Entomological Collections in the Age of Big Data

Short, Andrew Edward Z.; Dikow, Torsten; Moreau, Corrie S.

doi:10.1146/annurev-ento-031616-035536

Cited by 59 publications

(52 citation statements)

References 151 publications

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“…To facilitate trait‐based research on terrestrial arthropods, there is an abundance of information in the literature and extensive physical and digital collections (Short, Dikow & Moreau, ). The trait‐based approach is also an avenue for overcoming the many taxonomic impediments that have long plagued ecological research on terrestrial arthropods.…”

Section: Introductionmentioning

confidence: 99%

Trait‐based ecology of terrestrial arthropods

2018

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In focusing on how organisms' generalizable functional properties (traits) interact mechanistically with environments across spatial scales and levels of biological organization, trait-based approaches provide a powerful framework for attaining synthesis, generality and prediction. Trait-based research has considerably improved understanding of the assembly, structure and functioning of plant communities. Further advances in ecology may be achieved by exploring the trait-environment relationships of non-sessile, heterotrophic organisms such as terrestrial arthropods, which are geographically ubiquitous, ecologically diverse, and often important functional components of ecosystems. Trait-based studies and trait databases have recently been compiled for groups such as ants, bees, beetles, butterflies, spiders and many others; however, the explicit justification, conceptual framework, and primary-evidence base for the burgeoning field of 'terrestrial arthropod trait-based ecology' have not been well established. Consequently, there is some confusion over the scope and relevance of this field, as well as a tendency for studies to overlook important assumptions of the trait-based approach. Here we aim to provide a broad and accessible overview of the trait-based ecology of terrestrial arthropods. We first define and illustrate foundational concepts in trait-based ecology with respect to terrestrial arthropods, and justify the application of trait-based approaches to the study of their ecology. Next, we review studies in community ecology where trait-based approaches have been used to elucidate how assembly processes for terrestrial arthropod communities are influenced by niche filtering along environmental gradients (e.g. climatic, structural, and land-use gradients) and by abiotic and biotic disturbances (e.g. fire, floods, and biological invasions). We also review studies in ecosystem ecology where trait-based approaches have been used to investigate biodiversity-ecosystem function relationships: how the functional diversity of arthropod communities relates to a host of ecosystem functions and services that they mediate, such as decomposition, pollination and predation. We then suggest how future work can address fundamental assumptions and limitations by investigating trait functionality and the effects of intraspecific variation, assessing the potential for sampling methods to bias the traits and trait values observed, and enhancing the quality and consolidation of trait information in databases. A roadmap to guide observational trait-based studies is also presented. Lastly, we highlight new areas where trait-based studies on terrestrial arthropods are well positioned to advance ecological understanding and application. These include examining the roles of competitive, non-competitive and (multi-)trophic interactions in shaping coexistence, and macro-scaling trait-environment relationships to explain and predict patterns in biodiversity and ecosystem functions across space and time. We hope this review will spur a...

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Section: Introductionmentioning

confidence: 99%

Trait‐based ecology of terrestrial arthropods

2018

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“…Most existing databases include single view digitisations of pinned specimens (68), while datasets of insect specimens recorded using multiple sensors, 3D models, and databases on living insect specimens are only just emerging (69,70). The latter could be particularly relevant for deep learning models.…”

Section: Digitizing Specimens and Natural History Collectionsmentioning

confidence: 99%

Deep learning and computer vision will transform entomology

Høye

Ärje

Bjerge

et al. 2020

Preprint

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ABSTRACTMost animal species on Earth are insects, and recent reports suggest that their abundance is in drastic decline. Although these reports come from a wide range of insect taxa and regions, the evidence to assess the extent of the phenomenon is still sparse. Insect populations are challenging to study and most monitoring methods are labour intensive and inefficient. Advances in computer vision and deep learning provide potential new solutions to this global challenge. Cameras and other sensors that can effectively, continuously, and non-invasively perform entomological observations throughout diurnal and seasonal cycles. The physical appearance of specimens can also be captured by automated imaging in the lab. When trained on these data, deep learning models can provide estimates of insect abundance, biomass, and diversity. Further, deep learning models can quantify variation in phenotypic traits, behaviour, and interactions. Here, we connect recent developments in deep learning and computer vision to the urgent demand for more cost-efficient monitoring of insects and other invertebrates. We present examples of sensor-based monitoring of insects. We show how deep learning tools can be applied to the big data outputs to derive ecological information and discuss the challenges that lie ahead for the implementation of such solutions in entomology. We identify four focal areas, which will facilitate this transformation: 1) Validation of image-based taxonomic identification, 2) generation of sufficient training data, 3) development of public, curated reference databases, and 4) solutions to integrate deep learning and molecular tools.Significance statementInsect populations are challenging to study, but computer vision and deep learning provide opportunities for continuous and non-invasive monitoring of biodiversity around the clock and over entire seasons. These tools can also facilitate the processing of samples in a laboratory setting. Automated imaging in particular can provide an effective way of identifying and counting specimens to measure abundance. We present examples of sensors and devices of relevance to entomology and show how deep learning tools can convert the big data streams into ecological information. We discuss the challenges that lie ahead and identify four focal areas to make deep learning and computer vision game changers for entomology.

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“…The amount and quality of isolated genomic DNA from insect collections depend on a myriad of factors, including killing reagents, method of preservation of specimens in the field, and final voucher storage conditions (Kanda et al, 2015;Short, Dikow & Moreau, 2018). For example, Dillon et al, (1996) (see also Reiss, Schwert & Ashworth, 1995;Gilbert et al, 2007b) found that specimens killed with ethanol yielded significantly higher quantities of high quality DNA compared to other killing/preservation agents such as ethyl acetate vapor, formalin or ethylene glycol.…”

Section: Rule 4: Treat Your Specimens Well To Enhance Their Usementioning

confidence: 99%

Biodiversity seen through the perspective of insects: 10 simple rules on methodological choices and experimental design for genomic studies

Matos‐Maraví

Ritter

Barnes

et al. 2019

Preprint

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Massively parallel DNA sequencing opens up opportunities for bridging multiple temporal and spatial dimensions in biodiversity research, thanks to its efficiency to recover millions of nucleotide polymorphisms. Here we identify the current status, discuss the main challenges, and look into future perspectives on biodiversity genomics focusing on insects, which arguably constitute the most diverse and ecologically important group among all animals. We suggest 10 simple rules that provide a succinct step-by-step guide and best-practices to anyone interested in biodiversity research through the study of insect genomics. To this end, we review relevant literature on biodiversity and evolutionary research in the field of entomology. Our compilation is targeted at researchers and students who may not yet be specialists in entomology or molecular biology. We foresee that the genomic revolution and its application to the study of non-model insect lineages will represent a major leap to our understanding of insect diversity.

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Entomological Collections in the Age of Big Data

Cited by 59 publications

References 151 publications

Trait‐based ecology of terrestrial arthropods

Trait‐based ecology of terrestrial arthropods

Deep learning and computer vision will transform entomology

Biodiversity seen through the perspective of insects: 10 simple rules on methodological choices and experimental design for genomic studies

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