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The hypothesis that network structure, network plasticity and environment frequency predict population variation generates testable genomic predictions where these factors are known.
And city and state planners study the information to direct help to needy communities and predict population trends.
Therefore, in order to accurately predict population persistence under future climate scenarios, it is necessary to incorporate pond hydrology.
These functions were used in Integral Projection Models to predict population growth under CC scenarios and different anthropogenic disturbance regimes.
An inability to predict population response to future habitat projections is a shortcoming in bird conservation planning.
Besides, based on the information as regards transient vital rates, a model should be able to predict population dynamics for any time.
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This study aimed to determine the predictive power of cofactors and to predict population-based normative grip and pinch strength.
We tested whether a battery of subcellular HTS tests could be used to accurately predict population-level effects of engineered metal nanoparticles (ENPs) on marine phytoplankton, important primary producers that support oceanic food webs.
Dispersal exerts an important influence on population genetics and demography, as well as on our ability to predict population-level responses to environmental disturbance [1], [2].
In addition, comorbidity indices have become an important tool for policymakers, administrators and researchers to predict population-based outcomes.
In addition to providing a valuable individual service to people with curable diseases, the proposed system will also predict population-level disease spread risk for infectious diseases automatically using the crowd-sourced symptoms from the population.
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