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opencc-by-4.0Jan 2019View details →

Spatial aggregation and seedling survival of the Borneo Ironwood (Eusideroxylon zwageri)

Description: This dataset was collected by the Imperial College London MRes Tropical Forest Ecology Field Course as a group project in Maliau Basin. Fieldwork was carried out on 29 Jan - 1 Feb 2018. A 4-ha plot was set up to assess the spatial aggregation of the Borneo Ironwood (Eusideroxylon zwageri, local name Belian) and seedling survival. All trees with DBH >1cm were mapped with XY coordinate rounded to nearest 1m. DBH class was rounded down to the nearest 10cm, e.g. size 10, 20, 30 etc. Size class 1 refers to trees with DBH between 1 and 10cm. A swamp area on the north and northeast of ~1-ha was not assessible hence data defficient, but as Belian is not known to growth in water logged conditions the species was presumed to be absent in this swamp area. Belian seedlings were surveyed in 540 5x5m subplots in the centre zone of the 4-ha plot. In each seedling plot, healthy and damaged (primarily herbivory damage by mammals, defined as damaged when top shoot, i.e. apical meristem, was missing). Other variables recorded in seedling plots include slope angle and ground vegetation cover.Project: This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/152">MRes Tropical Forest Ecology Field Course</a>Funding: These data were collected as part of research funded by: <ul><li>Natural Environment Research Council (Directed grant, NE/P00363X/1, <a href="https://gtr.ukri.org/projects?ref=NE%2FP00363X%2F1">https://gtr.ukri.org/projects?ref=NE%2FP00363X%2F1</a>)</li><li>Imperial College London (MRes Tropical Forest Ecology field course program)</li></ul>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.XML metadata: GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=277">here</a>Files: This consists of 1 file: Belian_data_for_SAFE_upload_LQ_10Jan_2019.xlsxBelian_data_for_SAFE_upload_LQ_10Jan_2019.xlsxThis file contains dataset metadata and 2 data tables:<ol><li>Belian tree coordinates (described in worksheet Belian trees)Description: All trees with DBH >1cm were mapped in a 4-ha plot with XY coordinate rounded to nearest 1m. Each row corresponds to a Belian tree. DBH class was rounded down to the nearest 10cm, e.g. size 10, 20, 30 etc. Size class 1 refers to trees with DBH between 1 and 10cm. A swamp area on the north and northeast of ~1-ha was not assessible hence data defficient, but as Belian is not known to growth in water logged conditions the species was presumed to be absent in this swamp area.Number of fields: 9Number of data rows: 91Fields: <ul><li>X10: X coordinate of the southwest corner of 10x10 m subplot (Field type: Numeric)</li><li>Y10: Y coordinate of the southwest corner of 10x10 m subplot (Field type: Numeric)</li><li>X: X coordinate within the 10x10m subplot (local X) (Field type: Numeric)</li><li>Y: Y coordinate within the 10x10m subplot (local Y) (Field type: Numeric)</li><li>class: DBH rounded down to the nearest 10cm, e.g. size class 10, 20, 30 etc. Size class 1 refers to trees with DBH between 1 and 10cm (Field type: Numeric Trait)</li><li>remark: remark on whether tree is a resprout from the base of an older tree and other tree condition information (Field type: Comments)</li><li>collected_by: members of field teams: 4 groups of 4 students each (Field type: Comments)</li><li>X_coord: X coordinate within the 4-ha plot (global X) (Field type: Numeric)</li><li>Y_coord: Y coordinate within the 4-ha plot (global Y) (Field type: Numeric)</li></ul></li><li>Belian seedling plots (described in worksheet Belian seedlings)Description: Belian seedlings were surveyed in 540 5x5m subplots in the centre zone of the 4-ha plot. Each row corresponds to a seedling plot. In each seedling plot, healthy and damaged (primarily herbivory damage by mammals, defined as damaged when top shoot, i.e. apical meristem, was missing). Other variables recorded in seedling plots include slope angle and ground vegetation cover.Number of fields: 10Number of data rows: 540Fields: <ul><li>X10: X coordinate of the southwest corner of 10x10 m subplot (Field type: Numeric)</li><li>Y10: Y coordinate of the southwest corner of 10x10 m subplot (Field type: Numeric)</li><li>healthy: count of healthy Belian seedlings within 5x5m plot. "healthy" is defined as opposed to "damaged" (see next data column) (Field type: Abundance)</li><li>damaged: count of damaged Belian seedlings within 5x5m plot. "damaged" is defined as when a seedling suffered severe herbivory damage (including dead seedlings) with the apical meristem entirely missing (eaten by mammals) (Field type: Abundance)</li><li>slope: mean slope angle for each seedling subplot, a measure of habitat topography (Field type: Numeric)</li><li>veg_cover: ground vegetation cover for each subplot, a measure of habitat and interspecific competition (Field type: Ordered Categorical)</li><li>cache: when seedlings occur in dense clusters especially under fallen tree or in buttress crevices, this may indicate they were deposited there by rodents, i.e. caching (Field type: Comments)</li><li>remark: remark on habitat. seedling plots on footpath or water should be excluded from data analysis (Field type: Comments)</li><li>collected_by: members of field teams: 4 groups of 4 students each (Field type: Comments)</li><li>total: total number of Belian seedlings (Field type: Abundance)</li></ul></li></ol>Date range: 2018-01-29 to 2018-02-01Latitudinal extent: 4.7383 to 4.7383Longitudinal extent: 116.9713 to 116.9713Taxonomic coverage: 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.<div>Plantae &ensp;-&ensp;Tracheophyta &ensp;-&ensp;&ensp;-&ensp;Magnoliopsida &ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;Laurales &ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;Lauraceae &ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;Eusideroxylon &ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;Eusideroxylon zwageri </div>

opencc-by-4.0Jul 2019View details →

Retrotransposon insertions can initiate colorectal cancer and are associated with poor survival

This dataset is related to "Retrotransposon insertions can initiate colorectal cancer and are associated with poor survival" (Cajuso et al.). Abstract: Genomic instability pathways in colorectal cancer (CRC) have been extensively studied, but the role of retrotransposition in colorectal carcinogenesis remains poorly understood. Although retrotransposons are usually repressed, they become active in several human cancers, in particular those of the gastrointestinal tract. Here we characterize retrotransposon insertions in 202 colorectal tumor whole genomes and investigate their associations with molecular and clinical characteristics. We find highly variable retrotransposon activity among tumors and identify recurrent insertions in 15 known cancer genes. In approximately 1% of the cases we identify insertions in APC, likely to be tumor-initiating events. Insertions are positively associated with the CpG island methylator phenotype and the genomic fraction of allelic imbalance. Clinically, high number of insertions is independently associated with poor disease-specific survival. Sample description:  A signed informed consent was obtained for as many human participants as possible. In cases without a signed informed consent, an authorization from the National Supervisory Authority for Welfare and Health (Dnro 421/04/044/06, Dnro 8048/06.01.03.01/2014, Dnro 358/32/300/05, Dnro 1476/06.01.03.01/2012) was obtained as stated in Finnish law. The study has been reviewed by the Ethics Committee of the Hospital district of Helsinki and Uusima (Dnro 133/E8/03, 408/13/03/03/2009). Permission to use patient information was obtained from the National Institute for Health and Welfare (Dnro 53/07/2000, Dnro THL/1071/5.05.00/2011, Dnro THL/151/5.05.00/2017).   RNA sequencing: Total RNA from consecutive cryosections was extracted using RNeasy Mini Kit (Qiagen) from 34 tumors that displayed more than 50% of cancer cell percentage (HE staining of cryosections) and RNA integrity>6 (Agilent RNA 6000, Agilent 2100 Bioanalyzer). Paired-end RNA sequencing was performed on the Illumina Hiseq 2000. RNA-seq data was processed using Kallisto (version 0.43.0) software. Kallisto quantification was executed in paired-end mode and aligned against the Ensembl Human reference transcriptome (GRCh37_79). Quantification results from Kallisto were normalized and aggregated to gene-level utilizing sleuth (version 0.28.1) R package with default filtering settings.

Figure 6 in Monitoring the survival rate of released chukars: a case study in Elmalı Cedar Forest, Antalya, Turkey

Figure 6. Weekly numbers of monitored chukars released into the study area.

Figure 3 in Monitoring the survival rate of released chukars: a case study in Elmalı Cedar Forest, Antalya, Turkey

Figure 3. Monitoring station in the research area.

Figure 5 in Monitoring the survival rate of released chukars: a case study in Elmalı Cedar Forest, Antalya, Turkey

Figure 5. Responses of birds to project team members during the first month after release.

Figure 4 in Monitoring the survival rate of released chukars: a case study in Elmalı Cedar Forest, Antalya, Turkey

Figure 4. Natural predators of the chukars that visited the aviary on the first day of the study.

Figure 1 in Monitoring the survival rate of released chukars: a case study in Elmalı Cedar Forest, Antalya, Turkey

Figure 1. Geographical location of the study area.

Figure 2 in Monitoring the survival rate of released chukars: a case study in Elmalı Cedar Forest, Antalya, Turkey

Figure 2. Attaching transmitters to monitor chukar individuals.

opencc-by-4.0Apr 2024View details →

Figure 2 in Assessing population size and survival rate of Pelophylax bedriagae caralitanus, in a well-protected Nature Park in Türkiye

Figure 2. The view of the habitat surrounding Gölcük Lake.

opencc-by-4.0Aug 2024View details →

Source population and time spent in captivity affect survival and reproduction of long-distance translocated northern bobwhites

Northern bobwhites (Colinus virginianus) have become a species of great conservation priority because of widespread and ongoing population declines. Long-distance translocations are becoming increasingly used to access a source population with densities high enough to support translocation. Two key uncertainties exist regarding the efficacy of long-distance translocations: choosing a source population with adaptations that will be successful in a novel environment and mitigating the stress response common during the translocation process. We translocated bobwhites from the South Texas Plains and the Floridian Coastal Plain to a recipient site in the Floridian Coastal Plain in 2021 and 2022 to compare the survival and productivity of bobwhites translocated from two different source populations. We also evaluated how varying holding times during the translocation process influenced the success of the translocated individuals. Breeding season survival, nest propensity, and fecundity were greater for Florida resident and Florida translocated bobwhites relative to Texas translocated bobwhites. We observed high rates of mortality during the transport and holding processes, but holding time did not affect breeding season survival of Texas translocated bobwhites. Both nest success and fecundity of Texas translocated bobwhites were negatively affected by holding time. Bobwhites translocated long distances may have the adaptive capacity to be successful in novel environments, but the consequences of translocation stress can be detrimental. Future translocation planning should consider choosing source populations from similar ecoregions to simultaneously decrease translocation distances and potential stress from translocation.

opencc-by-4.0Sep 2024View details →

Predictors of outplanted marine bivalve survival in restoration: a review and synthesis

Global declines in marine shellfish have resulted in widespread efforts to restore populations. Previous research has predominantly focused on substrate-limited rather than recruitment-limited systems, yet given increased use of aquaculture-produced stock to restore marine bivalves, there is a need to understand differences in the survival of hatchery-produced and translocated wild stock. We conducted a systematic review and synthesis of studies that quantified the survival of outplanted marine bivalves. The systematic review identified 893 unique stocking events across 111 studies for 29 species across 10 families. Most research has occurred in temperate regions (73%), across four bivalve families (Ostreidae 37%, Pectinidae 20%, Veneridae 16%, and Mytilidae 11%). More stockings have outplanted hatchery-produced (66%) than translocated stock (34%). We conducted quantitative analyses for five species to determine how stock origin, size at outplant, outplant density, substrate co-deployment, predator exclusion, and time since outplant influences survival. Survival consistently decreased through time across all species. Substrate co-deployment, stock origin, and size at outplant did not influence survival, while predator exclusion and outplant density affected some species. Our analyses broadly demonstrate variability in the survival of outplanted bivalves through time, however predicted survival was poor after two years (<3%). Generally low survival highlights difficulties associated with conducting scalable restoration in recruitment-limited systems. Based on our findings, using hatchery-produced stock, mitigating predation, and outplanting epifaunal bivalves at high densities may increase survival probabilities when outplanting stock for restoration. Further exploration is needed to understand whether use of aquaculture-produced stock results in similar ecosystem structure, function, and service provisioning to natural and restored reefs in recruitment-limited systems.   This data set is in support of the article published with the same title in the Journal of Applied Ecology.

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>