Age and Size Selectivity of the Gaspereau River Alewife Fishery

AGE AND SIZE SELECTIVITY OF THE GASPEREAU RIVER ALEWIFE FISHERY:

IMPLICATIONS FOR THE ASSESSMENT OF THIS STOCK

Mark C. Billard

Thesis submitted in partial fulfillment of the

requirements for the Degree of

Bachelor of Science with

Honours in Biology

Acadia University

April 17, 2017

This thesis by Mark C. Billard

is accepted in its present form by the

Department of Biology

as satisfying the thesis requirements for the degree of

Bachelor of Science with Honours

Approved by the Thesis Supervisors

______Dr. Anna Redden Date

______Dr. Jamie Gibson Date

Approved by the Head of the Department

______Dr. Brian Wilson Date

Approved by the Chair of the Honours Committee

______Dr. Jun Yang Date

iii

I, Mark C. Billard, grant permission to the University Librarian at Acadia University to reproduce, loan or distribute copies of my thesis in microform, paper or electronic formats on a non-profit basis. I, however, retain the copyright in my thesis.

______Signature of Author

______Date

ACKNOWLEDGEMENTS

I would like to thank my supervisors, Dr. Jamie Gibson and Dr. Anna Redden for their support and instruction with this project and thesis. Dr. Gibson’s lessons on fishery science, modeling, and R provided me with a great learning experience. I greatly appreciate the one-on-one lessons that have vastly improved my skills and knowledge on those matters. Dr. Redden’s support and encouragement were invaluable for completion of this project.

I would also like to thank my lab mate Michael Adams, students Lita O’Halloran and Connor Sanderson, and Heather Bowlby of Fisheries and Oceans Canada for their help with the field and lab work components for this project. I thank the Acadia Centre for Estuarine Research and Fisheries and Oceans Canada for providing funding and materials for this project, as well as the George Baker Tidal Energy and Environment

Scholarship for funding my studies. Thanks also goes to Nova Scotia Power for maintaining and providing access to the White Rock Fish Ladder, and to Peter Croft of the Gaspereau River Square Net Fishermen’s Association for providing alewife to sample at his fishing stand.

Finally, I would like to thank my friends and family for their support over the course of this project.

vii

viii

TABLE OF CONTENTS

ACKNOWLEDGEMENTS ...... VII LIST OF TABLES ...... XI LIST OF FIGURES ...... XIII ABSTRACT ...... XV 1.0 INTRODUCTION ...... 1 1.1 FISHERIES ...... 2 1.1.1 Objectives of Fisheries Management ...... 3 1.2 STOCK ASSESSMENT ...... 4 1.2.1 Biological Reference Points ...... 5 1.2.2 Estimating Biological Reference Points ...... 7 1.2.3 Exploitation and Fishing Mortality ...... 10 1.3 SELECTIVITY OF FISHERIES ...... 11 1.3.1 Introduction to Selectivity ...... 11 1.3.2 Selectivity and Reference Points ...... 12 1.4 ALEWIFE ...... 15 1.4.1 Alewife Distribution ...... 16 1.4.2 Alewife Life Cycle ...... 16 1.4.3 Ecological Role of Alewife ...... 17 1.5 GASPEREAU RIVER ...... 18 1.5.1 Study Site Overview ...... 18 1.5.2 Previous and Current Alewife Studies ...... 18 1.5.3 Biological Reference Points for Gaspereau River Alewife ...... 19 1.5.4 Commercial Fishery ...... 20 1.6 OBJECTIVES ...... 21 2.0 METHODS ...... 23 2.1 SAMPLING OF BIOLOGICAL CHARACTERISTICS ...... 23 2.2 LABORATORY METHODS ...... 26 2.3 STATISTICS AND DATA ANALYSIS ...... 27 2.3.1 Abundance Estimate ...... 27 2.3.2 Weighting and Analyzing Biological Characteristics ...... 28 2.4 CALCULATION OF SELECTIVITY ...... 30 2.5 CALCULATION OF BIOLOGICAL REFERENCE POINTS WITH SELECTIVITY ...... 32 2.6 EXPLORING CAUSE OF SELECTIVITY ...... 34 3.0 RESULTS ...... 37 3.1 ABUNDANCE ESTIMATE ...... 37 3.2 EVIDENCE OF SELECTIVITY ...... 37 3.3 ESTIMATED SELECTIVITY CURVE ...... 38 3.4 EFFECTS OF SELECTIVITY ON REFERENCE POINTS ...... 39 3.5 EXPLORING THE CAUSE OF SELECTIVITY ...... 40 4.0 DISCUSSION ...... 41 4.1 SOURCES OF ERROR ...... 41

4.2 EFFECTS ON REFERENCE POINTS ...... 41 4.3 EXPLORING CAUSES OF SELECTIVITY ...... 44 4.4 NATURAL VARIABILITY ...... 45 5.0 CONCLUSIONS ...... 47 REFERENCES ...... 49 TABLES ...... 53 FIGURES ...... 65

LIST OF TABLES Table 1. Definitions of the reference points for alewife fisheries provided by Gibson and Myers (2003)………………………………………………………………………………………………………………..53

Table 2. Three assumed selectivities for the hypothetical marine fishery…………………….54

Table 3. Biological reference points for a hypothetical fish population exploited using three hypothetical selectivity scenarios……………………………………………………………………….55

Table 4. Biological reference points for the Gaspereau River alewife stock………………….56

Table 5. Start and end times of the five strata used for selection of video samples, as well as the total duration in hours of each stratum………………………………...…………………..56

Table 6. Historical summary of catch data, estimated escapement, estimated run size, and exploitation rate for alewife on the Gaspereau River, Nova Scotia………………………..57

Table 7. Summary statistics for the biological characteristics data for alewife sampled from the White Rock fish ladder and Gaspereau River fishery in 2016………………………….58

Table 8. Values for the boxplots characterizing fork length frequency distributions for Gaspereau River alewife……………………………………………………………………………………………….59

Table 9. Proportions-at-age, and numbers-at-age for the Gaspereau River alewife….....59

Table 11. Biological reference points for the Gaspereau River alewife stock estimated under assumptions of a non-selective fishery, and a fishery that is selective……………….61

Table 12. Number of alewife and day of the spawning run for three peaks in abundance that occurred for the Gaspereau River alewife stock in 2016…………………………….…………61

Table 13. Weekly means and standard errors of fork length for the Gaspereau River alewife………………………………………………………………………………………………………………………….62

Table 14. Means and standard errors of fork length for the Gaspereau River alewife …63

Table 15. Means and standard errors of age for the Gaspereau River alewife..…….…….64

xii

LIST OF FIGURES Figure 1. A visual description of population dynamics for a hypothetical fishery……….…65

Figure 2. The relationship between the exploitation rate, U, and the instantaneous fishing mortality rate, F………………………………………………………………………………………………..66

Figure 3. Three selectivity curves used to illustrate the effects of selectivity on reference points for the hypothetical marine fish stock……………………………………………………………….67

Figure 4. Yield per recruit (YPR) curves for the hypothetical marine fish stock……………..68

Figure 5. Spawner biomass per recruit (SPR) curves for the hypothetical marine fish stock…………………………………………………………………………………………………………………………….69

Figure 6. A stock recruitment (SR) curve for the hypothetical marine fish stock ………….70

Figure 7. Map of the study area on the Gaspereau River in Nova Scotia, showing the sampling locations in this study…...... 71

Figure 8. Photos of the two study locations where data were collected during the 2016 Gaspereau River alewife spawning run...... 72

Figure 9. Stock status plot for the Gaspereau River alewife stock………………………………..73

Figure 10. Boxplots characterizing fork length frequency distributions for Gaspereau River alewife………...... 74

Figure 11. Comparison of the sex ratio for Gaspereau River alewife…………………………….75

Figure 12. Comparison of proportions of age classes and proportions of repeat spawners for Gaspereau River alewife…………...... 76

Figure 13. Comparison of the composition of the repeat spawning component based on the number of times fish had previously spawned for Gaspereau River alewife………..…77

Figure 14. Comparison of the selectivity curves age for Gaspereau River alewife…………78

Figure 15. Comparison of spawner biomass per recruit (SPR) curves for Gaspereau River alewife………...... 79

Figure 16. Comparison of yield per recruit (YPR) curves for Gaspereau River alewife…..80

xiii

Figure 17. The spawner-recruit curve and replacement lines for the Gaspereau River alewife stock…………...... 81

Figure 18. Abundance plot for Gaspereau River alewife sampled at White Rock fish ladder and from the commercial fishery in 2016...... 82

Figure 19. Comparison of weekly means and standard errors for Gaspereau River alewife…………...... 83

Figure 20. A proportionate comparison of means and standard error of fork length for Gaspereau River alewife…………...... 84

Figure 21. A proportionate comparison of means and standard error of age for Gaspereau River alewife…………...... 85

xiv

ABSTRACT Biological reference points are metrics based on the biological characteristics of a fish stock and its fishery. They provide the link between stock assessment and management objectives, and are used to gauge whether management objectives are being achieved. Age and size can influence the impacts of a specific harvest rate depending on whether younger, smaller or older, larger fish are being harvested. A fishery that is selective for certain age classes would have different biological reference points for the stock, and could require different management of the fishery. In this study, I examined the selectivity of a commercial alewife fishery on the Gaspereau River,

Nova Scotia. Alewife (Alosa pseudoharengus) is a diadromous, herring-like fish species that spawn in the rivers and lakes along the eastern seaboard of North America. Alewife are fished throughout their range as they return to their natal rivers to spawn. In spring

2016, biological characteristics, including weight, fork length, sex, and age data were collected from alewife sampled at a commercial fisher’s stand, and at a fish ladder 4 km upriver. Data were used to reconstruct numbers-at-age for the total run. Significant differences were found in mean fork length, weight, age, and proportion of repeat spawners between the two sampling locations, indicating that the fishery is highly selective. Selectivity was calculated for age classes three through six, incorporated into the calculations for the biological reference points for this fishery, and compared to existing biological reference points based on the assumption the fishery is non-selective.

Although this study shows the fishery is selective, effects on the biological reference points were minor, likely due to the absence of immature fish in the spawning run.

xvi

xvii

1.0 Introduction

The role of fishery science is to study the nature of fish populations and their fisheries to ensure appropriate harvesting by the fisheries. Fishery stock assessments are the collection and analysis of various types of data from a fish stock to estimate stock status, and provide the scientific basis for the management of stocks (Hilborn and

Walters 1992). Biological reference points are reference values that can be compared to stock assessment results to determine the status of a stock (Gibson and Myers 2003a).

Biological reference points are based on the management objectives of the stock, the life-history characteristics of the population, and the characteristics of the fishery.

Management objectives of the stock are goals such as sustainable harvesting of the stock. The life-history characteristics include reproductive rates, maturity schedules, and natural mortality rates. Characteristics of the fishery include the instantaneous fishing mortality rate, indicative of the rate at which fish are removed from the stock, and the selectivity of the fishery, defined as the probability that fish of certain age or size classes are harvested by the fishery relative to other age or size classes. All these factors affect the sustainable harvesting of the population.

Alewife are a diadromous species of fish that are harvested throughout their range as they return to spawn each spring along the eastern seaboard of North America

(Loesch 1987). Alewife spawn alongside a similar species, blueback herring; these two species are collectively referred to as gaspereau or river herring (Loesch 1987). Stock assessments for river herring typically assume the fishery is non-selective, as selectivity

has not typically been calculated or included in models used for river herring (e.g. Gibson and Myers 2003a, ASMFC 2012). The goal of this thesis is to determine whether alewife fisheries are selective and the impact selectivity has on biological reference points for alewife stocks, and potentially the management of the stocks. The alewife fishery in the

Gaspereau River, Nova Scotia, was used as the study system for evaluating selectivity.

The following sections describe relevant aspects of fisheries management, fisheries models, and biological reference points. It also describes selectivity of a fishery, and discusses the causes of selectivity. Finally, an example is provided to show the impacts selectivity can have on a stock and its fishery.

1.1 Fisheries

Fish are an important resource for humans, ecologically and economically. As an economic resource, fish and other aquatic animals are caught for sustenance, bait, and other uses. Issues arise when fish are caught in such great numbers that there is a noticeable negative effect on the productivity of the population, or the stock is depleted.

Effects of overfishing include reduced yields from fisheries, with associated social and economic impacts, as well as effects on the ecosystem if the species is an important predator or prey. Due to the interconnectedness of aquatic environments, overfishing one population may significantly affect a different species or population in a different area, or in a different time (Swain and Sinclair 2000).

1.1.1 Objectives of Fisheries Management

Fisheries management uses fisheries science to sustainably manage fish stocks, ensuring they are harvested and conserved at rates that will not deplete the stocks

(Hilborn and Walters 1992). For this study, a fish stock is defined as a population of a single fish species that reproduces together and has shared biological characteristics such as the reproductive rate, natural mortality rate, or maturity schedule (Gibson et al.

2016). Fisheries managers aim to fulfill many objectives when making decisions on a stock, and those objectives fall into four categories: biological, economic, recreational, and social (Hilborn and Walters 1992).

To meet these objectives, fisheries managers typically aim to ensure that a stock is being harvested in a way that produces, or does not exceed, maximum sustainable yield (MSY) (Hilborn and Walters 1992). MSY is defined as the maximum yield the fishery can harvest from the stock indefinitely (Hilborn and Walters 1992). Although there are exceptions, MSY can be very difficult to determine without exceeding MSY and potentially depleting the stock (Hilborn and Walters 1992). However, it is still a useful concept for managing fish stocks.

From an economic perspective, fisheries managers could aim to ensure that fishers have the highest profit margin possible from their harvests. For fishers, maximizing profits of a fishery is common sense. Operating a fishing boat or fleet has many fixed costs such as insurance or rent, and variable costs that depend on time fished, such as wages and fuel. Therefore, the cost of fishing rises from a base cost proportionally to the time fished. As well, the economic value of a fish decreases with as

3 additional fish are caught. In theory, there exists a point where an individual will produce the highest ratio of fish caught to operation costs, however, this point may not be near the MSY (Hilborn and Walters 1992). It should be noted that few fisheries are managed purely or primarily from an economic perspective.

In addition to fulfilling biological and economic goals, fisheries managers may aim to make a stock available to the public for recreation. Recreational fishing, such as sport fishing, can also provide non-essential sustenance for the public (Hilborn and Walters

1992).

From a social perspective, fisheries managers aim to have a stock that can provide subsistence and jobs for those people that rely on it. This can be difficult to achieve when a stock is overfished. The fisheries manager must make decisions that affect current jobs and conservation of the fish population (Hilborn and Walters 1992).

The Fisheries Act of Canada (Minister of Justice 1985) prioritizes the conservation of stock first, providing subsistence for First Nations communities second, and then providing for commercial and sport fisheries. In reality, it is a complex balancing act to fulfill these objectives in the most effective way possible for all the different stocks that exist (Hilborn and Walters 1992).

1.2 Stock Assessment

A stock assessment is the collection and analysis of different types of data from a fish stock to estimate stock status, and to predict future stock status (Hilborn and

Walters 1992). The data collected can indicate the relative or absolute abundance, the age structure of the stock, productivity of the stock, natural and fishing mortality rates of

4 the stock, and any other important indices of the stock (Wallace and Fletcher 2001). The results of a stock assessment can be compared to previously established biological reference points to estimate stock status (Hilborn and Walters 1992). Stock assessments serve as a source of information on the characteristics of the stock and fishery, including the fishing mortality rate, selectivity, or determinations of whether the stock is overexploited (Hilborn and Walters 1992). A stock assessment provides fisheries managers with biological information about the stock, which the managers can weigh against social and economic factors to determine the best management of the stock

(Hilborn and Walters 1992).

When harvesting a fish stock, it is important to be mindful of stock recruitment

(also called spawner-recruit) relationships. Stock recruitment relationships are the processes in which spawning adults will produce young, which will in turn be recruited into the fishery (Hilborn and Walters 1992). The relationship between spawners and recruits is not always obvious when observing landings data. However, an overfished population will typically exhibit clearer connections between spawners and recruits

(Hilborn and Walters 1992). Stock recruitment relationships are described in detail in

Section 1.2.2.

1.2.1 Biological Reference Points

Biological reference points (BRPs) are metrics based on the biological characteristics of a fish stock, the characteristics of the stock’s fishery, and objectives for managing the fishery (Caddy and Mahon 1995, Gibson and Myers 2003a). BRPs are used as reference values to determine the status of the stock and fishery by comparing

5 estimates of stock abundance or exploitation of the fishery to established BRPs. These indicate whether the stock is healthy, or possibly over-exploited, and are used to determine if management objectives are being achieved (Gibson and Myers 2003a).

Fisheries and Oceans Canada (DFO) has developed a framework that serves as a guide to help ensure fish stock are not overfished. Within this framework, stock status can be in the critical, cautious, or healthy zones (DFO 2006, DFO 2012). When a fish stock is in the healthy zone, there is no immediate danger and the stock can be harvested at or near MSY. When a fish stock is in the cautious zone there is a risk of harming the stock, and efforts should be made to promote growth of the stock, while still allowing harvesting to occur. When a fish stock is in the critical zone the productivity of the stock is sufficiently impaired to cause serious harm. When in the critical zone, efforts must be taken to rebuild the stock, and harvesting must be kept to the minimum possible (DFO 2006).

The Upper Stock Reference (USR) is the boundary between the cautious and healthy zones. The Lower Reference Point (LRP) is the boundary between the critical and cautious zones of a fish stock. A Target Reference Point (TRP) that is equal to or greater than the USR may be defined for the stock. The TRP is the level of abundance considered optimal to meet management objectives (DFO 2006). The default reference points for a stock set the LRP at 40% of the biomass at MSY (BMSY) and the USR at 80% BMSY (DFO

2012). As more information on a stock becomes available, these reference points may change.

A Removal Reference (RR) is the rate at which fish are harvested from the population. The RR changes depending on stock status, and is always greater when in the healthy zone than when in the cautious or critical zones (DFO 2006). The reference point used is usually Fmsy, which is the instantaneous fishing mortality rate at MSY (DFO

2012). A Removal Reference Level (RRL) and Lower Removal Reference Level (LRRL) exist for some stocks (DFO 2006), and have been established for alewife in DFO’s Maritimes

Region (Gibson et al. 2016). If the corresponding fishing mortality rate of the fishery is above the RRL the fishery is being over exploited; if it is between RRL and LRRL it is fully exploited; and if it is less than LRRL the fishery is considered partially exploited (Gibson et al. 2016). A fishing mortality rate matching the RRL or slightly less than the RRL is generally desirable.

1.2.2 Estimating Biological Reference Points

BRPs can be generated for a stock using a variety of fisheries models. One such model is a production model consisting of three parts: a stock recruitment model (SR), a spawner biomass per recruit model (SPR), and a yield per recruit model (YPR), as described by Gibson and Myers (2003a).

A SR model can be used to model the stock recruitment relationship component of the population dynamics of a stock (Gibson and Myers 2003a). For stock recruitment relationships, “spawner” refers to all sexually mature adults that successfully spawn in a particular year, and “recruit” refers to all individuals of a specific age or stage within the resulting cohort. The age or stage of recruitment should be old enough that all density dependent processes have occurred but young enough that fish are still immature and

7 are not being captured in the fishery (Gibson and Myers 2003a). The rate at which spawners produce recruits is density dependent; for example, the commonly used

Beverton-Holt stock recruitment model shows that as more spawners enter the system more recruits are produced, however, fewer recruits are produced per spawner (Figure

1a, Hilborn and Walters 1992). The Beverton-Holt model has two parameters. The first parameter is the carrying capacity which is the number of recruits approached asymptotically as spawner abundance approaches infinity (Myers et al. 2001, Gibson and

Myers 2003b). The second parameter is the slope at the origin of the SR curve and is the maximum rate that spawners can produce recruits at low abundance in the absence of density dependence (Myers et al. 1999).

Recruits become spawners as they grow and reach maturity, processes that are assumed to be density independent (Gibson and Myers 2003a). As mortality rates increase, recruits will produce fewer spawners (Figure 1b). A replacement line, the slope of which is the inverse of the rate at which recruits produce spawners throughout their lives, can be used to characterize the population dynamics of a stock in conjunction with the stock recruitment relationship. The slope of the replacement line (Figure 1b) can be calculated with a SPR model. The SPR model describes how much spawning stock biomass is produced on average by a single recruit throughout its life for a given fishing mortality rate (Gibson and Myers 2003a).

The yield produced by each recruit can be described as a YPR model. The YPR model describes the yield for the fishery produced by an average recruit at any given

8 fishing mortality rate (Gibson and Myers 2003a). This production model is described in more detail in Section 2.5.

The production model can determine the values of BRPs when the stock is at equilibrium. A population is considered to be at equilibrium when the rate of recruits producing spawners is equal to the rate of spawners producing recruits (Figure 1c,

Gibson et al. 2016). If the mortality rate of spawners increases as shown by the shallower sloped line in Figure 1b, the number of recruits will decrease and the population will have a lower equilibrium point (Figure 1c). Stock recruitment relationships can be incorporated into models to describe the population dynamics of the stock.

Several BRPs can be calculated using the production model. Examples include various spawning stock biomass amounts or instantaneous fishing mortality rates.

SSBmsy, which is the spawning stock biomass that will produce MSY for the fishery, is an example, as well as SSB20%, which is the spawning stock biomass at 20% that of the equilibrium SSB at no fishing (Gibson and Myers 2003a). Instantaneous fishing mortality rate examples are Fmsy, the instantaneous fishing mortality rate that would produce MSY for the fishery and the SSBmsy, or Fcol, the instantaneous fishing mortality rate that would lead to the extirpation of the stock (Gibson and Myers 2003a). Reference points F25%,

F30%, and F35% represent the instantaneous fishing mortality rate when SPRF is 25-35% that of SPRF when F=0 (Gibson and Myers 2003a). It is common for the instantaneous fishing mortality rate BRPs to be expressed in terms of an exploitation rate. A variety of

BRPs can be generated from production models, as well as other models (Table 1,

Gibson et al. 2016). The BRPs generated from the production model can be compared to information from stock assessments to inform management of a stock.

1.2.3 Exploitation and Fishing Mortality

The terms instantaneous fishing mortality rate F and exploitation rate U can be used interchangeably, but the values for each are not directly interchangeable. F comes from the simple differential equation model (Hilborn and Walters 1992):

= −��, (Eq. 1) where N is the population size. This equation can be integrated over time to yield a difference equation:

� = �� . (Eq. 2)

This equation can be rearranged to solve for F:

� = −ln , (Eq. 3) which can easily yield a value greater than 1. The exploitation rate, U, is always between

0 and 1 and represents the proportion of fish that are removed from the stock. A U of 1 would mean the fishery harvested the entire stock, and a U of 0 would mean no fish were removed from the stock. The exploitation rate can be calculated as 1 minus the proportion of fish that survive (s):

� = 1 − �. (Eq. 4)

-F In Eq. 2, e can be replaced with s so that the current population N1 is equal to the previous population N0 times the survival rate s, which can be substituted in Eq. 4 to yield:

� = 1 − �. (Eq. 5)

Since e-F can never be greater than 1, neither can U (Hilborn and Walters 1992). The relationship between U and F is graphically represented in Figure 2.

1.3 Selectivity of Fisheries

1.3.1 Introduction to Selectivity

Selectivity of a fishery refers to the probability that a fishery selects, or harvests, a specific portion of a stock and can refer to how the fishery selects for different sizes, ages, or weights of fish (Millar and Fryer 1999). Three general types of selectivity, defined by Millar and Fryer (1999), are population, availability, and contact selectivity.

Population selectivity refers to the portion of the population that could be available to the fishery. In the case of an anadromous fish stock whose fishery is located on the river, only the sexually mature portion of the population will be selected for, as sexually immature fish will not enter the river. Availability selectivity refers to the portion of the population that is available to the fishery but can avoid the fishing gear (e.g. fish that enter the river but pass the fishing gear when the gear is not in use). Contact selectivity refers to the portion of the population that is available to the fishery and contacts the fishing gear, but can escape (e.g. passes through a net or was caught in a net but escapes). Selectivity of a fishery can result from the gear type, the time of year a fishery harvests, or the harvesting location (Millar and Fryer 1999). When not accounted for, selectivity can cause miscalculations of BRPs or stock status that, depending on the degree of selectivity, could affect the health and management of the stock (Hilborn and

Walters 1992) 11

1.3.2 Selectivity and Reference Points

Selectivity can affect estimations of the fishing mortality rates, equilibrium points of populations, and reference points set in relation to those rates and points. Using computer code for a production model of a marine fish stock developed by Dr. Jamie

Gibson (personal communication), I compared different selectivity scenarios to illustrate these effects. The model represents a hypothetical herring-like fish stock that lives and is fished entirely in a marine environment. I modified the model to include curves for three different selectivity scenarios, and to compare BRPs for the three scenarios. The hypothetical stock and fishery will serve as an example of how selectivity can affect

BRPs.

Three different selectivity scenarios were applied within the production model, all using the same parameters of growth, natural mortality, age classes, and probabilities of maturation-at-age. The three selectivity scenarios are compared for a hypothetical fish stock of age classes 1 through 9:

• Selectivity 1 matches the maturity rate of the fish, and selects only mature fish,

• Selectivity 2 is fully selective for fish in age class 5 and above, and

• Selectivity 3 is fully selective for all age classes.

For each age class, selectivity is represented as a value between 0 and 1, corresponding the ratio of the age-specific instantaneous fishing mortality rate to the fishing mortality rate of the fully selected age class (Figure 3, Table 2).

The term age class, not age, is used to reflect fish spawning on a yearly basis, meaning all fish are either the same age, a year apart, or multiple years apart, but not

12 half a year. Ages from a population are grouped into discrete age classes rather than a continuous age spectrum.

Using the production model, a YPR curve was produced for the three selectivity scenarios (Figure 4). When there is no fishing, there is no yield per recruit. As the fishing mortality rate increases, the yield and yield per recruit both increase until the yield per recruit reaches a maximum value. The instantaneous fishing mortality rate that produces the maximum yield per recruit is known as Fmax. After the peak at Fmax is reached in this model, yield increases as the instantaneous fishing mortality rate increases, but the yield per recruit gradually begins to decrease, as is apparent with the curve associated with

Selectivity 3. There are cases in which Fmax occurs at the highest possible fishing mortality rate, so the highest possible yield and the highest possible yield per recruit are both achieved at Fmax. Yield per recruit decreases under a high instantaneous fishing mortality rate because fish that are being harvested at a high rate cannot grow to an optimal size for harvest. If the instantaneous fishing mortality rate was infinitely high and the relationship of spawners producing recruits was ignored, in theory, all fish would be harvested as soon as they were recruited to the fishery, so the yield per recruit would be equal to the average weight of the recruits as they enter the fishery. In the section of the curve to the left of Fmax (Figure 4), biomass loss from fishing is less than natural growth of the fish, which is why the yield per recruit increases with an increasing instantaneous fishing mortality rate. Although once commonly used, YPR reference points are now rarely used because the YPR curve does not account for whether the abundance of fish is in a healthy zone or in a cautious or critical zone.

A SPR curve was produced from the same production model (Figure 5). An SPR curve shows the relationship of the biomass of spawners produced by each recruit throughout its life at different fishing mortality rates. At a fishing mortality rate of zero, the SSB per recruit is at its highest. This is the spawner biomass produced by a recruit throughout its lifespan. As the instantaneous fishing mortality rate increases, the curves quickly decrease and asymptotically approach a value specific to each curve. The curves decrease in SSB since fish are being removed from the stock before they can reach their maximum weight. In the case of Selectivity 3, the SSB per recruit is effectively zero at an instantaneous fishing mortality rate of 0.8. Since Selectivity 3 fully selects for all age classes, recruits are being removed from the stock before they can contribute to the SSB, providing the steep negative slope. Selectivity 1 approaches a SSB per recruit of 0 more gradually. This is because it selects all mature fish but not recruits. Under a high instantaneous fishing mortality rate recruits will remain but all spawners will be fished, providing an SSB of 0. Selectivity 2, on the other hand, selects only fish in age classes 6 and older. Since half of the fish in age class 3 are spawners and half are recruits, the SSB of Selectivity 2 at a high instantaneous fishing mortality rate will be the average weight of all spawners in age class 3 that are not selected.

A SR relationship and four replacement lines were produced from the production model (Figure 6). The replacement lines represent the three different selectivities and the replacement line when there is no fishing mortality. Intersection points of each line represent the equilibrium point of the population under those conditions; if conditions change, the stock will shift towards a new equilibrium point (Gibson et al. 2016).

Selectivity 3 selects for the entire stock, and has a SSBmsy of 28.78, Selectivity 1 selects for only mature fish and has a SSBmsy of 29.87, and Selectivity 2 selects for only fish aged

5 and greater and has a SSBmsy of 39.06 (Table 3). SSBmsy values are in terms of relative weight. Equilibrium points with greater spawner and recruit values correlate with a lower proportion of the stock that is selected.

Fishing mortality rate reference points (Table 3) for the hypothetical production model correlate with the SSBmsy values: Selectivity 3 has the lowest Fmsy and Fcol of 0.17 and 0.43 respectively, followed by Selectivity 1 with a Fmsy and Fcol of 0.32 and 1.06, and finally Selectivity 2 with the highest Fmsy and Fcol of 1.11 and 4.61. These values demonstrate how selectivity can affect the fishing mortality reference points and how fishing of immature fish can reduce productivity.

In this thesis, I adapt a similar production model, specific to Alosa life history, to incorporate age and size selectivity of a fishery. I apply the model to the Gaspereau River alewife stock to determine reference points with and without selectivity, and thus gauge the impact selectivity has on the alewife stock.

1.4 Alewife

Alewife, Alosa pseudoharengus (Wilson 1811), and blueback herring, Alosa aestivalis (Mitchill 1814), are two species of fish collectively referred to as gaspereau or river herring (Loesch 1987). Alewife and blueback herring occupy much of the same region and share many life history characteristics, but differ slightly in morphology, spawning substrate preference, and range (Loesch 1987). By far, the majority of gaspereau in the Gaspereau River are alewife (Jessop and Parker 1988, Gibson and

Daborn 1997). This river is not thought to support a population of blueback herring

(Rulifson 1994).

1.4.1 Alewife Distribution

Alewives are distributed across the eastern seaboard, as far south as South

Carolina (Berry 1964) and as far north as Newfoundland (Rulifson 1994). Adults overwinter off the coast of these regions and in the spring migrate up rivers to lakes and still bodies of water to spawn. Migration of the most southern populations to their natal rivers begins in late February, and begins as late as June in the Gulf of St. Lawrence, the most northern alewife population (Loesch 1987).

1.4.2 Alewife Life Cycle

The life cycle of alewives starts with sexually mature fish swimming up their natal rivers to spawn in the head ponds or lakes that feed those rivers. The spawning run can continue for up to two months (Scott and Scott 1988). Alewives will remain in the spawning grounds for several days to several weeks (Scott and Scott 1988). When spawning ceases the alewives head back to sea and begin feeding again (Durbin et al.

1979). Young alewives hatch after 3-8 days (Gibson and Myers 2001), at 5 mm in length

(Bigelow and Schroeder 1953). After about a month of feeding in fresh water, the juvenile alewives, 50-100 mm in length, head downstream to the sea (Bigelow and

Schroeder 1953). These young alewives stay at sea until they reach sexual maturity which is generally at 4-5 years of age, but can be as young as 3 or as old as 6 years of age

(Bigelow and Schroeder 1953).

1.4.3 Ecological Role of Alewife

Alewives can significantly impact the freshwater ecosystems they enter in several different ways. Alewives prey on a variety of zooplankton, as well as fish eggs, larvae, insects, and amphipods (Mills et al. 1992, Lent 1999). Adult alewives will input nutrients into the river system via mortality, and to a lesser degree via excretion and release of gametes (Durbin et al. 1979). Of the fish that escape a fishery downstream, 39-57% die in the spawning grounds (Durbin et al. 1979). In the Pausacacao Pond in Rhode Island,

USA, in 1959, 4,530 kg of Carbon, 728 kg of Nitrogen, and 115 kg of Phosphorus was added to the pond by an escapement of 202,500 alewife (Durbin et al. 1979). Some nutrients are removed from the system with the juveniles as they migrate to sea. Total dry weight of juvenile alewife leaving the Pausacacao Pond spawning grounds was two orders of magnitude less than the dry weight of adult alewives that died at the spawning grounds, indicating that nutrient removal from the system via juveniles was far less than the input by adults. Interestingly, the influx of nitrogen and phosphorus due to mortality and decay of adult alewives stimulates a bloom of mycoflora within the river bed leaf litter, which in turn stimulates detritivore activity in the leaf litter (Durbin et al. 1979).

This increase in microbial activity serves to jump start the transfer of nutrients from leaf litter up the food chain, and thus increases the overall productivity of these freshwater ecosystems (Durbin et al. 1979).

1.5 Gaspereau River

1.5.1 Study Site Overview

The alewife stock studied for this project spawns in the Black River-Gaspereau

River watershed, Nova Scotia (Figure 7). The Black River-Gaspereau River system has been modified over the past 80 years with dams, diversions, fish ladders, and in-stream turbines. Nova Scotia Power manages the modifications. There are five in-stream turbines on the Black River-Gaspereau River system. The turbine furthest downstream is located at White Rock. The White Rock diversion dam and White Rock fish ladder are located approximately 2 km upstream from the White Rock turbine. The White Rock diversion dam diverts most of the water flow from the Gaspereau River to the White

Rock canal which feeds the turbine downstream (Gibson and Myers 2001). Next to the

White Rock diversion dam is a concrete fish ladder that was built following the 2001 stock assessment, replacing an older wooden fish ladder (McIntyre et al. 2007). The

White Rock fish ladder allows easy access to the fish for counting via video camera and biological sampling (Figure 8).

1.5.2 Previous and Current Alewife Studies

The Gaspereau River alewife stock is an ideal stock to study due to its ease of access for sampling at the White Rock fish ladder. Prior stock assessments were conducted for this population in 1982-84, 1997-2007, and 2015 by Fisheries and Oceans

Canada (DFO), the Acadia Centre for Estuarine Research, and/or Nova Scotia Power

(Jessop and Parker 1988, Gibson and Daborn 1997, Gibson 1999, Gibson 2000a, Gibson

2000b, Gibson and Myers 2001, McIntyre et al. 2007, and Bonang 2016). Spawning run estimates and commercial fishery landings data were used in all assessments, and biological data including age data were collected in all years except 2007 and 2015.

Similar data was collected in 2016 for the present study which focuses on the assessment of age and size selectivity of the alewife fishery.

Contact selectivity of the fishing gear for capturing alewife on the Gaspereau

River has not been previously assessed, and was assumed constant, or non-selective, across age classes (ASMFC 2012). If non-selective, the average age, weight, length, and sex ratio of alewife caught by the fishery should match the averages of the entire population.

Although contact selectivity has not been evaluated, the Gaspereau River alewife fishery is managed in a way that could lead to some population selectivity: the fishery is only open for a portion of the alewife spawning run, so it selects for alewife passing through the river during the fishing season (see Section 1.5.4), and against alewife passing though the river after the fishing season. As well, the fishery does not catch alewife that are sexually immature, as they do not enter the river to spawn. Maturation- related selectivity is inherent in all in-river fisheries for anadromous Alosa.

1.5.3 Biological Reference Points for Gaspereau River Alewife

For alewife in the Gaspereau River, the current BRPs, under an assumption of no selectivity, have already been established based on previous work (Table 4). The USR is set to SSBMSY which is 85.8 tonnes or 400,000 fish escaped from the fishery (Gibson and

Myers 2003a, McIntyre et al. 2007). The LRP is set to SSB10%, or 10% of the unfished

19 spawning stock biomass, which is lower than some other fisheries, but is appropriate for alewife due to their high productivity (Gibson et al. 2016). The LRP is 54.7 tonnes or

235,000 fish escaped from the fishery (Gibson et al. 2016). A URR of 0.53 and a ULRR of

0.35 has also been established for the Gaspereau River alewife fishery (Gibson et al.

2016). The USR of 400,000 fish escaped from the fishery was achieved in 2015 and 2016

(Bonang 2016).

1.5.4 Commercial Fishery

McIntyre et al. (2007) report there are 18 commercial fishers on the Gaspereau

River, all between the tidal section of the river and the White Rock turbine (McIntyre et al 2007). Not all fishers fish every year. Most of the fishers use a dip-net to catch the alewife, while some others use a gillnet or a drift net (McIntyre et al. 2007). A dip-net

(Figure 8), also called a square-net, is submerged in a modified section of the river that corrals fish into a deeper section of water directly over the net, and prevents them from easily moving further upriver. The net is then hoisted out of the water bringing up all the fish that were in the water column directly above it. Fish are then harvested and processed for packaging on site. The alewife fishery is open from March 15 to May 31, but very few fish are caught prior to mid-April (McIntyre et al. 2007). Currently, the fishery is open four days per week: from 0530 hours on Monday to 2130 hours on

Tuesday, and from 0530 hours on Thursday to 2130 hours on Friday (McIntyre et al.

2007). Prior to 2002 the fishery was open on Wednesdays, but was closed in 2002 and onwards to reduce exploitation of the stock that was indicated by the 2001 stock assessment conducted by Gibson and Myers (McIntyre et al. 2007). Recreational fishing

20 is permitted on the river, but is considered insignificant and thus not taken into consideration (McIntyre et al. 2007). The additional closure on Wednesdays reduced exploitation (McIntyre et al. 2007), leading to increased spawning escapement that exceeded the USR in 2015 (Bonang 2016).

1.6 Objectives

The primary objective of this study was to determine if the Gaspereau River alewife dip-net fishery exhibits age or size selectivity, and if so, to determine the effects of selectivity on the BRPs for Gaspereau River alewife that are used in the stock assessment.

Field work undertaken for this thesis was part of the field program for updating the assessment of the alewife stock. It included escapement counts from the White Rock fish ladder and landings data from the fishery to estimate abundance of the alewife stock and the exploitation rate of the fishery. In addition to evaluating whether the fishery is selective, I have provided an update of the assessment for this stock for 2016.

2.0 Methods

To complete this study, daily and total abundance information as well as biological characteristics data were collected from alewife from two locations on the

Gaspereau River, Nova Scotia, in spring of 2016. Scales collected from alewife were read to obtain age and previous spawning information for a subsample of alewife from both locations. Biological characteristic data were weighted in proportion to daily abundance for each location, and then data were compared between the two locations to check for selectivity. Numbers-at-age for each location were used to calculate selectivity-at-age which was incorporated into a production model. BRPs were generated from the production model with and without selectivity to compare the effects selectivity has on

BRPs.

2.1 Sampling of Biological Characteristics

Alewife were sampled from two locations along the Gaspereau River during the spawning run of 2016: Peter Croft’s fish stand (45.069812°N, 64.352930°W), referred to within the thesis as the fishery, and the White Rock fish ladder (45.056195°N,

64.39623°W), referred to as the ladder (Figure 7). The fishery is located about 4 km downstream from the ladder.

At the fishery, alewife were sampled from the catch on the days the fishery was active – Monday, Tuesday, Thursday, and Friday. On these days, 100 fish were sampled for biological data (described below) then returned to the fisher. Sampling of biological data began on April 25th and ended on May 30th.

At the ladder, live alewife were sampled from May 4th to June 12th. Alewife were removed from the fish ladder using a handheld net, and placed in a bucket of water in groups of 4-5 to rest for several minutes before biological measurements and samples were collected. Once fish had been sampled, they were placed in recovery buckets with well oxygenated water from the ladder to ensure fish recovery. Once fish appeared suitably active, they were returned to the ladder.

Data collection from both locations included fork length, weight, and sex, followed by the collection of scale samples for aging. Fork lengths were measured by placing fish on a wooden board, inlayed with a ruler, with two raised edges perpendicular to each other. Fish were held on the board with their anterior and dorsal end uptight to the edges; length was measured at the fork of their caudal fin to a tenth of a centimeter. Weights were measured to the nearest gram using a portable scale. Sex was determined by squeezing the ventral side of the fish near the cloaca, starting at about 3 cm anterior of the cloaca and moving towards the cloaca. If milt was released the fish was recorded as male, and if row was released the fish was recorded as female.

Scale samples were taken using a flat dull knife with a rounded tip and no sharp edges.

The scales were removed by dragging the tip repeatedly over the side of the fish, slightly above the lateral line and several centimeters posterior of the dorsal fin. Once 10-15 scales were detached they were placed inside a labeled envelope lined with acid free paper, and later examined for age.

Escapement was estimated using video recorded at the ladder. Video was recorded continuously using a camera mounted at the head of the fish ladder, facing

24 down into a 1 m x 1 m channel with a white bottom to allow easy identification of alewife passing through (Figure 8). For an alewife to successfully pass through the fish ladder, they had to pass through the monitored 1 m x 1 m channel. Video was stored on the hard drive of a computer supplied by DFO in the fish counting building at the top of the White Rock fish ladder. Video files were 15 minutes in length. The files were copied to an external hard drive and viewed using Windows Movie Maker on a laptop.

At the beginning of the alewife spawning run there were issues at night with fish milling around the areas lit by lights. This was resolved on May 8th by repositioning the lights at night to encourage fish to swim past the camera, making it easier to count.

There were also some issues with the camera throughout the spawning run. On

May 27th and 28th, the camera would periodically lose connection with the computer recording video, such that some of the video files lacked images. In this situation, the next available working video files was used as a replacement.

Towards the end of the alewife spawning run, some white suckers, Catostomus commersonii (Lacépède 1803) were seen swimming up the ladder. They were identified by a broader head and wider body, and swam slowly in a straight line with little movement from the anterior end of the body. In contrast, alewife have a more pointed head and narrower body, move their entire body, and move erratically changing speed and direction frequently. When a white sucker was identified in the video, it was recorded, but not included in any count.

2.2 Laboratory Methods

Ages of alewife from both the ladder and the fishery were determined by counting annuli on the scales. Subsamples of 500 fish from each location were randomly chosen from the samples collected, based on simulations that showed that 500 subsamples provided adequate representation of the alewife sampled (Gibson et al.

2016).

Scales were aged based on methods described by Cating (1953). Scales were washed and several from each sample were mounted on clear plastic microscope slides with a thin clear plastic covering over top to hold the scales in place. Scales were viewed using a dissection scope under low magnification. All scale aging was done primarily by me, with reviews of uncertain scales conducted by Dr. Jamie Gibson until a consensus was reached on age and number of previous spawning events.

Scales were aged by counting the annuli, or growth rings, from the center of the scale to the edge. The number of annuli plus the edge of the scale was recorded as the age of that fish. Annuli appeared as dark, narrower bands between wider, lighter bands.

Spawning marks are also considered annuli but appear as a jagged clear line near the edge of the scale, with no growth around it. When spawning, alewife do not produce a clear broad annulus, as they consume part of their scale for energy and do not feed when spawning (Cating 1953). A scale with three annuli and one spawning mark would indicate a 5-year-old fish that spawned once previously.

2.3 Statistics and Data Analysis

All data analysis was undertaken using software environment R (version 3.3.2).

2.3.1 Abundance Estimate To obtain an abundance estimate for alewife in 2016 two values were required: the escapement from the fishery and the catch by the fishery. Landings from the fishery were provided by Peter Croft after the fishery was closed. Escapement was estimated using count data from the ladder.

The number of alewife that escaped the fishery on the Gaspereau River was estimated using video counts at the White Rock fish ladder. Video of the entire run was recorded and samples of the video were counted and used to estimate the total escapement. Video was sampled using a two-way stratified random sample design

(Nelson 2006). The total run was stratified first by day, then each day was stratified into five time periods or strata (Table 5). The strata were selected to roughly represent time periods when the number of fish ascending the ladder per unit time was expected to be roughly similar, and followed methods used by Bonang (2016). More fish move during warmer periods of the day (afternoon and evening) rather than cooler periods of the day

(night and early morning). Relative to random sampling, the two-way stratification scheme has the effect of reducing the variance of both the daily escapement estimates and the estimate of spawning escapement of the entire run (Nelson 2006).

Four 5-minute video segments were randomly chosen from each stratum for a total viewing of 20 five minute segments a day, every day of the spawning run. Fish were

27 visually counted using a tally counter. Escapement for each segment was calculated by subtracting fish that swam down the ladder from fish that swam up the ladder.

Escapement was estimated by estimating the mean escapement in each of the five periods (strata) in a day. Means for each stratum were then summed for each day of the run, May 4th to June 19th. Variances and 95% confidence intervals for the number of fish ascending the ladder each day, as well as the total for the season, were calculated following the methods of Nelson (2006), as was done in 2015 by Bonang (2016) for the

Gaspereau River alewife stock.

Once escapement was estimated, annual abundance was calculated by adding the landings data to the escapement estimate.

2.3.2 Weighting and Analyzing Biological Characteristics

Statistics including sample mean, �, variance with corrected bias, �, and standard error of the mean, ��, were calculated for data collected in 2016. The standard formulas for those statistics are as follows:

� = , (Eq. 6) and

( ) � = , (Eq. 7) and

�� = . (Eq. 8)

However, since the data was sampled with the intention of representing the total numbers of fish that passed through the sampling area each day and for the entire run,

28 and because sampling a set number of fish each day does not provide a representative sample of the spawning run, weighted statistics were calculated to better estimate the biological characteristics of the population (Madansky and Alexander, n.d.).

For the biological data taken from sampled fish at the ladder and the fishery to be representative of the population, the data were weighted corresponding to the amount of fish that passed through the ladder, or was caught by the fishery, in that day.

A weight, �, was calculated for each day by dividing the total amount of fish that passed through that day, �, divided by the number sampled that day, �:

� = . (Eq. 9)

If any data were missing, such as fork lengths (e.g. not recorded on that day), weights were calculated by dividing the estimate of daily population size by the daily sample size, minus the number of missing data points for that day, ��:

� = . (Eq. 10)

The weights were used to calculate the weighted means, weighted variances, and weighted standard errors of the means for each type of data. The formulas used for

weighted mean, �, weighted variance with corrected bias, �, are as follows

� = , (Eq. 11) and

() � = , (Eq. 12) and

�� = [ �� − �� (Eq. 13) 29

−2� � − � �� − ��

+� � − � ] .

The formula for the weighted mean is simply each value multiplied by its respective weights summed, divided by the sum of the weights (Eq. 11). An estimator with corrected bias of weighted standard variance was chosen since the sample is small relative to the population (Eq. 12). The formula for weighted standard error of the mean was chosen based on analysis of three different methods of determining weighted standard error (Gatz 1995). Gatz determined that the Cochran formulation (Eq. 13) was closest to the true value, as determined using a bootstrap method.

2.4 Calculation of Selectivity

To determine whether the fishery is selective, the biological characteristics of alewife captured in the fishery were compared with those captured at the ladder. After weighting the data from the ladder and the fishery, statistical comparisons were done between the weighted means using a linear model. A chi square test was done to compare the sex ratios between the two locations using weighted numbers of each sex, rather than observed numbers.

A boxplot for fork length data was created for both locations to visually compare medians and distributions and check for outliers. The sex ratio at both locations, over the study period, was also examined. Proportions of age and repeat spawners at each location were also calculated and plotted.

As described in the results, the fishery was found to be highly selective. Given this result, selectivity curves for the Gaspereau River alewife fishery were developed in terms of age for the production model. Selectivity was calculated using the age data from both locations.

Selectivity curves relative to the age structure of the entire run were developed to calculate selectivity for each age class. Proportions-at-age from the biological characteristics data were multiplied by the total escapement or catch for their respective location to proportionally represent numbers-at-age for the entire run. Fish in age class

7 comprised 0.2% of the sub-sample, so were combined with the closest available age class, 6, to avoid misrepresentation of the population via small sample size.

Proportional numbers-at-age were used to calculate exploitation for each age class:

� = , (Eq. 14) where Ua is the exploitation rate-at-age, Ca is the catch-at-age from the fishery, and Ea is the escapement-at-age from the fishery counted at the ladder, each for an age class a.

The exploitation rate-at-age was then converted to an instantaneous fishing mortality rate-at-age using Eq. 5 in Section 1.2.2.

A general formula for applying selectivity to an instantaneous fishing mortality rate, based on the separability assumption (Deriso et al. 1985), is:

�, = ��, (Eq. 15) where Ft,a is the instantaneous fishing mortality rate and sa is the selectivity for age class a, and Ft is the fully selected instantaneous fishing mortality rate in year t. To calculate

31 selectivity for each age class, Eq. 15 was rearranged for sa, and the instantaneous fishing mortality rate for each age class a was divided by the highest instantaneous fishing mortality rate for an age class a.

Fish of ages 3 through 7 were found at the fishery and the ladder, and the selectivity for age classes 3 through 6 was calculated. Fish aged 7 were combined with age class 6. Fish of ages 1 and 2 are not sexually mature for this population, so none were found at either location. No fish aged 8 or older were found.

2.5 Calculation of Biological Reference Points with Selectivity

A production model specific to the Gaspereau River alewife stock was used to demonstrate the effect of selectivity on BRPs. The model consisted of a SR component, a

SPR component, and a YPR component. Computer code for a model that did not include selectivity was provided by Dr. Jamie Gibson, and selectivity was incorporated into the model as part of this thesis. The model was used to compare BRPs for a non-selective and selective Gaspereau River alewife fishery.

The production model with selectivity incorporated is as follows. The SR component is a Beverton-Holt model, given as:

� = , (Eq. 16) ( ) where SSBt is the spawner biomass in year t, Rt is number of recruits in year class t, � is the slope at the origin or the maximum rate that spawners produce recruits, and Rasy, the asymptotic recruitment level defined as the limit approached by Rt as SSBt approaches infinity (Gibson and Myers 2003a, Gibson et al. 2016). Rasy is analogous to

32 the carrying capacity of an ecosystem for the population (Myers et al. 2001, Gibson and

Myers 2003b).

The SPR component is calculated as a function of the instantaneous fishing mortality rate F:

�� = �� , (Eq. 17) where SSa is given as

�� = �

( ) �� = �� + (1 − �)� �

( ) �� = �� + (1 − �)(1 − �)� �

( ) �� = �� + (1 − �)(1 − �)(1 − �)� �

( ) () �� = �� + (1 − �) 1 − � … (1 − �)� �

juv where a is the age of the fish, ma is the probability of maturity-at-age a, and M is the juvenile mortality rate (Gibson and Myers 2003a).

The calculation for the YPR component is given as:

�� = �� (1 − �) , (Eq. 18) similar to the SPR calculation.

A value of 0.4 was used for Mjuv, 0.53 for Madult (the adult mortality rate), 1 for the age of recruitment and 9 for the max age, 96.1 for �, and 1,563,665 for Rasy, all taken from Gibson and Myers (2003a).

The SSB can be found by multiplying SPR, the spawning stock biomass per recruit, by the number of recruits, for a given year t and instantaneous fishing mortality rate F.

SSB, number of recruits, and catch at equilibrium can be found by manipulating the equation for SSB (Gibson and Myers 2003a). All values at equilibrium are indicated by *:

( ) ��∗ = , (Eq. 19)

∗ ∗ � = ∗ , (Eq. 20) and

∗ ∗ � = � ∙ ��. (Eq. 21)

The calculation for SSB* is a modified Beverton-Holt stock recruitment relationship

(Gibson et al. 2016). Fmsy can be found by finding the instantaneous fishing mortality rate that corresponds with the maximum catch at equilibrium (Gibson and Myers 2003a).

Reference points such as SSBmsy or Recmsy can be found by calculating SPR and YPR for all values F, then performing a grid search to find the value of F, or to find a F for a desired

SSB or another metric (Gibson and Myers 2003a).

2.6 Exploring Cause of Selectivity

Additional analysis of biological characteristic data was done in an effort to determine the cause of the selectivity. With respect to life history characteristics, alewife spawning runs are structured; older, larger fish will tend to run first and younger, smaller fish tend to run later (Gibson et al. 2016). Since sampling at the ladder continued for approximately two weeks after sampling ceased at the fishery, data from both locations were compared over time to investigate if the differences between locations were possibly due to gear selectivity, or run structuring. Determining the source of selectivity

34 could indicate whether selectivity may be present in fisheries that follow similar practices.

Fork length data was averaged by week for both locations and compared directly with standard errors. However, this comparison of weekly averages has two issues. The first issue is that only 4 weeks (weeks 2 through 5) have means from both locations that can be directly compared. Second, this type of comparison does not account for the distance between the fishery and the ladder. The ladder is located approximately 4 km upstream from the fishery, so it takes some time for the fish to reach the ladder. Not accounting for the distance could result in indirect comparisons of different components of the run between the two locations. Given these two issues, data from both locations were directly compared by dividing each dataset into 10 equal proportions, and comparing the means of corresponding proportions. Since sampling began and ended at different times for the two locations, the two data sets had to be made comparable to each other.

Sampling ended on May 30th at the fishery and on June 12th at the ladder. To make both datasets comparable, some of the data sampled at the ladder after the fishery had closed had to be removed. To determine how much data should be removed from the ladder dataset, the time it takes for fish to travel from the fishery to the ladder had to be estimated. Daily abundances at each location were compared to estimate the travel time between locations.

To make the ladder dataset comparable to the fishery dataset, only six days of data for the ladder was kept after the last day of sampling at the fishery, May 30th. Data

35 for June 6th up to and including June 12th from the ladder was not included for comparing proportions of the spawning run. It is assumed that any fish passing through the ladder on those dates not included would not have had a chance to be sampled at the fishery. In total, 175 data points, representing approximately 7% of the dataset for the ladder, were not included in the comparison.

Once the travel time had been accounted for, fork lengths of alewife were compared by taking the weighted mean and weighted standard error of the first 10% of fish that passed through the fishery and comparing it to the first 10% of fish that passed through the ladder. The next 10% of fish, and rest of the proportions were compared in the same fashion, for a total of 10 equal portions of 10% each. It is assumed that although it takes about 6 days for fish to travel between the two locations, the fish will in general move the same amount each day, so that the first 10% of fish that passed through the fishery will be for the most part the same first 10% of fish that pass through the ladder. The same comparison of proportions was done for the age data as well.

3.0 Results 3.1 Abundance Estimate

The 2016 spawning run passed through the ladder from May 4th to June 19th, a total of 47 days. Within this period, 940 five-minute video samples were examined for alewife counts for a total viewing of 78 hours and 20 minutes of video. This represented

6.9% of the run which was observed and used to estimate the total escapement. A mean of 455,745 alewife, with a 95% confidence interval of 50,472, was estimated to have escaped from the fishery. A catch of 769,133 fish was reported, for a total run estimate of 1,224,878 fish (Table 6). The 2016 spawning run had the highest escapement and largest total run of any recorded year, although the stock is still considered slightly overexploited (Figure 9).

3.2 Evidence of Selectivity

The biological characteristics of alewife sampled at the ladder differed from those sampled from the fishery for most but not all characteristics. Significant differences between locations were determined for weighted means of fork length, weight, age, and proportion of repeat spawners (Table 7). Generally, fish sampled from the fishery were older, longer, heavier, and included more repeat spawners than fish sampled from the ladder. The fork length frequency distributions for the two locations were similar, and only a single possible outlier was found at the ladder (Figure 10, Table

8). Both locations had a similar sex ratio of approximately 54% male and 46% female, indicating the fishery is not selective for sex (Figure 11, Table 7).

Age proportions for age classes 3 to 6 differed between locations (Figure 12,

Table 9). A greater proportion of 3 and 4 year olds were found at the ladder, while proportionately more 5 and 6 year olds were found at the fishery. More first time, second time, and third time repeat spawners were found at the fishery than the ladder; overall, 45% more repeat spawners were found at the fishery than the ladder (Figure 13,

Table 7). The mean age of first time spawners and of repeat spawners did not significantly differ, but the number of first time spawners and repeat spawners did differ between locations (Table 7). These differences suggest that the fishery is highly selective for older, larger fish.

3.3 Estimated Selectivity Curve

Selectivity of the fishery is represented in terms of selectivity for age. Analyses of the age-specific instantaneous fishing mortality rate (Table 10) indicated that age class 6 had the highest instantaneous fishing mortality rate. Age-6 was therefore used as the fully selected age class. The instantaneous fishing mortality rates of age classes 3, 4 and

5 were 50%, 65%, and 83%, respectively (Table 10). Too few fish in age class 7 were captured to estimate selectivity for this age class, and no fish older than age 7 were captured. Selectivity for these older age classes were assigned a selectivity of one like age class 6, because the older age classes are known to be most abundant early in the run when the fishery is open. The final selectivity curve is shown in Figure 14.

3.4 Effects of Selectivity on Reference Points

The modified production model (Section 2.5) was used to evaluate the changes in reference points that would occur with selectivity included in the model. The non- selective curve shows that under non-selective conditions, the fishery would be expected to harvest more young fish than under the selective conditions (Figure 14).

SPR curves were generated for each selectivity (Figure 15). For a given F, under selective conditions, more spawning stock biomass is produced per recruit compared to non-selective conditions at the same F, resulting in higher Fx% BRPs.

YPR curves were also generated for each selectivity (Figure 16). At medium to high instantaneous fishing mortality rates, the selective fishery would produce slightly less yield in kg per recruit than the non-selective fishery. This lower yield would lead to a corresponding increase in SPR. Fmax occurs at a near infinite instantaneous fishing mortality rate and is identical for both selectivity scenarios (Table 11).

A SR curve was generated from the production model (Figure 17). The replacement lines for both selectivity curves are very similar. The instantaneous fishing mortality rate that would produce these reference points is Fmsy (Table 11). The model incorporating selectivity indicates that a higher instantaneous fishing mortality rate (1.33 compared to 1.01) and slightly more recruits and more spawning stock biomass are required to produce MSY than in the scenario where the fishery is assumed non- selective.

3.5 Exploring the Cause of Selectivity

Fish sampled at the ladder were consistently smaller and younger on average than fish sampled at the fishery, throughout the 2016 spawning run. To accurately compare proportions of the run, as described in Section 2.6, three peaks in abundance were identified at the ladder and the fishery. Abundance peaks were on average six days apart, suggesting that alewife take six days to travel the 4 km distance between locations (Figure 18, Table 12).

When fork length means were compared between the two locations over time, the size of alewife at the fishery was larger than at the ladder for each time segment directly compared (Figure 19, Table 13). When proportions of the run were compared for fork length and age, means of fork length and age from the fishery was still larger and older than the means of fork length and age from the ladder for most proportions

(Figure 20, Table 14, Figure 21, Table 15).

4.0 Discussion

The Gaspereau River alewife fishery was found to be highly selective for older and larger alewife. However, the effect of this selectivity on BRPs is relatively minor. To the best of my knowledge, this is the first study in which selectivity of an in-river fishery for an anadromous population has been estimated and incorporated into a production model (Section 2.5).

4.1 Sources of Error

Section 2.1 describes some corruption of video files that were sampled to estimate escapement. In these cases, adjacent video files within the same strata were used as an estimate. It is unlikely that any lost counts from corrupt video files would greatly increase the degree of error in the escapement estimate, since the lost video files constituted only 6 (<1%) of the 940 video segments viewed. Fortunately, none of the lost video files occurred during times of high fish passage.

Of the total number of alewife examined for sex at the ladder (n=2449), only four were post spawners. It is assumed that the post spawner numbers during our study were low overall, and did not contribute significantly to the escapement estimate.

4.2 Effects on Reference Points

When selectivity was incorporated into the production model for Gaspereau

River alewife notable changes in reference points were found for Fmsy, Fcol, F25%, F30%,

F35%, and their related exploitation rates. Other reference points changed very little.

It is important to note that the values of fishing mortality reference points from the two models are not directly comparable. Reference points for the non-selective model account for all the fish in the river (all age classes are harvested at the same rate), whereas Fmsy for the selective model is the instantaneous fishing mortality rate at MSY on the fully selected age class. Fishing mortality rates for alewife in age classes 3, 4, and

5 that are not fully selected by the fishery are lower. Since the instantaneous fishing mortality rate affects age classes less if they are not fully selected, the Fmsy for the selective model is higher than the non-selective model while still removing a similar amount of biomass from the stock. Fish are harvested at different rates across age classes for the same F, therefore biomass is removed across age classes at different rates as well.

Fishing mortality rates are higher for the selective model because fewer younger alewife are harvested by the fishery than previously thought. Since the fishery is more selective for older fish, a notably higher Fmsy (1.33 compared to 1.01) and a marginally higher MSY (148,971 compared to 148,610) is the result, since fishing pressure affects the stock less than under the non-selective assumption. A much higher Fcol is also due to the reduced effect that fishing pressure has. Since a greater number of younger fish are able to escape the fishery, collapsing the stock requires greater fishing effort than under the non-selective assumption.

The reason that the Fx% values are higher in the selective scenario than under the non-selective assumption is because young fish have less of a chance of being captured than older fish, so a higher fishing mortality rate under selective conditions is required to

42 allow the same percentage of the SSB to escape per recruit, compared to non-selective conditions.

Given that a fishery is selective, calculating the exploitation rate for that fishery can be more complex. Generally, the easiest data to obtain is catch or count data, and an exploitation rate can be calculated from that data. However, if the fishery is known to catch larger fish, and the average weights are known for the catch and the escaped fish, an exploitation rate based on biomass can be calculated by multiplying the numbers of catch and count by their respective average weight. To further increase the accuracy of the exploitation rate, numbers-at-age could be collected from the catch and escaped fish to provide exploitation rates for each age class based on numbers, or biomass by age class if the biomass by age class differs between locations. There is a trade-off between accuracy and efficiency, as a greater number of personnel and greater funds are required to collect all the data necessary for estimating biomass and numbers-at-age.

Selectivity was found not to impact BRPs for the Gaspereau River alewife as much as it would for a marine stock, since alewife are available for harvest in their natal river only when sexually mature, whereas immature fish are available for harvest in many marine fisheries. Since immature fish are not available to the Gaspereau River alewife fishery, the fishery is at less of a risk of collapse due to overfishing young or immature age classes. Since the placement of the Gaspereau River alewife fishery and life history of alewife result in inherent selectivity of mature fish, the impact the estimated selectivity has is reduced compared to the impact selectivity can have on a marine fishery.

4.3 Exploring Causes of Selectivity

Section 3.5 demonstrated that older larger fish were found at the fishery compared to the ladder when both sites were compared over time and over proportions. This consistent difference over time suggests that the timing of the fishing season (the fishery closes before the spawning run ends) is not the only cause for the selectivity described in this thesis.

To compare means for fork length and age between the ladder and fishery for different proportions of the run, travel time between the two sampling locations was estimated by identifying three abundance peaks at both locations and taking the average time (six days) between each pair of abundance peaks. There are inherent issues with estimating travel time based on abundance peaks: the exploitation of the fishery affects how many fish will escape the fishery that day, high river flows will slow fish from passing upstream, and it must be assumed that all alewife travel at the same speed for the same amount of time. Tagging fish would provide a better estimation of travel time between the locations. However, since tagging was not done in 2016, and the fact that it must take some amount of time for fish to travel from the fishery to the ladder, estimation of travel time by comparing abundance peaks was the best method available.

To further evaluate the cause of the selectivity, biological characteristics of catch from different fishers could be compared to investigate a link between selectivity and fishing gear used. Additionally, fish could be sampled for the entire run at both the

44 ladder and the fishery to remove any effect the start and end dates of the fishing season would have on selectivity.

Determining the cause of the selectivity could prove valuable, as it could indicate whether fisheries with similar practices could be selective in the same manner, providing information on the stocks fished that may be relevant to management.

4.4 Natural Variability

A universal issue of basing management decisions for stocks off models is that models cannot account for all the variables that exist. Models serve as useful estimation tools, but can be wrong. For example, fish fulfill both predator and prey roles in their environments, and different species are connected in many different ways. MacCall

(2002) describes how adding additional factors to models such as temperature, or adding in a predator species with a variable abundance tied to the environment and the prey species abundance, can markedly affect the output of the model. Similarly, one species of fish altering another population may in turn alter its own population, both directly and indirectly. For example, Atlantic cod (Gadus morhua) feeds on forage species such as herring (Clupea harengus) and mackerel (Scomber scombrus), but herring and mackerel also feed on cod eggs (Swain and Sinclair 2000). To maintain a stable cod population, enough adult cod must exist to keep the herring and mackerel populations low enough so that enough cod eggs survive to recruitment to replenish the stock. If the adult cod are dramatically decreased, a boom in forage species could reduce cod juveniles, significantly delaying efforts to rebuild the stock (Walters and Kitchell 2001).

The ecology of oceans and freshwater systems are complex and interconnected; both

45 systems can change independently, can affect each other, as well as be affected by terrestrial activity. While the production model used for assessing the Gaspereau River alewife stock does not include time varying parameters, it does provide an evaluation of the productivity and status of the population and fishery at present. Ongoing assessments are needed to determine whether population dynamics of the stock is changing over time.

Selectivity of a fishery is a factor in population dynamics models that can markedly change the output of a model as much as environmental factors, or abundance of predators and prey. If the Gaspereau River alewife fishery was selective towards younger fish, the reproductive potential of the stock would have been less than expected rather than more, and would have warranted changes in management of the fishery. If an environmental variable such as temperature was incorporated into the model, it is much more difficult to respond to the changes by altering management.

Once selectivity has been estimated, it will hold true until the fishing method changes, whereas environmental factors will vary independently.

Estimating selectivity and incorporating it into a model makes the model more accurate. It does not guarantee total accuracy of the model, but removes an assumption and replaces it with actual estimated values, lowering the degree of error the model could have. It is feasibly impossible to model an entire ecosystem, but including the most important parts can yield a model that provides useful information.

5.0 Conclusions

This study found the Gaspereau River alewife fishery to be highly selective for larger, older fish. This finding of selectivity is a new result, and replaces the previous assumption that the fishery is non-selective. Selectivity was estimated for each age class available to the fishery, and incorporated into a population dynamics model. The model was used to generate new reference points for the alewife stock. Biomass and abundance reference points such as SSBmsy and RECmsy increased slightly, while fishing mortality reference points such as Fmsy and Fcol increased much more. ESCmsy increased from 350,000 fish to 360,000 fish, which remains below the USR of 400,000.

References ASMFC. 2012. Stock Assessment Report No. 12-02 of the Atlantic States Marine Fisheries Commission River Herring Benchmark Stock Assessment Vol. I.

Berry, F.H. 1964. Review and emendation of family Clupeidae. Copeia, 720-730.

Bigelow, H.B., and Schroeder, W.C. 1953. Fishes of the Gulf of Maine. Vol. 53: Washington D.C.: United States Government Printing Office.

Bonang, J.J. 2016. Assessing stock and fishery status of spawning Alewife, Alosa pseudoharengus, in the Gaspereau River, Nova Scotia, by using video counts to estimate spawning run size and escapement. B.Sc. (Hon.) Thesis. Acadia University, Canada.

Caddy, J.F., and Mahon, R. 1995. Reference points for fisheries management. FAO Fish. Tech. Pap. No. 347.

Cating, J.P. 1953. Determining the age of Atlantic shad from their scales. U.S. Fishery Bulletin 54 (85):187-199

Deriso, R.B., Quinn II, T.J., Neal, P.R. 1985. Catch-Age Analysis with Auxiliary Information. Canadian Journal of Fisheries and Aquatic Sciences 42(4): 815-824.

DFO. 2006. A Harvest Strategy Compliant with the Precautionary Approach. DFO Can. Sci. Advis. Sec. Sci. Advis. Rep. 2006/023.

DFO. 2012. Reference points consistent with the precautionary approach for a variety of stocks in the Maritimes Region. DFO Can. Sci. Advis. Sec. Sci. Advis. Rep. 2012/035.

Durbin, A.G.S., Nixon, S.W., and Oviatt, C.A. 1979. Effects of the spawning migration of the alewife on freshwater ecosystems. Ecology 60: 8-17.

Gatz, D.F., and Smith, L. 1995. The standard error of a weighted mean concentration – I. Bootstrapping vs other methods. Atmospheric Environment. 29(11): 1185-1193

Gibson, A.J.F. 1999. Characteristics of the Gaspereau River alewife stock and fishery – 1998. Acadia Centre for Estuarine Research Publication No 49. Wolfville, N.S. 91p.

Gibson, A.J.F. 2000a. Characteristics of the Gaspereau River alewife stock and fishery – 1999. Acadia Centre for Estuarine Research Publication No 56. Wolfville, N.S. 48p.

Gibson, A.J.F. 2000b. The Gaspereau River Alewife Stock and Fishery 2000: Data Summary. Acadia Centre for Estuarine Research Publication No 58. Wolfville, N.S. 22p.

Gibson, A.J.F. and Daborn, G.R. 1997. The 1997 Alewife Spawning migration in the Gaspereau River, Nova Scotia, Final Report. Acadia Centre for Estuarine Research Publication No 45. Wolfville, N.S. 68p.

Gibson, A.J.F., and Myers, R.A. 2001. Gaspereau river alewife stock status report. Canadian Science Advisory Secretariat Research Document 2001/061. Fisheries and Oceans Canada. Ottawa.

Gibson, A.J.F., and Myers, R.A. 2003a. Biological Reference Points for Anadromous Alewife (Alosa pseudoharengus) Fisheries in the Maritime Provinces. Canadian Technical Report of Fisheries and Aquatic Sciences No. 2468.

Gibson, A.J.F., and Myers, R.A. 2003b. A meta-analysis of the habitat carrying capacity and the maximum lifetime reproductive rate of anadromous alewife in eastern North America. In Biodiversity and conservation of shads worldwide. Edited by K.E. Limburg and J.R. Waldman. American Fisheries Society Symposium Series. American Fisheries Society, Bethesda, Md. 211–221.

Gibson, A.J.F., Bowlby, H.D., and Keyser, F.M. 2016. A Framework for the Assessment if the Status of River Herring Populations and Fisheries in DFO’s Maritime Region. Unpublished Manuscript.

Hilborn, R., and Walters, C.J. 1992. Quantitative Fisheries Stock Assessment. New York, NY: Chapman and Hall.

Jessop, B.M., and Parker, H.A. 1988. The alewife in the Gaspereau River, Kings County, Nova Scotia. 1982-1984. Canadian Manuscript Report of Fisheries and Aquatic Sciences No. 1992: 29.

Lent, J.N. 1999. Selective predation by young-of-the-year alewives (Alosa pseudoharengus) on Zooplankton in Gaspereau Lake, Nova Scotia. B.Sc. (Hon.) Thesis. Acadia University, Canada.

MacCall, A.D. 2002. Fishery-management and stock-rebuilding prospects under conditions of low-frequency environmental variability and species interactions. Bulletin of Marine Science, 70(2): 613-628.

Madansky A. and Alexander, H.G.B. Alternative Approaches to Significance Testing with Weighted Means. Accessed on March 20 2017.

McIntyre, T.M., Bradford, R.G., Davies, T.D., and Gibson, A.J.F. 2007. Gaspereau River alewife stock status report. Canadian Science Advisory Secretariat Research Document 2007/032. Fisheries and Oceans Canada. Ottawa. 1-35.

Millar, R.B., Fryer, R.J. 1999. Estimating the size-selection curves of towed gears, traps, nets and hooks. Reviews in Fish Biology and Fisheries 9: 89-116.

Mills, E.L., O’Gorman, R., DeGisi, J., Heberger, R.F., and House, R.A. 1992. Food of the alewife (Alosa pseudoharengus) in Lake Ontario before and after the establishment of Bythotrephes cederstroemi. Canadian Journal of Fisheries and Aquatic Sciences 49: 2009-2019.

Minister of Justice. 1985. Fisheries Act. Retrieved from Justice Laws Website. Accessed on March 20 2017.

Myers, R.A., Bowen, K.G., and Barrowman, N.J. 1999. The maximum reproductive rate of fish at low population sizes. Canadian Journal of Fisheries and Aquatic Sciences 56: 2404–2419.

Myers, R.A., MacKenzie, B.R., Bowen, K.G., and Barrowman, N.J. 2001. What is the carrying capacity of fish in the ocean? A meta-analysis of population dynamics of North Atlantic cod. Canadian Journal of Fisheries and Aquatic Sciences 58: 1464– 1476.

Nelson, G.A. 2006. A Guide to Statistical Sampling for the Estimation of River Herring Run Size Using Visual Counts. Massachusetts Division of Marine Fisheries Department of Fish and Game, Executive Office of Environmental Affairs and Commonwealth of Massachusetts. Massachusetts Division of Marine Fisheries Technical Report TR- 25, 1-25.

Rulifson, R.A. 1994. Status of anadromous Alosa along the east coast of North America. Pages 108-112 In J. E. Cooper, R. T. Eades, R. J. Klauda, and J. G. Loesch, editors. Anadromous Alosa Symposium, Tidewater Chapter, American Fisheries Society, Bethesda, Maryland.

Scott, W.B., and Scott, M.G. 1988. Atlantic Fishes of Canada. Canadian Bulletin of Fisheries and Aquatic Sciences 219: 731.

Swain, D.P. and Sinclair, A.F. 2000. Pelagic fishes and the cod recruitment dilemma in the Northwest Atlantic. Canadian Journal of Fisheries and Aquatic Sciences 57(7): 1321-1325.

Wallace, R.K., and Fletcher, K.M. 2001. Understanding fisheries management, Second Edition. Mississippi-Alabama Sea Grant Consortium. 00-005.

Walters, C., and Kitchell, J.F. 2001. Cultivation/depensation effects on juvenile survival and recruitment: implications for the theory of fishing. Can. J. Fish. Aquat. Sci. 58: 39-50.

Tables Table 1. Definitions of the reference points for alewife fisheries provided by Gibson and Myers (2003). Adapted from Gibson et al. (2016). Theoretical basis BRP Definition Yield per Recruit Fmax The fishing mortality rate that maximizes the yield per recruit.

Umax The exploitation rate that corresponds with Fmax.

YPRmax The yield per recruit that results from Fmax.

Spawner per Fx% The fishing mortality rate where the SPR is reduced to Recruit x% that of SPRF=0

Life Cycle Model Fcol The fishing mortality rate that would drive the population to extinction (the fishing mortality rate that produces a replacement line equal to the inverse of the maximum likelihood estimate of the slope at the origin of the stock-recruitment relationship).

Ucol The exploitation rate that corresponds with Fcol.

Fmsy The fishing mortality rate that produces the maximum sustainable yield (based on the maximum likelihood estimates of the stock recruitment parameters).

Umsy The exploitation rate that corresponds with Fmsy.

SSBmsy The spawner biomass that produces the maximum sustainable yield (based on the maximum likelihood estimates of the stock recruitment parameters).

Recmsy The number of recruits to the stock required to produce maximum sustainable yield.

Escmsy The number of fish that must escape from the fishery to produce maximum sustainable yield.

Yieldmsy The yield of the fishery that will be obtained with a fishing mortality rate of Fmsy and when the stock is at equilibrium.

Table 1. (continued from previous page) Theoretical basis BRP Definition SSB20% The spawner biomass corresponding to 20% of the unfished equilibrium spawner biomass (based on the maximum likelihood estimates of the stock recruitment parameters).

SSBF=0 The spawning stock biomass produced under no fishing.

Table 2. Three assumed selectivities for the hypothetical marine fishery in Section 1.3, depicted in Figure 2. Each selectivity scenario has a selectivity value between 0 and 1 for all age classes from 1 to 9. The value represents the proportion of that age class that are subject to the hypothetical fishery. Age class Selectivity 1 Selectivity 2 Selectivity 3 1 0 0 1

2 0 0 1

3 0.5 0 1

4 0.9 0 1

5 1 0 1

6 1 1 1

7 1 1 1

8 1 1 1

9 1 1 1

Table 3. Biological reference points for a hypothetical fish population exploited using three hypothetical selectivity scenarios. Reference points correspond with Figures 3, 4, 5, and 6 described in Section 1.3.2. Definitions for reference points can be found in Table 1. Reference point Selectivity 1 Selectivity 2 Selectivity 3 Fmax 0.76 4.61 0.36

Umax 0.53 0.99 0.30

YPRmax (kg per recruit) 0.17 0.18 0.13

Fcol 1.06 4.61 0.43

Ucol 0.655 0.99 0.35

Fmsy 0.32 1.11 0.17

Umsy 0.28 0.67 0.16

SSBmsy (kg) 29.87 39.06 28.78

Recmsy (Number of fish) 74.92 79.62 74.21

Yieldmsy (kg) 11.13 14.33 8.80

SSB20% (kg) 20.00 20.00 20.00

SSBF=0 (kg) 99.98 99.98 99.98

Table 4. Biological reference points for the Gaspereau River alewife stock consistent with DFO’s precautionary framework (from Gibson et al. 2016). Assessment results can be compared against these reference points to determine stock status. Reference point Acronym Description Value Upper Stock Reference USR Spawning stock biomass 400,000 fish Point or number of spawners above which stock is healthy; if below but still above the LRP stock is in the cautious zone. Can be SSBMSY or similar value.

Lower Stock Reference LRP Spawning stock biomass 235,000 fish Point or numbers of spawners below which the stock is in the critical zone.

Exploitation Rate URR Exploitation rate above 0.53 Removal Reference which the stock is over Level exploited

Exploitation Rate ULRR Exploitation rate above 0.35 Lower Removal which stock is fully Reference Level exploited, below which stock is under exploited

Table 5. Start and end times of the five strata used for selection of video samples of escapement for the Gaspereau River alewife spawning run in 2016, as well as the total duration in hours of each stratum. Video was taken at the head of the White Rock fish ladder on the Gaspereau River, Nova Scotia. Stratum Start time End time Duration (hours) 1 0000 0600 6

2 0600 1200 6

3 1200 1600 4

4 1600 2000 4

5 2000 0000 4

Table 6. Historical summary of catch data, estimated escapement, estimated run size, and exploitation rate for alewife on the Gaspereau River, Nova Scotia. Data from 1970 through 2006 is from McIntyre et al. (2007), data from 2015 and 2016 was provided by Dr. Jamie Gibson. Asterisks indicates escapement estimates obtained by sub-sampling video. Total counts were used in other years. Year Catch estimate Escapement Estimated run Exploitation (number of estimate (number size (number of rate (%) fish) of fish) fish) 2016 769,133 *455,745± 1,224,878 62.7 25,236 2015 705,500 *436,879± 1,142,379 61.8 24,230 2006 282,589 *209,064± 491,653 57.5 35,540 2005 219,173 *265,705± 484,878 45.2 39,855 2004 268,820 *175,046± 443,866 60.6 17,504 2003 416,335 *435,832 852,167 48.9 2002 391,278 310,746 702,024 55.7 2001 119,348 238,842 358,190 33.3 2000 754,585 98,883 852,468 88.4 1999 698,600 81,236 779,926 89.6 1998 372,400 171,639 544,039 68.5 1997 611,520 95,443 706,953 86.5 1995 954,960 126,933 >1,081,892 <88.3 1984 212,966 111,100 324,066 65.7 1983 150,408 114,800 265,208 56.7 1982 254,068 50,400 304,468 83.4 1970 480,000 60,527 540,527 88.8

Table 7. Summary statistics for the biological characteristics data for alewife sampled from the White Rock fish ladder and Gaspereau River fishery in 2016. Biological data includes sex ratio, as well as means and standard deviations of fork length, weight, age, and age at first spawning. Age data consisted of a random subset of 500 individuals from both locations, whereas all other data was from the total amount sampled. Both data sets were weighted proportional to the daily escapement for the ladder and daily catch for the fishery, so that weighted summary statistics are representative of the run, P- values are indicative of the statistical significance of the differences in the weighted means between the ladder and the fishery. A chi-square test was conducted to compare proportions of males and females between the two locations, and a general linear model was generated to compare percent repeat spawners between locations. Ladder Ladder Fishery Fishery P-values weighted Weighted Number sampled 2449 2449 1651 1651

Sex Female 1151(47.1%) 1122(45.9%) 811(49.1%) 766(46.4%) Male 1297(52.9%) 1326(54.1%) 840(50.9%) 885(53.6%) Unknown 1 1 0 0 Ratio F/M 0.887/1 0.846/1 0.965/1 0.865/1 0.723

Mean Fork 24.89 24.92 25.11 25.35 3.91x10-3 Length (cm), (SE) (0.0264) (0.0272) (0.0314) (0.0500)

Mean Weight (g) 216.9 218.7 228.2 233.3 1.22x10-3 (SE) (0.805) (0.886) (0.960) (1.46)

Mean Age (year) 4.43 4.59 1.40x10-4 (SE) (0.029) (0.031)

Mean Age (year) 4.17 4.19 0.496 at First Spawn (0.031) (0.021) (SE)

Mean Age (year) 5.25 5.30 0.432 Repeat Spawners (0.047) (0.038) (SE)

Repeat Spawners 116/500 168/500 2.83x10-4 (%) 23.2% 33.6%

Table 8. Values for the boxplots characterizing fork length (FL) frequency distributions for Gaspereau River alewife sampled at White Rock fish ladder and from the commercial fishery in 2016. Boxplots are shown in Figure 10. Value Ladder Fishery FL (cm) FL (cm) Upper Fence 29.3 29.5 Upper Quartile 26.7 26.9 Median 24.9 25.2 Lower Quartile 23.1 23.5 Lower Fence 20.5 20.9 Outliers 29.8 NA

Table 9. Proportions-at-age, and numbers–at-age for the Gaspereau River alewife sampled at White Rock fish ladder and from the commercial fishery in 2016. Proportions and numbers-at-age for each age class are categorized by number of times previously spawned. Proportions-at-age are represented in a bar plot in Figure 12, and proportions of numbers of times spawned are represented in a bar plot in Figure 13. Ladder Fishery Age Number of times spawned Proportion Number Proportion Number 3 First time spawner 0.028 14 0.016 8 First time repeat spawner 0 0 0 0 Second time repeat spawner 0 0 0 0

4 First time spawner 0.572 286 0.474 237 First time repeat spawner 0.006 3 0.004 2 Second time repeat spawner 0 0 0 0

5 First time spawner 0.168 84 0.174 87 First time repeat spawner 0.158 79 0.222 111 Second time repeat spawner 0.006 3 0.008 4

6 First time spawner 0 0 0 0 First time repeat spawner 0.036 18 0.042 21 Second time repeat spawner 0.024 12 0.056 28 Third time repeat spawner 0 0 0.002 1

7 First time spawner 0 0 0 0 First time repeat spawner 0 0 0 0 Second time repeat spawner 0.002 1 0 0 Third time repeat spawner 0 0 0.002 1

Table 10. Numbers-at-age, age-specific exploitation rates, age-specific instantaneous fishing mortality rates, and selectivity for age classes 3 through 6 for the Gaspereau River alewife stock, as estimated using data collected in 2016. Age data were collected at the White Rock fish ladder and from the commercial fishery. Too few fish older than age- 6 were captured to estimate selectivity. Selectivity for older age classes was assumed to be similar to age class 6. Age class Ladder: Fishery: Exploitation Instantaneous Selectivity number of number of rate fishing fish fish mortality rate 3 12,761 12,306 0.491 0.675 0.504

4 263,421 367,646 0.583 0.874 0.653

5 151,307 310,730 0.673 1.12 0.834

6 27,345 76,913 0.738 1.34 1.00

Table 11. Biological reference points for the Gaspereau River alewife stock estimated under assumptions of a non-selective fishery, and a fishery that is selective using the selectivity-at-age (Table 10). The production model that produced the reference points is described in Section 2.5. Definitions for reference points can be found in Table 1. Biological reference point Non-selective Selective Fmax 4.61 4.61

Umax 0.99 0.99

YPRmax (kg per recruit) 0.133 0.132

F25% 0.78 1.01

F30% 0.65 0.84

F35% 0.55 0.71

Fcol 2.60 3.65

Ucol 0.93 0.97

Fmsy 1.01 1.32

Umsy 0.63 0.73

SSBmsy (kg) 86,332 86,447

Recmsy (Number of fish) 1,312,002 1,312,279

Escmsy (Number of fish) 350,614 358,229

Yieldmsy (kg) 148,610 148,971

SSB20% (kg) 109,215 109,215

SSBF=0 (kg) 546,075 546,075

Table 12. Number of alewife and day of the spawning run for three peaks in abundance that occurred for the Gaspereau River alewife stock in 2016. Abundance data were collected from the commercial fishery and the White Rock fish ladder. Lag time is the time in days it takes for an abundance peak to appear at the ladder after it has appeared at the fishery. Abundance data and peaks are shown in Figure 18. Peak number Location Day Number of fish Lag time in days 1 Fishery 3 186,000 1 Ladder 8 22,224 5

2 Fishery 9 101,438 2 Ladder 16 58,002 7

3 Fishery 16 118,404 3 Ladder 22 22,230 6

Table 13. Weekly means and standard errors of fork length for the Gaspereau River alewife sampled at White Rock fish ladder and from the commercial fishery in 2016. Week 1 begins on April 26th, and ends on May 2nd, but includes data from April 25th for the ladder. Week 7 ends on June 12th. NAs indicate that sampling was not done on those weeks for that location. Means and standard errors are represented in Figure 19. Week Statistic Ladder (cm) Fishery (cm) 1 Mean NA 25.51 Standard Error NA 0.0817

2 Mean 25.37 25.59 Standard Error 0.0588 0.0723

3 Mean 25.03 25.11 Standard Error 0.0437 0.0782

4 Mean 24.56 24.91 Standard Error 0.0496 0.0761

5 Mean 24.45 24.6 Standard Error 0.110 0.0735

6 Mean 24.57 NA Standard Error 0.128 NA

7 Mean 24.73 NA Standard Error 0.114 NA

Table 14. Means and standard errors of fork length for the Gaspereau River alewife sampled at White Rock fish ladder and from the commercial fishery in 2016. Means and standard errors were calculated for the first 10% of alewife that passed through each location, then the next 10%, and so on until 10 equal proportions of the spawning run from each location had been assessed. Means and standard errors are represented in Figure 20. Percentile Statistic Ladder (cm) Fishery (cm) 0-0.1 Mean 25.60 26.04 Standard error 0.0911 0.183

0.1-0.2 Mean 25.20 26.19 Standard error 0.0782 0.228

0.2-0.3 Mean 25.05 25.61 Standard error 0.0839 0.168

0.3-0.4 Mean 25.11 25.18 Standard error 0.0909 0.144

0.4-0.5 Mean 25.26 25.14 Standard error 0.0859 0.114

0.5-0.6 Mean 24.84 25.34 Standard error 0.0958 0.101

0.6-.07 Mean 24.68 25.35 Standard error 0.0847 0.096

0.7-0.8 Mean 24.37 25.13 Standard error 0.0710 0.158

0.8-0.9 Mean 24.64 24.78 Standard error 0.0827 0.148

0.9-1.0 Mean 24.50 24.76 Standard error 0.0863 0.058

Table 15. Means and standard errors of age for the Gaspereau River alewife sampled at White Rock fish ladder and from the commercial fishery in 2016. Means and standard errors were calculated for the first 10% of alewife that passed through each location, then the next 10%, and so on until 10 equal proportions of the spawning run from each location had been assessed. Means and standard errors are represented in Figure 21. Percentile Statistic Ladder (age) Fishery (age) 0-0.1 Mean 4.84 4.8 Standard error 0.0142 0.0128

0.1-0.2 Mean 4.62 4.8 Standard error 0.0133 0.0134

0.2-0.3 Mean 4.26 4.62 Standard error 0.0089 0.0133

0.3-0.4 Mean 4.68 4.66 Standard error 0.0137 0.0132

0.4-0.5 Mean 4.44 4.7 Standard error 0.0129 0.0129

0.5-0.6 Mean 4.38 4.68 Standard error 0.0133 0.0143

0.6-.07 Mean 4.32 4.54 Standard error 0.0119 0.0157

0.7-0.8 Mean 4.32 4.54 Standard error 0.0137 0.0135

0.8-0.9 Mean 4.22 4.36 Standard error 0.0130 0.0126

0.9-1.0 Mean 4.2 4.36 Standard error 0.0107 0.0150

Figures

a) Increasing habitat amount b) Carrying Capacity

Decreasing habitat amount Decreasing Mortality

Increasing Mortality Number of Recruits Number of Spawners

Number of Spawners Number of Recruits

c) Carrying Capacity

Equilibirum Points Number of Recruits

Number of Spawners

Figure 1. A visual description of population dynamics for a hypothetical fishery consisting of a stock recruitment relationship (a), two rates at which recruits produce spawners throughout their lives at different mortality rates (b), and the stock recruitment relationship combined with the two replacement lines, which are the inverse of the rates at which recruits produce spawners throughout their lives (c). The stock recruitment relationship is modeled from the Beverton-Holt recruitment model. The population dynamics model is described in more detail in Section 1.2.2.

2 Fishing Mortality (F)

0.0 0.2 0.4 0.6 0.8 1.0

Exploitation (U)

Figure 2. The relationship between the exploitation rate, U, and the instantaneous fishing mortality rate, F. The figure is explained in Section 1.2.3.

1.0 3

0.8

0.6

0.4 Age Specific Selectivity

0.2 1 2

0.0

2 4 6 8

Ages

Figure 3. Three selectivity curves used to illustrate the effects of selectivity on reference points for the hypothetical marine fish stock described in Section 1.3.2.

0.15

0.10

Yield per Recruit (kg) Yield

0.05 3 1 2

0.00

0 1 2 3 4

Figure 4. Yield per recruit (YPR) curves for the hypothetical marine fish stock described in Section 1.3.2. The three lines correspond to the three assumed selectivity scenarios shown in Figures 3 and 4. The X-axis (F) shows the instantaneous fishing mortality rate. The Y-axis shows the yield produced by an average recruit. The vertical lines represent the instantaneous fishing mortality rate which would provide the highest yield per recruit for each selectivity (Fmax).

1.0

0.8

0.6

SSB per Recruit (kg) 0.4

0.2 1

3 0.0

0 1 2 3 4

Figure 5. Spawner biomass per recruit (SPR) curves for the hypothetical marine fish stock described in Section 1.3.2. The three lines correspond to the three assumed selectivity scenarios shown in Figure 3. The X-axis (F) shows the instantaneous fishing mortality rate. The Y-axis shows the spawning stock biomass (SSB) produced by a recruit throughout its life.

120

100

80 ● 2 ● 3 ● 1

40 Number of Recruits

0 0 20 40 60 80 100 120 Spawner Biomass (kg)

Figure 6. A stock recruitment (SR) curve for the hypothetical marine fish stock described in Section 1.3.2. The solid line represents the rate which spawners produce recruits. The thick dashed line is the replacement line at an instantaneous fishing mortality rate of 0. Lines 1, 2, and 3 correspond to the three assumed selectivity scenarios shown in Figures 3, 4, and 5, and represent the replacement lines at Fmsy for their respective selectivities.

Fishery

Ladder

Figure 7. Map of the study area on the Gaspereau River in Nova Scotia, showing the sampling locations in this study. The “Fishery” refers to Peter Croft’s dip-net stand, and the “Ladder” refers to the White Rock fish ladder. Abundance data and biological characteristics of alewife were collected from both locations.

Figure 8. Photos of the two study locations where data were collected during the 2016 Gaspereau River alewife spawning run. Top left: a dip-net used to harvest alewife. Top right: camera mounted at the head of the White Rock fish ladder from which video was sampled to estimate alewife spawning escapement. Bottom right and left: White Rock fish ladder.

Spawning escapement critical cautious healthy

2.0

1999 ● ● 2000 ● 1997

1982 ● 1.5 exploited over

1984 ● ● 1998 2016 ●● ● 2004 2015 ● 1983 ● 2006 RLL 1.0

U/U ● 2003 ● 2005 fully Exploitation rate ● 2001 exploited

● 2002

0.5 partially exploited

0.0

0.0 0.5 1.0 1.5 2.0 Esc/EscUSR U URR = 0.53 Gaspereau River U fully = 0.35 Esc USR = 400000 Alewife Esc LRP = 234549

Figure 9. Stock status plot for the Gaspereau River alewife stock. The largest black point is the stock status for 2016, and the second largest black point is the stock status for 2015. Stock status from 1982-1984 are represented in open circles, and status from 1997-2006 are represented in closed circles. The plot was taken from Gibson et al. 2016 and updated with data for 2015 and 2016.

30 ●

24 Length (cm)

Ladder Fishery

Figure 10. Boxplots characterizing fork length frequency distributions and median values for Gaspereau River alewife sampled at the White Rock fish ladder and the commercial fishery in 2016. Values for boxplots are shown in Table 8.

1.0 ●

0.8

0.6 n=1326 n=864 n=787 Ladder

n=1122

Fishery 0.4 Proportion

0.2

0.0

Male Female

Figure 11. Comparison of the sex ratio for Gaspereau River alewife sampled at the White Rock fish ladder and the commercial fishery in 2016. Sample size of males and females from both locations were weighted with abundance data from each sampling location as described in Section 2.3.1 to and added above each bar.

0.6 n=289

Ladder Fishery

0.5 n=239 1st Time Spawner

1st Time Respawner n=202 0.4 2nd Time Respawner

n=166 3rd Time Respawner

0.3

Proportion 0.2

n=50 0.1 n=30 n=14 n=8 n=1 n=1 0.0

3 4 5 6 7 Age (years)

Figure 12. Comparison of proportions of age classes and proportions of repeat spawners for Gaspereau River alewife sampled at the White Rock fish ladder and the commercial fishery in 2016. Two 3rd-time repeat spawners of age-6 and age-7 were found at the fishery. Proportions and numbers-at-age, are found in Table 8.

1.0 ●

0.8 3rd Time Repeat Spawner

2nd Time Repeat Spawner 0.6

1st Time Repeat Spawner

0.4 Proportion

0.2 n=116 n=168

0.0

Ladder Fishery Location

Figure 13. Comparison of the composition of the repeat spawning component of the Gaspereau River alewife stock, based on the number of times fish had previously spawned. Alewife were sampled at White Rock fish ladder and the commercial fishery in 2016.

1.0

Non−selective

0.9

0.8 Estimated

0.7

Age Specific Selectivity 0.6

0.5

0.4

3 4 5 6 7 8 9

Ages

Figure 14. Comparison of the selectivity curves-at-age for Gaspereau River alewife sampled at the White Rock fish ladder and the commercial fishery in 2016. Curves are relative to the age structure of the mature fish that comprise the spawning run. The dashed line represents a non-selective fishery, and the dashed dotted line represents a selective fishery. The estimated selectivity curve was smoothed by combining age classes 6 and 7, since age seven fish only made up 0.2% of the sample size. Selectivity for ages 3 through 9 are represented since fish age 1 and 2 are not available to the fishery.

0.35

0.30

0.25

0.20

0.15 SSB per Recruit (kg)

0.10

Estimated 0.05

Non−selective

0.00 0 1 2 3 4

Figure 15. Comparison of spawner biomass per recruit (SPR) curves for Gaspereau River alewife sampled at the White Rock fish ladder and the commercial fishery in 2016. The two selectivity scenarios represented in Figure 14 are shown: a non-selective selectivity (dashed line), and the selectivity curve as estimated in this study using data from 2016 (dashed and dotted line). The X-axis (F) is the instantaneous fishing mortality rate. The Y- axis shows the spawning stock biomass (SSB) produced by a recruit throughout its life.

0.12 Non−selective

Estimated 0.10

0.08

0.06 Yield per Recruit (kg) Yield 0.04

0.02

0.00

0 1 2 3 4

Figure 16. Comparison of yield per recruit (YPR) curves for Gaspereau River alewife sampled at the White Rock fish ladder and the commercial fishery in 2016. The two selectivity scenarios represented in Figure 14 and 15 are shown: a non-selective selectivity (dashed line), and the selectivity curve as estimated in this study using data from 2016 (dashed and dotted line). The X-axis (F) is the instantaneous fishing mortality rate. The Y-axis shows the yield produced by an average recruit.

Non− selective 1500

● Estimated

1000

500 Recruits (Thousands of Fish)

0 10 20 30 40 50 60 Spawner Biomass (Thousands of kg)

1320

1315

● Non−selective ● Estimated

1310

1305

Recruits (Thousands of Fish) 1300 8.2 8.4 8.6 8.8 9 Spawner Biomass (Thousands of kg)

Figure 17. Top panel: The spawner-recruit curve (solid line) for the Gaspereau River alewife stock (from Gibson and Myers 2003a), and replacement lines for three fishing scenarios: no fishing (thick dashed line), for a non-selective fishery (thin dashed line), and for a fishery with the selectivity estimated in this study (dashed and dotted line) using data from the 2016 alewife spawning run. Replacement lines for scenarios with fishing correspond with their respective Fmsy. The point where the replacement lines with selective or non-selective fishing cross the solid spawner line indicates the number of recruits (RECmsy) on the y-axis and the biomass of spawners (SSBmsy) on the X-axis that would produce MSY for that selective or non-selective fishery. Bottom panel: As per the top panel but providing a closer view of the equilibrium points for the scenarios with fishing. Values are provided in Table 10.

200

5 Days 7 Days 6 Days ●

150 Fishery Ladder ● ●

● ● 100 ●

●

● 50 ●

● ● ● ● Number of Fish (Thousands) ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

25 30 5 10 15 20 25 30 5 12 April May June

Date

Figure 18. Abundance plot based on Gaspereau River alewife sampled at the White Rock fish ladder (solid line) and the commercial fishery (dashed line) in 2016. Coloured lines represent the three peaks in abundance identified at both locations, and the time in days between those peaks. Dates of and number of fish in the abundance peaks can be found in Table 11.

26.0 Fishery Ladder

● ● ● 25.5 ● ●

● ● 25.0 ●

●

Length (cm) ● ● ● 24.5 ●

24.0

1 2 3 4 5 6 7 Week

Figure 19. Comparison of weekly means and standard errors for Gaspereau River alewife sampled at the White Rock fish ladder and the commercial fishery in 2016. Week 1 begins on April 26th and ends on May 2nd, but includes data from April 25th for the ladder. Week 7 ends on June 12th. The horizontal lines represent total weighted means for each sampling location. Values for means and standard errors can be found in Table 12.

27.0

Fishery Ladder

26.5 ● ●

● 26.0 ●

● ● 25.5 ● ● ● ● ● ● ● ● 25.0 ● Length (cm) ● ● ● ● ● 24.5 ● ●

24.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Proportion of Run

Figure 20. A proportionate comparison of means and standard error of fork length for Gaspereau River alewife sampled at the White Rock fish ladder and the commercial fishery in 2016. The first 10% of fish were averaged with weights and compared between locations, then proportion 10% to 20%, and so on. The dashed and solid lines represent overall averages for the dataset. Each point for the fishery represents 165 individuals, and each point for the ladder represents 228 individuals. The portion of the 2016 spawning run represented is from April 25th to June 5th in 2016. Data from June 6th to June 12th for the ladder were not included, as no comparable data was available from the fishery for that time.

5.0 Fishery Ladder

● ● ● 4.8 ● ●

● ● ● ● ● ● 4.6 ●

● ● 4.4 ● ● ● ● ● Average Age Average ● ● 4.2 ●

4.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Proportion of Run

Figure 21. A proportionate comparison of means and standard error of age for Gaspereau River alewife sampled at the White Rock fish ladder and the commercial fishery in 2016. The first 10% of fish were averaged with weights and compared between locations, then proportion 10% to 20%, and so on. The dashed and solid lines represent overall averages for the dataset. Each point for the fishery represents 165 individuals, and each point for the ladder represents 228 individuals. The portion of the 2016 spawning run represented is from April 25th to June 5th in 2016. Data from June 6th to June 12th for the ladder were not included, as no comparable data was available from the fishery for that time.