Plotting GBIF occurrence data on a map using OpenStreetMap

In a previous post, I dicussed how to get occurrence data from the Global Biodiversity Information Facility (GBIF). For my current project at the Natural History Museum in Berlin, I work on penguins. In this post, I will plot occurrences of penguin species on a map. Occurrence maps show the geographical position of occurrences.

The penguin map
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Spectrograms are a common visualization of sound data. Visualizing sound data can be useful when doing a presentation or for publication. Additionally, machine learning algorithms for classifying sound data generally use spectrograms as their starting point, instead of the sound data itself, as many advanced algorithnms for classifying images are readily available. The example uses the R packages warbleR (Araya-Salas & Smith-Vidaurre, 2017), seewave (Sueur, Aubin, Simonis, 2008) and tuneR (Ligges et al., 2018).

This example draws the spectrogram of the call of a tawny owl (Strix aluco).

Tawny owl (Strix aluco). Alvaro Ortiz Troncoso, XC494801. Accessible at
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Hearing and vocalization ranges of marine animals

For a project on noise pollution in the oceans at the Natural History Museum in Berlin, I recently made this plot of the hearing and vocalization ranges of selected marine animals. Range plots are generally not-so-common plots. In this example, I plotted the hearing and vocalization range (frequency) for several species of whales, dolphins, seals, turtles and fishes.

Hearing and vocalization ranges of marine animals
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Ecological data sets in R-core

If you are using R, then you are probably familiar with the mtcars data set, that is used in many R tutorials. The “car analogy” is so common in text books, that this so-called technique has its own Wikipedia page. For the rest of us, who don’t understand anything about motor cars, R-core comes with a wide selection of example data sets, some of which relate to ecology or biodiversity.

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