Zeyu Yang, Lixia Chen, Ru Jia, Hongying Xu, Yihua Wang, Xuelei Wei, Dongping Liu, Huajin Liu, Yulin Liu, Peiyu Yang, Guogang Zhang. 2023: Migration routes of the endangered Oriental Stork (Ciconia boyciana) from Xingkai Lake, China, and their repeatability as revealed by GPS tracking. Avian Research, 14(1): 100090. DOI: 10.1016/j.avrs.2023.100090
Citation: Zeyu Yang, Lixia Chen, Ru Jia, Hongying Xu, Yihua Wang, Xuelei Wei, Dongping Liu, Huajin Liu, Yulin Liu, Peiyu Yang, Guogang Zhang. 2023: Migration routes of the endangered Oriental Stork (Ciconia boyciana) from Xingkai Lake, China, and their repeatability as revealed by GPS tracking. Avian Research, 14(1): 100090. DOI: 10.1016/j.avrs.2023.100090

Migration routes of the endangered Oriental Stork (Ciconia boyciana) from Xingkai Lake, China, and their repeatability as revealed by GPS tracking

Funds: This work was supported by National Key Research and Development Program of China (No. 2019YFA0607103) and the National Forestry and Grassland Administration of China (91217-2022, 202999922001, 213023721203)
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  • Corresponding author:

    E-mail address: zm7672@126.com (G. Zhang)

  • 1 These authors contributed equally to the work.

  • Received Date: 22 Aug 2022
  • Rev Recd Date: 18 Dec 2022
  • Accepted Date: 13 Feb 2023
  • Available Online: 14 Jun 2023
  • Publish Date: 24 Feb 2023
  • The Oriental Stork (Ciconia boyciana) is listed as ‘Endangered’ on the International Union for the Conservation of Nature (IUCN) Red List of Threatened Species and is classified as a first category nationally protected bird species in China. Understanding this species' seasonal movements and migration will facilitate effective conservation to promote its population. We tagged 27 Oriental Stork nestlings at Xingkai Lake on the Sanjiang Plain in Heilongjiang Province, China, used GPS tracking to follow them over the periods of 2014–2017 and 2019–2022, and confirmed their detailed migratory routes using the spatial analysis function of ArcGIS 10.7. We discovered four migration routes during autumn migration: one common long-distance migration route in which the storks migrated along the coastline of Bohai Bay to the middle and lower reaches of the Yangtze River for wintering, one short-distance migration route in which the storks wintered in Bohai Bay and two other migration routes in which the storks crossed the Bohai Strait around the Yellow River and wintered in South Korea. There were no significant differences in the number of migration days, residence days, migration distances, number of stopovers and average number of days spent at stopover sites between the autumn and spring migrations (P > 0.05). However, the storks migrated significantly faster in spring than in autumn (P ​= ​0.03). The same individuals did not exhibit a high degree of repetition in their migration timing and route selection in either autumn or spring migration. Even storks from the same nest exhibited considerable between-individual variation in their migration routes. Some important stopover sites were identified, especially in the Bohai Rim Region and on the Songnen Plain, and we further explored the current conservation status at these two important sites. Overall, our results contribute to the understanding of the annual migration, dispersal and protection status of the endangered Oriental Stork and provide a scientific basis for conservation decisions and the development of action plans for this species.

  • In the 21st century, the earth is undergoing the sixth mass extinction, particularly for terrestrial vertebrates (Ceballos et al., 2015; Tilman et al., 2017). Nevertheless, our ability to forecast the effects of these losses and to create practical mitigation plans is constrained by taxonomic and geographic information (Gardner et al., 2014; Miller et al., 2014). Thus, it is essential to record biodiversity as thoroughly as possible (Hromada et al., 2015). Fortunately, specimen data can provide both temporal and spatial information of species occurrence, and they are reliable data that can document past states of biodiversity (Hromada et al., 2015; Page et al., 2015; Schmitt et al., 2019; He et al., 2021). To solve the biodiversity crisis, advanced ecological and population analyses are needed, and more than 60% of the data for these analyses are provided by specimen collections archived in museums (Kress, 2014; Hromada et al., 2015). For instance, a study in southern Finland examined the association between feather color and climate change using skin specimens from wild brown owls and field survey information, and revealed that recent climate changes have impacted natural selection in wild populations (Karell et al., 2011). IUCN Red List criteria requires specific and detailed information about life history and biology, especially for widely distributed species (Hromada et al., 2015). Therefore, without voucher specimens, the extinction risk of many species cannot be properly assessed (Rocha et al., 2014). In summary, specimens and their related data are crucial for biodiversity research and conservation (Hromada et al., 2015; Zizka et al., 2021). They are also very instrumental and indispensable in fields such as taxonomy, biogeography, molecular biology, ecomorphology and others (Peterson et al., 1998).

    However, specimen collections themselves are also at risk (Hromada et al., 2015). Thousands of unidentified and unclassified specimens in museums around the world, sometimes unrecognized for a century or longer, are forgotten, misidentified and unlabeled (Winker et al., 1991; Hromada et al., 2015). Several studies have demonstrated that the value of museum collections is declining and deteriorating (Kress, 2014; Minteer et al., 2014; Rocha et al., 2014; Kemp, 2015). In addition, the rate of animal specimens being collected by natural museums has slowed down significantly in recent times (Prather et al., 2004; Kemp, 2015). Aside from financial and personnel constraints, some individuals and institutions think that removing biological specimens from natural populations may negatively influence community structure, population density, or the survivability of rare species, endangering the integrity of natural populations (Patterson, 2002; McLean et al., 2016; Hope et al., 2018). However, studies have also shown that scientific collection has a negligible contribution to mortality compared with other natural or human-caused impacts (Hope et al., 2018). A survey in Bolivia discovered that the influence of bird specimen collection on local bird populations was insignificant (Remsen, 1995). In particular, continued specimen collection is still crucial in identifying species-level categorization in birds; and conservation priorities could not be determined accurately until species limits are established correctly (Barrowclough et al., 2016; Keck, 2020).

    With the development of science and technology, observation data appear in large numbers (Johnston et al., 2020), but studies have shown that observation data can not completely replace the specimen data (Krell and Wheeler, 2014; Daru and Rodriguez, 2023). Although alternative forms of documentation, like photographs, are useful in recording distribution, they may be insufficient for other ecological research aspects (Peterson et al., 1998). Besides, most of the collectors of observational data are from different unprofessional teams (Johnston et al., 2020; Daru and Rodriguez, 2023). They may collect data in areas with strong human influence, so the human bias of data collection is large (Daru and Rodriguez, 2023). The specimen record is of great importance because it allows repeated examination for scientific verification and further study (Peterson et al., 1998). The primary goal of an animal collection is to preserve the species record, serving as a vital resource for both current and future research (Peterson et al., 1998). Each specimen is unique and irreplaceable (Winker et al., 1991). Consequently, it is essential to continuously and scientifically collect biological specimens for biodiversity investigation and research.

    The morphological, physiological, and behavioral characteristics that a species develops to adapt to its environment are known as species traits (Killen et al., 2013; Wilman et al., 2014; Isaksson, 2018). These characteristics/traits are closely related to species distribution, range change, and extinction risk (Croci et al., 2008; Maclean and Beissinger, 2017). For example, the extinction probability for birds might increase with larger body size and sensitive hunting vulnerability (Price and Gittleman, 2007; Wang et al., 2018). In addition, birds with wide geographic ranges and high trophic levels are more prone to shift their ranges (Davey et al., 2013; Yang et al., 2020). Collectors have tended to focus on more easily detectable groups of species in the field as well as larger, charismatic groups of organisms (Graham et al., 2004; Newbold et al., 2009). Therefore, the species' traits could also affect specimen number. A study in West Africa found that the long-term, patrol-based wildlife monitoring system's observations are biased, and larger, diurnal and gregarious species (e.g., larger ungulates and primates) are more detectable (Burton, 2012). Unfortunately, few studies have linked species traits to the animal specimen number at regional scales. Environmental factors (e.g., climate and elevation, etc.) can also affect the collection of species specimens (Yang et al., 2014; Monsarrat et al., 2019; Vargas et al., 2022). These factors can influence the distribution, abundance, and behavior of organisms, ultimately affecting the availability and accessibility of specimens for collection (McGill et al., 2006; Vargas et al., 2022). Furthermore, certain studies have observed a phenomenon that specimen collectors and researchers tend to focus their collection efforts on high-altitude areas characterized by greater environmental heterogeneity and richer biodiversity (Parnell et al., 2003; Sánchez-Fernández et al., 2008; Engemann et al., 2015; de Araujo and Ramos, 2021). However, this will cause bias in specimen collection. Studies have indicated that species with narrow environmental niches, including endemic species that are limited to specific geographic areas, can be particularly vulnerable to collection bias (Inman et al., 2021; Baker et al., 2022).

    As one of the world's richest countries in biodiversity resources, China has currently recorded 1505 avian species (Zheng, 2023) and abundant biological specimens (He et al., 2021). According to statistics, the National Specimen Information Infrastructure (NSII, http://www.nsii.org.cn/) alone holds more than 20 million animal specimens until 2022. However, few studies have been conducted on the factors that influence bird specimen number in China. Therefore, this study offers the first quantitative analysis of the link between species traits, climate niche breadth, and specimen number in Chinese birds to enhance the use of bird specimen data to study and preserve biodiversity. The following three questions are our focus: 1) which order and family have the most specimens? 2) what is the link between bird specimen number and species' temperature niche breadth and precipitation niche breadth? 3) which species trait is most associated with bird specimen number in China?

    Bird specimen data, comprising 22, 185 specimens, was collected from the NSII (http://www.nsii.org.cn/2017/home.php). The data cover 21 orders, 82 families, and 941 species. Mean annual temperature and mean annual precipitation were obtained from the WorldClim database (https://worldclim.org/). The temperature/precipitation niche breadth of each bird species was calculated as the difference between the maximum and minimum mean annual temperature/precipitation in its distribution area.

    Parameters of eleven bird traits were compiled from a recent publication (Wang et al., 2018). These traits include geographic range size (km2), habitat specificity (the number of habitats a species is recorded in), hunting vulnerability (rarely/never hunted or killed, occasionally hunted or killed, and often hunted or killed), body size (mm), clutch size (median number of eggs per nest), nest site (cavity, tree, shrub, water and ground), trophic level (omnivores, granivores, frugivores, nectarivores, insectivores, piscivores and carnivores), migrant status (resident, partial resident and full migrant), nest type (exposed or not), flocking tendency (strictly solitary, occasionally social and strictly social), and dispersal ratio (mean wing length divided by the cube root of mean mass).

    To conduct the phylogenetic generalized linear mixed models (PGLMM), a phylogeny for the 941 bird species was pruned from a mega bird phylogeny, which was based on a global species pool and composed of 10,000 trees with 9993 Operational Taxonomic Units (Jetz et al., 2014). The Maximum Clade Credibility tree was built using mean node height, and this process involved sampling 5000 pseudo-posterior distributions and utilizing Tree Annotator in the BEAST package (version 1.8.2) (Drummond and Rambaut, 2007).

    The correlations between specimen number and climate niche breadth and traits were examined using the ordinary least squares (OLS) models. The independent variable (bird specimen number) was logarithmically transformed to achieve normally distributed residuals.

    Since the dependent variable comprised discrete data and the response variable was counting data, the generalized linear model's (GLM) negative binomial model was utilized to test the links between bird traits and specimen number. Phylogenetic generalized linear mixed models (PGLMM) were also used to correct phylogenetic effects, whereby closely related species tend to share similar traits. In addition, we standardized (mean = 0 and SD = 1) species trait data and climate data to eliminate the effect of the different dimensions of the explanatory variable on the regression coefficient.

    Multiple variable analyses were also conducted to test the relative importance of different combinations of species traits. We only used the significant variables based on the previous PGLMM single variable analyses, i.e., geographic range size, habitat specificity, hunting vulnerability, body size and clutch size (Table 1).

    Table  1.  Results of single variable phylogenetic generalized linear mixed models (PGLMM) and generalized linear models (GLM). The standardized coefficient (Coef) of PGLMM and GLM and Akaike's information criterion (AIC) of PGLMM and GLM were listed. **p < 0.01; *p < 0.05.
    Model description Coef-GLM AIC-GLM Coef-PGLMM AIC-PGLMM
    Geographic range size 0.56** 7590 0.26** 1140
    Habitat specificity 0.25** 7779 0.11** 1327
    Hunting vulnerability 0.21** 7802 0.05* 1360
    Body size −0.20** 7804 −0.09** 1349
    Clutch size 0.16** 7809 0.08** 1350
    Nest site −0.13** 7813 −0.03 1364
    Migrant status −0.11* 7817 −0.01 1366
    Trophic level −0.03 7824 −0.02 1365
    Nest type −0.02 7824 −0.01 1366
    Flocking tendency 0.02 7824 −0.02 1365
    Dispersal ratio 0.01 7825 −0.01 1366
     | Show Table
    DownLoad: CSV

    Some trait data are not continuous values but discrete grade variables. For instance, the trophic level of birds was represented by numbers, that is, 1 stand for omnivores, 2–granivores, 3–frugivore, 4–nectarivores, 5–insectivores, 6–piscivores and 7–carnivores. Therefore, this study further examined the effects of nest type, nest site, flocking tendency, hunting vulnerability, trophic level and migration status by one-way Analysis of Variance (one-way ANOVA), as OLS and GLM fitting results did not clearly show the relationships among species traits groups.

    Standardized coefficients and Akaike's information criterion (AIC) of each PGLMM and GLM model were listed to assess their relative importance. PGLMM was conducted using the function 'pglmm' in the 'phyr' R package, and GLM was conducted using the function 'glm. nb' in the 'MASS' R package. All analyses were conducted in R 4.2.1.

    Among the collected bird specimens, Passeriformes accounted for the most specimens, with 12, 904 specimens (63% of all bird specimens). This was followed by Charadriiforme, Falconiformes, Piciformes, Anseriformes, Columbiformes, Galliformes and Gruiformes (Fig. 1), which accounted for more than 17% of the total bird specimen. The family with the most specimens was the Muscicapidae, with 4991 specimens (23% of all bird specimens). This was followed by Fringillidae, Motacillidae, Scoiopacidae, Paridae, Corvidae, Anatidae and Picidae (Fig. 1), which account for about 27% of the total specimen number.

    Figure  1.  Specimen number from birds of different orders and families. Only the eight families and orders with the highest values were shown.

    The results of the Ordinary Least Squares (OLS) analysis showed significant positive correlations between bird specimen number and both precipitation niche breadth and temperature niche breadth (Fig. 2). The traits that exhibited the strongest correlations with the bird specimen number were geographic range size (R2 = 0.25**) and body size (R2 = 0.02**) in OLS analysis (Fig. 2). Notably, climate niche breadth had greater influence than species traits, with precipitation niche breadth (R2 = 0.34**) showing a stronger correlation with specimen number compared to temperature niche breadth (R2 = 0.28**, Fig. 2).

    Figure  2.  Relationships between specimen number and the climatic niche breadth and species traits (only the two traits with the highest correlation were shown). **p < 0.01; *p < 0.05.

    Single variable phylogenetic generalized linear mixed models for discrete data (PGLMM) and generalized linear models (GLM) showed similar patterns regarding the associations between bird species traits and specimen number, i.e., the five traits with the lowest Akaike's Information Criterion (AIC) and highest standardized coefficients were geographic range size, habitat specificity, hunting vulnerability, body size and clutch size (Table 1). However, nest sites and migrant status were only significant in GLM (Table 1). Notably, specimen number showed a positive association with geographic range size, habitat specificity, hunting vulnerability and clutch size, but a negative association with body size.

    Multiple variable GLM models revealed that the combination of species traits most strongly associated with specimen number included geographic range size, habitat specificity, hunting vulnerability and body size (Table 2). Notably, the AIC (7456–7554) of GLM for the best seven combinations of variables tended to be lower than the AIC (7590) of the best single variable, i.e., geographic range size (Table 1 and 2).

    Table  2.  Results of multiple variables generalized linear models (GLM). The standardized coefficient and Akaike's information criterion (AIC) of GLM were listed. **p < 0.01; *p < 0.05.
    GLM
    Model description AIC
    Geographic range size (0.58**) + Habitat specificity (0.12**) + Hunting vulnerability (0.35**) + Body size (−0.39**) 7456
    Geographic range size (0.58**) + Habitat specificity (0.13**) + Hunting vulnerability (0.36**) + Body size (−0.38**) + Clutch size (−0.04) 7457
    Geographic range size (0.61**) + Hunting vulnerability (0.35**) + Body size (−0.40**) 7468
    Geographic range size (0.55**) + Habitat specificity (0.12**) + Body size (−0.26**) 7537
    Geographic range size (0.55**) + Habitat specificity (0.12**) + Clutch size (0.02) + Body size (−0.26**) 7540
    Geographic range size (0.58**) + Body size (−0.26**) 7548
    Geographic range size (0.58**) + Hunting vulnerability (0.23**) 7554
    Hunting vulnerability (0.29**) + Body size (−0.31**) 7764
     | Show Table
    DownLoad: CSV

    One-way variance analysis showed significant differences in the mean values of nest site, flocking tendency, hunting vulnerability and trophic level. Multiple comparisons revealed significant differences between hunting vulnerability and trophic level groups, while no significant variation was observed among flocking tendency groups. Notably, the specimen number of insectivores and omnivores was significantly higher compared to frugivores (Fig. 3) Specimen number of occasionally hunted was higher than often hunted and rarely hunted (Fig. 3).

    Figure  3.  Results of one-way analysis of variance. Only multiple comparison results for trophic level and hunting vulnerability were shown.

    This study linked bird specimen number with climatic niche breadth and species traits in China. The results showed that bird specimen number was positively associated with species' temperature niche breadth and precipitation niche breadth. For species traits, bird specimen number was positively associated with geographic range size, habitat specificity, hunting vulnerability and clutch size, but negatively associated with body size.

    Previous studies indicated that the total number of bird specimens in the world systematic collections surpassesed 9 × 106 (Remsen, 1995; Peterson et al., 1998). If this number was evenly among species, approximately 1000 specimens would exist for each of the 9600 bird species in the world (Peterson et al., 1998). However, despite the record of 1505 bird species in China, the NSII holds only 22, 185 bird specimen records. The bird specimen number recorded in NSII is significantly below the average level, insufficient to support ecological research on geographic variation, species limits, and numerous other life history details (Remsen, 1995). Our analysis indicates that Passeriformes specimens constitute 63% of the total specimen. Passerines are relatively common and well-collected species, indicating that specimens for non-Passeriformes species are considerably less.

    Niche breadth typically refers to the range of environmental conditions in which a species can survive and reproduce (Wellborn and Langerhans, 2015), and is generally positively correlated with the distribution range of species (Köckemann et al., 2009; Boucher-Lalonde and Currie, 2016). A study about 3277 bird species and 1659 mammal species in the Americas found a strong positive correlation between species' distribution ranges and ecological niche breadth (Boucher-Lalonde and Currie, 2016). Therefore, larger temperature and precipitation niches can indicate that species may have broader ecological niches, allowing them to occupy a wider range of habitats and exhibit a more extensive geographic distribution (Brown, 1984; Boucher-Lalonde and Currie, 2016). Species that occur in a broader range of climatic conditions may hold greater tolerance to climatic variability, and could therefore face less risk of extinction (Maclean and Beissinger, 2017; Hällfors et al., 2023). In this study, we tried to link species' temperature niche breadth and precipitation niche breadth to specimen number and found that species' temperature niche breadth and precipitation niche breadth were positively correlated with bird specimen number. For example, Alcedo atthis has larger temperature niche breadth (range from −4.26 ℃ to 23.37 ℃) and precipitation niche breadth (range from 41.61 mm to 2064.64 mm), as well as most specimen number (302 specimens). The temperature and precipitation niche breadth of Melanocorypha calandra and Lagopus muta were smaller (4.41–22.64 ℃ and 211.16–229.10 mm, 4.41–29.14 ℃ and 179.04–211.16 mm, respectively), and these two species only had one specimen. In line with these findings, many studies have shown that species with narrow environmental niches (e.g., endemic species) were more vulnerable to collection bias (Inman et al., 2021; Baker et al., 2022).

    Geographic range size is used as a criterion by the IUCN Red List of Threatened Species in estimating species extinction risk (Gupta et al., 2020). Previous studies have indicated that species with narrow geographic ranges are more prone to extinction due to limited population sizes resulting from local ecological constraints (Cohen and Johnston, 1987; Maurer, 1999). In addition, research has demonstrated that species with large geographic ranges generally exhibit higher local abundances (Brown, 1984; Köckemann et al., 2009; Dallas et al., 2017). Positive associations between species abundance and range size have been observed in many groups of organisms (Gaston, 1996; Kotze et al., 2003; Boucher-Lalonde and Currie, 2016). Birds with a wide geographic range often possess greater tolerance to their environment because of their ecological, behavioral, or physiological flexibility (Bonier et al., 2007). Consequently, birds with wide geographic ranges are more likely to be captured as specimens (Meineke and Daru, 2021). For birds, geographic range size provides an effective prediction of collection frequency (Meineke and Daru, 2021), consistent with our results. For instance, Motacilla alba is mainly distributed in Eurasia and widely distributed also in China, with 302 specimens deposited (the highest specimen number in this study).

    Habitat specificity is based on the number of habitats used or the number of habitats in which a species is recorded, and a lower value indicates higher habitat specificity (Martin and Boruta, 2013). In general, a species with a broader ecological niche has a higher ability to tolerate climate change by increasing the likelihood of finding a suitable habitat with sufficient resources, thereby enhancing species' survival in the new environment (Angert et al., 2011; Ding et al., 2013; Estrada et al., 2016). A study using a dataset of Danish terrestrial birds found a positive correlation between habitat breadth and species abundance (Bowler et al., 2018). Consistent with these previous studies, the results of this study showed a positive association between bird specimen number and habitat specificity. This suggests that birds with wider habitat breadth are more likely to be detected and collected by collectors.

    Previous studies have shown that birds most vulnerable to extinction are those that are heavily hunted and persecuted (Wang et al., 2018; Chen et al., 2019). While our findings showed a positive correlation between hunting vulnerability and specimen number. And the one-way ANOVA revealed that occasionally hunted birds were more likely to be collected as specimens than rarely and often hunted birds. This could be related to the fact that local hunters were once used by researchers to collect bird specimens (Boutan, 1993; Keck, 2020). A study in Spain discovered that hunting is still a severe problem for endangered birds; throughout the study period (1989–2016), for species "Near Threatened", "Vulnerable" and "Endangered", a stable number of individuals are hunted each year, but there has been a decrease in the number of "Least Concern" shot over the years (Balmori, 2019). Hunters generally prefer threatened species, and threatened species are less numerous (Altrichter, 2006; Balmori, 2019; Delany et al., 2003). Therefore, the common species that are occasionally hunted are more likely to be collected as specimens.

    Body size has traditionally been regarded as the most informative trait in avian species due to its direct correlation with several other traits in bird species, including dispersion capabilities, resource needs, trophic levels, extinction vulnerability, and reproductive strategies (Ding et al., 2013; Estrada et al., 2016). A study conducted in North America revealed a positive relationship between body size and abundance in bird communities with no closely related species (ancient lineages), indicating that larger species tend to be more abundant than smaller ones (Cotgreave, 1994). Conversely, another study using a dataset on the abundance and distribution of breeding birds in the UK showed a negative correlation between abundance and body size across bird species (Gregory, 1995). Furthermore, previous research has consistently demonstrated that smaller bird species exhibit greater adaptability to climate change and lower extinction risk compared to larger birds (Spooner et al., 2018; Wang et al., 2018; Xu et al., 2023). Small birds often possess shorter generation times, higher reproductive rates, and are generally easier to be detected (Coetzee and Chown, 2016). Hence, in theory, smaller birds with lower extinction risk and high reproductive rates are more abundant (Collen et al., 2011; Harnik et al., 2012). In alignment with these previous findings, this research found a negative correlation between specimen number and body size, meaning that smaller birds were more likely to be collected as specimens.

    Clutch size refers to the median number of eggs laid in a single brood by a nesting pair of birds, and it has been shown to have a significant predictive value for species extinction risk (Lyon, 2003; Wang et al., 2018). Smaller clutch sizes can render animals more vulnerable to random events and anthropogenic activities, increasing their sensitivity to habitat change and extinction risk (Lino et al., 2019). On the contrary, a study on capercaillie in European Russia indicated that clutch size reflects the degree of saturation of the entire environment as a whole by birds, and the population reduction was always followed by an increase in clutch size when the habitat is stable (Borchtchevski, 2017). In this study, the clutch size showed strong positive correlations with bird specimen number. Large clutch sizes indicate bird populations with a flush of resources, low competition, and high reproduction rates (Alerstam and Högstedt, 1982; Lundblad and Conway, 2021). This also indirectly supports our conclusion.

    Higher trophic levels increase species' vulnerability to extinction since they are expected to be less likely to respond to climate changes (Purvis et al., 2000; Buckley and Kingsolver, 2012). The one-way ANOVA findings revealed that omnivorous and insectivorous birds were more likely to be collected as specimens than frugivorous birds. What is noteworthy is that insectivorous birds have higher trophic levels than frugivorous birds. The majority of insectivorous birds are small in body size, which supports our conclusion of smaller birds were more likely to be collected as specimens. Insectivorous birds mainly belong to the order Passeriformes and Muscicapidae (Supahan, 2022), which explains patterns of Chinese bird specimen number, i.e., Passeriformes and Muscicapidae have the most specimens. Omnivorous birds, like sparrows, dwell in urban or rural areas and interact directly with the local community (Isaksson, 2018). They are more prevalent and more detectable to zoologists.

    This study analyzed the associations between bird specimen number and species traits as well as climate niche breadth in China. Although there are many other important factors affecting bird specimen number, e.g., social economic and technological development conditions, these factors were not the focus of this study. The current analyses based on available datasets could also provide some useful results. Specifically, the results showed that bird specimen number was significantly correlated with both species' traits and the species' temperature niche breadth and precipitation niche breadth. In the future, modern monitoring technologies (e.g., camera traps and bird watching) should pay more attention to the occurrence data of birds with limited ecological niches, limited geographic range size, low hunting vulnerability, large body size, and small clutch size, which could help to compensate for the bias in specimen collection. This study helps to better utilize bird specimen data for the study and preservation of biodiversity. Future research may pay more attention to the relationships between specimen number and environmental and socio-economic development to amplify specimen data implication in biodiversity research.

    These authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

    Ethics approval was not required for this study, though institutional guidelines for the care and use of animals were followed.

    Xiaoyu Duan: Methodology, Software, Writing – original draft, Visualization. Xiongwei Huang: Data curation, Formal analysis, Writing – review & editing, Investigation. Jingya Zhang: Formal analysis, Software, Validation. Shuo Lv: Methodology, Validation, Visualization. Gang Song: Conceptualization, Supervision, Writing – review & editing. Yanping Wang: Conceptualization, Supervision, Writing – review & editing. Gang Feng: Funding acquisition, Project administration, Supervision, Validation, Writing – review & editing.

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