Anthropogenic resource subsidies determine space use by Australian arid zone dingoes: an improved resource selection modelling approach.

• Main Authors: TM Newsome, G-A Ballard, CR Dickman, PJS Fleming, C Howden
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: 2013
• Subjects: Animals; Australia; Behavior; Animal; Ecosystem; Environment; Female; Geographic Information Systems; Geographic Mapping; Geography; Human Activities; Humans; Male; Models; Statistical; Predatory Behavior; Remote Sensing Technology; Spatio-Temporal Analysis; Wolves; 050206 Environmental Monitoring; MD Multidisciplinary; Canis lupus
• Online Access: http://hdl.handle.net/10536/DRO/DU:30089974; https://figshare.com/articles/journal_contribution/Anthropogenic_resource_subsidies_determine_space_use_by_Australian_arid_zone_dingoes_an_improved_resource_selection_modelling_approach_/20871331

Dingoes (Canis lupus dingo) were introduced to Australia and became feral at least 4,000 years ago. We hypothesized that dingoes, being of domestic origin, would be adaptable to anthropogenic resource subsidies and that their space use would be affected by the dispersion of those resources. We tested this by analyzing Resource Selection Functions (RSFs) developed from GPS fixes (locations) of dingoes in arid central Australia. Using Generalized Linear Mixed-effect Models (GLMMs), we investigated resource relationships for dingoes that had access to abundant food near mine facilities, and for those that did not. From these models, we predicted the probability of dingo occurrence in relation to anthropogenic resource subsidies and other habitat characteristics over ∼ 18,000 km(2). Very small standard errors and subsequent pervasively high P-values of results will become more important as the size of data sets, such as our GPS tracking logs, increases. Therefore, we also investigated methods to minimize the effects of serial and spatio-temporal correlation among samples and unbalanced study designs. Using GLMMs, we accounted for some of the correlation structure of GPS animal tracking data; however, parameter standard errors remained very small and all predictors were highly significant. Consequently, we developed an alternative approach that allowed us to review effect sizes at different spatial scales and determine which predictors were sufficiently ecologically meaningful to include in final RSF models. We determined that the most important predictor for dingo occurrence around mine sites was distance to the refuse facility. Away from mine sites, close proximity to human-provided watering points was predictive of dingo dispersion as were other landscape factors including palaeochannels, rocky rises and elevated drainage depressions. Our models demonstrate that anthropogenically supplemented food and water can alter dingo-resource relationships. The spatial distribution of such resources is therefore critical for ...