Modelling impacts of drivers on biodiversity and ecosystems

Biodiversity and ecosystem dynamics are inherently complex, and so is their response to environmental drivers - including both natural and anthropogenic drivers. Models are powerful tools for addressing complex systems as they can be used to assess and predict the impacts of drivers on biodiversity and ecosystems, and hence the impacts on ecosystem services and human well-being.

Biodiversity and ecosystem responses to environmental change can assume many forms as a consequence of the inherent complexity; one way of addressing this diversity is to reduce it to a few meaningful dimensions. Biodiversity and ecosystem variables can be arranged along dimensions representing key aspects of biodiversity complexity: biological organisation levels (species, populations, ecosystems, etc.) and biodiversity attributes (composition, structure and function). These two dimensions define a conceptual space that can be useful for identifying relevant response variables (see Table 1).

Table 1: Examples of biological levels for modelling (compositional, structural and functional biodiversity variables, from (Noss, 1990; Dale and Beyeler, 2001), selected to represent levels of biodiversity that warrant attention in environmental monitoring and assessment programmes.

Models of biodiversity and ecosystem function are critical to our capability to predict and understand responses to environmental change. There is a variety of modelling approaches which address different biological levels that are of relevance to IPBES:

• Individual-level models and evolutionary adaptation
• Species- or population-level models
• Community-level models
• Ecological interaction networks
• Ecosystem-level models and integrated assessment models

Individual-level models and evolutionary adaptation

Populations are not static but evolve. As a consequence, species may be able to adapt to conditions different from those previously experienced. Evolution can alter dispersal patterns, physiology and biotic interactions, and this poses a clear problem for predictive modelling at all levels, from genes to ecosystems: how to make predictions that go beyond current conditions?

There has been considerable research aimed at addressing this question, notably theoretical models that explicitly account for biological processes such as mutation, dispersal and interactions within and between species (e.g. mating and competition). Such models can account for environmental change and allow projections about future scenarios, beyond the range of what is currently observed. They also provide a means of assessing the robustness of predictions across uncertain parameters and processes.

An example of evolutionary models are models that examine how the arrangement of populations and migration rates among them influence evolutionary processes in the face of a changing environment. Such models have explored the process of evolution to a new or altered environment in the face of migration from the rest of the species range and can inform policy decisions about the maintenance of gene flow and the importance of migration corridors.

Species- or population-level models

Populations are groups of organisms, all of the same species, that live in a given area and interact. Biodiversity change at the species or population level is often measured using data on population demography and species distribution (i.e. the distribution of populations within a species). Populations change in size and distribution due to the interaction between internal (e.g. growth rate, reproduction) and external (e.g. resources, predation, diseases) factors. Models building from the simple exponential function, including the logistic population model, life table matrix modelling, the Lotka-Volterra models of community ecology, meta-population theory, and the equilibrium model of island biogeography and many variations thereof, are the basis for ecological population modelling to predict changes over time.

Community-level models

Community-level modelling offers an opportunity to move beyond species-level predictions and to predict broader impacts of environmental changes, which may be relevant in certain decision-making contexts. For example, it can be used to predict the impact of losing a top predator in the structure of a trophic network or the impacts of land-use change in native communities. Community-level approaches are also recommended when: time and financial resources are limited; when existing data are spatially sparse; when the knowledge on individual species distribution is limited or even absent, and when species diversity is beyond what can feasibly be modelled at the individual species level. Overall, assessing changes in community composition, including both species presence and abundance, and how those changes affect ecosystem processes, provides a more detailed understanding of the impacts of drivers. Moreover, species richness - a community-level metric – is a commonly used biodiversity indicator.

Ecological interaction networks

Ecological interaction networks include, among other examples, trophic webs and plant-pollinator webs. Species interactions within communities can be explicitly modelled using process-based approaches that describe the links between species and the dynamics that determine species coexistence in the network, such as predator-prey oscillations.

Correlative approaches are also frequent in studies of interaction networks, due to their lower information requirements, but it is generally recommended to pursue more mechanistic approaches that build on first principles and ecological theory. Similarly, applications in modelling marine ecosystems will require the coupling of different trophic levels that may have different characterisations. One way to represent biodiversity in complex marine systems would be to concentrate the detail of representation at the target species level and their main interactions at the community level. Community interaction network approaches have been used to assess the impacts of, for example, invasive species, the overfishing of top predators, biodiversity and ecosystem function relationships, freshwater pollution and global warming.

Ecosystem-level models and integrated assessment models

Ecosystem-level models may focus on the biophysical dimension of ecosystems (e.g. dynamic global vegetation models), or they can be developed to also include economic and social aspects (e.g. Ecopath with Ecosim model).

Dynamic Global Vegetation Models (DGVMs) are process-based models that simulate various biogeochemical, biogeophysical and hydrological processes such as photosynthesis, heterotrophic respiration, autotrophic respiration, evaporation, transpiration and decomposition. They are the most advanced tool for estimating the impact of climate change on vegetation dynamics at the global scale. The basic structure of a DGVM is shown in Figure 1.

Figure 1: Structure of Dynamic Global Vegetation Models (Modified from: http://seib-dgvm.com/oview.html).

Adding a further level of complexity beyond ecosystem modelling is achieved through integrated assessment models (IAMs, see Figure 2), which are defined as ‘an interdisciplinary process that combines, interprets, and communicates knowledge from diverse scientific disciplines from the natural and social sciences to investigate and understand causal relationships within and between complicated systems’.

Figure 2: Schematic representation of a typical full-scale integrated assessment model. Red labels and arrows represent existing model components and interactions, while grey labels and grey dashed arrows indicate important components and interactions not currently included (Modified from Harfoot et al., 2014a. Integrated assessment models for ecologists: the present and the future. Copyright c 2014 by John Wiley Sons, Inc. Reprinted by permission of John Wiley & Sons, Inc).

There are generally two main principles to integrated assessments: integration over a range of relevant disciplines and the provision of information suitable for decision making. IAMs, therefore, aims to describe the complex relationships between environmental, social and economic drivers that determine current and future states of the system and the effects of climate change, in order to derive policy-relevant insights. One of the essential characteristics of integrated assessment is the simultaneous consideration of the multiple dimensions of environmental problems. At the global level, IAMs could potentially be a valuable tool for modelling biodiversity dynamics under different drivers; however, current IAMs are not developed for this application. Existing IAMs are largely used for modelling climate change and investigating options for climate mitigation. Key outputs from IAMs include anthropogenic greenhouse gas emissions. However, these also provide projections for other variables, such as land cover and land use (including deforestation rates).

Examples of Integrated Assessment Models:

• IMAGE (Integrated Model to Assess the Global Environment)
• DICE (Dynamic Integrated model of Climate and the Economy)
• FUND (Climate Framework for Uncertainty, Negotiation and Distribution)
• MERGE (A Model for Evaluating the Regional and Global Effects of GHG Reduction Policies)