Lijun Chen, Zufei Shu, Wutao Yao, Yong Ma, Wenhong Xiao, Xiaoqun Huang. 2019: Combined effects of habitat and interspecific interaction define co-occurrence patterns of sympatric Galliformes. Avian Research, 10(1): 29. DOI: 10.1186/s40657-019-0169-2
Citation: Lijun Chen, Zufei Shu, Wutao Yao, Yong Ma, Wenhong Xiao, Xiaoqun Huang. 2019: Combined effects of habitat and interspecific interaction define co-occurrence patterns of sympatric Galliformes. Avian Research, 10(1): 29. DOI: 10.1186/s40657-019-0169-2

Combined effects of habitat and interspecific interaction define co-occurrence patterns of sympatric Galliformes

Funds: 

the National Key Research and Development Program of China 2017YFC0503802

China Postdoctoral Science Foundation 2017M 620905

More Information
  • Corresponding author:

    Lijun Chen, chenlijun@ioz.ac.cn

  • Received Date: 31 Mar 2019
  • Accepted Date: 24 Jul 2019
  • Available Online: 24 Apr 2022
  • Publish Date: 30 Jul 2019
  • Background 

    Disentangling the relative importance of environmental variables and interspecific interaction in modulating co-occurrence patterns of sympatric species is essential for understanding the mechanisms of community assembly and biodiversity. For the two sympatric Galliformes, Silver Pheasants (Lophura nycthemera) and White-necklaced Partridges (Arborophila gingica), we know little about the role of habitat use and interspecific interactions in modulating their coexistence.

    Methods 

    We adopted a probabilistic approach incorporating habitat preference and interspecific interaction using occupancy model to account for imperfect detection, and used daily activity pattern analysis to investigate the co-occurrence pattern of these two sympatric Galliformes in wet and dry seasons.

    Results 

    We found that the detection probability of Silver Pheasant and White-necklaced Partridge were related to habitat variables and interspecific interaction. The presence of Silver Pheasant increases the detection probability of White-necklaced Partridge in both the wet and dry season. However, the presence of White-necklaced Partridges increases the detection probability of Silver Pheasants in the wet season, but decreases the probability in the dry season. Further, Silver Pheasants were detected frequently in the sites of high values of enhanced vegetable index (EVI) in both the wet and dry season, and in sites away from human residential settlement in the wet season. White-necklaced partridges were mainly detected in low EVI sites. The site use probabilities of two Galliformes were best explained by habitat variables, Silver Pheasants and White-necklaced Partridges preferred steeper areas during the wet and dry season. Both species mainly occurred in low EVI areas during the wet season and occupied sites away from the resident settlement during the dry season. Moreover, the site use probabilities of two species had opposite relationships with forest canopy coverage. Silver Pheasants preferred areas with high forest canopy coverage whereas White-necklaced Partridges preferred low forest canopy coverage in the dry season, and vice versa in the wet season. Species interaction factor (SIF) corroborated weak evidence of the dependence of the site use of one species on that of the other in the either dry or wet season. Temporally, high overlapping of daily activity pattern indicated no significantly temporal niche differentiation between sympatric Galliformes in both wet and dry seasons.

    Conclusions 

    Our results demonstrated that the presence of two species influenced the detection probability interactively and there was no temporal partitioning in activity time between Silver Pheasants and White-necklaced Partridges in the wet and dry seasons. The site use probability of two Galliformes was best explained by habitat variables, especially the forest canopy coverage. Therefore, environmental variables and interspecific interaction are the leading drivers regulating the detection and site use probability and promoting co-occurrence of Silver Pheasants and White-necklaced Partridges.

  • In temperate zone, passerine birds time their breeding to track the availability of their food (Lack 1968), because birds have to raise their young at times with highest food abundance to achieve the highest reproductive output (Nager and van Noordwijk 1995; Dias and Blondel 1996). Many studies have found higher food availability results in increased growth rates (Keller and van Noordwijk 1993), and larger fledgling mass improves offspring survival (Gebhardt-Henrich and van Noordwijk 1991; Verboven and Visser 1998). The underlying mechanism of this relationship between food availability and offspring fitness remains largely unexplored. Some studies have explained this relationship from the point of malnutrition. It has been found in some passerine bird species such as Great Tit (Parus major) that the optimal food (e.g. Lepidoptera) has higher quality nutritional profile than the alternative food (e.g. Diptera and Coleoptera) (Arnold et al. 2010). Some studies found plasma metabolite level can predict changes in the individual body condition, growth rates and survival rate of nestlings (Nadolski et al. 2006; Amat et al. 2007; Albano et al. 2011). However, there is little work to reveal the physiological responses of the nestlings hatched asynchronized with the food abundance peak, which is important for understanding the causal link between the timing of breeding and the fitness of offspring.

    Food shortage is a major environmental factor inducing stress of wild animals (Merino et al. 2002; Herring et al. 2011). Understanding the stress status of the nestlings hatched in different food conditions is important to reveal the biological mechanism linking the food availability and fitness of offspring. Cells are the fundamental units of organisms, and the survival of the whole organism depends on the ability of cells to maintain homeostasis. The cellular stress response, an important molecular mechanism involved in maintaining cellular homeostasis, can offset the effects of stress and thereby allow other processes to restore cellular homeostasis (Kultz 2005; Somero 2010). Heat shock proteins (HSPs) are molecular chaperones that help maintain regular cellular functions by playing a crucial role in protein folding, unfolding, aggregation, degradation and transport (Beissinger and Buchner 1998; Sørensen et al. 2003; Arya et al. 2007). HSPs increase in response to acute and chronic stress, injury, and disease (Santoro 2000; Al-Aqil et al. 2013; Zulkifli et al. 2014). In particular, HSP60, 70 and 90 have been shown to be indicators of stress in several avian species, including Barn Swallows (Hirundo rustica), Broiler Chickens, and House Finches (Haemorhous mexicanus) (Merino et al. 2002; Beloor et al. 2010; Hill et al. 2013; Wein et al. 2016). Therefore, the levels of HSPs can be used as markers to reflect physiological status of the nestlings hatched under different food condition.

    Asian Short-toed Lark (Calandrella cheleensis) is the most common passerine species living on the grasslands of northeastern Inner Mongolia of China (Tian et al. 2015; Zhang et al. 2017). The breeding of the species begins in mid-April and ends in mid-June, and the grasshopper nymph (Orthoptera) is the main component (about 80%) of the nestlings' diet (Tian et al. 2015). Because there is only one significant nymph abundance peak once a year in the study area (Chen and Gong 2005; Guo et al. 2009), and the hatching time of nestlings has to be synchronized with the time of nymph abundance peak. According to our previous study, the species displays significantly within year individual variation in the timing of breeding behavior (Zhang et al. 2017; Zhao et al. 2017). Although there was an obvious hatching peak in one breeding season, there were some nestlings hatched outside this peak (Zhang et al. 2017). In addition, the synchronization between the nestling hatching peak and nymph abundance peak varied with the spring temperature. In 2014 and 2016, the peak of nestling hatching was asynchronized with the nymph abundance peak for the extremely high and low spring temperature, and nestling's food shortage induced by such mismatch could finally reduce the survival rate of nestlings (Zhang et al. 2017). Therefore, this species is an appropriate species to study the effect of food availability on the physiological status of nestlings.

    In this paper, we hypothesize that the nestlings of Asian Short-toed Lark hatched under poor optimal food condition would activate cell stress response. To test this hypothesis, we compared the gene expression of HSP70 and HSP90 in the blood cells of Asian Short-toed Lark nestlings hatched under different nymph abundance conditions in 2017.

    Our study area was in the Hulun Lake National Nature Reserve (47°45′50″‒49°20′20″N, 116°50′10″‒118°10′10″E), which is in the northeastern part of the Inner Mongolian Autonomous Region, China. This is a semiarid, steppe region where the mean annual temperature, precipitation and potential evaporation are - 0.6 ℃, 283 mm and 1754 mm, respectively. The dominant plant species are Stipa krylovii, Leymus chinesis and Cleistogenes squarrosa. Winter is longer than summer and the approximate average maximum daytime temperatures in January and July are - 20.02 ℃ and 22.72 ℃, respectively.

    Asian Short-toed Larks nest on the ground. From 20 April to 27 June in 2017, we surveyed nests of larks and grasshopper nymph in the study area every day. From 8 June to 21 June, we took blood samples of 64 four-day-old nestlings from different nests and recorded their hatching date. Considering the daily temperature variation (Fig. 1), each sampled nestling was blooded on 5:00 and 14:00 respectively. The relative abundance of grasshopper nymphs in the study area was quantified by catching them in an insect net on 10 parallel, 2 m × 100 m sampling transects, spaced 10 m apart, every 2 days. Captured nymphs were dried and weighed to determine their biomass. The mean daily nymph biomass was the average daily biomass obtained from all 10 transects. Annual variations in the timing of hatching and daily nymph biomass are shown in Fig. 2.

    Figure  1.  Temperature at 5:00 and 14:00 of the days sampling the blood of Asian Short-toed Lark nestlings
    Figure  2.  Annual trends of grasshopper nymph biomass and nestling hatching ratio at Hulun lake national nature reserve in 2017

    About 100 μL of whole blood was collected from 64 four-day-old nestlings to measure HSP70 and HSP90 gene expression levels. A brachial wing vein of each nestling was punctured with a disinfected 23 G needle within 1-3 min of capture and blood that exuded from the puncture site was collected into heparinized microcapillary tubes. The skin around the puncture site was disinfected with medical alcohol before, and after, puncturing. Pressure was applied to the puncture site for 1 min with an alcohol-soaked cotton wool swab to staunch bleeding. Blood sampling procedures complied with ARRIVE guidelines and were approved by the Animal Research Ethics Committee of the Hainan Provincial Education Centre for Ecology and Environment, Hainan Normal University (permit no. HNECEE-2013-002). The study is carried out with the permissions of Hulun Lake National Nature Reserve.

    Total RNA from the blood samples was isolated from 1 mL TRIzol reagent in 200 μL chloroform, centrifuged at 12, 000 r/min for 15 min at 4 ℃ after which about 500 μL of the supernatant was transferred to a clean tube, adding 500 μL isopropanol to the tube and centrifuging at 12, 000 r/min for 15 min at 4 ℃. The resultant RNA pellet was washed with 1 mL 75% ethanol (dissolved in DEPC water) two times. The RNA pellet was briefly air dried for 5 min and dissolved in 30 μL DEPC water. Total RNA quality and quantity were evaluated using agarose gel electrophoresis (AGE) and a NanoDrop 2000 spectrophotometer, and 2 μg total RNA sample was reversely transcribed to cDNA using M-MLV Reverse Transcriptase with oligo dT primers. The cDNA was used as a template in RT-qPCR. Primers were designed based on HSP70 and HSP90 mRNA sequences in NCBI using Primer3 software (Table 1). The procedures for RT-qPCR were as follows: pre-degeneration at 95 ℃ for 1 min, degeneration at 95 ℃ for 15 s, annealing at 59 ℃ for 15 s and extension at 72 ℃ for 40 s, totally 40 cycles. After the reaction had completed, a melting curve analysis from 55 to 95 ℃ was applied to ensure consistency and specificity of the amplified product. The expression of β-actin was confirmed to be stable under all treatments and this gene was consequently used as the reference gene to normalize mRNA levels among samples. RT-qPCR was performed twice, in triplicate. The values of the average cycle threshold (Ct) were determined and Ct scores for gene transcripts in each sample were normalized using the ΔCt scores for β-actin and expressed as the fold change in gene expression using the equation 2-ΔΔCT.

    Table  1.  Primer sequences and anticipated size of the amplified products
    Gene Forward primer Size (bp) Accession no.
    HSP70 F:5-TTTCCTTGTGCCCTGCTGTA-3
    R: 5-ACCTAACTTGTCCCAAAACTGC-3
    163 AY466445
    HSP90 F:5-CGATTGGTTGGTCCTGAGTT-3
    R:5-AAGGTTCCCGATGCTTCTG-3
    186 HQ162267
    β-actin F:5-CACAATGTACCCTGGCATTG-3
    R:5-ACATCTGCTGGAAGGTGGAC-3
    158 L08165
     | Show Table
    DownLoad: CSV

    We used "nymph biomass proportion (NBP)", the percentage of the daily nymph biomass relative to the total biomass measured over all survey days, as a measure of grasshopper nymph abundance in the statistical analysis. The effects of nymph biomass, sample time and their interaction on the blood HSP70 and HSP90 gene expression levels in nestlings are analyzed with a linear mixed model (LMM) in which the individual nestling is a random factor. Regression analysis is used to analyze the relationship between nymph biomass and the gene expression level of HSP70 and HSP90 if the effect of nymph biomass is significant in the LMMs. We used α = 0.05 as the significant level for all statistical tests. All analyses were performed using SPSS version 24 (SPSS Inc., Chicago, USA).

    The results of the LMMs for the effects of nymph biomass and sample time on gene expression level of HSP70 and HSP90 indicate that nymph biomass, sample time and interaction of the two factors significantly influenced the blood HSP70 and HSP90 gene expression level of Asian Short-toed Lark nestlings (Table 2). According to this result, the relationships between nymph biomass and HSP70 and HSP90 gene expression level at 5:00 and 14:00 were analyzed separately. Simple linear regression analysis showed that the nymph biomass negatively correlated with the gene expression level of HSP70 (5:00, n = 64, R2 = 0.811, Fig. 3a; 14:00, n = 64, R2 = 0.636, Fig. 3c) and HSP90 (5:00, n = 64, R2 = 0.666, Fig. 3b; 14:00, n = 64, R2 = 0.743, Fig. 3d). The gene expression levels of HSP70 and HSP90 increase with the decrease of nymph biomass (Fig. 3). In addition, both HSP70 and HSP90 gene expression levels at 14:00 are significantly higher than those at 5:00 (Fig. 3).

    Table  2.  Results of linear mixed models (LMMs) on the effects of nymph biomass, sample time and the interactions of the two factors on gene expression levels of blood HSP70 and HSP90 in nestlings of wild Asian Short-toed Lark in the Hulun Lake NatureReserve, the Inner Mongolian Autonomous Region, China
    HSPs Explanatory variable F p
    HSP70 Nymph biomass 88.36 <  0.001
    Sample time 213.65 <  0.001
    Nymph biomass × sample time 4.77 <  0.001
    HSP90 Nymph biomass 99.49 <  0.001
    Sample time 265.12 <  0.001
    Nymph biomass × sample time 8.97 <  0.001
     | Show Table
    DownLoad: CSV
    Figure  3.  The simple liner regression between nymph biomass proportion (NBP) and gene expression of HSP70 (a: 5:00, n = 64, R2 = 0.811; c: 14:00, n = 64, R2 = 0.636) and HSP90 (b: 5:00, n = 64, R2 = 0.666; d: 14:00, n = 64, R2 = 0.743) in the blood of Asian Short-toed Lark (Calandrella cheleensis)

    The LMM results indicate that the nymph abundance is a factor which significantly influences the gene expressions of HSP70 and HSP90 of Asian Short-toed Lark nestlings. Negative correlation between nymph biomass and gene expressions of HSP70 and HSP90 suggests that poor food condition is an important environment factor inducing cell stress of Asian Short-toed Lark nestlings. Similar results about the relationship between food availability and stress protein level of nestlings have been found in other species such as White Ibis which showed that the HSP60 levels increased predictably in response to food limitation (Herring and Gawlik 2013). Our results and the available study jointly show that food shortage during post embryo development can induce the stress in nestlings. Several studies have compared the response of HSP60, HSP70 and HSP90 to the ecological factors. While some of the studies found HSP60 is more sensitive than other HSPs (Martínez-Padilla et al. 2004; Moreno et al. 2005), other studies found HSP70 was more sensitive to environmental perturbations than HSP60 or HSP90 (Ulmasov et al. 1992; Fader et al. 1994; McMillan et al. 2011). Our study showed that HSP70 and HSP90 of nestlings respond to the optimal food condition similarly. The different responses of the HSPs might be species specific, therefore we suggest that multiple HSPs should be used as cell stress markers in the evaluation of cell stress status.

    In addition to the food availability, the LMM results of our study also indicate the sample time is also an important factor inducing the variation of HSPs level. HSP70 and HSP90 gene expressions in the blood sampled at 14:00 were significantly higher than those sampled at 5:00, which could be explained by the daily temperature variation in the habitat. Our study area was in the northeastern part of the Inner Mongolian Autonomous Region, where the daily temperature variation between minimum and maximum is 10‒15 ℃ (Chen and Gong 2005). The temperature at 14:00 was higher than that at 5:00 (Fig. 1). Temperature variation has been found to be an important environment factor inducing the HSPs expression (Arya et al. 2007). Therefore, the expression level of HSPs at 14:00 increasing in our study indicates the nestlings could cope with the temperature variation to maintain homeostasis by cell stress response. The response of blood HSPs to the marked temperature variation in the study area has been found in our previous study of adult Asian Short-toed Larks (Qin et al. 2017). These results indicate that cell stress response is an important physiological mechanism for birds to cope with highly stochastic ecosystems not only in adult but also in nestlings. Our LMM model suggested that poor food condition enhanced the level of cell stress of nestlings at both low and high temperature.

    Although the HSPs can maintain the homeostasis of the cells, overexpression of these stress proteins is known to have deleterious consequences (Feder and Hofmann 1999). Their synthesis represents a significant energetic cost (Hamdoun et al. 2003), which can lead to reduced fecundity (Parsons 1996). In addition, their response usually results in a concomitant reduction in the synthesis of antibodies, thus synthesizing more HSPs to mitigate stress has been found in passerine birds to be traded-off against mounting humoral and cell-mediated immune responses (Morales et al. 2006). The result of our previous study on the Asian Short-toed Lark showed that the survival rate of the nestlings in 2014 and 2016 when the peak of nestling hatching mismatched with the peak of nymph abundance peak was significantly lower than that in 2015 when the two peaks thoroughly match (Zhang et al. 2017), which suggests that overexpression of HSPs could be related with lower nestling survival. As a bird species living in the harsh weather condition, the nestlings of Asian Short-toed Lark have to cope with the marked weather variation by cell stress response, and poor food condition could impose additional stress to make HSPs overexpression, which consequently could result in negative effect on the nestling survival. Therefore, over cell stress response may be one of physiological factor mediating the effect of optimal food availability and the nestling's survival.

    Shifts in phenology induced by climate change are altering ecological relationships and processes around the world (Visser and Both 2005; Cleland et al. 2007; Forrest and Miller-Rushing 2010) and there is a growing body of literature on resultant trophic mismatches between nestlings and their food resources (Visser et al. 1998; Visser and Both 2005; Thackeray et al. 2010). Although such trophic mismatches have been found to result in nestlings having lower body mass, smaller body size and reduced survival rates (Verboven and Visser 1998; Visser et al. 1998; Durant et al. 2007; Ovaskainen et al. 2013; Dunn and Møller 2014), there is little information to reveal the physiological mechanism how the optimal food condition influences the fitness of the nestlings. Our research indicates that cell stress biomarkers have the potential to respond directly and predictably to optimal food conditions. Under the phenological mismatch scenario, the birds living in the fluctuating environment would encounter more stress challenges, which implicates that these birds could be more vulnerable to the phenological mismatch. Therefore, we suggest the vulnerability of the species to the phenological mismatch induced by climate change should be estimated combining the physiological and ecological data.

    The results of this study indicate that poor food condition is an important environment factor inducing cellular stress of Asian Short-toed Lark nestlings. The interactive effect of the nymph abundance and sample time on the HSPs response may be related with the daily temperature variation of the grassland. Over cell stress response may be one of physiological factor mediating the effect of food availability and the nestling's fitness.

    We are grateful to Manquan Gui, Muren Wu, Songtao Liu in Hulun Lake National Nature Reserve for their help on the field study.

    SZ and WL conceived the study and designed the experiments. L. Zhaang, L. Zhao and XZ conducted the experiments. LZ wrote the first draft of the article. SZ supervised the research and revised the draft. All authors read and approved the final manuscript.

    The data used in the present study are available from the corresponding author on reasonable request.

    Our experimental procedures complied with the current laws on animal welfare and research in China and had the approval of the Animal Research Ethics Committee of Hainan Normal University. In addition, all procedures followed standard protocols, such as the ARRIVE guidelines for reporting animal research.

    The authors declare that they have no competing interests.

    Not applicable.

  • Ahumada JA, Hurtado J, Lizcano D. Monitoring the status and trends of tropical forest terrestrial vertebrate communities from camera trap data: a tool for conservation. PLoS ONE. 2013;8:e73707.
    Bailey LL, MacKenzie DI, Nichols JD, Cooch E. Advances and applications of occupancy models. Methods Ecol Evol. 2014;5:1269-79.
    Bastianelli G, Wintle BA, Martin EH, Seoane J, Laiolo P. Species partitioning in a temperate mountain chain: segregation by habitat vs. interspecific competition. Ecol Evol. 2017;7:2685-96.
    Bu H, Wang F, McShea WJ, Lu Z, Wang D, Li S. Spatial co-occurrence and activity patterns of mesocarnivores in the temperate forests of Southwest China. PLoS ONE. 2016;11:e0164271.
    Burnham KP, Anderson DR. Multimodel inference: understanding AIC and BIC in model selection. Sociol Method Res. 2004;33:261-304.
    Burton CA, Neilson E, Moreira D, Ladle A, Steenweg R, Fisher JT, et al. Wildlife camera trapping: a review and recommendations for linking surveys to ecological processes. J Appl Ecol. 2015;52:675-85.
    Cai DS, Song XJ. Bioresource and protection countermeasure in National Reserve of Chebaling in Guangdong province. Ecol Sci. 2005;24:282-5 (in Chinese).
    Chen YH, Luiselli L. Species richness and co-occurrence patterns of Galliformes in China at three large spatial scales: does scale size matter? Revue D Ecologie. 2009;64:251-60.
    Crowley PH, Cox JJ. Intraguild mutualism. Trends Ecol Evol. 2011;26:627-33.
    D'Amen M, Mod HK, Gotelli NJ, Guisan A. Disentangling biotic interactions, environmental filters, and dispersal limitation as drivers of species co-occurrence. Ecography. 2018;41:1233-44.
    Davies TJ, Meiri S, Barraclough TG, Gittleman JL. Species co-existence and character divergence across carnivores. Ecol Lett. 2007;10:146-52.
    Davis CL, Rich LN, Farris ZJ, Kelly MJ, Di Bitetti MS, Blanco YD, et al. Ecological correlates of the spatial co-occurrence of sympatric mammalian carnivores worldwide. Ecol Lett. 2018;21:1401-12.
    Di Bitetti MS, De Angelo CD, Di Blanco YE, Paviolo A. Niche partitioning and species coexistence in a neotropical felid assemblage. Acta Oecol. 2010;36:403-12.
    Dröge E, Creel S, Becker MS, M'Soka J. Spatial and temporal avoidance of risk within a large carnivore guild. Ecol Evol. 2017;7:189-99.
    Estevo CA, Nagy-Reis MB, Nichols JD. When habitat matters: habitat preferences can modulate co-occurrence patterns of similar sympatric species. PLoS ONE. 2017;12:e0179489.
    Frey S, Fisher JT, Burton AC, Volpe JP, Rowcliffe M. Investigating animal activity patterns and temporal niche partitioning using camera-trap data: challenges and opportunities. Remote Sens Ecol Conserv. 2017;3:123-32.
    Haynes TB, Schmutz JA, Lindberg MS, Wright KG, Uher-Koch BD, Rosenberger AE. Occupancy of yellow-billed and Pacific loons: evidence for interspecific competition and habitat-mediated co-occurrence. J Avian Biol. 2014;45:296-304.
    HilleRisLambers J, Adler PB, Harpole WS, Levine JM, Mayfield MM. Rethinking community assembly through the lens of coexistence theory. Annu Rev Ecol Evol Syst. 2012;43:227-48.
    Hubbell SP. The unified neutral theory of biodiversity and biogeography. Princeton: Princeton University Press; 2001.
    Hutchinson GE. Concluding remarks. Cold Spring Harb Symp Quant Biol. 1957;22:415-27.
    Jankowski JE, Robinson SK, Levey DJ. Squeezed at the top: interspecific aggression may constrain elevational ranges in tropical birds. Ecology. 2010;91:1877-84.
    Karanth KU, Srivathsa A, Vasudev D, Puri M, Parameshwaran R, Kumar NS. Spatio-temporal interactions facilitate large carnivore sympatry across a resource gradient. Philos Trans R Soc Lond B Biol Sci. 2017. doi: .
    Kendall WL, White GC. A cautionary note on substituting spatial subunits for repeated temporal sampling in studies of site occupancy. J Appl Ecol. 2009;46:1182-8.
    Kraft NJB, Adler PB, Godoy O, James EC, Fuller S, Levine JM, et al. Community assembly, coexistence and the environmental filtering metaphor. Funct Ecol. 2015;29:592-9.
    Kronfeld-Schor N, Dayan T. Partitioning of time as an ecological resource. Annu Rev Ecol Evol Syst. 2003;34:153-81.
    Kronfeld-Schor N, Visser ME, Salis L, van Gils JA. Chronobiology of interspecific interactions in a changing world. Philos Trans R Soc Lond B Biol Sci. 2017. doi: .
    Latif QS, Ellis MM, Amundson CL. A broader definition of occupancy: comment on Hayes and Monfils. J Wildl Manag. 2016;80:192-4.
    Lima SL, Bednekoff PA. Temporal variation in danger drives antipredator behavior: the predation risk allocation hypothesis. Am Nat. 1999;153:649-59.
    Luo G, Yang C, Zhou H, Seitz M, Wu Y, Ran J. Habitat use and diel activity pattern of the Tibetan Snowcock (Tetraogallus tibetanus): a case study using camera traps for surveying high-elevation bird species. Avian Res. 2019;10:4.
    MacKenzie DI, Hines JE. Package.RPresence. R Interface for program PRESENCE. R Package Version 2.12.20. 2018.
    MacKenzie DI, Nichols JD, Lachman GB, Droege S, Royle JA, Langtimm CA. Estimating site occupancy rates when detection probabilities are less than one. Ecology. 2002;83:2248-55.
    MacKenzie DI, Bailey LL, Nichols JD. Investigating species co-occurrence patterns when species are detected imperfectly. J Anim Ecol. 2004;73:546-55.
    MacKenzie DI, Nichols JD, Royle JA, Pollock KH, Bailey L, Hines JE. Occupancy estimation and modeling: inferring patterns and dynamics of species cccurrence. San Diego: Elsevier; 2017.
    Mahendiran M. Coexistence of three sympatric cormorants (Phalacrocorax spp.); partitioning of time as an ecological resource. R Soc Open Sci. 2016;3:160175.
    Maphisa DH, Smit-Robinson H, Altwegg R. Dynamic multi-species occupancy models of birds of high altitude grasslands in eastern South Africa. PeerJ Prepr. 2018;6:e26932v1.
    Martin EH, Ndibalema VG, Rovero F. Does variation between dry and wet seasons affect tropical forest mammals' occupancy and detectability by camera traps? Case study from the Udzungwa Mountains, Tanzania. Afr J Ecol. 2017;55:37-46.
    Meredith M, Ridout MS. Estimates of coefficient of overlapping for animal activity patterns. R Package Version 0.3.2. 2018.
    Niedballa J, Sollmann R, bin Mohamed A, Bender J, Wilting A. Defining habitat covariates in camera-trap based occupancy studies. Sci Rep. 2015;5:17041.
    O'Connell AF, Nichols JD, Karanth KU. Camera traps in animal ecology: methods and analyses. New York: Springer; 2010.
    O'Connor KM, Nathan LR, Liberati MR, Tingley MW, Vokoun JC, Rittenhouse TAG. Camera trap arrays improve detection probability of wildlife: investigating study design considerations using an empirical dataset. PLoS ONE. 2017;12:e0175684.
    Petersen WJ, Savini T, Steinmetz R, Ngoprasert D. Periodic resource scarcity and potential for interspecific competition influences distribution of small carnivores in a seasonally dry tropical forest fragment. Mammal Biol. 2019;95:112-22.
    R Core Team. R: a language and environment for statistical computing. Vienna, Austria. R Foundation for Statistical Computing. 2018. . Accessed 5 Oct 2018.
    Reif J, Reifová R, Skoracka A, Kuczyński L. Competition-driven niche segregation on a landscape scale: evidence for escaping from syntopy towards allotopy in two coexisting sibling passerine species. J Anim Ecol. 2018;87:774-89.
    Rich LN, Miller DAW, Robinson HS, McNutt JW, Kelly MJ. Carnivore distributions in Botswana are shaped by resource availability and intraguild species. J Zool. 2017;303:90-8.
    Richmond OM, Hines JE, Beissinger SR. Two-species occupancy models: a new parameterization applied to co-occurrence of secretive rails. Ecol Appl. 2010;20:2036-46.
    Ridout MS, Linkie M. Estimating overlap of daily activity patterns from camera trap data. J Agric Biol Environ Stat. 2009;14:322-37.
    Rota CT, Wikle CK, Kays RW, Forrester TD, McShea WJ, Parsons AW, et al. A two-species occupancy model accommodating simultaneous spatial and interspecific dependence. Ecology. 2016;97:48-53.
    Rowcliffe JM. Package activity. Animal activity statistics R Package Version 1.1. 2016.
    Rowcliffe JM, Kays R, Kranstauber B, Carbone C, Jansen PA, Fisher D. Quantifying levels of animal activity using camera trap data. Methods Ecol Evol. 2014;5:1170-9.
    Santos F, Carbone C, Wearn OR, Rowcliffe JM, Espinosa S, Lima MGM, et al. Prey availability and temporal partitioning modulate felid coexistence in neotropical forests. PLoS ONE. 2019;14:e0213671.
    Schuette P, Wagner AP, Wagner ME, Creel S. Occupancy patterns and niche partitioning within a diverse carnivore community exposed to anthropogenic pressures. Biol Conserv. 2013;158:301-12.
    Shu Z, Lyu J, Song X, Huo Z, Chao Z, Chen M, et al. Statistic of the vascular plant specimens from Chebaling National Nature Reserve in Guangdong province. For Environ Sci. 2017;33:61-5 (in Chinese).
    Song XJ, Zou FS. A guide to birds of Chebaling National Nature Reserve. Guangzhou: Guangdong Science and Technology Press; 2017 (in Chinese).
    Steenweg R, Hebblewhite M, Kays R, Ahumada J, Fisher JT, Burton C, et al. Scaling-up camera traps: monitoring the planet's biodiversity with networks of remote sensors. Front Ecol Environ. 2017;15:26-34.
    Sukumal N, Savini T. Altitudinal differences in habitat use by Siamese fireback Lophura diardi and silver pheasant Lophura nycthemera in Khao Yai National Park, Thailand. Int J Galliformes. Conserv. 2009;1:18-22.
    Tambling CJ, Minnie L, Meyer J, Freeman EW, Santymire RM, Adendorff J, et al. Temporal shifts in activity of prey following large predator reintroductions. Behav Ecol Sociobiol. 2015;69:1153-61.
    Thakur MP, Wright AJ. Environmental filtering, niche construction, and trait variability: the missing discussion. Trends Ecol Evol. 2017;32:884-6.
    Valeix M, Chamaille-Jammes S, Fritz H. Interference competition and temporal niche shifts: elephants and herbivore communities at waterholes. Oecologia. 2007;153:739-48.
    Webb CO, Ackerly DD, McPeek MA, Donoghue MJ. Phylogenies and community ecology. Annu Rev Ecol Evol Syst. 2002;33:475-505.
    Yackulic CB, Reid J, Nichols JD, Hines JE, Davis R, Forsman E. The roles of competition and habitat in the dynamics of populations and species distributions. Ecology. 2014;95:265-79.
    Zhao ZJ. Avifauna of China, volume 1: non-Passerines. Changchun: Jilin Science and Technology Press; 2001 (in Chinese).
    Zheng G. A checklist on the classification and distribution of the birds of China. 3rd ed. Beijing: Science Press; 2017 (in Chinese).
    Zheng G. Pheasants in China. Beijing: Higher Education Press; 2015 (in Chinese).
    Zuckerberg B, Fink D, La Sorte FA, Hochachka WM, Kelling S. Novel seasonal land cover associations for eastern North American forest birds identified through dynamic species distribution modelling. Divers Distrib. 2016;22:717-30.

Catalog

    Figures(4)  /  Tables(3)

    Article Metrics

    Article views PDF downloads Cited by()

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return