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Supplementary material 5 from: Tichit P, Brickle P, Newton RJ, Convey P, Dawson W (2024) Introduced species infiltrate recent stages of succession after glacial retreat on sub-Antarctic South Georgia. NeoBiota 92: 85-110. https://doi.org/10.3897/neobiota.92.117226
Summary of effects of time since deglaciation for each glacier site on all variables modelled with Bayesian Inference (including quadratic and interaction terms if retained in the best model)
Supplementary material 10 from: Tichit P, Brickle P, Newton RJ, Convey P, Dawson W (2024) Introduced species infiltrate recent stages of succession after glacial retreat on sub-Antarctic South Georgia. NeoBiota 92: 85-110. https://doi.org/10.3897/neobiota.92.117226
Effect of time since deglaciation on the presence of plant and invertebrate species at tidewater and inland glacier sites modelled with Bayesian Inference
Supplementary material 1 from: Tichit P, Brickle P, Newton RJ, Convey P, Dawson W (2024) Introduced species infiltrate recent stages of succession after glacial retreat on sub-Antarctic South Georgia. NeoBiota 92: 85-110. https://doi.org/10.3897/neobiota.92.117226
Positions of all transects (black dots) along former deglaciation fronts (from 1993 to 2017, light to dark purple) in the vicinity of tidewater glaciers or in recent and old deglaciation areas in the vicinity of inland glaciers
Supplementary material 8 from: Tichit P, Brickle P, Newton RJ, Convey P, Dawson W (2024) Introduced species infiltrate recent stages of succession after glacial retreat on sub-Antarctic South Georgia. NeoBiota 92: 85-110. https://doi.org/10.3897/neobiota.92.117226
Average cover (in %) across transects of the 10 most common vascular plants around tidewater glaciers (a, n = 21 transects), inland glaciers (b, n = 18 transects)
TABLE 2 in Australia's deep-water octocoral fauna: historical account and checklist, distributions and regional affinities of recent collections
<p><b>TABLE 2.</b> Seven bathomes—ecologically meaningful depth zones <i>sensu</i> Last <i>et al.</i> (2005) sampled during the biodiversity surveys 1997–2008. The numbers and depth range of sampling stations where octocoral were collected are s hown separately.</p><table><tbody><tr><th>Bathome</th><th>Bathome depth range (m)1)</th><th>Number of sampling stations</th><th></th><th>Min. sampling depth (m)</th><th>Max. sampling depth (m)</th></tr></tbody><tbody><tr><th></th><td></td><td>TOTAL</td><td>SP-Sample2)</td><td></td><td></td></tr><tr><th>Outer shelf</th><td>50–150</td><td>63</td><td>58</td><td>77</td><td>133</td></tr><tr><th>Shelf-break</th><td>150–250</td><td>17</td><td>16</td><td>150</td><td>249</td></tr><tr><th>Upper slope (shallow)</th><td>250–500</td><td>51</td><td>45</td><td>252</td><td>498</td></tr><tr><th>Upper slope (deep)</th><td>500–800</td><td>31</td><td>26</td><td>527</td><td>800</td></tr><tr><th>Mid-slope (shallow)</th><td>800–1100</td><td>61</td><td>56</td><td>825</td><td>1092</td></tr><tr><th>Mid-slope (deep)</th><td>1100–1500</td><td>69</td><td>56</td><td>1104</td><td>1500</td></tr><tr><th>Lower slope</th><td>>1500</td><td>44</td><td>34</td><td>1502</td><td>3950</td></tr></tbody></table><p>1) inclusive of upper limit</p><p>2) SP-Sample identifies the number of sampling stations from which octocorals were identified to species-level</p>
Data and code for "Topography and climate drive recent shifts in vegetation productivity dynamics"
<p>This zip file includes data and code for manuscript "<span><span>Topography and climate drive recent shifts in vegetation productivity dynamics</span></span>".</p>
Recent genetic drift in the co-diversified gut bacterial symbionts of laboratory mice
<p>Daniel D. Sprockett (1), Brian A. Dillard (1), Abigail A. Landers (2), Jon G. Sanders (1), Andrew H. Moeller (1,2)*</p> <p>1 Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, NY 14853, USA<br>2 Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08540, USA<br>*To whom correspondence should be addressed: andrew.moeller@princeton.edu</p> <p> </p> <p><strong>Abstract:</strong></p> <p>Laboratory mice (<em>Mus musculus domesticus</em>) harbor gut bacterial strains that are distinct from those of wild mice but whose evolutionary histories are unclear. Understanding the divergence of laboratory-mouse gut microbiota (LGM) from wild-mouse gut microbiota (WGM) is critical, because LGM and WGM have been previously shown to differentially affect mouse immune-cell proliferation, infection resistance, cancer progression, and ability to model drug outcomes for humans. Here, we show that laboratory mice have retained gut bacterial symbiont lineages that diversified in parallel (co-diversified) with rodent species for > 25 million years, but that LGM strains of these ancestral symbionts have experienced accelerated accumulation of genetic load during the past ~ 120 years of captivity. Compared to closely related WGM strains, co-diversified LGM strains displayed significantly faster genome-wide rates of fixation of nonsynonymous mutations, indicating elevated genetic drift, a difference that was absent in non-co-diversified symbiont clades. Competition experiments in germ-free mice further indicated that LGM strains within co-diversified clades displayed significantly reduced fitness in vivo compared to WGM relatives to an extent not observed within non-co-diversified clades. Thus, stochastic processes (e.g., bottlenecks), not natural selection in the laboratory, have been the predominant evolutionary forces underlying divergence of co-diversified symbiont strains between laboratory and wild house mice. Our results show that gut bacterial lineages conserved in diverse rodent species have acquired novel mutational burdens in laboratory mice, providing an evolutionary rationale for restoring laboratory mice with wild gut bacterial strain diversity.</p>
Data and code for Recent observations of Thwaites Glacier, West Antarctica are consistent with high rates of loss in next 50 years
<p><span>This repository contains the code and input files needed for running the MITgcm and ISSM experiments in this manuscript, and the relevant outputs from the experiments.</span></p> <p><span>1. STREAMICE</span></p> <p><span>the STREAMICE folder contains the following subfolders:</span></p> <p><span>code: contains all MITgcm source files for calibration experiments that are not included in the MITgcm repository</span></p> <p><span>code_proj: similar to code/ but for projection experiments</span></p> <p><span>model_input: contains subdirectories containing binary inputs and parameter files for all experiments in the manuscript</span></p> <p><span>model_output: output from each projection experiment is contained in a .mat file with the following arrays:</span></p> <ul> <li><span> X, Y: x-and y-coordinates in WGS 84 / Antarctic Polar Stereographic (EPSG:3031)</span></li> <li><span> BED: bed elevation (m)</span></li> <li><span> BETA: \beta described in the manuscript (units: Pa^(1/2) (m/a)^{-1/6})</span></li> <li><span> BGLEN: \overline{B} described in the manuscript (units: Pa^(1/2) a^{-1/6})</span></li> <li><span> SURF: ice surface elevation (m)</span></li> <li><span> THICK: ice thickness (m)</span></li> <li><span> THICK0: initial ice thickness (m)</span></li> <li><span> VX: x-velocity (m/a)</span></li> <li><span> VY: y-velocity (m/a)</span></li> <li><span> times:<span> </span>time in years after 2004</span></li> </ul> <p><span>checkpoint 68y of the MITgcm source code (mitgcm.org) was used for this study.</span></p> <p><span>m1qn3 (https://who.rocq.inria.fr/Jean-Charles.Gilbert/modulopt/optimization-routines/m1qn3/m1qn3.html) was used for optimisation.</span></p> <p><span>details of how to compile and run the forward and adjoint STREAMICE code can be found at https://mitgcm.readthedocs.io/en/latest/</span></p> <p><span>-------------------------------------------</span></p> <p><span>2. ISSM</span></p> <p><span>runme.m contains all ISSM instructions to run the experiments.</span></p> <p><span>output/ contains mat files for all experiments, with array descriptions as above.</span></p> <p><span>ISSM source and documentation can be found at https://issm.jpl.nasa.gov/.</span></p>
Distribution. Widespread in the C and W regions of S Africa, reaching to about 15° N in SW Angola. Occupies mainly arid and semi-arid areas, but also occurs in regions with higher precipitation and denser vegetation, such as the fynbos biome of South Africa's Western Cape Province. Cape Foxes have expanded their range over recent decades to the SW, where the species reaches the Atlantic and Indian Ocean coastlines. May occur in SW Swaziland, and possibly also in Lesotho. in Canidae
Distribution. Widespread in the C and W regions of S Africa, reaching to about 15° N in SW Angola. Occupies mainly arid and semi-arid areas, but also occurs in regions with higher precipitation and denser vegetation, such as the fynbos biome of South Africa's Western Cape Province. Cape Foxes have expanded their range over recent decades to the SW, where the species reaches the Atlantic and Indian Ocean coastlines. May occur in SW Swaziland, and possibly also in Lesotho.
Subspecies and Distribution. B. c. crassicauda Peters, 1852 — DR Congo, Malawi, Mozambique, Zambia, and Zimbabwe. B. c. nigrescens Sale & Taylor, 1970 — Kenya (Lukenya Hill). B. c. omnivora Heller, 1913 — coastal forests of Kenya and extreme NE Tanzania. B. c. puisa Peters, 1852 — NE Mozambique and E Tanzania. B. c. tenuis Thomas & Wroughton, 1908 — Tanzania (Zanzibar I). A few recent records from Yemen, but these need confirmation. in Herpestidae
Subspecies and Distribution. B. c. crassicauda Peters, 1852 — DR Congo, Malawi, Mozambique, Zambia, and Zimbabwe. B. c. nigrescens Sale & Taylor, 1970 — Kenya (Lukenya Hill). B. c. omnivora Heller, 1913 — coastal forests of Kenya and extreme NE Tanzania. B. c. puisa Peters, 1852 — NE Mozambique and E Tanzania. B. c. tenuis Thomas & Wroughton, 1908 — Tanzania (Zanzibar I). A few recent records from Yemen, but these need confirmation.
Data from: The transcriptomics of crushing jaw convergence in cichlid fishes: comparative gene expression in recent sympatric versus older allopatric trophic adaptations
<p>How gene expression diverges during adaptation might be strongly influenced by the geographic setting and timeframe over which species evolve. To contrast transcriptomic patterns of replicate trophic adaptations that evolved convergently during both allopatric and sympatric contexts, we conducted RNA-seq on the trophically important lower pharyngeal jaws of two sympatrically and four allopatrically diverged species pairs of cichlid fishes. We first show that all of these species pairs have convergently diverged along a crushing trophic axis and that the sympatric pairs are as phenotypically divergent as the allopatric pairs. Then, we found that distinct sets of genes were differentially expressed in the jaws of sympatrically diverging pairs as compared to jaws in older allopatric species pairs. The genes that were differentially expressed in the jaws of allopatric pairs also were more highly expressed on average than in the sympatric pairs. Finally, for genes that were differentially expressed, the magnitude of differences in expression between the jaws were greater for sympatrically diverging species pairs. The particular genes, their expression levels, and the magnitude of expression differences between sympatrically originating adaptations might all play an important role in generating and maintaining boundaries to gene flow during the rapid ecological divergence that often characterizes sympatric speciation.</p>
FIGURE 1. Locations investigated for arboreal tiger beetles. 1 in The arboreal tiger beetles (Coleoptera: Cicindelidae) of Sri Lanka recorded from recent investigations
FIGURE 1. Locations investigated for arboreal tiger beetles. 1—Kilinochchi, 2—Vellankulam, 3—Padavi Sri Pura, 4—Padaviya Wewa, 5—Trincomali, 6—Kantalai, 7—Mahakanadarawa Wewa, 8—Tambuttegama, 9—Tabbowa Wewa, 10—Karuwalagaswewa, 11—Mahakumbukkadawala, 12—Ritigala, 13—Aukana, 14—Habarana, 15—Giritale Wewa, 16—Pidurangala, 17—Andiyagala, 18—Devahuwa Wewa, 19—Gonawela, 20—Elahara, 21—Aralaganwila, 22—Thoppigala, 23—Kalkudah Beach, 24—Kalkudah, 25—Pitawala Pathana, 26—Riveston, 27—Punchi Rattota, 28—Rattota, 29—Nalanda Watta, 30— Moragollagama, 31—Mahawa, 32—Magalla Wewa, 33—Melsiripura, 34—Dambadeniya, 35—Giriulla, 36—Kurulu Kele, 37—Naranbedda, 38—Naranbedda Forest East, 39—Rambukkana, 40—Udawaththa Forest, 41—Dunumadalawa Rainforest, 42—Rambukwella, 43—Peradeniya, 44—Hanthana hiking area, 45—Thaladuwa, 46—Wadumulla, 47—Veyangoda, 48—Muthurajawela Beach, 49—Muthurajawela, 50—Amithirigala, 51—Makandawa Forest, Kitulgala, 52—Malwana,, 53—Kaduwela, 54—Seethawaka, 55—Labugama, 56—Kuruwita, 57—Bodhinagala, 58—Opanayaka, 59—Pokunuthanna, 60—Waulpane, 61—Rakwana, 62—Morning Side, Suriyakanda, 63—Sinharaja Rain Forest, 64—Sinharaja Aranya Senasanaya, 65—Weddagala, 66—Pelawatta, 67—Mathugama, 68—Horawala, 69—Yagirala Rain Forest, 70—Kanneliya Rain Forest, 71—Kottawa Rain Forest, 72—Rumassala, 73—Weligama Beach, 74—Akuressa, 75—Kamburupitiya, 76—Matara, 77—Beliatta, 78—Kirinda, 79—Kochchipathana Wewa, 80—Sithulpawwa, 81—Yala Sanctuary, 82—Pottuvil, 83—Gal Oya National Park, 84—Bibile, 85—Kalmunai
Figure 3 in Potomida littoralis (Bivalvia, Unionidae) evolutionary history: slow evolution or recent speciation?
Figure 3. Chronogram obtained with BEAST using two independent calibrations: COI substitution rate (scale above) and the opening of the Gibraltar Strait (scale below). The x-axis scale is in million years. Bars indicate 95% HPD intervals.
Figure 2 in Potomida littoralis (Bivalvia, Unionidae) evolutionary history: slow evolution or recent speciation?
Figure 2. Bayesian tree reconstruction based on the two mitochondrial genes analysed. Stars on the branches indicate well supported clades, one star for each method: Bayesian inference, maximum-parsimony and maximum-likelihood.
Figure 4 in Potomida littoralis (Bivalvia, Unionidae) evolutionary history: slow evolution or recent speciation?
Figure 4. Network of P. littoralis haplotypes. I, Iberian Peninsula; K, Turkey; G, Greece; M, Morocco; T, Tunisia; F, France.
Figure 4 in Recent mitochondrial lineage extinction in the critically endangered Javan rhinoceros
Figure 4. Neighbor-Joining network. The analysis of 14 modern and historical Javan and Indian rhinoceros mitochondrial DNA sequences was conducted using the 'Integer Neighbor-Joining' algorithm implemented in POPART. We used the partial D-loop region of 413 bp to maximize the number of available Javan rhino samples. 'Javan_annamiticus' represents the recently extinct (since 2010) Vietnamese subspecies Rhinoceros sondaicus annamiticus. Each circle represents a certain haplotype; smaller black circles indicate median vectors. Small black lines connecting branches between the haplotypes denote the number of mutation steps separating the haplotypes. Sample identities and regions of origin for the Javan rhinoceros samples are indicated next to each circle. Numbers in the legend indicate the number of sequences in each group.
Figure 3 in Recent mitochondrial lineage extinction in the critically endangered Javan rhinoceros
Figure 3. Bayesian phylogeny of the historical Javan rhinoceros mitochondrial DNA lineages, using the Indian rhino (Rhinoceros unicornis; GenBank ID: X97336) as the outgroup and including the partial (7606-bp-long) mitogenome of the Javan rhino sample collected in Bhutan (JR524). The blue bars represent the 95% highest posterior density (HPD) intervals of the divergence times. Node labels in bold show the Bayesian posterior probability (PP) values for the major clades. All specimens in the blue clade originated from Indonesia (apart from JR27, which has largely unknown 'Calcutta?' label) and are clearly divergent from the Bhutanese sample.
Figure 1 in Recent mitochondrial lineage extinction in the critically endangered Javan rhinoceros
Figure 1. Map of Javan rhino distribution in Southeast Asia, showing their approximate historical (yellow) and current (red) distribution (Fernando et al., 2006; Groves & Leslie, 2011). The green dots indicate the approximate locations of historical samples used in this study. Two samples with unknown locations are not mentioned on the plot (Table 1).
Figure 2 in Recent mitochondrial lineage extinction in the critically endangered Javan rhinoceros
Figure 2. Bayesian phylogeny of all available (N = 36) rhino whole mitochondrial DNA sequences (published + new sequences from this study). Two Equus mitochondrial DNA sequences were included as the outgroup. The blue bars represent the 95% highest posterior density (HPD) intervals of the divergence times. All branches within a species level have been collapsed to improve readability. Node labels in bold show the Bayesian posterior probability (PP) values for two nodes; the rest of the nodes had PP values of one. Geological time scale abbreviations: Q, Quaternary; Ple, Pleistocene; Pli, Pliocene. Extinct rhino lineages are shown with daggers after species names.
Distribution. Balabac, Ramos, and Bugsuk Is, Palawan region, Phillipines. In the late 1990s, a small stock of eight Balabac Chevrotains escaped from their enclosures on Calauit Island, a small island off the coast of Busuanga Island, north of Palawan, where the species had been maintained and bred since 1982. These animals were reported to have increased to at least 21 free-living individuals by 2006. Apparently the species was also introduced to the larger island of Palawan. No recent records confirm that it is still extant there, although unconfirmed reports suggest that it may survive in southern Palawan. in Tragulidae
Distribution. Balabac, Ramos, and Bugsuk Is, Palawan region, Phillipines. In the late 1990s, a small stock of eight Balabac Chevrotains escaped from their enclosures on Calauit Island, a small island off the coast of Busuanga Island, north of Palawan, where the species had been maintained and bred since 1982. These animals were reported to have increased to at least 21 free-living individuals by 2006. Apparently the species was also introduced to the larger island of Palawan. No recent records confirm that it is still extant there, although unconfirmed reports suggest that it may survive in southern Palawan.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.