Factors influencing nest site selection in Laughing Dove (Spilopelia senegalensis) in an urban landscape in Karaj, Iran

Factors influencing nest site selection in Laughing Dove (Spilopelia senegalensis) in an urban landscape in Karaj, Iran | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Factors influencing nest site selection in Laughing Dove (Spilopelia senegalensis) in an urban landscape in Karaj, Iran Morteza Banisaffar, Afshin Alizadeh Shabani This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4082159/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 Dec, 2024 Read the published version in Ornis Hungarica → Version 1 posted You are reading this latest preprint version Abstract Urban habitats, such as natural ones, are rapidly changing. Thus, conservation and management of species adapted to urban environments can be challenging. Nest site selection is a pivotal point in the process of habitat selection and breeding in bird species. We investigated the influence of several spatial and structural factors on the nest site selection of Laughing Dove ( Spilopelia senegalensis ) in an urban landscape in Karaj, Iran. We also surveyed the feasibility of occupying artificial nest boxes (n = 17) by Laughing Doves between February and September 2019. We recorded 32 nest presence sites and 64 random nest absence sites. To model nest site selection, we conducted a spline binary logistic regression analysis. Three variables were identified as significant factors influencing the nest site selection of Laughing Dove: Nest height from the ground ( p = 0.04), with an optimal range of 290–350 cm; nest detection chance ( p = 0.06), invisible places from the front and sides were most favorable; and distance to opposite building ( p = 0.07), with an optimal range of 15–38 m. The occupancy rate of the artificial nest boxes was 35.3%. This study showed that nest site selection of the urban-adapted Laughing Dove is highly dependent on the security and food provided by humans. Laughing Dove Nest site selection Artificial nest boxes Spline Urban ecology Conservation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction The biodiversity of urban areas provides various environmental services, and this importance requires the conservation of its species. The study of urban ecology has emerged as a key element of conservation research (Fischer et al., 2015 ). As the world becomes more urbanized, artificial infrastructure increasingly replaces natural habitats (Lowry et al., 2013 ). The impact of urbanization on the environment is substantial and can result in substantial changes to ecosystem structure and processes (Soulsbury and White, 2015 ). Some species adapt to these changes while others may irrevocably reduce their ranges and populations (Iezekiel et al., 2017 ). The urban space is a permanently changing ecosystem, suffering from decreasing biodiversity, but also providing new anthropogenic habitats for some adaptable species (Sumasgutner et al., 2014 ). Even species considered to be extremely abundant globally may dramatically reduce their numbers, like the House Sparrow ( Passer domesticus ) in Europe (Iezekiel et al., 2017 ). Hence, in a rapidly changing world, it is critical to better understand even those species considered to be abundant to conserve them for future generations (Iezekiel et al., 2017 ). Nest site selection is an important step in the process of habitat selection and territory establishment in bird species (Suvorov et al., 2014 ). Since birds select specific habitats through a series of choices on different spatial scales (Fasola and Canova, 1991 ), knowledge of environmental characteristics can better represent their habitats (Graveland, 1998 ; Shabani et al., 2009 ). Information about nest sites of bird populations is necessary for effective conservation and management strategies (Olah et al., 2014 ). The Laughing Dove ( Spilopelia senegalensis ) has an extremely large range in the world. This species is native to most parts of Africa, the Middle East, South and Central Asia and has also been introduced to parts of Western Australia. This species is associated with cultivation, trees (but not forests) and human habitation. The nest is a frail, thin platform of roots, twigs and petioles placed in bushes, trees, on buildings under the eaves, on drainpipes or in cracks in walls (BirdLife International, 2023 ). There is information about factors influencing Laughing Dove nesting in the agricultural areas of North Africa (Brahmia et al., 2015 ; Hanane, 2015 ; Boukhriss and Selmi, 2019 ). However, the microhabitat selection of this species is different in an urban landscape with a different climate and ecosystem. Unlike the agricultural areas of North Africa, because of the presence of predatory species such as Magpies ( Pica pica ), Crows ( Corvus cornix ), and Cats ( Felis catus ), and the hospitable behavior of humans, this species does not nest in green spaces and trees in the urban landscape of Karaj. Rather, it builds nests in close connection with the human settlements. As the presence of Laughing Doves can be considered desirable in some parts of the city, it can be problematic in others. The presence of this species in parks and green spaces can have positive aesthetic and recreational aspects, whereas in some residential areas, it can cause health problems, noise pollution, and even create problems for building facilities, such as ventilators. A study conducted on the effect of wild bird droppings as a source of Campylobacter jejuni in children's playgrounds showed that the presence of wild birds, including Laughing Doves, can cause acute gastroenteritis in humans, especially in children (Abdollahpour et al., 2015 ). In addition, Laughing Doves can cause damage by transmitting the Newcastle disease virus (NDV) to the poultry industry (Okpanachi et al., 2020 ; Hirschinger et al., 2021 ). Therefore, with a better understanding of the characteristics of Laughing Dove nest sites, if necessary, we can take steps to make some places unsuitable for nesting this species and avoid possible damages. In the present study, we investigated the influence of several factors on the nest site selection of Laughing Dove in an urban landscape in Karaj according to different spatial and structural variables. In addition, the feasibility of occupying the artificial nest box by the Laughing Dove was investigated as the secondary objective of this study. 2. Methods 2.1. Study area A part of Karaj, located in the Alborz province in Iran, was considered for this study (35°49'36N'', 50°58'6''E; 239 ha) (Fig. 1 ). Karaj is a densely populated city located at the foot of the Alborz mountain range. The study area is located in the Central part of Karaj and has main streets, side streets, and alleys. Most of the buildings are multi-storey apartments (usually three or four storeys). Human traffic was higher in the main and side streets than in the alleys. Approximately 11% of this area is green space, and the altitude ranges from 1297 to 1355 m a.s.l.; therefore, the area has a slight slope. The prevailing climate in Karaj is known as a local steppe climate, with a mean annual temperature of 14.2° C (maximum mean temperature of 32.8° C in summer and minimum mean temperature of -4.6° C in winter) and precipitation of around 283 mm per year, although it varies greatly throughout each season. The driest season is summer (4 mm), whereas the rainiest season is winter (127 mm) (Climate Data, 2022 ). 2.2. Data collection We did strip transect sampling on foot from February 25 to April 3, 2019. Also, reports of people living in the study area were used to find the nests. We recorded 32 nest presence sites and 64 randomly selected nest absence sites. We also recorded abandoned nests' locations as nest presence sites. Ten environmental variables were measured in this study (Table 1 ). The tools and devices used in sampling included tape measure, Global Positioning System (GPS, Garmin®, USA), sliding ladder, and closed-circuit television (CCTV). Table 1 Variables used to model the nest site selection of Laughing Dove. * Diameter at breast height. ** A: Invisible from the front and sides; B: Invisible from the front and visible from one side; C: Visible from the front and invisible from the sides. *** The variable was not used in the model because of high redundancy (Pearson’s r > 0.70). Variable Unit Abbreviation Additional Description Nest height from ground cm HEIGHT The vertical distance of the base of the nest from the ground Nest to ceiling height cm CEILING The vertical distance of the base of the nest to the ceiling Nest horizontal distance to ceiling edge cm H_CEILING The horizontal distance from the edge of the nest to the edge of the ceiling Distance to nearest tree m TREE_DIST The horizontal distance from the nest to the nearest tree > 10 cm dbh * Distance to opposite building m BUILD_DIST The horizontal distance from the nest to the opposite building Distance to green space m GREEN_DIST The distance from the nest to the centroid of the nearest green space (area > 2 km 2 ) Distance to nearest counterpart nest m NEST_DIST The distance to the nearest nest presence site Nest detection chance # DETECT Ability to detect the nest from a distance of 4 m and along its horizontal; A,B,C ** Base structure *** # B_STRUCT A: Shop Board; B: Building Frontispiece; C: Balcony Base aspect # B_ASPECT A: North; B: East; C:South; D:West 2.3. Artificial nest boxes To verify the feasibility of occupying the artificial nest box by Laughing Doves, we designed a nest box (Fig. 2 ) and installed 17 of them in the study area. Furthermore, after building the model, we used a dataset of artificial nest box sites to validate the model. Because we had a time limit for conducting this study and had not yet built the model, we designed the artificial nest box and selected its installation locations according to our previous observations and descriptive statistics of the nest presence sites. Artificial nest boxes were made using a raw 8-mm MDF sheet. In our observations, we noticed that Laughing Doves did not nest on relatively wide flat surfaces but rather involved their nesting materials. Therefore, a piece of wood was placed inside each artificial nest box (Fig. 2 ; Fig. 5 ). Seventeen artificial nest boxes were installed from April 15 to April 22, 2019. Seven were installed in the vicinity of the shops, two on the frontispieces of the buildings, and eight on the balconies of the buildings. 2.4. Model building We implemented a spline binary logistic regression analysis to model nest site selection and identify significant factors influencing nest site selection of Laughing Dove (α = 0.10). Because the influence of a continuous predictor variable on the probability of the occurrence of the response variable (here, the presence of a nest) can be nonlinear, we decided to use the spline binary logistic regression method. In this method, the continuous predictor variable is divided into bins using knots. Therefore, we can determine the nonlinear influence of the continuous predictor variable if any. By using the spline method, the problem of information loss that occurs when categorizing continuous variables will be solved. We performed a knots-placing procedure based on different numbers of quantiles (2–5 quantiles) for all continuous predictor variables and using data of nest presence sites (n = 32) (2 quantiles = 1 knot, …, 5 quantiles = 4 knots). Quantiles are cut points dividing the range of a probability distribution into continuous intervals with equal probabilities or dividing the observations in a sample in the same way. To determine the most appropriate number of knots (or even no knots) for each continuous predictor variable, we performed logistic regression using the single predictor separately and with different numbers of knots (up to four knots and no knots). We then selected the number of knots that provided the equation with the lowest Akaike information criterion (AIC) value to enter into the model (Harrell Jr, 2017 ). Since the stepwise method is not valid (Thayer, 2002 ; Whittingham et al., 2006 ; Flom and Cassell, 2007 ), we created a global model using all predictor variables. We decided to eliminate redundant predictor variables (Pearson’s r ≥ 0.70) before creating the model, and nine predictor variables were retained. The first level for each categorical predictor variable was set as the reference level. 2.5. Model validation The accuracy of the model was evaluated using the receiver operating characteristic (ROC) curve (Swets, 1988 ; Fielding and Bell, 1997 ). The ROC plots correctly identified presence data at a given locality (sensitivity) against wrongly classified cases (1-specificity) for all possible thresholds and distinguishes between omission (i.e. predicted absence in places of actual presence) and commission errors (i.e. predicted presence in places of actual absence) (Ringani et al., 2022 ). The resultant area under the ROC curve (AUC) gives an indication of the model performance, and the AUC values can range from 0 to 1 (Phillips and Dudík, 2008 ). A model whose predictions are 100% wrong has an AUC of 0; one whose predictions are 100% correct has an AUC of 1. Due to the small sample size (n = 96), we used all observations as the training dataset. To perform cross-validation, and considering that we did not have a test dataset, we used the dataset of nest box sites (n = 17) as the test dataset to validate the model. For this purpose, we built a classification table using the model and the dataset of nest box sites. A classification table describes the predicted number of successes compared to the number of successes actually observed. Similarly, it compares the predicted number of failures with the number actually observed (cut value = 0.5). 2.6. Used software and packages All analyses were conducted in R (R Core Team, 2022 ). The rms R package (Harrell Jr, 2023 ) was used to perform spline binary logistic regression analysis. The receiver operating characteristic curve (ROC) analysis was performed using the pROC R package (Robin et al., 2011 ), and visualization of descriptive statistics (Fig. 4 ) was done using the R package Hmisc (Harrell Jr and Dupont, 2021 ). We used ArcGIS® v10.2 (ESRI, 2013) to perform map-related processes, including calculating the distance to the nearest counterpart nest and the distance to the nearest green space. SketchUp® Pro v15.2 (Trimble Navigation Limited, 2014) was used to design the nest box. 3. Results After obtaining the model, three variables, including nest height from the ground ( χ 2 = 8.44, df = 3, p = 0.04), nest detection chance ( χ 2 = 5.69, df = 2, p = 0.06), and distance to opposite building ( χ 2 = 6.92, df = 3, p = 0.07), were identified as significant factors influencing the nest site selection of Laughing Dove (Table 2 ). Although the range of nest height from the ground was high (273–1230 cm), according to the nest site selection model, the most favorable nesting height was approximately 290–350 cm (Fig. 3 a). In terms of nest detection chance, places that were invisible from the front and sides were most preferable for Laughing Dove nesting (Fig. 3 b). The most favorable distance from the nest of the Laughing Dove to the opposite building was approximately 15–38 m (Fig. 3 c). The high value of the area under the ROC curve (AUC = 0.91) showed that the model had a good performance. After obtaining the classification table, it was found that the model predicted 70.6% of artificial nest box site cases in the correct classes (Table 3 ). As of September 22, 2019, six of the 17 artificial nest boxes installed were occupied by Laughing Doves (Fig. 1 ). Four nest boxes were occupied in the vicinity of the shops, and two on the balconies of the buildings (Fig. 5 ). Table 2 Coefficients and Wald statistics of predictor variables used in the nest site selection model of Laughing Dove Factors β S.E. Wald Z Pr( > |Z| ) Wald Statistics (Overall Effect) Intercept -39.400 13.495 -2.92 0.004 χ 2 d.f. P HEIGHT ( 367cm) 0.076 0.029 2.57 0.010 DETECT = B -1.624 0.895 -1.81 0.070 5.69 2 0.058 DETECT = C -1.622 0.888 -1.83 0.068 BUILD_DIST ( 25m) -0.245 0.151 -1.62 0.105 CEILING ( 49cm) 0.162 0.074 2.19 0.028 TREE_DIST 0.117 0.079 1.47 0.142 2.16 1 0.142 NEST_DIST -0.004 0.004 -0.97 0.331 0.94 1 0.331 GREEN_DIST -0.002 0.005 -0.52 0.604 0.27 1 0.604 H_CEILING -0.002 0.008 -0.23 0.818 0.05 1 0.818 B_ASPECT = B 0.657 1.079 0.61 0.543 0.79 3 0.852 B_ASPECT = C -0.339 1.211 -0.28 0.780 B_ASPECT = D 0.033 0.987 0.03 0.973 Table 3 Classification table based on the nest site selection model of Laughing Dove using the dataset of artificial nest box sites Predicted Absence Presence Percentage Correct Observed Absence 7 4 63.6 Presence 1 5 83.3 Overall Percentage 70.6 4. Discussion Some factors influencing the nest site selection of Laughing Doves in agricultural habitats of North Africa were studied. However, no previous studies have investigated the nest site selection of this species in an urban environment. Hence, in this study, we gained a better understanding of the preferred nest sites for Laughing Doves in an urban landscape in Karaj. We also surveyed the feasibility of occupying artificial nest boxes by Laughing Doves. According to the high correlation between nest height from the ground and base structure variable, as well as the results of the nest site selection model, it seemed that shop boards had the most favorable height for Laughing Dove nesting. The height of Laughing Dove nests on shop boards in the urban environment of the present study was close to the height of nests of this species in trees in an olive orchard in the Guelma region in Northeastern Algeria (Brahmia et al., 2015 ), in the agricultural area of Tadla in Central Morocco (Hanane, 2015 ), and in a grove in the oasis of Kettana in Southeastern Tunisia (Boukhriss and Selmi, 2019 ). However, in some cases, the Laughing Dove nested at a relatively high height, especially in the balconies of apartments, which indicates the high adaptability of this species to various environmental conditions. In previous researches conducted in agricultural habitats of North Africa, they reported the average height of Laughing Dove nests from the ground, due to relatively low spatial heterogeneity in those environments, this value describes the optimal nesting height well. However, considering the relatively high range of height of Laughing Dove nests in the urban environment of the present study, the average value could not provide an accurate description of the optimal nesting height of this species. Therefore, we used the results of the nest site selection model to determine the optimal nesting height of the Laughing Dove in the urban environment of the present study. It seemed that the chance of nest detection was a very important factor in the nest site selection of Laughing Dove, and it preferred such places to build nests that could not be detected from the sides and above, especially by predatory birds such as Magpies ( P. pica ) and Crows ( C. cornix ). In some cases, the nest of Laughing Dove was visible to predatory mammals such as Cats ( F. catus ) and Free-ranging Dogs ( Canis lupus familiaris ), but it seemed impossible for these predators to access the nest. All nest sites had a ceiling, which appeared to be essential to prevent direct sunlight, rainfall, and predatory birds. Laughing Doves made almost no attempt to hide themselves or their nests from the human sight. This behavior could be attributed to the friendly disposition that most people in this area of Karaj have towards this species. Distance to the opposite building, which depends on the location of the nesting site in the main or side streets or alleys, was also recognized as another factor influencing the nest site selection of Laughing Dove. It seemed that the Laughing Dove preferred streets that had a lot of shops and human traffic for nesting. Shop boards were favorable places for Laughing Dove nesting, and the presence of humans provides food and security to their nests against predatory birds. The distance between Laughing Dove nests did not follow a particular pattern and depended on the availability of suitable nesting sites as well as the availability of food and security due to the presence of humans. Considering the abundance of habitat resources for Laughing Doves in the urban area of the present study, the very short distance between counterpart nests could be justified in some cases. However, because we also measured abandoned Laughing Dove nests in this study, it was hard to obtain an accurate understanding of the distance between active nests. We observed that all Laughing Dove nests were located in close proximity to humans. To further ensure this behavior, we installed two artificial nest boxes in abandoned buildings far from human traffic, none of which were occupied by Laughing Doves. Some people, especially shopkeepers, used to feed Laughing Doves. While species that are potential enemies of the Laughing Dove, such as Magpies ( P. pica ) and Crows ( C. cornix ), were considered undesirable by humans due to noise pollution and cultural beliefs. Hanane ( 2015 ) reported a short distance between Laughing Dove nests and human habitation in agricultural areas of Central Morocco. The dependence of Laughing Dove on being close to human habitats, both in urban and agricultural areas, could be due to the inability of this short-winged species to fly long distances to access water and food (Shoham et al., 1997 ). Since there were trees in the vicinity of almost all the buildings in the study area, it was difficult to determine the positive or negative influence of the distance to the nearest tree variable on the nest site selection of Laughing Dove. Although nest predators of Laughing Dove usually nest in urban green spaces, the distance to green space variable did not have a significant influence on the nest site selection of this species. Considering that approximately 35% of artificial nest boxes were occupied by Laughing Doves, it is concluded that it is practicable to design and install artificial nest boxes for this species for conservation, research, or even recreational purposes. We noticed that small shops are quickly giving way to shopping malls, and modern shop boards and facilities usually do not provide nesting sites for Laughing Doves. Therefore, artificial nest boxes can be useful tools to conserve, especially the future generations of this species. We suggest that nest site selection of Laughing Dove should be investigated in other urban and rural environments, in different cities and villages, in larger study areas, and with more samples. In addition, investigating other aspects of Laughing Dove life, including its ethology and breeding biology in different ecosystems, can increase our knowledge of this species. Declarations Acknowledgments We thank the University of Tehran for providing the financial support for this study. We also thank all the people who cooperated with us, especially Mohammadreza Kashfi, who gave us the initial idea of this study. Funding Declaration This research was done without funding. Ethical Statement 1) This material is the authors' own original work, which has not been previously published elsewhere. 2) The paper is not currently being considered for publication elsewhere. 3) The paper reflects the authors' own research and analysis in a truthful and complete manner. 4) The paper properly credits the meaningful contributions of co-authors and co-researchers. 5) The results are appropriately placed in the context of prior and existing research. 6) All sources used are properly disclosed (correct citation). Literally copying of text must be indicated as such by using quotation marks and giving proper reference. 7) All authors have been personally and actively involved in substantial work leading to the paper, and will take public responsibility for its content. I agree with the above statements and declare that this submission follows the policies of Solid State Ionics as outlined in the Guide for Authors and in the Ethical Statement. We have no conflicts of interest to disclose . Availability of data and materials Data have been uploaded and are freely available via Dryad: https://datadryad.org/stash/share/WIFsuJVK5uQIJd_zyHlWvbWtxdVvqRjUxkTWnKoziJQ References Abdollahpour, N., Zendehbad, B., Alipour, A., Khayatzadeh, J., 2015. Wild-bird feces as a source of Campylobacter jejuni infection in children's playgrounds in Iran. Food Control 50, 378-381. BirdLife International. 2023. Species factsheet: Spilopelia senegalensis . Downloaded from . Boukhriss, J., Selmi, S., 2019. Drivers of nest survival rate in a southern Tunisian population of Laughing Doves ( Spilopelia senegalensis ). Avian Research 10, 1-6. Brahmia, H., Zeraoula, A., Bensouilah, T., Bouslama, Z., Houhamdi, M., 2015. Breeding biology of sympatric Laughing Streptopelia senegalensis and Turtle Streptopelia turtur Dove: a comparative study in northeast Algeria. Zoology and ecology 25, 220-226. Climate Data. 2022. Karaj Climate by . 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Landscape and Urban Planning 127, 94-103. Suvorov, P., Svobodová, J., Albrecht, T., 2014. Habitat edges affect patterns of artificial nest predation along a wetland-meadow boundary. Acta Oecologica 59, 91-96. Swets, J. A., 1988. Measuring the accuracy of diagnostic systems. Science 240, 1285-1293. Thayer, J. D., 2002. Stepwise Regression as an Exploratory Data Analysis Procedure. Whittingham, M. J., Stephens, P. A., Bradbury, R. B., Freckleton, R. P., 2006. Why do we still use stepwise modelling in ecology and behaviour? Journal of animal ecology 75, 1182-1189. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4082159","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":279879824,"identity":"c833eeef-6b7b-4d12-b6d3-3e9bc9e85cd8","order_by":0,"name":"Morteza Banisaffar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIie3OMQrCMBSA4RdEXVq6xiVe4UmH0sG71L06ioNIp7oUZ8FjeIGEgC5R14pXcNBFOhSxVgtOseDikB8C74V8EACT6Q/D4nAA5pTb5X3LaxC3ExUDWZZ7DQIVaVjVrstr74TIpkidFW+u+3EOzpwTOdEQPxkF0togpfugdRrGCFQFIJTuYzxECa18BgpeBFIAEenI4YwiuyPtPolfkO5XkobI7RgpPgkpCH4j/vKM0l4g7SkSH5O9a/XUINISzwnda3ZDylRjk2ZjxthWyquOfETKd1Y1mEwmk+mHHhIoTkk7jSwhAAAAAElFTkSuQmCC","orcid":"","institution":"University of Tehran","correspondingAuthor":true,"prefix":"","firstName":"Morteza","middleName":"","lastName":"Banisaffar","suffix":""},{"id":279879825,"identity":"2b689f57-693d-4967-a9ab-a5c0dd722fa4","order_by":1,"name":"Afshin Alizadeh Shabani","email":"","orcid":"","institution":"University of Tehran","correspondingAuthor":false,"prefix":"","firstName":"Afshin","middleName":"Alizadeh","lastName":"Shabani","suffix":""}],"badges":[],"createdAt":"2024-03-12 08:47:47","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4082159/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4082159/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.2478/orhu-2024-0026","type":"published","date":"2024-12-07T00:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":52981432,"identity":"c4e0e5e0-5959-41c9-9a1b-102a9116a47f","added_by":"auto","created_at":"2024-03-19 10:17:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":288717,"visible":true,"origin":"","legend":"\u003cp\u003eStudy area; Contains modified Copernicus Sentinel data processed by Sentinel Hub (2022).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4082159/v1/5e16c0742cb56814735ab40e.png"},{"id":52981431,"identity":"7a31c5ad-8fdf-4cc7-9100-6a2025efa70c","added_by":"auto","created_at":"2024-03-19 10:17:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":68167,"visible":true,"origin":"","legend":"\u003cp\u003eLaughing Dove artificial nest box structure\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4082159/v1/09789ee5fcf881a4b426ba4f.png"},{"id":52981434,"identity":"a649256f-1c01-48e0-a0a1-0d531535231f","added_by":"auto","created_at":"2024-03-19 10:17:58","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":48604,"visible":true,"origin":"","legend":"\u003cp\u003eInfluence of three significant factors of the model on the probability of nest site occupancy by Laughing Dove (adjusted to the median of other predictor variables). Nest height from the ground (a), Nest detection chance (b), and Distance from nest to opposite building (c).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4082159/v1/7a48e973662c87756c8c60d4.png"},{"id":52981433,"identity":"f9e87d6d-4c3f-45ba-a999-5232bfa9a985","added_by":"auto","created_at":"2024-03-19 10:17:58","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":22517,"visible":true,"origin":"","legend":"\u003cp\u003eDescriptive statistics of all measured variables of nest presence sites of the Laughing Dove (n = 32)\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4082159/v1/90d6f7342d9b86c3f81950ac.png"},{"id":52981435,"identity":"51ac5a57-f5ff-438d-988e-9d972e16d6fe","added_by":"auto","created_at":"2024-03-19 10:17:58","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":160978,"visible":true,"origin":"","legend":"\u003cp\u003eAn artificial nest box installed in a balcony occupied by The Laughing Dove.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4082159/v1/5855059309a12c755f6cd7b4.png"},{"id":70969343,"identity":"29412502-5074-4b6e-9246-1f1b635fffdb","added_by":"auto","created_at":"2024-12-09 17:05:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1182315,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4082159/v1/341fbbe1-37e1-47d4-9437-da5c40e411e8.pdf"},{"id":52981437,"identity":"0807b659-b94b-47c8-9159-083a4162a124","added_by":"auto","created_at":"2024-03-19 10:17:59","extension":"zip","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":31036071,"visible":true,"origin":"","legend":"","description":"","filename":"Video.zip","url":"https://assets-eu.researchsquare.com/files/rs-4082159/v1/ace1bef3cb5f5d712ed37b71.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Factors influencing nest site selection in Laughing Dove (Spilopelia senegalensis) in an urban landscape in Karaj, Iran","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe biodiversity of urban areas provides various environmental services, and this importance requires the conservation of its species. The study of urban ecology has emerged as a key element of conservation research (Fischer et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). As the world becomes more urbanized, artificial infrastructure increasingly replaces natural habitats (Lowry et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The impact of urbanization on the environment is substantial and can result in substantial changes to ecosystem structure and processes (Soulsbury and White, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Some species adapt to these changes while others may irrevocably reduce their ranges and populations (Iezekiel et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe urban space is a permanently changing ecosystem, suffering from decreasing biodiversity, but also providing new anthropogenic habitats for some adaptable species (Sumasgutner et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Even species considered to be extremely abundant globally may dramatically reduce their numbers, like the House Sparrow (\u003cem\u003ePasser domesticus\u003c/em\u003e) in Europe (Iezekiel et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Hence, in a rapidly changing world, it is critical to better understand even those species considered to be abundant to conserve them for future generations (Iezekiel et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNest site selection is an important step in the process of habitat selection and territory establishment in bird species (Suvorov et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Since birds select specific habitats through a series of choices on different spatial scales (Fasola and Canova, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1991\u003c/span\u003e), knowledge of environmental characteristics can better represent their habitats (Graveland, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Shabani et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Information about nest sites of bird populations is necessary for effective conservation and management strategies (Olah et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Laughing Dove (\u003cem\u003eSpilopelia senegalensis\u003c/em\u003e) has an extremely large range in the world. This species is native to most parts of Africa, the Middle East, South and Central Asia and has also been introduced to parts of Western Australia. This species is associated with cultivation, trees (but not forests) and human habitation. The nest is a frail, thin platform of roots, twigs and petioles placed in bushes, trees, on buildings under the eaves, on drainpipes or in cracks in walls (BirdLife International, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThere is information about factors influencing Laughing Dove nesting in the agricultural areas of North Africa (Brahmia et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Hanane, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Boukhriss and Selmi, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, the microhabitat selection of this species is different in an urban landscape with a different climate and ecosystem. Unlike the agricultural areas of North Africa, because of the presence of predatory species such as Magpies (\u003cem\u003ePica pica\u003c/em\u003e), Crows (\u003cem\u003eCorvus cornix\u003c/em\u003e), and Cats (\u003cem\u003eFelis catus\u003c/em\u003e), and the hospitable behavior of humans, this species does not nest in green spaces and trees in the urban landscape of Karaj. Rather, it builds nests in close connection with the human settlements.\u003c/p\u003e \u003cp\u003eAs the presence of Laughing Doves can be considered desirable in some parts of the city, it can be problematic in others. The presence of this species in parks and green spaces can have positive aesthetic and recreational aspects, whereas in some residential areas, it can cause health problems, noise pollution, and even create problems for building facilities, such as ventilators. A study conducted on the effect of wild bird droppings as a source of \u003cem\u003eCampylobacter jejuni\u003c/em\u003e in children's playgrounds showed that the presence of wild birds, including Laughing Doves, can cause acute gastroenteritis in humans, especially in children (Abdollahpour et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In addition, Laughing Doves can cause damage by transmitting the Newcastle disease virus (NDV) to the poultry industry (Okpanachi et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Hirschinger et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Therefore, with a better understanding of the characteristics of Laughing Dove nest sites, if necessary, we can take steps to make some places unsuitable for nesting this species and avoid possible damages.\u003c/p\u003e \u003cp\u003eIn the present study, we investigated the influence of several factors on the nest site selection of Laughing Dove in an urban landscape in Karaj according to different spatial and structural variables. In addition, the feasibility of occupying the artificial nest box by the Laughing Dove was investigated as the secondary objective of this study.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study area\u003c/h2\u003e \u003cp\u003eA part of Karaj, located in the Alborz province in Iran, was considered for this study (35\u0026deg;49'36N'', 50\u0026deg;58'6''E; 239 ha) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Karaj is a densely populated city located at the foot of the Alborz mountain range. The study area is located in the Central part of Karaj and has main streets, side streets, and alleys. Most of the buildings are multi-storey apartments (usually three or four storeys). Human traffic was higher in the main and side streets than in the alleys. Approximately 11% of this area is green space, and the altitude ranges from 1297 to 1355 m a.s.l.; therefore, the area has a slight slope.\u003c/p\u003e \u003cp\u003eThe prevailing climate in Karaj is known as a local steppe climate, with a mean annual temperature of 14.2\u0026deg;\u003csup\u003eC\u003c/sup\u003e (maximum mean temperature of 32.8\u0026deg;\u003csup\u003eC\u003c/sup\u003e in summer and minimum mean temperature of -4.6\u0026deg;\u003csup\u003eC\u003c/sup\u003e in winter) and precipitation of around 283 mm per year, although it varies greatly throughout each season. The driest season is summer (4 mm), whereas the rainiest season is winter (127 mm) (Climate Data, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Data collection\u003c/h2\u003e \u003cp\u003eWe did strip transect sampling on foot from February 25 to April 3, 2019. Also, reports of people living in the study area were used to find the nests. We recorded 32 nest presence sites and 64 randomly selected nest absence sites. We also recorded abandoned nests' locations as nest presence sites. Ten environmental variables were measured in this study (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The tools and devices used in sampling included tape measure, Global Positioning System (GPS, Garmin\u0026reg;, USA), sliding ladder, and closed-circuit television (CCTV).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVariables used to model the nest site selection of Laughing Dove. \u003csup\u003e*\u003c/sup\u003eDiameter at breast height. \u003csup\u003e**\u003c/sup\u003eA: Invisible from the front and sides; B: Invisible from the front and visible from one side; C: Visible from the front and invisible from the sides. \u003csup\u003e***\u003c/sup\u003eThe variable was not used in the model because of high redundancy (Pearson\u0026rsquo;s r\u0026thinsp;\u0026gt;\u0026thinsp;0.70).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAbbreviation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdditional Description\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNest height from ground\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHEIGHT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe vertical distance of the base of the nest from the ground\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNest to ceiling height\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCEILING\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe vertical distance of the base of the nest to the ceiling\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNest horizontal distance to ceiling edge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eH_CEILING\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe horizontal distance from the edge of the nest to the edge of the ceiling\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance to nearest tree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003em\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTREE_DIST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe horizontal distance from the nest to the nearest tree\u0026thinsp;\u0026gt;\u0026thinsp;10 cm dbh\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance to opposite building\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003em\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBUILD_DIST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe horizontal distance from the nest to the opposite building\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance to green space\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003em\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGREEN_DIST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe distance from the nest to the centroid of the nearest green space (area\u0026thinsp;\u0026gt;\u0026thinsp;2 km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance to nearest counterpart nest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003em\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNEST_DIST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe distance to the nearest nest presence site\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNest detection chance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e#\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDETECT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAbility to detect the nest from a distance of 4 m and along its horizontal; A,B,C\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBase structure\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e#\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB_STRUCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA: Shop Board; B: Building Frontispiece; C: Balcony\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBase aspect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e#\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB_ASPECT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA: North; B: East; C:South; D:West\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Artificial nest boxes\u003c/h2\u003e \u003cp\u003eTo verify the feasibility of occupying the artificial nest box by Laughing Doves, we designed a nest box (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) and installed 17 of them in the study area. Furthermore, after building the model, we used a dataset of artificial nest box sites to validate the model.\u003c/p\u003e \u003cp\u003eBecause we had a time limit for conducting this study and had not yet built the model, we designed the artificial nest box and selected its installation locations according to our previous observations and descriptive statistics of the nest presence sites.\u003c/p\u003e \u003cp\u003eArtificial nest boxes were made using a raw 8-mm MDF sheet. In our observations, we noticed that Laughing Doves did not nest on relatively wide flat surfaces but rather involved their nesting materials. Therefore, a piece of wood was placed inside each artificial nest box (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Seventeen artificial nest boxes were installed from April 15 to April 22, 2019. Seven were installed in the vicinity of the shops, two on the frontispieces of the buildings, and eight on the balconies of the buildings.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Model building\u003c/h2\u003e \u003cp\u003eWe implemented a spline binary logistic regression analysis to model nest site selection and identify significant factors influencing nest site selection of Laughing Dove (α\u0026thinsp;=\u0026thinsp;0.10).\u003c/p\u003e \u003cp\u003eBecause the influence of a continuous predictor variable on the probability of the occurrence of the response variable (here, the presence of a nest) can be nonlinear, we decided to use the spline binary logistic regression method. In this method, the continuous predictor variable is divided into bins using knots. Therefore, we can determine the nonlinear influence of the continuous predictor variable if any. By using the spline method, the problem of information loss that occurs when categorizing continuous variables will be solved.\u003c/p\u003e \u003cp\u003eWe performed a knots-placing procedure based on different numbers of quantiles (2\u0026ndash;5 quantiles) for all continuous predictor variables and using data of nest presence sites (n\u0026thinsp;=\u0026thinsp;32) (2 quantiles\u0026thinsp;=\u0026thinsp;1 knot, \u0026hellip;, 5 quantiles\u0026thinsp;=\u0026thinsp;4 knots). Quantiles are cut points dividing the range of a probability distribution into continuous intervals with equal probabilities or dividing the observations in a sample in the same way.\u003c/p\u003e \u003cp\u003eTo determine the most appropriate number of knots (or even no knots) for each continuous predictor variable, we performed logistic regression using the single predictor separately and with different numbers of knots (up to four knots and no knots). We then selected the number of knots that provided the equation with the lowest Akaike information criterion (AIC) value to enter into the model (Harrell Jr, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Since the stepwise method is not valid (Thayer, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Whittingham et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Flom and Cassell, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), we created a global model using all predictor variables. We decided to eliminate redundant predictor variables (Pearson\u0026rsquo;s r\u0026thinsp;\u0026ge;\u0026thinsp;0.70) before creating the model, and nine predictor variables were retained. The first level for each categorical predictor variable was set as the reference level.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Model validation\u003c/h2\u003e \u003cp\u003eThe accuracy of the model was evaluated using the receiver operating characteristic (ROC) curve (Swets, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; Fielding and Bell, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). The ROC plots correctly identified presence data at a given locality (sensitivity) against wrongly classified cases (1-specificity) for all possible thresholds and distinguishes between omission (i.e. predicted absence in places of actual presence) and commission errors (i.e. predicted presence in places of actual absence) (Ringani et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The resultant area under the ROC curve (AUC) gives an indication of the model performance, and the AUC values can range from 0 to 1 (Phillips and Dud\u0026iacute;k, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). A model whose predictions are 100% wrong has an AUC of 0; one whose predictions are 100% correct has an AUC of 1.\u003c/p\u003e \u003cp\u003eDue to the small sample size (n\u0026thinsp;=\u0026thinsp;96), we used all observations as the training dataset. To perform cross-validation, and considering that we did not have a test dataset, we used the dataset of nest box sites (n\u0026thinsp;=\u0026thinsp;17) as the test dataset to validate the model. For this purpose, we built a classification table using the model and the dataset of nest box sites. A classification table describes the predicted number of successes compared to the number of successes actually observed. Similarly, it compares the predicted number of failures with the number actually observed (cut value\u0026thinsp;=\u0026thinsp;0.5).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Used software and packages\u003c/h2\u003e \u003cp\u003eAll analyses were conducted in R (R Core Team, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The rms R package (Harrell Jr, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) was used to perform spline binary logistic regression analysis. The receiver operating characteristic curve (ROC) analysis was performed using the pROC R package (Robin et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), and visualization of descriptive statistics (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) was done using the R package Hmisc (Harrell Jr and Dupont, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe used ArcGIS\u0026reg; v10.2 (ESRI, 2013) to perform map-related processes, including calculating the distance to the nearest counterpart nest and the distance to the nearest green space. SketchUp\u0026reg; Pro v15.2 (Trimble Navigation Limited, 2014) was used to design the nest box.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eAfter obtaining the model, three variables, including nest height from the ground (\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;8.44, df\u0026thinsp;=\u0026thinsp;3, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04), nest detection chance (\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;5.69, df\u0026thinsp;=\u0026thinsp;2, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.06), and distance to opposite building (\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;6.92, df\u0026thinsp;=\u0026thinsp;3, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.07), were identified as significant factors influencing the nest site selection of Laughing Dove (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough the range of nest height from the ground was high (273\u0026ndash;1230 cm), according to the nest site selection model, the most favorable nesting height was approximately 290\u0026ndash;350 cm (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). In terms of nest detection chance, places that were invisible from the front and sides were most preferable for Laughing Dove nesting (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). The most favorable distance from the nest of the Laughing Dove to the opposite building was approximately 15\u0026ndash;38 m (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003eThe high value of the area under the ROC curve (AUC\u0026thinsp;=\u0026thinsp;0.91) showed that the model had a good performance. After obtaining the classification table, it was found that the model predicted 70.6% of artificial nest box site cases in the correct classes (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs of September 22, 2019, six of the 17 artificial nest boxes installed were occupied by Laughing Doves (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Four nest boxes were occupied in the vicinity of the shops, and two on the balconies of the buildings (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCoefficients and Wald statistics of predictor variables used in the nest site selection model of Laughing Dove\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS.E.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWald \u003cem\u003eZ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePr(\u003cem\u003e\u0026gt; |Z|\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eWald Statistics (Overall Effect)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-39.400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ed.f.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHEIGHT (\u0026lt;\u0026thinsp;313cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e8.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHEIGHT' (313\u0026ndash;367cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHEIGHT'' (\u0026gt;\u0026thinsp;367cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDETECT\u0026thinsp;=\u0026thinsp;B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e5.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDETECT\u0026thinsp;=\u0026thinsp;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUILD_DIST (\u0026lt;\u0026thinsp;16m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e6.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUILD_DIST' (16\u0026ndash;25m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.756\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUILD_DIST'' (\u0026gt;\u0026thinsp;25m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEILING (\u0026lt;\u0026thinsp;24cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e5.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEILING' (24\u0026ndash;49cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCEILING'' (\u0026gt;\u0026thinsp;49cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTREE_DIST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNEST_DIST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.331\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGREEN_DIST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.604\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH_CEILING\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.818\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB_ASPECT\u0026thinsp;=\u0026thinsp;B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.852\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB_ASPECT\u0026thinsp;=\u0026thinsp;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.780\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB_ASPECT\u0026thinsp;=\u0026thinsp;D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.973\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClassification table based on the nest site selection model of Laughing Dove using the dataset of artificial nest box sites\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003ePredicted\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAbsence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePresence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePercentage Correct\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eObserved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbsence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e63.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePresence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e83.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eOverall Percentage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eSome factors influencing the nest site selection of Laughing Doves in agricultural habitats of North Africa were studied. However, no previous studies have investigated the nest site selection of this species in an urban environment. Hence, in this study, we gained a better understanding of the preferred nest sites for Laughing Doves in an urban landscape in Karaj. We also surveyed the feasibility of occupying artificial nest boxes by Laughing Doves.\u003c/p\u003e \u003cp\u003eAccording to the high correlation between nest height from the ground and base structure variable, as well as the results of the nest site selection model, it seemed that shop boards had the most favorable height for Laughing Dove nesting. The height of Laughing Dove nests on shop boards in the urban environment of the present study was close to the height of nests of this species in trees in an olive orchard in the Guelma region in Northeastern Algeria (Brahmia et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), in the agricultural area of Tadla in Central Morocco (Hanane, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and in a grove in the oasis of Kettana in Southeastern Tunisia (Boukhriss and Selmi, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, in some cases, the Laughing Dove nested at a relatively high height, especially in the balconies of apartments, which indicates the high adaptability of this species to various environmental conditions. In previous researches conducted in agricultural habitats of North Africa, they reported the average height of Laughing Dove nests from the ground, due to relatively low spatial heterogeneity in those environments, this value describes the optimal nesting height well. However, considering the relatively high range of height of Laughing Dove nests in the urban environment of the present study, the average value could not provide an accurate description of the optimal nesting height of this species. Therefore, we used the results of the nest site selection model to determine the optimal nesting height of the Laughing Dove in the urban environment of the present study.\u003c/p\u003e \u003cp\u003eIt seemed that the chance of nest detection was a very important factor in the nest site selection of Laughing Dove, and it preferred such places to build nests that could not be detected from the sides and above, especially by predatory birds such as Magpies (\u003cem\u003eP. pica\u003c/em\u003e) and Crows (\u003cem\u003eC. cornix\u003c/em\u003e). In some cases, the nest of Laughing Dove was visible to predatory mammals such as Cats (\u003cem\u003eF. catus\u003c/em\u003e) and Free-ranging Dogs (\u003cem\u003eCanis lupus familiaris\u003c/em\u003e), but it seemed impossible for these predators to access the nest. All nest sites had a ceiling, which appeared to be essential to prevent direct sunlight, rainfall, and predatory birds. Laughing Doves made almost no attempt to hide themselves or their nests from the human sight. This behavior could be attributed to the friendly disposition that most people in this area of Karaj have towards this species.\u003c/p\u003e \u003cp\u003eDistance to the opposite building, which depends on the location of the nesting site in the main or side streets or alleys, was also recognized as another factor influencing the nest site selection of Laughing Dove. It seemed that the Laughing Dove preferred streets that had a lot of shops and human traffic for nesting. Shop boards were favorable places for Laughing Dove nesting, and the presence of humans provides food and security to their nests against predatory birds.\u003c/p\u003e \u003cp\u003eThe distance between Laughing Dove nests did not follow a particular pattern and depended on the availability of suitable nesting sites as well as the availability of food and security due to the presence of humans. Considering the abundance of habitat resources for Laughing Doves in the urban area of the present study, the very short distance between counterpart nests could be justified in some cases. However, because we also measured abandoned Laughing Dove nests in this study, it was hard to obtain an accurate understanding of the distance between active nests.\u003c/p\u003e \u003cp\u003eWe observed that all Laughing Dove nests were located in close proximity to humans. To further ensure this behavior, we installed two artificial nest boxes in abandoned buildings far from human traffic, none of which were occupied by Laughing Doves. Some people, especially shopkeepers, used to feed Laughing Doves. While species that are potential enemies of the Laughing Dove, such as Magpies (\u003cem\u003eP. pica\u003c/em\u003e) and Crows (\u003cem\u003eC. cornix\u003c/em\u003e), were considered undesirable by humans due to noise pollution and cultural beliefs. Hanane (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) reported a short distance between Laughing Dove nests and human habitation in agricultural areas of Central Morocco. The dependence of Laughing Dove on being close to human habitats, both in urban and agricultural areas, could be due to the inability of this short-winged species to fly long distances to access water and food (Shoham et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1997\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSince there were trees in the vicinity of almost all the buildings in the study area, it was difficult to determine the positive or negative influence of the distance to the nearest tree variable on the nest site selection of Laughing Dove. Although nest predators of Laughing Dove usually nest in urban green spaces, the distance to green space variable did not have a significant influence on the nest site selection of this species.\u003c/p\u003e \u003cp\u003eConsidering that approximately 35% of artificial nest boxes were occupied by Laughing Doves, it is concluded that it is practicable to design and install artificial nest boxes for this species for conservation, research, or even recreational purposes. We noticed that small shops are quickly giving way to shopping malls, and modern shop boards and facilities usually do not provide nesting sites for Laughing Doves. Therefore, artificial nest boxes can be useful tools to conserve, especially the future generations of this species.\u003c/p\u003e \u003cp\u003eWe suggest that nest site selection of Laughing Dove should be investigated in other urban and rural environments, in different cities and villages, in larger study areas, and with more samples. In addition, investigating other aspects of Laughing Dove life, including its ethology and breeding biology in different ecosystems, can increase our knowledge of this species.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the University of Tehran for providing the financial support for this study. We also thank all the people who cooperated with us, especially Mohammadreza Kashfi, who gave us the initial idea of this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was done without funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1) This material is the authors\u0026apos; own original work, which has not been previously published elsewhere.\u003c/p\u003e\n\u003cp\u003e2) The paper is not currently being considered for publication elsewhere.\u003c/p\u003e\n\u003cp\u003e3) The paper reflects the authors\u0026apos; own research and analysis in a truthful and complete manner.\u003c/p\u003e\n\u003cp\u003e4) The paper properly credits the meaningful contributions of co-authors and co-researchers.\u003c/p\u003e\n\u003cp\u003e5) The results are appropriately placed in the context of prior and existing research.\u003c/p\u003e\n\u003cp\u003e6) All sources used are properly disclosed (correct citation). Literally copying of text must be indicated as such by using quotation marks and giving proper reference.\u003c/p\u003e\n\u003cp\u003e7) All authors have been personally and actively involved in substantial work leading to the paper, and will take public responsibility for its content.\u003c/p\u003e\n\u003cp\u003eI agree with the above statements and declare that this submission follows the policies of Solid State Ionics as outlined in the Guide for Authors and in the Ethical Statement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWe have no conflicts of interest to disclose\u003c/strong\u003e\u003cem\u003e.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData have been uploaded and are freely available via Dryad: https://datadryad.org/stash/share/WIFsuJVK5uQIJd_zyHlWvbWtxdVvqRjUxkTWnKoziJQ\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbdollahpour, N., Zendehbad, B., Alipour, A., Khayatzadeh, J., 2015. 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Journal of animal ecology 75, 1182-1189.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Laughing Dove, Nest site selection, Artificial nest boxes, Spline, Urban ecology, Conservation","lastPublishedDoi":"10.21203/rs.3.rs-4082159/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4082159/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUrban habitats, such as natural ones, are rapidly changing. Thus, conservation and management of species adapted to urban environments can be challenging. Nest site selection is a pivotal point in the process of habitat selection and breeding in bird species. We investigated the influence of several spatial and structural factors on the nest site selection of Laughing Dove (\u003cem\u003eSpilopelia senegalensis\u003c/em\u003e) in an urban landscape in Karaj, Iran. We also surveyed the feasibility of occupying artificial nest boxes (n\u0026thinsp;=\u0026thinsp;17) by Laughing Doves between February and September 2019. We recorded 32 nest presence sites and 64 random nest absence sites. To model nest site selection, we conducted a spline binary logistic regression analysis. Three variables were identified as significant factors influencing the nest site selection of Laughing Dove: Nest height from the ground (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04), with an optimal range of 290\u0026ndash;350 cm; nest detection chance (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.06), invisible places from the front and sides were most favorable; and distance to opposite building (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.07), with an optimal range of 15\u0026ndash;38 m. The occupancy rate of the artificial nest boxes was 35.3%. This study showed that nest site selection of the urban-adapted Laughing Dove is highly dependent on the security and food provided by humans.\u003c/p\u003e","manuscriptTitle":"Factors influencing nest site selection in Laughing Dove (Spilopelia senegalensis) in an urban landscape in Karaj, Iran","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-19 10:17:53","doi":"10.21203/rs.3.rs-4082159/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"277102aa-4753-4d55-bfb8-72d54731da00","owner":[],"postedDate":"March 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-12-09T17:05:06+00:00","versionOfRecord":{"articleIdentity":"rs-4082159","link":"https://doi.org/10.2478/orhu-2024-0026","journal":{"identity":"ornis-hungarica","isVorOnly":true,"title":"Ornis Hungarica"},"publishedOn":"2024-12-07 00:00:00","publishedOnDateReadable":"December 7th, 2024"},"versionCreatedAt":"2024-03-19 10:17:53","video":"","vorDoi":"10.2478/orhu-2024-0026","vorDoiUrl":"https://doi.org/10.2478/orhu-2024-0026","workflowStages":[]},"version":"v1","identity":"rs-4082159","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4082159","identity":"rs-4082159","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}