Biology subjects

Niedballa, J.

Publications and source records attributed to Niedballa, J..

3 recordsLinked to original sources

bioRxiv2023

Can snare removal safeguard protected areas caught in the Southeast Asian snaring crisis? Promises and perspectives

Unsustainable snaring is causing biodiversity declines across tropical protected areas, resulting in species extinctions and jeopardizing the health of forest ecosystems. Here, we used 11 years of ranger collected data to assess the impact of intensive snare removal on snaring levels in two protected areas in Viet Nam. Snare removal resulted in significant declines in snare occupancy (36.9, 95% BCI [4.6, 59.0] reduction in percent area occupied), but snaring levels nonetheless remained high (31.4, [23.6, 40.8] percent area occupied), and came with a substantial financial cost. Our results indicate that snare removal remains an important component of efforts to protect tropical protected areas, but by itself, is likely insufficient to address this threat. To stop snaring in protected areas, a multifaceted approach will be necessary that combines short-term reactive snare removal with long-term proactive programs that address the underlying drivers behind snaring.

ecology↗

bioRxiv2022

Global disparity of research allocation and the Aichi biodiversity conservation targets

Tracking progress towards global biodiversity conservation targets requires appropriate allocation of research and monitoring efforts. We conducted a global review of camera trap research on mammals as a proxy for biodiversity research and monitoring over the last two decades. We assessed how 3395 research locations from 2324 studies tracked priority regions for attaining the 2020 Aichi Biodiversity Targets. We used a geospatial distribution modelling approach to predict the spatial allocation of biodiversity research and to identify its key drivers. We show that conservation research in the past two decades has often failed to target areas important for conservation, and that 76.8% of the global research allocation can be attributed to country income, biome, mammal richness and accessibility. We predicted lowest probabilities of research allocation in low income countries. The Amazon and Congo Forest basins -- two highly biodiverse ecosystems facing unprecedented human alteration -- received inadequate research attention. Even in the most researched regions, an average of 51.4% of the research locations were outside the top 20% most important areas for the global biodiversity Aichi Targets. To support biodiversity conservation targets, policy and practice, more research and monitoring is required in regions with high importance for conservation.

ecology↗

bioRxiv2021

imageseg: an R package for deep learning-based image segmentation

O_LIConvolutional neural networks (CNNs) and deep learning are powerful and robust tools for ecological applications. CNNs can perform very well in various tasks, especially for visual tasks and image data. Image segmentation (the classification of all pixels in images) is one such task and can for example be used to assess forest vertical and horizontal structure. While such methods have been suggested, widespread adoption in ecological research has been slow, likely due to technical difficulties in implementation of CNNs and lack of toolboxes for ecologists. C_LIO_LIHere, we present R package imageseg which implements a workflow for general-purpose image segmentation using CNNs and the U-Net architecture in R. The workflow covers data (pre)processing, model training, and predictions. We illustrate the utility of the package with two models for forest structural metrics: tree canopy density and understory vegetation density. We trained the models using large and diverse training data sets from a variety of forest types and biomes, consisting of 3288 canopy images (both canopy cover and hemispherical canopy closure photographs) and 1468 understory vegetation images. C_LIO_LIOverall classification accuracy of the models was high with a Dice score of 0.91 for the canopy model and 0.89 for the understory vegetation model (assessed with 821 and 367 images, respectively), indicating robustness to variation in input images and good generalization strength across forest types and biomes. C_LIO_LIThe package and its workflow allow simple yet powerful assessments of forest structural metrics using pre-trained models. Furthermore, the package facilitates custom image segmentation with multiple classes and based on color or grayscale images, e.g. in cell biology or for medical images. Our package is free, open source, and available from CRAN. It will enable easier and faster implementation of deep learning-based image segmentation within R for ecological applications and beyond. C_LI

ecology↗