Correlation to Next Generation Science Standards (NGSS) Shop by NGSS »

Performance Expectations:

MS-LS2-2. Construct an explanation that predicts patterns of interactions among organisms across multiple ecosystems.
HS-LS2-6. Evaluate the claims, evidence, and reasoning that the complex interactions in ecosystems maintain relatively consistent numbers and types of organisms in stable conditions, but changing conditions may result in a new ecosystem.

• Science & Engineering Practices

  Constructing Explanations - Construct an explanation that includes qualitative or quantitative relationships between variables that predict phenomena.

• Disciplinary Core Ideas

  LS2.A: Interdependent Relationships in Ecosystems
  Predatory interactions may reduce the number of organisms or eliminate whole populations of organisms. Mutually beneficial interactions, in contrast, may become so interdependent that each organism requires the other for survival. Although the species involved in these competitive, predatory, and mutually beneficial interactions vary across ecosystems, the patterns of interactions of organisms with their environments, both living and nonliving, are shared.

  LS2.C: Ecosystem Dynamics, Functioning, and Resilience
  A complex set of interactions within an ecosystem can keep its numbers and types of organisms relatively constant over long periods of time under stable conditions. If a modest biological or physical disturbance to an ecosystem occurs, it may return to its more or less original status (i.e., the ecosystem is resilient), as opposed to becoming a very different ecosystem. Extreme fluctuations in conditions or the size of any population, however, can challenge the functioning of ecosystems in terms of resources and habitat availability.

• Crosscutting Concepts

  Patterns - Patterns can be used to identify cause and effect relationships.

  Stability and Change - Much of science deals with constructing explanations of how things change and how they remain stable.

  Cause and Effect - Cause and effect relationships may be used to predict phenomena in natural or designed systems.