Many studies have shown how intensification of farming is the main cause of loss biodiversity in these environments. During the last decades, agroecosystems in Europe have changed drastically, mainly due to mechanization of agriculture. In this work, species richness in bird communities was examined on a gradient of spatial heterogeneity of farmlands, in order to quantify its effects. Four categories of farmland spatial heterogeneity were defined, based on landscape and landuse parameters. The impact of features increasing the spatial heterogeneity was quantified comparing the similarity indexes between bird communities in several farmlands of Central Italy. The effects of environmental variables on bird richness were analyzed using GLM. The results highlighted that landscape features surrogates of high nature values (HNVs) of farmlands can increase more than 50% the bird species richness. The features more related to bird richness were hedgerows, scattered shrubs, uncultivated patches, and powerlines. The results confirm that the approach based on HNV for evaluating the farmlands is also suitable in order to study birds’ diversity. However, some species are more sensitive to heterogeneity, while other species occupy mainly homogeneous farmlands. As a consequence, different conservation methods must be considered for each farmland bird species.
Agricultural intensification is one of the main drivers of biodiversity decline. During the last twenty-five years a rapid and large scale change of the agricultural landscape occurred in Europe, caused by the intensification and mechanization of agricultural activities [
Many studies have shown that organic systems may enhance bird species diversity over nonorganic counterparts as a result of increased complexity in landscape features [
The spatial heterogeneity in an agroecosystems is measurable through landscape features that can be used also to assess bird species richness, as a surrogate of biodiversity [
The first aim of the present study is examining the relationships between bird species richness and spatial heterogeneity of farmlands in Central Italy and quantifying the impact of spatial heterogeneity on biodiversity, through the use of an index easy to apply and useful for conservation strategies.
The second aim is identifying the main environmental characteristics associated with the heterogeneity of farmlands and investigating whether the species living in heterogeneous agricultural areas are also present also in more homogeneous farmlands, to help in this way to identify the bird species most related to landscape heterogeneity as possible indicators of HNV farmlands.
The study area was an agricultural area of the North Eastern Marches region (Central Italy, 43°45′47.30′′N; 12°45′5.20′′E) that was 2,500 ha wide, ranged from 0 to 800 m/a.s.l. This area was selected because it includes farmlands representative of the different farming practices in the region. Within the area 80 sites were selected randomly and surveyed. There sites were located uniformly and each far at least 500 m from one other.
To survey the bird community composition during the breeding period of the year 2010, the sites were visited in the morning within 06:00 AM and 10:00 AM, with sunny weather conditions, between mid-April and the end of July. Each visit lasted 10 min, and all birds detected visually and acoustically within a radius of 100 m from the observer were recorded [
Description
In order to classify the farmland heterogeneity, an approach in line with that recently used for evaluating high nature value (HNV) farmland in Europe [
Environmental parameters used to describe the farmlands in Central Italy. The spatial scale of measurement was 100 m radius around a single point count.
Parameters | Abbreviation | Level | Details |
---|---|---|---|
Altitude | alt | Landscape | Altitude of the point count (m/a.s.l.) |
Terrain slope | slo | Landscape | No slope (less than 3 degrees): 0, slight slope (between 3 and 8 degrees): 1, mean slope (greater than 8 degrees): 2 |
Roads | roa | Landscape | Presence and type of roads (paved and unpaved) |
Power lines | pow | Landscape | Number of electricity wires |
Urban | urb | Land-use | % |
Forest | for | Land-use | % |
Shrubs | shr | Land-use | % |
Uncultivated | unc | Land-use | % |
Badland | bad | Land-use | % |
Grassland | gra | Land-use | % |
Hedgerows | hed | Land-use | % |
Isolated trees | tre | Land-use | % |
Vineyards | vin | Land-use | % |
Olive | oli | Land-use | % |
Cultivated total | cul | Land-use | Sum of all crop types, % |
|
ofo | Crop type | % |
|
whe | Crop type | % |
|
alf | Crop type | % |
|
hay | Crop type | % |
|
sun | Crop type | % |
|
cri | Crop type | % |
|
sug | Crop type | % |
|
cor | Crop type | % |
|
gar | Crop type | % |
|
oat | Crop type | % |
|
bra | Crop type | % |
|
let | Crop type | % |
To define the landscape diversity the following parameters were used: road presence and type: paved, unpaved, or mixed; power lines: presence and number; terrain slope, classified as no slope (less than 3 degrees): 0, slight slope (between 3 and 8 degrees): 1, and mean slope (greater than 8 degrees): 2; marginal vegetation level on cultivated, taking into account the presence (isolated or simultaneous) of natural and semi-natural features: max level (linear features (hedgerows) + point features (scattered shrubs)), mid-level (linear features (hedgerows)), low level (point features (scattered shrubs)), and min level (without hedgerows and scattered shrubs) (see Figure
Scheme of the criteria used to classify the level of marginal vegetation of farmlands in Central Italy. The point features represent scattered shrubs while linear features represent hedgerows.
The land-use diversity in each site was calculated using Shannon diversity index on land-uses,
Crop diversity was estimated for each site, applying the Shannon diversity index on crop types into the cultivated category (Table
Four categories of spatial heterogeneity of farmland were defined considering both landscape and land-use data collected
Ranking of farmland spatial heterogeneity according to landscape and land-uses characteristics, where FSH is farmland spatial heterogeneity.
FSH description | FSH rank | Terrain slope | Marginal vegetation | Land-use diversity |
---|---|---|---|---|
Very heterogeneous |
|
None, slight, mean | Linear + point | >1.0 |
Heterogeneous |
|
None, slight, mean | Linear | 0.8 < 1.0 |
Homogeneous |
|
None, slight, mean | Point | 0.6 < 0.8 |
Very homogeneous |
|
None, slight | None | <0.6 |
Biodiversity was estimated as bird species richness at each sampled site and was estimated for each farmland heterogeneity level too. Total biodiversity was the sum of all bird species recorded in the entire study area. To test the effects of environmental parameters (associated with farmland spatial heterogeneity) on biodiversity, bird richness in different spatial heterogeneity categories was compared using ANOVA (after doing the normality and homoscedasticity tests). Similarity between bird communities in different heterogeneity categories was explored using the Sorensen Similarity Index. The Sorensen similarity index (SI) [
The nature and strength of the relationship between bird richness and environmental parameters on farmlands were examined using GLM [
Sixty-six bird species were recorded during the breeding period. For the analysis, a total of 1133 records were collected. The maximum potential bird richness for the studied farmland was fifty-five species (Tables
Total frequency (%), relative frequency, and concern status of bird species [
|
FSH category |
|
| |||
---|---|---|---|---|---|---|
4 ( |
3 ( |
2 ( |
1 ( |
|||
|
90 | 96.3 | 84.6 | 62.5 | 86.3 | LC |
|
66.7 | 85.2 | 92.3 | 75 | 76.3 | LC |
|
63.3 | 74.1 | 92.3 | 100 | 73.8 | VU |
|
86.7 | 88.9 | 46.2 | 25 | 72.5 | LC |
|
70 | 77.8 | 84.6 | 50 | 71.3 | LC |
|
76.7 | 66.7 | 53.8 | 12.5 | 61.3 | LC |
|
76.7 | 77.8 | 23.1 | 25 | 61.3 | LC |
|
56.7 | 63 | 53.8 | 100 | 61.3 | NT |
|
80 | 66.7 | 15.4 | 0 | 55 | LC |
|
76.7 | 55.6 | 30.8 | 12.5 | 53.8 | LC |
|
53.3 | 51.9 | 76.9 | 25 | 52.5 | LC |
|
66.7 | 55.6 | 38.5 | 12.5 | 51.3 | LC |
|
46.7 | 51.9 | 61.5 | 12.5 | 46.3 | NT |
|
40 | 51.9 | 46.2 | 25 | 42.5 | LC |
|
33.3 | 40.7 | 38.5 | 50 | 37.5 | NT |
|
63.3 | 37 | 0 | 0 | 36.3 | LC |
|
30 | 25.9 | 46.2 | 62.5 | 33.8 | VU |
|
36.7 | 29.6 | 53.8 | 12.5 | 33.8 | DD |
|
50 | 29.6 | 23.1 | 0 | 32.5 | LC |
|
40 | 25.9 | 30.8 | 0 | 28.8 | LC |
|
30 | 37 | 7.7 | 0 | 25 | LC |
|
26.7 | 33.3 | 15.4 | 12.5 | 25 | LC |
|
43.3 | 22.2 | 0 | 0 | 23.8 | LC |
|
30 | 33.3 | 0 | 0 | 22.5 | LC |
|
33.3 | 18.5 | 15.4 | 0 | 21.3 | LC |
|
20 | 22.2 | 7.7 | 12.5 | 17.5 | LC |
|
20 | 22.2 | 7.7 | 12.5 | 17.5 | LC |
|
30 | 11.1 | 0 | 12.5 | 16.3 | VU |
|
6.7 | 14.8 | 30.8 | 25 | 15 | NA |
|
23.3 | 11.1 | 7.7 | 0 | 13.8 | VU |
|
13.3 | 14.8 | 7.7 | 12.5 | 12.5 | DD |
|
23.3 | 7.4 | 0 | 0 | 11.3 | LC |
|
10 | 3.7 | 15.4 | 25 | 10 | LC |
|
10 | 7.4 | 15.4 | 0 | 8.8 | LC |
|
0 | 11.1 | 15.4 | 12.5 | 7.5 | VU |
|
10 | 3.7 | 7.7 | 0 | 6.3 | LC |
|
13.3 | 0 | 7.7 | 0 | 6.3 | LC |
|
10 | 7.4 | 0 | 0 | 6.3 | LC |
|
6.7 | 7.4 | 0 | 0 | 5 | LC |
|
10 | 3.7 | 0 | 0 | 5 | LC |
|
6.7 | 3.7 | 7.7 | 0 | 5 | LC |
|
6.7 | 3.7 | 0 | 0 | 3.8 | LC |
|
6.7 | 0 | 0 | 0 | 2.5 | LC |
|
6.7 | 0 | 0 | 0 | 2.5 | DD |
|
3.3 | 3.7 | 0 | 0 | 2.5 | VU |
|
3.3 | 3.7 | 0 | 0 | 2.5 | DD |
|
3.3 | 3.7 | 0 | 0 | 2.5 | LC |
|
0 | 0 | 0 | 25 | 2.5 | VU |
|
6.7 | 0 | 0 | 0 | 2.5 | LC |
|
3.3 | 3.7 | 0 | 0 | 2.5 | LC |
|
0 | 3.7 | 0 | 0 | 1.3 | NT |
|
3.3 | 0 | 0 | 0 | 1.3 | LC |
|
3.3 | 0 | 0 | 0 | 1.3 | EN |
|
0 | 3.7 | 0 | 0 | 1.3 | LC |
|
3.3 | 0 | 0 | 0 | 1.3 | LC |
The most common farmland types had high or medium spatial heterogeneity (over 73%), whereas farmlands with lower heterogeneity were only 27% (Table
Descriptive elements of agricultural mosaic along the gradient of spatial heterogeneity of farmland in Central Italy.
|
FSH category |
|
df |
| ||||
---|---|---|---|---|---|---|---|---|
1 | 2 | 3 | 4 | Total | ||||
Altitude (m) | 122.7 ± 77.2 | 183.3 ± 156.2 | 298.8 ± 168.2 | 382.5 ± 129.8 | 292.4 ± 168.8 | 10.1 | 3 | 0.000*** |
Terrain slope (mode) | Low | Low | Low | Low-medium | Low | — | — | — |
Roads (mode) | Paved | Paved | Paved | Paved | Paved | — | — | — |
Powerlines (mean) | 3.1 ± 2.5 | 2.9 ± 2.0 | 2.5 ± 1.6 | 3.8 ± 2.8 | 3.1 ± 2.3 | 1.5 | 3 | 0.221 |
Crop diversity (mean) | 0.632 ± 0.3 | 0.554 ± 0.3 | 0.533 ± 0.4 | 0.489 ± 0.4 | 0.530 ± 0.3 | 0.3 | 3 | 0.790 |
Significance codes:
***
Similarity index pairwise of the bird species richness among the farmlands according to their spatial heterogeneity gradient.
FSH category | Similarity index | ||||
1 | 2 | 3 | 4 | Total | |
| |||||
1 | — | 0.780 | 0.667 | 0.605 | 0.472 |
2 | — | — | 0.815 | 0.776 | 0.764 |
3 | — | — | — | 0.959 | 0.922 |
4 | — | — | — | — | 0.962 |
Number of bird species (total) | 25 | 34 | 47 | 51 | 55 |
The bird species richness on different farmland spatial heterogeneity categories was different, being higher in farmlands with greater heterogeneity (
Relationship between bird species richness and gradient of spatial heterogeneity of farmlands in Central Italy.
The impact of FSH on biodiversity was quantified by means of similarity index pairwise between the FSH categories. The homogenization of farmlands was able to reduce the similarity index on bird richness up to a half compared to very heterogeneous farmlands. The total effect was quantified as a fall of circa 53% of diversity in comparison with the total amount of species and 40% between very heterogeneous and very homogeneous farmlands (Table
Following the results of this work, the linear features as hedgerows were more important than point features as shrubs for the total bird richness (more dissimilarities from index were found between 1 and 3 than between 1 and 2 FSH categories) (Table
The model that best described the relationships between bird species richness and environmental parameters in farmlands included three land-use variables (shrubs, uncultivated, and hedges) and one landscape variable (presence of powerlines). The ratio between explained and total deviance for the best model was 0.83. These four parameters were statistically significant for all the farmland typologies, and if compared to the initial model including all the variables, the model with only these four variables appeared to be the most suitable. Altitude had not effect on bird diversity in the study area. The results of backwise procedure have not selected altitude as variable related to the bird species richness (Tables
Different candidate models, ranked according to the AIC values, used to select the best models to relate bird richness to landscape, land-use, and crop types in farmlands of Central Italy.
Variables included in the model | Total number of variables | Scale level | AIC |
---|---|---|---|
Alt + slo + pow + shr + for + unc + bad + gra + hed + tre + urb + cul + ofo + whe + alf + hay + sun + cri + sug + cor + gar + oat + bra + let + land-use div | 25 | Landscape, land-use, crop type | 424.53 |
Alt + slo + pow + shr + for + unc + bad + gra + hed + tre + urb + cul | 12 | Landscape, land-use | 429.73 |
Alt + pow + shr + unc + bad + gra + hed + urb | 8 | Landscape, land-use | 422.51 |
Pow + shr + unc + hed | 4 | Landscape, land-use | 417.31 |
Results of Poisson regression for the best model relating the bird species richness to environmental parameters of farmlands in Central Italy. The table shows the most significant variables selected after a stepwise backward procedure using AIC criterion. A comparison of models with all possible variables, classified in the four different categories of spatial heterogeneity, showed that the significant parameters are the same for all the evaluated levels.
Environmental parameter | Estimate | SE |
|
|
---|---|---|---|---|
Farmlands ( |
||||
|
0.02805 | 0.01242 | 2.257 | 0.0240* |
|
1.23966 | 0.50681 | 2.446 | 0.0144* |
|
0.94663 | 0.53777 | 1.760 | 0.0784 |
|
1.51644 | 0.33585 | 4.515 |
|
|
2.21632 | 0.09018 | 24.576 |
|
Significance codes: ***
The bird species most frequently observed in heterogeneous farmlands were
The bird species most frequently observed in homogeneous farmlands were
Some species, such as
The use of environmental variables collected
The results showed how in Central Italy bird richness is related to spatial heterogeneity of farmlands, highlighting how the lack of several features, such hedgerows or scattered shrubs, causes the loss of more than 53% of the total potential bird species richness. These findings point out the important role of these few marginal components, typical of most heterogeneous mosaics, corresponding to HNV farmlands. The results also show that diversity of land-use rather than crop types seems to be important for bird species richness in farmlands. However, because these two variables are correlated, is hard determinate the real contribution of each one. Shannon index of crop types decreases with farmland heterogeneity, because diversity of crop types corresponds to more extended and less complex farmlands from a structural point of view. The characteristics of farmlands seeming to support high numbers of bird species are hedgerows, scattered shrubs, uncultivated patches, and the presence of power lines. The first three elements could improve the land-use diversity, offering habitat availability to several species [
The comparison between similarities index provided a quantitative measure of the total influence of the spatial heterogeneity on bird community. The approach by mean of Sorensen index can constitute an easy and fast decisional tool, useful to compare and quantify how a gradient of urbanization can modify the biodiversity of ecosystems [
The results of this work can be useful to select the most important environmental parameters to maintain the biodiversity and to characterize the bird communities or the presence of threatened species on different farmland types too. The bird species frequencies on farmland types are useful to develop any bioindicator approach (farmland bird index, monitoring of HNV farmlands, farmland focal species, etc.). For example Italian Sparrow, in decreasing throughout the whole Italian peninsula [
Finally, our results help filling the lack of data about farmland bird ecology in southern Europe [
The author is very grateful to the following people: Dan Chamberlain, Jon Mc Kean, Marco Girardello, Raffaele Secchi, Maria Balsamo, Yanina Benedetti, and anonymous reviewers for their suggestions or valuable comments on earlier versions of the manuscript or in the field. He also thanks ION Proofreading for the critical revision of English version of the paper.