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Prey Capture by Carnivorous Plants Worldwide 1923-2007
Available phylogenetic data illustrate that in all carnivorous lineages, the ancestral trap type is a sticky, flypaper-type trap (Ellison and Gotelli, 2001). In the Caryophyllales, pitfall traps (Nepenthes) and snap traps (Dionaea and Aldrovanda) are derived relative to the sticky pads of Drosera. Similarly, in the Lamiales, the sticky-leaved Pinguicula is ancestral to Genlisea with its eel (or lobster-pot) traps and Utricularia with its vacuum traps. In the Ericales, the Sarraceniaceae with its pitfall traps are derived relative to Roridula, another species with flypaper traps. Muller et al. (2004) hypothesed that carnivorous genera with rapidly evolving genomes (Genlisea and Utricularia) have more predictable and frequent captures of prey than do genera with more slowly evolving genomes; by extension it could be hypothesized that in general, carnivorous plants with more complex traps should have more predictable and frequent captures of prey than do those with relatively simple traps. Increases in predictability and frequency of prey capture could be achieved by evolving more elaborate mechanisms for attracting prey, by specializing on particular types of prey, or, as Darwin suggested, by specializing on particular (large) sizes of prey. In all cases, one would expect that prey actually captured would not be a random sample of the available prey. Furthermore, when multiple species of carnivorous plants co-occur, one would predict, again following Darwin that interspecific competition would lead to specialization on particular kinds of prey. Because the traps of carnivorous plants accumulate identifiable remains of prey, analysis of trap contents can provide an aggregate record of the prey that have been successfully "sampled" by the plant. Such samples could be used to begin to test the hypothesis that carnivorous plant genera differ in prey composition and to look for evidence of specialization in prey capture. Over the past 80 years, numerous ecologists have gat
Construction Costs of Carnivorous and Non-Carnivorous Plants at Harvard Forest 2006-2008
Leaf traits, including photosynthetic rates, leaf mass area, and leaf nutrient content covary in a coordinated way for a wide range of plant taxa. This covariation results from trade-offs between the costs of constructing plant tissues and the benefits accrued from photosynthesis. Carnivorous plants have been found to be outliers in the "universal spectrum of leaf traits" because they have very low photosynthetic rates for the amount of nitrogen in their leaves and traps. But no studies have measured simultaneously the actual construction costs of carnivorous traps and rates of photosynthesis to determine the amortization required to recover the investment (the "payback time") and thereby calculate the "marginal gain" of "investing" in carnivorous structures. The objective of this study was to measure construction costs (CCmass, grams of glucose required to build 1g of ash-free dry mass of tissue) and photosynthesis (Amass, nmol CO2 g-1 s-1) for traps, leaves, roots, and rhizomes of 15 carnivorous plant species with differing mechanisms of prey capture and consumption (pitfall traps, snap-traps, sticky pads) grown under greenhouse conditions. Payback time (h) was calculated as the quotient of CCmass and Amass after conversion to nmol of carbon per gram of ash-free dry mass. There were highly significant differences amongst species for CCmass of traps but there were no significant differences for CCmass amongst traps, roots and rhizomes. Mean (+- SD) CCmass for traps (1.14 +- 0.24 g glucose g-1) was significantly lower than the mean CCmass of leaves of 267 non-carnivorous plant species (1.47 +- 0.17 g glucose g-1). However, all 15 carnivorous plants examined in this study had low Amass and thus, the marginal gain of carnivory is small with a long payback time (524-1641 h). Our results of low CCmass for carnivorous traps is contrary to the oft-stated expectation of a high cost to construct elaborate carnivorous traps. Payback time integrates traits used to assess leaf
Ecophysiology of Carnivorous Plants Worldwide 1980-2011
Identification of trade-offs among physiological and morphological traits and their use in cost-benefit models and ecological or evolutionary optimization arguments have been hallmarks of ecological analysis for at least 50 years. Carnivorous plants are model systems for studying a wide range of ecophysiological and ecological processes and the application of a cost-benefit model for the evolution of carnivory by plants has provided many novel insights into trait-based cost-benefit models. Central to the cost-benefit model for the evolution of botanical carnivory is the relationship between nutrients and photosynthesis; of primary interest is how carnivorous plants efficiently obtain scarce nutrients that are supplied primarily in organic form as prey, digest and mineralize them so that they can be readily used, and allocate them to immediate versus future needs. Most carnivorous plants are terrestrial - they are rooted in sandy or peaty wetland soils - and most studies of cost-benefit trade-offs in carnivorous plants are based on terrestrial carnivorous plants. However more than 10% of carnivorous plants are unrooted aquatic plants. By examining data published between 1980 and 2011, we ask whether the cost-benefit model applies equally well to aquatic carnivorous plants and what general insights into trade-off models are gained by this comparison. Nutrient limitation is more pronounced in terrestrial carnivorous plants, which also have much lower growth rates and much higher ratio of dark respiration to photosynthetic rates than aquatic carnivorous plants. Phylogenetic constraints on ecophysiological trade-offs among carnivorous plants remain unexplored. Despite differences in detail, the general cost-benefit framework continues to be of great utility in understanding the evolutionary ecology of carnivorous plants. We provide a research agenda that if implemented would further our understanding of ecophysiological trade-offs in carnivorous plants and also would pro
Species Distribution Modeling of Carnivorous Plants Worldwide
Forecasting how carnivorous plant species will respond to climatic change is a key issue in their conservation and management but presents a number of challenges. These challenges derive from interactions between the relatively simplistic statistical methods typically used to forecast species responses to climatic change, which to date have been limited mainly to species distribution models (“SDMs) and particular aspects of the ecology of carnivorous plants, including their rarity, habitat specialization, and limited dispersal ability. The small ranges and oftentimes low local abundance of carnivorous plants provide few occurrence records, which increase the potential for poorly or over-fitted SDMs and misspecification of relationships with their “optimal” environments. The unique habitats in which carnivorous plants often grow also are difficult to characterize using the basic temperature and precipitation data that often undergird SDMs. Rather, habitats in which carnivorous plants are common often are decoupled from broader climatic patterns (e.g., many retain high soil moisture even during seasonal drought) and may be associated with frequent disturbance. Last, dispersal limitation also may constrain range shifts of carnivorous plants as the climate changes. These three issues raise two related questions that are critical for understanding and forecasting the future of carnivorous plants. First, to what extent are current carnivorous plants distributions constrained by climate; and second, how readily, if at all, might carnivorous plants disperse to colonize new habitat as it becomes climatically suitable? We estimated the vulnerability of carnivorous plants to climatic change in light of challenges identified with SDMs in general and their particular application to these unique species. We combined two approaches: “ensembles of small models”, which attempt to deal with the challenges of fitting SDMs for data-limited species; and “bioclimatic velocity”, which is
Distribution of large carnivores in Europe 2012 - 2016: Distribution map for Golden Jackal (Canis aureus)
<p><strong>Abstract</strong></p> <p>Regular assessments of species’ status are an essential component of conservation planning and adaptive management. They allow the progress of past or ongoing conservation actions to be evaluated and can be used to redirect and prioritize future conservation actions. Most countries perform periodic assessments for their own national adaptive management procedures or national red lists. Furthermore, the countries of the European Union have to report on the status of all species listed on the directives of the Habitats Directive every 6 years as part of their obligations under Article 17. However, these national level assessments are often made using non-standardized procedures and do not always adequately reflect the biological units (i.e., the populations) which are needed for ecologically meaningful assessments.</p> <p>Since the early 2000’s the Large Carnivore Initiative for Europe (a Specialist Group of the IUCN’s Species Survival Commission) has been coordinating periodic surveys of the status of large carnivores across Europe (e.g., von Arx et al. 2004; Salvatori & Linnell 2005, Kaczensky et al. 2013). These have covered the Eurasian lynx (<em>Lynx lynx</em>), the wolf (<em>Canis lupus</em>), the brown bear (<em>Ursus arctos</em>) and the wolverine (<em>Gulo gulo</em>). The golden jackal (<em>Canis aureus</em>) has been added to the LCIE prerogatives in 2014. The species is rapidly expanding in Europe (Trouwborst <em>et al.</em> 2015; Männil & Ranc 2022), a large-scale phenomenon that resembles that of the other large carnivores. Golden jackals are thriving in human-dominated landscapes (Ćirović <em>et al.</em> 2016; Lanszki <em>et al.</em> 2018; Fenton <em>et al.</em> 2021), where they are often functioning as the top predators, despite having smaller body size that is typical for large carnivores. The expansion of the species triggers many questions among scientists, stakeholders, and policy makers (Trouwborst <em>et al.</em> 2015; Hatlauf <em>et al.</em> 2021), that are closely connected to those raised by the other large carnivores (e.g., potential conflicts with livestock or hunting). In this context, monitoring the species’ expansion, delineating populations, assessing the species' legal and protection status, and addressing the concerns raised by this rapidly expanding carnivore requires a high level of coordination among regional experts.</p> <p>These surveys involve the contributions of the best available experts and sources of information. While the underlying data quality and field methodology varies widely across Europe, these coordinated assessments do their best to integrate the diverse data in a comparable manner and make the differences transparent. They also endeavor to conduct the assessments on the most important scales. This includes the continental scale (all countries except for Russia, Belarus, Moldova and the parts of Ukraine outside the Carpathian Mountain range), the scale of the EU 28 (where the Habitats Directive operates) and of the biological populations which reflect the scale at which ecological processes occur (Linnell et al. 2008). In this way, the independent LCIE assessments provide a valuable complement to the ongoing national processes.</p> <p>Our last assessments covered the period 2006-2011 (Kaczensky et al. 2013; Chapron et al. 2014) but, at the time, did not include golden jackals. The current assessment is based on the period 2012-2016 and broadly follows the same methodology. Explicit distinctions are made between classification based on empirical data and expert opinion. The population definitions used in this report follow those proposed in (Ranc <em>et al.</em> 2018); areas whose presence category was defined by expert opinion were not assigned to a specific population, though. </p> <p> </p> <p><strong>Methods</strong></p> <p>The mapping approach follows the methods described in Chapron et al. (2014) and Kaczensky et al. (2013). It updates the published Species Online Layers (SPOIS) to the period 2012-2016.</p> <p>In short, large carnivore presence was mapped at a 10x10 km ETRS89-LAEA Europe grid scale. This grid is widely used for the Flora-Fauna-Habitat reporting by the European Union (EU) and can be downloaded at: http://www.eea.europa.eu/data-and-maps/data/eea-reference-grids-2</p> <p>The map encompasses the EU countries plus the non-EU Balkan states, Switzerland, Norway, and the Carpathian region of Ukraine. Presence in a grid cell was ideally mapped based on carnivore presence and frequency in a cell resulting in:</p> <p>1 = Permanent (presence confirmed in >= 3 years in the last 5 years OR in >50% of the time OR reproduction confirmed within the last 3 years)</p> <p>3 = Sporadic (highly fluctuating presence) (presence confirmed in <3 years in the last 5 years OR in <50% of the time)</p> <p>5 = Expert-based presence (high confidence) (expert-based opinion; very suitable habitat near permanent presence areas)</p> <p>6 = Expert-based presence (low confidence or unconfirmed records) (expert-based opinion; suitable habitat near presence areas or unconfirmed C3 records of jackal presence)</p> <p>7 = Expert-based absence (high confidence) (jackal presence according to coarse-resolution hunting bag data but experts think, with high confidence, the species is not present)</p> <p>8 = Expert-based absence (low confidence) (jackal presence according to coarse-resolution hunting bag data but experts think the species is not present)</p> <p>Where grid cells were assigned different values between neighboring countries; the “disputed” cells were given the “higher” presence values e.g., a cell categorized as “sporadic” by one country and “permanent” by another was categorized as “permanent”. Data-based categories (1,3) were given priority over expert-based categories (5 through 8).</p> <p>To assess the quality of carnivore signs we used the SCALP criteria developed for the standardized monitoring of Eurasian lynx (<em>Lynx lynx</em>) in the Alps (Molinari-Jobin et al. 2012):</p> <p>Category 1 (C1): “Hard facts”, verified and unchallenged large carnivore presence signs (e.g., dead animals, DNA, verified camera trap images);</p> <p>Category 2 (C2): Large carnivore presence signs controlled and confirmed by a large carnivore expert (e.g., trained member of the network), which requires documentation of large carnivore signs; and</p> <p>Category 3 (C3): Unconfirmed category 2 large carnivore presence signs and all presence signs such as sightings and calls which, if not additionally documented, cannot be verified.</p> <p>See Hatlauf and Böcker (2022) for best practices regarding golden jackal records.</p> <p> </p> <p><strong>Usage Notes</strong></p> <p>The data available consists of a shapefile at a 10 x 10 km resolution compiled for the period 2012-2016 for the Large Carnivore Initiative of Europe IUCN Specialist Group and for the IUCN Red List Assessment.</p> <p> </p> <p><strong>References</strong></p> <p>Boitani, L., F. Alvarez, O. Anders, H. Andren, E. Avanzinelli, V. Balys, J. C. Blanco, U. Breitenmoser, G. Chapron, P. Ciucci, A. Dutsov, C. Groff, D. Huber, O. Ionescu, F. Knauer, I. Kojola, J. Kubala, M. Kutal, J. Linnell, A. Majic, P. Mannil, R. Manz, F. Marucco, D. Melovski, A. Molinari, H. Norberg, S. Nowak, J. Ozolins, S. Palazon, H. Potocnik, P.-Y. Quenette, I. Reinhardt, R. Rigg, N. Selva, A. Sergiel, M. Shkvyria, J. Swenson, A. Trajce, M. Von Arx, M. Wolfl, U. Wotschikowsky and D. Zlatanova. 2015. Key actions for Large Carnivore populations in Europe. Institute of Applied Ecology (Rome, Italy). Report to DG Environment, European Commission, Bruxelles. Contract no. 07.0307/2013/654446/SER/B3</p> <p>Ćirović, D., A. Penezić and M. Krofel. 2016. Jackals as cleaners: Ecosystem services provided by a mesocarnivore in human-dominated landscapes. <em>Biological Conservation</em>, 199: 51–55.</p> <p>Chapron, G., Kaczensky, P., Linnell, J.D.C., von Arx, M., Huber, D., Andrén, H., López-Bao, J.V., Adamec, M., Álvares, F., Anders, O., Balčiauskas, L., Balys, V., Bedő, P., Bego, F., Blanco, J.C., Breitenmoser, U., Brøseth, H., Bufka, L., Bunikyte, R., Ciucci, P., Dutsov, A., Engleder, T., Fuxjäger, C., Groff, C., Holmala, K., Hoxha, B., Iliopoulos, Y., Ionescu, O., Jeremić, J., Jerina, K., Kluth, G., Knauer, F., Kojola, I., Kos, I., Krofel, M., Kubala, J., Kunovac, S., Kusak, J., Kutal, M., Liberg, O., Majić, A., Männil, P., Manz, R., Marboutin, E., Marucco, F., Melovski, D., Mersini, K., Mertzanis, Y., Mysłajek, R.W., Nowak, S., Odden, J., Ozolins, J., Palomero, G., Paunović, M., Persson, J., Potočnik, H., Quenette, P.-Y., Rauer, G., Reinhardt, I., Rigg, R., Ryser, A., Salvatori, V., Skrbinšek, T., Stojanov, A., Swenson, J.E., Szemethy, L., Trajçe, A., Tsingarska[1]Sedefcheva, E., Váňa, M., Veeroja, R., Wabakken, P., Wölfl, M., Wölfl, S., Zimmermann, F., Zlatanova, D. and Boitani, L. 2014. Recovery of large carnivores in Europe’s modern human-dominated landscapes. <em>Science</em> 346: 1517-1519.</p> <p>Fenton, S., Moorcroft, P.R., Ćirović, D., Lanszki, J., Heltai, M., Cagnacci, F., Breck, S., Bogdanović, N., Pantelić, I., Ács, K. and Ranc, N. 2021. Movement, space-use and resource preferences of European golden jackals in human-dominated landscapes: insights from a telemetry study. <em>Mammalian Biology</em>, 101: 619–630.</p> <p>Hatlauf, J. and Böcker, F. 2022. Recommendations for the documentation and assessment of golden jackal (<em>Canis aureus</em>) records in Europe. BOKU reports on wildlife research and willdife management 27. Ed: Institute of Wildlife Biology and Game Management (IWJ), University of Natural Resources and Life Sciences, Vienna. ISBN: 978-3-900932-94-7</p> <p>Hatlauf, J., Bayer, K., Trouwborst, A. and Hackländer, K. 2021. New rules or old concepts? The golden jackal (<em>Canis aureus</em>) and its legal status in Central Europe. <em>European Journal of Wildlife Research</em>, 67, 25.</p> <p>Kaczensky, P., Chapron, G., Von Arx, M., Huber, D., Andrén, H. and Linnell, J. 2013. Status, management and distribution of large carnivores - bear, lynx, wolf and wolverine - in Europe. Istituto di Ecologia Applicata, Rome, Italy.</p> <p>Lanszki, J., Schally, G., Heltai, M. and Ranc, N. 2018. Golden jackal expansion in Europe: first telemetry evidence of a natal dispersal. <em>Mammalian Biology</em>, 88: 81–84.</p> <p>Linnell, J.D.C., Salvatori, V. and Boitani, L. 2008. Guidelines for population level management plans for large carnivores in Europe. A Large Carnivore Initiative for Europe report prepared for the European Commission (contract 070501/2005/424162/MAR/B2).</p> <p>Männil, P. and Ranc, N. 2022. Golden jackal (<em>Canis aureus</em>) in Estonia: development of a thriving population in the boreal ecoregion. <em>Mammalian Research,</em> 67: 245-250.</p> <p>Molinari-Jobin, A., Kéry, M., Marboutin, E., Molinari, P., Koren, I., Fuxjäger, C., Breitenmoser-Würsten, C., Wölfl, S., Fasel, M., Kos, I., Wölfl, M. and Breitenmoser, U. 2012. Monitoring in the presence of species misidentification: the case of the Eurasian lynx in the Alps. <em>Animal Conservation </em>15: 266-273.</p> <p>Ranc, N., Krofel, M. and Cirovic, D. 2018. IUCN Red List Mapping for the regional assessment of the Golden Jackal (<em>Canis aureus</em>) in Europe. IUCN Red List Threatened Species, 13.</p> <p>Salvatori, V. and Linnell, J.D.C. 2005. Report on the conservation status and threats for wolf (Canis lupus) in Europe. Council of Europe Report T-PVS/Inf (2005) 16.</p> <p>Trouwborst, A., Krofel, M. and Linnell, J.D.C. 2015. Legal implications of range expansions in a terrestrial carnivore: the case of the golden jackal (<em>Canis aureus</em>) in Europe. <em>Biodiversity Conservation</em>, 24: 2593–2610.</p> <p>von Arx, M., Breitenmoser-Würsten, C., Zimmermann, F. and Breitenmoser, U. 2004. Status and conservation of the Eurasian lynx (<em>Lynx lynx</em>) in Europe in 2001. KORA Report 19e: 1-330.</p> <p> </p> <p><strong>Contact information</strong></p> <p>Nathan Ranc, nathan.ranc@inrae.fr</p>
The terrestrial carnivorous plant Utricularia reniformis sheds light on environmental and life-form genome plasticity: Annotation, Gene Ontology and raw data
<p><strong>Description:</strong> In this work, we deeply sequenced (genome and transcriptome of different organs), assembled, and analyzed the 311-Mbp genome of the terrestrial carnivorous plant <em>U. reniformis</em> (Lentibulariaceae). This project presents great importance to the understanding of genomic, evolutive and functional aspects of<em> U. reniformis</em>, which may, with the next-generation sequencing and computational biology approaches shed light to a better understanding not only for the biology and evolution of <em>Utricularia</em> genus, but also for other genera and lineages of the Lentibulariaceae family. Here we present all the raw data generated, including annotation and gene ontology files.</p> <p><strong>External Information</strong></p> <p><a href="https://genomevolution.org/coge/GenomeInfo.pl?gid=54799">Genome Browser</a> avaliable at CoGe Portal (https://genomevolution.org/coge/GenomeInfo.pl?gid=54799)</p> <p><a href="http://https://www.ncbi.nlm.nih.gov/bioproject/290588">GenBank </a><a href="http://https://www.ncbi.nlm.nih.gov/bioproject/290588">Bioproject</a> (https://www.ncbi.nlm.nih.gov/bioproject/290588) for raw genomic and transcriptomic reads</p> <p><a href="https://bv.fapesp.br/en/auxilios/84264/genomics-and-transcriptomics-of-utricularia-reniformis-lentibulariaceae-an-evolutive-and-function/">FAPESP grant website</a> contaning the project abstract and other information.</p> <p><strong>Papers published related to <em>Utricularia reniformis</em> genome</strong></p> <pre><strong>[1]</strong> Silva SR, Diaz YC, Penha HA, Pinheiro DG, Fernandes CC, Miranda VF, MichaelTP, Varani AM. <strong>The Chloroplast Genome of Utricularia reniformis Sheds Light on the Evolution of the ndh Gene Complex of Terrestrial Carnivorous Plants from the Lentibulariaceae Family</strong>. PLoS One. 2016 Oct 20;11(10):e0165176. doi:<strong><a href="https://www.ncbi.nlm.nih.gov/pubmed/27764252">10.1371/journal.pone.0165176</a></strong>. </pre> <pre><strong>[2] </strong>Silva SR, Alvarenga DO, Aranguren Y, Penha HA, Fernandes CC, Pinheiro DG, Oliveira MT, Michael TP, Miranda VFO, Varani AM. <strong>The mitochondrial genome of the terrestrial carnivorous plant Utricularia reniformis (Lentibulariaceae): Structure, comparative analysis and evolutionary landmarks.</strong> PLoS One. 2017 Jul19;12(7):e0180484. doi: <strong><a href="https://www.ncbi.nlm.nih.gov/pubmed/28723946">10.1371/journal.pone.0180484</a></strong>.</pre> <pre><strong>[3] </strong>Silva SR, Moraes AP, Penha HA, Julião MHM, Domingues DS, Michael TP, Miranda VFO, Varani AM. <strong>The Terrestrial Carnivorous Plant Utricularia reniformis Sheds Light on Environmental and Life-Form Genome Plasticity.</strong> Int J Mol Sci. 2019 Dec 18;21(1). pii: E3. doi: <strong><a href="https://www.ncbi.nlm.nih.gov/pubmed/31861318">10.3390/ijms21010003</a></strong>.</pre> <p><strong>Acknowledgements</strong></p> <p>This work was supported by Sao Paulo Research Foundation FAPESP, Grant ID: [1325164-6]</p> <p> </p> <p><strong>---------------------------------------------------------</strong><br> <strong>FILES DESCRIPTION</strong><br> <strong>---------------------------------------------------------</strong><br> <br> ----------------<br> <strong>ANNOT-vFinal.sql: </strong>MySQL database containing all integrated annotation information of Urenif and Ugibba<br> ----------------<br> <strong>TABLE fields description</strong><br> gene_name gene name generated by EVidence Modeler + PASA<br> length gene lenght<br> status duplicate_gene_classifier status (0:singleton, 1:dispersed, 2:proximal, 3: tandem, 4:WGD)<br> product gene product <br> GOterms Blast2GO/OmicsBox GOterms<br> GO_mapping Blast2GO/OmicsBox GOterms derived from direct mapping (UniProt)<br> GO_annotation Blast2GO/OmicsBox annotated GOterms<br> GO_interpro Blast2GO/OmicsBox derived from InterProScan<br> EC Blast2GO/OmicsBox EC number<br> EC_name Blast2GO/OmicsBox enzyme name<br> NOG_annot EggNOG annotation description<br> NOG_EC EggNOG EC number<br> NOG_GO EggNOG GOterms<br> NOG_class EggNOG COG/KOG classfication<br> KEGG_Pathway EggNOG KEGG pathyways<br> KEGG_ko EggNOG KEGG ko<br> CAZy EggNOG CAZy enzymes<br> TAIR_gene Closest A. thaliana gene name (homologous) TAIR database lasted version<br> TAIR_annot Closest A. thaliana gene product (homologous) TAIR database lasted version <br> ortho MCL clustering among Vvinifera, Athaliana, and Slycopersicum (S:singleton, C: clustered, Y: shared)<br> ortho_two MCL clustering among Urenif and Ugibba (S:singleton, C: clustered, Y: shared)<br> -<br> -<br> ----------------<br> <strong>CEGs.zip </strong> 336 shared and concatenated CEGs from Urenif, U. gibba, Genlisea nigrocaulis, G. hispidula, G. aurea, G. pygmaea, and G. repens.<br> ----------------</p> <p><strong>ProcessRepeats_mod</strong> Modified version of RepeatMasker, ProcessRepeats script for detection of plant evolutionary lineages<br> ----------------</p> <p><strong>----------------------------------------------------------------------------------------------------------------------------------------------<br> <em>Utricularia gibba</em> files<br> ----------------------------------------------------------------------------------------------------------------------------------------------</strong><br> <strong>Ugibba</strong><strong>-no-masked.fa </strong> Ugibba genome excluding organellar genomes (provided by Lan et al., 2017)<br> <strong>Ugibba-softmasked.fa</strong> Ugibba genome RepeatMasker softmasked and excluding organellar genomes (provided by Lan et al., 2017)<br> <strong>Ug.collinearity </strong> MCScanX collinearity file<br> <strong>Ug-duplicates.txt</strong> MCScanX duplicate_gene_classifier short report<br> <strong>Ug.gene_type </strong> MCScanX duplicate_gene_classifier full report<br> <strong>Ug.tandem </strong> Ugibba tandem genes generated by MCScanX tool<br> <strong>Ugibba_annot.annot </strong> Blast2GO/OmicsBox annotation file (eudicotyledons filtered and Viridiplantae GOSlim) <strong>Ugibba_annot-</strong><strong>noclean</strong><strong>.</strong><strong>annot</strong><strong> </strong> Blast2GO/OmicsBox annotation file (not filtered)<br> <strong>Ugibba</strong><strong>.cDNA</strong> Ugibba cDNAs fasta file<br> <strong>Ugibba</strong><strong>.CDS </strong> Ugibba CDSs fasta file<br> <strong>Ugibba</strong><strong>-EVM.all-no-TEs-PASA-ANNOTATED.gff3</strong> Ugibba GFF3 file fully annotated (including gene products and GO terms)</p> <p><strong>Ugibba</strong><strong>-EVM.all-no-TEs-PASA.gff3</strong> Ugibba GFF3 file fully annotated (genes only)<br> <strong>Ugibba_export.txt</strong> Blast2GO/OmicsBox full exported table<br> <strong>Ugibba_fasta.fasta</strong> Blast2GO/OmicsBox Ugibba fasta proteins containg annotation (product and GO terms)<br> <strong>ugibba_frozen_cleaned-validated.box</strong> Full Blast2GO/OmicsBox file</p> <p><strong>ugibba_frozen.box</strong> Full Blast2GO/OmicsBox file (containing TEs genes annotation)</p> <p><strong>ugibba_nogs_emapper_annotations.box</strong> Full Blast2GO/OmicsBox EggNOG file (containing TEs genes annotation)</p> <p><strong>Ugibba_GAF.txt</strong> GAF file<br> <strong>Ugibba</strong><strong>.gene</strong> Ugibba gene fasta file<br> <strong>Ugibba_GOstat.txt </strong> GOstat file<br> <strong>Ugibba</strong><strong>-PASA-assemblies.fasta </strong> Ugibba PASA assemblies<br> <strong>Ugibba</strong><strong>-PASA.stats </strong> Ugibba annotation STATS<br> <strong>Ugibba</strong><strong>.</strong><strong>prot</strong><strong> </strong> Ugibba protein fasta file<br> <strong>Ugibba</strong><strong>-RepeatMasker.gff </strong> Ugibba RepeatMasker gff file<br> <strong>Ugibba</strong><strong>-RepeatMasker.gff3 </strong> Ugibba RepeatMasker gff3 file<br> <strong>Ugibba</strong><strong>-RepeatMasker.tbl </strong> Ugibba RepeatMasker results<br> <strong>Ugibba</strong><strong>-RepeatMasker-v2.gff3</strong> Ugibba RepeatMasker gff3 second version file<br> <strong>Ugibba</strong><strong>-RNAseq-assembled.fasta </strong> Ugibba RNAseq assembled transcriptome (Trinity)<br> <strong>Ugibba_TEs_DANTE_2019.fa </strong> Ugibba TEs library, detected by REPET and annotated by PASTEC and DANTE<br> <strong>Ugibba_WEGO.txt </strong> WEGO file</p> <p><strong>----------------------------------------------------------------------------------------------------------------------------------------------<br> <em>Utricularia reniformis</em> files<br> ----------------------------------------------------------------------------------------------------------------------------------------------</strong><br> <strong>Urenif</strong><strong>-no-masked.fa </strong> Urenif genome excluding organellar genomes<br> <strong>Urenif</strong><strong>-</strong><strong>softmasked</strong><strong>.fa</strong> Urenif genome RepeatMasker softmasked and excluding organellar genomes<br> <strong>Ur.collinearity </strong> MCScanX collinearity file<br> <strong>Ur-duplicates.txt </strong> MCScanX duplicate_gene_classifier short report<br> <strong>Ur.gene_type</strong> MCScanX duplicate_gene_classifier full report<br> <strong>Ur.tandem</strong> Urenif tandem genes generated by MCScanX tool<br> <strong>Urenif_annot.annot</strong> Blast2GO/OmicsBox annotation file (eudicotyledons filtered and Viridiplantae GOSlim)<br> <strong>Urenif_annot-</strong><strong>noclean</strong><strong>.</strong><strong>annot</strong> Blast2GO/OmicsBox annotation file (not filtered)<br> <strong>Urenif</strong><strong>.cDNA</strong> Urenif cDNAs fasta file<br> <strong>Urenif</strong><strong>.CDS </strong> Urenif cDNAs fasta file<br> <strong>Urenif</strong><strong>-EVM.all-no-TEs-PASA-ANNOTATED.gff3</strong> Urenif GFF3 file fully annotated (including gene products and GO terms)</p> <p><strong>Urenif</strong><strong>-EVM.all-no-TEs-PASA.gff3</strong> Urenif GFF3 file fully annotated (genes only)<br> <strong>Urenif_export.txt</strong> Blast2GO/OmicsBox full exported table<br> <strong>Urenif_fasta.fasta</strong> Blast2GO/OmicsBox Urenif fasta proteins containg annotation (product and GO terms)<br> <strong>urenif_frozen_cleaned-validated.box</strong> Full Blast2GO/OmicsBox file</p> <p><strong>urenif_frozen.box</strong> Full Blast2GO/OmicsBox file (containing TEs genes annotation)</p> <p><strong>urenif_nogs_emapper_annotations.box</strong> Full Blast2GO/OmicsBox EggNOG file (containing TEs genes annotation)<br> <strong>Urenif_GAF.txt </strong> GAF file<br> <strong>Urenif</strong><strong>.gene</strong> Urenif gene fasta file<br> <strong>Urenif_GOStat.txt </strong> GOstat file<br> <strong>Urenif</strong><strong>-PASA-assemblies.fasta</strong> Urenif PASA assemblies<br> <strong>Urenif</strong><strong>-PASA.stats </strong> Urenif annotation STATS<br> <strong>Urenif</strong><strong>.</strong><strong>prot</strong><strong> </strong> Urenif protein fasta file<br> <strong>Urenif</strong><strong>-RepeatMasker.gff </strong> Urenif RepeatMasker gff file<br> <strong>Urenif</strong><strong>-RepeatMasker.gff3 </strong> Urenif RepeatMasker gff3 file<br> <strong>Urenif</strong><strong>-RepeatMasker.tbl </strong> Urenif RepeatMasker results<br> <strong>Urenif</strong><strong>-RepeatMasker-v2.gff3 </strong> Urenif RepeatMasker gff3 second version file<br> <strong>Urenif</strong><strong>-RNAseq-assembled.fasta </strong> Urenif RNAseq assembled transcriptome (Trinity)<br> <strong>Urenif_TEs_DANTE_2019.fa </strong> Urenif TEs library, detected by REPET and annotated by PASTEC and DANTE<br> <strong>Urenif_WEGO.txt </strong> WEGO file<br> <strong>----------------------------------------------------------------------------------------------------------------------------------------------<br> ----------------------------------------------------------------------------------------------------------------------------------------------</strong></p>
FIG. 6. — Cynelos stenos n in A new species of the amphicyonid carnivore Cynelos Jourdan, 1862 from the early Miocene of North America
FIG. 6. — Cynelos stenos n. sp., UNSM 44723, Runningwater Formation, Runningwater Quarry (early Hemingfordian), Box Butte Co., Nebraska. Palatal view of the cranium with right P2-M3 (broken P1) and left P3-M3, partial P2, and P1 alveolus. Plant roots have eroded the enamel on the right M1-M2. Scale bar: 3 cm.
FIG. 5. — Cynelos stenos n in A new species of the amphicyonid carnivore Cynelos Jourdan, 1862 from the early Miocene of North America
FIG. 5. — Cynelos stenos n. sp., UNSM 44723, Runningwater Formation, Runningwater Quarry (early Hemingfordian), Box Butte Co., Nebraska. Right and left associated mandibles. Scale bar: 5 cm.
FIG. 2 in A new species of the amphicyonid carnivore Cynelos Jourdan, 1862 from the early Miocene of North America
FIG. 2. — Dental measurements for species of the amphicyonid Cynelos Jourdan, 1862. Abbreviations: a, greatest length of p4; b, m1 trigonid length from mesial edge of paraconid to central distal base of protoconid; c, greatest length of m1; d, greatest width of the m1 talonid (also for m2); e, greatest width of the m1 trigonid (also for m2); f, distal width of p4 (and for p2-p3).
FIG. 1 in A new species of the amphicyonid carnivore Cynelos Jourdan, 1862 from the early Miocene of North America
FIG. 1. — Dental measurements for species of the amphicyonid Cynelos Jourdan, 1862. Abbreviations: a, greatest length of P4 from mesial base of paracone to distal limit of metastylar blade; b, greatest width of P4 from lingual border of protocone to labial base of paracone; c, greatest labial length of M2; d, greatest M2 width from paracone to lingual cingulum; e, greatest labial length of M1; f, greatest M1 width from paracone to lingual cingulum.
FIG. 9 in A new species of the amphicyonid carnivore Cynelos Jourdan, 1862 from the early Miocene of North America
FIG. 9. — Bivariate graph of m1 dimensions (in mm) of Cynelos helbingi (Dehm, 1950) (Wintershof-West, southern Germany) relative to m1 of Cynelos stenos n. sp. (UNSM 44723, western Nebraska). Cynelos helbingi measurements from Dehm (1950).
FIG. 8 in A new species of the amphicyonid carnivore Cynelos Jourdan, 1862 from the early Miocene of North America
FIG. 8. — Variation in size and occlusal form of m1, m2, and m3 of Cynelos helbingi (Dehm, 1950), Wintershof-West, southern Germany (from Dehm 1950: figs 18, 21-25): A, B, holotype m1-m2 of the species, occlusal and lingual views (12293); C, D, large m1 in occlusal and labial views (12315); E, F, small m1, occlusal and labial views (12338); G, right m2, intermediate size (12842); H, right m2, large (12836); I, left m2, small (12834), all m2s in occlusal view. Catalog numbers from Sammlung München 1937 II. Scale bar: 1 cm.
FIG. 4. — Cynelos stenos n in A new species of the amphicyonid carnivore Cynelos Jourdan, 1862 from the early Miocene of North America
FIG. 4. — Cynelos stenos n. sp., UNSM 44723, Runningwater Formation, Runningwater Quarry (early Hemingfordian), Box Butte Co., Nebraska. Cranium in dorsal view. Note the narrow cranium, constricted rostrum, broad frontal region, and tall thin sagittal crest above the small braincase. Scale bar: 5 cm.
FIG. 7 in A new species of the amphicyonid carnivore Cynelos Jourdan, 1862 from the early Miocene of North America
FIG. 7. — Variation in size and occlusal form of P4, M1, and M2 of Cynelos helbingi (Dehm, 1950), Wintershof-West, southern Germany (from Dehm 1950: figs 29, 30; 35-37, 39): A, P4 mesial border indented, protocone prominent (12395); B, P4 mesial border not indented, protocone recessed (12387); C, large M1, labial border long (12347); D, small M1(12361); E, M2, mesial border straight, metacone reduced (12376); F, M2, mesial border irregular, metacone not as reduced (12379). Catalog numbers from Sammlung München 1937 II. Scale bar: 1 cm.
Figure 8 in Carnivores of Syria
Figure 8. • Otter, Lutra lutra (Linnaeus, 1758); · Egyptian mongoose or ichneumon, Herpestes ichneumon (Linnaeus, 1758).
Figure 4 in Carnivores of Syria
Figure 4. • RÜppell's sand fox, Vulpes rueppellii (Schinz, 1825); · Weasel, Mustela nivalis L., 1766.
Data from: Metabarcoding analysis provides insight into the link between prey and plant intake in a large alpine cat carnivore, the snow leopard
<p>Species of the family Felidae (a group represented by cats) are thought to be obligate carnivores, specialized for hunting and consuming other animals. However, the detection of plants in the feces of felids raises questions about the role of plants in their diet. This is particularly true for the snow leopard (Panthera uncia), a big cat native to central and South Asia's high mountains. Our study aimed to comprehensively identify the prey and plants consumed by snow leopards as well as six other sympatric mammals. We applied DNA metabarcoding methods on 126 fecal samples collected from the Sarychat-Ertash Nature Reserve in Kyrgyzstan. We found that among the three most common plant families in snow leopard feces, Tamaricaceae (genus Myricaraia) was consumed often by snow leopards. The genus Myricaria frequently appeared in samples lacking any animal prey DNA, indicating that snow leopards might have consumed this plant especially when their digestive tracts were empty. We also observed a significant difference in plant composition between male and female snow leopards, and potentially between sampling seasons. We provide a comprehensive overview of the prey and plants detected in the feces of snow leopards and sympatric mammals. We believe our findings will help in formulating hypotheses and guiding future research to understand the adaptive significance of plant-eating behavior in felids and animal-plant relationships in the ecosystem.</p>
Intraguild interactions and abiotic conditions mediate occupancy of mammalian carnivores: co-occurrence of coyotes-fishers-martens
<p>The widespread eradication of large carnivores and subsequent expansion of top mesopredators have the potential to impact species and community interactions with ecosystem-wide implications. An example of these trophic dynamics is the widespread establishment of coyotes following the extirpation of wolves and mountain lions in eastern North America. Here, we examined the occupancy of three carnivores in northern New York considering both environmental/habitat factors and interspecific interactions. We estimated the co-occurrence of coyotes, fishers, and martens from a landscape-scale winter camera trap survey repeatedly annually for three years. Martens occurred independently of both coyotes and fishers, while fishers and coyotes displayed positive intraguild interactions that were constant across the landscape. Both marten and fisher first-order occupancy was driven by a combination of biotic and abiotic factors, with both species displaying positive associations with forest cover but antithetical responses to average snow depth. The integral and antithetical role of snow depth in driving the occurrence of martens (positive) and fishers (negative) in the landscape indicates that future climatic warming could reduce the availability of current spatial refuges for martens created by severe winter conditions. Climate-driven alterations to established competitive interactions and co-existence patterns between marten and fishers have critical implications for the species' survival and conservation. We provide correlational evidence consistent with the potential for positive top-down effects of dominant mesocarnivores on subordinate species, with fisher occupancy increasing conditional on the presence of coyotes across the landscape. These findings align with the hypothesis that under certain conditions, coyotes may facilitate certain subordinate carnivores. The evidence produced here is consistent with hypotheses on the dynamic nature of trophic niches. We demonstrate the need to consider the interplay between climate, habitat, and interspecific interactions to understand wildlife occupancy patterns and inform wildlife management in a rapidly changing world.</p>
Fig. 3 in Road Mortality Of Carnivores (Mammalia, Carnivora) In Belarus
Fig. 3. Seasonal dynamics of mortality rates of carnivorous mammals on national highways in Belarus, 2007– 2018.
Contrasting effects of anthropogenic disturbance on the interaction among sympatric carnivores
Interaction among species is central to the stability of community structure. However, anthropogenic pressures alter interactions, disrupt trophic levels, and threaten ecosystem stability. Understanding interactions across human land-use gradients is fundamental to mitigating anthropogenic threats effectively and better managing threatened species. Using data from a large-scale camera trap survey, we developed a multispecies occupancy model for a carnivore guild comprising tiger, leopard, and dhole to investigate the effects of environmental (forest and prey abundance) and anthropogenic (settlement) variables on the interspecific interaction. Human settlement density had a strong but contrasting effect on interaction: as settlement density increased, tigers and leopards were less likely to coexist whereas leopards and dholes were more likely to occur together. Tiger and dhole occupancy was negatively associated with settlement density whereas, the leopard was positively associated. Per cent forest cover and large prey abundance had ubiquitous positive effects on carnivore occupancy. Our results indicate that human presence alters available niche space and spatial overlap among predators affecting interactions. The duality in the effect of the settlement on interacting pairs suggests that humans create a landscape of fear for apex predators but promotes coexistence between subordinate species partially supporting the intermediate disturbance hypothesis.
ScienceDex guides
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.