Monitoring” aims at the assessment Sorafenib cost of the current status of the coastal environment and short term trends, and their (deterministic) short-term forecasts. Such routine analyses and short-term forecasts are required for dealing with all sorts of practical problems such as coastal risk management (coastal flooding and extreme wave conditions), combating ocean pollution (Soomere et al., 2014 and Xi

et al., 2012), search and rescue operations. Similar as with marine spatial planning, monitoring is not a scientific task itself; but, again, the task of monitoring is supported by coastal science in providing methods – in this case, of observations, analysis and prediction. Also, science

is a stakeholder in monitoring efforts as well: Chances to disentangle complex oceanic processes and phenomena are considerably increased if a good state description in space and time is available. For spatial domains and time intervals of practical interest the space–time detailed state of the coastal sea can hardly be determined from observations alone, because a sustainable data acquisition is too expensive. However, amalgamating observations and output of dynamical models enables efficient, consistent and realistic estimations and forecasting of the ocean state (Robinson et al., 1998). selleck The challenge of such an amalgamation, also named data assimilation, is the extraction of the most important information from relatively sparse observations, and the propagation of this information in an optimal way into predictive models accounting for errors in the models and observations. There exist still a number of challenges in coastal ocean data assimilation. Diagnostics and metrics for assessing performance of the coastal assimilation models need further improvements.

Coupling between coastal and open-ocean assimilation systems is still an open problem. Alanine-glyoxylate transaminase Forecasting biogeochemistry state in the coastal ocean, although much asked for, is still in infancy. Treatment of river flows, mixing, bottom roughness and small-scale topography is still an issue. Non-homogeneity in space and time of model error statistics needs further consideration. Of particular importance is the optimal use of non-homogeneous data from different origin and platforms. Another application, which is still under development, is the design of observational networks. In numerical “Observation System Simulation Experiments” (OSSEs) possible monitoring networks can be tested, how accurate and efficient field estimates may become, given a certain number or quality of observing stations (Schulz-Stellenfleth and Stanev, 2010). Such OSSEs prepare the ground for designing sustained coastal ocean observing systems, advance the planning and design targeted scientific coastal observations.

[9, 22 and 23]) and click here once elevated stress levels have subsided. Previous work on intergroup conflict has shown that losing groups might be prevented from using certain areas because of exclusion by winners [9 and 23] or may avoid areas of agonistic interaction if prior experience reliably predicts future conflict [22]. This reduced involvement in agonistic interactions parallels the “loser effect” often found in dyadic contests, whereby individuals become less likely to escalate future conflicts following a defeat (reviewed in [24]). Even where loser effects are not found, previous fights can reduce aggression and discourage home-range overlap [25 and 26]. Here, however, we found the opposite

effect: the woodhoopoe groups in our study used roosts in zones of conflict more often following intergroup conflicts, especially conflicts that were lost, and arrived at roost sites earlier on such occasions. This greater usage may represent defense of a limiting resource; as in many other species [ 23, 27 and 28], there is a risk that highly productive or important parts of a territory will be annexed by successful rival groups [ 29]. Despite this risk, groups may continue to use other roosts outside the zone of conflict if they provide greater thermoregulatory benefits [ 13], provide more protection from predators

[ 29], or are less likely selleck inhibitor to accumulate water on rainy nights [ 30], or if switching roosts is important for minimizing the buildup of parasites [ 31]. Occasions when members of the same group roost in different

places probably reflect unresolved between-individual conflicts of interest over group decisions [32 and 33]. Our results suggest that an earlier conflict with a rival group enhances the likelihood that a consensus will be reached later on, i.e., that all group members roost together. Since all adult woodhoopoe group members contribute see more to the majority of IGIs [1] and the outcome of extended IGIs is strongly determined by relative group size [15], an increased need for collective defense may override within-group disagreements about roost site. Previous work on the factors influencing group fissions has focused on environmental variability and uncertainty, as well as within-group factors such as individual energetic state, the social relationships between group members, and the ways in which information is gathered and shared [34, 35 and 36]. Our study suggests that external factors—in this case, intergroup conflict—also play an important role and should be considered in future work on consensus decision-making. Extended intergroup conflicts appear to cause short-term increases in stress, which may be responsible for previously documented changes in allopreening and other behavior in the immediate aftermath [7 and 37].

Shuanggen Jin (Shanghai Astronomical Observatory CAS, China) ■ Dr Danijela Joksimovic (Institute of Marine Biology, Kotor, Montenegro) ■ Dr Juan Junoy (Universidad de Alcalá, Spain)

■ Dr Genrik S. Karabashev (P. P. Shirshov Institute of Oceanology RAS, Moscow, Russia) ■ Dr Bengt Karlson (Swedish Meteorological and Hydrological Institute (SMHI), Gothenburg, Sweden) ■ Dr Monika Kędra (Institute of Oceanology PAS, Sopot, Poland ) ■ Dr Agnieszka Kijewska (Institute of Oceanology PAS, Sopot, Poland ) ■ Dr Are Kont (Tallinn University, Estonia) ■ Dr Oleg V. Kopelevich (P. P. Shirshov selleck screening library Institute of Oceanology RAS, Moscow, Russia) ■ Dr Matthew S. Kornis (Smithsonian Environmental Research Center, Edgewater, USA) ■ Dr Vladimir E. Kostylev (Natural Resources, Dartmouth, Canada) ■ Prof. Grażyna Kowalewska (Institute of Oceanology PAS, Sopot, Vemurafenib in vivo Poland ) ■ Dr Marek Kowalewski (University of Gdańsk, Poland) ■ Prof. Adam Krężel (University of Gdańsk, Poland ) ■ Dr Adam Kubicki (Senckenberg am Meer, Wilhelmshaven, Germany) ■ Prof. Natalia Kuczyńska-Kippen (Adam Mickiewicz

University, Poznań, Poland ) ■ Prof. Ewa Kulczykowska (Institute of Oceanology PAS, Sopot, Poland ) ■ Dr Jolanta Kuśmierczyk-Michulec (Netherlands Organization for Applied Scientific Research (TNO), The Hague, The Netherlands) ■ Dr Jaan Laanemets (Tallinn University of Technology, Estonia) ■ Dr Troels Laier (Geological Survey of Denmark and Greenland (GEUS), Copenhagen, Denmark ) ■ Prof. Timothy Leighton (University of Southampton, United Kingdom) ■ Dr Thomas Leipe (Baltic Sea Research Institute, Warnemünde, Germany) ■ Dr Elżbieta Łysiak-Pastuszak (Institute of Meteorology and Water Management, Gdynia, Poland ) ■ Prof. Artur Magnuszewski (Warsaw University, Poland ) ■ Dr Wojciech Majewski Chlormezanone (Institute of Paleobiology PAS, Warszawa, Poland ) ■ Prof. Richard Manasseh (University of Melbourne, Australia) ■ Prof. Roman Marks (University of Szczecin, Poland ) ■ Prof. Stanisław R. Massel (Institute of Oceanology PAS, Sopot, Poland ) ■ Dr Mauro Mazzola (National Research Council, Bologna, Italy) ■ Dr David McKee (University of Strathclyde, Glasgow, United Kingdom) ■ Prof. Mirosław Miętus (University

of Gdańsk, Poland ) Leonardo K. Miyashita (University of São Paulo, Brazil ) ■ Prof. Jacek Namieśnik (Gdańsk University of Technology, Poland ) ■ Dr Leo Nykjaer (Institute for Environment and Sustainability, Joint Research Centre of the European Commission, Ispra, Italy) ■ Dr J. Pablo Ortiz de Galisteo (Meteorological State Agency, Valladolid, Spain) ■ Prof. Ilia Ostrovsky (Israel Oceanographic and Limnological Research, Migdal, Israel ) ■ Prof. Marianna Pastuszak (National Marine Fisheries Research Institute, Gdynia, Poland ) ■ Prof. Ksenia Paz■ Dro (Institute of Oceanology PAS, Sopot, Poland ) ■ Prof. Janusz Pempkowiak (Institute of Oceanology PAS, Sopot, Poland ) ■ Prof. Vladimir Pešić (University of Montenegro, Podgorica, Montenegro) ■ Prof.

The comparison was done click here at locations of oceanographic monitoring stations that characterize open sea conditions of the corresponding sub-basins (Figure 2). The results of the comparison do not differ significantly when instead of a single grid point the average of several contiguous grid points is considered. As the resolution of the grid on which the SMHI observations are interpolated is rather coarse and as observations over the sea are sparse (only a few stations are located on islands), RCA3-ERA40 model results are not necessarily worse than SMHI data. We focused on the analysis of the mean seasonal cycles at these stations, the interannual variability as expressed by the mean seasonal cycles of the corresponding standard deviations

and on maps of the entire Baltic Sea area showing seasonal mean atmospheric and oceanic surface variables. The quantitative assessment http://www.selleckchem.com/products/Y-27632.html of atmospheric surface fields is based upon mean biases of atmospheric surface variables at the five selected monitoring stations (Figure 2). We concentrated on variables that are necessary to force an ocean model, i.e. 2 m air temperature, 2 m specific humidity, SLP, adjusted wind speed, total cloudiness and precipitation. Figure 5 shows the mean seasonal cycles

and their variability of 2 m air temperature, SLP, adjusted 10 m wind speed, 2 m specific humidity, total cloudiness and precipitation over the Gotland Deep, characterizing open sea conditions of the eastern Gotland Basin (see Figure 2). Qualitatively similar results were found in the other sub-basins. Further, Figures 6 and 7 show maps of winter mean SLP and of winter and summer mean 2 m air temperature for the entire Baltic Sea area respectively. The mean biases of five

selected variables at five selected monitoring stations (Figure 2) are listed in Tables 3 to 7. We found very good agreement between RCA3-ERA40 model results and the SMHI Morin Hydrate data for 2 m air temperature, SLP, cloudiness and precipitation (Figures 5 to 7 and Tables 3 to 7). Also, the horizontal distributions for SLP (Figure 6) and 2 m air temperature (Figure 7) in the RCA3-ERA40 simulation are close to the gridded observations. However, in winter RCA3 simulated land-sea temperature gradients are larger than observed values. In addition, simulated air temperatures over the sea are about 1°C higher in winter and about 1°C lower in summer than in the observations. Further, the interannual variability of the 2 m air temperature is smaller in the RCA3-ERA40 than in the SMHI data. These results could be explained by biases in the observational data set, because the SMHI data contain only observations from land. The mean adjusted wind speed and its interannual variability are smaller in the RCA3-ERA40 than in the SMHI data (Figure 5). The largest annual mean biases are found in the northern Baltic Sea, where the simulated mean wind speed is underestimated by about 30% compared to the mean 10 m wind speed calculated from observations (Table 5).

49; 95% CI, 0.30–0.83; p < 0.01) and LOS (mean difference −2.22; 95% CI, −2.99 to −1.45; p < 0.01). There was no statistically significant reduction in noninfectious complications (OR = 0.81; 95% CI, 0.53–1.23; p = 0.32) or wound infections (OR = 0.69; 95% CI, 0.43–1.10; p = 0.12) (Fig. 3). This meta-analysis demonstrates no significant difference in effect of preoperative IN as compared with standard ONS on postoperative clinical outcomes. Given the high costs, poor palatability, and limited retail

availability of IN products, standard ONS can be a reasonable preoperative alternative. Standard ONS are inexpensive, widely available, and manufactured by multiple vendors in a variety of flavors to suite various tastes. Given the heterogeneity of the Alectinib research buy existing IN literature, the precise role of preoperative IN has not been clearly defined. Our results suggest that preoperative standard ONS is similar to IN. The literature for postoperative IN is much stronger. Postoperative IN has been demonstrated in many trials and several meta-analyses to reduce infectious complications, ventilator

days, and anastomotic leaks.4, 24, 25, 26, 27, 28 and 29 The theoretical grounding for IN is strong, particularly in concert with an early enteral feeding algorithm.30 Arginine, one of the key components of an IN strategy, is rapidly depleted in surgery and after major metabolic stresses.6 Supplementation can promote cell growth and differentiation and microvascular perfusion in these patients. Omega-3 fatty acids in several Pexidartinib research buy Janus kinase (JAK) perioperative randomized trials have been demonstrated to modulate proinflammatory and anti-inflammatory mediators in the heart, gut, liver, and in tumor tissue.31, 32, 33 and 34 Antioxidants are typically the other key ingredient

in IN products. Preoperative antioxidants have been shown to increase serum and tissue antioxidant levels, but the clinical benefit is unclear.35 Because these are combination products, it is challenging to sort out the effects of the various ingredients. The literature suggests the synergism of effects by combining distinct immune-modulating nutrients, especially arginine and fish oil. Several other investigators have performed meta-analyses examining various aspects of perioperative IN. Existing literature has often blurred the lines between preoperative, postoperative, and perioperative (pre- and post-) regimens.36 Many preoperative IN studies do not use isocaloric or isonitrogenous controls.37 Only one preoperative trial has ever demonstrated a statistically significant reduction in infectious complications when IN is compared with an isocaloric, isonitrogenous control oral supplement.11 This trial and two others without isonitrogenous controls also published by the same group in the same year are responsible for much of the signal of benefit detected in multiple previously published meta-analyses.

Dose response curves were measured in triplicate, and controls (1 nM dihydrotestosterone (DHT) Protein Tyrosine Kinase inhibitor and 0.1% ethanol, respectively) were repeated 6-fold. Measurement of luciferase activity was performed in cellular crude extracts using a Synergy HT plate reader from BioTek (Bad Friedrichshall, Germany). Cells were lysed in situ using 50 μl of lysis buffer (0.1 M tris–acetate, 2 mM EDTA, and 1% triton-x, pH 7.8), shaking the plate moderately for 20 min at room temperature. Following cellular lysis 150 μl

of luciferase buffer (25 mM glycylglycine, 15 mM MgCl2 and 4 mM EGTA, 1 mM DTT, 1 mM ATP, pH 7.8) and 50 μl of luciferin solution (25 mM glycylglycine, 15 mM MgCl2 and 4 mM EGTA, 0.2 mM luciferin, pH 7.8) were added automatically to each well in order to measure luminescence. All values were corrected for the mean of the negative control and then related to the positive control which was set to 100%. Cell line HeLa9903 was obtained from the JCRB (JCRB-No. 1318). These cells contain stable expression constructs for human ERα and firefly luciferase, respectively. The Daporinad latter is under transcriptional control of five ERE promoter elements from the vitellogenin gene. The transcription of ERα was confirmed by RT-PCR, as was the absence of AR-transcripts (Fig. S1). The assay was performed according

to the OECD test guideline TG455 (OECD, 2009) as follows. Cells were cultivated in phenol red free MEM containing 10% (v/v) of charcoal stripped FCS at 37 °C in an atmosphere with 5% CO2. For the actual assay cells were seeded into white 96-well polystyrene plates at a concentration of 104 cells per 100 μl and well (Costar/Corning, Amsterdam, Netherlands). Test substances were added 3 h after seeding by adding 50 μl of triple concentrated substance stocks to each well. As before dose response curves for treated samples were measured Acesulfame Potassium in triplicate, while controls (1 nM E2 or 0.1% ethanol, respectively) were repeated 6-fold. After 24 h of stimulation, cells were washed with PBS and then lysed using 50 μl

of lysis buffer and moderate shaking for 20 min at room temperature. Subsequent measurement of luciferase activity was performed analogous to the aforedescribed androgen reporter gene assay. All values were corrected for the mean of the negative controls and then related to the positive controls set as 100%. Cell line MCF-7 was obtained from the ATCC (ATCC-No. HTB-22) and checked with RT-PCR for transcription of ER, AR, GPR30 and AhR (Fig. S1). Cells were routinely passaged in RPMI 1640 medium containing 10% FCS (v/v), 100 U/ml Penicillin and 100 μg/ml streptomycin and grown at 37 °C in an atmosphere with 5% CO2. Prior to the actual assays the cells were transferred into hormone-free medium (phenol red free RPMI 1640 with 5% of charcoal stripped FCS).

Rice yield reductions from drought in rainfed areas range from 20 to 100%. Similarly, salt stress is the second most important abiotic stress limiting rice productivity, particularly in coastal areas and some inland rice fields. It is estimated that 20–50% of the irrigated rice lands are somewhat salt-affected [9]. Frequently, drought goes hand in hand with salinity in many areas of Asia where irrigation is used to reduce soil salt in rice paddy fields. For instance, reduced fresh water in irrigation often induces secondary salinization and aggravates the effects of salinity. Alternatively, secondary salinization worsens the effects of drought on rice. To achieve

high yield (HY) and yield stability through breeding, breeders have to develop high yielding rice varieties with significantly improved tolerances Buparlisib supplier to drought and salinity. Challenges then arise from the fact that HY, drought tolerance (DT) and salt tolerance (ST) are all complex traits controlled by polygenes, possible negative associations of rice DT or ST with HY, and different genetic and physiological mechanisms of the same traits at different developmental stages [10], [11] and [12]. Epacadostat supplier In addition,

selection of the right parental lines as donors for target traits has been difficult in real breeding programs. For instance, many rice landraces have good levels of DT and ST, but are low yielding [11]. Genetic drag is another major concern to breeders when they are making decisions in choosing landraces as trait

donors, particularly when the conventional pedigree breeding method is used [13]. While commonly used to improve single highly heritable traits, backcross (BC) breeding and strong phenotypic selection have been proven to be effective for improving single complex traits, particularly abiotic stress tolerances in rice [14], [15] and [16]. However, when aiming at improving multiple complex traits using phenotypic selection in a real BC breeding program, breeders are facing several important and tricky issues regarding what selection strategy should be used. This is particularly true when breeders have to deal with trait selection in two contrasting environments — the normal summer PAK5 crop season(s) in the target environments (TEs) and short-day winter nurseries of the tropical climate in Hainan, in order to speed up the breeding process. Thus, it remains unclear to most breeders as to what traits or trait combinations should be selected in each of the breeding environments. In particular, in what order and what environments, should different target traits be selected to achieve the best overall genetic gain within the shortest time, when multiple complex target traits have to be improved. In this study, we tried to answer these questions by presenting results from an effort for simultaneously improving HY, DT and ST of rice using introgression breeding.

These model descriptions enable the above quantum yields Φfl(z) and Φph(z) to be estimated GKT137831 cell line from the three main environmental parameters governing phytoplankton growth in the sea: basin trophicity, assumed to be

the surface concentration of chlorophyll a, Ca(0); the light conditions in the sea, the index of which are values of the irradiance PAR(z) at various depths; and the temperature temp(z) at different depths. These models are based on empirical material collected in the surface layer of waters, i.e. from the surface down to a depth of ca 60 m. This is equivalent to the water masses in roughly half the euphotic zone in basins with Ca(0) < 1 mg m−3, and almost the whole of the euphotic zone or even transgressing it in other basins. The measurements were carried out in basins of different trophicity and at temperatures ranging from ca 5°C to ca 30°C. We can therefore assume that the relationships are practically universal: to a good approximation they quantitatively describe the processes of photosynthesis and the natural fluorescence

of phytoplankton in any ocean or sea basin. The modelling of the yields of heat processes presented in this work is based on the same principles as the above models of fluorescence and photosynthesis. The appropriately modified assumptions of this modelling are as follows: • Assumption 1: The model quantum yields of the heat production ΦH(z) at particular

Epigenetic inhibitor depths in the sea are complementary to the unity of the sum of the quantum yields of photosynthesis Φph(z) and fluorescence Φfl(z), as emerges from equation (1). The set of equations, derived from assumptions 1–4, describing the models of the dependences of the quantum yield of heat production in the sea on environmental factors, is given in Table 1. where Ca(0) – total chlorophyll a concentration in the surface water layer [mg m− 3], The mathematical description of the relationship between the quantum yields of processes of the deactivation of phytoplankton pigment excitation energy new and environmental factors, presented in this paper (see (2), (3) and (4) and Table 1), enables their variability under different conditions in the water column to be tracked down to a depth of ca 60 m. On this basis Figure 1 illustrates the dependences of the quantum yields of all three sets of processes by which excited states in the molecules of all phytoplankton pigments are dissipated on the PAR irradiance in different trophic types of water. Apart from the dependence of the yield ΦH ( Figure 1b), the figure also shows the dependence of the quantum yield of fluorescence Φfl ( Figure 1a) and the quantum yield of photosynthesis Φph ( Figure 1c). In order to compare the strongly differentiated ranges of variability of these three yields, their values are presented on a logarithmic scale.

, 2011, Zhou et al., 2008 and Costa et al., 2004). As regards the mechanism by which BDNF protect the brain against cerebral ischemia, a chronic increase in BDNF levels increases the number of GABAergic synapses (Hong et al.,

2008), and enhances the likelihood of GABA release (Baldelli et al., 2005). Therefore, a chronic increase in BDNF levels in the brain can act as a neuroprotectant by increasing GABA release during ischemia. Regarding differential efficacy among the treated groups, a medium dose Roxadustat mouse of AGL alone – a dose equivalent to the standard dose for treatment of human DM-2 – displayed an evident reduction in volumes of infarcted lesions. Administration of a DPP-4 inhibitor, sitagliptin, with an excessive dose (100 mg/kg/day, i.e. 50–100 times larger than the effective dose used for human DM-2) for 12 weeks, paradoxically increased tau phosphorylation

in the Etoposide cell line hippocampus of DM-2 rats (Kim et al., 2012). It has also been shown that excessive BDNF levels impair learning and memory (Nakajo et al., 2008 and Yanamoto et al., 2008). Although the mechanism is unknown, excessive doses may be ineffective or unsafe when DPP-4 inhibitors are used as neuroprotectants or a neurotrophins. Although AGL treatment for three weeks did not induce significant weight loss in normal mice (p=0.117), increased BDNF in the brain has the ability to normalize excessive appetite and obesity ( Tsao et al., 2007 and Nakagawa et

al., 2003). Further investigations check are needed to clarify whether AGL treatment may be a good choice for the risk reduction of ischemic stroke in individuals who have obesity. In summary, AGL might be useful as a neuroprotectant, or an enhancer of BDNF production in the brain, aiming to halt or minimize brain injury due to first-ever or recurrent ischemic stroke. This protocol of study was approved by the Animal Care and Use Committee of the NCVC. Every effort was made to minimize both the number of animals used and their suffering. In the assessment of infarcted lesions, BDNF levels in the brain or rCBF, sample sizes were calculated to detect a 30–35% alteration with 95% confidence considering the corresponding mean and the standard deviation (S.D.) in our previous studies (Yuan et al., 2009). We used computer-generated randomization schedules for the randomization of experimental animals. By using our three-vessel occlusion (3VO)-technique for the induction of temporary focal ischemia, there was no need to make selection criteria and exclude animals (Yanamoto et al., 2003). The induction of ischemia and the assessment of volumes of infarcted lesions or neurological deficits were performed by a trained neurosurgeon who was blind to the treatment.

The Relate statistic, which reflects the relationship between the similarity matrices of living and dead assemblages was significant (p = 0.01),

although Rho = 0.563. The species that were most responsible for the similarity within each of the study areas generally reflect the dominant species. The SIMPER analysis of the live assemblages of the two study areas shows that St Helena Bay samples showed a similarity of 45% as a result of A. parkinsoniana, Buliminella eleganitissima, elongated bolivinids, Rosalina globularis and E. articulatum ( Fig. 3). Table Bay (60.61% similarity) samples were characterised by E. articulatum, C. lobatulus, R. globularis, Miliolinella subrotunda and Q. seminulum. The average dissimilarity between the two study areas was 68.7% which was mainly a result of the differences in the average abundance of A. parkinsoniana, PI3K inhibitor M. subrotunda, Q. seminulum and E. articulatum. The richness of samples from TB (14 ± 0.5) was significantly

greater than in SHB (9 ± 0.5) (p < 0.0001; F (1, 113) = 33.87). Patterns in taxon diversity were similar to those of richness: H′ being significantly (p < 0.0001; F (1, 113) = 36.92) lower in SHB than TB (1.69 ± 0.06 and 2.17 ± 0.04, respectively). The abundance of foraminifera, however were not significantly different. The pipeline sites of SHB had a significantly lower species Nabilone richness (p = 0.0001; F (1, 66) = 46.53), diversity (p = 0.001;

F (1, 66) = 15.85) and abundance (p = 0.0001; F (1, 66) = 32.69) than the non-pipeline Epacadostat solubility dmso sites. The pipeline and non-pipeline sites of TB were not significantly different regarding these measures. Significant negative correlations were found between species richness and Cd, Cu and Zn, whilst diversity was negatively correlated with Cd, Cr, Cu, Fe and Zn: abundance was not significantly correlated with any of the measured environmental variables (Supplementary data Table 4a). The inclusion of % N in the analyses did not change the aforementioned results, and it was not significantly correlated with diversity, richness or abundance (Supplementary data Table 4b). The marginal tests of the DISTLM showed significant relationships between the foraminiferal assemblages and the environmental variables (Supplementary data Table 6) and including the % N (Supplementary data Table 7) showed no significant effect. The BEST fit option revealed Cd (20.3%) as an important contributor to the percentage variation within the species data, and that all environmental variables together account for 30.1% of the variation. When including the % N in the analyses it showed that 62% of the variation could be explained by the environmental variables, although, %N was not a significant contributor on its own.