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Beyond Species Richness: Empirical Assessment of a Consumption-Weighted Functional Diversity Index Across Aquatic Ecosystems

This study empirically evaluates a consumption-weighted Functional Diversity Index (FDI) across aquatic ecosystems, demonstrating its utility as a complementary descriptor of trophic organization that captures patterns distinct from taxonomic diversity, while highlighting its sensitivity to measurement choices and noting that it does not necessarily outperform simpler models in predicting ecosystem functioning.

Original authors: Aryan Ayyanger

Published 2026-10-20

📖 5 min read🧠 Deep dive

CC BY 4.0

Original authors: Aryan Ayyanger

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 ⚕️ This is an AI-generated explanation of a preprint that has not been peer-reviewed.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

Biodiversity is often measured by a simple headcount: how many different species live in a place. But a forest with a hundred trees of the same species is ecologically very different from a forest with a hundred trees of a hundred different species. Scientists have long known that the number of species is only part of the story. The real question is how those species interact with their environment. Do they all eat the same food? Do they all consume it at the same rate? Or do some eat a little while others eat a lot, and do they rely on different resources? Understanding these patterns helps us see how an ecosystem functions, not just how many lives it holds.

For years, researchers have tried to create a single number that captures both the variety of diets and the amount of food being eaten. A new study takes a specific mathematical tool, designed to measure this "consumption-weighted" diversity, and tests it against real-world data from three very different aquatic environments. The goal was not to prove the tool perfect, but to see if it could actually be calculated from field data, what it tells us about nature that a simple species count cannot, and whether it helps predict how well an ecosystem performs its daily work, such as breaking down fallen leaves.

The researchers began by looking at the lower Jinsha River in China, a massive waterway regulated by a large dam. They examined data from four distinct time periods, covering times before and after the annual floods. In one of these periods, the river was teeming with a record number of fish species, showing high taxonomic diversity. However, when the researchers applied their new index, which weighs the food intake of different groups like herbivores, carnivores, and filter-feeders, that same period scored the lowest. This revealed a crucial insight: a river can be full of many different kinds of fish, yet those fish might all be eating the same things in similar proportions. The new index caught this lack of dietary separation, while a standard species count missed it entirely. The study also showed that the results were sensitive to how the data was weighed; if the researchers used the weight of the fish instead of their estimated food consumption, the ranking of the best and worst periods changed completely.

Next, the team turned to twelve reservoirs in Singapore, ranging from urban settings to forested areas. Here, they faced a data limitation: while they knew what fish ate, they did not know exactly how much each fish consumed. Because the index requires knowing the total amount of food eaten by every group to work correctly, they could not calculate the full score. Instead, they focused on just the dietary part of the equation. They found that when looking only at fish, the diets in forest reservoirs seemed more distinct than in urban ones. But when they expanded their view to include the entire food web, including insects and other small creatures, that difference vanished. This highlighted that the answer to "how diverse are the diets?" depends entirely on which animals you decide to include in your count.

Finally, the researchers tested the index in five small experimental streams in North Carolina, where scientists had added extra nutrients to the water to see how the ecosystem would respond. In these streams, they had detailed records of exactly how much food different types of insects and other small creatures were eating. As expected, adding nutrients caused the total amount of food flowing through the streams to increase dramatically. Yet, the new index did not always go up. In some streams, the extra food was eaten by the same few dominant species, causing the index to drop. In others, the food was shared more evenly among different species with different diets, and the index rose. This proved that a surge in total food consumption does not automatically mean a more diverse or balanced ecosystem.

To see if this complex index was actually useful for predicting real-world outcomes, the researchers compared it to a separate measurement: how fast leaves decomposed in the streams. They asked if knowing the index value helped them predict the speed of decomposition better than just knowing the time of year. The answer was no. Adding the index to their predictions made the results slightly less accurate, not more. This suggests that while the index is a sophisticated way to describe the balance of eating habits in a community, it does not necessarily act as a crystal ball for ecosystem health or efficiency.

The study concludes that this consumption-weighted index is a valid and calculable tool, but it is not a magic bullet. It offers a different, complementary view of nature that highlights how food is shared and how diets differ, details that a simple species count overlooks. However, it is highly sensitive to the choices researchers make about which animals to include and how to weigh their contributions. It describes the structure of the community with precision, but it does not automatically tell us if that community is healthy or resilient. The value of the tool lies in its ability to reveal these hidden patterns of resource use, provided scientists interpret it alongside other measures and understand its specific limitations.

Technical Summary: Beyond Species Richness: Empirical Assessment of a Consumption-Weighted Functional Diversity Index Across Aquatic Ecosystems

Problem Statement
Conventional taxonomic diversity indices (e.g., species richness, Shannon entropy) describe community composition but fail to explicitly integrate the distribution of resource consumption with differences in dietary niche use. While organisms may occupy similar numbers of species, they can differ significantly in how much they consume and how their diets overlap. Existing functional diversity measures, such as Rao's quadratic entropy or Pianka's overlap index, address weighted differences or dietary overlap separately but do not inherently combine these dimensions with the magnitude of resource consumption. The Functional Diversity Index (FDI), previously introduced in a NetLogo simulation study (Ayyanger and Vijay, 2026), proposes a consumption-weighted quadratic formulation to address this gap. However, its behavior in empirical communities, its sensitivity to measurement choices, and its utility in predicting ecosystem processes remained untested.

Methodology
This study evaluates the FDI using three distinct aquatic datasets, each offering different temporal, spatial, and experimental structures. The FDI is defined as the product of two components:

Consumption Evenness ( $E$ ): A Gini–Simpson form applied to consumption shares ( $p_i$ ) rather than species abundances.
Dietary Dissimilarity ( $D$ ): A Rao-style, consumption-weighted quadratic entropy based on Pianka's dietary overlap ( $O_{ij}$ ).
The index is calculated as $FDI = E \times D$ .

The three datasets used for assessment are:

Lower Jinsha River (China): Four time periods (May 2022, Oct 2022, May 2023, June 2023) derived from Ecopath food-web models. Inputs included modeled annual food-consumption flux ( $Q = B \times Q/B$ ) and diet matrices for six consumer groups.
Singapore Reservoirs (Singapore): Twelve reservoirs (urban, estuarine, forest) using gut-content data. Due to the lack of absolute consumption data, only the unweighted dietary dissimilarity component was calculated for fish-only and whole-food-web scopes.
Coweeta Experimental Streams (USA): Five headwater streams over three years (pre-treatment and two enrichment years). Data included estimated resource-specific food consumption for macroinvertebrate taxa. This dataset allowed for the calculation of the complete FDI and an independent test against litter-decomposition rates ( $K$ ).

Key Results

Jinsha River: The period with the highest fish Shannon diversity (May 2023) exhibited the lowest consumption-weighted FDI (0.05516). The ranking of periods was sensitive to the weighting method; replacing consumption shares with biomass shares altered which period ranked highest. Correlations between FDI and isotopic niche breadth (SEAc) were weaker than those of its individual components.
Singapore Reservoirs: Dietary dissimilarity varied significantly among reservoir types when considering fish only (permutation $p = 0.0366$ ), but this pattern disappeared when analyzing the whole food web ( $p = 0.8508$ ). The study confirmed that full FDI could not be calculated due to missing absolute intake data, highlighting the sensitivity of niche metrics to community boundaries.
Coweeta Streams: Total consumption increased during enrichment years, but FDI changed in mixed directions across streams. The index was sensitive to consumer coverage; restricting analysis to taxa present across all periods preserved 8 of 10 before-after directional comparisons but reversed the trend in Stream 6.
Predictive Power: In the Coweeta dataset, adding FDI (or its components) to a model predicting litter decomposition ( $K$ ) did not improve predictive accuracy compared to a period-only model. Leave-one-stream-out cross-validation showed an increase in Root Mean Square Error (RMSE) from 0.001720 (period only) to 0.002016 (period + FDI).

Key Contributions

Empirical Computability: The study demonstrates that the FDI framework can be applied to real-world food-web data where appropriate inputs (consumption fluxes and diet matrices) exist, while identifying specific data gaps (e.g., absolute intake) that prevent full calculation in other contexts.
Sensitivity Analysis: The research quantifies how FDI values and rankings depend critically on the definition of consumer contributions (consumption vs. biomass) and the scope of the food web (fish-only vs. whole web).
Descriptive vs. Predictive Utility: The paper establishes that FDI describes trophic patterns distinct from taxonomic diversity or total resource flow but does not demonstrate superiority in predicting independent ecosystem processes (specifically litter decomposition) in the tested datasets.
Component Decomposition: By reporting $E$ , $D$ , and $FDI$ separately, the study clarifies that the index is a deliberate synthesis of two related properties (evenness and differentiation) rather than an independent ecological signal.

Significance and Claims
The paper positions the FDI as a complementary, interpretable descriptor of consumption-weighted trophic organization, rather than a replacement for established biodiversity indices. Its primary significance lies in its ability to describe the joint balance of consumer participation and dietary separation, a dimension not captured by species counts alone.

The author explicitly states that the findings do not prove the superiority of FDI in predicting ecosystem functioning. The lack of improved predictive accuracy in the litter-decomposition test suggests that the index should not be interpreted as a direct indicator of ecosystem health, resilience, or stability without further evidence. The study concludes that FDI is most useful when the research question specifically concerns the distribution of resource consumption alongside dietary differentiation, provided that the limitations regarding weighting choices and data resolution are acknowledged.

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