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Article

Ecological Traits and Intraguild Competition Mediate Spatial and Temporal Overlaps Among Sympatric Mesocarnivores

by
Hiroshi Tsunoda
1,*,
Stanislava Peeva
2,
Evgeniy Raichev
2 and
Yayoi Kaneko
3
1
Center for Environmental Science in Saitama, Kamitanadare 914, Kazo-shi, Saitama 347-0115, Japan
2
Faculty of Agriculture, Trakia University, Student’s Campus, 6000 Stara Zagora, Bulgaria
3
Faculty of Agriculture, Tokyo University of Agriculture and Technology, Saiwaicho 3-5-8, Fuchu-shi, Tokyo 183-8509, Japan
*
Author to whom correspondence should be addressed.
Diversity 2025, 17(2), 108; https://doi.org/10.3390/d17020108
Submission received: 21 November 2024 / Revised: 16 January 2025 / Accepted: 27 January 2025 / Published: 1 February 2025
(This article belongs to the Special Issue Conservation and Management of Wild-Living Carnivorous Mammals)
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Abstract

:
In terrestrial mammalian carnivore guilds, interspecific competitions (interferences and resource competitions) among sympatric species induce their ecological and behavioral patterns and population dynamics, thereby shaping community structures. Competitive species must partition their ecological niches for sympatry, while the extent of niche overlaps is mediated by either the ecological traits (e.g., body size differences) or environmental features. We aimed to elucidate the patterns of spatial and temporal niche overlaps in mesocarnivore guilds, which are mediated by their ecological traits, regional environments, and anthropogenic disturbances. We investigated the spatial occurrence and diel activities of six mesocarnivore species and estimated their spatial and temporal overlap indices in various landscapes with different anthropogenic disturbances in central Bulgaria. Statistical modeling demonstrated that spatial overlap among mesocarnivores declined when mesocarnivore pairs were within the same family and when large carnivores (particularly wolves, Canis lupus) were present. Furthermore, we found that the extent of their temporal overlaps was associated with taxonomic and body size differences in mesocarnivore pairs as well as their trophic competitions. Our findings indicated that the intensity of interferences among mesocarnivore species was key for the decline of spatial or temporal niche overlap to relax antagonistic interactions.

1. Introduction

Interspecific competition and niche partitioning have long been central topics of animal ecology, and they have received growing interest in biodiversity conservation [1,2,3,4]. In communities of predatory animals, the theory of interspecific competition has been extended to intraguild predation in which sympatric species not only compete directly (i.e., kill and aggression) and indirectly (i.e., resource competition) but also predate each other [5,6]. This type of interspecific interaction is commonly observed in various taxa in terrestrial and aquatic systems [6]. The typical example is a guild of terrestrial predatory mammals (Order Carnivora, Mammalia), whereby intraguild interactions among sympatric species alter their ecological and behavioral patterns and population dynamics with each other, thereby shaping the community structures [1,2,7,8]. In general, mammalian carnivores occupy high trophic levels in terrestrial ecosystems and affect ecosystem dynamics broadly through trophic cascades, potentially contributing to biodiversity conservation and providing ecosystem services [7,9,10,11]. Therefore, understanding the mechanism structuring mammalian carnivore guilds is necessary [2,3].
In carnivore guilds, the difference in body size among competitive species is a key role for the interspecific interaction that a large species is generally predominant over a small one in direct competitions [7,12,13]. Therefore, large carnivores (particularly large canids, felids, and hyaenids) are typically dominant in local carnivore guilds, suppressing and controlling the abundance of smaller species in natural ecosystems [1,13,14]. However, intense human activities have degraded and modified the structuring of the ecological community in the Anthropocene [3,9]. Large carnivores first disappeared from regional ecosystems because their home ranges largely overlap with human-used areas, resulting in conflicts with humans [15]. Consequently, small–medium carnivores (also known as “mesocarnivores” [16,17]) are released from the top–down effects of larger predators in human-modified environments (i.e., “meso-predator release” [2,18]). Despite the similar body sizes (typically less than 15 kg [16,17]), direct competition among sympatric mesocarnivores is generally based on the differences in body size [12,13], resulting in the hierarchical structure in which a large, dominant mesocarnivore constrains a subordinate (i.e., smaller) species, in turn, the subordinate one suppresses a small carnivore [14,19,20].
Competitive species must partition their ecological niches for successful sympatry in an environment in accordance with the theory of competitive exclusion [21]. Among sympatric carnivores, partitioning in trophic (i.e., food), spatial (i.e., space use), and temporal (i.e., time active) niches are fundamental dimensions for the coexistence of multiple species [1,3,7,22]. However, mesocarnivores are typically trophic generalists [16,17], and they often consume common prey animals (e.g., rodents [23]), having high trophic overlap even in their sympatry [24,25,26]. Therefore, spatial and/or temporal niche partitioning is a key mechanism to mitigate competition and facilitate their coexistence. Furthermore, current accumulated knowledge has shown that the intensity of competition and the extent of niche partitioning among sympatric mesocarnivores are associated with the presence of apex large carnivores [19,27,28], resource availabilities [20,29], and anthropogenic disturbances [30,31]. However, previous studies assessed that niche partitioning among competitive carnivores is mediated by either their ecological traits (e.g., body size or trophic niches [19]) or environmental features (e.g., resource availabilities or human disturbances [3,32]) separately. The integrative assessment of both aspects is challenging, but it provides a comprehensive understanding of the structuring carnivore guilds in the Anthropocene.
In this study, we aimed to elucidate the patterns of spatial and temporal niche overlaps in mesocarnivore guilds, which are mediated by their ecological traits and regional environments. Our focal mesocarnivore guild included six species (three mustelids, European badger Meles meles, pine marten Martes martes, and stone marten M. foina; two canids, golden jackal Canis aureus and red fox Vulpes vulpes; and one felid, European wildcat Felis sylvestris) with different body sizes and trophic requirements in central Bulgaria (Figure 1 and Figure S1 in the Supplementary Materials). We surveyed the spatial and temporal overlaps in mesocarnivore guilds across several different landscapes in central Bulgaria and statistically assessed four hypotheses (H1–4; Figure 1):
  • H1—The spatial and temporal overlaps will decrease in carnivore pairs (1) with the increase of body size differences (BSD), (2) in the same family, and (3) in carnivores with highly trophic overlap because such ecological traits increase the probability of direct interferences (i.e., kill, harassment, and intraguild predation), provoking spatiotemporal avoidances [1,12,13,22];
  • H2—The temporal overlap will increase with the increase of human disturbances because wild carnivores typically shift their diel activities to nocturnal to avoid human presence [33], while either positive or negative effects on the spatial overlap [3];
  • H3—The spatial and temporal overlaps will decrease in the cold season because their trophic niches overlap under seasonal limited resource conditions (e.g., sharing small mammals, carrion, and fallen fruits [20,34]), promoting their spatiotemporal avoidances to decline direct encounter probabilities [14,35,36];
  • H4—Spatial and/or temporal overlaps will decrease in the absence of large carnivores (wolves and brown bears, Ursus arctos) because the effects of large-dominant mesocarnivores (i.e., the jackal in our focal species) may increase because of ecological release [2,18]. Consequently, small, subordinate species must segregate spatiotemporal niches from the dominant predator to decline antagonistic encounters [7,20,22].

2. Materials and Methods

2.1. Study Area

The study area was central Bulgaria, covering approximately 10,000 km2 in total (Figure 2). The climate across the region was transitional between moderate–continental and continental–Mediterranean, with average annual temperatures, precipitation, and snow cover ranging from 6.5 °C to 11.0 °C, 750 mm to 1100 mm, and 8 to 30 cm, respectively, depending on altitude [37]. Our studied areas included the Central Stara Planina Mts., the Upper Thracian Plain, and the Eastern Rhodope Mts. (ERM), which were subdivided into eight studied sites with different landscape features (Figure 2 and Table 1). In the Central Stara Planina Mts., three studied sites (NSP1–3) were located in the northern slopes of the mountain with elevations ranging from 550 to 1500 m above sea level (a.s.l.), whereas two sites were located in the southern slopes with elevations ranging from 550 to 1250 m a.s.l. (SSP1) and the foothill (<500 m a.s.l.; SSP2) (Table 1). These sites were predominantly covered with secondary oak forests (Quercus sp.) up to 1000 m a.s.l. and natural beech forests (Fagus sylvestris) at higher elevations, and a small proportion (<5% of landcovers) of these sites were covered with grasslands, shrubland, agricultural fields, and buildings (Table S1 in the Supplementary Materials). In the Upper Thracian Plain, two sites (UTP1 and 2) were located in the surroundings of Stara Zagora City (ca. 150,000 residents), with elevations ranging from 150 to 450 m a.s.l. (Table 1). These sites were human-dominated mosaic landscapes covered with agricultural fields (i.e., croplands, orchards, and pastures), buildings, and small forest patches of secondary oak (Quercus dalechampii) and oriental hornbeam (Carpinus orientalis) shrubland (Figure 2 and Table S1 in the Supplementary Materials). A site of the ERM was along the Arda River valley covered with mixed landscapes of agricultural fields, pastures, oriental hornbeam shrubs, black pine (Pinus nigra) plantations, and secondary oak forests (Q. dalechampii), with elevations ranging from 150 to 500 m a.s.l. (Figure 2 and Table S1 in the Supplementary Materials). The ERM site was a traditional agroforest landscape with rich and endemic biodiversity, registered as the European “Natura 2000” protected area network, and facilitated the rewilding program [38].

2.2. Camera Trapping

The spatial occurrence and time active of the focal carnivore species were surveyed using camera trapping methods between 2015 and 2022. Camera trapping surveys were conducted as different projects to investigate carnivores’ spatiotemporal activities in the studied sites [20,31,39,40]. The survey durations were different at each site (Table 1), and seven of the eight studied sites (except for UTP1) were covered during warm and cold snowy seasons, whereas UTP1 was surveyed in the warm season only (Table 1). Camera trapping stations in each site were located along unpaved forestry roads, human footpaths, or animal trails in forests or shrubs; therefore, such stations were not randomly or systematically spaced because of steep terrains in mountainous sites or highly fragmented forest patches in human-dominated lowlands (Figure 2). Although a camera-trapping protocol was developed to locate two camera-trapping stations at least 500 m apart, some cameras were located in closer straight distances than the criteria because of steep slopes, sheer cliffs, winding roads, or zig-zag footpaths/trails (Figure 2) [20]. Furthermore, we relocated the mounted cameras to prevent theft in human-modified areas when human presence was recorded [39]. Straight distances between two cameras ranged from 46 m to 23,369 m (mean and standard deviation: 6102 ± 5116 m; see also Table S2 in the Supplementary Materials). The cameras were mounted on trees approximately 1.0–1.5 m above the ground level, without any baits or scent lures, and angled to take pictures of the whole body of medium and large animals. Three models of passive infrared cameras were used (Keep Guard Cam KG690NV, Keepway Industrial Inc., Hong-Kong, China; Ltl Acorn 6310-3G, Zhuhai Ltl Acorn Electronics Inc., Zhuhai, China; SG560K-14mHC, Boly Inc., Shenzhen, China), with trigger speeds ranging from 0.8 to 1.2 s. We basically employed a single model (Keep Guard Cam KG690NV) for all camera trapping projects, while the remaining two models were backup cameras for failure and accident. The cameras were programmed to take three pictures in a capture event with a 5 min delay. Batteries were changed, and memory cards were replaced every 2 weeks (in UTP1 and 2) or for 2–3 months (other sites).
After the field survey, all animal images were identified to the species level as much as possible, and the time and date of the capture were recorded in the laboratory. Among the studied sites, both pine and stone martens are distributed in high-altitudinal zones (>1000 m a.s.l.) in the Central Stara Planina Mts., whereas the stone marten is the only species observed in low-altitudinal areas [41,42]. Although our datasets may include both martens specifically at high altitudes, it was substantially difficult to distinguish them correctly from camera-trapping data, which typically consisted of black and white images for night and twilight [20]. Therefore, the two martens were treated as a single category, genus Martes, in this study. When we found consecutive images of the same species (or genus Martes) within 30 min at a station, treated as a single capture event [43].
We also observed and identified 12 wild mammal species other than eight focal carnivores by camera trapping survey: red deer (Cervus elaphus), roe deer (Capreolus capreolus), fallow deer (Dama dama), wild boar (Sus scrofa), chamois (Rupicapra rupicapra), brown hare (Lepus europaeus), northern white-breasted hedgehog (Erinaceus roumanicus), European edible dormouse (Glis glis), red squirrel (Sciurus vulgaris), and least weasel (Mustela nivalis), as well as two introduced species, nutria (Myocastor coypus) and American mink (Neovison vison). We also recorded unidentified small mammals (Order Rodentia), several domestic animals, and humans.

2.3. Data Analyses

2.3.1. Dataset Preparation for Niche Overlap Analysis

For analyses of spatial and temporal overlaps, we divided our entire dataset into subsets of site-species units in warm (May–October) or cold (November–April) seasons. However, some subsets were insufficient, with small sample sizes for statistical robustness to estimate spatial and temporal overlaps. Therefore, we treated the data in accordance with the following criteria: (1) when the seasonal sample sizes (i.e., data in either warm or cold seasons) of a carnivore in a studied site were <20 detections, they were summed up into annual data, and (2) when the annual sample size of a carnivore in a studied site was <20 detections, we removed it from the dataset. We defined the standardized sample size as n < 20, according to a previous study [44]. Consequently, 27 seasonal subsets (i.e., meeting the first criteria) and nine annual subsets (i.e., meeting the second criteria) were compiled for focal species, whereas three cases were removed (Table 2).

2.3.2. Spatial and Temporal Overlap Estimation

We estimated Pianka’s index [45] for spatial overlap and Dhat as the coefficient of temporal overlap [46] among all possible pairs of focal mesocarnivores in each studied site and season (if applicable and, if not, annual data were used), where both indices can range from 0 (complete separation) to 1 (complete overlap). When one species in a carnivore pair was compiled with annual data only, while the other was seasonal, we calculated the overlap indices using annual data only in accordance with our criteria. In accordance with this criterion, we calculated overlap indices for a total of 97 carnivore-pairs (annual, n = 25; warm season, n = 39; cold season, n = 33; Table 2 and Figure 2). For analysis of spatial overlap, we used the relative activity index, which was estimated as the sum of captures for a species per 100 camera-trapping days at each station [47] and Pianka’s indices for all carnivore pairs were estimated using “EcoSimR” package ver.0.1.0 [48] and R ver.4.3.1 [49]. For temporal overlap analysis, we used the time of day (0:00–23:59) when the animal images were captured and transformed to circular data (i.e., 0–2π). We adopted Dhat1 for datasets containing <50 samples (i.e., detections for a single species), whereas Dhat4 for both species in a carnivore pair containing >50 samples only [50]. For this analysis, we used “overlap” package ver.0.3.9 [50] in R ver.4.3.1.

2.3.3. Statistical Modeling

In assessing our hypotheses (H1–4) on the factors associated with either spatial or temporal overlap in mesocarnivore guilds, we performed multivariate analyses using the generalized linear mixed-effect model (GLMM). We developed two models for spatial or temporal partitioning using Pianka’s index (index of spatial overlap) or Dhat (the coefficient of temporal overlap) as response variables. We used seven covariates relevant to our four hypotheses (H1–4) in statistical modeling. With regard to the first hypothesis (H1), three covariates were used, that is, differences in body size, differences in family, and the extent of trophic competition among mesocarnivores (Figure 1). For the differences in body size, we estimated the arcsine root squared transformed index on the relative BSD [12] using the following equation:
BSD = (BML − BMS)/BML,
where BML is the body mass of the larger species, and BMS is the mass of the smaller one [12] (Table S3 in the Supplementary Materials). We used a binary variable for the differences in family, which is defined as intra- (0, for jackal–fox and badger–marten pairs) or inter-family pair (1, for other pairs). For the extent of trophic competition, we divided the focal carnivore pairs into three groups: high trophic overlap (i.e., pairs among foxes, wildcats, and martens); intermediate trophic overlap (i.e., pairs between jackals and foxes/wildcats/martens); and low trophic overlap (i.e., pairs between badgers and the remaining four carnivores) in accordance with the trophic niches of focal carnivores [51,52,53,54,55]. The covariates were strings with “low trophic overlap” as the base. With regard to the second hypothesis (H2), we used the human footprint index (HFI) [56] at each camera-trapping station. We recorded the coordinates of each camera station using a portable GPS device (e-Trex 20, Garmin Inc., Schaffhausen, Switzerland) in the field and the median values within a 1 km radius of the camera stations were calculated using QGIS ver.3.28.11 [57] and the open-source dataset for HFI [58]. With regard to the third hypothesis (H3), we used a binary variable for the sampling duration of the cold season only (1) or not (i.e., warm or annual: 0). With regard to the fourth hypothesis (H4), we used observation records of large carnivores by camera trapping in the studied sites for the sampled durations (Table 2 and Table S1 in the Supplementary Materials): we divided into three groups (strings) of no large carnivores (base); brown bear only; and both bear and wolf. The identifiers of the studied sites were also used as a random effect. We performed GLMM analyses using the “glmmTMB” package ver.1.1.8 in R ver.4.3.1 [59].
We then performed model selection to determine that covariates with statistical significance (p < 0.05) were included with the high-ranked model (adjusted Akaike’s Information Criteria, AICc < 2) (Table S4 in the Supplementary Materials). Finally, we assessed and confirmed the goodness of fit of the models using the Kolmogorov–Smirnov test (spatial overlap, p = 0.175; temporal overlap, p = 0.276). For these analyses, we used “MuMIn” ver. 1.48.415.1 and base packages in R ver.4.3.1 [60].

3. Results

3.1. Spatial Overlap

The Pianka’s index of spatial overlap among mesocarnivore pairs ranged from 0.007 to 0.979 (Figure 3a). Based on the results of the GLMM analysis, the Pianka’s indices for spatial overlaps were positively associated with taxonomic differences in mesocarnivore pairs (z = 2.080, p = 0.038) and the presence of large carnivores (z = 2.188, p = 0.029; Table 3). Both covariates were included in the best model with the smallest AICc (Table S4 in the Supplementary Materials). The results indicated that the spatial overlap increased when the family of mesocarnivore pairs was different and both bears and wolves were present (Figure 4).

3.2. Temporal Overlap

The coefficients of temporal overlap (Dhat) among mesocarnivores ranged from 0.375 to 0.886 (Figure 3b). The results of GLMM analysis indicated that the coefficients of temporal overlap were positively associated with BSD (z = 2.812, p = 0.005) but negatively associated with taxonomic differences (z = −2.335, p = 0.020) and the extent of trophic competitions (z = −3.065, p = 0.002; Table 4). All three covariates were included in the selected models with delta AICc < 2 of the best model (Table S4 in the Supplementary Materials). The results indicated that temporal overlap increased with increasing the BSD in mesocarnivore pairs but decreased when mesocarnivores were in different families and of intermediate trophic overlap (i.e., pairs of golden jackals and fox/wildcat/martens; Figure 5).

4. Discussion

4.1. Ecological Trait Effects on Spatial and Temporal Overlaps (H1)

We demonstrated that ecological traits affected the spatial and temporal overlaps among sympatric mesocarnivores, whereas the effects of the covariates were different between the two indices. Spatial overlap among mesocarnivores decreased when they were in the same family (i.e., within canids or mustelids in our focal species), whereas the temporal overlap increased (Figure 4 and Figure 5). These results were partly consistent with our hypothesis (H1), indicating that mesocarnivores in the same family partitioned their spatial niches but overlapped the temporal niches. Among focal mesocarnivores, golden jackals substantially suppress red foxes [61,62], thereby facilitating their spatial partitioning to avoid antagonistic encounters [20,31,39,63,64]. Despite little knowledge on interferences between European badgers and martens [12,13], previous studies have reported their spatial avoidance [19,65,66]. Conversely, the temporal overlap among mesocarnivores increased in the same family pairs (Figure 5 and Table 4), which is inconsistent with our hypothesis regarding the avoidance of interspecific interferences (H1). Circadian rhythms or diel activity patterns in mammals are largely relevant to anatomical and morphological adaptation in visual organs and eye shapes through evolution [67,68]. In general, diel activities in carnivorous mammals are shaped by the prey activities (to maximize hunting success) [23,68]. Among focal species, both golden jackals and red foxes predate rodents as primary foods [26,69], potentially synchronizing their activities with their common prey. We previously found highly dietary overlaps between jackals and foxes in our study area (Pianka’s index of dietary overlap: 0.35–0.99) [34,69]. Moreover, European badgers and martens are nocturnal [20,23,39,40,65] with an eye morphology that can adapt to darkness [70].
Temporal overlap among mesocarnivores increased with increasing BSDs in mesocarnivore pairs (Figure 5), whereas little effect on spatial overlap was observed (Table 3). This result indicated that mesocarnivore pairs with large BSD overlapped their temporal niches. In this study, the smallest BSDs were observed in jackal–badger pairs (arcsine square root BSD = 7.8; Table S3 in the Supplementary Materials). They typically partition their temporal niches because jackals are crepuscular, whereas badgers are predominantly nocturnal species [20,23,39]. The mean value of Dhat between jackals and badgers was the lowest among the mesocarnivore pairs observed in this study (Figure 3). For other species, mesocarnivores other than jackals are typically nocturnal [20], and they showed relatively high temporal overlaps (Figure 3). This result was contrary to our hypothesis (H1), that is, temporal overlap declined with increasing BSD to mitigate interspecific interferences [22]. The likelihood of interspecific killing increases with increasing BSDs in carnivore pairs, indicating that the extent of direct interference is intensified in pairs with large BSD [12]. However, their interspecific killing was associated with the interaction between body size and taxon (i.e., intra-family) as well as trophic niches (i.e., predatory species). In particular, large predaceous species (e.g., large canids and felids) killed others more frequently than omnivores [12,13,14]. The focal mesocarnivores, except for the European wildcat, are typically omnivorous [51,53,54,55], whereas wildcats are facultative predators [52] but the second smallest species in our mesocarnivore guilds (Figure 1). Therefore, BSD might have little effect on their temporal partitioning in our studied guilds.
Temporal overlap decreased in carnivore pairs with intermediate trophic overlap, that is, pairs between golden jackals and smaller carnivores other than European badgers (Table 4 and Figure 5). Golden jackals exhibit flexible dietary habits by consuming various foods, for example, small mammals (rodents), fruits, and carrion [54,71], competing for food resources with smaller carnivores [69,72]. Furthermore, observational evidence shows direct killing and potential harassment by jackals on smaller species [61,62,73,74]. Therefore, direct (i.e., interference) and indirect (i.e., resource) competitions with larger, dominant jackals enforced temporal partitions by small, subordinate carnivores to decline antagonistic encounters through various habitats in Bulgaria.

4.2. Effects of Anthropogenic Disturbances on Spatial and Temporal Overlaps (H2)

We found no effects of anthropogenic disturbances on temporal overlap in mesocarnivore guilds, although this finding was inconsistent with our hypothesis (H2) as well as numerous previous reports on other carnivore guilds [30,75,76]. However, human disturbances altered the diel activities of large carnivores (e.g., wolves) and increased their nocturnality, whereas the changes in mesocarnivores were unclear, and nocturnal species (e.g., red fox) did not change their diel activities [77]. Our focal mesocarnivores (other than golden jackals) are also typically nocturnal across various landscape types [40]. Therefore, the low responses to anthropogenic disturbances might be attributed to the relatively high temporal overlaps among them across our studied sites.
Anthropogenic disturbances also showed no significant effects on spatial overlap, which was inconsistent with our hypothesis (H2). Theoretically, anthropogenic disturbances enforce co-occurrences of carnivores in small, fragmented habitats (e.g., urban green space) that function as refugia from human activities, thereby facilitating their spatial overlaps [3]. However, the human-modified landscapes in our studied sites were suburbs and rural landscapes (Figure 2). Our focal species are typically tolerant of moderate human activities, for example, agricultural landscape modifications [40,78,79], which have little effect on their spatial (and may also be temporal) overlap.

4.3. Effects of Season on Spatial and Temporal Overlaps (H3)

We demonstrated that the cold season has no effect on either spatial or temporal overlap in mesocarnivore guilds, which is inconsistent with our predictions in the third hypothesis (H3). The extent to which mesocarnivores overlap their spatial or temporal niches changes seasonally, but such changes are context-dependent in species pairs [20]. For example, European badgers are typically less active, or they hibernate during the cold season [80], strictly moving around their setts [81]. This phenomenon resulted in the spatial partitioning of badgers from sympatric carnivores that were active during the season [20]. In our studied areas, golden jackals primarily scavenge ungulate carcasses in winter and separate the trophic niches from small rodentivores (i.e., foxes, wildcats, and martens) [34]. Consequently, the potential risks of antagonistic encounters relaxed with the subordinates and facilitated their spatial overlap [1,20]. The three small carnivores (fox, wildcat, and martens) are competitive for small mammals as shared prey [52,53,55], facilitating spatial or temporal partitioning in the cold season [20]. Consequently, there might be no unified patterns in spatial and temporal overlaps in mesocarnivore guilds relevant to seasonal changes.

4.4. Effects of the Presence of Large Carnivores on Spatial and Temporal Overlaps (H4)

Consistent with our prediction in the fourth hypothesis (H4), spatial overlap decreased when large carnivores were absent (Figure 4). The effect of the presence of large carnivores was substantially significant when wolves were present, but little effect was observed when brown bears were only present (Figure 4). Wolves are the predominant competitor of golden jackals, constraining their spatial distribution [82,83,84,85]; this, in turn, mitigated suppressive effects of jackals on small carnivores (i.e., foxes, wildcats, and martens) and maybe enabled their spatial overlap, namely, “behavioral release” [77]. Some previous reports have indicated that wolves sometimes constrain red foxes [13,86], potentially having similar positive effects on small species such as martens [64,87]. Our findings indicated that constraining the spatial distribution of jackals (and also foxes) by wolves could trigger positive cascading effects toward small carnivores [27,64,88]. Another possible explanation is that wolves provided the carrion of their prey killed (typically wild ungulates) to mesocarnivores as food resources. Mesocarnivores, such as jackals, foxes, and martens (but not common in badgers and wildcats), are typical scavengers that are attracted to wolf kill sites [14,86,89]. It might facilitate their spatial overlap for consuming the shared resources (i.e., carrion) at wolf kill sites.

4.5. Methodological Limitations

Our datasets were composed of several research projects that used different camera numbers in various landscapes (Table 1 and Figure 1), and our survey protocol was consistent throughout the overall projects [43]. The placement and number of cameras installed are critical issues in the study design of camera trapping [43]. We adopted “deliberately biased placement” of cameras in which cameras were installed at focal points (i.e., footpaths and animal trails) to maximize animal detections [43], but spatial autocorrelation may occur [90]. Prior to the main statistical analyses using GLMM, we statistically assessed and confirmed the absence of significant spatial autocorrelation in our dataset using Mantel correlation test [91] (Supplementary S1 in the Supplementary Materials).
Given the methodological limitations in camera trapping, we treated both pine and stone martens as a single genus, Martes, in this study (see Section 2). In sympatry, pine and stone martens partition their spatial niches to reduce their competition, whereas they are commonly nocturnal [92,93]. However, behavioral responses to competitors at the fine-spatial scale are not different between the two species, and they commonly avoid the space and/or time that the competitor was active to reduce antagonistic encounters [20,28,36,94].

5. Conclusions

We demonstrated that ecological traits (i.e., differences in taxonomy, body sizes, and trophic niches) relevant to intraguild interactions (e.g., direct interference and resource competition) among mesocarnivores shaped patterns in their spatiotemporal niche overlap in central Bulgaria. Our findings also indicated that the cascading effects from large carnivores, particularly wolves, mediated the interactions in mesocarnivore guilds through behavioral alterations of large, predominant species such as golden jackals, thereby facilitating the coexistence of multi-species [14,64,88]. Therefore, our findings suggest that the presence of large carnivores is a key role in structuring and maintaining the richness of the carnivore community, resulting in redundant ecosystem services by their ecological functions [3,9,11]. Further studies, including empirical data on large carnivores (i.e., wolves and bears) and small species (such as the genus Mustela), are necessary to comprehensively understand the interspecific interaction and community structure of regional carnivore guilds.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/d17020108/s1, Supplementary S1: Mantel correlogram for the assessment of spatial autocorrelation in camera trap datasets; Figure S1: Schematic diagram of interspecific interactions between medium and large carnivores in Bulgaria; Table S1: Overview on environments of studied sites and focal carnivores detected by camera trapping; Table S2: Mean and standard deviation of the straight distance between two cameras in each studied site; Table S3: Relative body size differences (BSD, arcsine root squared transformed value) among mesocarnivores; Table S4: Results of model selection.

Author Contributions

Conceptualization, H.T.; methodology, H.T.; software, H.T.; validation, H.T.; formal analysis, H.T.; investigation, H.T., S.P., E.R. and Y.K.; resources, H.T., S.P. and E.R.; data curation, H.T., S.P. and E.R.; writing—original draft preparation, H.T.; writing—review and editing, H.T., S.P., E.R. and Y.K.; visualization, H.T.; supervision, H.T., S.P., E.R. and Y.K.; project administration, H.T., S.P., E.R. and Y.K.; funding acquisition, H.T., S.P., E.R. and Y.K. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Japan Society for Promotion of Science (JSPS-KAKENHI, grant number JP26257404).

Institutional Review Board Statement

Not applicable.

Data Availability Statement

The datasets that support the findings of this study are available from the corresponding author on reasonable request.

Acknowledgments

We are grateful to Dian Georgiev, Thomas Kronawetter, Krasimir B. Kirilov, Kairi Ito, Kurumi Noda, and Katelina Uzunowa for their cooperative support during the field surveys. We are grateful to anonymous reviewers for their insightful comments that improved the quality of the manuscript. We would like to thank Enago (www.enago.jp (accessed on 28 January 2025)) for the English language review.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Schematic diagram of assessing hypotheses (H1–4) in the present study.
Figure 1. Schematic diagram of assessing hypotheses (H1–4) in the present study.
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Figure 2. Map of the study area, central Bulgaria (represented by a red square; (a)); land cover and locations of eight camera trapping sites (b); and locations of camera traps (c–f).
Figure 2. Map of the study area, central Bulgaria (represented by a red square; (a)); land cover and locations of eight camera trapping sites (b); and locations of camera traps (c–f).
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Figure 3. Observed values of (a) Pianka’s index (for spatial overlap) and (b) Dhat (for temporal overlap) in mesocarnivore pairs. Sample sizes of carnivore pairs are given within parentheses. Colored dots represent row data in each pair.
Figure 3. Observed values of (a) Pianka’s index (for spatial overlap) and (b) Dhat (for temporal overlap) in mesocarnivore pairs. Sample sizes of carnivore pairs are given within parentheses. Colored dots represent row data in each pair.
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Figure 4. Relationships between Pianka’s index for spatial overlap in mesocarnivore pairs and (a) their family or (b) the occurrence of large carnivores (horizontal line, median; colored bar, quartile range; vertical lines, 1.5-fold ranges of quartile; dots, row data of Pianka’s index in each group).
Figure 4. Relationships between Pianka’s index for spatial overlap in mesocarnivore pairs and (a) their family or (b) the occurrence of large carnivores (horizontal line, median; colored bar, quartile range; vertical lines, 1.5-fold ranges of quartile; dots, row data of Pianka’s index in each group).
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Figure 5. Relationship between Dhat for temporal overlaps of mesocarnivore pairs and (a) their body size differences (shaded area: 95% confidence intervals), (b) family, and (c) extents of trophic competition (horizontal line, median; colored bar, quartile range; vertical lines, 1.5-fold ranges of quartile; dots, row data of Dhat in each group).
Figure 5. Relationship between Dhat for temporal overlaps of mesocarnivore pairs and (a) their body size differences (shaded area: 95% confidence intervals), (b) family, and (c) extents of trophic competition (horizontal line, median; colored bar, quartile range; vertical lines, 1.5-fold ranges of quartile; dots, row data of Dhat in each group).
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Table 1. Camera trapping protocol in eight subregions in central Bulgaria.
Table 1. Camera trapping protocol in eight subregions in central Bulgaria.
Study SiteSite IDElevational RangeStudied Duration
(Days)		Total Trapping
Efforts (Days)		Number of Camera Traps Installed
Stara Planina Mts.,
northern slope		NSP1752–1471 mMay 2018–October 2019
(492)		16624
NSP2554–1203 mOctober 2018–November 2019
(398)		19966
NSP3584–964 mSeptember 2018–November 2019
(413)		15205
Stara Planina Mts.,
southern slope		SSP1581–1250 mJuly 2016–October 2017
(454)		661215
SSP2393–464 mOctober 2021–October 2022
(338)		14245
Upper Thracian PlainUTP1164–361 mJune–July 2015 (43),
March–August 2016 (175)		43117
UTP2158–281 mNovember 2021–October 2022
(327)		18358
Eastern Rhodope Mts.,
Alda River valley		ERM171–499 mJune 2019–March 2020
(254)		170711
Table 2. Number of seasonal occurrence (warm/cold) of mesocarnivores by camera trapping survey with large carnivores observed by camera-trapping in each site (B, brown bear; W, wolf; N, non). Shaded numbers represent data that treated as annual data in analysis due to ineffective seasonal sample size (i.e., <20) according to our protocol. Red numbers represent data excluding from analysis due to small sample size. In UTP1, field survey was conducted in warm season only.
Table 2. Number of seasonal occurrence (warm/cold) of mesocarnivores by camera trapping survey with large carnivores observed by camera-trapping in each site (B, brown bear; W, wolf; N, non). Shaded numbers represent data that treated as annual data in analysis due to ineffective seasonal sample size (i.e., <20) according to our protocol. Red numbers represent data excluding from analysis due to small sample size. In UTP1, field survey was conducted in warm season only.
Site ID.Jackal Badger Fox Wildcat Marten
SeasonWarmColdWarmColdWarmColdWarmColdWarmCold
NSP1
(B,W)		1916420121016831
NSP2
(B,W)		84121346987551944319
NSP3
(N)		893021819623221112916
SSP1
(B)		2541911923714454905419862
SSP2
(B,W)		740.05011625461772639
UTP1
(N)		87-0.036-38-0-29-
UTP2
(N)		1987570.22146649136022670
ERM
(N)		39400.38065705931243154
Table 3. Results of GLMM analysis for covariates associated with the Pianka’s index for spatial overlap in mesocarnivore guilds.
Table 3. Results of GLMM analysis for covariates associated with the Pianka’s index for spatial overlap in mesocarnivore guilds.
Hypo.CovariateCoefficientStandard Errorzp
–(Intercept)−0.7910.234−3.383<0.001
H1Taxon (different family)0.3050.1472.0800.038
Body size differences−0.1780.184−0.9680.333
Trophic overlap (intermediate)−0.1740.139−1.2550.210
Trophic overlap (high)0.0500.1420.3500.727
H2Human footprint index0.0360.0820.4390.661
H3Cold season−0.1350.125−1.0750.282
H4Large carnivore (Bear)0.2210.2201.0080.314
Large carnivore (Bear + Wolf)0.3800.1742.1880.029
Table 4. Results of GLMM analysis for covariates associated with the temporal overlap index (Dhat) in mesocarnivore guilds.
Table 4. Results of GLMM analysis for covariates associated with the temporal overlap index (Dhat) in mesocarnivore guilds.
Hypo.CovariateCoefficientStandard Errorzp
–(Intercept)−0.3910.057−6.850<0.001
H1Taxon (different family)−0.0880.038−2.3350.020
Body size differences0.1320.0472.8120.005
Trophic overlap (intermediate)−0.1090.036−3.0650.002
Trophic overlap (high)0.0440.0371.1980.231
H2Human footprint index0.0110.0150.7760.438
H3Cold season0.0280.0310.9060.365
H4Large carnivore (Bear)−0.0210.037−0.5560.578
Large carnivore (Bear + Wolf)−0.00030.035−0.0110.992
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Tsunoda, H.; Peeva, S.; Raichev, E.; Kaneko, Y. Ecological Traits and Intraguild Competition Mediate Spatial and Temporal Overlaps Among Sympatric Mesocarnivores. Diversity 2025, 17, 108. https://doi.org/10.3390/d17020108

AMA Style

Tsunoda H, Peeva S, Raichev E, Kaneko Y. Ecological Traits and Intraguild Competition Mediate Spatial and Temporal Overlaps Among Sympatric Mesocarnivores. Diversity. 2025; 17(2):108. https://doi.org/10.3390/d17020108

Chicago/Turabian Style

Tsunoda, Hiroshi, Stanislava Peeva, Evgeniy Raichev, and Yayoi Kaneko. 2025. "Ecological Traits and Intraguild Competition Mediate Spatial and Temporal Overlaps Among Sympatric Mesocarnivores" Diversity 17, no. 2: 108. https://doi.org/10.3390/d17020108

APA Style

Tsunoda, H., Peeva, S., Raichev, E., & Kaneko, Y. (2025). Ecological Traits and Intraguild Competition Mediate Spatial and Temporal Overlaps Among Sympatric Mesocarnivores. Diversity, 17(2), 108. https://doi.org/10.3390/d17020108

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