Publications de Nina Marion Aurélia Van Tiel

Global patterns in plant environmental breadths

The latitudinal gradient in plant diversity is one of the most famous patterns in ecology. It is hypothesised that narrow niche breadths and restricted geographic ranges in the tropics allow more species to coexist with minimal overlap relative to high-lat ...

WILEY2025

MaskSDM: Adaptive Species Distribution Modeling Through Data Masking

Devis Tuia, Nina Marion Aurélia Van Tiel, Robin Adrien Zbinden

Species distribution models (SDMs) correlate species occurrences with environmental conditions and underpin much of ecological research. A key consideration in developing SDMs is selecting the optimal set of environmental predictor variables, which vary de ...

Springer Science and Business Media Deutschland GmbH2025

KABR: In-Situ Dataset for Kenyan Animal Behavior Recognition from Drone Videos

Nina Marion Aurélia Van Tiel

We present a novel dataset for animal behavior recognition collected in-situ using video from drones flown over the Mpala Research Centre in Kenya. Videos from DJI Mavic 2S drones flown in January 2023 were acquired at 5.4K resolution in accordance with IA ...

IEEE2024

Regional uniqueness of tree species composition and response to forest loss and climate change

Devis Tuia, Nina Marion Aurélia Van Tiel, Loïc Pellissier

The conservation and restoration of forest ecosystems require detailed knowledge of the native plant compositions. Here, we map global forest tree composition and assess the impacts of historical forest cover loss and climate change on trees. The global oc ...

Nature Portfolio2024

Multi-Scale and Multimodal Species Distribution Modeling

Devis Tuia, Nina Marion Aurélia Van Tiel, Robin Adrien Zbinden, Benjamin Alexander Kellenberger

Species distribution models (SDMs) aim to predict the distribution of species by relating occurrence data with environmental variables. Recent applications of deep learning to SDMs have enabled new avenues, specifically the inclusion of spatial data (envir ...

Springer Nature Link2024

Deep dive into KABR: a dataset for understanding ungulate behavior from in-situ drone video

Nina Marion Aurélia Van Tiel

In this paper, we extend the dataset statistics, model benchmarks, and performance analysis for the recently published KABR dataset, an in situ dataset for ungulate behavior recognition using aerial footage from the Mpala Research Centre in Kenya. The data ...

2024

On the selection and effectiveness of pseudo-absences for species distribution modeling with deep learning

Devis Tuia, Nina Marion Aurélia Van Tiel, Robin Adrien Zbinden, Benjamin Alexander Kellenberger, Lloyd Haydn Hughes

Species distribution modeling is a highly versatile tool for understanding the intricate relationship between environmental conditions and species occurrences. However, the available data often lacks information on confirmed species absence and is limited ...

2024

Fungal community composition predicts forest carbon storage at a continental scale

Nina Marion Aurélia Van Tiel

Forest soils harbor hyper-diverse microbial communities which fundamentally regulate carbon and nutrient cycling across the globe. Directly testing hypotheses on how microbiome diversity is linked to forest carbon storage has been difficult, due to a lack ...

Nature Portfolio2024

A Framework for Autonomic Computing for In Situ Imageomics

Nina Marion Aurélia Van Tiel

In situ imageomics is a new approach to study ecological, biological and evolutionary systems wherein large image and video data sets are captured in the wild and machine learning methods are used to infer biological traits of individual organisms, animal ...

The Institute of Electrical and Electronics Engineers, Inc2023

Exploring neural networks and their potential for species distribution modeling

Devis Tuia, Nina Marion Aurélia Van Tiel, Robin Adrien Zbinden, Benjamin Alexander Kellenberger, Lloyd Haydn Hughes

Species distribution models (SDMs) relate species occurrence data with environmental variables and are used to understand and predict species distributions across landscapes. While some machine learning models have been adopted by the SDM community, recent ...

2023