Volume 14 Issue 1
Feb.  2023
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Article Contents
Abstract
1.   Introduction
2.   Materials and methods
3.   Results
4.   Discussion
5.   Conclusions
CRediT authorship contribution statement
Ethics statements
Declaration of competing interest
Appendix A. Supplementary data
References
Related Articles
Aichun Xu, Ji Zhang, Qian Li, Zhongqiu Li, Qin Zhu. 2023: The benefits of being smaller: Consistent pattern for climate-induced range shift and morphological difference of three falconiforme species. Avian Research, 14(1): 100079. DOI: 10.1016/j.avrs.2023.100079
Citation: Aichun Xu, Ji Zhang, Qian Li, Zhongqiu Li, Qin Zhu. 2023: The benefits of being smaller: Consistent pattern for climate-induced range shift and morphological difference of three falconiforme species. Avian Research, 14(1): 100079. DOI: 10.1016/j.avrs.2023.100079
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The benefits of being smaller: Consistent pattern for climate-induced range shift and morphological difference of three falconiforme species

  • a.

    School of Life Sciences, China Jiliang University, Hangzhou, 310018, China

  • b.

    Lab of Animal Behavior and Conservation, School of Life Sciences, Nanjing University, Nanjing, 210023, China

More Information
  • Corresponding author:

    E-mail address: qinzhu9@mail.ustc.edu.cn (Q. Zhu)

  • Received Date: 14 Oct 2022
  • Rev Recd Date: 23 Dec 2022
  • Accepted Date: 07 Jan 2023
  • Available Online: 14 Apr 2023
  • Publish Date: 20 Jan 2023
    Graphical Abstract
    • Abstract

  • Climate exerts a dominant control over the distribution of species. Generally, species migrate to higher elevations to track thermal niches, but variations in morphological traits can result in trait-specific responses to climate change. Here we attempted to explore how three sympatrically distributed raptor species (the Upland Buzzard Buteo hemilasius, UB; the Common Kestrel, also called Eurasian ​Kestrel Falco tinnunculus, EK; and the Saker Falcon Falco cherrug, SF) would respond to climate change over time, and whether their responses would bias by different morphology. We tested the alternative hypotheses for Allen's rule for UB, EK, and SF in Qinghai Province, China, by modeling their current and future habitat suitability and confirming whether a consistent pattern exists between climate-induced range shifts and morphological differences among species. The extent of the projected distribution range within protected areas was also calculated for each species. We identified the future downward elevation shift for all the species, but with the notable northeastward shifting of the suitable climate space for UB and SF. Climate change would induce range contraction in the future, and the most acute influence is always the result of the pessimistic SSP585 scenario. No obvious pattern in climate-induced range shift was found for EK, for whom the morphological traits were significantly smaller all the time. More seriously, the ratios of highly suitable habitats being protected for our three raptor species were almost at a deficient level (below 1%). This study firstly tested the alternative hypothesis of Allen's rule among raptors in Qinghai Province unprecedently, confirmed the morphological basis for different responses to changing climate across species, and demonstrated the protection deficiency under the current protected area design. We advocate more related studies in the future to verify our findings across more taxa.

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  • Light is an essential environmental cue that affects the daily and seasonal activities of most free-living animals (Dixit and Singh, 2011; Dominoni, 2015; Grunst et al., 2023). Hence, the endogenous circadian clocks of animals are modulated to synchronize behavioral and physiological processes in response to exogenous diurnal cycles (Prabhat et al., 2020). However, changes in daily photoperiod regimes, such as exposure to artificial light at night, can disrupt this synchronization and lead to circadian rhythm desynchrony (Wyse et al., 2011; Spoelstra et al., 2018). This disparity between the internal physiology, behavior, and endogenous circadian clock of an animal can negatively affect its overall fitness, leading to energy loss, metabolic dysfunction, and neuroendocrinological disorders (Regas, 1998; West et al., 2017; Porcu et al., 2018). Body mass, behavioral activity, and core and peripheral body temperatures are primary indicators of energy metabolism and proper functioning of the neuroendocrinological system. By monitoring the changes in these parameters in response to abnormal photoperiod treatments, we can better understand the behavioral strategies and underlying metabolic mechanisms of animals. For example, the body temperature, body mass, and activity rhythms of birds may either be maintained or modified when the environmental photoperiod changes, especially under constant light (LL) or darkness (DD) conditions (Budki et al., 2012). However, the mechanisms by which birds adjust these parameters in response to abnormal photoperiod conditions are not fully understood.

    In vertebrates, the 24-h light–dark (LD) cycle is maintained through humoral and neural loops in a cascading manner (Tosini and Menaker, 1995; Buhr and Takahashi, 2013). The retinae, suprachiasmatic nuclei (SCN) of the hypothalamus, and pineal gland of non-mammalian vertebrates possess photoreceptors and are in close cooperation to ensure well-coordinated circadian precision and amplitude (Tosini and Menaker, 1995; Cassone et al., 2009; Cassone, 2014); in contrast, the SCN solely acts as the master pacemaker in mammals (Bronstein et al., 1990). Meanwhile, the hypothalamus and pineal gland of passerines play significant roles in resynchronizing their rhythms when the timing of LD cycles suddenly changes (Binkley and Mosher, 1986). On the other hand, the retina has a significant effect on the circadian rhythm of some non-passerine birds, such as the Japanese Quail (Coturnix japonica) and Rock Pigeon (Columba livia) (Brandstaetter, 2002). Generally, by integrating external LD cycles with humoral and neural loops, animals can predict and adapt to different times of the day (24 ​h) and, thus, maintain the circadian rhythms regulating their body mass, body temperature, and locomotion. Previous studies reported significant circadian rhythms influencing the locomotor activity and body temperature of mammals and birds (both passerines and non-passerines). A closely coupled oscillation between the core body temperature and the activity of an animal has been detected (Refinetti and Menaker, 1992; Depres-Brummer et al., 1995; Fonken et al., 2010). For example, improving the foraging success rate and utilization efficiency of food resources is closely synchronized with metabolism (Montoya et al., 2010), and the body temperature and activity of mammals were positively correlated (van Jaarsveld et al., 2019).

    However, the behavioral and physiological rhythms of animals are disrupted under LL conditions (Le Tallec et al., 2013; Fonken and Nelson, 2014). The circadian rhythms of body temperature and daily activities are two fundamental variables derived from the radical changes in neuroendocrinology, metabolism (Zhang et al., 2019), and behavior (Jiang et al., 2020). For instance, the circadian rhythm affecting the activity and core body temperature of mammals becomes arrhythmic and dissociated under LL conditions (Depres-Brummer et al., 1995; Benstaali et al., 2001; Fonken et al., 2010). In contrast, the responses of birds to LL conditions are highly phylogeny-dependent. For example, in Columbiformes, Pigeons (Aplopelia Bonaparte) maintain activity and body temperature rhythm and closely coupled vibration under LL conditions, however, their rhythmicity is lost (Yamada et al., 1988). Moreover, passerines such as House Sparrows (Passer domesticus) lose their activity and body temperature rhythms (Binkley et al., 1971); however, White-crowned Sparrows (Zonotrichia leucophrys gambelii) retain their locomotor rhythms for the first three days (Coverdill et al., 2008). Similarly, the activity rhythms of Java Sparrows (Padda oryzivora) and Indian Weaverbirds (Ploceus philippinus) persist (Ebihara and Kawamura, 1981; Pandey and Bhardwaj, 2011). These findings indicate that activity and body temperature rhythms are tightly coupled in passerines; however, such a phenomenon has not been consistently observed in different passerine species.

    Under DD conditions, some mammals maintain their circadian rhythm oscillations. For example, the diurnal Desert Goat (Capra hircus) maintains its activity and body temperature rhythms but exhibits a free-running circadian rhythm (Farsi et al., 2020). Nocturnal mice and rats also maintain the circadian rhythms of activity, body temperature, melatonin, and neurons under DD conditions (Wideman and Murphy, 2009b; Stenvers et al., 2016). Galliformes species such as the diurnal Japanese Quails (Underwood et al., 1999) and laying Hens (Gallus gallus domesticus) maintain the circadian rhythm of body temperature; however, the amount of locomotor activity was almost undetectable, and the activity rhythm was not sustained (Boshouwers and Nicaise, 1987). Passeriformes species such as diurnal House Sparrows exhibit rhythmic locomotor activity and body temperature (Gaston and Menaker, 1968), and diurnal European Starlings (Sturnus vulgaris) maintain their activity, feeding and melatonin rhythms (Gwinner, 1978). Therefore, nocturnal and diurnal animals can maintain circadian rhythms under DD conditions.

    The Eurasian Tree Sparrow (Passer montanus, ETS) is a representative diurnal resident bird species that is widely distributed across Eurasia (Dixit and Singh, 2012). It is commonly used as a model species to investigate the circadian rhythms of avian behavior and physiology (Ravikumar and Tewary, 1990), seasonal rhythms of baseline and stress-induced corticosterone levels (Li et al., 2008, 2011), and testosterone and gonadal hormone-releasing hormone levels (Li et al., 2012, 2017; Dixit et al., 2022; Wang et al., 2022). They also exhibit a typical circadian rhythm of hormone secretion (Jiang et al., 2020). The closely related species of ETS, the House Sparrow lost its activity and body temperature rhythms under LL conditions (Binkley et al., 1971), but could maintain those under DD conditions (Gaston and Menaker, 1968). The ETSs are therefore predicted to exhibit a similar pattern of circadian rhythms of behavioral activity and body temperature under abnormal photoperiod conditions. To validate this hypothesis, we examined the circadian rhythms of activity and core and peripheral body temperatures of ETSs under LD, LL, and DD conditions. We predicted that the rhythmicity of activity and body temperature under the DD conditions could be maintained, but their rhythms would disappear under the LL conditions.

    2.1   Bird collection

    Through the use of mist nets, 21 free-living juvenile ETSs were caught in the wild in Shijiazhuang City, Hebei Province, China (38.03° N, 114.48° E; elevation: 112 ​m) in June 2018. After the ETSs were captured, they were randomly assigned to three groups (n ​= ​7/group) and individually housed in similar-sized (width ​× ​length ​× ​height: 30 ​cm ​× ​40 ​cm ​× ​20 ​cm) cages. The ETSs were then acclimatized for five days under the normal LD cycle (12 ​h: 12 ​h) in three climate-controlled chambers under uniform temperature (28 ​℃) and humidity (40–60%). Food (millets) and water were provided ad libitum. All protocols in this study were approved by the Institutional Animal Care and Use Committee and the Ethics and Animal Welfare Committee of Hebei Normal University, China.

    2.2   Bird treatment

    After acclimatization, the light cycles of one climate-controlled chamber remained unchanged (LD group). In contrast, the other two climate-controlled chambers were adjusted to either constant bright light (LL group) or constant darkness (DD group). The light intensity was 300–400 lx (similar to sunset and sunrise intensities) in the LL and LD groups. In order to record the activity, the DD group was exposed to 850 ​nm wavelength infrared light. The ZT0/ZT12 (zeitgeber, lights on/off) for the LD group was 6:40 and 18:40, respectively. Food and water were provided ad libitum to the ETSs in the three groups.

    2.3   Body mass, activity, and body temperature monitoring

    The core and peripheral body temperatures, activity, and body mass of the ETSs were monitored for seven consecutive days. The activity in the three groups was monitored for 24 ​h each day using a Big Brother circadian rhythm system (COULBOURN, ACT-400, Actimetrics, Wilmette, IL, USA). In each climate room, a surveillance camera was mounted to record the activity of the ETSs under dark conditions, and infrared light was irradiated to replenish light. The bird cages were placed on three 3D shelves, and the activity of each bird was monitored using cameras for 24 ​h for seven consecutive days. All data were exported to the Big Brother Viewer software, and the activity rhythms were analyzed. The body mass of each bird was measured using a portable digital balance (nearest to 0.01 ​g) at 2:00, 6:00, 10:00, 14:00, 18:00, and 22:00 each day. The temperature of the cloaca (representing the core body temperature) was measured using a handheld thermometer (nearest to 0.01 ​℃; HH-25KC; Omega Engineering, Norwalk, CT, USA) at 2:00, 6:00, 10:00, 14:00, 18:00, and 22:00 each day. The eye and tarsometatarsus surface temperatures (representing the peripheral body temperature) were measured using an infrared thermal imager (TiS20+; Fluke Corporation, Everett, WA, USA) at 2:00, 6:00, 10:00, 14:00, 18:00, and 22:00 each day. To ensure accurate temperature measurements and obtain the best image quality, we preheated the thermal imager for 10 ​min before placing it on the eyes and tarsometatarsus.

    2.4   Statistical analysis

    The effects of different light treatments and time points on the activity, body mass, core, tarsometatarsus, and eye temperatures of the ETSs were analyzed using a mixed linear model. Multiple comparisons in each treatment and six-time points were performed using Bonferroni post-hoc tests, with time points defined as repeating variables. The JTK_Cyclev.3.1 function in the R package (Hughes et al., 2010) was used to calculate the circadian rhythm parameters, including acrophase (φ), amplitude (A), and Benjamini–Hochberg Q value (BH.Q, a method for optimizing false discovery rates), for the activity, body mass, and core, eye, and tarsometatarsus temperatures. A BH.Q ​ < ​0.05 means that there is a significant circadian rhythm. All cosine curves were fitted using the formula f(t) ​= ​M ​+ ​Acos (tπ/12−φ), including the mesor (M), amplitude (A), and acrophase (φ). All statistical analyses were performed in IBM SPSS Statistics v.23 (IBM Inc., NY, USA) and R 4.3.0 (Hughes et al., 2010). All figures were plotted using GraphPad Prism 8.0 (GraphPad Software, Inc., CA, USA). The data are expressed as the mean ​± ​SEM.

    3.1   Activity, body mass, and core and peripheral temperatures in response to abnormal photoperiod

    The activity, body mass, and core, tarsometatarsus, and eye temperatures of the ETSs varied significantly with treatment, time, and the interaction between treatment and time; however, there was no significant change in body mass in response to the interaction between time and treatment (Table 1; Appendix Table S1). The post-hoc results revealed that regardless of time, the birds in the LL group were significantly more active than those in the LD and DD groups (Fig. 1; Table 2; Appendix Table S2). The birds in the LD group were significantly more active during the day and less active at 22:00 than those in the DD group; however, there was no significant difference in activity between the LD and DD groups at 2:00 and 6:00 (Fig. 1; Table 2; Appendix Table S2).

    Table  1.  The statistical results of treatment, time, and the interaction of treatment and time on activity, body mass, core temperature, eye temperature, and tarsometatarsus temperature of Eurasian Tree Sparrows (Passer montanus) in mixed linear models.
    Variable Activity Body mass Core temperature Tarsometatarsus temperature Eye temperature
    Factor F df P F df P F df P F df P F df P
    Intercept 3946.259 1 < 0.001 109680.823 1 < 0.001 1989362.351 1 < 0.001 140432.869 1 < 0.001 153764.24 1 < 0.001
    Time 12.894 5 < 0.001 10.896 5 < 0.001 62.932 5 < 0.001 5.630 5 < 0.001 2.722 5 0.020
    Group 2206.145 2 < 0.001 44.889 2 < 0.001 3.221 2 0.040 15.715 2 < 0.001 204.796 2 < 0.001
    Group × Time 13.939 10 < 0.001 0.463 10 0.914 3.611 10 < 0.001 2.551 10 0.006 2.219 10 0.017
    Significant values (P < 0.05) are shown in bold.
     | Show Table
    DownLoad: CSV
    Figure  1.  Changes of activity of Eurasian Tree Sparrows (Passer montanus) under constant light (LL) and darkness (DD), and normal light-dark conditions (LD). (A) Average activity of each bird for eight consecutive days under a 24-h cycle (2:00, 6:00, 10:00, 14:00, 18:00, and 22:00). (B) Double-plotted actogram of daily rhythms of activity under a 48-h cycle. The cosine curve fitted in the figure represents a significant rhythm (BH.Q < 0.05). Data are shown as means ± SEM.
    DownLoad: Full-Size Img PowerPoint
    Table  2.  Statistical results of the average of activity, body mass, core temperature, tarsometatarsus temperature, and eye temperature of Eurasian Tree Sparrows (Passer montanus) under constant light (LL) and darkness (DD), and normal light-dark conditions (LD) at different time points (2:00, 6:00, 10:00, 14:00, 18:00, and 22:00).
    Factor Time df F P
    Activity 2:00 2105 1275.355 < 0.001
    6:00 2158 2185.044 < 0.001
    10:00 2123 119.227 < 0.001
    14:00 2141 263.255 < 0.001
    18:00 2159 203.929 < 0.001
    22:00 2141 1537.932 < 0.001
    Body mass 2:00 2177 5.786 0.004
    6:00 2198 11.570 < 0.001
    10:00 2189 11.770 < 0.001
    14:00 2196 5.735 0.004
    18:00 2184 7.355 0.001
    22:00 2193 5.848 0.003
    Core temperature 2:00 2199 6.002 0.003
    6:00 2219 0.009 0.991
    10:00 2216 3.084 0.048
    14:00 2217 0.832 0.437
    18:00 2215 2.268 0.106
    22:00 2213 7.878 < 0.001
    Tarsometatarsus temperature 2:00 2143 8.236 < 0.001
    6:00 2158 7.553 0.001
    10:00 2155 0.998 0.371
    14:00 2162 1.121 0.329
    18:00 2167 1.197 0.305
    22:00 2159 7.596 0.001
    Eye temperature 2:00 2143 39.786 < 0.001
    6:00 2157 52.551 < 0.001
    10:00 2154 23.777 < 0.001
    14:00 2162 28.110 < 0.001
    18:00 2167 22.433 < 0.001
    22:00 2158 50.003 < 0.001
    Significant values (P < 0.05) are shown in bold.
     | Show Table
    DownLoad: CSV

    The initial body mass of the birds in the three groups did not significantly differ (F ​= ​0.557, P ​= ​0.583; Fig. 2). However, the birds in the DD group were significantly heavier than those in the LD and LL groups; meanwhile, the body mass of the birds in the LL and LD groups did not significantly differ at any time point (Fig. 2; Table 2; Appendix Table S2).

    Figure  2.  Comparisons of initial body mass (A) and the changes of body mass (B) at six-time points (2:00, 6:00, 10:00, 14:00, 18:00, and 22:00) in a 24-h cycle under constant light (LL) and darkness (DD), and normal light-dark conditions (LD) of Eurasian Tree Sparrows (Passer montanus). Data are shown as means ± SEM.
    DownLoad: Full-Size Img PowerPoint

    The core temperature of the LL group was significantly higher than that of the DD group at 2:00 (P ​= ​0.002), and that of the LD (P ​= ​0.003) and DD groups (P ​= ​0.001) at 22:00; in contrast, it was significantly lower than that of the LD group at 10:00 (P ​= ​0.041). The core temperature of the three groups did not significantly differ at other time points (Fig. 3; Table 2; Appendix Table S2). Although there were no significant differences in tarsometatarsus temperature across the groups at other time points, the LL group had a significantly higher tarsometatarsus temperature than the LD group at 2:00, 6:00, and 22:00. The eye temperature of the LL and LD groups was significantly lower than that of the DD group at all time points; moreover, the eye temperature of the LL group was significantly higher than that of the LD group at all time points except at 10:00 (Fig. 3; Table 2; Appendix Table S2).

    Figure  3.  Daily changes of average values in core temperature (A), tarsometatarsus temperature (B), and eye temperature (C) at six-time points (2:00, 6:00, 10:00, 14:00, 18:00, and 22:00) under constant light (LL) and darkness (DD), and normal light-dark conditions (LD) of Eurasian Tree Sparrows (Passer montanus). The cosine curve fitted in the figure represents a significant rhythm (BH.Q < 0.05). Data are shown as means ± SEM.
    DownLoad: Full-Size Img PowerPoint

    3.2   Circadian rhythms of activity, body mass, and core and peripheral temperatures in response to constant photoperiod

    Except for body mass, the ETSs in the LD group exhibited significant circadian rhythms in activity and core and tarsometatarsus temperatures, with acrophase at 12:00, and in eye temperature, with acrophase at 14:00 (Table 3). The rhythmicity of these measured parameters varied with the treatment duration. There were no significant circadian rhythms in the activity, body mass, and tarsometatarsus and eye temperatures in the LL group; however, the core temperature exhibited significant rhythmicity, with acrophase at 14:00 (delayed by 2 ​h relative to the LD group; Table 3). Lastly, there were no significant circadian rhythms in the activity, body mass, and eye temperature in the DD group; however, the core and tarsometatarsus temperatures exhibited significant rhythmicity, with acrophases at 12:00 and 16:00, respectively (Table 3). Compared with the LD group, the amplitudes of all measured parameters decreased in the LL and DD groups (Table 3).

    Table  3.  Circadian rhythm parameters of activity, body mass, core temperature, tarsometatarsus temperature, and eye temperature of Eurasian Tree Sparrows (Passer montanus) under constant light (LL) and darkness (DD), and normal light-dark cycle (LD) conditions.
    Factors Variable BH.Q ADJ.P Acrophase (φ, h) Amplitude (A) df F P
    Activity LL 1.000 1.000 22 6.393 5275 0.725 0.606
    LD < 0.05 < 0.001 12 34.850 5276 24.163 < 0.001
    DD 0.072 0.035 12 5.922 5276 1.184 0.323
    Body mass LL 1.000 0.759 16 0.362 5364 3.027 0.013
    LD 0.679 0.144 16 0.540 5372 8.655 < 0.001
    DD 1.000 0.882 16 0.360 5401 2.089 0.071
    Core temperature LL < 0.05 < 0.001 14 0.478 5413 7.669 < 0.001
    LD < 0.05 < 0.001 12 0.889 5422 37.124 < 0.001
    DD < 0.05 < 0.001 12 0.714 5444 32.552 < 0.001
    Tarsometatarsus temperature LL 1.000 1.000 12 0.191 5391 0.480 0.790
    LD < 0.05 < 0.001 12 1.332 5363 9.814 < 0.001
    DD 0.029 0.019 16 0.470 5190 0.878 0.502
    Eye temperature LL 0.170 0.113 18 0.452 5388 0.587 0.710
    LD < 0.05 < 0.001 14 1.048 5363 24.730 < 0.001
    DD 0.793 0.793 14 0.171 5190 0.243 0.941
    Significant values (BH.Q < 0.05; P < 0.05) are shown in bold.
     | Show Table
    DownLoad: CSV

    As predicted, the activity, core and peripheral body temperatures of the ETSs had significant circadian rhythms and were closely coupled under LD conditions; however, eye temperature was acrophase-delayed by approximately 2 ​h. In response to an abnormal photoperiod, the amplitude of concussion for each index under all treatments weakened. Under LL conditions, only the core temperature exhibited significant rhythmicity, whereas under DD conditions, the core and tarsometatarsus temperatures displayed significant rhythmicity. These results indicate that the circadian rhythm of the core body temperature of ETSs is endogenous, well-maintained, and independent of the photoperiod.

    4.1   Circadian rhythms under normal conditions

    Under LD conditions, the ETSs were extremely active during the day and inactive at night; in particular, locomotor activity was significantly rhythmic and acrophased at approximately 12:00. They fed during the day, significantly gained weight while fasting, and lost weight at night. Unlike nocturnal animals, diurnal animals forage, digest food, and absorb nutrients to promote anabolism during the day (Wideman and Murphy, 2009). Diurnal birds are inactive at night and rely on the mobilization of their glycogen and fat stores to enhance catabolism (Lucia et al., 2010). Our results confirm that diurnal animals are highly photosensitive: their activities rapidly increase when lights are on, but their activities end when lights are off (Dawson et al., 2001; Trivedi et al., 2005; Dawson and Sharp, 2007). Although no significant rhythmicity was detected in the body mass of the ETSs, there were significant differences in body mass at daytime and nighttime. Our results are congruent with the reports that Light-vented Bulbul (Pycnonotus sinensis), White-throated Sparrow (Z. albicollis) (Kontogiannis, 1967), and Marsh Tit (Poecile palustris) (Hurly, 1992) gained weight during the day, but lost weight at night. Such circadian rhythms in activity and the corresponding changes in body mass indicate endogenous behavioral and physiological processes of animals to adapt to the external diurnal cycles of light signals (Prabhat et al., 2020).

    Meanwhile, we found that both the core and peripheral body temperatures—tarsometatarsus and eye temperatures—were remarkably rhythmic, with highly synchronized acrophases at 12:00 and 14:00, respectively. The core body temperature rhythm was synchronized with locomotor activity, indicating that they are highly coupled. The tarsometatarsus temperature had a higher amplitude and reached its peak synchronizing with the core body temperature, while the eye temperature reached its peak approximately 2 ​h later. Although birds can maintain an equilibrium of heat production and loss at a constant temperature in their surrounding environment, heat loss generally lags behind the core temperature (Giloh et al., 2012). Since the core body temperature is coupled with metabolic rate, the synchronicity of body temperature with locomotor activity is essential in controlling metabolic processes, ensuring that endogenous shock is synchronized with the external environment (Al-Hasani et al., 2003). All these temporal synchronizations to predictable LD cycles are essential in optimizing the fitness of organisms (Ramkisoensing and Meijer, 2015; Plano et al., 2017).

    4.2   Circadian rhythms under constant light conditions

    Compared with the birds under LD conditions, the birds under LL conditions lost the rhythmicity of their locomotor activity. Our results indicate that their activities are highly light signal–dependent (Dawson et al., 2001). Furthermore, our results are consistent with the findings for other avian species such as Rock Pigeons (Yamada et al., 1988), Domestic Chicken (Gallus domesticus), Ptarmigans (Lagopus muta) (Appenroth et al., 2021), and resident House Sparrows (Binkley et al., 1971). However, our results differ from the finding for migratory White-crowned Sparrows: under LL conditions, they maintain the rhythmicity of their locomotor activity for three days (Coverdill et al., 2008). Therefore, it seems that migratory and resident passerines exhibit different behavioral strategies in response to certain environmental signal changes that may be caused by different interactions between the output of the circadian oscillation and light signal.

    The ETSs under LL conditions had significantly higher core and peripheral body temperatures during the subjective night than those of the LD group during the dark period. However, their body mass did not change. Increased body temperature and activity, coupled with constant body mass, denote that the ETSs increased their food intake under constant light conditions at night; however, they demonstrated a similar trend under normal conditions. In contrast, nocturnal mice increased food intake and gained weight under light-disturbed conditions (Coomans et al., 2013). Furthermore, the core body temperature of the ETSs remained rhythmic, consistent with those of Pigeons (Ebihara et al., 1984; Oshima et al., 1989). However, the peripheral body temperature was arrhythmic. These results indicate that the rhythmicity of the core body temperature is endogenous, and peripheral body temperatures and activity can be decoupled under LL conditions. Relative to normal light conditions, the core body temperature of the ETSs under LL conditions was acrophase-delayed for approximately 2 ​h, indicating that its rhythmicity was affected by light, and its oscillation process was under the strict control of the endogenous biological clock. The core body temperature of the ETSs in the LL group during the subjective night was higher than that in the LD group, and the trough of the core body temperature rhythm occurred during the subjective night. This downregulation of metabolism has also been observed in nocturnal rats exposed to dim light (Borniger et al., 2014). In terms of energy expenditure, constant light exposure is a costly inconvenience because core body temperature rhythms can help an animal conserve energy; for example, a rhythmic core temperature helps House Sparrows save 7% of their energy (Regas, 1998).

    Generally, light signals can serve as external stimuli that induce internal physiological and behavioral changes by acting on the retinal photoreceptors of birds. When continuously exposed to constant light, the ETSs demonstrated behavioral changes and a higher level of activity, resulting in increased metabolic heat production and peripheral temperature. Unlike the core body temperature, controlled by the endogenous biological clock, the peripheral temperature strongly depends on exogenous light. Under LL conditions, the surface body temperature of the ETSs remained high, which could be induced by the increased activity at subjective night and enhanced thermogenesis through catabolism (Hohtola and Stevens, 1986). Therefore, the activity and body temperature rhythms of birds are disrupted under continuous exposure to intense light. Such a disruption may alter the homeostasis of energy metabolism and further impair the overall health of an organism. Considering the limited information on variations in peripheral body temperature, the underlying mechanisms of the decoupled relationship between peripheral and core body temperature require further investigation.

    4.3   Circadian rhythms in constant dark conditions

    The ETSs remained inactive at all time points and lost their activity rhythm under DD conditions. Similarly, White-crowned Sparrows lose their activity rhythm under dim light conditions; however, they maintain a high level of activity for night migration (McMillan, 1972; Coverdill et al., 2008). Our results differ from the findings that locomotor activity rhythms are maintained under DD conditions in House Sparrows (Cassone, 2014) and European Starlings (Gwinner, 1978). These results indicate that maintaining the activity rhythm under DD conditions is largely species-specific, not phylogenetic-dependent. In particular, the ETSs in the DD group were significantly heavier than those in the LD and LL groups, although they exhibited a similar trend in body mass as those in the LD group. Despite the disruption of their activity and foraging rhythms, an endogenous pattern of weight loss and gain was observed when the activities and energy consumption levels of the ETSs were low. The mechanism behind this phenomenon remains largely unknown and should be determined through ecological and physiological research.

    The ETSs in the DD group had comparable core body and tarsometatarsus temperatures with those in the LD and LL groups; however, their eye temperature was significantly higher than those in the other two groups. Owing to weak vision under dark conditions, an increase in eye temperature under DD conditions is critical for better foraging, thereby ensuring adequate food intake and maintenance of energy balance (Harmening, 2017). Furthermore, the core body and tarsometatarsus temperatures exhibited a significant circadian rhythm, whereas eye temperature did not. Similarly, the circadian rhythm of the core body temperature under DD conditions has been observed in Japanese Quails and House Sparrows (Gaston and Menaker, 1968; Underwood et al., 1999). The core temperature of the ETSs with an acrophase at 12:00 indicates that constant darkness is not necessary to induce changes in the rhythmicity of the core body temperature of birds because it is controlled by an endogenous clock. However, our results showed that the acrophase of the tarsometatarsus temperature was delayed by 4 ​h relative to the core body temperature. Although birds can maintain an equilibrium of heat production and loss at a constant temperature in their surrounding environment, heat loss generally lags behind the core temperature (Giloh et al., 2012). In birds, the tarsometatarsus is a vital organ that regulates thermal dissipation (Mosher and White, 1978). Considering that limited information is available on the circadian rhythm of tarsometatarsus temperature under dark conditions, how birds regulate their thermal equilibrium by adjusting the surface temperature of their peripheral organs remains to be elucidated.

    In this study, we examined the circadian rhythms of activity, body mass, and core and peripheral body temperatures of ETS in response to LD, LL, and DD conditions. We found that the core body temperature maintained significant rhythmicity when the external photoperiods changed; however, the amplitude of the concussion of each index under all treatments weakened, indicating that the core body temperature is an endogenous index. The peripheral body temperature and activity decoupled when the sparrows were exposed to constant light, whereas increased eye temperature enabled adequate food intake to maintain an energy balance under DD conditions. Therefore, our results suggest that the maintenance of the core body temperature rhythm is highly rhythmic and endogenous—it is not completely dependent on light; however, but the activity and peripheral body temperature rhythms are greatly affected by photoperiod. Our results differ from the research findings for other diurnal passerines such as House Sparrows and White-throated Sparrows. These discrepancies suggest that the coping mechanisms of body condition and core and peripheral body temperatures in response to constant light signals are species-dependent, not phylogeny-dependent. Our results contribute to a better understanding of the diversity and plasticity of physiology and behavior of passerines under abnormal photoperiods. The underlying causes of such species-dependent variations in the rhythmicity of physiology and behavior in response to unfavorable photoperiod warrant further study.

    Lirong Zuo: Formal analysis, Writing original draft, Writing review & editing. Ibrahim M. Ahmad: Formal analysis, Writing original draft, Writing review & editing. Yuanyuan Liu: Investigation, Methodology. Limin Wang: Investigation, Methodology. Shu Fang: Investigation, Methodology. Dongming Li: Conceptualization, Supervision, Funding acquisition, Writing review & editing.

    All experimental materials were conducted in accordance with the Institutional Committee for Animal Care and Use of Hebei Normal University, China, and was carried out under the auspices of scientific collecting permits issued by the Departments of Wildlife Conservation (Forestry Bureau) of Hebei Province, China.

    The authors declare no conflicts of interest.

    Supplementary data to this article can be found online at https://doi.org/10.1016/j.avrs.2023.100126.

    • References (121)
  • Aguirre-Liguori, J.A., Ramírez-Barahona, S., Gaut, B.S., 2021. The evolutionary genomics of species' responses to climate change. Nat. Ecol. Evol. 5, 1350-1360. .
    Albrich, K., Rammer, W., Seidl, R., 2020. Climate change causes critical transitions and irreversible alterations of mountain forests. Global Change Biol. 26, 4013-4027. .
    Allen, J.A., 1876. Geographical Variation Among North American Mammals: Especially in Respect to Size. Government Printing Office, Washington.
    Anderson, A.M., Friis, C., Gratto-Trevor, C.L., Morrison, R.I.G., Smith, P.A., Nol, E., 2019. Consistent declines in wing lengths of Calidridine sandpipers suggest a rapid morphometric response to environmental change. PLoS One 14, e0213930. .
    Anderson, R.P., Gonzalez, I., 2011. Species-specific tuning increases robustness to sampling bias in models of species distributions: an implementation with MaxEnt. Ecol. Model. 222, 27962811.
    Araújo, M.B., Rahbek, C., 2006. How does climate change affect biodiversity? Science 313, 1396-1397. .
    Bascompte, J., García, M.B., Ortega, R., Rezende, E.L., Pironon, S., 2019. Mutualistic interactions reshuffle the effects of climate change on plants across the tree of life. Sci. Adv. 5, eaav2539. .
    Bergmann, C., 1848. Über die Verhältnisse der Wärmeökonomie der Thiere zu ihrer Grösse. Göttinger Studien 3, 595-708.
    BirdLife International, 2021a. IUCN Red List for Birds. . (Accessed 18 September 2021).
    BirdLife International, Handbook of the Birds of the World, 2021a. Buteo hemilasius. The IUCN Red List of Threatened Species 2021: e. T22695967A202604414. .
    BirdLife International, 2021b. Species Factsheet: Falco cherrug. . (Accessed 18 September 2021).
    BirdLife International, Handbook of the Birds of the World, 2021b. Falco cherrug. The IUCN Red List of Threatened Species 2021: e. T22696495A204182473. .
    Birdlife International, 2021c. Species Factsheet: Falco tinnunculus. . (Accessed 18 September 2021).
    BirdLife International, Handbook of the Birds of the World, 2021c. Falco tinnunculus. The IUCN Red List of Threatened Species 2021: e. T22696362A206316110. .
    Bock, W.J., 1994. Concepts and methods in ecomorphology. J. Biosci. 19, 403-413.
    Bowler, D.E., Hof, C., Haase, P., Kröncke, I., Schweiger, O., Adrian, R., et al., 2017. Cross-realm assessment of climate change impacts on species' abundance trends. Nat. Ecol. Evol. 1, 0067. .
    Brown, P.T., Caldeira, K., 2017. Greater future global warming inferred from Earth's recent energy budget. Nature 552, 45-50. .
    Brown, E.D., Williams, B.K., 2019. The potential for citizen science to produce reliable and useful information in ecology. Conserv. Biol. 33, 561-569. .
    Brown, C.J., O'Connor, M.I., Poloczanska, E.S., Schoeman, D.S., Buckley, L.B., Burrows, M.T., et al., 2016. Ecological and methodological drivers of species' distribution and phenology responses to climate change. Global Change Biol. 22, 1548-1560. .
    Burgas, D., Byholm, P., Parkkima, T., 2014. Raptors as surrogates of biodiversity along a landscape gradient. J. Appl. Ecol. 51, 786-794. .
    Bustamante, J., Seoane, J., 2004. Predicting the distribution of four species of raptors (Aves: Accipitridae) in southern Spain: statistical models work better than existing maps. J. Biogeogr. 31, 295-306. .
    Casajus, N., Périé, C., Logan, T., Lambert, M.C., de Blois, S., Berteaux, D., 2016. An objective approach to select climate scenarios when projecting species distribution under climate change. PLoS ONE 11, e0152495. .
    Castro, L.C., Cetina-Heredia, P., Roughan, M., Dworjanyn, S., Thibaut, L., Chamberlain, M.A., et al., 2020. Combined mechanistic modelling predicts changes in species distribution and increased co-occurrence of a tropical urchin herbivore and a habitat-forming temperate kelp. Divers. Distrib. 26, 1211-1226. .
    Chen, I.C., Hill, J.K., Ohlemüller, R., Roy, D.B., Thomas, C.D., 2011. Rapid range shifts of species associated with high levels of climate warming. Science 333, 1024-1026. .
    Colwell, R.K., Brehm, G., Cardelús, C., Gilman, A.C., Longino, J.T., 2008. Global warming, elevational range shifts, and lowland biotic attrition in the wet tropics. Science 322, 258-261. .
    Cooney, C.R., Bright, J.A., Capp, E.J., Chira, A.M., Hughes, E.C., Moody, C.J., et al., 2017. Mega-evolutionary dynamics of the adaptive radiation of birds. Nature 542, 344-347. .
    Costantini, D., Dell'Omo, G., La Fata, I., Casagrande, S., 2014. Reproductive performance of Eurasian Kestrel Falco tinnunculus in an agricultural landscape with a mosaic of land uses. Ibis 156, 768-776.
    Crase, B., Liedloff, A., Vesk, P.A., Fukuda, Y., Wintle, B.A., 2014. Incorporating spatial autocorrelation into species distribution models alters forecasts of climate-mediated range shifts. Global Change Biol. 20, 2566-2579. .
    Davis, A.J., Jenkinson, L.S., Lawton, J.H., Shorrocks, B., Wood, S., 1998. Making mistakes when predicting shifts in species range in response to global warming. Nature 391, 783-786. .
    del Río, S., Canas, R., Cano, E., Cano-Ortiz, A., Musarella, C., Pinto-Gomes, C., et al., 2021. Modelling the impacts of climate change on habitat suitability and vulnerability in deciduous forests in Spain. Ecol. Indic. 131, 108202. .
    Du, L., Ma, M., 2013. Habit difference of Lesser kestrel and Common kestrel during breeding season. Sichuan J. Zool. 32, 766-769.
    Dykstra, C.R., 2018. City lifestyles: behavioral ecology of urban raptors. In: Boal, C.W., Dykstra, C.R. (Eds.), Urban Raptors. Island Press, Washington, DC, pp. 18–35.
    Elith, J., Catherine, H.G., Robert, P.A., Miroslav, D., Simon, F., Antoine, G., et al., 2006. Novel methods improve prediction of species' distributions from occurrence data. Ecography 29, 129-151. .
    Elsen, P.R., Tingley, M.W., 2015. Global mountain topography and the fate of montane species under climate change. Nat. Clim. Change 5, 772-776. .
    Eyring, V., Bony, S., Meehl, G.A., Senior, C.A., Stevens, B., Stouffer, R.J., et al., 2016. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosic. Model Dev. 9, 1937-1958. .
    Fan, L., Cai, T., Xiong, Y., Song, G., Lei, F., 2019. Bergmann's rule and Allen's rule in two passerine birds in China. Avian Res. 10, 34. .
    Fei, S., Desprez, J.M., Potter, K.M., Jo, I., Knott, J.A., Oswalt, C.M., 2017. Divergence of species responses to climate change. Sci. Adv. 3, e1603055. .
    Forrest, J.R.K., Cross, R., CaraDonna, P.J., 2019. Two-year bee, or not two-year bee? How voltinism is affected by temperature and season length in a high-elevation solitary bee. Am. Nat. 193, 560-574. .
    Gardner, J.L., Rowley, E., de Rebeira, P., de Rebeira, A., Brouwer, L., 2017. Effects of extreme weather on two sympatric Australian passerine bird species. Philos. T. Roy. Soc. B. 372, 20160148. .
    Geng, R., Zhang, X., Ou, W., Sun, H., Lei, F., Gao, W., et al., 2009. Diet and prey consumption of breeding Common Kestrel (Falco tinnunculus) in Northeast China. Prog. Nat. Sci. 19, 1501-1507. .
    Greenberg, R., Danner, R.M., 2012. The influence of the California marine layer on bill size in a generalist songbird. Evolution 66, 3825-3835. .
    Hansen, W.D., Fitzsimmons, R., Olnes, J., Williams, A.P., 2020. An alternate vegetation type proves resilient and persists for decades following forest conversion in the north American boreal biome. J. Ecol. 109, 85-98. .
    Harris, J.B.C., Dwi Putra, D., Gregory, S.D., Brook, B.W., Prawiradilaga, D.M., Sodhi, N.S., et al., 2014. Rapid deforestation threatens mid-elevational endemic birds but climate change is most important at higher elevations. Divers. Distrib. 20, 773-785. .
    He, X., Burgess, K.S., Yang, X., Ahrends, A., Gao, L., Li, D., 2019. Upward elevation and northwest range shifts for alpine Meconopsis species in the Himalaya-Hengduan Mountains region. Ecol. Evol. 9, 4055-4064. .
    Hickling, R., Roy, D.B., Hill, J.K., Fox, R., Thomas, C.D., 2006. The distributions of a wide range of taxonomic groups are expanding polewards. Global Change Biol. 12, 450-455. .
    Hope, A.G., Malaney, J.L., Bell, K.C., Salazar-Miralles, F., Chavez, A.S., Barber, B.R., et al., 2016. Revision of widespread red squirrels (genus: Tamiasciurus) highlights the complexity of speciation within North American forests. Mol. Phylogenet. Evol. 100, 170-182. .
    Hu, J., Jiang, Z., 2011. Climate change hastens the conservation urgency of an endangered ungulate. PLoS One 6, e22873. .
    Huchler, K., Schulze, C.H., Gamauf, A., Sumasgutner, P., 2020. Shifting breeding phenology in Eurasian kestrels Falco tinnunculus: effects of weather and urbanization. Front. Ecol. Evol. 8, 247.
    IUCN, 2021. The IUCN Red List of Threatened Species. Version 2021-3. . (Accessed 10 December 2021).
    Jha, R., Jha, K.K., 2021. Habitat prediction modelling for vulture conservation in Gangetic-Thar-Deccan region of India. Environ. Monit. Assess. 193, 532. .
    Johnston, A., Hochachka, W.M., Strimas-Mackey, M.E., Gutierrez, V.R., Robinson, O.J., Miller, E.T., et al., 2019. Best practices for making reliable inferences from citizen science data: case study using eBird to estimate species distributions. BioRxiv 574392. .
    Kassara, C., Gangoso, L., Mellone, U., Piasevoli, G., Hadjikyriakou, T.G., Tsiopelas, N., et al., 2017. Current and future suitability of wintering grounds for a long-distance migratory raptor. Sci. Rep. 7, 8798. .
    Kerr, J.T., Pindar, A., Galpern, P., Packer, L., Potts, S.G., Roberts, S.M., et al., 2015. Climate change impacts on bumblebees converge across continents. Science 349, 177-180. .
    Kramer-Schadt, S., Niedballa, J., Pilgrim, J.D., Schröder, B., Lindenborn, J., Reinfelder, V., et al., 2013. The importance of correcting for sampling bias in MaxEnt species distribution models. Divers. Distrib. 19, 1366-1379. .
    Kübler, S., Kupko, S., Zeller, U., 2005. The kestrel (Falco tinnunculus L.) in Berlin: investigation of breeding biology and feeding ecology. J. Ornithol. 146, 271-278.
    La Sorte, F.A., Jetz, W., 2012. Tracking of climatic niche boundaries under recent climate change. J. Anim. Ecol. 81, 914-925. .
    Lenoir, J., Svenning, J.C., 2015. Climate-related range shifts - a global multidimensional synthesis and new research directions. Ecography 38, 15-28. .
    Lenoir, J., Gégout, J.C., Guisan, A., Vittoz, P., Wohlgemuth, T., Zimmermann, N.E., et al., 2010. Going against the flow: potential mechanisms for unexpected downslope range shifts in a warming climate. Ecography 33, 295-303. .
    Lester, S.E., Ruttenberg, B.I., Gaines, S.D., Kinlan, B.P., 2007. The relationship between dispersal ability and geographic range size. Ecol. Lett. 10, 745-758. 10.1111/j. 1461-0248.2007.01070. x.
    Li, R., 2019. Protecting rare and endangered species under climate change on the Qinghai Plateau, China. Ecol. Evol. 9, 427-436. .
    Liu, J., Zhuang, D., Wang, J., Zhou, W., Wu, S., 2013. 1: 100000 Landuse Dataset of Qinghai Province (2000). National Tibetan Plateau Data Center. .
    Liu, H., Mi, Z., Lin, L., Wang, Y., Zhang, Z., Zhang, F., et al., 2018. Shifting plant species composition in response to climate change stabilizes grassland primary production. P. Natl. Acad. Sci. 115, 4051-4056. .
    Loarie, S.R., Duffy, P.B., Hamilton, H., Asner, G.P., Field, C.B., Ackerly, D.D., 2009. The velocity of climate change. Nature 462, 1052-1055. .
    Marchetti, K., Price, T., 1989. Differences in the foraging of juvenile and adult birds: the importance of developmental constraints. Biol. Rev. 64, 51-70. .
    Mariette, M.M., Buchanan, K.L., 2016. Prenatal acoustic communication programs offspring for high posthatching temperatures in a songbird. Science 353, 812-814. .
    Martín, B., Ferrer, M., 2013. Assessing biodiversity distribution using diurnal raptors in Andalusia, southern Spain. Ardeola 60, 15-28. .
    McMahon, K.W., Michelson, C.I., Hart, T., McCarthy, M.D., Patterson, W.P., Polito, M.J., 2019. Divergent trophic responses of sympatric penguin species to historic anthropogenic exploitation and recent climate change. P. Natl. Acad. Sci. 116, 25721-25727. 10.1073/pnas. 1913093116.
    Miles, D.B., Ricklefs, R.E., 1984. The correlation between ecology and morphology in deciduous forest passerine birds. Ecology 65, 1629-1640. .
    Morelli, F., Benedetti, Y., Jerzak, L., Kubecka, J., Delgado, J.D., 2020. Combining the potential resilience of avian communities with climate change scenarios to identify areas of conservation concern. Ecol. Indic. 116, 106509. .
    Moritz, C., Agudo, R., 2013. The future of species under climate change: resilience or decline? Science 341, 504-508.
    Muscarella, R., Galante, P.J., Soley-Guardia, M., Boria, R.A., Kass, J.M., Uriarte, M., et al., 2015. ENMeval: an R package for conducting spatially independent evaluations and estimating optimal model complexity for Maxent ecological niche models. Method. Ecol. Evol. 5, 1198-1205. .
    Onley, I.R., Gardner, J.L., Symonds, M.R., 2020. Spatial and temporal variation in morphology in Australian whistlers and shrike-thrushes: is climate change causing larger appendages? Biol. J. Linn. Soc. 130, 101-113. .
    Pachauri, R.K., Allen, M.R., Barros, V.R., Broome, J., Cramer, W., Christ, R., et al., 2014. Climate Change 2014: Synthesis Report. Contribution of Working Groups Ⅰ, Ⅱ and Ⅲ to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. IPCC, Geneva.
    Pacifici, M., Visconti, P., Butchart, S.H.M., Watson, J.E.M., Cassola, F.M., Rondinini, C., 2017. Species' traits influenced their response to recent climate change. Nat. Clim. Change 7, 205-208. .
    Parmesan, C., Yohe, G., 2003. A globally coherent fingerprint of climate change impacts across natural systems. Nature 421, 37-42. .
    Pearson, R.G., Dawson, T.P., 2003. Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful? Global Ecol. Biogeogr. 12, 361-371. .
    Peters, R.L., Darling, J.D.S., 1985. The greenhouse effect and nature reserves. BioScience 35, 707-717. .
    Peterson, M.L., Doak, D.F., Morris, W.F., 2019. Incorporating local adaptation into forecasts of species' distribution and abundance under climate change. Global Change Biol. 25, 775-793. .
    Phillips, S.J., Anderson, R.P., Schapire, R.E., 2006. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190, 231-259. .
    Phillips, S.J., Dudik, M., Elith, J., Graham, C.H., Lehmann, A., Leathwick, J.R., et al., 2009. Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecol. Appl. 19, 181-197. .
    Piersma, T., van Gils, J.A., 2011. The Flexible Phenotype: A Body-Centred Integration of Ecology, Physiology, and Behaviour. Oxford University Press, Oxford.
    Pigot, A.L., Tobias, J.A., 2013. Species interactions constrain geographic range expansion over evolutionary time. Ecol. Lett. 16, 330-338. .
    R Core Team, 2019. R: a Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna.
    Raxworthy, C.J., Pearson, R.G., Rabibisoa, N., Rakotondrazafy, A.M., Ramanamanjato, J.B., Raselimanana, A.P., et al., 2008. Extinction vulnerability of tropical montane endemism from warming and upslope displacement: a preliminary appraisal for the highest massif in Madagascar. Global Change Biol. 14, 1703-1720. .
    Ricklefs, R.E., Travis, J., 1980. A morphological approach to the study of avian community organization. Auk 97, 321-338. .
    Riddell, E.A., Iknayan, K.J., Wolf, B.O., Sinervo, B., Beissinger, S.R., 2019. Cooling requirements fueled the collapse of a desert bird community from climate change. P. Natl. Acad. Sci. 116, 21609-21615. .
    Romano, A., Séchaud, R., Roulin, A., 2020. Geographical variation in bill size provides evidence for Allen's rule in a cosmopolitan raptor. Global Ecol. Biogeogr. 29, 65-75. .
    Ruan, L., Xu, W., Han, Y., Zhu, C., Guan, B., Xu, C., et al., 2018. Gene flow from multiple sources maintains high genetic diversity and stable population history of Common Moorhen Gallinula chloropus in China. Ibis 160, 855-869. .
    Rubidge, E.M., Monahan, W.B., Parra, J.L., Cameron, S.E., Brashares, J.S., 2011. The role of climate, habitat, and species co-occurrence as drivers of change in small mammal distributions over the past century. Global Change Biol. 17, 696-708. .
    Schmid, S.F., Stöcklin, J., Hamann, E., Kesselring, H., 2017. High-elevation plants have reduced plasticity in flowering time in response to warming compared to low-elevation congeners. Basic Appl. Ecol. 21, 1-12. .
    Schröter, D., Cramer, W., Leemans, R., Prentice, I.C., Araújo, M.B., Arnell, N.W., et al., 2005. Ecosystem service supply and vulnerability to global change in Europe. Science 310, 1333-1337. .
    Selwood, K.E., McGeoch, M.A., Mac Nally, R., 2015. The effects of climate change and land-use change on demographic rates and population viability. Biol. Rev. 90, 837-853. .
    Sheard, C., Neate-Clegg, M.H.C., Alioravainen, N., Jones, S.E.I., Vincent, C., MacGregor, H.E.A., et al., 2020. Ecological drivers of global gradients in avian dispersal inferred from wing morphology. Nat. Commun. 11, 2463. .
    Siegel, R., Pyle, P., Thorne, J., Holguin, A., Howell, C., Stock, S., et al., 2014. Vulnerability of birds to climate change in California's Sierra Nevada. Avian Conserv. Ecol. 9, 7. .
    Spence, A.R., Tingley, M.W., 2020. The challenge of novel abiotic conditions for species undergoing climate-induced range shifts. Ecography 43, 1571-1590. .
    Sumasgutner, P., Nemeth, E., Tebb, G., Krenn, H.W., Gamauf, A., 2014. Hard times in the city-attractive nest sites but insufficient food supply lead to low reproduction rates in a bird of prey. Front. Zool. 11, 48.
    Sumasgutner, P., Terraube, J., Coulon, A., Villers, A., Chakarov, N., Kruckenhauser, L., et al., 2019. Landscape homogenization due to agricultural intensification disrupts the relationship between reproductive success and main prey abundance in an avian predator. Front. Zool. 16, 31.
    Sundev, G., Yosef, R., Birazana, O., 2009. Brandt's vole density affects nutritional condition of Upland buzzard Buteo hemilasius on the Mongolian grassland steppe. Ornis Fenn. 86, 131-139.
    Synes, N.W., Osborne, P.E., 2011. Choice of predictor variables as a source of uncertainty in continental-scale species distribution modelling under climate change. Global Ecol. Biogeogr. 20, 904-914. .
    Thuiller, W., 2007. Climate change and the ecologist. Nature 448, 550-552. .
    Tinajero, R., Barragan, F., Chapa-Vargas, L., 2017. Raptor functional diversity in scrubland-agricultural landscapes of northern-central-Mexican dryland environments. Trop. Conserv. Sci. 10, 1940082917712426. .
    Tobias, J.A., Sheard, C., Pigot, A.L., Devenish, A.J.M., Yang, J., Sayol, F., et al., 2022. AVONET: morphological, ecological and geographical data for all birds. Ecol. Lett. 25, 581-597. .
    Townsend Peterson, A., Papeş, M., Eaton, M., 2007. Transferability and model evaluation in ecological niche modeling: a comparison of GARP and Maxent. Ecography 30, 550-560. .
    Turner, M.G., Calder, W.J., Cumming, G.S., Hughes, T.P., Jentsch, A., LaDeau, S.L., et al., 2020. Climate change, ecosystems and abrupt change: science priorities. Philos. T. Roy. Soc. B. 375, 20190105. .
    Valladares, F., Matesanz, S., Guilhaumon, F., Araújo, M.B., Balaguer, L., Benito-Garzón, M., et al., 2014. The effects of phenotypic plasticity and local adaptation on forecasts of species range shifts under climate change. Ecol. Lett. 17, 1351-1364. .
    van Gils, J.A., Lisovski, S., Lok, T., Meissner, W., Ożarowska, A., De Fouw, J., et al., 2016. Body shrinkage due to Arctic warming reduces red knot fitness in tropical wintering range. Science 352, 819-821. .
    van Nes, E.H., Arani, B.M., Staal, A., van der Bolt, B., Flores, B.M., Bathiany, S., et al., 2016. What do you mean, 'tipping point. Trends Ecol. Evol. 31, 902-904. .
    Vizentin-Bugoni, J., Debastiani, V.J., Bastazini, V.A., Maruyama, P.K., Sperry, J.H., 2020. Including rewiring in the estimation of the robustness of mutualistic networks. Method. Ecol. Evol. 11, 106-116. .
    Walther, G-R., Post, E., Convey, P., Menzel, A., Parmesan, C., Beebee, T.J., et al., 2002. Ecological responses to recent climate change. Nature 416, 389-395.
    Warren, D.L., Seifert, S.N., 2011. Ecological niche modeling in Maxent: the importance of model complexity and the performance of model selection criteria. Ecol. Appl. 21, 335–342. .
    White, A.E., 2016. Geographical barriers and dispersal propensity interact to limit range expansions of Himalayan birds. Am. Nat. 188, 99-112.
    Williams, S.E., Shoo, L.P., Isaac, J.L., Hoffmann, A.A., Langham, G., 2008. Towards an integrated framework for assessing the vulnerability of species to climate change. PLoS Biol. 6, 2621-2626. .
    Wu, J., 2015. Detecting and attributing the effect of climate change on the changes in the distribution of Qinghai-Tibet plateau large mammal species over the past 50 years. Mammal Res. 60, 353-364. .
    Wu, T.W., Lu, Y.X., Fang, Y.J., Xin, X.G., Li, L., Li, W.P., et al., 2019. The Beijing Climate Center climate system model (BCC-CSM): the main progress from CMIP5 to CMIP6. Geosic. Model Dev. 12, 1573-1600.
    Xia, S., Liu, P., Jiang, Z., Cheng, J., 2021. Simulation evaluation of AMO and PDO with CMIP5 and CMIP6 models in historical experiment. Adv. Earth Sci. 36, 58-68. .
    Xin, X.G., Wu, T.W., Zhang, J., Zhang, F., Li, W.P., Zhang, Y.W., et al., 2019. Introduction of BCC models and its participation in CMIP6. Adv. Clim. Change Res. 15, 533.
    Xu, J., Grumbine, R.E., 2014. Building ecosystem resilience for climate change adaptation in the Asian highlands. WIREs. Clim. Change 5, 709-718. .
    Zhang, K., Yao, L., Meng, J., Tao, J., 2018. Maxent modeling for predicting the potential geographical distribution of two peony species under climate change. Sci. Total Environ. 634, 1326-1334. .
    Zhang, J., Jiang, F., Li, G., Qin, W., Li, S., Gao, H., et al., 2019. Maxent modeling for predicting the spatial distribution of three raptors in the Sanjiangyuan National Park, China. Ecol. Evol. 9, 6643-6654. .
    Zhang, J., Jiang, F., Li, G., Qin, W., Wu, T., Xu, F., et al., 2021. The four antelope species on the Qinghai-Tibet plateau face habitat loss and redistribution to higher latitudes under climate change. Ecol. Indic. 123, 107337. .
    Zhu, Q., Hobson, K.A., Zhao, Q., Zhou, Y., Damba, I., Batbayar, N., et al., 2020. Migratory connectivity of Swan Geese based on species' distribution models, feather stable isotope assignment and satellite tracking. Divers. Distrib. 26, 944-957. .
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