Bat species occurrences in Nyungwe National Park, Rwanda

Enregistrements de données

Les données de cette ressource occurrence ont été publiées sous forme d'une Archive Darwin Core (Darwin Core Archive ou DwC-A), le format standard pour partager des données de biodiversité en tant qu'ensemble d'un ou plusieurs tableurs de données. Le tableur de données du cœur de standard (core) contient 392 enregistrements.

Cet IPT archive les données et sert donc de dépôt de données. Les données et métadonnées de la ressource sont disponibles pour téléchargement dans la section téléchargements. Le tableau des versions liste les autres versions de chaque ressource rendues disponibles de façon publique et permet de tracer les modifications apportées à la ressource au fil du temps.

Versions

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Comment citer

Les chercheurs doivent citer cette ressource comme suit:

Flanders J, Frick W, Nziza J, Nsengimana O, Kaleme P, Dusabe M C, Ndikubwimana I, Twizeyimana I, Kibiwot S, Ntihemuka P, Cheng T, Muvunyi R, Webala P (2022): Bat species occurrences in Nyungwe National Park, Rwanda. v1.9. Bat Conservation International. Dataset/Occurrence. http://ipt.vertnet.org:8080/ipt/resource?r=bci_rwanda&v=1.9

Droits

Les chercheurs doivent respecter la déclaration de droits suivante:

L’éditeur et détenteur des droits de cette ressource est Bat Conservation International. En vertu de la loi, l'éditeur a abandonné ses droits par rapport à ces données et les a dédié au Domaine Public (CC0 1.0). Les utilisateurs peuvent copier, modifier, distribuer et utiliser ces travaux, incluant des utilisations commerciales, sans aucune restriction.

Enregistrement GBIF

Cette ressource a été enregistrée sur le portail GBIF, et possède l'UUID GBIF suivante : e9bf7d9e-8b31-4a02-8203-f7153b5d64c6.  Bat Conservation International publie cette ressource, et est enregistré dans le GBIF comme éditeur de données avec l'approbation du VertNet.

Mots-clé

occurrence; Occurrence; Observation

Contacts

Couverture géographique

The dataset includes bat occurrence records from Nyungwe National Park in southwestern Rwanda. Nyungwe National Park is the second-largest national park in Rwanda, protecting 1,019 square kilometers of Afromontane rainforest habitat in the Albertine Rift region of Africa. The park is recognized for exceptionally high biodiversity with 1,068 recorded plant species, 322 bird species, 75 known mammal species, including 13 primates. Nyungwe National Park is managed by African Parks in a management agreement with the Rwanda Development Board since October 2020.

Couverture taxonomique

The dataset includes occurrence records from Class Mammalia and Order Chiroptera, including 13 taxonomic records representing 10 genera and 5 families. Three records were identified to genus with the remaining 10 identified to species.

Couverture temporelle

Données sur le projet

We conducted bat surveys in Nyungwe National Park from 2013-2020 to document bat species occurrences and search for an extant population of the IUCN Critically Endangered Hill’s horseshoe bat (Rhinolophus hilli). Our dataset includes three notable species occurrences in Nyungwe National Park, including the first detection of Rhinolophus hilli since 1981, the first record of Lander's horseshoe bat (Rhinolophus landeri) in Nyungwe National Park, and the first record of the Damara woolly bat (Kerivoula argentata) in Rwanda. Bat Conservation International, a non-profit organization dedicated to protecting the world’s bat populations, organized a multi-national team of scientists and conservation biologists from Rwanda, Kenya, the Democratic Republic of Congo, and the United States to survey for bat species in Nyungwe National Park. Bat Conservation International partners with the Rwanda Wildlife Conservation Association on bat conservation in Rwanda and worked closely with staff at Nyungwe National Park on all aspects of this project. All work was carried out with research permits from Rwanda Development Board in collaboration with the Rwanda Wildlife Conservation Association.

Les personnes impliquées dans le projet:

Méthodes d'échantillonnage

Cave surveys: We surveyed caves by visually searching with the aid of bright lights all accessible areas for the presence of bats or signs of bat use. We noted the presence of bat guano or wall staining if present. At sites with areas inaccessible to human observers, we deployed acoustic detectors (SongMeter 4BAT, Wildlife Acoustics) at entrances for 1-2 nights and used Kaleidoscope Pro (version 5.4.2, Wildlife Acoustics, Inc) to identify the presence of bat echolocation activity during crepuscular and nocturnal hours. If bats were present during an internal search, we captured bats with hand nets or placed harp traps at the entrance prior to evening emergence. Capture surveys in forest habitats: Capture surveys were conducted with harp traps (a 2-bank 4.2 m2 harp trap by Ausbat and the ‘cave-catcher’ 2-bank 0.9m2 harp trap by Bat Conservation and Management) and use of three to five mist-nets of 2m, 6m and 12m lengths (Avinet). We placed harp traps and mist-nets parallel or perpendicular to forest trails in locations selected to maximize capture probability. Harp traps were deployed from sunset until sunrise. We opened mist-nets at sunset and monitored for approximately 4 hours and then reopened between 1-2 hours before sunrise. We monitored mist-nets continuously while open every 10-15 mins. We held bats individually in clean, cloth bags until processed and then released bats at the location of capture. See ‘Step Description’ for the description of data collected from captured bats. Acoustic sampling: Nyungwe Park Rangers deployed SongMeter 4BAT acoustic recorders (Wildlife Acoustics, Inc) at locations along forest trails or near cave entrances during multi-day patrols and collected recorders when returning from patrol. The SM4BAT recorders were programmed to record in full-spectrum at 384 kilohertz sampling frequency. The SM4BATs were set to record 30 minutes before sunset to 30 minutes after sunrise and were typically deployed for 3 to 5 nights at each location. We embedded geo-location coordinates on all files using the GPS attachment available from Wildlife Acoustics. Data were transferred to external hard drives and sent to Bat Conservation International in the USA for processing. See ‘Step Description’ for the description of the processing of acoustic data for species identification of R. hilli.

Description des étapes de la méthode:

1. We assessed captured bats for age (juvenile/sub-adult/adult), sex, and reproductive condition (females: non-breeding/pregnant/lactating/post-lactating; males: reproductively active/non-reproductively active as determined by enlarged testes) (Racey 2009). We measured standard morphometrics, including forearm length, tibia length, hindfoot length, tail length, ear length, tragus length, body length, and mass. We used the Mammals of Africa Volume IV (Hedgehogs, Shrews and Bats) (Kingdon 2013) as the primary key for species identification. We sampled skin tissue using a 3-mm biopsy punch from the wing membrane and stored skin tissue in desiccant until the DNA was extracted.
2. We recorded voucher echolocation calls upon release for each echolocating bat species using an M500 full-spectrum bat detector (Pettersson Electronics) at a sampling rate of 500 kHz. For constant-frequency (CF) bats (e.g. Rhinolophus spp.), we recorded resting echolocation calls while the bat was in hand. For species using frequency-modulated (FM) echolocation, we recorded echolocation activity in flight immediately upon release while visually following the bat with a light. Hand-recorded bat echolocations were analyzed using BatSound v.4.1 (Pettersson Electronics) to determine the following parameters for each pulse: duration (D), maximum frequency (FMAX), minimum frequency (FMIN), peak frequency (PF), and interpulse interval (IPI). We measured these parameters (D, FMAX, FMIN, and IPI) from spectrograms and the peak frequency (PF) from the power spectrum. Acoustic data collected by Nyungwe Park Rangers from July 2019 through November 2020 resulted in a total of 379,982 files recorded from 35 locations over 166 nights. We removed noise files and filtered the remaining files for constant frequency acoustic signatures (>15ms call duration) using Kaleidoscope Pro (version 5.4.2, Wildlife Acoustics). Echolocation calls matching those of voucher calls collected from Rhinolophus hilli, R. landeri, and R. clivosus were identified. All data are preserved to allow for future analysis once other call signatures are identified.
3. DNA extraction from wing biopsy punches was carried out at CIBIO-InBIO, University of Porto, Portugal, using Qiagen DNeasy kits (Qiagen, Crawley, UK) and stored at -20 oC. Mitochondrial cytochrome b (cyt b) gene was amplified by polymerase chain reaction (PCR) using the primers MOLCIT‐F (5′-AATGACAT-GAAAAATCACCGTTGT-3′) (Ibñez 2006) and MVZ16-R (5′-AAATAGGAARTATCAYTCTGGTTTRAT-3′) (Smith 1993). PCR’s were performed in a 10 μL volume, which included 1 μL of DNA extract, 0.4 μL of each primer (10 μM), 5 μL of Qiagen Master Mix, and double-distilled water was added until final volume was reached. Reactions were performed under the following conditions: 95 oC for 15 min; 40 cycles of 95 oC for 30 s, 50 oC for 45 s, 72o C for 1 min; 60 oC for 10 min, and DNA sequencing performed on an ABI3700 DNA sequencer (Applied Biosystems). Chromatograms were edited aligned using Mega X (Kumar 2018) with sequences submitted using a via Standard Nucleotide BLAST search on the NCBI website. For phylogenetic comparison, edges of incomplete sequences were trimmed to reduce missing data. Models of sequence evolution were explored in jModel test v.2.1.10 (Darriba 2012) using the Bayesian Information Criterion (BIC). Bayesian inference (BI) was performed using MrBayes v.3.2.7 (Ronquist 2003, Huelsenbeck 2001). BI trees were run with 4 simultaneous chains, each of 1×107 generations, sampled every 1000 generations, and with the first 25% of trees discarded as burn-in. Convergence was assessed using effective sampling size in Tracer v.1.7.1 (Rambaut 2018).

Citations bibliographiques

1. Aellen, V. (1973) Un Rhinolophus nouveau d’Afrique centrale. Periodicum biologorum 101–105.
2. Baeten, B., Cakenberghe, V. V. & Vree, F. D. (1984) An annotated inventory of a collection of bats from Rwanda (Chiroptera). Revue de Zoologie et de Botanique Africaines 183–196.
3. Darriba, D., Taboada, G. L., Doallo, R. & Posada, D. (2021) jModelTest 2: more models, new heuristics and parallel computing. Nat Methods 9, 772–772.
4. Huelsenbeck, J. P. & Ronquist, F. (2001) MRBAYES: Bayesian inference of phylogenetic trees. Bioinformatics 17, 754–755.
5. Ibñez, C., Garca-Mudarra, J. L., Ruedi, M., Stadelmann, B. & Juste, J. (2006) The Iberian contribution to cryptic diversity in European bats. Acta Chiropterologica 8, 277–297.
6. Kingdon, J. (2013) Mammals of Africa Volume IV (Hedgehogs, Shrews and Bats). Bloomsbury Publishing, London, UK.
7. Kumar, S., Stecher, G., Li, M., Knyaz, C. & Tamura, K. (2018) MEGA X: Molecular Evolutionary Genetics Analysis across Computing Platforms. Mol Biol Evol 35, 1547–1549.
8. Racey, P. Reproductive assessment of bats. (2009) IN: Ecological and behavioral methods for the study of bats (eds. Kunz, T. H. & Parsons, & S.) 249–264. The Johns Hopkins University Press, New York, USA.
9. Rambaut, A., Drummond, A. J., Xie, D., Baele, G. & Suchard, M. A. (2018) Posterior Summarization in Bayesian Phylogenetics Using Tracer 1.7. Systematic Biol 67, 901–904.
10. Ronquist, F. & Huelsenbeck, J. P. (2003) MrBayes 3: Bayesian phylogenetic inference under mixed models. Bioinformatics 19, 1572–1574.
11. Smith, M. F. & Patton, J. L. (1993) The diversification of South American murid rodents: evidence from mitochondrial DNA sequence data for the akodontine tribe. Biol J Linn Soc 50, 149–177.

Métadonnées additionnelles