The authors trace the problem through three generations of biodiversity metrics. Species-based indices describe community composition with precision but offer no mechanism for prediction. Functional-diversity indices, which characterise the distribution of traits such as body size, feeding mode or dispersal ability within a community, still depend on inventories of species that have already been recorded. Habitat-based metrics, the third generation, replace laborious species surveys with habitat classification, dramatically improving scalability, and they now underpin global policy. The Kunming–Montreal Global Biodiversity Framework, adopted at COP15 in December 2022, formalises habitat-based measures in its monitoring targets for 2030. England’s mandatory Biodiversity Net Gain requirement, in force since 2024 under the Environment Act 2021, calculates standardised biodiversity units from habitat distinctiveness and condition. Yet the evidence for these proxies is sobering: studies show the English metric is associated with measurable gains in plant biodiversity but not in birds or butterflies, and it predicts invertebrate biodiversity poorly.

The deeper issue, the authors contend, is that all three metric generations measure patterns rather than the processes that generate them. Even the ambitious European Biodiversity Observation Network roadmap, with its 84 Essential Biodiversity Variables spanning genes to ecosystems, remains observational by design. Such systems can standardise how biodiversity state and change are measured, but they cannot supply the causal prediction needed to evaluate an individual management decision. Correlative models fitted to observed occurrences project associations between recorded species and recorded conditions, with the projected species pool bounded by the region and period used for fitting. They output expected occurrence, not the conditions required for persistence, and they cannot identify which ecological constraint prevents a management response. The distinction the authors draw is not between description and prediction, but between observation-anchored prediction and mechanistic prediction.

The empirical motivation for FTC comes from a stubborn and poorly explained pattern in two decades of agri-environment research: the same intervention produces different, sometimes opposite, biodiversity responses in different landscape contexts. Meta-analyses indicate that organic farming increases species richness by roughly 30 percent on average relative to conventional management, yet 16 percent of studies report negative effects. Benefits tend to be greater in intensively managed, structurally homogeneous landscapes than in heterogeneous ones. A global analysis of 60 crops across six continents confirmed that ecological benefits were greatest in intensive agricultural landscapes, while the same landscape variable could have contrasting effects on different sustainability outcomes. Agri-environment schemes show the same context dependence: a landmark five-country study found substantial variation among countries, taxa and scheme types, with common species benefiting more than species of conservation concern, and a subsequent meta-analysis showed that species richness increased in simple cropland landscapes but not in complex ones.

Spatially explicit modelling offers independent support for a capacity-based interpretation. Across 611 simulated landscapes, landscape structure explained 84 percent of variation in mean beetle density and 70 percent of variation in pesticide impact, with mitigation effects also strongly landscape dependent. The authors argue that these variable outcomes are unlikely to be failures of intervention design. Instead, an intervention acts on biodiversity capacity, a property generated by the landscape as a system, whereas evaluation usually records species occurrence at particular places and times. A predictive metric must represent the processes that generate capacity, not only the patterns those processes have already produced.

So what exactly is Functional Trait Capacity? The authors define it as the range of viable functional-trait combinations that a landscape can sustain, given specified landscape configuration, management regime and assessment horizon. The object of measurement shifts from the community currently observed to the breadth of ecological strategies and life-history solutions for which the landscape provides viable conditions. A wider trait space can support a greater range of species and functional groups, because landscapes act on the resources, disturbance regimes and environmental constraints that filter trait combinations; species composition is one historically contingent outcome of that filtering. The concept builds on Hutchinson’s classic n-dimensional hypervolume and on modern functional-trait ecology, joining them by asking which trait combinations a landscape can sustain rather than which happen to be present.

The framework distinguishes three capacity states. Theoretical FTC is the capacity expected from a site’s physical characteristics and natural ecological trajectory in the absence of deliberate management, a declared reference rather than an absolute biophysical maximum. Management-determined FTC is the capacity produced by a specified land-use and management regime, and it is not necessarily lower than the reference: management can create conditions absent from the natural trajectory, for example by maintaining open habitat where woodland would otherwise develop. Realised biodiversity is the community currently present after historical filtering, the state measured by existing metrics. The gaps between these states represent biodiversity debts or opportunities, separating capacity lost or created through management from capacity that is viable but not yet realised. Crucially, FTC is designed to complement, not replace, observational systems: monitoring tracks realised state and trend, while FTC asks the adjacent causal question of what functional capacity specified conditions should produce.

To show the concept is calculable, the team built a deterministic demonstration using 80 virtual strategies generated from 16 archetypes defined by trophic role, fast–slow life history and disturbance response. Four traits determine viability: spatial access, temporal resource requirement, disturbance tolerance and biotic dependency, with rules for phenological overlap and acyclic food-web structure excluding biologically arbitrary combinations. Four 50-by-50-cell agricultural landscapes varied field configuration and non-crop area in a two-by-two design. The striking result: the researchers selected the same 32-strategy realised community in all four landscapes, yet full assessment of all 80 admissible strategies gave FTC values of 46, 67, 44 and 65. Identical observed richness concealed a difference of up to 23 viable strategies, strategies that occur in none of the observations and therefore could never be recovered by a model fitted only to them. When the same intervention, converting 5 percent of cropped cells to semi-natural habitat along field edges, was applied everywhere, FTC rose by 14 strategies in one landscape, four in another, and not at all in the remaining two, even though mean viable area increased in every case.

Robustness analyses suggest the pattern is not an artefact. Across 60 combinations of persistence thresholds, conversion intensities and placement rules, the high-non-crop landscape always had greater FTC than its low-non-crop counterpart, and intervention effects differed among landscapes in 50 of 60 combinations. In sensitivity ensembles varying the sampled strategies and seasonal resource profiles, baseline FTC differed among landscapes in every replicate, and the intervention response differed among landscapes in all 20 combined replicates. The authors are careful about what this proves: the demonstration shows FTC can be calculated, not that it is empirically valid. Observations remain essential to parameterise resource use, movement and disturbance responses, to test predicted viability and to estimate uncertainty. Their role changes rather than disappears, informing and testing the ecological processes that the model then uses to predict viable strategies under conditions not yet observed.

The path to operational use runs through existing infrastructure. The authors envisage a process-based estimation layer supplying maps, scenario contrasts, limiting-factor diagnoses and uncertainty-bounded indicators to advisers, conservation managers and policy analysts, who would never need to run the underlying model. Inputs correspond to established data streams: Integrated Administration and Control System records for cropping, remote sensing and habitat inventories for configuration, and open trait compilations. Spatially explicit platforms such as the Animal Landscape and Man Simulation System, already applied in eleven European countries, could serve as the mechanistic engine. Practical constraints remain, including restricted access to management records in some member states and incomplete representation of pesticide mixtures and habitat condition. But the conceptual stakes are high. As the EU Nature Restoration Regulation and nature-related financial disclosure frameworks demand choices about future outcomes rather than statements of present state, FTC offers something monitoring alone never can: a falsifiable, scenario-conditioned estimate of what our landscapes could sustain if we managed them differently.

Subject of Research: A computational framework for predicting landscape biodiversity capacity from functional traits and management scenarios

Article Title: Functional Trait Capacity: from observing biodiversity to predicting landscape capacity

Article References: Topping, C. J., Dalgaard, T., Rasmussen, C., & Dupont, Y. L. (2026). Functional Trait Capacity: from observing biodiversity to predicting landscape capacity. npj Sustainable Agriculture, 4(1), Article 79. https://doi.org/10.1038/s44264-026-00190-5

Image Credits: AI Generated

DOI: 10.1038/s44264-026-00190-5

Keywords: biodiversity, functional traits, agriculture, landscape ecology, ecological modelling, agri-environment schemes, conservation policy, Kunming-Montreal Global Biodiversity Framework, sustainable farming, ecosystem management, ALMaSS, biodiversity metrics