Why Biology Thrives on a Diversity of Explanations
Have you ever wondered why a simple question like "Why are there so many different species in a tropical rainforest?" can be answered in so many different ways? One biologist might explain the complex interplay of predator and prey, another might trace the evolutionary history of the species, while a third might model the competition for sunlight and nutrients mathematically. In biology, the quest for understanding rarely follows a single path.
This article explores the fascinating realm of biological explanation. We will journey from the philosophical foundations that recognize this diversity to the concrete tools and experiments that make it possible. You will discover that in biology, a "one-size-fits-all" explanation is often insufficient; instead, a tapestry of causal, mechanistic, and even non-causal mathematical explanations is woven together to create a more complete picture of life's processes.
Biological phenomena can be understood through different explanatory lenses
Different explanations often complement rather than compete with each other
Life's complexity demands diverse explanatory frameworks
For much of the history of science, a model known as the Deductive-Nomological (DN) model was influential. It proposed that scientific explanations are essentially deductive arguments, where a phenomenon is explained by showing it is a logical consequence of general laws and initial conditions 6 .
Early philosophy of science emphasized universal laws and deductive reasoning as the hallmark of scientific explanation.
Biologists realized their discipline often lacks strict universal laws, with explanations being context-dependent and probabilistic.
The focus shifted toward causal explanations that cite the causes producing effects, accommodating biology's causal regularities.
The dominant model became mechanistic explanation, detailing multi-level causal mechanisms.
Recognition grew that not all powerful biological explanations are causal, with mathematical explanations gaining prominence.
Philosopher Wesley Salmon illustrated problems with the DN model using the famous "flagpole problem": while the height of a flagpole explains the length of its shadow (using the sun's angle and optical laws), the DN model incorrectly suggests the shadow's length could also explain the flagpole's height 6 . This paradox highlighted the need for a different approach.
A growing consensus in the philosophy of biology now recognizes that not all powerful biological explanations are causal. Some of the most profound insights come from non-causal, mathematical explanations 6 .
Causal explanations form the backbone of much biological research. They answer "why" by pointing to antecedent events or conditions that bring about an effect. Within this category, several distinct patterns have emerged.
This is perhaps the most familiar pattern in molecular and cellular biology. A mechanistic explanation decomposes a system into its relevant parts and operations and shows how they are organized to produce a phenomenon of interest 6 .
In fields like ecology and developmental biology, explanations often trace the pathways or cascades of events through which an effect is produced.
In any complex biological system, a multitude of causes are at work. A key part of crafting an explanation is causal selection—deciding which cause or causes are the most explanatory in a given context 6 .
For example, if we ask why a person has lung cancer, a doctor might cite smoking as the primary cause. A molecular biologist might point to a specific mutation in a tumor suppressor gene. An environmental epidemiologist might highlight air pollution levels. All are valid causal factors, but the most explanatory one depends on the context and the specific question being asked.
While causal stories are powerful, some of the most elegant and surprising explanations in biology do not work by citing causes. Instead, they show that a phenomenon was inevitable or necessary due to mathematical, topological, or geometric constraints.
Why are the blood vessels in our retinas arranged in a pattern that minimizes the shadow they cast on our photoreceptors? This can be explained by an optimization process—natural selection favoring the arrangement that maximizes visual acuity. The explanation hinges on a mathematical principle of efficiency, not just the causal details of how the vessels grow 6 .
Similarly, the explanation for why larger animals have proportionally thicker legs rests on the mathematical square-cube law: an animal's volume (and weight) increases with the cube of its linear dimensions, but the strength of its legs increases with the cross-sectional area, which is a square. To support the increased weight, legs must become proportionally thicker, a necessity dictated by geometry, not a specific genetic cause.
In systems biology and neuroscience, a growing class of explanations shows that the behavior of a complex network is determined more by its overall topology—the pattern of connections—than by the detailed causal dynamics of each individual component 6 .
For example, the robustness of certain food webs to species loss might be explained by the network's "small-world" property rather than by studying each predator-prey interaction in isolation. The network's architecture itself, a topological feature, does the explanatory work.
These explanations demonstrate that some biological phenomena are emergent properties of the system's form, a revelation that shifts the focus from microscopic causes to global, organizing principles.
| Explanation Type | Core Question | Example in Biology | Key Feature |
|---|---|---|---|
| Mechanistic | How does it work? | Explanation of muscle contraction via the sliding filament model of actin and myosin. | Decomposes a system into its causal parts and operations. |
| Evolutionary | Why did it evolve? | Explanation of the giraffe's long neck as an adaptation for accessing high foliage. | Appeals to natural selection and historical processes. |
| Mathematical | Why is it necessary? | Explanation of the hexagonal honeycomb as the most efficient way to partition a plane. | Relies on mathematical, geometric, or optimization principles. |
| Teleological | What is it for? | Explanation that the function of the heart is to pump blood. | Appeals to the purpose or function of a trait. |
| Systemic/Network | How does the structure dictate function? | Explanation of ecosystem stability based on food web connectivity. | Focuses on the properties of the whole system's organization. |
To see how these different explanatory patterns come to life in practice, let's examine a classic type of ecological investigation: quantifying biodiversity in a forest fragment. While not a single historic experiment, this methodology is crucial for understanding and explaining the patterns of life on our planet.
To determine the impact of forest fragmentation on species diversity by comparing the richness and evenness of beetle species in a large, intact forest versus a small, isolated forest patch.
| Site | Total Individuals | Total Species (Richness) | Shannon Index (H) |
|---|---|---|---|
| Large Forest | 450 | 30 | 2.85 |
| Forest Fragment | 300 | 18 | 2.10 |
| Species Rank (Most to Least Abundant) | Large Forest: % of Total Population | Forest Fragment: % of Total Population |
|---|---|---|
| 1 | 12% | 25% |
| 2 | 10% | 18% |
| 3 | 8% | 15% |
| 15 | 1% | <0.5% |
So, how do we explain the lower diversity in the fragment? A complete picture requires weaving together multiple explanatory threads:
The "edge effects" of the fragment create different microclimates (drier, windier), which act as a causal filter, preventing sensitive beetle species from surviving.
The fragment is a relic of a once-larger forest. Its current state is the result of a historical, human-caused disturbance that fragmented the habitat.
Island Biogeography Theory provides a mathematical model predicting that smaller, more isolated "islands" of habitat will support fewer species.
This experiment showcases how biologists do not simply collect data; they interpret it through a versatile lens of explanatory frameworks to build a compelling and multi-faceted narrative.
The diversity of biological explanations is made possible by an equally diverse toolkit. Modern biology relies on a wide array of research reagents and methodological approaches, each enabling a different line of inquiry.
| Reagent/Method | Primary Function in Research | Role in Explanation |
|---|---|---|
| CRISPR-Cas9 | Precision gene editing; allows scientists to knock out, knock in, or modify specific genes. | Enables mechanistic explanations by testing the causal role of specific genes in a trait or disease. |
| Patient-Derived Xenografts | Implanting human tumor tissue into immunodeficient mice to study cancer biology and drug responses. | Provides a platform for personalized, causal explanations of tumor growth and treatment efficacy. |
| Molecular Imaging Probes | Tagging molecules (e.g., antibodies, dyes) to visualize and track biological processes in real-time. | Supports dynamic causal explanations by allowing researchers to visually trace mechanisms as they unfold. |
| In Silico (Computer) Models | Using computational simulations to model everything from protein folding to ecosystem dynamics. | Facilitates mathematical and systems-level explanations by testing theories and predicting outcomes based on abstract principles. |
| Polymerase Chain Reaction (PCR) | Amplifying specific segments of DNA, making millions of copies for analysis. | A foundational tool for gene-based explanations, enabling the measurement of gene expression and genetic variation. |
AI and big data analytics are now used to synthesize the vast amounts of data generated by these methods, helping to identify patterns that might suggest new mechanistic or mathematical explanations 4 .
A growing emphasis on the 3Rs (Replacement, Reduction, Refinement) in animal research is driving the development of sophisticated in silico approaches, which themselves represent a new form of model-based explanation 4 .
The search for a single, universal method of explanation in biology has given way to a celebration of its rich diversity. From the intricate causal mechanisms of molecular biology to the historical narratives of evolution and the elegant necessities of mathematical proofs, each explanatory pattern provides a unique and invaluable perspective on the phenomena of life. They are not in competition; they are in concert.
The next time you read about a biological discovery, listen for the different explanatory voices. You will hear a symphony of reasoning—a powerful, multi-angled conversation that gets us ever closer to the fascinating truth of the living world.
Through multiple explanatory frameworks, biology continues to unravel the magnificent complexity of the living world.