Brian J. Irwin

Detecting Temporal Trends in Freshwater Fisheries Surveys: Statistical Power and the Important Linkages between Management Questions and Monitoring Objectives

ABSTRACT Monitoring to detect temporal trends in biological and habitat indices is a critical component of fisheries management. Thus, it is important that management objectives are linked to monitoring objectives. This linkage requires a definition of what constitutes a management-relevant “temporal trend.” It is also important to develop expectations for the amount of time required to detect a trend (i.e., statistical power) and for choosing an appropriate statistical model for analysis. We provide an overview of temporal trends commonly encountered in fisheries management, review published studies that evaluated statistical power of long-term trend detection, and illustrate dynamic linear models in a Bayesian context, as an additional analytical approach focused on shorter term change. We show that monitoring programs generally have low statistical power for detecting linear temporal trends and argue that often management should be focused on different definitions of trends, some of which can be better...

An Operating Model for the Integrated Pest Management of Great Lakes Sea Lampreys

Models of entire managed systems, known as operating models or management strategy evaluation (MSE) models, have been developed in recent years to more fully account for uncertainty in multiple steps of fishery manage- ment. Here we describe an operating model of sea lamprey management in the Great Lakes and use the model to compare alternative management strategies for sea lamprey control in Lake Michigan. Control of sea lampreys is mainly achieved through the application of chemical lampricides that target stream-dwelling larvae before they become parasites. The op- erating model simulated uncertainty due to process variation in larval population dynamics, the accuracy of population as- sessments used to direct selection of areas to be chemically treated, and the effectiveness of these treatments. We used the operating model to compare the performance of stream selection strategies that either rely on assessments to direct chemi- cal treatments or eliminate the assessment process altogether by relying on prior but uncertain knowledge of stream-level sea lamprey growth rates to specify a fixed schedule for chemical treatments. The fixed schedule strategy led to a modest improvement in expected suppression of parasitic sea lamprey abundance over the assessment-based strategy so long as assessment cost savings were allocated to chemical treatment when assessment was not used to select streams for treat- ment. We also evaluated the sensitivity of the assessment-based strategy to differing but plausible levels of assessment uncertainty. A moderate reduction in assessment uncertainty led to a large increase in suppression of parasitic sea lamprey abundance for the assessment-based selection strategy, emphasizing the importance of both accurately measuring and re- ducing assessment uncertainty.

Adapting Inland Fisheries Management to a Changing Climate

Natural resource decision makers are challenged to adapt management to a changing climate while balancing short-term management goals with long-term changes in aquatic systems. Adaptation will require developing resilient ecosystems and resilient management systems. Decision makers already have tools to develop or ensure resilient aquatic systems and fisheries such as managing harvest and riparian zones. Because fisheries management often interacts with multiple stakeholders, adaptation strategies involving fisheries managers and other partners focused on land use, policy, and human systems, coupled with long-term monitoring, are necessary for resilient systems. We show how agencies and organizations are adapting to a changing climate in Minnesota and Ontario lakes and Montana streams. We also present how the Florida Fish and Wildlife Commission created a management structure to develop adaptation strategies. These examples demonstrate how organizations and agencies can cope with climate change effects on...

Estimating Spatial and Temporal Components of Variation for Fisheries Count Data Using Negative Binomial Mixed Models

Abstract Partitioning total variability into its component temporal and spatial sources is a powerful way to better understand time series and elucidate trends. The data available for such analyses of fish and other populations are usually nonnegative integer counts of the number of organisms, often dominated by many low values with few observations of relatively high abundance. These characteristics are not well approximated by the Gaussian distribution. We present a detailed description of a negative binomial mixed-model framework that can be used to model count data and quantify temporal and spatial variability. We applied these models to data from four fishery-independent surveys of Walleyes Sander vitreus across the Great Lakes basin. Specifically, we fitted models to gill-net catches from Wisconsin waters of Lake Superior; Oneida Lake, New York; Saginaw Bay in Lake Huron, Michigan; and Ohio waters of Lake Erie. These long-term monitoring surveys varied in overall sampling intensity, the total catch ...

Transboundary Fisheries Science: Meeting the Challenges of Inland Fisheries Management in the 21st Century

Managing inland fisheries in the 21st century presents several obstacles, including the need to view fisheries from multiple spatial and temporal scales, which usually involves populations and resources spanning sociopolitical boundaries. Though collaboration is not new to fisheries science, inland aquatic systems have historically been managed at local scales and present different challenges than in marine or large freshwater systems like the Laurentian Great Lakes. Therefore, we outline a flexible strategy that highlights organization, cooperation, analytics, and implementation as building blocks toward effectively addressing transboundary fisheries issues. Additionally, we discuss the use of Bayesian hierarchical models (within the analytical stage), due to their flexibility in dealing with the variability present in data from multiple scales. With growing recognition of both ecological drivers that span spatial and temporal scales and the subsequent need for collaboration to effectively manage heterog...

Consideration of reference points for the management of renewable resources under an adaptive management paradigm

The success of natural resource management depends on monitoring, assessment and enforcement. In support of these efforts, reference points (RPs) are often viewed as critical values of management-relevant indicators. This paper considers RPs from the standpoint of objective-driven decision making in dynamic resource systems, guided by principles of structured decision making (SDM) and adaptive resource management (AM). During the development of natural resource policy, RPs have been variously treated as either ‘targets’ or ‘triggers’. Under a SDM/AM paradigm, target RPs correspond approximately to value-based objectives, which may in turn be either of fundamental interest to stakeholders or intermediaries to other central objectives. By contrast, trigger RPs correspond to decision rules that are presumed to lead to desirable outcomes (such as the programme targets). Casting RPs as triggers or targets within a SDM framework is helpful towards clarifying why (or whether) a particular metric is appropriate. Further, the benefits of a SDM/AM process include elucidation of underlying untested assumptions that may reveal alternative metrics for use as RPs. Likewise, a structured decision-analytic framework may also reveal that failure to achieve management goals is not because the metrics are wrong, but because the decision-making process in which they are embedded is insufficiently robust to uncertainty, is not efficiently directed at producing a resource objective, or is incapable of adaptation to new knowledge.

Detecting Unusual Temporal Patterns in Fisheries Time Series Data

AbstractLong-term sampling of fisheries data is an important source of information for making inferences about the temporal dynamics of populations that support ecologically and economically important fisheries. For example, time series of catch-per-effort data are often examined for the presence of long-term trends. However, it is also of interest to know whether certain sampled locations are exhibiting temporal patterns that deviate from the overall pattern exhibited across all sampled locations. Patterns at these “unusual” sites may be the result of site-specific abiotic (e.g., habitat) or biotic (e.g., the presence of an invasive species) factors that cause these sites to respond differently to natural or anthropogenic drivers of population dynamics or to management actions. We present a Bayesian model selection approach that allows for detection of unique sites—locations that display temporal patterns with documentable inconsistencies relative to the overall global average temporal pattern. We applie...

Using Variance Structure to Quantify Responses to Perturbation in Fish Catches

AbstractWe present a case study evaluation of gill-net catches of Walleye Sander vitreus to assess potential effects of large-scale changes in Oneida Lake, New York, including the disruption of trophic interactions by double-crested cormorants Phalacrocorax auritus and invasive dreissenid mussels. We used the empirical long-term gill-net time series and a negative binomial linear mixed model to partition the variability in catches into spatial and coherent temporal variance components, hypothesizing that variance partitioning can help quantify spatiotemporal variability and determine whether variance structure differs before and after large-scale perturbations. We found that the mean catch and the total variability of catches decreased following perturbation but that not all sampling locations responded in a consistent manner. There was also evidence of some spatial homogenization concurrent with a restructuring of the relative productivity of individual sites. Specifically, offshore sites generally becam...

Optimization of scat detection methods for a social ungulate, the wild pig, and experimental evaluation of factors affecting detection of scat

Collection of scat samples is common in wildlife research, particularly for genetic capture-mark-recapture applications. Due to high degradation rates of genetic material in scat, large numbers of samples must be collected to generate robust estimates. Optimization of sampling approaches to account for taxa-specific patterns of scat deposition is, therefore, necessary to ensure sufficient sample collection. While scat collection methods have been widely studied in carnivores, research to maximize scat collection and noninvasive sampling efficiency for social ungulates is lacking. Further, environmental factors or scat morphology may influence detection of scat by observers. We contrasted performance of novel radial search protocols with existing adaptive cluster sampling protocols to quantify differences in observed amounts of wild pig (Sus scrofa) scat. We also evaluated the effects of environmental (percentage of vegetative ground cover and occurrence of rain immediately prior to sampling) and scat characteristics (fecal pellet size and number) on the detectability of scat by observers. We found that 15- and 20-m radial search protocols resulted in greater numbers of scats encountered than the previously used adaptive cluster sampling approach across habitat types, and that fecal pellet size, number of fecal pellets, percent vegetative ground cover, and recent rain events were significant predictors of scat detection. Our results suggest that use of a fixed-width radial search protocol may increase the number of scats detected for wild pigs, or other social ungulates, allowing more robust estimation of population metrics using noninvasive genetic sampling methods. Further, as fecal pellet size affected scat detection, juvenile or smaller-sized animals may be less detectable than adult or large animals, which could introduce bias into abundance estimates. Knowledge of relationships between environmental variables and scat detection may allow researchers to optimize sampling protocols to maximize utility of noninvasive sampling for wild pigs and other social ungulates.