DOI: 10.5061/dryad.fn2z34v1n
title: Invasion dynamics of the European Collared-Dove are explained by combined effects of habitat and climate

description: Global biodiversity is increasingly threatened by the spread of invasive
 species. Understanding the mechanisms influencing the initial colonization
 and persistence of invaders is therefore needed if conservation actions
 are to prevent new invasions or strive to slow their spread. The Eurasian
 collared-dove (Streptopelia decaocto, EUCO) is one of the most successful
 avian invasive species in North America; however, to our knowledge, no
 study has simultaneously examined the role that climate-matching, human
 activity, directional propagation, and local density have in this invasion
 process. Our research expands upon a cellular-automata-based hierarchical
 model developed to assess directional invasion dynamics to further
 quantify the impacts of climate, elevation, and land cover type on the
 spread of EUCO in North America. Our results suggest that EUCO’s dispersal
 patterns can largely be explained by the effects of habitat, climate, and
 environmental conditions at different stages of the invasion process
 rather than some innate preferred north-westerly spread. Specifically,
 EUCO initially colonized warm and wet grassland habitats and tended to
 persist in urban areas. We also found that while EUCO were more likely to
 spread to the northeast of existing habitats, directional preference did
 not drive persistence and recolonization events. These findings highlight
 the importance of incorporating both neighbourhood effects and
 environmental factors in the modelling of range-expanding species, adding
 to the toolset available to researchers to model invasive species spread.
 Further, our research demonstrates that historical records of invasive
 species occurrences from citizen science projects can provide the data
 resources needed to disentangle the characteristics driving species
 invasion and enable predictions that are of critical importance to
 resource managers.
description: <strong>Dataset Collection:</strong>
 The primary dataset in this study consists of detection records
 of Eurasian collared doves (Streptopelia decaocto) spanning from 1988 to
 2021 in North America. These records were sourced from the Project
 FeederWatch database. Accompanying these records are the total number of
 bird count surveys conducted over this period, enriched with geographic
 and environmental descriptors of each location.     
 <strong>Processing and Gridding:   
  </strong> Temporal Grouping: The detection
 records were grouped into annual recordings.   Spatial
 Gridding: Detection records were aligned using the grid cell structure
 adopted by the Christmas Bird Count (CBC).   
 Filtering: We filtered out grid cells that had no surveys, and
 cells that were predominantly water-covered (&gt; 50% of their area).
      <strong>Environmental Descriptors:   
  </strong> Climate Data: Average temperature and
 precipitation data were integrated from ScienceBase North America Climate
 data (1950–2000).   Elevation: Elevation data was
 sourced from ScienceBase North America Elevation data.  
 Land Cover: Land cover descriptors, which included forest, shrub,
 crop, urban, and other land cover types, were integrated using the North
 American Land Change Monitoring System (NALCMS) land cover map from
 2015.
description: # Data from: Invasion dynamics of the European Collared-Dove are explained
 by combined effects of habitat and climate [Access this dataset on
 Dryad](https://doi.org/10.5061/dryad.w0vt4b8z3) This dataset comprises
 Eurasian collared dove detection records from Project FeederWatch,
 enriched with geographic and environmental descriptors of each location.
 Our research expands upon a cellular-automata-based hierarchical model
 developed to assess directional invasion dynamics to quantifiably
 demonstrate how climate, elevation, and land cover impact the dove's
 spread. ## Description of the data and file structure ###
 Merged\_allCells\_obsCovLatlon\_1998-2021\_US&CA.csv 1. OBJECTID
 (int): A unique identifier representing the specific grid cell within the
 Christmas Bird Count (CBC) structure, enabling spatial referencing. 2.
 year (int): The year in which the observation was recorded, providing
 temporal context for the dove's presence. 3. eucdov (int): The number
 of survys with Eurasian collared doves observations records within the
 grid cell during the given year, forming the core of the dataset. 4.
 noEucdov (int): The total number of bird count surveys conducted within
 the grid cell during the specified year, serving as a measure of survey
 effort. 5. Elev\_Mean (float): The elevation of the grid cell's
 location, aiding in understanding altitudinal preferences of the species.
 6. Temp\_Mean (float): Average temperature recorded within the grid cell,
 contributing to the analysis of climatic influences on species
 distribution. 7. Prec\_Mean (float): Total precipitation measured within
 the grid cell, an important climatic factor impacting the dove's
 habitat. 8. forest\_per (float): Percentage of forest cover within the
 grid cell, representing a key land cover type affecting the species'
 habitat. 9. crop\_per (float): Percentage of crop-covered area within the
 grid cell, offering insights into anthropogenic landscape impacts. 10.
 shrub\_per (float): Percentage of shrub cover within the grid cell,
 contributing to the assessment of diverse habitat preferences. 11.
 urban\_per (float): Percentage of urbanized land within the grid cell,
 highlighting potential interactions with urban environments. 12.
 water\_per(float): Percentage of water body within the grid cell,
 supporting the removal and evaluation of cells covered mostly with water.
 13. other\_per (float): Percentage of other land cover types within the
 grid cell, providing a comprehensive view of habitat diversity. 14.
 centroid\_long (float): The longitude coordinate of the grid cell's
 center, complementing the latitude for accurate spatial referencing. 15.
 centroid\_lat (float): The latitude coordinate of the grid cell's
 center, aiding in precise geographic localization. ## Sharing/Access
 information Data was derived from the following sources: * [Project
 FeederWatch](https://feederwatch.org/explore/raw-dataset-requests/) *
 [ScienceBase North America Climate
 data](https://www.sciencebase.gov/catalog/item/4fb545b6e4b04cb937751d3d) *
 [ScienceBase North America Elevation
 data](https://www.sciencebase.gov/catalog/item/4fb5495ee4b04cb937751d6d) *
 [North American Land Change Monitoring System (NALCMS) land cover map
 2015](http://www.cec.org/north-american-environmental-atlas/land-cover-30m-2015-landsat-and-rapideye/) ## Code/Software ### NIMBLEcode\_fullModel\_forPredict.txt The main model in study, written in the BUGS language, which can be invoked and driven from R using the NIMBLE package. ### nimbleDriver\_FromRDS.R The R code used to drive the models in the study, reads `jags_data_file.RDS` as input which can be generated with `GenerateJAGSData.R` from `Merged_allCells_obsCovLatlon_1998-2021_US&CA.csv` and `N1ForAll.csv`. Package versions: * nimble\_0.13.1 ### GenerateJAGSData.R The R code used to filter and reformat the observation data into a list that is readable by the R driver code that runs the models. Package versions: * tidyverse\_2.0.0 ### N1ForAll.csv A table listing the cell neighbourhood relationships. Each row represents a cell, with its row number (first column) indicating its OBJECTID. Each row has 8 columns, indicating the OBJECTIDs of its 8 direct neighbouring cells. An NA entry in this table indicates that a cell has no neighbour in that specific direction. This occurs when a cell lies along the boundary of the study area or borders a body of water.