Dataset Information

New Litter Trap Devices Outperform Pitfall Traps for Studying Arthropod Activity.

ABSTRACT: 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.

SUBMITTER: Ruiz-Lupion D

PROVIDER: S-EPMC6571813 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

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New Litter Trap Devices Outperform Pitfall Traps for Studying Arthropod Activity.

Ruiz-Lupión Dolores D Pascual Jordi J Melguizo-Ruiz Nereida N Verdeny-Vilalta Oriol O Moya-Laraño Jordi J

Insects 20190523 5

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 ...[more]

PMID: 31126093

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Project description:Background and aimsCarnivorous plants of the genus Nepenthes possess modified leaves that form pitfall traps in order to capture prey, mainly arthropods, to make additional nutrients available for the plant. These pitchers contain a digestive fluid due to the presence of hydrolytic enzymes. In this study, the composition of the digestive fluid was further analysed with regard to mineral nutrients and low molecular-weight compounds. A potential contribution of microbes to the composition of pitcher fluid was investigated.MethodsFluids from closed pitchers were harvested and analysed for mineral nutrients using analytical techniques based on ion-chromatography and inductively coupled plasma-optical emission spectroscopy. Secondary metabolites were identified by a combination of LC-MS and NMR. The presence of bacteria in the pitcher fluid was investigated by PCR of 16S-rRNA genes. Growth analyses of bacteria and yeast were performed in vitro with harvested pitcher fluid and in vivo within pitchers with injected microbes.Key resultsThe pitcher fluid from closed pitchers was found to be primarily an approx. 25-mm KCl solution, which is free of bacteria and unsuitable for microbial growth probably due to the lack of essential mineral nutrients such as phosphate and inorganic nitrogen. The fluid also contained antimicrobial naphthoquinones, plumbagin and 7-methyl-juglone, and defensive proteins such as the thaumatin-like protein. Challenging with bacteria or yeast caused bactericide as well as fungistatic properties in the fluid. Our results reveal that Nepenthes pitcher fluids represent a dynamic system that is able to react to the presence of microbes.ConclusionsThe secreted liquid of closed and freshly opened Nepenthes pitchers is exclusively plant-derived. It is unsuitable to serve as an environment for microbial growth. Thus, Nepenthes plants can avoid and control, at least to some extent, the microbial colonization of their pitfall traps and, thereby, reduce the need to vie with microbes for the prey-derived nutrients.

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.

Dataset Information

New Litter Trap Devices Outperform Pitfall Traps for Studying Arthropod Activity.

Publications

New Litter Trap Devices Outperform Pitfall Traps for Studying Arthropod Activity.

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