Kyle L. Wilson

Understanding and Managing Social–Ecological Feedbacks in Spatially Structured Recreational Fisheries: The Overlooked Behavioral Dimension

Recreational fisheries are empirically tractable examples of social–ecological systems (SESs) that are characterized by complex interactions and feedbacks ranging from local to regional scales. The feedbacks among the three key compartments of the recreational fisheries SES—individual fish and populations, regionally mobile anglers, and regional and state-level fisheries managers—are strongly driven by behavior, but they are poorly understood. We review and identify factors, antecedents to behaviors, and behaviors most important to the outcomes of the coupled SES of recreational fisheries, which emerge from a range of social–ecological interactions. Using this information, we identify data gaps, suggest how to reduce uncertainty, and improve management advice for recreational fisheries focusing on open-access situations in inland fisheries. We argue that the seemingly micro-scale and local feedbacks between individual fish, fish populations, anglers, and managers lead to the emergence of important macro-s...

Combining Samples from Multiple Gears Helps to Avoid Fishy Growth Curves

AbstractSize-at-age information is critical in estimating growth parameters (e.g., the von Bertalanffy growth function [VBGF]) that are used to assess fish populations. Due to gear selectivity, single sampling methods rarely sample all ages or all sizes equally well. Most growth estimates rely on samples from a single gear or a haphazard combination of gears, potentially leading to biased and imprecise growth parameter estimates. We evaluated the efficacy of combining samples from two gears with different size selectivity to estimate VBGF parameters; we then applied that approach to a case study on the Lochloosa Lake (Florida) population of Black Crappies Pomoxis nigromaculatus. Simulated age- and size-structured populations were randomly sampled with two gears characterized by different size-selectivity curves (one gear was selective for smaller fish; the other was selective for larger fish). Maximum likelihood VBGF estimates obtained for each gear separately were compared with estimates from a combined ...

Interaction of ecological and angler processes: experimental stocking in an open access, spatially structured fishery.

Effective management of socioecological systems requires an understanding of the complex interactions between people and the environment. In recreational fisheries, which are prime examples of socioecological systems, anglers are analogous to mobile predators in natural predator-prey systems, and individual fisheries in lakes across a region are analogous to a spatially structured landscape of prey patches. Hence, effective management of recreational fisheries across large spatial scales requires an understanding of the dynamic interactions among ecological density dependent processes, landscape-level characteristics, and angler behaviors. We focused on the stocked component of the open access rainbow trout (Oncorhynchus mykiss) fishery in British Columbia (BC), and we used an experimental approach wherein we manipulated stocking densities in a subset of 34 lakes in which we monitored angler effort, fish abundance, and fish size for up to seven consecutive years. We used an empirically derived relationship between fish abundance and fish size across rainbow trout populations in BC to provide a measure of catch-based fishing quality that accounts for the size-abundance trade off in this system. We replicated our experimental manipulation in two regions known to have different angler populations and broad-scale access costs. We hypothesized that angler effort would respond to variation in stocking density, resulting in spatial heterogeneity in angler effort but homogeneity in catch-based fishing quality within regions. We found that there is an intermediate stocking density for a given lake or region at which angler effort is maximized (i.e., an optimal stocking density), and that this stocking density depends on latent effort and lake accessibility. Furthermore, we found no clear effect of stocking density on our measure of catch-based fishing quality, suggesting that angler effort homogenizes catch-related attributes leading to an eroded relationship between stocking density and catch-based fishing quality at the timescale of annual surveys. We conclude that declines in fishing quality resulting from understocking (due to declines in catch rate with low fish abundance) and overstocking (due to suppressed growth and limited recruitment at high density) give an optimal stocking rate that depends on accessibility and latent effort.

Use of underwater video to assess freshwater fish populations in dense submersed aquatic vegetation

Underwater video cameras (UVC) provide a non-lethal technique to sample fish in dense submersed aquatic vegetation. Fish often inhabit densely vegetated areas, but deficiencies of most sampling gears bias relative abundance estimates that inform fisheries management. This study developed methods using UVC to estimate relative abundance in dense vegetation using three experimental ponds covered with surface-matted hydrilla (Hydrilla verticillata) stocked at different densities of Lepomis spp. and largemouth bass (Micropterus salmoides). We conducted UVC point counts over 13 weeks to measure relative fish abundance and occurrence from video analysis. Ponds were then drained to obtain true fish densities. In total, fish were detected in 55% of all counts and juvenile and adult Lepomis spp. and largemouth bass were enumerated. End-of-season true fish densities ranged across ponds (from 52 to 37000 fishha � 1 ). Additionally, pond 2s true density changed substantially from 370 to 12300 fishha � 1 . True population size was accurately reflected in differences in estimated relative abundances obtained from fish counts, as in pond 2 where mean fish counts increased from 0.10 in week 1 to 2.33 by week 13. Underwater video accurately and precisely quantified relative abundance at naturally-occurring fish densities, but this success was reduced at low densities. Additional keywords: complex habitats, fish sampling, Hydrilla verticillata, invasive aquatic plants, largemouth bass, sunfish, video cameras.

Growing the biphasic framework: Techniques and recommendations for fitting emerging growth models

1.Several new growth models have been proposed to account for the life-history tradeoffs that occur when indeterminately-growing species allocate energy between somatic growth and reproduction. These models can improve the understanding of lifetime growth and life-history, but can be more difficult to fit than conventional growth models. Increased data demands, multiple growth phases, and increased parameterization all serve as barriers to the adoption and proper use of these new models. 2.We review and comment on confounding issues during model fitting for several of these models, and provide advice on surmounting such issues. We then simulation test an example model, the Lester biphasic growth model, using several common fitting approaches. We highlight the biases and precision of each approach, and provide guiding documents using R and JAGS code. 3.The Bayesian MCMC and likelihood profiling approaches generally provided the best fits. Simpler approaches can be unbiased and precise if sampled data are of relatively high quality (e.g., moderate sample sizes for juvenile and adult phases) and model assumptions are met. Bayesian hierarchical approaches can accommodate more complicated data scenarios (e.g., unbalanced design across multiple populations); we provide an example of such an approach by recovering multi-population growth trajectories and inferring growth-associated trait variation and environmental effects across populations. 4.Conventional growth models provide limited inference on life-history. Many biphasic growth models can provide direct inference on multiple life-history traits, but they can be difficult to fit. The recommended approaches herein provide a path forward for fitting biphasic growth models in a variety of scenarios, allowing for wider application and tests of life-history and ecological theory. This article is protected by copyright. All rights reserved.

How to Navigate Fisheries Education and Employment

How to Navigate Fisheries Education and Employment Andrew K. Carlson, Karen M. Dunmall, Ross E. Boucek, Nicholas W. Cole, Janice A. Kerns, Rebecca M. Krogman, M. Clint Lloyd, Vivian M. Nguyen, Tracy R. Wendt, Shannon L. White & Kyle L. Wilson a South Dakota State University, Department of Natural Resource Management, NPBL 138, Box 2140B, Brookings, SD 57007. E-mail: b University of Manitoba, Department of Biological Sciences, Winnipeg, MB, Canada c Florida International University, Department of Biology, Miami, FL d University of Nebraska, School of Natural Resources, Fisheries and Wildlife Cooperative Research Unit, Lincoln, NE e Wisconsin Cooperative Fishery Research Unit, College of Natural Resources, University of Wisconsin–Stevens Point, Stevens Point, WI f Iowa Department of Natural Resources, Chariton, IA g Mississippi State University, Department of Wildlife, Fisheries & Aquaculture, Mississippi State, MS h Carleton University, Ottawa, ON, Canada i University of Montana, Department of Ecosystem & Conservation Science, Missoula, MT j Department of Ecosystem Science Management, The Pennsylvania State University, University Park, PA k University of Calgary, Department of Biological Sciences, Calgary, AB, Canada Published online: 21 May 2015.