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TitleSpecies Conservation and Management: Case Studies
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LanguageEnglish
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Table of Contents
                            Contents
Contributing Authors
1 Using Models for Species Conservation and Management: An Introduction
I: Plants
	2 Strategies for Plant Population Viability Modeling: An Overview
	3 Australian Shrub Grevillea caleyi: Recovery through Management of Fire and Predation
	4 Whitebark Pine (Pinus albicaulis) in Mt. Rainier National Park, Washington, USA: Response to Blister Rust Infection
	5 Monkey Puzzle Tree (Araucaria araucana) in Southern Chile: Effects of Timber and Seed Harvest, Volcanic Activity, and Fire
	6 Banksia goodii in Western Australia: Interacting Effects of Fire, Reproduction, and Plant Growth on Viability
	7 Erodium paularense in Spain: Relevance of Microhabitats in Population Dynamics
	8 Australian Heath Shrub (Epacris barbata): Viability under Management Options for Fire and Disease
II: Invertebrates
	9 Modeling Invertebrates: An Overview
	10 Carnivorous Land Snail Tasmaphena lamproides in Tasmania: Effects of Forest Harvesting
	11 Bush Cricket Metrioptera bicolor in Sweden: Estimating Interpatch Dispersal Rates
	12 Puritan Tiger Beetle (Cicindela puritana) on the Connecticut River: Habitat Management and Translocation Alternatives
	13 Giant Velvet Worm (Tasmanipatus barretti) in Tasmania, Australia: Effects of Planned Conversion of Native Forests to Plantations
	14 Hermit Beetle (Osmoderma eremita) in a Fragmented Landscape: Predicting Occupancy Patterns
	15 Woodland Brown Butterfly (Lopinga achine) in Sweden: Viability in a Dynamic Landscape Maintained by Grazing
	16 Tapeworm Echinococcus multilocularis in Kazakhstan: Transmission Dynamics in a Patchy Environment
III: Fishes
	17 Modeling Viability of Fish Populations: An Overview
	18 European Mudminnow (Umbra krameri) in the Austrian Floodplain of the River Danube: Conservation of an Indicator Species for Endangered Wetland Ecosystems in Europe
	19 Chinook Salmon (Oncorhynchus tshawytscha) in Puget Sound: Effects of Spatially Correlated Catastrophes on Persistence
	20 Trout Cod (Muccullochella macquariensis) in the Murray River, Southeast Australia: Prioritizing Rehabilitation Efforts
	21 Yellowtail Flounder (Limanda ferruginea) off the Northeastern United States: Implications of Movement among Stocks
	22 Atlantic Herring (Clupea harengus) in the Northwest Atlantic Ocean: Dynamics of Nested Population Components under Several Harvest Regimes
	23 Pacific Herring (Clupea pallasi) in Canada: Generic Framework for Evaluating Conservation Limits and Harvest Strategies
IV: Amphibians and Reptiles
	24 Modeling Amphibians and Reptiles: An Overview
	25 Great Crested Newts (Triturus cristatus) in Europe: Effects of Metapopulation Structure and Juvenile Dispersal on Population Persistence
	26 Houston Toad (Bufo houstonensis) in Bastrop County, Texas: Need for Protecting Multiple Subpopulations
	27 Eastern Indigo Snakes (Drymarchon couperi) in Florida: Influence of Edge Effects on Population Viability
	28 Frillneck Lizard (Chlamydosaurus kingii) in Northern Australia: Determining Optimal Fire-Management Regimes
	29 Sand Lizard (Lacerta agilis) in Central Sweden: Modeling Juvenile Reintroduction and Spatial Management Strategies for Metapopulation Establishment
	30 Southern Great Barrier Reef Green Sea Turtle (Chelonia mydes) Stock: Consequences of Local Sex-Biased Harvesting
V: Birds
	31 Modeling Birds: An Overview
	32 Yellow-Shouldered Parrot (Amazona barbadensis) on the Islands of Margarita and La Blanquilla, Venezuela: Poaching and the Survival of a Threatened Species
	33 Golden-Cheeked Warbler (Dendroica chrysoparia) in Texas: Importance of Dispersal toward Persistence in a Metapopulation
	34 Capercaillie (Tetrao urogallus) in Western Switzerland: Viability and Management of an Endangered Grouse Metapopulation
	35 Seaside Sparrows (Ammodramus maritimus) in Connecticut: Projected Effects of Sea-Level Rise
	36 Helmeted Honeyeater (Lichenostomus melanops cassidix) in Southern Australia: Assessing Options for Establishing a New Wild Population
	37 Wandering Albatross (Diomedea exulans chionoptera) in the Southern Oceans: Effects of Dispersal and Density Dependence on the Persistence of an Island Metapopulation
VI: Mammals
	38 Mammal Population Viability Modeling: An Overview
	39 Snowshoe Hares (Lepus americanus) in the Western United States: Movement in a Dynamic Managed Landscape
	40 Florida Key Deer (Odocoileus virginiaus clavium): Effects of Urban Development and Road Mortality
	41 Turkish Mouflon (Ovis gmelinii anatolica) in Central Anatolia: Population Viability under Scenarios of Harversting for Trophy
	42 Sindh Ibex (Capra aegagrus blythi) in Kirthar National Park, Pakistan: Sensitivity of a Habitat and Population Model
	43 Steller Sea Lions (Eumetopias jubatus) in the Pacific Rim: Biological Uncertainty and Extinction Risk
	44 Florida Panther (Puma concolor coryi): Using Models to Guide Recovery Efforts
Appendix: Using RAMAS GIS
Index
	A
	B
	C
	D
	E
	F
	G
	H
	I
	J
	K
	L
	M
	N
	O
	P
	Q
	R
	S
	T
	U
	V
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	Y
                        
Document Text Contents
Page 2

Species Conservation
and Management:

Case Studies

H. RESIT AKÇAKAYA, et al.,
Editors

OXFORD UNIVERSITY PRESS

Page 275

PACIFIC HERRING (CLUPEA PALLASI) IN CANADA 257

below the long-term average for extended periods (Fu et al. 2000), although they may
entail a high variance in annual catch (Lande et al. 1997). Cutoff thresholds are often
set at 20% of unfished equilibrium abundance (Francis 1993, Thompson 1993). Some-
what arbitrarily, in 1985 a cutoff threshold of 25% of an early estimate of unfished
equilibrium biomass was introduced for herring fisheries in British Columbia to restrict
harvest during periods of reduced abundance (Schweigert and Fort 1999).

Risk analyses are necessary to evaluate the likely consequences and trade-offs associ-
ated with alternative decisions about harvest rates and cutoff thresholds. Ideally Monte Carlo
simulations to evaluate these trade-offs should be performed with models designed to cap-
ture the unique features of a particular fishery and its target species (e.g., Hollowed and
Megrey 1993, Zheng et al. 1993, Fu et al. 2000). However, the time required to design
species-specific programs may impede progress where conservation limits and harvest strat-
egies are required for a large number of species. Moreover, individual species-specific
programs often fail to consistently address more complicated problems that involve environ-
mental variability, spatial structure, and changes in productivity due to habitat modification.

In the case study reported here, we show that RAMAS GIS software (Akçakaya 2002)
is adequate to evaluate conservation limits and proportional harvesting controls for
Pacific herring populations, and it has sufficient power to address ecological problems,
including demographic and environmental stochasticity, spatial structure, and habitat
issues. We developed a generic simulation framework to derive conservation limits NL

and µL, and the optimal harvest strategy (combination), using RAMAS Metapop to
evaluate performance measures over a broad spectrum of values for initial population
size and the control variables.

Methods

Stage Structure

After hatching in February and March, Pacific herring spend 6 months inshore as age 0
juveniles. During August and September, many age 0 herring migrate offshore and join
schools of immature age 1 and 2 herring. As they mature, most at age 3, they join schools
of older mature fish on their fall/winter inshore migration to the spawning grounds (Ware
1996). Age 1 abundance has not been estimated in stock assessments to date, so we
modeled nine stages (age 2, 3, . . . , 10+) for each of five herring stocks. We used the
subscripts i, t, and a to represent stock, year, and age, respectively. Fecundity varies
with weight such that an average of 200,000 eggs per kilogram of mature herring are
produced annually (P |) in each stock (Hay 1985). Mean weight at age was averaged over
all stocks (Wi,a; Table 23.1), because empirical measurements showed little variation
among stocks; in contrast, maturity parameters (Oi,a, the proportion of individuals ma-
turing at age a, Table 23.1) did vary among stocks (Schweigert 2001). Accordingly,
egg production for stock i in year t was calculated as

E N W O Pi t i t a a i a
a

, , , ,=∑

where Ni,t,a are estimates of abundance from an age-structured stock assessment
(Schweigert 2001).

Page 276

258 FISHES

Survival rate from egg to age 2 recruits was calculated as

S
N

E
i t

i t

i t
, ,

, ,

,
0

2 2= +

The first row elements of the transition matrix (recruitment to age 2) for stock i at
age a were calculated as

Ri,a = WaOi,a P |S|i,o

where S|i,o is the average of Si,t,o across the time period from 1977 to 2001 (Table 23.1).
Diagonal elements of the transition matrix (survival rate) were assumed not to change
with age after age 2 but were estimated independently for each stock by Schweigert
(2001).

Spatial Structure and Dispersal

The herring metapopulation in British Columbia (B.C.) comprises five major “stocks”
denoted by area: the Queen Charlotte Islands (QCI), the Prince Rupert District (PRD),
the Central Coast (CC), the Strait of Georgia (GS), and the west coast of Vancouver
Island (WCVI) (Beacham et al. 2001, Ware and Schweigert 2001). Ware and Schweigert
(2001) concluded that despite variations in spawner biomass, the dispersal pattern for

Table 23.1 Average weight at age (kg) across all five stocks (Wa), maturity parameters (the
proportion that mature, Oi,a), age composition (σi,a), and recruitment to age 2 (Ri,a) at age a
for each stock i

Age (years)

2 3 4 5 6+ 7 8 9 10
Wa 0.05 0.08 0.11 0.13 0.14 0.16 0.17 0.18 0.18

Maturity proportion at age a (Oi,a)
QCI 0.1 0.4 0.64 0.90 1 1 1 1 1
PRD 0.10 0.48 0.71 0.90 1 1 1 1 1
CC 0.15 0.56 0.78 0.97 1 1 1 1 1
GS 0.12 0.71 0.94 0.97 1 1 1 1 1
WCVI 0.09 0.69 0.90 0.99 1 1 1 1 1

Age composition (σi,a)
QCI 0.0 0.27 0.25 0.21 0.27
PRD 0.0 0.21 0.23 0.21 0.35
CC 0.0 0.17 0.2 0.2 0.42
GS 0.0 0.29 0.26 0.2 0.24
WCVI 0.0 0.25 0.24 0.21 0.3

Recruitment to age 2 per individual at age a (Ri,a)
QCI 0.06 0.37 0.78 1.32 1.65 1.80 1.93 2.03 2.09
PRD 0.04 0.30 0.58 0.89 1.11 1.21 1.30 1.37 1.41
CC 0.05 0.27 0.49 0.73 0.85 0.93 0.99 1.05 1.08
GS 0.07 0.62 1.08 1.35 1.56 1.71 1.83 1.93 1.98
WCVI 0.03 0.38 0.64 0.86 0.98 1.07 1.15 1.21 1.24

Note: QCI = Queen Charlotte Islands; PRD = Prince Rupert District; CC = Central Coast; GS = Strait of Georgia; WCVI
= West Coast of Vancouver Island.

Page 550

532 INDEX

Wandering albatross (Diomedea exulans
chionoptera) (continued)

capture-mark-recapture protocols, 423
carrying capacities, 424
demographic stochasticity, 423
density dependence, 424, 427–428
density dependence and decline risk, 425–

426
description, 421
dispersal, 423, 424–425
environmental stochasticity, 423
life cycle, 423
metapopulation dynamic, 426–427
metapopulation function, 428
methods, 422–424
persistence of populations, 424–425
population parameters, 422–423
probability of decline from initial

abundance, 426, 427
survival rates, 423
vulnerable, 421

Wanzenböck, Josef, European mudminnow,
200–206

Warbler. See Golden-cheeked warbler
(Dendroica chrysoparia)

Weather, modeling invertebrates, 109
Weimerskirch, Henri, wandering albatross,

421–428
Whitebark pine (Pinus albicaulis)

basidiospores, 37
blister rust and population, 43, 45
blister rust by fungus Cronartium ribicola,

37
blister rust catastrophe, 42–43
calibration, 41–42
consequences of loss, 45
decline, 36
environmental and demographic

stochasticity, 42
family and location, 36–37
growth rate, 44
input of subpopulation structure, 38
interpopulation dispersal, 43
life history with blister rust, 39
metapopulation decline, 43–45
modeling, 38–43
population correlation, 43
sapling survival, 40
seed, 38–39
seedlings, 39
severally infected trees, 40–41

subpopulation distribution, 44–45
transition to nonreproductive adults, 40
trees with branch canker, 40
uninfected trees, 40
vital rates, 38

Wildfires
giant velvet worm, 154
management, 312–313
predation management with, 30
prevention and fire management, 31
probability, 28
records and prevention, 34–35

Wild populations, helmeted honeyeater, 411,
412

Wood, Chris C.
modeling fish viability, 193–197
Pacific herring, 256–267

Woodland brown butterfly (Lopinga achine)
average number of local populations, 176
average population growth rate, 173
dispersal, 175–176
distribution area, 173
estimates of maximum density, 174
extinction risk, 176–177
habitat succession, 177
initial population size, 175
inverse cumulative distribution of

migration, 175
landscape scenario, 175
parameterization, 172–176
population ceiling, 174
RAMAS GIS, 172–176
species, 172
stochasticity, 174
threatened species, 171–172
trusting predictions, 177–178

World Conservation Union (IUCN),
population, 256

Yamada, Kuniko, Sindh ibex, 469–481
Yellow-shouldered parrot (Amazona

barbadensis)
carrying capacity, 365
catastrophe, 365
classifying populations, 366
conservation program, 361–363, 368
demographic data and parameter values,

364–365
extreme weather, 365
habitat conversion, 368–369
harvesting, 366

Page 551

INDEX 533

impact of nestling poaching, 367
joint extinction risk, 363
maximum allowable poaching intensity,

363
methods, 363–366
persistence, 366
poaching intensity, 366–367
population viability analysis, 363
regional pride, 362
risk of extinction, 361
selling, 366
sensitivity, 367–368
simulations, 365–366
study sites, 363–364
survival, 365
vulnerable, 361

Yellowtail flounder (Limanda ferruginea)
abundance and mortality, 231
carrying capacity, 233
density dependence, 233

description, 230
dispersal, 235–236
distribution, 232
exploitation, 230–231, 243
fishery management, 231
fishing mortality rates, 243
geographic information, 231
harvest rates, 234–235
mark-recapture observations, 242
metapopulation structure, 232
modeling complications, 242
model runs, 239
projections of stock abundances, 240
RAMAS input parameters, 234
rebuilding simulations, 237–238, 241
retrospective simulations, 237, 238
sensitivity analysis, 241–242
simulation scenarios, 236
stage matrix, 232–233
stochasticity, 233

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