Managing and Analysing Camera Trapping Data: An Advanced Toolbox

Camera trapping has become a prime source of information about wild terrestrial mammals over the recent years, particularly for rare and elusive species and in challenging habitats. Key inference from camera-trapping encompasses species habitat associations, density and abundance estimations, or species interactions, amongst others. The rapid development of those methods and the large amount of data collected entail new challenges in terms of data management and analysis. The aim of this thesis was to contribute to the development of new methods for managing (Chapter 2) and analysing (Chapter 3 and 4) camera trapping data and to thus increase the efficiency and effectiveness of the use of camera-trapping data for practitioners both in academia and conservation. Camera-trapping can generate vast volumes of data over short periods of time, making efficient yet flexible data management imperative. In my first manuscript (Chapter 2), I developed a free and open-source R package for camera trap data management, camtrapR. It is the first such toolbox in the popular R language and was designed to offer a comprehensive and flexible workflow from data acquisition to creating input for well-developed downstream analytical tools, e.g. in occupancy or spatial capture-recapture frameworks. The package has quickly gained popularity and is now being used worldwide in scientific and conservation work, while it is still being actively maintained and developed. Species occurrence data from camera-traps can be combined with habitat information at camera traps via occupancy models in order to identify habitat associations of species while explicitly accounting for imperfect detection. The spatial scale at which habitat information are collected (grain and extent) will influence results heavily. In my second manuscript (Chapter 3), I assessed the influence of spatial scale on estimates of species-habitat relationships by varying the spatial resolution and extent of habitat covariates used in single-species occupancy models for six mammal species from Sabah, Malaysian Borneo. Habitat data from high-resolution (5-m RapidEye) satellite imagery had considerably higher model support than lower resolution data (≥30 m). Likewise, habitat data from patches of 50 meters around camera traps had higher model support than smaller (10 m) or larger (100 – 500 m) habitat patches. This study was the first to use 5-m RapidEye imagery in occupancy models and demonstrated the potential of such high-resolution satellite imagery for obtaining more realistic species-habitat associations in occupancy modelling, particularly in heterogeneous landscapes. The flexibility high-resolution satellite imagery offer in defining suitable spatial scales further add to their value. Species distributions in space and time are not only shaped by habitat preferences, but also by interactions between species, such as predator-prey relationships or various forms of competition. Discovering such spatiotemporal interactions in camera trapping data is challenging due to the sparseness and randomness of camera trapping data and further exacerbated by a lack of systematic comparisons of methods to assess such interactions. Therefore, in my third manuscript (Chapter 4), I developed a method to flexibly simulate camera trapping records of two interacting species. These simulated data are used for the first comparative assessment of the statistical power and robustness of a suite of statistical tests for spatiotemporal interactions. Linear models were the most powerful and flexible method to discover such interactions. Nevertheless, only strong interactions could be detected reliably with any of the methods tested. This novel simulation approach and the recommendations given can serve as guidelines for practitioners wishing to assess interactions between or within species from camera trapping data.Kamerafallen haben sich in den letzten Jahren zu einer der wichtigsten Datenquellen über wildlebende terrestrische Säugetiere entwickelt, insbesondere für seltene und schwer beobachtbare Arten sowie in herausfordernden Habitaten. Wichtige Rückschlüsse, welche aus Kamerafallendaten gewonnen werden können, sind unter anderem Habitatassoziationen von Arten, Schätzungen von Dichte und Abundanz, oder Interaktionen zwischen Arten. Die rasante Entwicklung dieser Methoden und die enormen Datenmengen, die dabei entstehen, hatten neue Herausforderungen hinsichtlich Datenverwaltung und –analyse zur Folge. Das Ziel dieser Arbeit war, zur Entwicklung von neuen Methoden zum Verwalten (Kapitel 2) und Analysieren (Kapitel 3 und 4) von Kamerafallendaten beizutragen und damit sowohl Effizienz als auch die Effektivität der Nutzung von Kamerafallendaten in Wissenschaft und Naturschutzarbeit zu verbessern. Da Kamerafallenstudien in kurzer Zeit riesige Datenmengen produzieren können, ist effizientes und flexibles Kamerafallendatenmanagement zwingend erforderlich. In meinem ersten Manuskript (Kapitel 2) habe ich ein frei verfügbares und quelloffenes R-Paket für die Verwaltung von Kamerafallendaten entwickelt, camtrapR. Es ist das erste derartige Paket in der weitverbreiteten Programmiersprache R, und es wurde konzipiert, um einen umfassenden und flexiblen Arbeitsfluss von der Datenerhebung bis zum Bereitstellen von Daten für weitergehende Analysen zu gewährleisten, z.B. mit Occupancy- oder Spatial Capture-Recapture-Methoden. Das Paket wird weiterhin gepflegt und weiterentwickelt, hat schnell an Popularität gewonnen und wird weltweit in Wissenschaft und Naturschutzarbeit genutzt. Daten über das Vorkommen von Arten aus Kamerafallen kann mit Habitatinformationen an Kamerafallenstandorten mit Hilfe von Occupancy-Modellen kombiniert werden, um Habitatassoziationen von Arten zu identifizieren und gleichzeitig für die unvollständige Detektierbarkeit von Arten zu korrigieren. Das räumliche Ausmaß (scale), in dem Habitatinformationen gesammelt werden, beeinflusst die Modellergebnisse erheblich. In meinem zweiten Manuskript (Kapitel 3) habe ich den Einfluss des räumlichen Ausmaßes von Habitatdaten auf die Abschätzung von Habitatassoziationen von Arten anhand von sechs Säugetierarten aus einem Kamerafallendatensatz aus Sabah, Borneo, Malaysia untersucht. Das geschah, indem ich die räumliche Auflösung und die Ausdehnung von Habitatinformationen in Occupancy-Modellen für die individuellen Arten variiert habe. Habitatinformationen aus hochauflösenden Satellitenbildern (5-m RapidEye) hatten deutlich höheren Modellsupport als niedrig aufgelöste Daten (≥30 m). Habitatdaten mit einem Radius von 50 m um Kamerafallen hatten gleichermaßen höheren Modellsupport als Daten aus kleineren (10 m) oder größeren (100 – 500 m) Radien. Dies war die erste Studie, die 5-m RapidEye Satelllitendaten in Occupancy-Modellen verwendet und demonstriert den Eignung dieser hochauflösenden Satellitendaten, insbesondere in heterogenen Landschaften mit Hilfe von Occupancy-Modellen zu realistischeren Habitatassoziationen zu gelangen. Die Flexibilität, mit der geeignete räumliche Ausdehnungen von Habitatdaten festgelegt werden können, ist ein weiterer Vorteil dieser Daten. Die Verbreitung von Arten in Raum und Zeit hängt nicht nur von Habitatpräferenzen ab, sondern auch von Interaktionen zwischen Arten, etwa in Räuber-Beute Beziehungen oder Konkurrenz zwischen oder innerhalb von Arten. Solche Beziehungen in Kamerafallendaten zu identifizieren ist herausfordernd aufgrund der Spärlichkeit und Zufälligkeit in Kamerafallendaten, und weiter erschwert durch das Fehlen eines systematischen Vergleiches von Methoden, um solche Interaktionen zu untersuchen. Deswegen habe ich in meinem dritten Manuskript (Kapitel 4) eine Methode entwickelt, mit der sich Kamerafallendaten zweier interagierender Arten flexibel simulieren lassen. Diese simulierten Kamerafallendaten wurden verwendet für die erste vergleichende Bewertung der statistischen Teststärke (power) und Robustheit einer Reihe von statistischen Tests zur Untersuchung räumlich-zeitlicher Interaktionen. Lineare Modelle hatten die höchste Teststärke und sind die flexibelste Methode, um solche Interaktionen festzustellen. Dennoch konnten mit allen untersuchten Methoden nur starke Interaktionen zwischen Arten zuverlässig nachgewiesen werden. Dieser neuartige Simulationsansatz und die daraus folgenden Empfehlungen können als Richtlinien für die Untersuchung von Interaktionen zwischen Arten oder innerhalb von Arten in Kamerafallendaten dienen

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

Planning tiger recovery: Understanding intraspecific variation for effective conservation

Although significantly more money is spent on the conservation of tigers than on any other threatened species, today only 3200 to 3600 tigers roam the forests of Asia, occupying only 7% of their historical range. Despite the global significance of and interest in tiger conservation, global approaches to plan tiger recovery are partly impeded by the lack of a consensus on the number of tiger subspecies or management units, because a comprehensive analysis of tiger variation is lacking. We analyzed variation among all nine putative tiger subspecies, using extensive data sets of several traits [morphological (craniodental and pelage), ecological, molecular]. Our analyses revealed little variation and large overlaps in each trait among putative subspecies, and molecular data showed extremely low diversity because of a severe Late Pleistocene population decline. Our results support recognition of only two subspecies: the Sunda tiger, Panthera tigris sondaica, and the continental tiger, Panthera tigris tigris, which consists of two (northern and southern) management units. Conservation management programs, such as captive breeding, reintroduction initiatives, or trans-boundary projects, rely on a durable, consistent characterization of subspecies as taxonomic units, defined by robust multiple lines of scientific evidence rather than single traits or ad hoc descriptions of one or few specimens. Our multiple-trait data set supports a fundamental rethinking of the conventional tiger taxonomy paradigm, which will have profound implications for the management of in situ and ex situ tiger populations and boost conservation efforts by facilitating a pragmatic approach to tiger conservation management worldwid

Fine-scale distributions of carnivores in a logging concession in Sarawak, Malaysian Borneo

Coarse-scale patterns of distribution and abundance of species are the outcome of processes occurring at finer spatial scales, hence the conservation of species depends on understanding their fine-scale ecology. For Bornean carnivores, little is known about fine-scale predictors of species occurrence and it is assumed that the two main threats to wildlife on Borneo, habitat disturbance and hunting, also impact their occurrence. To increase our understanding of the Borneo carnivore community, we deployed 60 cameras in a logging concession in northern Sarawak, Malaysian Borneo, where different landscape covariates, both natural and anthropogenic, were present. We built single-species occupancy models to investigate fine-scale carnivore occupancy. Overall, forest disturbance had a negative effect on Hose's civet (Diplogale hosei), banded civet (Hemigalus derbyanus) and yellow-throated marten (Martes flavigula). Further, banded civet had greater occupancy probabilities in more remote areas. Logging roads had the most diverse effect on carnivore occupancy, with Hose's civet and masked palm civet (Paguma larvata) negatively affected by roads, whereas Malay civet (Viverra tangalunga), short-tailed mongoose (Herpestes brachyurus) and leopard cat (Prionailurus bengalensis) showed higher occupancy closer to roads. Canopy height, canopy closure, number of trees with holes (cavities) and distance to nearest village also affected occupancy, though the directions of these effects varied among species. Our results highlight the need to collect often overlooked habitat variables: moss cover and ‘kerangas’ (tropical heath forest) were the most important variables predicting occurrence of Hose's civet. The preservation of such forest conditions may be crucial for the long-term conservation of this little-known species. Our results confirm that logged forests, when left to regenerate, can host diverse carnivore communities on Borneo, provided less disturbed habitat is available nearby, though human access needs to be controlled. We recommend easy-to-implement forest management strategies including maintaining forest next to logging roads; preserving fruiting trees and trees with cavities, both standing and fallen; and blocks of remote, less disturbed, mid- to high-elevation forest with understorey vegetation

Identifying refuges for Borneo's elusive Hose's civet

Human-induced environmental changes, particularly climate change, pose a threat to many tropical montane species, making the identification of optimal future habitat a conservation priority. Here we used maximum entropy (Maxent) and boosted regression trees to predict suitable habitat of the threatened Bornean highland endemic Hose's civet (Diplogale hosei), that is currently available, and for future time periods (2050s and 2080s), considering future land cover and climate change predictions. Next, we identified areas that were consistently suitable under current and future model predictions as forest refuges. Our analysis predicted that Hose's civet is restricted mainly to the highlands of Borneo to an area less than 20,000 km2 (about 2% of the entire island of Borneo). Changes in land cover have little impact on predicted suitable area for the species. However, we predicted habitat loss due to climate change to approximate 86% by 2080, except under a “green economy scenario” which showed stable or increasing suitable habitat. Refuges were small, about 11% of 2010 habitat, and mostly restricted to lower montane forest. About 28–35% of refuges lie within the current protected area network though much is designated as commercial forests within the proposed Heart of Borneo (HoB). For the conservation of Hose's civet and likely other Bornean highland endemics, we recommend increased wildlife and forest law enforcement in identified protected refuges and sustainable timber harvesting practices in surrounding commercial forests, both within the HoB and the extensions we identified. Results of our green model showed that efforts to reduce greenhouse gas emissions will likely contribute immensely to the long-term conservation of highland species such as Hose's civet

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

Global disparity of camera trap research allocation and defaunation risk of terrestrial mammals

Abstract Quantifying and monitoring the risk of defaunation and extinction require assessing and monitoring biodiversity in impacted regions. Camera traps that photograph animals as they pass sensors have revolutionized wildlife assessment and monitoring globally. We conducted a global review of camera trap research on terrestrial mammals over the last two decades. We assessed if the spatial distribution of 3395 camera trap research locations from 2324 studies overlapped areas with high defaunation risk. We used a geospatial distribution modeling approach to predict the spatial allocation of camera trap research on terrestrial mammals and to identify its key correlates. We show that camera trap research over the past two decades has not targeted areas where defaunation risk is highest and that 76.8% of the global research allocation can be attributed to country income, biome, terrestrial mammal richness, and accessibility. The lowest probabilities of camera trap research allocation occurred in low‐income countries. The Amazon and Congo Forest basins – two highly biodiverse ecosystems facing unprecedented anthropogenic alteration – received inadequate camera trap research attention. Even within the best covered regions, most of the research (64.2%) was located outside the top 20% areas where defaunation risk was greatest. To monitor terrestrial mammal populations and assess the risk of extinction, more research should be extended to regions with high defaunation risk but have received low camera trap research allocation