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Article

Potential Distribution, Density and Abundance Estimate of the European Turtle Dove Streptopelia turtur (Linnaeus, 1758) in Apulia

by
Simona Tarricone
1,
Giuseppe La Gioia
2,
Maria Antonietta Colonna
1,*,
Nicolò De Vito
1,
Massimo Lacitignola
1,
Domenico Gerardi
1,
Gianpasquale Chiatante
3,
Domenico Campanile
4,
Mariarosaria Fortunato
1 and
Marco Ragni
1
1
Department of Soil, Plant and Food Sciences, University of Bari Aldo Moro, 70126 Bari, Italy
2
Ornitologia Mediterranea (Or.Me.), 73100 Lecce, Italy
3
Department of Ecological and Biological Sciences, University of Tuscia, 01100 Viterbo, Italy
4
Regione Puglia, Dipartimento Agricoltura, Sviluppo Rurale ed Ambientale, Sezione Gestione Sostenibile e Tutela delle Risorse Forestali e Naturali, 70124 Bari, Italy
*
Author to whom correspondence should be addressed.
Birds 2026, 7(1), 20; https://doi.org/10.3390/birds7010020
Submission received: 30 January 2026 / Revised: 1 March 2026 / Accepted: 5 March 2026 / Published: 10 March 2026
Browse Figures
Versions Notes

Simple Summary

The European Turtle Dove (Streptopelia turtur) is a migratory bird species that arrives in Apulia in spring to nest. Its presence is of strong management interest as, despite being in an unfavourable state of conservation, the species remains a legally hunted game bird in the first two weeks of September. National action plans have recently been implemented to bring the species back to a satisfactory state of conservation, to balance the impact of agricultural practices and hunting. For Apulia, there is recent information on the distribution and trends of breeding pairs, but not on their abundance. The sampling method adopted for the FBI calculation was intensified, thanks to the financial support of the Apulia Region, to obtain more homogeneous coverage of the sampling points and, therefore, to calculate the average density and estimate the abundance of the breeding population at the regional level. The preliminary results collected in Apulia provide an estimated density of 0.87–1.16 birds/km2, corresponding to 17,337–24,303 birds. Olive orchards, needle-leaved woodlands, and evergreen broad-leaved woodlands had a positive effect on the species occurrence.

Abstract

The Turtle Dove is a regular migratory species widely distributed in Italy, though the information on its abundance in each Italian region is modest; thus, action plans have been implemented to improve its conservation. This is a preliminary study meant to provide information on the distribution and abundance of the TD in Apulia. We analyzed data collected during 2019–2023 within the Farmland Bird Index (FBI) project, whose sampling design was intensified to achieve more homogeneous coverage of the region. The survey method was based on unlimited-distance point counts lasting 10 min. Counts were carried out between 15 May and 15 June of every year, beginning from dawn until 12:00 AM, with each station visited once. A total of 211 TD birds were recorded across 147-point counts. The MaxEnt analysis showed that olive orchards, needle-leaved woodlands, and evergreen broad-leaved woodlands had a positive effect on species occurrence, whereas winter precipitation had a negative effect. The total estimate of pairs fell within the range 47.14–66. The estimated density for TD in Apulia was 0.87–1.16 birds/km2, while that of pairs was 0.69–0.97/km2. By relating the estimated densities to the area suitable for the species’ presence, the abundance of TD was estimated at approximately 17,337–24,303 birds.

1. Introduction

Over the past 40 years, deep changes in agriculture—such as heavy mechanization, increased use of fertilizer, shifts in crops along with the decline of hedges, scrub, and woodland [1,2]—have influenced the density of many farmland birds, including the European Turtle Dove (TD, Streptopelia turtur). This species, during the breeding season, occupies a wide range of habitats located at low altitudes (mostly below 1000 m a.s.l.), where arable land or grassland with hedges, trees or small woods are combined in an integrated agro-forestry system [3,4,5,6,7]. Usually, the TD avoids forests, preferring young plantations and woodlands, which allow optimal breeding conditions and active search of feed [8,9]. In Italy, the TD is a regular migratory nesting species, and an irregular winter visitor [10]. The most recent estimate of the Italian population suggests the presence of 150,000–300,000 breeding pairs [11,12]. From 2000 to 2023, a moderate decline has been observed in hilly areas, as well as in the pre-Alpine and Apennine regions [13]; meanwhile, an increase has been recorded in Mediterranean pseudo-steppe habitats, widely represented in North African countries [14,15,16] and in South Italy [17]. In the Apulian region, during the breeding season, the species is mainly associated with farmland habitats, as it requires seed-rich and productive agricultural areas for forage [8,18,19,20,21]. Hence, the population in Italy has been categorized as “Least Concern” in 2021, reflecting a secure conservation status [12,22]. The National Species Management Plan [23] outlines short-term objectives that include preserving and growing national and migratory populations, while enhancing knowledge of the species. To achieve these goals, the support of monitoring project plans aimed at estimating population abundance is essential. However, to date no population estimates have been carried out for the Apulian region, and, notably, no overwintering records have been reported [24]. Thus, this study aims to (i) define the potential spatial distribution of the Turtle Dove in Apulia by analyzing climate and land cover variables, and (ii) to provide a preliminary estimate of the breeding population size of the species in the region during the 2019–2023 period.

2. Materials and Method

2.1. Study Area

The present study was conducted in the Apulia region, situated in south-eastern Italy, covering an area of approximately 19,350 km2, excluding the islands. Apulia is predominantly bordered by the Adriatic and Ionian Seas. As the least mountainous region in Italy, 50% of its territory consists of flatlands, including small plains interspersed with moderate hills. The region’s low mountains are found in the Sub-Appennino Dauno and the Gargano promontory. The Murgia plateau spans 4000 km2, while the Tavoliere plain, Italy’s second-largest plain, covers 3000 km2 in the north-central areas of the region. Mount Cornacchia, the region’s highest peak, rises to 1152 m in the Sub-Appennino Dauno, while the Gargano promontory reaches 1055 m a.s.l. The region is characterized by a Mediterranean climate typical of semi-arid environments, ranging from dry and hot summers to mild and rainy winters. Annual rainfall averages between 450 and 550 mm, with the Tavoliere plain recording the lowest values (about 400 mm) and the Gargano area exceeding 900 mm annually [25,26]. Seasonal temperature variations are significant: in the Tavoliere, summer temperatures can reach 40 °C, while winter mornings sometimes drop to −3 °C [27]. Figure 1 shows the different land cover types of the Apulia region. Agriculture dominates land use, covering 81.4% of the territory, while forests and semi-natural areas represent 13.3% (Figure 2). Water bodies, including natural lakes and artificial reservoirs, account for 1.2% of the region’s surface [28,29].

2.2. Study Design

In the present study, the Farmland Bird Index (FBI), a metric system committed by the Ministry of Agriculture for monitoring population trends of bird species present in agricultural areas, was used [20]. The analysis relied on a robust and up-to-date dataset, which included FBI project data from the 2019–2023 period (37 meshes, where each mesh is a UTM 10 × 10 km, containing 100 cells, 1 × 1 km). Figure 3 shows the meshes studied as part of the FBI project coloured blue, while the red meshes show the 17 additional areas monitored during 2023, thanks to financial support from the Apulia Region, which has allowed us to increase the number of meshes studied with a more homogeneous distribution across the regional territory.
To evaluate potential sampling bias related to monitoring effort, we tested the relationship between the number of monitored years per cell and species detection. No significant correlation was found neither between effort and presence (Spearman’s rank correlation, rs = 0.12), nor between effort and the total number of individuals recorded (rs = 0.13).
The survey method employed in the FBI project, and replicated in the additional surveys, was the point counts technique without distance limits [30]. Each point was monitored for 10 min [31]. Counts were carried out between 15 May and 15 June every year, and when weather conditions were favourable (days without strong wind or rain), starting shortly after sunrise and ending no later than 12:00 AM. Each station was visited only once annually. The exploration of each mesh involved conducting, where feasible, 15 point counts distributed across cells, selected through a statistical randomization process. To preserve the randomized nature of mesh selection, point counts were placed as close as possible to the centre of each cell, compatibly with the site conditions. In Apulia, 2471 point counts were conducted as part of the FBI project, with additional 255 point counts specifically made for this study, resulting in a total of 2726 point counts. Considering that the species typically lays eggs from mid-April to mid-July, with both parents incubating for approximately 14 days and fledglings taking flight after about 20 days [32], it is expected that during the study period the birds observed were mainly adults, although the presence of a juvenile’s minority cannot be excluded.

2.3. Environmental Variables

The suitability of Apulia for breeding TDs was assessed using 21 variables considered relevant to the species’ ecology, including climatic and land cover predictors (Table 1). Specifically, eight bioclimatic variables were obtained at a spatial resolution of 30 arc-seconds (approximately 1 km2) from WorldClim version 2.1 (available at www.worldclim.org), a database of high-resolution global climate data frequently used in species distribution modelling [33]. In addition, 13 land use variables were derived from a regional habitat map provided as a vector layer (scale 1:25,000; minimum cartographic unit: 0.25–0.50 ha) [34]. To account for the spatial ecology of the species [35], environmental predictors were harmonized to a spatial resolution of 158 m (~2.5 ha), corresponding to the average breeding home-range size of TD (1.91–3.08 ha) [35,36,37]. A 158 m vector grid was generated over the study area. Mean values of climatic variables were extracted for each grid cell using zonal statistics, while habitat variables were quantified as percentage cover per cell by intersecting the grid with the habitat polygons. Collinearity among predictors was assessed using the variance inflation factor (VIF), applying a threshold of 3 to remove highly correlated variables [38,39]. After this procedure, 16 predictors (four climatic and 12 land cover variables) were retained for modelling (Table 1). Spatial analyses were performed using QGIS v3.34.13 and R version 4.5.2 [40], with the packages raster [41], terra [42], and usdm [43].

2.4. Species Distribution Model (SDM)

Using presence data collected during 2019 and 2023, we modelled the spatial distribution of the TD using the MaxEnt algorithm [44], a machine-learning method that applies the principle of maximum entropy to estimate species’ potential distributions from presence-only data [45,46]. MaxEnt was implemented using only linear and quadratic feature classes, which were selected a priori to ensure ecologically interpretable response curves and to limit model complexity [47,48]. Feature classes were therefore not included in the calibration process. All other parameters were kept at their default settings (background points = 10,000; maximum number of iterations = 5000; convergence threshold = 10−5) [45,46]. Because spatial autocorrelation may violate the assumption of independence among occurrence records in species distribution modelling [49,50], duplicate presence points within the same grid cell were removed, retaining a single record per cell [51,52]. To further assess potential spatial autocorrelation among occurrence records, Moran’s I test was calculated. No significant spatial autocorrelation was detected (p = 0.105), likely due to the broad and random spatial distribution of sampling points. To reduce model variance, we ran 100 bootstrap replicates and obtained the final prediction by averaging the outputs of these replicates. Model calibration focused exclusively on the regularization multiplier (β), as default regularization settings may produce overfitted models [53,54]. The β parameter was tuned by testing a set of candidate values (0.01, 0.1, 0.5, 1.0, 1.5, 2.0, 3.0, and 5.0), while keeping feature classes fixed (LQ). This approach allowed us to optimize model complexity within a predefined and ecologically interpretable model structure. During the tuning phase, model selection was based on Akaike’s Information Criterion corrected for small sample sizes (AICc), ΔAICc values, and omission-rates, which were used to identify the optimal regularization multiplier while minimizing over-parameterization. After selecting the best-performing model, predictive performance was evaluated using the area under the Receiver Operating Characteristic curve (AUC) [55]. Omission error rates were additionally calculated at a fixed sensitivity threshold of 0.95, meaning that 95% of the training occurrence localities were included in the prediction [56]. Models with omission error rates ≤ 5% were considered to have good predictive performance [57]. Variable importance was assessed using both percentage contribution and permutation importance, which quantify the relative influence of each predictor in the final model [45]. Finally, we generated the suitability map with a spatial resolution of 158 m, the same resolution used for model calibration. Additionally, the “equal training sensitivity and specificity” (ETSS) threshold was applied to convert continuous suitability maps into binary maps of suitable and unsuitable areas [58,59]. Although this discretization may entail some loss of information [60], binary outputs are often more practical for conservation planning and environmental management purposes than continuous suitability values [61,62]. All statistical analyses were conducted using R version 4.5.2 (R Core Team, Vienna, Austria, 2025) and the packages dismo [62], ENMeval [63], and PresenceAbsence [64].

2.5. Density and Abundance Estimation

Although behaviour indicative of sex determination (e.g., singing and reproductive activities) was recorded during the surveys, such information was not available for data provided by the FBI project. Consequently, a value of one pair was assigned to individual sightings. For observations of 2, 4, or 6 birds, the number of pairs was calculated as the number of birds divided by two. In cases where three birds were observed, a value of two pairs was assigned. The potential bias arising from the inclusion of birds not engaged in nesting may be counterbalanced by the assumption that two birds detected at the same listening point do not invariably represent the same breeding pair. Given the TD’s preference for tree-dense environments and its relatively weak vocalizations [65], it is estimated that each listening-point can adequately cover a maximum average area of 200 m of radius (~12.6 ha). The same monitoring strategy, made according to an expert-based approach, was used also by Bani et al. [66] for the study of the abundance of TD in Lombardy. Therefore, the low detectability of TD as compared to other bird species suggests that the monitoring results are underestimated by about 30–50%. The abundance was calculated by dividing the number of observations and the number of pairs by0.5 and 0.7, respectively, leading to a more realistic range. Consequently, we estimated TD densities between 2019 and 2023 by relating the number of birds counted during surveys to this surface. Finally, we estimated the total number of birds and breeding pairs in the region by multiplying the estimated densities by the area of the region, with islands excluded.

3. Results and Discussion

Figure 4 shows the distribution of sampling effort during the years 2019–2023. Among a total of 55 meshes, 55% have been monitored for 5 consecutive years, 2% for 4 years, 9% for 3 years and 34% were detected only for 1 year (details in Appendix A).
During 2019, a total of 211 TDs were counted across 147 point counts, representing the 5.39% of 2726 surveys conducted in Apulia, equivalent to approximately one bird per 12.92 points (Table 2).
A maximum of six birds was recorded at a single point count on two occasions (6 June 2021, and 18 May 2023) in two different meshes. These two points were the only ones where counts ≥ 4 occurred in every year of the study. Maximum counts at remaining points were lower, with only two points reaching four birds and the rest a maximum of three.
The MaxEnt analysis showed that among the most important variables shaping the distribution of the TD in Apulia, three land-use variables and one climatic variable were identified (Table 3). Specifically, olive orchards, needle-leaved woodlands, and evergreen broad-leaved woodlands had a positive effect on the species occurrence.
In contrast, winter precipitation showed a negative effect (Table 3; Figure 5).
Model performance was good, with an AUC of 0.833 ± 0.02 (SD) and an omission error rate of 4.6%. The model predicted an average suitability of 0.290 ± 0.001 (mean ± SD), with higher suitability values in the Gargano Promontory and in the central-southern lowlands of the region, particularly along the Adriatic coast (Figure 6). The ETSS threshold was equal to 0.44, and the resulting binary map indicated that approximately 25% of the region (4932 km2) was classified as suitable for the TD (Figure 6).
Overall, the set of predictors retained by the MaxEnt model supports a habitat-selection pattern typical of the European Turtle Dove, i.e., a preference for structurally heterogeneous farmland landscapes where nesting substrates (woody vegetation) occur in close proximity to profitable foraging habitats [4,18]. In this context, the positive association with olive orchards is ecologically plausible: Mediterranean orchards can provide both nesting structures (trees) and nearby seed resources, depending on ground-cover and farming practices, and they are frequently reported among the farmland habitats used by TDs during breeding [2,67]. At the same time, it is important to note that “orchard suitability” may strongly depend on management intensity (e.g., herbicide use and ground cover), which can modulate food availability at the foraging stage; therefore, within-habitat variation may partly explain local differences in occupancy and density [68]. The positive contribution of needle-leaved and evergreen broad-leaved woodlands suggests that, in Apulia, TD occurrence may be enhanced by the availability of sheltered nesting and resting sites, consistent with evidence that small woodlands, tree plantations, riparian forests and other wooded patches can substantially increase occurrence in intensive agroecosystems by providing key structural resources [69]. More generally, recent work based on tracking and habitat quantification indicates that the density of “small woody features” within agricultural landscapes can be a limiting factor and may show threshold-type relationships with habitat selection, reinforcing the importance of conserving woody elements embedded in farmland [70]. The negative relationship with winter precipitation may reflect indirect effects on habitat suitability, for instance, through impacts on vegetation phenology, seed availability, and/or landscape-level productivity in subsequent months. Although climatic variables are important drivers in Turtle Dove distribution, their effects can vary geographically and may interact with land management; hence, this result should be interpreted cautiously and tested further using temporally explicit covariates and (where possible) abundance-aware approaches [70,71].
The estimated number of breeding pairs for the observed birds ranged from 24 to 48 (Table 4). The estimated density of the TD in Apulia was of 0.87–1.16 birds per km2, while the density of breeding pairs was estimated at 0.69–0.97 pairs per km2. By relating the estimated densities to the area suitable for the species’ presence, abundance of TD was estimated at approximately 17,337–24,303 birds. The number of breeding pairs was estimated to range between about 13,390 and 18,730.
The results obtained represent an initial step towards defining the density and abundance of the TD in Apulia, which is a species of undeniable management importance but still understudied, particularly at a regional level. Similar studies have been carried out also in other Italian regions. In Lombardy, for example, a study conducted during 1992–2016, estimated the mean annual population ranging from 7132 birds in 1996 to 32,820 recorded in 2009 [66]. A recent investigation in Tuscany, evidenced the presence of 5.33 pairs/km2 during 2024 [72]. Besides these two regions, no information is available regarding the abundance of TD in the other Italian regions.
Further surveys and a more in-depth analysis of the data could refine these preliminary findings, potentially incorporating correlations with land use to achieve a more accurate quantitative distribution assessment. Landscape and land use are factors that can affect TD populations and especially their abundance [73,74,75] and all these factors and their temporal variation can substantially differ across regions. Importantly, the suitable area derived from the SDM provides a transparent spatial framework for scaling survey-derived densities, but future refinements should evaluate detectability and the contribution of spatial variation in sampling effort, as well as the role of key resources (e.g., availability of foraging seeds and water) known to affect breeding performance and habitat use.

Limit of the Study

The Turtle Dove usually prefers closed environments in order to find protection. Therefore, these areas are often hardly accessible to man (such as hunters, farmers, etc.). The male TD emits a song that has a relatively low volume, mainly during the first hours of light, followed by a progressive decrease in vocalizations, as well as the other daily activities in which it is involved. The sampling method used for data collection consists of the bird census until 12:00 AM, although bird observation rarely goes beyond 10:00 AM because the period between dawn and the early hours of the morning are the most intense for the TD’s activities. For this reason, the number of birds observed may be underestimated; therefore, the results require a corrective factor that is expert-based but still subjective. Furthermore, the impossibility of evaluating the distance of the sighted subject, since it is often only heard, does not allow the use of more precise methods for density estimation, such as distance sampling.
Randomized selection of meshes and their listening points, although improved with the choice of some additional meshes in the less covered areas of the region, may have determined that some habitat types are under-reported in the analysis, although sampling effort appears sufficient to minimize this issue.
Lack of information regarding subjects’ behaviour during observation can lead to errors in determining ecological preferences, especially during flights between trophic and nesting areas.
In order to obtain a more robust estimate of the species’ abundance in the region, the possibility of using N-mixture models could be evaluated in the future, perhaps with a stratification of listening points.

4. Conclusions

Our study represents an initial but important step towards quantifying the European Turtle Dove population size and habitat associations within a Mediterranean agricultural context. The results highlight the need for conservation actions that promote landscape heterogeneity, including the protection and restoration of mixed farmland–woodland mosaics, maintenance of semi-natural habitats, and incorporation of small woody elements within agricultural matrices. Future research should integrate multi-scale habitat analyses and demographic monitoring to refine abundance estimates and inform targeted management strategies that address both habitat quality and broader climatic influences on the Turtle Dove population.

Author Contributions

Conceptualization, S.T., G.L.G. and M.R.; methodology, M.F., M.L., N.D.V., D.G. and G.L.G.; software, G.C. and G.L.G.; validation, S.T., M.A.C. and M.R.; formal analysis, G.C.; investigation, M.L., N.D.V., D.G. and G.L.G.; data curation, G.L.G.; writing—original draft preparation, G.C., G.L.G., M.F., M.A.C. and S.T.; writing—review and editing, G.C., G.L.G., M.A.C. and S.T.; supervision, D.C. and M.R.; project administration, M.R.; funding acquisition, D.C., M.R. and S.T. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Apulian Region, grant number Z203D58E41.

Institutional Review Board Statement

Ethical review and approval were waived for this study since there was only an observation activity carried out in respect to animal welfare.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are contained within the article.

Acknowledgments

The authors are grateful to the LIPU association for sharing the data collected and analysed within the Farmland Bird Index project, funded by the Italian Ministry of Agriculture, Food Sovereignty and Forests (RRN 2014/2022, FEASR—Fondo europeo agricolo per lo sviluppo rurale).

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Table A1. Annual sampling effort during the years 2019–2023.
Table A1. Annual sampling effort during the years 2019–2023.
Year20192020202120222023Total
BK45xx x3
BK57xxxxx5
BK62xxxxx5
BK74xxxxx5
BK76xxxxx5
WF18 x1
WF19 x1
WF27xxxxx5
WF46xxxxx5
WF48xxxxx5
WF64x 1
WF65 x1
WF67 x1
WF69xxxxx5
WF76 x1
WF77xxxxx5
WF86 x1
WF87x 1
WF92xxxxx5
WF94xxxxx5
WF96xxxxx5
WG02xxxxx5
WG21xxxxx5
WG41xxxxx5
WG43 x1
WG52 x1
WG60xxxxx5
WG71xx x3
WG72xxxx 4
WG82xxxxx5
WG93xx x3
XE59xxxxx5
XE98xxxxx5
XF13xxxxx5
XF15xxxxx5
XF21 x1
XF25xxxxx5
XF33 x1
XF34xxxxx5
XF41xxxxx5
XF44 x1
XF52 x1
XF53xxxxx5
XF62 x1
XF64 x1
XF71xxxxx5
XF91xxxxx5
YE18 x1
YE36xx x3
YE46xxxxx5
YE47xxxxx5
YE55xx x3
YF10xxxxx5
WF25 x1
WG00 x1

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Figure 1. Different land cover types of the Apulia region.
Figure 1. Different land cover types of the Apulia region.
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Figure 2. Different ecosystem sets analyzed in Apulian region: (A), Pynus halepensis woods; (B), woody perennials (Quercus spp.) in arable crop system; and (C), Mediterranean agro-silvo-pastoral system, combining shrubs, pasture and arable lands.
Figure 2. Different ecosystem sets analyzed in Apulian region: (A), Pynus halepensis woods; (B), woody perennials (Quercus spp.) in arable crop system; and (C), Mediterranean agro-silvo-pastoral system, combining shrubs, pasture and arable lands.
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Figure 3. Location of Apulia region in Italy. Study conducted during 2019–2023 using UTM 10 × 10 km meshes, divided into those carried out within the FBI project (blue) and those added thanks to financial support from the Apulia Region (red).
Figure 3. Location of Apulia region in Italy. Study conducted during 2019–2023 using UTM 10 × 10 km meshes, divided into those carried out within the FBI project (blue) and those added thanks to financial support from the Apulia Region (red).
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Figure 4. The map shows the spatial sampling effort; the different colours indicate the number of years the meshes were monitored (green: 1 year; blue: 3 years; red: four years; violet: 5 years).
Figure 4. The map shows the spatial sampling effort; the different colours indicate the number of years the meshes were monitored (green: 1 year; blue: 3 years; red: four years; violet: 5 years).
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Figure 5. The response curves of the most important variables included in the MaxEnt model for the TD breeding in Apulia. The line and the shaded area represent the mean and the standard error of the 100 replicates obtained by the bootstrap with MaxEnt.
Figure 5. The response curves of the most important variables included in the MaxEnt model for the TD breeding in Apulia. The line and the shaded area represent the mean and the standard error of the 100 replicates obtained by the bootstrap with MaxEnt.
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Figure 6. Suitability of Apulia for the TD during the breeding season. The map shows the average suitability of 100 replicates obtained by the bootstrap with MaxEnt.
Figure 6. Suitability of Apulia for the TD during the breeding season. The map shows the average suitability of 100 replicates obtained by the bootstrap with MaxEnt.
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Table 1. Environmental variables used to define the suitability of Apulia for breeding the Turtle Dove. Descriptive statistics (mean, SD, range) calculated for the study area are shown (values are measured in the 158 m grid), as well the variance inflation factor (VIF) before (VIFbef) and after (VIFaft) the variable omission from the model.
Table 1. Environmental variables used to define the suitability of Apulia for breeding the Turtle Dove. Descriptive statistics (mean, SD, range) calculated for the study area are shown (values are measured in the 158 m grid), as well the variance inflation factor (VIF) before (VIFbef) and after (VIFaft) the variable omission from the model.
Environmental VariablesMeanSDRangeVIFbefVIFaft
Annual mean temperature (°C)15.31.710–17.2799.57-
Temperature seasonality (SD)6.10.580–7.0680.101.60
Mean temperature of warmest quarter (°C)23.22.120–24.7217.73-
Mean temperature of coldest quarter (°C)8.41.570–10.8576.801.55
Annual precipitation (mm)495.7103.830–680166.93-
Precipitation seasonality (CV)33.610.660–61.063.55-
Precipitation of warmest quarter (mm)69.516.050–9946.371.75
Precipitation of coldest quarter (mm)147.440.310–234129.211.82
Urban areas (% cover)6.221.630–1005.681.39
Intensively cultivated croplands (% cover)18.837.500–1001.69-
Extensively cultivated croplands (% cover)21.736.420–1001.522.12
Olive orchards25.138.870–10016.632.10
Orchards and vineyards10.326.410–1008.461.47
Deciduous broad-leaved forests5.019.850–1005.271.60
Evergreen broad-leaved forests1.712.060–1002.861.16
Needle-leaved forests1.911.970–1003.051.09
Shrublands0.56.250–1001.561.05
Mediterranean maquis1.49.880–1001.931.07
Natural grasslands4.117.540–1004.731.22
Water bodies1.510.950–1002.211.06
Table 2. Annual survey data showing the number of point counts, total TD detections, point counts with TD, and the percentage of point counts with TD.
Table 2. Annual survey data showing the number of point counts, total TD detections, point counts with TD, and the percentage of point counts with TD.
Year20192020202120222023TotalMeanSD
Point counts4976244384377302726545.20114.79
Observed individuals314146375621142.208.47
Observed pairs243332283114829.603.26
Point counts with TD203126254514729.408.55
% point counts with TD4.02%4.97%5.94%5.72%6.16%5.39%5.36%0.78%
Table 3. The MaxEnt model for breeding TDs in Apulia (southern Italy). Mean lambdas (λ), standard error (SE), and lower (LCI) and upper (UCI) 95% confidence intervals are calculated on the 100 replicates. Mean lambdas of both linear (L) and quadratic (Q) features are given, as well their contribution (Contr) and permutation (Perm) importances (% ± SD). Environmental variables are ordered by contribution importance.
Table 3. The MaxEnt model for breeding TDs in Apulia (southern Italy). Mean lambdas (λ), standard error (SE), and lower (LCI) and upper (UCI) 95% confidence intervals are calculated on the 100 replicates. Mean lambdas of both linear (L) and quadratic (Q) features are given, as well their contribution (Contr) and permutation (Perm) importances (% ± SD). Environmental variables are ordered by contribution importance.
Environmental VariableλMeanSELCIUCIContributionPermutation
(% ± SD)
Olive orchardsL3.940.123.704.1837.1 ± 0.9840.6 ± 4.79
Needle-leaved woodlandL9.700.209.2910.0919.1 ± 0.9911.7 ± 3.21
Q−5.510.20−5.91−5.10--
Evergreen broad-leaved woodlandL6.260.255.776.769.9 ± 0.936.4 ± 2.59
Precipitation of the coldest quarterL−0.980.16−1.29−0.669.8 ± 0.957.9 ± 3.41
Orchards and vineyardsL2.670.132.412.926.2 ± 0.338.7 ± 2.51
Extensively cultivated croplandsL2.360.142.082.643.8 ± 0.299.4 ± 2.38
Urban areasL−1.610.23−2.07−1.163.7 ± 0.312.1 ± 2.14
Natural grasslandsL−6.980.16−7.30−6.652.9 ± 0.334.1 ± 1.80
Mean temperature of the coldest quarterL2.050.231.582.512.0 ± 0.282.1 ± 1.72
Deciduous broad-leaved woodlandL2.440.231.972.902.0 ± 0.244.0 ± 2.07
Precipitation of the warmest quarterL0.610.150.310.911.1 ± 0.091.1 ± 1.37
Mediterranean maquisL2.000.251.512.490.9 ± 0.220.8 ± 0.76
Temperature seasonalityL3.510.332.854.170.8 ± 0.081.1 ± 1.09
Water bodiesL−0.880.07−1.01−0.740.7 ± 0.020.00
ShrublandsL−0.870.07−1.01−0.740.1 ± 0.020.00
Table 4. Observed individuals and pairs, along with density and abundance estimates of the TD in Apulia.
Table 4. Observed individuals and pairs, along with density and abundance estimates of the TD in Apulia.
Year20192020202120222023MeanSD
Point counts497624438437730545.2114.79
Surveyed area (km2)62.4578.4155.0454.9291.7368.5114.42
Observed birds314146375642.28.47
Esteemed individuals44.29–62.0058.57–82.0065.71–92.0052.86–74.0080.00–112.0060.29–84.4012.10–16.94
Esteemed individuals/km20.71–0.990.75–1.051.19–1.670.96–1.350.87–1.220.87–1.160.17–0.24
Observed pairs243332284833.008.15
Esteemed pairs34.29–48.0047.14–66.0045.71–64.0040.00–56.0068.57–96.0047.14–66.0011.64–16.30
Esteemed pairs/km20.55–0.770.60–0.840.83–1.160.73–1.020.75–1.050.69–0.970.10–0.14
Total birds in Apulia13,738.50–19,156.5014,512.50–20,317.5023,026.50–32,314.5018,576.00–26,122.5016,834.50–23,607.0017,337.60–24,303.603319.19–4697.87
Total pairs in Apulia10,642.50–14,899.5011,610.00–16,254.0016,060.50–22,446.0014,125.50–19,737.0014,512.50–20,317.5013,390.20–18,730.801982.41–2762.11
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Tarricone, S.; La Gioia, G.; Colonna, M.A.; De Vito, N.; Lacitignola, M.; Gerardi, D.; Chiatante, G.; Campanile, D.; Fortunato, M.; Ragni, M. Potential Distribution, Density and Abundance Estimate of the European Turtle Dove Streptopelia turtur (Linnaeus, 1758) in Apulia. Birds 2026, 7, 20. https://doi.org/10.3390/birds7010020

AMA Style

Tarricone S, La Gioia G, Colonna MA, De Vito N, Lacitignola M, Gerardi D, Chiatante G, Campanile D, Fortunato M, Ragni M. Potential Distribution, Density and Abundance Estimate of the European Turtle Dove Streptopelia turtur (Linnaeus, 1758) in Apulia. Birds. 2026; 7(1):20. https://doi.org/10.3390/birds7010020

Chicago/Turabian Style

Tarricone, Simona, Giuseppe La Gioia, Maria Antonietta Colonna, Nicolò De Vito, Massimo Lacitignola, Domenico Gerardi, Gianpasquale Chiatante, Domenico Campanile, Mariarosaria Fortunato, and Marco Ragni. 2026. "Potential Distribution, Density and Abundance Estimate of the European Turtle Dove Streptopelia turtur (Linnaeus, 1758) in Apulia" Birds 7, no. 1: 20. https://doi.org/10.3390/birds7010020

APA Style

Tarricone, S., La Gioia, G., Colonna, M. A., De Vito, N., Lacitignola, M., Gerardi, D., Chiatante, G., Campanile, D., Fortunato, M., & Ragni, M. (2026). Potential Distribution, Density and Abundance Estimate of the European Turtle Dove Streptopelia turtur (Linnaeus, 1758) in Apulia. Birds, 7(1), 20. https://doi.org/10.3390/birds7010020

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