Species-Specific Flash Patterns Track the Nocturnal Behavior of Sympatric Taiwanese Fireflies

Goh, King-Siang; Lee, Chia-Ming; Wang, Tzi‐Yuan

doi:10.3390/biology11010058

Cited by 4 publications

(18 citation statements)

References 43 publications

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“…The specimens were collected as previously described in Goh, Lee & Wang (2022) . The material collected in this study was identified by LJ Wang on the species level through the use of available references ( Ballantyne et al, 2013 ; Chen, 2003 ; Jeng, Lai & Yang, 2003 ; Jeng, Yang & Lai, 2003 ; Jeng et al, 1998 ).…”

Section: Methodsmentioning

confidence: 99%

“…The material collected in this study was identified by LJ Wang on the species level through the use of available references ( Ballantyne et al, 2013 ; Chen, 2003 ; Jeng, Lai & Yang, 2003 ; Jeng, Yang & Lai, 2003 ; Jeng et al, 1998 ). Five morphological characteristics of the specimens (body length, pronotum length, pronotum width, front wing length, and front wing width) were measured (see Table S3 ) using a dissecting microscope and photographed with a digital video camera as previously described in Goh, Lee & Wang (2022) . During the survey, the specimens were chilled on ice.…”

Section: Methodsmentioning

confidence: 99%

“…Fifty-six species have been described from Taiwan to date ( Jeng, Lai & Yang, 2003 ; Jeng, Yang & Engel, 2007 ; Jeng et al, 1998 ), but few reports have been made on their biodiversity, ecological habitats, comparative morphology, life cycle, or behavior ( Ballantyne et al, 2013 ; Ballantyne et al, 2015 ; Ballantyne & Lambkin, 2013 ; Ballantyne et al, 2019 ; Goh, Lee & Wang, 2022 ; Goh & Li, 2011 ; Ho et al, 2010 ; South et al, 2008 ). Taiwan has Luciola ; Curtos ; the reassessed Aquatica ( Fu, Ballantyne & Lambkin, 2010 ), Abscondita ( Ballantyne et al, 2013 ; Ballantyne et al, 2019 ), and Sclerotia ( Ballantyne et al, 2016 ) species of Luciolinae; and some Lampyrinae species ( Ballantyne et al, 2013 ; Goh, Lee & Wang, 2022 ; Jeng, Lai & Yang, 1999 ; Jeng, Yang & Engel, 2007 ; Jeng et al, 1998 ; Ohba & Yang, 2003 ; Wang, Wu & Wang, 2021 ). The endemic Abscondita cerata (formerly known as Luciola cerata ) is the most abundant species, widely distributed from low altitude to medium-high altitude (1,500 m) in Taiwan.…”

Section: Introductionmentioning

confidence: 99%

“…The endemic Abscondita cerata (formerly known as Luciola cerata ) is the most abundant species, widely distributed from low altitude to medium-high altitude (1,500 m) in Taiwan. During its breeding season, several sympatric fireflies could be found ( Goh, Lee & Wang, 2022 ; Jeng, Lai & Yang, 1999 ; Ohba & Yang, 2003 ). A recent study revealed that LED light intensity can influence the flash pattern of Aquatica ficta ( Owens, Meyer-Rochow & Yang, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

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Luminescent characteristics and mitochondrial COI barcodes of nine cohabitated Taiwanese fireflies

Goh

Wang

et al. 2022

PeerJ

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Background Over 50 Taiwanese firefly species have been discovered, but scientists lack information regarding most of their genetics, bioluminescent features, and cohabitating phenomena. In this study, we focus on morphological species identification and phylogeny reconstructed by COI barcoding, as well as luminescent characteristics of cohabited Taiwanese firefly species to determine the key factors that influenced how distinct bioluminescent species evolved to coexist and proliferate within the same habitat. Methods In this study, 366 specimens from nine species were collected in northern Taiwan from April to August, 2016–2019. First, the species and sex of the specimens were morphologically and genetically identified. Then, their luminescent spectra and intensities were recorded using a spectrometer and a power meter, respectively. The habitat temperature, relative humidity, and environmental light intensity were also measured. The cytochrome oxidase I (COI) gene sequence was used as a DNA barcode to reveal the phylogenetic relationships of cohabitated species. Results Nine species—eight adult species (Abscondita chinensis, Abscondita cerata, Aquatica ficta, Luciola curtithorax, Luciola kagiana, Luciola filiformis, Curtos sauteri, and Curtos costipennis) and one larval Pyrocoelia praetexta—were morphologically identified. The nine species could be found in April–August. Six of the eight adult species shared an overlap occurrence period in May. Luminescent spectra analysis revealed that the λmax of studied species ranged from 552–572 nm (yellow–green to orange–yellow). The average luminescent intensity range of these species was about 1.2–14 lux (182.1–2,048 nW/cm2) for males and 0.8–5.8 lux (122.8–850 nW/cm2) for females, and the maximum luminescent intensity of males was 1.01–7.26-fold higher than that of females. Compared with previous studies, this study demonstrates that different λmax, species-specific flash patterns, microhabitat choices, nocturnal activity time, and/or an isolated mating season are key factors that may lead to the species-specific courtship of cohabitated fireflies. Moreover, we estimated that the fireflies start flashing or flying when the environmental light intensity decreased to 6.49–28.1 lux. Thus, based on a rough theoretical calculation, the sensing distance between male and female fireflies might be 1.8–2.7 m apart in the dark. In addition, the mitochondrial COI barcode identified species with high resolution and suggested that most of the studied species have been placed correctly with congeners in previous phylogenies. Several cryptic species were revealed by the COI barcode with 3.27%–12.3% variation. This study renews the idea that fireflies’ luminescence color originated from the green color of a Lampyridae ancestor, then red-shifted to yellow-green in Luciolinae, and further changed to orange–yellow color in some derived species.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Luminescent characteristics and mitochondrial COI barcodes of nine cohabitated Taiwanese fireflies

Goh

Wang

et al. 2022

PeerJ

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“…can be of immense value in identifying firefly populations on the decline. These programs should be continued, expanded, and where possible unified into a single standardized platform that supports species identification (by trained observers or machine-learning algorithms, e.g., Goh et al, 2022). The responses of fireflies to ALAN are highly species-specific, and measures of general firefly activity without accompanying species information can be compromised by changes in species composition.…”

Section: Conservation Recommendationsmentioning

confidence: 99%

Behavioral responses of bioluminescent fireflies to artificial light at night

Owens

Broeck²,

Cock³

et al. 2022

Front. Ecol. Evol.

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Bioluminescent insects have been the subject of scientific interest and popular wonder for millennia. But in the 21st century, the fireflies, click beetles, and cave glow-worms that brighten our nights are threatened by an unprecedented competitor: anthropogenic light pollution. Artificial lights can obscure the light-based signals on which these and other bioluminescent organisms rely to court mates, deter predators, and attract prey. In the following review we summarize a recent influx of research into the behavioral consequences of artificial light at night for firefly beetles (Coleoptera: Lampyridae), which we organize into four distinct courtship signaling systems. We conclude by highlighting several opportunities for further research to advance this emerging field and by offering a set of up-to-date lighting recommendations that can help land managers and other stakeholders balance public safety and ecological sustainability.

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Embracing firefly flash pattern variability with data-driven species classification

Martin,

Nguyen,

Sarfati

et al. 2024

Sci Rep

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Many nocturnally active fireflies use precisely timed bioluminescent patterns to identify mates, making them especially vulnerable to light pollution. As urbanization continues to brighten the night sky, firefly populations are under constant stress, and close to half of the species are now threatened. Ensuring the survival of firefly biodiversity depends on a large-scale conservation effort to monitor and protect thousands of populations. While species can be identified by their flash patterns, current methods require expert measurement and manual classification and are infeasible given the number and geographic distribution of fireflies. Here we present the application of a recurrent neural network (RNN) for accurate automated firefly flash pattern classification. Using recordings from commodity cameras, we can extract flash trajectories of individuals within a swarm and classify their species with an accuracy of approximately seventy percent. In addition to its potential in population monitoring, automated classification provides the means to study firefly behavior at the population level. We employ the classifier to measure and characterize the variability within and between swarms, unlocking a new dimension of their behavior. Our method is open source, and deployment in community science applications could revolutionize our ability to monitor and understand firefly populations.

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Species-Specific Flash Patterns Track the Nocturnal Behavior of Sympatric Taiwanese Fireflies

Cited by 4 publications

References 43 publications

Luminescent characteristics and mitochondrial COI barcodes of nine cohabitated Taiwanese fireflies

Luminescent characteristics and mitochondrial COI barcodes of nine cohabitated Taiwanese fireflies

Behavioral responses of bioluminescent fireflies to artificial light at night

Embracing firefly flash pattern variability with data-driven species classification

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