problem-solving in conservation biology and wildlife management (gibbs/problem-solving in...

Index

Index entries are arranged in word-by-word sequence; page numbers in italics refer to figures;page numbers in bold refer to tables.

Adirondack Mountains (New YorkState, US), 233

Administrative Procedure Act (US), 296–7adult population size (Nc), concept of, 36–7Africa, threatened species, 257–63AIC see Akaike information criterion (AIC)Akaike information criterion (AIC), 112,

114, 120, 123–4AICc, 133weights, 115, 116

allele frequencies, 33drift, 36

alleles, 32autozygous, 42–3, 44

allozyme electrophoresis, 50allozymes, 32, 33Altai (Russia), 279Amazonia, 192Amstrup, S. C., 138Anderson, D. R., 123–4animals, sightability, 88ants, biodiversity, 141, 155applied demographic analysis, 59Arachis spp. (groundnuts), climate envelope

modeling, 245–54Arachis hypogaea (peanut), 245Arachis kuhlmannii (groundnut), 249Arachis stenosperma (groundnut), 249, 251ArcExplorer (computer program), 234

predictions, 237–42

Australia, 41autocorrelation, spatial, 230automobiles, and natural resource

overconsumption, 264–8autozygous alleles, 42–3, 44averages

means, 265–6weighted, 193

baits, nest predation experiments, 179–81bandicoots, 41Barbados, threatened vertebrates, 23Batang Ai National Park (Sarawak,

Malaysia), 89–104bats

biodiversity estimation, 142–55inbreeding, 43–6species, 152, 154

bees, 84bias avoidance, in surveys, 271–2binary connectivity matrices, 49BIOCLIM model, 246, 248bioclimatic envelopes

analytical resources, 243use of term, 233see also climate envelope modeling

biodiversityconcept of, 3ecosystem fragmentation effects, 187–95effects of driving on, 264–8

Gibbs / Problem-Solving in Conservation Biology 9781405152877_6_index Final Proof page 317 12.10.2007 11:47am Compositor Name: pananthi

Problem-Solving in Conservation Biology and Wildlife Management: Exercises for Class, Field, and LaboratoryJames P. Gibbs, Malcolm L. Hunter, and Eleanor J. Sterling© 2008 James P. Gibbs, Malcolm L. Hunter, Jr., and Eleanor J. Sterling ISBN: 978-1-405-15287-7

field of study, 3–12importance of, 21–7indices, 192legislation, 296–303sampling sites compared, 6–7threats to, 21–7

human society, 257–63use of term, 3

biodiversity conservation, 201issues, 304–9

biodiversity control variables, 258biodiversity estimation, 141–55

procedures, 142–54biodiversity hotspots, 15–19, 279, 280

data processing, 282–5datasets, 282identification, 281–8ranking criteria, 282resources, 288

biodiversity indicators, 258biodiversity loss

conservation prioritization, 279–88determinants, 257–63proximate causes, 257public attitudes, 269–70ultimate causes, 257

biodiversity maintenance, in forests, 196–200biodiversity security, 279biodiversity threat, 279

see also Red List of Threatened SpeciesBiological Conservation (journal), 20biological diversity see biodiversitybiological exploration, and climate

envelope modeling, 244–54birds

foraging energetics, 195human societal effects on, 258–63threatened species, 23, 258–63see also seabirds

bootstrap methods, 147Bray–Curtis index, 153Brazil, 21, 31, 192–3, 196breeding

captive, 42see also inbreeding

Buckland, S. T., 104Burnham, K. P., 123–4by-catch, seabirds, 59–65

cactiendangered species, 83–7inbreeding effects, 84, 86

camera trap data, analysis, 105–24Canada, 296

Cape Floristic Region (South Africa), 15,16–17

captive breeding, 42Captive Breeding Specialist Group

(CBSG), 47captive populations, 172CAPTURE (computer software)

goodness of fit tests, 135and MARK compared, 134–6model selection algorithm, 135resources, 138

capture histories, 127capture-mark-recapture studies see

mark-recapture studiescarbon dioxide emissions, 264, 266carbon sequestration, landscape-level, 192–3carbon-light technologies, 233Carothers, A. D., 138carrying capacity (K ), 60, 69, 70Catopuma temminckii (golden cat)

camera trap data, 108–10occupancy analysis, 108–20occurrence, 109

cattle, and cacti decline, 84, 87Caucasus, 15, 17Caughley, G., 83–7CBSG (Captive Breeding Specialist

Group), 47CCJ see Jaccard coefficient of community

similarity (CCJ)CEPF see Critical Ecosystem Partnership

Fund (CEPF)Chao, Anne, 147, 152–3Chao’s abundance-based Jaccard index, 153Chelonia mydas (green turtle), 23chi-squared tests, 97, 182–3Chihuahuan Desert (US/Mexico), 84China, 15, 18–19climate

datasets, 234–6models, 236predictions, 237–42see also global climate change

climate envelope modeling, 244–54applications, 244data preparation, 245–6overview, 248resources, 254single species, 249–53see also bioclimatic envelopes

climate parameters, 233climate spaces, 236

analytical resources, 243use of term, 233

318

Index


close-ended survey questions, 273closed captures analyses, 129–30closed populations, 128, 138Coastal Forests (Kenya and Tanzania),

15–16coefficient of determination, 230collector-bias problem, 230collector’s curves, 144

construction, 6interpretation, 5–6

Colwell, R. K., 155commercial fishing see fisheriescommunities

defining, 142evenness, 8

community distinctivenessanalysis, 8–10patterns, 10

community diversity, 9analysis, 7–8see also Jaccard coefficient of community

similarity (CCJ)confidence intervals, 265–6conservation

geographic information systems,221–32

see also biodiversity conservationconservation biology

conservation genetics in, 42definition, 13field of study, 13–20human dimensions of, 13issues, 269schematic models, 13–14, 15–19

Conservation Biology (journal), 20, 184conservation dependent species, 158Conservation Directory, The, 305conservation efforts

prioritization, 6–7, 9–10, 12, 279–88see also ex situ conservation programs

conservation genetics, applications, 42conservation goals, 203conservation groups, 12Conservation International, 15

biodiversity hotspots, 279, 280, 282–5Rapid Assessment Program, 12

conservation laws, 296–303, 305conservation planning

and climate envelope modeling, 244–54ecoregions, 201–12and population genetics, 31, 32–4prioritization, 212resources, 212see also protected areas

conservation policies, 304–9participation, 306–8

conservation values, 267public attitudes, 269–78surveys, 269–78

consumptionand biodiversity loss, 257–63see also overconsumption

contingency tables, 181–2control variables, 258Convention on Biological Diversity, 15Cooch, E., 138core habitats, 74–82corruption, 281Corruptions Perception Index (CPI), 260–1Costa Rica, 141Costanza, R., 27CPI (Corruptions Perception Index), 260–1Cramer–von Mises test, 97criteria matrices, 285, 286, 287Critical Ecosystem Partnership Fund

(CEPF)funding priorities, 13–20goals, 15strategies, 13–20

critically endangered species, 158cultural values, 24

Darwin, Charles, 289data deficient species, 158debates

agendas, 291–4international, 289–95preparation, 290structure, 290–1

deforestation, 279degrees of freedom, 182deltaAIC, 123–4democracy, 304demographic analysis, applied, 59density function, 91Department of Natural Resources (US), 305Dermochelys coriacea (leatherback turtle), 23detectability, factors affecting, 88detection function, 90–1determination, coefficient of, 230Dinerstein, E., 279–81, 282, 286DISTANCE (computer program)

analysis procedures, 95–8data importation, 93–5data set truncation, 98–9models, 99–102obtaining, 92–3population estimation, 88–104

Index

319

Index


resources, 104results, 103–4zone analysis, 102

distance samplingcomputer programs, 89concepts, 89–91orang utans, 91–2and population estimation, 88–104species selection, 104

distinctiveness, 8see also community distinctiveness;

evolutionary distinctivenessdistribution modeling, 246, 247–9DIVA-GIS (computer program), 254

data importation, 222–3data projections, 226–7obtaining, 222, 245richness estimation, 230

diversityconcept of, 228faunal, 194measurement, 228see also biodiversity; community diversity;

genetic diversity; species diversityDOMAIN model, 246, 248, 254doubletons, 146driving

effects on biodiversity, 264–8and road-kills, 266

Drosophila hemipeza (fruit fly), 40duplicates, 146

East Africa, 43Eastern Arc Mountains (Tanzania), 13,

15–16Echinocereus chisoensis var. chisoensis (Chisos

Mountains hedgehog cactus), 84–7endangered species status, 84seed dispersal, 84, 86

ecological traps, 74–82ecological values, 24economic values, 24ecoregions, conservation planning, 201–12ecosystem diversity, landscape analysis, 192–3ecosystem fragmentation

effects on biodiversity, 187–95see also landscape fragmentation

ecosystem function, change estimation, 194ecosystems, 185–254Ecuador

government, 289see also Galapagos Islands (Ecuador)

edge effects, 76, 179–84statistical tests, 181–3

edge habitats, 74–82and game, 179and predation, 179–84

edge species, 142methodological, 142spatial, 142temporal, 142

effective population size (Ne)concept of, 36–7definition, 37and genetic variation, 37–8

effective population size–adult populationsize ratios (Ne/Nc), 38, 39

effective strip width (ESW), 91, 97–8El Nino

and global climate change, 67occurrence, 68and penguin population persistence, 66–73

electrophoresis, 32electrophoresis gels, banding patterns, 32, 33elephants, population estimation, 88–9encounter rate, 97–8endangered species, decline, 83–7Endangered Species Act (US), 303environmental envelopes, 246–7environmental problems, determinants,

257–63enzymes, 32Epinephelus itajara (goliath grouper), 23Epinephelus striatus (Nassau grouper), 23Eretmochelys imbricata (hawksbill turtle), 23estimated means, 265–6estimates, 122, 216–18

naıve, 113population size, 125–38simulation-based, 118see also biodiversity estimation;

population estimationEstimateS (computer software), 141–55

obtaining, 143–4resources, 155

estimators, 228richness, 230

ESW (effective strip width), 91, 97–8eucalyptus, 197–8Europe, threatened species, 257–63evenness, 8evolution, theory of, 289evolutionary distinctiveness, 10–11ex situ conservation programs, 156–73

candidate species, 158–72guidelines, 172resources, 157–8roles, 157

320

Index


Excel see Microsoft Excelextinct species, 158extinct in the wild, 158extinctions, 24

islands, 213see also quasi-extinction thresholds

extrapolation, 147

FA see fluctuating asymmetry (FA)false absence, 105faunal diversity, estimation, 193Federal Endangered Species Act

(US), 296Federal Register (US), 305fish, 59

age distribution, 63harvesting, 59–65

strategies, 63–4threatened species, 23

fisheriesGalapagos Islands, 289–95long-line, 60seasonal restrictions, 65vs. seabird by-catch, 59–65

Fisher’s alpha index, 151fixed-width transects, 89flagship species, 172fluctuating asymmetry (FA)

definition, 45and inbreeding, 42–7index construction, 46measurement, 45–6

foraging, energetics, 194forest management, 196–200forests

biodiversity maintenance, 196–200fragmentation, 75–82, 187–94gallery, 188harvesting, 196–200landscape composition, 75seasonally inundated, 188wetland, 188

fragmentationmanaged, 191–2uncontrolled, 190, 193undisturbed, 188–9, 193see also ecosystem fragmentation;

habitat fragmentation; landscapefragmentation

Frankham, R., 41, 47frequency distributions, 246frogs, 40–1

cold-adapted, and global climate change,233–43

occupancy analysis, 105, 106occupancy studies, 119–20see also Rana septentrionalis (mink frog)

Fst see Wright’s fixation index (Fst)

Galapagos Islands (Ecuador), 59, 66, 67conservation issues, 289fisheries, 289–95

Galapagos National Park (Ecuador),289–95

gallery forests, 188game, and edge habitats, 179gametes, 44, 46Gascon, C., 193Gaussian distribution, 96gene flow, 48, 49, 52generation time

definition, 38determination, 62–3sampling, 38

genesprotein coding, 32see also alleles

genetic differentiation, 31, 48determination, 33–4

genetic distance matrices, 49, 52–4genetic distances, calculation, 50–1genetic distance–environment relationships,

statistical significance, 54genetic diversity

among vs. within populations, 31–5loss limitation, 36–41trees, 195

genetic drift, 36–41, 48genetic similarity

indices, 48matrices, 48–9

genetic structure, of populations, 48genetic variation

distribution, 48and effective population size, 37–8isolated groups, 36structure, 31–5see also heterozygosity

genetics, 29–56see also landscape genetics; population

geneticsgeographic coordinates, 223geographic distance matrix, 52, 53–5geographic information systems (GIS), for

conservation, 221–32giant anteaters, 21GIS (geographic information systems), for

conservation, 221–32

Index

321

Index


global climate change, 264and cold-adapted frogs, 233–43and El Nino, 67

Global Environment Facility, 15Glyptemys insculpta (wood turtle), 75

habitat connectivity, 78–82habitat loss and fragmentation, 78–80

golden cats, occupancy analysis, 108–20goodness-of-fit, 96

tests, 135Gore, Al, 264Gotelli, N. J., 65, 155governance

and biodiversity loss, 257–63integrity indices, 258quality of, 258–61

government agencies, letter to, 307–8governments, conservation policies, 304–9gross domestic product (GDP) per capita,

258–61Guaiacum officinale (commoner

lignum vitae), 23

habitat composition, 75habitat connectivity, 74–82

turtles, 78–80habitat corridors, 48habitat fragmentation, 74–82, 187habitat loss, 74–82, 187habitat management, parrots, 196–200habitat patches, occupancy, 105, 106habitats

continuous, occupancy, 107core, 74–82see also edge habitats

Hadley Centre (UK), 236Hardy–Weinberg equilibrium, 34harp traps, 142, 143harvest management, 65harvest rates, seabirds, 63harvesting

forests, 196–200strategies, 63–4sustainable, 59

Hawaiian Islands, 40herding species, population viability

analysis, 193–4heterozygosity, 37

determination, 33–4, 50, 194heterozygotes, 33

frequency, 34Hijmans, R. J., 221Hill, M. O., 155histograms, 96–7

homozygotes, 33Hornaday, William, 269hotspots see biodiversity hotspotsHudson Bay (Canada), 296human density, datasets, 260human society, effects on biodiversity,

257–63hunting

bag limits, 65seasonal restrictions, 65

husbandry, 158–72

ICE (Information Center for theEnvironment) (US), 214

imperiled species see threatened speciesimportance scores, 285–6, 287inbreeding

and fluctuating asymmetry, 42–7levels, 44–5

inbreeding coefficient, 42–3inbreeding effects, 45–6

cacti, 84, 86India, 128–38indicators, biodiversity, 258Indo-Pacific countries, biodiversity

hotspots, 279, 281Information Center for the Environment

(ICE) (US), 214inherent values, 24Intergovernmental Panel on Climate

Change, 236International Biogeography Society, 220international debates, 289–95International Monetary Fund, World

Economic Outlook Database, 258–61International Species Information System

(ISIS), 172International Union for the Conservation

of Nature (IUCN), 21–7, 158,258, 282

see also Red List of Threatened Speciesinterpolation, 149intrinsic rate of increase, 63invertebrates, mark-recapture studies, 138ISIS (International Species Information

System), 172island biogeography, 213–20

resources, 220theory, 214

islands, extinctions, 213isoclines, 235isolated groups, genetic variation, 36IUCN see International Union for the

Conservation of Nature (IUCN)

322

Index


Jaccard coefficient of community similarity(CCJ), 151–3, 208–9

averaging, 9determination, 8–9

jackknife methods, 147Jacobson, Susan, 13–14, 15, 19, 309Japan, Government of, 15Jarvis, A., 245javelinas see peccariesJohn D. and Catherine T. MacArthur

Foundation, 15Joint Photographic Experts Group

( JPEG), 242Jules, E. S., 76

K (carrying capacity), 60, 69, 70Kanha National Park (India), 128–38Karanth, K. U., 138Kenya, 15–16Kihansi Gorge (Tanzania), 13Kingstone, T., 155Kolmogorov–Smirnov test, 97

La Selva Biological Station (Costa Rica), 141Lambert Equal Area Azimuthal projection,

226, 227land use, guidelines, 192landscape analysis, ecosystem diversity, 192–3landscape composition, forests, 75landscape fragmentation, 187–95

land use guideline-based, 191–2scattered disturbances, 188–9uncontrolled, 191see also ecosystem fragmentation

landscape genetics, 48–56field of study, 48

landscapes, 185–254latitude, calculation, 222–3Laurance, W. F., 192laws, conservation, 296–303, 305LCL (lower confidence limit), 216–18leaf morphometry, 35least concern species, 158Leopold, Aldo, 179letters

to elected officials, 307to government agencies, 307–8to newspaper editors, 306–7

lichens, occupancy analysis, 107life table analysis, 59–65

freeware, 65life tables, construction, 60–1likelihood, 120–4line transects, population estimation, 88–104

linear predictors, 112Liophis perfuscus (Barbados racer), 23List of World Heritage in Danger, 290Loagan Bunut National Park (Sarawak,

Malaysia), 142–55logit links, 112long-line fisheries, 60longevity, seabirds, 60, 61Longino, J. T., 141, 155longitude, calculation, 222–3lower confidence limit (LCL), 216–18lower risk species, 158

M0 model, 130, 135–6MacArthur, R. H., 220MacArthur Foundation{,} John D. and

Catherine T., 15macaws, 196MacKenzie, Darryl, 120Madhya Pradesh (India), 128–38Magurran, A. E., 155mahogany, 197–8maintenance costs, 158Malaysia, 88, 89–104

bat studies, 142–55golden cat studies, 108–20

Maludam National Park (Sarawak,Malaysia), 149–55

Manaus (Brazil), 192–3Mantel test, 54Mao Chang Xuan, 145Mao Tau curves, 146, 148–9map projections, 226maps, ecosystem fragmentation analysis,

187–95MARK (computer software), 125–38

analyses, 130–2experimental, 136–7

and CAPTURE compared, 134–6data entry, 129–30models

combining, 133–4comparisons, 132–3

obtaining, 128resources, 138

mark-recapture studiesanalysis, 125–6concepts, 126–8invertebrates, 138methodology, 125population size estimation, 125–38taxicabs, 138vertebrates, 137–8

Massachusetts (US), 174

Index

323

Index


maternity ratesdetermination, 62seabirds, 60, 61

matricesbinary connectivity, 49criteria, 285, 286, 287genetic distance, 49, 52–5genetic similarity, 48–9

Maxent model, 248maximum likelihood estimation, 120–4Mb model, 130, 132mean generation time, determination, 62–3means, estimated, 265–6methodological edge species, 142Mexico, 40Mh model, 130, 133, 135–6Michaelis–Menton equation, 147, 148–9Microsoft Excel, 92, 109, 142, 222–3

add-ins, 54, 204, 216, 261datasets, 234–6, 237paired t-test, 86population viability analysis, 69–72

Middlesex Fells (Massachusetts, US), 174migration routes, 48, 49minimum representation problem, 201model averaging, 124model coefficients, 261model fit, 261model significance, 261Morisita–Horn index, 153mortality rates, 62Mountains of Southwest China, 15, 18–19movement corridors, identification, 48–56MS Excel see Microsoft ExcelMt model, 130, 132multiple linear regression, 260–1

interpretation, 261muntjac deer, 89–91

naıve estimates, 113National Biological Service, 65National Park Service (US), 214National Wildlife Federation (US), 305natural resources, overconsumption,

264–8natural selection, 48Nc (adult population size), concept of, 36–7Ne see effective population size (Ne)Nepal, 40nest predation experiments, 179–84

analysis, 181–3design, 180procedures, 180–1

net reproductive rate, determination, 62

New York State (US), 74, 257, 264automobiles, 265–7mink frogs, 233–43

New York State Amphibian and ReptileAtlas, 234–6

Newmark, W. D., 220newspapers, letters to, 306–7NGOs (nongovernmental organizations), 290Nichols, J. D., 138nongovernmental organizations

(NGOs), 290normal (Gaussian) distribution, 96Nuevo Leon (Mexico), 40null hypothesis, 181, 182Numenius borealis (Eskimo curlew), 23

Oahu (Hawaiian Islands), 40observations, 121occupancy

analysis, 105–20concept of, 105–6continuous habitats, 107habitat patches, 105, 106

O’Hara, R. B., 155open-ended survey questions, 273OpenOffice Suite, 86operational taxonomic units, 4Opuntia spp. (cacti), 83orang utans, 88

distance sampling, 91–2orchids, 31

conservation, 32–4Orchis isozymus (orchid), 32, 33Orchis polyzymus (orchid), 32, 33ORCHIS1, 32, 33organizations, 255–309overconsumption, 257

issues, 267natural resources, 264–8

overland dispersal, 52, 54

paired t-test, 86Palmer, A. R., 47parameters, 122parrots, habitat management, 196–200peanuts

climate envelope modeling, 245–54distribution modeling, 246modeled vs. observed diversity, 251–3

peccariesand cacti decline, 84, 87population decline, 84population viability analysis, 193–4and seed dispersal, 84

324

Index


pedigree management, 42–7pedigrees, construction, 43penguins, 66

population dynamics, 68–9population viability analysis, 67–72

percentiles, 247Petersen method, 126–7phylogenetic relationships,

spiders, 10–11Picea martinezii (Martinez spruce), 40Pinna, M. de, 12Pinzon Island (Galapagos Islands,

Ecuador), 289Pithecellobium elegans (canopy tree), 195planning units (PUs), 201–10

realistic costs, 209–10plant reintroductions, 174–8

design issues, 175–6equipment, 176record keeping, 176–7

plots, 89poachers, 84, 86, 87, 197polar bears

resources, 303as threatened species, 296–303

policies, 255–309public, 304–9see also conservation policies

policy analysis, 305–6politicians, letters to, 307pollinators, 84polls, 278

see also surveyspopulation decline

determinants, 83diagnosis, 83–7estimation, 86

population densityand biodiversity loss, 257–63datasets, 260

population dynamics, modeling, 68–9population estimation

and distance sampling, 88–104line transects, 88–104

population genetics, 31–5and conservation planning,

31, 32–4population growth rate, 63population management, 59population management targets,

establishment, 36–41population persistence

penguins, 66–73trilliums, 76–8

population size estimation, withmark-recapture data, 125–38

population trajectories, graphing, 71population viability analysis (PVA), 66–73

herding species, 193–4issues, 73models, 66

iteration, 72procedures, 66–72spreadsheet simulations, 69–72

populations, 57–138captive, 172closed, 128, 138diversity within vs. diversity

amongst, 31–5genetic structure, 48idealized, 37sample, 270–1vital statistics, 59see also effective population size (Ne)

predation, and edge habitats, 179–84predators

human, 179non-human, 179

PRESENCE (computer program),105–24, 131

analyses, 111–17complex models, 113–15data importation, 110–11model fit assessment, 115–17obtaining, 107–8simulations, 117–18

prioritizationconservation efforts, 9–10, 12, 279–88in conservation planning, 212

probability, 121protected area size, and species richness,

213–20protected areas, 201–12

protection status, 214public attitudes

biodiversity loss, 269–70conservation values, 269–78resources, 278

public hearings, 305public policies, 304–9PUs see planning units (PUs)PVA see population viability analysis (PVA)

q-q plots, 96, 97quadrats (plots), 89quality of governance, 258–61Quammen, D., 220quasi-extinction thresholds, 70, 71

Index

325

Index


RAMAS (software), 73Rana septentrionalis (mink frog), 233

and global climate change, 233–43life cycle, 234occurrence, 234

range, 158RAP (Rapid Assessment Program), 12Rapid Assessment Program (RAP), 12rarefaction, 150, 228, 230Red List of Threatened Species, 21–7

birds, 258database searching, 35global perspectives, 25–6Internet access, 22status, 158structure, 23

regression analysis, 216, 217regression coefficients, 216regulations, 305reintroduction potential, 172reintroductions, plants, 174–8reserve design, 201–12

wild potatoes, 221–32reserve selection, 231richness estimators, 230road-kills, driving and, 266Roraima (Brazil), 21, 31, 196Royal Chitwan National Park (Nepal), 40Rubis, June, 91–2rule-making, 296–303

caucus, 299comment presentation, 297–9final presentations, 299group assignments, 297individual assignments, 299–302issue framing, 299

Russia, 279

Sabah (Malaysia), 88salamanders, gene flow, 49sample populations, 270–1sampling

collector’s curves, 5–6see also distance sampling

sampling sites, 4biodiversity contrasting, 6–7evaluation, 5–6

Sarawak (Malaysia), 89–104bat studies, 142–55

Schleibe, S., 303seabird by-catch, vs. fisheries, 59–65seabirds

harvest rates, 63incidental drowning, 64

longevity, 60, 61maternity rates, 60, 61vital statistics, 62–4

Sepilok Rehabilitation Center(Sabah, Malaysia), 88

SEs (standard errors), 134Shannon’s index, 151Shannon–Weiner index, 192Shavla River (Russia), 279Sierra Madre Oriental (Mexico), 40sightability, animals, 88similarity indices, abundance-based, 153Simpson’s reciprocal index, 7–8, 9,

150–1, 192simulation-based estimates, 118Sinclair, A. R. E., 83–7singletons, 146–7site similarity, estimation, 151–3snakes, threatened species, 23Sobs (species observed), 144–5, 149Society for Conservation Biology, 13,

20, 278Solanum sect. Petota (wild potato), 221–32Solanum tuberosum (cultivated potato), 221Sørensen classic index, 152–3South Africa, 15, 16–17Southern Mesoamerica, 15, 18spatial autocorrelation, 230spatial edge species, 142species, 139–84

conservation dependent, 158critically endangered, 158data deficient, 158extinct, 158flagship, 172importance of, 24least concern, 158lower risk, 158vulnerable, 158see also edge species; threatened species

species accumulation curves, 144–6species diversity

determination, 7–8patterns, 10

species observed (Sobs), 144–5, 149species richness, 7, 9, 279

estimation, 141, 142, 147–9, 230estimator plots, 148–9intersite comparisons, 150patterns, 10and protected area size, 213–20

species richness datasets, 214–15interpretation, 216–18plotting, 216, 217

326

Index


species richness datasets (Contd )regression analysis, 216, 217transformation, 215

species–area relationship, 213–14Spheniscus mendiculus (Galapagos

penguin), 66population viability analysis, 67–72

spider collectionsclassification, 4–5representative, 5sorting, 4–5

spidersbiodiversity, 3–12, 141phylogenetic relationships, 10–11

Spooner, D. M., 221stacks, 251standard errors (SEs), 134start-up costs, 158State University of New York College of

Environmental Science and Forestry(SUNY-ESF), 264, 265–7

Stein, Gertrude, 31Stiassny, M. L. J., 12streams distance matrix, 52–5strip transects, 89SUNY-ESF (State University of New York

College of Environmental Scienceand Forestry), 264, 265–7

survey questions, 271–2close-ended, 273demographic, 275open-ended, 273sample, 273–5

surveysanalysis, 276–7bias avoidance, 271–2conservation values, 269–78design, 271–2implementation, 276objectives, 270–1

survival rates, 62survivorship values, 62sustained yields, 60Syracuse (New York, US), 257, 264

t-test, paired, 86Tanzania, 13, 15–16taxicabs, mark-recapture studies, 138taxonomy, field of study, 3Tayassu tajacu (collared peccary), 84temporal edge species, 142Thomas, L., 104threat analysis, and climate envelope

modeling, 244–54

threat scores, 285–6, 287threatened species

determinants, 24–6, 257–63evaluation, 24–6polar bears, 296–303values assessment, 22–4see also Red List of Threatened Species

TI (Transparency International), 258–61tigers, population size estimation, 128–38timber production, 196–200timber productivity, determination,

200toads

genetic distance, 52landscape genetics, 49–56overland dispersal, 52

tortoises, 289transect walks, 89transects

fixed-width, 89line, 88–104

Transparency International (TI), 258–61trees

genetic diversity, 195threatened species, 23

Triaenops persicus (triple nose-leaf bat), 43–6Trilliaceae (trilliums), 76Trillium ovatum (western white trillium), 74

habitat loss and fragmentation, 75–8population persistence, 76–8

turtles, 75habitat connectivity, 78–80threatened species, 23

UCL (upper confidence limit), 216–18Udzungwa Mountains (Tanzania), 13UNESCO (United Nations Educational,

Scientific, and CulturalOrganization), 290

uniques, 146–7United Kingdom (UK), climate models, 236United Nations Educational, Scientific,

and Cultural Organization(UNESCO), 290

United States (US)conservation legislation, 296–303conservation policies, 305protected areas, 214–20

United States Fish and Wildlife Service(USFWS), 299–303

United States Geological Survey, 235Biological Resources Discipline, 214

United States Man and the Biosphere(US MAB) program, 214

Index

327

Index


Universal Transverse Mercator (UTM)projection, 226

University of California at Davis (US), 214upper confidence limit (UCL), 216–18Urera kaalae (nettle), 40Ursus maritimus (polar bear)

resources, 303as threatened species, 296–303

Urticaceae (nettles), 40US MAB (United States Man and the

Biosphere) program, 214USFWS (United States Fish and Wildlife

Service), 299–303UTM (Universal Transverse Mercator)

projection, 226

Vane-Wright, R. I., 12vertebrates

mark-recapture studies, 137–8threatened species, 23

vital statistics, seabirds, 62–4Vortex (software), 73vulnerability, 207vulnerable species, 158

weighted averages, 193wetland forests, 188

wetlands, 188orchid conservation, 32–4

WHC (World Heritage Committee), 290–5White, G., 138Wikramanayake, E. D., 279–81, 282, 286Wilcove, D. S., 27wild cats, occupancy analysis, 107wild potatoes

distribution, 223–6reserve design, 221–32species richness distribution, 227–31

Wilson, E. O., 220wolves, 269, 270, 271wood frogs, 40–1World Bank, 15World Heritage Committee (WHC), 290–5World Heritage Sites, 290World Zoo Conservation Strategy, 157Wright’s fixation index (Fst)

determination, 33–4estimation, 50–1

zoosdesigning, 156–73historical background, 156–7issues, 173roles, 157

328

Index


problem-solving in conservation biology and wildlife management (gibbs/problem-solving in...

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