Browse Subject Areas

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

The Importance of Rotational Crops for Biodiversity Conservation in Mediterranean Areas

The Importance of Rotational Crops for Biodiversity Conservation in Mediterranean Areas

  • Gianpasquale Chiatante, 
  • Alberto Meriggi


Nowadays we are seeing the largest biodiversity loss since the extinction of the dinosaurs. To conserve biodiversity it is essential to plan protected areas using a prioritization approach, which takes into account the current biodiversity value of the sites. Considering that in the Mediterranean Basin the agro-ecosystems are one of the most important parts of the landscape, the conservation of crops is essential to biodiversity conservation. In the framework of agro-ecosystem conservation, farmland birds play an important role because of their representativeness, and because of their steady decline in the last Century in Western Europe. The main aim of this research was to define if crop dominated landscapes could be useful for biodiversity conservation in a Mediterranean area in which the landscape was modified by humans in the last thousand years and was affected by the important biogeographical phenomenon of peninsula effect. To assess this, we identify the hotspots and the coldspots of bird diversity in southern Italy both during the winter and in the breeding season. In particular we used a scoring method, defining a biodiversity value for each cell of a 1-km grid superimposed on the study area, using data collected by fieldwork following a stratified random sampling design. This value was analysed by a multiple linear regression analysis and was predicted in the whole study area. Then we defined the hotspots and the coldspots of the study area as 15% of the cells with higher and lower value of biodiversity, respectively. Finally, we used GAP analysis to compare hotspot distribution with the current network of protected areas. This study showed that the winter hotspots of bird diversity were associated with marshes and water bodies, shrublands, and irrigated crops, whilst the breeding hotspots were associated with more natural areas (e.g. transitional wood/shrubs), such as open areas (natural grasslands, pastures and not irrigated crops). Moreover, the results underlined the negative effects of permanent crops, such as vineyards, olive groves, and orchards, in particular during the winter season. This research highlights the importance of farmland areas mainly for wintering species and the importance of open areas for breeding species in the Mediterranean Basin. This may be true even when the species’ spatial distribution could be affected by biogeography. An important result showed that the hotspots for breeding species cannot be used as a surrogate for the wintering species, which were often not considered in the planning of protected areas.


Biodiversity preservation and restoration are the most important goals of conservation biology, and nature reserves play a vital role in achieving this goal [1]. However, the effectiveness of protected areas in representing biodiversity has been frequently questioned [24], and it is accepted that existing conservation areas usually provide inadequate coverage to biodiversity [510]. Thus, selection of critical areas for biodiversity conservation needs to prioritize areas on the basis of their biodiversity value, selecting those that have the highest priority, and needs to set precise prescriptions [1115].

In Italy, conservation measures and the selection of protected areas have always been localized in areas characterized by low levels of human presence and intervention [16]. In particular, Italian protected areas tend to over-represent mountainous areas and other regions with low economic values, while the coverage offered by protected areas in the Mediterranean part is limited [16]. As Maiorano et al. [16] stated, there is a clear indication that the existing protected areas cannot be considered to be fully representative of Italian vertebrate biodiversity. Besides, Italy and the Mediterranean basin have seen thousands of years of intense human presence, with a complex integration of traditional human activities and natural ecosystems leading to high environmental diversity and also to high fragmentation. As a result, a complex and ecologically rich cultural landscape has formed [17].

Thus, in the Mediterranean region more than anywhere else, the protected areas must be planned and managed in conjunction with the traditional agricultural and husbandry activities, and the only viable option for conservation is that of considering human presence and human activities as an integral part of the system [16,18,19].

The main goal of this research was to define if a crop dominated landscape represents an important context for bird diversity conservation, in order to prioritize areas for conservation purposes. To this end, we identify the hotspots and the coldspots of bird diversity in central Apulia (southern Italy), both during the winter and the breeding season. The landscape of this very poorly known region is dominated by agro-ecosystems, and it is inside one of the “hottest hotspots” of the world, the Mediterranean Basin [12,20,21].

Farmland bird species represent a large proportion of European avifauna, and the populations of several of those species have suffered a dramatic decline in the last decades, especially in Western Europe [22,23]. The causes of this decline have been identified mostly as changes of agricultural practices, such as heavy mechanization, input increase, temporal shift of cereal sowing from spring to autumn, and loss of landscape heterogeneity determined by the destruction of hedgerows, shrub and tree patches and other natural areas, which follows intensification [2226]. These changes led both to reduction of refuge and reproduction areas and to decrease of invertebrate prey, the latter largely prompted by the increase in biocide use [23,2729]. A further cause of farmland species’ decline is represented by land abandonment [22,30,31], which is now threatening important farmland bird populations in mountain areas [32].

In addition, this study could be interesting because of biogeographical aspects. Indeed, within the study area the peninsular effect is evident which leads to a decline in species richness as a function of distance from the mainland toward the distal tip of a peninsula [3335]. Because of its geographical position the Apulia region could be defined as a peninsula within a peninsula, so the spatial pattern of bird species could be affected by this effect. Therefore, testing farmland bird response to agro-ecosystem features in a biogeographically interesting context in the Mediterranean Basin hotspot may potentially contribute to increase practical knowledge for a prioritised conservation issue. Other research aims were (i) to establish if hotspots of breeding birds can be used as surrogates of wintering bird hotspots and vice versa, and (ii) to compare the hotspots and the coldspots with the current protected area network of the region. We expected that a landscape dominated by agro-ecosystems could be an important and interesting framework for biodiversity maintenance, even when the species distribution could be affected not only by human modifications but also by biogeography. We also expected different hotspots for wintering and breeding birds, and a good cover of protected areas, though only for breeding bird hotspots.

Materials and Methods

Study area

The study area occupies the central part of the Apulia region in southern Italy (N 41°0’ E 16°34’), more precisely the Bari and Barletta-Andria-Trani provinces, over an area of 5406 km2 (Fig 1). The altitude ranges from sea level to 679 m a.s.l. (Mt. Caccia), with 35% of the altitude comprised between sea level and 200 m a.s.l. and 40% between 201 and 400 m a.s.l. The climate is typically Mediterranean: along the coastal side and in the lowlands the summers are warm, windy, and dry, whilst the winters are mild and rainy. The minimum temperature varies between 2–5°C in January-February and 16–19°C in July-August. The maximum temperature varies between 10–13°C in January-February and 28–30°C in July-August. Precipitation, concentrated during the late autumn and winter, is scarce and in the form of rain. The average values of rainfall vary between 27–28 mm in July and 67 mm in October. The landscape is characterized mainly by not irrigated cereal crops (32.4%) and olive groves (27.6%). Vineyards represent 8.7% of the surface, followed by urban areas (8.5%), grasslands, pastures and fallows (7.5%), orchards (5.7%), and forests (4.5%). The study area comprised 51 municipalities with a total resident population of 1,638,743 (ISTAT data, 2013) and a density equal to 303 per km2. In the study area there are 18 protected areas which extend over 978 km2 (18.1% of the study area). Moreover, there are 9 Sites of Community Importance (1595 km2, 29.5% of the study area) and 2 Special Protection Areas (1296 km2, 24.0% of the study area).

Sampling design

To collect data for identifying the bird diversity hotspots we used a stratified random sampling design, with proportional allocation of samples to guarantee the same sampling effort in each stratum [36,37]. Thus, we partitioned the study area into 5655 squares of size 1 km2. Then we identified homogeneous areas, referred to as landscape units (LU) by a clustering of similar squares by the help of k-means cluster analysis [3739]. To this purpose we measured in each cell the percentages of land use types by a geographic information system (GIS) platform (ArcGIS 10.2.1, ESRI, Redlands, CA) using a regional land use map at fourth level of CORINE land cover 1:5000 (2011 update, SIT–Regione Puglia; S1 Table). Then we tested the goodness of the LU classification by the non parametric Kruskal-Wallis test and Discriminant Function Analysis on the same environmental variables used for clustering [39].

Field data collection

To collect data for the wintering hotspots of bird diversity we carried out linear transects (ca. 500, 1000, and 2000 m long) randomly placed in the study area and in number proportional to LU extension [37,40]. Transects were straight lines randomly placed in the LU both in the starting point and in direction using the DNR Sampling Tool v2.8 extension for ArcView 3.2 (Minnesota Department of Natural Resources). In particular, 264 transects (S2 Table) were walked once during December and January of the wintering seasons from 2011 to 2014 (2011–2012: 110 transects; 2012–2013: 91 transects; 2013–2014: 63 transects). To collect data on breeding hotspots of bird diversity, we carried out call counts in 301 hearing points randomly placed in the study area according to the stratified sampling between late April and early June of the breeding season 2012 (170 sampling points) and 2013 (131 sampling points) (see S2 Table) [4042]. Each point was surveyed once from dawn to 11:00 and the count lasted for 10 minutes [42,43]. A species was considered a breeder if it was observed at least in territorial behavior, such as song. In this way it was possible to discard the migratory species. Surveys were not conducted on windy or rainy days. We used a binocular Vortex Viper 10x40 and a GPS tracker Garmin eTrex Venture to position the observations in the area. For the nomenclature we followed the International Ornithologists’ Union list [44].

The effectiveness of the sampling design was assessed comparing the observed number of species in each cluster with the expected species number obtained by the non parametric Chao Index [43,45]. In particular, if the number of observed species was included in the 95% confidence interval of expected species the sampling design and the sampling effort were considered appropriate.

Statistical analysis

To identify the bird diversity hotspots we used a method proposed by Rey Benayas and de la Montaña [46]. In particular, for each point or transect (precisely its geometric centroid) carried out in the fieldwork, we calculated four parameters to identify areas of high-value of bird diversity: 1) the species richness Sr,, i.e. the number of bird species occurring in each sample. 2) The rarity index Rr, defined by the species geographical range measured as the inverse of the number of samples where it was present (1/ni); for a sample r, the rarity index was Σi = 1 (1/nri)/Sr, where Sr was the species richness in the sample r. 3) The vulnerability index Vr, quantified using the categories of the Red List of Italian Breeding Birds [47]. A score was assigned to every species related to its degree of vulnerability: 3 for Endangered (EN), 2 for Vulnerable (VU) and Near Threatened (NT) species, 1 for Least Concern (LC) species, 0 for Alien and Data Deficient (DD) species. Moreover, if the species was listed in the Annex I of the Birds Directive 2009/147/CE a value of 1 it was added, and considering the SPEC categories (Species of European Conservation Concern) suggested by BirdLife International [48], 1.5 was added for SPEC 1 (species of global conservation concern), 1 for SPEC 2 (species whose world populations are concentrated in Europe and which have an unfavorable conservation status), and 0.5 for SPEC 3 (species whose world populations are not concentrated in Europe, but which have an unfavorable conservation status in Europe). For a sample r, the vulnerability index was Σi = 1 Vri/Sr, where Vri was the vulnerability score of the species i present in the sample r. 4) The combined index of bird diversity Cr, which summarized the species richness, the rarity index, and the vulnerability index, calculated for the sample r as Σi = 1 (1/nri) Vri.

We used the combined index obtained in each sample to predict its value on the whole study area both during the winter and the breeding season formulating a Multiple Linear Regression Analysis (MLRA) [39]. In particular we investigated the relationships between the combined index and the percentage cover of land use classes (land use map at the 4th level of CORINE 1:5000, SIT–Regione Puglia, Table 1) measured in the 1 km2 cells in which the samples where located. When more than one sample falls in the cells, we used the mean values of their combined index.

Table 1. Land use classes with significant differences between the Landscape Units obtained by the cluster analysis (Kruskal-Wallis test).

Considering the non normality of the dependent variable both during the winter (Kolmogorov-Smirnov test, P < 0.001) and the breeding season (Kolmogorov-Smirnov test, P < 0.001), we used the logarithmic transformation making it normal (Kolmogorov-Smirnov test, Pwintering = 0.250, Pbreeding = 0.133) [39,49].

We simplified the model following a backward stepwise approach using an Information Theoretic Approach [50] selecting the variables by the Akaike Information Criterion (AIC, [51]. The Variance Inflation Factor was measured for the model with a threshold of 3, to test the variables’ collinearity [5254]. The goodness-of-fit of the predicted values and the observed ones was tested by the Pearson’s correlation test [39]. Moreover we tested the residuals for normality by the Kolmogorov-Smirnov test [39] and for independence by the Durbin-Watson test [55,56].

Finally, we used the predicted values of the combined index in each cell of the 1 km spaced grid to identify the biodiversity hotspots and coldspots. More precisely, we classified as bird diversity hotspots the 15% of the cells (848 cells) with the highest value of the combined index and as coldspots the 15% of the cells (848 cells) with the lowest values of this index [46].

Hotspots and protected areas

To define the protected surface of the study area, we considered each 1 km2 cell as currently “protected” when at least 50% of its surface was covered by a protected area [57]. To establish the protected area network, every type of protection was accounted for, the Natura 2000 Network (Special Protection Areas and Sites of Community Importance) comprised. Then, we measured how the existing protected areas represented the bird diversity (both wintering and breeding hotspots). In particular we compared through the Mann-Whitney U test [39] the combined index between protected cells and hotspot cells and correlated the percentage of protected areas in the 5655 cells and the combined index by Pearson’s correlation test, both during the wintering and the breeding season. Then, to establish the concordance between the hotspots and the protected areas, the Cohen Kappa statistic for agreement was calculated [58,59]. Kappa statistic ranges between 0, when there is no agreement between the cases, to 1, when there is a complete agreement between the cases [60]. Finally, to calculate the overlap between protected areas and hotspots, GAP analysis was used [61,62].

Ethics statement

This research was conducted with ethical approval from the University of Pavia (Department of Earth and Environmental Sciences). Bird surveys were conducted with permission from local landowners where necessary. Data collection did not involve sampling procedure and experimental manipulation of birds and the field work was conducted under the Law of the Republic of Italy on the Protection of Wildlife (February 25, 1992).


Habitat classification

Considering the land use types the 5655 cells were grouped in ten groups of homogeneous Landscape Units (LU). The Kruskal-Wallis test showed that the most representative land use types in the study area differed significantly between the Landscape Units (Table 1). The canonical correlations of the Discriminant Function Analysis were highly significant (P < 0.001) and 94.8% of the cells were correctly reclassified in the resulting clusters. For the description of Landscape Units obtained see S3 Table.

Bird diversity during the winter and the breeding season

The non parametric Chao Index showed that both during the winter and the breeding season the observed number of species in each LU was comparable to that expected, so the sampling effort could be considered good (Table 2).

Table 2. Observed and expected species number (95% confidence intervals) of each LU obtained by non parametric Chao Index.

During the winter season we recorded 124 species in the study area (S4 Table), out of which 24 species (19.4%) are listed in the Annex I of the Bird Directive 2009/147/CE, 31 species (25.0%) are listed in the Red List of Italian Breeding Birds as endangered (5 species), vulnerable (16 species) or near threatened (10 species), and 43 species (34.7%) are SPEC (SPEC 1 = 1 species, SPEC 2 = 14 species, SPEC 3 = 28 species). Considering the total number of observations in the wintering season (N = 9657), the most common species was Pica pica (7.5%, N = 721), followed by Fringilla coelebs (7.1%, N = 686), Erithacus rubecula (7.1%, N = 683), Serinus serinus (3.8%, N = 367), Cyanistes caeruleus (2.8%, N = 268), Sylvia melanocephala (2.7%, N = 262), Turdus philomelos (2.7%, N = 261), Parus major (2.7%, N = 259), and Passer italiae (2.7%, N = 257). The most rare species (0.01%, N = 1) were Anas acuta, Platarea leucorodia, Falco columbarius, Rallus aquaticus, Recurvirostra avosetta, Calidris alba, Calidris minuta, Philomachus pugnax, Limosa limosa, Tringa glareola, Larus audouinii, Anthus spinoletta, Remiz pendulinus, and Lanius excubitor.

During the breeding season we recorded 108 species in the study area (S4 Table), out of which 27 species (25.0%) are listed in the Annex I of the Bird Directive 2009/147/CE, 35 species (32.4%) are listed in the Red List of Italian Breeding Birds as endangered (8 species), vulnerable (19 species) or near threatened (8 species), and 46 species (42.6%) are SPEC (SPEC 1 = 1 species, SPEC 2 = 13 species, SPEC 3 = 32 species). Considering the total number of observations in the breeding season (N = 8762), the most common species was P. pica (10.0%, N = 878), followed by P. italiae (6.2%, N = 545), Emberiza calandra (5.1%, N = 449), Galerida cristata (5.0%, N = 440), P. major (4.2%, N = 370), S. serinus (4.1%, N = 362), Hirundo rustica (3.7%, N = 320), Passer montanus (3.5%, N = 306), and Streptopelia decaocto (3.2%, N = 277). The most rare species (0.01%, N = 1) were Tadorna tadorna, Anas platyrhynchos, Ixobrychus minutus, Fulica atra, Glareola pratincola, Larus melanocephalus, Larus michahellis, Gelochelidon nilotica, Sterna sandvicensis, Motacilla cinerea, Riparia riparia, Phylloscopus collybita, and Emberiza melanocephala.

Wintering hotspots of bird diversity

Considering the species observed during the winter season, we calculated the four indexes for each sample. The species richness ranged from 1 to 35 species (mean ± SE: 11.5 ± 0.31), the rarity index ranged from 0.007 to 0.46 (mean ± SE: 0.02 ± 0.002), the vulnerability index ranged from 0.67 to 2.5 (mean ± SE: 1.29 ± 0.01), and the combined index ranged from 0.02 to 8.50 (mean ± SE: 0.37 ± 0.04).

Four land use variables entered the best regression model obtained by AIC selection (Table 3). The most reliable effects were the positive one of marshes, rivers, water bodies, and irrigated crops, and the negative one of olive groves. The positive effects of shrublands were not so reliable because their confidence interval encompassed the 0 value. There was no collinearity between variables (VIF < 3) and the goodness-of-fit was fair (r = 0.354, P < 0.001). The residuals were normally distributed (Kolmogorov-Smirnov test, D = 0.05, P = 0.518) and independent (Durbin-Watson test, DW = 1.91, P = 0.243).

Table 3. The best model obtained by MLRA for the wintering hotspots of bird diversity.

In the study area there were many wintering hotspots of bird diversity, localized mainly in the north (in the Margherita di Savoia salt flats and along the Ofanto and Locone rivers), in the west (along the boundary with the Basilicata region), and in the south (along the boundary with Taranto province). In contrast, two main coldspots were predicted: the biggest in the central and coastal part of the study area, the smallest in south-eastern part (Fig 2).

Fig 2. Wintering hotspots of bird diversity in central Apulia and protected area boundaries.

Breeding hotspots of bird diversity

Considering the species observed during the breeding season, the species richness ranged from 0 to 17 (mean ± SE: 8.3 ± 0.17), the rarity index ranged from 0 to 0.46 (mean ± SE: 0.03 ± 0.003), the vulnerability index ranged from 0 to 3 (mean ± SE: 1.62 ± 0.02), and the combined index ranged from 0 to 12.35 (mean ± SE: 0.42 ± 0.05).

Five land use variables entered the best regression model obtained by AIC selection (Table 4). The most reliable effects were the positive one of transitional wood/shrubs and the negative one of orchards and olive groves. The negative effects of urban areas and vineyards were not so reliable because their confidence interval encompassed the 0 value. There was no collinearity between variables (VIF < 3) and the goodness-of-fit was fair (r = 0.399, P < 0.001). The residuals were normally distributed (Kolmogorov-Smirnov test, D = 0.07, P = 0.147) and independent (Durbin-Watson test, DW = 1.91, P = 0.198).

Table 4. The best model obtained by MLRA for the breeding hotspots of bird diversity.

In the study area breeding hotspots of bird diversity resulted in the north (the Margherita di Savoia salt flats), in the center (corresponding to the Alta Murgia Plateau), and in the west (along the boundary with Basilicata region). In addition, coldspots were localized mainly along the Adriatic Sea coast (Fig 3).

Fig 3. Breeding hotspots of bird diversity in central Apulia and protected area boundaries.

Hotspots and protected areas

The cells falling in protected areas had the combined index higher than the hotspot cells both during the winter (U = 1,295,338, P < 0.001) and the breeding seasons (U = 1,207,084, P < 0.001) such that the percentage of protected areas was positively correlated with the combined index both during the winter (r = 0.388, n = 5655, P < 0.001) and the breeding season (r = 0.571, n = 5655, P < 0.001). Nevertheless, the Kappa statistic was equal to 0.125 and to 0.314 during the winter and the breeding season respectively, showing a slight and a fair agreement between the cells considered as hotspots and the cells falling in protected areas. The GAP analysis showed that 45.9% of the area considered as wintering hotspots was inside protected areas while 68.7% of the area considered as breeding hotspots was protected. In contrast, only the 2.1% and 1.3% of land considered as coldspots were included in protected areas.


The main goal of this research was to define if crop dominated landscapes could be useful for bird diversity conservation, both in the winter and in the breeding season, in a biogeographically important area of the Mediterranean Basin. In the study area the species richness was greater during the winter than during the breeding season (77 vs 72 species respectively). Contrasting results were found in another Mediterranean study area, where the species richness was greater during the breeding season than in the wintering one [57]. The field data collection support the peninsula effect because many common species in the mainland of Italy (i.e. Columba palumbus, Cuculus canorus, Picus viridis, Dendrocopos major, Delichon urbicum, Troglodytes troglodytes, E. rubecula, Luscinia megarhynchos, and Turdus merula) [63] are very scarce as breeders in the study area. Our results showed that during the winter higher bird diversity was found in irrigated crops and marshes, rivers, and water bodies, while lesser diversity was found in olive groves. On the other hand, during the breeding season the highest values were found in transitional wood/shrubs areas, while the lowest were found in permanent crops, such as olive groves and orchards. Considering that not irrigated crops are the most important land use in the study area (32.4%), these negative effects underlined the importance of this habitat, as also showed by the importance for breeding birds of the Alta Murgia Plateau (in the central part of the study area and comprised in the Alta Murgia National Park), one of the largest steppe areas of Italy. This importance was noted, mainly for larks, by a recent research carried out in this area [64]. Further, the importance of wetlands for wintering birds is usual in the Mediterranean Basin [6568] and the Margherita di Savoia salt flats are one of the most important wintering areas for waterbirds in Italy [69,70].

The major differences between the two seasons were in the selection of more crop dominated areas during the wintering and more natural areas during the breeding season. This was due to the fact that during the winter, migrant species more related with humans and agricultural land come into the study area, such as Motacilla alba, E. rubecula, T. merula, T. philomelos, Sylvia atricapilla, Sturnus vulgaris, F. coelebs, S. serinus, and Linaria cannabina. On the other hand, during the breeding season, more migrant steppe species (i.e. Falco naumanni, Coracias garrulus, Melanocorypha calandra, Calandrella brachydactyla, Anthus campestris, Oenanthe hispanica, Sylvia conspicillata, Lanius minor, Lanius senator, and E. calandra) come into the study area. Higher species richness during the breeding season was found in open areas in Spain [71] as well as outside of the Western Palearctic [72]. In contrast, in Atlantic France cereal crops did not favor species richness [73]. Moreover, in the present study we found a positive effect of transitional wood/shrubs areas on bird diversity. Similar results were found in Spain and in France, where greater species richness was observed during the breeding season in woodlands and shrublands [71,7375]. These results were in concordance with a general rule of breeding birds in the Mediterranean Basin, where the highest number of species are found in steppes (25.7%) and in forests (24.3%) [20]. In general, olive groves, in particular the winter fruits, provide important resources for birds [76,77], such as E. rubecula, T. merula, T. philomelos, S. atricapilla, S. melanocephala, and S. vulgaris, very abundant in the study area during the wintering season. The negative effect of olive groves on species diversity during the winter and the large winter coldspot in the north of the study area, where olive groves were present, could be due to the fact that the herbaceous ground cover, which affects positively the species richness providing seeds and insect prey for foraging birds [78], was usually absent, because of the intensive agricultural practices (i.e. the use of herbicides and the frequent soil harrowing).

Olive groves and orchards hosted more widespread and generalist species, while in the transitional wood/shrubs areas, pastures, natural grasslands, and in the not irrigated crops there were more localized and specialized species. Furthermore, the importance of the Alta Murgia plateau highlighted the positive relationships between open areas and the more vulnerable species according to international and national lists. This relationship agrees with the findings of other studies, which pose the bird species of open areas as the most threatened in Mediterranean Europe [79,80].

This research showed that bird conservation can also be done in crop-dominated landscapes, where the best areas for the wintering and breeding birds are located. This was true in the Mediterranean regions, where traditional agricultural landscapes and extensively managed mosaics are characteristic [8183]. The importance of agricultural land in the protected areas network was highlighted in other researches [9,18,74,84]. In order to render agricultural landscapes efficient in complementing protected areas, agro-environmental practices that increase landscape heterogeneity and structural complexity should be emphasized [23,75,8587]. These may include mixed farming and the presence of natural vegetation in field margins, hedgerows, or in-field strips [88]. Improving the management of open and agricultural habitats within protected areas, could be a good way to achieve the goal of farmland bird conservation [24,25,8991].

Identifying gaps in the representation of species in protected areas, although important, was only one step, among others, toward building more effective networks of protected areas for conservation [11]. Designing boundaries for protected areas would require fine resolution species data (currently unavailable for most taxa) or the downscaling of individual species distributions [92]. In this view, the approach applied in this research to use a 1-km grid cell was very advantageous both to identify high value areas and to compare them with the current protected areas. Other studies have used a more coarse resolution, with larger grids, even if the researches were conducted at regional or national levels [46,9397]. A complete design would also include socioeconomic information to estimate management, acquisition, and opportunity costs associated with the implementation of conservation programs [98100].


In this research we showed the importance of crop dominated landscapes in the Mediterranean Basin for bird diversity conservation. In particular we highlighted the importance of open areas, such as natural grasslands, pastures, and not irrigated cereal crops, for conservation of breeding birds. Conversely, other researches provide insights that open habitat and habitat homogenization could be deleterious for bird diversity [26,73], even though the study areas investigated were not sited in Mediterranean regions. Moreover, the hotspots for breeding birds cannot be used as a surrogate for the wintering bird species, as observed in other places of the Mediterranean Basin and of the world [57,101]. Thus, the need to plan the protected areas network to take into account the wintering species is emphasized as well. Many studies have helped to establish priority conservation areas based only on the distribution of breeding bird species [9,18,46,85,102104]. Nevertheless, worldwide, few studies have dealt with wintering species distribution in order to evaluate the effectiveness of conservation reserves [71,105,106]. Likewise, research indicates that protection of non-breeding habitats may be crucial to avian conservation, because mortality was exacerbated in this period [66,107109] and many species appear to be primarily constrained by survival on their wintering grounds [110,111]. Petit [112] found that some habitats which were relatively unoccupied during the breeding season become important during the winter. Furthermore, large-scale conservation plans should consider other taxonomic groups, not only avian species, so that the final design of the protected areas network should cover the complete scope of biodiversity. This was true, because of the overlap of hotspots between different taxa was generally low, especially when groups have very different ecological requirements; this mismatch has been reported for many taxa in different parts of the world [95,113115]. Therefore a strategy for protected areas designation based solely on a few limited numbers of taxa may fail to provide adequate protection for many other organisms [116120].

Supporting Information

S1 Table. Land use variables used for cluster analysis and as predictors in the multiple linear regression to assessing bird diversity hotspots in Southern Italy.


S2 Table. Landscape Units (LU), length of transects and number of point counts carried out in the research.

Density of sampling transects (1 km/km2) and sampling points (1 point/km2) are showed as well.


S3 Table. Landscape Units defined by cluster analysis and used to randomly allocate sampling transects and sampling point counts.


S4 Table. Species observed during the winter (W) and the breeding season (B).

For each species is indicated if it is listed in the Italian Red List; EN = Endangered, VU = Vulnerable, NT = Near Threatened, LC = Least Concern, DD = Data Deficient, NA = Not Applicable), in the Annex I of the Birds Directive 2009/147/CE, and the SPEC category.



We are grateful to Giovanni Ferrara of Hunting District “Bari” for his help during the project. This study was funded by the project “Aggiornamento della carta delle vocazioni faunistiche dell’Ambito Territoriale di Caccia Bari e Barletta-Andria-Trani” (Hunting District“Bari”, Apulia, Italy). We appreciate the improvements in English usage made by Andrew Sturgeon. We thank three anonymous referees for reviewing the manuscript.

Author Contributions

Conceived and designed the experiments: GC AM. Performed the experiments: GC. Analyzed the data: GC. Contributed reagents/materials/analysis tools: GC. Wrote the paper: GC.


  1. 1. World Parks Congress. Available at: 2003.
  2. 2. Andelman SJ, Willig MR. Present patterns and future prospects for biodiversity in the Western Emisphere. Ecol Lett. 2003;6: 818–824.
  3. 3. Gaston KJ, Charman K, Jackson SF, Armsworth PR, Bonn A, Briers RA, et al. The ecological effectiveness of protected areas: the United Kingdom. Biol Conserv. 2006;132: 76–87.
  4. 4. Scott JM, Davis FW, McGhie RG, Wright RG, Groves C, Estes J. Nature reserves: do they capture the full range of America’s biological diversity? Ecol Appl. 2001;11: 999–1007.
  5. 5. Hoctor TS, Carr MH, Zwick PD. Identifying a linked reserved system using a regional landscape approach: the Florida Ecological Network. Conserv Biol. 2000;14: 984–1000.
  6. 6. Meynard CN, Howell CA, Quinn JF. Comparing alternative systematic conservation planning strategies against a politically driven conservation plan. Biodivers Conserv. 2009;18: 3061–3083.
  7. 7. Oldfield TEE, Smith RJ, Harrop SR, Leader-Williams N. A gap analysis of terrestrial protected areas in England and its implications for conservation policy. Biol Conserv. 2004;120: 303–309.
  8. 8. Rodrigues ASL, Andelman SJ, Bakarr MI, Boitani L, Brooks TM, Cowling RM, et al. Effectiveness of the global protected area network in representing species diversity. Nature. 2004;428: 640–643. pmid:15071592
  9. 9. Troupin D, Carmel Y. Can agro-ecosystems efficiently complement protected area network? Biol Conserv. 2014;169: 158–166.
  10. 10. Wiersma YF, Nudds TD. Efficiency and effectiveness in representative reserve design in Canada: the contribution of existing protected areas. Biol Conserv. 2009;142: 1639–1646.
  11. 11. Margules CR, Pressey RL. Systematic conservation planning. Nature. 2000;405: 243–253. pmid:10821285
  12. 12. Myers N, Mittermeier RA, Mittermeier CG, da Fonseca GAB, Kent J. Biodiversity hotspots for conservation priorities. Nature. 2000;403: 853–858. pmid:10706275
  13. 13. Pimm SL, Ayers M, Balmford A, Branch G, Brandon K, Brooks T, et al. Can we defy nature’s end? Science. 2001;293: 2207–2208. pmid:11567124
  14. 14. Soulé ME, Sanjayan MA. Conservation targets: do they help? Science. 1998;279: 2060–2061. pmid:17742319
  15. 15. Underwood EC, Klausmeyer KR, Cox RL, Busby SM, Morrison SA, Shaw MR. Expanding the global network of protected areas to save the imperiled Mediterranean biome. Conserv Biol. 2009;23: 43–52. pmid:18950475
  16. 16. Maiorano L, Falcucci A, Boitani . Gap analysis of terrestrial vertebrates in Italy: priorities for conservation planning in a human dominated landscape. Biol Conserv. 2006;133: 455–473.
  17. 17. Blondel J, Aronson J. Biology and wildlife of the Mediterranean Region. Oxford: Oxford University Press; 1999.
  18. 18. Campedelli T, Tellini Florenzano G, Londi G, Cutini S, Fornasari L. Effectiveness of the Italian National protected areas system in conservation of farmland birds: a gap analysis. Ardeola. 2010;57: 51–64.
  19. 19. Maiorano L, Falcucci A, Garton EO, Boitani L. Contribution of the Natura 2000 Network to biodiversity conservation in Italy. Conserv Biol. 2007;21: 1433–1444. pmid:18173467
  20. 20. Covas R, Blondel J. Biogeography and history of the Mediterranean bird fauna. Ibis. 1998;140: 395–407.
  21. 21. Shi H, Singh A, Kant A, Zhu Z, Waller E. Integrating habitat status, human population pressure, and protection status into biodiversity conservation priority setting. Conserv Biol. 2005;19: 1273–1285.
  22. 22. Donald PF, Pisano G, Rayment MD, Pain DJ. The Common Agricultural Policy, EU enlargment and the conservation of Europe’s farmland birds. Agr Ecosyst Environ. 2002;89: 167–182.
  23. 23. Benton TG, Vickery JA, Wilson JD. Farmland biodiversity: is habitat heterogeneity the key? Trends Ecol Evol. 2003;18: 182–188.
  24. 24. Fuller RJ, Gregory RD, Gibbons DW, Marchant JH, Wilson JD, Baillie SR, et al. Population decline and range contractions among lowland farmland birds in Britain. Conserv Biol. 1995;9: 1425–1441.
  25. 25. Newton I. The recent declines of farmland bird populations in Britain: an appraisal of causal factors and conservation actions. Ibis. 2004;146: 579–600.
  26. 26. Jeliazkov A, Mimet A, Chargé R, Jiguet F, Devictor V, Chiron F. Impacts of agricultural intensification on bird communities: new insights from a multi-level and multi-facet approach of biodiversity. Agr Ecosyst Environ. 2016;216: 9–22.
  27. 27. Wilson JD, Morris AJ, Arroyo BE, Clark SC. A review of the abundance and diversity of invertebrate and plant foods of granivorous birds in northern Europe in relation to agricultural change. Agr Ecosyst Environ. 1999;75: 13–30.
  28. 28. Boatman ND, Brickle NW, Hart JD, Milsom TP, Morris AJ, Murray AWA, et al. Evidence for indirect effects of pesticides on farmland birds. Ibis. 2004;146: 131–143.
  29. 29. Genghini M, Gellini S, Gustin M. Organic and integrated agriculture: the effects on bird communities in orchard farms in northern Italy. Biodivers Conserv. 2006;15: 3077–3094.
  30. 30. Rippa D, Maselli V, Soppelsa O, Fulgione D. The impact of agro-pastoral abandonment on the Rock Partridge Alectoris graeca in the Apennines. Ibis. 2011;153: 721–734.
  31. 31. Suárez-Seone S, Osborne PE, Baundry PE. Responses of birds of different biogeographic origins and habitat requirements to agricultural land abandonment in northen Spain. Biol Conserv. 2002;105: 333–344.
  32. 32. Brambilla M, Casale F, Bergero V, Bogliani G, Crovetto GM, Falco R, et al. Glorious past, uncertain present, bad future? Assessing effects of land-use changes on habitat suitability for a threatened farmland bird species. Biol Conserv. 2010;143: 2770–2778.
  33. 33. Simpson GG. Species density of North American recent mammals. System Zool. 1964;13: 57–73.
  34. 34. Lawlor TE. The peninsular effect on mammalian species diversity in Baja California. The American Naturalist. 1983;121: 432–439.
  35. 35. Brown JW, Opler PA. Patterns of butterfly species density in peninsular Florida. J Biogeogr. 1990;17: 615–622.
  36. 36. Barabesi L, Fattorini L. Random versus stratified location of transects or points in distance sampling: theoretical results and practical considerations. Environ Ecol Stat. 2013;20: 215–236.
  37. 37. Krebs CJ. Ecological methodology. 2nd ed. Menlo Park: Benjamin/Cummings; 1999.
  38. 38. Chuman T, Romportl D. Multivariate classification analysis of cultural landscapes: An example from the Czech Republic. Landscape Urban Plan. 2010;98: 200–209.
  39. 39. Legendre P, Legendre L. Numerical ecology. 2nd English ed. Amsterdam; New York: Elsevier; 1998.
  40. 40. Bibby CJ, Burgess ND, Hill DA, Mustoe SH. Bird census techniques. 2nd ed. London: Academic Press; 2000.
  41. 41. Buckland ST. Point-transect surveys for songbirds: robust methodologies. The Auk. 2006;123: 345.
  42. 42. Chamberlain D, Rolando A. The effects of a settling-down period on estimates of bird species richness and occurrence from point counts in the Alps. Bird Study. 2014;61: 121–124.
  43. 43. Colwell RK, Coddington JA. Estimating terrestrial biodiversity through extrapolation. Phil Trans R Soc B. 1994;345: 101–118. pmid:7972351
  44. 44. Gill F, Donsker D, editors. IOC World Bird List (v 4.3). 2014.
  45. 45. Magurran AE. Measuring biological diversity. Malden, Ma: Blackwell Pub; 2004.
  46. 46. Rey Benayas JM, la Montaña E de. Identifying areas of high-value vertebrate diversity for strengthening conservation. Biol Conserv. 2003;114: 357–370.
  47. 47. Peronace V, Cecere JG, Gustin M, Rondinini C. Lista Rossa 2011 degli uccelli nidificanti in Italia. Avocetta. 2012;36: 11–58.
  48. 48. BirdLife International. Birds in Europe. Population estimates, trends and conservation status. Cambridge; 2004.
  49. 49. Zuur AF, Ieno EN, Walker NJ, Saveliev AA, Smith GM. Mixed effects models and extensions in ecology with R. New York: Springer; 2009.
  50. 50. Burnham KP, Anderson DR. Model selection and multimodel inference: a practical information-theoretic approach. 2nd ed. New York: Springer; 2002.
  51. 51. Akaike H. Information theory as an extension of the maximum likelihood principle. In: Petrov BN, Csaki F, editors. Second International Symposium on Information Theory. Budapest: Akademiai Kiado; 1973. pp. 267–281.
  52. 52. Fox J, Monette G. Generalized collinearity diagnostics. J Am Stat Assoc. 1992;87: 178.
  53. 53. Guisan A, Edwards TC, Hastie T. Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecol Model. 2002;157: 89–100.
  54. 54. Zuur AF, Ieno EN, Elphick CS. A protocol for data exploration to avoid common statistical problems: Data exploration. Methods Ecol Evol. 2010;1: 3–14.
  55. 55. Savin NE, White KJ. The Durbin-Watson test for serial correlation with extreme sample sizes or many regressors. Econometrica. 1977;45: 1989.
  56. 56. Crawley MJ. GLIM for ecologists. Oxford; Boston: Blackwell Scientific Publications; 1993.
  57. 57. Marfil-Daza C, Pizarro M, Moreno-Rueda G. Do hot spots of breeding birds serve as surrogate hot spots of wintering birds? An example from central Spain. Anim Conserv. 2013;16: 60–68.
  58. 58. Cohen J. A coefficient of agreement for nominal scales. Educ Psychol Meas. 1960;20: 37–46.
  59. 59. Fielding AH, Bell JF. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ Conserv. 1997;24: 38–49.
  60. 60. Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics. 1977;33: 159–174. pmid:843571
  61. 61. Prendergast JR, Quinn RM, Lawton JH. The gaps between theory and practice in selecting nature reserves. Conserv Biol. 1999;13: 484–492.
  62. 62. Scott JM, Davis FW, Csuti B, Noss R, Butterfield B, Groves C, et al. Gap analysis: a geographic approach to protection of biological diversity. Wildlife Monogr. 1993;123: 3–41.
  63. 63. Fornasari L, Londi G, Buvoli L, Tellini , La Gioia G, Pedrini P, et al., editors. Distribuzione geografica e ambientale degli uccelli comuni nidificanti in Italia, 2000–2004 (dati del progetto MITO2000). Avocetta. 2010;34: 5–224.
  64. 64. Campedelli T, Londi G, Gioia GL, Frassanito AG, Florenzano GT. Steppes vs. crops: is cohabitation for biodiversity possible? Lessons from a national park in southern Italy. Agr Ecosyst Environ. 2015;213: 32–38.
  65. 65. Goutner V, Papakostas G. Evaluation of the ornithological importance of the Alyki Kitrous wetland, Macedonia, Greece: a priority for conservation. Biol Conserv. 1992;62: 131–138.
  66. 66. Rendón MA, Green AJ, Aquilera E, Almaraz P. Status, distribution and long-term changes in the waterbird community wintering in Doñana, south-west Spain. Biol Conserv. 2008;141: 1371–1388.
  67. 67. Liordos V, Pergantis F, Perganti I, Roussopoulos Y. Long-term population trends reveal increasing importance of a Mediterranean wetland complex (Messolonghi lagoons, Greece) for wintering waterbirds. Zoological Studies. 2014;53: 12.
  68. 68. Boucheker A, Samraoui B, Prodon R, Amat JA, Rendon-Martos M, Baccetti N, et al. Connectivity between the Algerian population of Greater Flamingo Phoenicopterus roseus and those of the Mediterranean basin. Ostrich. 2011;82: 167–174.
  69. 69. Baccetti N, Dall’Antonia P, Magagnoli P, Melega L, Serra L, Soldatini C, et al. Risultati dei censimenti degli uccelli acquatici svernanti in Italia: distribuzione, stima e trend delle popolazioni nel 1991–2000. Biol Cons Fauna. 2002;111: 1–240.
  70. 70. Zenatello M, Baccetti N, Borghesi F. Risultati dei censimenti degli uccelli acquatici svernanti in Italia. Distribuzione, stima e trend delle popolazioni nel 2001–2010. Roma: ISPRA; 2014.
  71. 71. Santos KC, Pino J, Rodà F, Guirado M, Ribas J. Beyond the reserves: The role of non-protected rural areas for avifauna conservation in the area of Barcelona (NE of Spain). Landscape Urban Plan. 2008;84: 140–151.
  72. 72. Cerezo A, Conde MC, Poggio SL. Pasture area and landscape heterogeneity are key determinants of bird diversity in intensively managed farmland. Biodivers Conserv. 2011;20: 2649–2667.
  73. 73. Gil-Tena A, De Cáceres M, Ernoult A, Butet A, Brotons L, Burel F. Agricultural landscape composition as a driver of farmland bird diversity in Brittany (NW France). Agr Ecosyst Environ. 2015;205: 79–89.
  74. 74. la Montaña E de, Rey Benayas JM, Vasques A, Razola I, Cayuela L. Conservation planning of vertebrate diversity in a Mediterranean agricultural-dominant landscape. Biol Conserv. 2011;144: 2468–2478.
  75. 75. Jiguet F, Julliard R, Couvet D, Petiau A. Modeling spatial trends in estimated species richness using breeding bird survey data: a valuable tool in biodiversity assessment. Biodivers Conserv. 2005;14: 3305–3324.
  76. 76. Rey PJ. The role of olive orchards in the wintering of frugivorous birds in Spain. Ardea. 1993;81: 151–160.
  77. 77. Rey PJ. Preserving frugivorous birds in agro-ecosystems: lessons from Spanish olive orchards: Frugivorous birds in agro-ecosystems. J Appl Ecol. 2011;48: 228–237.
  78. 78. Castro-Caro JC, Barrio IC, Tortosa FS. Is the effect of farming practices on songbird communities landscape dependent? A case study of olive groves in southern Spain. J Ornithol. 2014;155: 357–365.
  79. 79. Burfield IJ. The conservation status of steppic birds in Europe. In: Bota G, Morales MB, Mañosa S, Camprodon J, editors. Ecology and conservation of steppe-land birds. Barcelona: Lynx Edicions and Centre Tecnològic Forestal de Catalunya; 2005. pp. 119–139.
  80. 80. Tucker GM, Heath MF. Birds in Europe: their conservation status. Cambridge: BirdLife International; 1994.
  81. 81. Olsvig-Whittaker L, Sternberg M. The changing Mediterranean landscape: an editorial view. J Plant Sci. 2005;53: 149–150.
  82. 82. Sancho Comins J, Bosque Sendra J, Moreno Sanz F. Crisis and permanence of the traditional Mediterranean landscapes in the central region of Spain. Landscape Urban Plan. 1993;23: 155–166.
  83. 83. Serra P, Pons X, Sauri D. Land-cover and land-use change in a Mediterranean landscape: a spatial analysis of driving forces integrating biophysical and human factors. Appl Geogr. 2008;28: 189–209.
  84. 84. Farina A. Landscape structure and breeding bird distribution in a sub-Mediterranean agro-ecosystem. Landscape Ecol. 1997;12: 365–378.
  85. 85. Bacaro G, Santi E, Rocchini D, Pezzo F, Puglisi L, Chiarucci A. Geostatistical modelling of regional bird species richness: exploring environmental proxies for conservation purpose. Biodivers Conserv. 2011;20: 1677–1694.
  86. 86. Haslem A, Bennet AF. Birds in agricultural mosaics: the influence of landscape pattern and countryside heterogeneity. Ecol Appl. 2008; 185–196. pmid:18372565
  87. 87. Morelli F, Pruscini F, Santolini R, Perna P, Benedetti Y, Sisti D. Landscape heterogeneity metrics as indicators of bird diversity: determining the optimal spatial scales in different landscapes. Ecol Indic. 2013;34: 372–379.
  88. 88. Wright HL, Lake IR, Dolman PR. Agriculture—a key element for conservation in the developing world. Conserv Lett. 2012;5: 11–19.
  89. 89. Chamberlain DE, Fuller RJ, Bunce RGH, Duckworth JC, Shrubb M. Changes in the abundance of farmland birds in relation to the timing of agricultural intensification in England and Wales. J Appl Ecol. 2000;37: 771–788.
  90. 90. Reif J, Voříšek P, Štastný K, Bejček V, Petr J. Agricultural intensification and farmland birds: new insights from a central European country. Ibis. 2008;150: 596–605.
  91. 91. Wretenberg J, Lindström Å, Svensson S, Thierfelder T, Pärt T. Population trends of farmland birds in Sweden and England: similar trends but different patterns of agricultural intensification. J Appl Ecol. 2006;43: 1110–1120.
  92. 92. Araújo MB, Thuiller W, Williams PH, Reginster I. Downscaling European species atlas distributions to a finer resolution: implications for conservation planning. Global Ecol Biogeogr. 2005;14: 17–30.
  93. 93. Traba J, García de la Morena EL, Morales MB, Suárez F. Determining high value areas for steppe birds in Spain: hot spots, complementarity and the efficiency of protected areas. Biodivers Conserv. 2007;16: 3255–3275.
  94. 94. Mendoza AM, Arita HT. Priority setting by sites and by species using rarity, richness and phylogenetic diversity: the case of neotropical glassfrogs (Anura: Centrolenidae). Biodivers Conserv. 2014;23: 909–926.
  95. 95. Schouten MA, Barendregt A, Verweij PA, Kalkman VJ, Kleukers RMJC, Lenders HJR, et al. Defining hotspots of characteristic species for multiple taxonomic groups in the Netherlands. Biodivers Conserv. 2010;19: 2517–2536.
  96. 96. Sólymos P, Fehér Z. Conservation prioritization Based on Distribution of Land Snails in Hungary. Conserv Biol. 2005;19: 1084–1094.
  97. 97. Williams P, Gibbons D, Margules C, Rebelo A, Humphries C, Pressey R. A comparison of richness hotspots, rarity hotspots, and complementary areas for conserving diversity of British birds. Conserv Biol. 1996;10: 155–174.
  98. 98. Wilson KA, Westphal MI, Possingham HP, Elith J. Sensitivity of conservation planning to different approaches to using predicted species distribution data. Biol Conserv. 2005;122: 99–112.
  99. 99. Frazee SR, Cowling RM, Pressey RL, Turpie JK, Lindenberg N. Estimating the costs of conserving a biodiversity hotspot: a case study of the Cape Floristic Region, South Africa. Biol Conserv. 2003;112: 275–290.
  100. 100. Williams PH, Moore JL, Kamden Toham A, Brooks TM, Strand H, Amico J D’, et al. Integrating biodiversity priorities with conflicting socio-economic values in the Guinean-Congolian forest region. Biodivers Conserv. 2003;12: 1297–1320.
  101. 101. Thompson BC, Hughes MA, Anderson MC. Effects of including non-breeding bird species on predicted bird distributions for conservation planning in New Mexico. Biol Conserv. 2001;100: 229–242.
  102. 102. Araújo MB, Lobo JM, Moreno JC. The effectiveness of Iberian protected areas in conserving terrestrial biodiversity: performance of Iberian protected areas. Conserv Biol. 2007;21: 1423–1432. pmid:18173466
  103. 103. Araújo MB, Williams PH. The bias of complementarity hotspots toward marginal populations. Conserv Biol. 2002;15: 1710–1720.
  104. 104. Bishop J, Myers W. Associations between avian functional guild response and regional landscape properties for conservation planning. Ecol Indic. 2005;5: 33–48.
  105. 105. Jackson SF, Evans KL, Gaston KJ. Statutory protected areas and avian species richness in Britain. Biodivers Conserv. 2009;18: 2143–2151.
  106. 106. Stralberg D, Cameron DR, Reynolds MD, Hickey CM, Klausmeyer K, Busby SM, et al. Identifying habitat conservation priorities and gaps for migratory shorebirds and waterfowl in California. Biodivers Conserv. 2011;20: 19–40.
  107. 107. Mezquida ET, Villarán A. Abudance variations, survival and site fidelity of reed buntings Emberiza schoeniclus wintering in central Spain. Ornis Fennica. 2006;83: 11–19.
  108. 108. Mihoub JB, Gimenez O, Pilard P, Sarrazin F. Challenging conservation of migratory species: Sahelian rainfalls drive first-year survival of the vulnerable lesser kestrel Falco naumanni. Biol Conserv. 2010;143: 839–847.
  109. 109. Pace RM. Winter survival rates of American woodcock in south central Louisiana. J Wildlife Manage. 2000;64: 933–939.
  110. 110. Fretwell SD. Populations in a seasonal environment. Princeton: Princeton University Press; 1972.
  111. 111. Huertas DL, Diaz JA. Winter habitat selection by a montane forest bird assemblage: the effects of solar radiation. Can J Zool. 2001;79: 279–284.
  112. 112. Petit DR. Weather-dependent use of habitat patches by wintering woodland birds. J Field Ornithol. 1989;60: 241–247.
  113. 113. Lombard AT. The problems with multi-species conservation: do hotpsots, ideal reserves and existing reserves coincide? South Afr J Zool. 1995;30: 145–163.
  114. 114. Kerr JT. Species richness, endemism, and the choice of areas for conservation. Conserv Biol. 1997;11: 1094–1100.
  115. 115. Yip JY, Corlett RT, Dudgeon D. A fine-scale gap analysis of the existing protected area system in Hong Kong, China. Biodivers Conserv. 1994;13: 943–957.
  116. 116. Kati V, Devillers P, Dufrêne M, Legakis A, Vokou D, Lebrun P. Hotspots, complementarity or representativeness? designing optimal small-scale reserves for biodiversity conservation. Biol Conserv. 2004;120: 471–480.
  117. 117. Prendergast JR, Quinn RM, Lawton JH, Eversham BC, Gibbons DW. Rare species, the coincidence of diversity hotspots and conservation strategies. Nature. 1993;365: 335–337.
  118. 118. Danielsen F, Treadaway CG. Priority conservation areas for butterflies (Lepidoptera: Rhopalocera) in the Philippine islands. Anim Conserv. 2004;7: 79–92.
  119. 119. Das A, Krishnaswamy J, Bawa KS, Kiran MC, Srinivas V, Kumar NS, et al. Prioritisation of conservation areas in the Western Ghats, India. Biol Conserv. 2006;133: 16–31.
  120. 120. Estrada A, Real R, Vargas JM. Assessing coincidence between priority conservation areas for vertebrate groups in a Mediterranean hotspot. Biol Conserv. 2011;144: 1120–1129.