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Article

Dynamic Interdependence between Anglers and Fishes in Spatially Coupled Inland Fisheries

by
Scott F. Collins
1,*,
Matthew J. Diana
2,3 and
David H. Wahl
2
1
Department of Natural Resources Management, Texas Tech University, Lubbock, TX 79409, USA
2
Kaskaskia Biological Station, Illinois Natural History Survey, University of Illinois, 1235 County Road 1000N, Sullivan, IL 61951, USA
3
Michigan Department of Natural Resources, 621 N. 10th Street, Plainwell, MI 49080, USA
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(16), 10218; https://doi.org/10.3390/su141610218
Submission received: 24 July 2022 / Revised: 9 August 2022 / Accepted: 12 August 2022 / Published: 17 August 2022
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Abstract

:
The cumulative harvest pressure exerted by recreational anglers can be intense in some locations. Sustainable management and conservation of inland fisheries requires an understanding of the spatial ecology of fish-angler interactions (e.g., direct, indirect, and feedback). Advancement towards this goal requires study of the complex interdependencies of human and natural systems, which can be achieved, in part, by looking beyond the wetted confines of individual waterbodies towards the broader angling landscape. It has been hypothesized that fish stocks should experience strong reductions in areas near large aggregations of recreational anglers where fishing effort is presumed to be greatest. To test this hypothesis, we examined a complex of direct, indirect, and feedback effects among recreational anglers, bluegill sunfish Lepomis macrochirus, and largemouth bass Micropterous salmoides across inland recreational fisheries (n = 29 reservoirs) using path analysis and structural equation modeling. We found that recreational anglers imparted detectable effects on recreational bluegill (direct) and bass (indirect) fisheries across the landscape, which we attributed to (1) short travel distances of individuals at local scales (<40 km), and (2) a spatially and numerically heterogeneous distribution of anglers (i.e., anglers within counties) at the regional scale. Our study identified the presence of an emergent landscape-scale feedback, driven by angler numbers, mediated via angling effects on bluegill and bass populations, and which manifested as spatially variable movements of anglers. These dynamics collectively shaped inland fisheries across the landscape via a suite of direct, indirect, and feedback effects and highlight the complex relationships between fishes and anglers. Consideration of direct and indirect effects of angling pressure from the landscape should aid in prioritizing or identifying areas in need of management, conservation, public outreach, and education, and improve understanding of how changes to one or many species may feedback to other social, ecological, and economic systems.

1. Introduction

Of the >7 billion people on Earth, an estimated ~10% engage in recreational angling, and in some countries participation rates can be far greater [1,2,3,4]. Harvest by recreational anglers was once considered benign when compared to large commercial fisheries operations [5]. In actuality, the cumulative pressure exerted by recreational anglers can have detrimental effects on inland fisheries [6,7,8,9]. Collectively, the growth of cities in size and number [10], the importance of water and people [11,12], the popularity of recreational angling [1,2,13,14], and the critical importance of freshwater fishes for food security [14,15,16] suggests that inland fisheries near large or growing populations may be more prone to overexploitation. Sustainable management and conservation of fishes within these fisheries therefore requires an understanding of the ecology of fish-angler interactions (e.g., direct, indirect, and feedback). Advancement towards this goal will require study of the complex interdependencies of human and natural systems, which can be achieved, in part, by looking beyond the wetted confines of individual waterbodies towards the broader angling landscape [9,17,18].
Recreational fisheries are spatially structured social-ecological systems where individual waterbodies are linked by the movements of anglers [19,20,21,22,23]. At micro-scales, both catch and non-catch related factors including the proximity of available waterbodies, the costs of travel (e.g., time and money), the quality or reputation of a fishery, and site amenities collectively influence where anglers decide to go fishing [4,24,25]. At macro-scales that exceed the movements of individual anglers, the spatial distribution and number of anglers should affect patterns of fish abundance across fisheries, and be more pronounced for fishes with longer life spans, caught in large numbers, or harvested for consumption. Understanding the dynamics of spatially coupled inland fisheries requires knowledge of the direct, indirect, and feedback linkages among fishes and anglers; however, studies describing the complex interdependencies of anglers and fish across many inland fisheries are few but growing [23,24,25,26,27]. Such a knowledge gap hinders the capacity to understand and anticipate how changing social (e.g., attitudes towards fishing), economic (e.g., costs of recreational angling), and ecological (e.g., human-mediated effects of climate change) systems may affect, or be affected by, the dynamics of inland fisheries.
It has been hypothesized that the reduction or collapse of fish stocks should occur in areas near large aggregations of recreational anglers where fishing effort is presumed to be greatest [6,20,28]. If the effect of the surrounding angling population is strong, patterns should emerge across the landscape, as top-down effects of predation often supersede bottom-up processes (i.e., asymmetrical; [29,30]) even in enriched environments [31]. Although examinations of angler-fish dynamics across a landscape of freshwater fisheries is limited [20,24,28], findings indicate that angling effort originating from large population centers can be substantial. To date, study of the spatial coupling of inland fisheries has focused on anglers with a single fish species, yet most fisheries contain many species of interest to anglers. Any changes to one fish species may directly or indirectly affect the numbers of other species within the community (e.g., by-catch or food web alterations). Such changes may also affect fishing effort at other nearby lakes as anglers decide which waterbody to visit [20].
Here, we examined regional-scale linkages between human and natural systems using inland recreational fisheries as a model system. We examined a complex of direct, indirect, and feedback effects among recreational anglers, bluegill sunfish Lepomis macrochirus (Rafinesque, 1819), and largemouth bass Micropterous salmoides (Lacepède, 1802). Recreational fisheries (n = 29 reservoirs) were distributed across the state of Illinois (USA), and were located near small rural communities, medium sized cities, and a large urban agglomeration (i.e., population >5 million; city and its contiguous suburbs; Figure 1). The Chicago Metropolitan Statistical Area (CMSA) is the most densely populated area in Illinois, with an estimated ~9.5 million people. Notably, there is no commercial fishery for either fish species; thus, angling pressure is assumed to be solely by recreational means. Our objectives were to: (1) characterize the recreational angling populace across the landscape; and (2) examine the direct, indirect, and feedback relationships between angler and fish populations across the landscape.

2. Materials and Methods

2.1. Characteristics of the Angling Landscape

Data from publicly available sources were used to characterize the recreational angling landscape of Illinois, USA. The abundance and spatial distribution of recreational anglers across the state of Illinois was determined from resident license sales (ages 16+) by county for 2006 using data from the Illinois Department of Natural Resources. Henceforth, license sales are referred to as angler abundance. Population sizes for Illinois counties for 2006 were obtained from the U.S. Census Bureau (www.census.gov) and used in concert with angler abundance to estimate angler participation rates (% anglers county−1; [4]). Angler fishing effort for each county (total days fished by county) was estimated by multiplying the number of resident anglers by the average angling effort per angler (20 days angler−1 [32]). Average one-way distances (km) traveled by recreational anglers to each reservoir were obtained from technical reports [33,34,35] and used to examine spatial patterns in angler movements in relation to fish populations, as the movements of anglers can vary spatially [36,37]. Landscape position was defined as the proximity of a reservoir to Chicago, IL (Lat. 41.8337329, Long., −87.7321555), and was determined by calculating the shortest one-way driving distance (km) in Google Maps.

2.2. Fish Abundance

Catch per unit effort is a metric of relative abundance and is often used to characterize broad scale patterns of overfishing [7]. The relative abundance (catch per unit effort) of bluegill and largemouth bass in each reservoir was determined in the fall of each year via a boat electrofishing unit (Smith Root Type VI, three phase 240 V AC). The fall season was examined to best reflect the cumulative effects of angling pressure over a year. Spring seasons were avoided to minimize disturbing fish reproduction. Populations of bluegill and largemouth bass, hereafter bass, were quantified annually in 29 reservoirs from 2001 to 2005 [38]. At each reservoir, three 0.5-h shoreline transects were sampled (1.5 h sampling effort per lake). Relative abundances of each species were then averaged across years.

2.3. Model Development and Analyses

Following an initial examination of plausible models (Supplementary Materials: Table S1, Figure S1), a candidate model was identified to describe relationships among a reservoir’s proximity to a large population center (proximity to the CMSA), angler populations (angler-abundance county−1; mean angler travel distances (one-direction, km)), and bluegill and bass populations. The a priori model provided a framework for the analysis of data using path analysis and structural equation modeling [39] using AMOS v.24. The densely populated CMSA is in the northern part of the state, with comparatively smaller cities distributed to the south. In our model, the proximity of a reservoir to the CMSA directly relates to the number of recreational anglers (i.e., more anglers near the CMSA). In turn, the abundance of anglers surrounding a waterbody controls the numbers of bluegill and bass [24,28,40]. We interpreted paths as top-down harvest effects when standardized regression coefficients (β) were negative. We predicted stronger top-down effects on bluegill (relative to bass) because they are easy to catch and commonly harvested for consumption whereas bass fishing is considered more specialized (e.g., catch-release; trophy fishing) (e.g., [41,42,43]).
In coupled socio-economic and socio-ecological systems, variation in human behaviors can reflect feedback responses to changing ecological systems [44]. In pursuit of favorable fishing experiences, patterns of angler travel distances to waterbodies may track fish abundances across the landscape in the same way that consumers in retail markets often travel further for specialized goods, but not generic goods, because shoppers place a higher intrinsic value on specialized items (e.g., ‘range effect’ [45]). This phenomenon has also been observed in recreational anglers [26,46]. We predicted that the strength of relationships between angler movements and fishes would differ. Given the specialized nature of bass fishing, we predicted paths to bass will be stronger than paths to bluegill [42,43]. We interpreted paths as bottom-up attraction when standardized regression coefficients (β) were positive.
A network of variables can produce emergent properties, such as feedbacks, which are not expressed along individual pathways. We considered the potential for emergent feedback in the landscape. Specifically, we tested if top-down effects by abundant anglers on fishes further affects spatial patterns in angler travel distances. To assess this, we included reciprocal paths between anglers and fishes and interpreted the significance and directionality of paths.
Goodness-of-fit tests were used to determine the overall predictive performance of the model (Chi-squared statistic (χ2, p > 0.05); Normed Fit Index (NFI: range 0 to 1)). Model stability was assessed via the Stability Index Value (SIV: values between −1 and 1 indicate a stable model) because the model was non-recursive (i.e., inclusion of reciprocal paths between fish and anglers). Standardized regression coefficients (β) were deemed important if they were statistically different from zero (p < 0.05). To determine the significance of indirect effects (Standardized Indirect Effect, SIE), we estimated confidence intervals (95%; 1000 iterations) and associated p-values via bootstrap approximation. Variables were log10 transformed, as modeling necessitated linear relationships [39]. Finally, we used the results of the path analysis to generalize relationships between anglers and fishes by creating response surfaces (JMP Pro 13.0; Gaussian process model, Gaussian correlation function). A nugget parameter of 0.001 was used to smooth the interpolation of the response surface.

3. Results

3.1. Characterization of the Recreational Angling Landscape

Statewide, recreational anglers constituted 5.9 ± 3% (average ± SD; n = 102 counties) of a county’s population. As a county’s population increased, there was a corresponding non-linear increase in recreational anglers (Figure 2a), with participation rates varying from <1−15% (Figure 2a). Disparities among the most populated counties were pronounced, as there were 2 and 3-fold difference in the numbers of recreational anglers. Although angler participation rates were low (1.3–3.7%) in the CMSA, the CMSA harbored 62% of the state’s population and approximately 34% of its recreational anglers. Estimates of fishing effort originating from the CMSA exceeded 2.5 million fishing days (Figure 2a). The numbers of anglers within a county reflects where most fishing effort originated, but not necessarily where fishing pressure was focused. The distances traveled by recreational anglers (one-way direction; average ± SD) to reservoirs averaged 37 ± 19 km, indicating that excursions were generally within an hour of travel time. For comparison, Illinois counties (n = 102, average area of 1416 ± 573 km2) are roughly 37.6 km (square-root of average area) in dimension, and the state is approximately 630 km long (north to south) and 340 km wide (east to west). Taken together, large numbers of recreational anglers and short travel distances suggests fishing effort is localized to within a county or those close nearby.

3.2. Model Estimation and Interpretation

The non-recursive a priori model was stable (SIV = 0.39) and yielded an acceptable fit (χ2 = 0.75; d.f. = 1; p = 0.78; NFI = 0.99). Reservoirs closest in proximity to the CMSA were generally located in counties with greater numbers of recreational anglers (β = −0.95, p < 0.001), and more anglers corresponded with fewer bluegill (β = −0.79, p < 0.001) but not bass (p > 0.05; Figure 2b, Table 1). A positive indirect effect of a reservoir’s proximity to a large city, mediated via angler numbers, influenced the number of bluegill within waterbodies (SIE, 0.62; p = 0.005; Figure 2c, μ = −0.61, σ2 = 16.3). Across waterbodies, bluegill, and bass numbers positively tracked one-another (β = 0.73, p = 0.05; Figure 2b), and the effect of many anglers on bluegill populations had an indirect and negative effect on largemouth bass (SIE, −0.44; p = 0.03; Figure 2b,d, μ = −2.34, σ2 = 38.8). Bass abundance within a reservoir positively related with the travel distances of anglers to the reservoir (β = 0.68, p = 0.04; Figure 2b) whereas bluegill had no such effect (p > 0.05; Table 1, Figure 2b). Generally, reservoirs with greater numbers of bass (and bluegill) attracted anglers from further distances in the landscape (Figure 2e, μ = −0.88, σ2 = 29.9). Collectively, the distances traveled by anglers were shaped, directly and indirectly, by the top-down effects of angler abundance on the numbers of bass (Figure 2f, μ = 1.24, σ2 = 41.9). Such a pattern indicates the presence of a landscape-scale feedback where the numbers of anglers alter multiple fish populations to such a degree that patterns of angler movements are affected differently across the landscape.

4. Discussion

Through a suite of direct, indirect, and feedback effects, our study describes the complex spatial interdependencies of fishes and anglers, and, more broadly, of humans with nature [18,47]. The recreational angling landscape was characterized by a heterogeneous distribution of anglers that ranged three orders of magnitude between the lowest and highest populated counties. We found that the numbers of recreational anglers near a waterbody impart detectable changes to inland fisheries across the landscape, which we attribute to (1) short travel distances of individuals at local scales (<40 km) and (2) a spatially and numerically heterogeneous distribution of anglers (i.e., anglers within counties) at the regional scale. Recently, Ward et al. [25] argued that micro-scale and local feedbacks between individual fish, fish populations, and anglers can cause the emergence of macro-scale patterns. Our analysis supports these general predictions.
Our landscape-scale examination of anglers and fishes supported the hypothesis that spatially heterogeneous aggregations of recreational anglers (as inferred via angler abundance per county) affect the numbers of fishes in nearby waterbodies [20,24,28], creating patterns across fisheries that are indicative of heavily fished systems [7]. Bluegill are relatively easy to catch and often harvested in large numbers by some individuals [41,43,48]. Our findings support this general observation, as we detected a strong and negative relationship between anglers and the numbers of bluegill. Fewer bass were also observed in lakes surrounded by many anglers; however, the effect of angler abundance was indirect, negative, and mediated via changes to bluegill populations. Such a pattern could arise from the disruption of predator-prey dynamics between bluegill and bass (e.g., fewer bluegill prey for bass). The role of catch-release angling may also play a role. By releasing bass, bluegill may experience reductions in abundance because of bass predation and fishing pressure. Alternatively, or perhaps concurrently, top-down effects may be stronger in densely populated counties if there are simply more anglers harvesting fish for food, as consumption-oriented anglers are generally less species-selective and tend to harvest more fish [26,46,49,50]. Or, incidental fish mortalities (e.g., swallowing hook) may be more common with increased fishing effort at some lakes. Interestingly, the direct and indirect effects of anglers on bluegill and bass did not end in the reservoir. Rather, by changing the numbers of bluegill and bass, an emergent feedback appears to influence the movement attributes of anglers across the landscape.
Complexity in coupled human and natural systems can often produce unexpected and even counterintuitive patterns due to the emergence of feedbacks [18,44]. Synthesis of all paths in our study suggests the presence of a landscape feedback, driven by angler numbers and mediated via harvest effects on multiple fish populations, which manifested as spatially variable movements of anglers. Indeed, the variability of angler travel distances across Illinois corresponded with bass abundance in reservoirs, such that lakes with more abundant bass populations attracted anglers from greater distances in the landscape. Our findings suggest that specialized anglers, like shoppers seeking specialized products [45], will travel further for a certain species, whereas casual anglers tend to travel shorter distances and tend to harvest more fish regardless of species [26,46]. Assuming bass fishing is more ‘specialized’ (i.e., trophy fishing, catch-release, investment in boat and trailer; [42]) whereas bluegill fishing appeals to the casual angler, the contrasting effects of both angler abundance and travel distances between bluegill and bass align with these general observations. Because our study is one of only a few to assess regional scale fish-angler dynamics across inland freshwaters, whether the observed feedback is a generalizable feature of other fisheries landscapes is unclear. Although we detected a pattern, the process requires more testing. Moreover, changes to the numbers and behaviors of anglers and fish may create or reinforce additional feedbacks with other social (e.g., contaminant consumption and human health issues) and ecological systems (e.g., altered food web structure and function) [22,49,51].
Naturally, there are some caveats that warrant consideration. For instance, our study did not model angler preference. Yet, the decisions made by individuals (e.g., where, when, and why anglers go fishing) are complex and drive the macro-behaviors of the recreational angling population across the landscape. Terms like ‘specialized’, ‘generic’, and ‘casual’ were used as tractable means of framing a priori predictions about the interactions of anglers and fish across the landscape. However, scientists are continually demonstrating that generalizing the behaviors of individual anglers into categories is a decidedly difficult task, which could lead to incorrect inference [23]. Second, our study makes no presumption about the influence of time. Our analysis is a snapshot of inland fisheries relative to conditions in the surrounding landscape, and, consequently, inferences about the timescales required to produce the observed patterns, or the presence of any time lags, are unknown [18]. Others have theorized that exploitation of fish populations should drive spatial shifts of angling effort from one waterbody to another (e.g., a domino effect), sequentially collapsing nearby fisheries as anglers seek alternative fishing opportunities [20]. Critical tests of this important idea are needed [24], but were not possible in the present study. Finally, angling for consumption is a strong motivation for fishing in some countries [16,52]. Consequently, the strength of top-down effects may be similar for some species, and in some fisheries, but potential feedbacks or indirect effects (e.g., effects on other fish species; effects on angler behaviors) may be decoupled or manifest through differing social or food web pathways.
Earth’s population more than doubled during the latter half of the 20th century (1950–2000) and is expected to grow another 59% over the next fifty years 2000–2050 [53]. By 2030, the number of cities with populations exceeding one and ten million people will increase by 29% and 32%, respectively [53]. As cities grow faster than the surrounding rural communities, even more fishing pressure will presumably originate from urban rather than rural areas. Consequently, the looming shadow cast over nearby inland waters by large and growing cities will challenge conventional approaches to fisheries management across the globe. For instance, managers can lessen individual-angler impacts by implementing harvest restrictions (e.g., bag limits) on a fishery. Yet, the effectiveness of such an approach may be limited if there is high angler turnover [54]. Even if all anglers are compliant with harvest regulations, the fishery may still be susceptible to overexploitation if it is continually fished by many anglers. Theoretically, these anglers should move to other locations (e.g., ideal free distribution) in pursuit of better fishing opportunities. However, travel constraints (e.g., time and money) and other non-catch related factors can lead to deviations from this generalization [24,55].
Natural resource management efforts often rely on historic baselines for guiding decision making. Given the drastic increases in human population growth, can past conditions provide context and guidance for managing recreational fisheries [56]? The population of Illinois has increased from 8.7 to 12.4 million people between 1950–2000. During this time, the number of recreational anglers increased by 43% (assuming 5.9% participation rate) and represents a net increase of 218,300 individual anglers and 4.3 million days of fishing effort. Although these estimates are admittedly coarse, assuming constant rates (i.e., participation; fishing days angler−1), they reflect a substantial increase in recreational anglers through time and highlight that population growth has altered the recreational fishing landscape, and in turn the conditions of the fishery, by simply adding more participants. Yet, these simple assumptions may not hold, as urban inhabitants can choose from one of many recreational opportunities besides fishing. Cities may increase in population, but participation rates may not track population growth. How such demographic shifts influence sustainable management of recreational fisheries warrants further study.
Freshwaters provide crucial services to humans, and they are among the most threatened by rapidly growing human populations [12,57]. Sustainable management and conservation of inland fisheries susceptible to overexploitation will benefit from a clearer understanding of the complex social-ecological interdependences that shape the interaction of anglers and fishes [9,12,17,24,58]. Scientists face the daunting task of conserving natural resources within the shifting mosaic of the human populace [59,60]. Consideration of direct and indirect effects of angling pressure from the landscape should aid in prioritizing or identifying areas in need of management, conservation, public outreach and education, and protection and improve understanding of how changes to one or many species may feedback to other social, ecological, and economic systems [58].

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/su141610218/s1, Table S1: Path analysis parameter estimates for three a priori hypotheses examining plausible landscape drivers of fish abundance in Illinois (USA) reservoirs. β = standardized regression weights, R.W. = regression weight, S.E. = Standard error of regression weight, C.R. = critical ratio, Figure S1: Path analysis results of a three a priori models examining direct effects between landscape variables and populations of bluegill (Lepomis macrochirus) and largemouth bass (Micropterus salmoides) in Illinois (USA) reservoirs. Reference [39] is cited in Supplementary Materials.

Author Contributions

Conceptualization, S.F.C.; methodology, S.F.C.; formal analysis, S.F.C.; resources, S.F.C., D.H.W. and M.J.D.; data curation, S.F.C. and M.J.D.; writing—original draft preparation, S.F.C.; writing—review and editing, S.F.C., D.H.W. and M.J.D. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. Data used for modeling were obtained from publicly available sources [32,33,34,35,38].

Acknowledgments

We thank members of the Kaskaskia and Sam Parr Biological Stations, as well as graduate students from Texas Tech University and the University of Illinois-Urbana Champaign for their insights and feedback. Data pertaining to the sampling of fish populations were partially funded by the Illinois Department of Natural Resources (IDNR) and through Federal Aid in Sport Fish Restoration, Project F-128-R.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Map of Illinois (USA) showing the location (circles) of the lakes included in the study.
Figure 1. Map of Illinois (USA) showing the location (circles) of the lakes included in the study.
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Figure 2. (a) Relationships between county population size (x-axis) and resident angler abundance and estimated fishing effort (millions of days per county) in Illinois, USA. Inset shows the distribution of angler participation rates by county in relation to the population size of each county. (b) Path analysis results of a non-recursive a priori model (χ2 = 0.75; d.f. = 1; p = 0.78; NFI = 0.99; SIV = 0.39) examining direct, indirect, and feedbacks effects between recreational angling populations and populations of bluegill (Lepomis macrochirus) and largemouth bass (Micropterus salmoides) across the landscape. See Table 1 for values of all paths. Solid lines = direct effects, dashed lines = indirect effects. (c–f) Generalized relationships describing the factors affecting fish abundances and angler travel distances across the landscape. (c) Bluegill abundance in relation to a waterbodies proximity to the CMSA and the top-down effects of angler abundance. (d) Largemouth bass abundances in relation to the numbers of bluegill and the indirect influence of angler abundance. (e) Largemouth bass abundances relative to changes in the abundance of bluegill and the influence of angler travel distances. (f) Angler travel distances relative to changes in angler and bass abundances across the landscape.
Figure 2. (a) Relationships between county population size (x-axis) and resident angler abundance and estimated fishing effort (millions of days per county) in Illinois, USA. Inset shows the distribution of angler participation rates by county in relation to the population size of each county. (b) Path analysis results of a non-recursive a priori model (χ2 = 0.75; d.f. = 1; p = 0.78; NFI = 0.99; SIV = 0.39) examining direct, indirect, and feedbacks effects between recreational angling populations and populations of bluegill (Lepomis macrochirus) and largemouth bass (Micropterus salmoides) across the landscape. See Table 1 for values of all paths. Solid lines = direct effects, dashed lines = indirect effects. (c–f) Generalized relationships describing the factors affecting fish abundances and angler travel distances across the landscape. (c) Bluegill abundance in relation to a waterbodies proximity to the CMSA and the top-down effects of angler abundance. (d) Largemouth bass abundances in relation to the numbers of bluegill and the indirect influence of angler abundance. (e) Largemouth bass abundances relative to changes in the abundance of bluegill and the influence of angler travel distances. (f) Angler travel distances relative to changes in angler and bass abundances across the landscape.
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Table
Table 1. Path analysis parameter estimates for the a priori model (χ2, 0.75; d.f. = 1; p = 0.78; NFI, 0.99; SIV = 0.39). β = standardized regression weights, R.W. = regression weight, S.E. = Standard error of regression weight, C.R. = critical ratio, bass = largemouth bass Micropterus salmoides. Angler abundance estimated as the number of resident fishing sales within the county surrounding the lake. Angler movement represents the average one-way distance (km) traveled by recreational anglers to each lake.
Table 1. Path analysis parameter estimates for the a priori model (χ2, 0.75; d.f. = 1; p = 0.78; NFI, 0.99; SIV = 0.39). β = standardized regression weights, R.W. = regression weight, S.E. = Standard error of regression weight, C.R. = critical ratio, bass = largemouth bass Micropterus salmoides. Angler abundance estimated as the number of resident fishing sales within the county surrounding the lake. Angler movement represents the average one-way distance (km) traveled by recreational anglers to each lake.
Response βR.W.S.E.C.R.p
Angler abundanceProximity−0.95−2.290.48−4.70<0.001
Angler travelProximity0.600.510.173.110.002
bluegillAngler abund.−0.79−0.210.05−4.32<0.001
bassbluegill0.730.950.491.950.050
bassAngler travel−0.37−0.360.58−0.620.536
Angler travelbass0.680.700.342.050.041
bassAngler abund.0.180.510.530.950.340
Angler abundancebluegill0.160.600.960.630.530
Angler travelbluegill−0.33−0.440.39−1.140.250
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Collins, S.F.; Diana, M.J.; Wahl, D.H. Dynamic Interdependence between Anglers and Fishes in Spatially Coupled Inland Fisheries. Sustainability 2022, 14, 10218. https://doi.org/10.3390/su141610218

AMA Style

Collins SF, Diana MJ, Wahl DH. Dynamic Interdependence between Anglers and Fishes in Spatially Coupled Inland Fisheries. Sustainability. 2022; 14(16):10218. https://doi.org/10.3390/su141610218

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Collins, Scott F., Matthew J. Diana, and David H. Wahl. 2022. "Dynamic Interdependence between Anglers and Fishes in Spatially Coupled Inland Fisheries" Sustainability 14, no. 16: 10218. https://doi.org/10.3390/su141610218

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