How Does Biomass Change During Succession

How does biomass change during succession is a central question for ecologists studying ecosystem development. Biomass—the total mass of living organisms in a given area—shifts predictably as communities move from pioneer species to mature, stable assemblages. Understanding these patterns helps predict carbon storage, habitat quality, and the resilience of landscapes after disturbance.

Introduction Ecological succession describes the orderly process by which species composition and structure change over time following a disturbance such as fire, logging, or glacial retreat. While species turnover is the most visible aspect, the accompanying fluctuations in biomass reveal how energy and nutrients are accumulated, stored, and redistributed. In early successional stages, fast‑growing, high‑turnover organisms dominate, producing relatively low standing biomass but high productivity. As succession proceeds, slower‑growing, longer‑lived species increase, leading to a buildup of biomass that often peaks in mid‑ to late‑successional forests before stabilizing or slightly declining in old‑growth systems. This article outlines the typical trajectory of biomass change, explains the underlying mechanisms, and answers common questions about the phenomenon.

Steps of Biomass Change During Succession

1. Initial Colonization (Pioneer Stage)

   • Characteristics: Bare substrate, high light, limited nutrients.
   • Organisms: Grasses, herbs, lichens, mosses, and nitrogen‑fixing shrubs.
   • Biomass Trend: Low standing biomass (often < 5 % of mature ecosystem) but high net primary productivity (NPP) per unit biomass because individuals are small and rapidly turnover.

2. Early Successional Stage

   • Characteristics: Rapid canopy closure, increasing leaf area index.
   • Organisms: Fast‑growing trees (e.g., Populus, Betula), shrubs, and herbaceous understory.
   • Biomass Trend: Biomass begins to rise sharply as woody individuals accumulate wood and leaf mass; NPP remains high, but a larger fraction of photosynthate is allocated to growth rather than reproduction.

3. Mid‑Successional Stage

   • Characteristics: Canopy stratification develops; shade‑tolerant species appear.
   • Organisms: Mix of early‑successional pioneers and mid‑successional species (e.g., Acer rubrum, Quercus spp.).
   • Biomass Trend: Biomass accumulation continues, often reaching 60‑80 % of the potential climax value. Mortality of pioneers creates gaps that are filled by slower‑growing, more shade‑tolerant individuals, increasing structural complexity.

4. Late Successional (Climax) Stage

   • Characteristics: Relatively stable species composition, multi‑layered canopy, deep soil development.
   • Organisms: Long‑lived, shade‑tolerant trees (e.g., Tsuga, Fagus, Pseudotsuga), abundant woody debris, and diverse understory.
   • Biomass Trend: Biomass peaks, frequently exceeding 150‑200 t ha⁻¹ in temperate forests and surpassing 300 t ha⁻¹ in tropical rainforests. Net primary productivity declines toward zero as growth balances respiration and mortality; the ecosystem stores carbon primarily in woody biomass and soil organic matter.

5. Old‑Growth / Disturbance‑Reset Stage

   • Characteristics: Gap dynamics dominate; occasional large disturbances reset succession. - Organisms: Similar to climax but with a higher proportion of dead wood and coarse woody debris.
   • Biomass Trend: Standing live biomass may plateau or slightly decline due to increased mortality, yet total ecosystem biomass (live + dead) often remains high because dead wood accumulates.

Note: The exact shape of the biomass curve varies with climate, soil fertility, disturbance regime, and species pool, but the general pattern—low → rapid increase → asymptotic peak → possible slight decline—holds across many terrestrial systems.

Scientific Explanation

Resource Allocation and Life‑History Strategies

Pioneer species exhibit r‑selected traits: high reproductive output, rapid growth, and low investment in defensive tissues. Consequently, a large proportion of assimilated carbon is allocated to leaves and fine roots, producing high specific leaf area (SLA) and fast turnover. As succession proceeds, K‑selected species dominate, investing more carbon in structural tissues (wood, bark) and defensive compounds. This shift increases wood density and longevity, allowing biomass to accumulate even when NPP declines.

Canopy Development and Light Interception

Leaf area index (LAI) rises sharply during early and mid‑succession, enhancing light capture and thus gross primary productivity (GPP). Once canopy closure is achieved, additional leaf area yields diminishing returns because light becomes limiting lower in the canopy. The point where GPP ≈ ecosystem respiration (Rₑ) marks the transition from net carbon gain to net carbon neutrality, coinciding with peak biomass.

Nutrient Cycling and Soil Feedback

Early successional plants often facilitate nitrogen fixation or rapid nutrient cycling, enriching soils and enabling later‑successional species with higher nutrient demands. As organic matter builds, soil microbial communities shift toward slower decomposition rates, increasing the residence time of carbon in humus and further supporting biomass accumulation.

Disturbance and Reset Mechanisms

Disturbances such as fire, windthrow, or insect outbreaks remove biomass, releasing nutrients and creating open patches that restart the successional cycle. The frequency and intensity of these events determine the average biomass landscape: high disturbance frequency favors lower average biomass, while long intervals allow systems to approach their biomass ceiling.

Mathematical Representation

A simplified logistic model captures the biomass trajectory:

[ \frac{dB}{dt}= rB\left(1-\frac{B}{K}\right)-mB ]

where B is biomass, r is the intrinsic growth rate (high in pioneers, low in climax), K is the carrying capacity (maximum biomass set by climate and soil), and m represents mortality losses. Early succession is dominated by the rB term; as B approaches K, the logistic term slows growth, and mortality balances production, yielding a steady state.

FAQ

Q1: Does biomass always increase monotonically during succession?
A: Not necessarily. While live biomass generally rises, disturbances, senescence, or shifts to more open canopies can cause temporary declines. Total ecosystem biomass (live + dead) often shows a smoother increase because dead wood accumulates.

Q2: How does biomass change differ between terrestrial and aquatic succession?
A: In aquatic systems (

A: In aquatic systems (e.g., lakes), biomass accumulation often follows a different trajectory. Early succession may be characterized by rapid phytoplankton blooms fueled by nutrient inputs, leading to high pelagic biomass. Over time, sedimentation and organic matter buildup can shift systems toward benthic dominance (macrophytes, detritus). However, eutrophication can cause biomass peaks followed by crashes due to hypoxia, contrasting with the more gradual, structural buildup in terrestrial forests. Aquatic biomass is also more directly influenced by hydrological connectivity and water chemistry.

Conclusion

Successional biomass dynamics reflect a fundamental trade-off between rapid colonization and efficient resource storage. Early stages prioritize fast growth and high turnover, while later stages emphasize structural investment and longevity, driving a net increase in ecosystem carbon stocks. This trajectory is modulated by canopy development, soil feedbacks, and disturbance regimes, which together set the upper limits of biomass as defined by climate and nutrient availability. The logistic model underscores that biomass approaches a carrying capacity not through endless growth, but via a balance between productivity and mortality. Ultimately, the pattern of biomass accumulation—whether in a forest, grassland, or lake—reveals how living systems reorganize over time to maximize stability and resource use, while remaining vulnerable to resets from disturbance. Understanding these processes is critical for predicting carbon cycle feedbacks in a changing world, where altered disturbance frequencies and climatic conditions may shift successional pathways and the global distribution of biomass.

The trajectory toward that equilibrium is rarely linear. In many ecosystems, stochastic events—such as pest outbreaks, fire, or extreme weather—can temporarily depress B well below its asymptotic level, only for subsequent pulses of recruitment to push the system back toward the carrying capacity. Moreover, the shape of the logistic curve is sensitive to the initial B₀ and to the magnitude of m. When mortality spikes—perhaps due to drought or invasive pathogens—m rises, flattening the curve and extending the time needed to reach the steady state. Conversely, periods of favorable conditions can lower m and accelerate the climb, effectively compressing succession into a few decades rather than centuries.

These dynamics have profound implications for ecosystem services. In early‑stage communities, the rapid turnover of biomass translates into high nutrient fluxes that support diverse herbivore populations and facilitate nutrient recycling. As the system matures, the accumulation of woody material and deep organic horizons enhances water retention, soil stability, and carbon sequestration—benefits that are increasingly valued in the context of climate mitigation. Yet, the same structural complexity can also render mature stands more vulnerable to catastrophic disturbances; a single high‑intensity fire or pest invasion may collapse a large portion of the stored biomass, releasing carbon back into the atmosphere and resetting the successional clock.

From a management perspective, recognizing the non‑monotonic nature of biomass accumulation encourages a more nuanced approach to restoration. Rather than aiming for a prescriptive “ climax” target, practitioners can focus on fostering functional attributes that accelerate the approach to the carrying capacity: promoting soil microbial diversity, maintaining heterogeneous microhabitats, and minimizing chronic stressors that elevate m. Such strategies not only enhance the speed of biomass buildup but also improve the resilience of the emerging community to future perturbations.

Looking ahead, integrating remote sensing of vegetation structure with mechanistic models of the rB – mB(1‑B/K) form promises to refine our predictions of biomass trajectories under varying climate scenarios. Machine‑learning frameworks that ingest high‑frequency phenological data can capture subtle shifts in growth phenology and mortality rates that traditional models might miss, thereby offering a more dynamic picture of how biomass will respond to rising temperatures, altered precipitation patterns, and changing disturbance regimes.

In sum, the logistic formulation of successional biomass encapsulates a fundamental principle: growth is initially exponential but is ultimately constrained by environmental limits and self‑generated mortality. This principle holds across terrestrial and aquatic realms, albeit with habitat‑specific nuances in the drivers of r, K, and m. By appreciating the interplay between rapid early colonization, gradual resource consolidation, and the ever‑present risk of disturbance, ecologists and managers can better anticipate the pathways that ecosystems will follow in a rapidly changing world—and can design interventions that steer those pathways toward outcomes that are both ecologically sound and socially beneficial.