For some, locomotor diversity refers to an ability of an animal to use many different aspects of the environment (e.g. These data further highlight the challenges faced by arboreal animals and raise the possibility that being able to switch fluidly and frequently between many different locomotor modes may be advantageous for animals moving in an arboreal milieu.Ī working definition of locomotor diversity is troublesome and means many different things depending on the specific field or investigator (Van Valkenburgh, 1985 Gebo, 1987 Gatesy & Middleton, 1997). Findings from this study suggest that movement on arboreal substrates requires all mammals, regardless of taxonomic affiliation, to demonstrate high locomotor diversity. Furthermore, primates do not inherently have greater locomotor diversity than other mammalian taxa. Anatomical specialization has no effect on locomotor diversity in primates. However, this effect becomes non-significant when accounting for differences in substrate-use (i.e. Phylogenetic analyses of these locomotor diversity indices reveal that within primates, small-bodied species demonstrate greater locomotor diversity than large-bodied taxa. Using previously published locomotor repertoire data from 110 mammalian species, this study co-opted the Shannon–Wiener diversity index to calculate a singular measure of locomotor diversity. None of these claims have been tested in any sort of comparative framework. Some of these claims include that: (1) arboreal primates demonstrate higher locomotor diversity than terrestrial species (2) anatomically generalized and small-bodied primates demonstrate higher locomotor diversity than anatomically specialized and large-bodied taxa and (3) primates demonstrate higher locomotor diversity compared to non-primate mammals. While no concrete definition of locomotor diversity is currently available, this has not stopped researchers from making a number of assertions about the underlying mechanisms that determine whether a species will have high or low locomotor diversity. (Figure courtesy Du et al.Locomotor diversity has meant many different things depending on the subject area or investigator. See the examples folder for more information. On this input (NB, you might need to zoom in to see the individual pixels): datathief ( filename, xlim = xlim, ylim = ylim ) It will warn you if too many or too few pixels are detected.įor example, running this code: import datathief as dt filename = 'du_fig1a_annotated.png' xlim = ylim = data = dt. This function will then return the x and y coordinates of each data point. Then one pixel for each data point you wish to extract (default color: pure green). Do the same for the y-axis (default color: pure red). To use this tool, first annotate the plot by adding a single pixel at the start and end of the x-axis in a specified color that does not exist anywhere else in the image (default color: pure blue). If you want to extract a lot of data, or extract data from a continuous line, you are better off using the original Java DataThief package, or one of the many online tools that do exactly this. However, it might be annoying for a large amount of data. This makes it more transparent how the data are being read and makes the results more reproducible. Unlike the Java DataThief package and similar online tools, here the user manually annotates the figure with the data points of their choosing. Inspired by the Java package of the same name. Small utility for retrieving data from figures.
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