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Background Despite evidence that liver resection improves survival in patients with colorectal cancer liver metastases (CRCLM) and may be potentially curative, there are no population‐level data examining utilization and predictors of liver resection in the United States. Methods This is a population‐based cross‐sectional study. We ed data on patients with synchronous CRCLM using California Cancer Registry from 2000 to 2012 and linked the records to the Office of Statewide Health Planning Inpatient Database. Quantum Geographic Information System (QGIS) was used to map liver resection rates to California counties. Patient‐ and hospital‐level predictors were determined using mixed‐effects logistic regression. Results Of the 24 828 patients diagnosed with stage‐IV colorectal cancer, 16 382 (70%) had synchronous CRCLM. Overall liver resection rate for synchronous CRCLM was 10% (county resection rates ranging from 0% to 33%) with no improvement over time. There was no correlation between county incidence of synchronous CRCLM and rate of resection (R2 = .0005). On multivariable analysis, sociodemographic and treatment‐initiating‐facility characteristics were independently associated with receipt of liver resection after controlling for patient disease‐ and comorbidity‐related factors. For instance, odds of liver resection decreased in patients with black race (OR 0.75 vs white) and Medicaid insurance (OR 0.62 vs private/PPO); but increased with initial treatment at NCI hospital (OR 1.69 vs Non‐NCI hospital), or a high volume (10 + cases/year) (OR 1.40 vs low volume) liver surgery hospital. Conclusion In this population‐based study, only 10% of patients with liver metastases underwent liver resection. Furthermore, the study identifies wide variations and significant population‐level disparities in the utilization of liver resection for CRCLM in California. In this population‐based study, only 10% of patients with liver metastases underwent liver reseaction. Further, the study identifies wide variations and significant population‐level disparities in the utilization of liver resection for CRCLM in California.

In this viewpoint, we highlight two issues that we believe deserve more emphasize in the ongoing discussions on fish welfare. On the basis of the naturally or artificially selected proximate behavioural mechanism, an animal today may attempt to reach goals that are not necessarily equal to the functions that yielded higher fitness in the past process of evolution. These attempts lead to proximate ‘needs’ of animals. Accordingly, we can increase fish welfare by asking what goals fish are trying to reach (‘wanting’) and which results will satisfy their resulting needs (‘liking’). This can be done independently of the hard question about their subjective experiences. Because answering such questions of wanting and liking relies on highly experimental procedures, we should additionally think about approaches to assess fish welfare in practice in a way that goes beyond health aspects, too. Recently developed techniques open exciting avenues to tap into judgement biases of populations that may indicate welfare and may be applicable in large‐scale fish production systems. Being aware of these two issues hopefully helps to temper the conflict between the two current extreme poles of either negating or assuming a high level of fish sentience in the discussion of fish welfare. Animal welfare is likely to depend on affective states also in fish. Given that motivation (wanting) and goal assessment (liking) are important contributors to affective states, it is worthwhile studying and understanding the specific behavioural needs of farmed fish species. This helps to ensure that holding conditions allow them to follow their wants and reach states that they like.

Wildlife telemetry data may be used to answer a diverse range of questions relevant to wildlife ecology and management. One challenge to modeling telemetry data is that animal movement often varies greatly in pattern over time, and current continuous‐time modeling approaches to handle such nonstationarity require bespoke and often complex models that may pose barriers to practitioner implementation. We demonstrate a novel application of treed Gaussian process (TGP) modeling, a Bayesian machine learning approach that automatically captures the nonstationarity and abrupt transitions present in animal movement. The machine learning formulation of TGPs enables modeling to be nearly automated, while their Bayesian formulation allows for the derivation of movement descriptors with associated uncertainty measures. We demonstrate the use of an existing R package to implement TGPs using the familiar Markov chain Monte Carlo algorithm. We then use estimated movement trajectories to derive movement descriptors that can be compared across individuals and populations. We applied the TGP model to a case study of lesser prairie‐chickens (Tympanuchus pallidicinctus) to demonstrate the benefits of TGP modeling and compared distance traveled and residence times across lesser prairie‐chicken individuals and populations. For broad usability, we outline all steps necessary for practitioners to specify relevant movement descriptors (e.g., turn angles, speed, contact points) and apply TGP modeling and trajectory comparison to their own telemetry datasets. Combining the predictive power of machine learning and the statistical inference of Bayesian methods to model movement trajectories allows for the estimation of statistically comparable movement descriptors from telemetry studies. Our use of an accessible R package allows practitioners to model trajectories and estimate movement descriptors, facilitating the use of telemetry data to answer applied management questions. We apply a recently developed Bayesian machine learning model to both increase the accessibility of complex animal movement models and simultaneously achieve advanced modeling of highly varied telemetry data. By nesting this model within a widely applicable inferential framework and utilizing an accessible R package for modeling, we facilitate application by practitioners to model trajectories, estimate movement descriptors, and answer applied management questions.

Animal distribution maps are a key tool for wildlife conservation, guiding high‐profile decisions, such as legally designating priority habitat or building highway crossing structures. GPS tracking data enhances these efforts but requires balancing statistically robust sample sizes with minimizing researcher impacts on wildlife and costs. Nevertheless, rigorous guidelines that leverage a priori information are still lacking on how to determine the optimal number of tracked animals (i.e. sample size) for accurately mapping migration corridors and seasonal ranges at the population level, particularly in the context of ungulate conservation. We used a cumulative curve resampling approach to evaluate the consequences of reduced animal sample size, assessed sample size sufficiency and extrapolated where sample size sufficiency might occur outside of the observed data. We illustrate our approach with simulated data. We then compiled GPS data from 77 ungulate populations and aggregated individuals' spatial distributions in each study area to create population‐level migration and seasonal distributions and examined whether known explanatory variables (e.g. population abundance, environmental metrics) could predict sufficient sample sizes to map population distribution. Our simulated and empirical analyses to assess and model sample size sufficiency demonstrated that sample size varies depending on the species, season, population‐level percent volume contour of interest and population abundance. For example, for migration distributions at the 95% volume contour, the interquartile range for number of individuals needed to reach an adequate sample was 10–23 for bighorn sheep, 51–93 for elk and 58–164 for mule deer. Practical implication. For existing datasets, the resampling approach quantifies the sensitivity of population distribution maps to sample size. To guide study design for future GPS tracking projects aimed at mapping population distributions, our models provide specific sample size recommendations incorporating known population covariates. If adequate model training data are available, our approach can be extended across a wide range of taxa and populations to inform sample size requirements for estimating robust distribution patterns. Population‐level distribution maps are essential for wildlife conservation, yet clear guidelines for how many GPS‐tracked animals are needed remain lacking. Using simulated and empirical data from 77 ungulate populations, we evaluated how sample size affects distribution estimates and developed models that predict sufficient sample sizes based on species, season and population covariates. Our approach provides flexible, data‐driven recommendations to inform study design and, with adequate model training data, can be extended across taxa to support robust distribution mapping.

Understanding the environmental and genetic mechanisms underlying locally adaptive trait variation across the ranges of species is a major focus of evolutionary biology. Combining transcriptome sequencing with common garden experiments on populations spanning geographical and environmental gradients holds promise for identifying such mechanisms. The South African shrub Protea repens displays diverse phenotypes in the wild along drought and temperature gradients. We grew plants from seeds collected at 19 populations spanning this species’ range, and sequenced the transcriptomes of these plants to reveal gene pathways associated with adaptive trait variation. We related expression in co‐expressed gene networks to trait phenotypes measured in the common garden and to source population climate. We found that expression in gene networks correlated with source‐population environment and with plant traits. In particular, the activity of gene networks enriched for growth related pathways correlated strongly with source site minimum winter temperature and with leaf size, stem diameter and height in the garden. Other gene networks with enrichments for photosynthesis related genes showed associations with precipitation. Our results strongly suggest that this species displays population‐level differences in gene expression that have been shaped by source population site climate, and that are reflected in trait variation along environmental gradients.