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      Movement, resting, and attack behaviors of wild pumas are revealed by tri-axial accelerometer measurements

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          Abstract

          Background

          Accelerometers are useful tools for biologists seeking to gain a deeper understanding of the daily behavior of cryptic species. We describe how we used GPS and tri-axial accelerometer (sampling at 64 Hz) collars to monitor behaviors of free-ranging pumas ( Puma concolor), which are difficult or impossible to observe in the wild. We attached collars to twelve pumas in the Santa Cruz Mountains, CA from 2010-2012. By implementing Random Forest models, we classified behaviors in wild pumas based on training data from observations and measurements of captive puma behavior.

          Results

          We applied these models to accelerometer data collected from wild pumas and identified mobile and non-mobile behaviors in captive animals with an accuracy rate greater than 96%. Accuracy remained above 95% even after downsampling our accelerometer data to 16 Hz. We were further able to predict low-acceleration movement behavior (e.g. walking) and high-acceleration movement behavior (e.g. running) with 93.8% and 92% accuracy, respectively. We had difficulty predicting non-movement behaviors such as feeding and grooming due to the small size of our training dataset. Lastly, we used model-predicted and field-verified predation events to quantify acceleration characteristics of puma attacks on large prey.

          Conclusion

          These results demonstrate that accelerometers are useful tools for classifying the behaviors of cryptic medium and large-sized terrestrial mammals in their natural habitats and can help scientists gain deeper insight into their fine-scale behavioral patterns. We also show how accelerometer measurements can provide novel insights on the energetics and predation behavior of wild animals. Lastly we discuss the conservation implications of identifying these behavioral patterns in free-ranging species as natural and anthropogenic landscape features influence animal energy allocation and habitat use.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s40462-015-0030-0) contains supplementary material, which is available to authorized users.

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          Most cited references30

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          An introduction to recursive partitioning: rationale, application, and characteristics of classification and regression trees, bagging, and random forests.

          Recursive partitioning methods have become popular and widely used tools for nonparametric regression and classification in many scientific fields. Especially random forests, which can deal with large numbers of predictor variables even in the presence of complex interactions, have been applied successfully in genetics, clinical medicine, and bioinformatics within the past few years. High-dimensional problems are common not only in genetics, but also in some areas of psychological research, where only a few subjects can be measured because of time or cost constraints, yet a large amount of data is generated for each subject. Random forests have been shown to achieve a high prediction accuracy in such applications and to provide descriptive variable importance measures reflecting the impact of each variable in both main effects and interactions. The aim of this work is to introduce the principles of the standard recursive partitioning methods as well as recent methodological improvements, to illustrate their usage for low and high-dimensional data exploration, but also to point out limitations of the methods and potential pitfalls in their practical application. Application of the methods is illustrated with freely available implementations in the R system for statistical computing. (c) 2009 APA, all rights reserved.
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            Moving towards acceleration for estimates of activity-specific metabolic rate in free-living animals: the case of the cormorant.

            1. Time and energy are key currencies in animal ecology, and judicious management of these is a primary focus for natural selection. At present, however, there are only two main methods for estimation of rate of energy expenditure in the field, heart rate and doubly labelled water, both of which have been used with success; but both also have their limitations. 2. The deployment of data loggers that measure acceleration is emerging as a powerful tool for quantifying the behaviour of free-living animals. Given that animal movement requires the use of energy, the accelerometry technique potentially has application in the quantification of rate of energy expenditure during activity. 3. In the present study, we test the hypothesis that acceleration can serve as a proxy for rate of energy expenditure in free-living animals. We measured rate of energy expenditure as rates of O2 consumption (VO2) and CO2 production (VCO2) in great cormorants (Phalacrocorax carbo) at rest and during pedestrian exercise. VO2 and VCO2 were then related to overall dynamic body acceleration (ODBA) measured with an externally attached three-axis accelerometer. 4. Both VO2 and VCO2 were significantly positively associated with ODBA in great cormorants. This suggests that accelerometric measurements of ODBA can be used to estimate VO2 and VCO2 and, with some additional assumptions regarding metabolic substrate use and the energy equivalence of O2 and CO2, that ODBA can be used to estimate the activity specific rate of energy expenditure of free-living cormorants. 5. To verify that the approach identifies expected trends in from situations with variable power requirements, we measured ODBA in free-living imperial cormorants (Phalacrocorax atriceps) during foraging trips. We compared ODBA during return and outward foraging flights, when birds are expected to be laden and not laden with captured fish, respectively. We also examined changes in ODBA during the descent phase of diving, when power requirements are predicted to decrease with depth due to changes in buoyancy associated with compression of plumage and respiratory air. 6. In free-living imperial cormorants, ODBA, and hence estimated VO2, was higher during the return flight of a foraging bout, and decreased with depth during the descent phase of a dive, supporting the use of accelerometry for the determination of activity-specific rate of energy expenditure.
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              Random forests for classification in ecology.

              Classification procedures are some of the most widely used statistical methods in ecology. Random forests (RF) is a new and powerful statistical classifier that is well established in other disciplines but is relatively unknown in ecology. Advantages of RF compared to other statistical classifiers include (1) very high classification accuracy; (2) a novel method of determining variable importance; (3) ability to model complex interactions among predictor variables; (4) flexibility to perform several types of statistical data analysis, including regression, classification, survival analysis, and unsupervised learning; and (5) an algorithm for imputing missing values. We compared the accuracies of RF and four other commonly used statistical classifiers using data on invasive plant species presence in Lava Beds National Monument, California, USA, rare lichen species presence in the Pacific Northwest, USA, and nest sites for cavity nesting birds in the Uinta Mountains, Utah, USA. We observed high classification accuracy in all applications as measured by cross-validation and, in the case of the lichen data, by independent test data, when comparing RF to other common classification methods. We also observed that the variables that RF identified as most important for classifying invasive plant species coincided with expectations based on the literature.
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                Author and article information

                Contributors
                yixwang@ucsc.edu
                bnickel@ucsc.edu
                rutishauser@gmail.com
                cbryce@ucsc.edu
                williams@biology.ucsc.edu
                elkaim@soe.ucsc.edu
                cwilmers@ucsc.edu
                Journal
                Mov Ecol
                Mov Ecol
                Movement Ecology
                BioMed Central (London )
                2051-3933
                22 January 2015
                22 January 2015
                2015
                : 3
                : 1
                : 2
                Affiliations
                [ ]Environmental Studies Department, Center for Integrated Spatial Research, University of California, 1156 High Street, Santa Cruz, CA 95064 USA
                [ ]Wildlife Computers, 8345 154th Ave. NE, Redmond, WA 98052 USA
                [ ]Ecology and Evolutionary Biology Department, University of California, 1156 High Street, Santa Cruz, CA 95064 USA
                [ ]Computer Engineering Department, Autonomous Systems Lab, University of California, 1156 High Street, Santa Cruz, CA 95064 USA
                Article
                30
                10.1186/s40462-015-0030-0
                4337468
                25709837
                a5811418-1b50-4277-9d2b-83469943f7a0
                © Wang et al.; licensee BioMed Central. 2015

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. 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.

                History
                : 26 October 2014
                : 12 January 2015
                Categories
                Research
                Custom metadata
                © The Author(s) 2015

                puma concolor,accelerometer,behavior,random forest,predation

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