A review of extensive variation in the design of pitfall traps and a proposal for a standard pitfall trap design for monitoring ground-active arthropod biodiversity.
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ABSTRACT:
SUBMITTER: Brown GR
PROVIDER: S-EPMC4867678 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Soil fauna play a key role in nutrient cycling and decomposition, and in recent years, researchers have become more and more interested in this compartment of terrestrial ecosystems. In addition, soil fauna can act as ecosystem engineers by creating, modifying, and maintaining the habitat for other organisms. Ecologists usually utilize live catches in pitfalls traps as a standard method to study the activity of epigeic fauna in addition to relative abundance. Counts in pitfall traps can be used as estimates of relative activity to compare among experimental treatments. This requires taking independent estimates of abundance (e.g., by sifting soil litter, mark-recapture), which can then be used as covariates in linear models to compare the levels of fauna activity (trap catches) among treatments. However, many studies show that the use of pitfall traps is not the most adequate method to estimate soil fauna relative abundances, and these concerns may be extensible to estimating activity. Here, we present two new types of traps devised to study activity in litter fauna, and which we call "cul-de-sac" and "basket traps", respectively. We experimentally show that, at least for litter dwellers, these new traps are more appropriate to estimate fauna activity than pitfall traps because: (1) pitfall traps contain 3.5× more moisture than the surrounding environment, potentially attracting animals towards them when environmental conditions are relatively dry; (2) cul-de-sac and basket traps catch ca. 4× more of both meso- and macrofauna than pitfall traps, suggesting that pitfall traps are underestimating activity; and (3) pitfall traps show a bias towards collecting 1.5× higher amounts of predators, which suggests that predation rates are higher within pitfall traps. We end with a protocol and recommendations for how to use these new traps in ecological experiments and surveys aiming at estimating soil arthropod activity.
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Project description:BackgroundPitfall traps are commonly used to assess ground dwelling arthropod communities. The effects of different pitfall trap designs on the trapping outcome are poorly investigated however they might affect conclusions drawn from pitfall trap data greatly.MethodsWe tested four pitfall trap types which have been used in previous studies for their effectiveness: a simple type, a faster exchangeable type with an extended plastic rim plate and two types with guidance barriers (V- and X-shaped). About 20 traps were active for 10 weeks and emptied biweekly resulting in 100 trap samples.ResultsPitfall traps with guidance barriers were up to five times more effective than simple pitfall traps and trap samples resulted in more similar assemblage approximations. Pitfall traps with extended plastic rim plates did not only perform poorly but also resulted in distinct carabid assemblages with less individuals of small species and a larger variation.DiscussionDue to the obvious trait filtering and resulting altered assemblages, we suggest not to use pitfall traps with extended plastic rim plates. In comprehensive biodiversity inventories, a smaller number of pitfall traps with guidance barriers and a larger number of spatial replicates is of advantage, while due to comparability reasons, the use of simple pitfall traps will be recommended in most other cases.
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Project description:Insect populations are changing rapidly, and monitoring these changes is essential for understanding the causes and consequences of such shifts. However, large-scale insect identification projects are time-consuming and expensive when done solely by human identifiers. Machine learning offers a possible solution to help collect insect data quickly and efficiently.Here, we outline a methodology for training classification models to identify pitfall trap-collected insects from image data and then apply the method to identify ground beetles (Carabidae). All beetles were collected by the National Ecological Observatory Network (NEON), a continental scale ecological monitoring project with sites across the United States. We describe the procedures for image collection, image data extraction, data preparation, and model training, and compare the performance of five machine learning algorithms and two classification methods (hierarchical vs. single-level) identifying ground beetles from the species to subfamily level. All models were trained using pre-extracted feature vectors, not raw image data. Our methodology allows for data to be extracted from multiple individuals within the same image thus enhancing time efficiency, utilizes relatively simple models that allow for direct assessment of model performance, and can be performed on relatively small datasets.The best performing algorithm, linear discriminant analysis (LDA), reached an accuracy of 84.6% at the species level when naively identifying species, which was further increased to >95% when classifications were limited by known local species pools. Model performance was negatively correlated with taxonomic specificity, with the LDA model reaching an accuracy of ~99% at the subfamily level. When classifying carabid species not included in the training dataset at higher taxonomic levels species, the models performed significantly better than if classifications were made randomly. We also observed greater performance when classifications were made using the hierarchical classification method compared to the single-level classification method at higher taxonomic levels.The general methodology outlined here serves as a proof-of-concept for classifying pitfall trap-collected organisms using machine learning algorithms, and the image data extraction methodology may be used for nonmachine learning uses. We propose that integration of machine learning in large-scale identification pipelines will increase efficiency and lead to a greater flow of insect macroecological data, with the potential to be expanded for use with other noninsect taxa.
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Project description:A novel design of a portable funnel light trap (PFLT) was presented for collecting insects in ecological studies. The trap consists of a compact plastic box equipped with a light source and power source, along with two plastic polypropylene interception vanes. The PFLT costs 18.3 USD per unit and weighs approximately 300 g. A maximum of six PFLT units can be packed in one medium-sized backpack (32 cm × 45 cm × 15 cm, 20 L), making it easier to set up multiple units in remote areas wherein biodiversity research is needed. The low cost and weight of the trap also allows for large-scale deployment. The design is customizable and can be easily manufactured to fit various research needs. To validate the PFLT's efficacy in collecting insects across different habitat types, a series of field experiments were conducted in South Korea and Laos, where 37 trials were carried out. The PFLT successfully collected 7497 insects without experiencing battery issues or damage by rain or wind. Insect compositions and abundances differed across the three sampled habitat types: forests, grasslands, and watersides. This new FLT trap is an important tool for studying and protecting insect biodiversity, particularly in areas wherein conventional light traps cannot be deployed.
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Project description:Central-place foragers, such as social insects or nesting birds, repeatedly use the same routes from and to their nests when foraging for food. Such species forage more efficiently after accumulating experience. We examined, here, a relatively neglected aspect of such an improvement with experience-the avoidance of pitfall traps. Similar pits are built by antlions, which co-occur with the ants, but they also resemble other natural obstacles. We used the desert ant Cataglyphis niger, common in sandy habitats, and allowed it to forage for three successive runs for a food reward. Ant workers discovered food more slowly and in smaller numbers when pits were in their path. Pit presence also led to longer tracks by ants and slower movement. However, with experience, the ants fell into such pits less often and reached the food more quickly. To understand how past conditions affect current behavior, we investigated whether removing or adding pits led to a different result to that with a constant number of pits. Workers adjusted their behavior immediately when conditions changed. The only carryover effect was the longer tracks crossed by workers after pit removal, possibly resulting from the mismatch between the past and current conditions. Finally, the workers were more likely to fall into pits that were closer to the nest than those that were further away. This is a good example of the advantage that ambush predators can derive from ambushing their prey in specific locations.
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Project description:The ecological functioning of dryland ecosystems is closely related to the spatial pattern of the vegetation, which is typically structured in patches. Ground arthropods mediate key soil functions and ecological processes, yet little is known about the influence of dryland vegetation pattern on their abundance and diversity. Here, we investigate how patch size and cover, and distance between patches relate to the abundance and diversity of meso-and microarthropods in semi-arid steppes. We found that species richness and abundance of ground arthropods exponentially increase with vegetation cover, patch size, and patch closeness. The communities under vegetation patches mainly respond to patch size, while the communities in the bare-soil interpatches are mostly controlled by the average distance between patches, independently of the concurrent changes in vegetation cover. Large patches seem to play a critical role as reserve and source of ground arthropod diversity. Our results suggest that decreasing vegetation cover and/or changes in vegetation pattern towards small and over-dispersed vegetation patches can fast lead to a significant loss of ground arthropods diversity in drylands.
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