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    Hygienic quality of soil in the Gemer region (Slovakia) and the impact of risk elements contamination on cultivated agricultural products

    SoilContents of risk metals in soilsLands of localities from which soil and plant samples were taken belong to agricultural lands.Soil reaction is one of the factors that most affects the behaviour of heavy metals in soil. Low pH values pose a risk of reduced nutrient intake and increase the availability of heavy metals for plants29,30.The presence of risk elements in the soil was evaluated based on their contents in bioavailable form (mobile forms), determined in soil extracts NH4NO3, and the total contents of risk elements were determined in soil extract by aqua regia (Table 1).Table 1 The contents of risk elements (Cu, Ni, Pb, Cd, Hg, Mn) in soil (mg/kg).Full size tableAccessible heavy metals for plants are those which are present in the soil solution as soluble components or those which are easily dissolved by root exudates31. The highest Cu contents determined in soil extract by NH4NO3, were in the cadastre of Gemerská Poloma (max. 0.390 mg/kg) (Table 1). However, even the highest determined concentration of Cu in its bioavailable form did not exceeded the determined critical value for this element18. Nickel is a beneficial element for plants. Elevated Ni concentrations in soils have a potential negative effect on plants32. Content of bioavailable forms of nickel is lower than the determined critical value in all analysed samples. Cadmium and lead present a risk to agricultural activity in this area. Cadmium in soil is highly bioavailable and has higher mobility in plants compared to other heavy metals. It is easily transported by roots to shoots. In contrast, lead is one of the least mobile heavy metals. It is naturally concentrated in the upper layers of the soil33. The contents of the available forms of cadmium and lead exceed the critical values for these elements. In case of lead, the determined contents are from 0.257 Henckovce to 0.676 Gemerská Poloma. Takáč et al.34 determined in 20 soil samples from the Central Spiš region 7.2–257.6 mg Cu/kg soil and 1.0–84.8 mg Pb/kg in their potentially mobilizable form and 0.4–1.4 mg Cu/kg soil and 4.3–7.1 mg Pb/kg in their mobile form. In comparison with our results, Vilček et al.35 determined a lower content of Cd (0.04), Pb (0.17), Ni (0.15) and higher Cu content (0.48) mg/kg in forms accessible to plants in 16 soil samples from locality Nižná Slaná in the years 2006–2008. However, high concentrations of metals in soil do not necessarily mean the availability of metals for plants36. As a result, extractable Mn is often a better indicator of Mn availability. Mn2+ is generally considered to be bioavailable22. The highest concentration of Mn was measured in soil samples from the cadastre of Nižná Slaná. On the contrary, the lowest concentrations were detected in samples from Gemerská Poloma cadastre, which is the furthest cadastre from the source. No critical limit is set up for manganese according to Slovak legislation, it is not possible to classify these soils as contaminated/uncontaminated. For comparison, the EDTA-extractable content of Mn ranged from 22.7 to 127 mg/kg dry soil (China)29; the mobile concentrations between 0.32 and 202.0 mg/kg and the available concentrations from 5.4 to 126.3 mg/kg (Egypt)37.Based on results of statistical analysis, significant higher content of Cu, Pb and Cd can be stated in samples from Gemerská Poloma cadastre. These soils are classified as gley fluvisols, soils from the other two localities are cambisols (from medium heavy to light) and acid cambisols (Henckovce), cambisols from medium heavy to light and typically acid cambisols (Nižná Slaná). The soil profile of fluvisols is repeatedly disrupted by floods, which often enriches them with a new layer of sludge sediments2.Another method for determination of metal content in soil is mineralisation using aqua regia, which dissolves most of the soil constituents except those strongly bound in silicate minerals. This content is sometimes referred to as pseudototal (determined in aqua regia). In this way, all elements that are likely to become bioavailable in the long term are determined38.Pseudototal contents of risk metals (Table 1) determined in soil extract using aqua regia were higher than their limit value in case of Cu (Gemerská Poloma cadastre), Cd (all cadastres) and Hg (cadastre of Henckovce and Gemerská Poloma).Due to the fact that the hygienic condition of agricultural soils is assessed according to the exceeding of the limit values of at least one risk substance, the monitored plots can be classified as contaminated (Cu  > 60.0, Cd  > 0.7, Hg  > 0.5 mg/kg soil).Manganese is not classified as risk element in Slovak legislation.Tóth et al.39 classified European soils into four categories: (1) no detectable content of HM, (2) the concentration of the investigated element is above the threshold value (Hg 0.5, Cd 1, Cu 100, Pb 60 and Ni 50 mg/kg), but below the lower guideline value (Hg 2, Cd 10, Cu 150, Pb 200 and Ni 100 mg/kg), (3) concentration of one or more element exceeds the lower guideline value but is below the higher guideline value (Hg 5, Cd 20, Cu 200, Pb 750 and Ni 150 mg/kg), (4) samples having concentrations above the higher guideline value.In comparison with the threshold and guideline values, soils in cadastres of Gemerská Poloma (Cu), Henckovce, Nižná Slaná, Gemerská Poloma (Cd, Hg) represent the ecological risk. Threshold and guideline values for Mn were not defined.The Spiš region and the northern part of the Gemer region belong to the most polluted areas in Slovakia in terms of soil contamination due to mining and metallurgical activities that have been carried out here in the past. Soils near the sludge in Nižná Slaná contain 3.17–53.3 (14.2–301, 0.71–20.6, 3.33–177, 12.9–223 and 675–11,510, respectively) mg Cd (Cu, Hg, Ni, Pb and Mn, respectively)/kg of soil14. In loaded area of Dongchuan, (China), contained Cd (Cu, Hg, Ni and Pb, resp.) 0.20–3.57 (45.38–2026, 0.02–0.23, 24.06–95.9 and 6.83–146.6, resp.) mg/kg40. In contrast, in the agricultural area of Punjab of the India, the soil contamination was caused by an excessive use of agrochemicals and polluted irrigation sources. Increased Cu (Pb and Cd) contents were determined in the soil samples: 9.0–48.5 (5.5–9.67 and 0.516–1.58, resp.) mg/kg41.However, in most cases, a large portion of the total element content is not available for immediate uptake by plants. Available forms represent a small proportion of this total content which is potentially available to plants. Availability is affected by many factors, including pH, redox state, macronutrient levels, available water content and temperature29,33,36,38.Indicators of soil contaminationContamination factors and degree of contaminationThe contamination character may be described in a uniform, adequate and standardised way by means of the contamination factor and the degree of contamination. Hakanson24 reported four Contamination degrees of individual metal (({mathrm{C}}_{mathrm{f}}^{mathrm{i}})) – low (({mathrm{C}}_{mathrm{f}}^{mathrm{i}}) < 1), moderate (1 ≤ ({mathrm{C}}_{mathrm{f}}^{mathrm{i}})   More

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    Heterodissemination: precision insecticide delivery to mosquito larval habitats by cohabiting vertebrates

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    An evolutionary perspective on kin care directed up the generations

    ParticipantsData were drawn from the NCDS, which is a nationally representative study that has followed a cohort of participants all born in a single week in the United Kingdom since 1958. Since birth, they have been followed up a total of 11 times at ages 7, 11, 16, 23, 33, 42, 44, 46, 50 and 55. As data on time spent caring for grandchildren is only available from the most recent interview, all analyses here are cross-sectional, with all women included in the sample being aged either 55 or 56 (depending on whether the interview was conducted in 2013 or 2014) and representing the third generation of women in Fig. 1. The sample was limited to women who had at least one parent alive and at least one grandchild (n = 934). Data from the NCDS are available from the UK Data Service, and the participant characteristics shown in Supplementary Table S1.VariablesHours spent helping parents per weekInformation regarding parental caregiving was included as a count variable. In the most recent interviews, participants were asked whether they ever do various activities for their parents (e.g. shopping for them, helping with basic personal needs, giving them lifts, etc.), and if they do, how many hours on average per week do they spend doing so. Any women who reported not helping their parents do any of the activities were coded as helping their parents for zero hours per week.Hours spent caring for grandchildren per monthThe number of hours spent caring for grandchildren per month was also included as a count variable. Women were asked whether they ever look after their grandchildren without the grandchild’s parents being present, and if they do, at what frequency and for how many hours. Women who stated that they did not care for their grandchildren or did so less often than monthly were coded as caring for their grandchildren for zero hours per month. This measure also includes overnight stays.Fecundity status at age 55Fecundity status was derived from information on age, year and reason for last menstrual period, which was collected at ages 44, 50, and 55. Based on this, a binary categorical variable was derived where women were coded as either ‘Still menstruating’ or ‘No longer menstruating’. The latter category comprised of women who were post-menopausal or who had stopped menstruating for another reason, such as a surgical menopause. Women who had stopped menstruating due to menopause or other reasons were grouped together as the direct fitness implications of no longer menstruating are the same, regardless of the reason for it.Control variablesCovariates included were selected based on their expected effect on the woman’s ability to help other family members. As a proxy of socioeconomic status, the age at which the woman left education was included. Employment status was utilised to give an indication of the woman’s time constraints (i.e. if she was employed, it can be expected she had less time to care for kin)24, with women being coded as either employed, unemployed, or other, with the latter category including those who are doing something other than formal employment but do not classify themselves as unemployed (e.g. retired, volunteering, studying, etc.). Self-perceived health was used as a measure of how physically able the woman is to help family members25, and number of grandchildren was also included to adjust for how many grandparenting responsibilities a woman had. We also included information on the mortality status of the woman’s parents (i.e. whether she had both parents alive or not), which was derived from interviews at ages 7, 11, 16, 23, 42, 46, 50 and 55. The focal woman’s mother’s and father’s age at birth (collected in the perinatal interview) were also included to control for the amount of help her parents may need, as older parents would expected to be more in need of assistance. Finally, in models predicting hours spent caring for parents, time spent caring for grandchildren was adjusted for, and vice versa for models where hours spent caring for grandchildren was the outcome.AnalysesTime spent helping parents and caring for grandchildren were both modelled using zero-inflated negative binomial regression (ZINB). This modelling procedure was selected both due to the over-dispersed nature of the data with excess zeros, and because zero-inflated models allow for zeros to be generated through two distinct processes. Here, the model distinguishes between excess zeroes, which occur when the event could not have happened, and true zeros, which occur when there could have been an event. Therefore, the model estimates a binary outcome (does not care versus does care) and a count outcome (the number of hours spent caring). This method is theoretically appropriate, as there are many different reasons people would offer no care to kin: while some people may choose to invest less, for some people the choice is out of their control, with external factors influencing caring behaviours, such as living far away from kin26. In addition to this, ZINB was found to better fit the data than negative binomial regression (Supplementary Table S2).Time spent helping parents was first modelled. A ‘base’ model was first made containing the age the woman left education, employment status, marital status, self-perceived health, number of grandchildren, parent mortality status, age of parents, and time spent caring for grandchildren. Fecundity status was subsequently added, and model fitting then carried out on these two models, utilising their Akaike Information Criterion (AIC) value to understand whether a model including fecundity better fit the data than one without. The model with the lowest AIC value is taken to best fit the data. As AIC values penalise models for complexity, it means the model with the most terms will not automatically be selected as the best. The ΔAIC was also calculated, which is the difference between the candidate models AIC and the AIC value of the best fitting candidate model. If the ΔAIC value is ≤ 2, then it indicates that there is still good evidence to support the candidate model, meaning that a candidate model with a ΔAIC of ≤ 2 is almost as good as the best fitting model. A ΔAIC value of between 4 and 7 is taken to indicate the candidate model has considerably less support, and a ΔAIC of greater than 10 indicates there is no support for the candidate model27. 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    From natural capital accounting to natural capital banking

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    Neutral Theory is a tool that should be wielded with care

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    Different environmental variables predict body and brain size evolution in Homo

    Body and brain size databaseThe fossil dataset consists of the hitherto largest collection of body (n = 204) and brain size estimates (n = 166) from Homo in the past ~1.0 Ma (Fig. 1). The data on hominin body size estimates are derived from our own previous study6 plus additional estimates60 and updated chronometric ages from more recent literature. Individual body size estimates are provided by specimen in Supplementary Data 1 with data sources. The bulk of data on hominin brain sizes (endocranial volume, in cm3) is derived from recent meta-analyses7,12,13,61,62 and updated chronometric information. Specific sources of these data are indicated in Supplementary Data 2, with some assessments bearing larger errors due to the incomplete state of the crania on which they are based (e.g., Arago 21, Vértesszőlős, Zuttiyeh). Each body and brain size estimate is associated with information on estimated chronometric age (dating method and data source), geographical location (longitude and latitude), and taxonomic attribution. For the exact locations per specimen, see interactive map in Supplementary Note 1. We divided the dataset into three taxonomic units: Pleistocene H. sapiens, Neanderthals, and Mid-Pleistocene Homo. Whereas hypodigms of H. sapiens and Neanderthal remains are generally agreed upon, we use “Mid-Pleistocene Homo” as a strictly analytical unit to denote African and European Middle Pleistocene hominins that predate Neanderthals and are not assigned to Homo naledi, between ~800 and 130 ka. We refrained from further division of this group due to the often fragmentary nature of fossils, unclear alpha taxonomy, and small sample size. Analyses performed within these taxonomic units minimize phylogenetic effects of, e.g., significantly different brain sizes (e.g., ref. 2). Specimens from H. naledi and Homo floresiensis had to be excluded from this analysis as for each taxon they derive from a single location and age bracket, precluding assessment of paleoclimatic variation. Limitations to the fossil datasets (see e.g., refs. 2,3,4,6) include imprecision of brain and body size estimates due to methodical and taphonomic problems, uncertainties of absolute ages that translate into uncertainties of the associated climate, and unequal sampling of hominin fossils across time and space. These limitations were incorporated into the construction of the synthetic dataset to assess the extent of their effects on the overall results for the actual fossil dataset. For all further analyses, brain and body size values were log-transformed as they increase multiplicatively.Climate reconstructionsEach body and brain size estimate required corresponding estimates of relevant climatic variables. Our climate records are numerical model estimates based on global climate reconstructions for the past 1 Ma using the global climate model emulator GCMET27. The main idea behind GCMET is that global climate model (GCM) simulations of the past 120,000 years contain sufficient information about long-term climatic changes on time scales of ≥1000 years. Given that we know the external boundary conditions, we can reconstruct previous glacial–interglacial climatic changes. The Quaternary climate is largely determined by dynamics of the Northern Hemisphere ice sheets, which, in turn, are affected by orbital variations of the Earth around the Sun and variations of atmospheric CO2. Using these factors as external boundary conditions, GCMET can emulate the climate of the Quaternary in a similar way as a state-of-the-art GCM27.The atmospheric CO2 record of the past 1 Ma that we use in this study is a composite of the EPICA CO2 record from an Antarctic ice core63 and of output from a carbon cycle model (CYCLOPS)64. The EPICA record covers the past ~800 ka, whereas we use the CYCLOPS model output to cover the time up until 1.0 Ma. Orbital variations are based on calculations by Berger and Loutre65. Ice-sheet extents for the past 800 ka are based on numerical ice-sheet model output66. For the period before 800 ka, we assumed present-day ice-sheet configurations. This is an appropriate assumption given that all but one specimen of the fossil record before 800 ka in our datasets are within Africa or southeast Asia and thus far away from ice-sheet margins, with the local GCMET climate reconstructions not affected by this simplification.For each fossil site location from the body and brain size database, we extracted a time series of the relevant climate variables, see Table 1 (also Supplementary Figs. 10 and 11). The time series were used to attach the value of each climate variable to the fossil record, both for the actual fossil data as well as for the synthetic fossil datasets.Linear modelsThe null and two alternative linear models used throughout this manuscript are defined as follows. The null model simply estimates the mean for each taxonomic group, and we refer to this model as LM-T (linear model with taxa):$$Y={beta }_{0}+{beta }_{1}times {rm{taxon}}$$
    (1)
    Here Y corresponds to either body or brain size (or the log-transformed thereof), whereas β0 is the intercept, which is equivalent to the mean size of the reference taxon, and β1 is a factor that reflects the deviation from this mean size for a taxonomic group (thus giving independent intercepts for Mid-Pleistocene Homo, Neanderthals, or Pleistocene H. sapiens).The first alternative model contains the effect of the climate variable X (across all taxa):$$Y={beta }_{0}+{beta }_{1}times {rm{taxon}}+{beta }_{2}times X$$
    (2)
    Here β0 and β1 are the intercept terms, giving taxon-specific values, and β2 is the slope, which is the same across all taxa. We refer to this model as LM-TC (linear model with taxonomic differences plus a climate effect).The second alternative model takes taxonomic differences for the slope of the climate effect into account. This is done via an interaction term, β3, which acts as a modifier for the slopes (i.e., different intercepts, given by β0 and β1, and slopes, given by β2 and β3, for each taxonomic group):$$Y={beta }_{0}+{beta }_{1}times {rm{taxon}}+{beta }_{2}times X+{beta }_{3}times {rm{taxon}}times X$$
    (3)
    We refer to this model as LM-T*C (linear model with taxonomic differences plus a taxon-specific climate effect). The slopes, β2 and β3 in Eqs. (2) and (3), respectively, are presented in the main text in Tables 2 and 3 (for the log-transformed sizes) and in Supplementary Tables 1 and 2 (for the natural units of the sizes).Synthetic datasets and power analysisApart from determining the smallest sample size suitable to detect the effect of a given test at the desired level of significance, power analysis can also be used as a formal way to test whether a relationship between dependent and independent variables can be detected with the available data and proposed methods (i.e., linear models in our case) assuming that such a relationship exists. Before testing for any true association between local climate and the fossil record, we use such a power analysis to assess our power to detect relationships of different effect sizes given the uncertainties, for example, in body/brain sizes, dating, and climate reconstructions. We generated 1000 synthetic datasets for each of the ten climate variable associations (MAP, MAT, NPP, mean temperature of coldest quarter, mean precipitation of driest quarter, and the logarithm of their running standard deviation over a 10,000-year window) with body and brain size. For each association, we assumed a strong, a medium, and a weak relationship between size and climate.By strong, we refer to 1/4 of the maximum possible slope given by the range of the climate and size. Subsequently, medium is half the slope of the strong relationship, (1/8 maximum possible slope), and weak is half the slope of the medium relationship (1/16 maximum possible slope). For example, the strong association between MAT and body size (Bergmann’s rule) is −0.34 kg/°C, based on the above defined rule. This is close to the estimated association between temperature and body size of about −0.4 kg/°C found for modern humans in a recent study (ref. 31, their Fig. 5A). Unfortunately, there are no empirical data about other climatic relationships and body (or brain) size. For simplicity, we therefore applied the same rule of strong, medium, and weak associations for all other climate variables and for brain size.Before generating a synthetic dataset, we estimated the intercepts β0 and β1 and the slope β2 for the LM-TC model, Eq. (2). However, for the real fossil analysis we used the model with the interaction term, LM-T*C, Eq. (3). First, we looked up the climate record for each location and time from the climate time series and attached it to the respective empirical fossil records. We calculate the maximum slope from the X and Y ranges as β1 = range(Y)/range(X). Assigning an actual relationship factor, e.g., strong (=1/4), the intercept β0 can be calculated using the X- and Y-midpoints, β0 = Ymidpoint − 1/4β1Xmidpoint.For the synthetic fossil datasets, we assume an age uncertainty range of 10% (±5%) for radiocarbon-dated fossils, i.e., younger than 50 ka cal BP (e.g. ref. 67), and 20% (±10%) for fossils older than 50 ka coming from other dating methods with higher uncertainty such as luminescence, U-series, or ESR (e.g., ref. 68). Furthermore, we assume a standard error of 2 K for mean annual and mean temperature of coldest quarter. For all other climate variables, we assume a 20% error range (±10%). The 2 K and the 20% are in line with climate model biases as estimated in a recent study69. Within a taxonomic unit of the genus Homo, we assume a coefficient of variation (CV) of 7% for body size (average of intrapopulation means of 19 global Holocene hunter-gatherer populations, n = 510, data from JTS) and 3.5% for brain size (from ref. 28 populations, dataset: http://volweb.utk.edu/~auerbach/HOWL.htm; ref. 70, see also ref. 32). Previous research has demonstrated that the range of body size variation in Holocene human populations is larger than any taxonomic unit of earlier hominin and encompasses the range of variation found within earlier hominins6 and that sexual dimorphism in size among Mid-Pleistocene hominins is comparable to that of modern humans71. While there are significant differences in brain shape through recent hominin evolution, the range of size variation within Pleistocene hominin taxa remains comparable to that observed among modern humans60. These observations suggest that modeling the intrapopulation variation among hominin taxa upon modern human coefficients of variation provides a reasonable estimate of variation within hominin taxa that are often presented only by much smaller sample sizes. To create a synthetic dataset that has a mean and a variance as close to the fossil dataset, we introduced taxonomic size differences (β1 in Eq. (2)) that is based on the taxonomic differences in the mean size. This difference was estimated directly from the fossil dataset.The procedure to generate a single synthetic dataset is as follows. First, we selected a relationship strength, e.g., strong, and calculated the slope and intercepts. For each synthetic data point, we:

    1.

    Looked up the age and added a randomly sampled error (±5% or ±10%).

    2.

    Looked up the fossil site and selected the climate record from the previously calculated time series for that location and sampled age.

    3.

    Added a randomly sampled error (i.e., S.D. of 2 K or 20%) to the climate record. This is now the X value.

    4.

    Multiplied X with the slope β2 and added the intercept β1 with the respective taxonomic correction. This translates the climate record X into a size estimate Y.

    5.

    Added a random term to Y based on the CV, i.e., 3.5% for brain and 7% for body size.

    6.

    Repeated steps (1)–(5) for each fossil record and saved all locations, ages, Xs, and Ys to a file. This is a single synthetic dataset in the same format as the original fossil dataset.

    We repeated this N times to generate N synthetic datasets and repeated the same procedure for the other relationship strengths, i.e., medium, and weak and for all other climate variables. Panels of exemplary synthetic datasets for body and brain sizes in comparison with the original data are shown in Supplementary Figs. 10 and 11.We use the same thinning approach as described in the main text (n = 1000) for the synthetic datasets. These are then used for a power analysis to test whether the linear relationship between any climate variable and body or brain size can be detected. We fitted both the LM-TC model, Eq. (2) (in which the slope defining the relationship between climate and size is the same for the three taxonomic groups, which can differ in their intercept), and the LM-T*C model, Eq. (3) (different slopes and intercepts for the three groups). A climate effect was deemed present if the null model had a higher AIC value compared to either of the alternative models, LM-TC or LM-T*C, (ΔAIC  > 2), i.e., LM-T, Eq. (1), in which the three groups differ in size but there is no effect of climate. Figure 2 in the main text shows the power to detect a true relationship between size and climate. Individual records are color-coded according to the AIC difference between the LM-T and the alternative models, LM-TC or LM-T*C, ranging from −2 (red) to +15 (blue) with 2 as midpoint (white).All statistical tests were undertaken in Python version 3.8.5 using the following Python packages: statsmodels 0.12 (for linear models), pandas 1.1.3 (for dataframes, reading/writing CSV/Excel files), netCDF4 1.5.3 (reading NetCDF files), matplotlib 3.3.2 (for plotting), and numpy 1.19.2 (numerics).Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More