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Under harsh Pleistocene climates, migration and other forms of seasonally patterned landscape use were likely critical for reproductive success of mastodons ( Mammut americanum ) and other megafauna. However, little is known about how their geographic ranges and mobility fluctuated seasonally or changed with sexual maturity. We used a spatially explicit movement model that coupled strontium and oxygen isotopes from two serially sampled intervals (5+ adolescent years and 3+ adult years) in a male mastodon tusk to test for changes in landscape use associated with maturation and reproductive phenology. The mastodon’s early adolescent home range was geographically restricted, with no evidence of seasonal preferences. Following inferred separation from the matriarchal herd (starting age 12 y), the adolescent male’s mobility increased as landscape use expanded away from his natal home range (likely central Indiana). As an adult, the mastodon’s monthly movements increased further. Landscape use also became seasonally structured, with some areas, including northeast Indiana, used only during the inferred mastodon mating season (spring/summer). The mastodon died in this area (>150 km from his core, nonsummer range) after sustaining a craniofacial injury consistent with a fatal blow from a competing male’s tusk during a battle over access to mates. Northeast Indiana was likely a preferred mating area for this individual and may have been regionally significant for late Pleistocene mastodons. Similarities between mammutids and elephantids in herd structure, tusk dimorphism, tusk function, and the geographic component of male maturation indicate that these traits were likely inherited from a common ancestor.

(2015) 'A Bayesian mixed shrinkage prior procedure for spatialstochastic basis selection and evaluation of gPC expansions : applications to elliptic SPDEs.', Journal of computational physics., 284 . pp. 528-546. Further information on publisher's website: Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. AbstractWe propose a new fully Bayesian method to efficiently obtain the spectral representation of a spatial random field, which can conduct spatial-stochastic basis selection and evaluation of generalized Polynomial Chaos (gPC) expansions when the number of the available basis functions is significantly larger than the size of the training data-set. We develop a fully Bayesian stochastic procedure, called mixed shrinkage prior (MSP), which performs both basis selection and coefficient evaluation simultaneously. MSP involves assigning a prior probability on the gPC structure and assigning conjugate priors on the expansion coefficients that can be thought of as mixtures of Ridge-LASSO shrinkage priors, in augmented form. The method offers a number of advantages over existing compressive sensing methods in gPC literature, such that it recovers possible sparse structures in both stochastic and spatial domains while the resulted expansion can be re-used directly to economically obtain results at any spatial input values. Yet, it inherits all the advantages of Bayesian model uncertainty methods, e.g. accounts for uncertainty about basis significance and provides interval estimation through posterior distributions. A unique highlight of the MSP procedure is that it can address heterogeneous sparsity in the spatial domain for different random dimensions. Furthermore, it yields a compromise between Ridge and LASSO regressions, and hence combines a weak (l 2 -norm) and strong (l 1 -norm) shrinkage, in an adaptive, data-driven manner. We demonstrate the good performance of the proposed method, and compare it against other existing compressive sensing ones on elliptic stochastic partial differential equations.