On this page there are some descriptions of various citizen science (e.g. Android app) and quantitative tool development (e.g. R packages) I have been involved in:

Citizen science

Android app AviNest

In 2018 my group developed the AviNest citizen science app. This Android app allows volunteers to enter data on nest of all bird species breeding in the Netherlands on their mobile phone or tablet. Users can enter data on the location (using the GPS from their phone), habitat and parents of the nest (if banded), and record data on the number of eggs, chicks and fledglings. For some species, like the oystercatchers there are additional features, such as automatic estimation of egg-laying dates based on egg measurements and weights. The app also offers the opportunities to easily share data among volunteers, such that people working in groups receive the data of the nest visits each person has done on the same nest. Data can be uploaded from the field to an online sever and is stored in the online NestKaart database, which offers additional inbuilt features for simple analyses of reproductive success. The App has a google forum for asking (and answering) questions, and a manual and instruction videos can be found here (in Dutch). AviNest was programmed by Mario Huizinga (RingIT) and co-developed by Sovon, with funding from BirdLife Netherlands.

My inner nerd

For some weird reason I really like playing with numbers, doing statistics, population modeling, and computer coding. You can wake me up (almost) any time for a project that involves any (or preferably more) of these topics. Yes, I have learned to embrace my inner nerd.

I mainly code in R and Python (with a bit of C) and am particularly interested in the joint analysis of longitudinal data collected on multiple subjects (individuals, populations, species). Most recently, I have spent a lot of time on the use of machine learning tools in the analysis of ecological data and on state space / structural equation models.

R package climwin

climwin is a freely available software package designed in the R statistical environment by Liam Bailey and me. It is used for conducting climate window analyses. With the greater availability of high resolution climatic data there has been a growing interest in understanding how biological systems respond to climatic variation. However, the sensitivity of organisms to climatic perturbations will tend to vary across a year making it necessary to specify a smaller ‘window’ within a year over which climatic data is measured and summarised. Often, this choice of climate window has involved the demarcation of climate along seasonal lines (e.g. mean spring temperature), yet this regularly occurs with little a priori knowledge on a systems climatic sensitivity making such decisions somewhat arbitrary. Arbitrary selection of climate windows can compromise our ability to make meaningful conclusions from our analyses, as we cannot be sure if the response of a biological system to climate in the chosen window represents the strongest or most relevant impact of climate across a year. climwin provides an exploratory approach for climate window analysis that tests and compares all potential climate windows, thus removing the need for arbitrary climate window decisions. This wiki will provide a guide on how to conduct analysis with climwin, from most basic to more advanced features.

The package can be downloaded from CRAN and GitHub and has several vignettes with worked examples, a google forum for asking (and answering) questions and accompanying scientific papers [1,2,3]

R package odprism

odprism is a toolbox that allows for determining the optimal design and power of random intercept and slope models. It includes functions that allow for easy simulation of datasets with different amounts of among-subject heterogeneity and of number of samples per subject and subjects and automatically calculates the bias, precision and power that can be obtained by analyzing such data using random regression models. The package is no longer updated and is archived at CRAN, but some updates were done on GitHub , see also its accompanying paper.