Ontogeny shapes individual specialization

Ontogeny shapes individual specialization | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Help Center Sign In Submit a Preprint Cite Share Download PDF Article Ontogeny shapes individual specialization Anne Hertel, Jörg Albrecht, Nuria Selva, Agnieszka Sergiel, Keith Hobson, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2926801/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Nov, 2024 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Abstract Individual dietary specialization, where individuals occupy a subset of a population’s wider dietary niche, is of key importance for species’ resilience against environmental change. However, the ontogeny of individual specialization, as well as associated underlying social learning, genetic, and environmental drivers remain poorly understood. Using a multigenerational dataset of female European brown bears ( Ursus arctos ) followed since birth, we discerned the relative contributions of social learning, genetic predisposition, environmental forcings, and maternal effects to individual dietary specialization. Individual specialization varied from omnivorous to carnivorous diets spanning half a trophic position. The main determinants of this dietary specialization were maternal learning during rearing (13%), environmental similarity (12%), maternal effects (11%), and permanent individual effects (8%), whereas the contribution of genetic heritability was negligible. Importantly, the offspring’s trophic position closely resembled the trophic position of their mothers during the first 3-4 years after separation from the mother, but this relationship ceased with increasing time since separation. Our study reveals that social learning and maternal effects are as important for individual dietary specialization as environmental forcings. We propose a tighter integration of social effects into future studies of range expansion and habitat selection under global change that, to date, are mostly explained by environmental drivers. Biological sciences/Ecology/Behavioural ecology Biological sciences/Ecology/Stable isotope analysis Biological sciences/Developmental biology/Differentiation Earth and environmental sciences/Ecology/Evolutionary ecology Dietary specialization heritability maternal effects maternal learning trophic position trophic niche omnivore stable isotopes nitrogen-15 Ursus arctos Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Among individuals of the same species, niche variation is common and may occur when availability of food resources or habitat structure change across the species’ range. Ecological generalists, species with a wide niche, also seem to exhibit more individual specialization 1 and are hence particularly well adapted to persist under shifts in resource availability or composition enabling them to occupy larger distributional ranges than ecological specialists 2 . Individual variation is key for making species resilient towards changing resource availabilities in a rapidly changing world and may ultimately determine local persistence or extinction of species 3 . Inter- and intraspecific competition, predation and ecological opportunity, alter resource availability and have been identified as the main ecological drivers explaining variation in the degree of individual specialization among populations 4 . Yet, how individual variation emerges and is maintained within populations has been rarely quantified in the wild. In principle, four potential sources of variation exist: social and individual learning, genetic inheritance, the environment, and maternal effects. Individual differences in resource preference or competence to secure a resource may therefore be determined during early ontogeny through social (e.g., maternal) learning via imitation 5, 6, 7, 8, 9 leading to similarities between the offspring’s and their mother’s dietary phenotype. Effects of maternal learning can be lifelong or are subsequently modified through individual experiential learning 10 . Resource preferences have also been suggested to be genetically determined through genes inherited from both mother and father, where closely related individuals have more similar diets than distantly related individuals 11 . In addition, maternal effects account for lifelong similarities in dietary phenotype among offspring of the same mother 12 . Such similarities can arise from social interactions, maternal genotype, or maternal environment. Statistically, maternal effects are quantified as the similarity of repeated samples from siblings of the same mother but not as the similarity of behavioral expression between mother and offspring (i.e., “maternal learning”). In range resident species, where individuals occupy a subset of a population’s range, the environment, in terms of habitat composition or availability of particular food resources, may differ among home ranges and lead to individual specialization 11 . Accounting for the environmental heterogeneity when studying the drivers of individual specialization is therefore essential in range resident species 13, 14 . Attributing variation in diet to the individual level, to isolate its sources and to identify developmental drivers of diet preferences requires multigenerational datasets of repeated measures of the diet of individuals throughout their life. We used a 30-year longitudinal dataset of 72 female Scandinavian brown bears ( Ursus arctos ) of known mothers with repeated annual isotopic estimates of trophic position to assess whether individual dietary specialization occurs. Using information about their mother’s diet, a genetic pedigree, and individual movement data we then attributed individual variation in diet to its sources: maternal learning, genetic heritability, environment, and maternal effects. Brown bears are ecological generalists with a species range spanning the northern hemisphere from tundra to deserts, paralleled by extensive variation in diet: from populations tracking food resource pulses, such as spawning fish 15 , scavenging on ungulate carcasses or preying on ungulates neonates 16 , or feeding extensively on invertebrates, to populations using primarily fruiting plant based diets 17, 18 . Given this extreme dietary plasticity, it is not surprising that great dietary variation has been found within populations 19, 20 , however, the determinants and ontogeny of this variation at the individual level remain largely unknown. In ecology, differences in diet are often primarily attributed to differences in resource availability and abundance. Even within populations inhabiting a continuous biome, home range scale variation in habitat composition 21 can lead to variation in resource availability. The most parsimonious source of variation in diet are, therefore, differences in the environment. Brown bears maintain non-territorial home ranges but live a solitary lifestyle except for the period of offspring rearing involving up to three years 22 of maternal care, after which female offspring often settle close to their mother’s home range 23 . In their first years of life, bear cubs accompany their mother and it is therefore reasonable to assume that brown bear offspring learn behaviors such as habitat, den site, or diet selection from their mothers and hence show similar behavior to them. If mothers differ in their dietary selection, these differences may hence be maintained in the population through learning by imitation of the mother (hereafter "maternal learning”), even after offspring gain independence, however, such similarities may wane over time 24 . On the other hand, genetic heritability or maternal effects can have lifelong effects on offspring phenotype. Body size has been shown to be genetically heritable in our study population 25 suggesting greater similarity among closely related individuals also in other linked traits, such as trophic position. Alternatively, maternal effects (i.e., maternal genotype or maternal environment) alone can shape the phenotype of offspring. For example, milk quantity or quality 26 can vary among females either due to genetic differences or differences in the environments, leading to greater similarity among all offspring from the same mother (e.g. being smaller or larger in body size), which in turn could cause similarities in trophic position among siblings. To assess individual specialization along a continuum from a more plant-based to a more meat- or insect-based diet, we analyzed annual trophic positions from stable- nitrogen isotopes ( δ 15 N) in bear hair keratin 27 . Stable isotopes reflect cumulative diet intake and are deposited into the hair during growth with, a delay of approximately one month (i.e. a growing hair in June reflects the diet intake in May, 28 ). Bear hair is regularly renewed through molting in June, regrows over the summer and fall and stops growing during winter hibernation ( Fig. 1A , 29, 30 ). Guard hair samples collected in spring and early summer (April - June) therefore reflect an individual’s diet during the previous active season prior to hibernation 29 . Using repeated samples of known mother-daughter pairs, we fit a spatially explicit Bayesian hierarchical model (i.e. ´animal model´) 31, 32, 33 to disentangle the relative contributions of maternal learning, genetic relatedness, the environment, and maternal effects as determinants of individual specialization. Specifically, the model accounted for genetic relatedness with a pedigree and for environmental similarity of bear home ranges with pairwise habitat similarity encompassing the proportion of mature habitat such as old and mid-successional forests, disturbed habitat such as clearcuts and regenerating young forest, and habitat diversity (measured as Simpson’s diversity index) in a bear’s home range. The model also accounted for maternal effects by incorporating the mother’s ID as a random effect (i.e. if daughters from the same mother behaved in a similar fashion throughout life), and for maternal learning as the fixed effect of a mother’s trophic positions on her daughter’s trophic position. To this end, we determined maternal trophic positions from a population-wide model accounting for sexual dimorphism, age, and individual consistency in diet ( Supplement 3 ). Because bears may alter diet selection over time through individual learning, we allowed the effect of maternal learning to shift with time since the offspring gained independence. Last, we also accounted for permanent individual effects that could not be attributed to any of the aforementioned sources, by including a random effect for bear ID. We focused on the effect of maternal trophic position on female offspring trophic position, because male offspring were only monitored for a short period after family breakup. In the supplementary material we provide an additional analysis of the relationship between maternal and both female and male offspring trophic position in the first 4 years after family breakup and of the relationship between paternal trophic position and offspring trophic position. We also provide an alternative analysis accounting for spatial correlation via a spatial distance instead of a habitat similarity matrix, as well as a reduced model excluding the effect of environmental similarity to test whether spatial and genetic effects were confounded in philopatric female bears. Last, we validated our effect of maternal learning by refitting the model to a reduced dataset with observed maternal trophic positions during rearing, instead of modeled-averaged maternal trophic positions. RESULTS We analyzed annual trophic positions in 213 hair samples collected from 71 female brown bears born to 33 unique mothers (1–7 daughters per mother; median 2 daughters). Repeated sampling (median 3 years; range 1–11 years) revealed that female trophic position was unaffected by age (explained variance = 1% [0–4%]) and that individuals showed long-term individual specialization, accounting for 48% [31–61%] (median [89% equal tails credible interval]) of the total variance in trophic position (Fig. 2 , Basic model ). Individual specialization spanned half a trophic position ranging from 2.7 to 3.1 for individual females (Fig. 1 B), which is equivalent to the difference between an omnivore feeding on a mix of plants and animal prey and a carnivore feeding predominantly on animal prey. Individual specialization was primarily driven by initial maternal learning, the environment, and maternal effects. Maternal trophic position dynamic over the time since separation accounted for 13% [5% − 23%] of variation in trophic position, while environmental similarity accounted for 9% [0.1–5%] of the total phenotypic variation in trophic position. Additionally, maternal effects accounted for 11% [0.5% − 30%] of variation in trophic position, indicating that siblings (full and half) of the same mother were more similar in trophic position throughout life as compared to non-siblings. A remaining 8% [0.3–26%] of variance in trophic position was attributed to permanent individual effects (Fig. 2 ). Genetically more closely related individuals did not share a more similar trophic position (3% [< 0.1% – 17%] of variance explained) providing no evidence that dietary specialization could be heritable in this population (Fig. 2 ). After separating from their mother, female offspring initially maintained a similar trophic position as their mother (Pearson’s r = 0.66 in the first two years after separation), which gradually became more dissimilar over time (Pearson’s r = 0.31 in year 3–4 after separation, Fig. 3 ). In the first years, offspring of more carnivorous mothers also had a high trophic position while offspring of less carnivorous mothers had a lower trophic position. About five years after the separation of the mother, this correlation ceased to exist. Bears inhabiting home ranges with a similar composition of mature and disturbed forest, as well as a similar habitat diversity in the home range, also had more similar trophic positions. The distance between pairwise home range centroids ranged from 0.7 to 172 km with a median pairwise distance of 48 km and individuals living in closer proximity had a more similar trophic position than individuals living farther apart ( Supplementary material S6 ). Spatial distance and maternal effects seemed to be confounded in this female philopatric species: After excluding spatial distance, maternal learning and maternal effects but not heritability explained more variance in trophic position, corroborating that spatial proximity is confounded with philopatric females forming clusters of mothers and daughter in space, so called matrilines ( Supplementary material S7 ). In a separate analysis ( Supplementary material S8 ) we could also show that the relationship between maternal and offspring trophic position in the first years after family breakup was not sex-specific. Both male (n = 31, Pearson correlation coefficient = 0.4) and female (n = 69, Pearson correlation coefficient = 0.45) offspring’s trophic positions resembled their mother’s trophic position in the first 4 years of independence, corroborating our findings that initial maternal learning determines foraging behavior in the early years after family breakup. Conversely, paternal trophic position had no effect on offspring trophic position in the first 4 years of independence (Pearson correlation coefficient = 0.13, Supplementary material S9 ). While the modelled maternal trophic position correlated strongly with the observed trophic position in any given year ( Supplementary material S3 ), maternal learning explained even more of the phenotypic variance in daughter trophic position (22% [8% − 27%] instead of 13%, Supplementary material S10 ) when fitting the observed maternal trophic position during rearing, instead of the modelled posterior average maternal trophic position to a reduced dataset (62 hair samples collected from 38 daughters). Our estimates of maternal learning are therefore likely conservative and may underestimate the true effect of maternal learning on dietary specialization. DISCUSSION Our multigenerational dataset reveals unique insights into the ontogeny of individual dietary specialization along a continuum from a more herbivorous to a more carnivorous diet in a long-lived omnivore. Specifically, the foraging strategy of sons and daughters was intimately tied to the foraging strategy of their mother, a relationship that lasted up to four years after independence. We interpret this relationship as evidence that maternal learning plays an important role in shaping an individual’s dietary specialization. Five years into independence, the similarity between the mothers’ and their daughters’ trophic position slowly faded, likely due to individual learning and experience. In addition, siblings of the same mother also shared lifelong similarities in their trophic position, potentially mediated through maternal genetic or environmental effects on body size 25 . In general, previous ecological studies have mainly concentrated on resource availability as the main driver of resource selection 34 and individual specialization 4 , however our results show that, within populations, the environment is only one of several components shaping individual dietary variation. We conclude that early-life imitation of maternal dietary preferences and maternal effects (i.e., maternal genotype and environment), which together explained about 24% of the variation in trophic position, play a pivotal role in spreading and maintaining feeding strategies within populations, even in species with otherwise solitary lifestyles. In addition, variation solely linked to individual variation (in our study 8 %) demonstrates potential for behavioral innovation and the potential to adapt to changing conditions. Our findings are particularly relevant for species in which dietary specialization impacts individual fitness 7, 35, 36 . For example, protein-rich diets may promote greater offspring survival or mass gain 37 . Maternal and social learning in general therefore present an important, yet understudied pathway by which alternative behavioral strategies can establish and spread more rapidly within populations than by genetic evolution alone 38 . Species more adept in social learning of dietary strategies may therefore show greater behavioral variability at the population level, which could give them an advantage when adapting to changing environments due to landscape modification or urbanization, climatic variations or global change in general. Moreover, there is evidence that the strength of social learning in shaping individual phenotypes is not only species-specific, but can also vary among populations or individuals of the same species 39 . Our research also points to several aspects of maternal learning that warrant future research. First, there is little information on whether maternal care and maternal learning tend to be more prevalent in species or populations with greater dietary specialization. There is some evidence that within populations, dietary generalists (i.e. those with a wider dietary niche) seem to provide more intense parental care 40 , than their conspecific dietary specialists (i.e. ones with a narrower dietary niche), but the links to parental learning of foraging preferences remains unclear. Second, while generalist species with a wide ecological niche have been frequently shown to be more successful under changing environmental conditions, such as urban environments or fragmented landscapes, than specialist species 41, 42, 43 , it is currently unknown whether this success could be partially mediated by social or maternal learning. Last, social learning could alternatively limit behavioral innovation and adaptation due to adherence to social traditions 44 . We therefore suggest that alternative hypotheses should be evaluated that consider how social learning impacts individual specialization and in turn the adaptability of species under global change. Our findings that dietary specialization can be socially learned and transmitted are particularly relevant for species where specialization is related to human-wildlife conflict 45 . For example, the removal of single individuals which are known to cause conflict is an effective strategy to halt the spread of problematic behavior, increase societal acceptance by effectively mitigating the conflict, while minimizing the impact for species conservation goals 45 . Foraging behavior that causes conflict has also been shown to change in ursids across life time, remarking the crucial role of individuality and plasticity in behavior 46 . Maternal learning of behavior 47 , including dietary specialization and foraging on anthropogenic food resources is commonly observed in ursids 48, 49, 50, 51 . However, none of these studies tracked offspring diet over their lifetimes or were able to simultaneously account for the mother’s diet, genetics, the environment, and other maternal effects, that could explain similar patterns of dietary specialization. While some of the aforementioned studies suggest either the environment or maternal learning as primary drivers of individual specialization, we suggest using caution in assigning causality in dietary specialization, when potentially confounding alternative sources cannot be accounted for. Specifically, in female-biased philopatric species, spatial proximity does not only encode for spatial variation in resource abundance but is also conflated with relatedness and, in particular, with maternal effects. In brown bears, some daughters settle close to their mother’s home range 23 creating spatial clusters of closely related females, so called matrilinear assemblages 52 . Due to spatial dependence of these assemblages, it can therefore be difficult to disentangle maternal learning from other maternal effects (i.e., maternal genotype or maternal environment) or the ambient environment. Our study population spanned over 170 km with spatial proximity explaining 59% of the total phenotypic variation in trophic position of female bears: individuals further apart tended to have more different diets. However, when replacing spatial proximity with environmental similarity among home ranges, the explanatory power was attributed to maternal learning and maternal effects along with the environment. Our results therefore demonstrate that individual dietary specialization is not caused by a single driver in isolation but the product of many factors, namely maternal learning, maternal effects, and the environment. Our finding that maternal learning has a similar impact on resource selection as the environment provides important insights for a range of studies on habitat selection, dispersal, and range expansion. For example, a popular theory known as “natal habitat preference induction” suggests that dispersing animals select areas for settlement that resemble their natal habitat, even at fine habitat scales 21 . Our results challenge the notion that habitat similarity alone drives natal settlement strategies and rather suggest that maternally induced diet preferences, and hence the selection for food resources themselves, could play an important role in producing similar patterns of settlement selection like induced natal habitat preferences. Recent studies of migration and short stopover behavior in whooping cranes ( Grus americana ) have also observed that social learning rather than environmental conditions 53 or genetic inheritance 54 led to the emergence and establishment of alternative migratory behavior. Similar to what our study shows with respect to dietary specialization, social learning of migration strategies primarily determined behavior in early life whereas individual-experiential learning shaped behavior later in life 55 . Conclusion Drivers of dietary specialization are well documented among populations of the same species, however, systematic studies delineating the sources of individual specialization within populations are lacking, likely because suitable datasets including multigenerational, genetic, environmental, and life-history information are rare. We show here that in addition to the environment, maternal learning and (other) maternal effects can be important sources of dietary specialization. METHODS Bear sample collection We collected brown bear hair samples in south-central Sweden (~ N61°, E15°) as part of a long-term, individual-based monitoring project (Scandinavian Brown Bear Research Project; www.bearproject.info ). Hair samples were collected from known individuals and their offspring during bear captures in spring (April - June) 1993–2016 after bears emerged from hibernation. Bears were immobilized from a helicopter (Arnemo & Fahlman, 2011). A vestigial premolar tooth was collected from all bears not captured as a yearling to estimate age based on the cementum annuli in the root 56 . Bears were weighed in a stretcher suspended beneath a spring scale. Tissue samples (stored in 95% alcohol) were taken for DNA extraction to assign parentage and construct a genetic pedigree 52 . Guard hairs and follicles were plucked with pliers from a standardized spot between the shoulder blades and archived at the Swedish National Veterinary Institute. All animal captures and handling were performed in accordance with relevant guidelines and regulations and were approved by the Swedish authorities and ethical committee (Uppsala Djurförsöksetiska Nämnd: C40/3, C212/9, C47/9, C210/10, C7/12, C268/12, C18/ 15. Statens Veterinärmediciniska Anstalt, Jordbruksverket, Naturvårdsverket: Dnr 35–846/03, Dnr 412-7093-08 NV, Dnr 412-7327- 09 Nv, Dnr 31-11102/12, NV-01758-14). We used data of adult bears (solitary or with offspring) and of offspring after separation from their mother. Bear cubs are born in January or February during winter hibernation and are typically first captured together with their mother as yearlings at the age of ~ 15 months. Cubs in this population separate from their mother during the mating season in May or June after 1.5 or 2.5 years 57 . Only hair samples of solitary, independent offspring taken in spring and early summer at least 10 months after separation from the mother were included in this study. A hair sample taken in spring reflects the summer-fall diet of the bear in the previous active season (Fig. 1 A). Food sample collection We collected samples of the natural foods most important for brown bear in the study area, including 21 samples of moose hair ( Alces alces ), the most common meat source in the brown bears’ diet in our study area 58 , in the spring-autumn field season of 2014 ( Fig S1 ). Samples were placed in a paper envelope and dried at ambient temperature. Stable isotope analyses Hair samples were rinsed with a 2:1 mixture of chloroform:methanol or washed with pure methanol to remove surface oils 59 . Dried samples were ground with a ball grinder (Retsch model MM-301, Haan, Germany). We weighed 1 mg of ground hair into pre-combusted tin capsules and combusted at 1030°C in a Carlo Erba NA1500 elemental analyser. N 2 and CO 2 were separated chromatographically and introduced to an Elementar Isoprime isotope ratio mass spectrometer (Langenselbold, Germany). Two reference materials were used to normalize the results to VPDB and AIR: BWB III keratin ( δ 13 C =- 20.18‰, δ 15 N = 14.31‰, respectively) and PRC gel (δ 13 C =-13.64‰, δ 15 N = 5.07‰, respectively). Measurement precisions as determined from both reference and sample duplicate analyses were ± 0.1‰ for both δ 13 C and δ 15 N. Bear trophic position We calculated the trophic position of each bear hair sample relative to the average δ 15 N value of moose (mean ± sd = 1.8 ± 1.26‰, n = 21, Fig S1 ). Trophic position is calculated as the discrepancy of δ 15 N in a secondary consumer and its food source divided by the enrichment of δ 15 N per trophic level, plus lambda, the trophic position of the food source (e.g. 1 for primary producers, 2 for primary consumers, 3 for secondary consumer, 4 for tertiary consumers) 60 . We used an average trophic enrichment factor of 3.4‰ 60 and added a lambda of 2 given that the moose baseline trophic position as a strict herbivore. Bear trophic position = ( δ 15 N Ursus arctos – average( δ 15 N Alces alces )) / 3.4 + 2 Under an omnivorous diet including the consumption of herbivores (in particular moose but also ants such as Formica spp., Camponotus herculeanus with average δ 15 N indistinguishable from moose), bear trophic position values were expected to fall between 2 and 3. Values approaching 4 indicate a trophic enrichment through consumption of other omnivorous or carnivorous animals. Genetic pedigree and parentage assignment A genetic pedigree based on 16 microsatellite loci was available for the population including 1614 individual genotypes 61 . Genotyping followed the protocols of Waits, Taberlet 62 , Taberlet, Camarra 63 , and Andreassen, Schregel 64 . All female offspring in this study were genotyped and included in the population’s genetic pedigree. All females included in this study had a known mother that was also captured and followed. We used Cervus 3.0 65 for assignment of fathers and COLONY 66 for creating putative unknown mother or father genotypes and sibship reconstruction (see 61 for details). Maternal trophic position Based on repeated hair samples of 115 female (n female = 335) and 98 male (n male = 219) bears, we fitted a basic linear mixed effects model for female and male bears respectively, to estimate sex-specific among individual variation in trophic position ( Supplementary analysis 3 ). We modelled trophic position as a function of a quadratic relationship with age and we controlled for individual random intercepts. Female trophic position did not vary with age but was highly repeatable over multiple years. For all daughters, we extracted their mother’s (and father’s) trophic position as the median of the posterior distribution of their respective random intercept. The modelled posterior trophic position and the observed trophic position in a given sampling year were highly positively correlated (Pearson correlation coefficient r = 0.78, t = 22.63, df = 336, p < 0.001). Environmental similarity Resources may not be distributed evenly in space. For moose, population density and hunting quotas (which determine availability of slaughter remains) vary across the study area. For ants, the availability of old forests and clearcuts determine their abundance 67 . Further, brown bear daughters are often philopatric with limited dispersal and settle close to their mother’s home range 23 . Genetic, spatial, and maternal learning effects may therefore be confounded with related bears occupying adjacent ranges with similar environments and resource availability. Elsewhere, accounting for environmental similarity through spatial autocorrelation in animal models has revealed that a major portion of variance may be attributed to environmental similarity rather than genetic heritability 31, 32, 68, but see also 69 . Here, we accounted for environmental similarity by extracting habitat composition in each bear’s lifetime home range. For individuals with sufficient locations (> 1000 GPS locations or VHF locations on at least 25 days) we constructed home ranges using a 95% kernel density estimator. We used a Corine landcover map (25 m resolution) which we updated annually with polygons of newly emerged clearcuts (data obtained from the Swedish Forest Agency). We extracted home range composition in the year when diet was assessed. When individuals were monitored for multiple years, we extracted the home range composition for the median year. We calculated the proportion of mid-aged and old forest and proportion of disturbed forest (clearcuts and regenerating young forest) within the 95% utilization distribution. Additionally, we calculated habitat diversity using the Simpson diversity index from the R package landscapemetrics 70 . Following Thomson et al. 31 we calculated the Euclidean distance between scaled and centered habitat composition and habitat diversity in multivariate space, assuming equal importance of each component. Pairwise distances were scaled between 0 and 1, where increasing values indicated more similar habitat composition. In the supplementary material we provide an alternative analysis accounting for spatial autocorrelation in dietary specialization with a pairwise spatial distance matrix (S matrix; Supplementary analysis 5, Fig S5 ). Statistical analysis We applied a two-step modelling approach. First, we fitted a basic linear mixed effects model to estimate individual specialization as among individual variation in annual trophic position. We accounted for a nonlinear effect of age (second order polynomial) and for repeated measures of the same individual with individual random intercepts. We extracted the variance in fitted values (variance explained by fixed effects), among-individual, and residual variance and estimated the proportional contribution of fixed and random effects on the total phenotypic variance through variance standardization (i.e. repeatability 71 , marginal and conditional R 2 -values 72 ). Second, we used a spatially explicit Bayesian hierarchical model (i.e. ‘animal model’) 31, 33 to partition among-individual variance in trophic position into environmental similarity (σ 2 env ), additive genetic (σ 2 a ), permanent among-individual (σ 2 ind ), maternal (σ2 mat ), and residual within-individual effects (σ 2 r ). Similar to the basic model, we accounted for a nonlinear effect of age on trophic position (fitted as time since separation of mother and daughter scaled by the standard deviation, true age and time since separation were perfectly correlated: Pearson correlation coefficient > 0.99). We tested for maternal effects on offspring trophic position by incorporating the mother’s trophic position as a covariate into the model. To account for a potential decrease of the maternal effect over time, we let maternal trophic position interact with the time since separation of mother and daughter (both scaled by their standard deviation and centered). We partitioned the variance explained by the two components of the fixed effect, the effect of maternal learning over time (i.e. maternal trophic position and the interaction between maternal trophic position and time since separation) and age (i.e. the main effect of time since separation), respectively, by calculating the independent contribution of each component to the total variance explained by the fixed effects, following the approach by Stoffel, Nakagawa 73 adapted to a Bayesian framework (see code under 74 ). All models were fit using the R package “brms” 75 based on the Bayesian software Stan 76, 77 . We ran four chains to evaluate convergence which were run for 6,000 iterations, with a warmup of 3,000 iterations and a thinning interval of 10. All estimated model coefficients and credible intervals were therefore based on 1200 posterior samples and had satisfactory convergence diagnostics with \(\widehat{R}\) 400 78 . Posterior predictive checks recreated the underlying Gaussian distribution of trophic position well. For all parameters, we report the median and 89% credible intervals, calculated as equal tail intervals, as measure of centrality and uncertainty 79 . We deemed explained variance proportions as inconclusive when the lower credible interval limit was < 0.001 (i.e., < 0.1%) 80 . All statistical analyses were performed in R 4.0.0 81 . Primary data and code to reproduce all analyses are provided under ( https://doi.org/10.17605/OSF.IO/68B9U , 74 ). Declarations ACKNOWLEDGEMENTS AGH has received funding from the European Union's Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie Grant agreement No 793077 and from the German Science Foundation (HE 8857/1-1). The study was further funded by the Norway Grants under the Polish-Norwegian Research Programme administered by the National Research Centre for Research and Development in Poland and the Norwegian Research Council (JA, NS, AS, and AZ; GLOBE No POL-NOR/198352/85/2013). Isotope analyses were funded through a Robert Bosch Foundation grant to TM and the GLOBE project and conducted by KH, DJ and AS. We thank the Scandinavian Brown Bear Research Project (SBBRP) for providing access to the data. The SBBRP was funded by the Norwegian Environment Agency, the Swedish Environmental Protection Agency, the Austrian Science Fund, and the Norwegian Research Council. AUTHOR’S CONTRIUTIONS AH, JA, and TM developed the work. AZ, JK, KH, NS and AS provided the data. AZ managed the sample collection. AS managed the hair samples database and prepared samples for stable isotope analyses by KH. DJ provided laboratory space and resources and supervised preparatory procedures. SF constructed and provided the genetic pedigree. JH provided home range centroids. AM and JA advised to the analysis and interpretation of stable isotope data. TM, NS and AZ secure project funding. AH performed the statistical analyses with input from JA. AH wrote the manuscript with help from TM, JA, and input from all authors. References Bolnick DI, Svanbäck R, Araújo MS, Persson L. Comparative support for the niche variation hypothesis that more generalized populations also are more heterogeneous. Proc Natl Acad Sci U S A 104 , 10075-10079 (2007). Huang S, Tucker MA, Hertel AG, Eyres A, Albrecht J. Scale-dependent effects of niche specialisation: The disconnect between individual and species ranges. Ecology Letters 24 , 1408-1419 (2021). Forsman A, Wennersten L. Inter-individual variation promotes ecological success of populations and species: evidence from experimental and comparative studies. Ecography 39 , 630-648 (2016). Araújo MS, Bolnick DI, Layman CA. 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Van de Walle J, Pigeon G, Zedrosser A, Swenson JE, Pelletier F. Hunting regulation favors slow life histories in a large carnivore. Nature Communications 9 , 1100 (2018). Stenset NE , et al. Seasonal and annual variation in the diet of brown bears Ursus arctos in the boreal forest of southcentral Sweden. Wildlife Biology 22 , 107-116 (2016). Sergiel A , et al. Compatibility of preparatory procedures for the analysis of cortisol concentrations and stable isotope (δ(13)C, δ(15)N) ratios: a test on brown bear hair. Conserv Physiol 5 , cox021-cox021 (2017). Post DM. Using stable isotopes to estimate trophic position: models, methods, and assumptions. Ecology 83 , 703-718 (2002). Frank SC , et al. Harvest is associated with the disruption of social and fine-scale genetic structure among matrilines of a solitary large carnivore. Evolutionary Applications n/a . Waits L, Taberlet P, Swenson JE, Sandegren F, Franzén R. 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Shared spatial effects on quantitative genetic parameters: accounting for spatial autocorrelation and home range overlap reduces estimates of heritability in wild red deer. Evolution 66 , 2411-2426 (2012). Regan CE, Pilkington JG, Bérénos C, Pemberton JM, Smiseth PT, Wilson AJ. Accounting for female space sharing in St. Kilda Soay sheep (Ovis aries) results in little change in heritability estimates. Journal of Evolutionary Biology 30 , 96-111 (2017). Hesselbarth MHK, Sciaini M, Nowosad J, Hanss S. landscapemetrics: Landscape Metrics for Categorical Map Patterns. R package version 1.0.) (2019). Dingemanse NJ, Kazem AJN, Réale D, Wright J. Behavioural reaction norms: animal personality meets individual plasticity. Trends Ecol Evol 25 , 81-89 (2010). Nakagawa S, Schielzeth H. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods in Ecology and Evolution 4 , 133-142 (2013). Stoffel MA, Nakagawa S, Schielzeth H. partR2: partitioning R2 in generalized linear mixed models. PeerJ 9 , e11414 (2021). Hertel AG. Data&Code: The ontogeny of individual specialization. (2023). Bürkner P-C. brms: An R package for Bayesian multilevel models using Stan. Journal of Statistical Software 80 , 1-28 (2017). Stan Development Team. RStan: the R interface to Stan. R package version 2.17.3.) (2018). Carpenter B , et al. Stan: A Probabilistic Programming Language. 2017 76 , 32 (2017). Vehtari A, Gelman A, Simpson D, Carpenter B, Bürkner P-C. Rank-normalization, folding, and localization: An improved R for assessing convergence of MCMC. Bayesian Analysis , (2020). Kruschke J. Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan. (2014). Bonnet T , et al. Genetic variance in fitness indicates rapid contemporary adaptive evolution in wild animals. Science 376 , 1012-1016 (2022). R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing. (2020). Additional Declarations There is NO Competing Interest. Supplementary Files 2023AGHMaternalLearningSupplement.docx rs.pdf Reporting Summary Cite Share Download PDF Status: Published Journal Publication published 29 Nov, 2024 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2926801","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":201736332,"identity":"738e70f0-8f73-45c1-9d4d-5c3869d84453","order_by":0,"name":"Anne 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Stable isotopes are deposited into the growing hair with a delay of approximately one month. The quiescent phase, when hair ceases growing, lasts through hibernation, followed by emergence from the winter den and molting in late May-early June. Hair samples were taken in April - June and reflect the bears’ diet in the previous year; \u003cstrong\u003eB\u003c/strong\u003e) Posterior distribution of the population trophic niche (bold line) and individual specialization indicated by each individual’s posterior trophic position (modelled distribution with individual posterior means indicated by black dots). Scientific illustration by Juliana D. Spahr, SciVisuals.com.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-2926801/v1/d3c74b873cc5c5e423741344.png"},{"id":37255409,"identity":"d16a8c92-23b8-47ec-8ebf-c3de9b136f5d","added_by":"auto","created_at":"2023-05-19 18:48:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":65366,"visible":true,"origin":"","legend":"\u003cp\u003eProportion of variance (median of the posterior distribution) in brown bear trophic position explained by age, age-sensitive maternal learning, permanent individual effects, environmental similarity, permanent maternal effects, genetic heritability, and residual components.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-2926801/v1/4eec7e60edef8168ae2127f8.png"},{"id":37255411,"identity":"ad65dc13-f2a8-41d9-a38e-d6497fe6baf2","added_by":"auto","created_at":"2023-05-19 18:48:18","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":311112,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between female brown bear trophic position and their mother’s trophic position over number of years since separation (i.e. since the female became independent, usually at 1.5 years of age in our population). The females’ trophic position resembled their mothers’ in the first years after separation but this similarity ceased after 4 years. Lines indicate predicted posterior mean estimates with ribbons corresponding to the estimated standard error, raw data are shown as points.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-2926801/v1/4e3b49473bb3a156db921045.png"},{"id":70241090,"identity":"e78a1be7-5630-4f0a-9716-0370df3acc18","added_by":"auto","created_at":"2024-11-30 08:07:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1285259,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2926801/v1/ef6204ad-75c8-499c-88b4-f90b6c7f0f3f.pdf"},{"id":37255412,"identity":"19577c9a-406c-49cf-bd48-a79f0184460c","added_by":"auto","created_at":"2023-05-19 18:48:18","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1858363,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"2023AGHMaternalLearningSupplement.docx","url":"https://assets-eu.researchsquare.com/files/rs-2926801/v1/ea661eec5e586625a8f9ce96.docx"},{"id":37255591,"identity":"b0ceea92-547b-4c30-8131-11f5395be2b3","added_by":"auto","created_at":"2023-05-19 18:56:18","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":121060,"visible":true,"origin":"","legend":"\u003cp\u003eReporting Summary\u003c/p\u003e","description":"","filename":"rs.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2926801/v1/d2aaf65e5476b984811c53c8.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Ontogeny shapes individual specialization","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eAmong individuals of the same species, niche variation is common and may occur when availability of food resources or habitat structure change across the species\u0026rsquo; range. Ecological generalists, species with a wide niche, also seem to exhibit more individual specialization \u003csup\u003e1\u003c/sup\u003e and are hence particularly well adapted to persist under shifts in resource availability or composition enabling them to occupy larger distributional ranges than ecological specialists \u003csup\u003e2\u003c/sup\u003e. Individual variation is key for making species resilient towards changing resource availabilities in a rapidly changing world and may ultimately determine local persistence or extinction of species \u003csup\u003e3\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eInter- and intraspecific competition, predation and ecological opportunity, alter resource availability and have been identified as the main ecological drivers explaining variation in the degree of individual specialization among populations \u003csup\u003e4\u003c/sup\u003e. Yet, how individual variation emerges and is maintained within populations has been rarely quantified in the wild. In principle, four potential sources of variation exist: social and individual learning, genetic inheritance, the environment, and maternal effects. Individual differences in resource preference or competence to secure a resource may therefore be determined during early ontogeny through social (e.g., maternal) learning via imitation \u003csup\u003e5, 6, 7, 8, 9\u003c/sup\u003e leading to similarities between the offspring\u0026rsquo;s and their mother\u0026rsquo;s dietary phenotype. Effects of maternal learning can be lifelong or are subsequently modified through individual experiential learning \u003csup\u003e10\u003c/sup\u003e. Resource preferences have also been suggested to be genetically determined through genes inherited from both mother and father, where closely related individuals have more similar diets than distantly related individuals \u003csup\u003e11\u003c/sup\u003e. In addition, maternal effects account for lifelong similarities in dietary phenotype among offspring of the same mother \u003csup\u003e12\u003c/sup\u003e. Such similarities can arise from social interactions, maternal genotype, or maternal environment. Statistically, maternal effects are quantified as the similarity of repeated samples from siblings of the same mother but not as the similarity of behavioral expression between mother and offspring (i.e., \u0026ldquo;maternal learning\u0026rdquo;). In range resident species, where individuals occupy a subset of a population\u0026rsquo;s range, the environment, in terms of habitat composition or availability of particular food resources, may differ among home ranges and lead to individual specialization \u003csup\u003e11\u003c/sup\u003e. Accounting for the environmental heterogeneity when studying the drivers of individual specialization is therefore essential in range resident species \u003csup\u003e13, 14\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAttributing variation in diet to the individual level, to isolate its sources and to identify developmental drivers of diet preferences requires multigenerational datasets of repeated measures of the diet of individuals throughout their life. We used a 30-year longitudinal dataset of 72 female Scandinavian brown bears (\u003cem\u003eUrsus arctos\u003c/em\u003e) of known mothers with repeated annual isotopic estimates of trophic position to assess whether individual dietary specialization occurs. Using information about their mother\u0026rsquo;s diet, a genetic pedigree, and individual movement data we then attributed individual variation in diet to its sources: maternal learning, genetic heritability, environment, and maternal effects.\u003c/p\u003e \u003cp\u003eBrown bears are ecological generalists with a species range spanning the northern hemisphere from tundra to deserts, paralleled by extensive variation in diet: from populations tracking food resource pulses, such as spawning fish \u003csup\u003e15\u003c/sup\u003e, scavenging on ungulate carcasses or preying on ungulates neonates \u003csup\u003e16\u003c/sup\u003e, or feeding extensively on invertebrates, to populations using primarily fruiting plant based diets \u003csup\u003e17, 18\u003c/sup\u003e. Given this extreme dietary plasticity, it is not surprising that great dietary variation has been found within populations \u003csup\u003e19, 20\u003c/sup\u003e, however, the determinants and ontogeny of this variation at the individual level remain largely unknown. In ecology, differences in diet are often primarily attributed to differences in resource availability and abundance. Even within populations inhabiting a continuous biome, home range scale variation in habitat composition \u003csup\u003e21\u003c/sup\u003e can lead to variation in resource availability. The most parsimonious source of variation in diet are, therefore, differences in the environment. Brown bears maintain non-territorial home ranges but live a solitary lifestyle except for the period of offspring rearing involving up to three years \u003csup\u003e22\u003c/sup\u003e of maternal care, after which female offspring often settle close to their mother\u0026rsquo;s home range \u003csup\u003e23\u003c/sup\u003e. In their first years of life, bear cubs accompany their mother and it is therefore reasonable to assume that brown bear offspring learn behaviors such as habitat, den site, or diet selection from their mothers and hence show similar behavior to them. If mothers differ in their dietary selection, these differences may hence be maintained in the population through learning by imitation of the mother (hereafter \"maternal learning\u0026rdquo;), even after offspring gain independence, however, such similarities may wane over time \u003csup\u003e24\u003c/sup\u003e. On the other hand, genetic heritability or maternal effects can have lifelong effects on offspring phenotype. Body size has been shown to be genetically heritable in our study population \u003csup\u003e25\u003c/sup\u003e suggesting greater similarity among closely related individuals also in other linked traits, such as trophic position. Alternatively, maternal effects (i.e., maternal genotype or maternal environment) alone can shape the phenotype of offspring. For example, milk quantity or quality \u003csup\u003e26\u003c/sup\u003e can vary among females either due to genetic differences or differences in the environments, leading to greater similarity among all offspring from the same mother (e.g. being smaller or larger in body size), which in turn could cause similarities in trophic position among siblings. To assess individual specialization along a continuum from a more plant-based to a more meat- or insect-based diet, we analyzed annual trophic positions from stable- nitrogen isotopes (\u003cem\u003eδ\u003c/em\u003e\u003csup\u003e15\u003c/sup\u003eN) in bear hair keratin \u003csup\u003e27\u003c/sup\u003e. Stable isotopes reflect cumulative diet intake and are deposited into the hair during growth with, a delay of approximately one month (i.e. a growing hair in June reflects the diet intake in May, \u003csup\u003e28\u003c/sup\u003e). Bear hair is regularly renewed through molting in June, regrows over the summer and fall and stops growing during winter hibernation (\u003cb\u003eFig.\u0026nbsp;1A\u003c/b\u003e, \u003csup\u003e\u003cb\u003e29, 30\u003c/b\u003e\u003c/sup\u003e). Guard hair samples collected in spring and early summer (April - June) therefore reflect an individual\u0026rsquo;s diet during the previous active season prior to hibernation \u003csup\u003e29\u003c/sup\u003e. Using repeated samples of known mother-daughter pairs, we fit a spatially explicit Bayesian hierarchical model (i.e. \u0026acute;animal model\u0026acute;) \u003csup\u003e31, 32, 33\u003c/sup\u003e to disentangle the relative contributions of maternal learning, genetic relatedness, the environment, and maternal effects as determinants of individual specialization. Specifically, the model accounted for genetic relatedness with a pedigree and for environmental similarity of bear home ranges with pairwise habitat similarity encompassing the proportion of mature habitat such as old and mid-successional forests, disturbed habitat such as clearcuts and regenerating young forest, and habitat diversity (measured as Simpson\u0026rsquo;s diversity index) in a bear\u0026rsquo;s home range. The model also accounted for maternal effects by incorporating the mother\u0026rsquo;s ID as a random effect (i.e. if daughters from the same mother behaved in a similar fashion throughout life), and for maternal learning as the fixed effect of a mother\u0026rsquo;s trophic positions on her daughter\u0026rsquo;s trophic position. To this end, we determined maternal trophic positions from a population-wide model accounting for sexual dimorphism, age, and individual consistency in diet (\u003cb\u003eSupplement 3\u003c/b\u003e). Because bears may alter diet selection over time through individual learning, we allowed the effect of maternal learning to shift with time since the offspring gained independence. Last, we also accounted for permanent individual effects that could not be attributed to any of the aforementioned sources, by including a random effect for bear ID. We focused on the effect of maternal trophic position on female offspring trophic position, because male offspring were only monitored for a short period after family breakup. In the supplementary material we provide an additional analysis of the relationship between maternal and both female and male offspring trophic position in the first 4 years after family breakup and of the relationship between paternal trophic position and offspring trophic position. We also provide an alternative analysis accounting for spatial correlation via a spatial distance instead of a habitat similarity matrix, as well as a reduced model excluding the effect of environmental similarity to test whether spatial and genetic effects were confounded in philopatric female bears. Last, we validated our effect of maternal learning by refitting the model to a reduced dataset with observed maternal trophic positions during rearing, instead of modeled-averaged maternal trophic positions.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eWe analyzed annual trophic positions in 213 hair samples collected from 71 female brown bears born to 33 unique mothers (1\u0026ndash;7 daughters per mother; median 2 daughters). Repeated sampling (median 3 years; range 1\u0026ndash;11 years) revealed that female trophic position was unaffected by age (explained variance\u0026thinsp;=\u0026thinsp;1% [0\u0026ndash;4%]) and that individuals showed long-term individual specialization, accounting for 48% [31\u0026ndash;61%] (median [89% equal tails credible interval]) of the total variance in trophic position (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cem\u003eBasic model\u003c/em\u003e). Individual specialization spanned half a trophic position ranging from 2.7 to 3.1 for individual females (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB), which is equivalent to the difference between an omnivore feeding on a mix of plants and animal prey and a carnivore feeding predominantly on animal prey.\u003c/p\u003e \u003cp\u003eIndividual specialization was primarily driven by initial maternal learning, the environment, and maternal effects. Maternal trophic position dynamic over the time since separation accounted for 13% [5% \u0026minus;\u0026thinsp;23%] of variation in trophic position, while environmental similarity accounted for 9% [0.1\u0026ndash;5%] of the total phenotypic variation in trophic position. Additionally, maternal effects accounted for 11% [0.5% \u0026minus;\u0026thinsp;30%] of variation in trophic position, indicating that siblings (full and half) of the same mother were more similar in trophic position throughout life as compared to non-siblings. A remaining 8% [0.3\u0026ndash;26%] of variance in trophic position was attributed to permanent individual effects (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Genetically more closely related individuals did not share a more similar trophic position (3% [\u0026lt;\u0026thinsp;0.1% \u0026ndash; 17%] of variance explained) providing no evidence that dietary specialization could be heritable in this population (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAfter separating from their mother, female offspring initially maintained a similar trophic position as their mother (Pearson\u0026rsquo;s \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.66 in the first two years after separation), which gradually became more dissimilar over time (Pearson\u0026rsquo;s \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.31 in year 3\u0026ndash;4 after separation, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In the first years, offspring of more carnivorous mothers also had a high trophic position while offspring of less carnivorous mothers had a lower trophic position. About five years after the separation of the mother, this correlation ceased to exist. Bears inhabiting home ranges with a similar composition of mature and disturbed forest, as well as a similar habitat diversity in the home range, also had more similar trophic positions. The distance between pairwise home range centroids ranged from 0.7 to 172 km with a median pairwise distance of 48 km and individuals living in closer proximity had a more similar trophic position than individuals living farther apart (\u003cb\u003eSupplementary material S6\u003c/b\u003e). Spatial distance and maternal effects seemed to be confounded in this female philopatric species: After excluding spatial distance, maternal learning and maternal effects but not heritability explained more variance in trophic position, corroborating that spatial proximity is confounded with philopatric females forming clusters of mothers and daughter in space, so called matrilines (\u003cb\u003eSupplementary material S7\u003c/b\u003e). In a separate analysis (\u003cb\u003eSupplementary material S8\u003c/b\u003e) we could also show that the relationship between maternal and offspring trophic position in the first years after family breakup was not sex-specific. Both male (n\u0026thinsp;=\u0026thinsp;31, Pearson correlation coefficient\u0026thinsp;=\u0026thinsp;0.4) and female (n\u0026thinsp;=\u0026thinsp;69, Pearson correlation coefficient\u0026thinsp;=\u0026thinsp;0.45) offspring\u0026rsquo;s trophic positions resembled their mother\u0026rsquo;s trophic position in the first 4 years of independence, corroborating our findings that initial maternal learning determines foraging behavior in the early years after family breakup. Conversely, paternal trophic position had no effect on offspring trophic position in the first 4 years of independence (Pearson correlation coefficient\u0026thinsp;=\u0026thinsp;0.13, \u003cb\u003eSupplementary material S9\u003c/b\u003e). While the modelled maternal trophic position correlated strongly with the observed trophic position in any given year (\u003cb\u003eSupplementary material S3\u003c/b\u003e), maternal learning explained even more of the phenotypic variance in daughter trophic position (22% [8% \u0026minus;\u0026thinsp;27%] instead of 13%, \u003cb\u003eSupplementary material S10\u003c/b\u003e) when fitting the observed maternal trophic position during rearing, instead of the modelled posterior average maternal trophic position to a reduced dataset (62 hair samples collected from 38 daughters). Our estimates of maternal learning are therefore likely conservative and may underestimate the true effect of maternal learning on dietary specialization.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eOur multigenerational dataset reveals unique insights into the ontogeny of individual dietary specialization along a continuum from a more herbivorous to a more carnivorous diet in a long-lived omnivore. Specifically, the foraging strategy of sons and daughters was intimately tied to the foraging strategy of their mother, a relationship that lasted up to four years after independence. We interpret this relationship as evidence that maternal learning plays an important role in shaping an individual’s dietary specialization. Five years into independence, the similarity between the mothers’ and their daughters’ trophic position slowly faded, likely due to individual learning and experience. In addition, siblings of the same mother also shared lifelong similarities in their trophic position, potentially mediated through maternal genetic or environmental effects on body size\u0026nbsp;\u003csup\u003e25\u003c/sup\u003e. In general, previous ecological studies have mainly concentrated on resource availability as the main driver of resource selection\u0026nbsp;\u003csup\u003e34\u003c/sup\u003e and individual specialization\u0026nbsp;\u003csup\u003e4\u003c/sup\u003e, however our results show that, within populations, the environment is only one of several components shaping individual dietary variation. We conclude that early-life imitation of maternal dietary preferences and maternal effects (i.e., maternal genotype and environment), which together explained about 24% of the variation in trophic position, play a pivotal role in spreading and maintaining feeding strategies within populations, even in species with otherwise solitary lifestyles. In addition, variation solely linked to individual variation (in our study 8 %) demonstrates potential for behavioral innovation and the potential to adapt to changing conditions.\u003c/p\u003e\n\u003cp\u003eOur findings are particularly relevant for species in which dietary specialization impacts individual fitness\u0026nbsp;\u003csup\u003e7, 35, 36\u003c/sup\u003e. For example, protein-rich diets may promote greater offspring survival or mass gain\u0026nbsp;\u003csup\u003e37\u003c/sup\u003e. Maternal and social learning in general therefore present an important, yet understudied pathway by which alternative behavioral strategies can establish and spread more rapidly within populations than by genetic evolution alone\u0026nbsp;\u003csup\u003e38\u003c/sup\u003e. Species more adept in social learning of dietary strategies may therefore show greater behavioral variability at the population level, which could give them an advantage when adapting to changing environments due to landscape modification or urbanization, climatic variations or global change in general. Moreover, there is evidence that the strength of social learning in shaping individual phenotypes is not only species-specific, but can also vary among populations or individuals of the same species\u0026nbsp;\u003csup\u003e39\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur research also points to several aspects of maternal learning that warrant future research. First, there is little information on whether maternal care and maternal learning tend to be more prevalent in species or populations with greater dietary specialization. There is some evidence that within populations, dietary generalists (i.e. those with a wider dietary niche) seem to provide more intense parental care\u0026nbsp;\u003csup\u003e40\u003c/sup\u003e, than their conspecific dietary specialists (i.e. ones with a narrower dietary niche), but the links to parental learning of foraging preferences remains unclear. Second, while generalist species with a wide ecological niche have been frequently shown to be more successful under changing environmental conditions, such as urban environments or fragmented landscapes, than specialist species\u0026nbsp;\u003csup\u003e41, 42, 43\u003c/sup\u003e, it is currently unknown whether this success could be partially mediated by social or maternal learning. Last, social learning could alternatively limit behavioral innovation and adaptation due to adherence to social traditions\u0026nbsp;\u003csup\u003e44\u003c/sup\u003e. We therefore suggest that alternative hypotheses should be evaluated that consider how social learning impacts individual specialization and in turn the adaptability of species under global change.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur findings that dietary specialization can be socially learned and transmitted are particularly relevant for species where specialization is related to human-wildlife conflict\u0026nbsp;\u003csup\u003e45\u003c/sup\u003e. For example, the removal of single individuals which are known to cause conflict is an effective strategy to halt the spread of problematic behavior, increase societal acceptance by effectively mitigating the conflict, while minimizing the impact for species conservation goals\u0026nbsp;\u003csup\u003e45\u003c/sup\u003e. Foraging behavior that causes conflict has also been shown to change in ursids across life time, remarking the crucial role of individuality and plasticity in behavior\u0026nbsp;\u003csup\u003e46\u003c/sup\u003e. Maternal learning of behavior\u0026nbsp;\u003csup\u003e47\u003c/sup\u003e, including dietary specialization and foraging on anthropogenic food resources is commonly observed in ursids\u0026nbsp;\u003csup\u003e48, 49, 50, 51\u003c/sup\u003e. However, none of these studies tracked offspring diet over their lifetimes or were able to simultaneously account for the mother’s diet, genetics, the environment, and other maternal effects, that could explain similar patterns of dietary specialization. While some of the aforementioned studies suggest either the environment or maternal learning as primary drivers of individual specialization, we suggest using caution in assigning causality in dietary specialization, when potentially confounding alternative sources cannot be accounted for. Specifically, in female-biased philopatric species, spatial proximity does not only encode for spatial variation in resource abundance but is also conflated with relatedness and, in particular, with maternal effects. In brown bears,\u0026nbsp;some daughters settle close to their mother’s home range\u0026nbsp;\u003csup\u003e23\u003c/sup\u003e creating spatial clusters of closely related females, so called matrilinear assemblages\u0026nbsp;\u003csup\u003e52\u003c/sup\u003e.\u0026nbsp;Due to spatial dependence of\u0026nbsp;these assemblages, it can therefore be difficult to disentangle maternal learning from other maternal effects (i.e., maternal genotype or maternal environment) or the ambient environment. Our study population spanned over 170 km with spatial proximity explaining 59% of the total phenotypic variation in trophic position of female bears: individuals further apart tended to have more different diets. However, when replacing spatial proximity with environmental similarity among home ranges, the explanatory power was attributed to maternal learning and maternal effects along with the environment. Our results therefore demonstrate that individual dietary specialization is not caused by a single driver in isolation but the product of many factors, namely maternal learning, maternal effects, and the environment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur finding that maternal learning has a similar impact on resource selection as the environment provides important insights for a range of studies on habitat selection, dispersal, and range expansion. For example, a popular theory known as “natal habitat preference induction” \u0026nbsp;suggests that dispersing animals select areas for settlement that resemble their natal habitat, even at fine habitat scales\u0026nbsp;\u003csup\u003e21\u003c/sup\u003e. Our results challenge the notion that habitat similarity alone drives natal settlement strategies and rather suggest that maternally induced diet preferences, and hence the selection for food resources themselves, could play an important role in producing similar patterns of settlement selection like induced natal habitat preferences. Recent studies of migration and short stopover behavior in whooping cranes (\u003cem\u003eGrus americana\u003c/em\u003e) have also observed that social learning rather than environmental conditions\u0026nbsp;\u003csup\u003e53\u003c/sup\u003e or genetic inheritance\u0026nbsp;\u003csup\u003e54\u003c/sup\u003e led to the emergence and establishment of alternative migratory behavior. Similar to what our study shows with respect to dietary specialization, social learning of migration strategies primarily determined behavior in early life whereas individual-experiential learning shaped behavior later in life\u0026nbsp;\u003csup\u003e55\u003c/sup\u003e. \u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eDrivers of dietary specialization are well documented among populations of the same species, however, systematic studies delineating the sources of individual specialization within populations are lacking, likely because suitable datasets including multigenerational, genetic, environmental, and life-history information are rare. We show here that in addition to the environment, maternal learning and (other) maternal effects can be important sources of dietary specialization.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003e\u003cstrong\u003eBear sample collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe collected brown bear hair samples in south-central Sweden (~\u0026thinsp;N61\u0026deg;, E15\u0026deg;) as part of a long-term, individual-based monitoring project (Scandinavian Brown Bear Research Project; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.bearproject.info\u003c/span\u003e\u003c/span\u003e). Hair samples were collected from known individuals and their offspring during bear captures in spring (April - June) 1993\u0026ndash;2016 after bears emerged from hibernation. Bears were immobilized from a helicopter (Arnemo \u0026amp; Fahlman, 2011). A vestigial premolar tooth was collected from all bears not captured as a yearling to estimate age based on the cementum annuli in the root \u003csup\u003e56\u003c/sup\u003e. Bears were weighed in a stretcher suspended beneath a spring scale. Tissue samples (stored in 95% alcohol) were taken for DNA extraction to assign parentage and construct a genetic pedigree \u003csup\u003e52\u003c/sup\u003e. Guard hairs and follicles were plucked with pliers from a standardized spot between the shoulder blades and archived at the Swedish National Veterinary Institute. All animal captures and handling were performed in accordance with relevant guidelines and regulations and were approved by the Swedish authorities and ethical committee (Uppsala Djurf\u0026ouml;rs\u0026ouml;ksetiska N\u0026auml;mnd: C40/3, C212/9, C47/9, C210/10, C7/12, C268/12, C18/ 15. Statens Veterin\u0026auml;rmediciniska Anstalt, Jordbruksverket, Naturv\u0026aring;rdsverket: Dnr 35\u0026ndash;846/03, Dnr 412-7093-08 NV, Dnr 412-7327-\u003c/p\u003e\n\u003cp\u003e09 Nv, Dnr 31-11102/12, NV-01758-14). We used data of adult bears (solitary or with offspring) and of offspring after separation from their mother. Bear cubs are born in January or February during winter hibernation and are typically first captured together with their mother as yearlings at the age of ~\u0026thinsp;15 months. Cubs in this population separate from their mother during the mating season in May or June after 1.5 or 2.5 years \u003csup\u003e57\u003c/sup\u003e. Only hair samples of solitary, independent offspring taken in spring and early summer at least 10 months after separation from the mother were included in this study. A hair sample taken in spring reflects the summer-fall diet of the bear in the previous active season (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFood sample collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe collected samples of the natural foods most important for brown bear in the study area, including 21 samples of moose hair (\u003cem\u003eAlces alces\u003c/em\u003e), the most common meat source in the brown bears\u0026rsquo; diet in our study area \u003csup\u003e58\u003c/sup\u003e, in the spring-autumn field season of 2014 (\u003cstrong\u003eFig \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/strong\u003e). Samples were placed in a paper envelope and dried at ambient temperature.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStable isotope analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHair samples were rinsed with a 2:1 mixture of chloroform:methanol or washed with pure methanol to remove surface oils \u003csup\u003e59\u003c/sup\u003e. Dried samples were ground with a ball grinder (Retsch model MM-301, Haan, Germany). We weighed 1 mg of ground hair into pre-combusted tin capsules and combusted at 1030\u0026deg;C in a Carlo Erba NA1500 elemental analyser. N\u003csub\u003e2\u003c/sub\u003e and CO\u003csub\u003e2\u003c/sub\u003e were separated chromatographically and introduced to an Elementar Isoprime isotope ratio mass spectrometer (Langenselbold, Germany). Two reference materials were used to normalize the results to VPDB and AIR: BWB III keratin (\u003cem\u003e\u0026delta;\u003c/em\u003e\u003csup\u003e13\u003c/sup\u003eC =- 20.18\u0026permil;, \u003cem\u003e\u0026delta;\u003c/em\u003e\u003csup\u003e15\u003c/sup\u003eN = 14.31\u0026permil;, respectively) and PRC gel (\u0026delta;\u003csup\u003e13\u003c/sup\u003eC =-13.64\u0026permil;, \u003cem\u003e\u0026delta;\u003c/em\u003e\u003csup\u003e15\u003c/sup\u003eN = 5.07\u0026permil;, respectively). Measurement precisions as determined from both reference and sample duplicate analyses were \u0026plusmn;\u0026thinsp;0.1\u0026permil; for both \u003cem\u003e\u0026delta;\u003c/em\u003e\u003csup\u003e13\u003c/sup\u003eC and \u003cem\u003e\u0026delta;\u003c/em\u003e\u003csup\u003e15\u003c/sup\u003eN.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBear trophic position\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe calculated the trophic position of each bear hair sample relative to the average \u0026delta;\u003csup\u003e15\u003c/sup\u003eN value of moose (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;sd\u0026thinsp;=\u0026thinsp;1.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.26\u0026permil;, n\u0026thinsp;=\u0026thinsp;21, \u003cstrong\u003eFig \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/strong\u003e). Trophic position is calculated as the discrepancy of \u003cem\u003e\u0026delta;\u003c/em\u003e\u003csup\u003e15\u003c/sup\u003eN in a secondary consumer and its food source divided by the enrichment of \u003cem\u003e\u0026delta;\u003c/em\u003e\u003csup\u003e15\u003c/sup\u003eN per trophic level, plus lambda, the trophic position of the food source (e.g. 1 for primary producers, 2 for primary consumers, 3 for secondary consumer, 4 for tertiary consumers) \u003csup\u003e60\u003c/sup\u003e. We used an average trophic enrichment factor of 3.4\u0026permil; \u003csup\u003e60\u003c/sup\u003e and added a lambda of 2 given that the moose baseline trophic position as a strict herbivore.\u003c/p\u003e\n\u003cp\u003eBear trophic position = (\u003cem\u003e\u0026delta;\u003c/em\u003e\u003csup\u003e15\u003c/sup\u003eN\u003csub\u003e\u003cem\u003eUrsus arctos\u003c/em\u003e\u003c/sub\u003e \u0026ndash; average(\u003cem\u003e\u0026delta;\u003c/em\u003e\u003csup\u003e15\u003c/sup\u003eN\u003csub\u003e\u003cem\u003eAlces alces\u003c/em\u003e\u003c/sub\u003e)) / 3.4\u0026thinsp;+\u0026thinsp;2\u003c/p\u003e\n\u003cp\u003eUnder an omnivorous diet including the consumption of herbivores (in particular moose but also ants such as \u003cem\u003eFormica\u003c/em\u003e spp., \u003cem\u003eCamponotus herculeanus\u003c/em\u003e with average \u003cem\u003e\u0026delta;\u003c/em\u003e\u003csup\u003e15\u003c/sup\u003eN indistinguishable from moose), bear trophic position values were expected to fall between 2 and 3. Values approaching 4 indicate a trophic enrichment through consumption of other omnivorous or carnivorous animals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenetic pedigree and parentage assignment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA genetic pedigree based on 16 microsatellite loci was available for the population including 1614 individual genotypes \u003csup\u003e61\u003c/sup\u003e. Genotyping followed the protocols of Waits, Taberlet \u003csup\u003e62\u003c/sup\u003e, Taberlet, Camarra \u003csup\u003e63\u003c/sup\u003e, and Andreassen, Schregel \u003csup\u003e64\u003c/sup\u003e. All female offspring in this study were genotyped and included in the population\u0026rsquo;s genetic pedigree. All females included in this study had a known mother that was also captured and followed. We used Cervus 3.0 \u003csup\u003e65\u003c/sup\u003e for assignment of fathers and COLONY \u003csup\u003e66\u003c/sup\u003e for creating putative unknown mother or father genotypes and sibship reconstruction (see \u003csup\u003e61\u003c/sup\u003e for details).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaternal trophic position\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on repeated hair samples of 115 female (n\u003csub\u003efemale\u003c/sub\u003e = 335) and 98 male (n\u003csub\u003emale\u003c/sub\u003e = 219) bears, we fitted a \u003cem\u003ebasic\u003c/em\u003e linear mixed effects model for female and male bears respectively, to estimate sex-specific among individual variation in trophic position (\u003cstrong\u003eSupplementary analysis 3\u003c/strong\u003e). We modelled trophic position as a function of a quadratic relationship with age and we controlled for individual random intercepts. Female trophic position did not vary with age but was highly repeatable over multiple years. For all daughters, we extracted their mother\u0026rsquo;s (and father\u0026rsquo;s) trophic position as the median of the posterior distribution of their respective random intercept. The modelled posterior trophic position and the observed trophic position in a given sampling year were highly positively correlated (Pearson correlation coefficient r\u0026thinsp;=\u0026thinsp;0.78, t\u0026thinsp;=\u0026thinsp;22.63, df\u0026thinsp;=\u0026thinsp;336, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEnvironmental similarity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResources may not be distributed evenly in space. For moose, population density and hunting quotas (which determine availability of slaughter remains) vary across the study area. For ants, the availability of old forests and clearcuts determine their abundance \u003csup\u003e67\u003c/sup\u003e. Further, brown bear daughters are often philopatric with limited dispersal and settle close to their mother\u0026rsquo;s home range \u003csup\u003e23\u003c/sup\u003e. Genetic, spatial, and maternal learning effects may therefore be confounded with related bears occupying adjacent ranges with similar environments and resource availability. Elsewhere, accounting for environmental similarity through spatial autocorrelation in animal models has revealed that a major portion of variance may be attributed to environmental similarity rather than genetic heritability \u003csup\u003e31, 32, 68,\u003c/sup\u003e but see also \u003csup\u003e69\u003c/sup\u003e. Here, we accounted for environmental similarity by extracting habitat composition in each bear\u0026rsquo;s lifetime home range. For individuals with sufficient locations (\u0026gt;\u0026thinsp;1000 GPS locations or VHF locations on at least 25 days) we constructed home ranges using a 95% kernel density estimator. We used a Corine landcover map (25 m resolution) which we updated annually with polygons of newly emerged clearcuts (data obtained from the Swedish Forest Agency). We extracted home range composition in the year when diet was assessed. When individuals were monitored for multiple years, we extracted the home range composition for the median year. We calculated the proportion of mid-aged and old forest and proportion of disturbed forest (clearcuts and regenerating young forest) within the 95% utilization distribution. Additionally, we calculated habitat diversity using the Simpson diversity index from the R package landscapemetrics \u003csup\u003e70\u003c/sup\u003e. Following Thomson et al. \u003csup\u003e31\u003c/sup\u003e we calculated the Euclidean distance between scaled and centered habitat composition and habitat diversity in multivariate space, assuming equal importance of each component. Pairwise distances were scaled between 0 and 1, where increasing values indicated more similar habitat composition. In the supplementary material we provide an alternative analysis accounting for spatial autocorrelation in dietary specialization with a pairwise spatial distance matrix (S matrix; Supplementary analysis 5, \u003cstrong\u003eFig S5\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe applied a two-step modelling approach. First, we fitted a \u003cem\u003ebasic\u003c/em\u003e linear mixed effects model to estimate individual specialization as among individual variation in annual trophic position. We accounted for a nonlinear effect of age (second order polynomial) and for repeated measures of the same individual with individual random intercepts. We extracted the variance in fitted values (variance explained by fixed effects), among-individual, and residual variance and estimated the proportional contribution of fixed and random effects on the total phenotypic variance through variance standardization (i.e. repeatability \u003csup\u003e71\u003c/sup\u003e, marginal and conditional R\u003csup\u003e2\u003c/sup\u003e-values \u003csup\u003e72\u003c/sup\u003e). Second, we used a spatially explicit Bayesian hierarchical model (i.e. \u0026lsquo;animal model\u0026rsquo;) \u003csup\u003e31, 33\u003c/sup\u003e to partition among-individual variance in trophic position into environmental similarity (\u0026sigma;\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eenv\u003c/sub\u003e), additive genetic (\u0026sigma;\u003csup\u003e2\u003c/sup\u003e\u003csub\u003ea\u003c/sub\u003e), permanent among-individual (\u0026sigma;\u003csup\u003e2\u003c/sup\u003e\u003csub\u003eind\u003c/sub\u003e), maternal (\u0026sigma;2\u003csub\u003emat\u003c/sub\u003e), and residual within-individual effects (\u0026sigma;\u003csup\u003e2\u003c/sup\u003e\u003csub\u003er\u003c/sub\u003e). Similar to the basic model, we accounted for a nonlinear effect of age on trophic position (fitted as time since separation of mother and daughter scaled by the standard deviation, true age and time since separation were perfectly correlated: Pearson correlation coefficient\u0026thinsp;\u0026gt;\u0026thinsp;0.99). We tested for maternal effects on offspring trophic position by incorporating the mother\u0026rsquo;s trophic position as a covariate into the model. To account for a potential decrease of the maternal effect over time, we let maternal trophic position interact with the time since separation of mother and daughter (both scaled by their standard deviation and centered). We partitioned the variance explained by the two components of the fixed effect, the effect of maternal learning over time (i.e. maternal trophic position and the interaction between maternal trophic position and time since separation) and age (i.e. the main effect of time since separation), respectively, by calculating the independent contribution of each component to the total variance explained by the fixed effects, following the approach by Stoffel, Nakagawa \u003csup\u003e73\u003c/sup\u003e adapted to a Bayesian framework (see code under \u003csup\u003e74\u003c/sup\u003e).\u003c/p\u003e\n\u003cp\u003eAll models were fit using the R package \u0026ldquo;brms\u0026rdquo; \u003csup\u003e75\u003c/sup\u003e based on the Bayesian software Stan \u003csup\u003e76, 77\u003c/sup\u003e. We ran four chains to evaluate convergence which were run for 6,000 iterations, with a warmup of 3,000 iterations and a thinning interval of 10. All estimated model coefficients and credible intervals were therefore based on 1200 posterior samples and had satisfactory convergence diagnostics with \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\widehat{R}\\)\u003c/span\u003e\u003c/span\u003e \u0026lt; 1.01, and effective sample sizes \u0026gt; 400 \u003csup\u003e78\u003c/sup\u003e. Posterior predictive checks recreated the underlying Gaussian distribution of trophic position well. For all parameters, we report the median and 89% credible intervals, calculated as equal tail intervals, as measure of centrality and uncertainty \u003csup\u003e79\u003c/sup\u003e. We deemed explained variance proportions as inconclusive when the lower credible interval limit was \u0026lt; 0.001 (i.e., \u0026lt; 0.1%) \u003csup\u003e80\u003c/sup\u003e. All statistical analyses were performed in R 4.0.0 \u003csup\u003e81\u003c/sup\u003e. Primary data and code to reproduce all analyses are provided under (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.17605/OSF.IO/68B9U\u003c/span\u003e\u003c/span\u003e, \u003csup\u003e74\u003c/sup\u003e).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAGH has received funding from the European Union's Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie Grant agreement No 793077 and from the German Science Foundation (HE 8857/1-1). The study was further funded by the Norway Grants under the Polish-Norwegian Research Programme administered by the National Research Centre for Research and Development in Poland and the Norwegian Research Council (JA, NS, AS, and AZ; GLOBE No POL-NOR/198352/85/2013). Isotope analyses were funded through a Robert Bosch Foundation grant to TM and the GLOBE project and conducted by KH, DJ and AS. We thank the Scandinavian Brown Bear Research Project (SBBRP) for providing access to the data. The SBBRP was funded by the\u0026nbsp;Norwegian Environment Agency, the Swedish Environmental Protection Agency, the Austrian Science Fund, and the Norwegian Research Council.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHOR’S CONTRIUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAH, JA, and TM developed the work. AZ, JK, KH, NS and AS provided the data. AZ managed the sample collection. AS managed the hair samples database and prepared samples for stable isotope analyses by KH. DJ provided laboratory space and resources and supervised preparatory procedures. SF constructed and provided the genetic pedigree. JH provided home range centroids. AM and JA advised to the analysis and interpretation of stable isotope data. TM, NS and AZ secure project funding. AH performed the statistical analyses with input from JA. AH wrote the manuscript with help from TM, JA, and input from all authors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBolnick DI, Svanb\u0026auml;ck R, Ara\u0026uacute;jo MS, Persson L. Comparative support for the niche variation hypothesis that more generalized populations also are more heterogeneous. \u003cem\u003eProc Natl Acad Sci U S A\u003c/em\u003e \u003cstrong\u003e104\u003c/strong\u003e, 10075-10079 (2007).\u003c/li\u003e\n \u003cli\u003eHuang S, Tucker MA, Hertel AG, Eyres A, Albrecht J. Scale-dependent effects of niche specialisation: The disconnect between individual and species ranges. \u003cem\u003eEcology Letters\u003c/em\u003e \u003cstrong\u003e24\u003c/strong\u003e, 1408-1419 (2021).\u003c/li\u003e\n \u003cli\u003eForsman A, Wennersten L. 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(2020).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Dietary specialization, heritability, maternal effects, maternal learning, trophic position, trophic niche, omnivore, stable isotopes, nitrogen-15, Ursus arctos","lastPublishedDoi":"10.21203/rs.3.rs-2926801/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2926801/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIndividual dietary specialization, where individuals occupy a subset of a population’s wider dietary niche, is of key importance for species’ resilience against environmental change. However, the ontogeny of individual specialization, as well as associated underlying social learning, genetic, and environmental drivers remain poorly understood. Using a multigenerational dataset of female European brown bears (\u003cem\u003eUrsus arctos\u003c/em\u003e) followed since birth, we discerned the relative contributions of social learning, genetic predisposition, environmental forcings, and maternal effects to individual dietary specialization. Individual specialization varied from omnivorous to carnivorous diets spanning half a trophic position. The main determinants of this dietary specialization were maternal learning during rearing (13%), environmental similarity (12%), maternal effects (11%), and permanent individual effects (8%), whereas the contribution of genetic heritability was negligible. Importantly, the offspring’s trophic position closely resembled the trophic position of their mothers during the first 3-4 years after separation from the mother, but this relationship ceased with increasing time since separation. Our study reveals that social learning and maternal effects are as important for individual dietary specialization as environmental forcings. We propose a tighter integration of social effects into future studies of range expansion and habitat selection under global change that, to date, are mostly explained by environmental drivers.\u003c/p\u003e","manuscriptTitle":"Ontogeny shapes individual specialization","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-05-19 18:48:13","doi":"10.21203/rs.3.rs-2926801/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"760f4f60-b1e8-432f-8938-807169fb0eee","owner":[],"postedDate":"May 19th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":21626645,"name":"Biological sciences/Ecology/Behavioural ecology"},{"id":21626646,"name":"Biological sciences/Ecology/Stable isotope analysis"},{"id":21626647,"name":"Biological sciences/Developmental biology/Differentiation"},{"id":21626648,"name":"Earth and environmental sciences/Ecology/Evolutionary ecology"}],"tags":[],"updatedAt":"2024-11-30T08:07:44+00:00","versionOfRecord":{"articleIdentity":"rs-2926801","link":"https://doi.org/10.1038/s41467-024-54722-z","journal":{"identity":"nature-communications","isVorOnly":false,"title":"Nature Communications"},"publishedOn":"2024-11-29 05:00:00","publishedOnDateReadable":"November 29th, 2024"},"versionCreatedAt":"2023-05-19 18:48:13","video":"","vorDoi":"10.1038/s41467-024-54722-z","vorDoiUrl":"https://doi.org/10.1038/s41467-024-54722-z","workflowStages":[]},"version":"v1","identity":"rs-2926801","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2926801","identity":"rs-2926801","version":["v1"]},"buildId":"wLkW0s4AflPzk-lpfg-fK","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}