Transcriptional profiling pinpoints the heterogeneity of T helper cells in human nasal polyps at the clonal level.

Identifying important demographic drivers of population dynamics is fundamental for understanding life-history evolution and implementing effective conservation measures. Integrated population models (IPMs) coupled with transient life table response experiments (tLTREs) allow ecologists to quantify the contributions of demographic parameters to observed population change. While IPMs can estimate parameters that are not estimable using any data source alone, for example, immigration, the estimated contribution of such parameters to population change is prone to bias. Currently, it is unclear when robust conclusions can be drawn from them. We sought to understand the drivers of a rebounding southern elephant seal population on Marion Island using the IPM-tLTRE framework, applied to count and mark-recapture data on 9500 female seals over nearly 40 years. Given the uncertainty around IPM-tLTRE estimates of immigration, we also aimed to investigate the utility of simulation and sensitivity analyses as general tools for evaluating the robustness of conclusions obtained in this framework. Using a Bayesian IPM and tLTRE analysis, we quantified the contributions of survival, immigration and population structure to population growth. We assessed the sensitivity of our estimates to choice of multivariate priors on immigration and other vital rates. To do so we make a novel application of Gaussian process priors, in comparison with commonly used shrinkage priors. Using simulation, we assessed our model's ability to estimate the demographic contribution of immigration under different levels of temporal variance in immigration. The tLTRE analysis suggested that adult survival and immigration were the most important drivers of recent population growth. While the contribution of immigration was sensitive to prior choices, the estimate was consistently large. Furthermore, our simulation study validated the importance of immigration by showing that our estimate of its demographic contribution is unlikely to result as a biased overestimate. Our results highlight the connectivity between distant populations of southern elephant seals, illustrating that female dispersal can be important in regulating the abundance of local populations even when natal site fidelity is high. More generally, we demonstrate how robust ecological conclusions may be obtained about immigration from the IPM-tLTRE framework, by combining sensitivity analysis and simulation. This in-depth study considers the contribution of immigration to population growth, when immigration is estimated indirectly in an integrated population model. The authors demonstrate the importance of simulation and sensitivity analysis for obtaining robust conclusions, and introduce an elegant prior to the problem along the way.image

Historical data on chinstrap penguin (Pygoscelis antarctica) breeding population sizes are sparse and sometimes highly uncertain, making it hard to estimate true population trajectories. Yet, information on population trends is desirable as changes in population size can help inform conservation assessments. Recently, Krüger (2023) (Diversity 2023, 15, 327) used chinstrap penguin nest count data to predict breeding colony size trends between 1960 and 2020, to estimate whether the level of population change within three generations exceeded IUCN Red List Criteria for “Vulnerable” populations. Chinstrap penguin population trends are an important research topic, but we caution that Krüger (2023)’s statistical analyses (intended to form the foundation for drawing valid, evidence-based inferences from sparse data) contain fundamental errors that invalidate that paper’s findings. We discuss oversights in several key steps (data processing, exploratory data analysis, model fitting, model evaluation, and prediction) of that paper’s analysis to help others detect and avoid some of the pitfalls associated with estimating population trends via mixed models. We also show through reanalysis that improved statistical modelling can yield better predictions of chinstrap penguin population trends, at least within the range of observed data. This case study highlights (1) the profound influence that seemingly minor differences in modelling procedures (both unintentional errors and other decisions) can have on predictions of population trends, and (2) the substantial inherent uncertainty in population trend predictions derived from sparse, heterogenous data.

Mark–recapture models are the primary framework for estimating demographic parameters in wild organisms. Complex life histories and sampling processes demand complex model formulations, yet these are vulnerable to errors in implementation and unanticipated biases. Simulation is a powerful tool to diagnose potential issues, but as the size of models and data increase, the computational cost of simulation‐based model evaluation may discourage its use. A common phenomenon in mark–recapture studies is ‘transients’: the number of individuals that are only captured once exceeds that expected by the model. To avoid biasing survival estimates, transients should be accounted for. Mixture models offer a flexible, extensible and interpretable way to do so, but current implementations of transient mixture models as multievent models can have long runtimes for large datasets. Here, we describe a novel likelihood calculation for transient mixture models, which allows any multistate model with an efficient product‐multinomial likelihood to account for transients at negligible extra computational cost. The resulting ‘extended product‐multinomial’ (EPM) likelihoods are inferentially equivalent to multievent likelihoods, but faster to fit, often by an order of magnitude or more. A guide to implementation in R and Stan is provided. We illustrate our approach with a case study comparing survival of wild‐raised and hand‐reared African Penguins Spheniscus demersus . Facilitated by our EPM likelihood, we used simulation to assess our model's ability to disentangle survival differences from confounding factors whilst accounting for trap dependence and transience in the data. Hand‐rearing was associated with higher juvenile survival and lower adult survival compared with wild‐raised birds.