A state–space multistage life cycle model to evaluate population impacts in the presence of density dependence: illustrated with application to delta smelt (Hyposmesus transpacificus)

Multiple factors acting on different life stages influence population dynamics and complicate the assessment and management of populations. To provide appropriate management advice, the data should be used to determine which factors are important and what life stages they impact. It is also important to consider density dependence because it can modify the impact of some factors. We develop a state–space multistage life cycle model that allows for density dependence and environmental factors to impact different life stages. Models are ranked using a two-covariates-at-a-time stepwise procedure based on AICc model averaging to reduce the possibility of excluding factors that are detectable in combination, but not alone. Impact analysis is used to evaluate the impact of factors on the population. The framework is illustrated by application to delta smelt ( Hyposmesus transpacificus ), a threatened species that is potentially impacted by multiple anthropogenic factors. Our results indicate that density dependence and a few key factors impact the delta smelt population. Temperature, prey, and predators dominated the factors supported by the data and operated on different life stages. The included factors explain the recent declines in delta smelt abundance and may provide insight into the cause of the pelagic species decline in the San Francisco Estuary.

Download Full-text

Disentangling risks to an endangered fish: using a state-space life cycle model to separate natural mortality from anthropogenic losses

Canadian Journal of Fisheries and Aquatic Sciences ◽

10.1139/cjfas-2020-0251 ◽

2021 ◽

Author(s):

William Everett Smith ◽

Leo Polansky ◽

Matthew L. Nobriga

Keyword(s):

Life Cycle ◽

State Space ◽

Process Variation ◽

Estuarine Fish ◽

Observation Error ◽

Natural Mortality ◽

Life Cycle Model ◽

Cycle Model ◽

Multiple Sources ◽

Delta Smelt

State-space population models are becoming a common tool to guide natural resource management, because they address the statistical challenges arising from high observation error and process variation while improving inference by integrating multiple, disparate datasets. A hierarchical state-space life cycle model was developed, motivated by delta smelt (Hypomesus transpacificus), an estuarine fish experiencing simultaneous risks of entrainment mortality from out-of-basin water export and natural mortality. Notable model features included a covariate-dependent instantaneous rates formulation of survival, allowing estimation of multiple sources of mortality, and inclusion of relative observation bias parameters, allowing integration of differently scaled abundance indices and entrainment estimates. Simulation testing confirmed that two sources of mortality, process variation, and data integration parameters could be estimated. Delta smelt entrainment mortality was associated with environmental conditions used to manage entrainment, and recruitment and natural mortality were related to temperature, outflow, food, and predators. Although entrainment mortality was reduced in recent years, ecosystem conditions did not appear to support robust spawning or over-summer survival of new recruits, manifesting as a 98% reduction of adults during 1995-2015.

Download Full-text

Impact of repeated hospital accreditation surveys on quality and reliability, an 8-year interrupted time series analysis

BMJ Open ◽

10.1136/bmjopen-2018-024514 ◽

2019 ◽

Vol 9 (2) ◽

pp. e024514 ◽

Cited By ~ 5

Author(s):

Subashnie Devkaran ◽

Patrick N O’Farrell ◽

Samer Ellahham ◽

Randy Arcangel

Keyword(s):

Time Series ◽

Life Cycle ◽

Interrupted Time Series ◽

Quality Measures ◽

Simple Random Sample ◽

Regression Equations ◽

Life Cycle Model ◽

Cycle Model ◽

Hospital Accreditation ◽

The Impact

ObjectiveTo evaluate whether hospital re-accreditation improves quality, patient safety and reliability over three accreditation cycles by testing the accreditation life cycle model on quality measures.DesignThe validity of the life cycle model was tested by calibrating interrupted time series (ITS) regression equations for 27 quality measures. The change in the variation of quality over the three accreditation cycles was evaluated using the Levene’s test.SettingA 650-bed tertiary academic hospital in Abu Dhabi, UAE.ParticipantsEach month (over 96 months), a simple random sample of 10% of patient records was selected and audited resulting in a total of 388 800 observations from 14 500 records.Intervention(s)The impact of hospital accreditation on the 27 quality measures was observed for 96 months, 1-year preaccreditation (2007) and 3 years postaccreditation for each of the three accreditation cycles (2008, 2011 and 2014).Main outcome measure(s)The life cycle model was evaluated by aggregating the data for 27 quality measures to produce a composite score (YC) and to fit an ITS regression equation to the unweighted monthly mean of the series.ResultsThe results provide some evidence for the validity of the four phases of the life cycle namely, the initiation phase, the presurvey phase, the postaccreditation slump and the stagnation phase. Furthermore, the life cycle model explains 87% of the variation in quality compliance measures (R2=0.87). The best-fit ITS model contains two significant variables (β1 and β3) (p≤0.001). The Levene’s test (p≤0.05) demonstrated a significant reduction in variation of the quality measures (YC) with subsequent accreditation cycles.ConclusionThe study demonstrates that accreditation has the capacity to sustain improvements over the accreditation cycle. The significant reduction in the variation of the quality measures (YC) with subsequent accreditation cycles indicates that accreditation supports the goal of high reliability.

Download Full-text