Life on Earth is astonishingly diverse. Biologists have so far discovered, named, and described approximately 1.9 million species of organisms (Ruggiero et al. 2015). But on most counts, this is only a very small part of the actual diversity of life on our planet. According to one estimate, there currently exist around 8.7 million (give or take 1.3 million) species of eukaryotic organisms (Mora et al. 2011) – not even counting bacteria and other prokaryotes, which make up by far the largest part of the biomass. Other estimates put the total number of extant species anywhere between 2 million and 1 trillion, with a recent rough estimate being 1–6 billion species (Larsen et al. 2017). But life on Earth is not only extremely diverse when it comes to organismal diversity. Biologists also talk about biodiversity in terms of genetic diversity (e.g., the genetic variation within a particular species, such as the variation of alleles within the human genome, or the genetic variation that exists in a particular local population), ecosystem diversity (the variety of ecosystems found in a particular geographic region, or on the planet as a whole), and the variety of trophic groups in a particular region, as well as other types of diversity (Faith 2016).

Biologists try to understand the living world at large by zooming in on tractable portions of it and by constructing and studying models. If you want to study long-term evolution in real time, you can follow finch populations in their natural habitats in the Galápagos (Grant & Grant 1993) or you can evolve microbial models in flasks in a laboratory (Lenski et al. 1991). If you want to understand how multicellularity first emerged in eukaryotes, you cannot go back in time to examine that event directly. Instead, you can construct and study a variety of models: for example, agent-based computer simulations, model organisms such as yeast or volvox, or model-based phylogenetic reconstructions (Bonner et al. 2016).

“Explaining” and “explanation” are words that tend to feature prominently in even the most basic descriptions of science. This is a big part of what science is about: generating explanations of our world (see also Chapter 3). It seems to be a big part of what biology is all about as well. Research in biology undoubtedly leads to practical applications in pursuits from medicine to agriculture to conservation, but one of its fundamental aims is to generate understanding of the living world around – and within – us.

It is well-known that the dividing line between facts and values can be blurry. This is the case for several reasons. One is that every statement about the world is expressed in human language, which always reflects values and judgments in human societies. Even if we use, for example, the seemingly neutral and factual word, “table,” we cannot fully understand the meaning of the word without knowing what people normally do with tables: eating or working on it, and not putting one’s dirty feet on it. Another reason that facts and values are not mutually exclusive is that statements about what happens in the world necessarily reflect how the world is organized and valued by those inhabiting it. For example, we could say that Mara is a reliable person. We could prove this “fact” by showing a record of all our interactions with Mara since we first met her to prove that she kept her promises and never let us down. At the same time, our statement that Mara is reliable is also a value statement in that it reflects societal (and possibly also personal) values in terms of what kinds of behavior are, or should be, expected from a person in our societies. Keeping a promise is valued by people because it is seen as contributing to the functioning of human interaction and social and political institutions.

Biological knowledge is not created by individuals in isolation but through a process of review and response within scientific communities. Criticism then is a normal and necessary part of this process. Occasionally, however, lasting disagreements arise during this process that become scientific controversies. In modern biology, some of the most well-known controversies have been relative significance disputes, which are disagreements about the relative importance of features of a biological system. These do not admit all-or-nothing resolutions, but instead often start as strongly stated opposing positions only to find resolution in some middle ground. In this chapter, we consider different views on how biological communities both disagree and resolve those disagreements as part of the social process of knowledge production.

So, we now have a book on philosophy of science written for biologists, which contains topics that its editors (both of them biologists) thought would be useful. Whereas we hope that many biologists will read it and reflect on philosophy of science, this is not the only role we envisaged for this book. We also believe that the book could be used in courses for biology undergraduates and graduates. It could be used in a course on philosophy of science, or as a supplement to a biology-focused course that would at the same time aim to promote philosophical reflection among participants. In either case, what is important for anyone who would use this book for teaching purposes is that the contributors to the present book have highlighted some major themes to be discussed.

Mainstream philosophers often ask questions about concepts that are of fundamental importance to our dealings with each other and with the world. They might ask what is knowledge, or justice, or art. Philosophers of science have asked similar questions about fundamental concepts that characterize science in general: What, for example, is explanation, or probability, or a law of nature? This kind of approach typically aims to understand key concepts in science better, but it does not always aim to inform or assist scientific practice. Moreover, this approach sometimes presupposes – unwisely, according to its critics – that scientists jointly employ some reasonably unified concept of explanation, probability, or a law of nature.

As biologists can certainly appreciate, it can be helpful to think of biological knowledge as a species belonging to the genus knowledge. A good starting point for understanding the features of biological knowledge is therefore to ask what features all instances of knowledge have in common.

The data-based study of long past events and processes is common throughout the sciences. Some examples are the astrophysical hypotheses that the universe began with a cosmic explosion (“big bang”), which is supported by measurements of the cosmic microwave background radiation pervading the modern universe; the hypothesis that the end-Cretaceous mass extinction was caused by a meteorite impact, which is supported by an iridium anomaly and large quantities of shocked quartz in K-Pg (Cretaceous-Paleogene) boundary sediments; and the hypothesis that all life on Earth shares a common ancestor, which is supported by analyses of shared segments of ribosomal RNA found in contemporary organisms. My interest in the methodology of the historical sciences and how it differs from that of stereotypical or “classical” (as I later dubbed it) experimental science was first piqued in the 1990s by the writings of so-called “scientific [more accurately, biblical] creationists.” Scientific creationists and their successors, members of the “Intelligent Design Network,”1 extol classical experimental research (the testing of hypotheses under controlled laboratory conditions) as the paradigm of good science, contending that historical scientific research is inferior because it uses “a form of abductive reasoning that produces competing historical hypotheses, that lead to an inference to the best current explanation rather than to an explanation that is logically compelled by experimental confirmation.”2 Proponents of intelligent design are not alone, however, in denigrating the work of historical scientists. Articulating a view held by a surprising number of experimentalists, Henry Gee, at the time a senior editor of Nature, declared that no science can be historical because conjectures about the past cannot be tested by means of controlled laboratory experiments (Gee 1999).

To many biologists, science and philosophy may appear an odd couple without much in common. Perhaps the word “philosophy” will even bring to mind endless arguments and speculation about whether the chicken or the egg came first, without ever getting anywhere. After all, are philosophers not still arguing over the same things as Aristotle and his fellow Greeks? Well, yes. But biologists too are concerned with the questions that occupied Aristotle: what living beings are and where they come from; how they develop, function, and interact with one another; and why there are so many forms and how those forms should be classified. There has been tremendous progress in biology, of course. But it does not appear that biologists will ever run out of questions. This is because good science does not only reveal new things about the world; it also reveals that there are things we did not even know we could know. So we want to know more.

What does scientific knowledge consist of? At the most basic level it is structured by concepts. Scientific concepts have important representational and heuristic roles in the acquisition and justification of scientific knowledge because they both represent natural entities, properties, and processes and also make their investigation possible (Arabatzis 2019; see also Nersessian 2008; MacLeod 2012). This is why concepts and their meanings have long been the focus of philosophical studies (see for instance the concepts analyzed in The Stanford Encyclopedia of Philosophy, https://plato.stanford.edu; or those biology concepts in edited collections such as Keller & Lloyd 1992; Hull & Ruse 1998; Hall & Olson 2003; Hull & Ruse 2007). This chapter deals with a central feature of scientific concepts: their metaphorical nature.

Scientific knowledge (and its transformation) is often presented in terms of models or overarching theories (Chapter 4). This chapter, in contrast, focuses on concepts as units and organizers of scientific knowledge. Concepts, on the one hand, are more fine-grained units in that a scientific theory contains many individual concepts. On the other hand – and this makes a look at concepts in biology particularly interesting – a concept can be used across several theories, and it can persist even when a theory has been discarded. The concept of a species continues to be used well after pre-Darwinian theories about species were abandoned, and this concept is used across all of biology, in such different theoretical contexts as vertebrate development and microbial ecology. The gene concept is likewise used in very different fields; it has survived despite the flaws of the original Mendelian theory of inheritance and a move toward molecular accounts.

This chapter is about the methods used in the life sciences. In it, I present a case that there are a limited number of methods that life scientists employ. On its face, this conclusion seems neither exciting nor novel; that is, unless we consider that the life sciences have long been engaged in a hidden battle between two opposing views regarding the number of scientific methods that exist. I am arguing against both sides. The two standard positions that I am arguing against are: (1) that there exists a single, if idealized, Scientific Method (i.e., unity), and (2) that the sciences are individualistic and, therefore, scientists operate without a common method (i.e., anarchy). I am asserting that in contrast to unity or anarchy, there are several but a limited number of methods possible in the bio-sciences. By combining general methods and submethods, there could be more than a dozen possible valid means by which to conduct a life sciences inquiry. I offer a table sketching out these methods and submethods at the conclusion of the chapter. But before I launch into explaining these two standard views and why a middle way, endorsing several but a limited number of methods, seems preferable, I want to begin with an example of modern life science in vivo, as it were.