Causes and short-term consequences of variation in milk composition in wild sheep

Ontogeny shapes individual dietary specialization in female European brown bears (Ursus arctos)

Individual dietary specialization, where individuals occupy a subset of a population's wider dietary niche, is a key factor determining a species resilience against environmental change. However, the ontogeny of individual specialization, as well as associated underlying social learning, genetic, and environmental drivers, remain poorly understood. Using a multigenerational dataset of female European brown bears (Ursus arctos) followed since birth, we discerned the relative contributions of environmental similarity, genetic heritability, maternal effects, and offspring social learning from the mother to individual specialization. Individual specialization accounted for 43% of phenotypic variation and spanned half a trophic position, with individual diets ranging from omnivorous to carnivorous. The main determinants of dietary specialization were social learning during rearing (13%), environmental similarity (5%), maternal effects (11%), and permanent between-individual effects (9%), whereas the contribution of genetic heritability (3%) was negligible. The trophic position of offspring closely resembled the trophic position of their mothers during the first 3-4 years of independence, but waned with increasing time since separation. Our study shows that social learning and maternal effects were more important for individual dietary specialization than environmental composition. We propose a tighter integration of social effects into studies of range expansion and habitat selection under global change.

Robust Physiological Metrics From Sparsely Sampled Networks

Physiological and biochemical networks are highly complex, involving thousands of nodes as well as a hierarchical structure. True network structure is also rarely known. This presents major challenges for applying classical network theory to these networks. However, complex systems generally share the property of having a diffuse or distributed signal. Accordingly, we should predict that system state can be robustly estimated with sparse sampling, and with limited knowledge of true network structure. In this review, we summarize recent findings from several methodologies to estimate system state via a limited sample of biomarkers, notably Mahalanobis distance, principal components analysis, and cluster analysis. While statistically simple, these methods allow novel characterizations of system state when applied judiciously. Broadly, system state can often be estimated even from random samples of biomarkers. Furthermore, appropriate methods can detect emergent underlying physiological structure from this sparse data. We propose that approaches such as these are a powerful tool to understand physiology, and could lead to a new understanding and mapping of the functional implications of biological variation.

Milk composition in a wild mammal: a physiological signature of phenological changes

Understanding how spring phenology influences early life can provide important insights into drivers of future development and survival. We combined unique, long-term data from a bighorn sheep population and satellite-derived phenology indices to quantify the relative importance of maternal and environmental influences on milk composition and lamb overwinter survival. Based on 216 milk samples from 34 females monitored over 6 years, we found that longer snow-free and vegetation growing seasons increased milk fatty acid, iron and lactose concentrations. Structural equation modelling revealed no causality between milk energy content, lamb weaning mass and lamb overwinter survival. Our results suggest that spring conditions can affect milk energy content, but we did not detect any effect on lamb overwinter survival either directly or indirectly through lamb weaning mass. The effect of green-up date on milk composition and energy content suggests that herbivores living in seasonal environments, such as the bighorn sheep, might rely on a strategy intermediate between 'capital' and 'income' breeding when energy demands are high.

Sources of variation in maternal allocation in a long-lived mammal

Life history theory predicts allocation of energy to reproduction varies with maternal age, but additional maternal features may be important to the allocation of energy to reproduction.

We aimed to characterize age‐specific variation in maternal allocation and assess the relationship between maternal allocation and other static and dynamic maternal features.

Mass measurements of 531 mothers and pups were used with Bayesian hierarchical models to explain the relationship between diverse maternal attributes and both the proportion of mass allocated by Weddell seal mothers, and the efficiency of mass transfer from mother to pup during lactation as well as the weaning mass of pups.

Our results demonstrated that maternal mass was strongly and positively associated with the relative reserves allocated by a mother and a pup's weaning mass but that the efficiency of mass transfer declines with maternal parturition mass. Birthdate was positively associated with proportion mass allocation and pup weaning mass, but mass transfer efficiency was predicted to be highest at the mean birthdate. The relative allocation of maternal reserves declined with maternal age but the efficiency of mass transfer to pups increases, suggestive of selective disappearance of poor‐quality mothers.

These findings highlight the importance of considering multiple maternal features when assessing variation in maternal allocation.

Causes and short-term consequences of variation in milk composition in wild sheep

Ecologists seek to understand the fitness consequences of variation in physiological markers, under the hypothesis that physiological state is linked to variability in individual condition and life history. Thus, ecologists are often interested in estimating correlations between entire suites of correlated traits, or biomarkers, but sample size limitations often do not allow us to do this properly when large numbers of traits or biomarkers are considered. Latent variables are a powerful tool to overcome this complexity. Recent statistical advances have enabled a new class of multivariate models-multivariate hierarchical modelling (MHM) with latent variables-which allow to statistically estimate unstructured covariances/correlations among traits with reduced constraints on the number of degrees of freedom to account in the model. It is thus possible to highlight correlated structures in potentially very large numbers of traits. Here, we apply MHM to evaluate the relative importance of individual differences and environmental effects on milk composition and identify the drivers of this variation. We ask whether variation in bighorn sheep milk affects offspring fitness. We evaluate whether mothers show repeatable individual differences in the concentrations of 11 markers of milk composition, and we investigate the relative importance of annual variability, maternal identity and morphological traits in structuring milk composition. We then use variance estimates to investigate how a subset of repeatable milk markers influence lamb summer survival. Repeatability of milk markers ranged from 0.05 to 0.64 after accounting for year-to-year variations. Milk composition was weakly but significantly associated with maternal mass in June and September, summer mass gain and winter mass loss. Variation explained by year-to-year fluctuations ranged from 0.07 to 0.91 suggesting a strong influence of environmental variability on milk composition. Milk composition did not affect lamb survival to weaning. Using joint models in ecological, physiological or behavioural contexts has the major advantage of decomposing a (co)variance/correlation matrix while being estimated with fewer parameters than in a "traditional" mixed-effects model. The joint models presented here complement a growing list of tools to analyse correlations at different hierarchical levels separately and may thus represent a partial solution to the conundrum of physiological complexity.