A harvest framework for a recovering American black bear population

Having reproducible and transparent science-based processes in wildlife management ensures the integrity of decision making. These processes are particularly important when establishing harvest frameworks, as guiding information in the peer-reviewed literature is limited. We provide an example using multiple data sets, whose products guided aspects of the development of a harvest framework for a population of recolonizing American black bears (Ursus americanus) in Missouri, USA. To characterize the spatial distribution of harvest, we used 10 years (2010–2019) of black bear global positioning system (GPS) location data and 30 years (1991–2020) of sightings data to assess spatial vulnerability to harvest as the intersection among information on bear occurrence, bear sightings, and hunter land-use tendencies (i.e., the avoidance of steep slopes, large distances from roads). We then used the spatial vulnerability assessment, information on the distribution of public and private lands, and easily discernable boundaries (i.e., major highways, rivers) to suggest boundaries for bear management zones. Additionally, to identify the timing of harvest that would limit female harvest bias, we assessed the temporal vulnerability of harvest using sex-based changes in average daily step lengths and monthly utilization distribution sizes during fall. Black bear occurrence and sighting propensity was greater in southwestern Missouri, and potential hunter land use appeared pervasive across the landscape given the lack of landscape features that would disincentivize use. Given the influence of black bear occurrence and sighting propensity, spatial harvest vulnerability diminished from southern and southeastern to central portions of Missouri, with areas north of the Missouri River not a part of the established black bear range. We consequently divided areas south of the Missouri River into 3 black bear management zones: a small southwestern zone with primarily private lands and high harvest vulnerability, a southeastern zone that encompassed considerable public lands and moderate amounts of vulnerability, and a central zone that was composed mainly of areas of low vulnerability. Temporally, males did not exhibit movement-based changes, but females became less active after the first week of October and used 63.9% less area through fall. Based on movements rates of males and females, a hunting season after the first week of October could reduce the likelihood of females being harvested. Harvests from the black bear harvest season in 2021 suggest that the proportion of bears harvested in each zone was similar in distribution to the proportion of permits allocated across zones with no harvest sex bias, which was aligned with agency goals. Animal movement and space use data products can guide harvest framework decision-making.     

Genetic variation and population structure in native Americans

We examined genetic diversity and population structure in the American landmass using 678 autosomal microsatellite markers genotyped in 422 individuals representing 24 Native American populations sampled from North, Central, and South America. These data were analyzed jointly with similar data available in 54 other indigenous populations worldwide, including an additional five Native American groups. The Native American populations have lower genetic diversity and greater differentiation than populations from other continental regions. We observe gradients both of decreasing genetic diversity as a function of geographic distance from the Bering Strait and of decreasing genetic similarity to Siberians--signals of the southward dispersal of human populations from the northwestern tip of the Americas. We also observe evidence of: (1) a higher level of diversity and lower level of population structure in western South America compared to eastern South America, (2) a relative lack of differentiation between Mesoamerican and Andean populations, (3) a scenario in which coastal routes were easier for migrating peoples to traverse in comparison with inland routes, and (4) a partial agreement on a local scale between genetic similarity and the linguistic classification of populations. These findings offer new insights into the process of population dispersal and differentiation during the peopling of the Americas.     

Protein secondary structure prediction for a single-sequence using hidden semi-Markov models

Background  

The accuracy of protein secondary structure prediction has been improving steadily towards the 88% estimated theoretical limit. There are two types of prediction algorithms: Single-sequence prediction algorithms imply that information about other (homologous) proteins is not available, while algorithms of the second type imply that information about homologous proteins is available, and use it intensively. The single-sequence algorithms could make an important contribution to studies of proteins with no detected homologs, however the accuracy of protein secondary structure prediction from a single-sequence is not as high as when the additional evolutionary information is present.     

Bayesian clustering using hidden Markov random fields in spatial population genetics

We introduce a new Bayesian clustering algorithm for studying population structure using individually geo-referenced multilocus data sets. The algorithm is based on the concept of hidden Markov random field, which models the spatial dependencies at the cluster membership level. We argue that (i) a Markov chain Monte Carlo procedure can implement the algorithm efficiently, (ii) it can detect significant geographical discontinuities in allele frequencies and regulate the number of clusters, (iii) it can check whether the clusters obtained without the use of spatial priors are robust to the hypothesis of discontinuous geographical variation in allele frequencies, and (iv) it can reduce the number of loci required to obtain accurate assignments. We illustrate and discuss the implementation issues with the Scandinavian brown bear and the human CEPH diversity panel data set.     

Genetic population structure of the Dakota skipper (Lepidoptera: Hesperia dacotae): A North American native prairie obligate

A range-wide survey of Dakotaskipper (Hesperia dacotae) populationsassessed levels of genetic variability andgeographic scale of population structure inthis species of conservation concern. Thisspecies exists on isolated patches of nativetall- and mixed-grass prairie within a highlymodified landscape dominated by agriculture. It has been extirpated from the southernportion of its range and has sufferedrange-wide declines. Nine populations weresampled from western Minnesota, eastern SouthDakota, and southern Manitoba. Starch gelelectrophoresis was used to resolve 21 isozymeloci in 278 Dakota skippers. Dakota skipperpopulations were approximately as variable asother lepidopterans found in isolated habitats. Genetic distances indicated that Manitobapopulations were somewhat distinct from ones inMinnesota and South Dakota. Isolation-by-distance was detected range-wideand among the seven southern-most populations. Genetically effective immigration rates weresmall at both range-wide and regional scalesand effective populations sizes were lowsuggesting that Dakota skipper populations aregenetically isolated from one another, althoughthey were likely more connected in the recentpast. Genotype assignment tests revealed twoclusters of populations in Minnesota and SouthDakota that were not apparent from theisolation-by-distance results. Significantheterozygote deficiencies relative toHardy-Weinberg expectations and high inbreedingcoefficients suggest structure within samplelocations. Management recommendations includethe maximization of effective population sizein each Dakota skipper population to offset theeffects of drift and habitat corridors in somecases. Habitat management should consider thewithin-site population structure and possibletemporal population structure detected in thisstudy.     

Genetic variation and fine-scale population structure in American pikas across a human-modified landscape

Natural resource extraction can represent a major human modification to the landscape. Habitat reclamation is becoming an increasingly important strategy for abating the loss of biodiversity associated with these developments; however, the demographic and genetic consequences of colonizing artificial habitat remain unknown in many species. Here, we investigated the genetic consequences of landscape modifications for the American pika (Ochotona princeps) relative to two major developments in British Columbia, Canada: a large open-pit copper mine (Highland Valley Copper) under partial reclamation and a bisecting major highway (97C). We assessed microsatellite genotypic data for 109 individuals across 15 sites located either within the mine on artificial habitat or on adjacent natural habitat both north and south of the highway. There were no significant differences in levels of heterozygosity, allelic richness or inbreeding between natural (n?=?7) and artificial sites (n?=?8). However, pikas residing on artificial habitat exhibited significantly higher relatedness estimates. Bayesian clustering analyses revealed two distinct genetic units corresponding to north and south of the highway, with further substructure detected in the south. Likewise, high genetic friction was detected in the central region of the area, largely corresponding to the highway and modified landscape associated with the mine. At a finer scale, pairwise estimates of differentiation and migration rates suggest little gene flow may be occurring among sites across the sampling area, with some evidence for directional migration from artificial to natural sites. Overall, artificial habitat has been successful in promoting occupancy for American pikas, however, barriers to gene flow likely associated with resource extraction and road construction limit connectivity across the landscape.     

Evolutionary information hidden in a single protein structure

The knowledge of conserved sequences in proteins is valuable in identifying functionally or structurally important residues. Generating the conservation profile of a sequence requires aligning families of homologous sequences and having knowledge of their evolutionary relationships. Here, we report that the conservation profile at the residue level can be quantitatively derived from a single protein structure with only backbone information. We found that the reciprocal packing density profiles of protein structures closely resemble their sequence conservation profiles. For a set of 554 nonhomologous enzymes, 74% (408/554) of the proteins have a correlation coefficient > 0.5 between these two profiles. Our results indicate that the three-dimensional structure, instead of being a mere scaffold for positioning amino acid residues, exerts such strong evolutionary constraints on the residues of the protein that its profile of sequence conservation essentially reflects that of its structural characteristics.     

Surname analysis of the Corsican population reveals an agreement with geographical and linguistic structure

The surname is a cultural trait that is extremely useful for historical and linguistic studies and can effectively be used as a genetic marker. In many human populations the surname is inherited in the paternal lineage, and can therefore be considered a marker for the Y chromosome. In this study, surnames were recorded from the white pages of telephone directories in current use in Corsica in 1993. All surnames present in thirteen villages scattered over the whole island and covering the main historical regions were transcribed. Surname variability was found to be higher in coastal villages, and lower in more isolated communities. The isonymy detected among the thirteen villages allowed the calculation of kinship values, visualized in a tree showing two main clusters, one referring to the northern villages and one encompassing the villages of the south. The pattern reflects the administrative division of the island, with the exception of Vico, which belongs to the southern administrative region but is geographically close to the northern villages, and Ghisoni, which belongs to the northern district but is more similar to the village of Bastelica in the southern district. The data presented here show a structure in the surname distribution that is in substantial agreement with the geographical patterns. The kinship values are consistent with a moderated gene flow among villages producing a surname structure according to the geographic features of the territory.     

Folding Trp-cage to NMR resolution native structure using a coarse-grained protein model

We develop a coarse-grained protein model with a simplified amino acid interaction potential. Using this model, we perform discrete molecular dynamics folding simulations of a small 20-residue protein--Trp-cage--from a fully extended conformation. We demonstrate the ability of the Trp-cage model to consistently reach conformations within 2-angstroms backbone root-mean-square distance from the corresponding NMR structures. The minimum root-mean-square distance of Trp-cage conformations in simulations can be <1 angstroms. Our findings suggest that, at least in the case of Trp-cage, a detailed all-atom protein model with a molecular mechanics force field is not necessary to reach the native state of a protein. Our results also suggest that the success of folding Trp-cage in our simulations and in the reported all-atom molecular mechanics simulation studies may be mainly due to the special stabilizing features specific to this miniprotein.     

Joint model selection of marginal mean regression and correlation structure for longitudinal data with missing outcome and covariates

This work develops a joint model selection criterion for simultaneously selecting the marginal mean regression and the correlation/covariance structure in longitudinal data analysis where both the outcome and the covariate variables may be subject to general intermittent patterns of missingness under the missing at random mechanism. The new proposal, termed “joint longitudinal information criterion” (JLIC), is based on the expected quadratic error for assessing model adequacy, and the second‐order weighted generalized estimating equation (WGEE) estimation for mean and covariance models. Simulation results reveal that JLIC outperforms existing methods performing model selection for the mean regression and the correlation structure in a two stage and hence separate manner. We apply the proposal to a longitudinal study to identify factors associated with life satisfaction in the elderly of Taiwan.     

On the effect of misclassification on bias of perfectly measured covariates in regression

This note clarifies under what conditions a naive analysis using a misclassified predictor will induce bias for the regression coefficients of other perfectly measured predictors in the model. An apparent discrepancy between some previous results and a result for measurement error of a continuous variable in linear regression is resolved. We show that similar to the linear setting, misclassification (even when not related to the other predictors) induces bias in the coefficients of the perfectly measured predictors, unless the misclassified variable and the perfectly measured predictors are independent. Conditional and asymptotic biases are discussed in the case of linear regression, and explored numerically for an example relating birth weight to the weight and smoking status of the mother.     

Estimating population size for a continuous time frailty model with covariates in a capture-recapture study

A continuous time frailty capture-recapture model is proposed for estimating population size of a closed population with the use of observed covariates to explain individuals' heterogeneity in presence of a random effect. A conditional likelihood approach is used to derive the estimate of parameters, and the Horvitz-Thompson estimator is adopted to estimate the unknown population size. Asymptotic normality of the estimates is obtained. Simulation results and a real example show that the proposed method works satisfactorily.     

Evidence for a single-chain structure of native human ceruloplasmin using sodium dodecyl sulfate-polyacrylamide-crossed immunoelectrophoresis

A simple method is described for the analysis of proteins by sodium dodecyl sulfate-polyacrylamide gel electrophoresis combined with crossed immunoelectrophoresis. The technique allows the identification of specific proteins and estimation of their molecular weights after the electrophoresis of complex mixtures such as blood plasma. Human ceruloplasmin was estimated to have a molecular weight of 128,000 using this method. Reduction of plasma with 2-mercaptoethanol in the presence of protease inhibitors did not result in dissociation of ceruloplasmin into detectable smaller subunits, suggesting that native ceruloplasmin is composed of a single polypeptide chain.     

Regression analysis when covariates are regression parameters of a random effects model for observed longitudinal measurements

We consider regression analysis when covariate variables are the underlying regression coefficients of another linear mixed model. A naive approach is to use each subject's repeated measurements, which are assumed to follow a linear mixed model, and obtain subject-specific estimated coefficients to replace the covariate variables. However, directly replacing the unobserved covariates in the primary regression by these estimated coefficients may result in a significantly biased estimator. The aforementioned problem can be evaluated as a generalization of the classical additive error model where repeated measures are considered as replicates. To correct for these biases, we investigate a pseudo-expected estimating equation (EEE) estimator, a regression calibration (RC) estimator, and a refined version of the RC estimator. For linear regression, the first two estimators are identical under certain conditions. However, when the primary regression model is a nonlinear model, the RC estimator is usually biased. We thus consider a refined regression calibration estimator whose performance is close to that of the pseudo-EEE estimator but does not require numerical integration. The RC estimator is also extended to the proportional hazards regression model. In addition to the distribution theory, we evaluate the methods through simulation studies. The methods are applied to analyze a real dataset from a child growth study.     

Numerical equivalence of imputing scores and weighted estimators in regression analysis with missing covariates

Imputation, weighting, direct likelihood, and direct Bayesian inference (Rubin, 1976) are important approaches for missing data regression. Many useful semiparametric estimators have been developed for regression analysis of data with missing covariates or outcomes. It has been established that some semiparametric estimators are asymptotically equivalent, but it has not been shown that many are numerically the same. We applied some existing methods to a bladder cancer case-control study and noted that they were the same numerically when the observed covariates and outcomes are categorical. To understand the analytical background of this finding, we further show that when observed covariates and outcomes are categorical, some estimators are not only asymptotically equivalent but also actually numerically identical. That is, although their estimating equations are different, they lead numerically to exactly the same root. This includes a simple weighted estimator, an augmented weighted estimator, and a mean-score estimator. The numerical equivalence may elucidate the relationship between imputing scores and weighted estimation procedures.     

Environmentally-based maternal effects: a hidden force in insect population dynamics?

Summary The nutritional environment of the parental generation of the polyphagous gypsy moth, Lymantria dispar, can significantly influence the growth and reproductive potential of the next generation through environmentally-based maternal effects. In the first experiment, members of the parental generation were reared on red oak trees (Quercus rubra L.) with known defoliation and phenolic levels. Diet in the offspring generation was homogeneous (synthetic diet). With genetic effects accounted for 1) offspring attained greater pupal weights when their mothers fed on trees with higher leaf damage levels, 2) daughters had a shorter prefeeding stage, a trait associated with dispersal tendency, when their mothers experienced higher condensed tannin levels, and 3) sons had lower pupal weights when their mothers experienced greater condensed tannin levels. In the second experiment, members of the parental generation were reared on either red or black oak (Q. velutina) trees. Offspring of each mother were divided among four diets: red oak, chestnut oak (Q. prinus L.), a standard synthetic diet, and a low-protein synthetic diet. The parental host species accounted for 24% of the variation in daughters' development time; offspring diet accounted for 52%. When mothers were reared on black oak rather than red oak, their offspring developed significantly faster when the F1 diet was chestnut oak. Environmentally-based maternal effects can significantly influence the expression of offspring dispersal potential, growth rate, and offspring fecundity. These traits contribute to natality and survival in natural populations and, hence, to population growth potential. Theoretical models of insect population dynamics demonstrate that the presence of a time delay in a density dependent response can induce destabilization. Maternal effects act on a time delay and may participate in the destabilization characteristic of outbreak species.