The ecological niche is one of the foundational concepts in ecology, but its definition has been contested for a century. Charles Elton in 1927 framed the niche as a species’ functional role within a community, emphasizing interactions with other species. Joseph Grinnell in 1928 instead defined it as the physical space in which a species lives. The most widely used formulation came from G. Evelyn Hutchinson in 1957, who described the niche as a hypervolume in an n-dimensional environmental space, roughly spanning biotic and abiotic factors. Hurlbert later compiled more than twenty different definitions of the niche, and the debate continues. Crucially, almost all of these definitions operate at the species level or above, treating variation among individuals as noise around an ecological optimum.

That species-level focus is now recognized as a serious limitation. Foundational work over recent decades has shown that many species contain distinct phenotypes linked to age, sex, morphology, or behavior, and that consistent individual differences appear in cognition, antipredator behavior, breeding behavior, and responses to chemical cues. The Niche Variation Hypothesis, proposed by Van Valen in 1965, holds that the width of a species’ niche is driven by among-individual variation, a relationship demonstrated empirically across multiple taxa. Individual-level differences therefore matter for population-level dynamics, species coexistence, and the indefinite persistence of species. The term individualized niche emerged in the early 2010s to describe individual niche specialization, and it inherits the plasticity of the broader niche concept, which is precisely why its quantification has been considered a laborious task.

The new workflow, presented by researchers building on a formal definition published by Takola and Schielzeth, distinguishes between realized and potential individualized niches, following Hutchinson’s classic separation of the fundamental and realized niches. The realized individualized niche represents the environmental space where an individual actually is found, while the potential niche represents the space where it could be found. The workflow proceeds in three stages: data preparation, analysis, and output generation. Recorded GPS locations correspond to used locations, and available locations are generated randomly within a radius set by the organism’s dispersal rates. These points are then overlaid with environmental variables, which can be measured, inferred, or calculated with the aid of remote-sensing tools and grouped by resource type such as food, soil, and weather.

The analytical core is a mixed-effects resource selection function, or RSF, fitted for each resource-type group using a binomial model with a logit link. The response variable is binary, distinguishing used from available locations. Each model contains the relevant environmental predictors along with random intercepts and random slopes for each individual. The random intercepts capture baseline differences among individuals, whereas the random slopes, also known as reaction norms, estimate individual variation in the strength of selection along each environmental gradient. The model outputs represent population-average effects that describe selection and individualized deviations that quantify consistent among-individual differences along specific environmental axes. Fitted predictions are converted from the log-odds scale back to probabilities of use, and these predicted values are then scaled into relative selection probabilities, or RSPs, which set each individual’s maximum predicted selection value to one and express all other locations relative to that maximum.

These RSPs serve as observation-level weights in the construction of individualized niche hypervolumes. For each resource type, the workflow transforms the ecological group into a standardized multidimensional environmental space and estimates a weighted hypervolume separately for each individual using Gaussian kernel density estimation with Silverman’s bandwidth estimator. Because density-estimated hypervolumes have continuous boundaries, the workflow converts them to hard boundaries by selecting the region containing 95 percent of the probability estimates. Niche breadth is then defined as the volume of each individual’s 95 percent hypervolume, roughly corresponding to the within-individual variance of the individualized niche. Pairwise niche overlap between individuals is quantified with Jaccard and Sørensen similarity indices, which provide insights into intraspecific competition and niche partitioning along different environmental axes. Notably, the breadth of a potential niche is not necessarily larger than that of the realized niche, contrary to the traditional species-level definition, a phenomenon related to the so-called zero-niche paradox in which animals survive under conditions outside their species’ fundamental niche.

A key innovation is the use of repeatability estimates to address the problem of resource availability, which strongly influences individual variation in use. Repeatability quantifies the proportion of variance attributable to between-individual differences. The workflow calculates repeatability separately for realized niches, based on the environmental conditions individuals actually use, and for potential niches, based on the conditions available to them. Higher realized than potential repeatability indicates that individuals differ in use beyond what would be expected from their opportunity sets alone, supporting an interpretation of genuine individualized selection or specialization. Similar values suggest that differences in use largely reflect differences in availability, while higher potential repeatability points to spatial or home-range segregation. The workflow implements these tests with 1,000 bootstrap replicates and 1,000 permutations to obtain empirical 95 percent confidence intervals.

The case study showcases the workflow with northern lapwing tracking data publicly available through the Movebank database, covering the Netherlands, Belgium, England, France, and Spain from April 2021 to March 2023, with between 28 and 1,266 locations per individual. The environmental variables integrated into the lapwing niches were remarkably rich: temperature, precipitation, wind speed, the normalized difference vegetation index, crop type, cropping intensity, soil water index, soil organic carbon and its stock, soil nitrogen, pesticide application rates, population density, human footprint, and earthworm abundance. The mixed-effects RSFs showed that lapwings avoided densely populated areas and frequently cropped fields but preferred certain crop types, water, and high earthworm abundance, confirming what is already known about the species’ ecology while the random slopes revealed how each of the 13 tracked birds responded differently to each environmental variable.

The lapwing results illustrate the diagnostic power of the paired niche components. In many cases, for example in the selection of soil and human-presence variables, realized niche breadth was smaller than potential niche breadth, providing evidence of stronger individual specialization. High overlap among potential niches for soil variables suggested limited potential for individual differentiation in a highly suitable environment, while high overlap among realized niches for food resources, including the soil water index, earthworm abundance, and their interaction, indicated that individuals tended to use the same resources. Repeatability values ranged from 0 to 0.55 for potential niches and from 0 to 0.83 for realized niches, and in most cases realized repeatability exceeded potential repeatability, consistent with the hypothesis that northern lapwings exhibit individual variation in space use.

The authors emphasize that the workflow rests on explicit assumptions: individuals move in landscapes that on average match their phenotype, higher relative selection probabilities correspond to conditions providing greater expected lifetime reproductive success, and individualization is supported when realized niches show greater among-individual structure than potential niches. The framework is transferable across taxa of mobile organisms and responds to a decade of calls for incorporating individual-level variation into ecology and conservation. By quantifying individual niche variation, filtering for areas of high selection probability, and aggregating resource-use data into meaningful indices of intraspecific variation, the workflow promises more accurate and mechanistic individual-based models of wild population dynamics. Future research can focus on linking individualized niches to population growth rates, and because climatic niche breadth has been shown to predict recent range shifts across moths, butterflies, and birds, accounting for intraspecific variation may improve forecasts of persistence and redistribution under global change. As the individualized niche concept becomes established, quantifying it from movement data may prove an essential tool for behavioral ecologists seeking to draw population-level inferences from individual-level data.

Subject of Research: Individual specialization in habitat use quantified from animal movement data

Article Title: The individualized niche in motion: Quantifying individual specialisation with movement data

Article References: Takola, E. (2026). The individualized niche in motion: Quantifying individual specialisation with movement data. Individual-based Ecology, 2, Article e203247. https://doi.org/10.3897/ibe.2.203247

Image Credits: AI Generated

DOI: 10.3897/ibe.2.203247

Keywords: ecology, movement ecology, individualized niche, resource selection functions, hypervolumes, repeatability, northern lapwing, GPS tracking, niche breadth, niche overlap, individualized, niche

The study, published in Ecology and Evolution, took advantage of an extraordinary dataset gathered at the Lijiang Forest Ecosystem Research Station. Over the 2012 flowering season, researchers conducted nine censuses at two-week intervals in four alpine meadows spread along an elevation gradient from 2,725 meters to 3,910 meters above sea level. Across 320 hours of observation, they recorded 5,369 pollination events linking 101 plant species with 335 insect pollinator species, each insect identified through a combination of morphology and DNA barcoding. Crucially, the team filtered out insects carrying no pollen, ensuring the network represented genuine pollination rather than mere flower visitation.

What makes the analysis novel is its treatment of time. Rather than slicing the season into disconnected snapshots, the researchers built temporal multilayer networks, a framework borrowed from network science in which each two-week census forms a layer and species are connected both to their interaction partners within a layer and to themselves across adjacent layers. The weights of these interlayer links reflect changes in abundance and phenology, effectively encoding how strongly a species persists from one period to the next. This architecture allowed the team to ask questions that static networks cannot answer: which species hold the community together through time, and what biological traits make them capable of doing so?

The first major finding concerns modularity. Ecological networks are typically organized into modules—clusters of species that interact strongly with one another but weakly with outsiders—and in static networks this modularity is thought to buffer disturbances. Using the Infomap algorithm, which compresses the description of flows through a network to detect its natural divisions, the researchers confirmed that all four temporal networks were significantly modular compared with randomized null models. But the modules themselves were startlingly ephemeral. No single module persisted across all nine time layers at any site, and at three of the four meadows, roughly 60 to 69 percent of modules lasted only one or two census periods. Early-season and late-season modules became entirely distinct communities.

Yet the turnover of species with different flowering and flight phenologies turned out to be only half the story. On average, 47.6 percent of pollinator species changed their module affiliation at least once during the season, a property the authors call adjustability. More striking still, the number of modules a pollinator occupied was tightly correlated with the number of time layers in which it remained active, with Spearman correlations ranging from 0.67 to 0.90 across sites. Long-persisting species do not simply sit in a stable niche; they actively track floral resources as the plant community shifts, rewiring their interactions and migrating between modules. The bumblebee B. friseanus exemplified this flexibility, occupying up to six different modules at the highest-elevation sites.

The second pillar of the analysis is versatility, measured through multilayer PageRank centrality—the same algorithm that underlies Google’s search rankings, adapted to rank species by their importance across the entire temporal structure. Most pollinators scored low, but a select few stood out: between 4 and 14 species per site, or roughly 5 to 15 percent of the local pollinator fauna, showed versatility significantly exceeding random expectation. These super-generalists were dominated by Diptera and Hymenoptera, with hoverflies and anthomyiid flies taking center stage at high elevations, consistent with the established importance of flies in cold, alpine environments. Intriguingly, a hemipteran of the genus Nysius emerged as highly versatile at two sites, hinting that true bugs may be underappreciated pollinators in alpine meadows.

Having identified the network’s temporal linchpins, the researchers turned to the mechanistic question: what makes these species versatile? Their hypothesis drew on classical niche theory, which holds that species exploiting a broader range of resources and conditions persist better in fluctuating environments. They quantified each pollinator’s trophic niche space as the volume of floral trait space—corolla depth, flower units per plant, and plant height—spanned by the plants it visited, and its environmental niche space as the volume of temperature and humidity conditions under which it foraged, using convex hull volumes in multivariate trait space. Body traits likely to underpin these niches were also measured: proboscis length, head width, and forewing index.

Structural equation modeling revealed a consistent hierarchical pathway across all four meadows. Trophic niche space exerted a significant positive effect on versatility at every site, with standardized coefficients climbing from 0.31 at the lowest meadow to 0.51 at the highest. Environmental niche space acted both directly on versatility at the lower sites and indirectly, through its strong positive association with trophic niche space, at all sites—suggesting that pollinators tolerant of a wide range of microclimatic conditions also access a wider variety of flowers. Trait effects were more context-dependent: proboscis length broadened the trophic niche at the lowest site and the environmental niche at mid-high elevation, while head width showed a negative direct effect on versatility at one site, possibly because smaller-bodied insects can access more floral morphologies. The models explained between 23 and 48 percent of the variation in versatility.

The team took considerable care to rule out statistical artifacts. Because richer communities naturally offer larger interaction pools, they resampled the networks 100 times with fixed species richness and confirmed that the observed path coefficients generally fell within the null distributions, indicating genuine ecological signal rather than richness-driven inflation. A parallel resampling that standardized the number of individuals per pollinator species showed that the niche-versatility relationships were not merely a byproduct of abundant taxa accumulating more records. The consistency of the results across a 1,200-meter elevation gradient, spanning meadows with 58 to 169 pollinator species, strengthens the case that niche breadth is a general determinant of temporal network roles.

The implications reach well beyond Yulong Mountain. As climate change disrupts phenology and drives mismatches between flowering times and pollinator activity, the species most capable of holding ecosystems together may be precisely these broad-niched, module-switching generalists. The authors argue that highly versatile pollinators, by sustaining interactions across shifting temporal and ecological contexts, are likely to persist in changing environments and should be treated as priority candidates for conservation and further research into their functional replaceability. The multilayer framework itself invites extension to other interaction types—seed dispersal, herbivory, predation—and to temporal scales from hours to years, offering a general lens for understanding how ecological networks endure, reorganize, and sometimes collapse in a rapidly changing world.

Subject of Research: Temporal dynamics of plant-pollinator networks and the traits driving pollinator versatility across seasons

Article Title: Trophic and Environmental Generalists Maintain Pollinator Network Functional Continuity by Switching Modules Through Time

Article References: Li, H.-D., Zhao, Y.-H., Holyoak, M., Tao, Z., Xu, K., Wu, Z., Wang, H., & Li, D.-Z. (2026). Trophic and Environmental Generalists Maintain Pollinator Network Functional Continuity by Switching Modules Through Time. Ecology and Evolution, 16(9), Article e74263. https://doi.org/10.1002/ece3.74263

Image Credits: AI Generated

DOI: 10.1002/ece3.74263

Keywords: pollination networks, multilayer networks, modularity, versatility, niche breadth, bumblebees, alpine meadows, phenology, structural equation modeling, ecological networks, Himalaya-Hengduan Mountains, pollinator conservation