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Noninvasive fecal sampling in Itatiaia National Park, Brazil: wild mammal identification and parasite detection
<p class="CAPA3"><b>Background</b>: Noninvasive collection of feces is a cost-effective strategy for monitoring free-living wild mammals. The aim of this study was to search for carnivore and artiodactyl species and investigate the gastrointestinal parasites in their feces, in Itatiaia National Park, Brazil.</p> <p class="CAPA3"><b>Methodology/Main Findings:</b> Between 2017 and 2018, feces from carnivores and artiodactyls were collected along trails in the park. Host species were identified from these feces through macroscopic and trichological examination and through molecular biology using mitochondrial gene fragments. To investigate parasites, the Faust, Lutz and modified Ritchie and Sheather techniques and enzyme immunoassays were used to detect <i>Cryptosporidium</i> sp. antigens. A total of 244 stools were collected in three regions of the park. The species identified were <i>C. brachyurus</i> (39.7%), <i>L. guttulus</i> (21.3%), <i>C. familiaris</i> (5.3%), <i>C. thous</i> (0.8%), <i>Puma</i> <i>yagouaroundi</i> (0.8%), <i>L. pardalis</i> (0.4%), <i>P</i>. <i>concolor</i> (0.4%) and <i>S. scrofa</i> (4.9%). The overall positivity for parasites was 81.1%. Helminths were more frequently detected in carnivore feces (70.9%), especially eggs of the family Ascarididae and <i>Toxocara</i> sp. Protozoa presented higher frequency in artiodactyl feces (87.1%), especially coproantigens of<i> Cryptosporidium</i> sp. This zoonotic protozoon was detected in eight mammal species, including one that may be invasive: animals from crossbreeding of domestic pig with wild boar. High values for parasite structural richness (R'), Shannon (H') and Simpson (D) parasite diversity indices were observed, especially in the feces of <i>C. brachyurus</i> (R' = 12, H' = 2.2761, D = 0.887). Significant differences in parasite diversity were observed between <i>C. brachyurus </i>and <i>C. familiaris</i>; and between <i>C. brachyurus</i> and <i>S. scrofa</i> (pooled t test = 0.01 and 0.03, respectively). The highest values for parasite similarity (Sorensen test ≥ 0.8) were found among species that are taxonomically close, such as <i>C. brachyurus</i> and <i>L. guttulus</i>.</p> <p class="CAPA3"><b>Conclusions/Significance</b>: This was the first parasitological survey on carnivore and artiodactyl feces collected noninvasively in Brazil. It enabled animal identification through correlations using three techniques and diagnosis of highly frequent parasite structures that can infect these animals.</p>
Supplementary Structural Models (SARS-CoV-2 Spike-RBD:ACE2 complex and TMPRSS2) - SARS-CoV-2 spike protein predicted to form complexes with host receptor protein orthologues from a broad range of mammals
<p>Structural Models (PDB) of SARS-CoV-2 Spike RBD bound to ACE2 receptors of 215 animals.</p> <p>Structural model of Human TMPRSS2.</p> <p>Modelled using the FunMod pipeline and referenced in the preprint</p> <p><a href="https://www.biorxiv.org/content/10.1101/2020.05.01.072371v5">SARS-CoV-2 spike protein predicted to form complexes with host receptor protein orthologues from a broad range of mammals</a></p> <p> </p>
FIGURE 1 in Quantifying vertebrate zoogeographical regions of Australia using geospatial turnover in the species composition of mammals, birds, reptiles and terrestrial amphibians
FIGURE 1. Map of Australia with major clusters retrieved from the analysis.
FIGURE 8 in Quantifying vertebrate zoogeographical regions of Australia using geospatial turnover in the species composition of mammals, birds, reptiles and terrestrial amphibians
FIGURE 8. The interim zoogeographic provinces of Australia.
Supplementary material 2 from: Shivambu N, Shivambu TC, Downs CT (2020) Assessing the potential impacts of non-native small mammals in the South African pet trade. NeoBiota 60: 1-18. https://doi.org/10.3897/neobiota.60.52871
Table S2
Supplementary material 1 from: Shivambu N, Shivambu TC, Downs CT (2020) Assessing the potential impacts of non-native small mammals in the South African pet trade. NeoBiota 60: 1-18. https://doi.org/10.3897/neobiota.60.52871
Table S1
Maximizing the value of forest restoration for tropical mammals by detecting three-dimensional habitat associations
<b>Description: </b><p>Species detection data for 28 medium-large mammal species obtained using camera trap methods across a logging-induced degradation gradient. Cameras were deployed using a paired design across 74 sampling locations. Data were used to explore species-habitat associations with LiDAR-derived measures of forest structure.</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/26"><b>Understanding covariation between mammalian diversity and forest carbon across a human-modified tropical landscape</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>NERC (Standard grant, NE/K016407/1, <a href="http://gotw.nerc.ac.uk/list_full.asp?pcode=NE%2FK016407%2F1&classtype=ENRIs&classification=Biodiversity&cookieConsent=A">http://gotw.nerc.ac.uk/list_full.asp?pcode=NE%2FK016407%2F1&classtype=ENRIs&classification=Biodiversity&cookieConsent=A</a>)</li></ul><p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p><p></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=4010757">here</a></p><p><b>Files: </b>This consists of 1 file: DeereEtAl2020_PNAS.xlsx</p><p><b>DeereEtAl2020_PNAS.xlsx</b></p><p>This file contains dataset metadata and 2 data tables:</p><ol><li><p><b>Site metadata and deployment details</b> (described in worksheet Deployment)</p><p>Description: Details of sampling locations, operational dates and survey effort for each camera trap station (N=126)</p><p>Number of fields: 9</p><p>Number of data rows: 126</p><p>Fields: </p><ul><li><b>Site_ID</b>: Unique alphanumeric identifier of the location camera traps were deployed (Field type: location)</li><li><b>Habitat_Class</b>: Forest condition relative to logging-indiced degradation. Follows Putz and Redford Classification scheme (Putz, Francis E., and Kent H. Redford. "The importance of defining 'forest': tropical forest degradation, deforestation, long‐term phase shifts, and further transitions." Biotropica 42.1 (2010): 10-20) (Field type: categorical)</li><li><b>Latitude</b>: Geographic coordinate of camera trap location (Field type: latitude)</li><li><b>Longitude</b>: Geographic coordinate of camera trap location (Field type: longitude)</li><li><b>Date_On</b>: Date camera traps were deployed (Field type: date)</li><li><b>Time_On</b>: Time camera traps were deployed (Field type: time)</li><li><b>Date_Off</b>: Date camera traps were collected (Field type: date)</li><li><b>Time_Off</b>: Time camera traps were collected (Field type: time)</li><li><b>CTNs</b>: Total survey effort for camera trap station (Field type: numeric)</li></ul></li><li><p><b>Species detection data</b> (described in worksheet Detection)</p><p>Description: Raw camera trap detection data for 28 medium-large mammal species obtained from 126 camera trap stations deployed using a paired design across 74 sampling locations </p><p>Number of fields: 7</p><p>Number of data rows: 29008</p><p>Fields: </p><ul><li><b>Site</b>: Unique alphanumeric identifier of the location camera traps were deployed (Field type: location)</li><li><b>common_name</b>: Mammal species identifier (Field type: taxa)</li><li><b>Sp_ID</b>: Numeric species identifier, used to coerce dataframe into a 4D array (Field type: id)</li><li><b>Site_ID</b>: Numeric site identifier, used to coerce dataframe into a 4D array (Field type: id)</li><li><b>Spatial_Rep</b>: Spatial replicate indicative of the number of camera trap stations deployed at a site. Also used to coerce dataframe into a 4d array (Field type: replicate)</li><li><b>Temporal_Rep</b>: Temporal replicate, each comprising six camera trap nights (Field type: replicate)</li><li><b>Detection</b>: Presence/absence of species during survey period (Field type: abundance)</li></ul></li></ol><p><b>Date range: </b>2014-06-20 to 2017-10-09</p><p><b>Latitudinal extent: </b>4.5536 to 4.8121</p><p><b>Longitudinal extent: </b>117.4122 to 117.7398</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div> -  Animalia <br> -  -  Chordata <br> -  -  -  Mammalia <br> -  -  -  -  Rodentia <br> -  -  -  -  -  Sciuridae <br> -  -  -  -  -  -  <i>Rheithrosciurus</i> <br> -  -  -  -  -  -  -  <i>Rheithrosciurus macrotis</i> <br> -  -  -  -  -  Hystricidae <br> -  -  -  -  -  -  <i>Hystrix</i> <br> -  -  -  -  -  -  -  <i>Hystrix brachyura</i> <br> -  -  -  -  -  -  -  <i>Hystrix crassispinis</i> <br> -  -  -  -  -  -  <i>Trichys</i> <br> -  -  -  -  -  -  -  <i>Trichys fasciculata</i> <br> -  -  -  -  Proboscidea <br> -  -  -  -  -  Elephantidae <br> -  -  -  -  -  -  <i>Elephas</i> <br> -  -  -  -  -  -  -  <i>Elephas maximus</i> <br> -  -  -  -  Carnivora <br> -  -  -  -  -  Viverridae <br> -  -  -  -  -  -  <i>Viverra</i> <br> -  -  -  -  -  -  -  <i>Viverra tangalunga</i> <br> -  -  -  -  -  -  <i>Arctictis</i> <br> -  -  -  -  -  -  -  <i>Arctictis binturong</i> <br> -  -  -  -  -  -  <i>Paradoxurus</i> <br> -  -  -  -  -  -  -  <i>Paradoxurus hermaphroditus</i> <br> -  -  -  -  -  -  <i>Hemigalus</i> <br> -  -  -  -  -  -  -  <i>Hemigalus derbyanus</i> <br> -  -  -  -  -  -  <i>Paguma</i> <br> -  -  -  -  -  -  -  <i>Paguma larvata</i> <br> -  -  -  -  -  Felidae <br> -  -  -  -  -  -  <i>Pardofelis</i> <br> -  -  -  -  -  -  -  <i>Pardofelis marmorata</i> <br> -  -  -  -  -  -  <i>Prionailurus</i> <br> -  -  -  -  -  -  -  <i>Prionailurus bengalensis</i> <br> -  -  -  -  -  -  <i>Neofelis</i> <br> -  -  -  -  -  -  -  <i>Neofelis diardi</i> <br> -  -  -  -  -  Mustelidae <br> -  -  -  -  -  -  <i>Martes</i> <br> -  -  -  -  -  -  -  <i>Martes flavigula</i> <br> -  -  -  -  -  Ursidae <br> -  -  -  -  -  -  <i>Helarctos</i> <br> -  -  -  -  -  -  -  <i>Helarctos malayanus</i> <br> -  -  -  -  -  Herpestidae <br> -  -  -  -  -  -  <i>Herpestes</i> <br> -  -  -  -  -  -  -  <i>Herpestes brachyurus</i> <br> -  -  -  -  -  Mephitidae <br> -  -  -  -  -  -  <i>Mydaus</i> <br> -  -  -  -  -  -  -  <i>Mydaus javanensis</i> <br> -  -  -  -  Primates <br> -  -  -  -  -  Cercopithecidae <br> -  -  -  -  -  -  <i>Macaca</i> <br> -  -  -  -  -  -  -  <i>Macaca fascicularis</i> <br> -  -  -  -  -  -  -  <i>Macaca nemestrina</i> <br> -  -  -  -  -  Hominidae <br> -  -  -  -  -  -  <i>Pongo</i> <br> -  -  -  -  -  -  -  <i>Pongo pygmaeus</i> <br> -  -  -  -  Artiodactyla <br> -  -  -  -  -  Suidae <br> -  -  -  -  -  -  <i>Sus</i> <br> -  -  -  -  -  -  -  <i>Sus barbatus</i> <br> -  -  -  -  -  Tragulidae <br> -  -  -  -  -  -  <i>Tragulus</i> <br> -  -  -  -  -  -  -  <i>Tragulus napu</i> <br> -  -  -  -  -  -  -  <i>Tragulus kanchil</i> <br> -  -  -  -  -  Cervidae <br> -  -  -  -  -  -  <i>Muntiacus</i> <br> -  -  -  -  -  -  -  <i>Muntiacus atherodes</i> <br> -  -  -  -  -  -  -  <i>Muntiacus muntjak</i> <br> -  -  -  -  -  -  <i>Rusa</i> <br> -  -  -  -  -  -  -  <i>Rusa unicolor</i> <br> -  -  -  -  Pholidota <br> -  -  -  -  -  Manidae <br> -  -  -  -  -  -  <i>Manis</i> <br> -  -  -  -  -  -  -  <i>Manis javanica</i> <br> -  -  -  -  Erinaceomorpha <br> -  -  -  -  -  Erinaceidae <br> -  -  -  -  -  -  <i>Echinosorex</i> <br> -  -  -  -  -  -  -  <i>Echinosorex gymnura</i> <br></div><p></p>
Data from: A Preliminary Survey of Medium and Large-Sized Mammals from Lebu Natural Protected Forest, Southwest Showa, Ethiopia
<p>Abstract: This study was conducted to determine the species composition and diversity of medium and large-sized mammals from Lebu Natural Protected Forest, Ethiopia. Surveys were conducted to record mammals through direct observation and indirect evidence from three habitat types, namely: Natural forest, Bushland, and Riverine forest. A total of 15 mammalian species was recorded. The species recorded were Papio anubis, Chlorocebus aethiops, Tragelaphus scriptus, Canis aureus, Crocuta crocuta, Panthera pardus, Procavia capensis, Colobus guereza, Sylvicapra grimmia, Orycteropus afer, Helogale parvula, Hystrix cristata, Lepus fagani, Potamochoerus larvatus and Phacochoeus africanus. A total of 223 records of observations was compiled. 74% of these records (N=167) were obtained from direct sight, whereas the rest was recorded through indirect evidence. The dominant order recorded was Order Primates (57.4%) followed by Order Artiodactyla (17.5 %) while the least record was Order Lagomorpha (1.34%). The species richness varied across the stratified habitat types. However, there is no significant difference in Shannon-Wiener Index values between the habitat types. The species diversity of the study area was H'=2. 119. The present study area is of great potential area for the conservation of the species. Long-term detailed studies should be carried out for effective conservation and management initiatives in the study area.</p>
Data from: Comparing life histories across taxonomic groups in multiple dimensions: how mammal-like are insects?
Explaining variation in life histories remains a major challenge because they are multi-dimensional and there are many competing explanatory theories and paradigms. An influential concept in life history theory is the 'fast-slow continuum', exemplified by mammals. Determining the utility of such concepts across taxonomic groups requires comparison of the groups' life histories in multidimensional space. Insects display enormous species richness and phenotypic diversity, but testing hypotheses like the 'fast-slow continuum' has been inhibited by incomplete trait data. We use phylogenetic imputation to generate complete datasets of seven life history traits in orthopterans (grasshoppers and crickets) and examine the robustness of these imputations for our findings. Three phylogenetic principal components explain 83-96% of variation in these data. We find consistent evidence of an axis mostly following expectations of a 'fast-slow continuum', except that 'slow' species produce larger, not smaller, clutches of eggs. We show that the principal axes of variation in orthopterans and reptiles are mutually explanatory, as are those of mammals and birds. Essentially, trait covariation in Orthoptera, with 'slow' species producing larger clutches, is more reptile-like than mammal-or-bird-like. We conclude that the 'fast-slow continuum' is less pronounced in Orthoptera than in birds and mammals, reducing the universal relevance of this pattern, and the theories that predict it.
Leech blood-meal iDNA reveals differences in Bornean mammal diversity across habitats
<b>Description: </b><p>This data set includes the data used in Drinkwater et al. (2020) Leech blood-meal iDNA reveals differences in Bornean mammal diversity across habitats, submitted to Molecular Ecology. There are three sets of data based on the biomonitoring of mammals using iDNA extracted from leeches collected across the SAFE project (and DVCA) in 2016. At each site in the SAFE landscape 20 minute handsearches took place within the boundaries of fixed 25m2 vegetation plots. For these analyses we only used Haemadipsa picta individuals, as previous studies have revealed species differences between H. picta and H. zeylanica in the SAFE area. With metabarcoding techniques, first we extracted and amplified the 16S rRNA region of mammal DNA, from site-matched pools of leeches using PCR and specific mammal primers. NGS sequencing was used and the short fragments were then identified using in silico PCR with ecoPCR and OBITOOLS (metabarcoding packages) to assign taxonomy to the unknown sequences. We then analysed diversity in different habitats across the landscape and included microclimate data, from LiDAR scans of the landscape as variables which could impact the detection of mammals. </p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/10"><b>The effects of rainforest fragmentation on mammal community assemblages using leech blood-meal analysis</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>NERC (Standard grant , NE/K016148/1)</li><li>NERC (Independent research grant, NE/S01537X/1)</li></ul><p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p><p></p><p><b>Permits: </b>These data were collected under permit from the following authorities:</p><ul><li>Sabah Biodiversity Council (Research licence JKM/MBS.1000 2/2 (34))</li><li>Sabah Biodiversity Council (Research licence JKM/MBS.1000 2/3 JLD.2 (107))</li><li>Sabah Biodiversity Council (Export licence JKM/MBS.1000 2/3 JLD.3 (44))</li><li>Danum Valley Conservation Area (Research licence YS/DVMC/2016/253)</li></ul><p></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=4095374">here</a></p><p><b>Files: </b>This consists of 1 file: Drinkwater2020-iDNA_diversity3.xlsx</p><p><b>Drinkwater2020-iDNA_diversity3.xlsx</b></p><p>This file contains dataset metadata and 3 data tables:</p><ol><li><p><b>UNFILTERED Taxonomic assignment of iDNA sequences</b> (described in worksheet ecoTAG_output_raw)</p><p>Description: UNFILTERED This dataset is the raw output of the in silico PCR using the programs ecoPCR and the OBITOOLS package. The exact primers are matched against all mammal sequences in GenBank (NCBI) using a minimum of three mismatches between primer and query sequence and a quality filter of a minimum identity of 0.95. This dataset was subsequently filtered for contaminant, geographically implausible mammals and collapsed by haplotype per pool</p><p>Number of fields: 15</p><p>Number of data rows: 3454</p><p>Fields: </p><ul><li><b>id</b>: Unique sequence ID within leech pool (Field type: id)</li><li><b>site</b>: The site at the SAFE project from which the pool of leeches was collected (Field type: id)</li><li><b>hab</b>: The habitat type of the site at the SAFE project from which the pool of leeches was collected (Field type: id)</li><li><b>count</b>: Count of the times this sequence was found - UNFILTERED (Field type: numeric)</li><li><b>best_identity</b>: Percent identity match between query and database sequence - UNFILTERED (Field type: numeric)</li><li><b>family</b>: Family taxid - following GenBank (Field type: id)</li><li><b>family_name</b>: Family name (Field type: id)</li><li><b>genus</b>: Genus taxid - following GenBank (Field type: id)</li><li><b>genus_name</b>: Genus name (Field type: id)</li><li><b>order</b>: Order taxid - following GenBank (Field type: id)</li><li><b>order_name</b>: Order name (Field type: id)</li><li><b>species</b>: Species taxid - following GenBank (Field type: id)</li><li><b>species_name</b>: Species name (Field type: id)</li><li><b>Assigned_name</b>: Assigned taxonomic name (Field type: id)</li><li><b>sequence</b>: Query sequence (Field type: id)</li></ul></li><li><p><b>Mammal detections recorded in each pool </b> (described in worksheet detections)</p><p>Description: From the taxonomic assignment list, the unique sequences identfied in each pool are are recorded as detections. The value is a count of the numebr of time the unique sequence for that taxon was recorded in the pool. Geographically implausible mammals have been removed and taxa which agree per site have been collapsed. This give a detections by pool matrix. For analyses these counts were converted into presence/absence data. </p><p>Number of fields: 19</p><p>Number of data rows: 57</p><p>Fields: </p><ul><li><b>pool</b>: This is the pool name given to the leech pool for sequencing (Field type: id)</li><li><b>site</b>: The site at the SAFE project from which the pool of leeches was collected (Field type: id)</li><li><b>leeches</b>: This is the number of individual leeches which make up the pool (Field type: numeric)</li><li><b>habitat</b>: Habitat type - classification used in the paper to describe the quality of forest in the sites where the leeches were collected (Field type: id)</li><li><b>Arctogalidia</b>: Occurrence of detections of this taxon in a leech pool, as determined from leech-based iDNA (Field type: abundance)</li><li><b>Elephas</b>: Occurrence of detections of this taxon in a leech pool, as determined from leech-based iDNA (Field type: abundance)</li><li><b>Felidae</b>: Occurrence of detections of this taxon in a leech pool, as determined from leech-based iDNA (Field type: abundance)</li><li><b>Helarctos</b>: Occurrence of detections of this taxon in a leech pool, as determined from leech-based iDNA (Field type: abundance)</li><li><b>Hemigalus</b>: Occurrence of detections of this taxon in a leech pool, as determined from leech-based iDNA (Field type: abundance)</li><li><b>Hystrix</b>: Occurrence of detections of this taxon in a leech pool, as determined from leech-based iDNA (Field type: abundance)</li><li><b>Macaca</b>: Occurrence of detections of this taxon in a leech pool, as determined from leech-based iDNA (Field type: abundance)</li><li><b>Manis</b>: Occurrence of detections of this taxon in a leech pool, as determined from leech-based iDNA (Field type: abundance)</li><li><b>Muntiacus</b>: Occurrence of detections of this taxon in a leech pool, as determined from leech-based iDNA (Field type: abundance)</li><li><b>Rusa</b>: Occurrence of detections of this taxon in a leech pool, as determined from leech-based iDNA (Field type: abundance)</li><li><b>Sus</b>: Occurrence of detections of this taxon in a leech pool, as determined from leech-based iDNA (Field type: abundance)</li><li><b>Paguma</b>: Occurrence of detections of this taxon in a leech pool, as determined from leech-based iDNA (Field type: abundance)</li><li><b>Tragulus</b>: Occurrence of detections of this taxon in a leech pool, as determined from leech-based iDNA (Field type: abundance)</li><li><b>Trichys</b>: Occurrence of detections of this taxon in a leech pool, as determined from leech-based iDNA (Field type: abundance)</li><li><b>Viverra</b>: Occurrence of detections of this taxon in a leech pool, as determined from leech-based iDNA (Field type: abundance)</li></ul></li><li><p><b>Microclimate variables</b> (described in worksheet microclimate)</p><p>Description: Mean and maximum temperature and mean and maximum VPD extracted at each of the second order points used in the study. These values were extracted from microclimate surfaces generated in Jucker et al., (2018), using the coordinates from the centre of each of the 25m2 plots. For the values in Danum Valley Conservation Area (DVCA), these were extracted from the nearest river point (coordinates given).</p><p>Number of fields: 6</p><p>Number of data rows: 92</p><p>Fields: </p><ul><li><b>Code</b>: SAFE second order points including LOMBOK points at RLFE and three river sites at DVCA (Field type: location)</li><li><b>site</b>: The site at the SAFE project from which the pool of leeches was collected (Field type: id)</li><li><b>T_max_raster</b>: The maximum daily temperature at each second order point (Field type: numeric)</li><li><b>T_mean_raster</b>: The mean daily temperature at each second order point (Field type: numeric)</li><li><b>VPD_max_raster</b>: The maximum daily vapour pressure deficit (VPD) at each second order point (Field type: numeric)</li><li><b>VPD_mean_raster</b>: The mean daily vapour pressure deficit (VPD) at each second order point (Field type: numeric)</li></ul></li></ol><p><b>Date range: </b>2016-01-01 to 2016-12-31</p><p><b>Latitudinal extent: </b>4.5000 to 5.0700</p><p><b>Longitudinal extent: </b>116.7500 to 117.8200</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div> -  Animalia <br> -  -  Chordata <br> -  -  -  Mammalia <br> -  -  -  -  Rodentia <br> -  -  -  -  -  Hystricidae <br> -  -  -  -  -  -  <i>Hystrix</i> <br> -  -  -  -  -  -  <i>Trichys</i> <br> -  -  -  -  -  -  -  <i>Trichys fasciculata</i> <br> -  -  -  -  Proboscidea <br> -  -  -  -  -  Elephantidae <br> -  -  -  -  -  -  <i>Elephas</i> <br> -  -  -  -  -  -  -  <i>Elephas maximus</i> <br> -  -  -  -  Primates <br> -  -  -  -  -  Cercopithecidae <br> -  -  -  -  -  -  <i>Macaca</i> <br> -  -  -  -  Carnivora <br> -  -  -  -  -  Felidae <br> -  -  -  -  -  Viverridae <br> -  -  -  -  -  -  <i>Viverra</i> <br> -  -  -  -  -  -  -  <i>Viverra tangalunga</i> <br> -  -  -  -  -  -  <i>Paguma</i> <br> -  -  -  -  -  -  -  <i>Paguma larvata</i> <br> -  -  -  -  -  -  <i>Arctogalidia</i> <br> -  -  -  -  -  -  -  <i>Arctogalidia trivirgata</i> <br> -  -  -  -  -  -  <i>Hemigalus</i> <br> -  -  -  -  -  -  -  <i>Hemigalus derbyanus</i> <br> -  -  -  -  -  Ursidae <br> -  -  -  -  -  -  <i>Helarctos</i> <br> -  -  -  -  -  -  -  <i>Helarctos malayanus</i> <br> -  -  -  -  Pholidota <br> -  -  -  -  -  Manidae <br> -  -  -  -  -  -  <i>Manis</i> <br> -  -  -  -  -  -  -  <i>Manis javanica</i> <br> -  -  -  -  Artiodactyla <br> -  -  -  -  -  Suidae <br> -  -  -  -  -  -  <i>Sus</i> <br> -  -  -  -  -  -  -  <i>Sus barbatus</i> <br> -  -  -  -  -  Tragulidae <br> -  -  -  -  -  -  <i>Tragulus</i> <br> -  -  -  -  -  Cervidae <br> -  -  -  -  -  -  <i>Muntiacus</i> <br> -  -  -  -  -  -  <i>Rusa</i> <br> -  -  -  -  -  -  -  <i>Rusa unicolor</i> <br></div><p></p>
Supplementary material 2 from: Bertolino S, Ancillotto L, Bartolommei P, Benassi G, Capizzi D, Gasperini S, Lucchesi M, Mori E, Scillitani L, Sozio G, Falaschi M, Ficetola GF, Cerri J, Genovesi P, Carnevali L, Loy A, Monaco A (2020) A framework for prioritising present and potentially invasive mammal species for a national list. In: Wilson JR, Bacher S, Daehler CC, Groom QJ, Kumschick S, Lockwood JL, Robinson TB, Zengeya TA, Richardson DM. NeoBiota 62: 31-54. https://doi.org/10.3897/neobiota.62.52934
This is the R-script and the output of the analyses
Supplementary material 1 from: Bertolino S, Ancillotto L, Bartolommei P, Benassi G, Capizzi D, Gasperini S, Lucchesi M, Mori E, Scillitani L, Sozio G, Falaschi M, Ficetola GF, Cerri J, Genovesi P, Carnevali L, Loy A, Monaco A (2020) A framework for prioritising present and potentially invasive mammal species for a national list. In: Wilson JR, Bacher S, Daehler CC, Groom QJ, Kumschick S, Lockwood JL, Robinson TB, Zengeya TA, Richardson DM. NeoBiota 62: 31-54. https://doi.org/10.3897/neobiota.62.52934
This is the database produced during the research
Supplementary material 3 from: Bertolino S, Ancillotto L, Bartolommei P, Benassi G, Capizzi D, Gasperini S, Lucchesi M, Mori E, Scillitani L, Sozio G, Falaschi M, Ficetola GF, Cerri J, Genovesi P, Carnevali L, Loy A, Monaco A (2020) A framework for prioritising present and potentially invasive mammal species for a national list. In: Wilson JR, Bacher S, Daehler CC, Groom QJ, Kumschick S, Lockwood JL, Robinson TB, Zengeya TA, Richardson DM. NeoBiota 62: 31-54. https://doi.org/10.3897/neobiota.62.52934
Complete ranking of the species
Data from: Movement and seasonal energetics mediate vulnerability to disturbance in marine mammal populations
<p>In marine environments noise from human activities is increasing dramatically, causing animals to alter their behavior and forage less efficiently. These alterations incur energetic costs that can result in reproductive failure, death, and may ultimately influence population viability; yet the link between population dynamics and individual energetics is poorly understood. We present an energy budget model for simulating effects of acoustic disturbance on populations. It accounts for environmental variability and individual state, while incorporating realistic animal movements. Using harbor porpoises (<i>Phocoena phocoena</i>) as a case study, we evaluated population consequences of disturbance from seismic surveys and investigated underlying drivers of vulnerability. The framework reproduced empirical estimates of population structure and seasonal variations in energetics. The largest effects predicted for seismic surveys were in late summer and fall, and were unrelated to local abundance, but instead to lactation costs, water temperature, and body fat. Our results demonstrate that consideration of temporal variation in individual energetics and their link to costs associated with disturbances is imperative when predicting disturbance impacts. These mechanisms are general to animal species, and the framework presented here can be used for gaining new insights into the spatiotemporal variability of animal movements and energetics that control population dynamics.</p>
Figure 1 from: Antoł A, Kozłowski J (2020) Scaling of organ masses in mammals and birds: phylogenetic signal and implications for metabolic rate scaling. ZooKeys 982: 149-159. https://doi.org/10.3897/zookeys.982.55639
Figure 1 PGLS (solid lines) and OLS (dashed lines) interspecific scaling of tissue/organ masses in mammals with log fat-free-body mass as the independent variable. For the scaling with log body mass as an independent variable, see Suppl. material 1: Figure S3
Figure 2 from: Antoł A, Kozłowski J (2020) Scaling of organ masses in mammals and birds: phylogenetic signal and implications for metabolic rate scaling. ZooKeys 982: 149-159. https://doi.org/10.3897/zookeys.982.55639
Figure 2 PGLS (solid lines) and OLS (dashed lines) interspecific scaling of tissue/organ masses in birds with log fat-free-body mass as the independent variable. For the scaling with log body mass as an independent variable, see Suppl. material 1: Figure S5.
Data from: Altitude shapes the environmental drivers of large-scale variation in abundance of a widespread mammal species
<p>Habitat quality and heterogeneity directly influence the distribution and abundance of organisms at different spatial scales. Determining the main environmental factors driving the variation in species abundance is crucial to understand the underlying ecological processes and this is especially important for widely distributed species living in contrasting environments. However, the responses to environmental variation are usually described at relatively small spatial scales. Here, we studied the variation in abundance of a widely distributed mustelid, the European badger (<i>Meles meles</i>), across France.<b> </b>We used (1) direct detections of 9,439 dead and living badgers, from 2006 to 2009, to estimate badger relative abundance in 703 small agricultural regions of metropolitan France and (2) a Bayesian modelling approach to identify the main environmental determinants influencing badger abundance.<b> </b>Despite a continuous distribution of badger in France, we found large variation in badger abundance between regions, explained by environmental factors. Among a set of 13 environmental variables, we demonstrated that badger abundance in lowlands (< 400 m a.s.l.) was mostly driven by biotic factors such as potential food resources (earthworm abundance and fruits crops) and forest fragmentation. Conversely, in mountainous areas, abiotic factors (i.e. soil texture and climate) drove the variation in badger relative abundance.<b> </b>These results underline the importance of mapping the abundance of wildlife species based on environmental suitability, and highlight the complexity of drivers influencing species abundance at such large spatial scales. Altitude shaped the environmental drivers (biotic <i>vs.</i> abiotic) that most influenced relative abundance of a widespread species. In the case of badger, such abundance maps are crucial to identify critical areas for species management as this mustelid is a main wild vector of bovine tuberculosis in several countries.</p>
Data from: Community-based wildlife management area supports similar mammal species richness and densities compared to a national park
<p>Community-based conservation models have been widely implemented across Africa to improve wildlife conservation and livelihoods of rural communities. In Tanzania, communities can set aside land and formally register it as Wildlife Management Area (WMA), which allows them to generate revenue via consumptive or non-consumptive utilization of wildlife. The key, yet often untested, assumption of this model is that economic benefits accrued from wildlife motivate sustainable management of wildlife. To test the ecological effectiveness (here defined as persistence of wildlife populations) of Burunge Wildlife Management Area (BWMA), we employed a participatory monitoring approach involving WMA personnel. At intermittent intervals between 2011 and 2018, we estimated mammal species richness and population densities of ten mammal species (African elephant, giraffe, buffalo, zebra, wildebeest, waterbuck, warthog, impala, Kirk's dik-dik, and vervet monkey) along line transectss . We compared mammal species accumulation curves and density estimates with those of time-matched road transect surveys conducted in adjacent Tarangire National Park (TNP). Mammal species richness estimates were similar in both areas, yet observed species richness per transect was greater in TNP compared to BWMA. Species-specific density estimates of time-matched surveys were mostly not significantly different between BWMA and TNP, but elephants occasionally reached greater densities in TNP compared to BWMA. In BWMA, elephant, wildebeest, and impala populations showed significant increases from 2011 to 2018. These results suggest that community-based conservation models can support mammal communities and densities that are similar to national park baselines. In light of the ecological success of this case study, we emphasize the need for continued efforts to ensure that the BWMA is effective. This will require adaptive management to counteract potential negative repercussions of wildlife populations on peoples' livelihoods. This study can be used as a model to evaluate the effectiveness of wildlife management areas across Tanzania. Community-based conservation models have been widely implemented across Africa to improve wildlife conservation and livelihoods of rural communities. In Tanzania, communities can set aside land and formally register it as Wildlife Management Area (WMA), which allows them to generate revenue via consumptive or non-consumptive utilization of wildlife. The key, yet often untested, assumption of this model is that economic benefits accrued from wildlife motivate sustainable management of wildlife. To test the ecological effectiveness (here defined as persistence of wildlife populations) of Burunge Wildlife Management Area (BWMA), we employed a participatory monitoring approach involving WMA personnel. At intermittent intervals between 2011 and 2018, we estimated mammal species richness and population densities of ten mammal species (African elephant, giraffe, buffalo, zebra, wildebeest, waterbuck, warthog, impala, Kirk's dik-dik, and vervet monkey) along line transectss . We compared mammal species accumulation curves and density estimates with those of time-matched road transect surveys conducted in adjacent Tarangire National Park (TNP). Mammal species richness estimates were similar in both areas, yet observed species richness per transect was greater in TNP compared to BWMA. Species-specific density estimates of time-matched surveys were mostly not significantly different between BWMA and TNP, but elephants occasionally reached greater densities in TNP compared to BWMA. In BWMA, elephant, wildebeest, and impala populations showed significant increases from 2011 to 2018. These results suggest that community-based conservation models can support mammal communities and densities that are similar to national park baselines. In light of the ecological success of this case study, we emphasize the need for continued efforts to ensure that the BWMA is effective. This will require adaptive management to counteract potential negative repercussions of wildlife populations on peoples' livelihoods. This study can be used as a model to evaluate the effectiveness of wildlife management areas across Tanzania.</p>
FIG. 9 in When ivory came from the seas. On some traits of the trade of raw and carved sea-mammal ivories in the Middle Ages
FIG. 9. — Chess piece, Castila, 12th century. Baltimore, Walters Art Museum: 71.145. Height: 7.1 cm (Photo Baltimore, Walters Art Museum, CC).
FIG. 11 in When ivory came from the seas. On some traits of the trade of raw and carved sea-mammal ivories in the Middle Ages
FIG. 11. — The Ainkhürn Schwert (Unicorn Sword) of Philip the Good of Burgundy, mid-15th century, Vienna, Imperial treasure, Hofburg (Kunsthistorisches Museum: SK XIV-3). Length: 104 cm (Photo KHM-Museumsverband).
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Annotated Behaviour and Observability Dataset (ABODe)
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