Index

Page numbers in bold refer to tables and those in italic refer to figures

abridged life tables, 117 abstract models, 317–18

Acanthaster planci, 60 accuracy, 23, 23 – 4, 33

population size estimation, 72

acorn woodpecker (Melanerpes formicivorus), 193

active adaptive management, 178 activity levels, 100

adaptive sampling, 60 – 6 application, 62–5 efficacy, 65 – 6

population size variance estimation, 61, 62

quadrat size, 65, 66 threshold rules, 61, 66

total population size estimation, 61 aerial surveys

double sampling, 71 transects, 68

age–size data, 133 – 4, 136 age-specific fecundity, 140, 141–2 age-specific survival, 132

death rates, 103, 104 Euler–Lotka equation, 140, 141

age structure, 6 – 8, 317, 318 comparison at different times, 131 conveyor belt analogy, 7, 7, 9 Leslie matrix model, 7– 8

McKendrick–von Forster equation, 8 –10 mark–recapture survival analysis, 121,

121

maturation period variable, 8 aggregated populations, 60

line transects, 77 predator–prey/host–parasitoid models,

252– 4

sampling strategies see adaptive sampling see also patchiness

Aimophila aestivalis (Bachman’s sparrow), 184

Akaike information criterion (AIC), 16, 238 mark–recapture study model selection,

123, 124

Alces alces (moose) see wolf–moose functional responses

ALEX, 47

Allee effect, 180, 181, 182 allometric relationships, 151–3, 152

ingestion rates, 258 –9

r (intrinsic rate of growth) estimation, 152, 152–3

transmission rates estimation, 297– 8

analysis of variance, 24

Ancylostoma duodenale, 308 Anopheles crucians, 305

Apodemus sylvaticus (wood mouse), 305 – 6 Ascaris lumbricoides, 308

Asterionella formosa, 239, 240 autocorrelation function (ACF), 170 auxiliary variables, 67, 68

Bachman’s sparrow (Aimophila aestivalis), 184

bank vole (Clethrionomys glareolus), 299, 305 – 6

basic reproductive number see R0 Bayesian methods, 44 – 6

depensation (inverse density dependence), 181

posterior probability, 45 prior probability, 45

belt transects, 50

see also sampling strategies Beverton–Holt model, 175, 176, 183

inverse density dependence, 181, 182 bias, 23, 29

quadrat sampling, 53, 73 binary collision model, 298 –9 binomial data

logistic regression, 26, 27 overdispersion, 27– 8 survival analysis, 118, 119

biological control, 285 host–parasitoid models, 251–2

bird–spruce budworm (Choristoneura fumiferana) predation, 260, 261

births, 102

bison brucellosis infection, 311, 311 black molly (Poecilia latipinna)–

Ichthyophthirius multifiliis infection, 29 –30, 30, 32

experimental infection, 294, 295 survival analysis, 109, 110, 113 –15

Weibull distribution, 109 –10, 111–12 bootstrap methods, 40, 42– 4

line transects sampling error, 78 stock–recruitment relationship models,

178

time series analysis

density dependence modelling, 163, 165, 168

dynamic competition modelling, 234 Box–Cox transformation, 168

bridled nailtail wallaby (Onychogalea fraenata), 188 –9

339

340 I N D E X
brucellosis in bison, 311, 311	computer software, 2, 46 – 8
brushtailed possum (Trichosurus vulpecula),	modelling, 47– 8
285	spreadsheets, 46 –7
Bubulus ibis ibis (cattle egret), 211	statistics, 47
bumble-bee competition modelling, 232–3	confidence intervals, 25 – 6
burrow counts, 99	Bayesian methods, 45 – 6
	bootstrap methods, 40, 42– 4
call counts, 99	jackknife methods, 40, 41–2, 44
Canis lupus see wolf–moose functional	line transects, 77– 8
responses	maximum likelihood estimates, 38 –9
captive breeding data, 103	profile likelihood, 39, 41, 119
CAPTURE, 86, 87	stock–recruitment relationship models,
capture histories, 85, 90, 92– 4, 121	178
subpopulation interchange models, 195	survival analysis, 118 –19
carnivore track counts, 100	continuous-time models, 5
carrying capacity, 157, 158, 160, 183	density dependence, 158
estimation, 161	step length, 5
catastrophic stochasticity, 11	convex polygon, 186, 188
catch per unit effort, 99	coral cover, 74
cattle egret (Bubulus ibis ibis), 211	Cormack–Jolly–Seber model, 96, 97, 98,
cellular automaton models, 184 –5	120, 195
Choristoneura fumiferana (spruce budworm),	capture probabilities, 120
260, 261	survival probabilities, 120
Clethrionomys glareolus (bank vole), 299,	cowpox, 299
305 – 6	effect on fecundity, 305 – 6
Clinocottus globiceps, 234, 235	credibility interval, 45 – 6
cluster sampling, 60, 66 –7	cross-validation, 169
see also adaptive sampling	crown of thorns starfish, 191
clutch size, 103	current life tables, 116
cohort life tables, 116, 117–19
collared dove (Streptopelia decaocto), 211	death rates, 103, 104, 138
competition, 216 – 43	survival curves, 105
agents, 216, 229	see also hazard function
defining interaction coefficients, 219 –21	deaths, 102
experimental approaches, 222–5, 241	delay-differential equations, 8
isocline mapping, 225 –30, 242	demographic stochasticity, 11
press experiments, 223	density dependence, 157– 83, 313
pulse experiments, 223, 242	definition, 158
resource competition, 224 –5	in demographic parameters, 173
time scales, 224 –5	host–parasite models, 307
Lotka–Volterra model, 216, 217–19	experimental manipulations, 183
mechanistic models, 238 – 41	density, 173 – 4
resource dynamics, 238 – 41, 239, 240,	resources, 174
242	importance in natural populations,
targets, 216, 229, 230	157– 8
competition coefficients	inverse, 180 –2, 183
from census data, 230 – 8, 243	logistic, 158, 159
dynamic approach, 230, 233 – 8	rate of population spread, 214
observation error, 235, 236	reasons for estimation, 158 –9
Pimm–Schoener method, 232, 233	rescaling, 160 –1
process error, 235 – 6	stochasticity, 178 – 80, 183
static approach, 230, 231–3	stock–recruitment relationship, 174 – 8
density manipulations, 222–3	time series analysis, 161–73
resource usage methods, 221–2, 224,	applications, 164 –7, 170 –2
241	bootstrapped estimates of parameters,
see also interaction coefficients	163, 165, 168
competitive isoclines	cross-validation of parameters, 169
mapping, 225 –30	environmental stochasticity, 162,
resource competition, 241, 242	173
complexity levels, 14 –15	exponential growth, 163, 163
computer-intensive methods, 318 –19	inverse density dependence, 181,
bootstrap methods, 40, 42– 4	182
jackknife methods, 40, 41–2, 44	lagged population size model, 168 –9

density dependence (contd ) length of time series, 172–3

logistic models, 163, 163 – 4, 183 noise, 169, 173

observation error, 162, 172 parameter estimates, 163 process error, 162

random walk, 162, 163 randomization method, 168

transmission of pathogen/parasite, 293, 299

density manipulations, 173 – 4, 183 Allee effect demonstration, 182 competition studies, 222–5

interaction coefficients estimation, 223 – 4

press experiments, 223 pulse experiments, 223 resource competition, 224 –5 time scales, 224 –5

stock–recruitment relationship, 178 density-vagueness, 178

depensation, 180

see also inverse density dependence desert rodent competition modelling, 232 deterministic models, 10 –12

Diaptomus sanguineus, 127

diatom resource competition, 239, 239 – 41,

240, 242

differential equation models, 5 – 6 diffusion coefficient, 210

diffusion models, 210 –14, 211, 215 observed versus predicted rates of spread,

213 –14, 214

range expansion rate, 211–13, 212 discrete-time models, 5, 6

density dependence, 158 –9, 159 dispersal, 186, 187, 190

dispersal distributions, 190 –3, 214 data collection methods, 191

finite study area effects, 192, 193 exponential models, 190, 191 Gaussain plume models, 191 power models, 190, 191

survival analysis methods, 192 DNA-based methods

microsatellite DNA, 200 –1 traces analysis, 100 –1

double sampling, 56, 69, 70 –1 Durbin–Watson test, 150

emigration, 102 see also migration

endangered species population dynamics, 208 –9

environmental stochasticity, 11

density dependence analysis, 162, 173 probability distribution, 11

rate of increase relationships, 154, 154 time series data, 147, 149

epidemic transmission, 294 –7, 296 estimation procedure, 14

bias, 23

I N D E X 341

Euler–Lotka equation, 140 – 6 parameters, 141–2, 144, 145 rmax (maximum rate of increase)

estimation, 144 – 6 solving equation, 142

spreadsheet application, 142–3 event-based models, 13

Exmouth Gulf prawns, 176 explanatory models, 3 exponential growth, 5, 149 extinction risk estimation, 160

extra sum of squares analysis, 38, 38 extra-binomial variation (heterogeneity),

32

F ratios, 15 factorial models, 21 failure time, 105

false fritillary butterfly (Melitaea diamina), 208 –9

fecundity, 102–3, 137 age/size-dependence, 103 definition, 103

density dependence, 173 macroparasites, 307, 308, 309

host–pathogen/parasite models, 305 – 6 parasite reduction experiments, 306,

306

stage-structured discrete models, 146 filter feeders, 245

fisheries biology, 1 depensation (inverse density

dependence), 180 –2 fecundity estimation, 103 growth rates modelling, 133 population size estimation, 99

stock–recruitment relationship, 174 –5 footprint tracking, 186

force of infection, 290 –3 fox–rabbit predation

diet analysis, 257, 259

estimated functional responses, 260 data-fitting procedure, 264 –5

fractional factorial models, 21 frequency-dependent transmission, 293,

299

frequency distribution, 12 FSTAT, 199

gene flow estimation, 197, 199 generation time, 6

genetic approaches

data collection methods, 198 generation time, 6 individual-based, 12 migration /dispersal, 187, 190

rates estimation, 200 –1, 215 spatial models, 196 –201, 197 variables rescaling, 18

genetic assignment test, 200 –1, 202 Geographic Information System (GIS), 184 gerbil competition modelling, 225 – 6, 226,

227, 228, 229, 230

342 I N D E X

Gerbillus allenbyi, 225 – 6, 226, 227, 228, 229, 230

Gerbillus pyramidum, 225 – 6, 226, 227, 228, 229, 230

gestation period, 103

ghost bat (Macroderma gigas), 124 – 6 Glanville fritillary butterfly (Melitaea cinxia),

182, 208 GLIM, 28, 47, 89, 96

grasshopper competition modelling, 228 –130, 231

grouse macroparasite burden fecundity effect, 306, 306 mortality effect, 303, 304

growth curves, 135 growth rates, 133 – 6, 138

age–size data, 133 – 4, 136 functional forms, 133, 134 growth curves, 135 recapture data, 133, 135 – 6 von Bertalanffy model, 133

habitat destruction modelling, 208 –9 hair DNA analysis, 100, 101 Hardy–Weinberg equilibrium, 200 harmonic mean distribution, 187, 189

Hanski model see incidence function models hazard function, 105, 108, 108

exponential survival function, 108, 109 functional form, 108

Weibull distribution, 108, 109 helminth parasite density-dependent

fecundity, 307, 308, 309 heterogeneity (extra-binomial variation),

32

home range, 185 – 6, 188 –9 definition, 186

harmonic mean, 187, 189 Jennrich–Turner bivariate normal

distribution, 187, 188 kernel estimation, 187, 189

minimum convex polygon, 186, 188 range data analysis, 186

host–parasite models, 284

algebraic sensitivity analysis, 19 –20 basic structure, 285, 287–9 demographic parasite parameters, 307 density dependence studies, 173

macroparasite fecundity, 307, 308, 309

free-living infective stages, 287, 288, 294 functional responses, 245

host fecundity effects, 305 – 6, 306 host population size, 287

threshold population (NT ), 289, 310 –11

host survival rate modelling, 109 –10, 111–12, 113 –15

maximum likelihood estimates, 29 –32,

30

mortality see parasite-induced mortality parameterization, 284 –5

parasite survival within host, 20, 307 parasites on hosts, 19, 287, 288, 294

R0 (basic reproductive number), 288, 309 –10

rescaling, 19

transmission see transmission process; transmission rate

host–parasitoid models, 244, 251–2 age/stage structure effects, 6 aggregation, 253

functional responses dimensional analysis, 255 – 6

experimental approaches, 254 – 6 habitat complexity, 256

heterogeneity, 252– 4 Nicholson–Bailey model, 252 spatial refuges, 253

host–pathogen models, 284 age structure effects, 6 asymptomatic carriers, 307

basic structure (SIR models), 285, 286 –7 epidemics, 294 –5

host fecundity effects, 305 – 6 incubation period, 306 latent period, 306, 307

microparasite parameters, 306 –7 mortality see parasite-induced mortality parameterization, 284 –5

R0 (basic reproductive number), 12, 286, 309 –10

recovery rates, 306, 307 threshold population for disease

transmission (NT), 286, 310 –11 transmission see transmission process;

transmission rate house finch, 180 hypothesis testing, 24

Icelandic summer spawning herring, 176 Ichthyophthirius multifiliis–black molly

infection see black molly (Poecilia latipinna)

immigration, 102 see also migration

incidence function models, 204 –7, 215 application, 208 –9

colonization modelling, 206 extinction modelling, 205 – 6 parameters, 205

rescue effect, 205 – 6 individual-based models, 12–13 individual genotype detection, 100 –1 ingestion rates, allometric relationships,

258 –9

insects

density dependent survival, 159 generation time, 6

survival rates, 104 interaction coefficients, 219 –21

estimation from perturbation experiments, 223 – 4

inverted negative Jacobian matrix, 220 –1 Jacobian matrix at equilibrium, 220

per capita direct effects, 220 removal matrix, 221

see also competition coefficients

interaction parameters, 157, 313 –14 intraspecific competition, 17

inverse density dependence, 180 –2, 183 island models, 196, 197

jackknife methods, 40, 41–2, 44 line transects sampling error, 78

stock–recruitment relationship models, 178

see also cross-validation Jennrich–Turner bivariate normal

distribution, 187, 188 JOLLY, 95

Jolly–Seber method, 84, 85, 90, 91 application, 92– 4

kangaroos

commercial harvesting, 21

rainfall–rate of increase relationship, 154, 154

Kaplan–Meier method, 118 –19 kernel estimation, 187, 189

Lagopus lagopus scoticus (red grouse), 291–2, 292

lambda, λ (finite rate of increase), 140 landscape (spatially explicit) models, 184,

318

larch budmoth (Zeiraphera diniana), 170 –2 large vertebrate strip transects, 74 Lasiorhinus kreffti (northern hairy-nosed

wombat), 100

Latin hypercube sampling, 22, 22–3 lattice models, 185

genetic approaches (stepping stone models), 196, 197

least squares estimation, 33 Lefkowitz matrices, 8 Leslie matrix model, 7– 8

life tables, 104, 116 –19, 117 abridged, 117

cohort, 116, 117–19 current, 116

lifespans, 102 likelihood function, 31 likelihood ratio tests

linear models, 15, 16

mark–recapture study model selection, 122–3

Lincoln–Petersen method, 83 – 4, 85, 87, 90, 97

line-intercept transects, 74 line transects, 50, 73 – 83

application, 78 – 82 clustered distributions, 77 confidence intervals, 77– 8 definitions, 74

effective half strip width, 76 effective strip width, 74 fitting curves to data, 76 –7 method, 75 –7

rationale, 73 – 4 replicate lines, 78 sampling error, 78

I N D E X 343

sightability curve, 75, 75 sighting difficulties, 74 see also sampling strategies

linear models, 24

drawbacks for ecological problems, 29 least squares estimation, 29, 37 likelihood ratio test, 15, 16

litter size, 103

lizard fish (Saurida), 305 logarithmic transformations, 24, 37

logistic density dependence, 158, 159 carrying capacity, 161

logistic regression, 23, 26 – 8

drawbacks for ecological problems, 27 maximum likelihood estimates, 29 – 39 overdispersed data, 27– 8

scale parameter φ specification, 28 long-lived vertebrate populations, 6 Lotka–Volterra model, 216, 238, 268

dynamic regression models, 234, 236 phase-plane analysis, 217–19 predator–prey models, 245, 247 process/experimental error, 278 –9

McKendrick–von Forster equation, 8 –10, 129

Macroderma gigas (ghost bat), 124 – 6

Macropus robustus (wallaroo), 78 – 82 Macropus rufus (red kangaroo), 269, 270, 272 marine bottom sampling, 59

MARK, 121, 124

mark–recapture methods, 82– 4, 313 capture histories, 85, 90, 92– 4, 121 capture probabilities, 85 – 6, 97, 99, 119,

120, 120, 194, 195 closed populations, 84 –9

effective sample area, 84 –5 Cormack–Jolly–Seber method, 97, 98 dispersal distribution models, 192 growth rates estimation, 133, 135 – 6 Jolly–Seber method, 84, 85, 90, 91, 92– 4 Lincoln–Petersen method, 83 – 4, 85, 87,

97

log-linear method, 95 – 6

migration rates estimation, 193 – 6, 215 subpopulation interchanges, 194 – 6

movement of individuals estimation, 186 patch-related movement, 187, 190

open-population models, 90 – 6 recovery probability, 95 removal methods, 98 –9 sample size, 96 –7

sampling effort allocation, 97– 8 sampling times, 97– 8, 98 Schnabel estimates, 87, 88 –9

survival analysis, 119, 120, 120, 121, 124 – 6

age-structured model, 121, 121 application, 124 – 6

likelihood ratios, 15, 16

model selection approaches, 122–3 time-dependent methods, 86 –7, 89

mark–resight methods see mark–recapture methods

344 I N D E X
Marmota flaviventris (yellow-bellied	nonlinear models, 37
marmot), 193	extra sum of squares analysis, 38, 38
matrix-based models, 7	logarithmic transformations, 24, 37
maturation period modelling, 8	northern hairy-nosed wombat (Lasiorhinus
maximum likelihood estimates, 29–39	kreffti ), 100
age–size data, 133, 134	northern spotted owl, 3
confidence intervals, 38 –9
bootstrap methods, 40, 42– 4	objective statistical methods, 14, 15, 45
jackknife methods, 40, 41–2	observation error, 314 –16
profile likelihood, 39, 41	dynamic competition modelling, 235, 236
gene flow estimation, 199	stochastic models, 11
nonlinear least squares, 37– 8	stock–recruitment relationship, 177, 177,
principle, 29, 30 –2	178
process, 31, 33 – 6	time series analysis, 278, 279, 280
residual deviance, 31–2	density dependence, 162, 172
stock–recruitment relationship models,	Oligocottus maculosus, 234, 235
178	Ondatra zibethicus (muskrat), 211
survival rates, 29 –32, 30, 32	Onychogalea fraenata (bridled nailtail
Weibull distribution, 111–12	wallaby), 188 –9
mechanistic models	Opisthorchis viverrini, 308
competition, 238 – 41	Ostertagia, 308
density dependence, 158, 159 – 60
Melanerpes formicivorus (acorn woodpecker),	Paramecium competition modelling, 234 – 6,
193	237, 237
Melanoplus sanguinipes, 231	parameter interaction, 18, 21
Melitaea cinxia (Glanville fritillary butterfly),	parameter numbers, 14 –15
182, 208	parameter uncertainty, 18 –23
Melitaea diamina (false fritillary butterfly),	parametric statistical tests, 24
208 –9	parasite-induced mortality, 299 –305
metapopulations, 201, 203 – 4, 215	experimental studies, 300 –2, 301
definition, 201	impact of disease on survival, 300 –1
incidence function models see incidence	laboratory experiments, 302
function models	observational methods, 302–5
microsatellite DNA-based methods,	case mortality in epidemics, 303
200 –1	macroparasite burden, 303 –5, 304
migration, 186, 187, 190	patch models, 185
rates, 190, 215	colonization/extinction patterns, 203,
genetic approaches, 200 –1	204
mark–recapture methods, 193 – 6	competition modelling, 225 – 6, 226, 227,
minimum convex polygon, 186, 188	228, 229
modelling objectives, 1	genetic approaches (island models), 196,
modelling software packages, 47– 8	197
moose (Alces alces) see wolf–moose	gene flow estimation, 197, 199
functional responses	incidence function models, 204 –9
movement of individuals, 185 –201	metapopulations, 201, 203 – 4
data collection, 186	patch-related movement, 187, 190
genetic approaches, 196 –201, 215	patchiness, 60
migration, 187, 190	population sampling strategies, 54
rates, 193 – 6, 215	see also aggregated populations
probability distributions, 187, 190 –3	pellets analysis, 257, 259
range data analysis, 186 –7, 188 –9	periodicity in data, 60, 60
MSSURVIV, 196	phase-plane analysis, 217–19
Mus musculus mark–recapture study, 88 –9	phocine distemper epidemic transmission,
Jolly–Seber population estimation	296 –7, 297
method, 92– 4	phytoplankton–cladoceran herbivory
muskrat (Ondatra zibethicus), 211	model, 278, 279 – 80, 280, 281
myxomatosis in rabbits, 303	‘pick-up’ sample, 131
	Pimm–Schoener method, 232
Necator americanus, 308	plant–herbivore models, 244, 268 –78
nested models, 15 –16	vertebrate herbivores, 268 –71, 272
Nicholson–Bailey model, 252	herbivore functional response, 269, 270
nonlinear least squares, 37– 8	herbivore numerical response, 269 –70,
growth parameters, 133, 135	270

I N D E X 345

plant–herbivore models (contd ) herbivore population density

dependence, 269

plant response function, 269, 270 plug-in estimate, 43

Poecilia latipinna see black molly– Ichthyophthirius multifiliis infection

polar bear migration, 201, 202 polymerase chain reaction, 100, 101 population age structure see age structure population dynamics, 102

overcompensation, 17 population growth rates, 102

early reproduction, 103 fecundity, 103

population sampling problems, 50 strategies, 50, 51–2 subareas definition, 50

subsampling methods, 50 population size estimation, 49 –101

accuracy, 72

actual population size, 49 catch per unit effort, 99 continuous-time models, 5

deterministic/stochastic models, 10 –12 DNA traces, 100

indexes, 49, 50, 100 indirect methods, 99 –100 line transects, 73 – 83

mark–recapture/resight, 82– 4 numerical algorithms, 5 population definition, 49 –50 precision, 54, 72 quadrat/transect shape, 72–3, 73 quadrat/transect size, 72

rare animals, 100 –1 ratio methods, 98 –9 removal methods, 98 –9

sampling see population sampling population viability analysis models, 2

sensitivity analysis, 23 software packages, 47 posterior probability, 45

post-sampling stratification, 59 double sampling, 71

precision, 23, 23 – 4, 29 population size estimation, 72

population sample size, 53 quadrat size, 54, 72

stratified random sampling, 55 predator–prey consumption rates allometric relationships, 258 –9

estimation from field data, 257, 259, 260 stomach contents, 259, 261–2

predator–prey models, 244 – 68 age/stage structure effects, 6

approximate methods for sparse data see vole–predator interactions

basic structure, 245

functional responses, 244, 245 –7, 246, 282, 283

data-fitting procedure, 264 –7

dimensional analysis, 255 – 6 experimental approaches, 254 – 6, 283 explicit forms, 247

field data, 256 – 68 habitat complexity, 256

predator-centred measurement approach, 257– 62, 263, 283

prey-centred measurement approach, 256 –7, 262–3, 283

type I, 245

type II, 245 – 6, 263 – 4, 283, 314 type III, 246, 263 – 4, 283

type IV, 246

Lotka–Volterra model, 245, 247 numerical response of predators, 244,

247–51, 249, 282, 283 field data, 268

ratio-dependent predation, 248, 249, 250 –1

total response, 248

spatial heterogeneity, 252– 4, 283 time series analysis, 278 – 82, 280, 281

observer error, 278, 279, 280 process error, 278

predictive models, 3, 4 press experiments, 223 prior probability, 45 probability distributions

computer-intensive methods, 39 – 40 dispersal distances, 187, 190 –3 stochastic models, 136 –7

PROC GENMOD, 32 process error, 314 –16

dynamic competition modelling, 235 – 6 stochastic models, 11

stock–recruitment relationship, 175, 177, 178

time series analysis, 278 density dependence, 162

production/biomass (P/B) ratios, 153 profile likelihood, 39, 41 proportions estimation, 26

Pseudomys gracilicaudatus, 233 pulse experiments, 223, 224

purposes (application) of models, 3, 4

quadrats, 50, 72–3 along transects, 67 location selection, 53 shape, 72–3, 73

size, 53 – 4

adaptive sampling, 65, 66 population size estimation, 54, 72

see also sampling strategies

r (intrinsic rate of growth), 5, 158 definitions, 139 – 40

R0 (basic reproductive number), 12, 286, 288

host–parasite interaction, 309 –10

rmax (maximum rate of increase), 139 – 40 estimation from Euler–Lotka equation,

144 – 6

346 I N D E X
rabbits	rescaling, 16 –18
fox predation see fox–rabbit predation	density dependence, 160 –1
myxomatosis, 303	host–parasite model, 19 –20
radioactive tracers, 186	residual deviance, 31–2
radiotelemetry, 186	resource manipulation, 174
distribution of dispersal distances, 192–3	resource usage, 221–2, 224
predator–prey functional responses,	Richards equation, 134, 136
262–3	Ricker model, 17, 175, 176, 183
RAMAS, 47, 169	ring-necked ducks, 62–5
random sampling	@Risk risk analysis package, 23, 46
sensitivity analysis, 21	rodent competition modelling, 233
Latin hypercube sampling, 22, 22–3	Runge–Kutta method, 234, 235
simple, 52– 4
population density confidence	S-Plus, 47
intervals, 53	St Paul island reindeer population, 149 –51
stratified see stratified random sampling	sample size for mark–recapture methods,
range expansion rate, 211–13	96 –7
parameters, 212	sampling distribution, 12
raptor diet analysis, 257, 259	sampling effort allocation
estimated functional responses, 260	double sampling, 70
rare animal population size estimation,	mark–recapture methods, 97– 8, 98
100 –1	random sample site location, 54
rate of increase, 139 –56	stratified random sampling, 56, 59
allometric approaches, 151–3	Taylor’s power law, 59
context dependence, 139, 155	sampling error
definitions, 139 – 40	line transects, 78
effect of age distribution, 140, 144, 145,	time series data, 147, 149
145	sampling strategies, 50, 51–2
estimation from life-history information,	adaptive sampling, 60 – 6
140 – 6	cluster sampling, 66 –7
estimation from time series, 147–9	double sampling, 70 –1
application, 149 –51	patchiness, 54
production/biomass (P/B) ratios, 153	periodicity in data, 60, 60
stage-structured populations, 146	ratio estimation, 68 –70
stochasticity, 153 – 4	simple random sampling, 52– 4
ratio-dependent predation, 248, 249, 250 –1	stratified random sampling, 54 –9
ratio estimation, 68 –70	systematic sampling, 59 – 60
auxiliary variables, 68	two-stage designs, 67– 8
double sampling, 70	sampling units see study areas
population size estimation, 98 –9	SAS, 28, 42, 47
Rattus lutreolus, 233	satellite tracking, 186
reaction-diffusion models, 185	Saurida (lizard fish), 305
recruitment	scat counts, 99, 257
mark–recapture/resight methods, 84, 97	Schaefer model, 175
stage-frequency data, 126	Schistosoma mansoni snail infection, 285, 291,
stock–relationship, 174 – 8	292–3, 293
red grouse (Lagopus lagopus scoticus), 291–2,	Schnabel estimates, 87, 88 –9
292	sensitivity analysis, 4
red kangaroo (Macropus rufus), 269, 270,	algebraic forms examination, 18, 19 –20
272	differential equation models, 5
redimensioning, 16	factorial models, 21
see also rescaling	one-at-a-time parameter variation, 18,
regression, 24, 70	21
remote sensing vegetation cover estimation,	parameter interaction, 18, 21
69	parameter uncertainty, 18 –23
removal methods	random sampling, 21
interaction coefficients, 221	Latin hypercube sampling, 22, 22–3
population size estimation, 98 –9	rescaling, 16
resampling	host–parasite interaction, 19 –20
computer-intensive methods, 39 – 44	sentinel animal approach, 290
confidence intervals for maximum	shrew dispersal, 201
likelihood estimates, 39	Sigara ornata, 305
line transects sampling error, 78	silvereyes (Zosterops lateralis), 164 –7

single species models, 2 SIR models, 286 –7

Skeena River sockeye salmon, 176 snowshoe hare, 262

lynx predation model, 278, 282 Soay sheep, 301, 301–2

sockeye salmon, 176 sparse data, 27 spatial models

diffusion models, 210 –14, 211, 215 genetic approaches, 196 –201 incidence function models, 204 –7, 215

application, 208 –9 metapopulations, 201, 203 – 4, 215

spatial parameters, 184 –215, 317 individual movements see movement of

individuals spatial refuges, 253 spatial scale, 317

spatially explicit (landscape) models, 184, 318

species interaction models, 2 spotted owl (Strix occidentalis), 184 spreadsheets, 2, 46 –7

risk-analysis add-ins, 46

spruce budworm (Choristoneura fumiferana),

260, 261 SPSS, 47

stable age distribution, 130, 131, 132 stage–frequency data, 126 –30, 127 stage-structured populations, 6

rate of increase (r) estimations, 146 staggered entry, 105, 107

standard error, 12, 38 –9 see also variance

standing age distribution, 132–3 starling (Sturnus vulgaris), 211 stationary age distribution, 131 Statistica, 47

statistical models complexity levels, 14 selection, 15 –16

statistical population, 50 statistical significance, 25, 26 statistical software packages, 47 statistical tools, 23 – 48

step length

continuous-time models, 5 discrete-time models, 6 matrix-based models, 7, 8

stepping stone models, 196, 197 stochastic density dependence, 160, 161,

178, 183

transition matrix, 178 –9 stochastic models, 4, 10 –12

event-based, 13

frequency distribution of parameters, 12 individual-based, 12–13

matrix-based, 8 observation error, 11

population viability analysis, 136 –7, 138 probability distributions, 11, 136 –7 process error, 11

I N D E X 347

r (intrinsic rate of growth), 153 – 4 sensitivity analysis, 23

stochasticity, 10, 11 stock–recruitment relationship, 183

Beverton–Holt model, 175, 176, 183 data variability, 175, 176, 177 density dependence, 174 – 8

Schaefer model, 175 depensation (inverse density

dependence), 181, 182 observation error, 177, 177, 178 process error, 175, 177, 178 Ricker model, 175, 176, 183

stochastic density dependence, 179, 179 stomach contents analysis, 259, 261–2 strata, 54 –5

mapping, 56 numbers, 56

strategic models, 3, 4, 155 parameter numbers, 14

stratified random sampling, 54, 69, 70 application, 57– 8

mean density estimation, 55 – 6 post-sampling stratification, 59

double sampling, 71

sampling effort allocation, 56, 59 variance estimation, 55 – 6

Streptopelia decaocto (collared dove), 211 strip transects, 74

study areas (sampling units), 53 adaptive sampling, 60 –1, 66 definition, 50

mark–recapture methods, 84 –5 problems with random points, 54 strata, 54 –5, 56

Sturnus vulgaris (starling), 211 SURGE, 124

survival analysis, 26, 31, 103 –33, 137– 8 age structure comparison at different

times, 131

censored observations, 105, 107, 118 density dependence studies, 173 explanatory variables, 109

failure time, 105

hazard function, 105, 108, 108 functional form, 108 –9, 109

life tables, 104, 116 –19, 117 mark–recapture/resight methods, 84, 94,

96, 97, 119 –23 application, 124 – 6

maximum likelihood estimates, 29 –32,

30

method selection, 106 methods of last resort, 130 –3 parametric, 108 –16 ‘pick-up’ sample, 131 probability, 108, 108

stage-frequency data, 126 –30, 127 staggered entry, 105, 107 standing age distribution, 132–3 survival curves, 104, 105, 107

survival curves, 104, 105 types, 105, 107

348 I N D E X

sustainability, 2

swine fever epidemic transmission, 295, 296 Synedra ulna, 239, 240

Systat, 47

systematic sampling, 59 – 60

problems with periodicity in density, 60, 60

t test, 24, 25 – 6 tactical models, 3, 155

parameter numbers, 14 Taylor series, 159 Taylor’s power law, 59 territory size, 185

see also home range

tidepool fish competition modelling, 233, 234, 235

time modelling, 5 – 6

time series analysis, 147–9, 158 autocorrelation, 147– 8, 150 density dependence see density

dependence

dynamic competition modelling, 234, 243 observation error, 147, 162, 278, 279, 280 predator–prey models, 278 – 82

process error, 147, 162, 278 total response, 248

Trachyrachys kiowa, 231 track counts, 99, 100 tracking methods, 186 transects, 50, 72–3, 74

location selection, 53 shape, 72–3, 73

size, 72

see also line transects transition matrix, 178 –9 translocated populations, 144 transmission process, 289 –99

contact stage, 289 –90, 298 density-dependent transmission, 293, 299 development stage, 290, 298

force of infection, 290 –3 age-prevalence/age-intensity

approach, 290 –3, 292, 293 sentinel animal approach, 290

frequency-dependent transmission, 293, 299

transmission rate, 293 –9 allometric approaches, 297– 8 binary collision model, 298 –9

estimation from observed epidemics, 294 –7, 296

experimental estimation, 294, 295 scaling problems, 298 –9

spatial structure, 298, 299

Trichostrongylus tenuis, 291–2, 292

two-stage designs, 67

two-way analysis of variance, 15

univoltine species, 6

variables rescaling, 16 –18 variance

adaptive sampling, 61, 62 double sampling, 71

line transects, 78 ratio estimation, 69

stratified random samples, 55 – 6 two-stage sampling, 68

vegetation cover line-intercept transects, 74

remote sensing estimation, 69 vertebrates

large, 74 long-lived, 6 survival rates, 104

vole–predator interactions, 271 carrying capacity estimation, 275 environmental fluctuations, 273, 274 functional responses

type II, 273

type III, 273, 276

generalist predators, 271, 273 parameters, 275 – 6, 276

maximum consumption rate estimation, 275

mustelid predators, 273 parameters, 274

predicted versus acutal dynamics, 277 r (intrinsic rate of growth) estimations,

274 –5

von Bertalanffy model, 133, 134 growth curves, 135

recapture data, 135 VORTEX, 47

wallaroo (Macropus robustus), 78 – 82 Weibull distribution, 108, 109

dispersal distribution models, 192 survival rate modelling, 109 –10, 113 –15

maximum likelihood estimate, 111–12 white-tailed deer, 145

wolf (Canis lupus)–moose (Alces alces) functional responses, 260, 263

data-fitting procedure, 266 –7

wood mouse (Apodemus sylvaticus), 305 – 6

yellow-bellied marmot (Marmota flaviventris), 193

Zeiraphera diniana (larch budmoth), 170 –2

Zosterops lateralis (silvereyes), 164 –7