Zoology – Visualizing biodiversity data

January 18, 2025January 18, 2025

Real-time arthropod taxonomy

Identifying arthropods in real-time is a hard problem. First, arthropods have an extremely large number of species, even narrowing the search to the biodiversity within specific biomes. Furthermore, they have a huge variety of forms, so classifiying at a higher level, such as at order level, is still a problem. Look at the incredible variety of the order Coleoptera for example.

Yet there are applications, e.g. in agriculture, biodiversity calculations, collection scanning, in general any application that benefits from large scale automation, that require just that: real-time arthropod taxonomy.

So here is a first try, comparing several machine learning methods, and evaluating the results in terms of accuracy and speed.

January 16, 2025January 16, 2025

Hearing in Penguins – Hörfähigkeiten von Pinguinen

Neue Publikation, beim Umweltbundesamt!

Das Projekt untersuchte das Hörvermögen von Humboldt-Pinguine. Darüber hinaus wurde eine Tieraudiogramm-Datenbank entwickelt, die den Vergleich zwischen den veröffentlichten Hörkurven verschiedener mariner Tiere erlaubt. Mit diesem Vorhaben wurde der Grundstein für zukünftige Studien über das Hörvermögen von tauchenden Vögeln gelegt hat und so zu einem größeren Verständnis beigetragen, inwiefern Meeresvögel von Unterwasserlärm betroffen sind.

https://www.umweltbundesamt.de/publikationen/hearing-in-penguins-hoerfaehigkeiten-von-pinguinen

August 18, 2023January 18, 2025

Refactoring the Animal Sound Archive

For my latest project at Museum für Naturkunde Berlin, I refactored the Animal Sound Archive search interface and API. The Animal Sound Archive contains thousands of high quality, scientifically checked recordings, which can be used freely for science or any purpose. Thanks to the wonderful colleagues of the Animal Sound Archive Team, it has been a pleasure.

Check it out on GBIF!

March 14, 2023March 14, 2023

The Animal Audiogram Database

Conference: Symposium “Hörvermögen von Pinguinen / Hearing in Penguins”, 153. Jahresversammlung der Deutschen Ornithologen-Gesellschaft, 19. und 20. September 2020. Full presentation DOI: 10.13140/RG.2.2.33907.14883

December 2, 2022December 4, 2022

Storing a taxonomic tree in a relational database

Taxonomic trees are ubiquitous in biodiversity software. A very common application is using a tree to allow the users to browse the data. Other applications are: training classification models, curating a collection, visualizing research results etc.

Data is often stored in a relational database, such as MySQL. Unfortunately, relational databases are not particularly well suited for storing tree structures. Yet the choice of a database may be guided by more important requirements, and so the taxonomic tree is sometimes implemented as an afterthought. The result can be a structure that is difficult to maintain and to query, sometimes requiring more work than expected to maintain and finally yielding a less satisfying experience for the end user.

I will show some counterexamples, and how get a better result by using a data structure called “nested set”.

October 4, 2020October 4, 2020

Comparing the distribution of Corvus corone and Corvus cornix

In this post, I will use a divergent color scale to plot two distributions on the same map. As an example, I chose to plot the European distribution of two species of corvids: the carrion crow (Corvus corone) and the hooded crow (Corvus cornix). There has been some adjustments to the taxonomical status of the hooded crow (see Parkin et al., 2003 for details), hoewever, currently, they are regarded as different species.

In this map, I will use a divergent color scale to show areas in Europe where each species is dominant, and also show areas where both species are present.

Distribution of Corvus corone and C. cornix in Europe

September 21, 2020September 21, 2020

Simple distribution maps using ggplot

In a previous post, I discussed how to plot GBIF occurrence data using OpenStreetMaps. Here, I will plot a distribution map. Distribution maps differ from occurrence maps in that occurrences are aggregated and plotted as a heat map. Additionally, the map has to be projected using an equal area projection.
I will illustrate these two features by plotting the distribution of the tawny owl (Strix aluco) in Europe.

September 19, 2020

Facets and time frames: plotting the migration of the stork

In a previous post, I discussed how to plot occurrence data from GBIF on a map. In this post, I will discuss how to plot a bird migration by producing an occurrence map for each month of the year. I will use the migration of the stork (Ciconia ciconia) as an example.

September 1, 2020September 1, 2020

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.

August 2, 2020September 21, 2020

Spectrograms

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).