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    Finding Selection in All the Right Places
    Lab introducing students to evolutionary genetics using bioinformatics and biocuration, published as GSA Learning Resource
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    Understanding Fish Habitat Selection Using Snorkel Surveys
    Identifying the habitat selection patterns of aquatic species is necessary for protecting threatened and endangered native fishes, as well as for producing satisfying and exciting sport fisheries....
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    A Quick and Simple Natural Selection Role Play
    Teaching evolution remains a challenging task in biology education. Students enter the classroom with stubborn misconceptions and many traditional examples of the process of evolution may not...
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    Riverbird distributions and habitat use
    Riverbirds are widespread, use diverse riparian habitats, and respond rapidly to changing conditions. Students can apply riverbird data to evaluate hypotheses about these responses, while gaining...
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    Winter is Coming! Can you handle the freeze?
    This publication contains the adaptations I made to the Data Nuggets module in order to use it with an online, introductory, non-majors botany class. I used this with a unit on natural and...
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    Altruism Simulation Activity
    An altruism activity that uses a NetLogo altruism simulation.
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    Exaggerated Traits and Breeding Success in Widowbirds
    Teaching Notes on the Exaggerated Traits and Breeding Success in Widowbirds
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    Exaggerated Traits and Breeding Success in Widowbirds
    National Center for Case Study Teaching in Science- this interrupted case focuses on sexual selection in widowbirds.
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    NIBLSE Learning Resource Collection
    NIBLSE Learning Resource Collection

    Keywords: database

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    An Introduction to Statistical Learning: with Applications in R
    This book provides an introduction to statistical learning methods and is aimed for upper level undergraduate students, masters students and Ph.D. students in the non-mathematical sciences.
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    Deme 2.0
    This workbook simulates the population genetics of a single gene with two alleles. It can simultaneously model up to three local populations, which may differ in their selection coefficients.
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    Evolution Through Natural Selection
    This Excel workbook simulates the population genetics of a single gene with two alleles, allowing the user to set the initial allele frequencies and enter parameters for a variety of different...
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    Teaching Selection Mechanisms to 9th Graders using "Are You My Species?" Data Nugget
    Teaching notes related to the use the of Data Nugget "Are You My Species?" to teach mechanisms of selection and quantitative skills in a 9th grade Biology classroom.
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    Exploring Fitness and Population Change under Selection: Avida-Ed Lab Book Exercise 3
    An introductory activity exploring the process of natural selection in action using the Avida-ED simulation.
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    Documenting teaching scholarship among undergraduate biology educators using OER
    Poster on the use, adaptation, and reuse of Open Educational Resources within the QUBES community at OpenEd 2018
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    Data Nugget- City Parks and Texas A&M University-Kingsville Camera Trap Assignment
    The Data Nugget- City Parks module was used in a sophomore-junior level undergraduate wildlife management techniques course (with lab) in Fall 2017. Modifications were made to the original Data...
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    Exploring Population Change without Selection: Avida-Ed Lab Book Exercise 4
    An introductory activity exploring how a population changes without natural selection using the Avida-ED simulation.
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    Birds and Lice: Reciprocal Natural Selection
    This exercise investigates the form and function of lice and their relationship to their bird host.
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    Allele and Phenotype Frequencies in Rock Pocket Mouse Populations
    A lesson that uses real rock pocket mouse data collected by Dr. Michael Nachman and his colleagues to illustrate the Hardy-Weinberg principle.
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    2015-Zhang-EtAl-On the Selection of ODE Models with Application to Predator-Prey Dynamical Models
    We propose a computationally inexpensive approach that employs statistical estimation of the full model, followed by a combination of a least squares approximation (LSA) and the adaptive Lasso.
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