DOI: 10.5061/dryad.mpg4f4r35
title: Data for: The role of landscape in shaping bird community and implications for landscape management at Nanjing Lukou International Airport

description: Understanding the patterns of bird diversity and its driving force is
 necessary for bird strike prevention. In this study, we investigated the
 effects of landscape attributes on phylogenetic and functional diversity
 of bird communities at Nanjing Lukou International Airport (NLIA). Bird
 identifications and counting of individuals were carried out from November
 2017 to October 2019. Based on the land cover data, the landscape was
 divided into four main types, including farmlands, woodlands, wetlands and
 urban areas. Bird phylogenetic and functional diversity were strongly
 affected by landscape matrix types. Bird species, phylogenetic and
 functional richness were highest in woodlands, while phylogenetic and
 functional structure were most complex in wetlands. We found that related
 carnivorous birds occupied most niches at the NLIA. Moreover, bird
 assemblages exhibited phylogenetic and functional clustering in all
 landscape matrix types, which meant environmental filtering governed
 community assembly. Landscape attributes of different matrix types
 influenced phylogenetic and functional diversity of bird communities.
 Landscape diversity affected bird functional structure positively, and the
 mean of patch area in the landscape had the strongest explanatory effects.
 To prevent bird strikes, we suggest that more small-area woodlands with
 low levels of aggregation can be considered and farmlands should be
 patchier and more regular. At landscape level, the number of patches can
 be adjusted to reduce the frequency of bird activities.
description: Wavelet analysis, a spectral decomposition technique, was used to
 determine the key spatial scale that was important to quantify the
 relative influence of matrix types on species' distributions. We
 generated 1, 000 random points within 8 km radius extent from the NLIA and
 extracted their land-cover types. Then we ran the wavelet analysis with
 MATLAB 9.9 (MathWorks, Natick, Massachusetts;
 https://ww2.mathworks.cn/products/matlab.html). The result showed that a
 288-m radius from the center of each point was appropriate. We then
 randomly selected 9 sampling points in farmlands, 7 in wetlands, 5 in
 woodlands and 4 in urban areas (25 points in total) to make sure that
 sampling effort on each matrix type was roughly proportional to its area.
 Sampling points were at least 576m apart. From November 2017 to October
 2019, we carried out point-count bird surveys monthly. Each observation
 was in 20 minutes. We usually surveyed birds on sunny and windless days.
 We recorded all birds seen or heard and excluded fly-overs to obtain the
 birds data. Considering that the combination of functional groups and
 landscape matrices enables a comprehensive assessment of bird diversity,
 we chose three main functional groups based on the traits data collected
 by Wang et al. 2021. We cut the global phylogenetic
 tree of birds by subsampling 5,000 'Hackett All species: a set of
 10,000 trees with 9993 OTUs each' trees from BirdTree
 (http://birdtree.org). Then we constructed a new maximum clade credibility
 tree with 0.5 posterior probability limit by the software TreeAnnonator
 v1.10.4 in the BEAST package v1.10.4. We focused on 4 kinds of functional
 traits relevant ecologically to bird strikes: morphological
 characteristics (body length, bill length, wing length, tarsus length and
 body mass), nest sites (ground, water, shrub, canopy and wall), feeding
 behaviors (carnivores, insectivores, omnivores, granivores and piscivores)
 and foraging strata (below water surface, ground, understory and midhigh).
 Morphological characteristics were continuous values and others were
 treated as binary traits.  Based on the new
 phylogenetic tree constructed, we calculated Faith's phylogenetic
 distance (Faith's PD) to describe the total sum of phylogenetic
 history. We also calculated mean pairwise distance (MPD) and mean nearest
 taxon distance (MNTD) to represent phylogenetic structure. We computed
 functional richness (FRic) and functional dispersion (FDis) to represent
 functional diversity and structure. All metrics were calculated using R
 package 'picante' and 'FD'. Species richness (SR),
 MPD, MNTD and FDis of functional groups were also calculated to evaluate
 phylogenetic and functional structure of different functional
 groups. We calculated landscape metrics of the class
 level and landscape level using R package 'landscapemetrics'. At
 the class level, number of patches, edge density, mean of patch area, mean
 perimeter-area ratio, mean fractal dimension index and aggregation index
 of each matrix type were calculated. At the landscape level, number of
 patches, mean of patch area, effective mesh size, mean fractal dimension
 index, mean perimeter-area ratio and Shannon's diversity index were
 calculated.
description: The data files included with our submission can be opened with R
 or RStudio for further analysis.