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.

Size‐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 VBGF fitted to data from both gears. In every simulated scenario, a combined‐gear approach reduced bias and increased precision for estimating the VBGF, but the gear‐specific proportions that improved VBGF estimates depended on size selectivity. The VBGF estimates for the Black Crappie population showed that the combined‐gear method yielded intermediate parameter values relative to single‐gear approaches based on (1) trawl sampling (fishery‐independent survey) and (2) angler harvest (as determined from carcass collections; fishery‐dependent data). Furthermore, the combined‐gear approach had greater precision in individual parameter estimates and much less variance than single‐gear approaches when estimating the VBGF. Combining data from two gears can increase sample representativeness, leading to improvements in VBGF estimation. Such approaches can reduce uncertainty in VBGF estimation and can provide insight into key demographic processes occurring in fish populations for which ontogeny and gear selectivity lead to imperfect sampling. Received January 22, 2015; accepted July 30, 2015

Sustainable management of fisheries resources requires an understanding of spatial and temporal interplay between targeted fish populations and anglers. We conducted a field study comparing spatial patterns in recreational angler effort to fish distribution in a Florida lake. Over one year, spatial locations of Largemouth Bass (Micropterus salmoides) anglers and Largemouth Bass were surveyed. Over 90% of anglers were fishing within 50 m from shore and one‐third of fish were located offshore at any given time. This spatial patterning suggested that fish located in areas not targeted by anglers were less vulnerable to angling and, thus, anglers were not distributed according to the ideal free distribution. However, tag return data of telemetered fish showed similar catch trends in both onshore and offshore habitats, indicating that all fish were equally vulnerable to angling and anglers were ideally distributed. Informed use of spatial and/or temporal fishery regulations should consider fish and angler behavior.

For many fish species, variation in somatic growth can drive changes in population productivity through the dependence of survival, fecundity, and reproductive schedules on size. Changes in growth arise from many density-dependent and-independent sources. Many assessments of temporal variation in somatic growth rely on methods that lack biological underpinning in the model structure to describe observed relationships between size and environmental conditions. However, biologically-based growth models are needed to examine how density-dependent andindependent factors influence the underlying process of growth (i.e., growth = anabolic factorscatabolic factors). Our objective was to extend biologically-based growth models to estimate temporal variation in somatic growth patterns. A set of hierarchical non-linear mixed effects models based off the von Bertalanffy model and length-weight relationship were developed. We applied the models to a Black Crappie (BC; Pomoxis nigromaculatus) population to assess the impacts of density, chlorophyll A concentration (Chl-a), water level, and temperature on somatic growth. Growth in length was influenced by temperature, with fastest growth at optimal temperatures and slower growth when temperatures were coldest (48% slower) or hottest (82% slower), and was negatively related to density, with 25% slower growth at high density. Weight of age-0 BC was negatively related to chlorophyll A, individuals were 18% lighter at high Chl-a, and positively to temperature, individuals were 10% lighter when water was cooler. Finally, growth in weight of age-1+ BC was negatively related to all factors, with 5-11% lighter fish at high densities, Chl-a, water levels, and temperatures. The model structure developed in this manuscript has broad applicability to populations that have time series data of size-at-age observations, growth increments, or back-calculated sizes and adequate environmental data.

Monitoring is an essential component in ecosystem management, and leveraging existing data sources for multiple species of interest can be one effective way to enhance information for management agencies. Here, we analyzed juvenile Chinook Salmon (Oncorhynchus tshawytscha) bycatch data that has been collected by the recently established Enhanced Delta Smelt Monitoring program (EDSM), a survey designed to estimate the abundance and distribution of the San Francisco Estuary’s (estuary) endangered Delta Smelt (Hypomesus transpacificus). Two key aspects of the EDSM program distinguish it from other fish surveys in the estuary: a stratified random sampling design and the spatial scale of its sampling effort. We integrated the EDSM data set with other existing surveys in the estuary, and used an occupancy model to assess differences in the probability of detecting Delta Smelt across gear types. We saw no large-scale differences in size selectivity, and while detection probability varied among gear types, cumulative detection probability for EDSM was comparable to other surveys because of the program’s use of replicate tows. Based on our occupancy model and sampling effort in the estuary during spring of 2017 and 2018, we highlighted under-sampled regions that saw improvements in monitoring coverage from EDSM. Our analysis also revealed that each sampling method has its own benefits and constraints. Although the use of random sites with replicates, as conducted by EDSM, can provide more statistically robust abundance estimates relative to traditional methods, the use of fixed stations and simple methods such as beach seining may provide a more cost-effective way to monitor salmon occurrence in certain regions of the estuary. Leveraging the strengths of each survey’s method can enable stronger inferences on salmon abundance and distribution. Careful consideration of these trade-offs is crucial as the management agencies of the estuary continue to adapt and improve their monitoring programs.

Bag and size limits are commonly used in recreational fisheries management, but these regulations are often treated as separate management tools. This effectively overlooks how bag and size limits can be simultaneously used to achieve multiple management outcomes (e.g., reduce exploitation, prevent overfishing, maximize angler acceptance, etc.). Our objectives were to combine data‐limited stock assessment methods with an angler catch simulation and a yield‐per‐recruit model to assess the effectiveness of bag and size limits to decrease exploitation rates and improve the spawning potential ratio (SPR). We then applied these methods to the Kipawa Lake Walleye Sander vitreus fishery that has experienced overfishing and poor fishing quality. Using data‐limited assessment methods, the exploitation rate was estimated at 0.45 (95% CI = 0.32–0.59) and the population was overfished (mean SPR = 0.06; 95% CI = 0.02–0.13). Bag limits significantly reduced total harvest when extremely restrictive (i.e., reduced to one fish per angler from the current limit of six), but changes in bag limits alone were not sufficient to prevent overharvest because SPR remained below 0.35. Size limits could be used to prevent overharvest with narrow harvest slots (up to a 14‐cm slot range with a minimum harvestable size greater than 32 cm) or large minimum size limits (>52 cm) at the current bag limit of six. When bag limits were reduced to one or two fish per day, harvest windows could be 3–13 cm larger and minimum length limits could be 3–12 cm lower to prevent overharvest. This analysis outlines a relatively simple and effective method that can be applied using data commonly collected in annual agency surveys to predict which regulatory combinations can be used to prevent overharvest, reduce exploitation rates, and maximize angler satisfaction and acceptance of regulations. Finally, the data and model code are included in the Supplement and can be easily applied to other data limited fisheries.

Monitoring is an essential component in ecosystem management, and leveraging existing data sources for multiple species of interest can be one effective way to enhance information for management agencies. Here, we analyzed juvenile Chinook Salmon (Oncorhynchus tshawytscha) bycatch data that has been collected by the recently established Enhanced Delta Smelt Monitoring program (EDSM), a survey designed to estimate the abundance and distribution of the San Francisco Estuary’s (estuary) endangered Delta Smelt (Hypomesus transpacificus). Two key aspects of the EDSM program distinguish it from other fish surveys in the estuary: a stratified random sampling design and the spatial scale of its sampling effort. We integrated the EDSM data set with other existing surveys in the estuary, and used an occupancy model to assess differences in the probability of detecting Delta Smelt across gear types. We saw no large-scale differences in size selectivity, and while detection probability varied among gear types, cumulative detection probability for EDSM was comparable to other surveys because of the program’s use of replicate tows. Based on our occupancy model and sampling effort in the estuary during spring of 2017 and 2018, we highlighted under-sampled regions that saw improvements in monitoring coverage from EDSM. Our analysis also revealed that each sampling method has its own benefits and constraints. Although the use of random sites with replicates, as conducted by EDSM, can provide more statistically robust abundance estimates relative to traditional methods, the use of fixed stations and simple methods such as beach seining may provide a more cost-effective way to monitor salmon occurrence in certain regions of the estuary. Leveraging the strengths of each survey’s method can enable stronger inferences on salmon abundance and distribution. Careful consideration of these trade-offs is crucial as the management agencies of the estuary continue to adapt and improve their monitoring programs.

No abstract