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Population dynamics are functions of several demographic processes including survival, reproduction, somatic growth, and maturation. The rates or probabilities for these processes can vary by time, by location, and by individual. These processes can co-vary and interact to varying degrees, e.g., an animal can only reproduce when it is in a particular maturation state. Population dynamics models that treat the processes as independent may yield somewhat biased or imprecise parameter estimates, as well as predictions of population abundances or densities. However, commonly used integral projection models (IPMs) typically assume independence across these demographic processes. We examine several approaches for modelling between process dependence in IPMs and include cases where the processes co-vary as a function of time (temporal variation), co-vary within each individual (individual heterogeneity), and combinations of these (temporal variation and individual heterogeneity). We compare our methods to conventional IPMs, which treat vital rates independent, using simulations and a case study of Soay sheep (Ovis aries). In particular, our results indicate that correlation between vital rates can moderately affect variability of some population-level statistics. Therefore, including such dependent structures is generally advisable when fitting IPMs to ascertain whether or not such between vital rate dependencies exist, which in turn can have subsequent impact on population management or life-history evolution.

Long-term fish survey monitoring programs use a variety of fishing gears to catch fish, and the resulting catches are the basis for status and trends reports on the condition of different fish stocks. These catches can also be part of the data used to set stock assessment models, which establish harvest regulations, and to fit population dynamics models, which are used to analyze population viability. However, most fishing gears are size-selective, and fish size — among other possible covariates, such as environmental conditions — affects the probability that a fish will be caught in the path the gear sweeps. Failing to properly account for selectivity can adversely affect the ability to interpret and use status and trends measures, stock-assessment models, and population-dynamics models. Our side-by-side gear comparison study evaluated the selectivity of multiple open-water trawl surveys that have provided decades worth of information on the imperiled fish species Delta Smelt (Hypomesus transpacificus). We used data from the study to estimate gear selectivity curves for multiple trawls using two methods. The first method examines the total number of fish-at-length caught across all gears, and does not directly use or estimate fish length distribution in the population. The second method examines the total number of fish caught by each gear separately, and explicitly estimates fish length distribution in the population. The results from the two methods were similar, and we found that one trawl was highly efficient at catching larger Delta Smelt. This is the first formal multi-gear evaluation of how well survey gear used to monitor Delta Smelt in the San Francisco Estuary selects fish by size, and we plan to incorporate the results into Delta Smelt population models.

For nearly 50 years, the California Department of Fish and Wildlife has used a midwater trawl to intensively monitor fish populations in the San Francisco Estuary during the fall, sampling over 100 locations each month. The data collected have been useful for calculating indices of fish abundance and for detecting and documenting the decline of the endangered fish species delta smelt (Hypomesus transpacificus). However, efforts to calculate estimates of absolute abundance have been hampered by the lack of information on gear efficiency, in particular questions about contact selectivity and the effect of tow method on catches. To answer these questions we conducted a study that used a covered cod end on a net towed either near the surface, referred to as a surface tow, or throughout the water column, referred to as an oblique tow. A contact selectivity model was fit to estimate the probability that a delta smelt that has come into contact with the net is retained in the cod end of the net conditional on its body length. Full retention of delta smelt was found to occur around 60 mm fork length. Delta smelt catch densities for the surface tows were an order of magnitude greater than densities in the oblique tows, suggesting a surface orientation at the sub-adult life stage. These results represent an important step in being able to calculate absolute abundance estimates of the delta smelt population size using decades’ worth of monitoring data.

Hydrodynamic models have been used to estimate rates of ichthyoplankton transport across marine and estuarine environments and subsequent geographic isolation of a portion of the population (i.e., entrainment). Combining simulated data from hydrodynamic models with data from fish populations can provide more information, including estimates of regional abundance. Entrainment of postlarval delta smelt (Hypomesus transpacificus), a threatened species endemic to California's Sacramento-San Joaquin Delta, caused by water export operations, was modeled using a Bayesian hierarchical model. The model was fit using data spanning years 1995-2015 from multiple sources: hydrodynamic particle tracking, fish length composition, mark-recapture, and count data from entrainment monitoring. Estimates of the entrainment of postlarval delta smelt ranged from 10 (SD = 23) in May 2006 to 561791 (SD = 246 423) in May 2002. A simulation study indicated that all model parameters were estimable, but errors in transport data led to biased estimates of entrainment. Using only single data sources rather than integration through hierarchical modeling would have underestimated uncertainty in entrainment estimates or resulted in bias if transport, survival, or sampling efficiency were unaccounted for. Des modeles hydrodynamiques ont ete utilises pour estimer les vitesses de transport d'ichtyoplancton dans des milieux marins et estuariens et l'isolement geographique subsequent d'une partie de la population (c.-a-d., l'entrainement). La combinaison de donnees de simulation obtenues de modeles hydrodynamiques a des donnees sur les populations de poissons peut fournir plus d'information, dont des estimations de l'abondance regionale. L'entrainement d'eperlans du delta (Hypomesus transpacificus), une espece menacee endemique au delta des rivieres Sacramento et San Joaquin, en Californie, au stade postlarvaire cause par des activites d'exportation d'eau a ete modelise en utilisant un modele bayesien hierarchique. Le modele a ete cale sur des donnees allant de 1995 a 2015 provenant de differentes sources, dont des donnees de suivi des particules hydrodynamiques, de composition de longueurs de poissons, de marquage-recapture et de denombrement provenant de la surveillance de l'entrainement. Les estimations de l'entrainement d'eperlans du delta post-larvaires vont de 10 (ET = 23) en mai 2006 a 561791 (ET = 246 423) en mai 2002. Une etude de simulation indique que tous les parametres du modele peuvent etre estimes, mais des erreurs dans les donnees de transport menent a des estimations biaisees de l'entrainement. L'utilisation d'une seule source de donnees plutot que l'integration par la modelisation hierarchique aurait sous-estime l'incertitude dans les estimations de l'entrainement ou se serait traduite par un biais si le transport, la survie ou l'efficacite d'echantillonnage n'avait pas ete pris en compte. [Traduit par la Redaction], Introduction Entrainment is a physical process whereby groups are advected and spatially redistributed through the action of moving water, resulting in geographic isolation of the entrained portion of the population. [...]

The decline of Sacramento River winter-run Chinook salmon (Oncorhynchus tshawytscha) remains one of the major water management issues in the Sacramento River. Few field studies have been published on winter-run, leaving gaps in our knowledge about their life history. This is especially true in the Sacramento–San Joaquin Delta, which provides essential rearing and migratory habitats for winter-run, and serves as the center of water operations for California. Using long-term monitoring data that identified winter-run-sized fish (“winter-run”) using length-at-date criteria, we examined patterns of juvenile migration in terms of geographic distribution, timing, numbers, and residence times. We analyzed the role of flow, turbidity, temperature, and adult escapement on the downstream movement (“migration”) of winter-run. Winter-run passed Knights Landing (rkm 144 or 51 rkm upstream of the Delta) between October and April, with substantial variation in peak time of entry that was strongly associated with the first high flows of the migration season. Specifically, the first day of flows of at least 400 m3 s-1 at Wilkins Slough (rkm 190) coincided with the first day that at least 5% of the annual total catch was observed at Knights Landing. While the period during which winter-run left the Delta spanned several months based on Chipps Island (rkm 29) catch data, the median catch typically occurred over a narrow window in March. Differences in timing of cumulative catch at Knights Landing and Chipps Island indicate that apparent residence time in the Delta ranges from 41 to 117 days, with longer apparent residence times for juveniles arriving earlier at Knights Landing. We discuss the potential importance of the Yolo Bypass floodplain as an alternative rearing and migratory corridor, contingent on the timing, duration, and magnitude of floodplain inundation. These results carry implications for habitat restoration and management of Sacramento River flows.

A sample design-based procedure for estimating pre-adult and adult delta smelt abundance is described. Using data from midwater trawl surveys taken during the months of September, October, November, and December for the years 1990 through 2006 and estimates of size selectivity of the gear from a covered codend experiment, stratified random sample ratio estimates of delta smelt abundance were made per month. The estimation procedure is arguably an improvement over the dimensionless delta smelt indices that have been used historically in that (1) the volume sampled is used in a manner that leads to directly interpretable numbers and (2) standard errors are easily calculated. The estimates are quite imprecise, i.e., coefficients of variation in the range of 100\% occurred. The point estimates are highly correlated with the monthly indices, and conclusions on abundance declines are quite similar. However, both the estimates and indices may suffer from selection biases if the trawl samples are not representative of the true densities. Future work is needed in at least three areas: (1) gathering additional information to determine the validity of assumptions made, in particular determining the possible degree of selection bias; (2) developing procedures that utilize survey data gathered from earlier life history stages, such as larval surveys; (3) embedding a life-history model into the population estimation procedure.

The resolution at which animal populations can be modeled can be increased when multiple datasets corresponding to different life stages are available, allowing, for example, seasonal instead of annual descriptions of dynamics. However, the abundance estimates used for model fitting can have multiple sources of error, both random and systematic, namely bias. We focus here on the consequences of, and how to address, differing and unknown observation biases when fitting models.State‐space models (SSMs) separate process variation and observation error, thus providing a framework to account for different and unknown estimate biases across multiple datasets. Here we study the effects on the inference of including or excluding bias parameters for a sequential life stage population dynamics SSM using a combination of theory, simulation experiments, and an empirical example.When the data, that is, abundance estimates, are unbiased, including bias parameters leads to increased imprecision compared to a model that correctly excludes bias parameters. But when observations are biased and no bias parameters are estimated, recruitment and survival processes are inaccurately estimated and estimates of process variance become biased high. These problems are substantially reduced by including bias parameters and fixing one of them at even an incorrect value. The primary inferential challenge is that models with bias parameters can show properties of being parameter redundant even when they are not in theory.Combining multiple datasets into a single analysis by using bias parameters to rescale data can offer significant improvements to inference and model diagnostics. Because their estimability in practice is dataset specific and will likely require more precise estimates than might be expected from ecological datasets, we outline some strategies for characterizing process uncertainty when it is confounded by bias parameters. [ABSTRACT FROM AUTHOR]

State‐space models are an increasingly common and important tool in the quantitative ecologists' armoury, particularly for the analysis of time‐series data. This is due to both their flexibility and intuitive structure, describing the different individual processes of a complex system, thus simplifying the model specification step.State‐space models are composed of two processes (a) the system (or state) process that describes the dynamics of the true underlying state of the system over time; and (b) the observation process that links the observed data with the current true state of the system at that time. Specification of the general model structure consists of considering each distinct ecological process within the system and observation processes, which are then automatically combined within the state‐space structure.There is typically a trade‐off between the complexity of the model and the associated model‐fitting process. Simpler model specifications permit the application of simpler model‐fitting tools; whereas more complex model specifications, with nonlinear dynamics and/or non‐Gaussian stochasticity often require more sophisticated model‐fitting algorithms to be applied.We provide a brief overview of general state‐space models before focusing on the different model‐fitting tools available. In particular for different general state‐space model structures we discuss established model‐fitting tools that are available. We also offer practical guidance for choosing a specific fitting procedure. [ABSTRACT FROM AUTHOR]