DOI: 10.5061/dryad.bzkh189r4
title: Functional diversity in agricultural landscapes: evidence of long-term clustering and multi-scale effects of land use on avian communities

description: Functional diversity (FD) is an essential community property connecting
 biodiversity, ecosystem functioning, and conservation objectives. In
 agricultural landscapes, avian communities, which play key functional
 roles, are facing large-scale biodiversity erosion, largely due to
 land-use changes. However, the long-term spatiotemporal dynamics of FD and
 its responses to land use remain poorly understood. This study addressed
 this gap by analysing bird community data collected from lowland
 agricultural landscapes at a regional scale between 2007 and 2021. We
 analysed temporal trends in FD metrics (richness, divergence, evenness),
 their relationships with species richness, changes in functional
 composition, patterns of clustering/overdispersion, and examined the
 multi-scale effects of land use across a local-to-landscape gradient. FD
 metrics were computed using a probabilistic hypervolume approach and
 standardised against null expectations. Cause-effect relationships were
 explored using Generalized Additive Models. Results revealed a decline in
 FD metrics, despite recent recoveries in richness and divergence. Species
 richness increased linearly and negatively influenced FD metrics. These
 findings, coupled with evidence of functional clustering, suggested a loss
 of functions and originality in the functional space, an increase of
 redundancy, and a shift towards traits associated with forest-dwelling,
 larger-bodied, or omnivorous species. Land use was found to affect FD,
 likely acting as an environmental filter promoting trait clustering.
 Built-up areas exerted a detrimental effect on all FD metrics at smaller
 scales, while arable land increased FD across all scales. Woody features
 increased FD primarily at the landscape level, while hedgerows showed a
 reversed effect on evenness. Meadows and shrubs were important to sustain
 functional divergence at the local scale. Wetlands increased functional
 richness and divergence without affecting evenness. These findings raise
 important questions on the relationships between FD and species richness,
 emphasise the value of long-term data, and highlight the benefit of a
 multi-scale spatial approach for guiding land-use planning and
 biodiversity conservation.
description: # Functional diversity in agricultural landscapes: evidence of long-term
 clustering and multi-scale effects of land use on avian communities
 Dataset DOI:
 [10.5061/dryad.bzkh189r4](https://doi.org/10.5061/dryad.bzkh189r4) ##
 Description of the data and file structure Bird data included in these
 datasets were derived from the Long-term Monitoring Program of Breeding
 Birds of Lombardy using data collected between 2007 and 2021 (for details,
 see Bani et al., 2009,
 [https://doi.org/10.1080/08927014.2009.9522509](https://doi.org/10.1080/08927014.2009.9522509); Tirozzi et al., 2021, [https://doi.org/10.3390/ani11123426](https://doi.org/10.3390/ani11123426)). Species traits were compiled using information from Pearman et al. 2014, [https://doi.org/10.1111/geb.12127](https://doi.org/10.1111/geb.12127); Wilman et al. 2014, [https://doi.org/10.1890/13-1917.1](https://doi.org/10.1890/13-1917.1); Storchová & Hořák 2018, [https://doi.org/10.1111/geb.12709](https://doi.org/10.1111/geb.12709); Tobias et al. 2022, [https://doi.org/10.1111/ele.13898](https://doi.org/10.1111/ele.13898)). For further details see the associated article. ### Files and variables #### File: Dfix.RData **Description:** The dfix dataset covers a 15-years sampling period (2007-2021) and includes fixed point counts with at least five years of repeated samplings. R workspace containing the following objects: 1. commun = dataframe of the avian community matrix (observation IDs in rows, species EURING codes in columns, where E = EURING, and the five-digit number identifies the unique code assigned to each species. EURING is the coordinating organisation for European bird ringing schemes. Species are codified using standardised codes; for further details and for correspondence between codes and species names visit https://euring.org/data-and-codes/euring-codes). Bird abundances are reported as the number of breeding pairs (see the associated article for details) 2. dat = dataframe of survey data: * ID: observation ID; a unique code for each observation collected at a specific site in a specific year * WGS84_long: longitude of the observation (WGS 84 Datum, EPSG 4326) * WGS84_lat: latitude of the observation (WGS 84 Datum, EPSG 4326) * site: point count ID identifying spatially distinct point counts * Group: primary sampling unit ID grouping point counts that fall within the same sampling unit  * Year: year of sampling 3. dat_FD = dataframe resulting from the analyses, containing the output data from section 1 of the R script FuncDiv_script.R 4. tbl_site = dataframe of no. of surveys per each site (rows) and year (columns) 5. traits.sel2 = species x traits matrix. A detailed explanation of the traits is provided in the Supporting Information S14 of the associated article and in the READMI for the file "traits_dfix.csv" 6. alpha_perm_trend = simulated functional richness under random expectation given species richness. These data were produced using the HPC Leonardo [https://leonardo-supercomputer.cineca.eu/](https://leonardo-supercomputer.cineca.eu/) * ID: observation ID; a unique code for each observation collected at a specific site in a specific year * the other columns (sim.x) represent each of the 1000 simulated values, where x is the index of the corresponding simulation 7. div_perm_trend = simulated functional divergence under random expectation given species richness. These data were produced using the HPC Leonardo [https://leonardo-supercomputer.cineca.eu/](https://leonardo-supercomputer.cineca.eu/) * ID: observation ID; a unique code for each observation collected at a specific site in a specific year * the other columns (sim.x) represent each of the 1000 simulated values, where x is the index of the corresponding simulation 8. eve_perm_trend = simulated functional evenness under random expectation given species richness. These data were produced using the HPC Leonardo [https://leonardo-supercomputer.cineca.eu/](https://leonardo-supercomputer.cineca.eu/) * ID: observation ID; a unique code for each observation collected at a specific site in a specific year * the other columns (sim.x) represent each of the 1000 simulated values, where x is the index of the corresponding simulation 9. lab_CWM_plot = dataframe to be used for generating Fig. 4. * ori: original name of the trait derived from the model output * trait_title: title for each subplot * trait_group: traits to be used for assigning colours in the plot #### File: Dfix_ran.RData **Description:** The dfix_ran dataset covers a 15-years sampling period (2007-2021) and includes both fixed and non-permanent point counts. R workspace containing the following objects: 1. commun: dataframe of the avian community matrix (observation IDs in rows, species EURING codes in columns, where E = EURING, and the five-digit number identifies the unique code assigned to each species. EURING is the coordinating organisation for European bird ringing schemes. Species are codified using standardised codes; for further details and for correspondence between codes and species names visit https://euring.org/data-and-codes/euring-codes). Bird abundances are reported as the number of breeding pairs (see the associated article for details) 2. traits.sel2 = species x traits matrix. A detailed explanation of the traits is provided in the Supporting Information S14 of the associated article and in the READMI for the file "traits_dfix.csv" 3. dat_FD = dataframe resulting from the analyses described in the R script FuncDiv_script.R, containing the output data and the variables required to perform land-use models. * ID: observation ID; a unique code for each observation collected at a specific site in a specific year * SES_rich: standardised effect size for functional richness (numeric) * SES_div: standardised effect size for functional divergence (numeric) * SES_eve: standardised effect size for fucntional evenness (numeric) * fric_obs: observed functional richness (numeric) * fdiv_obs: observed functional divergence (numeric) * feve_obs: observed functional evenness (numeric) * p.lower.rich_01: presence/absence of functional clustering for functional richness. 0 if absent, 1 if present * p.lower.div_01: presence/absence of functional clustering for functional divergence. 0 if absent, 1 if present * p.lower.eve_01: presence/absence of functional clustering for functional evenness. 0 if absent, 1 if present * WGS84_lat:  latitude of the observation (WGS 84 Datum, EPSG 4326) * WGS84_long:  longitude of the observation (WGS 84 Datum, EPSG 4326) * Year: year of sampling * Land-use variables were calculated as percentage of cover within a specified buffer around the point count. Below, the explanation of abbreviations and acronyms: * built: built-up areas * orch: orchards * woodycrops: timber plantations * fk: hedgerows' density * meadows: meadows * evo_shurbs: shrubs * forest: forests * water_wet: water bodies and wetlands * arable: arable land * rice: rice fields * b100: 100-m buffer radius * b500: 500-m buffer radius * b2000: 2000-m buffer radius #### File: Dfix_hv.list.RData **Description:** Dfix_hv.list.RData: R workspace containing the list of hypervolumes for each observation in the dataset Dfix. Names of the hypervolumes correspond to the observation IDs.  #### File: Dfix_ran_hv.list.RData **Description:** Dfix_ran_hv.list.RData: R workspace containing the list of hypervolumes for each observation in the dataset Dfix_ran. Names of the hypervolumes correspond to the observation IDs. #### File: FuncDiv_script.R **Description:** R script to run the analyses. All instructions and explanations are contained within the script. #### File: commun_dfix.csv **Description**: Community matrix for the dfix dataset. The dfix dataset covers a 15-years sampling period (2007-2021) and includes fixed point counts with at least five years of repeated samplings. * ID: observation ID; a unique code for each observation collected at a specific site in a specific year * All the other columns represent the EURING code for avian species (E = EURING, the five-digit number identifies the unique code assigned to each species. EURING is the coordinating organisation for European bird ringing schemes. Species are codified using standardised codes; for further details and for correspondence between codes and species names visit https://euring.org/data-and-codes/euring-codes). Bird abundances are reported as the number of breeding pairs (see the associated article for details) #### File: commun_dfix_ran.csv **Description**: Community matrix for the dfix_ran dataset. The The dfix_ran dataset covers a 15-years sampling period (2007-2021) and includes both fixed and non-permanent point counts. * ID: observation ID; a unique code for each observation collected at a specific site in a specific year * All the other columns represent the EURING code for avian species (E = EURING, the five-digit number identifies the unique code assigned to each species. EURING is the coordinating organisation for European bird ringing schemes. Species are codified using standardised codes; for further details and for correspondence between codes and species names visit https://euring.org/data-and-codes/euring-codes). Bird abundances are reported as the number of breeding pairs (see the associated article for details) #### File: traits_dfix.csv **Description:** Species x traits matrix for the dfix dataset. For further information on traits see Supporting Information S14 in the associated article. * Euring: EURING code for avian species (E = EURING, the five-digit number identifies the unique code assigned to each species. EURING is the coordinating organisation for European bird ringing schemes. Species are codified using standardised codes; for further details and for correspondence between codes and species names visit https://euring.org/data-and-codes/euring-codes) * Weight: mean weight in breeding season, unsexed (g) (numerical trait) * Clutch: mean clutch size, i.e. number of eggs (numerical trait) * Broods: mean number of broods per breeding season (numerical trait) * HWI: Hand-Wing Index, i.e. the Kipps' distance corrected fro wing size (numerical trait)  * Hab: main habitat where the species lives; categorical trait with six levels * Diet: main source of foods on which the species feeds on; categorical trait with five levels * Nest: nesting habits in relation to types of location where birds build their nests; categorical trait with five levels * FB_Sa: foraging behaviour "sally"; categorical binary trait; 0 if the characteristic is absent, 1 if it is present * FB_Fg: foraging behaviour "foliage glean"; categorical binary trait; 0 if the characteristic is absent, 1 if it is present * FB_Pp: foraging behaviour "pursuit/pounce"; categorical binary trait; 0 if the characteristic is absent, 1 if it is present * FB_Gpdop: foraging behaviour "graze/pick-peck-stab/dig/overturn object/probe"; categorical binary trait; 0 if the characteristic is absent, 1 if it is present * FS_Ca: foraging substrate "canopy"; categorical binary trait; 0 if the characteristic is absent, 1 if it is present * FS_Sh: foraging substrate "shrub.low.high"; categorical binary trait; 0 if the characteristic is absent, 1 if it is present * FS_Ve: foraging substrate "vegetation"; categorical binary trait; 0 if the characteristic is absent, 1 if it is present * FS_Ai: foraging substrate "air"; categorical binary trait; 0 if the characteristic is absent, 1 if it is present * FS_Wa: foraging substrate "water"; categorical binary trait; 0 if the characteristic is absent, 1 if it is present * FS_Gm: foraging substrate "ground/mud"; categorical binary trait; 0 if the characteristic is absent, 1 if it is present #### File: traits_dfix_ran.csv **Description**: Species x traits matrix for the dfix_ran dataset. For the content of the dataset, refer to the file "traits_dfix.csv". For further information on traits see Supporting Information S14 in the associated article. ## Code/software Analyses were run using R version 4.3.2 (R Core Team, 2023, [https://www.R-project.org/](https://www.r-project.org/)) and the HPC Leonardo ([https://leonardo-supercomputer.cineca.eu/](https://leonardo-supercomputer.cineca.eu/))