Models can be used to address the effects of changes in indirect drivers on direct drivers of change in nature.
Indirect drivers are drivers that operate diffusely by altering and influencing direct drivers as well as other indirect drivers (also referred to as ‘underlying causes’).
Indirect drivers play a major role in influencing direct drivers of biodiversity and ecosystem change, as well as strongly influencing other indirect drivers. Socio-economic and demographic trends heavily influence consumption patterns with subsequent environmental implications. In addition to interacting with socio-economic and demographic drivers, technological innovation can lead to the adoption of cleaner and more sustainable energy production, as well as indirectly contributing to environmental degradation through electronic and other waste as well as increased demand for the raw materials used in new technologies. While difficult to model, an understanding of the role of societal drivers such as culture and government is crucial to sustainable ecosystem management as these are strong drivers of value sets and decision frameworks that affect behaviours.
Direct drivers (natural and anthropogenic) are drivers that unequivocally influence biodiversity and ecosystem processes (also referred to as ‘pressures’).
Anthropogenic direct drivers are to a significant extent driven by the aforementioned indirect drivers. Direct drivers impact biodiversity and ecosystem change at a more proximate level, frequently involving synergies with other direct drivers, and ultimately feeding back into indirect drivers. Examples of direct drivers of biodiversity and ecosystem change are land-use change, climate change, pollution, natural resource use and exploitation, and invasive species.
Land-use change is the major human influence on habitats and can include the conversion of land cover (e.g. deforestation or mining), changes in the management of the ecosystem or agro-ecosystem (e.g. through the intensification of agricultural management or forest harvesting) or changes in the spatial configuration of the landscape (e.g. fragmentation of habitats).
At the regional scale, a variety of different models have emerged in the past decades to simulate changes in land use driven by demographic change, policies and changing demands for land-based commodities or urban use. Model structure and characteristics are often specific to the scale of application, the research questions and the dominant processes involved. Agent-based models have become popular tools for small areas and when it is important to explicitly represent diversity in land-use decision making. In such models, the changing landscape pattern emerges from the decisions of individual landowners and managers that respond to indirect drivers.
At larger spatial and temporal scales, a simpler conceptualisation of decision making is often applied and land-use change is simulated based on the suitability of locations for a specific land use, with the regional-level demands for the different land uses and spatial constraints resulting from regulations and land-use planning.
Direct driver pathways of climate change are related to changes in climate and weather patterns impacting in situ ecosystem functioning and causing the migration of species and entire ecosystems. There are indications that climate change-induced temperature increases may threaten as many as one in six species at the global level. Rising atmospheric CO2 concentrations leading to higher ocean temperatures and ocean acidification are expected to have profound effects upon marine ecosystems, particularly coral reefs and marine communities near the seafloor. Recent studies projecting reef contraction due to global warming are unanimous in their depiction of the negative impacts on the marine biodiversity that depend on these ecosystems, although the direct effects of ocean acidification are highly variable across different taxa.
The Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) used climate change projections to make impact assessments in different Earth system sectors and at different scales. Based on common background scenarios, uncertainties across multiple impact models have been derived. ISI-MIP aims to establish a longer-term coordinated impact assessment effort driven by the entire impact community covering all biodiversity and ecosystem services sectors on global scales and for selected regional and ecosystem-specific case studies. In this way, feedbacks between managing biodiversity and ecosystem services sectors, climate and Earth systems can be studied in a loosely coupled manner. A few groups are currently working on fully coupling all three model types (global circulation models, Earth system models and integrated assessment models), where the latter cover both the climate mitigation and adaptation functions of ecosystem management. Using such full coupling, climate drivers and their biodiversity and ecosystem services feedbacks can be consistently analysed. Decision-support tools can be expected to become more useful in the decades to come, as the temporal (including climate extremes) and spatial resolution of climate signals improve and more transient model runs become available.
Pollution is an important driver of biodiversity and ecosystem change throughout all biomes, with particularly devastating direct effects on freshwater and marine habitats.
At a global level, the atmospheric deposition of nitrogen has been recognised as one of the most important threats to the integrity of global biodiversity. Once nitrogen is deposited on terrestrial ecosystems, a cascade of effects can occur that often leads to overall declines in biodiversity. Within terrestrial biomes, nitrogen deposition through fossil fuels and fertiliser use has been found to impede decomposition and slow microbial growth, with a number of implications for terrestrial biodiversity. Changes in biotic or ecological characteristics are simulated in response to environmental drivers using mathematical representations of the most important processes. Such process-based models are useful for assessing temporal trends and response times, however, they often require a large amount of data for model calibration.
While terrestrial ecosystems have been affected by nitrogen-phosphorous fertilisers, these have had a far more pernicious effect on the biodiversity of freshwater and marine habitats, leading to eutrophication and hypoxic or ‘dead’ zones that support no aquatic life. Eutrophication and acidification occur when nitrogen and phosphorous are introduced, allowing algal blooms to proliferate which deplete the water of oxygen as well as frequently resulting in toxic algae. Integrated approaches to modelling nutrient emissions have been conducted on a global scale using the Millennium Ecosystem Assessment storylines and the Global Nutrient Export from Watersheds (NEWS) model highlighting the role of indirect drivers on future nutrient emissions.
Examples of models:
- Global Nutrient Export from Watersheds (NEWS) model
The anthropogenic exploitation of wildlife has occurred throughout human history, leading to biodiversity loss and extinctions; however, the recent rate of loss has accelerated sharply. The most overexploited species include marine fish, invertebrates, trees, tropical vertebrates hunted for bushmeat and species harvested for the medicinal and pet trade.
Human activities have severely affected ocean health through overfishing, although there are significant country level differences. As the primary driver of the decline in marine resources, the overexploitation of marine habitats has led to precipitous drops in commercially valuable species, as well as other species subject to bycatch and overfishing. The decision to exit a declining fishery is highly contingent on the socioeconomic status of the fisher, with poorer households less likely to leave. Furthermore, there is evidence at the local level that proximity to markets and market demand better predict overfishing than population density. Here, participatory modelling approaches with greater stakeholder involvement at the local level are highly appropriate for applications involving the sustainable governance of natural resources, with particular salience for the management of fisheries.
Trade in ornamental species, including vertebrates associated with traditional medicine, has led to significant biodiversity losses, particularly in the South East Asia. In addition, trade in aquatic ornamental fish serves as a vector for the spread of invasive species. As a direct driver, natural resource use and exploitation is heavily influenced by indirect drivers such as socio-economic and demographic trends, as well as societal and cultural influences. Indeed, per capita consumption levels are emerging as a potentially more important driver of biodiversity and ecosystem change than population growth. Models and scenarios of natural resource consumption and exploitation therefore need to be intimately tied to economic and sociocultural trends.
Invasive species may be indigenous and/or exotic/alien, and occur mostly in terrestrial and aquatic ecosystems (marine and freshwater), disrupting the ecological functioning of natural systems. Invasive species outcompete local and indigenous species for natural resources, with negative implications for biodiversity. A number of invasive and alien species or weeds have been reported in various parts of the world, resulting in loss of biodiversity at local and regional scales and causing significant economic damage.
A number of invasive species-related models have been developed and used in depicting invasive species spread, distribution in new areas, and also for quantifying their impacts on the environment. Climex, first published in the 1980s, is one of the earliest used models of invasive species. The primary output is a mapped prediction of the favourability of a set of locations for a given species, although the model also produces a suite of additional information to allow for a further understanding of species responses to climate. Bioclimatic envelope models such as Climex have been frequently employed to map species distribution, although the predictive accuracy of such models can vary substantially depending on the inclusion of topographic heterogeneity and CO2 concentrations.
Examples of models:
- Climex model