Subterms
Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. More
Soil status, plant development and root architecture
Measurements of the gravimetric soil water content of the subsoils in both variants of the soil rhizotrons showed clear differences depending on the irrigation scenario: For the variants with top-irrigation, the water availability in the topsoil corresponded to a pF value of 2.0 at the beginning of the experiment, while for the variants with sub-irrigation the pF value was 2.2 (Fig. 2).
Figure 2
Changes of (a) pF Values and (b) gravimetric soil moisture contents in dependency of the specific bulk density (topsoil 1.1 g cm3; subsoil 1.4 g cm) plotted over time for the two soil rhizotron trials (grey = sub-irrigation; black = top-irrigation).
Full size image
The initial pF value of the subsoils was approximately 2.1 in both variants. Irrigation affected the time course of pF values: It remained within the range of the field capacity (pF 2.1–2.2 at day 44) in the irrigated top- and subsoils, respectively (Fig. 2a). The other, non-irrigated complementary soil layers dried out and the pF values increased to 2.8–2.9 from approximately day 20 onwards. Changes in gravimetric water content reflected these scenarios: gravimetric soil moisture remained at 5% in the irrigated topsoil but dropped to 2% (the matric potential declined by − 53 kPa) in the variants with subsoil irrigation. Also, the irrigation of the subsoil almost maintained a constant water content (the matric potential declined by − 5 kPa, only), while the subsoil dried out upon top-irrigation (the matric potential declined by − 61 kPa). Consequently, our setup allowed a comparison of plant growth and related P acquisition from soil with either sufficient water supply in top- or subsoil, respectively.
The 10 cm thick layer of topsoil, which was implemented in all rhizotron types, supported similar developments of wheat plants in all rhizotrons. Progressing plant developmental changes in both the aboveground plant parts and the root architecture were observed once the sand was accessed by roots: Since then, plant growth was significantly reduced in the sandy rhizotrons compared with those filled with soil as illustrated by the measured plant parameters after 44 days (Table 1, quantitatively evaluated only for the end of the experiment).
Table 1 Characteristics of plants, 33P uptake and water inputs due to the different forms of irrigation from different rhizotron trials (n = 3) after 44 days; different letters indicate significant differences among different rhizotron trials (p More
Alencar LRV, Quental TB, Grazziotin FG, Alfaro ML, Martins M, Venzon M et al. (2016) Diversification in vipers: phylogenetic relationships, time of divergence and shifts in speciation rates. Mol Phylogenet Evol 105:50–62
PubMed Article Google Scholar
Anger GR, Dean R (2010) The birds of Panama: a field guide. Zona Tropical Publication, San José, Costa Rica
Google Scholar
Avise JC (1992) Molecular population structure and the biogeographic history of a regional fauna: a case history with lessons for conservation biology. Oikos 63:62–76
CAS Article Google Scholar
Bacon CD, Silvestro D, Jaramillo C, Smith BT, Chakrabarty P, Antonelli A (2015) Biological evidence supports an early and complex emergence of the Isthmus of Panama. Proc Natl Acad Sci 112:6110–6115
CAS PubMed Article Google Scholar
Bagley JC, Hickerson MJ, Johnson JB (2018) Testing hypotheses of diversification in Panamanian frogs and freshwater fishes using hierarchical approximate Bayesian computation with model averaging. Diversity 10:1–25
Article Google Scholar
Bagley JC, Johnson JB (2014) Phylogeography and biogeography of the lower Central American Neotropics: diversification between two continents and between two seas. Biol Rev 89:767–790
PubMed Article Google Scholar
Baird AB, Marchán-Rivadeneira MR, Pérez SG, Baker RJ (2012) Morphological analysis and description of two new species of Rhogeessa (Chiroptera: Vespertilionidae) from the neotropics. Occas Pap Mus Tex Tech Univ 307:1–25
Google Scholar
Balkenhol N, Cushman SA, Storfer AT, Waits LP (2008) Landscape Genetics. Concepts, Methods, Applications. Wiley-Blackwell, West Sussex, UK
Google Scholar
Beebee TJC (2005) Conservation genetics of amphibians. Heredity 95:423–7
CAS PubMed Article Google Scholar
Bloch JI, Woodruff ED, Wood AR, Rincon AF, Harrington AR, Morgan GS et al. (2016) First North American fossil monkey and early Miocene tropical biotic interchange. Nature 533:243–246
CAS PubMed Article Google Scholar
de Boer JZ, Drummond MS, Bordelon MJ, Defant MJ, Bellon H, Maury RC (1995) Cenozoic magmatic phases of the Costa Rican island arc (Cordillera de Talamanca). Geol Soc Am Spec Pap 295:35–56
Google Scholar
Bogarín D, Pupulin F, Arrocha C, Warner J (2013) Orchids without borders: studying the hotspot of Costa Rica and Panama. Lankesteriana 13:13–26
Google Scholar
Bolaños R, Watson V, Tosi J (2005) Mapa ecológico de Costa Rica (Zonas de Vida), según el sistema de clasificación de zonas de vida del mundo de LR Holdridge), Escala 1:750 000. Centro Científico Tropical, San José, Costa Rica. Centro Científico Tropical, San José, Costa Rica
Brumfield RT, Braun MJ (2001) Phylogenetic relationships in bearded manakins (Pipridae: Manacus) indicate that male plumage color is a misleading taxonomic marker. Condor 103:248–258
Article Google Scholar
Carnaval AC, Hickerson MJ, Haddad CFB, Rodrigues MT, Moritz C (2009) Stability predicts genetic diversity in the Brazilian Atlantic forest hotspot. Science 323:785–789
CAS PubMed Article Google Scholar
Coates AG, Jackson JBC, Collins LS, Cronin TM, Dowtsett HJ, Bybell LM et al. (1992) Closure of the Isthmus of Panama: the near-shore marine record of Costa Rica and western Panama. Geol Soc Am Bull 104:814–828
Article Google Scholar
Coen E (1991) Climate. In: Janzen DH (ed) Historia Natural de Costa Rica. Editorial de la Universidad de Costa Rica, San José, Costa Rica, p 822
Google Scholar
Collins RA, Boykin LM, Cruickshank RH, Armstrong KF (2012) Barcoding’s next top model: an evaluation of nucleotide substitution models for specimen identification. Methods Ecol Evol 3:457–465
Article Google Scholar
Collins RA, Cruickshank RH (2012) The seven deadly sins of DNA barcoding. Mol Ecol Resour 13:969–975
PubMed Google Scholar
Crawford AJ (2003) Huge populations and old species of Costa Rican and Panamanian dirt frogs inferred from mitochondrial and nuclear gene sequences. Mol Ecol 12:2525–2540
CAS PubMed Article Google Scholar
Crawford AJ, Bermingham E, Carolina PS (2007) The role of tropical dry forest as a long-term barrier to dispersal: a comparative phylogeographical analysis of dry forest tolerant and intolerant frogs. Mol Ecol 16:4789–807
CAS PubMed Article Google Scholar
Crawford AJ, Lips KR, Bermingham E (2010) Epidemic disease decimates amphibian abundance, species diversity, and evolutionary history in the highlands of central Panama. Proc Natl Acad Sci 107:13777–13782
CAS PubMed Article Google Scholar
Cruz-Piedrahita C, Navas CA, Crawford AJ (2018) Life on the edge: a comparative study of ecophysiological adaptations of frogs to Tropical semiarid environments. Physiol Biochem Zool 91:740–756
PubMed Article Google Scholar
Doebeli M, Dieckmann U (2003) Speciation along environmental gradients. Nature 421:259–264
CAS PubMed Article Google Scholar
Edgar RC (2004) MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res 32:1792–1797
CAS PubMed PubMed Central Article Google Scholar
Edwards S, Beerli P (2000) Perspective: gene divergence, population divergence, and the variance in coalescence time in phylogeographic studies. Evolution 54:1839–1854
CAS PubMed Google Scholar
Feldman CR, Spicer GS (2006) Comparative phylogeography of woodland reptiles in California: repeated patterns of cladogenesis and population expansion. Mol Ecol 15:2201–2222
CAS PubMed Article Google Scholar
Ferrier S, Manion G, Elith J, Richardson K (2007) Using generalized dissimilarity modelling to analyse and predict patterns of beta diversity in regional biodiversity assessment. Divers Distrib 13:252–264
Article Google Scholar
Fitzpatrick MC, Mokany K, Manion G, Lisk M, Ferrier S, Nieto-Lugilde D (2020) gdm: Generalized dissimilarity modeling. https://CRANR-project.org/package=gdm.
Fouquet A, Vences M, Salducci M-D, Meyer A, Marty C, Blanc M et al. (2007) Revealing cryptic diversity using molecular phylogenetics and phylogeography in frogs of the Scinax ruber and Rhinella margaritifera species groups. Mol Phylogenet Evol 43:567–582
CAS PubMed Article Google Scholar
Frost DR (2019) Amphibian species of the world: an online reference. Version 6.0 (Date of access). Electronic database accessible at. Amphib Species World an Online Ref Version 60.
Funk WC, Caminer M, Ron SR (2012) High levels of cryptic species diversity uncovered in Amazonian frogs. Proc R Soc B 279:1806–1814
PubMed Article Google Scholar
Gabb WM, Lücke OH, Gutiérrez V, Soto G (2007) On the geology of the Republic of Costa Rica. (Transcription of the original manuscript by: Oscar H. Lücke, Viviana Gutiérrez & Gerardo Soto). Rev Geológica América Cent 203:103–118
Google Scholar
Garrigues R, Dean R (2014) The Birds of Costa Rica. Zona Tropical Publication, San José, Costa Rica
Google Scholar
Grant T, Frost DR, Caldwell JP, Gagliardo R, Haddad CFB, Kok PJR et al. (2006) Phylogenetic systematics of dart-poison frogs and their relatives (Amphibia: Athesphatanura: Dendrobatidae). Bull Am Mus Nat Hist 299:1–262
Article Google Scholar
Guarnizo CE, Cannatella DC (2013) Genetic divergence within frog species is greater in topographically more complex regions. J Zool Syst Evol Res 51:333–340
Google Scholar
Hernandez PA, Graham CH, Master LL, Albert DL (2006) The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography 29:773–785
Article Google Scholar
Hijmans RJ, van Etten J (2010) raster: Geographic analysis and modeling with raster data. R Packag version 1.
Hijmans RJ, Phillips S, Leathwick J, Elith J (2012) Package ‘dismo’. Species distribution modeling. R Packag version 08-11.
Hilje B, Arévalo-Huezo E (2012) Aestivation in the cane toad Rhinella marina Linnaeus 1758 (Anura, Bufonidae) during the peak of a dry season in a tropical dry forest, Costa Rica. Herpetol Notes 5:533–534
Google Scholar
Holdridge LR (1987) Ecología basada en zonas de vida. Instituto Interamericano de Cooperación para la Agricultura (IICA), San Jose, Costa Rica
Google Scholar
Horn S (1990) Timing of deglaciation in the Cordillera de Talamanca. Costa Rica Clim Res 1:81–83
Article Google Scholar
Hudson RR, Coyne JA (2002) Mathematical consequences of the genealogical species concept. Evolution 56:1557–1565
PubMed Article Google Scholar
Islebe GA, Hooghiemstra H, van’t Veer R (1996) Holocene vegetation and water level history in two bogs of the Cordillera de Talamanca, Costa Rica. Vegetatio 124:155–171
Google Scholar
Jacobson S (2018) Reproductive behavior and male mating success in two species of glass frogs. Herpetologica 41:396–404
Google Scholar
Janzen DH (1991) Historia Natural de Costa Rica. Primera (D Janzen, Ed.). Editorial San José, Universidad de Costa Rica, San José, Costa Rica
Google Scholar
Kaky E, Nolan V, Alatawi A, Gilbert F (2020) A comparison between Ensemble and MaxEnt species distribution modelling approaches for conservation: a case study with Egyptian medicinal plants. Ecol Inf 60:101150
Article Google Scholar
Keigwin LD (1978) Pliocene closing of the Isthmus of Panama, based on biostratigraphic evidence from nearby Pacific Ocean and Caribbean Sea cores. Geology 6:630–634
Article Google Scholar
Kessing B, Croom H, Martin A, McIntosh C, Owen MW, Palumbi SP (2004) The Simple Fool’s Guide to PCR, Version 1. Department of Zoology, University of Hawaii, Honolulu, USA
Google Scholar
Kimura M (1980) A simple method for estimating evolutionary rate of base substitutions through comparative studies of nucleotide sequences. J Mol Evol 16:111–120
CAS Article Google Scholar
Kirby MX, Jones DS, MacFadden BJ (2008) Lower Miocene stratigraphy along the Panama Canal and its bearing on the Central American Peninsula. PLoS ONE 3:e2971
Article CAS Google Scholar
Kluge J, Kessler M (2006) Fern endemism and its correlates: contribution from an elevational transect in Costa Rica. Divers Distrib 12:535–545
Article Google Scholar
Kubicki B (2007) Ranas de Vidrio de Costa Rica, 1st edn. Instituto Nacional de Biodiversidad, Santo Domingo de Heredia, Costa Rica
Google Scholar
Li X, Wang Y (2013) Applying various algorithms for species distribution modelling. Integr Zool 8:124–135
PubMed Article Google Scholar
Luo A, Ling C, Ho SYW, Zhu CD (2018) Comparison of methods for molecular species delimitation across a range of speciation scenarios. Syst Biol 67:830–846
PubMed PubMed Central Article Google Scholar
Luteyn J (1999) Paramos: a checklist of plant diversity, geographical distribution, and botanical literature. Mem N. Y Bot Gard 84:138–141
Google Scholar
Manel S, Holderegger R (2013) Ten years of landscape genetics. Trends Ecol Evol 28:614–621
PubMed Article Google Scholar
Manel S, Schwartz MK, Luikart G, Taberlet P (2003) Landscape genetics: combining landscape ecology and population genetics. Trends Ecol Evol 18:189–197
Article Google Scholar
Manly B (1986) Randomization and regression methods for testing for associations with geographical, environmental and biological distances between populations. Res Popul Ecol 28:201–218
Article Google Scholar
Marshall JS (2007) The geomorphology and physiographic provinces of Central America. In: Bundschuh & Alvarado (eds) Central America: Geology, Resources and Hazards, vol 1, CRC Press, Florida, USA, pp 1–51
Marshall LG (1988) Land mammals and the Great American interchange. Am Sci 76:380–388
Google Scholar
Mayr E (1942) Systematics and the origin of species, 1st edn. Columbia University Press, New York, NY
Google Scholar
McCann S, Greenlees MJ, Newell D, Shine R (2014) Rapid acclimation to cold allows the cane toad to invade montane areas within its Australian range. Funct Ecol 28:1166–1174
Article Google Scholar
McRae BH (2006) Isolation by resistance. Evolution 60:1551–1561
PubMed Article Google Scholar
Michaux JR, Libois R, Filippucci MG (2005) So close and so different: Comparative phylogeography of two small mammal species, the Yellow-necked fieldmouse (Apodemus flavicollis) and the Woodmouse (Apodemus sylvaticus) in the Western Palearctic region. Heredity 94:52–63
CAS PubMed Article Google Scholar
Montes C, Cardona A, McFadden R, Morón SE, Silva CA, Restrepo-Moreno S et al. (2012) Evidence for middle Eocene and younger land emergence in central Panama: implications for Isthmus closure. Bull Geol Soc Am 124:780–799
CAS Article Google Scholar
Muscarella R, Galante PJ, Soley-Guardia M, Boria RA, Kass JM, Uriarte M et al.(2014) ENMeval: an R package for conducting spatially independent evaluations and estimating optimal model complexity for MaxEnt ecological niche models Methods Ecol Evol 5:1198–1205
Article Google Scholar
Myers N, Mittermeier RA, Mittermeier CG, Fonseca GAB, da, Kent E (2000) Biodiversity hotspots for conservation priorities. Nature 403:853–858
CAS PubMed Article Google Scholar
Naimi B, Hamm NAS, Groen TA, Skidmore AK, Toxopeus AG (2014) Where is positional uncertainty a problem for species distribution modelling? Ecography 37:191–203
Article Google Scholar
O’Dea A, Lessios HA, Coates AG, Eytan RI, Restrepo-Moreno SA, Cione AL et al. (2016) Formation of the Isthmus of Panama. Sci Adv 2:1–12
Article Google Scholar
Oliveira BF, São-Pedro VA, Santos-Barrera G, Penone C, Costa GC (2017b) AmphiBIO, a global database for amphibian ecological traits. Sci Data 4:170123
PubMed PubMed Central Article Google Scholar
Oliveira E, Martinez P, Sao-Pedro V, Gehara M, Burbrink FT, Mesquita D et al. (2017a) Climatic suitability, isolation by distance and river resistance explain genetic variation in a Brazilian whiptail lizard. Heredity 120:251–265
PubMed PubMed Central Article Google Scholar
Padial JM, De La Riva I (2009) Integrative taxonomy reveals cryptic Amazonian species of Pristimantis (Anura: Strabomantidae). Zool J Linn Soc 155:97–122
Article Google Scholar
Paz A, Crawford AJ (2012) Molecular-based rapid inventories of sympatric diversity: a comparison of DNA barcode clustering methods applied to geography-based vs clade-based sampling of amphibians. J Biosci 37:887–896
CAS PubMed Article Google Scholar
Paz A, González A, Crawford AJ (2019) Testing effects of Pleistocene climate change on the altitudinal and horizontal distributions of frogs from the Colombian Andes: A species distribution modeling approach. Front Biogeogr 11:e37055
Article Google Scholar
Paz A, Ibáñez R, Lips KR, Crawford AJ (2015) Testing the role of ecology and life history in structuring genetic variation across a landscape: a trait-based phylogeographic approach. Mol Ecol 24:3723–3737
PubMed Article Google Scholar
Pinto-Sánchez NR, Ibañez R, Madriñán S, Sanjur OI, Bermingham E, Crawford AJ (2012) The Great American biotic interchange in frogs: multiple and early colonization of Central America by the South American genus Pristimantis (Anura: Craugastoridae). Mol Phylogenet Evol 62:954–972
PubMed Article Google Scholar
Prum R, Rice NH, Mobley J, Dimmick W (2000) A preliminary phylogenetic hypothesis for the cotingas (Cotingidae) based on mitochondrial DNA. Auk 117:236–241
Article Google Scholar
Puillandre N, Lambert A, Brouillet S, Achaz G (2012) ABGD, Automatic Barcode Gap Discovery for primary species delimitation. Mol Ecol 21:1864–1877
CAS PubMed PubMed Central Article Google Scholar
Ramírez-Barahona S, Eguiarte LE (2013) The role of glacial cycles in promoting genetic diversity in the Neotropics: The case of cloud forests during the Last Glacial Maximum. Ecol Evol 3:725–738
PubMed PubMed Central Article Google Scholar
Rich PV, Rich T (1991) La ruta de dispersión centroamericana: Historia biotica y paleográfica. In: Janzen DH (ed) Historia Natural de Costa Rica. Editorial San José, Universidad de Costa Rica, San José, Costa Rica, p 13–34
Google Scholar
Riddle BR, Hafner DJ, Alexander LF, Jaeger JR (2000) Cryptic vicariance in the historical assembly of a Baja California Peninsular Desert biota. Proc Natl Acad Sci USA 97:14438–14443
CAS PubMed Article Google Scholar
Robertson JM, Duryea MC, Zamudio KR (2009) Discordant patterns of evolutionary differentiation in two Neotropical treefrogs. Mol Ecol 18:1375–1395
PubMed Article Google Scholar
Robertson JM, Lips KR, Heist EJ (2008) Fine scale gene flow and individual movements among subpopulations of Centrolene prosoblepon (Anura: Centrolenidae). Rev Biol Trop 56:13–26
PubMed Google Scholar
Rodríguez A, Börner M, Pabijan M, Gehara M, Haddad CFB, Vences M (2015) Genetic divergence in tropical anurans: deeper phylogeographic structure in forest specialists and in topographically complex regions. Evol Ecol 29:765–785
Article Google Scholar
Savage J, Bolaños F (2009) A checklist of the amphibians and reptiles of Costa Rica: additions and nomenclatural revisions. Zootaxa 2005:1–23
Article Google Scholar
Savage JM (2002) The Amphibians and Reptiles of Costa Rica: a herpetofauna between two continents, between two seas. University of Chicago press, Chicago, Illinois, USA
Sexton JP, Hangartner SB, Hoffmann AA (2014) Genetic isolation by environment or distance: which pattern of gene flow is most common? Evolution 68:1–15
CAS PubMed Article Google Scholar
Shah VB, McRae BH (2008) Circuitscape: a tool for landscape ecology. Proc 7th Python Sci Conf. 7:62–65
Slatkin M (1993) Isolation by distance in equilibrium and non-equilibrium populations. Evolution 47:264–279
PubMed Article Google Scholar
Smith AM, Green DM (2005) Dispersal and the metapopulation paradigm in amphibian ecology and conservation: are all amphibian populations metapopulations? Ecography 28:110–128
Article Google Scholar
Stamatakis A (2014) RAxML version 8: A tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30:1312–1313
CAS PubMed PubMed Central Article Google Scholar
Steele CA, Storfer A (2007) Phylogeographic incongruence of codistributed amphibian species based on small differences in geographic distribution. Mol Phylogenet Evol 43:468–479
CAS PubMed Article Google Scholar
Stehli FG, Webb SD (eds) (1985) The Great American Biotic Interchange. Plenum Press, New York, NY (FG Stehli and SD Webb, Eds.)
Google Scholar
Stiles G, Remsen JV, McGuire JA (2017) The generic classification of the Trochilini (Aves: Trochilidae): reconciling taxonomy with phylogeny. Zootaxa 4353:401–424
PubMed Article Google Scholar
Stine R (1995) Graphical interpretation of variance inflation factors. Am Stat 49:53–56
Google Scholar
Streicher JW, Crawford AJ, Edwards CW (2009) Multilocus molecular phylogenetic analysis of the montane Craugastor podiciferus species complex (Anura: Craugastoridae) in Isthmian Central America. Mol Phylogenet Evol 53:620–630
PubMed Article Google Scholar
Tamura K, Stecher G, Peterson D, Filipski A, Kumar S (2013) MEGA6: Molecular evolutionary genetics analysis version 6.0. Mol Biol Evol 30:2725–2729
CAS PubMed PubMed Central Article Google Scholar
Vences M, Thomas M, Van Der Meijden A, Chiari Y, Vieites DR (2005) Comparative performance of the 16S rRNA gene in DNA barcoding of amphibians. Front Zool 2:1–12
Article CAS Google Scholar
Wang I, Crawford AJ, Bermingham E (2008a) Phylogeography of the Pygmy Rain Frog (Pristimantis ridens) across the lowland wet forests of isthmian Central America. Mol Phylogenet Evol 47:992–1004
CAS PubMed Article Google Scholar
Wang IJ (2013) Examining the full effects of landscape heterogeneity on spatial genetic variation: A multiple matrix regression approach for quantifying geographic and ecological isolation. Evolution 67:3403–3411
PubMed Article Google Scholar
Wang IJ, Bradburd GS (2014) Isolation by environment. Mol Ecol 23:5649–5662
PubMed Article Google Scholar
Wang Y-H, Yang K-C, Bridgman CL, Lin L-K (2008b) Habitat suitability modelling to correlate gene flow with landscape connectivity. Landsc Ecol 23:989–1000
Google Scholar
Webb SD (2006) The Great American Biotic Interchange: patterns and processes. Ann Mo Bot Gard 93:245–257
Article Google Scholar
Weigt LA, Crawford AJ, Rand AS, Ryan MJ (2005) Biogeography of the túngara frog, Physalaemus pustulosus: a molecular perspective. Mol Ecol 14:3857–3876
CAS PubMed Article Google Scholar
Weir JT, Bermingham E, Schluter D (2009) The Great American biotic interchange in birds. Proc Natl Acad Sci USA 106:21737–21742
CAS PubMed Article Google Scholar
Werle E, Schneider C, Renner MV, W F (1994) Convenient single-step, one tube purification of PCR products for direct sequencing. Nucleic Acids Res 22:4354–4355
CAS PubMed PubMed Central Article Google Scholar
Weyl R (1980) Geology of Central America. Gebrueder Borntraeger, Berlin-Stuttgart
Google Scholar
Wilson JS, Carril OM, Sipes SD (2014) Revisiting the Great American Biotic Interchange through analyses of amphitropical bees. Ecography 37:791–796
Article Google Scholar
Wollenberg KC, Vieites DR, Glaw F, Vences M (2011) Speciation in little: the role of range and body size in the diversification of Malagasy mantellid frogs. BMC Evol Biol 11:217
PubMed PubMed Central Article Google Scholar
Wollenberg Valero KC (2015) Evidence for an intrinsic factor promoting landscape genetic divergence in Madagascan leaf-litter frogs. Front Genet 6:155
PubMed PubMed Central Article CAS Google Scholar
Wright S (1943) Isolation by distance. Genetics 28:114–138
CAS PubMed PubMed Central Google Scholar
Wright S (1951) The genetical structure of populations. Ann Eugen 15:323–354
CAS PubMed PubMed Central Article Google Scholar
Zhang J, Kapli P, Pavlidis P, Stamatakis A (2013) A general species delimitation method with applications to phylogenetic placements. Bioinformatics 29:2869–2876
CAS PubMed PubMed Central Google Scholar
Zuur AF, Ieno EN, Elphick CS (2010) A protocol for data exploration to avoid common statistical problems. Methods Ecol Evol 1:3–14
Article Google Scholar More
The evaluation of T. qataranse growth parameters suggests that Pb has no adverse effect on the plant at concentrations of less than 100 mg/L Pb. However, at 100 mg/L, the metal disturbed healthy growth and, in particular, interfered with root development. Consistent with our findings, a similar study using Z. fabago reported that Pb negatively affects root development14. The root plays a vital role in plant health and development, influencing other tissues’ response to stress conditions. Despite it being one of the most critical parameters in the assessment of plant health, a significant reduction in total chlorophyll content was observed (Fig. 1c). However, Pb toxicity symptoms (e.g., leaf chlorosis and root darkening) were not apparent across any of the treatments. Typically, Pb accumulation in plants raises the level of chlorophyllase, an enzyme that negatively affects chlorophyll. An increased level of chlorophyllase slows down photosynthesis and, therefore, affects overall growth and development. Consequently, due to slow metabolic activities, cell division is adversely affected and healthy growth is inhibited15.
Pb accumulates differently in plant tissue parts, especially in the root22. Concerning T. qataranse Pb accumulation, overall data across all treatments indicates that T. qataranse preferentially concentrates Pb in the root (up to 2,784 mg/kg). Our result is consistent with the reports of many similar studies. For instance, known plant species, including Nerium oleander L. and Brassica juncea, accumulate higher Pb concentrations in their roots than other tissue parts16. Also, in a study involving different plants, Finster, et al.17 determined that the roots always accumulate more Pb than other plant parts, including the fruits, where only traces of the metal translocate the shoot. Our result is also in agreement with the work of Langley-Turnbaugh and Belanger18. Kumar, et al.19 and Pourrut, et al.20 conducted several critical reviews of Pb toxicity in plants and determined that several factors contribute to restricted metal translocation in plants. Of such, Casparian strip endodermis restriction is by far the most limiting for Pb. Notheless, the ability of T. qataranse to accumulate more than 1000 mg/kg Pb suggests that it is a Pb hyperaccumulator21.
Additionally, the root BCF across all treatments was higher than that of the shoot (Fig. 2b). The BCF indicates that, to some degree, T. qataranse sequestrate Pb from growth medium that contains up to 1600 mg/kg Pb. However, it was optimal in the 50 mg/kg treatment. At this concentration, the growth medium had up to 800 mg/kg Pb. The TF under all treatments was less than 1 (Fig. 2c), meaning that T. qataranse can not sufficiently transfer Pb to its aerial parts. In the current study, the restriction of Pb translocation finding is consistent with our previous report on T. qataranse where field samples were analyzed for various metals accumulation, including Pb22. Some of the factors that affect metal bioavailability and uptake include plant and metal types; metal form, concentration, and age in the soil; pH; and organic matter content. However, pH and total organic matter content are the most critical in terms of metal bioavailability and uptake of Pb. The pH significantly affects the behavior of Pb by dictating its chemical form. Metals, including Pb, are more soluble at low or near-neutral pH values. At pH > 8, metals tend to precipitate in the soil. Similarly, a high TOC limits the bioavailability of Pb11. In this work, the pH and TOC in the growth medium were 7.35 and 1.87%, respectively. Therefore, given the neutral pH and low TOC, their effects on Pb bioavailability and uptake by T. qataranse was insignificant and can be eliminated.
Various response mechanisms enable plants to withstand metal toxicity, of such, metal avoidance and uptake are the most common. Before compartmentalization, Pb is translocated to a degree that can be described by the TF23. Pb mainly precipitates on the root cell wall and only the free ions are transported to other parts via the xylem and phloem cells24. Previous works confirmed that Pb disrupts cellular homeostasis by replacing essential cations and altering metal-containing enzyme activity. In plants, the primary sources of ROS are chloroplasts, mitochondria, and peroxisomes. Pb toxicity interferes with electron transport chains in turn increasing ROS accumulation. Nearly every stage of the central dogma of plants (DNA, RNA, protein) is affected by Pb toxicity25.
The antioxidant system is one mechanism used by plants for protection against metal toxicity. In this study, the result of the SOD, CAT, APX, GPX, and GR assay show increased activity of all five enzymes. SOD activity was the highest, up to ten times higher than the control (0 mg/kg Pb), particularly in the root (Fig. 3a), suggesting the critical role of SOD in T. qataranse antioxidative defense. Having the highest enzymatic activity be in the root changes root organic constituents due to Pb complexation. This indicates that, as suggested in our previous work, as an uptake mechanism, Pb ions bind to T. qataranse root by complexation through cationic exchange with hydroxyl and carboxyl functional groups7. This is a well-established complexation mechanism of transition metals, including Pb1. In comparison, GR demonstrated the least activity (Fig. 3e). Such differences can be attributed to the specific roles each enzyme has in ameliorating Pb stress. Many other studies reported a similar increase in the activities of one or all the enzymes analyzed in this work following plants’ exposure to Pb; examples are increases in the activity of CAT, SOD, APX, and GPX in Ceratophyllum demersum L.26 and the cotton plant27; SOD, APX, GPX, and GR in Oryza sativa L.28; and APX, CAT, and GR in Triticum aestivum9. Changes in enzymatic activity account for the elimination of ROS and the improvement of stress conditions in plants. Therefore, the enhanced activities of all enzymes suggest that their role is crucial in ameliorating Pb toxicity in T. qataranse. Other studies support this conclusion, including Ferrer, et al.16 who attribute enhanced CAT and APX activities to the efficient ROS scavenging capability of Z. fabago exposed to Pb. In addition, Nikalje and Suprasanna30 reviewed several other similar studies involving halophytes . However, to the best of our knowledge, our work is the first on T. qataranse. In addition, primarily due to GR activity suggesting the utilization of glutathione, we can conclude that Pb detoxification in T. qataranse may partly involve glutathione metabolism30. Glutathione, which exists in either the reduced (GSH) or oxidized form (GSSG), acts as an antioxidant and chelating bioligand majorly accountable for metals detoxification. Enzymes involved in glutathione metabolism mediates detoxification Glutathione-S-transferases (GSTs) are a major phase II GSH-dependent ROS scavenging enzymes. They play significant roles in GSH conjugation with exogenous and endogenous species found during oxidative stress, including H2O2 and lipid peroxides9,10. Consistent with our findings, a more recent review by Kumar and Prasad14 discussed several other studies, some of which include the use of model species, Arabidopsis thaliana, and Oryza sativa, all of which support our findings.
It is worth mentioning that part of the discussion presented in this work is limited to the perspective of deciphering Pb tolerance and uptake mechanisms from metal translocation and plant antioxidative systems. However, both molecular and biochemical mechanisms play significant roles in toxic metals detoxification, including Pb. For instance, glutathione metabolism is known to regulates the biosynthesis of phytochelatins (PC), which bind Pb and transports it to vacuoles where detoxification can occur. Additionally, genes, such as glutamate cysteine ligae 1 (GSH1), glutamate cysteine ligae 1 (GSH2), phytochelatins synthase 1 (PCS1), and phytochelatins synthase 2 (PCS2), are actively involved in GSH-dependent PCs synthesis. Other primary and secondary metabolites that act as antioxidants, such as Tocols, flavonoids, anthocyanidins, and ascorbic acid, are essential to protecting plants against oxidative damage as well. The functions of these metabolites are well documented8,9,20. Further, we recognize that proteins regulate ROS signaling and the expression of such proteins changes due to Pb exposure11. Due to metal stress in plants, increased protein synthesis is essential in cellular metabolic processes. Mitogen-activated protein (MAP) kinase pathways regulate such processes, serving as a signaling system against oxidative stress. Signaling occurs through multiple stages of the reaction, modifying gene expression and, ultimately, protein synthesis4. Therefore, our future work will focus on the differential expression of proteins, particularly those that are related to stress responses, such as the heat shock protein family, due to Pb exposure in T. qataranse. More
1.
IUCN. 50 Years of Working for Protected Areas: A brief history of IUCN World Commission on Protected Areas. (International Union for Conservation of Nature and Natural Resources, 2010).
2.
SCBD. The CBD Programme of Work on Protected Areas. http://www.cbd.int/protected/ (2004).
3.
SCBD. In COP 10 Decision X/2: Strategic Plan for Biodiversity 2011–2020 (Secretariat of the Convention on Biological Diversity, 2010).
4.
SCBD. Zero Draft of the Post-2020 Global Biodiversity Framework. https://www.cbd.int/doc/c/efb0/1f84/a892b98d2982a829962b6371/wg2020-02-03-en.pdf (2020).
5.
UNEP-WCMC, IUCN & NGS. Protected Planet Live Report 2020 (August update) https://livereport.protectedplanet.net/ (2020).
6.
IUCN. IUCN Red List of Threatened Species. Version 2020.2 (International Union for Conservation of Nature, 2020).
7.
Coad, L. et al. Widespread shortfalls in protected area resourcing undermine efforts to conserve biodiversity. Front. Ecol. Environ. 17, 259–264 (2019).
Article Google Scholar
8.
Gaston, K. J., Jackson, S. F., Cantú-Salazar, L. & Cruz-Piñón, G. The ecological performance of protected areas. Annu. Rev. Ecol. Evolution Syst. 39, 93–113 (2008).
Article Google Scholar
9.
Geldmann, J., Manica, A., Burgess, N. D., Coad, L. & Balmford, A. A global-level assessment of the effectiveness of protected areas at resisting anthropogenic pressures. Proc. Natl Acad. Sci. USA 116, 23209–23215 (2019).
ADS CAS Article Google Scholar
10.
Andam, K. S., Ferraro, P. J., Pfaff, A., Sanchez-Azofeifa, G. A. & Robalino, J. A. Measuring the effectiveness of protected area networks in reducing deforestation. Proc. Natl Acad. Sci. USA 105, 16089–16094 (2008).
ADS CAS Article Google Scholar
11.
Gray, C. L. et al. Local biodiversity is higher inside than outside terrestrial protected areas worldwide. Nat. Commun. 7, 12306 (2016).
ADS CAS Article Google Scholar
12.
Cazalis, V., Belghali, S. & Rodrigues, A. S. L. Using a Large-scale Biodiversity Monitoring Dataset to Test the Effectiveness of Protected Areas at Conserving North-American Breeding Birds. Preprint at bioRxiv https://doi.org/10.1101/433037 (2019).
13.
Hiley, J. R., Bradbury, R. B. & Thomas, C. D. Impacts of habitat change and protected areas on alpha and beta diversity of Mexican birds. Diversity Distrib. 22, 1245–1254 (2016).
Article Google Scholar
14.
Cazalis, V. et al. Effectiveness of protected areas in conserving tropical forest birds. Nat. Commun. 11, 4461 (2020).
CAS Article Google Scholar
15.
Mascia, M. B. & Pailler, S. Protected area downgrading, downsizing, and degazettement (PADDD) and its conservation implications. Conserv. Lett. 4, 9–20 (2011).
Article Google Scholar
16.
IUCN-WCPA Task Force on OECMs. Recognising and Reporting Other Effective Area-based Conservation Measures (IUCN, Gland, Switzerland, 2019).
17.
Visconti, P. et al. Protected area targets post-2020. Science 364, 239–241 (2019).
ADS CAS PubMed Google Scholar
18.
Hockings, M., Stolton, S., Dudley, N. & Phillips, A. Evaluating Effectiveness – A Framework for Assessing the Management of Protected Areas (IUCN – The World Conservation Union, 2000).
19.
UNEP-WCMC. Global Database on Protected Area Management Effectiveness (August update) https://pame.protectedplanet.net/ (2020).
20.
Joppa, L. N. & Pfaff, A. High and far: biases in the location of protected areas. PLoS ONE 4, e8273 (2009).
ADS Article Google Scholar
21.
Rodrigues, A. S. L. et al. Global gap analysis: priority regions for expanding the global protected-area network. BioScience 54, 1092–1100 (2004).
Article Google Scholar
22.
Ferraro, P. J. Counterfactual thinking and impact evaluation in environmental policy. N. Dir. Eval. 2009, 75–84 (2009).
Article Google Scholar
23.
Gill, D. A. et al. Capacity shortfalls hinder the performance of marine protected areas globally. Nature 543, 665–669 (2017).
ADS CAS Article Google Scholar
24.
Hillebrand, H. et al. Biodiversity change is uncoupled from species richness trends: consequences for conservation and monitoring. J. Appl. Ecol. 55, 169–184 (2018).
Article Google Scholar
25.
Geldmann, J. et al. A global analysis of management capacity and ecological outcomes in terrestrial protected areas. Conserv. Lett. 11, e12434 (2018).
Article Google Scholar More
In the present work, we performed quantitative metabolomic analysis for eleven biological tissues of S. lucioperca. The advantage of the quantitative approach over commonly used semi-quantitative measurements is that the obtained data on the metabolite concentrations expressed in nmoles per gram of a tissue can be directly used by any researcher as a reference to the baseline level of metabolites in that tissue. Quantitative data also allow for the comparing the tissues with very dissimilar metabolomic compositions.
The metabolomic analysis performed in the present work demonstrates that although the majority of metabolites are common for all tissues, their concentrations in tissues may vary at a large scale. Moreover, there are some tissue-specific compounds with very high abundance in only 1–2 types of tissue. The examples of such metabolites are glycine, histidine, creatine, and betaine in muscle, ovothiol A in lens, NAA in lens and brain, glucose in liver. Apparently, these compounds are important for biological functions specific for these particular tissues.
Two groups of metabolites, osmolytes and antioxidants, play the key role in the cell protection against osmotic and oxidative stresses. In this work, the following compounds were conventionally assigned to osmolytes: taurine, myo-inositol, NAH, NAA, betaine, threonine-phosphoethanolamine (Thr-PETA), and Ser-PETA. Obviously, this assignment is rather arbitrary: some of these compounds, besides osmotic protection, perform other cellular functions, including cell signaling, providing substrate for biosynthesis, and so on17,18,19. At the same time, the tissues under study contain metabolites with concentrations of the same level or even higher than the concentrations of compounds assigned to osmolytes: lactate, glucose, acetate, creatine. These metabolites are mostly related to the reactions of cellular energy generation, and their concentrations should strongly depend on the fish activity. For that reason, in this work we did not include them into the list of osmolytes. Figure 6 shows the concentrations of osmolytes in different fish tissues (excluding acellular tissues AH and VH), and demonstrates that the composition of osmolytes in tissues strongly depends on the cell type.
Figure 6
Concentrations of major osmolytes (in µmol/g) in S. lucioperca tissues.
Full size image
We found in the fish tissues the following compounds with antioxidative properties: glutathione (GSH), ascorbate, OSH, and NADH. The detection of minor amounts of one more well-known thiol antioxidant, ergothioneine, in the gills of another freshwater fish, R. rutilus lacustris, has recently been reported15; however, in the present work ergothioneine was not found neither by NMR nor by LC–MS in any of the studied tissues of S. lucioperca. NADH was found only in NMR spectra of liver, muscle, heart, and lens, and its concentration in these tissues does not exceed 15 nmol/g. GSH, OSH, and ascorbate are present in much higher concentrations in the majority of the fish tissues, so these three compounds play the main role in the cellular defense against the oxidative stress. The presence and the relative abundance of GSH and OSH in the fish tissues were confirmed by LC–MS data.
The metabolomic features of particular fish tissues are discussed below.
AH and VH
AH and VH are acellular fluids with minimal metabolic activity. AH is produced in the ciliary epithelium through both the active secretion and the passive diffusion/ultrafiltration of blood plasma20,21,22,23. Consequently, the metabolomic composition of AH is similar to that of plasma10. VH is also connected with blood via the hematoophthalmic barrier24 and with AH, and one can see (Table 1, Fig. 2) that the metabolomic compositions of AH and VH are close to each other. Thus, it is safe to assume that the levels of metabolites in AH and VH reflect their levels in blood plasma, which circulates through the majority of fish tissues. Significant deviations of metabolite levels in tissue as compared to plasma should be attributed to the intracellular metabolic activity specific for this particular tissue.
Lens
The eye lens is one of the most anatomically isolated tissues. The lens mostly consists of metabolically inert fiber cells without nuclei and organelles with the exception of metabolically active epithelial monolayer. The data present in Table 1 indicate that the lens contains very high levels of proteinogenic amino acids: for some amino acids (for example, branched-chain amino acids, glutamine, aspartate) their levels in the lens are more than ten-fold higher than that in AH. Moreover, the concentrations of the majority of amino acids in the lens are higher than in any other fish tissue. The elevated levels of amino acids in the lens has been noticed many years ago25, and it was attributed to the active amino acid transport from AH to lens26,27,28,29. These amino acids are presumably needed to synthesize high protein content (up to 40% of the total lens weight), which is in turn needed to provide high refraction coefficient.
The fish lens contains a unique set of osmolytes and antioxidants. The lens osmolytes are myo-inositol, NAH, NAA, Thr-PETA, and Ser-PETA. We have previously shown15 that the concentrations of osmolytes in the fish lens undergo significant seasonal variations. At the late winter time, when the fish was caught for this study, the most abundant lens osmolyte is myo-inositol. High concentrations of this compound are also found in other fish tissues, including brain, gill, and spleen. Thr-PETA and Ser-PETA are also among the most abundant metabolites in the majority of the fish tissues. In opposite, NAH and NAA are present in high concentrations only in the fish lens and brain. At the same time, the concentration of taurine, which is the most abundant osmolyte in all other fish tissues, in the lens is rather low.
The major antioxidant of the fish lens is OSH12. It has been shown that the level of OSH in S. lucioperca lens vary from 3 µmol/g at autumn to 1.5 µmol/g at winter15, which is in a good agreement with our present data (Table 1). The concentration of the second most abundant lens antioxidant, GSH, is 3–4 times lower than that of OSH. Taking into account the properties of OSH30,31,32,33, it has been proposed12,34 that OSH is a primary protector against the oxidative stress, while the main function of GSH in the lens is the maintenance of OSH in the reduced state. It should be noticed that although OSH was also found in other fish tissues (Table 1, Fig. 2), its concentration in these tissues is significantly lower than in the lens. Therefore, in respect to fish, OSH can truly be called “lenticular antioxidant”.
High concentrations of amino acids, osmolytes, antioxidants and some other compounds in the lens indicate that these metabolites are either synthesized in metabolically active epithelial cells, or pumped into the lens from AH against the concentration gradient with the use of specific transporters also located in the epithelial layer. The fiber cells of the lens are metabolically passive, and fresh metabolites can appear in these cells only due to the diffusion from the epithelial layer toward the lens center. Therefore, one can expect that the concentrations of the most important metabolites decrease from the lens cortex toward the lens nucleus. To check this assumption, we measured the metabolomic profiles for cortex and nucleus separately. The measurements were performed for three lenses; then the ratios of the metabolite concentrations in the cortex to that in the nucleus were calculated and averaged. The results of the calculations are shown in Fig. 7 (only for metabolites with the highest and the lowest cortex/nucleus ratios) and Supplementary Table S2 (for all metabolites). Indeed, the levels of the majority of metabolites in the lens nucleus are significantly lower than in the cortex. For five metabolites, namely ATP, NAA, inosinate, ADP, and GSH the difference exceeds the factor of thirty; that means that these compounds are almost completely depleted during their diffusion toward the lens nucleus.
Figure 7
Barplot for statistically significant differences in the metabolomic content of lens cortex and nucleus. Bars show the averaged ratio of metabolite concentrations in the cortex to that in the nucleus of the S. lucioperca lens.
Full size image
Brain
The brain tissue similarly to the lens is isolated from the vascular system by means of the hematoencephalic barrier. However, in opposite to the lens, brain is very metabolically active tissue, as in particular indicated by the high level of lactate (14 µmol/g). Similar lactate concentrations were found only in muscle and heart (Table 1). Besides lactate, the most abundant metabolites of the fish brain are osmolytes myo-inositol, Ser-PETA, taurine, Thr-PETA, NAH, and NAA; the concentrations of these compounds in brain are in the range from 2.5 to 13 µmol/g (Table 1). The brain tissue also contains high levels of glutamate and creatine, which are used by brain cells for the cellular energy generation. The level of antioxidant ascorbate in brain (400 nmol/g) is significantly higher than in other fish tissues, which indicates the importance of ascorbate for the brain correct operation. Besides ascorbate, the brain tissue also contains OSH and GSH, but at significantly lower concentrations (100–200 nmol/g).
Blood-rich organs: liver, spleen, milt, muscle, heart, gill, kidney
Figure 4 demonstrates that from the metabolomic viewpoint, gill, kidney, milt, and spleen are the most similar tissues. However, the quantitative analysis indicates significant differences. In particular, spleen does not contain measurable by NMR amounts of antioxidants OSH and GSH. The levels of osmolytes are also different: the concentration of taurine in spleen is threefold higher than in gill and milt, while the level of myo-inositol in milt is much lower than in spleen and gill (Fig. 6). Significant differences are also found for some amino acids (alanine, creatine), organic acids (lactate, GABA), and nucleosides (ATP, ADP, AMP, inosine).
One of the important liver functions is the maintaining the glucose level in blood regulated by producing glucose from stored glycogen. Correspondingly, the level of glucose in liver is extremely high (40 µmol/g), which is higher than in any other tissue by at least an order of magnitude. Liver also contains elevated (as compared to other tissues) concentrations of threonine, glutamate, succinate, fumarate, AMP, and nicotinamide.
The biological functions of muscle and heart are relatively similar; however, the metabolomic compositions of these tissues differ significantly. The main osmolyte in muscle cells is taurine (30 µmol/g), while in heart the osmotic protection is shared between taurine (15 µmol/g) and Ser-PETA (12 µmol/g). Muscle contains very high levels of glycine and histidine. Glycine is known to protect muscles from wasting under various wasting conditions35,36, while histidine and histidine-related compounds were reported to play the role of intracellular proton buffering constituents in vertebrate muscle37. Very likely that both glycine and histidine also participate in the osmotic protection of the muscle cells. The level of creatine—the energy source—in muscle (23 µmol/g) is five-fold higher than in heart. More
Quality control of the full-length transcriptomes
The FL transcriptomes for R. a. hainanus, R. a. himalayanus and Myotis ricketti were constructed based on sequencing data of three separated libraries on the PacBio Sequel platform. Specifically, a total of 3,444,947 subreads with 6,448,987,299 nucleotides, 3,255,638 subreads with 6,504,282,447 nucleotides and 3,403,451 subreads with 7,190,237,257 nucleotides were generated for R. a. hainanus, R. a. himalayanus and Myotis ricketti respectively. After quality control, we obtained 137,159 circular consensus sequencing (CCS) reads for R. a. hainanus, 137,160 CCS reads for R. a. himalayanus and 152,251 CCS reads for Myotis ricketti. With the standard IsoSeq. 3 classification and clustering pipeline, we identified 111,806 FLNC for R. a. hainanus, 105,713 FLNC for R. a. himalayanus and 122,222 FLNC for Myotis ricketti. After isoform-level polishing, 10384, 9984 and 10932 high quality isoforms were retained in R. a. hainanus, R. a. himalayanus and Myotis ricketti respectively. After removing redundancy with CD-HIT-EST and filtering isoforms shorter than 200 bp, the final FL transcriptomes for R. a. hainanus, R. a. himalayanus and Myotis ricketti (FL-CF-Rhai, FL-CF-Rhim and FL-FM-Myo, respectively) contain 10103, 9676 and 10504 FL isoforms with an average length of 2251, 2370 and 2530 bp, respectively (Table 2). Finally, the FL transcriptome from both CF and FM bats (FL-CF-FM) contains 26,342 transcripts with an average length of 2,405 bp (Table 2). BUSCO analysis revealed that a total of 2,354 (57.4%) BUSCOs were included in FL-CF-FM. We also found 39.9%, 38.1% and 41.9% BUSCOs in FL-CF-Rhai, FL-CF-Rhim and FL-FM-Myo, respectively (Table 4). Given the highly specialized function of the cochlea, we should not expect a high level of BUSCO value in FL transcriptome of cochlea. A recent single cell RNA-seq study has identified a similar number of genes expressed in the murine cochlea (a total of 12,944)30.
Table 4 Completeness of each of the four FL transcriptomes assessed by benchmarking universal single-copy ortholog (BUSCO) analysis.
Full size table
Quality control of annotation
Four FL transcriptomes (FL-CF-Rhai, FL-CF-Rhim, FL-FM-Myo, and FL-CF-FM) were functionally annotated by performing DIAMOND and BLASTx searches against the Nr and UniProt databases separately. For FL-CF-FM, 24,793 and 24,198 transcripts were annotated by Nr database and UniProt database, respectively (Table 3). After combining the annotation results from the two databases, a total of 24,833 transcripts were annotated in at least one database. We obtained similar annotation results for FL-CF-Rhai, FL-CF-Rhim and FL-FM-Myo (Table 3). Transcripts without annotations might be novel isoforms of echolocating animals or due to the lack of representative sequences for cochlea in public databases. More
These methods are an expanded version of those in our related work, Boakes et al.15.
The database was compiled over the period 2005–2008. Data collection equates to around 1500 person-days and data were gathered by a team of 21 people. Between them, team members were fluent in English, French, German, Mandarin, Russian, Spanish and Swedish. These languages were extremely helpful in transcribing museum specimen labels and in translating publications. However, the majority of publications were in English and we acknowledge that the database will be biased toward records published in English-language publications.
Our study focuses on the 130 galliform species that occur within the Palaearctic and Indo-Malay biogeographic realms22 (see Online-only Table 1). We have additionally included records of the Imperial Pheasant (Lophura imperialis) although it is now recognised that this is a hybrid and not a species. The geographic range of two of the species in the database, the Red Grouse (Lagopus lagopus) and the Rock Ptarmigan (Lagopus muta), extends to North America. North American data was often included in the information which museums sent us and in these instances we entered those records into the database since we thought they might be of use to researchers studying these species. However, it should be noted that we did not search exhaustively for records of these species in North America, we have merely included those that we came across.
We attempted to gather all species distribution data that could be accessed from five different sources; museum collections, literature records, banding (ringing) data, ornithological atlases and birdwatchers’ trip report websites. For each data source, exhaustive and systematic search strategies were adopted.
Museum collections
Using web-based searches and Roselaar23, 377 natural history collections were identified. We found contact details for 338 of these collections and requested by email or letter a list of the Galliformes in their holdings along with collection localities and dates. Non-respondents were recontacted. 135 museums were able to share data with us (see Online-only Table 2). Museum records were obtained through publicly available online databases e.g. ORNIS, electronic or paper catalogues sent to us by the museums or by visiting the museums and transcribing data directly from specimens or card catalogues. Almost half of the museums we contacted did not respond despite at least one follow-up enquiry, and there was substantial variation in the amount and format of data contributed by those that did reply. Altogether, over 50% of the records came from just six museums (Natural History Museum, London; Zoological Institute of the Russian Academy of Sciences, St Petersburg; Zoological Museum of Lomonosov Moscow State University; Field Museum of Natural History, Chicago; American Museum of Natural History, New York; National Museum of Natural History, Leiden), a single museum (the Natural History Museum, London) contributing nearly 20% of the museum records that could be georeferenced and dated15. Following databasing and/or georeferencing, records were returned to larger collections and to those who had requested the data.
Literature
Data from the literature were added to those previously collected by McGowan24. Entire series of key English-language international and regional ornithological journals such as Ibis, Bird Conservation International, Journal of the Bombay Natural History Society, and Kukila were scanned for relevant information, availability allowing. We began at the library of the Zoological Society of London and followed up missing journal issues at the BirdLife International library, Cambridge UK; the British Library, London, UK; the Edward Grey Institute, University of Oxford, UK. Relevant Chinese literature was also scanned. Additionally, data were obtained from regional reports, personal diaries, letters, newsletters etc stored in the archives of BirdLife International, Cambridge, UK; the World Pheasant Association, Newcastle, UK; the Edward Grey Institute, University of Oxford, UK. Several of the species/regional experts we consulted also contributed their personal records which were recorded in the database as ‘personal communications’. As far as it were possible, records were classed as primary or secondary data within the ‘dynamicProperties’ field of GalliForm14. It is important to note that some primary records or museum specimens will be duplicated within the database in the secondary data.
Banding records
Eighty-three ornithological banding groups were identified using web-based searches and were contacted via email. Thirty of these groups replied and only seven were able to provide us with data (see Table 1). The majority of galliform species tend not to be banded due to their large body sizes and spurs. Additionally, many of the banding groups kept their records on paper and were not able to send them to us. Nevertheless, we were able to access and georeference 15,152 banding records.
Table 1 The ringing groups that shared data with GalliForm.
Full size table
Ornithological atlases
We digitised location data from 20 ornithological atlases (see Table 2). Data from several other atlases were not used since the range of dates for the records was wider than 20 years.
Table 2 The atlases that were digitised to be included in GalliForm.
Full size table
Trip report website data
We used the two trip report websites that were popular with birders during the data recording period (2005–2008), www.travellingbirder.com and www.birdtours.co.uk. At that time, eBird (probably the most relevant current online source today) did not cover the majority of the countries within our study region, and our intention with the deposition of this dataset is to focus on pre-eBird data that are more difficult and time consuming to access. We extracted data from all trip reports of birdwatching visits to European, Asian and North African countries. Care was taken to enter reports that featured on both websites once only.
Criteria for data inclusion
To be included in the database, records had to meet the following criteria:
1.
The record identified the species of the bird concerned.
2.
The record contained either a verbal description of the locality at which the bird concerned was observed or the co-ordinates at which the bird was observed.
Records of captive birds were excluded. Records relating to non-native occurrences were included but were flagged in the ‘establishmentMeans’ field as “introduced”.
Data entry
GalliForm14 was originally compiled in the programme Microsoft Access 2003. To maximise uniformity in data entry, all data recorders were given thorough and consistent training and each was provided with a set of database guidelines. An Access Database form was created to standardise data entry and to enable multiple members of the team to collect data simultaneously.
Each entry in GalliForm14 corresponds to a single record of a single species recorded in a specific location. The data fields of GalliForm14 are described in Online-only Table 3. The taxonomy used has been updated to be consistent with the BirdLife International 2019 taxonomy (datazone.birdlife.org). All information was entered exactly as it was described in the data source, with as much information extracted as possible. Multiple records from different sources which recorded the same information were still included in the interest of completeness. The only exception to this is the trip report data in which we did not enter identical records which occurred on both the Travelling Birder and Bird Tours websites.
The source of the data, i.e. literature, museum, atlas, ringing or website trip report is recorded in the ‘dynamicProperties’ field under the code “dataSource”. For literature data, (where known) the nature of the record, i.e. primary or secondary, is recorded under the code “datatype”.
Taxonomy has of course changed considerably over time. To allow for this we recorded the taxonomy as it was described in the data source in the ‘originalNameUsage’ field. The current taxonomy was then selected from a look-up table. If at the time of data entry, the data compiler was unsure which species the synonym referred to, the species was tagged as “unknown” and the species was designated at a later date following further research on the synonym.
Identical localities can also be described in multiple ways. We recorded the locality as it was given in the data source in the ‘verbatimLocality’ field. If the ‘verbatimLocality’ clearly tallied with a locality already within the database, the record was linked to that locality in order to increase georeferencing efficiency.
It was rare for a source to record absence of evidence, i.e. a survey for a species at a particular locality which failed to find that species. However, in the few cases where we did come across such records, the locality and date of the survey were recorded and “absent” was recorded in the ‘occurrenceStatus’ field.
Each record refers to an independent observation. For museum and ringing records, this means a single individual. For literature, atlas or trip report records this may refer to a group of birds observed in one particular locality, on one particular day. If given, the number of total individuals is recorded in the ‘individualCount’ field. The number of males and females is recorded in the ‘sex’ field and the number of juveniles and adults in the ‘lifeStage’ field. If the ‘lifeStage’ field is blank, it is reasonable to assume the individual(s) is an adult.
Occasionally, additional information about the observation might be included in the data source, for example the habitat the bird was observed in or whether the bird was common or rare in that locality. These data are recorded in the ‘habitat’ and ‘organismQuantity’ fields, respectively. Any additional information which did not fit within the structure of the database was recorded in the ‘occurrenceRemarks’ field, along with any notes found on museum labels.
For the purposes of data deposition, the database was converted to a tab-delimited CSV file with all fields following Darwin Core format. A full summary of these fields is given in Online-only Table 3.
Georeferencing
Locality descriptions were converted to geographic co-ordinates using a wide range of atlases and gazetteers, co-ordinates generally only being assigned if accurate to one degree (although in the majority of cases the locations were accurate to within 30 minutes, Table 3). We would initially search for a locality within the gazetteers available to us at the time. If the locality was not listed within those gazetteers we would search for the locality using atlases. Since this fieldwork was conducted, MaNIS standards have become widely used for studies of this kind, but these weren’t fully developed at the time of data collection25. Named places, e.g. towns or counties, were georeferenced using their geographic centre and georeferencing uncertainty measured from the centre to the edge of the named place. Often localities were given simply as the name of a river, mountain or Protected Area. In these instances we used the midpoint of the river between source and mouth (uncertainty measured as distance from midpoint to source/mouth), the summit of the mountain (uncertainty measured as distance from summit to approximate mountain foot) and the rough centre of the Protected Area (uncertainty measured as distance from centre to Protected Area edge). If a particular locality description matched two or more places their midpoint was taken (uncertainty measured as distance from midpoint to place). Offsets from localities (e.g. “50 km N of Kuala Lumpur”; “8 miles along the road from Sheffield to Chesterfield”) were measured using a digital atlas (uncertainty was approximated at the georeferencer’s discretion in these instances, usually between 3 and 10 arc-minutes, depending on the vagueness of the offset.) For georeferencing done ‘in house’, the gazeteer/atlas used was recorded.
Table 3 Georeference and date completeness of the records.
Full size table
When possible, localities we could not georeference ourselves were sent to regional experts.
92% of our localities are georeferenced to an accuracy of 30 minutes, corresponding to 82% of occurrence records (see Table 3).
We had less success at georeferencing museum records than literature records15, due in part to difficulties in reading hand-writing on specimen labels. Older records were also harder to georeference, presumably due to changes in place names over time, and to some early ornithologists failing to document the collection locality. As might be expected, localities from countries that do not use the Roman alphabet were also harder to georeference.
Some records were excluded from the database based on their locality: records which we thought were trading localities, notably Malacca in Malaysia and Leadenhall Market in the UK; records from captive specimens, e.g. zoological gardens.
Dating
49% of records are dated to within an accuracy of one year. Where possible, we assigned date ranges to undated records. For example, if the name of the collector was given on a museum specimen and we knew when that collector was active in that region, we assigned a date range covering that period. There remain undated records which could perhaps be dated in this way. Undated literature records were designated as occurring before their publication date. We were able to date 89% of records to within 10 years. More
