DOI: 10.5061/dryad.12jm63z8h
title: Data from: An uncomfortable neighborhood: presence evolution of two competing carnivores in north-eastern Italy

description: Within ecological communities, larger predators typically limit
 mesocarnivore populations. On a continental scale, this may be the case
 for the grey wolf (Canis lupus) over the golden jackal (Canis aureus) in
 Europe. North-eastern Italy represents one of the first areas in Western
 Europe to experience golden jackal colonization, followed by wolf
 recolonization. Since few studies have investigated the spatial
 relationship between these two wild canids, this work aimed to analyze
 their distribution dynamics and investigate whether the wolf and
 environmental factors may have influenced golden jackal distribution at a
 local scale. We used systematic and opportunistic data collected over 11
 years (2013-2023) to assess the presence of both species on a 10x10 km
 square grid system. A multinomial logistic mixed model (MLMM) was applied
 to test whether the study period and landscape metrics (terrain ruggedness
 and habitat fragmentation indices) influenced both species’ presence at a
 broad scale (i.e., square grid units). Generalized linear mixed models
 (GLMMs) were used to test whether the wolf and landscape metrics
 influenced jackal presence at a finer scale (i.e., jackal howling calling
 stations). Both species showed an increase in their former distribution,
 with a small growth in sympatric areas. Models revealed that jackals
 preferred less rugged and more fragmented areas typical of the lowland,
 whereas wolves preferred rugged terrain with extensive forested patches in
 the Alpine range. Furthermore, our results showed that, at a local scale,
 golden jackal presence was negatively related to wolf presence. This study
 provides further insights into the coexistence of these two competing wild
 canids, suggesting a potential top-down effect of the wolf on golden
 jackal colonization dynamics. However, the wolf influence may vary in
 intensity depending on environmental context, with a weaker effect in
 areas of higher human pressure, such as the lowlands.
description: # Data from: An uncomfortable neighborhood: presence evolution of two
 competing carnivores in north-eastern Italy ## Description of the data and
 file structure This dataset contains the data and code required to
 replicate analyses in Frangini et al. (2025) ### Files and variables ####
 File: MLMM_dataset.csv **Description:** The dataset used to perform the
 MLMM as described in the manuscript. Each row refers to a single grid
 square for a specific year. This .csv file is semicolon-delimited with a
 comma decimal. ##### Variables * ID: the numeric ID of the grid square. *
 Species_c1: the presence category for that grid square in a specific year,
 based on C1 data: "Jackal" or "Wolf", when one of the
 two species has been found; "Sympatry", when both species have
 been found; "Absence", when neither jackal nor wolf has been
 found.  * LDI: the mean value of Landscape Division Index inside the grid
 square. * ENN: the mean Euclidean Nearest Neighbor distance among urban
 patches inside the grid square.  * TRI: the mean value of Terrain
 Ruggedness Index inside the square grid.  * Area: the biogeographical area
 where the grid square lies; i.e., within the Alpine or Lowland portion of
 the study area. Within the dataset, we mistakenly left the Italian terms:
 "Alpi" means Alps, and refers to the Alpine part of the study
 area, while "Pianura" means lowland and refers to the lowland
 part of the study area. * Year: the sampling year, between 2013 and 2023. 
 * NP: the number of urban patches inside the grid square.  * Pland: the
 percentage of urban patches inside the grid square. #### File: GLMM.csv
 **Description:** The dataset used to perform the GLMMs as described in the
 manuscript. Each row refers to a golden jackal howling stimulation in a
 given year. This .csv file is semicolon-delimited with a comma decimal.
 ##### Variables * Jackal_presence: the golden jackal status at each
 calling station, present or absent.  * ID_loc: numerical identifier of the
 jackal howling calling station * ENN: the mean Euclidean Nearest Neighbor
 distance among urban patches * NP: the number of urban patches inside the
 2km buffer around the calling station.  * Pland: the percentage of urban
 patches inside the 2km buffer around the calling station.  * LDI: the mean
 value of Landscape Division Index inside the 2km buffer around the calling
 station.  * TRI: the mean Terrain Ruggedness Index value inside the 2km
 buffer around the calling station.  * Zona: the biogeographical area of
 the study area, i.e., lowland or alpine * n_wolves: the number of grey
 wolf observations collected inside the 2km buffer around each calling
 station.  * year: the sampling year, i.e., from 2013 to 2023.  ##
 Code/software * "MLMM.R": the R code used to perform the MLMM
 analysis, as described in the manuscript.  * "GLMM.R": the R
 code used to perform the GLMMs analyses, as described in the manuscript.
 The packages used to perform the analyses are available within the code.