Mountain goat survival in coastal Alaska: Effects of age,sex, and climate

Ecological theory predicts that individual survival should vary between sex and age categories due to differences in allocation of nutritional resources for growth and reproductive activities. During periods of environmental stress, such relationships may be exacerbated, and affect sex and age classes differently. We evaluated support for hypotheses about the relative roles of sex, age, and winter and summer climate on the probability of mountain goat (Oreamnos americanus) survival in coastal Alaska. Specifically, we used known-fates analyses (Program MARK) to model the effects of age, sex, and climatic variation on survival using data collected from 279 radio-marked mountain goats (118 M, 161 F) in 9 separate study areas during 1977–2008. Models including age, sex, winter snowfall, and average daily summer temperature (during Jul–Aug) best explained variation in survival probability of mountain goats. Specifically, our findings revealed that old animals (9+ yr) have lower survival than younger animals. In addition, males tended to have lower survival than females, though differences only existed among prime-aged adult (5–8 yr) and old (9+ yr) age classes. Winter climate exerted the strongest effects on mountain goat survival; summer climate, however, was significant and principally influenced survival during the following winter via indirect effects. Furthermore, old animals were more sensitive to the effects of winter conditions than young or prime-aged animals. These findings detail how climate interacts with sex and age characteristics to affect mountain goat survival. Critically, we provide baseline survival rate statistics across various age, sex, and climate scenarios. These data will assist conservation and management of mountain goats by enabling detailed, model-based demographic forecasting of human and/or climate-based population impacts. © 2011 The Wildlife Society.     

Selection of haplotype variables from a high-density marker map for genomic prediction

Background

Using haplotype blocks as predictors rather than individual single nucleotide polymorphisms (SNPs) may improve genomic predictions, since haplotypes are in stronger linkage disequilibrium with the quantitative trait loci than are individual SNPs. It has also been hypothesized that an appropriate selection of a subset of haplotype blocks can result in similar or better predictive ability than when using the whole set of haplotype blocks. This study investigated genomic prediction using a set of haplotype blocks that contained the SNPs with large effects estimated from an individual SNP prediction model. We analyzed protein yield, fertility and mastitis of Nordic Holstein cattle, and used high-density markers (about 770k SNPs). To reach an optimum number of haplotype variables for genomic prediction, predictions were performed using subsets of haplotype blocks that contained a range of 1000 to 50 000 main SNPs.

Results

The use of haplotype blocks improved the prediction reliabilities, even when selection focused on only a group of haplotype blocks. In this case, the use of haplotype blocks that contained the 20 000 to 50 000 SNPs with the highest effect was sufficient to outperform the model that used all individual SNPs as predictors (up to 1.3 % improvement in prediction reliability for mastitis, compared to individual SNP approach), and the achieved reliabilities were similar to those using all haplotype blocks available in the genome data (from 0.6 % lower to 0.8 % higher reliability).

Conclusions

Haplotype blocks used as predictors can improve the reliability of genomic prediction compared to the individual SNP model. Furthermore, the use of a subset of haplotype blocks that contains the main SNP effects from genomic data could be a feasible approach to genomic prediction in dairy cattle, given an increase in density of genotype data available. The predictive ability of the models that use a subset of haplotype blocks was similar to that obtained using either all haplotype blocks or all individual SNPs, with the benefit of having a much lower computational demand.     

Black Bears Select Large Woody Structures for Dens in Southeast Alaska

Timber interests target coastal temperate rainforests, and within them stands composed of large trees potentially selected by American black bears (Ursus americanus) for denning. We identified the location of 75 black bear dens (used ≥14 days) in an intensively logged area on Prince of Wales Island, Alaska, USA. We ground-visited a subset (n = 43) of these sites to measure the diameter of living trees and woody structures used by black bears for denning. We contrasted dens with random trees available in the study area in a series of models to characterize black bear den selection. All but 1 of 43 ground-visited dens were located within woody structures, and all but 2 of these structures were >1 m diameter at breast height (dbh). We built resource selection functions (RSFs) to investigate black bear den selection across a range of spatial scales, though estimated selection was generally scale invariant. Black bears strongly selected large-diameter woody structures (dead or alive) as dens at the home-range scale, with the estimated relative strength of selection (RSS) for a 2-m-diameter tree approximately 166 times that of a 1-m-diameter tree. The estimated RSS of logged forest was unexpectedly greater (2.75 times) than that of the remaining commercially valuable old-growth forest. Selection for den structures within stumps in previously logged stands suggests features of the woody structure may be of greater importance to black bears selecting dens than attributes of the surrounding forest. There was no observed correlation between bear den selection and second-growth age, indicating that stumps may persist as suitable dens well after logging. Because denning is an important part of their life history and the denning structures used by black bears on Prince of Wales Island will eventually decay, retaining habitat value for bears in areas intensively managed for timber harvest requires strategies to recruit very large, old trees throughout the landscape. © 2021 The Wildlife Society.     

Mesenchymal Stem Cells Derived from Adipose Tissue vs Bone Marrow: In Vitro Comparison of Their Tropism towards Gliomas

Introduction

Glioblastoma is the most common primary malignant brain tumor, and is refractory to surgical resection, radiation, and chemotherapy. Human mesenchymal stem cells (hMSC) may be harvested from bone marrow (BMSC) and adipose (AMSC) tissue. These cells are a promising avenue of investigation for the delivery of adjuvant therapies. Despite extensive research into putative mechanisms for the tumor tropism of MSCs, there remains no direct comparison of the efficacy and specificity of AMSC and BMSC tropism towards glioma.

Methods

Under an IRB-approved protocol, intraoperative human Adipose MSCs (hAMSCs) were established and characterized for cell surface markers of mesenchymal stem cell origin in conjunction with the potential for tri-lineage differentiation (adipogenic, chondrogenic, and osteogenic). Validated experimental hAMSCs were compared to commercially derived hBMSCs (Lonza) and hAMSCs (Invitrogen) for growth responsiveness and glioma tropism in response to glioma conditioned media obtained from primary glioma neurosphere cultures.

Results

Commercial and primary culture AMSCs and commercial BMSCs demonstrated no statistically significant difference in their migration towards glioma conditioned media in vitro. There was statistically significant difference in the proliferation rate of both commercial AMSCs and BMSCs as compared to primary culture AMSCs, suggesting primary cultures have a slower growth rate than commercially available cell lines.

Conclusions

Adipose- and bone marrow-derived mesenchymal stem cells have similar in vitro glioma tropism. Given the well-documented ability to harvest larger numbers of AMSCs under local anesthesia, adipose tissue may provide a more efficient source of MSCs for research and clinical applications, while minimizing patient morbidity during cell harvesting.     

Isolation and Immunocytochemical Characterization of Three Tachykinin-Related Peptides from the Mosquito,Culex salinarius

Three myotropic peptides belonging to the Arg-amide insect tachykinin family were isolated from whole-body extracts of the mosquito, Culex salinarius. The peptides, APSGFMGMR-NH₂, APYGFTGMR-NH₂ and APSGFFGMR-NH₂ (designated culetachykinin I, II, and III) were isolated and purified on the basis of their ability to stimulate muscle contractions of isolated Leucophaea maderae hindgut. Biologically inactive methionine sulfoxides of two of the three peptides were isolated using an ELISA system based upon antiserum raised against APYGFTGMR-NH₂ and identified with mass spectrometry. Immunocytochemistry localized these peptides in cells in the brain, antennae, subesophageal, thoracic and abdominal ganglion, proventriculus and midgut. Nerve tracts containing these peptides were found in the median nerve of the brain, central body, nervi corpus cardiaci, cervical nerve, antennal lobe and on the surface of the midgut.     

Genomic prediction of genetic merit using LD-based haplotypes in the Nordic Holstein population

Background

A haplotype approach to genomic prediction using high density data in dairy cattle as an alternative to single-marker methods is presented. With the assumption that haplotypes are in stronger linkage disequilibrium (LD) with quantitative trait loci (QTL) than single markers, this study focuses on the use of haplotype blocks (haploblocks) as explanatory variables for genomic prediction. Haploblocks were built based on the LD between markers, which allowed variable reduction. The haploblocks were then used to predict three economically important traits (milk protein, fertility and mastitis) in the Nordic Holstein population.

Results

The haploblock approach improved prediction accuracy compared with the commonly used individual single nucleotide polymorphism (SNP) approach. Furthermore, using an average LD threshold to define the haploblocks (LD≥0.45 between any two markers) increased the prediction accuracies for all three traits, although the improvement was most significant for milk protein (up to 3.1 % improvement in prediction accuracy, compared with the individual SNP approach). Hotelling’s t-tests were performed, confirming the improvement in prediction accuracy for milk protein. Because the phenotypic values were in the form of de-regressed proofs, the improved accuracy for milk protein may be due to higher reliability of the data for this trait compared with the reliability of the mastitis and fertility data. Comparisons between best linear unbiased prediction (BLUP) and Bayesian mixture models also indicated that the Bayesian model produced the most accurate predictions in every scenario for the milk protein trait, and in some scenarios for fertility.

Conclusions

The haploblock approach to genomic prediction is a promising method for genomic selection in animal breeding. Building haploblocks based on LD reduced the number of variables without the loss of information. This method may play an important role in the future genomic prediction involving while genome sequences.