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      Space use and habitat selection of an invasive mesopredator and sympatric, native apex predator


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          Where mesopredators co-exist with dominant apex predators, an understanding of the factors that influence their habitat and space use can provide insights that help guide wildlife conservation and pest management actions. A predator’s habitat use is defined by its home range, which is influenced by its selection or avoidance of habitat features and intra- and inter-specific interactions within the landscape. These are driven by both innate and learned behaviour, operating at different spatial scales. We examined the seasonal home ranges and habitat selection of actively-managed populations of a native apex predator (dingo Canis dingo) and invasive mesopredator (feral cat Felis catus) in semi-arid Western Australia to better understanding their sympatric landscape use, potential interactions, and to help guide their management.


          We used kernel density estimates to characterise the seasonal space use of dingoes and feral cats, investigate inter- and intra-species variation in their home range extent and composition, and examine second-order habitat selection for each predator. Further, we used discrete choice modelling and step selection functions to examine the difference in third-order habitat selection across several habitat features.


          The seasonal home ranges of dingoes were on average 19.5 times larger than feral cats. Feral cat seasonal home ranges typically included a larger proportion of grasslands than expected relative to availability in the study site, indicating second-order habitat selection for grasslands. In their fine-scale movements (third-order habitat selection), both predators selected for roads, hydrological features (seasonal intermittent streams, seasonal lakes and wetlands), and high vegetation cover. Dingoes also selected strongly for open woodlands, whereas feral cats used open woodlands and grasslands in proportion to availability.

          Management recommendations

          Based on these results, and in order to avoid unintended negative ecological consequences (e.g. mesopredator release) that may stem from non-selective predator management, we recommend that feral cat control focuses on techniques such as trapping and shooting that are specific to feral cats in areas where they overlap with apex predators (dingoes), and more general techniques such as poison baiting where they are segregated.

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          Most cited references78

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          Predator interactions, mesopredator release and biodiversity conservation.

          There is growing recognition of the important roles played by predators in regulating ecosystems and sustaining biodiversity. Much attention has focused on the consequences of predator-regulation of herbivore populations, and associated trophic cascades. However apex predators may also control smaller 'mesopredators' through intraguild interactions. Removal of apex predators can result in changes to intraguild interactions and outbreaks of mesopredators ('mesopredator release'), leading in turn to increased predation on smaller prey. Here we provide a review and synthesis of studies of predator interactions, mesopredator release and their impacts on biodiversity. Mesopredator suppression by apex predators is widespread geographically and taxonomically. Apex predators suppress mesopredators both by killing them, or instilling fear, which motivates changes in behaviour and habitat use that limit mesopredator distribution and abundance. Changes in the abundance of apex predators may have disproportionate (up to fourfold) effects on mesopredator abundance. Outcomes of interactions between predators may however vary with resource availability, habitat complexity and the complexity of predator communities. There is potential for the restoration of apex predators to have benefits for biodiversity conservation through moderation of the impacts of mesopredators on their prey, but this requires a whole-ecosystem view to avoid unforeseen negative effects. 'Nothing has changed since I began. My eye has permitted no change. I am going to keep things like this.' From 'Hawk Roosting', by Ted Hughes.
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            The Rise of the Mesopredator

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              Application of random effects to the study of resource selection by animals.

              1. Resource selection estimated by logistic regression is used increasingly in studies to identify critical resources for animal populations and to predict species occurrence. 2. Most frequently, individual animals are monitored and pooled to estimate population-level effects without regard to group or individual-level variation. Pooling assumes that both observations and their errors are independent, and resource selection is constant given individual variation in resource availability. 3. Although researchers have identified ways to minimize autocorrelation, variation between individuals caused by differences in selection or available resources, including functional responses in resource selection, have not been well addressed. 4. Here we review random-effects models and their application to resource selection modelling to overcome these common limitations. We present a simple case study of an analysis of resource selection by grizzly bears in the foothills of the Canadian Rocky Mountains with and without random effects. 5. Both categorical and continuous variables in the grizzly bear model differed in interpretation, both in statistical significance and coefficient sign, depending on how a random effect was included. We used a simulation approach to clarify the application of random effects under three common situations for telemetry studies: (a) discrepancies in sample sizes among individuals; (b) differences among individuals in selection where availability is constant; and (c) differences in availability with and without a functional response in resource selection. 6. We found that random intercepts accounted for unbalanced sample designs, and models with random intercepts and coefficients improved model fit given the variation in selection among individuals and functional responses in selection. Our empirical example and simulations demonstrate how including random effects in resource selection models can aid interpretation and address difficult assumptions limiting their generality. This approach will allow researchers to appropriately estimate marginal (population) and conditional (individual) responses, and account for complex grouping, unbalanced sample designs and autocorrelation.

                Author and article information

                Mov Ecol
                Mov Ecol
                Movement Ecology
                BioMed Central (London )
                4 May 2020
                4 May 2020
                : 8
                : 18
                [1 ]GRID grid.1012.2, ISNI 0000 0004 1936 7910, School of Biological Sciences, University of Western Australia, ; Crawley, Perth, WA 6009 Australia
                [2 ]Present Address: Nyamba Buru Yawuru, 55 Reid road, Cable Beach, WA 6726 Australia
                [3 ]GRID grid.1008.9, ISNI 0000 0001 2179 088X, School of Biosciences, The University of Melbourne, ; Parkville, VIC 3010 Australia
                [4 ]GRID grid.1003.2, ISNI 0000 0000 9320 7537, Australian Research Council Centre of Excellence for Environmental Decisions, , School of Biological Sciences, The University of Queensland, ; St. Lucia, QLD 4072 Australia
                [5 ]GRID grid.452589.7, ISNI 0000 0004 1799 3491, Biodiversity and Conservation Science, Department of Biodiversity, Conservation and Attractions, ; Woodvale, WA 6946 Australia
                [6 ]GRID grid.1021.2, ISNI 0000 0001 0526 7079, Centre for Integrative Ecology, , School of Life and Environmental Sciences, Deakin University, ; Burwood, VIC 3125 Australia
                © The Author(s) 2020

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                : 13 March 2020
                : 7 April 2020
                Funded by: FundRef http://dx.doi.org/10.13039/100012169, Gorgon Barrow Island Net Conservation Benefits Fund;
                Funded by: FundRef http://dx.doi.org/10.13039/501100002287, Department of Biodiversity, Conservation and Attractions;
                Funded by: FundRef http://dx.doi.org/10.13039/100007615, Centre of Excellence for Environmental Decisions, Australian Research Council;
                Funded by: Australian Department of Education, Skills and Employment
                Custom metadata
                © The Author(s) 2020

                dingo (canis dingo),feral cat (felis catus),gps tracking,habitat selection,home range,kernel density estimation,movement ecology,predator interaction,step selection function


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