Modelling the Impacts of Habitat Changes on the Population Density of Eurasian Skylark (Alauda arvensis) Based on Its Landscape Preferences

Abstract

The dramatic decline of the abundance of farmland bird species can be related to the level of land-use intensity or the land-cover heterogeneity of rural landscapes. Our study area in central Europe (Hungary) included 3049 skylark observation points and their 600 m buffer zones. We used a very detailed map (20 × 20 m minimum mapping unit), the Hungarian Ecosystem Basemap, as a land-cover dataset for the calculation of three landscape indices: mean patch size (MPS), mean fractal dimension (MFRACT), and Shannon diversity index (SDI) to describe the landscape structure of the study areas. Generalized linear models were used to analyze the effect of land-cover types and landscape patterns on the abundance of the Eurasian skylark (Alauda arvensis). According to our findings, the proportions of arable land, open sand steppes, closed grassland patches, and shape complexity and size characteristics of these land cover patches have a positive effect on skylark abundance, while the SDI was negatively associated with the skylark population. On the basis of the used statistical model, the abundance density (individuals/km*) of skylarks could be estimated with 37.77% absolute percentage error and 2.12 mean absolute error. We predicted the skylark population density inside the Natura 2000 Special Protected Area of Hungary which is 0–6 individuals/km* and 23746 ± 8968 skylarks. The results can be implemented for the landscape management of rural landscapes, and the method used are adaptable for the density estimation of other farmland bird species in rural landscapes. According to our findings, inside the protected areas should increase the proportion, the average size and shape complexity of arable land, salt steppes and meadows, and closed grassland land cover patches.

Landscape and Stand Characteristics Influence on the Bird Assemblage in Nothofagus antarctica Forests of Tierra del Fuego

“…Landscape configuration (area, perimeter, and shape of patches; number of patches; largest patch index) and composition (area of open areas) variables significantly influenced the bird assemblages in the studied forests, in agreement with Calamari and Zaccagnini [9] and Carrara et al [2]. They are also in agreement with other studies, for example [7,66]; our results reject the habitat size hypothesis proposed by Fahrig [67] because it was found that not only the composition (OTA variable) but also the configuration (AREA, PERIM, SHAPE, FNP, ONP, OLPI, OCONNECT, TE, and ED) of the landscape influenced the assemblage of birds. As observed by Klingbeil and Willig [14], migratory birds (e.g., E. albiceps and T. leucopyga) showed a very weak association with landscape variables, which could be associated with the fact that these are long-distance migrants capable of adapting to many different environments [51,68].…”

An Overview of Fractal Geometry Applied to Urban Planning

“…However, when comparing fractal distributions to uniform ones, a fractal form will not necessarily mean higher density. Urban areas with similar density measurements can have very different fractal dimensions, as shown in Figure 3 in [81] Consequently, fractal dimension cannot be used as a proxy for density; rather, it can supplement density metrics to distinguish between urban and rural areas in large-scale urban regions [82][83][84][85][86][87].…”

Pixel and object-based land cover mapping and change detection from 1986 to 2020 for Hungary using histogram-based gradient boosting classification tree classifier

“…With the acceleration of LULC changes that occurred with urban expansion, economic growth, and explicitly/implicitly increasing human needs, the monitoring of LULC changes became a key tool that could be used in work associated with environmental protection and sustainable development. LULC maps are often used as base data in environmental studies conducted in fields such as agriculture (Bezdan et al, 2019), hydrology (Tobak et al, 2019), ecology (Csikós & Szilassi, 2021), urbanization (Steurer & Bayr, 2020), and change detection (Szilassi, 2017). LULC mapping is a widely discussed topic in the field of remote sensing because remote sensing can provide tools, and reliable and extensive data with a high temporal and spatial resolution for LULC mapping (Townshend et al, 1991).…”