How Scientists Decode Natural Mortality in Fish
Beneath the ocean's surface unfolds a continuous, silent drama that determines the fate of fish populations worldwide—a phenomenon scientists call natural mortality. This invisible force represents all forms of death not caused by fishing: predation, disease, starvation, and senescence. For fisheries managers, accurately estimating natural mortality isn't merely academic; it's fundamental to determining how many fish can be sustainably harvested without collapsing populations.
For decades, the problem was so intractable that scientists often simply assumed a constant value—typically 0.2—for most species, despite knowing this was biologically unrealistic 1 .
The groundbreaking work of Erik Ursin and colleagues in the 1970s revolutionized this field by introducing sophisticated formulae that acknowledged the complex reality of natural mortality. Their work transformed our understanding from seeing natural mortality as a simple, constant number to recognizing it as a dynamic process influenced by multiple ecological factors—a perspective that continues to shape how we manage and conserve marine resources today.
Ursin's fundamental insight was recognizing that natural mortality isn't a single monolithic force but rather the sum of distinct biological processes. His pioneering approach divided natural mortality (M) into two primary components: predation mortality (M2) and non-predation mortality (M1), expressed in the simple yet powerful equation M = M1 + M2 1 .
Includes deaths from disease, starvation, and old age—processes largely determined by a fish's physiology and environmental conditions 1 .
Represents death from being eaten by other animals—a process deeply embedded in the complex food web dynamics of marine ecosystems 1 .
This decomposition allowed scientists to study these components separately and understand how they interact—for instance, how changes in predator populations might affect the mortality rates of their prey. This was a radical departure from previous models that treated natural mortality as an unexplained "black box."
Ursin's theoretical framework found its most powerful application in Multispecies Virtual Population Analysis (MSVPA), developed by the International Council for the Exploration of the Sea (ICES) in the late 1970s 1 . This sophisticated modeling approach represented a quantum leap beyond previous single-species assessments.
Scientists systematically gathered stomach content data from predator species to determine who eats whom, and in what proportions 1 .
The model established mathematical relationships between predator and prey populations, quantifying how changes in predator abundance would affect prey mortality rates 1 .
Using extensive feeding data, researchers could now estimate predation mortality for each species and age group, something previously impossible 1 .
The model could simulate how fishing on one species might affect others through trophic cascades—for example, how removing a predator might increase survival of its prey 1 .
| Aspect of Natural Mortality | Traditional View | MSVPA Revelation |
|---|---|---|
| Variation across ages | Assumed constant | Varied significantly with life stage |
| Variation over time | Assumed constant | Fluctuated with ecosystem conditions |
| Predation importance | Largely ignored | Major component for many species |
| Species interactions | Not considered | Complex predator-prey relationships |
| Impact on recruitment estimates | Unquantified | Errors could exceed factor of 2 |
The MSVPA approach yielded startling revelations that overturned conventional wisdom. Analysis of North Sea populations revealed that natural mortality was far from constant—it varied significantly by species, age, and time 1 . For commercially important species like cod, haddock, and whiting, the models showed that predation accounted for a substantial portion of total natural mortality, with these species both preying upon and competing with each other in a complex web of interactions 1 .
Perhaps most importantly, the MSVPA demonstrated that assuming constant natural mortality could lead to significant errors—sometimes by a factor of two or more—in estimates of recruitment (the number of young fish entering the population) 1 .
Beyond Ursin's innovative approach, fisheries scientists have developed multiple methods to estimate natural mortality, each with particular strengths and limitations. These techniques can be broadly categorized into direct methods (using specific data collected from populations) and indirect methods (based on theoretical relationships and empirical correlations) 2 .
| Method Category | Specific Techniques | Key Principle | Limitations |
|---|---|---|---|
| Direct Methods | Tag-recapture studies | Tracking marked individuals over time | Tagging mortality, non-reporting of tags 2 |
| Catch-curve analysis | Age structure of catches | Confounding with fishing mortality 2 1 | |
| Indirect Methods | Life history invariants | Relationships between M and growth | Susceptible to assumption violations 2 |
| Maximum age methods | Linking M to longevity (Hoenig, 1983) | Requires reliable age data 2 | |
| Empirical relationships | Correlations with environmental factors | May not transfer well between systems 2 | |
| Multispecies Approaches | MSVPA (Ursin-inspired) | Stomach content analysis of predators | Data-intensive, complex implementation 1 |
Rely on actual observations and measurements from fish populations. These provide the most accurate estimates but are often expensive and time-consuming to implement.
Use theoretical relationships and correlations to estimate mortality. These are more practical for data-poor situations but rely on assumptions that may not always hold true.
Each method provides a different lens through which to view natural mortality, and modern fisheries science often combines multiple approaches to develop the most reliable estimates. The choice of method depends heavily on the available data, the specific characteristics of the stock, and the resources available for assessment.
Sometimes, unique historical circumstances have created ideal conditions for estimating natural mortality. One remarkable example occurred during World War II (1939-1945), when the North Sea was largely closed to fishing due to hostilities and the commandeering of fishing vessels for war efforts 1 .
This unexpected fishing moratorium created a perfect natural experiment. Scientists could compare survey data collected before and after the war to measure population declines that were almost entirely attributable to natural mortality rather than fishing 1 . The analysis was straightforward:
This unusual circumstance provided valuable, real-world measurements of natural mortality that helped validate the more theoretical approaches being developed.
The WWII fishing hiatus provided a unique opportunity to study natural mortality without the confounding effects of fishing pressure.
The revolutionary perspective introduced by Ursin continues to influence how we approach fisheries science and ecosystem management today. The recognition that natural mortality varies systematically with body size has led to the development of size-based models that predict how mortality changes as fish grow 1 . Similarly, the understanding that fishing changes ecosystem structure—and thus natural mortality rates—has become fundamental to ecosystem-based fisheries management 1 .
Modern assessments acknowledge that mortality rates change as fish grow, with smaller fish experiencing higher mortality rates than larger individuals of the same species.
Fisheries management now considers species within their ecological context, recognizing that fishing one species affects others through complex food web interactions.
| Research Tool | Function | Application in Ursin's Work |
|---|---|---|
| Stomach content analysis | Identifies predator-prey relationships | Foundation for estimating predation mortality (M2) 1 |
| Population surveys | Estimates abundance at age | Provided data for MSVPA calculations 1 |
| Tagging systems | Tracks individual fates | Enabled direct mortality estimates 2 |
| Multispecies models | Simulates species interactions | Operationalized Ursin's M1+M2 concept 1 |
| Size-spectrum analysis | Examines size-based patterns | Revealed mortality-body size relationships 1 |
Modern assessments increasingly acknowledge that natural mortality isn't static but responds to changing environmental conditions and human impacts. This has profound implications for sustainable management, particularly as climate change alters marine ecosystems. Fisheries managers now recognize that a management strategy that works under one set of ecosystem conditions may fail when those conditions change—in part because natural mortality rates may shift 1 .
Despite decades of advancement since Ursin's foundational work, natural mortality remains a challenging frontier in fisheries science. As one researcher noted, we're still "scrabbling around for understanding of natural mortality" 1 . The parameter continues to be highly uncertain and difficult to estimate directly, often becoming confounded with other factors in stock assessment models 2 .
Ursin's fundamental insight—that we must look beyond simple constants and embrace the complex, dynamic nature of ecological mortality—has permanently transformed the field.
Yet Ursin's fundamental insight—that we must look beyond simple constants and embrace the complex, dynamic nature of ecological mortality—has permanently transformed the field. His formulae and conceptual frameworks provided the foundation for recognizing that effective fisheries management requires understanding species within their ecological context, not as isolated populations.
As climate change, pollution, and other human impacts continue to reshape ocean ecosystems, Ursin's legacy reminds us that sustainable management requires acknowledging and accounting for the intricate web of interactions that determine the birth, growth, and death of every fish in the sea. The hidden dance of death in our oceans may be invisible to casual observation, but thanks to Ursin's pioneering work, we have essential tools to understand its steps and rhythm.