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Quantitatively predicting of the responses of freshwater ecosystems to the effects of potentially toxic metals and acidification is an ongoing challenge in ecotoxicology. The WHAM-FTOX model is based on the assumptions that toxic effects of protons and metal cations are additively related to their occupancies of binding sites on organisms, and that those binding sites can be represented by the binding sites of humic acid (HA). We applied the model to simulate the species richness (nsp) of crustacean zooplankton in acid- and metal-contaminated lakes near Sudbury, Ontario between 1973 and 2006. Historic emissions from metal smelters at Sudbury have caused contamination of surrounding lakes by acid deposition, while lakes closest to the smelters were also contaminated with metals, mainly Ni and Cu, and to lesser extents Zn, Cd, and Pb. Changes in water chemistry over the study period show partial recovery as a result of emission reductions. In application, binding of protons and metals to organisms is simulated by applying the WHAM7 model for each water sample. A combined dose term, FTOX,i, is computed for each species within a conceptual assemblage, assuming a distribution of species sensitivities to metals. The probability of finding the species in a sample is then computed from a fixed relationship with FTOX,i, and nsp is computed by summing these probabilities across all species. The distribution of species sensitivities is found by assuming them to be lognormally distributed and fitting their mean and standard deviation. The model was able to describe the variability in nsp well, with an R-squared value of 0.84 (p < 0.0001). Generally, the model reproduces the temporal patterns of nsp in individual lakes well, although it tends to underestimate the rate at which species richness increases as water chemistry recovers. The most important toxic cations were H, Al, Ni, and Cu, with a small contribution from Zn. The predicted contributions of protons and individual met

The WHAM-FTOX model quantifies cation toxicity towards freshwater organisms, assuming an additive toxic response to the amounts of protons and metals accumulated by an organism. We combined a parameterization of the model, using data from multi-species laboratory toxicity tests, with a fitted field species sensitivity distribution, to simulate the species richness (nsp) of crustacean zooplankton in acid- and metal-contaminated lakes near Sudbury, Ontario over several decades, and also in reference (uncontaminated) lakes. A good description of variation in toxic response among the zooplankton species was achieved with a log-normal distribution of a new parameter, β, which characterizes an organism’s intrinsic sensitivity towards toxic cations; the greater is β, the more sensitive is the species. The use of β assumes that while species vary in their sensitivity, the relative toxicities of different metals are the same for each species (common relative sensitivity). Unbiased agreements between simulated and observed nsp were obtained with a high correlation (r2 = 0.81, p < 0.0001, n = 217). Variations in zooplankton species richness in the Sudbury lakes are calculated to be dominated by toxic responses to H, Al, Cu and Ni, with a small contribution from Zn, and negligible effects of Cd, Hg and Pb. According to the model, some of the Sudbury lakes were affected predominantly by acidification (H and Al), while others were most influenced by toxic heavy metals (Ni, Cu, Zn); for lakes in the latter category, the relative importance of heavy metals, compared to H and Al, has increased over time. The results suggest that, if common relative sensitivity operates, nsp can be modelled on the basis of a single set of parameters characterizing the average toxic effects of different cations, together with a species sensitivity distribution.

Over the last two centuries, the landscape of many industrialised nations has been transformed by the spread and intensification of agriculture, by atmospheric pollution, by human waste (rising in line with population growth), and now by changes in the climate. The research presented here aims to understand and quantify how these long-term changes have impacted UK freshwaters and the flux of macronutrients to the sea. The Long Term Large Scale (LTLS) Freshwater Model presented here used readily-available driving data (climate, land-use, nutrient inputs, catchment topography) to understand and quantify how changes in the UK's macronutrient histories have impacted on freshwater stores and fluxes. Model-reconstructed sources and fluxes of carbon, nitrogen and phosphorus (C, N and P) from 1800 to 2010 indicate that the rapid increase in the use of agricultural fertilisers after the second world war, and the rising human population, led to a rapid rise in N & P fluxes to rivers. During this period, the modelling shows that the dominant source of N in rivers changed from improved grassland to arable, the dissolved N export to rivers quadrupled, and P from human waste increased by ~600%, despite waste water treatment. The simulations also indicate a net storage of nitrates in groundwater between the 1940s and 1990s, and a net release to coastal waters post-1990; but groundwater retention and later release of C&P are less significant. Overall, modelling indicates that >75% of C, N and P entering freshwaters goes directly to the coastal waters, with 15–20% of C & N removed in river processes. These results constitute the first process-based integrated modelling assessment of freshwater macronutrient change at a national scale. The LTLS approach provides a methodology to develop fully-coupled global models of terrestrial, freshwater, atmospheric and marine processes that can take account of changes in land-management and climate.

Carbon stores in the terrestrial biosphere globally represent over 50% of present-day organic carbon reservoirs and have significantly altered over the last three centuries owing to anthropogenic disturbances. Conversion of natural land to agricultural uses often results in a loss of soil carbon, whilst atmospheric deposition of pollutants such as nitrogen has increased carbon storage in both soil and biomass. Terrestrial carbon storage underpins a range of ecosystem services, including climate regulation, food production, and water services. This storage is crucial for sustainable land management. Quantification of terrestrial carbon cycling at regional and national scales, and understanding how human-induced drivers have impacted present-day carbon stores is therefore required to inform sustainable land use policy. This study applies the N14CP model, an integrated soil-plant biogeochemistry carbon-nitrogen-phosphorus model, across the United Kingdom to simulate changes in terrestrial carbon storage from 1700 to 2020. The analysis shows that change in anthropogenic terrestrial carbon storage is a complex picture comprising of gains in natural areas due to nitrogen deposition and afforestation, and losses in arable areas. We observed an overall net increase in total terrestrial carbon storage of 6.9%. We note, however, that continued increases in carbon storage cannot be assumed due to (i) reduced influence of future nitrogen deposition as these systems become limited by other nutrients, (ii) the need to continue enhanced nitrogen inputs to maintain carbon sequestered, and (iii) carbon declines in arable areas continuing alongside diminishing gains in other land use types. This research provides a full picture of anthropogenic impacts on terrestrial organic carbon storage, accounting for changing nutrient cycles at a national scale.

Carbon stores in the terrestrial biosphere globally represent over 50% of present-day organic carbon reservoirs and have significantly altered over the last three centuries owing to anthropogenic disturbances. Conversion of natural land to agricultural uses often results in a loss of soil carbon, whilst atmospheric deposition of pollutants such as nitrogen has increased carbon storage in both soil and biomass. Terrestrial carbon storage underpins a range of ecosystem services, including climate regulation, food production, and water services. This storage is crucial for sustainable land management. Quantification of terrestrial carbon cycling at regional and national scales, and understanding how human-induced drivers have impacted present-day carbon stores is therefore required to inform sustainable land use policy. This study applies the N14CP model, an integrated soil-plant biogeochemistry carbon-nitrogen-phosphorus model, across the United Kingdom to simulate changes in terrestrial carbon storage from 1700 to 2020. The analysis shows that change in anthropogenic terrestrial carbon storage is a complex picture comprising of gains in natural areas due to nitrogen deposition and afforestation, and losses in arable areas. We observed an overall net increase in total terrestrial carbon storage of 6.9%. We note, however, that continued increases in carbon storage cannot be assumed due to (i) reduced influence of future nitrogen deposition as these systems become limited by other nutrients, (ii) the need to continue enhanced nitrogen inputs to maintain carbon sequestered, and (iii) carbon declines in arable areas continuing alongside diminishing gains in other land use types. This research provides a full picture of anthropogenic impacts on terrestrial organic carbon storage, accounting for changing nutrient cycles at a national scale.

It is a well-attested fact that the uptake and toxicity of cationic metals to organisms are dependent on the chemistry of the exposure medium. Considerable research effort has been devoted to development of modelling tools to understand, explain and predict these medium effects. Predominant among the developed models is the Biotic Ligand Model (BLM), which considers exposure to be directly related to metal bound to specific uptake sites (biotic ligands) on the target organism, with chemical speciation in the medium and competition for binding to the biotic ligand(s) accounted for. Similarly to the BLM, WHAM-FTOX is based around the concept of computing amounts of metal bound to the organism. However, rather than computing amounts of metals bound to assumed specific biotic ligands, WHAM-FTOX assumes that exposure to metals is proportional to the amount of metal bound by all weak-acid coordination sites on or in the organism, in equilibrium with the surrounding medium. An overall toxic response for mixtures of cations (metals and protons) is quantified as a toxicity function FTOX, given by FTOX = ∑ αi ϴi where the exposure to each cation is given by ϴi (the fractional occupancy of binding sites) and αi is a toxicity coefficient specific to each cation. Amounts of bound cations are computed using the WHAM chemical speciation model, taking the amounts bound to humic acid (HA) as proxies for amounts bound to organisms. This approach has the advantage that constants for cation binding are already available, rather than needing to be derived as with the BLM, and that mixture effects are readily computed. Furthermore, the toxic effects of proton binding can be included in the mixture exposure modelling. Initial applications of WHAM-FTOX focused on describing field community effects in freshwaters impacted by acidification and metal contamination in a number of locations including the UK and North America. Subsequent work has focused on modelling accumulation and mixture eff

We used the WHAM chemical speciation model and the WHAM-FTOX toxicity model to analyse the published results of laboratory toxicity experiments covering 52 different freshwater biological test species and 24 different metals, a total of 2037 determinations of EC50 with accompanying data on solution composition. The key extracted parameter was αM, the parameter in WHAM-FTOX that characterises the toxic potency of a metal on the basis of its estimated metabolically active body burden. For 16 data sets applying to metal-test species pairs with appreciable variations in solution composition, values of EC50 back-calculated from averaged values of αM showed significantly (p < 0.001) less deviation from the measured EC50 values than did the simple average EC50, confirming that the modelling calculations could account for some of the dependence of toxicity on chemical speciation. Data for different exposure times permitted a simple parameterisation of temporal effects, enabling values of αM,max (values at infinite exposure time) to be obtained, and the effects of different exposure times to be factored out for further analysis. Comparison of averaged values of αM,max for different metals showed little difference among major taxa (invertebrates, plants, and vertebrates). For Cd, Cu, Ni and Zn (the four metals with most data) there were significant differences among αM,max values for different species, but within-species variabilities were greater. Reasonably similar species sensitivity distributions of standardised αM,max applied to Cd, Cu, Ni and Zn. The average values, over all species, of αM,max increased in the order Al < lanthanides < Zn ∼ UO2 < Ni ∼ Cu < Pb < Cd < Ag. Considering all the αM,max values, there was a strong dependence (r2 = 0.56, p < 0.001) on Pearson’s hardness-softness categories, and a slightly stronger relationship (r2 = 0.59) if ionic radius was included in the statistical model, indicating that softer, larger cations are the most effective toxicants

Soil organic matter (SOM) is a major ecosystem component, central to soil fertility, carbon balance, and other soil functions. To advance SOM modelling, we devised a steady‐state model of topsoil SOM, with explicit descriptions of physical states and properties, and used it to simulate SOM concentration, carbon:nitrogen:phosphorus (C:N:P) stoichiometry, bulk density, and radiocarbon content. The model classifies SOM by element stoichiometry (αSOM is poor in N and P, βSOM is rich), mean residence times (1–2000 years), and physical state (free, occluded, adsorbed, hypoxic). The most stable SOM is either βSOM preferentially adsorbed by mineral matter, or αSOM in strongly hypoxic zones. Soil properties were simulated for random combinations of plant litter input (amount and C:N:P stoichiometry), mineral sorption capacity, propensity for hypoxia, and bulk density of non‐adsorbed αSOM. To optimize model parameters, outputs from 5000 simulations were used to construct bivariate relations among soil variables, which were compared with those found in data for 835 survey sites, covering all common land uses. The bivariate relations, and patterns of data scatter, were reproduced, and also variations in soil radiocarbon with soil type, suggesting that apparent scatter in measured data might reflect SOM diversity. The temporal acquisition by soil of ‘bomb 14C’ could also be simulated. The steady‐state model is the basis for a dynamic version, suitable for simulating changes in SOM through time. It provides insight into the possible manipulation of SOC sequestration; for example increasing litter inputs might only increase moderately‐stable SOC pools, while encouraging the creation of βSOM by adsorption to mineral matter from deeper soil could lead to long‐term stabilization.

The transport of dissolved organic matter (DOM) across the land-ocean-aquatic-continuum (LOAC), from freshwater to the ocean, is an important yet poorly understood component of the global carbon budget. Exploring and quantifying this flux is a significant challenge given the complexities of DOM cycling across these contrasting environments. We developed a new model, UniDOM, that unifies concepts, state variables and parameterisations of DOM turnover across the LOAC. Terrigenous DOM is divided into two pools, T1 (strongly-UV-absorbing) and T2 (non- or weakly-UV-absorbing), that exhibit contrasting responses to microbial consumption, photooxidation and flocculation. Data are presented to show that these pools are amenable to routine measurement based on specific UV absorbance (SUVA). In addition, an autochtonous DOM pool is defined to account for aquatic DOM production. A novel aspect of UniDOM is that rates of photooxidation and microbial turnover are parameterised as an inverse function of DOM age. Model results, which indicate that ~ 5% of the DOM originating in streams may penetrate into the open ocean, are sensitive to this parameterisation, as well as rates assigned to turnover of freshly-produced DOM. The predicted contribution of flocculation to DOM turnover is remarkably low, although a mechanistic representation of this process in UniDOM was considered unachievable because of the complexities involved. Our work highlights the need for ongoing research into the mechanistic understanding and rates of photooxidation, microbial consumption and flocculation of DOM across the different environments of the LOAC, along with the development of models based on unified concepts and parameterisations.

Fertilization of nitrogen (N)-limited ecosystems by anthropogenic atmospheric nitrogen deposition (Ndep) may promote CO2 removal from the atmosphere, thereby buffering human effects on global radiative forcing. We used the biogeochemical ecosystem model N14CP, which considers interactions among C (carbon), N and P (phosphorus), driven by a new reconstruction of historical Ndep, to assess the responses of soil organic carbon (SOC) stocks in British semi-natural landscapes to anthropogenic change. We calculate that increased net primary production due to Ndep has enhanced detrital inputs of C to soils, causing an average increase of 1.2 kgCm−2 (c. 10%) in soil SOC over the period 1750–2010. The simulation results are consistent with observed changes in topsoil SOC concentration in the late 20th Century, derived from sample-resample measurements at nearly 2000 field sites. More than half (57%) of the additional topsoil SOC is predicted to have a short turnover time (c. 20 years), and will therefore be sensitive to future changes in Ndep. The results are the first to validate model predictions of Ndep effects against observations of SOC at a regional field scale. They demonstrate the importance of long-term macronutrient interactions and the transitory nature of soil responses in the terrestrial C cycle.

Natural biogeochemical cycles of the macronutrient elements carbon (C), nitrogen (N) and phosphorus (P) have been transformed by food and fuel production, through atmospheric pollution and climate change. Further, land disturbance has led to considerable losses of nutrients from terrestrial ecosystems. This investigation aims to explore and address several barriers to understanding natural organic matter cycling across terrestrial and aquatic ecosystems. Soil organic matter (SOM) turnover models are often constrained by C and N, while data on organic P is lacking. Twenty UK soils were used to provide the first investigation of organic P in density fractionated SOM pools. Organic matter in the mineral fraction was considerably more enriched in oP. Stoichiometric ratios agreed with a new classification model, which provides important constraints for models of nutrient cycles. Radiocarbon (14C) measurements of aquatic OM indicates sources and turnover on different timescales. Here, the first analysis of particulate O14C in UK rivers suggested topsoil was the major source. Significantly depleted material was found in a catchment with historical mining activity. Global, temporal analysis of dissolved O14C enabled quantification of different OM sources, and highlighted the importance of assessing the data against the changing atmospheric 14C signal. New dissolved O14C data for rural, arable and urban catchments were more depleted than the global averages. In industry, there is a growing need to manage aquatic nutrient enrichment through rapid and reliable monitoring. A model of UV absorbance was tested against freshwaters that were biased towards eutrophic conditions. The results demonstrated the weak absorbing components of algal DOM, and new variable model parameters were introduced, which quantified the contribution of algal DOM. This could have implications on model predictions of DOC concentration, and a generally applicable spectroscopic model is questionable. This

To predict ecosystem responses to anthropogenic change it is important to understand how and where plant productivity is limited by macronutrient availability. Nitrogen (N) is required in large quantities for plant growth, and is readily lost through leaching or gas fluxes, but reactive nitrogen can be obtained through dinitrogen fixation, and phosphorus (P) is often considered a more fundamental long-term constraint to growth and carbon sequestration in terrestrial ecosystems. Phosphorus limitation may be becoming more prevalent due to widespread pollution by atmospheric N. Assessments of the effects of macronutrient availability on productivity in natural ecosystems are however scarce. We measured standing biomass of bracken Pteridium aquilinum as a proxy for productivity across sites with similar climate but varied geology. Total above-ground biomass varied from 404 to 1947 g m− 2, yet despite 12-fold to 281-fold variation in soil macronutrient stocks these were remarkably poor at explaining variation in productivity. Soil total nitrogen, organic phosphorus, calcium, magnesium and zinc had no relationship with productivity, whether expressed as concentrations, stocks or element/C ratios, and nor did foliar N/P. Soil potassium (K) and molybdenum stocks both showed weak relationships with productivity. The stock of K in bracken biomass was considerably greater as a proportion of soil stock than for other nutrient elements, suggesting that this nutrient element can be important in determining productivity. Moisture availability, as indicated by environmental trait scores for plant species present, explained considerably more of the variation in productivity than did K stock, with less production in wetter sites. Soil N/C ratio and organic P stock were relatively unimportant in determining productivity across these bracken sites. It is possible that more-direct measures of N and P availability would explain variation in productivity, but the study shows the importanc

Understanding changes in plant-soil C, N and P using data alone is difficult due to the linkages between carbon, nitrogen and phosphorus cycles (C, N and P), and multiple changing long-term drivers (e.g. climate, land-use, and atmospheric N deposition). Hence, dynamic models are a vital tool for disentangling these drivers, helping us understand the dominant processes and drivers and predict future change. However, it is essential that models are tested against data if their outputs are to be concluded upon with confidence. Here, a simulation of C, N and P cycles using the N14CP model was compared with time-series observations of C, N and P in soils and biomass from the Rothamsted Research long-term experiments spanning 150 years, providing an unprecedented temporal integrated test of such a model. N14CP reproduced broad trends in soil organic matter (SOM) C, N and P, vegetation biomass and N and P leaching. Subsequently, the model was used to decouple the effects of land management and elevated nitrogen deposition in these experiments. Elevated N deposition over the last 150 years is shown to have increased net primary productivity (NPP) 4.5-fold and total carbon sequestration 5-fold at the Geescroft Wilderness experiment, which was re-wilded to woodland in 1886. In contrast, the model predicts that for cropped grassland conditions at the Park Grass site, elevated N deposition has very little effect on SOM, as increases in NPP are diverted from the soil. More broadly, these results suggest that N deposition is likely to have had a large effect on SOM and NPP in northern temperate and boreal semi-natural grasslands and forests. However, in cropped and grazed systems in the same region, whilst NPP may have been supported in part by elevated N deposition, declines in SOM may not have been appreciably counteracted by increased N availability.

Models are needed to understand how plant-soil nutrient stores and fluxes have responded to the last two centuries of widespread anthropogenic nutrient pollution and predict future change. These models need to integrate across carbon, nitrogen, and phosphorus (C, N, and P) cycles and simulate changes over suitable timescales using available driving data. It is also vital that they are constrainable against observed data to provide confidence in their outputs. To date, no models address all of these requirements. To meet this need, a new model, N14CP, is introduced, which is initially applied to Northern Hemisphere temperate and boreal ecosystems over the Holocene. N14CP is parameterized and tested using 88 northern Europe plot-scale studies, providing the most robust test of such a model to date. The model simulates long-term P weathering, based on the assumption of a starting pool of weatherable P (Pweath0, g m−2), which is gradually transformed into organic and sorbed pools. Nitrogen fixation (and consequently primary production) is made dependent on available P. In the absence of knowledge about the spatial variability of Pweath0, N14CP produces good average soil and plant variables but cannot simulate variations among sites. Allowing Pweath0 to vary between sites improves soil C, N, and P results greatly, suggesting that contemporary soil C, N, and P are sensitive to long-term P weathering. Most sites were found to be N limited. Anthropogenic N deposition since 1800 was calculated to have increased plant biomass substantially, in agreement with observations and consequently increased soil carbon pools.

Understanding changes in plant-soil C, N and P using data alone is difficult due to the linkages between carbon, nitrogen and phosphorus cycles (C, N and P), and multiple changing long-term drivers (e.g. climate, land-use, and atmospheric N deposition). Hence, dynamic models are a vital tool for disentangling these drivers, helping us understand the dominant processes and drivers and predict future change. However, it is essential that models are tested against data if their outputs are to be concluded upon with confidence. Here, a simulation of C, N and P cycles using the N14CP model was compared with time-series observations of C, N and P in soils and biomass from the Rothamsted Research long-term experiments spanning 150 years, providing an unprecedented temporal integrated test of such a model. N14CP reproduced broad trends in soil organic matter (SOM) C, N and P, vegetation biomass and N and P leaching. Subsequently, the model was used to decouple the effects of land management and elevated nitrogen deposition in these experiments. Elevated N deposition over the last 150 years is shown to have increased net primary productivity (NPP) 4.5-fold and total carbon sequestration 5-fold at the Geescroft Wilderness experiment, which was re-wilded to woodland in 1886. In contrast, the model predicts that for cropped grassland conditions at the Park Grass site, elevated N deposition has very little effect on SOM, as increases in NPP are diverted from the soil. More broadly, these results suggest that N deposition is likely to have had a large effect on SOM and NPP in northern temperate and boreal semi-natural grasslands and forests. However, in cropped and grazed systems in the same region, whilst NPP may have been supported in part by elevated N deposition, declines in SOM may not have been appreciably counteracted by increased N availability.

Models are needed to understand how plant-soil nutrient stores and fluxes have responded to the last two centuries of widespread anthropogenic nutrient pollution and predict future change. These models need to integrate across carbon, nitrogen, and phosphorus (C, N, and P) cycles and simulate changes over suitable timescales using available driving data. It is also vital that they are constrainable against observed data to provide confidence in their outputs. To date, no models address all of these requirements. To meet this need, a new model, N14CP, is introduced, which is initially applied to Northern Hemisphere temperate and boreal ecosystems over the Holocene. N14CP is parameterized and tested using 88 northern Europe plot-scale studies, providing the most robust test of such a model to date. The model simulates long-term P weathering, based on the assumption of a starting pool of weatherable P (Pweath0, g m−2), which is gradually transformed into organic and sorbed pools. Nitrogen fixation (and consequently primary production) is made dependent on available P. In the absence of knowledge about the spatial variability of Pweath0, N14CP produces good average soil and plant variables but cannot simulate variations among sites. Allowing Pweath0 to vary between sites improves soil C, N, and P results greatly, suggesting that contemporary soil C, N, and P are sensitive to long-term P weathering. Most sites were found to be N limited. Anthropogenic N deposition since 1800 was calculated to have increased plant biomass substantially, in agreement with observations and consequently increased soil carbon pools.