# Ken NewmanBiomathematics and Statistics Scotland; University of Edinburgh

Ken Newman

PhD, Statistics

## About

70

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Introduction

State-space models for ecological processes, including wildlife, fish, and insect population dynamics.
Statistical analysis, including calibration and history matching, of complex computer simulation models, e.g., crop growth models and agricultural runoff.

## Publications

Publications (70)

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 particul...

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 mode...

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 particul...

State–space models (SSMs) are an important modeling framework for analyzing ecological time series. These hierarchical models are commonly used to model population dynamics, animal movement, and capture‐recapture data, and are now increasingly being used to model other ecological processes. SSMs are popular because they are flexible and they model...

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 challeng...

State-space models (SSMs) are an important modeling framework for analyzing ecological time series. These hierarchical models are commonly used to model population dynamics and animal movement, and are now increasingly being used to model other ecological processes. SSMs are popular because they are flexible and they model the natural variation in...

In stratified sampling, the N population units are grouped into L strata, independent samples are selected from within each stratum, and unbiased estimation is achieved as a weighted average of stratum-specific estimates. Strata may be natural—pool, riffle, and run habitat unit types in a small stream—or strata may be constructed to ensure that som...

Similar to strata, population units may instead be grouped into clusters. Usually, units within clusters are geographically or genetically close to one another—all households on a city block, individuals within a single family. In (single-stage) equal size cluster sampling, the total population consists of N clusters, with equal numbers of populati...

Inexpensive and/or readily available auxiliary variable, x , values may often be available at little or no cost. If these variables are highly correlated with the target variable, y , then use of ratio or regression estimators may greatly reduce sampling variance. These estimators are not unbiased, but bias is generally small compared to the target...

This chapter presents a formal quantitative treatment of material covered conceptually in Chapter 2, all with respect to equal probability with replacement (SWR) and without replacement selection simple random sampling, (SRS) of samples of size n from a finite population of size N . Small sample space examples are used to illustrate unbiasedness of...

The abundance of rare species of plants and animals may often prove difficult to estimate due to the isolated patchy distribution of individuals. Adaptive sampling may prove more effective than other sampling strategies for such species. In adaptive cluster sampling an initial SRS of population units is selected. Further adaptive sampling in the ne...

Equal probability selection is a special case of the general theory of probability sampling in which population units may be selected with unequal probabilities. Unequal selection probabilities are often based on auxiliary variable values which are measures of the sizes of population units, thus leading to the acronym (PPS)—“Probability Proportiona...

Attention is restricted to two - phase or double sampling. A large first-phase sample is used to generate a very good estimate of the mean or total of an auxiliary variable, x , which is relatively cheap to measure. Then, a second-phase sample is selected, usually from the first-phase sample, and both auxiliary and target variables are measured in...

In multi-stage sampling, there are two or more stages of sampling and the simplest version, which the chapter emphasizes is called two-stage sampling. In two-stage sampling, an initial first-stage sample of n primary units (or clusters) is selected. Then, at the second stage of sampling, mi subunits are selected from the Mi subunits in the selected...

Many ecological research and resource monitoring programs must deliver good estimates of both current resource status and long-term trend. The simple two-occasion context frames the trade-offs in design of surveys to achieve these objectives. If the objective is to estimate change in status (trend), then most precise estimation is achieved by full...

In many contexts it is difficult or impossible to select a simple random sample. For example, the number of units in the finite population, N , may not be known in advance, or it may not be feasible to assign labels to all units in the population and to select an SRS from these labels (e.g., crabs within boxes on a fishing vessel). Instead, one may...

In many ecological and natural resource settings, there may be a high degree of spatial structure or pattern to the distribution of target variable values across the landscape. For example, the number of trees per hectare killed by a bark beetle infestation may be exceptionally high in one region of a national forest and near zero elsewhere. In suc...

This chapter provides a conceptual, visual and non-quantitative presentation of the basic principles of sampling theory which are developed in formal quantitative fashion in subsequent chapters. Included are summaries of (a) basic terminology used throughout the text (population, sample, estimator, estimate), (b) components of a sampling strategy (...

We present a rigorous but understandable introduction to the field of sampling theory for ecologists and natural resource scientists. Sampling theory concerns itself with development of procedures for random selection of a subset of units, a sample, from a larger finite population, and with how to best use sample data to make scientifically and sta...

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 challeng...

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 methodolo...

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 m...

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 th...

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...

Designing and implementing natural resource monitoring is a challenging endeavor undertaken by many agencies, NGOs, and citizen groups worldwide. Yet many monitoring programs fail to deliver useful information for a variety of administrative (staffing, documentation, and funding) or technical (sampling design and data analysis) reasons. Programs ri...

The previous two chapters have presented the state-space model as a general framework for modelling population dynamics and discussed alternative ways of fitting SSMs to data. In this chapter, we address model formulation and model evaluation.

In the previous chapter, a sequence of matrices was used to model the sequence of subprocesses, birth, survival, movement, etc., which characterize population dynamics. We find this building block perspective attractive for at least two reasons: (1) it allows one to mentally “divide and conquer” sometimes complicated population dynamics processes;...

In this chapter, we develop a “building block” approach to defining population dynamics models, in which each building block corresponds to one biological process, and is represented by one matrix (Lebreton 1973; Lebreton and Isenmann 1976; Buckland et al. 2004, 2007). Matrix models are usually defined within a deterministic framework, but we will...

Borchers et al. (2002) explored methods for estimating the abundance of closed populations. When that book was being written, a biologist’s puzzled reaction was “What is a closed population?”. In reality, there is no such thing—populations only appear closed if you look at them for a short enough time period. Look at them for longer and they will c...

In Chap. 5, we recommended that formulation of population dynamics models should be guided by aims to answer specific scientific questions or assess or predict the effects of management actions. Management actions might target a specific life stage. For example, we might ask “How does removing wetland plants (such as bulrush or cattail) that have s...

There are many reasons why we are interested in how long wild animals survive. A particularly pressing one is so that we can evaluate the effects of climate and anthropomorphic changes. In an early example, North and Morgan (1979) demonstrated a link between winter temperature and the survival of grey herons, Ardea cinerea, using point-process mode...

This chapter is an overview of methods for using available data to make inferences about states and parameters of a state-space model. We call this “model fitting”, or as Hilborn and Mangel (1997) say, “confronting models with data”. Given a general SSM [Eqs. (3.3)–(3.5)], $$\displaystyle\begin{array}{rcl} \mbox{ Initial state pdf}&:& g_{0}(\mathbf...

In terms of modelling population dynamics, the mark-recapture literature has in recent years been dominated by methods for estimating survival, as described in Chap. 7. In this chapter, we consider open-population mark-recapture methods for estimating abundance, survival and births. We first summarise conventional methods (Seber 1973, 1982).

Studying rare and sensitive species is a challenge in conservation biology. The problem is exemplified by the case of the imperiled delta smelt Hypomesus transpacificus, a small delicate fish species endemic to the San Francisco Estuary, California. Persistent record-low levels of abundance and relatively high sensitivity to handling stress pose co...

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 essent...

Four species of pelagic fish of particular management concern in the upper San Francisco Estuary, California, USA, have declined precipitously since ca. 2002: delta smelt (Hypomesus transpacificus), longfin smelt (Spirinchus thaleichthys), striped bass (Morone saxatilis), and threadfin shad (Dorosoma petenense). The estuary has been monitored since...

We examined trends in abundance of four pelagic fish species (delta smelt, longfin smelt, striped bass, and threadfin shad) in the upper San Francisco Estuary, California, USA, over 40 years using Bayesian change point models. Change point models identify times of abrupt or unusual changes in absolute abundance (step changes) or in rates of change...

Future development and climate change pose potentially serious threats to estuarine fish populations around the world. We
examined how habitat suitability for delta smelt (Hypomesus transpacificus), a state and federally protected species, might be affected by changes in outflow in the San Francisco Estuary due to future
development and climate cha...

A multiyear study was carried out in the Sacramento–San Joaquin Delta system to examine the relationship between the survival of out-migrating Chinook salmon Oncorhynchus tshawytscha and the amount of water exported from the system by the two major pumping stations in the southern portion of the delta. Paired releases of groups of coded-wire-tagged...

Four species of pelagic fish of particular management concern in the upper San Francisco Estuary, California, USA, have declined precipitously since ca. 2002: delta smelt (Hypomesus transpacificus), longfin smelt (Spirinchus thaleichthys), striped bass (Morone saxatilis), and threadfin shad (Dorosoma petenense). The estuary has been monitored since...

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 r...

We compare two Monte Carlo (MC) procedures, sequential importance sampling (SIS) and Markov chain Monte Carlo (MCMC), for making Bayesian inferences about the unknown states and parameters of state-space models for animal populations. The procedures were applied to both simulated and real pup count data for the British grey seal metapopulation, as...

Increasing pressures on the environment are generating an ever-increasing need to manage animal and plant populations sustainably, and to protect and rebuild endangered populations. Effective management requires reliable mathematical models, so that the effects of management action can be predicted, and the uncertainty in these predictions quantifi...

Hidden process models are a conceptually useful and practical way to simultaneously account for process variation in animal population dynamics and measurement errors in observations and estimates made on the population. Process variation, which can be both demographic and environmental, is modeled by linking a series of stochastic and deterministi...

Bayesian hierarchical state-space models are a means of modeling fish population dynamics while accounting for both demographic and environmental stochasticity, observation noise, and parameter uncertainty. Sequential importance sampling can be used to generate posterior distributions for parameters, unobserved states, and random effects for popula...

This paper proposes a unified framework for defining and fitting stochastic, discrete-time, discrete-stage population dynamics models. The biological system is described by a state-space model, where the true but unknown state of the population is modelled by a state process, and this is linked to survey data by an observation process. All sources...

Dose-response calibration.

A mandate of Title 34 – Central Valley Project Improvement Act (of Public Law 102-575), CVPIA, is to "develop within three years of enactment and implement a program which makes all reasonable efforts to ensure that, by the year 2002, natural production of anadromous fish in Central Valley rivers and streams will be sustainable, on a long term basi...

We estimated the consumption of juvenile salmon Oncorhynchus spp. and steelhead O. mykiss by smallmouth bass Micropterus dolomieu in the tailrace and forebay of the Lower Granite Dam and compared this consumption with that in the two major river arms of the upper Lower Granite Reservoir, Snake River, Idaho–Washington. We examined over 9,700 smallmo...

We develop a unified framework for jointly defining population dynamics models and measurements taken on a population. The framework is a state-space model where the population processes are modelled by the state process and measurements are modelled by the observation process. In many cases, the expected value for the state process can be represen...

Products of multinomial models have been the standard approach to analysing animal release-recovery data. Two alternatives, a pseudo-likelihood model and a Bayesian nonlinear hierarchical model, are developed. Both approaches can to some degree account for heterogeneity in survival and capture probabilities over and above that accounted for by cova...

Inference for stochastic process models: incorporating model uncertainty
Buckland, S T, Kösters, N, Newman, K B and Atkinson, R. Inference for stochastic process models: incorporating model uncertainty, Proceedings of the International Biometric Conference, 221-229.

A quasi-likelihood model with a ridge parameter was developed to understand the factorspossibly associated with the survival of juvenile chinook salmon smolts outmigrating throughthe lower portions of the Sacramento river system. Coded-wire-tagged (CWT) chinooksalmon smolts were released at various locations within the river between the years 1979...

Using fishery recoveries from a tagged cohort of coho salmon, the ocean spatial-temporal abundance of the cohort is predicted using a state-space model. The model parameters, which reflect spatial distribution, mortality, and movement, vary considerably between different cohorts. To evaluate the effect of proposed management plans on a future cohor...

Summary A general description of individual animal behavior and history consists of three components, initial spatial location, mortality, and spatial translation. Flexibility and generation of competing theories arise from alternative formulations for any component. Individual animal models can be modified for spatially (and temporally) aggregated...

We compared the performance of sequential Gaussian simulation (sGs) and Markov-Bayes simulation (MBs) using relatively small samples taken from synthetic datasets. A moderate correlation (approximately r = 0.70) existed between a continuous primary variable and a continuous secondary variable. Given the small sample sizes, our objective was to dete...

A statistical model was constructed for the survival of juvenile chinook salmon smolts outmigrating through the lower portions of the Sacramento river system. Coded-wire-tagged (CWT) chinook salmon smolts were released at various locations within the river system between the years 1979 and 1995. Recoveries of these juvenile salmon in a lower river...

Bayesian methods provide a means of explicitly accounting for uncertainty in the choice of model used to interpret fisheries data. The probability of a given model being the correct model conditional on the data, the posterior probability, is a measure of the degree of belief and strength of evidence for the model. Bayesian model averaging uses the...

Variation in the unit leaf rate in trees, i.e. the weight of wood increment per unit of leaf area, arises from disproportionate changes in their rates of net photosynthesis and in the allocation of carbohydrates. Changes in unit leaf rate in response to variations in canopy density were investigated in a thinning experiment established in a 36 year...

The authors thank California Department of Fish and Game and the CALFED Ecosystem Restoration Program for financial support. We also thank Lyman McDonald and Randy Bailey for helpful suggestions and Bailey Environmental and CH2M-Hill for financial support during earlier stages of this work.