Exploring Models in the Biology Classroom

Abstract

Models are simplified representations of more complex systems that help scientists structure the knowledge they acquire. As such, they are ubiquitous and invaluable in scientific research and communication. Because science education strives to make classroom activities more closely reflect science in practice, models have become integral teaching and learning tools woven throughout the Next Generation Science Standards (NGSS). Although model-based learning and curriculum are not novel in educational theory, only recently has modeling taken center stage in K-12 national standards for science, technology, engineering, and mathematics (STEM) classes. We present a variety of examples to outline the importance of various types of models and the practice of modeling in biological research, as well as the emphasis of NGSS on their use in both classroom learning and assessment. We then suggest best practices for creating and modifying models in the context of student-driven inquiry and demonstrate that even subtle incorporation of modeling into existing science curricula can help achieve student learning outcomes, particularly for English-language learners. In closing, we express the value of models and modeling in life beyond the classroom and research laboratory, and highlight the critical importance of “model literacy” for the next generation of scientists, engineers, and problem-solvers.

Full text

ABSTRACT
Models are simplified representations of more complex systems that help scientists
structure the knowledge they acquire. As such, they are ubiquitous and invaluable
in scientific research and communication. Because science education strives to
make classroom activities more closely reflect science in practice, models have
become integral teaching and learning tools woven throughout the Next
Generation Science Standards (NGSS). Although model-based learning and
curriculum are not novel in educational theory, only recently has modeling
taken center stage in K–12 national standards for science, technology,
engineering, and mathematics (STEM) classes. We present a variety of
examples to outline the importance of various types of models and the practice
of modeling in biological research, as well as the emphasis of NGSS on their
use in both classroom learning and assessment. We then suggest best practices
for creating and modifying models in the context of student-driven inquiry and
demonstrate that even subtle incorporation of modeling into existing science
curricula can help achieve student learning outcomes, particularly for English-
language learners. In closing, we express the value of models and modeling in
life beyond the classroom and research laboratory, and highlight the critical
importance of “model literacy”for the next generation of scientists,
engineers, and problem-solvers.
Key Words: Next Generation Science Standards;
model-based learning; inquiry-based science; scientific
practice; student learning; inquiry-based learning.
Introduction
The Next Generation Science Standards
(NGSS) aim to make the teaching of sci-
ence more closely aligned with the practice
of science. The NGSS highlight models,
which are simplified representations of
more complex phenomena, as central to
all aspects of learning in science, technol-
ogy, engineering, and mathematics (STEM; NGSS Lead States,
2013a). Mirroring the process of scientific research, the NGSS are
structured in three primary sections: Disciplinary Core Ideas (the
knowledge base that scientists need to do their work), Practices
(what scientists actually do), and Crosscutting Concepts (frameworks
scientists use to connect core ideas). Performance Expectations
(learning and skills assessment) within the NGSS are combinations
of these Crosscutting Concepts,Practices, and Disciplinary Core Ideas.
“Developing and using models”is one of seven NGSS Practices, and
“Systems and system models”is one of eight Crosscutting Concepts
within the NGSS (National Research Council, 2012a). Because
NGSS Performance Expectations emphasize student engagement in
using models to explicitly demonstrate knowledge of Disciplinary
Core Ideas (e.g., HS-LS1-5: Use a model to illustrate how photosyn-
thesis transforms light energy into stored chemical energy), it is crit-
ical that teachers regularly and clearly incorporate scientific models
in science lessons.
Models are key elements in daily practice for biologists, and
model-based learning has a rich history in educational theory
(Louca & Zacharia, 2012). Nevertheless, many biology teachers are
not well versed in the broad range of models used by scientists and
therefore find it difficult to envision how to incorporate them into
classroom instruction (Hoskinson et al., 2014).
This may be because instructors fail to realize that
models extend far beyond the familiar 3D physical
models of cell structure or the digestive system. In
fact, teachers and scientists alike use a variety of
model types in their instruction and research with-
out labeling them as such.
Here, we (1) highlight the diversity of ways in
which models are used to conduct and teach sci-
ence and (2) provide a framework for intentional
use of models in biology classroom activities as
emphasized by the NGSS. As practicing scientists
and educators working together to infuse inquiry-
based science curricula in local middle and high
school classrooms through a National Science Foundation GK–12
program (http://scwibles.ucsc.edu), we offer a perspective on the
use of models in the biology classroom that comes from both
Models are key
elements in daily
practice for biologists,
and model-based
learning has a rich
history in educational
theory.
The American Biology Teacher, Vol. 78, No 1, pages. 35–42, ISSN 0002-7685, electronic ISSN 1938-4211. ©2016 by the Regents of the University of California. All rights
reserved. Please direct all requests for permission to photocopy or reproduce article content through the University of California Press’s Reprints and Permissions web page,
www.ucpress.edu/journals.php?p=reprints. DOI: 10.1525/abt.2016.78.1.35.
THE AMERICAN BIOLOGY TEACHER EXPLORING MODELS
35
FEATURE ARTICLE Exploring Models in the Biology
Classroom
•CA LE B M. BRYCE, VIKRAM B. BALIGA,
KRISTIN L. DE NESNERA, DURAN FIACK,
KIMBERLY GOETZ, L. MAXINE TARJAN,
CATHERINE E. WADE, VERONICA YOVOVICH,
SARAH BAUMGART, DONALD G. BARD,
DORIS ASH, INGRID M. PARKER,
GREGORY S. GILBERT

biological research and educational theory. We
describe a range of ways in which models can be
used in the classroom, and how the NGSS empha-
size modeling as a central practice. We outline a
“modeling continuum,”analogous to Herron’s
(1971) inquiry continuum, and make suggestions
for how teachers can acknowledge and enhance
their use of models in the classroom in either subtle
or substantial ways to more effectively mirror the
essential scientific practice of modeling.
Models in Biology Research
Scientists primarily use models in two ways. First
and foremost, models are used to increase our
understanding about the world through evi-
dence-based testing. To evaluate the merits and
limitations of a model, it must be challenged with
empirical data. Models that are inconsistent with
empirical evidence must be either revised or dis-
carded. In this way, modeling is a metacognitive
tool used in the hypothesis-testing approach of
the scientific method (Platt, 1964). Second, sci-
entists use models to communicate and explain
their findings to others. This allows the broader sci-
entific community to further challenge and revise
the model. Furthermore, this dynamic quality of sci-
entific models allows researchers to test, retest, and
ultimately gain new understanding and insight.
Biologists use models in nearly every facet of
scientific inquiry, research, and communication.
Models are helpful tools for representing ideas
and explanations and are used widely by scientists
to help describe, understand, and predict processes occurring in
the natural world. All models highlight certain salient features of
a system while minimizing the roles of others (Starfield et al.,
1990; Hoskinson et al., 2014). By nature of their utility, models
can take many forms based on how they are created, used, or com-
municated. After reflecting on the types of models we use in our daily
work as biological researchers, we have identified three main catego-
ries of models used regularly in scientific practice: concrete, concep-
tual, and mathematical (Figure 1).
Development of scientific models of one type can prompt and
inform models of other types. For example, Watson and Crick
developed a physical model of DNA to help determine how dif-
ferent nucleotide bases can pair to produce a double-helix struc-
ture (Figure 1b), which in turn suggested a conceptual model for
DNA replication (Watson & Crick, 1953). Jacques Monod’s
observation of a “double growth curve”of bacteria that deviated
from the expected exponential growth model led to the develop-
ment of a new, more accurate model of cellular regulation of gene
expression (Figure 1e; Jacob & Monod, 1961). Ecologists James
Estes and John Palmisano developed conceptual models of popu-
lation growth and decline among marine predator–prey species
(Figure 1g) on the way to creating mathematical models of sea
otter, sea urchin, and kelp dynamics along the Alaskan coast
(Estes & Palmisano, 1974).
Models in Learning & Teaching
“Model-based learning”refers explicitly to the understanding gained
while creating or refining scientific models (Louca & Zacharia,
2012), but mental models are central to learning theory more broadly
and provide the foundation for all other types of models (Johnson-
Laird, 1983). Mental models often preexist instruction and are limited
to conceptual or mathematical forms. A person’s conceptual under-
standing of a process or relationship (i.e., mental model) directly
informs his or her creation of a model, whether that model is concrete,
conceptual, or mathematical. Through testing and experience, these
models can be updated to reflect reality more accurately. As students
iteratively draft scientific models, they inevitably modify their under-
lying mental models through analysis. In a classroom context, stu-
dents refine their own mental models as they observe, analyze, and
discuss the modeling work of others.
Learning theorists from the cognitivist school typically sought
ways to translate mental operations into visible forms called “repre-
sentations,”such as diagrams or flowcharts. The internal represen-
tations that comprise mental models are tightly linked to reasoning
associated with learning (Bauer & Johnson-Laird, 1993; Johnson-
Laird, 2010). To this end, the emphasis of NGSS on modeling in
the science classroom may present unique learning opportunities
for students who are English-language learners. Developing and
Figure 1. Scientific models may be concrete (physical representations in 2D or
3D), mathematical (expressed symbolically or graphically), or conceptual
(communicated verbally, symbolically, or visually). Concrete models can be
simplified representations of a system (a) or working-scale prototypes (b).
Mathematical models can be descriptive or predictive, and empirical or
mechanistic. A descriptive model, such as a regression line, depicts a pattern of
association that is derived from empirical data (c), whereas a predictive model uses
equations to represent a mechanistic understanding of a process (d); each can be
expressed both symbolically and visually. Conceptual models focus on an
understanding of how a process works and can be expressed as visual (e)or
symbolic (f) representations as well as through verbal descriptions or analogies (g).
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36

using models provides these students with nonverbal ways to
express understanding initially, and their consistent use in the
classroom gives these students practice and confidence in speaking
about how models explain observations (Quinn et al., 2011). The
interplay between representations (i.e., models) of a system and
the language used to describe them builds students’conceptual
understanding of the system in question while refining their science
literacy (Quinn et al., 2011; Stoddart et al., 2011).
Model-based learning has seen numerous interpretations in
theory and practice (Gobert & Buckley, 2000; Buckley et al., 2004;
Clement & Rea-Ramirez, 2008; Louca & Zacharia, 2012; Windschitl,
2013). Here, we adopt Gilbert’s (2004) taxonomy of five modes of
modeling: concrete, verbal, symbolic, visual, and gestural (Figure 2).
These closely overlap our categorization of models in biological
research (Figure 1), with the addition of gestural models, which scien-
tists use regularly to complement their verbal communications. A
key distinction is that the five modes of modeling (Figure 2) offer a
framework for how models are used in teaching, while our three cate-
gories of models (Figure 1) provide a structure for categorizing models
used routinely in science. This three-part taxonomy of model types is
useful for identifying things that are unknown (new hypotheses,
unexplored relationships among variables), whereas modeling used
in teaching often illustrates known concepts to help students make
sense of what scientists accept as supported by evidence.
Model-based learning typically consists of five steps: (1) observa-
tion and data collection, (2) construction of a preliminary model,
(3) application, (4) evaluation, and (5) revision of the preliminary
model (Fretz et al., 2002). In practice, model-based learning and
model-based inquiry are reflections and extensions of the scientific
method (Windschitl et al., 2008) and have been applied across a vari-
ety of disciplines in both computer-based learning environments and
classroom settings (Fretz et al., 2002; Clement & Rea-Ramirez, 2008).
A Modeling Continuum within the
Framework of NGSS
The Framework for K–12 Science Education (National Research Coun-
cil, 2012a) offers an outline for teachers to provide gradual exposure
to model development to students at each grade level. The use of
models for K–12 students progresses from simple (e.g., model dupli-
cation) to complex applications (e.g., tests of model reliability and
predictive power) as classroom activities transition from demonstra-
tions by the instructor toward student-directed inquiry (Figure 3). In
earlier grades (K–2), students largely focus on recognizing models as
tools that can be used to explain familiar structures (e.g., a plastic
skeleton or diagram of a plant) or scientific practices (e.g., measuring
quantities, comparing relationships). Students are presented with
Figure 2. Examples of biological concepts taught in the high school biology curriculum, represented by each of Gilbert’s (2004)
five modes of modeling at different scales.
THE AMERICAN BIOLOGY TEACHER EXPLORING MODELS
37

model-building activities that are designed to unveil common char-
acteristics of models and how they are used in STEM fields.
During the next stage of educational development (grades 3–5),
students start to build and revise simple models to design solutions
to problems or represent phenomena (e.g., 3-LS1-1: Develop models
to describe that organisms have unique and diverse life cycles, but all
have in common birth, growth, reproduction, and death; NGSS
Lead States, 2013b). Students begin to develop and apply models
to describe processes, explain relationships, and make predictions.
As students advance to middle school (grades 6–8), the use of
models expands to predicting and testing more abstract phenom-
ena (e.g., MS-LS1-7: Develop a model to describe how food is rear-
ranged through chemical reactions, forming new molecules that
support growth and/or release energy as this matter moves through
an organism; NGSS Lead States, 2013b). At this stage, students
undertake increasingly open-ended investigations of model struc-
ture. Such investigations include variable modification to validate
observed changes in a system, integration of uncertain and unob-
servable factors and/or variables, and the generation of data to test
hypotheses explicitly. Finally, in high school (grades 9–12), stu-
dents construct and use models for more advanced prediction
and to represent interactions between variables within a system
(e.g., HS-LS2-5: Develop a model to illustrate the role of photosyn-
thesis and cellular respiration in the cycling of carbon among the
biosphere, atmosphere, hydrosphere, and geosphere; NGSS Lead
States, 2013b). Inquiry at this stage is largely focused on the critical
evaluation and comparison of different models to improve predic-
tions and explanatory power.
This learning progression for “Developing and using models”
(NGSS Lead States, 2013a) offers a continuum of exposure to
modeling through inquiry. Students are initially taught how to rec-
ognize the use of models in STEM fields before advancing to more
complex activities in which they revise, compare, and evaluate
models on the basis of predictive and explanatory power. In this
framework, models are constructs that are useful to ask or answer
a question, rather than just to describe an object (e.g., a mathemat-
ical equation versus a physical model of a cell). Models are abstract
descriptions that can be refined through evidence-based testing by
examining the assumptions, domain, parameters, and structure of
the model (see Figure 4).
Inquiry & Learning to Create & Modify
Models: Classroom Best Practices
Inquiry encompasses more than just asking questions: it involves
expanding one’s depth of knowledge (Webb, 1997) through system-
atic exploration of a subject from various perspectives. A scientist or
student engaged in inquiry begins by distinguishing what is known
from what is unknown in the context of a specific learning outcome.
Figure 3. Asking students questions about their model can help them make subtle shifts toward more complex engagement
with models; students shift from simply identifying models, to using them, to constructing their own models. This progression of
how students engage with models parallels that which is established across grade levels (NGSS Lead States, 2013a).
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38

Creating models helps identify the most important features of com-
plex processes and is a productive exercise for inquiry-based activities.
Breaking down a complex process into its constituent parts helps stu-
dents derive the process itself rather than memorize a series of facts
about a process. Next, the student creates a model to represent and
simplify a phenomenon and/or relationship in order to develop ques-
tions and hypotheses, which are subsequently tested through data col-
lection. Data are used to reevaluate the initial model and develop
arguments based on evidence. Additionally, revising models provides
students with metacognitive opportunities –they better understand
their own thinking through evaluation. Initial models evolve to reflect
the learning that ultimately results from curiosity-driven investigations
to understand how a system operates (NGSS Lead States, 2013a).
Perhaps the most effective use of models and modeling in the
classroom is to have students create a model upon exposure to a
new idea, and then revisit and revise that model over an extended
period (Windschitl, 2013). Students return to their models multiple
times over the course of a unit to incorporate ideas learned from sub-
sequent readings, activities, tests, and discussions. In this way, stu-
dents revise and develop more nuanced models while using
critical-thinking skills to expand their depth of knowledge. For
example, after being introduced to the term biodiversity, high school
students devised their own conceptual and mathematical models to
assess biodiversity. Over the course of the school year, they tested
and refined these models by quantifying plant and insect diversity
before and after planting a native plant garden on the school’s cam-
pus (Yost et al., 2012).
This prolonged time frame may prove challenging for instruc-
tors who are just beginning to use model-based inquiry in their
classrooms. However, it deepens students’understanding of the
scientific process and, from our experience, becomes easier to
implement with practice. When considering this approach to
models and modeling, certain forms of models are better suited
for use in science classrooms than others. Models are most effec-
tive in science education when they offer clear visual representa-
tion of processes or phenomena, incorporate both observable
and unobservable features, are context-rich, and can easily be
revised (Windschitl, 2013). Unobservable features are not detect-
able by human senses or technology. Events or processes may
be unobservable because of their spatial scale (e.g., atoms, the
universe) or temporal scale (e.g., evolution, continental drift) or
because they are not accessible physically (e.g., Earth’score)or
temporally (e.g., geologic time; Ambitious Science Teaching, 2015).
Unobservable features also include inferred relationships, such as
Figure 4. Case study (Algebra I and Algebra II students): Models as predictive tools (Bryce et al., 2014).
THE AMERICAN BIOLOGY TEACHER EXPLORING MODELS
39

the slope of a regression line, which is not itself measured empirically
but, rather, relies on inference from data.
In the classroom, instructors generally rely on formative assess-
ment to evaluate student learning and performance. In the context
of model-based learning, assessment should evaluate the develop-
ment of student knowledge and the application of that knowledge
toward a deeper understanding of scientific practices (National
Research Council, 2012a). We offer four assessment criteria that
can be used to evaluate the composition, accuracy, predictive power,
and comprehension of models to determine the depth of student
knowledge and application of models in the classroom (Table 1).
We emphasize here that, while modeling is an essential scientific
and classroom practice for enhancing learning, it complements
rather than precludes the use of other demonstrated teaching tools.
Teachers should choose the correct teaching tool for their learning
objective. Therefore, their goals will determine how much time they
spend on modeling in the classroom. In other words, modeling is
the most appropriate learning tool in many, but not all, situations.
For example, if you want students to learn how to pipette, they
probably do not need to draw a conceptual model about pipetting.
However, drawing arrows to illustrate the interactions between
organisms can help tremendously in understanding food webs.
Subtle Shifts in the Classroom
It would be ideal to incorporate many full-scale, inquiry-based
modeling activities into science classes to encourage students to
explore and explain the natural world. However, limited time and
resources in existing science curricula mean that this is not always
practical. Fortunately, teachers can shift their lesson plans in subtle
ways to incorporate modeling exercises on a smaller scale while still
enhancing student learning. Even at small scales, the repetitive,
contextualized practice of model-building helps students acquire
knowledge, generate predictions and explanations, analyze and
interpret data, develop communication skills, and make evidence-
based arguments through active participation (Schwarz et al., 2009).
Many types of activities currently used in the classroom can be easily
adapted in small, manageable ways to teach students about models by
using “subtle shifts”(Figure 3). Here, we explore how to enhance lab
and classroom activities by engaging students with scientific modeling
in small but meaningful ways.
We often ask students to create simplified physical replicas of
objects, which supports active learning (i.e., “learning by doing”;
DuFour et al., 2006). In STEM courses, active learning with physical
objects increases student performance, particularly in historically
underrepresented populations (Eddy & Hogan, 2014; Freeman
et al., 2014), through engaging the tactile senses (Nersessian, 1991;
Begel et al., 2004). Active, hands-on learning also helps students ana-
lyze the organization and orientation of component parts (Haury &
Rillero, 1994).
Revisiting an example mentioned earlier, a common classroom-
learning activity is to have students construct a clay model of a cell
(Figure 5). Through some simple, scaffolded inquiry, this basic
physical representation can be a vehicle to a deeper understanding
of modeling as a process. Asking questions about the physical models
they have made can help students understand the context and justi-
fication for their model, as well as think critically about what their
model truly represents. What cell features did they include in the
clay cell model, and what features did they omit –and why? What
does this model demonstrate about a cell? Which aspects of a cell
are hard or impossible to represent with a clay model? Further,
teachers may try shifting the objective of building physical models
Table 1. Student model-assessment criteria.
Criterion Description Example
Composition Does the model include all the major
components of the process it describes?
Does a food web include all key species and
connections?
Accuracy Does the model accurately describe the
underlying process that generated your data?
Are cause-and-effect relationships appropriately
represented by the model?
Prediction Can you make predictions with your model? Does your regression of number of seeds on
mass of seeds accurately predict the number of
seeds in a new batch of seeds? (See Figure 4,
question 8)
Comprehension Does the student understand the assumptions
of the model?
Can a student use his or her model to describe
the process it represents?
Figure 5. Clay cell models with organelles are ubiquitous in
biology classrooms, but inquiry can be infused to illustrate the
process of modeling beyond simple physical representations.
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40

from serving as simple representations to addressing scientific ques-
tions. For instance, instead of building a model that reproduces the
features of plankton, have students construct models of plankton to
test the effect of structure on plankton sinking rates (Smith et al.,
2007). By generating hypotheses about the traits that affect buoy-
ancy, creating a series of models of different shapes, and then timing
their sinking rates through a viscous liquid (e.g., corn syrup),
students can use models to learn why high surface-area-to-volume
ratio is a common adaptation that reduces sinking rates of oceanic
plankton.
Biology students often learn about complex processes, such as
nutrient cycling or DNA transcription and translation, through system
models. System models are organized groups of related objects or
components that form a whole (National Research Council, 1996,
2012b). An example of a simple system model is the “Vitruvian
Man”figure used in some anatomy courses (Figure 1a). The Vitruvian
Man is an illustration created by Leonardo da Vinci that depicts a male
figure in two superimposed positions, simultaneously inscribed in
both a circle and a square. This image of the human figure is a model
that represents ideal human proportions as described by the ancient
Roman architect Vitruvius. On this illustration, da Vinci’snotes
describe 15 ideal human proportions, the most famous of which is
that the height of a person equals the length of his or her outspread
arms. Da Vinci’s visual model remains one of the most referenced
and reproduced images in the world, appearing in books and films
and even on coins, and presents an excellent opportunity for class-
room inquiry.
Beyond engaging the iconic Vitruvian Man image in a historical
and cultural context, students can explore it as a model by question-
ing its assumptions and testing its accuracy (Baliga & Baumgart,
2014). This activity gives students the opportunity to use a general
model to form a specific hypothesis, analyze data, and, ultimately,
argue whether the evidence they gathered supports their hypothesis.
Students can explore patterns in human anatomical scaling by taking
linear measurements of various body parts across many individuals
(i.e., fellow classmates). Using measured body dimensions to gener-
ate scatterplots and linear regressions, students can examine the rela-
tionships between the measurements. This provides students with a
visual representation of how variable their data are and allows them
to see whether ratios between body-part lengths are consistent across
individuals. Then they can assess whether people exhibit Vitruvian
proportions by comparing their data with predictions outlined by
da Vinci on the Vitruvian Man. This activity also gives students the
freedom to ask and answer other questions that arise and test their
own hypotheses, such as whether proportions between body parts
are consistent across individuals, or whether the proportions differ
across age groups or between males and females. This subtle shift
toward an intentional use of models in the classroom allows students
not only to learn what a model represents, but to develop the ability
to critically examine a model’s assumptions and limitations and even
design new models of their own.
Models & Modeling as an Essential
Life Skill
These examples illustrate the functionality of models in scientific
research for biologists and as effective learning tools for students,
yet the utility of modeling reaches far beyond research labs and
classrooms. Modeling forms an integral part of how we interpret and
understand a complex world (Hoskinson et al., 2014). Maps are two-
dimensional models that help us navigate three-dimensional cities.
Instruction manuals provide visual models of steps to help us assemble
furniture, install plumbing or light fixtures, or mount objects on the
wall. We create mental models when planning parties to predict how
much food to make, where guests will sit, and what activities they
may enjoy. Past experiences with friends are the “data”we use to model
and predict guest needs and behaviors. Models of many sorts help us
organize the information we gather as we identify patterns and pro-
cesses and, as a result, aid in refining our understanding over time.
The ability to create, manipulate, and communicate models
not only enhances students’science learning, but also provides a
foundational skill set that will be useful throughout life. “Model
literacy”empowers students to think critically by providing them
with a systematic way to explore “what if”and “how”questions about
the apparent processes that govern a system. By elucidating processes
and promoting dialogue, models can better inform decision making
and improve communication. Hence, model literacy is a vital tool
for answering many of the biggest questions that the next generation
of scientists, engineers, and other problem-solvers will face.
Acknowledgments
We thank the teachers, staff, and students of Watsonville High
School (CA) for the opportunity to develop and implement
inquiry-based science curricula in their classrooms over the past
5 years. We also thank Dr. Yiwei Wang, who provided animal illus-
trations for Figure 2, and Elias Gilbert for the clay model of a cell
(Figure 5). This work was funded by the National Science Founda-
tion (NSF GK-12 DGE-0947923).
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Available online at http://scwibles.ucsc.edu/Products/Yost_Garden.html.
CALEB M. BRYCE is a PhD candidate in Ecology and Evolutionary Biology at
the University of California, Santa Cruz, 100 Shaffer Rd., Santa Cruz, CA
95060; e-mail: cbryce@ucsc.edu. VIKRAM B. BALIGA is a PhD candidate in
Ecology and Evolutionary Biology at the University of California, Santa
Cruz, 100 Shaffer Rd., Santa Cruz, CA 95060; e-mail: vbaliga@ucsc.edu.
KRISTIN L. DE NESNERA is a PhD candidate in Ecology and Evolutionary
Biology at the University of California, Santa Cruz, 100 Shaffer Rd., Santa
Cruz, CA 95060; e-mail: kdenesne@ucsc.edu. DURAN FIACK is a PhD
candidate in Environmental Studies at the University of California, Santa
Cruz, 1156 High St., Santa Cruz, CA 95064; e-mail: dfiack@ucsc.edu.
KIMBERLY GOETZ is a PhD candidate in Ecology and Evolutionary Biology
at the University of California, Santa Cruz, 1156 High St., Santa Cruz, CA
95060; e-mail: kimtgoetz@gmail.com. L. MAXINE TARJAN is a PhD candidate
in Ecology and Evolutionary Biology at the University of California, Santa
Cruz, 100 Shaffer Rd., Santa Cruz, CA 95060; e-mail: ltarjan@ucsc.edu.
CATHERINE E. WADE has a PhD in Environmental Studies from the
University of California, Santa Cruz, 1156 High St., Santa Cruz, CA 95064;
e-mail: cwade@ucsc.edu. VERONICA YOVOVICH is a PhD candidate in
Environmental Studies at the University of California, Santa Cruz, 1156
High St., Santa Cruz, CA 95064; e-mail: vyovovic@ucsc.edu. SARAH
BAUMGART is a science teacher at Watsonville High School, 250 E. Beach
St., Watsonville, CA 95076; e-mail: Sarah_Baumgart@pvusd.net. DONALD G.
BARD is an Adjunct Professor of Biology at Cabrillo College, Aptos CA and
of Anatomy at Monterey Peninsula College, Monterey, CA, as well as a
Program Coordinator for the SCWIBLES GK–12 program at the University of
California, Santa Cruz, 1156 High St., Santa Cruz, CA 95064; e-mail:
dbard@ucsc.edu. DORIS ASH is an Associate Professor of Education at the
University of California, Santa Cruz, 1156 High St., Santa Cruz, CA 95064;
e-mail: dash5@ucsc.edu. INGRID M. PARKER is a Professor of Ecology and
Evolutionary Biology at the University of California Santa Cruz, 1156 High
St., Santa Cruz, CA 95064; e-mail: imparker@ucsc.edu. GREGORY S. GILBERT
is a Professor of Environmental Studies at the University of California
Santa Cruz, 1156 High St., Santa Cruz, CA 95064; e-mail: ggilbert@ucsc.edu
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