Dynamic omnivory shapes the functional role of large carnivores under global change

Domain: earth_science
Published: 2025-12-03
Editorial outcome: minor_revision

Abstract
Omnivory is increasingly recognized as a dynamic stabilizing force under environmental change. Despite its ubiquity across ecosystems, trophic levels and spatiotemporal scales, our empirical understanding of how omnivores respond to changing conditions in terrestrial ecosystems is limited. Here we combine macroecological and paleoecological approaches across seven bear species—the largest terrestrial carnivores—and discover they dynamically adapt their trophic position in food webs to resource availability. Throughout their ranges, bears shift to carnivory in unproductive ecosystems with short growing seasons and to herbivory in productive ecosystems with long growing seasons. In line with this, isotopic evidence from the Late Pleistocene and Holocene reveals a sharp decrease in the trophic position of the European brown bear in response to increasing net primary productivity and growing season length. These findings reveal a mechanism of trophic rewiring that alters the functional role of large carnivores in ecosystems and may simultaneously stabilize food web dynamics under global change.

Introduction
Global change fundamentally reshapes the structure of terrestrial and aquatic food webs, which can have profound effects on entire ecosystems1–3. Numerous studies have investigated the mechanisms behind changes in the structure of food webs under global change1. However, an important mechanism that has received little attention is changes in trophic interactions due to the dynamic foraging behavior of consumers4,5. This mechanism is probably widespread and should be first detected in large omnivores at higher trophic levels, because they are adapted to rely on a wider range of resources, exhibit high behavioral flexibility, and often respond rapidly to environmental change4,5. As omnivores are common in food webs, a deterministic shift in the functional role of omnivores due to global change could result in the rewiring of entire food webs4. In addition, changes in the functional role of omnivores, for instance from predation to herbivory, have direct consequences for food-web dynamics5 and key ecosystem functions such as nutrient cycling, energy flux, and biomass production6. Therefore, trophic responses of omnivores to global change could be a powerful indicator of critical transitions in food web structure and ecosystem functioning4,5.

Global change may exert particularly strong effects on the foraging behavior of omnivores via changes in resource availability. For instance, land-use intensification strongly reduces the availability of net primary productivity (NPP) to wildlife via the conversion of natural vegetation to livestock and crop production systems7. On the other hand, anthropogenic nutrient inputs to ecosystems and food subsidies can increase the availability of resources to wildlife8,9. Moreover, prolonged growing seasons due to climate warming10 can reduce seasonal bottlenecks in NPP and may release consumers from energetic constraints by reducing the metabolic costs of maintenance and foraging in warmer environments11,12. Accordingly, food web theory suggests that changes in resource availability and growing season length may cause shifts in the foraging behavior and trophic position of omnivores (‘dynamic omnivory’ hypothesis; Fig. 1)4,5,12–14. Yet, the trophic adaptation of large terrestrial omnivores to altered resource availability and climate remains poorly understood4,5 because, traditionally, omnivory has been considered as a static trait5 and studies that have investigated the dynamics of omnivory have mainly been conducted in micro- and mesocosm experiments or aquatic ecosystems4,5,15,16. In addition, most research has been conducted at local scales and over short periods, but little is known about the dynamics of omnivory at larger spatiotemporal scales4,5.

Here, we aim to fill these knowledge gaps by combining macroecological and paleoecological approaches to investigate how large terrestrial omnivores adapt their trophic position in food webs to changing NPP and growing season length at global and millennial scales. We leverage the unique insights gained by both approaches17,18 to infer how large terrestrial omnivores adapt their trophic position in food webs to global change. We focus on the seven extant terrestrial bear species (Order: Carnivora, Family: Ursidae), which are the largest terrestrial omnivores and occupy a wide range of biomes from the arctic tundra to tropical rainforests. Unlike most other large carnivores, bears show a preference for low-protein diets and have relatively weak craniodental adaptations to carnivory19–22, which allows them to maintain a high degree of dietary flexibility. Owing to their broad dietary niches, bears contribute to a multitude of ecosystem processes, such as predation, scavenging, frugivory, and herbivory that can have strong impacts on prey populations23, plant regeneration24,25, nutrient cycling26,27 and energy fluxes6 within and across terrestrial and aquatic ecosystems.

Results
Macroecological analysis
We first investigated how the trophic position of extant bears is related to NPP and growing season length across their geographic ranges (Fig. 2). To do so, we compiled a comprehensive database of dietary compositions based on micro-histological analyses of fecal and stomach contents throughout the geographic ranges of the seven extant terrestrial bear species, using 210 records from 155 studies (Fig. 2a)28. We used annual diets, which integrate foraging behavior across the entire active season of the focal species, to enable meaningful comparisons across study locations. We excluded the polar bear (U. maritimus) from our analysis, because this species almost exclusively hunts for marine prey on Arctic Sea ice, but fasts on land during the ice-free season29. Therefore, the minor contribution of terrestrial food sources during the ice-free season is not representative of the species’ trophic niche30–32. Based on the dietary data for the remaining seven terrestrial bear species, we used a Bayesian hierarchical model to estimate the trophic position as the percentage of the dietary energy that is contributed by animal prey (including vertebrates and invertebrates), while accounting for the effects of digestibility and energy content of different food sources (Fig. 2b; Methods; Supplementary Table 1). In a second step, the model related the estimated trophic position to NPP (kg C m−2 a−1) and meteorological growing season length33 (months with mean temperature T > 0 °C; hereafter growing season length) at each of the study locations (Fig. 2c, d). The temperature threshold of 0 °C demarcates the growth period of frost-resistant plants34, as well as the active period of bear populations in temperate and boreal biomes (e.g., Ursus arctos and U. americanus)35,36. To account for the potential effects of competition between sympatric bear species, we also included a factor indicating whether a focal population was located within the range of another larger (dominant) or smaller (subordinate) bear species37,38. The model included random factors for study and species identity to account for the non-independence of data from the same species and study and propagated all uncertainties associated with the estimated trophic position through the entire model.

The model revealed that the trophic position of extant bears across their geographic ranges is negatively related to NPP (−0.18 [−0.30, −0.066], pd = 0.99; median [90% Equal-Tailed Interval], and posterior probability of the relationship being negative) and growing season length (−0.41 [−0.57, −0.26], pd = 1; Fig. 2c, d; Supplementary Table 2). Thus, terrestrial bears generally occupy higher trophic positions in ecosystems with low productivity and short growing seasons and lower trophic positions in ecosystems with high productivity and long growing seasons. A decline in trophic position may be attributable to (i) a population effect, where local bear populations alter their foraging behavior relative to NPP and growing season length, or (ii) a species occurrence effect, where bear species with a certain trophic position simply do not occur in environments with a particular NPP and growing season length. To disentangle these two effects, we ran an additional model where we separated the effects of NPP and growing season length into these two components (Methods). The two-component model showed that variation in trophic position was exclusively explained by population effects, and not by the species occurrence effect (Supplementary Table 2). These effects were robust despite additional effects of competition with sympatric bear species, which caused a decrease in the trophic position of subordinate bear species (−0.50 [−0.84, −0.16], pd = 0.99) but no change in the trophic position of dominant bear species (0.059 [−0.26, 0.37], pd = 0.62; Supplementary Table 2). This indicates that the trophic position of extant bears is a flexible trait that emerges from adaptation to local resource availability, growing season length, and interspecific competition with other sympatric bear species at the population level.

Paleoecological analysis
In a second step, we investigated how European brown bears adapted their trophic position to the marked increases in NPP and growing season length at the transition from the Late Pleistocene to the Holocene (Fig. 3a). To do so, we used stable isotope analyses of collagen extracted from a comprehensive collection of fossil and subfossil bone and tooth remains of brown bears (n = 219) and red deer (Cervus elaphus, n = 372) across Europe, covering the last 55 ka BP (Fig. 3b)28. The low δ13C and δ15N values (‰) of brown bear collagen allowed us to rule out the consumption of marine resources (e.g., anadromous fish; see “Methods”). Therefore, we estimated the trophic position of brown bears based on δ15N of collagen through time using a Bayesian hierarchical model, in which the δ15N values of red deer from the same region were included as a dietary baseline for a strict herbivore (Fig. 3c; Methods). The model accounted for the effects of altitude39 and the type of sampled material (bone or teeth)38 on δ15N values of brown bears and red deer and propagated all uncertainties associated with these factors, as well as with trophic fractionation38,40 through the entire model (Methods; Supplementary Table 3). In the last step, the model estimated the relationship of the trophic position of brown bears with NPP and growing season length, which were derived from global climate and vegetation models (HadCM3 and BIOME4)41 (Fig. 3a).

We found that European brown bears occupied higher trophic positions during the Late Pleistocene than during the Holocene (Fig. 3c). This observed decrease in trophic position of European brown bears from the Late Pleistocene to the Holocene was closely associated with increasing NPP (−0.17 [−0.30, −0.029], pd = 0.97) and growing season length (−0.19 [−0.33, −0.041], pd = 0.98) at the onset of the Holocene (Fig. 3d, e; Supplementary Table 3). Consequently, the occupied trophic positions were highest during the period characterized by the lowest NPP and shortest growing seasons (period GS3 to GI8 in Fig. 3c) and lowest during those periods characterized by high NPP and long growing seasons (periods Greenlandian and Northgrippian in Fig. 3c). Most strikingly, the paleoecological patterns for brown bears closely resembled the macroecological patterns observed across the geographic ranges of the seven extant terrestrial bear species (Figs. 2 and 3). These results are in strong agreement with the ‘dynamic omnivory’ hypothesis, predicting that omnivores adapt their trophic position to resource availability and growing season length (Fig. 1).

Methods
Macroecological analysis
Trophic data. To obtain dietary data for the seven extant terrestrial bear species, we conducted a literature search in the Web of Science Core Collection for publications until 2018. We searched titles and abstracts using the keywords “(food OR feed* OR diet* OR forag* OR nutri* OR scat* OR fec* OR faec* OR stomach* OR gut*) AND (ursidae OR ursus OR melursus OR tremarctos OR helarctos OR ailuropoda OR bear* OR panda)”. We also checked the reference lists of the retrieved publications for additional studies that had not been identified by the keyword search. To be included in our analysis, the studies had to meet the following criteria: (i) Diet assessment was based on micro-histological analyses of food remains in scats or stomachs. (ii) Sample collection in the field covered the entire seasonal activity period of the focal species. (iii) All food items, instead of only a subset, were reported. (iv) Reported food items could be mapped onto established macroecological diet classification schemes60. (v) The number of collected samples was reported. (vi) Sufficient data to calculate one of the following measures was reported: relative frequency of occurrence of food items (F), calculated as the number of occurrences of food item i divided by the total number of occurrences of all food items (F = f / Σ f ); the relative volume of food items (V), calculated as the mean volume of food item i in scats or stomachs divided by the total volume of all food items, that is (V = v / Σ v ); the relative dietary contribution based on ingested dry weight of food items (D), calculated as the product of the relative volume of food item i and correction factors c that account for differences in the digestibility of food items and normalizing to proportions (D = c V / Σ c V ); or relative dietary energy contribution of food items (E), calculated as the product of the relative dietary dry weight contribution of food item i and correction factors c that account for differences in the energy content of food items and normalizing to proportions (E = c D / Σ c D ). We excluded the polar bear from our analysis, because this species almost exclusively hunts for marine prey on Arctic Sea ice, but fasts on land during the ice-free season29. Therefore, the minor contribution of terrestrial food sources during the ice-free season is not representative of the species’ trophic niche30–32.

In total, 155 publications met the above criteria, including articles in scientific journals, master’s and PhD theses, and gray literature (e.g., non-commercial publications in the form of technical reports or conference proceedings)28. For each publication, we extracted the geographic coordinates of the study location, the type of collected samples (i.e., scats or the stomachs of dead individuals), the number of samples, and the reported food items in scats or stomachs. We mapped the reported food items onto one of 20 dietary categories based on an established macroecological diet classification scheme60 (Supplementary Table 1) and recorded the quantity of food items in the diet using the above described variables. To convert estimates of relative volume into relative dietary contribution or dietary energy contribution, we compiled correction factors that account for differences in the digestibility and energy content of food items from the literature (Supplementary Table 1).

Environmental variables. For the geographic location of each record, we extracted data on net primary productivity (NPP, kg C m–2 a–1) (Terra MODIS17A3, spatial resolution: 0.5°)61 and near-surface daily average air temperature at monthly resolution (CHELSA, spatial resolution: 0.5°)62. We calculated growing season length as the number of months in which the near-surface daily average air temperature was above 0 °C. The temperature threshold of 0 °C demarcates the growth period of frost-resistant plants34, as well as the active period of bear populations in temperate and boreal biomes (e.g., Ursus arctos and U. americanus)35,36. NPP and growing season length were positively correlated (Pearson’s r = 0.61, t = 11.2, df = 208, P < 0.001, Supplementary Fig. 1), and variance inflation factors indicated that the correlation between both predictors did not affect estimates of effect sizes (see section Model implementation and assessment of model fit).

Species co-occurrence. We quantified species co-occurrences using IUCN species distribution maps63. For the geographic records of each species, we determined whether a location fell within the geographic range of one or more other bear species. Then we created a categorical variable with three levels (no co-occurrence, subordinate, and dominant), indicating whether the focal species at a given location co-occurred with another bear species and, if so, whether it was smaller (subordinate) or larger (dominant) than the co-occurring species.

We did not account for co-occurrence with other large carnivores in the analysis (e.g., felids or canids), as bears have evolved from carnivores with high-protein diets to omnivores that consume low-protein diets, thus, reducing interspecific competition with other large carnivores22. Moreover, bears are well-documented kleptoparasites, often monopolizing carrion resources and significantly limiting carrion access and predatory behavior in other large carnivores (e.g., wolves, cougars, and pumas)64–70. In interspecific encounters with other carnivores, bears typically dominate due to their large body size or are able to defend themselves by charging, as observed in encounters with tigers (Panthera tigris)71–73. In cases of interspecific killing, bears are typically the killer species, while rare instances of bears being killed usually involved denning adults, cubs of the year, or aggressive encounters with tigers in Indian tiger reserves and the Russian Far East71–76.

Model implementation. To analyze the trophic position of the seven extant terrestrial bear species, we used a Bayesian hierarchical model that consisted of two sub-models. Sub-model I was used to (i) represent the relative volume of food items in sampled materials for those datasets with missing information on relative volume (107 of 210 datasets), based on the close relationship with the relative frequency of occurrence of food items (Supplementary Fig. 2) and (ii) to estimate the relative dietary energy contribution of animal prey, including vertebrates and invertebrates (i.e., the trophic position). All uncertainties associated with the data imputation and estimation of trophic position were propagated through the entire model. Sub-model II was then used to estimate the relationship of the trophic position with NPP, growing season length, as well as co-occurrence with others subordinate or dominant bear species.

To inform sub-model I, we first quantified the relationship between the relative frequency of occurrence (F) and relative volume (V) for those datasets (i.e., unique study-year or study-location combinations), in which both variables had been reported (n = 106 datasets). For each dataset j, we quantified the relationship between F and V (both log-transformed) using geometric mean regression to account for the fact that both variables are subject to measurement error77. We calculated the geometric mean slope as β = sign(σ / σ ) * (σ / σ ) = σ / σ , and the associated intercept as β = V - F * β , where V and F are the mean values of both variables for dataset j. The distribution of geometric mean slopes and associated intercepts across the datasets was then used to inform the data imputation in sub-model I. In particular, sub-model I assumed that the slopes and intercepts follow a multivariate normal distribution around μ and variance-covariance matrix Σ :

β ~ Normal(μ , Σ )

μ ~ Normal(0, 10^4)

Σ ~ scaled-inverse-Wishart(s, df)

where Σ is a 2 × 2 variance–covariance matrix. We used a non-informative scaled inverse Wishart prior78 with scale s = 1 and d.f. = 2. This prior follows a half-t distribution for the standard deviation, σ ~ t+ , and has a marginal uniform prior distribution for the correlation parameter ρ . For each dataset j without information on V the model then drew an intercept and slope from the multivariate distribution and estimated values of V using the observed values of F based on the following regression formula:

μ = β + β * F

V ~ Normal(μ , σ^2 )

σ^2 ~ scaled-inverse-Gamma(s, df)

We modeled V using a normal distribution with mean μ and residual variance σ^2. For the residual variance, we used a weakly informative scaled inverse gamma prior79 with scale s = 1 and two degrees of freedom (d.f. = 2), which for the standard deviation follows a half-t distribution. Then the relative dietary energy contribution E of each food item was calculated for each dataset j by multiplying the values of V with correction factors c and c that account for differences in the digestibility and energy content of food items and normalizing to proportions:

E = c * c * V / Σ (c * c * V)

Based on the relative dietary energy contribution E of each food item, we calculated for each dataset j the trophic position as the relative dietary energy contribution of animal prey P j (including vertebrates and invertebrates). The process model then quantified the relationship of P with NPP, growing season length, as well as co-occurrence with other subordinate or dominant bear species. The model can be written as follows:

μ = Σ β * X + γ + δ

y ~ Normal(μ , σ^2 )

γ ~ Normal(0, σ^2 )

δ ~ Normal(0, σ^2 )

σ^2 , σ^2 , σ^2 ~ scaled-inverse-Gamma(s, df)

β ~ Normal(0, 10^4)

where μ is the expected value of the jth observation, X is a model matrix including an intercept and the explanatory variables (NPP [log-transformed], growing season length, and co-occurrence with others subordinate or dominant bear species [dummy-coded]) and an associated vector of parameters β, and γ is a random effect for the lth study, which is normally distributed around 0 with variance σ^2, and δ is a random effect for the mth species, which is normally distributed around 0 with variance σ^2. We modeled the trophic position with a normal distribution around μ and residual variance σ^2. For modeling purposes, we first compressed values of P which were in the closed interval (0 ≤ y ≤ 1) into the open interval (0 < y < 1) and subsequently applied a probit transformation: y = probit((P(n–1)+0.5)/n), in which n is the total number of observations80. The compression was necessary, because the probit function is defined on the open interval (0 < y < 1; range of P after transformation: 0.00034–0.99934). We used an informative normal prior with a mean of 0 and a variance of 10^4 for the fixed effects and weakly informative scaled inverse gamma priors with scale s = 1 and two degrees of freedom (d.f. = 2) for variances of random effects and residuals. To separate population effects, where local bear populations alter their foraging behavior relative to NPP and growing season length, from species occurrence effects, where bear species with a certain trophic position simply do not occur in environments with a particular NPP and growing season length, we ran an additional two-component model in which we applied group mean centering to these two predictor variables (Supplementary Table 2).

Paleoecological analysis
Database of subfossil and fossil remains. To reconstruct the trophic position of the European brown bear during the Late Pleistocene and Holocene, we compiled a database of 591 dated and georeferenced subfossil and fossil remains of brown bears and red deer using published sources (n = 483 samples) and material from museum collections (n = 108; see Data Availability Statement)28. Dates of specimens were based on direct radiocarbon (14C) dating (n = 237) or based on the context of the excavation sites (n = 354). Contextual dates were either obtained based on related artifacts (n = 196) or in case of three sites (El Miron, Kiputz IX, and Emine-Bair-Khosar) based on age-depth models (n = 158). Radiocarbon dating of material from museum collections (n = 55) was done using accelerator mass spectrometry at the Poznań Radiocarbon Laboratory (Poz), Poznań, Poland. Dated fossils without geolocations were geocoded manually using the name of the fossil site. The quality and reliability of all radiocarbon dates was assessed based on dating method, stratigraphy, association and material81. This resulted in 219 samples of brown bear and 372 samples of red deer. Age-depth models were fitted using package Bchron (version 4.7.6)82 in R (version 4.4.2)83. The 14C ages of these fossils were calibrated using package Bchron and the IntCal13 curve84.

Stable isotope analysis. Collagen was extracted from bone or tooth of subfossil and fossil specimens following previously established protocols85,86. The extraction process included a step of soaking in 0.125 M NaOH between the demineralization and solubilization steps to achieve the elimination of lipids and humic acids. Elemental analysis (C , N ) and isotopic analysis (δ13C , δ15N ) were conducted at two laboratories. At the Department of Geosciences of Tübingen University, Germany, an NC2500 CHN-elemental analyzer was coupled to a Thermo Quest Delta+ XL mass spectrometer. At the Environment Canada Stable Isotope Laboratory in Saskatoon, Canada, collagen was combusted at 1030 °C in a Carlo Erba NA1500 or Eurovector 3000 elemental analyser coupled with an Elementar Isoprime or a Nu Instruments Horizon isotope ratio mass spectrometer. The international standard for δ13C measurements was the Vienna Pee Dee Belemnite (VPDB) carbonate that for δ15N atmospheric nitrogen (AIR). Analytical error, based on within-run replicate measurement of laboratory standards (Saskatoon: BWB III keratin and PRC gel; Tubigen: albumen, modern collagen, USGS 24, IAEA 305A), was ±0.1‰ for δ13C values and ±0.2‰ for δ15N values. Reliability of the δ13C and δ15N values was established by measuring its chemical composition, with C/N atomic ratio ranging from 2.9 to 3.6 ref. 87., and percentage of C and N above 8% and 3% ref. 88., respectively.

Trophic discrimination factors and corrections for material type and elevation. Because of the general isotopic enrichment in nitrogen between trophic levels, consumers exhibit higher δ15N values than their food resources38. We accounted for this effect in the analyses using data on trophic discrimination factors based on bone collagen of predators and their prey from the literature40. Nitrogen isotope values (δ15N) generally decrease with increasing altitude39. We accounted for this effect of altitude in the analyses by correcting the raw δ15N values using reference data from the literature39 to model the relationship between elevation and the nitrogen isotope composition of plants and herbivores. In long-lived large mammals, adult bone collagen reflects the average isotopic composition of the diet over several years preceding death, whereas collagen in tooth dentine primarily accumulates during the period of tooth development38. Since brown bears possess brachydont teeth dental growth ceases early in life, during a stage when a substantial proportion of nutrients is still derived from lactation38. Consequently, collagen extracted from dentine may exhibit higher δ15N values than bone collagen from the same individual38. We accounted for this effect in the analyses by correcting the isotope ratio of teeth using data from the literature38 on the difference in δ15N values between collagen from tooth and bone material of the same individuals.

Potential consumption of marine resources by brown bears. To rule out the consumption of marine resources (e.g., anadromous fish) by brown bears, we compared the δ13C and δ15N values of the brown bear material with those of a range of marine mammal species from the literature89 (Supplementary Fig. 3). The δ13C and δ15N values of brown bear material were much lower than those of material from marine mammals, but very similar to those of the red deer material (Supplementary Fig. 3), indicating negligible consumption of marine resources by brown bears in our data. Therefore, we estimated the trophic position of brown bears based on δ15N of collagen through time using the δ15N values of red deer from the same region as a dietary baseline for a strict herbivore.

Environmental variables. We used package pastclim (version 2.2.0)90 to annotate each sample with spatiotemporally explicit data on net primary productivity (NPP, kg C m–2 a–1) from a global dynamic vegetation model (BIOME4, spatial resolution: 0.5°)41, near-surface daily average air temperature at monthly resolution from a global climate model (HadCM3, spatial resolution: 0.5°)41, and with data on elevation from a global elevation model (GMTED 2010, spatial resolution: 0.0083°)91. Analogous to the macroecological analysis, we calculated growing season length as the number of months in which the near-surface daily average air temperature was above 0 °C. NPP and growing season length were only very weakly correlated (Pearson’s r = −0.06, t = −0.14, df = 6, P = 0.89, Supplementary Fig. 4), and variance inflation factors indicated that the correlation between both predictors did not affect estimates of effect sizes (see section Model implementation and assessment of model fit).

Temporal resolution of analysis. For the analysis, we defined eight time periods that were used to bin the data (Fig. 3): Meghalayan (0–4.2 ka BP), Northgrippian (4.2–8.2 ka BP), Greenlandian (8.2–11.7 ka BP), Greenland Stadial 1 (GS-1, 11.7–12.8 ka BP), Greenland Interstadial 1 (GI-1, 12.8–14.6 ka BP), Greenland Stadial 2 to Greenland Interstadial 2 (GS-2 to GI-2, 14.6–23.3 ka BP), Greenland Stadial 3 to Greenland Interstadial 8 (GS-3 to GI-8, 23.3–38.2 ka BP), Greenland Stadial 9 to Greenland Interstadial 14 (GS-9 to GI-14, 43.8–54.2 ka BP). These time bins represent a trade-off between temporal resolution and sample size within each time period. The Subdivision of Late Pleistocene and Holocene time periods was based on current definitions of stratigraphic stages92,93.

Paleoecological model. To analyze changes in the trophic position of the brown bear during the past 55,000 years, we used a Bayesian hierarchical model that consisted of two sub-models. Sub-model I was used to estimate the trophic position of the brown bear within each time period using the δ15N values of red deer collagen as a dietary baseline for a strict herbivore. The model accounted for the effects of altitude39, the type of sampled material (bones or teeth)38 on δ15N values of brown bears and red deer, and propagated all uncertainties associated with these factors as well as with trophic fractionation38,40 through the entire model. Sub-model II then related the estimated trophic position to NPP and growing season length. Below, we describe the model in detail.

First, sub-model I estimated the averaged difference between the isotopic value of tooth and bone material based on paired samples of bone and teeth from the same individuals of different species of large carnivores (n = 35)28,38:

T ~ Normal(μ , σ^2 )

σ^2 ~ scaled-inverse-Gamma(s, df)

μ ~ Normal(0, 10^4)

We modeled the observed difference between the isotopic values of tooth and bone material T using a normal distribution with mean μ and residual variance σ^2. We used an informative normal prior with a mean of 0 and a variance of 10^4 for μ and a weakly informative scaled inverse gamma prior with scale s = 1 and two degrees of freedom (d.f. = 2) for σ^2.

Second, sub-model I estimated the effect of elevation on the isotopic values based on data that relates δ15N (‰) of plants and herbivorous animals to elevation (n = 69)28,39:

μ = type + β * E

N ~ Normal(μ , σ^2 )

σ^2 ~ scaled-inverse-Gamma(s, df)

type ~ Normal(0, 10^4)

β ~ Normal(0, 10^4)

where μ is the expected value of the δ15N value of the material (plant, herbivore), type is the mean isotopic value of the material, β is the effect of elevation, and E is the elevation at which sample i has been collected. We modeled the observed δ15N values N using a normal distribution with mean μ and variance σ^2. We used informative normal priors with a mean of 0 and a variance of 10^4 for the effects of material type and elevation and a weakly informative scaled inverse gamma prior with scale s = 1 and two degrees of freedom (d.f. = 2) for σ^2.

Third, sub-model I used the estimates of β and μ to estimate the mean bias-corrected δ15N values for red deer (baseline) and brown bear (consumer) in each of the eight time periods (Supplementary Fig. 5):

μ = baseline + β * E + μ * T

B ~ Normal(μ , σ^2 )

μ = consumer + β * E + μ * T

C ~ Normal(μ , σ^2 )

where μ and μ are the expected δ15N values of red deer and brown bear samples, baseline and consumer are the mean bias-corrected nitrogen signatures of red deer and brown bear in time period j, respectively, E is the elevation at which sample i has been collected, and T indicates whether the sample is a tooth (T = 1). We modeled the observed δ15N values of red deer B and brown bear C using normal distributions with means μ and μ and variances σ^2 and σ^2, respectively. We used informative normal priors with a mean of 0‰ and a variance of 10^4‰ for the mean bias-corrected δ15N values (baseline and consumer) and weakly informative scaled inverse gamma priors with scale s = 1 and two degrees of freedom (d.f. = 2) for the variances (σ^2 and σ^2):

σ^2, σ^2 ~ scaled-inverse-Gamma(s, df)

baseline, consumer ~ Normal(0, 10^4)

The trophic position of the brown bear in period j TP j was then estimated as the difference between the mean bias-corrected δ15N values of brown bear and red deer, divided by the trophic discrimination factor μ Δ plus an offset for the trophic level of the baseline (λ = 2)94:

TP = (consumer - baseline) / μ Δ + λ

The trophic discrimination factor was estimated based on data for pairs of large carnivores and their prey (n = 10 pairs)28,38:

Δ ~ Normal(μ Δ, σ^2 Δ)

σ^2 Δ ~ scaled-inverse-Gamma(s, df)

μ Δ ~ Normal(0, 10^4)

We modeled the observed trophic discrimination factor Δ using a normal distribution with mean μ Δ and residual variance σ^2 Δ. We used an informative normal prior with a mean of 0 and a variance of 10^4 for μ Δ and a weakly informative-scaled inverse gamma prior with scale s = 1 and two degrees of freedom (d.f. = 2) for σ^2 Δ.

In a final step, sub-model II related the estimated trophic position TP j in each period j to the NPP and growing season length in each period:

μ = Σ β * X

TP ~ Normal(μ , σ^2 )

σ^2 ~ scaled-inverse-Gamma(s, df)

β ~ Normal(0, 10^4)

where μ is the expected value of the jth observation, X is a model matrix including an intercept and the two explanatory variables (NPP [log-transformed] and growing season length) and an associated vector of parameters β. We used an informative normal prior with a mean of 0 and a variance of 10^4 for β and a weakly informative-scaled inverse gamma prior with scale s = 1 and two degrees of freedom (d.f. = 2) for the residual variance.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The manuscript provides evidence that bears can flexibly alter their trophic position (degree of omnivory) across large spatial and temporal gradients of resource availability. A global diet analysis showed that 7 bear species increased their consumption of lower trophic level resources (i.e. increasing omnivory) as NPP and growing season increased. Next, subfossil bone and tooth samples of brown bears were used to show that trophic positions decreased from the Late Pleistocene to Holocene as NPP and growing season increased.\nThese findings are noteworthy examples of dynamic omnivory as a behavioral response to changes in resource availability in both space and time. I imagine they will be of widespread interest to ecologists and will motivate future work in the field. The conclusions and claims are well supported. I did not note any flaws in the data analysis, interpretation or conclusions. The methods are sound and meet expected standards. There is sufficient detail provided in the methods and the supplement to reproduce the study. I do have a few minor clarifying questions and comments I’ve listed below. Overall, this is an interesting study that provides novel information about spatial and temporal flexibility in omnivory.\nSpecific Comments\n1) Could seasonal diet changes between animal and plant prey influence your results?\nIn the global dietary analysis, “samples covered the entire seasonal activity period of the focal species”, meaning that any seasonal diet changes would still allow you to say what the contribution of animal prey was for that location (because all seasons were considered), correct?\nWhat was the temporal resolution of your d15N data from each bone and teeth sample? Does the d15N extracted from collagen reflect the lifetime average trophic position of that individual bear? This might be worth mentioning in the text.\n2) For the temporal analysis, what was the number of individual European brown bears included in each of the 8 time bins? I could have missed this somewhere.\n3) Line 282-286: When comparing trophic positions to NPP and growing season across locations, the studies included in this analysis likely spanned a large date range. Can you clarify that data were pulled for NPP and monthly temperature from the same year in which the samples were collected for each study location, to account for interannual variation in environmental conditions?\n4) Fig 1 shows how omnivory can change from no omnivory to omnivory and back as resource availability increases. That makes sense, and I see how this is meant to show a continuous gradient of decreasing trophic position (especially with the grey diagonal arrows). But visually, it highlighted for me the extreme end cases of no omnivory, and gave me the sense that ‘dynamic omnivory’ refers to a shift from no omnivory to omnivory (and vice versa). The Figure caption also read to me as if these are 3 discrete stages and didn’t emphasize the continuous nature of the omnivory (intraguild predation) stage that is so well highlighted by your empirical findings (bears do not rely exclusively on plants or animals but increase plant consumption across the gradient, while still feeding as omnivores on both prey types).\nI wondered if adding a second intraguild predation motif might help show that dynamic omnivory also includes shifts in the degree of omnivory. These two motifs could show how the O-R interaction strengthens as the O-H interaction weakens as NPP increases. Visually, this might help the reader connect your conceptual figure with your empirical findings.\nFinally, this figure is not referenced again in the text that I could find. It would be worth adding one sentence to the results or discussion stating how your empirical findings relate to the conceptual framework.\n5) Fig. 2 and Fig. 3 are very effective and well done. I noticed some small boxes on both figures that are likely symbols that didn’t convert properly?", "reviewer_2": "The authors have assembled an impressive dataset and then followed a thoughtful modelling approach that allowed them to overcome unavoidable data limitations, to robustly estimate how the trophic position of bears in the Ursidae Family (as exemplar omnivores) changes across space with the length of the growing season and annual net primary productivity. They also considered the influence of competition from sympatric bear species. They then followed a similar approach to examine this relationship on a Paleoecological scale. As the authors note, omnivores play an important role in multitrophic communities and changes in their trophic position can have implications for the stability and functioning of an ecosystem. Therefore, understanding what influences their trophic position is a key ecological question and this study addresses it in a very convincing way.\nAll sections of the manuscript are very well written and easy to follow, clearly introducing the topic and explaining the methods that were used to answer the central questions of the project. This is also true for the script for the data preparation and analysis!\nMy main criticism is that, in their intro and discussion, the authors have placed a lot of emphasis on the effects that resource availability may have on bear diet, to the near exclusion of other potential mechanisms. I would argue that one of them, direct effects of temperature on bears, is already embedded in the data they are analyzing. I would expect that annual net primary productivity is strongly dependent on growth season length. (A plot showing the relationship of the two would be a useful addition to the supplement – I know the authors checked for variance inflation; this is just to help readers understand all the relevant relationships). The total effect of GSL on diet would be largely mediated by NPP. So, with both of these variables as predictors, the model is estimating the “direct” effect of GSL on diet (i.e. unmediated by NPP) and the effect of NPP on diet, accounting for the common influence of GSL on both NPP and TP. Here is a causal diagram showing the main effects of their model:\nNPP <- GSL -> TP\nNPP -> TP\n(I know the authors know all that; I am merely laying out so that we are all on the same page.)\nI agree that NPP and partly GSL are measures of plant diet availability during the year. So the authors’ conclusion, that bears rely less on animal diet because plant diet becomes increasingly available, is partly supported by these results. However, GSL is also strongly related to mean annual temperature. So I would expect that part of the effect of GSL on bear diet that is unmediated by NPP, is really the direct effect of temperature on bear physiology and activity. I would suggest that the larger the number of months with below-zero temperatures, the more bears would need to rely on animal diet to build fat reserves. Then additionally, with higher temperatures, heat loss is no longer a problem; in fact, the reduction of heat dissipation might restrict activity, further promoting a shift from hunting to foraging. Looking at Figures 3a and 3c I think this is also true for the Paleo analysis; trophic position seems to track changes in GSL more than NPP. Here is a more complete diagram:\nNPP <- GSL -> (PDA) -> TP\nNPP -> (PDA) -> TP\nGSL -> TP\nwhere (PDA) is the (unobserved) plant diet availability. In the absence of PDA, this causal model simplifies to the previous one, with the caveat that the arrow from GSL to TP (and the corresponding model estimate) captures both the effects of GSL that relate to plant diet availability during the year, as well as direct effects of temperature on bear trophic activity. I do not think that these two are separable in practice, so I am not asking that the authors fit a different model. I believe their model is the best model one could fit. I am merely asking, assuming they agree with my interpretation, that they elaborate a bit on the more complex nature of that GSL->TP effect.\nI know this complicates the take-home message of this work, but it is an important aspect of how omnivory relates to ecosystem processes; one of these mechanisms is a within-system adaptation (trophic behaviour influenced by resource availability), while the other one is external forcing (trophic behaviour influenced by temperature).\nI understand the logic behind the exclusion of U. maritimus but it would be interesting to have its trophic position included in the figures as a reference point, as the most carnivorous species in the Family; from a brief look at the diet data, they do consume some plant material.\nFigure 2c,d: I cannot reconcile the information in lines 354-358 of Methods about the compression to (0.00034-0.99934), with the scatterplot of these two panels. The trophic position data seem to be within a (0.1-0.8) range. Also, in panel B, the range for values is larger. Is it because this panel represents not compressed data?\nL114-115: I might have missed it, be I did not find how this overlapp was estimated and where it was used. It seems only mentioned in the SI with the dominant and subordinate factors. Also, would it not make sense to make this a continuous variable reflecting the proportion of overlap?\nL120-121: could you add a measure of representing an (any kind of) effect size to the posterior probabilities?\nSupplement: Could you add a plot showing the relationship of NPP to GSL?", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_4": "The study presents a novel and innovative application of the dynamic omnivory hypothesis (Figure 1) to large, terrestrial, mammalian carnivores by investigating spatial and temporal dietary variation in ursids. Overall the study was well organized and composed, and the methods and results were clearly explained. The references were comprehensive, and this paper has potential to serve as solid resource for future work on this topic. My substantive comments and questions are in relation to the interpretation of the findings – see below.\nFor the modern spatial analysis of global bear species TP I had three comments and questions;\n1) It appears in Figure 2 that really only brown bears are “dynamically adapting” their species-specific TP to resource availability… for 5 of the other species besides the black bear, there does not look like there is substantial TP variation to explain. And while the black bear TP distribution does extend, it is only due to a few individuals. To what extent does the spatial analysis and interpretation apply to bear species other than brown (and possibly black) bears?\n2) Along the same line of inquiry, the term dynamic implies an ability to respond – how does bear species morphology and body size factor into the interpretations? Are all 7 species able to respond to resource changes by altering TP, or are some more constrained than others by evolutionary legacy? This could be an important consideration before the model results are applied to all bear species.\n3) The model accounts for sympatric competition among bear species, but I did not see any discussion of the potential confounding effects of interactions with other members of the carnivore and omnivore guilds? Could variation in community assemblage influence the response of brown bears to changing resources?\nFor the fossil temporal analysis of European brown bear TP I had two comments and questions;\n1) Dietary resources of marine origin, such as anadromous fish or coastal invertebrates, could influence the interpretation of TP from brown bear d15N values (relative to red deer baseline, Figure 3). Is this something the authors considered? Specifically, are there spatial patterns in fossil location (proximity to coastline, river systems with geohistorical anadromous fish presence, etc.) that interact with the temporal bear TP patterns? The trend in brown bear TP through time is convincingly correlated to climate and NPP, but given the importance of marine resources for populations of brown bears today, it could be worth addressing in the discussion of these results.\n2) The covariation in d15N and d13C values of marine diet resources (both are generally higher than terrestrial) could be another way to query the isotopic data to identify individual bears for which TP (estimated from d15N values) might be influenced by marine resources, not solely climate/NPP factors. The methods refer to d13C analyses, but I did not see these data presented, is this something the authors considered?"}, "author_response": "Responses to reviewers\nWe would like to thank the Reviewers for their constructive comments. Below we provide a\npoint-by-point response to the comments, which are highlighted in blue font. To facilitate the\nreview process, we have highlighted the changes made to the manuscript in red font in the Word\ndocument. Please note that line numbers in our responses refer to lines in the revised version of\nthe manuscript. At the end of our responses, we provide a list of the cited literature.\nReviewer comments\nReviewer #1 (Remarks to the Author):\nThe manuscript provides evidence that bears can flexibly alter their trophic position (degree of\nomnivory) across large spatial and temporal gradients of resource availability. A global diet\nanalysis showed that 7 bear species increased their consumption of lower trophic level\nresources (i.e. increasing omnivory) as NPP and growing season increased. Next, subfossil\nbone and tooth samples of brown bears were used to show that trophic positions decreased\nfrom the Late Pleistocene to Holocene as NPP and growing season increased.\nThese findings are noteworthy examples of dynamic omnivory as a behavioral response to\nchanges in resource availability in both space and time. I imagine they will be of widespread\ninterest to ecologists and will motivate future work in the field. The conclusions and claims are\nwell supported. I did not note any flaws in the data analysis, interpretation or conclusions. The\nmethods are sound and meet expected standards. There is sufficient detail provided in the\nmethods and the supplement to reproduce the study. I do have a few minor clarifying questions\nand comments I’ve listed below. Overall, this is an interesting study that provides novel\ninformation about spatial and temporal flexibility in omnivory.\nResponse 1.1: We are grateful for the reviewer’s encouraging remarks and appreciation of our\nwork.\nSpecific Comments\n1) Could seasonal diet changes between animal and plant prey influence your results?\nIn the global dietary analysis, “samples covered the entire seasonal activity period of the focal\nspecies”, meaning that any seasonal diet changes would still allow you to say what the\ncontribution of animal prey was for that location (because all seasons were considered),\ncorrect?\nResponse 1.2: It is true that the diet of the species also varies seasonally. However, these\ndifferences do not influence our results, as we only included studies that covered the entire\nseasonal activity period of the focal species. We used the average annual diet, because it is\nmore representative of the diet than seasonal snapshots and only this approach allows for\nmeaningful comparisons across study locations. Previous work has shown that this approach\nintegrates very well across seasonal dietary changes (e.g., in temperate or boreal biomes)1. In\nfact, using seasonal dietary snapshots instead of average annual diet could lead to detection of\nspurious differences, for example, if the spring diet in one location is compared with autumn\ndiet in another location. We added a sentence into the main text outlining our rationale for\nusing annual diets (see lines 104-106). The sentence reads: “…We used annual diets, which\nintegrate foraging behavior across the entire active season of the focal species, to enable\nmeaningful comparisons across study locations. …”.\nWhat was the temporal resolution of your d15N data from each bone and teeth sample? Does\nthe d15N extracted from collagen reflect the lifetime average trophic position of that individual\nbear? This might be worth mentioning in the text.\nResponse 1.3: In long-lived large mammals, adult bone collagen reflects the average isotopic\ncomposition of the diet over several years preceding death, whereas collagen in tooth dentine\nprimarily accumulates during the period of tooth development2. Since brown bears possess\nbrachydont teeth dental growth ceases early in life, during a stage when a substantial\nproportion of nutrients is still derived from lactation2. Consequently, collagen extracted from\ndentine may exhibit higher δ15N values than bone collagen from the same individual2. We\naccounted for this effect in the analyses by correcting the isotope ratio of teeth using data from\nthe literature2 on the difference in δ15N values between collagen from tooth and bone material\nof the same individuals. Following your suggestion, we added this explanation to the methods\nsection of the manuscript (see lines 463-471).\n2) For the temporal analysis, what was the number of individual European brown bears\nincluded in each of the 8 time bins? I could have missed this somewhere.\nResponse 1.4: The sample sizes for the European brown bear and red deer (the isotopic\nbaseline for a strict herbivore) are indicated at the bottom in Fig. 3c. The information is also\nhighlighted in the figure legend. To make the link of the sample sizes to the species clearer, we\nadded the scientific names to the legend (see lines 932-933). It now reads: “…Sample sizes for\nbrown bear (n ) and red deer (n ) in each time bin are given at the bottom of the\nU. arctos C. elaphus\npanel….”.\n3) Line 282-286: When comparing trophic positions to NPP and growing season across\nlocations, the studies included in this analysis likely spanned a large date range. Can you\nclarify that data were pulled for NPP and monthly temperature from the same year in which the\nsamples were collected for each study location, to account for interannual variation in\nenvironmental conditions?\nResponse 1.5: Unfortunately, we were unable to obtain annually resolved data on NPP and\ngrowing season length for the macroecological analysis. As a result, our model does not\naccount for interannual variation in these environmental factors. Instead, we used NPP\nestimates from Terra MODIS17A3 and calculated growing season length based on CHELSA\nclimatologies of near-surface air temperature. Both datasets offer excellent spatial resolution\nand integrate data over extended time periods, minimizing the influence of individual extreme\nyears on our analysis and conclusions (temporal coverage: CHELSA – 30-year average; Terra\nMODIS17A3 – 16-year average).\nNevertheless, given the large spatial scale of our macroecological analysis, we expect that\ngeographic differences in climate between populations are more pronounced and have a greater\ninfluence on observed geographic patterns in trophic position than interannual climatic\nvariability. Furthermore, we expect the effects of NPP and growing season length on trophic\nposition in our macroecological analysis to be conservative estimates. If annually resolved\npredictor variables were available, these effects might be even stronger, as they would capture\ninterannual climatic variation in addition to geographic variation in climatic conditions.\n4) Fig 1 shows how omnivory can change from no omnivory to omnivory and back as resource\navailability increases. That makes sense, and I see how this is meant to show a continuous\ngradient of decreasing trophic position (especially with the grey diagonal arrows). But visually,\nit highlighted for me the extreme end cases of no omnivory, and gave me the sense that\n‘dynamic omnivory’ refers to a shift from no omnivory to omnivory (and vice versa). The\nFigure caption also read to me as if these are 3 discrete stages and didn’t emphasize the\ncontinuous nature of the omnivory (intraguild predation) stage that is so well highlighted by\nyour empirical findings (bears do not rely exclusively on plants or animals but increase plant\nconsumption across the gradient, while still feeding as omnivores on both prey types).\nI wondered if adding a second intraguild predation motif might help show that dynamic\nomnivory also includes shifts in the degree of omnivory. These two motifs could show how the\nO-R interaction strengthens as the O-H interaction weakens as NPP increases. Visually, this\nmight help the reader connect your conceptual figure with your empirical findings.\nFinally, this figure is not referenced again in the text that I could find. It would be worth adding\none sentence to the results or discussion stating how your empirical findings relate to the\nconceptual framework.\nResponse 1.6: We followed your suggestion and modified the figure by adding a second\nintraguild predation motif to highlight the continuous nature of dynamic omnivory. We also\nadded a sentence to the figure caption to highlight the shift in relative interaction strengths\nacross the environmental gradients on the x-axis (see revised Fig. 1). Moreover, we added\nreferences to this figure in the results section to relate our empirical findings back to the\nconceptual framework (see lines 172-174). The section reads: “…These results are in strong\nagreement with the ‘dynamic omnivory’ hypothesis, predicting that omnivores adapt their\ntrophic position to resource availability and growing season length (Fig. 1)….”\n5) Fig. 2 and Fig. 3 are very effective and well done. I noticed some small boxes on both\nfigures that are likely symbols that didn’t convert properly?\nResponse 1.7: Thank you for pointing to problems with figure conversion. Upon uploading the\nrevised version of our manuscript, we checked that symbols converted properly.\nReviewer #2 (Remarks to the Author):\nThe authors have assembled an impressive dataset and then followed a thoughtful modelling\napproach that allowed them to overcome unavoidable data limitations, to robustly estimate how\nthe trophic position of bears in the Ursidae Family (as exemplar omnivores) changes across\nspace with the length of the growing season and annual net primary productivity. They also\nconsidered the influence of competition from sympatric bear species. They then followed a\nsimilar approach to examine this relationship on a Paleoecological scale. As the authors note,\nomnivores play an important role in multitrophic communities and changes in their trophic\nposition can have implications for the stability and functioning of an ecosystem. Therefore,\nunderstanding what influences their trophic position is a key ecological question and this study\naddresses it in a very convincing way.\nAll sections of the manuscript are very well written and easy to follow, clearly introducing the\ntopic and explaining the methods that were used to answer the central questions of the project.\nThis is also true for the script for the data preparation and analysis!\nResponse 2.1: We appreciate the reviewer’s positive assessment of our study.\nMy main criticism is that, in their intro and discussion, the authors have placed a lot of\nemphasis on the effects that resource availability may have on bear diet, to the near exclusion\nof other potential mechanisms. I would argue that one of them, direct effects of temperature on\nbears, is already embedded in the data they are analyzing. I would expect that annual net\nprimary productivity is strongly dependent on growth season length. (A plot showing the\nrelationship of the two would be a useful addition to the supplement – I know the authors\nchecked for variance inflation; this is just to help readers understand all the relevant\nrelationships). The total effect of GSL on diet would be largely mediated by NPP. So, with both\nof these variables as predictors, the model is estimating the “direct” effect of GSL on diet (i.e.\nunmediated by NPP) and the effect of NPP on diet, accounting for the common influence of\nGSL on both NPP and TP. Here is a causal diagram showing the main effects of their model:\nNPP <- GSL -> TP\nNPP -> TP\n(I know the authors know all that; I am merely laying out so that we are all on the same page.) I\nagree that NPP and partly GSL are measures of plant diet availability during the year. So the\nauthors’ conclusion, that bears rely less on animal diet because plant diet becomes increasingly\navailable, is partly supported by these results. However, GSL is also strongly related to mean\nannual temperature. So I would expect that part of the effect of GSL on bear diet that is\nunmediated by NPP, is really the direct effect of temperature on bear physiology and activity. I\nwould suggest that the larger the number of months with below-zero temperatures, the more\nbears would need to rely on animal diet to build fat reserves. Then additionally, with higher\ntemperatures, heat loss is no longer a problem; in fact, the reduction of heat dissipation might\nrestrict activity, further promoting a shift from hunting to foraging. Looking at Figures 3a and\n3c I think this is also true for the Paleo analysis; trophic position seems to track changes in\nGSL more than NPP. Here is a more complete diagram:\nNPP <- GSL -> (PDA) -> TP\nNPP -> (PDA) -> TP\nGSL -> TP\nwhere (PDA) is the (unobserved) plant diet availability. In the absence of PDA, this causal\nmodel simplifies to the previous one, with the caveat that the arrow from GSL to TP (and the\ncorresponding model estimate) captures both the effects of GSL that relate to plant diet\navailability during the year, as well as direct effects of temperature on bear trophic activity. I do\nnot think that these two are separable in practice, so I am not asking that the authors fit a\ndifferent model. I believe their model is the best model one could fit. I am merely asking,\nassuming they agree with my interpretation, that they elaborate a bit on the more complex\nnature of that GSL->TP effect.\nI know this complicates the take-home message of this work, but it is an important aspect of\nhow omnivory relates to ecosystem processes; one of these mechanisms is a within-system\nadaptation (trophic behaviour influenced by resource availability), while the other one is\nexternal forcing (trophic behaviour influenced by temperature).\nResponse 2.2: Thank you for this constructive and critical comment. We added the point\nrelated to physiological effects of temperature on foraging behaviour to the introduction (see\nlines 74-76). The section in the introduction now reads: “… Moreover, prolonged growing\nseasons due to climate warming3 can reduce seasonal bottlenecks in NPP and may release\nconsumers from energetic constraints by reducing the metabolic costs of maintenance and\nforaging in warmer environments4,5.…”. In the revised version of the manuscript, we now also\nexplicitly highlight in the discussion section (see lines 179-186 and 229) physiological\ntemperature effects as a potential mechanism underlying altered foraging behavior. The section\nreads: “…We attribute the observed trophic adaptation to three nonexclusive mechanisms: (i)\ntracking seasonal peaks in plant productivity (e.g., fruit and seed production), facilitated by the\nmobility of these large omnivores6,7; (ii) shifts in foraging preferences if foraging for plant\nresources is energetically more efficient than hunting or scavenging, due to lower search and\nhandling times in dense vegetation8,9; and (iii) preferential foraging for plant resources in\nwarmer environments, where reduced metabolic costs of maintenance and foraging alleviate\nenergetic constraints4,5.…”\nI understand the logic behind the exclusion of U. maritimus but it would be interesting to have\nits trophic position included in the figures as a reference point, as the most carnivorous species\nin the Family; from a brief look at the diet data, they do consume some plant material.\nResponse 2.3: Thank you for this comment. We also initially considered adding the polar bear\nto the analysis as a reference point. However, this brings up several problems noted in the\nmanuscript: First, the scat samples collected during summer at coasts are not representative of\nthe species’ diet, because polar bears are effectively fasting during this period and acquire\nenergy mainly from predation of marine mammals on sea ice during autumn, winter, and\nspring10–12. More recent work indicates that observed population declines of polar bears are in\nfact associated with food shortage during prolonged periods ashore, suggesting that they cannot\npersist solely on plant-based diets13. Moreover, those dietary studies describing winter diets of\npolar bears, have been conducted using fatty acid analysis of adipose tissue14. Therefore, these\nresults are not directly comparable to the micro histological analyses of scat and stomach\ncontents used in our study. Finally, we do not have comparable data on net primary\nproductivity or a comparable variable describing growing season length for this species, as\npolar bears spend most of the year on sea ice and only fast on land during summer. Even\nthough we appreciate the suggestion of the reviewer, we would like to avoid including the polar\nbear as a reference point into the analysis for the reasons stated above.\nFigure 2c,d: I cannot reconcile the information in lines 354-358 of Methods about the\ncompression to (0.00034-0.99934), with the scatterplot of these two panels. The trophic\nposition data seem to be within a (0.1-0.8) range. Also, in panel B, the range for values is\nlarger. Is it because this panel represents not compressed data?\nResponse 2.4: The panels c,d show component + residual plots, which are conceptually similar\nto partial residual plots. Therefore, these two panels do not show the raw data, but the marginal\nrelationships between the response variable and each predictor variable after accounting for the\neffects of other variables in the model. This is why the distribution of the data in these plots is\nnot the same as in panel b, in which the raw data is shown. We already highlighted this in the\ncaptions of Figure 2 and 3 (see lines 908-911 and 934-935). We hope that this clarifies the\nquestion of the reviewer.\nL114-115: I might have missed it, be I did not find how this overlap was estimated and where it\nwas used. It seems only mentioned in the SI with the dominant and subordinate factors. Also,\nwould it not make sense to make this a continuous variable reflecting the proportion of\noverlap?\nResponse 2.5: Thank you for this comment. We added an explanation of how we quantified co-\noccurrence to the methods section (see lines 327-332). The section reads: “…We quantified\nspecies co-occurrences using IUCN species distribution maps15. For the geographic records of\neach species, we determined whether a location fell within the geographic range of one or\nmore other bear species. Then we created a categorical variable with three levels (no co-\noccurrence, subordinate, and dominant), indicating whether the focal species at a given\nlocation co-occurred with another bear species and, if so, whether it was smaller (subordinate)\nor larger (dominant) than the co-occurring species….”\nWhile we appreciate the advantages of using a quantitative measure of proportional range\noverlap in the context of species-centered analyses, this was not meaningful in the context of\nour population-centered analysis, because each population could have different values for the\nco-occurrence variable, so that some populations could be located within the geographic range\nof another species while this was not the case for other populations (see Table 1 below).\nTherefore, we used a categorical variable indicating the co-occurrence and the potential type of\ninteraction with other bear species based on their body mass.\nTable 1. Summary of co-occurrence pattern across the populations of each bear species\nincluded in the study.\nCo-occurrence\nFocal species smaller Focal species larger\nSpecies no (subordinate) (dominant)\nUrsus arctos 71 6 18\nUrsus americanus 52 11 0\nUrsus thibetanus 24 4 0\nHelarctos malayanus 2 0 0\nMelursus ursinus 11 0 3\nTremarctos ornatus 6 0 0\nAiluropoda melanoleuca 0 0 2\nL120-121: could you add a measure of representing an (any kind of) effect size to the posterior\nprobabilities?\nResponse 2.6: Following your suggestion, we added effect size measures to the posterior\nprobabilities. In particular, we added the median and the 90% Equal-Tailed Intervals (ETI) in\nline with the information given in the supplementary materials (see lines 126-128, 140-142,\nand 165-166).\nSupplement: Could you add a plot showing the relationship of NPP to GSL?\nResponse 2.7: We added plots of these two variables based on the macroecological and\npaleoecological data to the supplement and an explanatory sentence to the respective parts of\nthe methods section (see new Supplementary Figs. 1 and 4; lines 322-325 and 490-493).\nReviewer #2 (Remarks on code availability):\nCode is executable and extremely well commented. We did not carefully check for potential\nmistakes in it though\nResponse 2.8: Thank you for the positive feedback on the code.\nReviewer #3 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is\npart of the Nature Communications initiative to facilitate training in peer review and to provide\nappropriate recognition for Early Career Researchers who co-review manuscripts.\nResponse 3.1: We appreciate your time and effort in co-reviewing our manuscript.\nReviewer #4 (Remarks to the Author):\nThe study presents a novel and innovative application of the dynamic omnivory hypothesis\n(Figure 1) to large, terrestrial, mammalian carnivores by investigating spatial and temporal\ndietary variation in ursids. Overall the study was well organized and composed, and the\nmethods and results were clearly explained. The references were comprehensive, and this paper\nhas potential to serve as solid resource for future work on this topic. My substantive comments\nand questions are in relation to the interpretation of the findings – see below.\nResponse 4.1: Thank you for your valuable time and thoughtful review of our manuscript.\nFor the modern spatial analysis of global bear species TP I had three comments and questions;\n1) It appears in Figure 2 that really only brown bears are “dynamically adapting” their species-\nspecific TP to resource availability… for 5 of the other species besides the black bear, there\ndoes not look like there is substantial TP variation to explain. And while the black bear TP\ndistribution does extend, it is only due to a few individuals. To what extent does the spatial\nanalysis and interpretation apply to bear species other than brown (and possibly black) bears?\nResponse 4.2: Thank you for this comment. With the macroecological analysis we aimed to\ntest (i) whether differences in trophic position between bear species converge on the same\ngeneral relationship with NPP and growing season length, and (ii) whether geographic\ndifferences in trophic position between populations of the same species can be attributed to\ngeographic differences in NPP and growing season length between these populations.\nWith respect to question (i) our analysis nicely illustrates that there is a general relationship of\nthe trophic position of bears with NPP and growing season length (Fig. 2). This indicates that\nthe realized trophic positions of the different bear species are largely a result of the\nenvironmental conditions encountered by each species.\nWith respect to question (ii) our analysis provides the strongest evidence for those species with\nbroad distributions that consequently also experience a broad range of environmental\nconditions to which they need to adapt (i.e., brown bears, American black bears, Asiatic black\nbears, and sloth bears; see Fig. 2a,b in main text). However, our conclusions regarding the\nadaptive capacity of those species with smaller geographic distributions are weaker (i.e., sun\nbear, spectacled bear, and giant panda). Yet, previous work has shown that the majority of\nextant bear species exhibit significant dietary flexibility, enabling them to subsist on plant- or\nanimal-based diets for prolonged periods of time depending on resource availability16–19.\nBrown bears, American black bears, and Asiatic black bears are known to consume a wide\nrange of plant and animal matter18. Likewise, sloth bears and sun bears have been reported to\nadjust their diets seasonally between insects and fruits17,20,21. And even spectacled bears that\nstrongly rely on plant-based diets have been reported to scavenge or predate on smaller\nmammals17. This is also reflected in relatively weak morphological skull adaptations of these\nspecies, which have been interpreted as resulting from selective pressures favoring dietary\nflexibility over specialization17–19,22.\nPolar bears and giant pandas are indeed exceptions in this context as they represent the\nextremes of the carnivory–omnivory–herbivory gradient in our study: Polar bears are almost\nstrictly carnivorous, preying on a range of marine mammals on sea ice14, and consume\nvegetative material only in southern areas where they are forced ashore during summer by\nmelted sea ice10,16,23. Observed population declines associated with food shortage during\nprolonged periods ashore suggest that they cannot persist solely on terrestrial diets13. At the\nother extreme, the diet of giant pandas is limited almost entirely to bamboo24,25, and the species\nonly occasionally scavenges and predates on small mammals17. Therefore, we expect that only\nthose species that are omnivorous and have been shown to switch seasonally between animal-\nand plant-based diets depending on their availability are able to adapt their trophic position to\nchanges in resource availability. To clarify this aspect, we added this notion to the discussion\nsection of the manuscript (see lines 186-198). The section reads: “…These results are\nsupported by previous work reporting trophic adaptations to seasonal and annual variability in\nresource availability within populations of six of the seven terrestrial bear\nspecies22,17,19,9,26,16,18. The only exceptions among ursids in this context are the polar bear and\nthe giant panda (Ailuropoda melanoleuca), which represent the extremes of the carnivory–\nomnivory–herbivory gradient in our study (Fig. 1): Polar bears are almost strictly carnivorous,\npreying on a range of marine mammals on sea ice14 and consuming plant resources (e.g.,\nberries) only in southern areas where they are forced ashore during the ice-free period in\nsummer10,16,23. At the other extreme, giant pandas are strictly herbivorous, consuming almost\nexclusively bamboo24,25 and only occasionally scavenging or predating on small mammals17.\nTherefore, we expect that only those species that are omnivorous and have been shown to\nswitch seasonally between animal- and plant-based diets depending on availability are able to\nadapt their trophic position to changes in resource availability. …”. We hope that this addition\nto the discussion addresses the concerns of the reviewer.\n2) Along the same line of inquiry, the term dynamic implies an ability to respond – how does\nbear species morphology and body size factor into the interpretations? Are all 7 species able to\nrespond to resource changes by altering TP, or are some more constrained than others by\nevolutionary legacy? This could be an important consideration before the model results are\napplied to all bear species.\nResponse 4.3: As mentioned above, except for the polar bear and the giant panda, all other\nextant bear species have maintained a remarkable degree of dietary flexibility, which allows\nthem to subsist on plant or animal diets depending on resource availability16–19. Importantly, the\ngiant panda is the only ursid species that shows strong adaptations in skull morphology to its\nspecialized bamboo diet17,19. For all other ursids, including polar bears, skull morphology is\nless derived than would be expected by their diets17,19, suggesting that selective pressures on\nskull morphology are relatively weak17. Instead, it has been suggested in the literature that the\nrelatively weak adaptations for carnivory and herbivory observed in bears may be the result of\nselection for the ability to cope with temporal fluctuations in dietary components19. We\nelaborated on the potential constraints imposed by morphology in the discussion section (see\nlines 234-249). The section reads: “… Despite the signal of trophic adaptation of populations\nto environmental change in our study, it remains unclear to what extent species that show\nmorphological adaptations to specific diet types (e.g., herbivory in the giant panda)17,19,22,27 or\npopulations that occur at the physiological or ecological limits of their species’ range in\nextreme environments (e.g., arctic, desert, or alpine environments) are able to cope with future\nchanges in environmental conditions. The adaptive capacity of these species and populations\nwill depend on the magnitude and velocity of environmental change, as well as on whether\nalternative food sources meet their nutritional requirements6,28. For instance, it has been\nsuggested that the switch of polar bears from marine prey to terrestrial food sources (e.g.,\nberries or bird nests) in response to reductions in sea-ice extent is associated with declines in\nbody size, body condition, reproduction, and population sizes13. Similarly, due to their\nspecialization on bamboo, the capacity of giant pandas to adjust their trophic niche to\nvariations in resource availability is likely limited24. Investigating how the interplay of\nbehavioral, morphological, and physiological factors constrains or facilitates the trophic\nflexibility of omnivores may provide further insights into their adaptive capacity to\nenvironmental change. …”. We hope that this addresses the concerns of the reviewer.\n3) The model accounts for sympatric competition among bear species, but I did not see any\ndiscussion of the potential confounding effects of interactions with other members of the\ncarnivore and omnivore guilds? Could variation in community assemblage influence the\nresponse of brown bears to changing resources?\nResponse 4.4: We did not account for co-occurrence with other large carnivores in the analysis\n(e.g., felids or canids), as bears have evolved from carnivores with high-protein diets to\nomnivores that consume low-protein diets, thus, reducing interspecific competition with other\nlarge carnivores29. Moreover, bears are well-documented kleptoparasites, often monopolizing\ncarrion resources and significantly limiting carrion access and predatory behaviour in other\nlarge carnivores (e.g., wolves, cougars, and pumas)30–36. In interspecific encounters with other\ncarnivores, bears typically dominate due to their large body size or are able to defend\nthemselves by charging, as observed in encounters with tigers (Panthera tigris)37–39. In extreme\ncases of interspecific killing, bears are typically the killer species, while rare instances of bears\nbeing killed usually involve denning adults, cubs of the year, or aggressive encounters with\ntigers in Indian tiger reserves and the Russian Far East37–42.We added this justification to the\nmethods section of the manuscript (see lines 333-343).\nFor the fossil temporal analysis of European brown bear TP I had two comments and questions;\n1) Dietary resources of marine origin, such as anadromous fish or coastal invertebrates, could\ninfluence the interpretation of TP from brown bear d15N values (relative to red deer baseline,\nFigure 3). Is this something the authors considered? Specifically, are there spatial patterns in\nfossil location (proximity to coastline, river systems with geohistorical anadromous fish\npresence, etc.) that interact with the temporal bear TP patterns? The trend in brown bear TP\nthrough time is convincingly correlated to climate and NPP, but given the importance of marine\nresources for populations of brown bears today, it could be worth addressing in the discussion\nof these results.\n2) The covariation in d15N and d13C values of marine diet resources (both are generally higher\nthan terrestrial) could be another way to query the isotopic data to identify individual bears for\nwhich TP (estimated from d15N values) might be influenced by marine resources, not solely\nclimate/NPP factors. The methods refer to d13C analyses, but I did not see these data\npresented, is this something the authors considered?\nResponse 4.5: Thank you for these important comments. As suggested, we used the d15N and\nd13C stable isotope ratios to rule out any influence of marine diet resources, such as\nanadromous fish or coastal invertebrates. To do so, we compared the stable isotope ratios of the\nbrown bear in our data to those of other a range of different marine mammal species from the\nliterature43. Importantly, we found no indication of consumption of marine resources for the\nbrown bear samples in our data, as the d15N and d13C values are much lower than would be\nexpected, if the respective brown bear individuals had consumed marine resources (see new\nSupplementary Fig. 3). We added this analysis to the methods section of the main text and the\nsupplementary materials (see new Supplementary Fig. 3), and briefly refer to the analysis in\nthe results section (see lines 150-152 and 473-481). The section in the methods reads: “…To\nrule out the consumption of marine resources (e.g., anadromous fish) by brown bears, we\ncompared the δ13C and δ15N values of the brown bear material with those of a range of marine\nmammal species from the literature43 (Supplementary Fig. 3). The δ13C and δ15N values of\nbrown bear material were much lower than those of material from marine mammals, but very\nsimilar to those of the red deer material (Supplementary Fig. 3), indicating negligible\nconsumption of marine resources by brown bears in our data. Therefore, we estimated the\ntrophic position of brown bears based on δ15N of collagen through time using the δ15N values\nof red deer from the same region as a dietary baseline for a strict herbivore.…”.\nReferences\n1. Vulla, E. et al. Carnivory is positively correlated with latitude among omnivorous\nmammals: evidence from brown bears, badgers and pine martens. Ann. Zool. Fenn. 46,\n395–415 (2009).\n2. Bocherens, H. Isotopic tracking of large carnivore palaeoecology in the mammoth steppe.\nQuat. Sci. Rev. 117, 42–71 (2015).\n3. Liu, H., Lu, C., Wang, S., Ren, F. & Wang, H. Climate warming extends growing season\nbut not reproductive phase of terrestrial plants. Glob. Ecol. Biogeogr. 30, 950–960 (2021).\n4. Anderson, K. 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Acta 48, 625–639\n(1984)."}, {"round": 2, "reviewer_comments": {"reviewer_2": "The revised manuscript addresses all our previous concerns and we look forward to seeing it published.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_4": "All of my comments and questions have been thoughtfully and thoroughly addressed in the revised version of the manuscript."}, "author_response": ""}]

Decision letter