Museum specimens provide novel insights into changing plant–herbivore interactions

Meineke, Emily K.; Davies, T. Jonathan

doi:10.1098/rstb.2017.0393

Cited by 45 publications

(36 citation statements)

References 126 publications

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“…The millions of plant specimens stored in herbaria around the world serve as priceless historical records of global biodiversity (Funk, 2003). The careful study and use of these specimens have been crucial in answering an assortment of evolutionary and ecological questions (Pyke and Ehrlich, 2010;Lavoie, 2013), such as the resolution of taxonomic puzzles and phylogenetic relationships (Ames and Spooner, 2008;Ng and Smith, 2016;Ng et al, 2019), phenological and species distribution responses to climate change (reviewed in Willis et al, 2017;Jones and Daehler, 2018;Lang et al, 2019), and plant-insect interactions through time (Lees et al, 2011;Meineke and Davies, 2018). Despite the clear value of herbaria, many collections have been neglected in recent decades as funding and curatorial expertise have waned (Dalton, 2003;Prather et al, 2004).…”

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confidence: 99%

LeafMachine: Using machine learning to automate leaf trait extraction from digitized herbarium specimens

Weaver

Laport

2020

Appl Plant Sci

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Obtaining phenotypic data from herbarium specimens can provide important insights into plant evolution and ecology but requires significant manual effort and time. Here, we present LeafMachine, an application designed to autonomously measure leaves from digitized herbarium specimens or leaf images using an ensemble of machine learning algorithms. METHODS AND RESULTS: We trained LeafMachine on 2685 randomly sampled specimens from 138 herbaria and evaluated its performance on specimens spanning 20 diverse families and varying widely in resolution, quality, and layout. LeafMachine successfully extracted at least one leaf measurement from 82.0% and 60.8% of high-and low-resolution images, respectively. Of the unmeasured specimens, only 0.9% and 2.1% of high-and low-resolution images, respectively, were visually judged to have measurable leaves. CONCLUSIONS: This flexible autonomous tool has the potential to vastly increase available trait information from herbarium specimens, and inform a multitude of evolutionary and ecological studies.

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confidence: 99%

LeafMachine: Using machine learning to automate leaf trait extraction from digitized herbarium specimens

Weaver

Laport

2020

Appl Plant Sci

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“…The latter could be particularly relevant for deep learning models. There is also a valuable archive of entomological data in herbarium specimens in the form of signs of herbivory (71). The standard digitization of herbarium collections has proven suitable for extracting herbivory data using machine learning techniques (72).…”

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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“…Millions of herbarium specimens were collected prior to the intensification of human influence on the planet, including the acceleration of climate change. The unique, long-term data preserved within herbarium collections can now help us understand the past and predict the future of global change (Heberling and Isaac, 2017;Lang et al, 2019;Meineke et al, 2018a;Meineke, et al, 2018b).…”

Section: Introductionmentioning

confidence: 99%

Applying Machine Learning to Investigate Long Term Insect-Plant Interactions Preserved on Digitized Herbarium Specimens

Meineke

Tomasi

Yuan

et al. 2019

Preprint

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Premise of the study: Despite the economic importance of insect damage to plants, longterm data documenting changes in insect damage ('herbivory') and diversity are limited. Meineke, Tomasi, Yuan, and Pryer, p. 2 Millions of pressed plant specimens are now available online for collecting big data on plant-insect interactions during the Anthropocene.• Methods: We initiated development of machine learning methods to automate extraction of herbivory data from herbarium specimens. We trained an insect damage detector and a damage type classifier on two distantly related plant species. We experimented with 1) classifying six types of herbivory and two control categories of undamaged leaf, and 2) detecting two of these damage categories for which several hundred annotations were available.• Results: Classification models identified the correct type of herbivory 81.5% of the time.The damage classifier was accurate for categories with at least one hundred test samples. We show anecdotally that the detector works well when asked to detect two types of damage.• Discussion: The classifier and detector together are a promising first step for the automation of herbivory data collection. We describe ongoing efforts to increase the accuracy of these models to allow other researchers to extract similar data and apply them to address a variety of biological hypotheses.

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Museum specimens provide novel insights into changing plant–herbivore interactions

Cited by 45 publications

References 126 publications

LeafMachine: Using machine learning to automate leaf trait extraction from digitized herbarium specimens

LeafMachine: Using machine learning to automate leaf trait extraction from digitized herbarium specimens

Deep learning and computer vision will transform entomology

Applying Machine Learning to Investigate Long Term Insect-Plant Interactions Preserved on Digitized Herbarium Specimens

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