DOI: 10.5061/dryad.0zpc86776
title: Data from: Exotic megaherbivores as ecosystem engineers in Australian savannas: Do they facilitate predator movement?

description: An understanding of how terrestrial mammalian predators use their
 environment is critical for the development of effective management and
 monitoring. Mammalian predators often use anthropogenic linear
 features—such as roads, fencelines, and infrastructure corridors—to
 increase movement efficiency and prey encounter rates. However, there has
 been little investigation into how predators use more subtle linear
 features such as game trails (i.e., well‐trodden paths created by
 megaherbivores). This is despite native and exotic megaherbivores being
 abundant across many of Earth's most intact landscapes and
 conservation areas. We investigated how the two largest terrestrial
 mammalian predators in northern Australian savannas—the dingo (Canis
 familiaris, introduced ca. 4000 years ago) and cat (Felis catus,
 introduced ca. 200 years ago)—use game trails created by exotic
 megaherbivores (Asian water buffalo Bubalus bubalis and horse Equus
 caballus). We deployed two camera traps at 52 sites, with one camera
 positioned on a game trail and another in undisturbed vegetation
 &lt; 60 m away. We compared the activity of predators on game trails
 to adjacent undisturbed vegetation and explored how trail use varied with
 vegetation structure and prey activity. Dingoes and cats were 34 times and
 6 times more likely to be detected on game trails than in adjacent
 vegetation, respectively, suggesting these predators preferentially use
 game trails. We speculate that the extensive network of game trails
 created by exotic megaherbivores across northern Australia's vast
 savannas has potentially facilitated terrestrial mammalian predator
 movement at very large scales. Controlling exotic megaherbivores may,
 therefore, provide a means of disrupting the activity of dingoes and cats,
 thereby benefiting predation‐susceptible native species. However, further
 research is needed to understand the ecological implications of game
 trails in Australian savannas and other habitat types.
description: At each site there were two cameras. One camera was placed on a
 game trail termed 'on' and the other camera was placed
 approximately 60m away in undisturbed vegetation termed
 'off'. Data csv includes the the number of
 independent detections of a species on the 'on-trail' camera and
 'off-trail' camera. Detections were considered independent when
 &gt;30 minutes had elapsed since the previous detections of that
 species on that same camera. 
 'feral_herbivore' category includes detections of two
 species: <em>Bubalus bubalis </em>and<em> Equus
 caballus</em> 'sm_mam' category
 includes detections of <em>Sminthopsis </em>spp.,
 <em>Pseudomys </em>spp. and <em>Rattus
 </em>spp. 'all_prey_spp' category is
 the sum of independent detections of all native mammal species less than
 2.5kg i.e. all native mammal species excluding agile wallaby
description: # Exotic megaherbivores as ecosystem engineers in Australian savannas: Do
 they facilitate predator movement?
 [https://doi.org/10.5061/dryad.0zpc86776](https://doi.org/10.5061/dryad.0zpc86776) ## Description of the data and file structure The experimental method involved the deployment of 104 camera traps at 52 sites on Melville Island to compare faunal (both native and invasive spp.) activity along game trails created by exotic megaherbivores versus in undisturbed vegetation in savanna habitats. At each site, paired cameras were positioned: one facing along a game trail and another in undisturbed vegetation within 60 meters. The cameras were unbaited, set at a height of 50 cm, and programmed to capture five consecutive images per trigger. Detections were considered independent when &gt;30 minutes had elapsed since the previous detection of that species on the same camera. We investigated each species’ use of game trails using logistic generalised linear models. The modelled response variable was the proportion of independent on-trail detections relative to all independent detections recorded at each site. Sites with no detections of species being modelled on either camera were removed from the analysis. Models were fit with a quasibinomial error structure using the VGAM package (Yee, 2015) in R (version 4.3.1, R Core Team, 2023) to account for overdispersion. To examine how biotic variables influenced a species' use of game trails, we analysed all combinations of the explanatory variables outlined in Table S1, with no model interactions as none were deemed ecologically relevant. ### Files and variables #### File: Neave_2024_gametrail_cam_detections.csv **Description:**  ##### Variables * site: site identity * buff_off: number of independent buffalo detections off-trail camera * buff_on:  number of independent buffalo detections on-trail camera * dingo_off: number of independent dingo detections off-trail camera * dingo_on: number of independent dingo detections on-trail camera * horse_off: number of independent horse detections off-trail camera * horse_on: number of independent horse detections on the trail camera * cat_off: number of independent cat detections off-trail camera * cat_on: number of independent cat detections on the trail camera * bandi_off: number of independent northern brown bandicoot detections off-trail camera * bandi_on: number of independent northern brown bandicoot detections on-trail camera * wallaby_off: number of independent agile wallaby detections off-trail camera * wallaby_on: number of independent agile wallaby detections on-trail camera * treerat_off: number of independent black-footed tree-rat detections off-trail camera * treerat_on: number of independent black-footed tree-rat detections on-trail camera * deli_off: number of independent delicate mouse detections off-trail camera * deli_on: number of independent delicate mouse detections on the trail camera * tunneyi_off: number of independent pale field-rat detections off-trail camera * tunneyi_on: number of independent pale field-rat detections on-trail camera * dunnart_off: number of independent dunnart detections off-trail camera * dunnart_on: number of independent dunnart detections on-trail camera * poss_off: number of independent northern brushtail possum detections off-trail camera * poss_on: number of independent northern brushtail possum detections on-trail camera * feral_herbivore_off: number of independent horse and buffalo detections off-trail camera * feral_herbivore_on: number of independent horse and buffalo detections on-trail camera * all_prey_spp_off: sum of independent detections of all native mammal species &lt;2.5kg off-trail camera * all_prey_spp_on: sum of independent detections of all native mammal species &lt;2.5kg on-trail camera * sm_mam_off:  sum of dunnart, delicate mouse, and pale field-rat independent detections off-trail camera * sm_mam_on:  sum of dunnart, delicate mous,e and pale field-rat independent detections off-trail camera * veg_visibility: average visibility height at each site (cm). Vegetation visibility was a proxy for vegetation density and was quantified using a custom vegetation index based on visual obstruction measurements along a transect. * cam_nights_off: number of nights off-trail the camera was active * cam_nights_on: number of nights the on-trail camera was active ## Code/software **Neave_et_al_glm_qbinomial_code.R** : This code fits and compares multiple binomial and quasibinomial logistic regression models to analyze species’ trail-use preferences, using QAIC for model selection while checking for overdispersion. ``` R version 4.3.1 (2023-06-16 ucrt) -- "Beagle Scouts" ``` Packages required: VGAM car dplyr ggplot2