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39 results for “Neotoma”
Jornada Basin LTER/Jornada Experimental Range site, station Rodent trapping web in grassland vegetation zone, study of animal abundance of Neotoma albigula in units of numberPer3pt14HectareTrappingWeb on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Jornada Basin LTER/Jornada Experimental Range (JRN) contains animal abundance of Neotoma albigula measurements in numberPer3pt14HectareTrappingWeb units and were aggregated to a yearly timescale.
Jornada Basin LTER/Jornada Experimental Range site, station Rodent trapping web in grassland vegetation zone, study of animal abundance of Neotoma micropus in units of numberPer3pt14HectareTrappingWeb on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Jornada Basin LTER/Jornada Experimental Range (JRN) contains animal abundance of Neotoma micropus measurements in numberPer3pt14HectareTrappingWeb units and were aggregated to a yearly timescale.
Data from: Reliability of macrofossils in woodrat (Neotoma) middens for detecting low-density tree populations
Macrofossils from woodrat (Neotoma) middens serve as an important proxy for reconstructing past vegetation in arid and semiarid regions of North America. The presence/absence of plant macrofossils in middens can provide valuable information on temporal and spatial patterns of plant migration and range boundaries. The primary aim of this study was to determine how local plant abundance, distance of plant populations from midden sites, and species population density on the landscape affect the probability of occurrence of macrofossils in middens. The study was designed with the primary intent of determining the reliability of middens in detecting scattered populations of Pinus ponderosa. We analyzed macrofossil assemblages from 42 modern woodrat middens from West Carrizo Canyon in southeastern Colorado, near the current eastern range margin of Pinus ponderosa. We compared midden contents with composition of the surrounding vegetation, measuring distance from the midden to the nearest individual of selected plant species, and the percent cover of each species within 30 m of the midden. We used this information to model the probability of species presence in a midden across a range of population densities on the landscape. Macrofossils of Juniperus spp., Quercus gambelii, and Opuntia spp. were consistently found in middens regardless of their local abundance in vegetation, although populations occurred within 30 m of all middens. Pinus edulis and P. ponderosa occurred in nearly all middens within 20-30 m of individual trees. P. ponderosa was rare in middens >20-30 m away from individual trees. Results of a simple simulation model suggest that middens become absolutely reliable indicators of P. ponderosa presence on the landscape only when average tree density exceeds 50 stems ha-1. Woodrats reliably collected macrofossils of Pinus edulis, P. ponderosa, Juniperus spp., Quercus gambelii, and Opuntia spp. when populations of these taxa occur within 20-30 m of a midden site. Woodrats did not collect P. ponderosa when the nearest individuals were more than 30 m away. Low-density populations of these and other species may be difficult to detect in fossil woodrat-midden series owing to reduced probability that individuals grow within foraging distance of the middens. Data from this and similar studies can be used to construct and parameterize a forward model of macrofossil representation in woodrat middens.
Data from: Multilocus characterization of a woodrat (genus Neotoma) hybrid zone
In order to investigate hybridization between 2 species of woodrats, Neotoma floridana and Neotoma micropus, 103 specimens were collected, in March of 1988, from a known area of sympatry, and compared with reference collections from areas of allopatry. Ten genetic markers, consisting of 7 microsatellite loci, 1 mitochondrial gene (cytochrome-b [Cytb]), and 2 nuclear introns (intron 2 of the vertebrate alcohol dehydrogenase gene [Adh1-I2] and intron 7 of the beta-fibrinogen gene [Fgb-I7]) were used to develop a composite genotype for each individual and for detection of hybridization. Six individuals were identified as pure parental N. micropus, 96 as hybrids, and 1 as pure parental N. floridana. Hybrids were formed primarily through matings between complex genotypes, resulting in a high prevalence of individuals classified as backcrosses. The ratio of hybrid classes, population substructure, and presence of significant linkage disequilibrium within the zone of contact could not reject either the hybrid superiority or hybrid equilibrium model as responsible for maintenance of this hybrid zone. The collection date of this dataset (1988) provided not only a point in time assessment of the hybrid zone but also provided opportunities for future comparisons of temporal datasets with the purpose of examining hybrid zone characteristics over multiple generations.
On following pages: 184. Magdalena Woodrat (Xenomys nelsoni); 185. Bushy-tailed Woodrat (Neotoma cinerea); 186. Sonoran Woodrat (Neotoma phenax); 187. Goldman's Woodrat (Neotoma goldmani); 188. Allegheny Woodrat (Neotoma magisten; 189. Eastern Woodrat (Neotoma floridanus); 190. White-throated Woodrat (Neotoma albigula); 191. Black-tailed Woodrat (Neotoma melanura); 192. White-toothed Woodrat (Neotoma leucodon): 193. Southern Plains Woodrat (Neotoma micropus); 194. Nelson's Woodrat (Neotoma nelsoni); 195. Bolanos Woodrat (Neotoma palatina); 196. Tamaulipan Woodrat (Neotoma angustapalata); 197. Mexican Woodrat (Neotoma mexicana); 198. Painted Woodrat (Neotoma picta); 199. Guatemala Woodrat (Neotoma ferruginea); 200. Nicaraguan Woodrat (Neotoma chrysomelas); 201. Stephens's Woodrat (Neotoma stephensi); 202. Dusky-footed Woodrat (Neotoma fuscipes); 203. Big-eared Woodrat (Neotoma macrotis); 204. Bryant's Woodrat (Neotoma bryant); 205. Angel de la Guarda Woodrat (Neotoma insularis); 206. Desert Woodrat (Neotoma lepida); 207. Arizona Woodrat (Neotoma devia). in Cricetidae
On following pages: 184. Magdalena Woodrat (Xenomys nelsoni); 185. Bushy-tailed Woodrat (Neotoma cinerea); 186. Sonoran Woodrat (Neotoma phenax); 187. Goldman's Woodrat (Neotoma goldmani); 188. Allegheny Woodrat (Neotoma magisten; 189. Eastern Woodrat (Neotoma floridanus); 190. White-throated Woodrat (Neotoma albigula); 191. Black-tailed Woodrat (Neotoma melanura); 192. White-toothed Woodrat (Neotoma leucodon): 193. Southern Plains Woodrat (Neotoma micropus); 194. Nelson's Woodrat (Neotoma nelsoni); 195. Bolanos Woodrat (Neotoma palatina); 196. Tamaulipan Woodrat (Neotoma angustapalata); 197. Mexican Woodrat (Neotoma mexicana); 198. Painted Woodrat (Neotoma picta); 199. Guatemala Woodrat (Neotoma ferruginea); 200. Nicaraguan Woodrat (Neotoma chrysomelas); 201. Stephens's Woodrat (Neotoma stephensi); 202. Dusky-footed Woodrat (Neotoma fuscipes); 203. Big-eared Woodrat (Neotoma macrotis); 204. Bryant's Woodrat (Neotoma bryant); 205. Angel de la Guarda Woodrat (Neotoma insularis); 206. Desert Woodrat (Neotoma lepida); 207. Arizona Woodrat (Neotoma devia).
FIGURE 3 in A phylogenetic analysis of Neotoma varia (Rodentia: Cricetidae), a rediscovered, endemic, and threatened rodent from Datil Island, Sonora, Mexico
FIGURE 3. Oclusal view of the upper molars: (A) N. varia, (B) N. albigula albigula, (C) N. a. melanura, (D) N. a. seri, and (E) N. a. venusta. M1 is the first molar, M2 the second molar, and M3 the third molar. (I) Is the contact area between the lobes of the fist molar and the second. (II) Is the presence of three lobes in the third molar.
FIGURE 1. Localities for Neotoma varia and N in A phylogenetic analysis of Neotoma varia (Rodentia: Cricetidae), a rediscovered, endemic, and threatened rodent from Datil Island, Sonora, Mexico
FIGURE 1. Localities for Neotoma varia and N. albigula specimens examined in this study. The numbers correspond to specific localities in Table 1.
Data from: The sensitivity of Neotoma to climate change and biodiversity loss over the late Quaternary
<p>The late Quaternary was a time of considerable environmental change in North America. Not only was climate highly variable, but a megafaunal extinction at the terminal Pleistocene led to considerable loss of biodiversity. These combined perturbations likely had cascading effects across communities and ecosystems. Here, we focus on a detailed fossil record on the Edwards Plateau in Texas and the response of <em>Neotoma</em>, a genus of herbivorous rodents, to these environmental and ecological perturbations. We characterized changes in <em>Neotoma</em> body mass and diet across the past 20,000 years; body mass was estimated using measurements of fossil teeth and diet quantified using stable isotope analysis of carbon and nitrogen isotope from fossil bone collagen. We found that prior to ~7,000 cal yr BP, maximum mass was positively and significantly correlated to precipitation and negatively correlated to temperature. Independently, body mass was significantly and negatively correlated to communtiy composition becoming more similar to modern over time. Moreover, while <em>Neotoma</em> diet in the Pleistocene was primarily sourced from C<sub>3</sub> resources, it became progressively more reliant on C<sub>4</sub> (and potentially CAM) plants through the Holocene. The combination of decreasing population body mass and higher C<sub>4</sub>/CAM consumption was associated with a regional transition from a mesic forest to a xeric savanna grassland. Our results suggest that <em>Neotoma</em> during the terminal Pleistocene were responding to climatic factors through changes in body size, while changes in local resource availability during the Holocene likely led to changes in the relative abundance of different <em>Neotoma</em> species in the community. </p>
Data from: Reliability of macrofossils in woodrat (Neotoma) middens for detecting low-density tree populations
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Data from: The sensitivity of Neotoma to climate change and biodiversity loss over the late Quaternary
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Data from: Multilocus characterization of a woodrat (genus Neotoma) hybrid zone
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Data from: Transcriptome sequencing and microarray development for the woodrat (Neotoma spp.): custom genetic tools for exploring herbivore ecology
Massively parallel sequencing has enabled the creation of novel, in-depth genetic tools for nonmodel, ecologically important organisms. We present the de novo transcriptome sequencing, analysis and microarray development for a vertebrate herbivore, the woodrat (Neotoma spp.). This genus is of ecological and evolutionary interest, especially with respect to ingestion and hepatic metabolism of potentially toxic plant secondary compounds. We generated a liver transcriptome of the desert woodrat (Neotoma lepida) using the Roche 454 platform. The assembled contigs were well annotated using rodent references (99.7% annotation), and biotransformation function was reflected in the gene ontology. The transcriptome was used to develop a custom microarray (eArray, Agilent). We tested the microarray with three experiments: one across species with similar habitat (thus, dietary) niches, one across species with different habitat niches and one across populations within a species. The resulting one-colour arrays had high technical and biological quality. Probes designed from the woodrat transcriptome performed significantly better than functionally similar probes from the Norway rat (Rattus norvegicus). There were a multitude of expression differences across the woodrat treatments, many of which related to biotransformation processes and activities. The pattern and function of the differences indicate shared ecological pressures, and not merely phylogenetic distance, play an important role in shaping gene expression profiles of woodrat species and populations. The quality and functionality of the woodrat transcriptome and custom microarray suggest these tools will be valuable for expanding the scope of herbivore biology, as well as the exploration of conceptual topics in ecology.
Data from: Transcriptome sequencing and microarray development for the woodrat (Neotoma spp.): custom genetic tools for exploring herbivore ecology
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Data for: Reproductive success of captive-reared Allegheny Woodrats (Neotoma magister) released into genetically depauperate populations
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Temperature-mediated changes in hepatic gene expression of a mammalian herbivore (Neotoma lepida)
GEO Series GSE80595. Neotoma lepida. 16 samples. Type: Expression profiling by array.
Transcriptome sequencing and microarray development for the woodrat (Neotoma spp.): custom genetic tools for exploring herbivore ecology
GEO Series GSE43941. Neotoma bryanti; Neotoma lepida. 12 samples. Type: Expression profiling by array.
RAW264.7 macrophages infected with Brucella abortus, B. melitensis, B. neotomae, and B. ovis
GEO Series GSE8403. Mus musculus. 16 samples. Type: Expression profiling by SAGE.
Host RAW264.7 macrophage transcript profile following Brucella melitensis, B. neotomae, and B. ovis infections
GEO Series GSE8385. Mus musculus. 11 samples. Type: Expression profiling by array.
FIGURE 2 in A phylogenetic analysis of Neotoma varia (Rodentia: Cricetidae), a rediscovered, endemic, and threatened rodent from Datil Island, Sonora, Mexico
FIGURE 2. Phylogenetic trees of the 828-bp fragment of the Cyt b gene generated from the analyzed Neotoma specimens. The numbers of haplotypes and subspecies at the tip of each branch follow Table 1. The trees were constructed using the 50% majority rule consensus algorithm (except in maximum-likelihood). Values in the nodes are branch support for the analyses. The N. leucodon sequence was included as the ingroup and N. picta, N. isthmica, and N. mexicana as the outgroup. (A) Maximum-likelihood tree using the GTR+I model; (B) neighbor-joining tree, genetic distances were calculated using the Jukes-Cantor model, percentage differences per node are in table 2. (C) Bayesian inference performed using the GTR+I model.
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