Ecological modeling aims to create mathematical or computational representations of ecological systems to predict their behavior and dynamics over time.

Community models focus on the interactions and dynamics among different species within an ecological community.

The Lotka-Volterra model assumes that the prey population growth rate is independent of the predator population, while the predator population growth rate is proportional to the prey population.

Dispersal refers to the movement of individuals from one subpopulation to another within a metapopulation.

Landscape models focus on the spatial distribution and interactions of species or populations across a heterogeneous landscape.

Sensitivity analysis aims to determine which parameters in an ecological model have the greatest impact on the model's output.

The Akaike Information Criterion (AIC) is a statistical measure used to compare the goodness-of-fit of different ecological models.

Calibration involves adjusting the parameters of an ecological model to improve its agreement with observed data.

Ecosystem models focus on the interactions and dynamics of energy and nutrients within an ecosystem.

The logistic growth model assumes that the population growth rate is proportional to the population size, but decreases as the population approaches its carrying capacity.

A raster is a grid or lattice structure commonly used to represent the spatial domain in spatially explicit ecological models.

Model validation involves comparing the predictions of an ecological model with independent observed data to assess its accuracy.

Population models focus on the dynamics of a single population, including factors such as birth, death, and migration.

Model building involves the process of developing a mathematical or computational representation of an ecological system.

Linear regression is a statistical technique used to determine the relationship between two variables and test its significance.