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

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2022 The Authors. Methods in Ecology and Evolution published by John Wiley & Sons Ltd on behalf of British Ecological SocietyConvolutional neural networks (CNNs) and deep learning are powerful and robust tools for ecological applications and are particularly suited for image data. Image segmentation (the classification of all pixels in images) is one such application and can, for example, be used to assess forest structural metrics. While CNN-based image segmentation methods for such applications 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. Here, we present R package imageseg which implements a CNN-based workflow for general purpose image segmentation using the U-Net and U-Net++ architectures in R. The workflow covers data (pre)processing, model training and predictions. We illustrate the utility of the package with image recognition models for two forest structural metrics: tree canopy density and understorey vegetation density. We trained the models using large and diverse training datasets from a variety of forest types and biomes, consisting of 2877 canopy images (both canopy cover and hemispherical canopy closure photographs) and 1285 understorey vegetation images. Overall segmentation accuracy of the models was high with a Dice score of 0.91 for the canopy model and 0.89 for the understorey vegetation model (assessed with 821 and 367 images respectively). The image segmentation models performed significantly better than commonly used thresholding methods and generalized well to data from study areas not included in training. This indicates robustness to variation in input images and good generalization strength across forest types and biomes. The package and its workflow allow simple yet powerful assessments of forest structural metrics using pretrained models. Furthermore, the package facilitates custom image segmentation with single or multiple classes and based on colour or grayscale images, for example, for applications 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.publishedVersio

Camtrap DP: an open standard for the FAIR exchange and archiving of camera trap data

Camera trapping has revolutionized wildlife ecology and conservation by providing automated data acquisition, leading to the accumulation of massive amounts of camera trap data worldwide. Although management and processing of camera trap-derived Big Data are becoming increasingly solvable with the help of scalable cyber-infrastructures, harmonization and exchange of the data remain limited, hindering its full potential. There is currently no widely accepted standard for exchanging camera trap data. The only existing proposal, “Camera Trap Metadata Standard” (CTMS), has several technical shortcomings and limited adoption. We present a new data exchange format, the Camera Trap Data Package (Camtrap DP), designed to allow users to easily exchange, harmonize and archive camera trap data at local to global scales. Camtrap DP structures camera trap data in a simple yet flexible data model consisting of three tables (Deployments, Media and Observations) that supports a wide range of camera deployment designs, classification techniques (e.g., human and AI, media-based and event-based) and analytical use cases, from compiling species occurrence data through distribution, occupancy and activity modeling to density estimation. The format further achieves interoperability by building upon existing standards, Frictionless Data Package in particular, which is supported by a suite of open software tools to read and validate data. Camtrap DP is the consensus of a long, in-depth, consultation and outreach process with standard and software developers, the main existing camera trap data management platforms, major players in the field of camera trapping and the Global Biodiversity Information Facility (GBIF). Under the umbrella of the Biodiversity Information Standards (TDWG), Camtrap DP has been developed openly, collaboratively and with version control from the start. We encourage camera trapping users and developers to join the discussion and contribute to the further development and adoption of this standard. Biodiversity data, camera traps, data exchange, data sharing, information standardspublishedVersio

Mammal responses to global changes in human activity vary by trophic group and landscape

Wildlife must adapt to human presence to survive in the Anthropocene, so it is critical to understand species responses to humans in different contexts. We used camera trapping as a lens to view mammal responses to changes in human activity during the COVID-19 pandemic. Across 163 species sampled in 102 projects around the world, changes in the amount and timing of animal activity varied widely. Under higher human activity, mammals were less active in undeveloped areas but unexpectedly more active in developed areas while exhibiting greater nocturnality. Carnivores were most sensitive, showing the strongest decreases in activity and greatest increases in nocturnality. Wildlife managers must consider how habituation and uneven sensitivity across species may cause fundamental differences in human–wildlife interactions along gradients of human influence.Peer reviewe

Untersuchungen zum Raumnutzungsverhalten und zur Nahrungsökologie ausgewählter Raubsäugerarten im brandenburgischen Vogelschutzgebiet „Mittlere Havelniederung“ mit besonderem Blick auf am Boden brütende Vogelarten

Seit mehreren Jahrzehnten nehmen die Bestandszahlen von Bodenbrütern europaweit ab, ein Prozess, der hauptsächlich auf die Intensivierung der Landwirtschaft zurückzuführen ist. In diesem Zusammenhang könnte aber auch die zunehmende Prädation von Raubsäugern (Carnivora, Mammalia) eine wichtige Rolle spielen. Ziel dieser Studie war die Bewertung und Einschätzung des Prädationspotentials ausgewählter Raubsäugerarten auf Bodenbrüter im brandenburgischen Vogelschutzgebiet "Mittlere Havelniederung". Von Mai 2015 bis Juni 2017 wurde die Raumnutzung verschiedener Raubsäugerarten mittels Kamerafallen und einer Telemetriestudie untersucht und anschließend mit den Ergebnissen einer Vogelkartierung verglichen. Ergänzt wurden die Untersuchungen mit einer Losungsanalyse der am häufigsten beobachteten Raubsäugerarten Waschbär (Procyon lotor) und Rotfuchs (Vulpes vulpes). Waschbären wiesen eine hohe Standorttreue sowie eine hohe Präferenz für Gewässer und Feuchtgebiete auf, während Rotfüchse eine hohe intraspezifische Variabilität in Bezug auf ihre Habitatnutzung sowie ein hohes Abwanderungsverhalten zeigten. Die Ergebnisse lassen außerdem ein hohes Prädationspotential des Waschbären auf Wasservögel vermuten, während der Einfluss auf Wiesenbrüter geringer zu sein scheint. Diese scheinen den höchsten Prädationsdruck durch den Rotfuchs zu erfahren. Die Nahrungsanalysen bestätigen diese Ergebnisse und spiegeln auch räumliche Bewegungsmuster beider Arten wieder. Diese Studie liefert als eine der europaweit ersten Studien empirische Belege für ein starkes indirektes und direktes Prädationspotential des Waschbären insbesondere auf Wasservögel. Zudem hebt diese Studie die Notwendigkeit einer differenzierten Betrachtung potenzieller Auswirkungen von Raubsäugern auf Bodenbrüter hervor und gibt einen Hinweis darauf, dass das Prädationspotential einer Raubsäugerart eng mit der Strukturvielfalt eines Lebensraums und somit mit der Intensität der landwirtschaftlichen Bewirtschaftung zusammenhängt.Over recent decades, a general decline in ground-nesting bird species has been recorded all over Europe and this trend is mainly a result of agricultural intensification. However, increasing predation pressure by carnivores (Carnivora, Mammalia) might also play an important role in this context. The aim of this study was to assess and evaluate the predatory potential of selected carnivore species on ground-nesting birds in the Special Protection Area `Mittlere Havelniederung’ in Brandenburg, Germany. Between May 2015 and June 2017, camera- trapping and a telemetry study were carried out to investigate the spatial behavior of mesocarnivore species. Subsequently, spatial data were compared with results of a bird mapping and complemented by an analysis of scat contents of the most abundant carnivore species raccoon (Procyon lotor) and red fox (Vulpes vulpes). Camera trapping revealed a high diversity of carnivores. In addition, spatial distribution patterns showed high site fidelity and an exclusive preference for waters and swamplands in raccoons, while red foxes showed a high level of intraspecific variance in habitat use and a pronounced level of migratory activity. Predator-prey spatial overlap assumes a high potential impact of raccoons on water-associated bird species, while their impact on grassland birds appears not as important. Grassland birds seem to experience highest predatory pressure by red foxes. Dietary analysis support these results and confirm species-specific spatial patterns. As one of the first studies in Europe, this study provides empirical evidence of raccoons’ strong indirect and direct predatory potential in particular on water-associated bird species. Besides, this study highlights the need for a differentiated view on the potential impact of carnivore species on ground-nesting birds and suggests, that the predatory potential of a carnivore species is linked with landscape diversity and thus with intensity of agricultural land use practices