DOI: 10.5061/dryad.r4xgxd2n2
title: Landscape heterogeneity increases bird functional diversity within Neotropical vineyards

description: Conversion of lands to agroecosystems has resulted in a decline in bird
 biodiversity. Analyzing functional diversity is a central tool for
 detecting changes in the ecological functions performed by birds in these
 landscapes. This paper aims to investigate the responses of bird taxonomic
 and functional diversity to landscape heterogeneity and native forest
 cover in Neotropical vineyards. We sampled 19 vineyard landscapes in
 southeastern Brazil. These landscapes covered a gradient of forest cover
 and heterogeneity resulting from various land uses. To assess bird
 diversity, we considered both taxonomic diversity and functional diversity
 (i.e., functional richness, evenness, and divergence). To examine the
 potential interactions between landscapes and bird assemblages, we
 employed generalized linear models (GLM). Taxonomic diversity showed no
 correlation with any landscape metrics. On the other hand, variation in
 the three metrics of functional diversity was related to landscape
 heterogeneity. However, in heterogeneous landscapes, these communities can
 be structured by limiting similarity processes. We highlight the impact of
 landscape homogenization on the ecological functions performed by birds in
 vineyards while finding no significant effect on species diversity. These
 findings can provide valuable support for the formulation of public
 policies aimed at striking a balance between agricultural production and
 biodiversity conservation.
description: A. Landscape metrics Fieldwork was conducted
 in a rural area of ~40,000 ha in São Miguel Arcanjo, a significant region
 for grape production in Brazil. The study areas were carefully chosen
 based on high-resolution images obtained from ArcGIS 10.3 base map
 imagery, which was captured by DigitalGlobe satellites in 2016 with a
 scale of 1:5000 and a resolution of 0.5 m2. Seven distinct land-use
 classes (remnants of Atlantic Forest, regenerating forests, Eucalyptus
 plantations, grape plantations, other agricultures, open areas, and urban
 areas) were manually delineated using ArcGIS (ESRI) within the study area.
 To ensure accuracy, field validation was conducted in 2016 and 2017, and
 any errors in interpretation were rectified. For each polygon identified
 as a grape plantation, a central point within the vineyards was selected,
 and buffers with a radius of 100, 250, 500, 750, and 1,000 m were
 established around it. Nineteen sampling sites located at least 1,000 m
 from each other were selected using FRAGSTATS v4.2.1 with a forest cover
 percentage ranging from 18% to 55% and environmental heterogeneity
 assessed by Shannon's diversity index (SHDI) within a range of 0.95
 to 1.78. B. Bird Surveys Bird
 surveys were conducted during the grape harvest period, from January to
 April 2018. Four points were established at each study area (1,000-m
 radius buffer), ensuring a minimum distance of 200 m between each point
 and the center. To assess bird communities, we employed 50-m 10-min
 fixed-radius point counts at each point. These counts were carried out
 four times per area (40-min), spanning four different days in the mornings
 (between 6 am and 10 am). This resulted in 160 minutes of bird counts
 conducted at each area (i.e. 1,000 m radius buffer) and a cumulative
 duration of 3,040 minutes across all 19 sampled areas. All visually and
 acoustically detected birds were recorded, except those flying over. The
 abundance was determined by aggregating the total number of contacts for
 each species and dividing by 16, representing the average of four daily
 repetitions across each of the four sampling points within a
 landscape. C- Data analysis (all references in the
 original Manuscript) We used species richness and
 abundance as measures of TD. Functional traits that may indicate
 ecological functional diversity; Luck et al. 2012) were applied to
 characterize the functional structure (i.e., the distribution of species
 and their abundance in the space they occupy; Villéger et al. 2008). We
 considered traits related to seven categories to calculate the components
 of FD, which include the ability of resource use, diet, foraging stratum,
 foraging strategy, reproductive strategy (nest location), migratory
 status, and activity period (Table S1). These trait categories were
 selected based on previous studies by Barbaro et al. (2017), Luck et al.
 (2012, 2015), and information from Wilman et al. (2014), Del Hoyo et al.
 (2019), and BirdLife International (2019). The Gower
 distance (Gower 1971) was used to create a distance matrix from the
 trait's matrix using the ade4 package (Dray & Dufour 2007,
 Thioulouse et al. 2018). Subsequently, we calculated FD indexes according
 to the equations proposed by Villeger et al. (2008), using the FD package
 (Laliberté & Legendre 2010; Laliberté et al. 2015) in R v.3.5.1 (R
 Core Team 2018). Complementary indexes were employed to better represent
 the community by capturing its entire functional structure (Barbaro et al.
 2017). These indexes include functional richness (FRic), functional
 evenness (FEve), and functional divergence (FDiv) (Laliberté &
 Legendre 2010; Mason et al. 2005; Petchey & Gaston 2002; Villéger
 et al. 2008). FRic is the convex hull volume of the
 functional trait space, summarized by a principal coordinate analysis
 (Laliberté et al. 2015). This concept refers to the diversity of functions
 performed by various species within an ecosystem, representing the
 extensive array of roles and interactions these species play in a
 biological community. Theoretically, the greater the FRic, the more
 resilient and stable an ecosystem is, as the loss of a species can be
 compensated by others that perform similar functions. FEve refers to the
 uniformity in the distribution of the functional contributions of
 different species, assessing whether they perform similar functions in
 terms of their abundance or relative importance within an ecosystem. When
 FEve is high, their functions are well distributed and balanced,
 increasing stability. However, when FEve is low, some species may dominate
 and perform more important functions, increasing the vulnerability of the
 ecosystem. FDiv assesses the trait abundance distribution within this
 volume and increases with extreme trait values. It’s a more comprehensive
 measure that considers not only the number of functions, but also the
 variety and distribution of these functions in the ecosystem, assessing
 how different functions are distributed among species, and considering
 whether they overlap or are complementary (Mason et al. 2005; Villéger et
 al. 2008; Laliberté & Legendre 2010). Potential
 influences of species richness on some FD metrics were controlled using
 standardized effect sizes (SES) (Mason et al. 2013). For this purpose, we
 employed a simulation approach to create a null model with expected values
 by chance. This involved maintaining the number of species constant and
 randomizing the abundances among species, which generated 999 random
 communities per site (Mason et al. 2013). Subsequently, we calculated the
 FD metrics and used the means and standard deviations to compute the
 standard effect size (SES) for each metric, following the formula SES
 (observed values - mean of expected values) / standard deviation of
 expected values (Gotelli & McCabe 2002). We tested the
 significance of SES values using the p-value of a one-tailed z-score (less
 or greater depending on the SES value). Negative values of SES indicate
 that the observed metrics are less than expected by chance, suggesting
 stronger environmental filtering and greater species similarity within the
 community. Conversely, positive SES values indicate that the observed
 metrics are higher than expected by chance, suggesting greater niche
 complementarity and a lower level of similarity within the community
 (Petchey & Gaston 2007). Linear models with a
 Gaussian error distribution were conducted to analyze the impacts of
 forest cover and landscape heterogeneity on TD metrics, FD metrics, and
 standardized effect sizes for FD metrics. We used a full model structure
 with heterogeneity, forest cover, and their interaction, a model structure
 without the interaction, and simple models in which only heterogeneity or
 forest cover was used to explain diversity metrics. To determine the
 significance of each variable, we compared models and assessed the
 goodness of fit using the likelihood-ratio test (Quinn & Keouh
 2002). In addition, we examined the relationships between TD and FD
 metrics through Pearson correlations. To further understand the process of
 bird community assembly, we compared observed values of FD and expected
 values using the Wilcoxon test for paired samples. The comparison involved
 contrasting the observed values of FD with the mean expected values
 obtained from 999 randomizations. We also examined the relationship
 between expected values of FD and species richness using simple linear
 regression (SLR). To test the spatial autocorrelation
 of our results, we calculated Moran’s I for landscape predictors,
 diversity metrics, and model residuals using the spdep package (Bivand
 & Wong 2018).
description: # Landscape heterogeneity increases bird functional diversity within
 Neotropical vineyards
 [https://doi.org/10.5061/dryad.r4xgxd2n2](https://doi.org/10.5061/dryad.r4xgxd2n2) The dataset has landscape metrics (1), including forest cover and landscape heterogeneity in buffers of 100, 250, 500, 700, and 1000 m, and raw data from bird count points (2) around 19 vineyards in southeastern Brazil. ## Description of the data and file structure ### **Identity: (1) LandscapeMetrics.csv, (2) Birds\_CountPoints.csv** (1) 20 records (including header) and 11 fields. The total file size is 2 kb. (2) 150 records (including header) and 20 fields. The total file size is 9 kb. ## **Variable information** ### **Table 1. Summary of variable information for file (1) – Landscape metrics** | **Variable** | **Description** | **Type** | | :----------- | :-------------------------------------------------------------------------------------- | :----------- | | Point | Each of the 19 study areas (central point within the vineyards) | Alphanumeric | | pct100 | Proportion of forest cover in a buffer with a radius of 100 m around the center point | Numeric | | pct250 | Proportion of forest cover in a buffer with a radius of 250 m around the center point | Numeric | | pct500 | Proportion of forest cover in a buffer with a radius of 500 m around the center point | Numeric | | pct750 | Proportion of forest cover in a buffer with a radius of 750 m around the center point | Numeric | | pct1000 | Proportion of forest cover in a buffer with a radius of 1000 m around the center point | Numeric | | het100 | Environmental heterogeneity in a buffer with a radius of 100 m around the center point | Numeric | | het250 | Environmental heterogeneity in a buffer with a radius of 250 m around the center point | Numeric | | het500 | Environmental heterogeneity in a buffer with a radius of 500 m around the center point | Numeric | | het750 | Environmental heterogeneity in a buffer with a radius of 750 m around the center point | Numeric | | het1000 | Environmental heterogeneity in a buffer with a radius of 1000 m around the center point | Numeric | ### Table 2. Summary of variable information for file (2) – Bird count points | Variable | Description | Type | | :------- | :---------------------------------------------------------- | :-------- | | Species | Bird species  | Character | | P01 | Total count of each bird species recorded in study area P01 | Integer | | P02 | Total count of each bird species recorded in study area P02 | Integer | | P03 | Total count of each bird species recorded in study area P03 | Integer | | P04 | Total count of each bird species recorded in study area P04 | Integer | | P05 | Total count of each bird species recorded in study area P05 | Integer | | P06 | Total count of each bird species recorded in study area P06 | Integer | | P07 | Total count of each bird species recorded in study area P07 | Integer | | P08 | Total count of each bird species recorded in study area P08 | Integer | | P09 | Total count of each bird species recorded in study area P09 | Integer | | P10 | Total count of each bird species recorded in study area P10 | Integer | | P11 | Total count of each bird species recorded in study area P11 | Integer | | P12 | Total count of each bird species recorded in study area P12 | Integer | | P13 | Total count of each bird species recorded in study area P13 | Integer | | P14 | Total count of each bird species recorded in study area P14 | Integer | | P15 | Total count of each bird species recorded in study area P15 | Integer | | P16 | Total count of each bird species recorded in study area P16 | Integer | | P17 | Total count of each bird species recorded in study area P17 | Integer | | P18 | Total count of each bird species recorded in study area P18 | Integer | | P19 | Total count of each bird species recorded in study area P19 | Integer | ## Sharing/Access information NA ## Code/Software NA