Mathematical and statistical methods used to analyze biological data are powerful research tools that play several important roles in conceptualizing and understanding the structure and dynamics of ecological systems. Through the development of specialized and sophisticated quantitative tools and models, the complex nature of data arising from studies of ecological systems can be understood.
Scientist conduct research to develop and evaluate mathematical and statistical tools and models that abstract and accommodate the unique characteristics of ecological systems and data, while also allowing for maximum extraction of information about those systems. This research is critical for improving the information gained from time-consuming, logistically diffi cult, and resource-intense field studies.
Using Quantile Regression to Investigate Ecological Limiting Factors - Principal Investigator - Brian Cade
Unexplained heterogeneity in statistical models of animal responses to their physical environment is reasonable to expect because the measured habitat resources are a constraint on—but not the sole determinant of—abundance, survival, fecundity, or fitness. The ecological understanding and reliability of management predictions based on animal habitat models can be improved by shifting focus from estimating expected values (means) of responses to estimating intervals of responses associated with multiple percentiles of a distribution.
Quantitative and Statistical Research Collaboration - Principal Investigator - Brian Cade
Mathematical and statistical models are powerful research tools that play several important roles in conceptualizing and understanding the structure and dynamics of complicated ecological systems, including developing mechanistic hypotheses pertaining to ecological systems, designing studies that elucidate ecosystem structure and function, and extracting information from data.
Below are other science projects associated with this project.
Using Quantile Regression to Investigate Ecological Limiting Factors
Quantitative and Statistical Research Collaboration
Mathematical and statistical methods used to analyze biological data are powerful research tools that play several important roles in conceptualizing and understanding the structure and dynamics of ecological systems. Through the development of specialized and sophisticated quantitative tools and models, the complex nature of data arising from studies of ecological systems can be understood.
Scientist conduct research to develop and evaluate mathematical and statistical tools and models that abstract and accommodate the unique characteristics of ecological systems and data, while also allowing for maximum extraction of information about those systems. This research is critical for improving the information gained from time-consuming, logistically diffi cult, and resource-intense field studies.
Using Quantile Regression to Investigate Ecological Limiting Factors - Principal Investigator - Brian Cade
Unexplained heterogeneity in statistical models of animal responses to their physical environment is reasonable to expect because the measured habitat resources are a constraint on—but not the sole determinant of—abundance, survival, fecundity, or fitness. The ecological understanding and reliability of management predictions based on animal habitat models can be improved by shifting focus from estimating expected values (means) of responses to estimating intervals of responses associated with multiple percentiles of a distribution.
Quantitative and Statistical Research Collaboration - Principal Investigator - Brian Cade
Mathematical and statistical models are powerful research tools that play several important roles in conceptualizing and understanding the structure and dynamics of complicated ecological systems, including developing mechanistic hypotheses pertaining to ecological systems, designing studies that elucidate ecosystem structure and function, and extracting information from data.
Below are other science projects associated with this project.