We have developed a method to identify and quantify recharge episodes, along with their associated infiltration-related inputs, by a consistent, systematic procedure. Our algorithm partitions a time series of water levels into discrete recharge episodes and intervals of no episodic recharge. It correlates each recharge episode with a specific interval of rainfall, so storm characteristics such as intensity and duration can be associated with the amount of recharge that results. To be useful in humid climates, the algorithm evaluates the separability of events, so that those whose recharge cannot be associated with a single storm can be appropriately lumped together. Elements of this method that are subject to subjectivity in the application of hydrologic judgment are values of lag time, fluctuation tolerance, and master recession parameters. Because these are determined once for a given site, they do not contribute subjective influences affecting episode-to-episode comparisons. By centralizing the elements requiring scientific judgment, our method facilitates such comparisons by keeping the most subjective elements openly apparent, making it easy to maintain consistency. If applied to a period of data long enough to include recharge episodes with broadly diverse characteristics, the method has value for predicting how climatic alterations in the distribution of storm intensities and seasonal duration may affect recharge.

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Estuaries and CoastsSpatiotemporal models of an estuarine fish species to identify patterns and factors impacting their distribution and abundance Abstract: Understanding the distribution and abundance of organisms can be exceedingly difficult for pelagic fish species that live in estuarine environments. This is particularly so for fish that cannot be readily marked and released or otherwise tracked, such as the diminutive delta smelt, Hypomesus transpacificus, endemic to the San Francisco Estuary. The environmental factors that influence distribution operate at multiple scales, from daily tidal cycles and local perceptual fields to seasonal and annual changes in dominant environmental gradients spanning the entire San Francisco Estuary. To quantify scale specific patterns and factors shaping the spatiotemporal abundance dynamics of adult delta smelt, we fit a suite of models to an extensive, spatially resolved, catch survey time series from 13 annual cohorts. The best model included cohort-specific abundance indicators and daily mortality rates, a regional spatial adjustment, and haul-specific environmental conditions. The regional adjustment identified several density hotspots that were persistent across cohorts. While this model did include local environmental conditions, the gain in explained variation was relatively slight compared to that explained by the regional adjustment. Total abundance estimates were derived by multiplying habitat volume by catch density (design-based) and modeled density (model-based), with both showing severe declines in the population over the time period studied. The design-based approaches had lower uncertainty but potentially higher bias. We discuss the implications of our results for advancing the science and improving management of delta smelt, and future data collection needs. ABSTRACT: Understanding the distribution and abundance of organisms can be exceedingly 14 difficult for pelagic fish species that live in estuarine environments. This is particularly so for 15 fish that cannot be readily marked and released or otherwise tracked, such as the diminutive delta 16 smelt, Hypomesus transpacificus, endemic to the San Francisco Estuary. The environmental 17 factors that influence distribution operate at multiple scales, from daily tidal cycles and local 18 perceptual fields to seasonal and annual changes in dominant environmental gradients spanning 19 the entire San Francisco Estuary. To quantify scale specific patterns and factors shaping the 20 spatiotemporal abundance dynamics of adult delta smelt, we fit a suite of models to an extensive, 21 spatially resolved, catch survey time series from 13 annual cohorts. The best model included 22 cohort-specific abundance indicators and daily mortality rates, a regional spatial adjustment, and 23 haul-specific environmental conditions. The regional adjustment identified several density 24 hotspots that were persistent across cohorts. While this model did include local environmental 25 conditions, the gain in explained variation was ...

Population abundance indices and estimates of uncertainty are starting points for many scientific endeavors. However, if the indices are based on data collected by different monitoring programs with possibly different sampling procedures and efficiencies, applying consistent methodology for calculating them can be complicated. Ideally, the methodology will provide indices and associated measures of uncertainty that account for the sample design, the level of sampling effort (e.g., sample size), and the capture or detection probabilities. We develop and demonstrate consistent methodology for multiple monitoring programs that sample different life stages of Delta Smelt Hypomesus transpacificus, a critically endangered fish species endemic to the San Francisco Estuary, whose abundance indices have been at the center of much controversy given the regulatory consequences of their listed status. Current indices use different and incomparable methods, do not account for gear selectivity, and do not provide measures of uncertainty. Using recently available information on gear‐specific, length‐based conditional probabilities of capture given availability, we develop new abundance indices along with measures of uncertainty by means of a single methodological approach. These new indices are highly correlated with existing ones, but the approach taken here illuminates different sources of bias and quantifies between‐year variation using probabilistic statements where the previous indices cannot. Decomposition of uncertainty into its constituent sources reveals that early life stage uncertainty is dominated by gear inefficiency while later life stage uncertainty is dominated by sample size, thus providing guidance for improvements to existing surveys. An additional result of general methodological interest is a demonstration, via simulation intended to reflect realistic data properties, that a lognormal distribution is preferable to the normal distribution for making probabilistic statements about the indices. The work here facilitates the fitting of models attempting to identify factors associated with the dynamics and decline of the species.

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 the relative selectivity 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.

State-space models (SSMs) are a popular tool for modeling animal abundances. Inference difficulties for simple linear SSMs are well known, particularly in relation to simultaneous estimation of process and observation variances. Several remedies to overcome estimation problems have been studied for relatively simple SSMs, but whether these challenges and proposed remedies apply for nonlinear stagestructured SSMs, an important class of ecological models, is less well understood. Here we identify improvements for inference about nonlinear stage-structured SSMs fit with biased sequential life stage data. Theoretical analyses indicate parameter identifiability requires covariates in the state processes. Simulation studies show that plugging in externally estimated observation variances, as opposed to jointly estimating them with other parameters, reduces bias and standard error of estimates. In contrast to previous results for simple linear SSMs, strong confounding between jointly estimated process and observation variance parameters was not found in the models explored here. However, when observation variance was also estimated in the motivating case study, the resulting process variance estimates were implausibly low (near-zero). As SSMs are used in increasingly complex ways, understanding when inference can be expected to be successful, and what aids it, becomes more important. Our study illustrates (i) the need for relevant process covariates and (ii) the benefits of using externally estimated observation variances for inference for nonlinear stage-structured SSMs.

Water infiltrating into soil of natural structure often causes wetting patterns that do not develop in an orderly sequence. Because traditional unsaturated flow models represent a water advance that proceeds sequentially, they fail to predict irregular development of water distribution. In the source‐responsive model, a diffuse domain (D) represents flow within soil matrix material following traditional formulations, and a source‐responsive domain (S), characterized in terms of the capacity for preferential flow and its degree of activation, represents preferential flow as it responds to changing water‐source conditions. In this paper we assume water undergoing rapid source‐responsive transport at any particular time is of negligibly small volume; it becomes sensible at the time and depth where domain transfer occurs. A first‐order transfer term represents abstraction from the S to the D domain which renders the water sensible. In tests with lab and field data, for some cases the model shows good quantitative agreement, and in all cases it captures the characteristic patterns of wetting that proceed nonsequentially in the vertical direction. In these tests we determined the values of the essential characterizing functions by inverse modeling. These functions relate directly to observable soil characteristics, rendering them amenable to evaluation and improvement through hydropedologic development.

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 561 791 (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.