🧬 biology

Reconciling biodiversity–ecosystem functioning relationships across systems

This paper reconciles the discrepancy between positive biodiversity–ecosystem functioning relationships in controlled experiments and mixed patterns in natural communities by introducing a framework that attributes the divergence to the contrasting roles of covariance effects driven by species abundance distributions.

Original authors: Yonghui Wang, Wenhong Ma, Jianguo Wu, Shaopeng Wang, Bernhard Schmid, Nico Eisenhauer, Yi Tao, Jin-Sheng He, Chao Wang, Zhenhua Zhang, Huiying Liu, Bailing Miao, Bin Zhang, Huping Yang, Xinxin Dai, Xi

Published 2026-07-08

📖 5 min read🧠 Deep dive

CC BY 4.0

Original authors: Yonghui Wang, Wenhong Ma, Jianguo Wu, Shaopeng Wang, Bernhard Schmid, Nico Eisenhauer, Yi Tao, Jin-Sheng He, Chao Wang, Zhenhua Zhang, Huiying Liu, Bailing Miao, Bin Zhang, Huping Yang, Xinxin Dai, Xinhui Jia, Yann Hautier

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ⚕️ This is an AI-generated explanation of a preprint that has not been peer-reviewed. It is not medical advice. Do not make health decisions based on this content. Read full disclaimer

The Big Mystery: Why Do Experiments and Nature Disagree?

Imagine you are trying to figure out how a sports team performs.

The Experiments: Scientists have run many controlled "team-building" experiments. They take a pool of players and randomly assign them to teams of different sizes. They consistently find that larger teams perform better. If you have 10 players instead of 2, the team scores more points. This suggests that "more players = better results."
The Reality: However, when scientists look at real-world ecosystems (like actual grasslands in nature), the story is messy. Sometimes, having more species (players) helps the ecosystem. But often, it doesn't help at all, or it even seems to hurt. For example, when farmers add fertilizer (nitrogen) to a field, the grass grows huge (great ecosystem function), but the number of different plant species drops drastically (bad biodiversity).

The Question: Why do the controlled experiments say "more is better," while nature often says "more doesn't matter" or "more is bad"?

The Solution: A New Way to Look at the Team

The authors of this paper developed a new "lens" or framework to look at how ecosystems work. They realized that the total performance of an ecosystem isn't just one thing; it's made of two distinct parts, which they call the Average Effect and the Covariance Effect.

Think of a choir singing a song:

1. The Average Effect (The "Everyone Gets Louder" Scenario)

Imagine the choir director tells everyone to sing a little bit louder, but everyone sings at roughly the same volume as before.

What happens: The total volume of the song goes up.
The Result: Because everyone is singing well and equally, you can hear more different voices. The diversity of the choir stays high or even improves.
In Nature: This is what happens in the controlled experiments. Because scientists plant seeds in equal amounts, when the ecosystem gets a boost, everyone benefits equally. This creates a positive link: more species = better ecosystem.

2. The Covariance Effect (The "Star Singer Takes Over" Scenario)

Now, imagine the choir director doesn't tell everyone to sing louder. Instead, one or two "star" singers get really, really loud, while the rest of the choir gets quieter and quieter until they can barely be heard.

What happens: The total volume of the song might still go up (because the stars are so loud), but the balance is gone.
The Result: The "stars" dominate the sound. The quieter singers (subordinate species) get drowned out and might even disappear from the recording. The total volume is high, but the diversity of voices drops.
In Nature: This is what happens in the real world, especially when fertilizer is added. The fertilizer makes the "weeds" or dominant grasses grow massive (they become the stars), while the delicate wildflowers (the quiet singers) get crowded out and die. The ecosystem produces a lot of biomass (high volume), but it loses its variety (low diversity).

How This Solves the Mystery

The paper argues that the disagreement between experiments and nature isn't because the experiments are "fake" or nature is "broken." It's because the balance between these two effects is different in each setting.

In Biodiversity Experiments: Scientists carefully control the planting. They ensure no single species starts with a massive advantage. This suppresses the "Star Singer" effect (Covariance). The system is dominated by the "Everyone Gets Louder" effect (Average). Therefore, they always see a positive link between diversity and function.
In Natural Communities: Nature is messy. Some species are naturally better adapted to the current conditions. When the environment changes (like adding nitrogen), these "star" species explode in size, while others shrink. This triggers a strong "Star Singer" effect (Covariance). This effect boosts the total output (like grass growth) but crushes the diversity.

The Nitrogen Example

The paper specifically looked at what happens when nitrogen (fertilizer) is added to grasslands.

The Observation: The grass grows huge, but the number of plant species crashes.
The Explanation: The nitrogen didn't help all plants equally. It acted like a megaphone for the dominant grasses (increasing the Covariance Effect). These grasses grew so big they blocked the sun and resources from the smaller plants. The ecosystem function (biomass) went up, but because the "Covariance Effect" was so strong, the diversity went down.

The Takeaway

The paper reconciles the two worlds by showing they are actually following the same rules, just with different weights:

Both systems rely on two forces: a "fair" boost for everyone (Average) and a "unfair" boost for the strong (Covariance).
Both forces make the ecosystem produce more work (biomass).
The difference: The "fair" boost helps diversity, while the "unfair" boost hurts diversity.
The Conclusion: Experiments show a positive relationship because they are designed to minimize the "unfair" boost. Nature shows mixed or negative relationships because the "unfair" boost (where a few species dominate) is very common in the real world.

By understanding these two separate forces, scientists can finally explain why nature doesn't always look like the neat graphs from the lab, without dismissing the value of either.

Technical Summary: Reconciling Biodiversity–Ecosystem Functioning Relationships Across Systems

Problem Statement
A persistent discrepancy exists in ecological literature regarding the relationship between biodiversity and ecosystem functioning (BEF). Manipulative biodiversity experiments consistently demonstrate positive BEF relationships, where species perform better on average in species-rich communities compared to species-poor ones. Conversely, natural communities often exhibit mixed or even negative BEF relationships, where ecosystem functioning can increase despite species losses (e.g., under nitrogen enrichment). This divergence has fueled debates regarding whether biodiversity experiments accurately represent real-world ecosystem dynamics. The core unresolved question is why these two systems yield such contrasting patterns despite potentially sharing underlying mechanisms.

Methodology
To address this gap, the authors developed a unified analytical framework applicable to both natural and experimental systems. The framework partitions temporal deviations in ecosystem functioning (anomalies) into two distinct components based on the Price equation concept:

The Average Effect: Reflects random, proportionate fluctuations in species abundances. This component captures scenarios where all species increase or decrease in abundance equally, maintaining relative evenness.
The Covariance Effect: Reflects non-random, disproportionate fluctuations. This component captures abundance-dependent compositional changes where dominant species become more dominant and subordinate species become rarer (or vice versa), altering species-abundance unevenness.

The study utilized three major datasets:

Natural Grasslands: Data from 90 sites, including 33 sites in northern China (long-term surveys and nutrient experiments) and 57 sites from the global Nutrient Network (plant coverage data).
Biodiversity Experiments: Data from 20 experimental sites, comprising 19 sites from the Agrodiversity experiment and data from the Jena experiment (plant productivity data).
Nitrogen Addition Experiments: Specific subsets of the above datasets (37 grasslands) to analyze responses to nitrogen enrichment.

Statistical analyses involved linear and residual regressions to quantify the explanatory power ( $R^2$ and partial $R^2$ ) of the average and covariance effects on ecosystem functioning (productivity or coverage) and species richness. The framework also examined the contributions of dominant versus subordinate species to these effects and tested the sensitivity of the covariance effect to species-abundance distributions.

Key Results

Universal Drivers: Across both natural and experimental systems, ecosystem functioning was positively related to both the average and covariance effects. However, their relationships with species diversity were contrasting:
- The average effect was positively associated with species diversity (richness).
- The covariance effect was negatively associated with species diversity.
Divergent Mechanisms:
- In biodiversity experiments, ecosystem functioning was predominantly driven by the average effect. The experimental design, which assembles species with controlled abundances, minimizes the covariance effect. This results in the consistent positive BEF relationships observed in these studies.
- In natural communities, the covariance effect contributed comparably to the average effect in driving ecosystem functioning. Because the covariance effect is negatively associated with diversity (often driven by the dominance of a few species), its strong influence in nature generates mixed or negative BEF relationships.
Nitrogen Addition: In natural grasslands, nitrogen addition increased ecosystem functioning (productivity) but reduced species richness. The framework revealed that this increase in functioning was primarily driven by an intensified covariance effect (dominant species thriving while subordinates declined), which simultaneously reduced diversity.
Species Contributions: The average effect was relatively evenly driven by subordinate species, whereas the covariance effect was strongly driven by dominant species. The covariance effect was found to be highly sensitive to changes in species-abundance unevenness; reversing abundance ranks in experimental data reversed the sign of the estimated covariance effect.

Significance and Claims
The paper claims to resolve the longstanding debate regarding the representativeness of biodiversity experiments. The authors argue that the discrepancy between experimental and natural BEF relationships does not arise from fundamentally different underlying mechanisms. Instead, it stems from the relative importance of the average versus covariance effects in driving ecosystem functioning.

Reconciliation: The framework demonstrates that biodiversity experiments capture key processes (the average effect) operating in natural communities. The divergence in observed patterns is due to the suppression of the covariance effect in controlled experiments (via equalized abundances) versus its prominence in natural systems (where species respond disproportionately to environmental conditions).
Mechanistic Insight: The study provides a mechanistic basis for negative BEF relationships, attributing them to the covariance effect where increases in functioning are coupled with increased dominance and reduced diversity.
Generality: The consistency of these patterns across diverse contexts (natural grasslands, global nutrient networks, and controlled experiments) suggests that the partitioning framework offers a unified explanation for ecosystem dynamics under anthropogenic global changes, such as eutrophication.

The authors conclude that their framework successfully reconciles the contrasting BEF relationships by identifying the specific components (average vs. covariance) that dominate in different system types, thereby validating the utility of experiments while explaining the complexities of natural ecosystems.