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Understanding the causes of mortality for a declining species is essential for developing effective conservation and management strategies, particularly when anthropogenic activities are the primary threat. Using a competing hazards framework allows for robust estimation of the cause‐specific variation in risk that may exist across multiple dimensions, such as time and individual. Here, we estimated cause‐specific rates of severe injury and mortality for North Atlantic right whales (Eubalaena glacialis), a critically endangered species that is currently in peril due to human‐caused interactions. We developed a multistate capture–recapture model that leveraged 30 years of intensive survey effort yielding sightings of individuals with injury assessments and necropsies of carcass recoveries. We examined variation in the hazard rates of severe injury and mortality due to entanglements in fishing gear and vessel strikes as explained by temporal patterns and the age and reproductive status of the individual. We found strong evidence for increased rates of severe entanglement injuries after 2013 and for females with calves, with consequently higher marginal mortality. The model results also suggested that despite vessel strikes causing a lower average rate of severe injuries, the higher mortality rate conditional on injury results in significant total mortality risk, particularly for females resting from a recent calving event. Large uncertainty in the estimation of carcass recovery rate for vessel strike deaths permeated into the apportionment of mortality causes. The increased rates of North Atlantic right whale mortality in the last decade, particularly for reproducing females, has been responsible for the severe decline in the species. By apportioning the human‐caused threats using a quantitative approach with estimation of relevant uncertainty, this work can guide development of conservation and management strategies to facilitate species recovery. Our approach is relevant to other monitored populations where cause‐specific injuries from multiple threats can be observed in live and dead individuals. [ABSTRACT FROM AUTHOR]

Effective conservation of threatened populations requires identification of the processes limiting recovery. When multiple population processes are potentially limiting, they are sometimes analyzed independently, often using different datasets. Analytically, this is suboptimal, as processes are often correlated, which can lead to biased estimates of parameters and quantities of interest. Integrated population models (IPMs) can synthesize several data streams in the same probabilistic framework to circumvent these issues. Lack of prey was identified as one of the primary threats to recovery of critically endangered southern resident killer whales (SRKWs), Orcinus orca. Previous studies have correlated SRKW demographic rates with indices of Chinook salmon Oncorhynchus tshawytscha abundance, but these approaches have modeled reproduction and survival independently. We developed an IPM for SRKWs that models both processes simultaneously, as a function of Chinook salmon abundance along the west coast of North America. We used this model to evaluate the relationship between Chinook salmon abundance and demographic rates of SRKWs, with the goal of updating our understanding of a potential causal relationship between prey availability and SRKW population dynamics, and how these relationships may have changed over time. Results suggest that SRKW mortality rates are more strongly associated with Chinook salmon abundance than birth rates. Our analysis also suggests northern resident killer whale abundance could also be affecting SRKW carrying capacity, possibly through competition over shared prey resources. The IPM fit observed total abundance data well and predicted temporal changes in population demographics with reasonable accuracy, but small sample size may have opacified model selection. Our approach offers a valuable tool for predicting the response of the SRKW population to alternative management strategies involving the recovery of Chinook salmon stocks in the eastern Pacific. [ABSTRACT FROM AUTHOR]

Spatial capture–recapture (SCR) models have increasingly been used as a basis for combining capture–recapture data types with variable levels of individual identity information to estimate population density and other demographic parameters. Recent examples are the unmarked SCR (or spatial count model), where no individual identities are available and spatial mark–resight (SMR) where individual identities are available for only a marked subset of the population. Currently lacking, though, is a model that allows unidentified samples to be combined with identified samples when there are no separate classes of "marked" and "unmarked" individuals and when the two sample types cannot be considered as arising from two independent observation models. This is a common scenario when using noninvasive sampling methods, for example, when analyzing data on identified and unidentified photographs or scats from the same sites.Here we describe a "random thinning" SCR model that utilizes encounters of both known and unknown identity samples using a natural mechanistic dependence between samples arising from a single observation model. Our model was fitted in a Bayesian framework using NIMBLE.We investigate the improvement in parameter estimates by including the unknown identity samples, which was notable (up to 79% more precise) in low‐density populations with a low rate of identified encounters. We then applied the random thinning SCR model to a noninvasive genetic sampling study of brown bear (Ursus arctos) density in Oriental Cantabrian Mountains (North Spain).Our model can improve density estimation for noninvasive sampling studies for low‐density populations with low rates of individual identification, by making use of available data that might otherwise be discarded. [ABSTRACT FROM AUTHOR]

The various debates around model selection paradigms are important, but in lieu of a consensus, there is a demonstrable need for a deeper appreciation of existing approaches, at least among the end-users of statistics and model selection tools. In the ecological literature, the Akaike information criterion (AIC) dominates model selection practices, and while it is a relatively straightforward concept, there exists what we perceive to be some common misunderstandings around its application. Two specific questions arise with surprising regularity among colleagues and students when interpreting and reporting AIC model tables. The first is related to the issue of 'pretending' variables, and specifically a muddled understanding of what this means. The second is related to p -values and what constitutes statistical support when using AIC. There exists a wealth of technical literature describing AIC and the relationship between p -values and AIC differences. Here, we complement this technical treatment and use simulation to develop some intuition around these important concepts. In doing so we aim to promote better statistical practices when it comes to using, interpreting and reporting models selected when using AIC.