Constanta 1979 dataset contains zooplankton data collected monthly from January 1979 to december 1979 allong a 5 station transect in front of the city Constanta (44°10'N, 28°41.5'E - EC1; 44°10'N, 28°47'E - EC2; 44°10'N, 28°54'E - EC3; 44°10'N, 29°08'E - EC4; 44°10'N, 29°22'E - EC5). Zooplankton sampling was undertaken at 5 stations where samples were collected using a Juday closing net in the 0-10, 10-25, 25-50m layer (depending also on the water masses). The dataset includes samples analysed for mesozooplankton species composition and abundance. Sampling volume was estimated by multiplying the mouth area with the wire length. Taxon-specific mesozooplankton abundance was count under microscope. Total abundance is the sum of the counted individuals. Total biomass Fodder, Rotifera , Ctenophora and Noctiluca was estimated using a tabel with wet weight for each species an stage.

Abundance and biomass of zooplankton from the Romanian littoral collected in April, June and September 1999

The SHELF 1999 dataset contains zooplankton data collected in April, June and September 1999 allong 5 transect in front of the Romanian littoral. Zooplankton sampling was undertaken using a Juday closing net in the 0-10, 10-25, and 25-50m layer (depending also on the water masses). The dataset includes samples analysed for mesozooplankton species composition and abundance. Sampling volume was estimated by multiplying the mouth area with the wire length. Taxon-specific mesozooplankton abundance was count under microscope. Total abundance is the sum of the counted individuals. Total biomass Fodder, Rotifera , Ctenophora and Noctiluca was estimated using a tabel with wet weight for each species an stage.

Dissolved nitrogen in soil solution of the Jena Experiment (Main Experiment, year 2002)

This data set contains measurements of dissolved nitrogen (total dissolved nitrogen: TDN, dissolved organic nitrogen: DON, dissolved ammonium: NH4+, and dissolved nitrate: NO3-) in samples of soil water collected from the main experiment plots of a large grassland biodiversity experiment (the Jena Experiment; see further details below). In the main experiment, 82 grassland plots of 20 x 20 m were established from a pool of 60 species belonging to four functional groups (grasses, legumes, tall and small herbs). In May 2002, varying numbers of plant species from this species pool were sown into the plots to create a gradient of plant species richness (1, 2, 4, 8, 16 and 60 species) and functional richness (1, 2, 3, 4 functional groups). Plots were maintained by bi-annual weeding and mowing. In April 2002 glass suction plates with a diameter of 12 cm, 1 cm thickness and a pore size of 1-1.6 µm (UMS GmbH, Munich, Germany) were installed in depths of 10, 20, 30 and 60 cm to collect soil solution. The sampling bottles were continuously evacuated to a negative pressure between 50 and 350 mbar, such that the suction pressure was about 50 mbar above the actual soil water tension. Thus, only the soil leachate was collected. Cumulative soil solution was sampled biweekly and analyzed for nitrate (NO3-) and ammonium (NH4+) concentrations with a continuous flow analyzer (CFA, Skalar, Breda, The Netherlands). Nitrate was analyzed photometrically after reduction to NO2- and reaction with sulfanilamide and naphthylethylenediamine-dihydrochloride to an azo-dye. Our NO3- concentrations contained an unknown contribution of NO2- that is expected to be small. Simultaneously to the NO3- analysis, NH4+ was determined photometrically as 5-aminosalicylate after a modified Berthelot reaction. The detection limits of NO3- and NH4+ were 0.02 and 0.03 mg N L-1, respectively. Total dissolved N in soil solution was analyzed by oxidation with K2S2O8 followed by reduction to NO2- as described above for NO3-. Dissolved organic N (DON) concentrations in soil solution were calculated as the difference between TDN and the sum of mineral N (NO3- + NH4+). In 5% of the samples, TDN was equal to or smaller than mineral N. In these cases, DON was assumed to be zero.

Dissolved nitrogen in soil solution of the Jena Experiment (Main Experiment, year 2003)

This data set contains measurements of dissolved nitrogen (total dissolved nitrogen: TDN, dissolved organic nitrogen: DON, dissolved ammonium: NH4+, and dissolved nitrate: NO3-) in samples of soil water collected from the main experiment plots of a large grassland biodiversity experiment (the Jena Experiment; see further details below). In the main experiment, 82 grassland plots of 20 x 20 m were established from a pool of 60 species belonging to four functional groups (grasses, legumes, tall and small herbs). In May 2002, varying numbers of plant species from this species pool were sown into the plots to create a gradient of plant species richness (1, 2, 4, 8, 16 and 60 species) and functional richness (1, 2, 3, 4 functional groups). Plots were maintained by bi-annual weeding and mowing. In April 2002 glass suction plates with a diameter of 12 cm, 1 cm thickness and a pore size of 1-1.6 µm (UMS GmbH, Munich, Germany) were installed in depths of 10, 20, 30 and 60 cm to collect soil solution. The sampling bottles were continuously evacuated to a negative pressure between 50 and 350 mbar, such that the suction pressure was about 50 mbar above the actual soil water tension. Thus, only the soil leachate was collected. Cumulative soil solution was sampled biweekly and analyzed for nitrate (NO3-) and ammonium (NH4+) concentrations with a continuous flow analyzer (CFA, Skalar, Breda, The Netherlands). Nitrate was analyzed photometrically after reduction to NO2- and reaction with sulfanilamide and naphthylethylenediamine-dihydrochloride to an azo-dye. Our NO3- concentrations contained an unknown contribution of NO2- that is expected to be small. Simultaneously to the NO3- analysis, NH4+ was determined photometrically as 5-aminosalicylate after a modified Berthelot reaction. The detection limits of NO3- and NH4+ were 0.02 and 0.03 mg N L-1, respectively. Total dissolved N in soil solution was analyzed by oxidation with K2S2O8 followed by reduction to NO2- as described above for NO3-. Dissolved organic N (DON) concentrations in soil solution were calculated as the difference between TDN and the sum of mineral N (NO3- + NH4+). In 5% of the samples, TDN was equal to or smaller than mineral N. In these cases, DON was assumed to be zero.

Mineral nitrogen from KCl extractions of soil from the Jena Experiment (Main Experiment up to 30cm depth, year 2003)

As an estimate of plant-available N, this data set contains measurements of inorganic nitrogen (NO3-N and NH4-N, the sum of which is termed mineral N or Nmin) determined by extraction with 1 M KCl solution of soil samples from the main experiment plots of a large grassland biodiversity experiment (the Jena Experiment; see further details below). In the main experiment, 82 grassland plots of 20 x 20 m were established from a pool of 60 species belonging to four functional groups (grasses, legumes, tall and small herbs). In May 2002, varying numbers of plant species from this species pool were sown into the plots to create a gradient of plant species richness (1, 2, 4, 8, 16 and 60 species) and functional richness (1, 2, 3, 4 functional groups). Plots were maintained by bi-annual weeding and mowing. Soil sampling and analysis: Five soil cores (diameter 0.01 m) were taken at a depth of 0 to 0.15 m and 0.15 to 0.3 m of the mineral soil from each of the experimental plots in March, June, and October 2003. Samples of the soil cores per plot were pooled during each sampling campaign. NO3-N and NH4-N concentrations were determined by extraction of soil samples with 1 M KCl solution and were measured in the soil extract with a Continuous Flow Analyzer (CFA, Skalar, Breda, Netherlands).

Pollen analysis of soil samples from the A.D. 79 level. Boscoreale, Oplontis, and Pompeii

Fifty samples of Roman time soil preserved under the thick ash layer of the A.D.79 eruption of Mt Vesuvius were studied by pollen analysis: 33 samples from a former vineyard surrounding a Villa Rustica at Boscoreale (excavation site 40 x 50 m), 13 samples taken along the 60 m long swimming pool in the sculpture garden of the Villa of Poppaea at Oplontis, and four samples from the formal garden (12.4 x 17.5 m) of the House of the Gold Bracelet in Pompeii. To avoid contamination with modern pollen all samples were taken immediately after uncovering a new portion of the A.D. 79 soil. For comparison also samples of modern Italian soils were studied. Using standard methods for pollen preparation the pollen content of 15 of the archaeological samples proved to be too little to reach a pollen sum of more than 100 grains. The pollen spectra of these samples are not shown in the pollen tables. (Flotation with a sodium tungstate solution, Na2WO4, D = 2.05, following treatment with HCl and NaOH would probably have given a somewhat better result. This method was, however, not available as too expensive at that time.) Although the archaeological samples were taken a few meters apart their pollen values differ very much from one sample to the other. E.g., at Boscoreale (SW quarter). the pollen values of Pinus range from 1.5 to 54.5% resp. from 1 to 244 pine pollen grains per 1 gram of soil, the extremes even found under pine trees. Vitis pollen was present in 7 of the 11 vineyard samples from Boscoreale (NE quarter) only. Although a maximum of 21.7% is reached, the values of Vitis are mostly below 1.5%. Even the values of common weeds differ very much, not only at Boscoreale, but also at the other two sites. The pollen concentration values show similar variations: 3 to 3053 grains and spores were found in 1 g of soil. The mean value (290) is much less than the number of pollen grains, which would fall on 1 cm2 of soil surface during one year. In contrast, the pollen and spore concentrations of the recent soil samples, treated in exactly the same manner, range from 9313 to almost 80000 grains per 1 g of soil. Evidently most of the Roman time pollen has disappeared since its deposition, the reasons not being clear. Not even species which are known to have been cultivated in the garden of Oplontis, like Citrus and Nerium, plant species with easily distinguishable pollen grains, could be traced by pollen analysis. The loss of most of the pollen grains originally contained in the soil prohibits any detailed interpretation of the Pompeian pollen data. The pollen counts merely name plant species which grew in the region, but not necessarily on the excavated plots.

Chlorophyll pigments of the central Arctic Ocean during POLARSTERN cruise ARK-XXVII/3 from August-September 2012

During the RV Polarstern cruise ARK-XXVII/3 to the Arctic Ocean in summer 2012, when sea ice declined to a record minimum bottom, sediments were collected with a TV-guided multicorer at stations in the Nansen and Amundsen basin. Chlorophyll a and phaeopigments were extracted from sediments with acetone using three replicates per station. The concentrations in the acidified supernatants were measured with a Turner Trilogy fluorometer (Boetius and Damm, 1998). The sum of chlorophyll a and phaeopigments is expressed as chloroplast pigment equivalents (CPE) (Thiel, 1982).

Abundance of mesozooplankton from the S-IT3 cruise in the Sicily Channel in March 2008

The "SESAME_IT3_ZooAbundance_0-50-100m_SZN" dataset contains data of mesozooplankton species composition and abundance (ind. m-3) from samples collected in the Sicily Channel in the early spring of 2008 (17,18 March) during the SESAME-WP2 cruise IT3. Samples were collected by vertical tows with a closing WP2 net (56 cm diameter, 200 µm mesh size) in the following depth layers: 100-200 m, 50-100 m, 0-50 m. Sampling was always performed in light hours with the exception of station S-IT3-03 where zooplankton were collected in dark hours. A flowmeter was applied to the mouth of the net, however, due to its malfunctioning, the volume of filtered seawater was calculated by multiplying the the area by the height of the sampled layer from winch readings. After collection, each sample was split in two halves (1/2) after careful mixing with graduated beakers. Half sample was immediately fixed and preserved in a formaldehyde-seawater solution (4% final concentration) for species composition and abundance. The other half sample was kept fresh for biomass measurements (data already submitted to SESAME database in different files).Here, only the zooplankton abundance of samples in the upper layers 0-50 m and 50-100 m are presented. The abundance data of the samples in the layer 50-100 m will be submitted later in a separate file. The volume of filtered seawater was estimated by multiplying the the area by the height of the sampled layer from winch readings. Identification and counts of specimens were performed on aliquots (1/20-1/5) of the fixed sample or on the total sample (half of the original sample) by using a graduate large-bore pipette. Copepods were identified to the species level and separated into females, males and juveniles (copepodites). All other taxa were identified at the species level when possible, or at higher taxonomic levels. Taxonomic identification was done according to the most relevant and updated taxonomic literature. Total mesozooplankton abundance was computed as sum of all specific abundances determined as explained above.

Chapter 8 Location Choice of Japanese Multinationals: Country Characteristics or Region Characteristics?

In the present global era in which firms choose the location of their plants beyond national borders, location characteristics are important for attracting multinational enterprises (MNEs). The better access to countries with large market is clearly attractive for MNEs. For example, special treatments on tariffs such as the Generalized System of Preferences (GSP) are beneficial for MNEs whose home country does not have such treatments. Not only such country characteristics but also region characteristics (i.e. province-level or city-level ones) matter, particularly in the case that location characteristics differ widely between a nation's regions. The existence of industrial concentration, that is, agglomeration, is a typical regional characteristic. It is with consideration of these country-level and region-level characteristics that MNEs decide their location abroad. A large number of academic studies have investigated in what kinds of countries MNEs locate, i.e. location choice analysis. Employing the usual new economic geography model (i.e. constant elasticity of substitution (CES) utility function, Dixit-Stiglitz monopolistic competition, and ice-berg trade costs), the literature derives the profit function, of which coefficients are estimated using maximum likelihood procedures. Recent studies are as follows: Head, Rise, and Swenson (1999) for Japanese MNEs in the US; Belderbos and Carree (2002) for Japanese MNEs in China; Head and Mayer (2004) for Japanese MNEs in Europe; Disdier and Mayer (2004) for French MNEs in Europe; Castellani and Zanfei (2004) for large MNEs worldwide; Mayer, Mejean, and Nefussi (2007) for French MNEs worldwide; Crozet, Mayer, and Mucchielli (2004) for MNEs in France; and Basile, Castellani, and Zanfei (2008) for MNEs in Europe. At the present time, three main topics can be found in this literature. The first introduces various location elements as independent variables. The above-mentioned new economic geography model usually yields the profit function, which is a function of market size, productive factor prices, price of intermediate goods, and trade costs. As a proxy for the price of intermediate goods, the measure of agglomeration is often used, particularly the number of manufacturing firms. Some studies employ more disaggregated numbers of manufacturing firms, such as the number of manufacturing firms with the same nationality as the firms choosing the location (e.g., Head et al., 1999; Crozet et al., 2004) or the number of firms belonging to the same firm group (e.g., Belderbos and Carree, 2002). As part of trade costs, some investment climate measures have been examined: free trade zones in the US (Head et al., 1999), special economic zones and opening coastal cities in China (Belderbos and Carree, 2002), and Objective 1 structural funds and cohesion funds in Europe (Basile et al., 2008). Second, the validity of proxy variables for location elements is further examined. Head and Mayer (2004) examine the validity of market potential on location choice. They propose the use of two measures: the Harris market potential index (Harris, 1954) and the Krugman-type index used in Redding and Venables (2004). The Harris-type index is simply the sum of distance-weighted real GDP. They employ the Krugman-type market potential index, which is directly derived from the new economic geography model, as it takes into account the extent of competition (i.e. price index) and is constructed using estimators of importing country dummy variables in the well-known gravity equation, as in Redding and Venables (2004). They find that "theory does not pay", in the sense that the Harris market potential outperforms Krugman's market potential in both the magnitude of its coefficient and the fit of the model to be estimated. The third topic explores the substitution of location by examining inclusive values in the nested-logit model. For example, using firm-level data on French investments both in France and abroad over the 1992-2002 period, Mayer et al. (2007) investigate the determinants of location choice and assess empirically whether the domestic economy has been losing attractiveness over the recent period or not. The estimated coefficient for inclusive value is strongly significant and near unity, indicating that the national economy is not different from the rest of the world in terms of substitution patterns. Similarly, Disdier and Mayer (2004) investigate whether French MNEs consider Western and Eastern Europe as two distinct groups of potential host countries by examining the coefficient for the inclusive value in nested-logit estimation. They confirm the relevance of an East-West structure in the country location decision and furthermore show that this relevance decreases over time. The purpose of this paper is to investigate the location choice of Japanese MNEs in Thailand, Cambodia, Laos, Myanmar, and Vietnam, and is closely related to the third topic mentioned above. By examining region-level location choice with the nested-logit model, I investigate the relative importance of not only country characteristics but also region characteristics. Such investigation is invaluable particularly in the case of location choice in those five countries: industrialization remains immature in those countries which have not yet succeeded in attracting enough MNEs, and as a result, it is expected that there are not yet crucial regional variations for MNEs within such a nation, meaning the country characteristics are still relatively important to attract MNEs. To illustrate, in the case of Cambodia and Laos, one of the crucial elements for Japanese MNEs would be that LDC preferential tariff schemes are available for exports from Cambodia and Laos. On the other hand, in the case of Thailand and Vietnam, which have accepted a relatively large number of MNEs and thus raised the extent of regional inequality, regional characteristics such as the existence of agglomeration would become important elements in location choice. Our sample countries seem, therefore, to offer rich variations for analyzing the relative importance between country characteristics and region characteristics. Our empirical strategy has a further advantage. As in the third topic in the location choice literature, the use of the nested-logit model enables us to examine substitution patterns between country-based and region-based location decisions by MNEs in the concerned countries. For example, it is possible to investigate empirically whether Japanese multinational firms consider Thailand/Vietnam and the other three countries as two distinct groups of potential host countries, by examining the inclusive value parameters in nested-logit estimation. In particular, our sample countries all experienced dramatic changes in, for example, economic growth or trade costs reduction during the sample period. Thus, we will find the dramatic dynamics of such substitution patterns. Our rigorous analysis of the relative importance between country characteristics and region characteristics is invaluable from the viewpoint of policy implications. First, while the former characteristics should be improved mainly by central government in each country, there is sometimes room for the improvement of the latter characteristics by even local governments or smaller institutions such as private agencies. Consequently, it becomes important for these smaller institutions to know just how crucial the improvement of region characteristics is for attracting foreign companies. Second, as economies grow, country characteristics become similar among countries. For example, the LCD preferential tariff schemes are available only when a country is less developed. Therefore, it is important particularly for the least developed countries to know what kinds of regional characteristics become important following economic growth; in other words, after their country characteristics become similar to those of the more developed countries. I also incorporate one important characteristic of MNEs, namely, productivity. The well-known Helpman-Melitz-Yeaple model indicates that only firms with higher productivity can afford overseas entry (Helpman et al., 2004). Beyond this argument, there may be some differences in MNEs' productivity among our sample countries and regions. Such differences are important from the viewpoint of "spillover effects" from MNEs, which are one of the most important results for host countries in accepting their entry. The spillover effects are that the presence of inward foreign direct investment (FDI) aises domestic firms' productivity through various channels such as imitation. Such positive effects might be larger in areas with more productive MNEs. Therefore, it becomes important for host countries to know how much productive firms are likely to invest in them. The rest of this paper is organized as follows. Section 2 takes a brief look at the worldwide distribution of Japanese overseas affiliates. Section 3 provides an empirical model to examine their location choice, and lastly, we discuss future works to estimate our model.