Shan Tian, Jiliang Xu, Jianqiang Li, Zhengwang Zhang, Yong Wang. 2018: Research advances of Galliformes since 1990 and future prospects. Avian Research, 9(1): 32. DOI: 10.1186/s40657-018-0124-7
Citation: Shan Tian, Jiliang Xu, Jianqiang Li, Zhengwang Zhang, Yong Wang. 2018: Research advances of Galliformes since 1990 and future prospects. Avian Research, 9(1): 32. DOI: 10.1186/s40657-018-0124-7

Research advances of Galliformes since 1990 and future prospects

Funds: 

the funded by the National Key Programme of Research and 422 Development, Ministry of Science and Technology 2016YFC0503200

More Information
  • Corresponding author:

    xujiliang@bjfu.edu.cn

  • Received Date: 04 Jul 2017
  • Accepted Date: 18 Sep 2018
  • Available Online: 24 Apr 2022
  • Publish Date: 26 Sep 2018
  • Background 

    Galliformes are widely distributed throughout the world and economically important to humans as domesticated animals or gamebirds. They are at a unique position for advancing knowledge and techniques of wildlife conservation as the barometer of the status of applied ecology. Populations of many galliform species have declined mainly due to habitat loss and over-hunting. An assessment of knowledge of Galliformes could help to provide guidelines for future research and conservation strategies.

    Methods 

    Using the Web of Science search engine, we conducted a literature review of galliform-related articles published from 1990 to 2016. We used the "research area" option to filter articles focused on the zoology, environmental sciences ecology, biodiversity conservation, forestry, behavioral sciences, reproductive biology, biochemistry and molecular biology, cell biology, genetics and heredity, evolutionary biology, physiology and developmental biology. We then checked duplication based on the title, abstract and full text. In addition, we examined the reference lists of selected studies to include the publications that were missed by above searching.

    Results 

    We retained 1874 articles related to the Galliformes from the initial 243, 128 publications that were found. About 91.4% focused on one or two species, and 85.0% were conducted within a short duration, typically 1-2 years. The majority of the articles concentrated on macroscopic ecology (55.5%), mainly focusing on habitat selection or habitat use. With recent advances of molecular biology, the studies of taxonomy and phylogenetics rose quickly in last two decades. The study of physiology and biochemistry was no longer limited to simple description but expanded to the mechanisms of phenotype and micro-evolutionary potential. An additional area receiving increasing attention is the conservation of Galliformes, with the assessment of the conservation status and conservation management effectiveness of Galliformes (e.g. species diversity and genetic diversity) becoming the focus.

    Conclusions 

    The studies on Galliformes have made great achievements since 1990, but there are still gaps, particularly in macroscopic ecology, molecular genetics, and conservation. There is an urgent need to enhance long-term monitoring and analysis of population dynamics, and applying different disciplines to galliform conservation. Moreover, life history information of many galliform species is still lacking, which has hindered conservation efforts and effectiveness. In addition, multidiscipline studies and new technologies are not common for galliform studies, and should be encouraged.

  • Flight is the central avian adaptation and an extraordinary range of specialized modes of flight has been exploited during evolution (Gill 1995). Skeleton and wing muscles form the basis for avian flight; these anatomical features are also adapted to reflect the flight style and phylogeny (McKitrick 1991; Corvidae et al. 2006). Muscle architecture and the physiological properties of muscle fiber are important in the evaluation of muscle capacity. With few exceptions, muscle mass and volume distribution are considered key physical parameters in avian myology, controlling flight (Fisher 1946; Owre 1967). Additional studies are needed to investigate the relationship between fiber-type distribution and muscle function (e.g., Meyers 1992; Torrella et al. 1998; Meyers and Stakebake 2005; Corvidae et al. 2006; Welch and Altshuler 2009). Mass, volume and the type of fiber substantially affect the contractile properties of a muscle. However, more than any other factor, muscle architecture predicts muscle function (Lieber and Fridén 2000; Ward et al. 2009; Lieber and Ward 2011). The importance of muscle fiber architecture has long been recognized and related studies have been widely reported in human and other mammals (Sacks and Roy 1982; Payne et al. 2005, 2006; Williams et al. 2007, 2008; Eng et al. 2008; Channon et al. 2009; Rupert et al. 2014), but seldom in birds. Report on the hind limb of an ostrich by Smith et al. (2006) may have been the first comprehensive research on avian muscle architecture. More work, with a special focus on the functional relationship between muscle architecture and flight styles, is needed (Dial 1992a).

    Skeletal muscle architecture is defined as the arrangement of muscle fibers within a muscle (Gans 1982; Sacks and Roy 1982). The two most important architectural parameters are the physiological cross-sectional area (PCSA) and muscle fiber length (Lieber and Fridén 2000). The PCSA of a muscle is the only architectural parameter that is directly proportional to force generation, whereas muscle excursion and velocity are directly proportional to muscle fiber length. A greater serial sarcomere count (i.e., greater fiber length) leads directly to a larger muscle excursion, because serial excursions of individual sarcomeres are additive (Lieber and Ward 2011). Thus, long fibers are predicted to operate over a relatively wide range of muscle lengths, which can achieve greater velocities compared with shorter-fibered muscles. Due to these direct structure-function correlations, architectural features will undoubtedly reflect the functional properties and specializations of different muscles.

    The pectoralis and supracoracoideus play dominant roles in bird flight and are the most widely studied of the forelimb muscles. Electromyographic (EMG) data indicate that intrinsic muscles of the wing contribute little additional mechanical power for flight, but are important in modulating wing orientation and controlling wing shape (Dial 1992a, 1992b; Biewener 2011). Due to the difficulty of in vivo force measurements for smaller muscles located more distally in the wing, the roles of these muscles in adjusting the wing, as well as their functional specializations, remain largely unknown (Biewener 2011). Given this circumstance, analysis of muscle architecture can play an important role in evaluating the role of muscles. China is particularly rich in pheasants and their relatives, with the result that these receive more attention than any other group. The Golden Pheasant (Chrysolophus pictus) is an endemic pheasant of China, but with their number decreasing (Lei and Lu 2006). The flight of this species, as well as that of other phasianids involves an explosive take-off followed by a rapid and swift flapping flight (Askew and Marsh 2002). Many investigations have focused on the power output and function of wing muscles during non-steady flight (Dial 1992a, 1992b; Tobalske and Dial 2000; Askew et al. 2001; Askew and Marsh 2002). In this study we have attempted to quantify forelimb muscles architecture in the Golden Pheasant, with an emphasis on the brachial, antebrachial and manual segments and their varying functions in adjusting wing shape. An understanding of muscle architectural specialization illuminates the functional features of the different muscles used during flight and may provide additional information for further biomechanical and in vivo investigations.

    Five adult Golden Pheasants [three females, two males; mass 422±95.8 g (mean±SD)] were obtained from an accredited local farm for use in this study, killed by decapitation. The project was approved by the Animal Care and Ethics Committee of Capital Normal University. After the birds were sacrificed, each limb was skinned and individual muscles were identified, exposed and cleared of fascia. The muscles were then systematically removed and detailed dissections of muscle-tendon architecture were carried out. Muscle architecture was determined from methods described by Smith et al. (2006). Muscle mass measurements were obtained with an electronic scale (Ohaus, USA) to the nearest 0.0001 g, while lengths were measured with digital calipers (Workzone, Germany) to the nearest 0.01 mm. Muscle belly length was measured as the length from the most proximal fibers to the most distal fibers. Muscle volume was calculated by dividing muscle mass by muscle density (1.06 g∙cm-3; Mendez and Keys 1960; Brown et al. 2003) and its physiological cross-sectional area (PCSA) was then determined by dividing muscle volume by its mean fascicle length. To obtain fascicle length, the collagen between the muscle fibers was gradually dissolved in nitric acid (30% HNO3) for about 24 h and then the tissue was immersed in a 50% glycerol solution. At least five measurements of fascicle length were taken from randomly distributed areas and depths within the muscle belly. The maximum isometric force of a muscle, Fmax, was estimated by multiplying PCSA by the maximum isometric stress of a vertebrate skeletal muscle (0.3 MPa; Wells 1965). Pennation angles were not included in our measurements, because muscle bellies in most of the forelimb muscles are directly attached to the skeleton, without forming tendons of insertion. As well, among muscles with an insertion tendon, nearly all angles are smaller than 10°; the cosine of a 10° angle is very close to one and would thus have little effect on estimations of PCSA.

    For gross comparison, muscles were assigned to six functional groups: i.e., brachial depressors/elevators, antebrachial extensors/flexors, antebrachial depressors/elevators, manual extensors/flexors, manual depressors/elevators and manual intrinsics. For each functional muscle group, the sum of the separate muscle PCSAs and the total force were combined to result in one value for each group; the fascicle length was averaged among the containing muscles of that group. One way ANOVA was performed within each functional group. Functions of individual muscles have not been previously published for this species and are, therefore, based on anatomical positioning, our personal observations and references from George and Berger (1966).

    All 47 previously recognized muscles (Zhang and Yang 2013) were identified in the pectoral limb of the Golden Pheasant. The corresponding architectural data are presented in Table 1. On average, the unilateral forelimb muscle mass of the Golden Pheasant accounts for 12.00%±1.56% (mean±SD) of body mass, corresponding to 51.74 g of muscle per forelimb. Muscles were assigned to four groups according to their location; the distribution of muscle mass exhibited a sharp reduction from the proximal to the distal (Figure 1). Extrinsic muscles, brachial muscles, antebrachial muscles and manual muscles constitute 84.98%, 8.81%, 5.94% and 0.33%, respectively, of the total forelimb muscle mass. The pectoralis (PT), supracoracoideus (SP) and scapulohumeralis caudalis (SHC) are the three largest muscles of the forelimb. PT accounts for 13.4% of the total body mass and 55.86% of the total forelimb muscle mass. The corresponding data for SP are 3.9% and 16.31% and for SHC 1.3% and 5.33%. The largest brachial muscle was triceps brachii, accounting for 4.63% of the total forelimb muscle mass, closely followed by biceps brachii (BB, 1.66%) and deltoideus major (DMA, 1.41%). Triceps brachii is composed of two distinct heads, the humeral and scapular, with the humeral head (TH, 1.331±0.490 g) slightly larger than the scapular head (TS, 1.076±0.439 g). The extensor metacarpi radialis (EMR) and flexor carpi ulnaris (FCU) muscles form the bulk of the antebrachial group, weighing 0.614±0.284 g and 0.469±0.293 g, respectively, corresponding to 1.17 and 0.87% of the total forelimb muscle mass. Smallest were the distal manual muscles, which include the flexor alulae (FA, 0.004±0.002 g, 0.008%), the extensor brevis alulae (EBA, 0.007±0.005 g, 0.014%) and the flexor digiti minoris (FDMI, 0.011±0.006 g, 0.020%).

    Table  1.  Architectural properties of forelimb muscles in the Golden Pheasant
    Muscle Abbreviation Muscle mass (g) Fascicle length (cm) Belly length (cm) Volume (cm3) PCSA (cm2) Force (N)
    M. pectoralis PT 28.830±10.956 2.439±0.155 9.216±3.200 27.198 11.152 334.552
    M. supracoracoideus SC 8.495±3.547 2.330±0.601 10.141±0.520 8.014 3.632 108.951
    M. scapulohumeralis caudalis SHC 2.771±1.143 1.686±0.480 4.676±0.506 2.614 1.611 48.334
    M. coracobrachialis caudalis CBC 1.101±0.397 1.031±0.321 3.582±0.342 1.039 1.065 31.954
    M. subcoracoideus SUC 0.985±0.396 1.192±0.382 2.844±0.473 0.929 0.858 25.753
    M. subscapularis SS 0.177±0.065 0.487±0.056 1.434±0.244 0.167 0.339 10.177
    M. latissimus dorsi pars cranialis LDCR 0.126±0.029 2.080±0.351 3.245±0.486 0.119 0.058 1.729
    M. latissimus dorsi pars caudalis LDCA 0.387±0.203 2.238±0.212 3.237±0.396 0.365 0.162 4.870
    M. rhomboideus superficialis RS 0.247±0.115 0.773±0.204 1.100±0.215 0.233 0.291 8.742
    M. rhomboideus profundus RP 0.263±0.139 0.670±0.189 0.730±0.095 0.248 0.359 10.755
    M. serratus superficialis pars caudalis SSC 0.217±0.081 0.914±0.213 1.796±0.273 0.205 0.232 6.956
    M. serratus superficialis pars metapatagialis SSM 0.123±0.031 4.044±0.643 5.222±0.814 0.116 0.029 0.866
    M. serratus profundus SP 0.067±0.031 0.566±0.144 0.795±0.277 0.063 0.115 3.436
    M. scapulohumeralis cranialis SHA 0.017±0.004 0.560±0.243 0.968±0.298 0.016 0.031 0.935
    M. sternocoracoideus STC 0.073±0.059 0.396±0.021 0.600±0.118 0.068 0.171 5.142
    M. serratus superficialis pars cranialis SSA 0.047±0.015 1.113±0.221 1.810±0.351 0.044 0.040 1.191
    M. triceps brachii humeral head TH 1.331±0.490 1.220±0.137 5.173±0.277 1.256 1.022 30.672
    M. triceps brachii scapular head TS 1.076±0.439 1.503±0.390 5.250±0.298 1.015 0.675 20.236
    M. biceps brachii BB 0.850±0.449 1.293±0.772 4.048±0.670 0.802 0.846 25.387
    M. deltoideus major DMA 0.719±0.254 2.174±0.120 3.910±0.346 0.678 0.316 9.478
    M. tensor propatagialis TP 0.398±0.176 2.009±0.228 2.860±0.248 0.375 0.204 6.112
    M. deltoideus minor DMI 0.062±0.017 0.460±0.141 0.983±0.246 0.058 0.136 4.069
    M. expansor secondariorum ES 0.057±0.020 0.277±0.083 0.654±0.076 0.054 0.195 5.836
    M. brachialis BR 0.038±0.009 0.640±0.163 1.239±0.272 0.036 0.058 1.732
    M. extensor metacarpi radialis EMR 0.614±0.284 0.871±0.148 3.981±0.243 0.580 0.648 19.426
    M. pronator superficialis PS 0.351±0.112 0.644±0.097 3.550±0.079 0.332 0.509 15.282
    M. pronator profundus PP 0.297±0.101 0.915±0.332 2.997±0.294 0.280 0.326 9.772
    M. flexor carpi ulnaris FCU 0.469±0.293 0.541±0.092 3.670±0.157 0.442 0.804 24.129
    M. flexor digitorum profundus FDP 0.251±0.091 0.874±0.236 3.541±0.289 0.237 0.283 8.477
    M. extensor carpi ulnaris ECU 0.172±0.067 0.642±0.114 3.421±0.257 0.163 0.263 7.897
    M. ectepicondylo ulnaris ECTU 0.205±0.079 0.507±0.280 4.223±0.230 0.193 0.501 15.023
    M. extensor longus alulae ELA 0.158±0.067 1.440±0.566 3.949±0.277 0.149 0.104 3.135
    M. supinator SU 0.089±0.034 0.410±0.083 2.485±0.485 0.084 0.201 6.035
    M. extensor longus digiti majoris ELDM 0.088±0.037 0.819±0.244 2.406±0.270 0.083 0.101 3.025
    M. entepicondylo ulnaris ENU 0.077±0.042 0.395±0.114 2.166±0.181 0.072 0.185 5.552
    M. ulnometacarpalis ventralis UV 0.092±0.032 0.780±0.196 2.259±0.296 0.087 0.118 3.542
    M. ulnometacarpalis dorsalis UD 0.076±0.026 0.663±0.134 1.245±0.359 0.071 0.106 3.179
    M. extensor digitorum communis EDC 0.070±0.024 0.790±0.164 3.163±0.184 0.066 0.084 2.532
    M. flexor digitorum superficialis FDS 0.063±0.017 0.654±0.014 1.809±0.244 0.060 0.091 2.739
    M. abductor digiti majoris ABDM 0.043±0.025 0.259±0.056 2.098±0.198 0.041 0.157 4.712
    M. interosseus dorsalis ID 0.034±0.009 0.271±0.074 1.430±0.203 0.032 0.120 3.613
    M. interosseus ventralis IV 0.027±0.015 0.282±0.116 1.482±0.273 0.025 0.095 2.836
    M. abductor alulae ABA 0.034±0.019 0.436±0.158 0.999±0.183 0.032 0.080 2.410
    M. adductor alulae ADA 0.012±0.004 0.390±0.180 0.626±0.135 0.012 0.034 1.011
    M. extensor brevis alulae EBA 0.007±0.005 0.243±0.116 0.537±0.055 0.007 0.030 0.905
    M. flexor alulae FA 0.004±0.002 0.198±0.048 0.428±0.185 0.004 0.020 0.589
    M. flexor digiti minoris FDMI 0.011±0.006 0.164±0.032 1.588±0.344 0.010 0.064 1.912
     | Show Table
    DownLoad: CSV
    Figure  1.  Mean muscle mass (± SD) as a proportion of total forelimb muscle mass for all muscles.
    The pectoralis is omitted here because of its large percentage (55.86%) of the forelimb of the Golden Pheasant.

    Among muscles with an insertion tendon, tendon lengths were obtained only from those that were discernible. The proportional lengths of muscle belly and tendon lengths are shown in Figure 2. Most tendons in the distal limb are comparatively longer than those in the proximal limb and exceed belly lengths.

    Figure  2.  Comparison between tendon length and muscle belly length for both the proximal (dark red and dark blue) and distal (light red and light blue) forelimb muscles of the Golden Pheasant.

    The distribution of fascicle lengths showed a general trend, with proximal muscles having longer fascicles and distal muscles relatively short fascicles. The pectoralis and supracoracoideus had the longest mean fascicle lengths (2.44 cm and 2.33 cm, respectively), followed closely by the latissimus dorsi, deltoideus major, tensor propatagialis and scapulohumeralis caudalis. Intrinsic manual muscles displayed the shortest fascicle lengths, ranging from 0.16 to 0.44 cm. Among the intrinsics, the abductor and adductor of the alular digit were relatively high in this parameter.

    The pectoralis muscle had, on average, the largest PCSA (11.15 cm2), thus yielding the highest force-producing capacity of all the forelimb muscles (Fmax=334.55 N), followed by the supracoracoideus (3.63 cm2, 108.95 N). PCSAs of most of the intrinsics of the manus, i.e., latissimus dorsi pars cranialis, scapulohumeralis cranialis, serratus superficialis pars cranialis and brachialis, were the smallest, at less than 0.1 cm2.

    The fascicle length and PCSA of six functional groups are shown in Table 2. A significant difference (p < 0.05) was observed between the antebrachial depressors and elevators for PCSA and total force. The difference in PCSA and total force between the intrinsics of the alular digit and major digit was also significant (p < 0.05).

    Table  2.  Comparisons of muscle group architecture
    Functional group Mean FL (cm) Total PCSA (cm2)
    Brachium Depressors 1.815 12.010
    Elevators 1.662 5.694
    Antebrachium Extensors 1.088 2.501
    Flexors 0.999 2.823
    Antebrachium Depressors 0.688 1.336
    Elevators 0.402 0.386
    Manus Flexors 0.692 1.665
    Extensors 0.980 0.937
    Manus Depressors 0.465 0.466
    Elevators 0.629 0.321
    Intrinsics Alular digit 0.317 0.164
    Major digit 0.271 0.372
    Minor digit 0.164 0.064
     | Show Table
    DownLoad: CSV

    Disparities in architectural properties and mechanical function within a synergic group, or between different functional groups, are shown in Figure 3. The antebrachial extensors were characterized by high force-producing capacities, whereas the flexors showed more diversification in their fundamental design: FCU and BB would be expected to govern function because of their large PCSAs, while TP has the capacity to perform a large excursion. Considering the depressors (or pronators) vs. elevators (supinators) comparison, the other antagonistic groups of the antebrachium, the former possessed a significantly larger PCSA and relatively longer FL than the latter. The FCU appeared to control the flexion of the manus in force production. Extension of the manus tended to be more complicated than flexion for simultaneously possessing a large PCSA muscle (EMR) and a long FL muscle (ELA), features that suggest a design predicated on both force and speed. All intrinsic muscles of the manus are indicated on the bottom of the left-hand side of Figure 3 (low PCSA, short fascicles). They differ anatomically in that the muscles of the major digit are characterized by large PCSAs, whereas the muscles in the alular digit are remarkable for their long fascicle lengths.

    Figure  3.  Scatter graph of fascicle length and physiological cross-sectional areas (PCSAs) of muscles in the forelimb of the Golden Pheasant.

    Along with certain mammals and birds, such as horses and ostriches (Payne et al. 2005; Smith et al. 2006), the distribution of muscle mass throughout the Golden Pheasant forelimb demonstrated a proximal-to-distal reduction. This design minimizes the moment of inertia during locomotion (Hildebrand 1988), which, in turn, conserves metabolic energy (Steudel 1996). The tendon in the distal forelimb segment of the Golden Pheasant was relatively longer than that found in the proximal. This extra length enables muscles to control distal movements of the wing without the burden of extra muscle mass. Possessing a small and lightweight distal segment (Biewener 2011) provides a distinct advantage by decreasing limb inertia during flight. The ability of tendons to stretch and recoil enables storage and recovery of elastic energy, while allowing muscle fibers to sustain high forces (Roberts 2002). Longer tendons in the distal forelimb enhance muscle performance by increasing contraction efficiency and reducing metabolic costs.

    As the primary wing depressor and elevator, the pectoralis and supracoracoideus are the most widely studied of the forelimb muscles. In the Golden Pheasant, these two muscles possess the largest mass, PCSA and proportion of all the muscles in the forelimb. As expected, they also possess the longest fascicle lengths. Greenwalt (1962) predicted that the pectoralis muscle of a volant species should constitute 15.5% of the total body mass of a bird. In the Golden Pheasant, this muscle comprises 13.4% of total body mass. Regarding the supracoracoideus, this muscle represents about 1.6% of the total body mass in volant birds and most non-diving birds, 4%-5% in wing-propelled diving birds (e.g., Atlantic Puffin) and 10%-12% in penguins (Greenwalt 1962; Poore et al. 1997; Kovacs and Meyers 2000). Our results show that the supracoracoideus in the Golden Pheasant accounts for 3.9% of the total body mass of this species. The relatively large supracoracoideus in alcids and other wing-propelled diving birds most likely evolved to raise the wing against the resistive drag of water (Kovacs and Meyers 2000). The pheasants use high-frequency, high-amplitude wing beats during their explosive take-off flights and these attributes probably create a high inertial power requirement for elevating the wing. In the Golden Pheasant, it is likely that the supracoracoideus is large to meet this inertial-work or inertial-power requirement. A long fascicle results in greater excursion length, whereas PCSA corresponds to force production. The long fascicle and large PCSA of the pectoralis and supracoracoideus enable powerful upstrokes and downstrokes through a large excursion to achieve sufficient aerodynamic lift, particularly during takeoff and vertical ascending flights (Biewener 2011).

    Previous work (Dial 1992a; Berg and Biewener 2010) demonstrated that the brachial and antebrachial muscles act primarily as joint stabilizers and are not essential for normal extension and flexion of the wing during level flapping flights. However, during non-steady flights (e.g., takeoffs and landings), these muscles contribute to the performance of the wing by acting as an aerofoil, modulating wing orientation and wing shape (Dial 1992a, 1992b; Biewener 2011). In this study, we have demonstrated differences in architectural design within antagonistic groups of the antebrachial muscles (Table 2). For example, antebrachial elevators vs. depressors present significant differences in total PCSA and estimated isometric force (p < 0.05). Similar differences were also observed between intrinsic muscles of the major and alular digits, which have architectural features that facilitate force production (major digits) and excursion (alular digit). Pronounced development of the antebrachial depressors (pronator superficialis, pronator profundus and ectepicondylo ulnaris) suggests that ventrally rotating the distal half of the wing should profoundly affect shape change and orientation modulation during non-steady flights; these muscles are evidently capable of providing increased thrust for acceleration during takeoff and vertical ascending flights (Biewener 2011). A similar trend was also found in pigeons, where the pronator superficialis exhibited biphasic activities with EMG intensities at their maximum during takeoff and ascending flights (Dial 1992b). Three birds of prey (Cooper's Hawk Accipiter cooperii, Osprey Pandion haliaetus and Red-tailed Hawk Buteo jamaicensis) also exhibit these muscle-induced phenomena, which may explain observed differences in flight mode and hunting behavior (Corvidae et al. 2006). The manus intrinsics (Figure 3) may be related to joint stabilization or the execution of precision movements (Williams et al. 2008; Channon et al. 2009). The major digit provides support for the outer primaries that control forward thrust and aerodynamic performance, particularly during flapping flights (Combes and Daniel 2001; Swaddle and Lockwood 2003). The major digit intrinsics, with their greater PCSAs and force generation capability, may help stabilize the wing and provide powerful support for the primary feathers. The alula, or bastard wing, is a high lift device located at the leading edge of the wing that allows birds to fly at an acute attack angle and at a lower speed without stalling (Gill 1995; Alvarez et al. 2001). It features 3 to 5 small flight feathers originating from the first digit and moves independently of the rest of the wingtip. Four muscles attach to the alular digit and control the position of the alula; among them, the adductor and abductor appear to be dominant and more effective functionally due to their significantly longer fascicles. Their architectural properties are designed for excursion and velocity, as muscles with relatively long fibers operate over a large range of muscle lengths and can achieve faster velocities compared with a shorter-fibered muscle (Ward et al. 2009). This may imply that adjustment of the alula is paramount for rapid adduction and abduction during flight in the Golden Pheasant.

    Our results also revealed that muscle architecture varies widely within synergic groups (Figure 3, Table 2). The flexors of the manus, for example, are composed of six different muscles: i.e., flexor carpi ulnaris, flexor digitorum profundus, flexor digitorum superficialis, extensor carpi ulnaris, ulnometacarpalis ventralis and ulnometacarpalis dorsalis. They all contribute to flexion of the manus. The flexor carpi ulnaris (FCU), with the largest PCSA at nearly 8 times that of the flexor digitorum superficialis, generates very high forces and acts as the functionally dominant muscle in this group. Within the manual extensors group, extensor metacarpi radialis was designed for optimal force production, whereas extensor longus alulae evolved for fast velocity. These results are consistent with Dial's observation (1992a) that the extensor metacarpi radialis and flexor carpi ulnaris exhibited their greatest EMG activity during non-steady flights, indicating that the manus extension is significantly more complicated than flexion.

    The authors declare that they have no competing interests.

    ZZ designed the experiments, and YY and HW conducted the experiments. ZZ, HW and YY analyzed the data and wrote the paper. All authors read and approved the final manuscript.

    We thank Guangdi Si for her help with laboratory work. This work was supported by the National Natural Science Foundation of China (30870263, 31272259).

  • Akins CK, Zentall TR. Imitative learning in male Japanese quail (Coturnix japonica) using the two-action method. J Comp Psychol. 1996;110:316.
    Ancel A, Visschedijk AJ. Respiratory exchanges in the incubated egg of the domestic guinea fowl. Resp Physiol. 1993;91:31-42.
    Anich NM, Worland M, Martin KJ. Habitat use by spruce grouse in northern Wisconsin. Wildl Soc B. 2013;37:766-77.
    Apa AD, Wiechman LA. Captive-breeding of captive and wild-reared Gunnison sage-grouse. Zoo Biol. 2016;35:70-5.
    Ashizawa K, Kawaji N, Tanaka A, Nagase D, Matsumoto Y, Tatemoto H, Tatemoto H, Tsuzuki Y. Population fluctuation and habitat preference of Ijima's Copper Pheasant Syrmaticus soemmerringii ijimae: an endemic, 'near threatened' Japanese subspecies. Ornithol Sci. 2014;13:77-81.
    Barilani M, Bernard-Laurent A, Mucci N, Tabarroni C, Kark S, Perez G, Jose A, Randi E. Hybridisation with introduced chukars (Alectoris chukar) threatens the gene pool integrity of native rock (A. graeca) and red-legged (A. rufa) partridge populations. Biol Conserv. 2007a;137:57-69.
    Barilani M, Sfougaris A, Giannakopoulos A, Mucci N, Tabarroni C, Randi E. Detecting introgressive hybridisation in rock partridge populations (Alectoris graeca) in Greece through Bayesian admixture analyses of multilocus genotypes. Conserv Genet. 2007b;8:343-54.
    Baruch-Mordo S, Evans JS, Severson JP, Naugle DE, Maestas JD, Kiesecker JM, Falkowski MJ, Hagen CA, Reese KP. Saving sage-grouse from the trees: a proactive solution to reducing a key threat to a candidate species. Biol Conserv. 2013;167:233-41.
    Bellinger MR, Johnson JA, Toepfer J, Dunn P. Loss of genetic variation in greater prairie chickens following a population bottleneck in Wisconsin, USA. Conserv Biol. 2003;17:717-24.
    Benedict NG, Oyler-McCance SJ, Taylor SE, Braun CE, Quinn TW. Evaluation of the eastern (Centrocercus urophasianus urophasianus) and western (Centrocercus urophasianus phaios) subspecies of sage-grouse using mitochondrial control-region sequence data. Conserv Genet. 2003;4:301-10.
    Bernardo CSS, Desbiez ALJ, Olmos F, Collar NJ. Reintroducing the red-billed curassow in Brazil: population viability analysis points to potential success. Nat Conserv. 2014;12:53-8.
    Birks SM, Edwards SV. A phylogeny of the megapodes (Aves: Megapodiidae) based on nuclear and mitochondrial DNA sequences. Mol Phylogenet Evol. 2002;23:408-21.
    Block WB, Brennan LA. The habitat concept in ornithology theory and application. Curr Ornithol. 1993;11:35-91.
    Blomberg EJ. The influence of harvest timing on greater sage-grouse survival: a cautionary perspective. J Wildl Manag. 2015;79:695-703.
    Bourke JM, Witmer LM. Nasal conchae function as aerodynamic baffles: experimental computational fluid dynamic analysis in a turkey nose (Aves: Galliformes). Resp Physiol Neurobiol. 2016;234:32-46.
    Bouzat JL. The importance of control populations for the identification and management of genetic diversity. Genetica. 2000;110:109-15.
    Brautigam KJ, Osborne DC, White JD. Photographic evidence and chronology of nest parasitism by a Wild Turkey (Meleagris gallopavo). Wilson J Ornithol. 2016;128:204-7.
    Bro E, Mayot P, Corda E, Reitz F. Impact of habitat management on grey partridge populations: assessing wildlife cover using a multisite BACI experiment. J Appl Ecol. 2004;41:846-57.
    Brøseth H, Pedersen HC. Disturbance effects of hunting activity in a willow ptarmigan Lagopus lagopus population. Wildl Biol. 2010;16:241-8.
    Brymer ALB, Holbrook JD, Niemeyer RJ, Suazo AA, Wulfhorst JD, Vierling KT, Newingham BA, Link TE, Rachlow JL. A social-ecological impact assessment for public lands management: application of a conceptual and methodological framework. Ecol Soc. 2016;21:9.
    Bush KL, Strobeck C. Phylogenetic relationships of the Phasianidae reveals possible non-pheasant taxa. J Hered. 2003;94:472-89.
    Campbell-Staton SC, Cheviron ZA, Rochette N, Catchen J, Losos JB, Edwards SV. Winter storms drive rapid phenotypic, regulatory, and genomic shifts in the green anole lizard. Science. 2017;357:495-8.
    Capdevila J, Puigcerver M, López S, Pérez-Masdeu E, García-Galea E. The role of nest-site selection and cereal production in differential nest predation in Common Quail Coturnix coturnix and hybrid quail C. coturnix × C. japonica. Ibis. 2016;158:784-95.
    Caravaggi A, Banks PB, Burton CA, Finlay CMV, Haswell PM, Hayward MW, Rowcliffe MJ, Wood MD. A review of camera trapping for conservation behaviour research. Remote Sens Ecol Conserv. 2017;3:109-22.
    Carpio AJ, Guerrero-Casado J, Tortosa FS, Vicente J. Predation of simulated red-legged partridge nests in big game estates from South Central Spain. Eur J Wildl Res. 2014;60:391-4.
    Chang J, Wang B, Zhang YY, Liu Y, Liang W, Wang JC, Shi HT, Su WB, Zhang ZW. Molecular evidence for species status of the endangered Hainan peacock pheasant. Zoo Sci. 2008;25:30-5.
    Charvet CJ, Striedter GF. Developmental species differences in brain cell cycle rates between northern bobwhite quail (Colinus virginianus) and parakeets (Melopsittacus undulatus): implications for mosaic brain evolution. Brain Behav Evol. 2008;72:295-306.
    Chávez-león G, Velázquez A, Fregoso A, Bocco G. Habitat associations of the long-tailed wood-partridge (Dendrortyx macroura) in a managed coniferous forest in Michoacán, Mexico. Biodivers Conserv. 2004;13:1943-60.
    Chen D, Liu Y, Davison GWH, Dong L, Chang J, Gao SH, Li S-H, Zhang ZW. Revival of the genus Tropicoperdix Blyth 1859 (Phasianidae, Aves) using multilocus sequence data. Zool J Linn Soc Lond. 2015;175:429-38.
    Chen Y, An B, Liu N. Asymmetrical introgression patterns between rusty-necklaced partridge (Alectoris magna) and chukar partridge (Alectoris chukar) in China. Integr Zool. 2016;11:403-12.
    Clawson MV, Skalski JR, Isabelle JL, Millspaugh JJ. Trends in male wild turkey abundance and harvest following restoration efforts in the southeast region of Missouri, 1960-2010. Wildl Soc B. 2015;39:116-28.
    Clements JF, Schulenberg TS, Iliff MJ, Roberson DT, Fredericks A, Sullivan BL, Wood CL. The eBird/clements checklist of birds of the world: v2016 (2016). . Accessed 31 Dec 2016.
    Coates PS, Casazza ML, Ricca MA, Brussee BE, Blomberg EJ, Gustafson KB, Overton CT, Davis DM, Niell LE, Espinosa SP, Gardner SC, Delehanty DJ. Integrating spatially explicit indices of abundance and habitat quality: an applied example for greater sage-grouse management. J Appl Ecol. 2016;53:83.
    Cohen C, Wakeling JL, Mandiwana-Neudani TG, Sande E, Dranzoa C, Crowe TM, Bowie RCK. Phylogenetic affinities of evolutionarily enigmatic African galliforms: the Stone Partridge Ptilopachus petrosus and Nahan's Francolin Francolinus nahani, and support for their sister relationship with New World quails. Ibis. 2012;154:768-80.
    Corfield JR, Long B, Krilow JM, Wylie DR, Iwaniuk AN. A unique cellular scaling rule in the avian auditory system. Brain Struct Funct. 2016;221:2675-93.
    Crowe T. Phylogenetic affinities of enigmatic African galliforms: the Stone Partridge Ptilopachus petrosus and Latham's and Nahan's' Francolins' Francolinus lathami and F. nahani. Cladistics. 2010;26:206.
    Crowe TM, Bowie RCK, Bloomer P, Mandiwana TG, Hedderson TAG, Randi E, Pereira SL, Wakeling J. Phylogenetics, biogeography and classification of, and character evolution in, gamebirds (Aves: Galliformes): effects of character exclusion, data partitioning and missing data. Cladistics. 2006;22:495-532.
    Daley MA, Voloshina A, Biewener AA. The role of intrinsic muscle mechanics in the neuromuscular control of stable running in the guinea fowl. J Physiol. 2009;587:2693-707.
    del Hoyo J, Elliott A, Sargatal J. Handbook to the birds of the world. Vol. 2. New world vultures to Guineafowl. Barcelona: Lynx Edicions; 1994.
    Deng WH, Zheng GM. Landscape and habitat factors affecting cabot's tragopan Tragopan caboti, occurrence in habitat fragments. Biol Conserv. 2004;117:25-32.
    Dong L, Heckel G, Liang W, Zhang Y. Phylogeography of Silver Pheasant (Lophura nycthemera L.) across China: aggregate effects of refugia, introgression and riverine barriers. Mol Ecol. 2013;22:3376-90.
    Dzialak MR, Olson CV, Harju SM, Webb SL, Mudd JP, Winstead JB, Hayden-Wing LD. Identifying and prioritizing greater sage-grouse nesting and brood-rearing habitat for conservation in human-modified landscapes. PLoS ONE. 2011;6:e26273.
    Dzialak MR, Olson CV, Harju SM, Webb SL, Winstead JB. Temporal and hierarchical spatial components of animal occurrence: conserving seasonal habitat for greater sage-grouse. Ecosphere. 2012;3:1-17.
    Ellis-Felege SN, Burnam JS, Palmer WE, Sisson DC, Carroll JP. Fight or flight: parental decisions about predators at nests of northern bobwhites (Colinus virginianus). Auk. 2013;130:637-44.
    Fearer TM, Stauffer DF. Relationship of ruffed grouse (Bonasa umbellus) home range size to landscape characteristics. Am Midl Nat. 2003;150:104-14.
    Feng X, Lin CT, Qiao HJ, Ji LQ. Assessment of climatically suitable area for Syrmaticus reevesii under climate change. Endang Species Res. 2015;28:19-31.
    Follett BK, Kumar V, Juss TS. Circadian nature of the photoperiodic clock in Japanese quail. J Comp Physiol A. 1992;171:533-40.
    Follett BK, Pearce KA. Photoperiodic control of the termination of reproduction in Japanese quail (Coturnix coturnix japonica). Proc R Soc B Biol Sci. 1990;242:225-30.
    Franco P, Fierro-Calderón K, Kattan G. Population densities and home range sizes of the Chestnut Wood-quail. J Field Ornithol. 2006;77:85-90.
    Froese GZL, Contasti AL, Mustari AH, Brodie JF. Disturbance impacts on large rain-forest vertebrates differ with edge type and regional context in Sulawesi, Indonesia. J Trop Ecol. 2015;31:509-17.
    Fuller RA, Garson PJ. Pheasants: status survey and conservation action plan 2000‒2004. In: IUCN; 2000. p. 1‒23.
    Galla SJ, Johnson JA. Differential introgression and effective size of marker type influence phylogenetic inference of a recently divergent avian group (Phasianidae: Tympanuchus). Mol Phylogenet Evol. 2015;84:1-13.
    Gama GM, Malhado ACM, Bragagnolo C, Correia RA, Ladle RJ. Cultural viability of reintroducing the ecologically extinct Alagoas Curassow (Pauxi mitu Linnaeus, 1766) to Northeast Brazil. J Nat Conserv. 2016;29:25-32.
    Garcia M, Charrier I, Iwaniuk AN. Directionality of the drumming display of the ruffed grouse. Condor. 2012;114:500-6.
    Gee GF. Avian artificial insemination and semen preservation//IFCB Symposium on breeding birds in captivity. North Hollywood: Int Found Conserv Birds; 1983. p. 375-98.
    Gill F, Donsker D. IOC World Bird List (v 6.4) (2016). . . Accessed 31 Dec 2016.
    Goddard AD, Dawson RD. Seasonal changes in habitat features influencing nest survival of sharp-tailed grouse in northeastern British Columbia, Canada. Ecoscience. 2009;16:476-82.
    Gregory AJ, Beck JL. Spatial heterogeneity in response of male greater sage-grouse lek attendance to energy evelopment. PLoS ONE. 2014;9:e97132.
    Gu LY, Liu Y, Wang N, Zhang ZW. A panel of polymorphic microsatellites in the Blue Eared Pheasant (Crossoptilon auritum) developed by cross-species amplification. Chin Birds. 2012;3:103-7.
    Hämäläinen A, Alatalo RV, Lebigre C, Siitari H, Soulsbury CD. Fighting behaviour as a correlate of male mating success in black grouse Tetrao tetrix. Behav Ecol Soc. 2012;66:1577-86.
    Hanotte O, Burke T, Armour JA, Jeffreys AJ. Hypervariable minisatellite DNA sequences in the Indian peafowl Pavo cristatus. Genomics. 1991;9:587-97.
    Hayward LS, Wingfield JC. Maternal corticosterone is transferred to avian yolk and may alter offspring growth and adult phenotype. Gen Comp Endocr. 2004;135:365-71.
    He L, Dai B, Zeng B, Zhang X, Chen B, Yue B, Li J. The complete mitochondrial genome of the Sichuan Hill Partridge (Arborophila rufipectus) and a phylogenetic analysis with related species. Gene. 2009;435:23-8.
    Heller NE, Zavaleta ES. Biodiversity management in the face of climate change: a review of 22 years of recommendations. Biol Conserv. 2009;142:14-32.
    Hennache A. A review of captive Galliformes in European zoos. Int J Galliformes Conserv. 2009;1:23-8.
    Hernández F, Henke SE, Silvy NJ, Rollins D. The use of prickly pear cactus as nesting cover by northern bobwhites. J Wildl Manag. 2003;67:417-23.
    Herrington JA, Rodriguez Y, Lickliter R. Elevated yolk progesterone moderates prenatal heart rate and postnatal auditory learning in bobwhite quail (Colinus virginianus). Dev Psychobiol. 2016;58:784-8.
    Höglund J. Genetic studies of black grouse with special reference to conservation biology: a review. Folia Zool. 2009;58:135.
    Hörnell WM, Willebrand T, Smith AA. Seasonal movements and dispersal patterns: implications for recruitment and management of willow ptarmigan (Lagopus lagopus). J Wildl Manag. 2014;78:194-201.
    Huang ZH, Liu NF, Luo SX, Long J, Xiao YA. Ecological genetics of rusty-necklaced partridge (Alectoris magna): environmental factors and population genetic variability correlations. Korean J Genetic. 2007;29:115-20.
    Huang Z, Liu N, Zhou T. A comparative study of genetic diversity of peripheral and central populations of chukar partridge from northwestern China. Biochem Genet. 2005;43:613-21.
    Iqubal P, Mcgowan PJK, Carroll JP, Rahmani AR. Home range size, habitat use and nesting success of Swamp Francolin Francolinus gularis on agricultural land in northern India. Bird Conserv Int. 2003;13:127-38.
    Janke AK, Gates RJ. Home range and habitat selection of northern bobwhite coveys in an agricultural landscape. J Wildl Manag. 2013;77:405-13.
    Jankowski MD, Russell RE, Franson JC, Dusek RJ, Hines MK, Gregg M, Hofmeister EK. Corticosterone metabolite concentrations in Greater Sage-Grouse are positively associated with the presence of cattle grazing. Rangel Ecol Manag. 2014;67:237-46.
    Jansen R, Crowe TM. Relationship between breeding activity and rainfall for Swainson's Spurfowl, Pternistis swainsonii, within southern Africa, with specific reference to the Springbok Flats, Limpopo Province, South Africa. Ostrich J Afr Ornithol. 2005;76:190-4.
    Jetz W, Wilcove DS, Dobson AP. Projected impacts of climate and land-use change on the global diversity of birds. PLoS Biol. 2007;5:e157.
    Jiang L, Wang G, Peng R, Peng Q, Zou F. Phylogenetic and molecular dating analysis of Taiwan Blue Pheasant (Lophura swinhoii). Gene. 2014;539:21-9.
    Jiang PP, Ge YF, Lang QL, Ding P. Genetic structure among wild populations of Elliot's Pheasant Syrmaticus ellioti in China from mitochondrial DNA analyses. Bird Conserv Int. 2007;17:177-85.
    Jimenez AG, Van Brocklyn J, Wortman M, Williams JB. Cellular metabolic rate is influenced by life-history traits in tropical and temperate birds. PLoS ONE. 2014;9:e87349.
    Johnsgard PA. Pheasants of the World. Washington: Smithsonian Institution Press; 1999.
    Johnson FA, Hagan G, Palmer WE, Kemmerer M. Uncertainty, robustness, and the value of information in managing a population of northern bobwhites. J Wildl Manag. 2014;78:531-9.
    Johnson JA, Dunn PO. Low genetic variation in the heath hen prior to extinction and implications for the conservation of prairie-chicken populations. Conserv Genet. 2006;7:37-48.
    Jones J. Habitat selection studies in avian ecology: a critical review. Auk. 2001;118:557-62.
    Kadhim KK, Zuki ABZ, Noordin MM, Babjee SMA, Khamas W. Light and scanning electron microscopy of the intestine of the young Red Jungle Fowl (Gallus gallus). J Anim Vet Adv. 2010;9:2729-937.
    Kayvanfar N, Aliabadian M, Ghasempouri SM. Morphometric and morphological differentiation of the subspecies of Phasianus colchicus (Linnaeus, 1758) on the Iranian Plateau (Aves: Galliformes). Zool Middle East. 2015;61:9-17.
    Khalil S, Anwar M, Hussain I. Breeding biology of grey francolin (Francolinus pondicerianus) in salt range, Pakistan. Pak J Zool. 2016;48:115-23.
    Khan WA, Mian A. Comparative population biology of black (Francolinus francolinus) and grey (F. pondicerianus) Francolins under Lal Suhanra National Park (Pakistan) conditions. Pak J Zool. 2013;45:949-58.
    Kobayashi A, Nakamura H. Chick and juvenile survival of Japanese rock ptarmigan Lagopus muta japonica. Wildlife Biol. 2013;19:358-67.
    Krakauer AH, Blundell MA, Scanlan TN, Wechsler MS, McCloskey EA, Yu JH, Patricelli GL. Successfully mating male sage-grouse show greater laterality in courtship and aggressive interactions. Anim Behav. 2016;111:261-7.
    Kurhinen J, Danilov P, Gromtsev A, Helle P, Linden H. Patterns of black grouse, Tetrao tetrix distribution in northwestern Russia at the turn of the millennium. Folia Zool. 2009;58:168-72.
    Kvasnes MAJ, Pedersen HC, Storaas T, Nilsen EB. Large-scale climate variability and rodent abundance modulates recruitment rates in Willow Ptarmigan (Lagopus lagopus). J Ornithol. 2014;155:891-903.
    Lawes MJ, Fly S, Piper SE. Gamebird vulnerability to forest fragmentation: patch occupancy of the Crested Guineafowl (Guttera edouardi) in Afromontane forests. Anim Conserv. 2006;9:67-74.
    Li Y, Cui B, Qiu X, Ding C, Batool I. Management reference for nature reserve networks based on MaxEnt modeling and gap analysis: a case study of the brown-eared pheasant in China. Anim Biodiv Conserv. 2016;39:241-52.
    Lu X, Zheng GM. Dominance-dependent microroost use in flock-living Tibetan Eared-pheasants. Ardea. 2007;95:225-34.
    Lu X, Zheng GM. Reproductive ecology of Tibetan Eared Pheasant Crossoptilon harmani in scrub environment, with special reference to the effect of food. Ibis. 2003;145:657-66.
    Lu X. Hot genome leaves natural histories cold. Science. 2015;349:1064.
    Lupis SG, Messmer TA, Black T. Gunnison sage-grouse use of conservation reserve program fields in Utah and response to emergency grazing: a preliminary evaluation. Wildl Soc B. 2005;34:957-62.
    Lyly MS, Villers A, Koivisto E, Helle P, Ollila T, Korpimäki E. Guardian or threat: does golden eagle predation risk have cascading effects on forest grouse? Oecologia. 2016;182:487-98.
    Mantyka-pringle CS, Martin TG, Rhodes JR. Interactions between climate and habitat loss effects on biodiversity: a systematic review and meta-analysis. Global Change Biol. 2013;19:1642-4.
    Martin TE. Life history evolution in tropical and south temperate birds: what do we really know? J Avian Biol. 1996;27:263-72.
    Marzluff JM. A decadal review of urban ornithology and a prospectus for the future. Ibis. 2016;159:1-13.
    Matzke AJ, Varga F, Gruendler P, Unfried I, Berger H, Mayr B, Matzke MA. Characterization of a new repetitive sequence that is enriched on microchromosomes of turkey. Chromosoma. 1992;102:9-14.
    McGowan PJK, Owens LL, Grainger MJ. Galliformes science and species extinctions: what we know and what we need to know. Anim Biodiv Conserv. 2012;35:321-31.
    McGowan PJK, Garson PJ. Status survey and conservation action Plan 1995‒1999 Pheasants. In: IUCN; 1995.
    McGowan PJK, Zhang YY, Zhang ZW. Galliformes-barometers of the state of applied ecology and wildlife conservation in China. J Appl Ecol. 2009;46:524-6.
    McJunkin JW, Zelmer DA, Applegate RD. Population dynamics of wild turkeys in Kansas (Meleagris gallopavo): theoretical considerations and implications of rural mail carrier survey (RMCS) data. Am Midl Nat. 2005;154:178-87.
    McNew LB, Sandercock BK. Spatial heterogeneity in habitat selection: nest site selection by Greater Prairie-Chickens. J Wildl Manag. 2013;77:791-801.
    Mock KE, Theimer TC, Rhodes OE, Greenberg DL, Keim P. Genetic variation across the historical range of the wild turkey (Meleagris gallopavo). Mol Ecol. 2002;11:643-57.
    Moher D, Liberati A, Tetzlaff J, Altman DG. PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med. 2009;6:e1000097.
    Moss R, Storch I, Müller M. Trends in grouse research. Wildl Biol. 2010;16:1-11.
    Moulin S, Randi E, Tabarroni C, Hennache A. Mitochondrial DNA diversification among the subspecies of the Silver and Kalij Pheasants, Lophura nycthemera and L. leucomelanos, Phasianidae. Ibis. 2003;145:E1-11.
    Myles J, Cheng L. The social and cultural life of non-native English speaking international graduate students at a Canadian university. J Engl Acad Purp. 2003;2:247-63.
    O'Brien TG, Kinnaird MF. A picture is worth a thousand words: the application of camera trapping to the study of birds. Bird Conserv Int. 2008;18:S144-62.
    Onyeanusi BI, Ema AN, Ezeokoli CD, Onyeanusi JC. The structure of the Harderian gland of the guinea fowl at embryonic and post embryonic stages. Anat Histol Embryol. 1993;22:183-90.
    Onyeyili PA, Egwu GO, Jibike GI, Atori JO. Plasma and red cell cholinesterase concentrations in guinea-fowl (Numuida meleagris) and Nigerian domestic fowl (Gallus domesticus). Vet Res Commun. 1992;1:173-5.
    Persons NW, Hosner PA, Meiklejohn KA, Braun EL, Kimball RT. Sorting out relationships among the grouse and ptarmigan using intron, mitochondrial, and ultra-conserved element sequences. Mol Phylogenet Evol. 2016;98:123-32.
    Peters J, Lebrasseur O, Deng H, Larson G. Holocene cultural history of Red jungle fowl (Gallus gallus) and its domestic descendant in East Asia. Quat Sci Rev. 2016;142:102-19.
    Pike TW, Petrie M. Experimental evidence that corticosterone affects offspring sex ratios in quail. Proc R Soc B Biol Sci. 2006;273:1093-8.
    Powell W, Morgante M, Andre C, Hanafey M, Vogel J, Tingey S, Rafalski A. The comparison of RFLP, RAPD, AFLP and SSR (microsatellite) markers for germplasm analysis. Mol Breeding. 1996;2:225-38.
    Rao M, Htun S, Platt SG, Tizard R, Poole C, Myint T, Watson JEM. Biodiversity conservation in a changing climate: a review of threats and implications for conservation planning in Myanmar. Ambio. 2013;42:789-804.
    Ren QQ, Li XF, Yuan J, Chen DS, Zhang L, Guo WW, Jiang L, Wang P, Kan XZ. Complete mitochondrial genome of the Blue Eared Pheasant, Crossoptilon auritum (Galliformes: Phasianidae). Mitochondr DNA. 2016;27:615-7.
    Rhim SJ. Ecological factors influencing nest survival of hazel grouse Bonasa bonasia in a temperate forest, South Korea. For Ecol Manag. 2012;282:23-7.
    Rhim SJ. Spring-season social organsization of the Hazel Grouse (Bonasa bonasia) in relation to habitat type in temperate forests of South Korea. Ornis Fennica. 2010;87:160-7.
    Robel RJ, Hughes JP, Keane TD, Kemp KE. Do artificial nests reveal meaningful patterns of predation in kansas grasslands? Southwest Nat. 2003;48:460-4.
    Robinson SG, Haukos DA, Plumb RT, Hagen CA, Pitman JC, Lautenbach JM, Sullins DS, Kraft JD, Lautenbach JD. Lesser prairie-chicken fence collision risk across its northern distribution. J Wildl Manag. 2016;80:906-15.
    Rolstad J, Wegge P, Sivkov AV, Hjeljord O, Storaunet KO. Size and spacing of grouse leks: comparing capercaillie (Tetrao urogallus) and black grouse (Tetrao tetrix) in two contrasting Eurasian boreal forest landscapes. Can J Zool. 2009;87:1032-43.
    Ross AM, Johnson G, Gibbs JP. Spruce grouse decline in maturing lowland boreal forests of New York. For Ecol Manag. 2016;359:118-25.
    Sandoval L, Barrantes G. Characteristics of male Spot-bellied Bobwhite (Colinus leucopogon) song during territory establishment. J Ornithol. 2012;153:547-54.
    Schulwitz S, Bedrosian B, Johnson JA. Low neutral genetic diversity in isolated Greater Sage-Grouse (Centrocercus urophasianus) populations in northwest Wyoming. Condor. 2014;116:560-73.
    Selås V, Sonerud GA, Framstad E, Kålås JA, Kobro S, Pedersen HB, Spidso TK, Wiig O. Climate change in Norway: warm summers limit grouse reproduction. Popul Ecol. 2011;53:361-71.
    Shen YY, Dai K, Cao X, Murphy RW, Shen XJ, Zhang YP. The updated phylogenies of the Phasianidae based on combined data of nuclear and mitochondrial DNA. PLoS ONE. 2014;9:e95786.
    Shen YY, Liang L, Sun YB, Yue BS, Yang XJ, Murphy RW, Zhang YP. A mitogenomic perspective on the ancient, rapid radiation in the Galliformes with an emphasis on the Phasianidae. BMC Evol Biol. 2010;10:132.
    Smith JA, Whalen CE, Bomberger BM, Powell LA. Indirect effects of an existing wind energy facility on lekking behavior of greater prairie-chickens. Ethology. 2016;122:419-29.
    Stiver JR, Apa AD, Remington TE, Remington TE, Gibson RM. Polygyny and female breeding failure reduce effective population size in the lekking Gunnison sage-grouse. Biol Conserv. 2008;141:472-81.
    Storch I. Human disturbance of grouse-why and when? Wildl Biol. 2013;19:390-403.
    Sučić I. Rock Partridge (Alectoris graeca) Population size on mountain Tušnica in the period between 2000 and 2007. Šumar List. 2008;132:331-6.
    Sun YH, Fang Y, Jia CX, Klaus S, Swenson JE, Scherzinger W. Nest site selection of Chinese grouse Bonasa sewerzowi at Lianhuashan, Gansu, China. Wildl Biol. 2007;13:68-72.
    Tang CZ. The 4th International Pheasant Symposium was held in Beijing, China. Acta Zool Sin. 1990;36:104 (in Chinese).
    Tanner EP, Elmore RD, Fuhlendorf SD, Davis CA, Thacker ET, Dahlgren DK. Behavioral responses at distribution extremes: how artificial surface water can affect quail movement patterns. Rangel Ecol Manag. 2015;68:476-84.
    Thogmartin WE. Landscape attributes and nest-site selection in wild turkeys. Auk. 1999;116:912-23.
    Thomas GH. Evolution: an avian explosion. Nature. 2015;526:516-7.
    Tirpak J, Giuliano W, Miller A. Ruffed grouse brood habitat selection at multiple scales in Pennsylvania: implications for survival. Can J Zool. 2008;86:329-37.
    Van-Niekerk JH. Vocal behaviour of Crested Francolin Dendroperdix sephaena in response to playback calls. Ostrich. 2010;81:149-54.
    Vignal A, Milan D, SanCristobal M, Eggen A. A review on SNP and other types of molecular markers and their use in animal genetics. Genet Sel Evol. 2002;34:275-305.
    Walter JB. Presidential address: three centuries of international ornithology. Acta Zool Sin. 2004;50:880-912.
    Wang N, Kimball RT, Braun EL, Liang B, Zhang ZW. Assessing phylogenetic relationships among Galliformes: a multigene phylogeny with expanded taxon sampling in Phasianidae. PLoS ONE. 2013;8:e64312.
    Wang N, Zhang ZW. The novel primers for sex identification in the brown eared-pheasant and their application to other species. Mol Ecol Resour. 2009;9:186-8.
    Wang Y, Xu J, Carpenter JP, Zhang Z, Zheng G. Information-theoretic model selection affects home-range estimation and habitat preference inference: a case study of male Reeves's Pheasants Syrmaticus reevesii. Ibis. 2012a;154:273-84.
    Wang Y, Zhang ZW, Zheng GM, Li JQ, Xu JL, Ma ZJ, Biancucci AL. Ornithological research: past twenty years and future perspectives in China. Biodivers Sci. 2012b;20:119-37 (in Chinese).
    Watson JEM, Iwamura T, Butt N. Mapping vulnerability and conservation adaptation strategies under climate change. Nature. 2013;3:989-94.
    Watson JEM, Venter O. Ecology: a global plan for nature conservation. Nature. 2017. https: //doi.org/10.1038/nature24144.
    Wells DA, Jones DN, Bulger D, Brown C. Male brush-turkeys attempt sexual coercion in unusual circumstances. Behav Process. 2014;106:180-6.
    Williams CK, Lutz RS, Applegate RD. Winter survival and additive harvest in northern bobwhite coveys in Kansas. J Wildl Manag. 2004;68:94-100.
    Wu YQ, Xu X, Liu NF, Xu F. Seasonal Changes in Habitat Use of Blue-Eared Pheasant, Crossoptilon auritum. Pak J Zool. 2013;45:1699-704.
    Xiao H, Hu Y, Lang Z, Fang B, Guo W, Zhang Q, Pan X, Lu X. How much do we know about the breeding biology of bird species in the world? J Avian Biol. 2016;4:1-6.
    Xu JL, Zhang XH, Sun QH, Zheng GM, Wang Y, Zhang ZW. Home range, daily movements and site fidelity of male Reeves's pheasants Syrmaticus reevesii in the Dabie Mountains, central China. Wildl Biol. 2009;15:338-44.
    Xu JL, Zhang ZW. Home range and habitat composition of male Reeves's Pheasants in an agricultural-forest plantation landscape in central China: a preliminary report. Chin Birds. 2011;2:53-8.
    Xu Y, Ran JH, Zhou X, Yang N, Yue BS, Wang Y. The effect of temperature and other factors on roosting times of Szechenyi Monal Partridges Tetraophasis szechenyii during the breeding season. Ornis Fenn. 2008;85:126-34.
    Zhan XJ, Zhang ZW. Molecular phylogeny of avian genus Syrmaticus based on the mitochondrial cytochrome b gene and control region. Zool Sci. 2005;22:427-35.
    Zhang L, Dong T, Xu W, Ouyang Z. Assessment of habitat fragmentation caused by traffic networks and identifying key affected areas to facilitate rare wildlife conservation in China. Wildl Res. 2015;42:266-79.
    Zhang ZW, Ding CQ, Ding P, Zheng GM. The current status and a conservation strategy for species of Galliformes in China. Biodivers Sci. 2003;11:414-21 (in Chinese).
    Zheng GM. A checklist on the classification and distribution of the birds of world. Beijing: Science Press; 2002 (in Chinese).
    Zheng GM. Pheasants in China. Beijing: Higher Education Press; 2015 (in Chinese).
    Zhou CF, Xu JL, Zhang ZW. Dramatic decline of the Vulnerable Reeves's pheasant Syrmaticus reevesii, endemic to central China. Oryx. 2015a;49:529-34.
    Zhou TC, Sha T, Irwin DM, Zhang YP. Complete mitochondrial genome of the Indian peafowl (Pavo cristatus), with phylogenetic analysis in phasianidae. Mitochondr DNA. 2015b;26:912-3.
    Zhou ZT, Zhang YY. Isolation and characterization of microsatellite markers for Temminck's Tragopan (Tragopan temminckii). Conserv Genet. 2009;10:1633-5.
  • Related Articles

  • Cited by

    Periodical cited type(13)

    1. Lin Wang, Mingjie Liu, Zihui Zhang. Reversed sexual dimorphism in the leg muscle architecture of the Eurasian sparrowhawk. The Anatomical Record, 2023, 306(2): 437. DOI:10.1002/ar.25066
    2. Xinxin Liang, Mingjie Liu, Chenxi Ying, et al. Myological variation in the hindlimb of three raptorial birds in relation to foraging behavior. Avian Research, 2022, 13: 100053. DOI:10.1016/j.avrs.2022.100053
    3. James Charles, Roger Kissane, Tatjana Hoehfurtner, et al. From fibre to function: are we accurately representing muscle architecture and performance?. Biological Reviews, 2022, 97(4): 1640. DOI:10.1111/brv.12856
    4. Lin Wang, Xinsen Wei, Xinxin Liang, et al. Ontogenetic changes of hindlimb muscle mass in Cabot's tragopan (Galliformes, Phasianidae) and their functional implications. The Anatomical Record, 2021, 304(12): 2841. DOI:10.1002/ar.24609
    5. Fernanda Bribiesca-Contreras, Ben Parslew, William I. Sellers. Functional morphology of the forelimb musculature reflects flight and foraging styles in aquatic birds. Journal of Ornithology, 2021, 162(3): 779. DOI:10.1007/s10336-021-01868-y
    6. Meg L. Martin, Kenny J. Travouillon, Patricia A. Fleming, et al. Review of the methods used for calculating physiological cross-sectional area (PCSA) for ecological questions. Journal of Morphology, 2020. DOI:10.1002/jmor.21139
    7. Nadezhda V. Kryukova, Alexander N. Kuznetsov. Suboccipital muscle of sharpnose sevengill shark Heptranchias perlo and its possible role in prey dissection. Journal of Morphology, 2020. DOI:10.1002/jmor.21142
    8. Fernanda Bribiesca‐Contreras, Ben Parslew, William I. Sellers. A Quantitative and Comparative Analysis of the Muscle Architecture of the Forelimb Myology of Diurnal Birds of Prey (Order Accipitriformes and Falconiformes). The Anatomical Record, 2019, 302(10): 1808. DOI:10.1002/ar.24195
    9. S P Sullivan, F R McGechie, K M Middleton, et al. 3D Muscle Architecture of the Pectoral Muscles of European Starling (Sturnus vulgaris). Integrative Organismal Biology, 2019, 1(1) DOI:10.1093/iob/oby010
    10. Ashley M. Heers, Jeffery W. Rankin, John R. Hutchinson. Building a Bird: Musculoskeletal Modeling and Simulation of Wing-Assisted Incline Running During Avian Ontogeny. Frontiers in Bioengineering and Biotechnology, 2018, 6 DOI:10.3389/fbioe.2018.00140
    11. Daria Razmadze, Aleksandra A. Panyutina, Nikita V. Zelenkov. Anatomy of the forelimb musculature and ligaments ofPsittacus erithacus(Aves: Psittaciformes). Journal of Anatomy, 2018, 233(4): 496. DOI:10.1111/joa.12861
    12. H. Wang, J. Yan, Z. Zhang. Sexual dimorphism in jaw muscles of the Japanese sparrowhawk (Accipiter gularis ). Anatomia, Histologia, Embryologia, 2017, 46(6): 558. DOI:10.1111/ahe.12309
    13. Fernanda Bribiesca-Contreras, William I. Sellers. Three-dimensional visualisation of the internal anatomy of the sparrowhawk (Accipiter nisus) forelimb using contrast-enhanced micro-computed tomography. PeerJ, 2017, 5: e3039. DOI:10.7717/peerj.3039

    Other cited types(0)

Catalog

    Figures(6)  /  Tables(2)

    Article Metrics

    Article views (234) PDF downloads (8) Cited by(13)

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return