Models are simplified representations of reality. We use models in many different ways, for many different purposes: physical models are used to study urban planning, vehicle impacts, new inventions, and molecular structures; rats are used as model organisms to test new pharmaceuticals for side effects; conceptual models are used to simplify and understand concepts, ideas, and relationships; mathematical models are used to understand, quantify, and predict the behaviour of the natural world. All models are intended to capture some key feature of reality (shape, behaviour, relationships, etc.); the specific focus of a model will depend on the purpose for which the model is intended to be used.

Environmental data is more than a bunch of numbers. Meaningful information about the natural world is embedded in those numbers as patterns, cycles, trends, changes, and events. Arranging your data in different ways can highlight different features of the phenomena captured by your data set. This dual aspect of a data set is its power: data is quantifiable information. You can describe the key features of your data set using words, but you can also quantify, or characterize, critical information using numbers, mathematical equations, or statistical concepts.

We have used the Mauna Loa monthly CO2 data set to isolate and characterize changes in the anthropogenic and biological contributions to atmospheric CO2 concentrations between 1960 and 2017. We have also studied variability in the seasonal cycle, driven by changes in photosynthesis and respiration from year to year. But is the CO2 record from Mauna Loa, a mountain in Hawaii, representative of the global variability in atmospheric CO2?

The management and conservation of the world’s oceans require synthesis of spatial data on the distribution and intensity of human activities and the overlap of their impacts on marine ecosystems. We developed an ecosystem-specific, multiscale spatial model to synthesize 17 global data sets of anthropogenic drivers of ecological change for 20 marine ecosystems. Our analysis indicates that no area is unaffected by human influence and that a large fraction (41%) is strongly affected by multiple drivers. However, large areas of relatively little human impact remain, particularly near the poles. The analytical process and resulting maps provide flexible tools for regional and global efforts to allocate conservation resources; to implement ecosystem-based management; and to inform marine spatial planning, education, and basic research.

Large environmental changes are occurring all around us. As the human population has grown, the demand for food, energy, water, living space, and amenities has also grown, as has the production of industrial wastes, sewage, agricultural run-off, and carbon dioxide – the monster of all waste products. These changes are not always obvious to the naked eye, and the lack of a clear understanding is often a major barrier to taking appropriate action. This is where the environmental scientist is needed: to shed light on the what, where, when, why, and how of environmental change.

This book is both a resource and a practical guide to thinking about, doing, and communicating science. After you work through concrete examples of how to develop a scientific question, how to consider the complexity of natural phenomena, and how to align questions with data analysis, a scientific research proposal is used to demonstrate the degree to which critical environmental science concepts have been absorbed and applied. As an assignment, a research proposal is an effective way to integrate core concepts of scientific thinking while allowing students to engage with a topic of particular personal interest.

Communicating science is difficult. The challenge is to effectively summarize scientific results in a clear and informative way. Science is based on collecting empirical data (usually numbers) and often uses very long lists of numbers or spreadsheets with multiple columns and rows called “tables.” Though it is extremely important to make the raw data (the original lists of numbers) publicly available, raw data presented as such is not easy to absorb or understand.

Once you have a preliminary research question, it is time to investigate the existing data that could be used to address the question. To find existing data, start by searching the published peer-reviewed literature. Google Scholar or an open web search will not necessarily limit your search to peer-reviewed publications and can therefore be a waste of time or cause you to rely on inappropriate work, or both. The easiest way to find peer-reviewed science is to use a database at your institutional library that curates peer-reviewed publications. There are many databases that are useful. Web of Science is one of the most general useful databases, but you can also use databases that are more discipline specific like Georef or BioMed. Ask your librarians for advice to ensure that you are accessing the right papers for your purpose.

The way scientists work is not linear. A scientist does not think quietly to herself “I am following the scientific method” as she observes, hypothesizes, tests, and concludes. In fact, the process of science is much more iterative, circular, and creative than is implied by a linear model of the scientific method.

Fill in the following spreadsheet with an appropriate timescale (seconds, minutes, days, years, decades, centuries, millennia … billions of years) and spatial scale (micrometres, metres, kilometres, 100s kilometres, 1000s kilometres …) for each word or phrase on the list. You do not have to be exact but try to capture the scales within an order of magnitude.

Imagine that you have found a new planet and you are able to measure the temperature of the planet at various locations on the surface of the planet: 10 in the northern half of the planet and 10 in the southern half of the planet. The northern data and the southern data show opposite patterns. You have now been asked to characterize the temperature of the planet over one year.

Writing a proposal is the first step to getting a project approved and funded. In many cases, a call for proposals is like a competition where the most persuasive proposal will get approved and others will not. In science, persuasive writing is not hyperbolic or purposefully evasive. Taking a narrow view of a topic to elevate its importance is not an effective way to write a persuasive science proposal. A persuasive science proposal clearly and accurately articulates the motivating problem and outlines the methodology chosen to address the problem in a logical and systematic way. Scientists use references as supporting documents to authenticate statements. Effective referencing increases the quality of the proposal.

Modern-day Creationism, taking the Bible literally, lays claim to being the truly authentic Christianity dating back to the Gospels. But while it is true that there have always been those inclined to read scripture more literally than others, from the first there have been interpretations and more, taking one away from the actual words of the text. St. Augustine, the most influential figure in Western Christianity, was clear on this. The Bible is inspired, the Word of God. God, however, knew that He could not always talk literally. The ancient Jews were not sophisticated, fourth-century Romans and would have had little understanding if, say, rainbows were described in scientific terms. Metaphor or allegory was essential (Augustine 1982).

In 1962 Thomas Kuhn published one of the twentieth century’s most fruitful and most scandalous books, The Structure of Scientific Revolutions (Kuhn 1962). He argued that the form of inquiry we call natural science periodically undergoes more or less sudden shifts in what he called paradigms. His paradigmatic examples were the displacement of Ptolemaic astronomy by Copernican heliocentrism in the sixteenth century; and in the late eighteenth century the discrediting of alchemy, which sought to turn one element into another, by Lavoisier’s chemistry, which took the elements to be atomic and chemical change to consist of compounding these elements in particular ways. These are not especially disconcerting cases, since they helped give birth to modern science in the first place. What was disconcerting was Kuhn’s contention that, even after it was up and running, modern science – institutionalized practices of inquiry in which hypotheses and theories are rigorously tested by careful observation and experimentation – shows the same pattern. Disciplinary communities, he claimed, typically rally around a new paradigm until enough anomalies pile up to invite an even newer one.