DOI: 10.5061/dryad.5dv41nsf9
title: Drivers of interspecific spatial segregation in two closely related seabird species at a pan-Atlantic scale

description: Aim. Ecologically similar species living in sympatry are expected to
 segregate to reduce the effects of competition where such resources are
 limiting. Segregation from heterospecifics commonly occurs in space, but
 it is often unknown whether segregation has underlying environmental
 causes. Indeed, species could segregate because of different fundamental
 environmental requirements (i.e., ‘niche divergence’), because competitive
 exclusion at sympatric sites can force species to either change the
 habitat use they would have at allopatric sites (i.e., ‘niche
 displacement’) or to avoid certain areas, independently of habitat (i.e.,
 ‘spatial avoidance’). Testing these hypotheses requires the comparison
 between sympatric and allopatric sites. Understanding the competitive
 mechanisms that underlie patterns of spatial segregation could improve
 predictions of species responses to environmental change, as competition
 might exacerbate the effects of environmental change. Location. North
 Atlantic and Arctic. Taxa. Common guillemots Uria aalge and Brünnich’s
 guillemots Uria lomvia. Methods. Here, we examine support for these
 explanations for spatial segregation in two closely-related seabird
 species, common guillemots (Uria aalge) and Brünnich’s guillemots (U.
 lomvia). For this, we collated a pan-Atlantic data set of breeding season
 foraging tracks from 1046 individuals, collected from 20 colonies (8
 sympatric and 12 allopatric). These were analysed with habitat models in a
 spatially transferable framework to compare habitat preferences between
 species at sympatric and allopatric sites. Results. We found no effect of
 the distribution of heterospecifics on local habitat preferences of the
 focal species. We found differences in habitat preferences between
 species, but these were not sufficient to explain the observed levels of
 spatial segregation at sympatric sites. Main conclusions. Assuming we did
 not omit any relevant environmental variables, these results suggest a mix
 of niche divergence and spatial avoidance produces the observed patterns
 of spatial segregation.
description: The data consists in common guillemots (<em>Uria
 aalge</em>) and Brünnich’s guillemots (<em>U.
 lomvia</em>) GPS tracking data from multiple colonies. Details on
 data collection methodologies are available in the associated publication
 in Journal of Biogeography (https://doi.org/10.1111/jbi.15042).
description: # Drivers of interspecific spatial segregation in two closely related
 seabird species at a pan-Atlantic scale
 [https://doi.org/10.5061/dryad.5dv41nsf9](https://doi.org/10.5061/dryad.5dv41nsf9) Datasets and codes for running the analyses in [https://doi.org/10.1111/jbi.15042](https://doi.org/10.1111/jbi.15042). Common guillemots (*Uria aalge*) and Brünnich's guillemots (*U. lomvia*) were fitted with GPS loggers at 20 colonies across the North Atlantic and Arctic during the breeding season to study at-sea foraging habitat use. Overlap analyses were run to estimate the levels of spatial segregation between the two species at sympatric sites. Habitat models were run to evaluate the habitat preferences of each species separately, and test three hypotheses to explain the observed spatial segregation: niche divergence (different fundamental environmental requirements for the two species), niche displacement (competitive exclusion at sympatric sites forcing species to change the habitat use they would display at allopatric sites) and spatial avoidance (competitive exclusion at sympatric sites forcing species to avoid certain areas, independently of habitat). ### **Description of the data and file structure** #### **Data** ###### GPS data All files with names of the form "...GPS_data_formatted.csv", with columns: ring_no (individual ID), site (breeding colony), year, species (latin name), stage (at deployment, incubation or chick-rearing), stage_rec (at recapture, when available), dep_date_time (deployment date and time, when available), rec_date_time (recapture date and time, when available), longitude, latitude, col_lon (colony longitude), col_lat (colony latitude). Data used throughout the manuscript. To use these data in further analyses, please **contact the corresponding data owners** (list available in data_owners.xlsx). ###### Other data Colony count data: "colony_sizes.csv", with columns: full_name (name of the colony), site (abbreviated name), tbmu_pairs (number of Brünnich's guillemots pairs), comu_pairs (number of common guillemots pairs), country, col_lon (colony longitude), col_lat (colony latitude). Data used to model the effect of the number of pairs on distance from the colony and the effect of heterospecifics on habitat selection. Coastal shapefiles: "gsshg_north_atlantic" and "high_resolution_north_atlantic" files. Data used for mapping and for calculating distance to the coast. Environmental rasters: "composite_sst_2015.tif" provided as an example to run the R scripts. Other environmental datasets are not provided here, but download links are available in Table 1 of [https://doi.org/10.1111/jbi.15042](https://doi.org/10.1111/jbi.15042). #### **R codes** Run script "00_main_habitat_model_2023.R" to run the whole analysis. * Data preparation (separation of foraging bout, pseudo-absences simulation, calculation and extraction of environmental variables, ...): scripts "01_speed_classif_final_2023.R" to "07_stage_regression_2023.R". * Habitat models: "08_forward_selection_cross_validation_loop_2023.R". * Overlap analysis (linear interpolation of locations within foraging bouts, kernel overlap analysis): "09_interpolation within foraging bouts 2023.R" and "10_kernel overlap linear interpolation by year 2023.R". * Estimation of sample sizes: "11_sample_sizes_2023.R" and "12_number_of_bouts_locs_2023.R". * Figures: all scripts in subfolder "FINAL_FIGURES/". ### **Associated funding** Funding for the overall project (LOMVIA) and for data collection in Iceland was provided by UKRI/NERC in the UK (NE/R012660/1) and BMBF (Deutsche Forschungsgemeinschaft 03V01459) in Germany, with contributions from the Max Planck Society. Research at the Gannet Islands, Canada, was supported by Environment and Climate Change Canada, the Murre Fund, the Natural Sciences and Engineering Research Council, and Acadia University. Fieldwork at the colony at Sklinna and Hornøya was part of the SEAPOP programme ([www.seapop.no/](http://www.seapop.no/) en), which is financed by the Norwegian Ministry of Climate and Environment via the Norwegian Environment Agency, the Norwegian Ministry of Petroleum and Energy via the Norwegian Research Council (grant 192141), and the Norwegian Oil and Gas Association (now Offshore Norge). Data collection in Greenland was supported by the Greenland Government. Data collection on the Isle of May was funded by the FAME/STAR project and the Natural Environment Research Council.