Recreational fisheries (RF) are complex social-ecological systems that play an important role in aquatic environments while generating significant social and economic benefits around the world. The nature of RF is diverse and rapidly evolving, including the participants, their priorities and behaviors, and the related ecological impacts and social and economic benefits. RF can lead to negative ecological impacts, particularly through overexploitation of fish populations and spread of non-native species and genotypes through stocking. Hence, careful management and monitoring of RF is essential to sustain these ecologically and socioeconomically important resources. This special issue on recreational fisheries contains diverse research, syntheses, and perspectives that highlight the advances being made in RF research, monitoring, management, and practice, which we summarize here. Co-management actions are rising, often involving diverse interest groups including government and non-government organizations; applying collaborative management practices can help balance social and economic benefits with conservation targets. Technological and methodological advances are improving the ability to monitor biological, social, and economic dynamics of RF, which underpin the ability to maximize RF benefits through management actions. To ensure RF sustainability, much research focuses on the ecological aspects of RF, as well as the development of management and angling practices that reduce negative impacts on fish populations. For example, angler behavior can be influenced to conform to conservation-minded angling practices through regulations, but is often best accomplished through growing bottom-up social change movements. Anglers can also play an important role in fisheries monitoring and conservation, including providing data on fish abundance and assemblages (i.e., citizen science). The increasing impacts that growing human populations are having on the global environment are threatening many of the natural resources and ecosystem services they provide, including valuable RF. However, with careful development of research initiatives, monitoring and management, sustainable RF can generate positive outcomes for both society and natural ecosystems and help solve allocation conflicts with commercial fisheries and conservation. which ultimately determines the degree of environmental impacts across aquatic landscapes (

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.

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‐scale patterns—some of which may be undesirable, such as regional overfishing. Hence, understanding the scale at which the behavior‐mediated mechanisms and processes identified in this article operate is critical for managing for the sustainability of spatially structured recreational fisheries. We conclude our study by providing relevant research stimuli for the future.

PURPOSE Rural low-income African American patients with diabetes have traditionally poorer clinical outcomes and limited access to state-of-the-art diabetes care. We determined the effectiveness of a redesigned primary care model on patients' glycemic, blood pressure, and lipid level control. METHODSIn 3 purposively selected, rural, fee-for-service, primary care practices, African American patients with type 2 diabetes received point-of-care education, coaching, and medication intensifi cation from a diabetes care management team made up of a nurse, pharmacist, and dietitian. In 5 randomly selected control practices matched for practice and patient characteristics, African American patients received usual care. Using univariate and multivariate adjusted models, we evaluated the effects of the intervention on intermediate (median 18 months) and long-term (median 36 months) changes in glycated hemoglobin (hemoglobin A 1c ) levels, blood pressure, and lipid levels, as well as the proportion of patients meeting target values.RESULTS Among 727 randomly selected rural African American diabetic patients (368 intervention, 359 control), intervention patients had a signifi cantly greater reduction in mean hemoglobin A 1c levels at intermediate (-0.5 % vs -0.2%; P <.05) and long-term (-0.5% vs -0.10%; P <.005) follow-up in univariate and multivariate models. The proportion of patients achieving a hemoglobin A 1c level of less than 7.5% (68% vs 59%, P <.01) and/or a systolic blood pressure of less than 140 mm Hg (69% vs 57%, P <.01) was also signifi cantly greater in intervention practices in multivariate models.CONCLUSION Redesigning care strategies in rural fee-for-service primary care practices for African American patients with established diabetes results in significantly improved glycemic control relative to usual care.

Several new growth models have been proposed to account for the life‐history trade‐offs 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. 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. The Bayesian Markov chain Monte Carlo 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 growth trajectories and inferring growth‐associated trait variation and environmental effects across multiple populations. Conventional growth models provide limited inference on life history. Many biphasic growth models can provide direct inference on multiple life‐history traits, but 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.