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  • Morato, Ronaldo G.  (3)
  • 2015-2019  (3)
  • Biodiversity Research  (3)
  • 1
    Online Resource
    Online Resource
    ATLANTIC MAMMAL TRAITS : a data set of morphological traits of mammals in the Atlantic Forest of South America
    Wiley ; 2018
    In:  Ecology Vol. 99, No. 2 ( 2018-02), p. 498-498
    Details
    In: Ecology, Wiley, Vol. 99, No. 2 ( 2018-02), p. 498-498
    Abstract: Measures of traits are the basis of functional biological diversity. Numerous works consider mean species‐level measures of traits while ignoring individual variance within species. However, there is a large amount of variation within species and it is increasingly apparent that it is important to consider trait variation not only between species, but also within species. Mammals are an interesting group for investigating trait‐based approaches because they play diverse and important ecological functions (e.g., pollination, seed dispersal, predation, grazing) that are correlated with functional traits. Here we compile a data set comprising morphological and life history information of 279 mammal species from 39,850 individuals of 388 populations ranging from −5.83 to −29.75 decimal degrees of latitude and −34.82 to −56.73 decimal degrees of longitude in the Atlantic forest of South America. We present trait information from 16,840 individuals of 181 species of non‐volant mammals (Rodentia, Didelphimorphia, Carnivora, Primates, Cingulata, Artiodactyla, Pilosa, Lagomorpha, Perissodactyla) and from 23,010 individuals of 98 species of volant mammals (Chiroptera). The traits reported include body mass, age, sex, reproductive stage, as well as the geographic coordinates of sampling for all taxa. Moreover, we gathered information on forearm length for bats and body length and tail length for rodents and marsupials. No copyright restrictions are associated with the use of this data set. Please cite this data paper when the data are used in publications. We also request that researchers and teachers inform us of how they are using the data.
    Type of Medium: Online Resource
    ISSN: 0012-9658 , 1939-9170
    URL: Issue
    URL: Article
    DOI: 10.1002/ecy.2018.99.issue-2
    DOI: 10.1002/ecy.2106
    RVK:
    WA 15000
    Language: English
    Publisher: Wiley
    Publication Date: 2018
    detail.hit.zdb_id: 1797-8
    detail.hit.zdb_id: 2010140-5
    SSG: 12
    Location Call Number Limitation Availability
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    Online Resource
  • 2
    Online Resource
    Online Resource
    Jaguar movement database: a GPS‐based movement dataset of an apex predator in the Neotropics
    Wiley ; 2018
    In:  Ecology Vol. 99, No. 7 ( 2018-07), p. 1691-1691
    Details
    In: Ecology, Wiley, Vol. 99, No. 7 ( 2018-07), p. 1691-1691
    Abstract: The field of movement ecology has rapidly grown during the last decade, with important advancements in tracking devices and analytical tools that have provided unprecedented insights into where, when, and why species move across a landscape. Although there has been an increasing emphasis on making animal movement data publicly available, there has also been a conspicuous dearth in the availability of such data on large carnivores. Globally, large predators are of conservation concern. However, due to their secretive behavior and low densities, obtaining movement data on apex predators is expensive and logistically challenging. Consequently, the relatively small sample sizes typical of large carnivore movement studies may limit insights into the ecology and behavior of these elusive predators. The aim of this initiative is to make available to the conservation‐scientific community a dataset of 134,690 locations of jaguars ( Panthera onca ) collected from 117 individuals (54 males and 63 females) tracked by GPS technology. Individual jaguars were monitored in five different range countries representing a large portion of the species’ distribution. This dataset may be used to answer a variety of ecological questions including but not limited to: improved models of connectivity from local to continental scales; the use of natural or human‐modified landscapes by jaguars; movement behavior of jaguars in regions not represented in this dataset; intraspecific interactions; and predator‐prey interactions. In making our dataset publicly available, we hope to motivate other research groups to do the same in the near future. Specifically, we aim to help inform a better understanding of jaguar movement ecology with applications towards effective decision making and maximizing long‐term conservation efforts for this ecologically important species. There are no costs, copyright, or proprietary restrictions associated with this data set. When using this data set, please cite this article to recognize the effort involved in gathering and collating the data and the willingness of the authors to make it publicly available.
    Type of Medium: Online Resource
    ISSN: 0012-9658 , 1939-9170
    URL: Issue
    URL: Article
    DOI: 10.1002/ecy.2018.99.issue-7
    DOI: 10.1002/ecy.2379
    RVK:
    WA 15000
    Language: English
    Publisher: Wiley
    Publication Date: 2018
    detail.hit.zdb_id: 1797-8
    detail.hit.zdb_id: 2010140-5
    SSG: 12
    Location Call Number Limitation Availability
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    Online Resource
  • 3
    Online Resource
    Online Resource
    A comprehensive analysis of autocorrelation and bias in home range estimation
    Wiley ; 2019
    In:  Ecological Monographs Vol. 89, No. 2 ( 2019-05)
    Details
    In: Ecological Monographs, Wiley, Vol. 89, No. 2 ( 2019-05)
    Abstract: Home range estimation is routine practice in ecological research. While advances in animal tracking technology have increased our capacity to collect data to support home range analysis, these same advances have also resulted in increasingly autocorrelated data. Consequently, the question of which home range estimator to use on modern, highly autocorrelated tracking data remains open. This question is particularly relevant given that most estimators assume independently sampled data. Here, we provide a comprehensive evaluation of the effects of autocorrelation on home range estimation. We base our study on an extensive data set of GPS locations from 369 individuals representing 27 species distributed across five continents. We first assemble a broad array of home range estimators, including Kernel Density Estimation ( KDE ) with four bandwidth optimizers (Gaussian reference function, autocorrelated‐Gaussian reference function [ AKDE ], Silverman's rule of thumb, and least squares cross‐validation), Minimum Convex Polygon, and Local Convex Hull methods. Notably, all of these estimators except AKDE assume independent and identically distributed ( IID ) data. We then employ half‐sample cross‐validation to objectively quantify estimator performance, and the recently introduced effective sample size for home range area estimation () to quantify the information content of each data set. We found that AKDE 95% area estimates were larger than conventional IID ‐based estimates by a mean factor of 2. The median number of cross‐validated locations included in the hold‐out sets by AKDE 95% (or 50%) estimates was 95.3% (or 50.1%), confirming the larger AKDE ranges were appropriately selective at the specified quantile. Conversely, conventional estimates exhibited negative bias that increased with decreasing . To contextualize our empirical results, we performed a detailed simulation study to tease apart how sampling frequency, sampling duration, and the focal animal's movement conspire to affect range estimates. Paralleling our empirical results, the simulation study demonstrated that AKDE was generally more accurate than conventional methods, particularly for small . While 72% of the 369 empirical data sets had 〉 1,000 total observations, only 4% had an 〉 1,000, where 30% had an 〈 30. In this frequently encountered scenario of small , AKDE was the only estimator capable of producing an accurate home range estimate on autocorrelated data.
    Type of Medium: Online Resource
    ISSN: 0012-9615 , 1557-7015
    URL: Issue
    URL: Article
    DOI: 10.1002/ecm.2019.89.issue-2
    DOI: 10.1002/ecm.1344
    Language: English
    Publisher: Wiley
    Publication Date: 2019
    detail.hit.zdb_id: 2010129-6
    SSG: 12
    SSG: 14
    Location Call Number Limitation Availability
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