18.3 Ecosystem modeling and ecological forecasting
3 min read•august 7, 2024
Ecosystem modeling helps us understand how nature works. It looks at how elements move through ecosystems and how living things interact. These models show us how ecosystems function and how they might change over time.
Ecological forecasting takes modeling a step further. It predicts future changes in populations, species distributions, and habitats. This helps us plan for conservation and manage ecosystems in a changing world.
Ecosystem Modeling
Biogeochemical Cycles and Food Web Models
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- Biogeochemical cycles represent the movement and transformation of elements (carbon, nitrogen, phosphorus) through ecosystems
- Includes processes such as photosynthesis, respiration, decomposition, and nutrient uptake by organisms
- Models simulate the fluxes and storage of elements in different ecosystem compartments (atmosphere, soil, vegetation, water)
- Food web models depict the trophic interactions and energy flow between organisms in an ecosystem
- Represent the feeding relationships between producers (plants), consumers (herbivores, carnivores), and decomposers
- Help understand the structure, stability, and dynamics of ecological communities
- Can be used to assess the impacts of perturbations (species extinctions, invasive species) on ecosystem functioning
Ecosystem Services and Vegetation Dynamics Models
- Ecosystem services models quantify the benefits humans derive from ecosystems
- Includes provisioning services (food, water, timber), regulating services (climate regulation, water purification), and cultural services (recreation, aesthetic value)
- Models assess the value of ecosystem services and how they are affected by land use change, climate change, and other drivers
- Vegetation dynamics models simulate the growth, competition, and succession of plant communities over time
- Incorporate factors such as climate, soil properties, disturbances (fire, grazing), and species interactions
- Help predict changes in vegetation composition, , and carbon storage under different environmental conditions and management scenarios
- Examples include forest growth models (SORTIE) and (LPJ, ORCHIDEE)
Ecological Forecasting
Population and Species Distribution Models
- Population dynamics models predict changes in the abundance and structure of animal or plant populations over time
- Incorporate factors such as birth rates, death rates, migration, and density-dependence
- Help assess the viability of populations and the effectiveness of conservation strategies
- Examples include matrix population models and individual-based models (IBMs)
- Species distribution models (SDMs) predict the geographic distribution of species based on their environmental requirements
- Relate species occurrence data to environmental variables (climate, topography, land cover) using statistical or machine learning methods
- Help identify suitable habitats, potential range shifts under climate change, and priority areas for conservation
- Examples include MaxEnt, GLMs, and GAMs
Habitat Suitability and Land Use Change Models
- Habitat suitability models assess the quality and extent of habitats for species based on their ecological needs
- Consider factors such as vegetation structure, prey availability, and connectivity
- Help prioritize areas for habitat protection, restoration, or management
- Can be combined with SDMs to refine species distribution predictions
- Land use change models simulate the dynamics of land cover and land use over time
- Incorporate drivers such as population growth, economic development, and policy scenarios
- Help predict the impacts of land use change on biodiversity, ecosystem services, and climate
- Examples include cellular automata models (SLEUTH) and agent-based models (ABM)
Key Terms to Review (18)
Adaptive Capacity: Adaptive capacity refers to the ability of a system, such as an ecosystem or a community, to adjust and respond to changes and disturbances in its environment. This capacity is crucial for resilience, as it determines how well a system can withstand and recover from stressors like climate change, habitat loss, or invasive species. A higher adaptive capacity allows ecosystems and communities to maintain their functionality and continue to provide essential services despite ongoing environmental changes.
Biodiversity indices: Biodiversity indices are quantitative measures used to assess and compare the diversity of species in a given area or ecosystem. These indices help ecologists understand the health of ecosystems, track changes over time, and inform conservation efforts by summarizing complex ecological data into a single value. They play a vital role in ecosystem modeling and ecological forecasting by providing baseline metrics that can be used to evaluate the impacts of environmental changes and human activities on biodiversity.
Biomass: Biomass refers to the total mass of living matter within a specific area or volume, typically expressed in terms of weight per unit area, such as grams per square meter. It includes all forms of organic material, such as plants, animals, and microorganisms, and serves as a crucial component in ecosystem modeling and ecological forecasting by providing insights into energy flow, nutrient cycling, and overall ecosystem health.
Carbon sequestration rates: Carbon sequestration rates refer to the speed at which carbon dioxide (CO2) is captured and stored in various natural and artificial systems, such as forests, soils, oceans, and technological solutions. Understanding these rates is crucial for modeling ecosystem dynamics and forecasting ecological changes, especially as they relate to climate change mitigation efforts and the overall health of ecosystems.
Carrying capacity: Carrying capacity refers to the maximum number of individuals of a particular species that an environment can sustainably support without degrading the habitat. This concept emphasizes the balance between resource availability and population dynamics, influencing factors like energy flow, nutrient cycling, and ecological stability. When a population exceeds its carrying capacity, it can lead to resource depletion, environmental degradation, and ultimately population decline.
Dynamic global vegetation models: Dynamic global vegetation models (DGVMs) are computer simulations that represent the interactions between climate, land surface processes, and vegetation dynamics across the globe. These models help researchers understand how changes in climate and land use affect vegetation patterns and ecosystem functions over time. By simulating plant growth, competition, and biogeochemical cycles, DGVMs provide valuable insights into ecosystem responses to environmental changes.
Ecological niche models: Ecological niche models (ENMs) are computational tools used to predict the distribution of species across geographic areas based on environmental conditions and species occurrence data. These models integrate ecological and biogeographical information, allowing researchers to understand how species interact with their environment and how changes in climate or habitat can impact their distribution and survival.
Ecological succession: Ecological succession is the process by which ecosystems change and develop over time, involving a series of gradual changes in species composition and community structure. This dynamic process can occur in response to disturbances or environmental changes, leading to either primary succession, which starts on lifeless substrates, or secondary succession, which occurs in areas where an ecosystem has been disturbed but soil and organisms still exist. Understanding ecological succession is crucial for modeling ecosystems and predicting future ecological conditions.
Geographic information systems (gis): Geographic Information Systems (GIS) are powerful tools used to capture, store, analyze, manage, and visualize spatial or geographic data. These systems enable users to understand complex relationships within ecological and environmental data by layering various types of information, which is crucial for modeling ecosystems and forecasting ecological changes.
H. Bruce Walker: H. Bruce Walker is a notable figure in the field of ecosystem modeling and ecological forecasting, recognized for his contributions to understanding and predicting ecological dynamics. His work focuses on integrating mathematical modeling with ecological principles to simulate ecosystem responses to various environmental changes, which is crucial for effective conservation and management strategies.
Nutrient Cycling: Nutrient cycling refers to the continuous movement and exchange of essential nutrients through various components of the ecosystem, including soil, water, air, and living organisms. This process is vital for maintaining ecosystem health, supporting plant growth, and ensuring the sustainability of food webs.
Peter G. R. Smith: Peter G. R. Smith is a notable researcher in the field of ecosystem modeling and ecological forecasting. His work has significantly contributed to understanding how ecosystems function and respond to environmental changes, especially through the development and refinement of models that simulate ecological processes. His research plays a key role in addressing pressing environmental issues, providing valuable insights into biodiversity, climate change impacts, and sustainable management practices.
Predictive modeling: Predictive modeling is a statistical technique used to forecast future outcomes based on historical data and identified patterns. It involves creating a model that represents the relationships between variables, which can be used to predict how ecosystems might respond to various changes over time, helping in ecological forecasting and decision-making.
Primary productivity: Primary productivity is the rate at which energy is converted by photosynthetic and chemosynthetic autotrophs to organic substances in an ecosystem. It serves as the foundation of energy flow, as these primary producers harness sunlight or inorganic compounds to create organic matter that fuels the rest of the ecosystem. Understanding primary productivity is crucial for grasping nutrient cycling and ecosystem health, as it directly influences food webs and the availability of resources for various organisms.
Remote sensing: Remote sensing is the technology used to collect information about the Earth's surface and atmosphere from a distance, typically through satellites or aerial sensors. It provides crucial data for monitoring environmental changes, mapping resources, and understanding various Earth systems interactions.
Resilience: Resilience is the ability of an ecosystem to recover from disturbances and maintain its functions and structures over time. This characteristic is crucial for ecosystems as it influences their capacity to withstand changes, adapt to new conditions, and ensure sustainability in the face of environmental stressors such as climate change or human activities.
Scenario analysis: Scenario analysis is a strategic planning method used to evaluate and prepare for potential future events by analyzing different plausible scenarios. This approach helps decision-makers understand the range of possible outcomes and the uncertainties associated with ecological and environmental changes, making it a crucial tool in ecosystem modeling and ecological forecasting.
Trophic Levels: Trophic levels are the hierarchical positions that organisms occupy in a food chain, determined by their feeding relationships. They describe how energy and nutrients flow through ecosystems, starting from primary producers at the base to various levels of consumers. Understanding trophic levels is essential for examining the structure and function of ecosystems, as well as for predicting changes in biodiversity and ecosystem health.