Incorporating Biodiversity into Biogeochemistry Models to Improve Prediction of Ecosystem Services in Temperate Grasslands: Review and Roadmap
1.1. Grasslands as Major Providers of Ecosystem Services
1.2. Using Models to Explain and Predict Grassland ES
1.3. Goals and Outline of This Paper
- We review the current understanding of the impact of grassland biodiversity on ES, and the drivers of biodiversity itself (Section 2).
- We review the state of the art of models for grassland biogeochemistry, and of models for the dynamics of biodiversity (Section 3). We identify the main differences between the two modelling approaches.
- We discuss how BGMs can be modified to simulate both the dynamics of biodiversity itself and the impacts of biodiversity on ES (Section 4).
- We discuss our findings and propose a roadmap for further model development and data use (Section 5).
2. Why Consider Biodiversity in Grassland Models?
2.1. Impacts of Biodiversity on ES
- Biodiversity (observed species richness) affects the extent to which drought reduces growth and soil respiration . More generally, biodiversity (species number in manipulated grassland communities) increases resistance to various types of climatic event: dry or wet, moderate or extreme, short- or long-term . In marginal grasslands (e.g., on dry soils in the Mediterranean), high biodiversity may extend the growing season. This shortens the period in which soils are bare and therefore protects against soil loss by erosion. Despite the fact that high biodiversity tends to stabilize productivity by reducing climate sensitivity , there are examples where biodiversity reduces the stability of ecosystem functioning in response to extreme events .
- High biodiversity tends to lead to higher aboveground biomass, especially when species are from different functional types . In grazed grasslands, high biodiversity not only improves productivity, but also grass quality and milk production , although the improvement may be absent under already highly productive conditions .
- High biodiversity in intensively managed grasslands may suppress weeds , but a modest increase in conventional grassland plant diversity with legumes and forbs improves pollination and therefore productivity . In unfertilized grasslands, the presence of legumes increases productivity and soil organic matter, and improves soil texture . The soil improvement also increases resilience against drought.
- Permanent monocultures will eventually decline in productivity due to insect herbivory or pathogen load .
- High species richness reduces root decomposition by increasing root C:N ratios, except for legumes , with grassland species having widely varying turnover rates . However, high biodiversity (in mixtures that were compared to monocultures) does tend to stimulate soil microbial biomass and soil respiration , and it strongly increases carbon sequestration [50,51]. Overall, N-cycling processes are stimulated less by biodiversity than C-cycling , except when legumes are added to the mixture .
2.2. Impacts of Environmental Change on Biodiversity
- In mixtures subjected to an imposed extreme drought, grass species common to wetter soils (high value of the Ellenberg F index) suffered most senescence and mortality . Legume species suffered more than grasses, irrespective of their Ellenberg value. Recovery was also better in grasses than in the N-fixers.
- Experimental warming tends to decrease species richness, but its impact depends strongly on the specific ecosystem .
- Biodiversity reduction in a Californian grassland was due to decreasing winter rain, and not due to changes in grazing, fire, N-deposition, or invasive species . Losses especially concerned native annual forb species with traits indicative of low drought tolerance. In contrast, 13-year long manipulation of temperature and rainfall in infertile grasslands had only a minor effect on biodiversity (species richness and relative abundance of growth forms) and productivity with the exception of reduction from chronic summer drought .
- A review of herbaceous systems worldwide showed that nitrogen fertilization tends to reduce plant species richness, whereas irrigation mostly has little effect . Fertilization reduces plant species richness of mountain grasslands the most where summers are cool, where mowing is carried out, and overall where biomass has been increased most by the fertilization . The effects of phosphorus are generally less evident than those of nitrogen .
- Grazing tends to be more beneficial for biodiversity than mowing . Johansen et al.  observed that abandonment of grazing led to decline in species diversity in Norwegian semi-natural grasslands but the effects varied with climate and soil conditions. However, grazing affects biodiversity nonlinearly: minor grazing increases biodiversity, overgrazing reduces it [64,65,66], while also the timing and the grazing species are important .
- In short-rotation (3–4 years) grasslands, the diversity of plant species is initiated by farmers deciding what combination of species is sown and how growth of weeds is suppressed, but weed proportions generally do increase, thereby decreasing sward productivity when they replace high yielding grass, forb and legume varieties .
2.3. What Can We Learn from Experiments and Field Observations?
- Grace and colleagues  showed that the influence of biodiversity on productivity was very small in mature natural grasslands.
- Veen et al.  and Weisser et al.  showed that results from weeded experiments may differ from non-weeded experiments. See also Kardol et al.  who concluded that their species-removal experiment (non-weeded) allowed for better low-diversity performance than weeded species-addition experiments.
- Gruner et al.  found that the experimental set-up affected the response of biodiversity to warming.
3. Modelling the Biogeochemistry and Biodiversity of Grasslands
3.1. Biogeochemistry Models (BGMs)
3.2. Models for Biodiversity
3.3. The Main Differences between BGMs and Models for Biodiversity-ES Relations
- Model structure. The simulated state variables in BGMs tend to be pools of carbon, nitrogen and water in soils, vegetation and atmosphere, whereas ecological models tend to focus on population dynamical properties such as the abundance of PFTs, species, traits or individuals (and their size-age distribution). The processes that are modelled are biogeochemical/physiological and demographic, respectively. BGMs focus on abiotic environmental drivers whereas biodiversity models usually focus on biotic interactions. For BGMs, traits are static inputs (i.e., model parameters) while for biodiversity models they are dynamic state variables.
- Mathematical formulation. BMGs are never fully analytical models: trajectories over time of state variables must be numerically derived by computer modelling. Biodiversity models can be analytically solvable systems of differential equations, but even when they are computer models, their time step of calculation tends to be much longer than the daily or sub-daily time step of most BGMs. Moreover, biodiversity computer models tend to be discrete-event agent-based (or even individual-based) models in contrast to the ‘big-leaf’ continuum BGMs.
- Data-use. BGMs require detailed information on mainly abiotic conditions (weather, atmospheric [CO2], N-deposition, soil properties) as drivers for the flows of carbon, nitrogen and water, whereas biodiversity models predominantly need biotic information, such as the initial age-size distribution of organisms or the frequency distribution of traits.
- Spatial scale. BGMs tend to be one-dimensional models that are assumed to be applicable to spatially homogeneous fields, whereas the representation of space in biodiversity models may be poorer in the vertical direction (no leaf or soil layers) but richer horizontally, even extending to simulating heterogeneous landscapes rather than fields (see also Figure 5).
4. Modifying Existing BGMs to Simulate the Impacts and Dynamics of Biodiversity
4.1. Representing Biodiversity as a Constant Metric
4.2. Representing Multiple Species or PFTs without Simulating Competition
4.3. Representing Multiple Competing Species or PFTs
5.1. The Need and Scope for Introducing Biodiversity into BGMs
5.2. Reconciling Current BGMs and Models for Biodiversity
5.3. A Roadmap for Future Model Development
- Mechanistic modelling: nearby destinations.
- It will always be useful to have a choice of models, with different levels of complexity, and with different domains of applicability. The three different approaches to joint modelling of biogeochemistry and biodiversity that we distinguished in Section 3 are not mutually exclusive, but can be explored in parallel. Each approach comes with its own set of strengths and weaknesses, which can be assessed by frequent model comparisons against common data.
- It will be worthwhile to keep pursuing the various methods for managing model complexity that we described above (under “Reconciling Current BGMs and Models for Biodiversity”), in particular the identification of further constraints to trait-trait and trait-environment relationships. However, a trade-off between ease of implementation and parameterization on the one hand, and realistic representation of mechanisms on the other, will remain unavoidable.
- The biogeochemical modelling will be of immediate benefit to our understanding of biodiversity-ES relationships if it explores ways to reconcile the apparent mismatch between responses of ES to manipulated biodiversity and naturally evolved biodiversity (see Figure 4).
- It will also be important to use biodiversity-representing BGMs to explore why certain responses to environmental change are seen in some grasslands but not in others, as in the examples provided by De Boeck et al.  where high biodiversity did not necessarily lead to greater stability of ecosystem functioning in response to extreme events.
- As BGMs are dynamic models, they can, in principle, be employed to explain how the initial response of grasslands to disturbances (resistance phase) differs from the long-term response (recovery phase), given that biodiversity can affect the two phases differently .
- Mechanistic modelling: long-range destinations.
- In the long term, future biogeochemistry-biodiversity modelling may aim for more ambitious applications than those documented so far. This includes more comprehensive representation of environmental drivers (e.g., phosphorus, weeds, pests and diseases) and more detail in the representation of biodiversity itself along the different dimensions of PFTs, species and traits.
- Most current BGMs have limited or no representation of spatial processes. They tend to be one-dimensional models that can be run for different locations, with site-specific environmental drivers, but true spatial processes such as species migration and hydrology are generally not represented . Given the fact that the dynamics of biodiversity are only partly determined by within-field processes, future models for biodiversity and biogeochemistry may have to operate at the landscape-scale, as depicted in Figure 5. Applying process-based models to larger spatial scales requires that the interplay of species dynamics and grassland functioning is analyzed at local and regional scales, and assessed in virtual landscapes with heterogeneous soil, climate, management and natural disturbance and stress factors.
- Data collection and benchmarking.
- Model development cannot proceed without supporting data, and data analysis is hampered when reliable models are not available. The future modelling developments outlined above should thus go together with continuing increases in the quantity and diversity of data. We argued above for the parallel development of multiple modelling approaches, but this development should frequently be re-anchored in reality by comparing all models against rich benchmarking data sets, that cover multiple different ES rather than just productivity.
- Benchmarking data should be collected for the many different production situations that grasslands experience: potential growth, water-limitation, nutrient-limitation, weeds, pests, diseases, grazing, mowing. The data should cover extreme events (abiotic, biotic) as well as chronic stress conditions, in particular those that are expected to become more common in the future. Biodiversity may act to stabilize biogeochemical fluxes under extreme conditions, and BGMs need data to test their capacity to account for this.
- Remote sensing and eddy covariance measurements can be collected to assess the prevalence of biodiversity and its impact on GHG over wider areas. Eddy covariance towers have generally been placed in spatially homogeneous landscapes, to facilitate data interpretation despite variable wind directions, but this is no longer a necessity. Levy et al.  showed how Bayesian inference can be used to derive a spatial map of vegetation properties around a single measurement tower. A caveat is that eddy covariance measurements on grasslands tend to be unreliable during grazing events because carbon losses from animal respiration are not registered .
- The literature shows much evidence for an important role of biodiversity in grasslands, but uncertainties remain because of differences in environmental conditions between studies. Meta-analyses of available data should, for example, not conflate the impacts of experimentally manipulated vs. naturally evolved biodiversity. A Bayesian hierarchical approach to meta-analysis could be used to account for the interactions , which would prepare the data for use in model development.
- Overall, we advocate that data keep being collected in both biodiversity-manipulation experiments, and in monitoring studies where biodiversity is not imposed by the researcher (Figure 4). The first study type will provide data that elucidate causal pathways and can thus be used in the design of model structure. The second study type provides data from the actual grasslands for which our models need to be calibrated to issue reliable forecasts.
- Hybrid mechanistic-probabilistic modelling.
- We have focused here on mechanistic modelling of biogeochemistry and its relationship with biodiversity in order to predict ES under new conditions, and to explain observations of ES. However, we may want to predict a more comprehensive suite of grassland ES than is customary or even possible for biogeochemical modelling. For ES that are not outputs of BGMs, modelers could assess whether those ES are predictable from variables that the models do simulate. Aesthetic appeal, for example, may be a function of biodiversity. These added relationships are not mechanistic and may not be robust, but they can be implemented as conditional probability distributions to account for uncertainty.
- More generally, for practical application of our models and for ease of uncertainty quantification, we may want to summarize the input-output relationships of our biodiversity-enhanced BGMs in the form of graphical models (probabilistic networks), which will facilitate uncertainty and risk analysis .
Conflicts of Interest
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Van Oijen, M.; Barcza, Z.; Confalonieri, R.; Korhonen, P.; Kröel-Dulay, G.; Lellei-Kovács, E.; Louarn, G.; Louault, F.; Martin, R.; Moulin, T.; Movedi, E.; Picon-Cochard, C.; Rolinski, S.; Viovy, N.; Wirth, S.B.; Bellocchi, G. Incorporating Biodiversity into Biogeochemistry Models to Improve Prediction of Ecosystem Services in Temperate Grasslands: Review and Roadmap. Agronomy 2020, 10, 259. https://doi.org/10.3390/agronomy10020259
Van Oijen M, Barcza Z, Confalonieri R, Korhonen P, Kröel-Dulay G, Lellei-Kovács E, Louarn G, Louault F, Martin R, Moulin T, Movedi E, Picon-Cochard C, Rolinski S, Viovy N, Wirth SB, Bellocchi G. Incorporating Biodiversity into Biogeochemistry Models to Improve Prediction of Ecosystem Services in Temperate Grasslands: Review and Roadmap. Agronomy. 2020; 10(2):259. https://doi.org/10.3390/agronomy10020259Chicago/Turabian Style
Van Oijen, Marcel, Zoltán Barcza, Roberto Confalonieri, Panu Korhonen, György Kröel-Dulay, Eszter Lellei-Kovács, Gaëtan Louarn, Frédérique Louault, Raphaël Martin, Thibault Moulin, Ermes Movedi, Catherine Picon-Cochard, Susanne Rolinski, Nicolas Viovy, Stephen Björn Wirth, and Gianni Bellocchi. 2020. "Incorporating Biodiversity into Biogeochemistry Models to Improve Prediction of Ecosystem Services in Temperate Grasslands: Review and Roadmap" Agronomy 10, no. 2: 259. https://doi.org/10.3390/agronomy10020259