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Table 5 in Harbor seal pup dispersal and individual morphology, hematology, and contaminant factors affecting survival

Table 5. Model selection of apparent survival probability (Phi) for model set 3: morphology and sex.<table><tbody><tr><th></th><th></th><th></th><th>QAICc</th><th>Model</th><th>No.</th><th></th></tr></tbody><tbody><tr><th>Model</th><td>QAICc ΔQAICc weights likelihood parameters Deviance</td></tr><tr><th>{Φ (g + length)} 219.90</th><td>0.000</td><td>0.191</td><td>1.000</td><td>4</td><td>211.810</td></tr><tr><th>{Φ (g + mass)}</th><td>220.44</td><td>0.544</td><td>0.145</td><td>0.762</td><td>4</td><td>212.354</td></tr><tr><th>{Φ (g + sex + length)}</th><td>220.46</td><td>0.567</td><td>0.144</td><td>0.753</td><td>5</td><td>210.334</td></tr><tr><th>{Φ (g)}</th><td>220.57</td><td>0.672</td><td>0.136</td><td>0.715</td><td>3</td><td>214.517</td></tr><tr><th>{Φ (g + sex)}</th><td>220.83</td><td>0.935</td><td>0.120</td><td>0.627</td><td>4</td><td>212.745</td></tr><tr><th>{Φ (g + sex + mass)}</th><td>221.16</td><td>1.267</td><td>0.101</td><td>0.531</td><td>5</td><td>211.034</td></tr><tr><th>{Φ (g + bd)}</th><td>222.23</td><td>2.330</td><td>0.060</td><td>0.312</td><td>4</td><td>214.140</td></tr><tr><th>{Φ (g + cond)}</th><td>222.45</td><td>2.557</td><td>0.053</td><td>0.278</td><td>4</td><td>214.368</td></tr><tr><th>{Φ (g + girth)}</th><td>222.56</td><td>2.662</td><td>0.050</td><td>0.264</td><td>4</td><td>214.472</td></tr></tbody></table>Note: g = group (SF, TB, and TMMC), bd = blubber depth, cond = the body condition index. c ͡ adjustment = 1.15. Highest ranked model highlighted in bold.

Table 3 in Harbor seal pup dispersal and individual morphology, hematology, and contaminant factors affecting survival

Table 3. Model selection of apparent survival probability (Phi) for model set 1: group and time.<table><tbody><tr><th></th><th></th><th></th><th>QAICc</th><th>Model</th><th>No.</th><th></th></tr></tbody><tbody><tr><th>Model</th><td>QAICc</td><td>ΔQAICc</td><td>weights</td><td>likelihood</td><td>parameters</td><td>Deviance</td></tr><tr><th>{Φ (g)}</th><td>222.45</td><td>0.000</td><td>0.538</td><td>1.000</td><td>3</td><td>49.658</td></tr><tr><th>{Φ (SF and</th><td>222.95</td><td>0.501</td><td>0.419</td><td>0.779</td><td>2</td><td>52.184</td></tr><tr><th>TMMC v TB)}</th><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>{Φ (SF and</th><td>227.55</td><td>5.102</td><td>0.042</td><td>0.078</td><td>2</td><td>56.786</td></tr><tr><th>TB v TMMC)}</th><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>{Φ (.)}</th><td>233.43</td><td>10.983</td><td>0.002</td><td>0.004</td><td>1</td><td>64.684</td></tr><tr><th>{Φ (t)}</th><td>254.10</td><td>31.655</td><td>0.000</td><td>0.000</td><td>22</td><td>41.095</td></tr><tr><th>{Φ (g × t)}</th><td>320.74</td><td>98.291</td><td>0.000</td><td>0.000</td><td>66</td><td>0.000</td></tr><tr><th>Model</th><td>QAICc</td><td>ΔQAICc</td><td>weights</td><td>likelihood</td><td>parameters</td><td>Deviance</td></tr><tr><th>{Φ (mass)}</th><td>101.79</td><td>0.000</td><td>0.220</td><td>1.000</td><td>2</td><td>97.704</td></tr><tr><th>{Φ (T4)}</th><td>102.08</td><td>0.287</td><td>0.190</td><td>0.866</td><td>2</td><td>97.991</td></tr><tr><th>{Φ (.)}</th><td>102.42</td><td>0.625</td><td>0.161</td><td>0.732</td><td>1</td><td>100.387</td></tr><tr><th>{Φ (change in</th><td>102.80</td><td>1.013</td><td>0.133</td><td>0.603</td><td>2</td><td>98.717</td></tr><tr><th>mass)}</th><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>{Φ (days in rehab)}</th><td>103.47</td><td>1.679</td><td>0.095</td><td>0.432</td><td>2</td><td>99.383</td></tr><tr><th>{Φ (admit date)}</th><td>103.73</td><td>1.942</td><td>0.083</td><td>0.379</td><td>2</td><td>99.646</td></tr><tr><th>{Φ (logOH)}</th><td>104.38</td><td>2.593</td><td>0.060</td><td>0.273</td><td>2</td><td>100.297</td></tr><tr><th>{Φ (mass per day)}</th><td>104.46</td><td>2.666</td><td>0.058</td><td>0.264</td><td>2</td><td>100.370</td></tr></tbody></table>Note: t = time, g = group (SF = San Francisco, TB = Tomales Bay, and TMMC = The Marine Mammal Center). c ͡ adjustment = 1.15. Highest ranked model highlighted in bold.Note: T4 = total thryroxine, adjustment = 1.15.

Table 7 in Harbor seal pup dispersal and individual morphology, hematology, and contaminant factors affecting survival

Table 7. Model selection of apparent survival probability (Phi) for model set 5 incorporating top covariates from previous models.<table><tbody><tr><th></th><th></th><th></th><th>QAICc</th><th>Model</th><th>No.</th><th></th></tr></tbody><tbody><tr><th>Model</th><td>QAICc</td><td>ΔQAICc</td><td>weights</td><td>likelihood</td><td>parameters</td><td>Deviance</td></tr><tr><th>{Φ (g + T4)}</th><td>248.8571</td><td>0</td><td>0.37137</td><td>1</td><td>4</td><td>240.7709</td></tr><tr><th>{Φ (g + T4 +</th><td>250.1334</td><td>1.2763</td><td>0.19618</td><td>0.5283</td><td>5</td><td>240.0038</td></tr><tr><th>mass)}</th><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>{Φ (g + T4 +</th><td>250.298</td><td>1.4409</td><td>0.18068</td><td>0.4865</td><td>5</td><td>240.1684</td></tr><tr><th>logOH)}</th><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>{Φ (g + T4 +</th><td>251.8598</td><td>3.0027</td><td>0.08275</td><td>0.2228</td><td>6</td><td>239.678</td></tr><tr><th>mass + logOH)}</th><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>{Φ (g + mass)}</th><td>252.2936</td><td>3.4365</td><td>0.06662</td><td>0.1794</td><td>4</td><td>244.2074</td></tr><tr><th>{Φ (g)}</th><td>252.7458</td><td>3.8887</td><td>0.05314</td><td>0.1431</td><td>3</td><td>246.6942</td></tr><tr><th>{Φ (g + mass +</th><td>254.2608</td><td>5.4037</td><td>0.02491</td><td>0.0671</td><td>5</td><td>244.1312</td></tr><tr><th>logOH)}</th><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><th>{Φ (g + logOH)}</th><td>254.3069</td><td>5.4498</td><td>0.02434</td><td>0.0655</td><td>4</td><td>246.2207</td></tr></tbody></table>Note: g = group (SF, TB, and TMMC), T4 = total thyroxine, OH = PCB+DDT+ PBDE+HCH+CHLD, Phi = survival.

Table 4 in Harbor seal pup dispersal and individual morphology, hematology, and contaminant factors affecting survival

Table 4. Model selection of apparent survival probability (Phi) for model set 2: lipid weight contaminant classes.<table><tbody><tr><th></th><th></th><th></th><th>QAICc</th><th>Model</th><th>No.</th><th></th></tr></tbody><tbody><tr><th>Model</th><td>QAICc</td><td>ΔQAICc</td><td>weights</td><td>likelihood</td><td>parameters</td><td>Deviance</td></tr><tr><th>{Φ (g)}</th><td>220.57</td><td>0.000</td><td>0.226</td><td>1.000</td><td>3</td><td>214.517</td></tr><tr><th>{Φ (g + logPBDE)}</th><td>221.04</td><td>0.473</td><td>0.179</td><td>0.789</td><td>4</td><td>212.955</td></tr><tr><th>{Φ (g + logHCH)}</th><td>221.11</td><td>0.544</td><td>0.172</td><td>0.762</td><td>4</td><td>213.026</td></tr><tr><th>{Φ (g + logCHLD)}</th><td>221.67</td><td>1.104</td><td>0.130</td><td>0.576</td><td>4</td><td>213.586</td></tr><tr><th>{Φ (g + logPCB)}</th><td>222.06</td><td>1.491</td><td>0.107</td><td>0.475</td><td>4</td><td>213.973</td></tr><tr><th>{Φ (g × logHCH)}</th><td>222.25</td><td>1.683</td><td>0.098</td><td>0.431</td><td>6</td><td>210.070</td></tr><tr><th>{Φ (g + logDDT)}</th><td>222.46</td><td>1.894</td><td>0.088</td><td>0.388</td><td>4</td><td>214.376</td></tr></tbody></table>Note: g = group (SF, TB, and TMMC), PCB = polychlorinated biphenyls, DDT = summed dichlorodiphenyltrichloroethane and its metabolites, PBDE = polybrominated diphenylethers, CHLD = chlordanes, and HCH = hexachlorocyclohexanes. c ͡ adjustment = 1.15. Highest ranked model highlighted in bold.

Table 6 in Harbor seal pup dispersal and individual morphology, hematology, and contaminant factors affecting survival

Table 6. Model selection of apparent survival probability (Phi) for model set 4: blood variables.<table><tbody><tr><th></th><th></th><th></th><th>QAICc</th><th>Model</th><th>No.</th><th></th></tr></tbody><tbody><tr><th>Model</th><td>QAICc</td><td>ΔQAICc</td><td>weights</td><td>likelihood</td><td>parameters</td><td>Deviance</td></tr><tr><th>{Φ (g + T4)}</th><td>217.45</td><td>0.000</td><td>0.481</td><td>1.000</td><td>4</td><td>209.366</td></tr><tr><th>{Φ (g + T4 + T3)}</th><td>219.49</td><td>2.033</td><td>0.174</td><td>0.362</td><td>5</td><td>209.356</td></tr><tr><th>{Φ (g + T3)}</th><td>220.04</td><td>2.587</td><td>0.132</td><td>0.274</td><td>4</td><td>211.953</td></tr><tr><th>{Φ (g)}</th><td>220.57</td><td>3.116</td><td>0.101</td><td>0.211</td><td>3</td><td>214.517</td></tr><tr><th>{Φ (g + wbc)}</th><td>221.49</td><td>4.040</td><td>0.064</td><td>0.133</td><td>4</td><td>213.406</td></tr><tr><th>{Φ (g + IgG)}</th><td>222.04</td><td>4.589</td><td>0.048</td><td>0.101</td><td>4</td><td>213.955</td></tr></tbody></table>Note: g = group (SF, TB, and TMMC), T4 = total thyroxine, T3 = triiodothyronine, WBC = white blood cell count, IgG = total immunoglobulin. c ͡ adjustment = 1.15. Highest ranked model highlighted in bold.

Table 1 in Harbor seal pup dispersal and individual morphology, hematology, and contaminant factors affecting survival

Table 1. Mean and range for covariate values by group (SF = San Francisco, TB = Tomales Bay and TMMC = The Marine Mammal Center). The geometric mean is shown for contaminant concentrations (ng/g lipid weight).<table><tbody><tr><th></th><th>SF</th><th>TB</th><th>TMMC</th></tr></tbody><tbody><tr><th>Sample size</th><td>19</td><td>7</td><td>21</td></tr><tr><th>Morphology</th><td></td><td></td><td></td></tr><tr><th>Mass (kg)</th><td>19 (13–27)</td><td>20 (16–25)</td><td>18 (13–25)</td></tr><tr><th>Length (cm)</th><td>85 (72–100)</td><td>88 (83–95)</td><td>86 (79–96)</td></tr><tr><th>Girth (cm)</th><td>72 (61–86)</td><td>74 (65–83)</td><td>69 (58–93)</td></tr><tr><th>Blubber depth</th><td>19 (14–25)</td><td>19 (14–23)</td><td>18 (13–22)</td></tr><tr><th>(mm) Body condition</th><td>1.0 (− 3.0–7.5)</td><td>0.1 (− 1.9–1.9)</td><td>− 1.0 (− 5.4–2.8)a</td></tr><tr><th>Sex (male, female)</th><td>9, 10</td><td>5, 2</td><td>9, 12</td></tr><tr><th>Blood IgG (mg/mL) WBC (/μL) T4 (nmol/L)</th><td>24 (20–33) 7.4 (4.3–11.1)b 28 (10–58)</td><td>26 (21–29) 8.3 (4.9–13.6)a, b 46 (18–62)</td><td>29 (26–32)a 10.1 (6.2–15.0)a 19 (7 40)a –</td></tr><tr><th>T3 (nmol/L)</th><td>0.86 (0.53–2.32)</td><td>0.61 (0.39–0.94)</td><td>0.88 (0.52–1.46)</td></tr><tr><th>Contaminants PCB</th><td>9,777a</td><td>1,794</td><td>1,148</td></tr><tr><th>DDT</th><td>(2,594–30,075) 7,179</td><td>(668–10,627) 3,897</td><td>(229–7,528) 1,616a</td></tr><tr><th>PBDE CHLD HCH OH</th><td>(2,738–24,436) 1,053a (360–2,874) 373 (125–883) 27 (12–57) 18,601a</td><td>(992–17,380) 192 (40–1,117) 252 (126–817) 43a (20 70) – 6,302b</td><td>(318–5,885) 154 (61–876) 99a (47–461) 24 (17–42) 3,160</td></tr><tr><th></th><td>(5,836–57,855)</td><td>(1,952–27,880)</td><td>(672–14,599)</td></tr></tbody></table>Note: IgG = serum immunoglobulin, WBC = total white blood cell count, T4 = serum total thyroxine, T3 = serum total triiodothyronine, PCB = polychlorinated biphenyls, DDT = summed dichlorodiphenyltrichloroethane and its metabolites, PBDE = polybrominated diphenylethers, CHLD = chlordanes, HCH = hexachlorocyclohexanes, and OH = the five contaminant classes summed. Superscript letters represent significant differences among groups.

Table 8 in Harbor seal pup dispersal and individual morphology, hematology, and contaminant factors affecting survival

Table 8. Weekly survival estimate, standard error, and 95% confidence interval from the top model: {Φ (g + T4)}.<table><tbody><tr><th></th><th>Estimate</th><th>SE</th><th>LCL</th><th>UCL</th></tr></tbody><tbody><tr><th>SF</th><td>0.914</td><td>0.021</td><td>0.864</td><td>0.947</td></tr><tr><th>TMMC</th><td>0.886</td><td>0.028</td><td>0.820</td><td>0.930</td></tr><tr><th>TB</th><td>0.972</td><td>0.020</td><td>0.891</td><td>0.993</td></tr></tbody></table>

Table 2 in Harbor seal pup dispersal and individual morphology, hematology, and contaminant factors affecting survival

Table 2. Results for the significant generalized linear model of summed organohalogen contaminants (OH) controlling for group (SF = San Francisco, TB = Tomales Bay and TMMC = The Marine Mammal Center)<table><tbody><tr><th>Dependent</th><th>Model</th><th></th><th></th><th></th><th></th></tr></tbody><tbody><tr><th>variable</th><td>parameter</td><td>Estimate</td><td>SE</td><td>t</td><td>P</td></tr><tr><th>OH</th><td>intercept</td><td>13.853</td><td>1.626</td><td>8.52</td><td><0.005</td></tr><tr><th></th><td>location_TB</td><td>− 0.929</td><td>0.319</td><td>− 2.91</td><td>0.006</td></tr><tr><th></th><td>location_TMMC</td><td>− 1.706</td><td>0.226</td><td>− 7.554</td><td><0.005</td></tr><tr><th></th><td>length</td><td>− 0.047</td><td>0.019</td><td>− 2.486</td><td>0.017</td></tr></tbody></table>

opencc-by-4.0Mar 2024View details →

Prevalence and Factors Associated with the Use of Traditional Medicine in Individuals with Hypercholesterolemia, Hyperglycaemia, and Arterial Hypertension in Ecuador: Results from a Population-based Study in Two Health Districts

Open the record for dataset details and reuse information.

opencc-by-4.0Sep 2024View details →

Fusarium Pan-Annotations: improved individual and collective genome annotations

A 'pan-annotation' of genomes from 77 taxa (83 accessions) across the genus Fusarium, delivered as part of the Earlham Institute Strategic Programme Grant 'Decoding Biodiversity' (BBSRC). Citation: <a href="https://www.doi.org/10.1101/2025.03.12.642647" target="_blank" rel="noopener">Leveraging existing data to maximise quality and consistency across gene model annotations: a Fusarium pan-annotation. bioRxiv doi:10.1101/2025.03.12.642647</a>

opencc-by-4.0Oct 2024View details →

Genome-wide association study for circulating metabolites in 619,372 individuals

<h2>Genome-wide association study for circulating metabolites in 619,372 individuals</h2> Preprint:  <a href="https://doi.org/10.1101/2024.10.15.24315557">https://doi.org/10.1101/2024.10.15.24315557</a> meta_ALL - meta-analysis across Estonian Biobank and six genetic ancestry groups of the UK Biobank meta_EUR - meta-analysis across Estonian Biobank and EUR genetic acestry group of the UK Biobank *_all_lead_varaints.tsv - all genome-wide significant lead variants detected for each of the 249 tested metabolites *_independent_lead_variants.tsv - indepedendent lead variants that were in high LD with each other (r2 > 0.8).

opencc-by-4.0Nov 2024View details →

Minimal dataset for study, Spatiotemporal patterns of individual and multiple simultaneous severe weather events co-occurring with power outages in the United States, 2018-2020

These datasets are the minimal datasets for the study, Spatiotemporal patterns of individual and multiple simultaneous severe weather events co-occurring with power outages in the United States, 2018-2020. Study DOI: https://doi.org/10.21203/rs.3.rs-4752336/v1

opencc-by-4.0Jun 2021View details →

Dall'individuo alla relazione (From the individual to the relationship)

Audio recording of the seminar "From the individual to the relationship", in which a multidisciplinary path is presented that revolves around a fundamental paradigm shift for understanding and intervention for the improvement of social reality: the transition from a perspective focused on individual to a relationship focused. With the help of some passages taken from various texts by physicist Fritjof Capra and psychoanalysts and psychotherapists Renzo Carli and Rosa Maria Paniccia, we retrace some salient stages of the epistemological journey that leads us from centering on the individual dimension of human experience to that based on its relationship dimension, highlighting the value of this change of perspective in terms of the possibility of change for the improvement of individual and collective quality of life. Registrazione audio del seminario “Dall’individuo alla relazione”, in cui viene presentato un percorso multidisciplinare che ruota intorno ad un fondamentale cambio di paradigma per la comprensione e l’intervento per il miglioramento della realtà sociale: il passaggio da una prospettiva focalizzata sull’individuo ad una focalizzata sulla relazione. Con l’ausilio di alcuni brani tratti da diversi testi del fisico Fritjof Capra e degli psicoanalisti e psicoterapeuti Renzo Carli e Rosa Maria Paniccia, si ripercorrono alcune tappe salienti del percorso epistemologico che ci porta dalla centratura sulla dimensione individuale dell’esperienza umana a quella fondata sulla sua dimensione relazione, evidenziando il valore di questo cambio di prospettiva in termini di possibilità di cambiamento per il miglioramento dalla qualità della vita individuale e collettiva.

opencc-by-4.0Jun 2021View details →

In vitro protein and starch digestion kinetics of individual chickpea cells: from static to more complex in vitro digestion approaches

Attention has been given to more (semi-)dynamic in vitro digestion approaches ascertaining the consequences of dynamic in vivo aspects on in vitro digestion kinetics. As these often come with time and economical constraints, evaluating the consequence of stepwise increasing the complexity of static in vitro approaches using easy-to-handle digestion set-ups has been the center of our interest. Starting from the INFOGEST static in vitro protocol, we studied the influence of static gastric pH versus gradual gastric pH change (pH 6.3 to pH 2.5 in 2 h) on macronutrient digestion in individual cotyledon cells derived from chickpeas. Little effect on small intestinal proteolysis was observed comparing the applied digestion conditions. Contrary, the implementation of a gradual gastric pH change, with and without the addition of salivary α-amylase, altered starch digestion kinetics rates, and extents by 25%. The evaluation of starch and protein digestion, being co-embedded in cotyledon cells, did not only confirm but accounted for the interdependent digestion behavior. The insights generated in this study demonstrate the possibility of using a hypothesis-based approach to introduce dynamic factors to in vitro models while sticking to simple and cost-efficient set-ups.   The data used for the graphs in the paper: K. Pälchen, D. Michels, D. Duijsens, S. T. Gwala, A. K. Pallares Pallares, M. Hendrickx, A. Van Loey and T. Grauwet, Food Funct., 2021, DOI: 10.1039/D1FO01123E

opencc-zeroJul 2021View details →

Individual fish infection data from metapopulation epidemics used to calculate resistance, tolerance and competence

Host competence, defined as the likelihood that a host will transmit infection, may be affected by an individual's resistance to infection and its ability to withstand damage caused by infection (tolerance). Host competence may therefore be one of the most important factors to impact host-parasite dynamics, yet the relationships among resistance, tolerance, and competence are poorly understood. The objective of the present study was to determine whether individual host resistance (ability to resist or minimize infection) and/or tolerance (ability to withstand or minimize reduction in fitness due to infection) contributed to the competence (ability to spread infection) of hosts using guppies infected with the ectoparasite, Gyrodactylus turnbulli. This individual-fish level analysis used data collected from a previous metapopulation experiment that had tracked host-parasite dynamics at the metapopulation scale using individually marked guppies that were moved among experimental tanks within replicate metapopulations. Fish tolerance was measured as the residual from a fish's expected survival post-infection for a given parasite burden. Fish resistance was measured as the peak parasite load ( - log-transformed). Host competence was measured as the incidence (number of new infections over two days after the arrival of a fish to a tank) weighted by the density of available uninfected fish in the tank. In contrast to the assumption of a trade-off between resistance and tolerance, individual fish tolerance and resistance were both negatively associated with competence. Connectivity (the number of fish with which an individual came into contact) was not associated with competence. Our results indicate that resistance and tolerance are both important to disease spread. These findings highlight the importance of understanding how individual defence against parasites may contribute to its competence as a host, and therefore impact metapopulation-level dynamics.

opencc-by-4.0Jul 2021View details →

Data for Publication "Comparing individuals buried in flexed and extended positions at the Greek colony of Chersonesos (Crimea) using cranial metric, dental metric, and dental nonmetric traits"

Data for Publication: H. Rathmann, R. Stoyanov, and R. Posamentir, Comparing individuals buried in flexed and extended positions at the Greek colony of Chersonesos (Crimea) using cranial metric, dental metric, and dental nonmetric traits.

opencc-zeroDec 2016View details →

Data from: Habitat and social factors shape individual decisions and emergent group structure during baboon collective movement

For group-living animals traveling through heterogeneous landscapes, collective movement can be influenced by both habitat structure and social interactions. Yet research in collective behavior has largely neglected habitat influences on movement. Here we integrate simultaneous, high-resolution, tracking of wild baboons within a troop with a 3-dimensional reconstruction of their habitat to identify key drivers of baboon movement. A previously unexplored social influence – baboons' preference for locations that other troop members have recently traversed – is the most important predictor of individual movement decisions. Habitat is shown to influence movement over multiple spatial scales, from long-range attraction and repulsion from the troop's sleeping site, to relatively local influences including road-following and a short-range avoidance of dense vegetation. Scaling to the collective level reveals a clear association between habitat features and the emergent structure of the group, highlighting the importance of habitat heterogeneity in shaping group coordination.

opencc-zeroJul 2021View details →

Borrelia infection in bank voles Myodes glareolus is associated with specific DQB haplotypes which affect allelic divergence within individuals

The high polymorphism of Major Histocompatibility Complex (MHC) genes is generally considered to be a result of pathogen-mediated balancing selection. Such selection may operate in the form of heterozygote advantage, and/or through specific MHC allele–pathogen interactions. Specific MHC allele–pathogen interactions may promote polymorphism via negative frequency-dependent selection (NFDS), or selection that varies in time and/or space because of variability in the composition of the pathogen community (fluctuating selection; FS). In addition, divergent allele advantage (DAA) may act on top of these forms of balancing selection, explaining the high sequence divergence between MHC alleles. DAA has primarily been thought of as an extension of heterozygote advantage. However, DAA could also work in concert with NFDS though this is yet to be tested explicitly. To evaluate the importance of DAA in pathogen-mediated balancing selection, we surveyed allelic polymorphism of MHC class II DQB genes in wild bank voles (Myodes glareolus) and tested for associations between DQB haplotypes and infection by Borrelia afzelii, a tick-transmitted bacterium causing Lyme disease in humans. We found two significant associations between DQB haplotypes and infection status: one haplotype was associated with lower risk of infection (resistance), while another was associated with higher risk of infection (susceptibility). Interestingly, allelic divergence within individuals was higher for voles with the resistance haplotype compared to other voles. In contrast, allelic divergence was lower for voles with the susceptibility haplotype than other voles. The pattern of higher allelic divergence in individuals with the resistance haplotype is consistent with NFDS favouring divergent alleles in a natural population, hence selection where DAA works in concert with NFDS.

opencc-zeroJul 2021View details →

Numbers of individuals and endemicity for birds in three study sites of the Himalayas-Hengduan mountains of China

1. Describing the patterns and revealing the underlying mechanisms responsible for variations in community structure remains a central focus in ecology. However, important gaps remain, including our understanding of species abundance. Most studies on abundance-based relationships are from either temperate ecosystems or tropical ecosystems, and few have explicitly tested abundance-based relationships across a temperate to tropical ecotone. 2. Here, we use a comprehensive dataset of breeding birds across elevation spanning a temperate to subtropical gradient in the Himalayas-Hengduan mountains of China to examine the relationship between species abundance and (1) elevational range size, (2) body size, (3) elevational range center and, (4) endemicity. 3. We tested a priori predictions for abundance-elevational range size relationship, abundance-body size relationship, and abundance-elevational range center relationship and explored how these relationships change along this temperate to subtropical mountain ecosystem. 4. We found that species abundance was significantly positively correlated with elevational range size across the study sites, demonstrating the key importance of elevational range size towards species abundance. Body size and elevational range center are weakly correlated with abundance. A novel finding of our study is that the abundance-elevational range size relationship gradually weakened from temperate to subtropical ecosystems, adding to a growing body of evidence suggesting that abundance-elevational range size tracks a temperate to tropical ecotone. 5. Our study demonstrates that abundance range-size relationship can transition across ecotones where faunas of different evolutionary origins converge. Furthermore, measuring abundance relationships across different environmental variables at the same spatial scale with comparable biogeography is a key strategy that can reveal the underlying mechanisms behind abundance patterns.