Dataset – Visualizing biodiversity data

January 18, 2025February 19, 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.

December 20, 2023

Plant leaf classification

There are important potential applications for a machine learning system than can classify plants. For example, current research on crop protection is using machine learning for precision weed and plant disease detection. The importance of moving away from traditional pesticide-based crop protection methods cannot be overstated, as demonstrated by the alarming rate at which flowering plants are evolving away from insect pollination.

I obtained a dataset of plant leaf images (Hussain, 2023) and compared three machine learning algorithms for classification: multilayer perceptron, random forest and support vector machine. The classifiers’ accuracy vary between 74% and 84% .

The Jupyter notebook with the full Python code can be accessed on Kaggle.

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

March 14, 2022March 14, 2022

Computing α-diversity

Diversity indices are a common descriptive statistic used in biodiversity informatics. Diversity indices typically express the species richness of a given habitat or area. The α-diversity index is suitable when studying a single habitat and is expressed by a single number. There are several commonly used equations used to compute α-diversity. In this example, I will be using the Simpson’s diversity index, which is computed by the formula:

$D = 1 - \sum_{i=1}^{S}p_i^2$

Where S is the number of species in the sample and p is the proportion of a particular species. The Simpson’s diversity index is thus more influenced by common species rather than by rare species and is often considered to be an index reflecting the actual species diversity in a sample.

To illustrate this, I will use will use data obtained from GBIF. Remember, α-diversity is suitable for expressing the diversity within a single habitat, so I will obtain data accordingly. Here I chose the Tiergarten, a large (210 hectare) park in central Berlin.

February 20, 2022February 20, 2022

Ecological datasets in Python

Datasets included in library distributions are very practical for explaining concepts and for tutorials, as of course no extra download is required. A while ago, I posted a list of biodiversity datasets that come with R-core. Here I continue along the same line and list datasets coming with popular Python libraries.

September 22, 2021July 27, 2023

Using neural networks to classify 3D scans

For my capstone project in machine learning at EPFL, I wrote a classifier capable of sorting 3D scans of archaeological objects by culture.

Digitization of museum collections is currently a major challenge faced by cultural heritage and natural history museums. Museums are expected to digitize the collections to improve not only the documentation of artifacts, but also their availability for research, reconstruction and outreach activities, and to make these digital representations available online.

Machine learning setup

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.

August 27, 2020August 28, 2020

Getting occurrence data from GBIF

The Global Biodiversity Information Facility (GBIF) is a data aggregator for biodiversity data. The big advantage of using an aggregator like GBIF over getting data directly from the original data source is that an aggregator provides a single point of entry to many data sets, so analysing one data set is technically interoperable with any other data set.

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).