Building a science for landscape fisheries management

Transcription

1 Building a science for landscape fisheries management Nigel Lester Aquatic Research and Monitoring Section Science and Research Branch Ministry of Natural Resources nigel.lester@ontario.ca American Fisheries Society Ontario Chapter Winter Seminar Peterborough January 29 th,

2 Fish Fishers 2

3 Largemouth Bass

4 Muskellunge

5 Northern Pike

6 Brook Trout

7 Smallmouth Bass

8 Lake Trout

9 Walleye

10 Fish Fishers 10

11 Recreational Fishing Licenses 50,000 licenses 200,000 licenses 750,000 licenses

12 Recreational Fishing Licenses 50,000 licenses 200,000 licenses 500,000 licenses 750,000 licenses

13 Types of anglers Credit: W. Dunlop Credit: W. Dunlop Credit: S. Lawrence 13

14 Ontario Recreational Fishery p = 1 p = 0.5 Total harvest Number harvested (millions) (millions of fish) Other La. Bass Sunfish Sm. Bass Pike Perch Walleye Number caught Total catch (millions) (millions of fish)

15 Fish Fishers Live in lakes and rivers Everywhere Species abundance varies Temperature Nutrients Isolated stocks Live in settlements Clustered More in south Species preferences Edibility Fightability Abundance Mobile fishers 15

16 Fish Fishers What types? Spatial distribution How many fish? Where? Response to changes in: Fishing Habitat Community What types? Spatial distribution How much fishing? Where? Response to changes in: Fish abundance Regulations 16

17 Fish Landscape Fisheries Management Fishers Divide the landscape o Fisheries Management Zones Set goals for Zones (not lakes) o Involve fishers (and non-fishers) o FMZ Advisory Councils Evidence-based decision-making o 5-year management cycle o Monitoring o Science Development 17

18 Fish Landscape Fisheries Management Monitoring Programs Fishers 18

19 Fish Landscape Fisheries Management Monitoring Programs Fishers Inland Lakes Broad-scale Monitoring Lake surveys conducted by OMNR Data from lakes: Fish abundance and life history Contaminants Aquatic habitat Aquatic community Fishing activity 19

20 Fish BsM Cycle Landscape Fisheries Management Monitoring Programs Fishers 5-year cycle (since 2008) Stratified random sample of lakes Cycle 1 surveyed 700 lakes 8 % of lakes > 50 ha Cycle 2 ( ) Re-survey most lakes Survey additional random sample 20

21 Fish BsM Cycle Landscape Fisheries Management Monitoring Programs Fishers Survey of Recreational Fishing Federal-provincial mail survey Data from licensed anglers: Expenditures Opinions Fishing effort, catch and harvest 21

22 Fish BsM Cycle Landscape Fisheries Management Monitoring Programs Fishers RecFish year cycle (since 1975) Stratified random sample of anglers Survey ~30,000 anglers ~2% of licensed anglers Since 2005 Georeference the data Get estimates for each Zone 22

23 Building a science for Landscape Fisheries Management What s the question? An empirical answer Made in Ontario Theory Apply the theory

24 Building a science for Landscape Fisheries Management How much fishing effort is sustainable? An empirical answer Made in Ontario Theory Apply the theory

25 Building a science for Landscape Fisheries Management How much fishing effort is sustainable? An empirical answer Made in Ontario theory Apply the theory

26 Ontario MNR Fisheries Assessment Units Districts and Regions Policy and Research BsM Science team Other government agencies Quebec Other provinces US states Building a science for Landscape Fisheries Management Acknowledgements Academic partners University of Toronto University of Guelph NSERC Brian Shuter Peter Abrams

27 How much fishing effort is sustainable? Calculating fishing effort on lakes in each zone Total hours of angling in one year Total lake area (hectare) Effort = angler-hours / ha in one year RecFish

28 How much fishing effort is sustainable? Fishing effort angler-hours / (ha.year) Growing Degree Days > 5 o C RecFish

29 Fishing effort angler-hours / (ha.year) How much fishing effort is sustainable? RecFish Mail survey estimates Probably overestimate effort (Hogg et al. 2010) BsM data will be used to assess bias Values are used here to demonstrate approach

30 Impact of fishing effort depends on angling skill and regulations catchability (angling skill) Catch = Effort * q * Density (# fish/ha) (# fish/ha) Catch/Effort = q * Density CPUE (fish/hr) q = Fish density (# fish/ha) 30

31 Impact of fishing effort depends on angling skill and regulations Proportion kept Harvest = Catch * p Harvest = Effort * q * Density * p Harvest/Density = Effort * q * p F = Effort * q * p Fishing mortality rate 31

32 Impact of fishing effort depends on angling skill and regulations Proportion kept Harvest = Catch * p q = 0.02 Harvest = Effort * q * Density * p p = 1.0 Harvest/Density = Effort * q * p F = Effort * q * p p = 0.5 Fishing mortality rate 32

33 Impact of fishing effort depends on angling skill and regulations Proportion kept Harvest = Catch * p Harvest = Effort * q * Density * p Harvest/Density = Effort * q * p Exploitation rate = 1 e -F q = 0.02 p = 1.0 p = 0.5 F = Effort * q * p Fishing mortality rate 33

34 Building a science for landscape fisheries management Sustainable effort depends on: Impact on Fishing mortality rate (F) Modified by angling catchability (q) and regulations (p) How much F is sustainable? An empirical answer Desirable F < Natural mortality rate (M) 34

35 How much F is sustainable? Biomass density (kg/ha) B max F ext Fishing mortality rate (/year)

36 How much F is sustainable? Biomass density (kg/ha) B max Maximum Sustainable Yield Yield = F * B F ext Fishing mortality rate (/year)

37 How much F is sustainable? Biomass density (kg/ha) B max B msy F < F msy Yield = F * B F msy F ext Fishing mortality rate (/year)

38 Squeers Lake Experimental Lake Trout Fishery Quetico Mille Lacs FAU Squeers Lake (384 ha) near Thunder Bay Lake Trout fishery heavy winter fishing closed to fishing in 1979 Estimated natural mortality M = 0.24 (Ball 1988) Experimental fishery opened in days per year (winter) controlled # of anglers target = 2 kg/ha Monitoring Creel census to monitor harvest Mark-recapture to monitor abundance Calculated Fishing mortality rate

39 How much F is sustainable? Squeers Lake - lake trout (M = 0.24) Biomass (kg/ha) Fishing mortality rate (/yr)

40 How much F is sustainable? Squeers Lake - lake trout (M = 0.24) Biomass (kg/ha) Fishing mortality rate (/yr)

41 How much F is sustainable? Squeers Lake - lake trout (M = 0.24) Biomass (kg/ha) Fishing F msy mortality = 0.21 rate M (/yr)

42 How much F is sustainable? Squeers Lake - lake trout (M = 0.24) Biomass (kg/ha) F msy M Suggested rule of thumb (Gulland 1970) 8 F msy = 0.9 * M 6Meta-analysis of marine stocks (Zhou et al. 2012) 0.9 is mean for 179 Teleost species 4 F msy = 0.9 * M Predicted for walleye (Lester et al. 2014) 2 Assumes harvest mature fish only Fishing F msy mortality = 0.21 rate M (/yr)

43 Building a science for landscape fisheries management Sustainable effort depends on: Impact on Fishing mortality rate (F) Modified by angling catchability (q) and regulations (p) How much F is sustainable? F msy M (natural mortality rate) How does one predict M? Made in Ontario theory Biphasic growth model Thermal age M = f(climate, Body size) 43

44 Walleye growth and mortality Walleye tastes great, #1 in Ontario coolwater species broad geographical range Growing Degree Days > 5 o C North: 800 o C South: 5000 o C Lifetime growth pattern Effect of reproduction Effect of climate

45 Examples of walleye growth (female) Lac Regnault GDD = 1077 Rice Lake GDD = Mean total length (mm) immature mature Mean total length (mm) immature mature Age (years) Age (years)

46 Biphasic Growth Model Length (cm) t 1 Age (yr) Lester, Shuter and Abrams (2004)

47 L Biphasic Growth Model g L = 3 h / g Length (cm) L m t 1 T m Age (yr) Lester, Shuter and Abrams (2004)

48 L Biphasic Growth Model g L = 3 h / g Length (cm) L m Optimum length at maturity L m = h (2/M 1) M = 2 h h + L m t 1 T m Age (yr) Lester, Shuter, Venturelli and Nadeau (2014)

49 L Biphasic Growth Model g L = 3 h / g Length (cm) L m Optimum length at maturity L m = h (2/M 1) M = 2 h h + L m t 1 T m Age (yr) To estimate M need: Growth rate (h) when unexploited Length of maturity (L m ) when unexploited Lester, Shuter, Venturelli and Nadeau (2014)

50 Mean length at age (130 populations) Females Mean total length (mm) Age (years)

51 Mean length at age (130 populations) Females Mean total length (mm) GDD <1500 GDD = GDD > Age (years)

52 Mean length at age (by GDD zone) Female 800 Mean total length (mm) Age (yr) GDD < 1500 GDD = 1500 to 2500 GDD > 2500

53 Mean length at age (by GDD zone) Female 800 Mean total length (mm) Age (yr) GDD < 1500 GDD = 1500 to 2500 GDD > 2500 Thermal age = age x GDD x 10-3

54 Mean length at thermal age Female 800 Mean total length (mm) Thermal age Thermal age = age x GDD x 10-3

55 Predicting M for walleye Mean total length (mm) maturation L M 430 mm Thermal age M = 2 h h + L m Venturelli, Lester and Shuter (2010) Lester, Shuter, Venturelli and Nadeau (2014)

56 Predicting M for walleye Mean total length (mm) maturation L M 430 mm h = 0.03 GDD mm/yr Assume slowest growth approximates unexploited state (i.e., high density) Thermal age M = 2 h h + L m Venturelli, Lester and Shuter (2010) Lester, Shuter, Venturelli and Nadeau (2014)

57 Predicting M for walleye Mean total length (mm) maturation L M 430 mm h = 0.03 GDD mm/yr Assume slowest growth approximates unexploited state (i.e., high density) Thermal age M = M = 2 h h + L m 0.06 GDD 0.03 GDD Venturelli, Lester and Shuter (2010) Lester, Shuter, Venturelli and Nadeau (2014)

58 Total mortality versus predicted natural mortality (FWIN surveys Ontario and Quebec lakes) Estimated total mortality (Z, /y) Unexploited/Light Exploited Predicted natural mortality (M, /y) Lester et al. 2014

59 Predicted M for walleye (L mature = 430 mm) For walleye: 0.06 GDD M = 0.03 GDD

60 Building a science for landscape fisheries management Sustainable effort depends on: Impact on Fishing mortality rate (F) Modified by angling catchability (q) and regulations (p) How much F is sustainable? F msy M (natural mortality rate) How does one predict M? M = f(climate, Body size) Estimate Effort at MSY 60

61 Effort at MSY Effort msy = F msy q * p Angling skill Proportion kept 61

62 Effort at MSY For walleye: 0.06 GDD M = 0.03 GDD Effort msy = F msy q * p Angling skill Proportion kept 62

63 Effort at MSY For walleye: 0.06 GDD M = 0.03 GDD q = 0.02 Effort msy = F msy q * p Angling skill Proportion kept p=0.5 p=1.0 63

64 Effort at MSY Fishing effort angler-hours / (ha.year) RecFish 2010 q = p=0.5 p=1.0 64

65 Effort at MSY Fishing effort angler-hours / (ha.year) RecFish 2010 q = p=0.5 p=1.0 Mail survey estimates Probably overestimate effort (Hogg et al. 2010) BsM data will be used to assess bias 65 Values are used here to demonstrate approach

66 Effort at MSY Fishing effort angler-hours / (ha.year) RecFish 2010 q = Walleye < 10% of harvest Walleye > 70% of harvest p=0.5 p=1.0 Mail survey estimates Probably overestimate effort (Hogg et al. 2010) BsM data will be used to assess bias 66 Values are used here to demonstrate approach

67 Walleye > 70% of harvest RecFish 2010 Species composition of harvest Walleye < 10% of harvest Walleye alone cannot support high fishing effort requires very stringent regulations Smaller fish (panfish, perch) can sustain higher fishing effort because natural mortality increases as body size decreases In the south, smaller fish account for > 50% of the harvest 67

68 Building a science for landscape fisheries management How much fishing effort is sustainable? Addressing other questions as well: How much harvesting is sustainable (kg of fish)? How is the community size spectrum affected by: Environmental factors Fishing

69 Building a science for landscape fisheries management How much fishing effort is sustainable? How much harvesting is sustainable (kg of fish)? How is the community size spectrum affected by: Environmental factors Fishing Major symposium planned for AFS 2014

70 Building a science for landscape fisheries management Landscape Fisheries Management new scale of thinking no textbooks, we have to discover the science adaptive management process Success requires commitment to monitoring sharing of data public involvement 70

71 THE END 71