A comparison of different Malaise trap types

Recent reports on insect decline have highlighted the need for long-term data on insect communities towards identifying their trends and drivers. With the launch of many new insect monitoring schemes to investigate insect communities over large spatial and temporal scales, Malaise traps have become one of the most important tools due to the broad spectrum of species collected and reduced capture bias through passive sampling of insects day and night. However, Malaise traps can vary in size, shape, and colour, and it is unknown how these differences affect biomass, species richness, and composition of trap catch, making it difficult to compare results between studies. We compared five Malaise trap types (three variations of the Townes and two variations of the Bartak Malaise trap) to determine their effects on biomass and species richness as identified by metabarcoding. Insect biomass varied by 20%–55%, not strictly following trap size but varying with trap type. Total species richness was 20%–38% higher in the three Townes trap models compared to the Bartak traps. Bartak traps captured lower richness of highly mobile taxa but increased richness of ground-dwelling taxa. The white roofed Townes trap captured a higher richness of pollinators. We find that biomass, total richness, and taxa group specific richness are all sensitive to Malaise trap type. Trap type should be carefully considered and aligned to match monitoring and research questions. Additionally, our estimates of trap type effects can be used to adjust results to facilitate comparisons across studies

Disentangling effects of climate and land use on biodiversity and ecosystem services - a multi‐scale experimental design

Climate and land-use change are key drivers of environmental degradation in the Anthropocene, but too little is known about their interactive effects on biodiversity and ecosystem services. Long-term data on biodiversity trends are currently lacking. Furthermore, previous ecological studies have rarely considered climate and land use in a joint design, did not achieve variable independence or lost statistical power by not covering the full range of environmental gradients. Here, we introduce a multi-scale space-for-time study design to disentangle effects of climate and land use on biodiversity and ecosystem services. The site selection approach coupled extensive GIS-based exploration (i.e. using a Geographic information system) and correlation heatmaps with a crossed and nested design covering regional, landscape and local scales. Its implementation in Bavaria (Germany) resulted in a set of study plots that maximise the potential range and independence of environmental variables at different spatial scales. Stratifying the state of Bavaria into five climate zones (reference period 1981–2010) and three prevailing land-use types, that is, near-natural, agriculture and urban, resulted in 60 study regions (5.8 × 5.8 km quadrants) covering a mean annual temperature gradient of 5.6–9.8°C and a spatial extent of ~310 × 310 km. Within these regions, we nested 180 study plots located in contrasting local land-use types, that is, forests, grasslands, arable land or settlement (local climate gradient 4.5–10°C). This approach achieved low correlations between climate and land use (proportional cover) at the regional and landscape scale with |r ≤ 0.33| and |r ≤ 0.29| respectively. Furthermore, using correlation heatmaps for local plot selection reduced potentially confounding relationships between landscape composition and configuration for plots located in forests, arable land and settlements. The suggested design expands upon previous research in covering a significant range of environmental gradients and including a diversity of dominant land-use types at different scales within different climatic contexts. It allows independent assessment of the relative contribution of multi-scale climate and land use on biodiversity and ecosystem services. Understanding potential interdependencies among global change drivers is essential to develop effective restoration and mitigation strategies against biodiversity decline, especially in expectation of future climatic changes. Importantly, this study also provides a baseline for long-term ecological monitoring programs

Diversity and specialization responses to climate and land use differ between deadwood fungi and bacteria

Climate and land use are major determinants of biodiversity, and declines in species richness in cold and human exploited landscapes can be caused by lower rates of biotic interactions. Deadwood fungi and bacteria interact strongly with their hosts due to long-lasting evolutionary trajectories. However, how rates of biotic interactions (specialization) change with temperature and land-use intensity are unknown for both microbial groups. We hypothesize a decrease in species richness and specialization of communities with decreasing temperature and increasing land use intensity while controlling for precipitation. We used a full-factorial nested design to disentangle land use at habitat and landscape scale and temperature spanning an area of 300 × 300 km in Germany. We exposed four deadwood objects representing the main tree species in Central Europe (beech, oak, spruce, pine) in 175 study plots. Overall, we found that fungal and bacterial richness, community composition and specialization were weakly related to temperature and land use. Fungal richness was slightly higher in near-natural than in urban landscapes. Bacterial richness was positively associated with mean annual temperature, negatively associated with local temperature and highest in grassland habitats. Bacterial richness was positively related to the covariate mean annual precipitation. We found strong effects of host-tree identity on species richness and community composition. A generally high level of fungal host-tree specialization might explain the weak response to temperature and land use. Effects of host-tree identity and specialization were more pronounced in fungi. We suggest that host tree changes caused by land use and climate change will be more important for fungal communities, while changes in climate will affect bacterial communities more directly. Contrasting responses of the two taxonomic groups suggest a reorganization of deadwood microbial communities, which might have further consequences on diversity and decomposition in the Anthropocene

Relationship of insect biomass and richness with land use along a climate gradient

Recently reported insect declines have raised both political and social concern. Although the declines have been attributed to land use and climate change, supporting evidence suffers from low taxonomic resolution, short time series, a focus on local scales, and the collinearity of the identified drivers. In this study, we conducted a systematic assessment of insect populations in southern Germany, which showed that differences in insect biomass and richness are highly context dependent. We found the largest difference in biomass between semi-natural and urban environments (−42%), whereas differences in total richness (−29%) and the richness of threatened species (−56%) were largest from semi-natural to agricultural environments. These results point to urbanization and agriculture as major drivers of decline. We also found that richness and biomass increase monotonously with increasing temperature, independent of habitat. The contrasting patterns of insect biomass and richness question the use of these indicators as mutual surrogates. Our study provides support for the implementation of more comprehensive measures aimed at habitat restoration in order to halt insect declines

Dung‐visiting beetle diversity is mainly affected by land use, while community specialization is driven by climate

Dung beetles are important actors in the self‐regulation of ecosystems by driving nutrient cycling, bioturbation, and pest suppression. Urbanization and the sprawl of agricultural areas, however, destroy natural habitats and may threaten dung beetle diversity. In addition, climate change may cause shifts in geographical distribution and community composition. We used a space‐for‐time approach to test the effects of land use and climate on α‐diversity, local community specialization (H (2)′) on dung resources, and γ‐diversity of dung‐visiting beetles. For this, we used pitfall traps baited with four different dung types at 115 study sites, distributed over a spatial extent of 300 km × 300 km and 1000 m in elevation. Study sites were established in four local land‐use types: forests, grasslands, arable sites, and settlements, embedded in near‐natural, agricultural, or urban landscapes. Our results show that abundance and species density of dung‐visiting beetles were negatively affected by agricultural land use at both spatial scales, whereas γ‐diversity at the local scale was negatively affected by settlements and on a landscape scale equally by agricultural and urban land use. Increasing precipitation diminished dung‐visiting beetle abundance, and higher temperatures reduced community specialization on dung types and γ‐diversity. These results indicate that intensive land use and high temperatures may cause a loss in dung‐visiting beetle diversity and alter community networks. A decrease in dung‐visiting beetle diversity may disturb decomposition processes at both local and landscape scales and alter ecosystem functioning, which may lead to drastic ecological and economic damage

Insect Detect - insect classification dataset v2

The Insect Detect - insect classification dataset v2 contains mainly images of various insects sitting on or flying above an artificial flower platform. All images were automatically recorded with the Insect Detect DIY camera trap, a hardware combination of the Luxonis OAK-1, Raspberry Pi Zero 2 W and PiJuice Zero pHAT for automated insect monitoring. Most of the images were captured by camera traps deployed at different sites in 2023. For some classes (e.g. ant, bee_bombus, beetle_cocci, bug, bug_grapho, hfly_eristal, hfly_myathr, hfly_syrphus) additional images were captured with a lab setup of the camera trap. For some classes (e.g. bee_apis, fly, hfly_episyr, wasp) images from the first dataset version were transferred to this dataset. This dataset is also available on Roboflow Universe. The images in the dataset from Roboflow are automatically compressed, which decreases model accuracy when used for training. Therefore it is recommended to use this uncompressed Zenodo version and split the dataset into train/val/test subsets in the provided training notebook. Classes This dataset contains the following 27 classes: ant (Formicidae) bee (Anthophila excluding Apis mellifera and Bombus sp.) bee_apis (Apis mellifera) bee_bombus (Bombus sp.) beetle (Coleoptera excluding Coccinellidae and some Oedemeridae) beetle_cocci (Coccinellidae) beetle_oedem (visually distinct Oedemeridae) bug (Heteroptera excluding Graphosoma italicum) bug_grapho (Graphosoma italicum) fly (Brachycera excluding Empididae, Sarcophagidae, Syrphidae and small Brachycera) fly_empi (Empididae) fly_sarco (visually distinct Sarcophagidae) fly_small (small Brachycera) hfly_episyr (hoverfly Episyrphus balteatus) hfly_eristal (hoverfly Eristalis sp., mainly Eristalis tenax) hfly_eupeo (mainly hoverfly Eupeodes corollae and Scaeva pyrastri) hfly_myathr (hoverfly Myathropa florea) hfly_sphaero (hoverfly Sphaerophoria sp., mainly Sphaerophoria scripta) hfly_syrphus (mainly hoverfly Syrphus sp.) lepi (Lepidoptera) none_bg (images with no insect - background (platform)) none_bird (images with no insect - bird sitting on platform) none_dirt (images with no insect - leaves and other plant material, bird droppings) none_shadow (images with no insect - shadows of insects or surrounding plants) other (other Arthropods, including various Hymenoptera and Symphyta, Diptera, Orthoptera, Auchenorrhyncha, Neuroptera, Araneae) scorpionfly (Panorpa sp.) wasp (mainly Vespula sp. and Polistes dominula) For the classes hfly_eupeo and hfly_syrphus a precise taxonomic distinction is not possible with images only, due to a potentially high variability in the appearance of the respective species. While most specimens will show the visual features that are important for a classification into one of these classes, some specimens of Syrphus sp. might look more like Eupeodes sp. and vice versa. The images were sorted to the respective class by considering taxonomic and visual distinctions. However, this dataset is still rather small regarding the visually extremely diverse Insecta. Insects that are not included in this dataset can therefore be classified to the wrong class. All results should always be manually validated and false classifications can be used to extend this basic dataset and retrain your custom classification model. Deployment You can use this dataset as starting point to train your own insect classification models with the provided Google Colab training notebook. Read the model training instructions for more information. A insect classification model trained on this dataset is available in the insect-detect-ml GitHub repo. To deploy the model on your PC (ONNX format for fast CPU inference), follow the provided step-by-step instructions. License This dataset is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0). Citation You can cite this dataset as: Sittinger, M., Uhler, J., Pink, M. (2023). Insect Detect - insect classification dataset v2 [Data set]. Zenodo. https://doi.org/10.5281/zenodo.832538

Insect detect: An open-source DIY camera trap for automated insect monitoring.

Insect monitoring is essential to design effective conservation strategies, which are indispensable to mitigate worldwide declines and biodiversity loss. For this purpose, traditional monitoring methods are widely established and can provide data with a high taxonomic resolution. However, processing of captured insect samples is often time-consuming and expensive, which limits the number of potential replicates. Automated monitoring methods can facilitate data collection at a higher spatiotemporal resolution with a comparatively lower effort and cost. Here, we present the Insect Detect DIY (do-it-yourself) camera trap for non-invasive automated monitoring of flower-visiting insects, which is based on low-cost off-the-shelf hardware components combined with open-source software. Custom trained deep learning models detect and track insects landing on an artificial flower platform in real time on-device and subsequently classify the cropped detections on a local computer. Field deployment of the solar-powered camera trap confirmed its resistance to high temperatures and humidity, which enables autonomous deployment during a whole season. On-device detection and tracking can estimate insect activity/abundance after metadata post-processing. Our insect classification model achieved a high top-1 accuracy on the test dataset and generalized well on a real-world dataset with captured insect images. The camera trap design and open-source software are highly customizable and can be adapted to different use cases. With custom trained detection and classification models, as well as accessible software programming, many possible applications surpassing our proposed deployment method can be realized

Normalized confusion matrix for the EfficientNet-B0 insect classification model, validated on the dataset test split.

The cell values show the proportion of images that were classified to a predicted class (x-axis) to the total number of images per true class (y-axis). The model was trained on a custom dataset with 21,000 images (14,686 in train split). Metrics are shown on the dataset test split (2,125 images) for the converted model in ONNX format.</p

Mean PiJuice battery charge level and sum of the sunshine duration per day.

The PiJuice battery was charged by a second battery, connected to a 9W solar panel. Weather data was taken from the nearest weather station (source: Deutscher Wetterdienst).</p

Camera trap design.

(A) Field deployment of the camera trap and flower platform (35x20 cm) on wooden post. (B) Weatherproof camera trap enclosure with integrated hardware and connected solar panel.</p