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Plant fibre reference collection - Arecales

Arecales is the order that groups all palm trees, these plants are quite iconic of tropical context with coconut, rattan palm trees among others. Leaves and stem are the plant parts that are exploited for handicraft purposes. Palm trees, like all Monocotyledon plants, do not produce wood (no cambium, no secondary growth) therefore, their vascular system is never renewed and needs to be protected with fibrous caps. This anatomical arrangement is significant as there is vascular replacement through time vascular cells continue to thicken and lignify their wall for many years. Therefore, the plant grows taller without significantly increasing in diameter (Tomlinson et al., 2011: 13). This is particularly striking of coconut palm tree which stalk’s diameter will not exceed 30 centimeters while the plant height could reach more than 30 meters. Palm tree stems got unique features of growth that produce confounding variability in anatomy (Thomas&De Franceschi, 2013: 5). Of anatomical characters of interest is the vascular system which is compound of dorsal vascular fibrous caps are generally more developed in the cortical area (external parts) than in the central part and are of different shape. The morphology of these vascular caps (reniforma, lunata, etc.) among others vascular features (transverse commissure) are interesting as they can help to distinguish palm trees, sometimes to the genus level and could also inform us about plant mechanism and property (Thomas, 2011: 33). Thomas, R. 2011. Anatomie comparée des palmiers, identification-assistée par ordinateur. Application en paléobotanique et en archéobotanique. Thèse de doctorat, Sciences de la nature et de l’homme. Muséum National d’Histoire Naturelle, Paris. Thomas R.&De Franceschi, D. 2013. Palm stem anatomy and computer-aided identification: The Coryphoideae (Arecaceae), American Journal of Botany, 100(2), 289–313. Tomlinson, P.B., Horn, J.W., Fisher, J.B. 2011. Anatomy of Palm: Arecaceae- Palmae. OUP Oxford, 276 pp.

PhytoNodes for Environmental Monitoring: Stimulus Classification based on Natural Plant Signals in an Interactive Energy-efficient Bio-hybrid System

Cities worldwide are growing, putting bigger populations at risk due to urban pollution. Environmental monitoring is essential and requires a major paradigm shift. We need green and inexpensive means of measuring at high sensor densities and with high user acceptance. We propose using phytosensing: using natural living plants as sensors. In plant experiments we gather electrophysiological data with sensor nodes. We expose the plant Zamioculcas zamiifolia to five different stimuli: wind, temperature, blue light, red light, or no stimulus. Using that data we train ten different types of artificial neural networks to classify measured time series according to the respective stimulus. We achieve good accuracy and succeed in running trained classifying artificial neural networks online on the microcontroller of our small energy-efficient sensor node. To indicate later possible use cases, we showcase the system by sending a notification to a smartphone application once our continuous signal analysis detects a given stimulus.   Data repository for our paper "PhytoNodes for Environmental Monitoring: Stimulus Classification based on Natural Plant Signals in an Interactive Energy-efficient Bio-hybrid System", submitted to the GoodIT conference. Please refer to the paper for more information.   Contents of this repository <ul> <li>mu_interface: Code for our data collection plant experiments, based on Raspberry Pis and the <a href="http://cybertronica.co/?q=products/phytosensor">Cybertronica phytosensing and phytoactuating system</a>.</li> <li>raw_data: The datasets from our plant experiments for the stimuli wind, temperature, red light, blue light, and no stimulus.</li> <li>dl-4-tsc: Deep learning framework developed by <a href="https://doi.org/10.1007/s10618-019-00619-1">Fawaz et. al (Deep learning for time series classification: a review)</a> and adapted to our use case. Find the training and testing datasets in the archives folder as well as the trained classifiers in the results folder.</li> <li>classification_results.ods: Overview of the results from the deep learning framework (accuracy, precision, recall, training time).</li> <li>TFLite_Models: The trained classifiers in TensorFlow Lite Format.</li> <li>00_AI_BLE_MeasuringOnlyWind: Source code for classification on STM-based PhytoNodes (using MCDCNN two-class classifier) and Bluetooth communication. The code is written for the STM32WB55 Nucleo board and can be transferred to the dongle.</li> <li>zavrsniProjekt_iOS: Source code of the iOS app used to receive data from the STM-based PhytoNodes.</li> <li>Watchplant_application_documentation.pdf: Instructions to build and use the iOS app.</li> </ul>

Data from: Soil mesofauna may buffer the negative effects of drought on alien plant invasion

Although many studies have tested the direct effects of drought on alien plant invasion, less is known about whether drought affects alien plant invasion indirectly via interactions of plants with other groups of organisms such as soil mesofauna. To test for such indirect effects, we grew single plants of nine naturalized alien target species in pot-mesocosms with a community of five native grassland species under four combinations of two drought (well-watered vs drought) and two soil-mesofauna-inoculation (with vs without) treatments. We found that drought decreased the absolute and the relative biomass production of the alien plants, and thus reduced their competitive strength in the native community. Drought also decreased the abundance of soil mesofauna, particularly soil mites, but did not affect the abundance and richness of soil herbivores. Soil-fauna inoculation did not affect biomass of the alien plants but increased biomass of the native plant community, and thereby decreased the relative biomass production of the alien plants. This increased invasion resistance due to soil fauna, however, tended (p = 0.09) to be stronger for plants growing under well-watered conditions than under drought. Synthesis. Our multispecies experiment thus shows that soil fauna might help native communities to resist alien plant invasions, but that this effect might be weakened under drought. In other words, soil mesofauna may buffer the negative effects of drought on alien plant invasions.

Data from: Pyrophilic plants respond to post-fire soil conditions in a frequently burned longleaf pine savanna

Fire-plant feedbacks engineer recurrent fires in pyrophilic ecosystems like savannas. The mechanisms sustaining these feedbacks may be related to plant adaptations that trigger rapid responses to fire's effects on soil. Plants adapted for high fire frequencies should quickly regrow, flower, and produce seeds that mature rapidly and disperse post-fire. We hypothesized that offspring of such plants would germinate and grow rapidly, responding to fire-generated changes in soil nutrients and biota. We conducted an experiment using longleaf pine savanna plants that were paired based on differences in reproduction and survival under annual ("more" pyrophilic) vs. less frequent ("less" pyrophilic) fire regimes. Seeds were planted in different soil inoculations from experimental fires of varying severity. The "more" pyrophilic species displayed high germination rates followed by species specific, rapid growth responses to soil location and fire severity effects on soils. In contrast, the "less" pyrophilic species had lower germination rates that were not responsive to soil treatments. This suggests that rapid germination and growth constitute adaptations to frequent fires, and that plants respond differently to fire severity effects on soil abiotic factors and microbes. Further, variable plant responses to post-fire soils may influence plant community diversity and fire-fuel feedbacks in pyrophilic ecosystems.

Data from: Plant richness, land use and temperature differently shape invertebrate leaf-chewing herbivory on plant functional groups

Nutrient demands of leaf-chewing invertebrate herbivores change with temperature, which causes shifts in herbivores' diets. Temperature may act differently on herbivore species, so that factors shaping herbivore species richness may modulate temperature effects on invertebrate herbivory among plant functional groups with different nutrient composition (C:N ratio low to high: legumes, non-leguminous forbs, grasses). Global warming urges a deeper understanding of temperature effects on herbivory among plant functional groups in different habitats and landscapes. This study obtained measures on proportional leaf area loss to leaf-chewing invertebrate herbivores ('herbivory') on three plant functional groups on 80 plots of open herbaceous vegetation adjacent to different habitat types (forest, grassland, arable field, settlement) along climate and land-use gradients in Bavaria, Germany. Herbivory was analysed with regard to habitat characteristics (habitat type, plant richness at species and family level, local mean temperature), landscape characteristics (proportion of grassland, landscape diversity; 0.2–3.0-km), climate (multi-annual mean temperature, 'MAT') and interactive effects of plant functional group, temperature and habitat or landscape characteristics. Herbivory on plant functional groups changed differently in response to plant richness (family level only) and habitat type, but not to differences in landscape characteristics and temperature – only on grassland plots, multi-annual mean temperature differentially affected herbivory among plant functional groups. Thus, abiotic and biotic factors can differently affect leaf-chewing herbivory on plant functional groups. Under current conditions, plant richness and habitat type more strongly affected herbivory among legumes, forbs and grasses than temperature and landscape-scale land use.

Data from: Plant host traits mediated by foliar fungal symbionts and secondary metabolites

Fungal symbionts living inside plant leaves ("endophytes") can vary from beneficial to parasitic, but the mechanisms by which the fungi affect the plant host phenotype remain poorly understood. Chemical interactions are likely the proximal mechanism of interaction between foliar endophytes and the plant, as individual fungal strains are often exploited for their diverse secondary metabolite production. Here, we go beyond single strains to examine commonalities in how 16 fungal endophytes shift plant phenotypic traits such as growth and physiology, and how those relate to plant metabolomics profiles. We inoculated individual fungi on switchgrass, Panicum virgatum L. This created a limited range of plant growth and physiology (2–370% of fungus-free controls on average), but effects of most fungi overlapped, indicating functional similarities in unstressed conditions. Overall plant metabolomics profiles included almost 2000 metabolites, which were broadly correlated with plant traits across all the fungal treatments. Terpenoid-rich samples were associated with larger, more physiologically active plants and phenolic-rich samples were associated with smaller, less active plants. Only 47 metabolites were enriched in plants inoculated with fungi relative to fungus-free controls, and of these, LASSO regression identified 12 metabolites that explained from 14–43% of plant trait variation. Fungal long-chain fatty acids and sterol precursors were positively associated with plant photosynthesis, conductance, and shoot biomass, but negatively associated with survival. The phytohormone gibberellin, in contrast, was negatively associated with plant physiology and biomass. These results can inform ongoing efforts to develop metabolites as crop management tools, either by direct application or via breeding, by identifying how associations with more beneficial components of the microbiome may be affected.

Selection against early flowering in geothermally heated soils is associated with pollen but not prey availability in a carnivorous plant

This data set includes data on flowering phenology, rosette diameters and fitness of the perennial herb Pinguicula vulgaris, as well as data on soil temperature and experimental treatment applied. The data was collected during the summer of 2020 in 287 plant individuals located in a sub-arctic geothermal area in Ölfus municipality in SW-Iceland, Hengill (64°03’N; 21°18’W, ~360 m.a.s.l.).

opencc-zeroJul 2022View details →

Data from: Strong links between plant traits and microbial activities but different abiotic drivers in mountain grasslands

This dataset contains data and code that support the results in Weil, S.-S., Martinez-Almoyna, C., Piton, G., Renaud, J., Boulangeat, L., Foulquier, A., ... & Thuiller, W. (2021) Strong links between plant traits and microbial activities but different abiotic drivers in mountain grasslands (accepted in Journal of Biogeography). We used an extensive plant-soil dataset that covers 14 elevational gradients (between 1500 and 2800 m of elevation) distributed over the whole French Alps to analyse the spatial co-dependencies between the plant and soil compartments. We ran a Graphical Lasso that extracts the direct and indirect linkages between plant functional composition, soil microbial activities, and environmental conditions (local climate and soil properties). Our main results are 1) that plant traits are tightly associated with microbial activities, the former being driven by climate and the latter by soil properties; 2) that the dominance of specific plant traits was more important than their diversity to determine plant-soil linkages; and 3) that soil microbes invested strongly in nutrient acquisition in sites with conservative plant traits and reduced organic matter quality.

Data from: Plant community stability is associated with a decoupling of prokaryote and fungal soil networks

Data from the manuscript Plant community stability is associated with a decoupling of prokaryote and fungal soil networks: https://doi.org/10.1101/2022.06.21.496867

Presence data for vascular plant, bryophyte and lichen species in 100 vegetation plots (each 1 m2) from 32 shell-beds at Akerøya, Hvaler, SE Norway

We present a data set consisting of abundance data for 106 vascular plant species, 36 bryophyte species and 13 lichen species from 100 vegetation plots, each 1 m2, distributed on 32 shell-beds at Akerøya, Hvaler municipality, former Østfold (in 2022 Viken) county. The plots were analysed with respect to species composition in June 1979. These data formed the basis for the publication: Halvorsen, R. 1980. Numerical analysis and successional relationships of shell-bed vegetation at Akerøya, Hvaler, SE Norway. Norw. J. Bot. Vol. 27 pp. 71-95. Oslo. ISSN 0300-1156.

opencc-zeroOct 2021View details →

Opposing community assembly patterns for dominant and non-dominant plant species in herbaceous ecosystems globally

Biotic and abiotic factors interact with dominant plants —the locally most frequent or with the largest coverage— and non-dominant plants differently, partially because dominant plants modify the environment where non-dominant plants grow. For instance, if dominant plants compete strongly, they will deplete most resources, forcing non-dominant plants into a narrower niche space. Conversely, if dominant plants are constrained by the environment, they might not exhaust available resources but instead may ameliorate environmental stressors that usually limit non-dominants. Hence, the nature of interactions among non-dominant species could be modified by dominant species. Furthermore, these differences could translate into a disparity in the phylogenetic relatedness among dominants compared to the relatedness among non-dominants. By estimating phylogenetic dispersion in 78 grasslands across five continents, we found that dominant species were clustered (e.g., co-dominant grasses), suggesting dominant species are likely organized by environmental filtering, and that non-dominant species were either randomly assembled or overdispersed. Traits showed similar trends for those sites (<50%) with sufficient trait data. Furthermore, several lineages scattered in the phylogeny had more non-dominant species than expected at random, suggesting that traits common in non-dominants are phylogenetically conserved and have evolved multiple times. We also explored environmental drivers of the dominant/non-dominant disparity. We found different assembly patterns for dominants and non-dominants, consistent with asymmetries in assembly mechanisms. Among the different postulated mechanisms, our results suggest two complementary hypotheses seldom explored: (1) Non-dominant species include lineages adapted to thrive in the environment generated by dominant species. (2) Even when dominant species reduce resources to non-dominant ones, dominant species could have a stronger positive effect on some non-dominants by ameliorating environmental stressors affecting them, than by depleting resources and increasing the environmental stress to those non-dominants. These results show that the dominant/non-dominant asymmetry has ecological and evolutionary consequences fundamental to understand plant communities.

Great tits (Parus major) flexibly learn that herbivore-induced plant volatiles indicate prey location – an experimental evidence with two tree species

1. When searching for food, great tits (Parus major) can use herbivore-induced plant volatiles (HIPVs) as an indicator of arthropod presence. Their ability to detect HIPVs was shown to be learned, and not innate, yet the flexibility and generalization of learning remains unclear. 2. We studied if, and if so how, naïve and trained great tits (Parus major) discriminate between herbivore-induced and non-induced saplings of Scotch elm (Ulmus glabra) and cattley guava (Psidium cattleyanum). We chemically analysed the used plants and showed that their HIPVs differed significantly and overlapped only in a few compounds. 3. Birds trained to discriminate between herbivore-induced and non-induced saplings preferred the herbivore-induced saplings of the plant species they were trained to. Naïve birds did not show any preferences. Our results indicate that the attraction of great tits to herbivore-induced plants is not innate, rather it is a skill that can be acquired through learning, one tree species at a time. 4. We demonstrate that the ability to learn to associate HIPVs with food reward is flexible, expressed to both tested plant species, even if the plant species has not coevolved with the bird species (i.e. guava). Our results imply that the birds are not capable of generalising HIPVs among tree species but suggest that they either learn to detect individual compounds or associate whole bouquets with food rewards.

opencc-zeroJul 2022View details →

Can disease resistance evolve independently at different ages? Genetic variation in age-dependent resistance to disease in three wild plant species

1. Juveniles are typically less resistant (more susceptible) to infectious disease than adults, and this difference in susceptibility can help fuel the spread of pathogens in age-structured populations. However evolutionary explanations for this variation in resistance across age remain to be tested. 2. One hypothesis is that natural selection has optimized resistance to peak at ages where disease exposure is greatest. A central assumption of this hypothesis is that hosts have the capacity to evolve resistance independently at different ages. This would mean that hosts populations have a) standing genetic variation in resistance at both juvenile and adult stages, and b) that this variation is not strongly correlated between age-classes so that selection acting at one age does not produce a correlated response at the other age 3. Here we evaluated the capacity of three wild plant species (Silene latifolia, S. vulgaris, and Dianthus pavonius) to evolve resistance to their anther-smut pathogens (Microbotryum fungi), independently at different ages. The pathogen is pollinator-transmitted, and thus exposure risk is considered to be highest at the adult flowering stage. 4. Within each species we grew families to different ages, inoculated individuals with anther smut, and evaluated the effects of age, family and their interaction on infection. 5. In two of the plant species, S. latifolia and D. pavonius, resistance to smut at the juvenile stage was not correlated with resistance to smut at the adult stage. In all three species, we show there are significant age*family interaction effects, indicating that age-specificity of resistance varies among the plant families. 6. Synthesis: These results indicate that different mechanisms likely underlie resistance at juvenile and adult stages and support the hypothesis that resistance can evolve independently in response to differing selection pressures as hosts age. Taken together our results provide new insight into the structure of genetic variation in age-dependent resistance in three well-studied wild host-pathogen systems.

Satellite-derived chlorophyll-a concentrations for Western Water Treatment Plant (Melbourne, Australia) using Mixture Density Networks and Sentinel-2 and Landsat 8 imagery

This dataset contains satellite-derived chlorophyll-a data of the Western Water Treatment Plant (Melbourne, Australia) for the period 21 Mar. 2013 - 01 Feb. 2021. Chlorophyll-a concentrations have been calculated using Mixture Density Networks and Sentinel-2 and Landsat 8 imagery. Mixture Density Networks are a class of neural networks that tackle the inverse problem by modelling the multimodal distribution of target variables using a mixture of Gaussians. For more information, please refer to the following: <ul> <li>Pahlevan, N., Smith, B., Alikas, K., Anstee, J., et al. (2022). Simultaneous retrieval of selected optical water quality indicators from Landsat-8, Sentinel-2, and Sentinel-3. Remote Sensing of Environment, 270, 112860</li> <li>Smith, B., Pahlevan, N., Schalles, J., et al. (2021). A Chlorophyll-a Algorithm for Landsat-8 Based on Mixture Density Networks. Frontiers in Remote Sensing, 1</li> <li>Pahlevan, N., Smith, B., Schalles, J., et al. (2020). Seamless retrievals of chlorophyll-a from Sentinel-2 (MSI) and Sentinel-3 (OLCI) in inland and coastal waters: A machine-learning approach. Remote Sensing of Environment, 240, 111604</li> </ul>

opencc-zeroJul 2022View details →

Data from: Diversity among rare and common congeneric plant species from the Garry oak and Okanagan shrub-steppe ecosystems in British Columbia: implications for conservation

Using universal non-coding chloroplast DNA markers (cpDNA), we investigated genetic diversity and genetic structure in four rare and common plant species pairs inhabiting threatened ecosystems (Garry Oak and Okanagan shrub-steppe) in British Columbia. The species found in the Garry oak ecosystem are: Sanicula bipinnatifida (purple sanicle; Apiaceae; rare), Sanicula crassicaulis (Pacific sanicle; Apiaceae; common), and Balsamorhiza deltoidea (deltoid balsamroot; Asteraceae; rare). The species found in the Okanagan shrub-steppe ecosystem are: Balsamorhiza sagittata (arrowleaf balsamroot; Asteraceae; common), Orthocarpus barbatus (Grand Coulee owl-clover; Orobanchaceae; rare), Orthocarpus luteus (yellow owl-clover; Orobanchaceae; common), Phacelia ramosissima (branching phacelia; Hydrophyllaceae; rare), and Phacelia linearis (thread-leaved phacelia; Hydrophyllaceae; common). Eight cpDNA regions were sequenced for each study species. Sequences were aligned and concatenated within each species, and single nucleotide polymorphisms (SNPs) were used to analyze patterns of regional genetic diversity and phylogeographic structure within genera and species. Results include: total gene diversity (Ht), nucleotide diversity (π), number of private alleles, haplotype networks, isolation by distance, and analysis of molecular variance.

Fig. 30 in Review of the fritillary species systematically close to Melitaea lutko Evans, 1932 (Lepidoptera: Nymphalidae) with analysis of their geographic distribution and interrelations with host plants

Fig. 30 (continued on next page). Distribution of the lutko species group fritillaries in combination with the distribution of known and suspected species of host plants from the genus Phlomoides Blume. = Melitaea lutko Evans, 1932; = M. timandra timandra Coutsis & Oorschot, 2014; = M. timandra binaludica subsp. nov.; = M. shahvarica sp. nov.; = M. mimetica mimetica Higgins, 1940; = M. mimetica delerei Heidemann, 1954;? = unconfirmed findings of M. timandra; = M. timandra with an unclear subspecies status; = Phlomoides regeliana (Aitch. & Hemsl.) Adylov, Kamelin & Makhm.; = Phlomoides boissieriana (Regel) Adylov, Kamelin & Makhm.; = Phlomoides laciniata (L.) Kamelin & Makhm.; = Phlomoides labiosiformis (Popov) Adylov, Kamelin & Makhm.; = Phlomoides loasifolia (Benth.) Kamelin & Makhm.; = Phlomoides molucelloides (Bunge) Salmaki; = Phlomoides acaulis (Beck ex Rech.f.) Salmaki; = Phlomoides labiosa (Bunge) Adylov, Kamelin & Makhm. A. Pakistan, Chitral, Chaghbini CGNP, alt. 2700–3000 m. B. Pakistan, Khyber Pakhtunkhwa, Drosh. C. Pakistan, Khyber Pakhtunkhwa, Keon Nullah. D. Pakistan, Khyber Pakhtunkhwa, Malakand. E. Pakistan, Khyber Pakhtunkhwa, Birmoglasht. F. Turkmenistan, Badkhyz, Kepeli, alt. 700 m. G. Turkmenistan, Badkhyz, Kyzyl-Jar, alt. 700 m. H. Turkmenistan, Kushka, alt. 700 m. I. Turkmenistan, Murgab river, Sary-Yazy, alt. 300 m. J. Turkmenistan, 30 km E of BairamAli, Zahmet, alt. 240 m. K. Turkmenistan, Bairam-Ali, alt. 230 m. L. Turkmenistan, Kara-Kum desert, 30 km W of Mary, alt. 200 m.M. Turkmenistan, Dushak, alt. 250 m.N. Turkmenistan, Chaacha, alt. 400 m. O. Turkmenistan, Bakharden, alt. 200 m. P. Iran, Khorossan Razavi, Kuh-e-Binalud Mts, Qadamgah area, Gerina, alt. 2000 m. Q. Iran, Khorasan Razavi, Kuh-e-Binalud Mts, 15 km SW of Zoshk, alt. 2300– 2500 m. R. Iran, S Khorosan, 75 km N of Birjant, Sedeh, alt. 1500 m. S. Iran, S Khorosan, 35 km N of Birjant, alt. 1500 m. T. Afghanistan, Bamian, Band-e-Amir, Dzhudoi-Kvak Gorge, alt. 3200 m. U. Afghanistan, Bamian, Band-e-Amir, Hazarajat, alt. 3000–3200 m. V. Afghanistan, Bamian, Koh-iBaba Mts, Joshanak, alt. 2800 m. W. Afghanistan, Heart, Qala-i-Naw, Kashka pass. X. Iran, Semnan, Shahvar Mt., alt. 2200–2500 m. Y. Turkmenistan, Kara-Kala, Monjukly Ridge, 300–700 m. Z. Iran, Golestan, E Maraveh Tappeh, N Ghazan Ghayeh, Palizan Mts. A". Pakistan, Balochistan, Quetta, Urak, alt. 2500 m. B". Pakistan, Balochistan, Ziarat, alt. 2500 m. C". Pakistan, Balochistan, Khojak, alt. 1700 m. D". Pakistan, Balochistan, Zaghum, alt. 1600 m; E". Pakistan, Punjab, Gawar, alt. 500 m. F". Pakistan, Balochistan, Sheik Wazil, alt. 1600 m. G". Afghanistan, Bamian, Hushkak, alt. 2700–2800 m. H". Afghanistan, Bamian, Punjub Distr., 10 km NE of Varas, alt. 2400 m. I". Afghanistan, Ghor, 17 km E of Changcharan, 15 km S of Bandi-Ali, Gazak Mts, alt. 2400 m. J". Afghanistan, Ghor, Bayan Range, 15 km S of Changcharan, Kindival valley, alt. 2700 m. K". Afghanistan, Bamiyan, Kohi-Baba Mts, Panjao, alt. 3000 m. Afghanistan, Bamiyan, Koh-i-Baba Mts, Shah-tu-Kotal, alt. 4000 m. L". Afghanistan, Kapisa, Pandshir valley, alt. 2200–2800 m. M". Afghanistan, Kabul. N". Iran, Tehran, Elburz Ridge, Demavend Mt., Ask, alt. 1800 m. O". Iran, Semnan, Foulad Mohaleh, alt. 2200 m. P". Pakistan, Punjab, Murree.

Fig. 29 in Review of the fritillary species systematically close to Melitaea lutko Evans, 1932 (Lepidoptera: Nymphalidae) with analysis of their geographic distribution and interrelations with host plants

Fig. 29.Differences in the structure of valva and aedeagus of the lutko species group.A. Melitaea shahvarica sp. nov. B, D, H. M. timandra binaludica subsp. nov. C–E. M. mimetica Higgins, 1940. F. M. lutko Evans, 1932. G. M. timandra timandra Coutsis &van Oorschot, 2014. A. Iran, Semnan Prov., Shahrud area, S macroslope of Shahvar Mts, alt. 2200–2400 m. B. Iran, Rezavi Khorassan Prov., Kuh-e-Binalud Mts, Dorrud v. vicinity, alt. 2430 m. C. Afghanistan, Bamian Prov., Punjub Distr., 10 km NE of Varas v., alt. 2400 m. D. Afghanistan, Band-i-Amir, Hazarajat. E. Pakistan, Balochistan, Quetta, Urak, alt. 2400– 2700 m. F. Pakistan, Chitral, Gol National Park, alt. 2700 m. G. Turkmenistan, Sary-Yazy, alt. 700 m. H. Iran, Rezavi Khorassan Prov., Kuh-e-Binalud Mts, Dorrud v. vicinity, alt. 2430 m.

Fig. 28 in Review of the fritillary species systematically close to Melitaea lutko Evans, 1932 (Lepidoptera: Nymphalidae) with analysis of their geographic distribution and interrelations with host plants

Fig. 28.Eggs and caterpillars of Melitaea shahvarica sp. nov. in nature and in the laboratory.A–B. Freshly laid eggs under a leaf of a host plant, May 2018, Iran, Shahvar Mt., alt. 2200 m. C–D. IV–V instar caterpillars on the leaves of the host plant Phlomoides molucelloides (Bunge) Salmaki, July 2019, Iran, Shahvar Mt., alt. 2500 m. E. I instar caterpillars in the laboratory, Moscow, May 2018. F. VI instar caterpillars during diapause, Moscow, October 2018.

Fig. 18. Male genitalia and harpe. A–C in Review of the fritillary species systematically close to Melitaea lutko Evans, 1932 (Lepidoptera: Nymphalidae) with analysis of their geographic distribution and interrelations with host plants

Fig. 18. Male genitalia and harpe. A–C. Melitaea shahvarica sp. nov. D–E. M. lutko Evans, 1932. F–I. M. mimetica Higgins, 1940. A–C. Iran, Semnan Prov., Shahrud area, S macroslope of Shahvar Mts, alt. 2200–2400 m. D–E. Pakistan, Chitral, Chaghbini, CGNP [Chitral Gol National Park], alt. 2700 m. F–G. Afghanistan, Bamian Prov., Punjub Distr., 10 km. NE Varas v., alt. 2400 m. H–I. Afghanistan, Bamian Prov., Panjub Distr., 10 km. NE Varas vil., alt. 2400 m.

Fig. 8 in Review of the fritillary species systematically close to Melitaea lutko Evans, 1932 (Lepidoptera: Nymphalidae) with analysis of their geographic distribution and interrelations with host plants

Fig. 8. Distribution of Melitaea mimetica Higgins, 1940. For a description of the symbols with letters, see Fig. 30. = M. mimetica mimetica Higgins, 1940; = M. mimetica delerei Heidemann, 1954.