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Michael Bode ARC Centre of Excellence for Environmental Decisions, University of Melbourne [email protected] Larval dispersal in reef fishes: biology, ecology, economics

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Page 1: Larval dispersal in reef fishes: biology, ecology, economicsbioinformatics.org.au/ws13/wp-content/uploads/ws13/... · Doherty (1991) Ecology of Fishes on Coral Reefs. Population dynamics

Michael Bode ARC Centre of Excellence for

Environmental Decisions, University of Melbourne [email protected]

Larval dispersal in reef fishes: biology, ecology, economics

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Early research into tropical fish communities

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i

Population dynamics on a single patch

Nonlinear dynamics in the absence of dispersal:

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Population dynamics on a single patch

Recruitment limitation:

Doherty (1991) Ecology of Fishes on Coral Reefs

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Population dynamics on multiple patches

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Dispersal is essentially a linear coupling of a multidimensional nonlinear system

•  Dispersal is defined by connectivity matrix C •  Matrix elements are the proportion of

larvae from reef i that travel to reef j

1

23

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Dispersal is essentially a linear coupling of a multidimensional nonlinear system

•  Dispersal is defined by connectivity matrix C •  Matrix elements are the proportion of

larvae from reef i that travel to reef j

1

23

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P. maculatus. Harrison et al. (2012) Current Biology

Measuring dispersal

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•  Resource intensive •  Invasive – individual & species level •  Scale limited – (temporal and spatial)

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Part 1: Modelling dispersal

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Modelling dispersal

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Reef fish metapopulations and larval dispersal M. K. James and others 2083

(b)

14 16 18

(d )

14 16 18

18

16

14(c)

latitude (S) of sink reef

latit

ude

(S)

of s

ourc

e re

ef 18

16

14(a)

Figure 3. Images of the seasonal connectivity matrices for (a) 1985 (b) 1989 (c) 1996 and (d ) the matrix averaged over all 20seasons. Axes are labelled in degrees of latitude. Each element aij of the matrix is the proportion of all larval production in aseason on reef i that settles on reef j. These elements are coded by colour (and size) as follows: red, greater than 0.02; blue,between 0.01 and 0.02; green, between 0.005 and 0.01; pink, less than 0.005.

ever, some limited corroboration of the model comes froma comparison with adult distribution data. Williams et al.(1986) conducted a census of fish communities on a tran-sect of the shelf in the Central Section of the Marine Park(on the southern boundary of our study region). Thoseauthors found that for 16 out of 18 species, there was aclose relationship between the distributions of recruits andadults. Recruits tended to be found only in the same cross-shelf location as adults of the same species.

We analysed cross-shelf transport patterns underassumed spawner distributions representing differentspecies. Figure 6 shows the results for our baseline para-meters. Virtual larvae originating from reefs in one of threezones defined by Williams et al. (1986) tend to be trans-ported to reefs within the same zone.

Proc. R. Soc. Lond. B (2002)

4. DISCUSSION

(a) Model predictionsWhen focusing on physical transport processes, the

model predicts that most local populations in our studyregion depend largely on externally supplied larvae. Self-recruiting larvae comprise a smaller fraction of the settlingcohort on most reefs (mean self-recruitment less than 9%of settling cohort on 80% of reefs in the area). Moreover,the model predicts that the total proportion of larvae set-tling back onto their reef of origin is lower than the totalproportion settling on other reefs. These findings contrastwith those from the modelling study of Cowen et al.(2000) of populations on more isolated oceanic reefs inthe Caribbean.

14 16 18

Sour

ce r

eef

Destination reef

Connectivity patterns

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Inter-annual variation

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Inter-specific variation

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Part 2: Dispersal and coexistence

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Coexistence needs differences

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Reef fish community theory

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Metacommunity simulation •  Real distribution of reefs (P = 110) •  Variable dispersal matrices (t = 1, …, 32 years) •  Multiple species (S = 5) –  Identical competitors –  Identical niches –  Different dispersal behaviour

Normally we would expect monodominance

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(%)

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•  Same model •  Two species, identical at a local scale •  Larval dispersal stages of slightly different lengths. •  Three identical patches

Dispersal differences and coexistence

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Dispersal differences and coexistence

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Dispersal differences and coexistence

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Coexistence is possible if each

species is a superior disperser over one of

the inter-patch distances

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Coexistence is possible if each species is a superior disperser over one of the

inter-patch distances

Mechanism has high predictive power for

larger simulations

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Dispersal differences support coexistence that: •  Is simple and intuitive •  Driven by common factors •  Can create quite complex patterns •  Creates stable geographic replacement

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Mechanisms  are  not  locally  observable.  

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Part 3: Economic perspectives on dispersal

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Measuring dispersal on Manus Island

Plectropomus areolatus. Source: FAO

Timonai Mbunai

Pere Tawi

Locha

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Bioeconomic scales on Manus

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Bioeconomic scales on Manus

Community tenure areas

Spawning aggregation source areas

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Management question •  What is the maximum annual equilibrium

harvest rate from each spawning aggregation? •  How do dispersal externalities affect the

harvesting decision?

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Harvested population model

Plectropomus areolatus. Source: FAO

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Harvested population model

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Harvested population model

Simulation model Population estimates

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Independent communities •  Each community chooses: – a harvest rate on each of their

aggregations, – that maximises total equilibrium harvest. – given that other communities act rationally.

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Harvests under different coalitions

•  Communities are highly heterogeneous •  Describe coalition structures using

partitions e.g., C0 = {{1} {2} {3} {4} {5}} C1 = {{1} {2},{3} {4,5}} C2 = {{1,2,3} {4,5}} CG = {1,2,3,4,5}

–  52 unique coalition structures

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Harvest coalition size •  Non-cooperative groups remove 12-25% / FSA /

yr •  Cooperative harvests remove 10-17% •  Grand coalition improve overall catch (by 15%)

and equilibrium population levels (by 70%)

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Harvest coalition size •  The current scale of management on Manus

could lead to undesirable outcomes. •  A grand coalition would result in an increase in

catches in every community, for much lower effort

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Grand coalition stability Group 2 leaves

coalition

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Grand coalition stability •  The grand coalition surplus is insufficient for

a set of side-payments to yield rational cooperation.

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Smaller coalitions •  The coalition between Locha and Pere is the only

Nash equilibrium (internal & external stability). •  Almost all the resultant benefits are captured by

the adjacent communities: Tawi and Mbunai

Timonai Mbunai

Pere Tawi

Locha

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Economic impacts of larval dispersal

•  The scale of larval dispersal creates interconnections between communities

•  The dissonant scales causes problems. – Too much dispersal to ignore each other – Too much dispersal to want to cooperate – Not enough dispersal to provide necessary

surplus

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Collaborators •  Maurice James •  Paul Armsworth •  Glenn Almany •  Lance Bode •  Rick Hamilton •  Luciano Mason •  Geoff Jones •  David Williamson