Monitoring arthropods in protected grasslands: comparing pitfall trapping, quadrat sampling and video monitoring

Zaller, Johann G.; Kerschbaumer, G.; Rizzoli, R.; Tiefenbacher, Alexandra; Gruber, Edith; Schedl, Heimo

doi:10.5194/we-15-15-2015

Cited by 17 publications

(17 citation statements)

References 42 publications

(64 reference statements)

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“…Compared to the often used pitfall traps, camera traps sample more individuals (Collett & Fisher, 2017;Halsall & Wratten, 1988), and cause no depletion of specimens or habitat destruction (Digweed, Currie, Carcamo, & Spence, 1995;Zaller et al, 2015). Compared to the often used pitfall traps, camera traps sample more individuals (Collett & Fisher, 2017;Halsall & Wratten, 1988), and cause no depletion of specimens or habitat destruction (Digweed, Currie, Carcamo, & Spence, 1995;Zaller et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, camera traps require less maintenance (Caravaggi et al, 2017;Collett & Fisher, 2017). Rarely, but increasingly, camera traps have been used to monitor insects and other arthropods (Collett & Fisher, 2017;Dolek & Georgi, 2017;Zaller et al, 2015). Rarely, but increasingly, camera traps have been used to monitor insects and other arthropods (Collett & Fisher, 2017;Dolek & Georgi, 2017;Zaller et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…Even though identifications of species based on images are well known for mammals and birds (Norouzzadeh et al, 2018;Yu et al, 2013), camera trap studies designed for arthropods conclude that image-based species identification by humans is generally not possible (Collett & Fisher, 2017;Zaller et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…The camera trap method has distinct advantages over traditional methods in entomology. Compared to the often used pitfall traps, camera traps sample more individuals (Collett & Fisher, 2017;Halsall & Wratten, 1988), and cause no depletion of specimens or habitat destruction (Digweed, Currie, Carcamo, & Spence, 1995;Zaller et al, 2015). Furthermore, camera traps require less maintenance (Caravaggi et al, 2017;Collett & Fisher, 2017).…”

Section: Introductionmentioning

confidence: 99%

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Species‐level image classification with convolutional neural network enables insect identification from habitus images

Hansen

Svenning

Olsen

et al. 2019

Ecology and Evolution

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1. Changes in insect biomass, abundance, and diversity are challenging to track at sufficient spatial, temporal, and taxonomic resolution. Camera traps can capture habitus images of ground-dwelling insects. However, currently sampling involves manually detecting and identifying specimens. Here, we test whether a convolutional neural network (CNN) can classify habitus images of ground beetles to species level, and estimate how correct classification relates to body size, number of species inside genera, and species identity.2. We created an image database of 65,841 museum specimens comprising 361 carabid beetle species from the British Isles and fine-tuned the parameters of a pretrained CNN from a training dataset. By summing up class confidence values within genus, tribe, and subfamily and setting a confidence threshold, we trade-off between classification accuracy, precision, and recall and taxonomic resolution.3. The CNN classified 51.9% of 19,164 test images correctly to species level and 74.9% to genus level. Average classification recall on species level was 50.7%.Applying a threshold of 0.5 increased the average classification recall to 74.6% at the expense of taxonomic resolution. Higher top value from the output layer and larger sized species were more often classified correctly, as were images of species in genera with few species. 4. Fine-tuning enabled us to classify images with a high mean recall for the whole test dataset to species or higher taxonomic levels, however, with high variability.This indicates that some species are more difficult to identify because of properties such as their body size or the number of related species.5. Together, species-level image classification of arthropods from museum collections and ecological monitoring can substantially increase the amount of occurrence data that can feasibly be collected. These tools thus provide new opportunities in understanding and predicting ecological responses to environmental change. Jens-Christian Svenninghttps://orcid.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Species‐level image classification with convolutional neural network enables insect identification from habitus images

Hansen

Svenning

Olsen

et al. 2019

Ecology and Evolution

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show abstract

“…i , also reformulated as D = 1 − C, which is usually referred to as Gini-Simpson index of diversity (e.g., [13,14]). In 1961, Rènyi [15] outlined a generalization of Shannon entropy as a 1-parameter functional family, and Hill [16], about a decade later, used the exponential form of Rènyi's generalized entropy to derive what he called diversity numbers, which were shown to be related with Shannon and Simpson diversity measures.…”

Section: Introductionmentioning

confidence: 99%

Discussing Landscape Compositional Scenarios Generated with Maximization of Non-Expected Utility Decision Models Based on Weighted Entropies

Casquilho

Rego

2017

Entropy

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Abstract:The search for hypothetical optimal solutions of landscape composition is a major issue in landscape planning and it can be outlined in a two-dimensional decision space involving economic value and landscape diversity, the latter being considered as a potential safeguard to the provision of services and externalities not accounted in the economic value. In this paper, we use decision models with different utility valuations combined with weighted entropies respectively incorporating rarity factors associated to Gini-Simpson and Shannon measures. A small example of this framework is provided and discussed for landscape compositional scenarios in the region of Nisa, Portugal. The optimal solutions relative to the different cases considered are assessed in the two-dimensional decision space using a benchmark indicator. The results indicate that the likely best combination is achieved by the solution using Shannon weighted entropy and a square root utility function, corresponding to a risk-averse behavior associated to the precautionary principle linked to safeguarding landscape diversity, anchoring for ecosystem services provision and other externalities. Further developments are suggested, mainly those relative to the hypothesis that the decision models here outlined could be used to revisit the stability-complexity debate in the field of ecological studies.

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Environmental DNA metabarcoding reliably recovers arthropod interactions which are frequently observed by video recordings of flowers

Stothut,

Kühne,

Ströbele

et al. 2024

Environmental DNA

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Environmental DNA (eDNA) metabarcoding promises to be a cost‐ and time‐efficient monitoring tool to detect interactions of arthropods with plants. However, observation‐based verification of the eDNA‐derived data is still required to confirm the reliability of those detections, i.e., to verify whether the arthropods have previously interacted with the plant. Here, we conducted a comparative analysis of the performance of eDNA metabarcoding and video camera observations to detect arthropod communities associated with sunflowers (Helianthus annuus, L.). We compared the taxonomic composition, interaction type, and diversity by testing for an effect of arthropod interaction time and occupancy on successful taxon recovery by eDNA. We also tested if prewashing of the flowers successfully removed eDNA deposition from before the video camera recording, thus enabling a reset of the community for standardized monitoring. We find that eDNA and video camera observations recovered distinct communities, with about a quarter of the arthropod families overlapping. However, the overlapping taxa comprised ~90% of the interactions observed by the video camera. Interestingly, eDNA metabarcoding recovered more unique families than the video cameras, but approximately two‐thirds of those unique observations were of rare species. The eDNA‐derived families were biased toward plant sap‐suckers, showing that such species may deposit more eDNA than, for example, transient pollinators. We also find that prewashing of the flower heads did not suffice to remove all eDNA traces, suggesting that eDNA on plants may be more temporally stable than previously thought. Our work highlights the great potential of eDNA as a tool to detect plant‐arthropod interactions, particularly for specialized and frequently interacting taxa.

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Monitoring arthropods in protected grasslands: comparing pitfall trapping, quadrat sampling and video monitoring

Cited by 17 publications

References 42 publications

Species‐level image classification with convolutional neural network enables insect identification from habitus images

Species‐level image classification with convolutional neural network enables insect identification from habitus images

Discussing Landscape Compositional Scenarios Generated with Maximization of Non-Expected Utility Decision Models Based on Weighted Entropies

Environmental DNA metabarcoding reliably recovers arthropod interactions which are frequently observed by video recordings of flowers

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