Translator Disclaimer
27 March 2019 Roosting Ecology of Black-Headed Ibis (Threskiornis melanocephalus) in Urban and Rural Areas of Southern Rajasthan, India
Author Affiliations +
Abstract

The roosting ecology of most waterbird species is poorly known and even less is known from southern Asia, where many species inhabit human-modified areas. Roosting ecology of the Black-headed Ibis (Threskiornis melanocephalus) was studied in urban and rural settings in southern Rajasthan, India. Analyses focused on assessing whether site characteristics varied between nest sites, urban and rural roost sites, and paired sites (i.e., a waterbird roost site near Black-headed Ibis roosts but without Black-headed Ibis). Additionally, the hypothesis that factors affecting Black-headed Ibis numbers at roosts would be similar at urban and rural sites was tested. Tree characteristics (canopy cover, girth at breast height) were different (P < 0.05) between nest and roost sites. Urban roost sites experienced 2.3 times greater disturbance than rural roost sites. Paired site characteristics were similar to urban roost sites (multi response permutation procedure, significance of δ = 0.3), but were dissimilar to rural roost sites. Co-occurring roosting bird assemblages were significantly different between roosts and paired sites (significance of δ < 0.01) in urban and rural settings. Black-headed Ibis numbers at urban roosts were influenced by multiple variables, but models showed considerable ambiguity at rural sites. Results strongly suggest that including roost sites in a species status assessment is important.

Communal roosting is defined as an aggregation of unrelated conspecifics (more than two species or individuals) that spend diurnal or nocturnal resting time together. Individuals have the choice to use the same sites repeatedly alongside individuals of other species or conspecifics, or roost singly while not necessarily returning to the same place each time (Beauchamp 1999; Laughlin et al. 2014). It is a taxonomically widespread behavior occurring from lower invertebrates such as flatworms (e.g., Cura foreman and Dugesia tigrina) to mammals such as bats (e.g., Myotis sodalis), with aggregations numbering into the tens of thousands (Reynierse et al. 1969; Silvis et al. 2014). Communal roosting can improve fitness via reduced predation, increased thermoregulatory benefits, and foraging efficiency (Weatherhead 1983; Kramer 1985; Beauchamp 1999). Consequently, communal roosting sites can involve a considerable proportion of the species' population, making them an important facet of conservation measures (Donazar et al. 1996). The ecology of waterbird species are biased toward studies of foraging and breeding, while roosting behavior is poorly known for the majority of species. Roost sites can be different from breeding sites (Ogden 1990), suggesting that the absence of studies on this aspect constitutes a gap in understanding the ecology and conservation of waterbirds.

Characterizing roost sites can provide insights into habitat requirements for waterbirds during the non-breeding season. In natural settings, waterbird roost locations can be influenced by proximity to foraging habitats such as wetlands and grasslands (Pearson et al. 1992) and large, tall trees (Blanco 1996; Chevallier et al. 2010). However, when human activities dominate landscapes, presence of novel foraging sites such as rubbish dumps, well-watered gardens and swimming pools, and exotic tree species can be attractive for many bird species and eventually may alter roosting in natural habitats (Blanco 1996; Singh and Downs 2016). There is scarce information to assess whether the locations of roosting sites in exotic structures have similar characteristics to those found in natural structures (Bryan et al. 2002; Bowker and Downs 2012). Mixed species roosts have considerable advantages including improving feeding efficiency and reduced predation (Eiserer 1984). Composition of co-occurring roosting species can therefore be an important component of roosting behavior, but has not been well studied in waterbirds (Eiserer 1984). Another important but poorly understood aspect of waterbird roosting behavior is the size of flocks at roosts. In some waterbirds, roost sites with different flock sizes can share similar characteristics, especially in natural habitats (Bryan et al. 2002). Observational and experimental studies on non-waterbirds have suggested that flock size at roosts can be a function of resource distribution around roost sites (Ward and Zahavi 1973; Chapman et al. 1989), and represent individual roost site characteristics (Lambertucci 2013). Studies of waterbird roosting behavior have been conducted either in only natural or only urban settings, and it is not known if roosting ecology varies in different settings for the same species (Pearson et al. 1992; Chevallier et al. 2010; Singh and Downs 2016). Most studies on roosting ecology of waterbirds consider a small number of variables, and usually focus on a local scale (proximity to foraging habitats and roost tree characteristics).

Colonial waterbirds in southern Asia forage and breed in a variety of landscape and habitat conditions including natural wetlands, urban areas, and intensively cultivated rural landscapes (Sundar 2006; Koli et al. 2013; Sundar et al. 2016; Chaudhury and Koli 2018). Roosting behavior has been documented in several waterbirds species (Ali and Ripley 2007), but is very poorly understood for the near-threatened and colonial Black-headed Ibis (Threskionis melanocephalus; BirdLife International 2016). Black-headed Ibis use different habitats ranging from agricultural landscapes, densely-populated cities, and several kinds of natural and artificial wetlands (Balakrishnan and Thomas 2004; Sundar 2006; Chaudhury and Koli 2016; Chaudhary 2018) that represent a variety of conditions with differing levels of human disturbance and habitat availability. In this study, we describe Black-headed Ibis roosting ecology in southern Rajasthan, India, and evaluate whether roosting ecology varied between two disparate settings: urban and rural. No formal study of roosting ecology of a communal waterbird is available from southern Asia where human presence is ubiquitous and extensive.

Our objective was to document Black-headed Ibis roosting ecology focusing on site and tree characteristics, co-occurring roosting species, and factors affecting Black-headed Ibis flock sizes at roosts. We predicted that site and tree characteristics would vary between Black-headed Ibis roosting and nesting sites, between urban and rural Black-headed Ibis roost sites, and between roost sites and nearby “paired” sites that had waterbirds roosting without any Black-headed Ibis. We also predicted that assemblages of co-occurring roosting species would vary between roost and paired sites, but would be similar between Black-headed Ibis roosts located in urban and rural settings. Finally, we predicted that the same variables would affect Black-headed Ibis flock sizes at both urban and rural roosts.

Methods

Study Area

The study was conducted in six districts of southern Rajasthan in northwestern India covering an area of 40,055 km2 (Fig. 1). The region experiences strong seasonality with distinct winter (November-February), summer (March-June), and wet or monsoon (July-October) seasons. Total rainfall in the region averaged 867.8 mm. The highest temperature (42 °C) was recorded in summer and lowest (8 °C) in the winter season. The landscape was relatively heterogeneous and set within the old fold mountains of the Aravallis. Towns and cities in the focal districts had population densities (500-8,000 people/km2; Office of the Registrar General and Census Commissioner, India 2011) magnitudes higher than rural areas and constituted urban settings marked by a dense concentration of buildings and related impervious surfaces. Only relatively large reservoirs, temple ponds and lakes were retained as urban wetlands. Most of these had boundary walls, and were heavily used for fishing and recreational boating (Koli et al. 2013). Agricultural areas alongside sparsely populated villages constituted the rural areas (population density varied between 190-400 people/km2 at the district level; Office of the Registrar General and Census Commissioner, India 2011). Agriculture was the major rural land use supported by water from natural and artificial wetlands including reservoirs, marshes, village ponds, and lakes of various sizes (Choudhary 2018). Rural wetlands were used extensively year-round for grazing livestock, fishing and irrigation. Major crops were rice (Oryza sativa), maize (Zea mays), and soybean (Glycine max) during the monsoon, followed by wheat (Triticum aestivum) during the winter, and vegetables during the summer (Kulshreshtha et al. 2013). The region had few forested protected reserves, and outside these reserves, there were scattered trees both in the rural and urban areas. Trees outside reserves were exploited for timber, fruits, flowers, and leaves. A combination of utility-based attitudes, cultural norms, and formal protection ensured persistence of trees on the landscape. Urban and rural areas had sharp boundaries with no intermediate levels of variation in land use. Levels of urbanization varied between cities, and agricultural intensification varied between rural areas.

Figure 1.

Locations and sizes of Black-headed Ibis nesting and roosting sites in urban and rural settings in southern Rajasthan, India. Inset shows location of Rajasthan state (gray) and focal districts (black) in India.

f01_51.jpg

Locating Black-headed Ibis Nest, Roosts and Paired Sites

Black-headed Ibis roost sites were located by surveying along a road transect of a total of 2,561 km (356 km in Rajsamand; 563 km in Udaipur; 320 km in Dungarpur; 711 km in Banswara; 293 km in Pratapgarh; and 318 km in Chittorgarh) in June 2017 (see Chaudhury and Koli 2018). Nest sites were located during the breeding season in September 2017 when heronries were fully active (Chaudhury and Koli 2018). Roost and nest sites were located by direct observations and by following Black-headed Ibis flying to roosts or heronries. Presence of droppings below trees (roosts), nests on trees, and information given by local people helped identify potential nests and roosts. We visited each potential site to confirm nesting and roosting sites, and counted all Black-headed Ibis and other co-occurring roosting birds during visits. We identified a location as a roost site when two or more Black-headed Ibis assembled to spend the night, and a nest site when at least one active nest of the species was present. All roost and nesting sites were georeferenced using a hand-held Global Positioning System (Garmin eTrex 30x). At roosts, we counted Black-headed Ibis after birds stopped flying into the roost in the evening. We were interested in assessing whether Black-headed Ibis roost sites were distinct relative to roost sites that had other waterbirds, but no Black-headed Ibis. For these comparisons, we located paired roost sites without Black-headed Ibis using the same methods, but focusing on other species such as egrets, herons, other ibis species, and storks. Paired sites were located close to Black-headed Ibis urban and rural roost sites.

Variables

At each nest, roost and paired site, we measured variables corresponding to three broad aspects of roosting ecology. The first related to proximity to potential foraging habitat and human disturbance. We measured the distance (m) from each site to the nearest wetland, stream or river. We measured proximity to human disturbance as the distance (m) to the nearest road and human habitation. We used Google Earth Pro (2018) for all measurements. The second aspect related to tree and site characteristics at sites. We made a number of measurements to characterize the tree and the human disturbance level at each site. We measured tree height (m) using a clinometer (Brunton Omni-slope), and girth at breast height (m) using a regular measuring tape. We estimated canopy cover (m2) by measuring the longest and widest edges of canopies, averaging and halving the measures, and using the radius value to estimate the area of a circle. We counted the number of available trees within a 100 x 100-m area around nest, roost and paired sites. We computed an index of human disturbance at sites. We allocated a score for three different types of disturbance recorded within 30 m of the site between 07: 30 and 09: 00 hr (peak activity times; see Rao and Koli 2017) as follows: number of pedestrians (1: 1-30, 2: 31-60, 3: 61-90, 4: 91-120, 5: > 120); number of vehicles passing (1: 1-90, 2: 91-180, 3: 181-270, 4: 271-360, 5: > 360); and number of parked vehicles (1: 0-3, 2: 4-6, 3: 7-9, 4: 10-12, 5: > 12). The disturbance index was computed by summing scores across all three types of measured disturbance (Soh et al. 2002). Finally, we listed and counted all co-occurring roosting species at roost and paired sites.

Statistical Analysis

Comparing tree and site characteristics between nesting, roosting and paired sites. We carried out exploratory analyses to determine if variables differed between sampled sites. We carried out t-tests in statistical program SPSS (SPSS Inc. 2011) to identify individual variables that differed between sites across a range of paired comparisons. We used two-sample t-tests when comparisons had unequal sample sizes (e.g., nest sites vs. urban roost sites), and paired t-tests when sample sizes were equal (e.g., urban roost sites vs. paired sites). We used the multi-response permutation procedure (MRPP) to test the hypothesis of no difference in measurements of variables between sites. MRPP is a non-parametric multivariate procedure that does not require assumptions of multivariate normality and homogeneity of variance of the data (Cai 2006). We present both the effect size “A” (chance-corrected within-group agreement), and a significance test, “δ”, that is the outcome of 1,000 permutations. We used the package ‘vegan’ in statistical program R (Oksanen et al. 2018), specifying Bray-Curtis distance to compute dissimilarity matrices, for MRPP analyses.

Associations with co-occurring roosting birds. The abundance of birds was used to evaluate whether individual species and the full complement of co-occurring species differed between paired and Black-headed Ibis roost sites, and between Black-headed Ibis urban and rural roost sites. We computed the “simple index of association”, which is the probability that two individuals are observed together given that one of them has been seen, following Ginsberg and Young (1992). The simple index is free of biases of sample size, double counts and potential overestimations (Ginsberg and Young 1992; Hoppitt and Farine 2018). The index ranges from 0-1, with higher values indicating species found more often or associated more with the target species. We segregated species into three broad feeding guilds (carnivore, omnivore, piscivore) and two species from additional guilds (Asian Openbill Anastomus oscitans, obligate snail eater; Rose-ringed Parakeet Psittacula krameri, frugivore) and graphically assessed if associations with Black-headed Ibis varied across foraging guilds. We tested the hypothesis that waterbird assemblages at roosts would favor omnivores in two ways. First, we hypothesized that assemblages would vary between Black-headed Ibis roost sites and paired sites, but would be similar at urban and rural roost sites. We used abundance matrices of co-occurring species and conducted MRPP to test hypotheses. Secondly, we compared the number of species in each feeding guild between roosting and paired sites testing the null of no difference in guild-wise species richness using a χ2-test of independence. The χ2-test was carried out manually using Microsoft Excel.

Factors affecting Black-headed Ibis flock sizes at roosts. We assessed collinearity among variables by undertaking bivariate correlations separately for urban and rural roost sites, and retained only weakly correlated variables (Pearson's correlations, P > 0.05). For both urban and rural roost sites, these included number of co-occurring roosting species (CS), distance to wetland (DTW), tree height (TH), and number of available trees (ATS). We used generalized additive models (GAM) to relate variables with observed Black-headed Ibis flock sizes at roosts. GAM is a non-parametric extension of the more commonly used generalized linear models, and useful to fit models with over-dispersed data sets with non-linear relationships. We ran the full complement of single-variable models, the null model and the full model with all four variables. We also ran two additional models (CS+DTW and CS+TH+ATS) that we decided on a priori, for each setting. We used the multimodel information-theoretic inference framework and computed Akaike Information Criteria (AIC) to compare among competing models using package ‘gam’ in statistical package R (Hastie 2013). We used a difference of two AIC units between competing models to signify they were different (Burnham and Anderson 2002).

Results

Fifty Black-headed Ibis roosting sites (24 in urban and 26 in rural settings) and 13 nesting sites were located (Fig. 1). Cumulative Black-headed Ibis numbers were much higher at roost sites (n = 336 and 328 in urban and rural roost sites, respectively) compared to nesting sites (n = 207). Black-headed Ibis numbers were highest at nesting sites (average = 15.9 ± 8.7 SD; Range = 3-50), with fewer birds at each urban (average = 14.0 ± 8.7 SD; Range = 1-33) and rural (average =12.6 ± 7.0 SD; Range = 3-35) roost site.

Comparing Nest, Roost and Paired Site Characteristics

Black-headed Ibis nest (n = 13) and roost (n = 50) sites had similar measures for proximity to potential foraging habitat and human habitation (P > 0.05; Table 1). The disturbance index was similar between nest and urban roost sites (two-sample t-test; t = 0.20; P = 0.84), but was significantly different between urban and rural roost sites (two-sample t-test; t = -6.21; P < 0.01), with urban roost sites experiencing 2.3 times higher disturbance (Table 1). Except for the number of available trees, all other tree characteristics varied significantly (P < 0.05) between nest and roost sites, with nest sites having the lowest values (Table 1). Tree height, canopy cover and girth measurements were significantly higher (P < 0.05) at urban roost sites compared to rural roost sites.

Nest site characteristics varied significantly between both urban and rural roost sites (MRPP; A = 0.07; significance of δ < 0.01). Characteristics of urban roost sites also differed significantly from rural roost sites (A = 0.02; significance of δ < 0.04). Characteristics of roost sites were similar to paired sites in urban settings (A = 0.003; significance of δ = 0.3). Rural roost sites, however, were dissimilar from paired roost sites at the 94% significance level (A = 0.02; significance of δ = 0.06).

Associations With Co-occurring Roosting Species

The simple index of association computed for 16 species at Black-headed Ibis roost sites showed eight species were associated more with Black-headed Ibis (simple index > 0.2) at both urban and rural roost sites (Fig. 2). Simple index values for individual species varied similarly across urban and rural roost sites (Pearson's r = 0.88; P < 0.01). Black-headed Ibis were associated more with Lesser Cormorant (Microcarbo niger), Great Egret (Ardea alba) and Cattle Egret (Bubulcus ibis) and to a lesser degree with Oriental Darter (Anhinga melanogaster), Intermediate Egret (A. intermedia), Eurasian Spoonbill (Platalea leucorodia), Asian Openbill, and Black-necked Stork (Ephippiorhynchus asiaticus; Fig. 2). Painted Storks (Mycteria leucocephala) had five times higher index value in rural roost sites relative to urban roost sites. Urban and rural sites had similar assemblages of co-occurring roosting species (MRPP; A = -0.003; significance of δ = 0.63). However, assemblages at Black-headed Ibis roosts differed significantly from assemblages at paired sites at both urban (A = 0.08; significance of δ < 0.01) and rural (A = 0.08; significance of δ < 0.01) settings. Species richness across guilds varied between roost and paired sites with carnivore species dominating at all sites (Fig. 3), but this difference was not statistically significant (χ2 9 = 2.29; P > 0.1).

Factors Affecting Black-headed Ibis Flock Size at Urban and Rural Roosts

Model comparisons for Black-headed Ibis urban roosts showed the full model to be the best model, being 3.3 AIC units lower than the subsequent competing model (Table 2). All the next three best models included number of co-occurring species, were within 2 AIC units of each other, and had more support than the null model by > 10 AIC units. In contrast, models showed considerable uncertainty in rural settings with the null model being the model with the most support (lowest AIC at 178.4; Table 3), and subsequent competing models were within 2 AIC units of each other.

Table 1.

Summary statistics of variables measured at Black-headed Ibis nest and roost sites, and paired sites (without Black-headed Ibis) in southern Rajasthan, India. Settings with significantly different measures for a particular variable are indicated with the same superscripted letter (t-tests, P < 0.05). Distance to road, human habitation, and wetland, and disturbance index are variables measuring proximity to potential foraging sites and human presence. Numbers are mean ± SD (Range).

t01_51.gif

Figure 2.

Simple index values showing associations of species with Black-necked Ibis at urban (black) and rural (white) roost sites. Co-occurring species are categorized into feeding guilds.

f02_51.jpg

Discussion

Our results showed that urban and rural settings were equally important as roost sites for Black-headed Ibis, and that Black-headed Ibis numbers at roost sites were three times higher than at nest sites. In addition, roost site characteristics were different from nest sites suggesting that conservation planning for the species should also take into account preservation of roost sites. Black-headed Ibis still maintained roost sites in high disturbance urban sites. Our results agree with a study on heronry locations across India that also found a high human tolerance for heronries located in urban settings (Subramanya 1996). Absence of differences in proximity to potential foraging sites across urban and rural settings suggests adequacy of foraging sites in southern Rajasthan. Conversely, it also suggests that Black-headed Ibis likely forage in habitats that are not just assumed potential foraging sites such as wetlands or rivers. This finding matches observations in southern Rajasthan (Chaudhury and Koli 2018) and Uttar Pradesh (Sundar 2006) where Black-headed Ibis foraged in a variety of habitats throughout the year. Trees were much larger at roost sites relative to nest sites, particularly in urban settings. Roost and paired sites were different only in rural settings suggesting that conditions in rural landscapes for roosting waterbirds are more diverse.

Similarity in associations of roosting Black-headed Ibis with co-occurring species in urban and rural settings is a novel finding, and matched our expectations. In part, this suggests that urban and rural settings are equally hospitable for the observed 16 co-occurring species. The list includes globally near-threatened species such as Oriental Darter, Eurasian Spoonbill, Painted Stork and Black-necked Stork (Fig. 2). Assemblages of co-occurring species varied between Black-headed Ibis roost and paired sites suggesting that these assemblages are nonrandom.

Figure 3.

Species richness across feeding guilds of co-occurring bird assemblages at Black-headed Ibis roost sites and at paired sites.

f03_51.jpg

A combination of variables influenced flock sizes at Black-headed Ibis urban roosts. However, contrary to our prediction, measured variables were inadequate to explain flock sizes at rural roosts. Black-headed Ibis flock sizes increased at roosts as numbers of co-occurring species increased, further suggesting mutual benefits for all the species. Poor support for distance to wetlands alone influencing flock sizes is not easily explainable. However, results suggest that, in addition to foraging habitat, associating with other species is a key component of Black-headed Ibis roosting ecology in southern Rajasthan.

Our findings provide an understanding of roosting ecology for a large waterbird in southern Asia. The observed importance of co-occurring species at Black-headed Ibis roosts would seem to suggest that a behavioral study in southern Asia would yield novel insights. This study also supports the conservation value of roost sites, even in relatively busy urban settings, to Black-headed Ibis. Status assessments currently identify deterioration of foraging wetland habitat and disturbance at nesting colonies as important aspects of conservation of Black-headed Ibis (BirdLife International 2016). Assessments should include the identification and maintenance of roosting sites as an important conservation strategy. While previous studies (Sundar and Kittur 2012, 2013; Sundar et al. 2015, 2016) have discovered the high value of agricultural landscapes in southern Asia to waterbirds, the discovery of crowded urban settings supporting high populations of waterbird species outside of the breeding season is novel. This bodes well for species conservation in the region, and provides an optimistic overtone to Black-headed Ibis status assessments.

Table 2.

Model selection statistics for factors affecting Black-headed Ibis flock sizes at roosts in urban settings in southern Rajasthan, India. Variables are: CS – number of co-occurring roosting species; DTW – distance to the nearest wetland; TH – tree height; and ATS – number of trees around roost trees. The full model includes all four variables.

t02_51.gif

Table 3.

Model selection statistics for factors affecting Black-headed Ibis flock sizes at roosts in rural settings in southern Rajasthan, India. Variables are: CS – number of co-occurring roosting species; DTW – distance to the nearest wetland; TH – tree height; and ATS – number of trees around roost trees. The full model includes all four variables.

t03_51.gif

Acknowledgments

Field methods followed ethical guidelines laid out for observational studies of waterbirds outside of the protected area network as per the Wildlife Protection Act (1972), Government of India. We thank Virendra Singh Bedsa and Kamlesh Ji for help with fieldwork. This study was not funded by any agency. Authors contributed as indicated: project planning: Vijay Kumar Koli and Sunil Chaudhary; fieldwork: Sunil Chaudhary; data analyses: K. S. Gopi Sundar and Vijay Kumar Koli; manuscript writing: K. S. Gopi Sundar and Vijay Kumar Koli, with Sunil Chaudhary participating.

Literature Cited

  1. Ali, S. and S. D. Ripley. 2007. Handbook of the birds of India and Pakistan.Bombay Natural History Society and Oxford University Press, Bombay, India. Google Scholar

  2. Balakrishnan, M. and S. K. Thomas. 2004. Conserving the breeding habitat of the near threatened Oriental white ibis Threskiornis melanocephalus.Current Science87: 1190–1192. Google Scholar

  3. Beauchamp, G. 1999. The evolution of communal roosting in birds: origin and secondary losses.Behavioural Ecology10: 675–687. Google Scholar

  4. BirdLife International. 2016. Threskiornis melanocephalus.The IUCN Red List of Threatened Species 2012.e.T22697516A37830989.  https://doi.org/10.2305/IUCN.UK.2012-1.RLTS.T22697516A37830989.en, accessed 16 September 2016. Google Scholar

  5. Blanco, G. 1996. Population dynamics and communal roosting of White Storks foraging at a Spanish refuse dump.Colonial Waterbirds19: 273–276. Google Scholar

  6. Bowker, M. B. and C. T. Downs. 2012. Breeding of large, water-associated, colonially nesting birds of the north-eastern region of KwaZulu-Natal, South Africa.Waterbirds35: 270–291. Google Scholar

  7. Bryan, A. L., Jr. , K. F. Gaines and C. S. Eldridge. 2002. Coastal habitat use by Wood Storks during the non-breeding season.Waterbirds25: 429–435. Google Scholar

  8. Burnham, K. P. and D. R. Anderson. 2002. Model selection and multimodel inference: a practical information-theoretic approach, 2nd ed.Springer-Verlag, New York, New York. Google Scholar

  9. Cai, L. 2006. Multi-response permutation procedure as an alternative to the analysis of variance: an SPSS implementation.Behavior Research Methods38: 51–59. Google Scholar

  10. Chapman, C. A., L. J. Chapman and L. Lefebvre. 1989. Variability in parrot flock size: possible functions of communal roosts.Condor91: 842–847. Google Scholar

  11. Chaudhary, S. 2018. Study on the distribution, ecology and ethology of Black-headed Ibis (Threskiornis melanocephalus) in southern Rajasthan.Ph.D. Thesis, Mohanlal Sukhadia University, Udaipur, Rajasthan, India. Google Scholar

  12. Chaudhury, S. and V. K. Koli. 2016. Carcass feeding by Black-headed Ibis Threskiornis melanocephalus.Indian Birds12: 26. Google Scholar

  13. Chaudhury, S. and V. K. Koli. 2018. Population status, habitat preference, and nesting characteristics of Black-headed Ibis Threskiornis melanocephalus Latham, 1790 in southern Rajasthan, India.Journal of Asia-Pacific Biodiversity11: 223–228. Google Scholar

  14. Chevallier, D., R. Duponnois, F. Baillon, P. Brossault, J.M. Grégoire, H. Eva, Y. Le Maho and S. Massemin. 2010. The importance of roosts for Black Storks Ciconia nigra wintering in West Africa.Ardea98: 91–96. Google Scholar

  15. Donazar, J. A., O. Ceballos and J. L. Tella. 1996. Communal roosts of Egyptian Vulture (Neophron percnopterus): dynamics and implications for the species conservation. Pages 189–201inBiology and Conservation of Mediterranean Raptors (J.Muntaner and J.Mayol, Eds.). SEO/BirdLife Publisher, Madrid, Spain. Google Scholar

  16. Eiserer, L. A. 1984. Communal roosting in birds.Bird Behavior5: 61–80. Google Scholar

  17. Ginsberg, J. R. and T. P. Young. 1992. Measuring associations between individuals or groups in behavioural studies.Animal Behaviour44: 377–379. Google Scholar

  18. Google Earth Pro. 2018. Images of southern Rajasthan, 2D landscape/ Copernicus images, data 510, v. 7.3.2.5491.Google LLC, Mountain View, California.  https://www.google.com/earth/index.html , accessed 15 July 2018. Google Scholar

  19. Hastie, T.2013. ‘gam’: generalized additive models. R package v. 1.09.R Foundation for Statistical Computing, Vienna, Austria.  https://CRAN.R-project.org/package=gam, accessed 30 June 2018. Google Scholar

  20. Hoppitt, W. and D. R. Farine. 2018. Association indices for quantifying social relationships: how to deal with missing observations of individuals or groups.Animal Behaviour136: 227–238. Google Scholar

  21. Koli, V. K., M. Yaseen and C. Bhatnagar. 2013. Population status of Painted Stork Mycteria leucocephala and Black-headed Ibis Threskiornis melanocephalus in southern Rajasthan, India.Indian Birds8: 39–41. Google Scholar

  22. Kramer, D. L.1985. Are colonies supra optimal groups? Animal Behaviour33: 1031. Google Scholar

  23. Kulshreshtha, S., S. Sharma and B. K. Sharma. 2013. The majestic Rajasthan: an introduction. Pages 3–37inFaunal Heritage of Rajasthan, India: General Background and Ecology of Vertebrates (B. K.Sharma, S.Kulshreshtha and A. R.Rahmani, Eds.), vol. 1. Springer Science+Business Media, New York, New York. Google Scholar

  24. Lambertucci, A. A. 2013. Variability in size of groups in communal roosts: influence of age-class, abundance of individuals and roosting site.Emu113: 122–127. Google Scholar

  25. Laughlin, A. L., D. R. Sheldon, D. W. Winkler and C. M. Taylor. 2014. Behaviour drivers of communal roosting in a songbird: a combined theoretical and empirical approach.Behavioural Ecology25: 734–743. Google Scholar

  26. Office of the Registrar General and Census Commissioner, India. 2011. Census of India. 2011.New Delhi, India.  http://censusindia.gov.in/, accessed 28 June 2018. Google Scholar

  27. Ogden, J. C. 1990. Habitat management guidelines for the wood stork in the Southeast Region.Unpublished report, U.S. Department of the Interior, Fish and Wildlife Service, Southeast Region, Atlanta, Georgia. Google Scholar

  28. Oksanen, J., F. G. Blanchet, M. Friendly, R. Kindt, P. Legendre, D. McGlinn, P. R. Minchin, R. B. O'Hara, G. L. Simpson, P. Solymos and others. 2018. ‘vegan’: community ecology package. R package v. 2.5-2.R Foundation for Statistical Computing, Vienna, Austria.  https://CRAN.R-project.org/package=vegan, accessed 30 June 2018. Google Scholar

  29. Pearson, S. M., J. M. Walsh and J. Pickering. 1992. Wood Stork use of wetland habitats around Cumberland Island, Georgia.Colonial Waterbirds15: 33–42. Google Scholar

  30. Rao, S. and V. K. Koli. 2017. Edge effect of busy high traffic roads on the nest site selection of birds inside the city area: guild response.Transportation Research Part D51: 94–101. Google Scholar

  31. Reynierse, J. H., K. K. Gleason and R. Otteman. 1969. Mechanisms producing aggregations in planaria.Animal Behaviour17: 47–63. Google Scholar

  32. Silvis, A., A. B.Kniowski, S. D.Gehrt and W. M.Ford. 2014. Roosting and foraging social structure of the endangered Indiana bat (Myotis sodalis).PLOS ONE9: e96937. Google Scholar

  33. Singh, P. and C. T. Downs. 2016. Hadeda Ibis (Bostrychia hadeda) urban nesting and roosting sites.Urban Ecosystems19: 1295–1305. Google Scholar

  34. Soh, M. C. K., N. S. Sodhi, R. K. H. Seoh and B. W. Brook. 2002. Nest site selection of the house crow (Corvus splendens), an urban invasive bird species in Singapore and implications for its management.Landscape Urban Planning59: 217–226. Google Scholar

  35. SPSS Inc. 2011. Advanced statistics v. 20.0.SPSS Inc., Chicago, Illinois. Google Scholar

  36. Subramanya, S. 1996. Distribution, status and conservation of Indian heronries.Journal of Bombay Natural History Society93: 459–486. Google Scholar

  37. Sundar, K. S. G. 2006. Flock size, density and habitat selection of four large waterbird species in an agricultural landscape in Uttar Pradesh, India: implications for management.Waterbirds29: 365–374. Google Scholar

  38. Sundar, K. S. G. and S. Kittur. 2012. Methodological, temporal and spatial factors affecting modeled occupancy of resident birds in the perennially cultivated landscape of Uttar Pradesh, India.Landscape Ecology27: 59–72. Google Scholar

  39. Sundar, K. S. G. and S. Kittur. 2013. Can wetlands maintained for human use also help conserve biodiversity? Landscape-scale patterns of bird use of wetlands in an agricultural landscape in north India.Biological Conservation168: 49–56. Google Scholar

  40. Sundar, K. S. G., A. S. Chauhan, S. Kittur and S. Babu. 2015. Wetland loss and waterbird use of wetlands in Palwal district, Haryana, India: the role of agriculture, urbanization and conversion to fish ponds.Wetlands35: 115–125. Google Scholar

  41. Sundar, K. S. G., B. Maharjan, R. Koju, S. Kittur and K. R. Gosai. 2016. Factors affecting provisioning times of two stork species in lowland Nepal.Waterbirds39: 365–374. Google Scholar

  42. Ward, P. and A. Zahavi. 1973. The importance of certain assemblages of birds as ‘information centres’ for food finding.Ibis115: 517–534. Google Scholar

  43. Weatherhead, P. J. 1983. Two principal strategies in avian communal roosts.American Naturalist121: 237–243. Google Scholar

Vijay K. Koli, Sunil Chaudhary, and K. S. Gopi Sundar "Roosting Ecology of Black-Headed Ibis (Threskiornis melanocephalus) in Urban and Rural Areas of Southern Rajasthan, India," Waterbirds 42(1), 51-60, (27 March 2019). https://doi.org/10.1675/063.042.0106
Received: 21 July 2018; Accepted: 6 November 2018; Published: 27 March 2019
JOURNAL ARTICLE
10 PAGES


SHARE
ARTICLE IMPACT
Back to Top