DOI: 10.5061/dryad.zgmsbccn3
title: The influence of domestic dogs on the spatial and temporal distribution of tayra

description: The domestic dog (Canis familiaris) is an exotic species known to have
 impacts on native fauna and it may induce spatial and temporal segregation
 in other species as a mechanism to reduce agonistic interactions. The
 tayra (Eira barbara), a medium-sized, widely distributed mustelid, is one
 of the species that can be affected. To test this hypothesis, between 2020
 and 2022, we installed camera traps in an agricultural landscape composed
 of a protected area, agro-livestock areas, an urban area, and several
 rural households. We calculated the activity patterns, their overlapping
 coefficients and generated models to explain the species’s occupancy and
 verify possible interferences. We obtained 32 records of tayras and 100 of
 domestic dogs. In only 8 of 73 sampling points, the two species occurred
 concomitantly. Activity overlap was 68%; however, at the points where both
 species occurred, the overlap was 43%. Tayra showed to be positively
 associated with forest areas, and negatively with agricultural
 exploitation areas. Dogs predominantly occupied agricultural areas and
 were negatively related to forest areas. In addition to dog interference,
 which seems to affect tayra's use of the landscape, environmental
 conditions are also essential in describing the occupancy of the
 subordinate species. It is possible that their scansorial habit is
 important to avoid agonistic events.
description: # The influence of domestic dogs on the spatial and temporal distribution
 of tayra
 [https://doi.org/10.5061/dryad.zgmsbccn3](https://doi.org/10.5061/dryad.zgmsbccn3) ## Description of the data and file structure We obtained records of tayras and dogs using 40 camera traps between December 2020 and August 2022, totalling a sampling effort of 43,665 cameras per day. The camera traps were systematically arranged at 73 sampling points approximately 30-40 centimeters from the ground, with approximately 2 kilometers between the points. We programmed the traps to work continuously, 24 hours a day for at least 60 consecutive days. The photographic records were extracted as information on the location of species activity, their presence and absence histories and the temporal variations that interfere with detection and the spatial variations that interfere with occupation. "-" = n/a ### Files and variables #### File: Activitypattern2.csv **Description:** The spreadsheet contains the dates and locations where the species were recorded and the h values, which are formulas that convert time data into radians, that is, circular data. ##### Variables * Species;Date;Local;Ponto;Time;hdec;hday: #### File: occu\_dog.csv **Description:** Matrix of presence and absence of domestic dogs, where each occasion is a grouping of 5 consecutive days. Each line represents a sample point and each column an ​​occasion. ##### Variables * Name;1;2;3;4;5;6;7;8;9;10;11;12;13;14;15;16;17;18;19;20;21: * 0 = absence * 1 = presence #### File: varocc.csv **Description:** Matrix containing the climatic covariates that affect the detection of species. The average temperatures (t) and precipitation (p) were grouped for 5-day occasions. Each line represents a sample point and each column an ​​occasion. ##### Variables * t1;t2;t3;t4;t5;t6;t7;t8;t9;t10;t11;t12;t13;t14;t15;t16;t17;t18;t19;t20;t21;p1;p2;p3;p4;p5;p6;p7;p8;p9;p10;p11;p12;p13;p14;p15;p16;p17;p18;p19;p20;p21: #### File: occu\_tayra.csv **Description:** Matrix of presence and absence of tayra where each occasion is a grouping of 5 consecutive days. Each line represents a sample point and each column an ​​occasion. * Name;1;2;3;4;5;6;7;8;9;10;11;12;13;14;15;16;17;18;19;20;21: * 0 = absence * 1 = presence #### File: varsite.csv **Description:** Environmental covariates that affect the occupation of species. Land use percentages were extracted from land use and land cover maps at different scales. The data were normalized. Land use percentages were extracted from land use and land cover maps at different scales. The data were normalized. Land use percentages were extracted from land use and land cover maps at different scales. The data were normalized. Each line represents a sample point and each column shows the percentages of the coverage types at each sample point,. ##### Variables * Agriculture_200;Temporary_200;Agriculture_500;Forest_800;Pasture_800;Perennial_1000;Forest_1000;Temporary_1000;Agriculture_200ms;Temporary_200ms;Agriculture_500ms;Forest_800ms;Pasture_800ms;Perennial_1000ms;Forest_1000ms;Temporary_1000ms: