PK&8nds:s:refs.MYD <79Ludwig, J. A. Bastin, G. N. Wallace, J. F. McVicar, T. R.2007QAssessing landscape health by scaling with remote sensing: when is it not enough?163-169Landscape Ecology222_drylands; indicators; monitoring; rangelands; scale VEGETATION CONDITION; RANGELANDS; AUSTRALIAArticleFebAssessment of the health of landscapes, by monitoring their condition over space and time, is needed to better understand the processes for sustaining or, in many cases, repairing them. Remote sensing is a tool that can efficiently identify and assess areas of landscape damage at different scales and help land managers solve specific problems. Remote sensing may appear to be a panacea for all monitoring situations but sometimes the information it provides is not enough by itself. In this paper we give examples of both scenarios-when remote sensing alone is adequate and when it is not. When remotely sensed data alone is not sufficient, monitoring problems can be solved by incorporating additional finer scale data. We use a five-step procedure based on scaling to help land managers answer the question: when is remote sensing data alone not sufficient to underpin the information needs required to achieve a specific management goal?://000243823900001 ~ISI Document Delivery No.: 130UG Times Cited: 0 Cited Reference Count: 34 Cited References: *MILL EC ASS, 2003, EC HUM WELL BEING FR ASH A, 2004, HLTH RANGELANDS PRIN, P69 BARTLEY R, 2006, IN PRESS HYDROLOGY P BASTIN GN, 2005, AUSTR COLLABORATIVE BASTIN GN, 2006, ECOL MANAG RESTOR S1, V7, S71 BROOKER L, 2002, LANDSCAPE URBAN PLAN, V60, P185 CACCETTA PA, 2000, P 10 AUSTR REM SENS, P97 GUNDERSON LH, 2002, PANARCHY UNDERSTANDI HOBBS RJ, 1990, AUSTR ECOSYSTEMS 200, P93 LAMBECK RJ, 1993, ASSESSMENT CONSERVAT LAMBECK RJ, 1999, 2 URL DEP ENV HERT B LUDWIG JA, 2005, ECOL SOC, V10 LUDWIG JA, 2006, IN PRESS ECOL INDIC MCVICAR TR, 1998, AGR SYST, V57, P399 MCVICAR TR, 2002, IAR MONOGRAPH, V84, P205 MCVICAR TR, 2003, REV PREDICTIVE MODEL PATIL GP, 2002, MANAGING HLTH ECOSYS, P559 PRINCE SD, 2002, GLOBAL DESERTIFICATI, P23 PRINGLE HJR, 2006, IN PRESS LANDSC ECOL, V21 RYAN P, 2004, ECOL MANAGE RESTOR, V5, P85 SAUNDERS DA, 1987, NATURE CONSERVATION SAUNDERS DA, 1991, CONSERV BIOL, V5, P18 SMITH DMS, 2000, AUSTR J ENV MANAGEME, V7, P190 TONGWAY DJ, 2004, LANDSCAPE FUNCTION A WALLACE J, 2006, ECOL MANAG RESTOR S1, V7, S31 WALLACE JF, 1998, STATE ENV TECHNICAL WALLACE JF, 2004, AUSTRAL ECOL, V29, P100 WESSMAN CA, 2006, SCALING UNCERTAINTY, P147 WHITE DH, 2000, AGR SCI, V13, P27 WIENS JA, 1989, FUNCT ECOL, V3, P385 WOINARSKI JCZ, 2003, RANGELAND J, V25, P157 WOODCOCK CE, 1987, REMOTE SENS ENVIRON, V21, P311 WU J, 1999, CANADIAN J REMOTE SE, V25, P367 WU J, 2006, SCALING UNCERTAINTY, V3 0921-2973 Landsc. Ecol.ISI:000243823900001Trop Savannas Cooperat Res Ctr, Atherton, Qld 4883, Australia. CSIRO Sustainable Ecosyst, Atherton, Qld 4883, Australia. CSIRO Sustainable Ecosyst, Ctr Arid Zone Res, Alice Springs, NT 0871, Australia. CSIRO Math & Informat Sci, Floreat, WA 6151, Australia. CSIRO Land & Water, Canberra, ACT 2601, Australia. eWater Cooperat Res Ctr, Canberra, ACT 2601, Australia. Ludwig, JA, Trop Savannas Cooperat Res Ctr, POB 780, Atherton, Qld 4883, Australia. john.ludwig@csiro.auEnglish <7<Vogt, P. Riitters, K. H. Estreguil, C. Kozak, J. Wade, T. G.2007<Mapping spatial patterns with morphological image processing171-177Landscape Ecology222{morphological image processing; spatial pattern; forest fragmentation UNITED-STATES; FOREST FRAGMENTATION; LANDSCAPE; SCALEArticleFebhWe use morphological image processing for classifying spatial patterns at the pixel level on binary land-cover maps. Land-cover pattern is classified as 'perforated,' 'edge,' 'patch,' and 'core' with higher spatial precision and thematic accuracy compared to a previous approach based on image convolution, while retaining the capability to label these features at the pixel level for any scale of observation. The implementation of morphological image processing is explained and then demonstrated, with comparisons to results from image convolution, for a forest map of the Val Grande National Park in North Italy.://000243823900002 ISI Document Delivery No.: 130UG Times Cited: 0 Cited Reference Count: 21 Cited References: *HEINZ CTR HJ HEIN, 2002, STAT NAT EC MEAS LAN *MONTR PROC LIAIS, 2000, MONTR PROC YEAR 2000 *USDA FOR SERV, 2004, FS766 USDA FOR SERV BOGAERT J, 2004, ENVIRON MANAGE, V33, P62 CIVCO DL, 2002, PHOTOGRAMM ENG REM S, V68, P1083 ESTREGUIL CM, 2004, 21078EN EUR FORMAN RTT, 1995, LAND MOSAICS FORMAN RTT, 1995, LANDSCAPE ECOL, V10, P133 HEILMAN GE, 2002, BIOSCIENCE, V52, P411 LI HB, 2004, LANDSCAPE ECOL, V19, P389 MATHERON G, 1967, ELEMENTS THEORIE MIL METZGER JP, 1996, LANDSCAPE ECOL, V11, P65 METZGER JP, 1997, ACTA OECOL, V18, P1 MUSICK HB, 1991, QUANTITATIVE METHODS NEEL MC, 2004, LANDSCAPE ECOL, V19, P435 RIITTERS K, 2000, CONSERV ECOL, V4 RIITTERS KH, 2002, ECOSYSTEMS, V5, P815 RIITTERS KH, 2004, ENVIRON MONIT ASSESS, V91, P257 SOILLE P, 2003, MORPHOLOGICAL IMAGE TURNER MG, 2001, LANDSCAPE ECOLOGY TH ZIPPERER WC, 1993, LANDSCAPE ECOL, V8, P177 0921-2973 Landsc. Ecol.ISI:000243823900002European Commiss, DG Joint Res Ctr, IES, Land Management & Nat Hazards Unit LMNH, I-21020 Ispra, VA, Italy. US Forest Serv, So Res Stn, Res Triangle Pk, NC 27709 USA. Jagiellonian Univ, Inst Geog & Spatial Management, PL-30387 Krakow, Poland. US EPA, Div Environm Sci, Res Triangle Pk, NC 27711 USA. Vogt, P, European Commiss, DG Joint Res Ctr, IES, Land Management & Nat Hazards Unit LMNH, TP 261,Via E Fermi 1, I-21020 Ispra, VA, Italy. peter.vogt@jrc.itEnglish _<7=Wickham, J. D. Riitters, K. H. Wade, T. G. Coan, M. Homer, C.2007?The effect of Appalachian mountaintop mining on interior forest179-187Landscape Ecology222Appalachian mountains; coal mining; edge effects; forest loss; interior forest UNITED-STATES; LAND-USE; HABITAT FRAGMENTATION; NUTRIENT; SCALE; DYNAMICS; SERVICES; CLIMATEArticleFebSouthern Appalachian forests are predominantly interior because they are spatially extensive with little disturbance imposed by other uses of the land. Appalachian mountaintop mining increased substantially during the 1990s, posing a threat to the interior character of the forest. We used spatial convolution to identify interior forest at multiple scales on circa 1992 and 2001 land-cover maps of the Southern Appalachians. Our analyses show that interior forest loss was 1.75-5.0 times greater than the direct forest loss attributable to mountaintop mining. Mountaintop mining in the southern Appalachians has reduced forest interior area more extensively than the reduction that would be expected based on changes in overall forest area alone. The loss of Southern Appalachian interior forest is of global significance because of the worldwide rarity of large expanses of temperate deciduous forest.://000243823900003 yISI Document Delivery No.: 130UG Times Cited: 0 Cited Reference Count: 34 Cited References: *SAMAB, 1996, 5 SAMAB USDA FOR SER *US EPA, 2005, PUBL EPA BEAULAC MN, 1982, WATER RESOUR BULL, V18, P1013 BURNS SLS, 2005, THESIS W VIRGINIA U COSTANZA R, 1997, NATURE, V387, P253 FAHRIG L, 2002, ECOL APPL, V12, P346 FOSTER DR, 1998, ECOSYSTEMS, V1, P96 FOX J, 1999, ORGAN ENVIRON, V12, P163 FRINK CR, 1991, J ENVIRON QUAL, V20, P717 FRY J, 2005, LAND COVER CHANGE DE, P105 HARPER KA, 2005, CONSERV BIOL, V19, P768 HAYDEN BP, 1998, PHILOS T ROY SOC B, V353, P5 HINKLE CR, 1993, BIODIVERSITY SE US U HOMER C, 2004, PHOTOGRAMM ENG REM S, V70, P829 JONES KB, 2001, LANDSCAPE ECOL, V16, P301 LAURANCE WF, 2002, CONSERV BIOL, V16, P605 MARSHALL CH, 2004, MON WEATHER REV, V132, P28 ONEILL RV, 2000, BIOSCIENCE, V50, P333 PICKERING J, 2003, WILDERNESS EARTHS LA PIELKE RA, 2002, PHILOS T ROY SOC A, V360, P1705 RAMAHARITRA T, 2006, TROP RESOURCE B, V25, P32 RIITTERS K, 2000, CONSERV ECOL, V4 RIITTERS KH, 2002, ECOSYSTEMS, V5, P815 RIITTERS KH, 2003, J FOREST, V101, P18 ROBINSON SK, 1995, SCIENCE, V267, P1987 SCOTT JM, 1998, ANN MO BOT GARD, V85, P34 SKOLE D, 1993, SCIENCE, V260, P1905 SLONECKER ET, 2001, REMOTE SENS REV, V20, P293 SZWILSKI TB, 2001, INT J SURF MINING RE, V15, P73 VOGELMANN JE, 2001, PHOTOGRAMM ENG REM S, V67, P650 WEAKLAND CA, 2005, AUK, V122, P497 WEATHERS KC, 2001, CONSERV BIOL, V15, P1506 WESTMAN WE, 1977, SCIENCE, V197, P960 WICKHAM JD, 2005, LANDSCAPE ECOL, V20, P791 0921-2973 Landsc. Ecol.ISI:000243823900003US EPA, Natl Exposure Res Lab, Res Triangle Pk, NC 27711 USA. US Forest Serv, So Forest Res Stn, Res Triangle Pk, NC 27709 USA. US Geol Survey, Sci Applicat Int Corp, EROS Data Ctr, Sioux Falls, SD 57198 USA. Wickham, JD, US EPA, Natl Exposure Res Lab, E243-05, Res Triangle Pk, NC 27711 USA. wickham.james@epa.gov kriitters@fs.fed.us wade.timothy@epa.gov coan@usgs.gov homer@usgs.govEnglishc<7CLopez-Barrera, F. Manson, R. H. Gonzalez-Espinosa, M. Newton, A. C.2007]Effects of varying forest edge permeability on seed dispersal in a neotropical montane forest189-203Landscape Ecology222oak; Quercus; highlands of Chiapas; rodents; acorn dispersal; seed predation; masting; edge contrast OLD FIELD EDGE; SMALL MAMMALS; ECOLOGICAL BOUNDARIES; RODENT POPULATIONS; HABITAT EDGES; TREE SEED; PREDATION; CHIAPAS; MEXICO; OAKArticleFebHard (high-contrast with pastures) and soft (low-contrast with old-fields) forest edges created by slash-and-burn agriculture have become common landscape features in regions dominated by neotropical montane forest. However, little is know about the impacts of such edge types on forest regeneration dynamics. The consequences of varying forest edge permeability for oak acorn dispersal were investigated in a forest mosaic in the Highlands of Chiapas, Mexico. Rates of acorn production and removal, as well as the abundance and composition of small mammal seed consumers, were monitored along these different edge types (hard vs. soft) at specific distances from forest edges into forest patches and adjacent grasslands during two consecutive years. Results show that acorn removal declined significantly only in grasslands of sites characterised by hard edges (Logistic regression, P < 0.05). Movements of metal-tagged acorns support the hypothesis that soft edges are more permeable to small mammals, with rodents moving acorns up to 15 m into grasslands of sites with soft edges. In sites with hard edges, higher rates of acorn dispersal were recorded from the forest edge towards the forest interior. Peromyscus spp. were the main acorn predators and/or dispersers of acorns present in our study sites. Rates of acorn removal during a non-masting year were greater than the subsequent mast-seeding year (85% removal within 138 days vs. 75% within 213 days), demonstrating that mast seeding may allow some seeds to escape predation. The implications of these results for oak dispersal and regeneration along edges in fragmented tropical forest landscapes are discussed.://000243823900004 ISI Document Delivery No.: 130UG Times Cited: 0 Cited Reference Count: 57 Cited References: BONFIL C, 1999, APPL VEG SCI, V2, P189 BOWERS MA, 1993, OECOLOGIA, V94, P247 BRIGGS JM, 1989, J MAMMAL, V70, P35 BROTHERS TS, 1992, CONSERV BIOL, V6, P91 CADENASSO ML, 2000, J ECOL, V88, P31 CADENASSO ML, 2001, CONSERV BIOL, V15, P91 CADENASSO ML, 2003, BIOSCIENCE, V53, P750 DESROCHERS A, 2003, LANDSCAPE ECOL, V18, P543 DONOVAN TM, 1997, ECOLOGY, V78, P2064 DUELLI P, 1990, BIOL CONSERV, V54, P193 FOX GA, 2000, DESIGN ANAL ECOLOGIC, P235 GONZALEZESPINOSA M, 1991, J VEG SCI, V2, P351 GRIBKO LS, 1995, TREE PLANTERS NOTES, V46, P143 HARPER KA, 2005, CONSERV BIOL, V19, P768 HARRINGTON GN, 2001, J TROP ECOL 2, V17, P225 HOLMQUIST JG, 1998, OIKOS, V81, P558 HONNAY O, 2002, FOREST ECOL MANAG, V161, P109 HORVATH A, 2001, STUD NEOTROP FAUNA E, V36, P169 HOVLAND N, 1999, OECOLOGIA, V121, P236 HOWELL NG, 1999, GUIDE BIRDS MEXICO N HUBBARD JA, 1999, J VEG SCI, V10, P739 JANSEN PA, 2001, NOURAGUES DYNAMICS P, P275 JANZEN DH, 1971, ANNU REV ECOL SYST, V2, P465 JENSEN TS, 1982, OECOLOGIA, V54, P184 KALCOUNISRUPPELL MC, 2002, J MAMMAL, V83, P614 KOLLMANN J, 2002, PLANT ECOL, V164, P249 LAURANCE WF, 2001, TRENDS ECOL EVOL, V16, P70 LIDICKER WZ, 1999, LANDSCAPE ECOL, V14, P333 LOPEZBARRERA F, 2003, THESIS U EDINBURGH E LOPEZBARRERA F, 2005, FOREST ECOL MANAG, V217, P67 LOPEZBARRERA F, 2006, FOREST ECOL MANAG, V225, P234 MANSON RH, 1998, ECOSCIENCE, V5, P183 MANSON RH, 1998, OIKOS, V82, P37 MANSON RH, 1999, LANDSCAPE ECOL, V14, P335 MANSON RH, 2001, ECOLOGY, V82, P3320 MEINERS SJ, 2002, AM J BOT, V89, P466 MEINERS SJ, 2003, PLANT ECOL, V168, P45 MUELLERDOMBOIS D, 1974, AIMS METHODS VEGETAT NIXON KC, 1993, BIOL DIVERSITY MEXIC, P447 OSTFELD RS, 1996, BIOSCIENCE, V46, P323 OSTFELD RS, 1997, ECOLOGY, V78, P1531 PRICE MV, 1986, SEED DISPERSAL, P191 QUINTANAASCENCI.PF, 1992, B TORREY BOT CLUB, V119, P6 REID FA, 1997, FIELD GUIDE MAMMALS RIES L, 2004, ANNU REV ECOL EVOL S, V35, P491 SANTOS T, 1997, FOREST ECOL MANAG, V98, P181 SCHNURR JL, 2002, OIKOS, V96, P402 SOKAL RR, 1998, BIOMETRY PRINCIPLES SONG SJ, 1999, ECOSCIENCE, V6, P521 SORK VL, 1984, ECOLOGY, V65, P1020 STAMPS JA, 1987, AM NAT, V129, P533 STEELE MA, 2001, J MAMMAL, V82, P35 STEVENS SM, 1998, BIOL CONSERV, V85, P1 STRAYER DL, 2003, BIOSCIENCE, V53, P723 WEATHERS KC, 2001, CONSERV BIOL, V15, P1506 WOLFF JO, 1996, J MAMMAL, V77, P850 ZOLLNER PA, 2000, LANDSCAPE ECOL, V15, P523 0921-2973 Landsc. Ecol.ISI:000243823900004INECOL AG, Inst Ecol, Dept Funct Ecol, Xalapa 91070, Veracruz, Mexico. Univ Edinburgh, Sch Geosci, Inst Environm & Atmospher Sci, Edinburgh EH9 3JU, Midlothian, Scotland. Coll So Borderlands, ECOSUR, Dept Terr Ecol & Systemat, Biodivers Conservat Div, Chiapas 29200, Mexico. Bournemouth Univ, Sch Conservat Sci, Poole BH12 5BB, Dorset, England. Lopez-Barrera, F, INECOL AG, Inst Ecol, Dept Funct Ecol, Km 2-5,Carretera Antigua Coatepec 351, Xalapa 91070, Veracruz, Mexico. fabiola@ecologia.edu.mxEnglish<7Gagne, S. A. Fahrig, L.2007jEffect of landscape context on anuran communities in breeding ponds in the National Capital Region, Canada205-215Landscape Ecology222land use; urbanization; agriculture; forest cover; Amphibian conservation; species richness; abundance SPECIES RICHNESS; LAND-USE; AMPHIBIANS; FRAGMENTATION; FROG; CONSEQUENCES; BIODIVERSITY; POPULATIONS; PREDATION; ABUNDANCEArticleFebLand cover change, predominantly habitat conversion to agricultural use and urbanization, has recently been recognized as the primary cause of biodiversity loss in terrestrial ecosystems. We evaluated the relative effects of urban and agricultural landscapes on anuran species richness and the abundance of six anuran species found at breeding ponds in and around the cities of Ottawa, Ontario and Gatineau, Quebec. We performed six call surveys at 29 permanent focal ponds surrounded by one of three landscape contexts: primarily urban, primarily agricultural, and primarily forested. We also measured three local pond variables to control for the effects of local habitat quality in our analyses. We found that anuran species richness was significantly lower in breeding ponds in urban landscapes compared to forested and agricultural landscapes, which exhibited no significant difference in species richness. The abundances of individual anuran species were also consistently lower in urban landscapes for all species except one, which exhibited no response to landscape type. Three species had their highest abundances in ponds in forested landscapes, whereas two species had their highest abundances in ponds in agricultural landscapes. We conclude that ponds embedded in urban landscapes support lower biodiversity than ponds in agricultural settings. We suggest that landscapes composed of a mosaic of forest and open habitats surrounding wetlands would hold the highest biodiversity of these species.://000243823900005 ISI Document Delivery No.: 130UG Times Cited: 0 Cited Reference Count: 48 Cited References: *CAN BIOD INF INF, 2004, URB BIOD ENV CAN OTT *ENV SYST RES I, 1999, ARC VIEW 3 2 X *INS, 2001, S PLUS 6 0 *MILL EC ASS, 2005, EC HUM WELL BEING BI, P86 *SPSS, 2003, SPSS 12 0 WIND ATAURI JA, 2001, LANDSCAPE ECOL, V16, P147 BENTON TG, 2003, TRENDS ECOL EVOL, V18, P182 BONIN J, 1997, AMPHIBIANS DECLINE C, P141 BRADLEY G, 1995, URBAN FOREST LANDSCA, P3 CARR LW, 2001, CONSERV BIOL, V15, P1071 CZECH B, 2000, BIOSCIENCE, V50, P593 DAM A, 2005, THESIS CARLETON U OT, P69 DEFRIES RS, 2004, FRONT ECOL ENVIRON, V2, P249 DEMAYNADIER PG, 2000, NAT AREA J, V20, P56 DUGUAY S, 2004, THESIS CARLETON U OT, P53 DUNFORD W, 2001, EFFECTS AGR URBAN LA, P91 FAHRIG L, 1995, BIOL CONSERV, V73, P177 FICETOLA GF, 2004, BIOL CONSERV, V119, P219 FOLEY JA, 2005, SCIENCE, V309, P570 GIBBS JP, 2005, ECOL APPL, V15, P1148 GRAY MJ, 2004, CONSERV BIOL, V18, P1368 GRIMM NB, 2000, BIOSCIENCE, V50, P571 GUERRY AD, 2002, CONSERV BIOL, V16, P745 HECNAR SJ, 1998, J BIOGEOGR, V25, P763 HEINEN JT, 1993, AM MIDL NAT, V130, P184 HOULAHAN JE, 2003, CAN J FISH AQUAT SCI, V60, P1078 JOHNSON CM, 2002, PREDICTING SPECIES O, P157 JOLY P, 2001, CONSERV BIOL, V15, P239 KNIGHT RL, 1998, STEWARDSHIP BOUNDARI KNUTSON MG, 1999, CONSERV BIOL, V13, P1437 KOLOZSVARY MB, 1999, CAN J ZOOL, V77, P1288 LAAN R, 1990, BIOL CONSERV, V54, P251 LEHTINEN RM, 1999, WETLANDS, V19, P1 MCGARIGAL K, 2002, FRAGSTATS SPATIAL PA MENSING DM, 1998, J ENVIRON MANAGE, V53, P349 PHELPS AM, 2001, THESIS U OTTAWA OTTA, P176 PLATT RH, 2004, ENVIRONMENT, V46, P11 POPE SE, 2000, ECOLOGY, V81, P2498 RELYEA RA, 2005, ECOL APPL, V15, P1118 RICHTER KO, 1995, WETLANDS, V15, P305 RILEY SPD, 2005, CONSERV BIOL, V19, P1894 SANZO D, 2006, ENVIRON POLLUT, V140, P247 STUART SN, 2004, SCIENCE, V306, P1783 TRENHAM PC, 2003, ECOL APPL, V13, P1522 WEYRAUCH SL, 2004, BIOL CONSERV, V115, P443 WILCOX BA, 1985, AM NAT, V125, P879 WILSON JD, 2003, CONSERV BIOL, V17, P763 WOODS M, 2003, MAMMAL REV, V33, P174 0921-2973 Landsc. Ecol.ISI:000243823900005Carleton Univ, Dept Biol, Ottawa, ON K1S 5B6, Canada. Carleton Univ, Ottawa Carleton Inst Biol, Geomat & Landscape Ecol Res Lab, Ottawa, ON K1S 5B6, Canada. Gagne, SA, Carleton Univ, Dept Biol, Colonel Dr, Ottawa, ON K1S 5B6, Canada. saraanne.gagne@gmail.comEnglish<7cGonzalez-Abraham, C. E. Radeloff, V. C. Hammer, R. B. Hawbaker, T. J. Stewart, S. I. Clayton, M. K.2007HBuilding patterns and landscape fragmentation in northern Wisconsin, USA217-230Landscape Ecology222disturbance zone; landscape fragmentation; building density; spatial pattern of buildings; landscape legacies; rural sprawl; Wisconsin RESIDENTIAL DEVELOPMENT; LAKESHORE DEVELOPMENT; UNITED-STATES; PINE-BARRENS; LAND-COVER; FOREST; BIRDS; HABITAT; CONSEQUENCES; DISTURBANCEArticleFebHousing growth is prevalent in rural areas in the United States and landscape fragmentation is one of its many effects. Since the 1930s, rural sprawl has been increasing in areas rich in recreational amenities. The question is how housing growth has affected landscape fragmentation. We thus tested three hypotheses relating land cover and land ownership to density and spatial pattern of buildings, and examined whether building density or spatial pattern of buildings was a better predictor for landscape fragmentation. Housing locations were mapped from 117 1:24,000-scale USGS topographic maps across northern Wisconsin. Patch-level landscape metrics were calculated on the terrestrial area remaining after applying 50, 100 and 250 m disturbance zones around each building. Our results showed that building density and the spatial pattern of buildings were affected mostly by lake area, public land ownership, and the abundance of coniferous forest, agricultural land, and grassland. A full 40% of the houses were within 100 m of lakeshores. The clustering of buildings within 100 m of lakeshores limited fragmentation farther away. In contrast, agricultural and grassland areas were correlated with higher building density, higher fragmentation, and more dispersed building pattern possible legacies of agricultural settlement patterns. Understanding which factors influence building density and fragmentation is useful for landscape level planning and ecosystem management in northern Wisconsin and areas that share similar social and environmental constraints.://000243823900006 ISI Document Delivery No.: 130UG Times Cited: 0 Cited Reference Count: 58 Cited References: 1996, USGS NATL MAPPING PR *USFS, 2001, CHEQ NIC NAT FOR *WI DNR, 1998, WISCLAND LAND COV WL ANDERSON JR, 1976, 964 US DEP INT GEOL ANDREN H, 1994, OIKOS, V71, P355 ARENDT R, 1997, J AM PLANN ASSOC, V63, P137 BAWDEN DL, 1977, 4 PAPERS FARM FAMILY BLACK JD, 1925, DEP B USDA, V1295 BRITTINGHAM MC, 1983, BIOSCIENCE, V33, P31 BROWN DG, 2003, LANDSCAPE ECOL, V18, P777 CARSTENSEN VR, 1958, FARMS FOREST EVOLUTI CHATTERJEE S, 2000, REGRESSION ANAL EXAM CHRISTENSEN DL, 1996, ECOL APPL, V6, P1143 COLEMAN JS, 1993, WILDLIFE SOC B, V21, P381 DWYER JF, 2004, LANDSCAPE URBAN PLAN, V69, P153 FAHRIG L, 1997, J WILDLIFE MANAGE, V61, P603 FLADER SL, 1983, GREAT LAKES FOREST FORMAN RTT, 1986, LANDSCAPE ECOLOGY FORMAN RTT, 1995, LAND MOSAICS ECOLOGY FRIES RF, 1951, EMPIRE PINE STORY LU FRIESEN LE, 1995, CONSERV BIOL, V9, P1408 FUGUITT GV, 1985, ANNU REV SOCIOL, V11, P259 FUGUITT GV, 1990, DEMOGRAPHY, V27, P589 GARBER SD, 1995, ECOL APPL, V5, P1151 GOBSTER PH, 2000, J FOREST, V98, P9 GOBSTER PH, 2004, LANDSCAPE URBAN PLAN, V69, P165 GOODMAN RB, 1932, FOREST LAND USE WISC HAMMER RB, 2004, LANDSCAPE URBAN PLAN, V69, P183 HANSEN AJ, 2002, BIOSCIENCE, V52, P151 HAWBAKER TJ, 2005, LANDSCAPE ECOL, V20, P609 HEIMLICH RE, 2001, 803 USDA EC RES SERV HOCKIN D, 1992, J ENVIRON MANAGE, V36, P253 HOSTETLER M, 1999, LANDSCAPE URBAN PLAN, V45, P15 KAREIVA P, 1990, PHILOS T ROY SOC B, V330, P175 KLUZA DA, 2000, ANIM CONSERV 1, V3, P15 LINDSAY AR, 2002, BIOL CONSERV, V107, P1 LONG L, 1997, ENVIRON PLANN A, V29, P1355 MCGRANAHAN DA, 1999, 781 USDA AGR EC EC R MILLER JR, 2000, LANDSCAPE URBAN PLAN, V50, P227 MLADENOFF DJ, 1993, ECOL APPL, V3, P294 MURPHY RE, 1931, T WISC ACAD SCI, V26, P96 NAIMAN RJ, 1996, LANDSCAPE ECOL, V11, P193 NASSAUER JI, 1995, LANDSCAPE ECOL, V10, P229 RADELOFF VC, 1999, CAN J FOREST RES, V29, P1649 RADELOFF VC, 2001, FOREST SCI, V47, P229 RADELOFF VC, 2005, ECOL APPL, V15, P799 REESE HM, 2002, REMOTE SENS ENVIRON, V82, P224 RODGERS JA, 1995, CONSERV BIOL, V9, P89 SAUNDERS DA, 1991, CONSERV BIOL, V5, P18 SCHNAIBERG J, 2002, ENVIRON MANAGE, V30, P24 SCHULTE LA, 2002, CAN J FOREST RES, V32, P1616 STEARNS FW, 1997, NC189 GEN, P1 THEOBALD DM, 1997, LANDSCAPE URBAN PLAN, V39, P25 TURNER MG, 1989, ANNU REV ECOL SYST, V20, P171 TURNER MG, 2001, LANDSCAPE ECOLOGY TH UPTON G, 1985, SPATIAL DATA ANAL EX VOS CC, 2000, AM NAT, V183, P24 WOODFORD JE, 2003, BIOL CONSERV, V110, P277 0921-2973 Landsc. Ecol.ISI:000243823900006_Univ Wisconsin, Dept Forest Ecol & Management, Madison, WI 53706 USA. Univ Wisconsin, Dept Rural Sociol, Madison, WI 53706 USA. US Forest Serv, N Cent Res Stn, Evanston, IL 60201 USA. Univ Wisconsin, Dept Stat, Madison, WI 53706 USA. Radeloff, VC, Univ Wisconsin, Dept Forest Ecol & Management, 1630 Linden Dr, Madison, WI 53706 USA. radeloff@wisc.eduEnglish<7Galbraith, L. M. Burns, C. W.2007KLinking land-use, water body type and water quality in southern New Zealand231-241Landscape Ecology222wetlands; catchment; land cover; phosphorus; nitrogen; agriculture; eutrophication; drainage basin; lakes FRESH-WATER; TOTAL PHOSPHORUS; ORGANIC-MATTER; TOTAL NITROGEN; LAKES; ECOSYSTEMS; CHEMISTRYArticleFebyLand-use and vegetation cover have been linked to the nutrient levels (nitrogen, phosphorus) of surface waters in several countries. However, the links generally relate to streams and rivers, or to specific types of standing water, for example shallow lakes in a geologically defined region. We measured physical variables and nutrient chemistry of 45 water bodies representative of the wide range of lentic wetland environments (swamps, riverine wetlands, estuaries, reservoirs, shallow lakes, deep lakes) in Otago, New Zealand, and related these to catchment variables and land-use in order to assess the potential influence of catchment modification on water quality of these diverse wetlands. Catchment boundaries and land cover were derived from maps using ArcView GIS software. Our predictions that concentrations of nutrients and other components of water quality would correlate positively with the nature and intensity of catchment modification were confirmed in multivariate analyses. Physical and chemical measures were positively related to the extent of modification in the catchment (percentage of the catchment in pasture, planted forest, scrub and urban areas), and negatively related to lack of catchment modification (more of the catchment in bare ground, tussock grassland and indigenous forest). The strong negative correlations between nutrient concentrations, suspended sediment, water colour and the percentage of tussock cover in the catchment imply that increased conversion of the native tussock grassland to pastoral farming in Otago will increase nutrient concentrations and reduce water quality of the diverse lentic ecosystems.://000243823900007 ISI Document Delivery No.: 130UG Times Cited: 0 Cited Reference Count: 25 Cited References: *SPSS INC, 2000, SPSS WIND VERS 10 1 CUTHBERT ID, 1992, LIMNOL OCEANOGR, V37, P1319 DARCY P, 1997, CAN J FISH AQUAT SCI, V54, P2215 ELLIOTT S, 2002, LAKE MANAGERS HDB LA HURYN AD, 2003, NATURAL HIST SO NZ, P237 JOHNES P, 1996, FRESHWATER BIOL, V36, P451 LOUGHEED VL, 2001, CAN J FISH AQUAT SCI, V58, P1603 MABERLY SC, 2003, HYDROBIOLOGIA, V506, P83 MENZEL DW, 1964, LIMNOL OCEANOGR, V9, P138 MOSS B, 1998, ECOLOGY FRESH WATERS NEWSOME PFJ, 1987, WATER SOIL MISCELLAN, V112 PREPAS EE, 2001, CAN J FISH AQUAT SCI, V58, P1286 RASMUSSEN JB, 1989, LIMNOL OCEANOGR, V34, P1097 RILEY RH, 2003, NEW ZEAL J MAR FRESH, V37, P389 SCHINDLER DW, 1997, HYDROL PROCESS, V11, P1043 TERBRAAK CFJ, 1998, CANOCO REFERENCE MAN THOMAS JD, 1997, FRESHWATER BIOL, V38, P1 THOMPSON RM, 1998, TAIER CATCH S EC RES, P44 TONG STY, 2002, J ENVIRON MANAGE, V66, P377 TOWNSEND CR, 2001, UNPUB LAND USE IMPAC VALDERRAMA JC, 1981, MAR CHEM, V10, P109 WETZEL RG, 1991, LIMNOLOGICAL ANAL WETZEL RG, 1995, FRESHWATER BIOL, V33, P83 WICKHAM JD, 2005, LANDSCAPE ECOL, V20, P791 YOUNG RG, 1999, ECOL APPL, V9, P1359 0921-2973 Landsc. Ecol.ISI:000243823900007Univ Otago, Dept Zool, Dunedin, New Zealand. Burns, CW, Univ Otago, Dept Zool, Box 56,340 Great King St, Dunedin, New Zealand. carolyn.burns@stonebow.otago.ac.nzEnglishZ<7 =Hurme, E. Kurttila, M. Monkkonen, M. Heinonen, T. Pukkala, T.2007xMaintenance of flying squirrel habitat and timber harvest: a site-specific spatial model in forest planning calculations243-256Landscape Ecology222Finland; spatial forest planning; spatial objectives; stand neighborhood structure; suitable habitat PTEROMYS-VOLANS; WILDLIFE; EXTINCTION; OPTIMIZATION; CONSERVATION; BIODIVERSITY; CONSTRAINTS; LANDSCAPES; MOVEMENTS; OREGONArticleFebSpatial and temporal continuity of resources often benefits both ecological and economic goals in landscape management. Consideration of multiple and conflicting goals is also needed to view the future production possibilities of forests in successful forest management. Our aim was to estimate the production potential of a planning area in Finland by examining different forest management strategies from ecological and economic perspectives using long-term forest planning calculations. Economic objectives referred to timber production, whereas ecological objectives were based on suitable habitats for arboreal Siberian flying squirrel (Pteromys volans). Suitable habitats were defined using an empirical site-specific model, which includes a spatial variable reflecting the availability of habitat within an individual's activity area. Five alternative forest plans were worked out with different objectives for flying squirrel habitat and timber production. The alternative plans were compared with respect to values of objective variables at the end of the planning period of 60 years and against a production possibility frontier among net present value and flying squirrel habitat. Varying objective values in our analyses resulted from different utilization of production possibilities, and the changes were in line with the objectives used. The formation of flying squirrel habitat clusters in the landscape was enhanced, and it did not always incur severe reductions in harvestable timber volume. Possibilities to combine ecological and economic goals, both spatial and aspatial, in the planning process seems to be an encouraging alternative for the long-term forest management in the future.://000243823900008 ISI Document Delivery No.: 130UG Times Cited: 0 Cited Reference Count: 43 Cited References: 1994, METSANHOITOSUOSITUKS ARTHAUD GJ, 1996, CAN J FOREST RES, V26, P2191 BAILEY TC, 1995, INTERACTIVE SPATIAL BETTINGER P, 1999, ENVIRON MODEL ASSESS, V4, P143 BORGES JG, 1999, CAN J FOREST RES, V29, P1764 BOSTON K, 2001, FOREST ECOL MANAG, V145, P191 CALKIN DE, 2002, CAN J FOREST RES, V32, P1329 CARLSSON M, 1999, CAN J FOREST RES, V29, P1183 DOWSLAND KA, 1993, MODERN HEURISTIC TEC, P20 FORMAN RTT, 1986, LANDSCAPE ECOLOGY HAIGHT RG, 1995, ECOL APPL, V5, P767 HANSKI IK, 2000, J MAMMAL, V81, P798 HARRISON S, 1995, IALE STUDIES LANDSCA, V2, P293 HARRISON S, 1999, ECOGRAPHY, V22, P225 HEINONEN T, 2004, SILVA FENN, V38, P319 HOF J, 1994, FOREST SCI, V40, P177 HOF J, 1997, ECOL APPL, V7, P1160 HURME E, 2005, FOREST ECOL MANAG, V216, P241 JUUTINEN A, 2004, FOREST SCI, V50, P527 KANGAS J, 1996, CAN J FOREST RES, V26, P103 KARVONEN L, 2000, GUIDELINES LANDSCAPE KIRKPATRICK S, 1983, SCIENCE, V220, P4598 KURTTILA M, 2001, FOREST ECOL MANAG, V142, P127 KURTTILA M, 2002, FOREST ECOL MANAG, V166, P245 LANDE R, 1993, AM NAT, V142, P911 LICHTENSTEIN ME, 2003, LAND ECON, V79, P56 MASCOLELL A, 1995, MICROECONOMIC THEORY MICHALEWICZ Z, 2004, SOLVE IT MODERN HEUR MONKKONEN M, 1997, ECOGRAPHYS, V20, P634 MURADIAN R, 2001, ECOL ECON, V38, P7 MYKRA S, 1998, SILVA FENNICA, V32, P389 NALLE DJ, 2004, J ENVIRON ECON MANAG, V48, P997 OHMAN K, 2000, CAN J FOREST RES, V30, P1817 OHMAN K, 2003, FOREST ECOL MANAG, V176, P161 POLASKY S, 2005, ECOL APPL, V15, P1387 PUKKALA T, 1993, SCAND J FOR RES, V8, P560 PUKKALA T, 2004, UNPUB MONSU METSASUU RASSI P, 2001, SUOMEN LAJIEN UHANAL REUNANEN P, 2000, CONSERV BIOL, V14, P218 SCHUMAKER NH, 2004, ECOL APPL, V14, P381 SELONEN V, 2003, ECOGRAPHY, V26, P641 SESSIONS J, 1992, FOREST SCI, V38, P203 TILMAN D, 1994, NATURE, V371, P65 0921-2973 Landsc. Ecol.ISI:000243823900008GUniv Oulu, Dept Biol, FIN-90014 Oulu, Finland. Finnish Forest Res Inst, Joensuu Res Unit, FIN-01301 Joensuu, Finland. Univ Jyvaskyla, Dept Biol & Evnironm Sci, FIN-40014 Jyvaskyla, Finland. Univ Joensuu, Fac Forestry, FIN-80101 Joensuu, Finland. Hurme, E, Univ Oulu, Dept Biol, 3000, FIN-90014 Oulu, Finland. Eija.Hurme@oulu.fiEnglish,<7 Leyk, S. Zimmermann, N. E.2007OImproving land change detection based on uncertain survey maps using fuzzy sets257-272Landscape Ecology222predictive uncertainty modelling; fuzzy sets; land cover change analysis; classification bias; correction of survey maps; area estimation ACCURACY ASSESSMENT; THEMATIC MAPS; CLASSIFICATION; MODELSArticleFebIn this paper we present a method for correcting inherent classification bias in historical survey maps with which subsequent land cover change analysis can be improved. We linked generalized linear modelling techniques for spatial uncertainty prediction to fuzzy set based operations. The predicted uncertainty information was used to compute fuzzy memberships of forest and non-forest classes at each location. These memberships were used to reclassify the original map based on decision rules, which take into consideration the differences in identification likelihood during the historical mapping. Since the forest area was underestimated in the original mapping, the process allows to correct this bias by favouring forest, especially where uncertainty was high. The analyses were performed in a cross-wise manner between two study areas in order to examine whether the bias correction algorithm would still hold in an independent test area. Our approach resulted in a significant improvement of the original map as indicated by an increase of the Normalized Mutual Information from 0.26 and 0.36 to 0.38 and 0.45 for the cross-wise test against reference maps in Pontresina and St. Moritz, respectively. Consequently subsequent land cover change assessments could be considerably improved by reducing the deviations from a reference change by almost 50 percent. We concluded that the use of logistic regression techniques for uncertainty modelling based on topographic gradients and fuzzy set operations are useful tools for predictively reducing uncertainty in maps and land cover change models. The procedure allows to get more reliable area estimates of crisp classes and it improves the computation of the fuzzy areas of classes. The approach has limitations when the original map shows high initial accuracy.://000243823900009 IISI Document Delivery No.: 130UG Times Cited: 0 Cited Reference Count: 37 Cited References: *INS, 2001, S PLUS 6 WIND US GUI AHLQVIST O, 2003, INT J GEOGR INF SCI, V17, P223 ANDREFOUET S, 2000, IEEE T GEOSCI REMO 1, V38, P257 BAKER WL, 1989, LANDSCAPE ECOL, V2, P111 BEZDEK JC, 1981, PATTERN RECOGNITION BINAGHI E, 1999, PATTERN RECOGN LETT, V20, P935 BOLLIGER J, 2005, ECOGRAPHY, V28, P141 BROWN DG, 1998, INT J GEOGR INF SCI, V12, P105 BURROUGH P, 1998, PRINCIPLES GEOGRAPHI BURROUGH PA, 1989, J SOIL SCI, V40, P477 CHENG T, 2001, J GEOGRAPH INFORM SC, V15, P27 COHEN J, 1960, EDUC PSYCHOL MEAS, V20, P37 COPPIN P, 2004, INT J REMOTE SENS, V25, P1565 DUBOIS D, 2000, FUNDAMENTALS FUZZY S FISHER P, 2000, FUZZY SET SYST, V113, P7 FONTE CC, 2004, INT J GEOGR INF SCI, V18, P127 FOODY GM, 1996, INT J REMOTE SENS, V17, P1317 FORBES AD, 1995, J CLIN MONITOR, V11, P189 GOPAL S, 1994, PHOTOGRAMM ENG REM S, V60, P181 GUISAN A, 2000, ECOL MODEL, V135, P147 JAGER G, 2000, IEEE T GEOSCI REMOTE, V38, P1462 KIENAST F, 1993, LANDSCAPE ECOL, V8, P103 KLIR GJ, 1999, UNCERTAINITY BASED I KRISHNAPURAM R, 1993, IEEE T FUZZY SYST, V1, P98 LEWIS HG, 2001, INT J REMOTE SENS, V22, P3223 LEYK S, 2004, LECT NOTES COMPUT SC, V3234, P191 LEYK S, 2005, T GIS, V9, P291 LU D, 2004, INT J REMOTE SENS, V25, P2365 MATSAKIS P, 2000, REMOTE SENS ENVIRON, V74, P516 PLEWE B, 2002, T GIS, V6, P431 POWER C, 2001, INT J GEOGR INF SCI, V15, P77 ROBINSON VB, 1988, COMPUT ENVIRON URBAN, V12, P89 ROBINSON VB, 2003, T GIS, V7, P3 RUSPINI EH, 1969, INFORM CONTR, V15, P22 STEELE BM, 1998, REMOTE SENS ENVIRON, V66, P192 WOODCOCK CE, 2000, INT J GEOGR INF SCI, V14, P153 ZADEH LA, 1965, INFORM CONTR, V8, P338 0921-2973 Landsc. Ecol.ISI:000243823900009Univ Zurich, Dept Geog, CH-8057 Zurich, Switzerland. Swiss Fed Res Inst, Land Use Dynam, WSL, CH-8903 Birmensdorf, Switzerland. Leyk, S, Univ Zurich, Dept Geog, Winterhurerstr 190, CH-8057 Zurich, Switzerland. leyks@bluewin.chEnglish<7 *Howell, C. A. Dijak, W. D. Thompson, F. R.2007QLandscape context and selection for forest edge by breeding Brown-headed Cowbirds273-284Landscape Ecology222.brown-headed cowbird; edge effects; forest fragmentation; habitat selection; home range; Illinois; landscape context; Missouri; Molothrus ater; neotropical migrant bird NESTING SUCCESS; HABITAT SELECTION; NEOTROPICAL MIGRANT; MOLOTHRUS-ATER; PARASITISM; ABUNDANCE; DENSITY; BIRDS; MECHANISMS; SONGBIRDSArticleFebRWe evaluated support for four alternate hypotheses explaining the distribution of breeding Brown-headed Cowbirds (Molothrus ater) in forests at varying distances from the forest edge in three Midwestern USA landscapes with varying amounts of forest fragmentation (core forest area ranged from 5 to 70%). We focused on breeding cowbirds' use of forest because of the risk of nest parasitism to forest-dwelling hosts and to identify factors affecting breeding cowbird habitat selection. We compared distances of cowbird locations in the forest from the forest edge ("edge distances") to distances of random forest locations in the entire landscape or within individual cowbird home ranges. We analyzed 1322 locations of 84 cowbirds across three landscapes. We found support for the landscape context hypothesis that breeding cowbird preference for forest edge varied with landscape context. Ninety percent of cowbird locations were within 150-350 m of forest edge, despite the overall availability of forest at greater distances from edge (as far as 500-1450 m) both within cowbird home ranges and the entire forested landscape. Cowbird preference for edge varied by landscape context largely due to differences in the availability of forest edge. In a highly fragmented forest cowbirds utilized the entire forest and likely viewed it as "all edge." In less fragmented forests, cowbirds preferred edge. We consider how variation in cowbird edge preference might relate to patterns in host abundance, host diversity, and host quality because cowbird movements indicate they are capable of using forest farther from edges.://000243823900010 ISI Document Delivery No.: 130UG Times Cited: 0 Cited Reference Count: 44 Cited References: *SAS I, 2000, VERS 8 02 BRITTINGHAM MC, 1983, BIOSCIENCE, V33, P31 BURNHAM KP, 2002, MODEL SELECTION INFE CHASKO GG, 1982, WILDL MONOGR, V82 COKER DR, 1995, J WILDLIFE MANAGE, V59, P631 CURSON DR, 2000, AUK, V117, P795 CURSON DR, 2003, J WILDLIFE MANAGE, V67, P520 DONOVAN TM, 1997, ECOLOGY, V78, P2064 DONOVAN TM, 2000, ECOLOGY MANAGEMENT C, P255 ELLIOTT PF, 1980, CONDOR, V82, P138 EVANS DR, 1997, WILSON BULL, V109, P470 FLASPOHLER DJ, 2001, ECOL APPL, V11, P32 FORMAN RTT, 1986, LANDSCAPE ECOLOGY FRIESEN L, 1999, CONSERV BIOL, V13, P338 GATES JE, 1978, ECOLOGY, V59, P871 GATES JE, 1998, ECOL APPL, V8, P27 GUSTAFSON EJ, 2002, ECOL APPL, V12, P412 HAHN DC, 1995, CONSERV BIOL, V9, P1415 HANOWSKI JM, 1995, ENV CONCERNS RIGHTS, P276 HAUBER ME, 2001, CAN J ZOOL, V79, P1518 HOCHACHKA WM, 1999, STUDIES AVIAN BIOL, V18, P80 HOSOI SA, 2000, ANIM BEHAV 4, V59, P823 HOWELL CA, 2000, LANDSCAPE ECOL, V15, P547 JENSEN WE, 2005, OECOLOGIA, V142, P136 JONES J, 2001, AUK, V118, P557 KING DI, 1996, CONSERV BIOL, V10, P1380 LITELL RCC, 1996, SAS SYSTEM MIXED MOD MANOLIS JC, 2002, AUK, V119, P955 MCGARIGAL K, 2002, FRAGSTATS SPATIAL PA MORRIS DL, 1998, AUK, V115, P376 MORSE SF, 1999, CONSERV BIOL, V13, P327 MURCIA C, 1995, TRENDS ECOL EVOL, V10, P58 ORTEGA CP, 1998, COWBIRDS OTHER BROOD PATON PWC, 1994, CONSERV BIOL, V8, P17 ROBINSON SK, 1992, ECOLOGY CONSERVATION, P408 ROBINSON SK, 1994, BIRD CONSERV INT, V4, P233 ROBINSON SK, 1995, SCIENCE, V267, P1987 ROTHSTEIN SI, 1984, ECOLOGY, V65, P77 STEPHENS SS, 2003, BIOL CONSERV, V111, P101 TEMPLE SA, 1988, CONSERV BIOL, V2, P340 THOMPSON FR, 1994, AUK, V111, P979 THOMPSON FR, 2000, ECOLOGY MANAGEMENT C, P100 THOMPSON FR, 2000, ECOLOGY MANAGEMENT C, P271 WIENS JA, 1993, OIKOS, V66, P369 0921-2973 Landsc. Ecol.ISI:000243823900010Univ Missouri, Dept Biol, St Louis, MO 63121 USA. Univ Missouri, USDA, US Forest Serv, N Cent Forest Expt Stn, Columbia, MO 65211 USA. Howell, CA, PRBO Conservat Sci, 3820 Cypress Dr, Petaluma, CA 94954 USA. chowell@prbo.orgEnglish<7Maheu-Giroux, M. de Blois, S.2007QLandscape ecology of Phragmites australis invasion in networks of linear wetlands285-301Landscape Ecology222Iinvasive species; agricultural weed; common reed; corridor; linear habitat; autoregressive model; network-K function; road ecology; spatial point pattern analysis; autocorrelation POINT PATTERN-ANALYSIS; COMMON REED; SPATIAL AUTOCORRELATION; K-FUNCTION; AGRICULTURAL LANDSCAPE; CRYPTIC INVASION; SPREAD; MODEL; WATER; POPULATIONSArticleFebThe interaction between landscape structure and spatial patterns of plant invasion has been little addressed by ecologists despite the new insights it can provide. Because of their spatial configuration as highly connected networks, linear wetlands such as roadside or agricultural ditches, can serve as corridors facilitating invasion at the landscape scale, but species dynamics in these important habitats are not well known. We conducted a landscape scale analysis of Phragmites australis invasion patterns (1985-2002 and 1987-2002) in two periurban areas of southern Quebec (Canada) focusing on the interaction between the network of linear wetlands and the adjacent land-uses. Results show that, at the beginning of the reference period, the two landscapes were relatively non-invaded and populations occurred mostly in roadside habitats which then served as invasion foci into other parts of the landscape. The intrinsic rates of increase of P. australis populations in linear anthropogenic habitats were generally higher than those reported for natural wetlands. Riparian habitats along streams and rivers were little invaded compared to anthropogenic linear wetlands, except when they intersected transportation rights-of-way. Bivariate spatial point pattern analysis of colonization events using both Euclidian and network distances generally showed spatial dependence (association) to source populations. An autologistic regression model that included landscape and edaphic variables selected transportation rights-of-way as the best predictor of P. australis occurrence patterns in one of the landscapes. Given the high invasion rates observed, managers of linear wetlands should carefully monitor expansion patterns especially when roads intersect landscapes of conservation or economic value.://000243823900011 ISI Document Delivery No.: 130UG Times Cited: 0 Cited Reference Count: 63 Cited References: *AGR CAN, 1952, CART SOLS IL MONTR J *AGR CAN, 1991, CART GEOL COMT CHAMB *USDA, 2003, NAT RES CONS SERV AILSTOCK M, 2001, RESTOR ECOL, V21, P49 AKAIKE H, 1973, 2 INT S INF THEOR, P267 AUGUSTIN NH, 1996, J APPL ECOL, V33, P339 BADDELEY A, 2005, J STAT SOFTW, V12, P1 BEAUCHEMIN S, 1998, J ENVIRON QUAL, V27, P721 BETTS MG, 2006, ECOL MODEL, V191, P197 CATLING PM, 2003, CAN BOT ASS B, V36, P4 DEBLOIS S, 2004, ENVIRON MANAGE, V33, P606 DECKERS B, 2005, ECOGRAPHY, V28, P99 DELISLE F, 2003, J BIOGEOGR, V30, P1 DOMON G, 1993, LANDSCAPE URBAN PLAN, V25, P53 EKSTAM B, 1999, SEED SCI RES, V9, P157 FORMAN RTT, 1998, ANNU REV ECOL SYST, V29, P207 FOXCROFT LC, 2004, DIVERS DISTRIB, V10, P427 GRATTON C, 2005, RESTOR ECOL, V13, P358 GUMPERTZ ML, 2000, FOREST SCI, V46, P97 HAASE P, 1995, J VEG SCI, V6, P575 HAVENS KJ, 1997, ENVIRON MANAGE, V21, P599 HEWITT JE, 1997, J EXP MAR BIOL ECOL, V216, P77 HUDON C, 2005, ECOSCIENCE, V12, P347 JAMIESON A, 2003, CAN BIOSYST ENG, V45 KEITT TH, 2002, ECOGRAPHY, V25, P616 KELLER BEM, 2000, WETLANDS ECOLOGY MAN, V8, P391 KLUTE DS, 2002, PREDICTING SPECIES O, P335 KOTSCHY KA, 2000, FOLIA GEOBOT, V35, P363 KRISTENSEN SP, 2002, J ENVIRON MANAGE, V66, P171 KRUMSCHEID P, 1989, AQUAT BOT, V35, P57 LAMONTAGNE L, 1997, AGR AGROALIMENTAIRE, P59 LATHROP RG, 2003, ESTUARIES, V26, P423 LAVOIE C, 2003, J BIOGEOGR, V30, P537 LEE KW, 2004, KOREAN J REMOTE SENS, V20, P57 LEGENDRE P, 1989, VEGETATIO, V80, P107 LEGENDRE P, 1993, ECOLOGY, V74, P1659 LICHSTEIN JW, 2002, ECOL MONOGR, V72, P445 MAHEUGIROUX M, 2005, AQUAT BOT, V83, P310 MAL TK, 2004, CAN J PLANT SCI, V84, P365 MCKEE J, 1996, NEW PHYTOL, V133, P233 MORAN PAP, 1950, BIOMETRIKA, V37, P17 NAGELKERKE NJD, 1991, BIOMETRIKA, V78, P691 NAYLOR S, 2003, WATER SCI TECHNOL, V48, P215 OHSER J, 1983, MATH OPERATIONSFORSC, V14, P63 OKABE A, 2001, GEOGR ANAL, V33, P271 OKABE A, 2004, SANET TOOLBOX SPATIA RIPLEY BD, 1976, J APPL PROBAB, V13, P255 ROUGET M, 2003, AM NAT, V162, P713 SALTONSTALL K, 2002, P NATL ACAD SCI USA, V99, P2445 SALTONSTALL K, 2003, WETLANDS, V23, P1043 SCHRODER P, 2005, Z NATURFORSCH C, V60, P317 SPOONER PG, 2004, APPL VEG SCI, V7, P61 SPOONER PG, 2004, LANDSCAPE ECOL, V19, P491 SUN G, 1999, WATER SCI TECHNOL, V40, P139 VASQUEZ EA, 2005, MAR ECOL-PROG SER, V298, P1 WEINSTEIN MP, 1999, ESTUARIES, V22, P793 WEISSER PJ, 1981, BOTHALIA, V13, P553 WIEGAND T, 1998, AM NAT, V152, P321 WIEGAND T, 2004, OIKOS, V104, P209 WIEGAND T, 2006, J ECOL, V94, P825 WILCOX KL, 2003, J GREAT LAKES RES, V29, P664 WITH KA, 2002, CONSERV BIOL, V16, P1192 ZEDLER JB, 2004, CRIT REV PLANT SCI, V23, P431 0921-2973 Landsc. Ecol.ISI:000243823900011McGill Univ, Dept Plant Sci, Ste Anne De Bellevue, PQ H9X 3V9, Canada. de Blois, S, McGill Univ, Sch Environm, 3534 Univ St, Montreal, PQ H3A 2A7, Canada. sylvie.deblois@mcgill.caEnglishc<7'Zhang, N. Yu, Z. L. Yu, G. R. Wu, J. G.2007rScaling up ecosystem productivity from patch to landscape: a case study of Changbai Mountain Nature Reserve, China303-315Landscape Ecology222scaling; extrapolation; ecosystem modeling; biogeochemical cycles; landscape pattern; net primary productivity; Changbai Mountain Nature Reserve NET PRIMARY PRODUCTIVITY; MODELING APPROACH; GENERAL-MODEL; SIMULATION; GRASSLAND; DYNAMICS; ECOLOGY; BALANCEArticleFebFScaling up ecosystem processes from plots to landscapes is essential for understanding landscape structure and functioning as well as for assessing ecological impacts of land use and climate change. This study illustrates an upscaling approach to studying the spatiotemporal pattern of ecosystem processes in the Changbai Mountain Nature Reserve in northeastern China by integrating simulation modeling, GIS, remote sensing data, and field-based observations. The ecosystem model incorporated processes of energy transfer, plant physiology, carbon dynamics, and water cycling. Using a direct extrapolation scheme, the patch-level ecosystem model was scaled up to quantify the landscape-level pattern of primary productivity and the carbon source-sink relationship. The simulated net primary productivity (NPP) for the entire landscape, consisting of several ecosystem types, was 0.680 kg C m(-2) yr(-1). The most widely distributed ecosystem type in this region was the mixed broad-leaved and Korean pine (Pinus koraiensis) forest, which had the highest NPP (1.084 kg C m(-2) yr(-1)). The total annual NPP for all ecosystem types combined was estimated to be 1.332 Mt C yr(-1). These results suggest that the Changbai Mountain landscape as a whole was a carbon sink, with a net carbon sequestration rate of about 0.884 Mt C yr(-1) for the study period. The simulated NPP agreed reasonably well with available field measurements at a number of locations within the study landscape. Our study provides new insight into the relationship between landscape pattern and ecosystem processes, and useful information for improving management practices in the Changbai Mountain Nature Reserve, which is one of the most important forested landscapes in China. Several research needs are discussed to further refine the modeling approach and reduce prediction uncertainties.://000243823900012 ISI Document Delivery No.: 130UG Times Cited: 0 Cited Reference Count: 41 Cited References: ABER JD, 1999, INTEGRATING HYDROLOG, P335 BAND LE, 1991, ECOL MODEL, V56, P171 BURKE IC, 1990, LANDSCAPE ECOL, V4, P45 CHEN BR, 1984, FOREST ECOSYSTEM STU, P19 CHEN CG, 1989, MANUAL BIOMASS MAIN CHEN JM, 1999, ECOL MODEL, V124, P99 COUGHLAN JC, 1997, LANDSCAPE ECOL, V12, P119 DAI Y, 2004, AM METEOROL SOC, V6, P2281 GE JP, 1990, J NE FOR U, V18, P26 HONG BG, 2006, LANDSCAPE ECOL, V21, P195 JIN CJ, 1995, ACTA ECOLOGICA SI SB, V15, P86 JIN CJ, 2000, CHIN J APPL ECOL, V11, P19 KENNEDY RE, 2006, LANDSCAPE ECOL, V21, P213 KING AW, 1991, QUANTITATIVE METHODS, P479 LAW BE, 2006, SCALING UNCERTAINTY, P167 LI H, 2006, SCALING UNCERTAINITY, P45 LIU J, 1997, REMOTE SENS ENVIRON, V62, P158 LIU J, 1999, J GEOPHYS RES-ATMOS, V104, P27735 MLADENOFF DJ, 1999, SPATIAL MODELING FOR NORMAN JM, 1982, BIOMETEOROLOGY INTEG, P65 PARTON WJ, 1987, SOIL SCI SOC AM J, V51, P1173 PEI TP, 1981, FOREST ECOSYSTEM STU, V2, P189 RUNNING SW, 1988, ECOL MODEL, V42, P125 RUNNING SW, 1993, SCALING PHYSL PROCES, P141 SUN R, 2004, CAN J REMOTE SENS, V30, P731 TURNER MG, 2001, LANDSCAPE ECOLOGY TH TURNER MG, 2006, IN PRESS KEY TOPICS WARING RH, 1998, FOREST ECOSYSTEMS AN WU J, 1999, CANADIAN J REMOTE SE, V25, P367 WU J, 2006, SCALING UNCERTAINITY WU J, 2006, SCALING UNCERTAINITY, P17 WU JG, 1994, ECOL MONOGR, V64, P447 WU JG, 1995, Q REV BIOL, V70, P439 WU JG, 1997, ECOL MODEL, V101, P325 WU JG, 2002, LANDSCAPE ECOL, V17, P355 XING SP, 1988, FORESTS JILIN PROVIN YAN XD, 1995, ACTA ECOL SIN SB, V15, P86 ZHANG N, 2003, ACTA PHYTOECOL SIN, V27, P325 ZHANG N, 2003, CHIN J APPL ECOL, V14, P643 ZHANG N, 2003, CHINESE J APPL ECOLO, V14, P659 ZHANG N, 2003, J GEOGR SCI, V13, P139 0921-2973 Landsc. Ecol.ISI:000243823900012)Grad Univ Chinese Acad Med, Coll Resources & Environm, Beijing 100049, Peoples R China. Natl Nat Sci Fdn China, Beijing 100085, Peoples R China. Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China. Beijing Normal Univ, Sch Life Sci, Beijing 100875, Peoples R China. Arizona State Univ, Sch Life Sci, Tempe, AZ 85287 USA. Arizona State Univ, Global Inst Sustainabil, Tempe, AZ 85287 USA. Zhang, N, Grad Univ Chinese Acad Med, Coll Resources & Environm, 19A Yu Quan Rd, Beijing 100049, Peoples R China. zhangna@gucas.ac.cnEnglish<7_Turner, M. G. Barrett, G. W. Gardner, R. H. Iverson, L. R. Risser, P. G. Wiens, J. A. Wu, J. G.2007)In memoriam - Frank B. Golley (1930-2006)1-3Landscape Ecology221Biographical-ItemJan://000243619800001 |ISI Document Delivery No.: 127XO Times Cited: 0 Cited Reference Count: 1 Cited References: GOLLEY FB, PUBLICATION LIST 0921-2973 Landsc. Ecol.ISI:000243619800001Univ Wisconsin, Dept Zool, Madison, WI USA. Univ Georgia, Inst Ecol, Athens, GA 30602 USA. Univ Maryland, Ctr Environm Sci, Appalachian Lab, Frostburg, MD USA. USDA, Forest Serv, Deleware, OH USA. Oklahoma State Univ, Oklahoma City, OK USA. Nature Conservancy, Washington, DC USA. Arizona State Univ, Sch Life Sci, Tempe, AZ 85287 USA. Arizona State Univ, Global Inst Sustainabil, Tempe, AZ 85287 USA. Turner, MG, Univ Wisconsin, Dept Zool, Madison, WI USA. turnermg@wisc.eduEnglish)<7 Naveh, Z.2007-In memoriam of Francsco Di Castri (1930-2005)5-6Landscape Ecology221Biographical-ItemJan://000243619800002 }ISI Document Delivery No.: 127XO Times Cited: 0 Cited Reference Count: 1 Cited References: DICASTRI F, PUBLICATION LIST 0921-2973 Landsc. Ecol.ISI:000243619800002Technion Israel Inst Technol, Fac Civil & Environm Engn, IL-32000 Haifa, Israel. Naveh, Z, Technion Israel Inst Technol, Fac Civil & Environm Engn, IL-32000 Haifa, Israel. z.nave@hotmail.comEnglish Z<7Buyantuyev, A. Wu, J. G.2007<Effects of thematic resolution on landscape pattern analysis7-13Landscape Ecology221landscape characterization; image classification; thematic resolution; landscape metrics; landscape pattern analysis SCALING RELATIONS; CHANGING SCALE; METRICS; INDEXES; FRAGMENTATION; AGGREGATION; SENSITIVITY; BEHAVIOR; AREAArticleJanThe thematic resolution of mapped data determines the amount of detail of geospatial information, and influences various aspects of landscape classification and the relevance of derived pattern attributes to particular ecological questions. Here we show that changing thematic resolution may significantly affect landscape metrics and in turn their ability to detect landscape changes. The effects of thematic resolution on many landscape metrics tend to show consistent general patterns, but the details of these patterns are likely to be dependent on specific landscape patterns and classification criteria. Thus, the effects of thematic resolution, like those with regard to grain and extent, must be considered in landscape pattern analysis.://000243619800003 ISI Document Delivery No.: 127XO Times Cited: 0 Cited Reference Count: 24 Cited References: *FED GEOGR DAT COM, 1997, VEG CLASS STAND ANDERSON JR, 1976, LAND USE LAND COVER BALDWIN DJB, 2004, LANDSCAPE ECOL, V19, P255 BUYANTUYEV A, 2006, P 8 ANN S CENTR AR P, P11 HARGIS CD, 1998, LANDSCAPE ECOL, V13, P167 JELINSKI DE, 1996, LANDSCAPE ECOL, V11, P129 LI HB, 1993, LANDSCAPE ECOL, V8, P155 LI HB, 2004, LANDSCAPE ECOL, V19, P389 MCGARIGAL K, 1995, FRAGSTATS SPATIAL PA NEEL MC, 2004, LANDSCAPE ECOL, V19, P435 OPPENSHAW S, 1984, MODIFIABLE AREAL UNI RIITTERS KH, 1995, LANDSCAPE ECOL, V10, P23 SAURA S, 2004, LANDSCAPE ECOL, V19, P197 SCOTT JM, 1998, ANN MO BOT GARD, V85, P34 SHEN WJ, 2004, ECOGRAPHY, V27, P459 STEFANOV WL, 2000, 1985 1990 1993 1998 STEFANOV WL, 2001, REMOTE SENS ENVIRON, V77, P173 TURNER MG, 1989, LANDSCAPE ECOLOGY, V3, P153 WICKHAM JD, 1995, INT J REMOTE SENS, V16, P3585 WU J, IN PRESS KEY TOPICS WU J, 1995, LECT MODERN ECOLOGY, P1 WU J, 2000, GEOGRAPHICAL INFORMA, V6, P6 WU JG, 2002, LANDSCAPE ECOL, V17, P761 WU JG, 2004, LANDSCAPE ECOL, V19, P125 0921-2973 Landsc. Ecol.ISI:000243619800003Arizona State Univ, Sch Life Sci, Tempe, AZ 85287 USA. Arizona State Univ, Global Inst Sustainabil, Tempe, AZ 85287 USA. Buyantuyev, A, Arizona State Univ, Sch Life Sci, Tempe, AZ 85287 USA. Alexander.Buyantuyev@asu.eduEnglish<7Gardner, R. H. Urban, D. L.2007/Neutral models for testing landscape hypotheses15-29Landscape Ecology221neutral landscape models; pattern and process; landscape hypothesis testing; land cover analysis LAND-USE CHANGE; UNITED-STATES; SPATIAL-PATTERNS; CENTRAL AMAZON; NATURAL AREAS; PINE FORESTS; COVER CHANGE; USE HISTORY; METRICS; VEGETATIONArticleJanNeutral landscape models were originally developed to test the hypothesis that human-induced fragmentation produces patterns distinctly different from those associated with random processes. Other uses for neutral models have become apparent, including the development and testing of landscape metrics to characterize landscape pattern. Although metric development proved to be significant, the focus on metrics obscured the need for iterative hypothesis testing fundamental to the advancement of the discipline. We present here an example of an alternative neutral model and hypothesis designed to relate the process of landscape change to observed landscape patterns. The methods and program, QRULE, are described and options for statistical testing outlined. The results show that human fragmentation of landscapes results in a non-random association of land-cover types that can be describe by simple statistical methods. Options for additional landscape studies are discussed and access to QRULE described in the hope that these methods will be employed to advance our understanding of the processes that affect the structure and function in human dominated landscapes.://000243619800004 ISI Document Delivery No.: 127XO Times Cited: 0 Cited Reference Count: 73 Cited References: *NRC, 2001, GRAND CHALL ENV SCI *NRC, 2003, NEON ADDR NAT ENV CH ANDERSON DR, 2000, J WILDLIFE MANAGE, V64, P912 ANDERSON GS, 1997, LANDSCAPE ECOL, V12, P261 ANDOW DA, 1990, LANDSCAPE ECOL, V4, P177 BENNETT AF, 1994, BIOL CONSERV, V68, P155 BURNHAM KP, 1998, MODEL SELECTION INFE COPELAND JH, 1996, J GEOPHYS RES-ATMOS, V101, P7409 COSTANZA R, 1994, LANDSCAPE ECOL, V9, P47 DALE VH, 1994, CONSERV BIOL, V8, P1027 DELCOURT HR, 1983, QUATERNARY SCI REV, V1, P153 DISTEFANO J, 2004, FOREST ECOL MANAG, V187, P173 FAGAN WE, 1999, AM NAT, V153, P165 FAUTH PT, 2000, LANDSCAPE ECOL, V15, P621 FOLEY JA, 2005, SCIENCE, V309, P570 FORMAN RTT, 1986, LANDSCAPE ECOLOGY FORTIN MJ, 2003, OIKOS, V102, P203 FOSTER DR, 1998, ECOSYSTEMS, V1, P96 GARDNER RH, 1987, LANDSCAPE ECOL, V1, P19 GARDNER RH, 1990, QUANTITATIVE METHODS, P289 GARDNER RH, 1999, LANDSCAPE ECOLOGICAL, P280 GARNDER RH, IN PRSS KEY TOPICS P GOETZ SJ, 2004, ECOSYSTEMS LAND USE, P263 GOTELLI NJ, 1996, NULL MODELS ECOLOGY GUSTAFSON EJ, 1996, ECOLOGY, V77, P94 HANSKI I, 2005, EMBO REP, V6, P388 JOHNSON DH, 2002, J WILDLIFE MANAGE, V66, P272 JOHNSON NL, 1970, CONTINUOUS UNIVARIAT, V1 JOHNSON NL, 1970, CONTINUOUS UNIVARIAT, V2 KRUMMEL JR, 1987, OIKOS, V48, P321 LANGLOIS JP, 2001, LANDSCAPE ECOL, V16, P255 LAVOREL S, 1994, OIKOS, V71, P75 LINDBORG R, 2004, ECOLOGY, V85, P1840 MADDOX J, 1992, NATURE, V359, P35 MANLY BFJ, 1997, RANDOMIZATION BOOTST MCCAY DH, 2001, LANDSCAPE ECOL, V16, P89 MEYER WB, 1992, ANNU REV ECOL SYST, V23, P39 MLADENOFF DJ, 1999, SPATIAL MODELING FOR MOTZKIN G, 1999, J VEG SCI, V10, P903 NASSAUER JI, 1997, PACING NATURE CULTUR NEEL MC, 2004, LANDSCAPE ECOL, V19, P435 ONEILL RV, 1992, LANDSCAPE ECOL, V7, P55 ORWIG DA, 1994, CAN J FOREST RES, V24, P1216 OSHER LJ, 2003, BIOGEOCHEMISTRY, V65, P213 PEARSON SM, 1997, WILDLIFE LANDSCAPE E, P215 PLATT JR, 1964, SCIENCE, V146, P347 PLOTNICK RE, 1993, LANDSCAPE ECOL, V8, P201 PLOTNICK RE, 1993, LECT MATH LIFE SCI P, V23, P129 PLOTNICK RE, 1996, PHYS REV E B, V53, P5461 PYKE CR, 2004, FRONT ECOL ENVIRON, V2, P178 RIITTERS KH, 1995, LANDSCAPE ECOL, V10, P23 RIITTERS KH, 2002, ECOSYSTEMS, V5, P815 RIITTERS KH, 2005, ENVIRON MANAGE, V35, P483 RILEY RH, 1997, INT J REMOTE SENS, V18, P121 ROBINSON DH, 2002, J WILDLIFE MANAGE, V66, P263 ROMME WH, 1998, ECOSYSTEMS, V1, P524 STAUFFER D, 1992, INTRO PERCOLATION TH STOHLGREN TJ, 1998, GLOB CHANGE BIOL, V4, P495 TAVERNA K, 2005, LANDSCAPE ECOL, V20, P689 TISCHENDORF L, 2003, LANDSCAPE ECOL, V18, P41 TURNER MG, 1994, NAT AREA J, V14, P3 TURNER MG, 2001, LANDSCAPE ECOLOGY TH URBAN DL, 1987, BIOSCIENCE, V37, P119 URBAN DL, 2000, LANDSCAPE ECOL, V15, P603 URBAN DL, 2005, ECOLOGY, V86, P1996 VITOUSEK PM, 1997, SCIENCE, V277, P494 VOGELMANN JE, 2001, PHOTOGRAMM ENG REM S, V67, P650 WICKHAM JD, 1997, PHOTOGRAMM ENG REM S, V63, P397 WICKHAM JD, 2000, LANDSCAPE ECOL, V15, P171 WILLIAMS MR, 2004, BIOGEOCHEMISTRY, V68, P259 WITH KA, 1997, OIKOS, V79, P219 WU JG, 2002, LANDSCAPE ECOL, V17, P355 ZAR JH, 1996, BIOSTATISTICAL ANAL 0921-2973 Landsc. Ecol.ISI:000243619800004Univ Maryland, Ctr Environm Sci, Appalachian Lab, Frostburg, MD 21532 USA. Duke Univ, Nicholas Sch Environm & Earth Sci, Durham, NC 27708 USA. Gardner, RH, Univ Maryland, Ctr Environm Sci, Appalachian Lab, Frostburg, MD 21532 USA. gardner@al.umces.eduEnglishP<7Bodin, O. Norberg, J.2007YA network approach for analyzing spatially structured populations in fragmented landscape31-44Landscape Ecology221landscape fragmentation; compartments; graphs; network analysis; Madagascar; Lemur catta; spatial resilience; natural reserves HABITAT FRAGMENTATION; LEMUR-CATTA; CONNECTIVITY; METRICS; RESILIENCE; MADAGASCAR; DISPERSAL; PERSPECTIVE; THRESHOLDS; MAMMALSArticleJanWe extend the recently proposed graph-theoretical landscape perspective by applying some network-centric methods mainly developed in the social sciences. The methods we propose are suitable to (1) identify individual habitat patches that are disproportionally high in importance in preserving the ability of organisms to traverse the fragmented landscape, and (2) find internally well-connected compartments of habitat patches that contribute to a spatial compartmentalization of species populations. We demonstrate the utility of these methods using an agricultural landscape with scattered dry-forest patches in southern Madagascar, inhabited by the ring-tailed lemur, Lemur catta. We suggest that these methods are particularly suitable in landscapes where species' traversability is not fully inhibited by fragmentation, but merely limited. These methods are potentially highly relevant in studying spatial aspects of resilience and in the design of natural reserves.://000243619800005 ISI Document Delivery No.: 127XO Times Cited: 0 Cited Reference Count: 48 Cited References: BENDER DJ, 2003, LANDSCAPE ECOL, V18, P17 BENGTSSON J, 2003, AMBIO, V32, P389 BODIN O, 2006, ECOL APPL, V16, P440 BOGAERT J, 2003, CONSERV ECOL, V7 BORGATTI SP, 1990, SOC NETWORKS, V12, P337 BORGATTI SP, 2002, UCINET WINDOWS SOFTW BROOKS CP, 2003, OIKOS, V102, P433 BRUINDERINK GG, 2003, CONSERV BIOL, V17, P549 BUNN AG, 2000, J ENVIRON MANAGE, V59, P265 CALABRESE JM, 2004, FRONT ECOL ENVIRON, V2, P529 DEGENNE A, 1999, INTRO SOCIAL NETWORK ELMQVIST T, 2003, FRONT ECOL ENVIRON, V1, P488 ELMQVIST T, 2004, PLANT TALK, P29 FREEMAN LC, 1979, SOC NETWORKS, V1, P215 FREEMAN LC, 2004, DEV SOCIAL NETWORK A GARANT D, 2005, NATURE, V433, P60 GILPIN ME, 1991, METAPOPULATION DYNAM GIRVAN M, 2002, P NATL ACAD SCI USA, V99, P7821 GUSTAFSON EJ, 1998, ECOSYSTEMS, V1, P143 HANSKI I, 1994, J ANIM ECOL, V63, P151 HARGIS CD, 1998, LANDSCAPE ECOL, V13, P167 JOLLY A, 1999, INT J PRIMATOL, V20, P359 KEITT TH, 1997, CONSERV ECOL, V1, P1 KRAUSE AE, 2003, NATURE, V426, P282 LEE JT, 2005, LANDSCAPE URBAN PLAN, V71, P17 LEIBOLD MA, 2004, LIMNOL OCEANOGR 2, V49, P1278 LEVIN SA, 2000, FRAGILE DOMINION COM LI HB, 2004, LANDSCAPE ECOL, V19, P389 LILLESAND TM, 1994, REMOTE SENSING IMAGE LOREAU M, 2003, ECOL LETT, V6, P673 LUCZKOVICH JJ, 2003, J THEOR BIOL, V220, P303 MEFFE GK, 2002, ECOSYSTEM MANAGEMENT MELIAN CJ, 2004, ECOLOGY, V85, P352 NEWMAN MEJ, 2004, PHYS REV E, V69 NYSTROM M, 2001, ECOSYSTEMS, V4, P406 POSTMA E, 2005, NATURE, V433, P65 RICKETTS TH, 2001, AM NAT, V158, P87 ROBINSON GR, 1992, SCIENCE, V257, P524 ROSHIER DA, 2001, LANDSCAPE ECOL, V16, P547 SEIDMAN S, 1983, SOC NETWORKS, V5, P92 SUSSMAN RW, 1991, AM J PHYS ANTHROPOL, V84, P43 SUTHERLAND GD, 2000, CONSERV ECOL, V4 TENGO M, 2004, THESIS STOCKHOLM U S TISCHENDORF L, 2001, LANDSCAPE ECOL, V16, P235 URBAN D, 2001, ECOLOGY, V82, P1205 VERBEYLEN G, 2003, LANDSCAPE ECOL, V18, P791 WASSERMAN S, 1994, SOCIAL NETWORK ANAL WITH KA, 1999, LANDSCAPE ECOL, V14, P73 0921-2973 Landsc. Ecol.ISI:000243619800005Stockholm Univ, Dept Syst Ecol, S-10691 Stockholm, Sweden. Bodin, O, Stockholm Univ, Dept Syst Ecol, S-10691 Stockholm, Sweden. orjan@system.ecology.su.seEnglish<7Grear, J. S. Burns, C. E.2007Evaluating effects of low quality habitats on regional population growth in Peromyscus leucopus: Insights from field-parameterized spatial matrix models45-60Landscape Ecology221habitat quality; landscape; spatial; matrix model; Peromyscus; population; source; sink; white-footed mouse; North America; USA WHITE-FOOTED MICE; FOREST FRAGMENTS; MARKED ANIMALS; DENSITY; LANDSCAPE; SELECTION; DISPERSAL; RECAPTURE; DYNAMICS; SURVIVALArticleJanDue to complex population dynamics and source-sink metapopulation processes, animal fitness sometimes varies across landscapes in ways that cannot be deduced from simple density patterns. In this study, we examine spatial patterns in fitness using a combination of intensive field-based analyses of demography and migration and spatial matrix models of white-footed mouse (Peromyscus leucopus) population dynamics. We interpret asymptotic population growth rates from these spatial models as fitness-based measures of habitat-quality and use elasticity analysis to further explore model behavior and the roles of migration. In addition, we compare population growth rates at the spatial scale of single habitats and the landscape-level scale at which these habitats are assembled. To this end, we employ emerging techniques in multi-scale estimation of demography and movement and recently described vec-permutation methods for spatial matrix notation and analysis. Our findings indicate that the loss of low quality habitats or reductions in movement from these habitats into higher quality areas could negatively affect landscape-level population fitness.://000243619800006 5 ISI Document Delivery No.: 127XO Times Cited: 0 Cited Reference Count: 57 Cited References: *US FISH WILDL SER, 1981, EC SERV MAN, V103 ADLER GH, 1985, OIKOS, V45, P380 ANDERSON CS, 2003, CAN J ZOOL, V81, P897 BOWMAN JC, 2000, FOREST ECOL MANAG, V129, P119 BOWNE DR, 2004, LANDSCAPE ECOL, V19, P1 BROOKS RP, 1997, WILDLIFE SOC B, V25, P163 BURNHAM KP, 2002, MODEL SELECTION MULT BURNS CE, 2005, ECOLOGY, V86, P753 CASWELL H, 1994, ECOLOGY, V75, P1299 CASWELL H, 2001, MATRIX POPULATION MO DEGRAFF RM, 2001, NEW ENGLAND WILDLIFE DIAS PC, 1996, TRENDS ECOL EVOL, V11, P326 FRETWELL SC, 1970, ACTA BIOTHEOR, V19, P16 GREAR JS, 2005, ECOLOGY, V86, P960 HALAMA KJ, 1994, OIKOS, V69, P107 HARDING EK, 2002, BIOL CONSERV, V104, P227 HOWE RW, 1991, BIOL CONSERV, V57, P239 HUNTER CM, 2005, ECOL MODEL, V188, P15 KING JA, 1968, BIOL PEROMYSCUS RODE KOONS DN, 2005, ECOL MODEL, V185, P283 LEBRETON JD, 1992, ECOL MONOGR, V62, P67 LEBRETON JD, 2002, J APPL STAT, V29, P353 LEBRETON JD, 2003, OIKOS, V101, P253 LEFKOVITCH LP, 1985, ECOL MODEL, V30, P297 MIDDLETON DAJ, 1997, ECOL APPL, V7, P107 MILLAR JS, 1984, WINT ECOLOGY SMALL M, P253 MORRIS DW, 1989, EVOL ECOL, V3, P80 MORRIS DW, 1991, AM NAT, V138, P702 MORRIS DW, 2004, OIKOS, V107, P549 MORRIS WF, 2002, QUANTITATIVE CONSERV MURPHY MT, 2001, CONSERV BIOL, V15, P737 NUPP TE, 2000, J MAMMAL, V81, P512 NUPP TE, 2001, MAMM BIOL, V66, P345 OSTFELD RS, 1995, ECOL APPL, V5, P353 PICKETT STA, 1995, SCIENCE, V269, P331 PRADEL R, 1996, BIOMETRICS, V52, P703 PULLIAM HR, 1988, AM NAT, V132, P652 PULLIAM HR, 1991, AM NAT S, V137, P50 PULLIAM HR, 1992, ECOL APPL, V2, P165 PULLIAM HR, 1996, POPULATION DYNAMICS, P45 ROFF DA, 1974, OECOLOGIA, V15, P245 ROFF DA, 1974, OECOLOGIA, V15, P259 ROSENBLATT DL, 1999, AM MIDL NAT, V141, P115 SCHMIDHOLMES S, 2001, BIOL CONSERV, V99, P293 STEEN H, 2000, J ANIM ECOL, V69, P659 TERMAN CR, 1993, J MAMMAL, V74, P678 THOMAS CD, 1999, J ANIM ECOL, V68, P647 TILMAN D, 1997, SPATIAL ECOLOGY ROLE VANHORNE B, 1983, J WILDLIFE MANAGE, V47, P893 WATKINSON AR, 1995, J ANIM ECOL, V64, P126 WHEATLEY M, 2002, J MAMMAL, V83, P716 WHITAKER JO, 1998, MAMMALS E US WHITE GC, 1999, BIRD STUDY S, V46, P120 WIENS JA, 1989, FUNCT ECOL, V3, P385 WILLIAMS BK, 2002, ANAL MANAGEMENT ANIM WOLFF JO, 1985, CAN J ZOOL, V63, P2657 WOLFF JO, 1986, CAN FIELD NAT, V100, P186 0921-2973 Landsc. Ecol.ISI:000243619800006kUS EPA, Environm Res Lab, Off Res & Dev,Atlantic Ecol Div, Natl Hlth & Environm Effects Res Lab, Narragansett, RI 02882 USA. Yale Univ, Dept Ecol & Evolutionary Biol, New Haven, CT 06511 USA. Grear, JS, US EPA, Environm Res Lab, Off Res & Dev,Atlantic Ecol Div, Natl Hlth & Environm Effects Res Lab, 27 Tarzwell Dr, Narragansett, RI 02882 USA. grear.jason@epa.govEnglish<7 Thogmartin, W. E. Knutson, M. G.2007gScaling local species-habitat relations to the larger landscape with a hierarchical spatial count model61-75Landscape Ecology2216abundance map; black-billed cuckoo; hierarchical model; information-theoretic model selection; multi-level model; red-headed woodpecker; spatial count model; wood thrush BREEDING-BIRD DISTRIBUTION; FOREST BIRDS; CERULEAN WARBLERS; GAP ANALYSIS; ABUNDANCE; CLIMATE; BIODIVERSITY; VEGETATION; OCCUPANCY; PATTERNSArticleJanMuch of what is known about avian species-habitat relations has been derived from studies of birds at local scales. It is entirely unclear whether the relations observed at these scales translate to the larger landscape in a predictable linear fashion. We derived habitat models and mapped predicted abundances for three forest bird species of eastern North America using bird counts, environmental variables, and hierarchical models applied at three spatial scales. Our purpose was to understand habitat associations at multiple spatial scales and create predictive abundance maps for purposes of conservation planning at a landscape scale given the constraint that the variables used in this exercise were derived from local-level studies. Our models indicated a substantial influence of landscape context for all species, many of which were counter to reported associations at finer spatial extents. We found land cover composition provided the greatest contribution to the relative explained variance in counts for all three species; spatial structure was second in importance. No single spatial scale dominated any model, indicating that these species are responding to factors at multiple spatial scales. For purposes of conservation planning, areas of predicted high abundance should be investigated to evaluate the conservation potential of the landscape in their general vicinity. In addition, the models and spatial patterns of abundance among species suggest locations where conservation actions may benefit more than one species.://000243619800007 K ISI Document Delivery No.: 127XO Times Cited: 0 Cited Reference Count: 50 Cited References: *US FISH WILDL SER, 2002, BIRDS CONS CONC *US NACBI COMM, 2000, N AM BIRD CONS IN BI BRENNAN JM, 2002, INTEGRATING LANDSCAP, P68 BROOKS SP, 1998, J COMPUT GRAPH STAT, V7, P434 BURNHAM KP, 2002, MODEL SELECTION MULT, P353 DEATH G, 2000, ECOLOGY, V81, P3178 DIAMOND DD, 2003, NAT AREA J, V23, P129 DONOVAN TM, 2002, ECOL APPL, V12, P364 GALE GA, 2001, J FIELD ORNITHOL, V72, P291 GALLI AE, 1976, AUK, V93, P356 GEISSLER PH, 1990, US FISH WILDL SERV B, V90, P54 GELMAN A, 1995, BAYESIAN DATA ANAL, P526 GIESE CLA, 2003, FOREST ECOL MANAG, V179, P523 GUSTAFSON EJ, 1992, LANDSCAPE ECOL, V7, P101 GUSTAFSON EJ, 2002, ECOL APPL, V12, P412 HOLLAND JD, 2004, BIOSCIENCE, V54, P227 HUGHES JM, 2001, BIRD N AM, V587, P24 KOENIG WD, 2001, ECOLOGY, V82, P2636 LEE M, 2002, OIKOS, V96, P110 LICHSTEIN JW, 2002, ECOL APPL, V12, P836 LINK WA, 2002, ECOLOGY, V83, P2832 LINK WA, 2002, J WILDLIFE MANAGE, V66, P277 LONG JS, 1997, REGRESSION MODELS CA, P297 MARTIN TE, 1981, AUK, V98, P715 MCKENNEY DW, 2002, PREDICTING SPECIES O, P377 MCNAB WH, 1994, WOWSA5 US FOR SERV NOON BR, 2003, BIOSCIENCE, V53, P1217 OCONNOR RJ, 2004, AUK, V121, P604 PEARLSTINE LG, 2002, J ENVIRON MANAGE, V66, P127 REMPEL RS, 2003, LANDSCAPE ECOL, V18, P741 RICH TD, 2004, PARTNERS FLIGHT N AM, P84 RIITTERS K, 2000, CONSERV ECOL, V4, P2756 ROSEBERRY JL, 1998, J WILDLIFE MANAGE, V62, P895 ROTH RR, 1996, BIRDS N AM, V246, P28 SAMPLE DW, 1997, MANAGING HABITAT GRA SARGENT RA, 2003, SOUTHEAST NAT, V2, P217 SCOTT JM, 1993, WILDLIFE MONOGR, P1 SCOTT JM, 2002, PREDICTING SPECIES O, P868 SEOANE J, 2004, ECOL MODEL, V171, P209 SMITH KG, 1987, J WILDLIFE MANAGE, V51, P459 SMITH KG, 2000, BIRDS N AM, V518, P28 SPIEGELHALTER DJ, 2002, J ROY STAT SOC B 4, V64, P583 SPIEGELHALTER DJ, 2003, WINBUGS VERSION 1 4 THOGMARTIN WE, 2004, ECOL APPL, V14, P1766 THOGMARTIN WE, 2006, CONDOR, V108, P25 THOMAS A, 2002, GEOBUGS USER MANUAL VENIER LA, 1999, J BIOGEOGR, V26, P315 VENIER LA, 2004, J BIOGEOGR, V31, P315 WIENS JA, 1981, ESTIMATING NUMBERS T, P513 WIENS JA, 1987, OIKOS, V48, P132 0921-2973 Landsc. Ecol.ISI:000243619800007US Geol Survey, Upper Midw Environm Sci Ctr, La Crosse, WI 54603 USA. Thogmartin, WE, US Geol Survey, Upper Midw Environm Sci Ctr, 2630 Fanta Reed Rd, La Crosse, WI 54603 USA. wthogmartin@usgs.govEnglish<70Smithwick, E. A. H. Harmon, M. E. Domingo, J. B.2007zChanging temporal patterns of forest carbon stores and net ecosystem carbon balance: The stand to landscape transformation77-94Landscape Ecology221landscape; carbon; disturbance; fire; harvest; NECB; NPP; model PACIFIC-NORTHWEST; FIRE FREQUENCY; BOREAL FOREST; PRODUCTIVITY; DISTURBANCE; MODELS; SCALE; USA; SEQUESTRATION; ATMOSPHEREArticleJanShort- and long-term patterns of net ecosystem carbon balance (NECB) for small, relatively uniform forest stands have been examined in detail, but the same is not true for landscapes, especially those with heterogeneous disturbance histories. In this paper, we explore the effect of two contrasting types of disturbances (i.e., fire and tree harvest) on landscape level NECB by using an ecosystem process model that explicitly accounts for changes in carbon (C) stores as a function of disturbance regimes. The latter were defined by the average disturbance interval, the regularity of the disturbance interval (i.e., random, based on a Poisson frequency distribution, or regular), the amount of C removed by the disturbance (i.e., severity), and the relative abundance of stands in the landscape with unique disturbance histories. We used the model to create over 300 hypothetical landscapes, each with a different disturbance regime, by simulating up to 200 unique stand histories and averaging their total C stores. Mean NECB and its year-to-year variability was computed by calculating the difference in mean total C stores from one year to the next. Results indicated that landscape C stores were higher for random than for regular disturbance intervals, and increased as the mean disturbance interval increased and as the disturbance severity decreased. For example, C storage was reduced by 58% when the fire interval was shortened from 250 years to 100 years. Average landscape NECB was not significantly different than zero for any of the simulated landscapes. Year-to-year variability in landscape NECB, however, was related to the landscape disturbance regime; increasing with disturbance severity and frequency, and higher for random versus regular disturbance intervals. We conclude that landscape C stores of forest systems can be predicted using the concept of disturbance regimes, a result that may be a useful for adjusting estimates of C storage to broad scales that are solely based on physiological processes.://000243619800008 ISI Document Delivery No.: 127XO Times Cited: 0 Cited Reference Count: 41 Cited References: APPS MJ, 2000, GLOBAL CLIMATE CHANG, P107 BAKER WL, 1989, CAN J FOREST RES, V19, P700 BONDLAMBERTY B, 2004, GLOBAL CHANGE BIOL, V10, P473 BORMANN FH, 1979, AM SCI, V67, P660 CHAPIN FS, IN PRESS ECOSYSTEMS EUSKIRCHEN ES, 2002, ECOL MODEL, V154, P75 GOULDEN ML, 1996, GLOB CHANGE BIOL, V2, P169 HARMON ME, 1996, CLIMATIC CHANGE, V33, P521 HARMON ME, 2001, J FOREST, V99, P24 HARMON ME, 2001, USERS GUIDE STANDCAR HARMON ME, 2002, CAN J FOREST RES, V32, P863 HOUGHTON RA, 1999, TELLUS B, V51, P298 HOUGHTON RA, 2003, GLOBAL CHANGE BIOL, V9, P500 JANISCH JE, 2002, TREE PHYSIOL, V22, P77 JOHNSON EA, 1985, CAN J FOREST RES, V15, P214 JOHNSON EA, 1994, ADV ECOL RES, V25, P239 KUHLBUSCH TAJ, 1996, J GEOPHYS RES, V101, P23 KUHLBUSCH TAJ, 1996, J GEOPHYS RES, V101, P651 KUHLBUSCH TAJ, 1996, J GEOPHYS RES, V101, P666 KURZ WA, 1998, MITIGATION ADAPTATIO, V2, P405 LAW BE, 2000, GLOB CHANGE BIOL, V6, P155 LAW BE, 2004, GLOBAL CHANGE BIOL, V10, P1429 PACALA SW, 2001, SCIENCE, V292, P2316 PENG CH, 1999, ECOL MODEL, V122, P175 RAISON RJ, 1979, PL SOIL, V51, P73 ROMME WH, 1982, BIOSCIENCE, V32, P664 SCHIMEL DS, 1997, ECOL MONOGR, V67, P251 SCHIMEL DS, 2001, NATURE, V414, P169 SHUGART HH, 1981, AM SCI, V69, P647 SMITHWICK EAH, 2002, ECOL APPL, V12, P1303 SMITHWICK EAH, 2002, THESIS OREGON STATE SMITHWICK EAH, 2003, LANDSCAPE ECOL, V18, P701 SMITHWICK EAH, 2005, ECOSYSTEMS, V8, P163 SUN OJ, 2004, GLOBAL CHANGE BIOL, V10, P1470 TANS PP, 1990, SCIENCE, V247, P1431 THORNLEY JHM, 2004, TREE PHYSIOL, V24, P765 TURNER MG, 1993, LANDSCAPE ECOL, V8, P213 VANWAGNER CE, 1978, CANADIAN J FOREST RE, V8, P220 WATT AS, 1947, J ECOL, V35, P1 WIRTH C, 2002, PLANT SOIL, V242, P41 ZACKRISSON O, 1996, OIKOS, V77, P10 0921-2973 Landsc. Ecol.ISI:000243619800008&Univ Wisconsin, Dept Zool, Madison, WI 53706 USA. Oregon State Univ, Dept Forest Sci, Corvallis, OR 97330 USA. Univ Wisconsin, Dept Forest Ecol & Management, Madison, WI 53706 USA. Smithwick, EAH, Univ Wisconsin, Dept Zool, Birge Hall,430 Lincoln Dr, Madison, WI 53706 USA. easmithwick@wisc.eduEnglish0<7 "He, Z. B. Zhao, W. Z. Chang, X. L.2007The modifiable areal unit problem of spatial heterogeneity of plant community in the transitional zone between oasis and desert using semivariance analysis95-104Landscape Ecology221transitional zone between oasis and desert; spatial heterogeneity; the modifiable areal unit problem; scale effect; zoning effect LANDSCAPE ECOLOGYArticleJanThe modifiable areal unit problem has significant implications for ecological research that involve investigating and analyzing the spatial heterogeneity of plant community. In this paper, semivariance analysis was used to evaluate the spatial characteristics of plant community in the transitional zone between oasis and desert. The spatial structures of the plant community were characterized using exponent model variogram parameters, including nugget (C-0) range (A(0)) and sill (C-0+C). Two methods were employed to determine the scale effect of spatial heterogeneity. (1) A constant grain size (10x10 m(2)) and variational plot areas have been used to analyze spatial heterogeneity of the plant community. (2) The grain size was only changed to analyze spatial heterogeneity. In addition, the plot of 500x500 m(2) was clustered into nested units of different shapes and different directions to analyze zoning effect. Finally, using semivariance analysis, we obtained a suitable plot area and zoning approach to weaken the scale and zoning effects. The results showed that the effects of scale on different variogram parameters had significant difference. For example, C-0 and C-0+C were very sensitive at small scales, A(0) was influenced significantly by plot area at larger scales, and C-0 and A(0) were relatively sensitive to different zoning approaches. In order to get more representative characteristic of spatial heterogeneity of the plant community, the sampling area should be more than 200x200 m(2) for Nitraria sphaerocarpa populations, 100x100 m(2) for Reaumuria soongorica populations, and a grain size from 20x20 m(2) to 30x30 m(2) for both populations.://000243619800009 ISI Document Delivery No.: 127XO Times Cited: 0 Cited Reference Count: 19 Cited References: ARBIA G, 1989, ACCURACY SPATIAL DAT, P249 FORMAN RTT, 1986, LANDSCAPE ECOLOGY FOTHERINGHAM AS, 1991, ENVIRON PLANN A, V23, P1025 GEHLKE CE, 1934, J AM STAT ASSOC, V29, P169 HE ZB, 2004, CHINESE J APPL ECOL, V15, P947 JELINSKI DE, 1996, LANDSCAPE ECOL, V11, P129 KING AW, 1991, LANDSCAPE ECOL, V5, P239 LI YC, 1997, ACTA ECOL SIN, V17, P393 MARCEAU DJ, 1999, CANADIAN J REMOTE SE, V25, P347 MEISEL JE, 1998, LANDSCAPE ECOL, V13, P347 ONEILL RV, 1979, SYSTEMS ANAL ECOSYST, P140 OPENSHAW S, 1977, ENVIRON PLANN A, V9, P169 OPENSHAW S, 1984, CATMOG, V38 ROBINSON WS, 1950, AM SOCIOL REV, V15, P351 URBAN DL, 1987, BIOSCIENCE, V37, P119 WU J, 1995, LECT MODERN ECOLOGY, P1 WU JG, 1994, ECOL MONOGR, V64, P447 WU JG, 1995, Q REV BIOL, V70, P439 YULE GU, 1950, INTRO THEORY STAT 0921-2973 Landsc. Ecol.ISI:000243619800009Chinese Acad Sci, Cold & Arid Reg Environm & Engn Res Inst, Linze Inland River Basin Comprehens Res Stn, Chinese Ecosyst Network Res,Lab Basin Hydrol & Ec, Lanzhou 730000, Peoples R China. Yantai Normal Univ, Dept Geog & Tourism, Yantai 264025, Peoples R China. He, ZB, Chinese Acad Sci, Cold & Arid Reg Environm & Engn Res Inst, Linze Inland River Basin Comprehens Res Stn, Chinese Ecosyst Network Res,Lab Basin Hydrol & Ec, Lanzhou 730000, Peoples R China. hzbmail@ns.lzb.ac.cnEnglishH<7!Svoray, T. Mazor, S. Pua, B.2007xHow is shrub cover related to soil moisture and patch geometry in the fragmented landscape of the Northern Negev desert?105-116Landscape Ecology221 aerial photographs; digital elevation models (DEM); fuzzy logic; patch scale; geographical information systems (GIS) PINYON-JUNIPER WOODLAND; HABITAT FRAGMENTATION; SLOPE ASPECT; FUZZY-LOGIC; VEGETATION; ECOSYSTEM; CLIMATE; DESERTIFICATION; COLONIZATION; COEXISTENCEArticleJanAmong the major challenges of landscape ecologists is to develop relatively simple models to quantify ecological processes over large areas. Application of such models can be well demonstrated in fragmented semi-arid ecosystems where competition over resources is intense due to habitat loss, however, only a few studies have done so. Our aim was to model and study the integrated effect of spatial variation in potential soil moisture and patch size and shape on shrub-grass ratio (SGR) in a semi-arid fragmented environment. We specifically ask: (i) what factors most strongly relate to SGR in large remnant patches (> 1.6 ha), and (ii) do different factors more strongly relate to SGR in small patches (< 1.6 ha)? The study was carried out using 60 patches within a semi-arid fragmented environment in the Northern Negev of Israel. Aerial photographs and digital elevation models were used to map six environmental variables: wetness index, aspect, rock cover, rock pattern, patch area, and patch shape. The variables were designed in GIS and were modeled using fuzzy logic procedures to predict SGR, and these predictions were compared to shrub cover maps extracted using maximum likelihood classification of aerial photographs taken in September 2003. We found that in the study area, factors indicating potential soil moisture are most strongly related to SGR in large patches, whereas patch geometric attributes are more strongly relate to SGR in small patches.://000243619800010 ~ ISI Document Delivery No.: 127XO Times Cited: 0 Cited Reference Count: 51 Cited References: ACKERMANN O, 2004, CATENA, V57, P309 BARKUTIEL P, 2005, EUR IALE C LANDSC EC BARLING RD, 1994, WATER RESOUR RES, V30, P1029 BELSKY AJ, 1994, ECOLOGY, V75, P922 BOJORQUEZTAPIA LA, 2002, ENVIRON MANAGE, V30, P418 BRESHEARS DD, 1999, LANDSCAPE ECOL, V14, P465 BUCHBINDER B, 1996, GEOLOGICAL MAP HASHP BURROUGH PA, 2000, PRINCIPALS GEOGRAPHI CANFIELD HE, 2001, CATENA, V44, P1 COLLINGE SK, 2002, LANDSCAPE ECOL, V17, P647 DAN Y, 1988, MAN ENV SO SHEFELAH, P50 DANIN A, 1988, MAN ENV SO SHEFELAH, P59 DEBINSKI DM, 2000, CONSERV BIOL, V14, P342 DEBLOIS S, 2002, ECOGRAPHY, V25, P244 DEMERES MN, 2000, FUNDAMENTALS GEOGRAP EFRAT E, 1994, RURAL GEOGRAPHY ISRA FAHRIG L, 2003, ANNU REV ECOL EVOL S, V34, P487 FERNANDEZILLESCAS CP, 2003, ECOL MONOGR, V73, P207 FORMAN RTT, 1986, LANDSCAPE ECOLOGY GIBBS JP, 2001, BIOL CONSERV, V100, P15 HAILA Y, 2002, ECOL APPL, V12, P321 HILLERISLAMBERS R, 2001, ECOLOGY, V82, P50 HOUSE JI, 2003, J BIOGEOGR, V30, P1763 KUTIEL P, 1992, ISRAEL J BOT, V41, P243 LEHOUEROU HN, 1996, J ARID ENVIRON, V34, P133 LEVIN SA, 1974, AM NAT, V108, P207 MARSHALL EJR, 2002, AGR ECOSYST ENVIRON, V89, P5 MILNE BT, 1996, ECOLOGY, V77, P805 NOYMEIR I, 1973, ANNU REV ECOL SYST, V4, P25 POESEN J, 1994, CATENA, V23, P1 POESEN JW, 1998, GEOMORPHOLOGY, V23, P323 REID KD, 1999, SOIL SCI SOC AM J, V63, P1869 ROBINSON VB, 2003, T GIS, V7, P3 SANKARAN M, 2004, ECOL LETT, V7, P480 SCHLESINGER WH, 1990, SCIENCE, V247, P1043 SCHLESINGER WH, 1996, ECOLOGY, V77, P364 SCHOLES RJ, 1997, ANNU REV ECOL SYST, V28, P517 SCHUMAKER NH, 1996, ECOLOGY, V77, P1210 SHARON D, 2002, EARTH SURF PROC LAND, V27, P867 SHMIDA A, 1984, VEGETATIO, V58, P29 SHOSHANY M, 2002, REMOTE SENS ENVIRON, V82, P5 STERNBERG M, 2001, ECOL RES, V16, P335 SVORAY T, 2004, ECOL MODEL, V180, P537 SVORAY T, 2005, IEEE GEOSCI REMOTE S, V2, P211 VANWIJK MT, 2002, WATER RESOUR RES, V38 WALKER JP, 1999, WATER RESOUR RES, V35, P2259 WALTER H, 1971, ECOLOGY TROPICAL SUB WILBY A, 2004, OIKOS, V106, P209 YAIR A, 2002, GEOMORPHOLOGY, V42, P43 YAO J, 1999, ECOGRAPHY, V22, P715 ZANGVIL A, 1988, MAN ENV SO SHEFELAH, P42 0921-2973 Landsc. Ecol.ISI:000243619800010Ben Gurion Univ Negev, Dept Geog & Environm Dev, IL-84105 Beer Sheva, Israel. Svoray, T, Ben Gurion Univ Negev, Dept Geog & Environm Dev, IL-84105 Beer Sheva, Israel. tsvoray@bgu.ac.ilEnglish<7"Guirado, M. Pino, J. Roda, F.2007Comparing the role of site disturbance and landscape properties on understory species richness in fragmented periurban Mediterranean forests117-129Landscape Ecology221Mforest condition; forest fragmentation; forest patches; human-induced disturbance; multiple linear regressions; patch history; plant species richness; synanthropic species WESTERN CARPATHIAN FOOTHILLS; HABITAT FRAGMENTATION; WOODLAND PATCHES; HERBACEOUS-LAYER; PLANT DIVERSITY; LAND-USE; CONSERVATION; VEGETATION; ROADS; BIODIVERSITYArticleJan2We hypothesized that the spatial configuration and dynamics of periurban forest patches in Barcelona (NE of Spain) played a minor role in determining plant species richness and assemblage compared to site conditions, and particularly to both direct (measured at plot level) and potential (inferred from landscape metrics) human-associated site disturbance. The presence of all understory vascular plants was recorded on 252 plots of 100 m(2) randomly selected within forest patches ranging in size from 0.25 ha to 218 ha. Species were divided into 6 groups, according to their ecology and conservation status. Site condition was assessed at plot level and included physical attributes, human-induced disturbance and Quercus spp. tree cover. Landscape structure and dynamics were assessed from patch metrics and patch history. We also calculated a set of landscape metrics related to potential human accessibility to forests. Results of multiple linear regressions indicated that the variance explained for non-forest species groups was higher than for forest species richness. Most of the main correlates corresponded to site disturbance variables related to direct human alteration, or to landscape variables associated to indirect human effects on forests: Quercus tree cover (a proxy for successional status) was the most important correlate of non-forest species richness, which decreased when Quercus tree cover increased. Human-induced disturbance was an important correlate of synanthropic and total species richness, which were higher in recently managed and in highly frequented forests. Potential human accessibility also affected the richness of most species groups. In contrast, patch size, patch shape and connectivity played a minor role, as did patch history. We conclude that human influence on species richness in periurban forests takes place on a small scale, whereas large-scale effects attributable to landscape structure and fragmentation are comparatively less important. Implications of these results for the conservation of plant species in periurban forests are discussed.://000243619800011 ISI Document Delivery No.: 127XO Times Cited: 0 Cited Reference Count: 74 Cited References: BASCOMPTE J, 2001, ECOL LETT, V4, P417 BOLOS O, 1990, FLORA MANUAL PAISOS BROSOFSKE KD, 2001, FOREST ECOL MANAG, V146, P75 BROTHERS TS, 1992, CONSERV BIOL, V6, P91 CLIFF AD, 1981, SPATIAL PROCESSES MO DECONCHAT M, 2001, ANN FOR SCI, V58, P315 DZWONKO Z, 1988, VEGETATIO, V76, P15 DZWONKO Z, 1989, OIKOS, V56, P77 DZWONKO Z, 1992, J BIOGEOGR, V19, P195 ELKIE P, 1999, PATCH ANAL USERS MAN ERIKSSON O, 1996, OIKOS, V77, P248 FAHRIG L, 2003, ANNU REV ECOL EVOL S, V34, P487 FORMAN RTT, 1992, LANDSCAPE BOUNDARIES, P236 FORMAN RTT, 1995, LAND MOSAICS ECOLOGY FORMAN RTT, 1998, ANNU REV ECOL SYST, V29, P207 FORTIN MJ, 1989, VEGETATIO, V83, P209 GIBB H, 2002, BIOL CONSERV, V106, P91 GILLIAM FS, 1995, ECOL APPL, V5, P947 GILLIAM SF, 2002, FOREST ECOL MANAG, V155, P3 GODEFROID S, 2003, GLOBAL ECOL BIOGEOGR, V12, P287 GODEFROID S, 2004, BIOL CONSERV, V119, P405 GONDARD H, 2001, BIODIVERS CONSERV, V10, P189 GRACIA C, 2000, INVENTARI ECOLOGIC F, V5 GRAHAM MH, 2003, ECOLOGY, V84, P2809 GRASHOFBOKDAM C, 1997, J VEG SCI, V8, P21 GROVE AT, 1996, MEDITERRANEAN DESERT GUIRADO M, 2002, THESIS CREAF BARCELO GUIRADO M, 2006, GLOBAL ECOL BIOGEOGR, V15, P50 HANSKI I, 1999, METAPOPULATION ECOLO HARRIS LD, 1988, CONSERV BIOL, V2, P330 HARRISON S, 1999, ECOGRAPHY, V22, P225 HERSPERGER AM, 2003, OIKOS, V101, P279 HOBBS ER, 1988, LANDSCAPE ECOLOGY, V1, P141 HOBBS RJ, 2000, INVASIVE SPECIES CHA, P385 HOLT RD, 1995, ECOLOGY, V76, P1610 HONNAY O, 1999, BIOL CONSERV, V87, P73 HONNAY O, 1999, FOREST ECOL MANAG, V115, P157 HONNAY O, 2002, BIODIVERS CONSERV, V11, P213 INGHE O, 1985, OIKOS, V45, P400 JACQUEMYN H, 2003, ECOGRAPHY, V26, P768 JELINSKI DE, 1992, AM J BOT, V79, P728 LEGENDRE P, 2002, ECOGRAPHY, V25, P601 LEVENSON JB, 1981, FOREST ISLAND DYNAMI, P13 LUKEN JO, 1991, LANDSCAPE URBAN PLAN, V20, P315 MAESTRE FT, 2004, DIVERS DISTRIB, V10, P21 MCDONNELL MJ, 1990, ECOLOGY, V71, P1232 MCKINNEY ML, 2002, BIOSCIENCE, V52, P883 MEFFE R, 1994, PRINCIPLES CONSERVAT MOFFATT SF, 2004, PLANT ECOL, V174, P119 MOONEY AH, 1988, BIODIVERSITY, P156 MURCIA C, 1995, TRENDS ECOL EVOL, V10, P58 PARENDES LA, 2000, CONSERV BIOL, V14, P64 PARROTTA JA, 2002, FOREST ECOL MANAG, V169, P243 PAUCHARD A, 2004, CONSERV BIOL, V18, P1 PETERKEN GF, 1984, J ECOL, V72, P155 PETIT S, 2004, LANDSCAPE ECOL, V19, P463 PONS X, 2000, MIRAMON SISTEMA INFO PYSEK P, 1998, J BIOGEOGR, V25, P155 ROBERTS MR, 1995, J VEG SCI, V6, P903 ROSS KA, 2002, J BIOGEOGR, V29, P749 SAUNDERS DA, 1991, CONSERV BIOL, V5, P18 SELMANTS PC, 2003, FOREST ECOL MANAG, V185, P275 SPELLERBERG IF, 1998, GLOBAL ECOL BIOGEOGR, V7, P317 TELLERIA JL, 1999, ECOGRAPHY, V22, P13 TROMBULAK SC, 2000, CONSERV BIOL, V14, P18 VANRUREMONDE RHAC, 1991, J VEG SCI, V2, P377 VELLEND M, 2003, ECOLOGY, V84, P1158 VERDU JR, 2000, BIODIVERS CONSERV, V9, P1707 VERHEYEN K, 2004, ECOLOGY, V85, P3302 WATKINS RZ, 2003, CONSERV BIOL, V17, P411 WHITTAKER RJ, 2001, J BIOGEOGR, V16, P3 WIENS JA, 1992, LANDSCAPE BOUNDRIES, P216 WILCOVE DS, 1986, CONSERVATION BIOL SC, P237 WOOD A, 2000, ROOT CAUSES BIODIVER 0921-2973 Landsc. Ecol.ISI:000243619800011Autonomous Univ Barcelona, Ctr Ecol Res & Forestry Applicat CREAF, E-08193 Bellaterra, Spain. Univ Barcelona, Dept Plant Biol, E-08028 Barcelona, Spain. Guirado, M, Autonomous Univ Barcelona, Ctr Ecol Res & Forestry Applicat CREAF, E-08193 Bellaterra, Spain. m.guirado@creaf.uab.esEnglishT<7#Weir, J. E. S. Corlett, R. T.2007WHow far do birds disperse seeds in the degraded tropical landscape of Hong Kong, China?131-140Landscape Ecology221bulbuls; dispersal distance; frugivory; Garrulax; global warming; habitat fragmentation; landscape connectivity; pycnonotidae; seed shadows MONTANE FOREST; PATTERNS; TREE; FRAGMENTATION; CONSEQUENCES; MIGRATION; MOVEMENT; TRACKING; DISTANCE; BEHAVIORArticleJanInformation on seed dispersal distances is critical for understanding plant species persistence in habitat fragments and vegetation recovery when disturbance is reduced. In the degraded upland landscape of Hong Kong (22 degrees N), the bulbuls Pycnonotus sinensis and P. jocosus are responsible for a large proportion of seed movements. Dispersal distances were estimated from gut passage times (GPTs) and movement patterns determined by radio-telemetry. Estimates were also made for the hwamei, Garrulax canorus. Seven adult P. sinensis and four adult G. canorus were tracked in winter, and six juvenile P. sinensis, three juvenile P. jocosus and two juvenile G. canorus in summer. GPTs were 5-122 min in the bulbuls and 18-61 min in the hwamei. Most 10min movements were < 100 m for the bulbuls and < 50 m for the hwamei, but the largest were > 1300 m for both bulbuls and 940 m for the hwamei. Displacement-time graphs generally levelled off rapidly, with median displacements < 60 m after an hour, except with summer juvenile bulbuls. Median displacements during median gut passage times for seeds from single-seeded fruits were 40, 116 and 131 m, respectively, for winter adult P. sinensis and summer juvenile P. sinensis and P. jocosus. Maximum observed displacements during the maximum measured GPTs were > 1 km for all bulbuls. Estimated dispersal distances were shorter for hwameis. The radio-telemetry results were supplemented by 49 h of visual observations, during which 1,510 bird movements across open areas were observed, 64% by P. sinensis, 13% by P. jocosus, and 0.5% by G. canorus. The bulbuls, therefore, connect habitat fragments in upland Hong Kong for plants with fruits within their maximum gape width.://000243619800012 ISI Document Delivery No.: 127XO Times Cited: 0 Cited Reference Count: 42 Cited References: AU AYY, 2006, PLANT ECOL CORLETT RT, 1996, J TROP ECOL 6, V12, P819 CORLETT RT, 1998, FORKTAIL, V13, P23 CORLETT RT, 2002, SEED DISPERSAL FRUGI, P451 DUDGEON D, 2004, ECOLOGY BIODIVERSITY FUKUI A, 2003, ORNITHOLOGICAL SCI, V2, P41 FUKUI AW, 1995, RES POPUL ECOL, V37, P211 GODOY JA, 2001, MOL ECOL, V10, P2275 GRAHAM CH, 1995, BIOTROPICA, V27, P479 HEWITT N, 2002, J BIOGEOGR, V29, P337 HIGGINS SI, 2003, OIKOS, V101, P354 HOLBROOK KM, 2000, OECOLOGIA, V125, P249 HOUGHTON J, 2005, REP PROG PHYS, V68, P1343 HOWE HF, 2004, BIOSCIENCE, V54, P651 ISLER ML, 1999, TANAGERS NATURAL HIS IZHAKI I, 1992, CONDOR, V94, P912 JOHST K, 2002, OIKOS, V98, P263 KEITH S, 1992, BIRDS AFRICA, V4, P279 KO IWP, 1999, THESIS U HONG KONG H LEUNG YK, 2004, HONG KONG METEOROL S, V14, P21 LEVEN MR, 2000, THESIS U HONG KONG H LEVEY DJ, 2000, ECOLOGY, V81, P267 LEVIN SA, 2003, ANNU REV ECOL EVOL S, V34, P575 LEVINE JM, 2003, ANNU REV ECOL EVOL S, V34, P549 MAHLI Y, 2004, PHILOS T R SOC LON B, V359, P311 MANDONDALGER I, 2004, J TROP ECOL 6, V20, P635 NATHAN R, 2000, TRENDS ECOL EVOL, V15, P278 PEARSON RG, 2005, BIOL CONSERV, V123, P389 PEH KSH, 2002, RAFFLES B ZOOL, V50, P251 PIZO MA, 2004, ORNITOL NEOTROP S, V15, P117 RABINOWITZ AR, 1991, J ZOOL, V223, P281 RAYNER JMV, 1985, DICT BIRDS, P224 SCHABACKER J, 2000, ECOTROPICA, V6, P157 SO SNH, 1999, THESIS U HONG KONG H SUN C, 1997, OECOLOGIA, V112, P94 WANG BC, 2002, TRENDS ECOL EVOL, V17, P379 WANG H, 2001, MAMM BIOL, V66, P251 WEHNCKE EV, 2003, J ECOL, V91, P677 WESTCOTT DA, 2000, OECOLOGIA, V122, P249 WILLIAMS TC, 2001, AUK, V118, P389 YUMOTO T, 1999, ECOL RES, V14, P179 ZHUANG XY, 1997, J TROP ECOL 6, V13, P857 0921-2973 Landsc. Ecol.ISI:000243619800012Univ Hong Kong, Dept Ecol & Biodivers, Hong Kong, Hong Kong, Peoples R China. Corlett, RT, Univ Hong Kong, Dept Ecol & Biodivers, Pokfulam Rd, Hong Kong, Hong Kong, Peoples R China. Corlett@hkucc.hku.hkEnglish<7$2Gustafson, E. J. Lytle, D. E. Swaty, R. Loehle, C.2007ySimulating the cumulative effects of multiple forest management strategies on landscape measures of forest sustainability141-156Landscape Ecology221timber management; multiple owner landscapes; landscape pattern; HARVEST simulation model; sustainable forestry; biodiversity; forest products industry BIODIVERSITY; OWNERSHIP; FRAGMENTATION; CONSEQUENCES; DEERArticleJanWhile the cumulative effects of the actions of multiple owners have long been recognized as critically relevant to efforts to maintain sustainable forests at the landscape scale, few studies have addressed these effects. We used the HARVEST timber harvest simulator to predict the cumulative effects of four owner groups (two paper companies, a state forest and non-industrial private owners) with different management objectives on landscape pattern in an upper Michigan landscape managed primarily for timber production. We quantified trends in landscape pattern metrics that were linked to Montreal Process indicators of forest sustainability, and used a simple wildlife habitat model to project habitat trends. Our results showed that most trends were considered favorable for forest sustainability, but that some were not. The proportion of all age classes and some forest types moved closer to presettlement conditions. The trend for the size of uneven-aged patches was essentially flat while the average size of patches of the oldest and youngest age classes increased and the size of patches of the remaining age classes decreased. Forest fragmentation generally declined, but edge density of age classes increased. Late seral forest habitat increased while early successional habitat declined. The owners use different management systems that cumulatively produce a diversity of habitats. Our approach provides a tool to evaluate such cumulative effects on other landscapes owned by multiple owners. The approach holds promise for helping landowner groups develop and evaluate cooperative strategies to improve landscape patterns for forest sustainability.://000243619800013 PISI Document Delivery No.: 127XO Times Cited: 0 Cited Reference Count: 33 Cited References: *MI DEP NAT RES, 2001, IFMAP GAP UPP PEN LA *MONT PROC WORK GR, 1999, CRIT IND CONS SUST M ALVERSON WS, 1988, CONSERV BIOL, V2, P348 COMER PJ, 1995, MIGHIGANS PRESETTLEM DAVIS MB, 1981, FOREST SUCCESSION CO, P132 DAVIS MB, 1998, ECOLOGY, V79, P2641 DOEPKER RL, 2000, MIWILD MICHIGAN WILD FRELICH LE, 2002, FOREST DYNAMICS DIST GRUMBINE RE, 1994, CONSERV BIOL, V8, P27 GUSTAFSON EJ, 1999, LANDSCAPE ECOLOGICAL, P109 GUSTAFSON EJ, 1999, SPATIAL MODELING FOR, P309 GUSTAFSON EJ, 2002, COMPUT ELECTRON AGR, V33, P179 GUSTAFSON EJ, 2005, HARVEST WINDOWS V6 1 HANSEN AJ, 1991, BIOSCIENCE, V41, P382 HARPER KA, 2005, CONSERV BIOL, V19, P768 JORDAN JK, 2002, P LAND TYP ASS C DEV KEYS J, 1995, ECOLOGICAL UNITS E U KNIGHT RL, 1998, STEWARDSHIP BOUNDARI KURTTILA M, 2001, BOREAL ENVIRON RES, V6, P285 KURTTILA M, 2002, FOREST ECOL MANAG, V166, P69 KURTTILA M, 2003, LANDSCAPE ECOL, V18, P529 LUCIER AA, 1997, BIOMASS BIOENERG, V13, P193 MLADENOFF DJ, 1993, CONSERV BIOL, V7, P889 MLADENOFF DJ, 2004, APACK 2 23 ANAL SOFT MOORE MM, 1999, ECOL APPL, V9, P1266 PARKHURST GM, 2002, ECOL ECON, V41, P305 PETERSON A, 1998, GUIDEBOOK BEST MANAG POLASKY S, 2005, ECOL APPL, V15, P1387 REICE SR, 1994, AM SCI, V82, P424 SAUNDERS DA, 1991, CONSERV BIOL, V5, P18 SCHULTE LA, 2001, J FOREST, V99, P5 SWANSON FJ, 1994, ECOSYSTEM MANAGEMENT, V2 WOOTTON JT, 2001, ECOL LETT, V4, P46 0921-2973 Landsc. Ecol.ISI:000243619800013USDA, Forest Serv, N Cent Res Stn, Rhinelander, WI 54501 USA. USDA, Forest Serv, N Cent Res Stn, Grand Rapids, MN 55744 USA. Nature Conservancy, Upper Peninsula Conservat Off, Marquette, MI 49855 USA. Natl Council Air & Stream Improvement, Naperville, IL 60540 USA. Nature Conservancy, Ohio Chapter, Dublin, OH 43017 USA. Gustafson, EJ, USDA, Forest Serv, N Cent Res Stn, 5985 Highway K, Rhinelander, WI 54501 USA. egustafson@fs.fed.usEnglish<7%Verburg, P. H.2006=Simulating feedbacks in land use and land cover change models 1171-1183Landscape Ecology218land use and land cover change; model; feedback; complex systems HOUSEHOLD DECISION-MAKING; AGENT-BASED MODEL; TEMPORAL COMPLEXITY; MULTISCALE ANALYSIS; LANDCOVER CHANGE; DRIVING FORCES; SPATIAL MODELS; SYSTEMS; FUTURE; LEVELArticleNov%In spite of the many advances in land use and land cover change modelling over the past decade many challenges remain. One of these challenges relates to the explicit treatment of feedback mechanisms in descriptive models of the land use system. This paper argues for model-based analysis to explore the role of feedback mechanisms as determinants of land use dynamics and system evolution. Different types of feedbacks in the land use system are discussed addressing interactions over scales of analysis, feedbacks between impacts and driving forces of land use change and feedbacks between agents and land units. The inclusion of feedbacks in land use models will require new methods for model parameterization and calibration, but will ultimately increase our understanding of land use system dynamics.://000242089300001 ISI Document Delivery No.: 106GP Times Cited: 0 Cited Reference Count: 94 Cited References: *EEA, 1999, ENV EUR UN TURN CENT *GLP, 2005, 53IHDP IGBP ADGER WN, 2000, PROG HUM GEOG, V24, P347 AGARWAL C, 2001, REV ASSESSMENT LAND BAKER WL, 1999, SPATIAL MODELING FOR, P333 BERGER T, 2001, AGR ECON, V25, P245 BOLLIGER J, 2005, ECOL COMPLEX, V2, P107 BONABEAU E, 2002, P NATL ACAD SCI U S3, V99, P7280 BOSSEL H, 1999, INDICATORS SUSTAINAB BOUSQUET F, 2004, ECOL MODEL, V176, P313 BRAIMOH AK, 2004, EARTH INTERACT, V8, P1 BRIASSOULIS H, 2000, WEB BOOK REGIONAL SC BROWN DG, 2005, INT J GEOGR INF SCI, V19, P153 BROWN DG, 2005, J GEOGRAPHICAL SYSTE, V7, P25 BURGI M, 2004, LANDSCAPE ECOL, V19, P857 BYRNE D, 1998, COMPLEXITY THEORY SO CARVALHO G, 2004, ENV DEV SUSTAIN, V6, P163 CASTELLA JC, 2005, AGR SYST, V86, P312 CASTELLA JC, 2005, ECOLOGY SOC, V10, P27 COSTANZA R, 2003, FUTURES, V35, P651 COUCLELIS H, 2001, GEOGRAPHIC INFORM SY, P33 COUCLELIS H, 2005, ENVIRON PLANN A, V37, P1353 DEADMAN P, 2004, ENVIRON PLANN B, V31, P693 DENIJS TCM, 2004, J ENVIRON MANAGE, V72, P35 EVANS TP, 2001, ECOL MODEL, V143, P95 EVANS TP, 2004, J ENVIRON MANAGE, V72, P57 FISCHER G, 2001, AGR ECOSYST ENVIRON, V85, P163 FOLEY JA, 2003, FRONT ECOL ENVIRON, V1, P38 FOLEY JA, 2005, SCIENCE, V309, P570 GEIST HJ, 2002, BIOSCIENCE, V52, P143 GEIST HJ, 2004, BIOSCIENCE, V54, P817 GEOGHEGAN J, 1998, PEOPLE PIXELS LINKIN, P51 GREENE WH, 2000, ECONOMETRIC ANAL GRIFFITH DA, 2005, ANN ASSOC AM GEOGR, V95, P740 GRIMM V, 1999, ECOL MODEL, V115, P129 GUISAN A, 2000, ECOL MODEL, V135, P147 GUTMAN G, 2004, LAND CHANGE SCI OBSE HEISTERMANN M, 2006, AGR ECOSYST ENVIRON, V114, P141 HEUVELINK GBM, 1993, INT J GEOGR INF SYST, V7, P231 HIETEL E, 2004, LANDSCAPE ECOL, V19, P473 HOLLING CS, 1996, RIGHTS NATURE ECOLOG, P57 HOLLING CS, 2001, ECOSYSTEMS, V4, P390 HOSHINO S, 2001, LAND USE POLICY, V18, P75 LAMBIN EF, 2000, 48 IGBP LAMBIN EF, 2003, ANNU REV ENV RESOUR, V28, P205 LEVIN SA, 1998, ECOSYSTEMS, V1, P431 LIGTENBERG A, 2004, J ENVIRON MANAGE, V72, P43 LOW B, 1999, ECOL ECON, V31, P227 MANSON SM, 2001, GEOFORUM, V32, P405 MANSON SM, 2005, AGR ECOSYST ENVIRON, V111, P47 MEIJL HV, 2006, AGR ECOSYST ENVIRON, V114, P21 MILNE BT, 1998, ECOSYSTEMS, V1, P449 MUNROE DK, 2002, AGR ECON, V27, P355 NELSON GC, 2001, LAND ECON, V77, P187 NEPSTAD D, 2001, FOREST ECOL MANAG, V154, P395 ORESKES N, 1994, SCIENCE, V263, P641 OSULLIVAN D, 2000, ENVIRON PLANN A, V32, P1409 OSULLIVAN D, 2004, T I BRIT GEOGR, V29, P282 OVERMARS KP, 2005, INT J GEOGR INF SCI, V19, P125 OVERMARS KP, 2006, AGR SYST, V89, P435 PAN WKY, 2005, GLOBAL PLANET CHANGE, V47, P232 PARKER DC, 2003, ANN ASSOC AM GEOGR, V93, P314 PARKER DC, 2004, AGR ECOSYST ENVIRON, V101, P233 PIJANOWSKI BC, 2002, COMPUTERS ENV URBAN, V26, P553 POLSKY C, 2004, ANN ASSOC AM GEOGR, V94, P549 PONTIUS RG, 2004, ECOL MODEL, V179, P445 REIDSMA P, 2006, AGR ECOSYST ENVIRON, V114, P86 RINDFUSS RR, 2003, PEOPLE ENV APPROACHE, P1 RINDFUSS RR, 2004, P NATL ACAD SCI USA, V101, P13976 ROUCHIER J, 2001, J ECON DYN CONTROL, V25, P527 RUDEL TK, 2005, TROPICAL FORESTS REG SCHMIT C, 2006, ENVIRON SCI POLICY, V9, P174 SCHOORL JM, 2004, CATENA, V57, P35 SCOONES I, 1999, ANNU REV ANTHROPOL, V28, P479 STEPHENNE N, 2001, AGR ECOSYST ENVIRON, V85, P145 TURNER BL, 1995, 35 IGBP TURNER BL, 1997, ECUMENE, V4, P196 TURNER BL, 2004, LAND CHANGE SCI OBSE, P431 VANNOORDWIJK M, 2002, ECOL MODEL, V149, P113 VELDKAMP A, 2001, AGR ECOSYST ENVIRON, V85, P1 VERBURG PH, 1999, ECOL MODEL, V116, P45 VERBURG PH, 2000, ECOSYSTEMS, V3, P369 VERBURG PH, 2002, ENVIRON MANAGE, V30, P391 VERBURG PH, 2004, GEOJOURNAL, V61, P309 VERBURG PH, 2004, GEOPH MONOG SERIES, V153, P217 VERBURG PH, 2004, LANDSCAPE ECOL, V19, P77 VOINOV A, 1999, ENVIRON MODELL SOFTW, V14, P473 WALSH SJ, 1999, PHOTOGRAMM ENG REM S, V65, P97 WALSH SJ, 2001, AGR ECOSYST ENVIRON, V85, P47 WEAR DN, 1998, ECOSYSTEMS, V1, P575 WHITE R, 2000, COMPUTERS ENV URBAN, V24, P383 WU JG, 2002, ECOL MODEL, V153, P1 WU JG, 2004, LANDSCAPE ECOL, V19, P125 ZALIDIS GC, 2004, J ENVIRON MANAGE, V70, P315 0921-2973 Landsc. Ecol.ISI:000242089300001Univ Wageningen & Res Ctr, Dept Environm Sci, NL-6700 AA Wageningen, Netherlands. Verburg, PH, Univ Wageningen & Res Ctr, Dept Environm Sci, POB 37, NL-6700 AA Wageningen, Netherlands. Peter.Verburg@wur.nlEnglish <7&$Chen, X. W. Barrows, C. W. Li, B. L.2006SPhase coupling and spatial synchrony of subpopulations of an endangered dune lizard 1185-1193Landscape Ecology218Coachella Valley fringe-toed lizard; density; reproductive effort; spatial synchrony; phase coupling POPULATION-DYNAMICS; MOTH; OUTBREAKS; PATTERNSArticleNovExamining demographic phase coupling and spatial synchrony is important for understanding complicated spatiotemporal population dynamics. It is also necessary for protecting rare and endangered species; populations whose dynamics are controlled by resource flux will face increased extinction risk if environmental conditions that drive those resources become spatially synchronized. In this study, we studied the spatial synchrony of subpopulations of the threatened Coachella Valley fringe-toed lizard (Uma inonzata), on its remaining sand dune habitat in the Coachella Valley of California. Our results indicated that there is a high level of spatial synchrony between lizard density and their mean reproductive effort for two subpopulations separated by a relatively short distance. High levels of spatial synchrony also exist between the mean lizards' reproductive effort and annual precipitation. We measured spatial synchrony using four separate methods; using different methods allows understanding of complicated ecological interactions.://000242089300002 ISI Document Delivery No.: 106GP Times Cited: 0 Cited Reference Count: 23 Cited References: BARROWS CW, 1996, CONSERV BIOL, V10, P888 BARROWS CW, 1997, SOUTHWEST NAT, V42, P218 BJORNSTAD ON, 1999, TRENDS ECOL EVOL, V14, P427 BUONACCORSI JP, 2001, ECOLOGY, V82, P1668 GRENFELL BT, 1998, NATURE, V394, P674 HANSKI I, 1993, J ANIM ECOL, V62, P656 HAYDON DT, 2000, THEOR POPUL BIOL, V58, P239 HAYDON DT, 2001, P NATL ACAD SCI USA, V98, P13149 HEINO M, 1997, P ROY SOC LOND B BIO, V264, P481 HUDSON PJ, 1999, TRENDS ECOL EVOL, V14, P1 IMS RA, 1990, OIKOS, V57, P381 KOENIG WD, 1998, CONSERV BIOL, V12, P612 KROHNE DT, 1988, CAN J ZOOL, V66, P2170 LAMBIN X, 1998, P ROY SOC LOND B BIO, V265, P1491 MASON RR, 1978, ENVIRON ENTOMOL, V7, P672 NORRIS KS, 1958, B AM MUS NAT HIST, V114, P251 PALMQVIST E, 1998, OIKOS, V83, P359 PELTONEN M, 2002, ECOLOGY, V83, P3120 POLLARD E, 1991, OIKOS, V60, P7 POST E, 2004, P NATL ACAD SCI USA, V101, P9286 RANTA E, 1995, P ROY SOC LOND B BIO, V262, P113 STEBBINS RC, 1944, ECOL MONOGR, V14, P311 WILLIAMS DW, 1995, ENVIRON ENTOMOL, V24, P987 0921-2973 Landsc. Ecol.ISI:000242089300002fAlabama A&M Univ, Ctr Forestry & Ecol, Normal, AL 35762 USA. Univ Calif Riverside, Dept Bot & Plant Sci, Riverside, CA 92521 USA. Univ Calif Riverside, Ctr Conservat Biol, Riverside, CA 92521 USA. Ctr Nat Lands Management, La Quinta, CA 92253 USA. Chen, XW, Alabama A&M Univ, Ctr Forestry & Ecol, POB 1927, Normal, AL 35762 USA. xiongwen.chen@e-mail.aamu.eduEnglish <7'Bossuyt, B. Honnay, O.2006Interactions between plant life span, seed dispersal capacity and fecundity determine metapopulation viability in a dynamic landscape 1195-1205Landscape Ecology218clonality; extinction risk; population growth rate; RAMAS/Metapop; seed dispersal; stage structured model SCALE SPATIAL DYNAMICS; SUCCESSIONAL LANDSCAPES; EXTINCTION; PERSISTENCE; POPULATIONS; MODEL; ECOSYSTEMS; EVOLUTION; REMNANTArticleNovgClassical metapopulation models do not account for temporal changes in the suitability of habitat patches. In reality, however, the carrying capacity of most habitat types is not constant in time due to natural succession processes. In this study, we modeled plant metapopulation persistence in a successional landscape with disappearing and emerging habitat patches, based on a realistic dune slack landscape at the Belgian-French coast. We focused on the effects of the variation of different plant traits on metapopulation persistence in this changing landscape. Therefore, we used a stage based stochastic metapopulation model implemented in RAMAS/Metapop, simulating a large variation in plant traits but keeping landscape characteristics such as patch turnover rate and patch life span constant. The results confirm the conclusions of earlier modeling work that seed dispersal distance and seed emigration rate both have an important effect on metapopulation persistence. We also found that high population growth rate or high recruitment considerably decreased the extinction risk of the metapopulation. Additionally, a long plant life span had a strong positive effect on metapopulation persistence, irrespective of the plant's dispersal capacity and population growth rate. Plant species that invest in life span require less investment in offspring and dispersal capacity to avoid extinction, even in dynamic landscapes with deterministic changes in habitat quality. Moreover, metapopulations of long-lived plant species were found to be much less sensitive to high levels of environmental stochasticity than short-lived species.://000242089300003 tISI Document Delivery No.: 106GP Times Cited: 0 Cited Reference Count: 36 Cited References: AKCAKAYA HR, 2002, RAMAS METAPOP VIABIL AKCAKAYA HR, 2004, CONSERV BIOL, V18, P526 AMARASEKARE P, 2001, J THEOR BIOL, V209, P333 BAGUETTE M, 2004, BASIC APPL ECOL, V5, P213 BOSSUYT B, 2003, J VEG SCI, V14, P781 BOUGHTON D, 2002, CONSERV ECOL, V6 BRACHET S, 1999, J THEOR BIOL, V198, P479 BROWN JH, 1977, ECOLOGY, V58, P445 DRAKE JM, 2004, ECOL LETT, V7, P26 DURRETT R, 1994, THEOR POPUL BIOL, V46, P363 EHRLEN J, 1998, APPL VEG SCI, V1, P29 EHRLEN J, 2002, OIKOS, V98, P308 EHRLEN J, 2003, J ECOL, V91, P316 ELLNER SP, 2003, ECOLOGY, V84, P882 ERIKSSON O, 1994, ECOL RES, V9, P257 ERIKSSON O, 1996, OIKOS, V77, P248 ERIKSSON O, 2000, GLOBAL ECOL BIOGEOGR, V9, P443 ERIKSSON O, 2001, INTEGRATING ECOLOGY, P157 FRECKLETON RP, 2002, J ECOL, V90, P419 FRECKLETON RP, 2003, J ECOL, V91, P321 HANSKI I, 1994, J ANIM ECOL, V63, P151 HASTINGS A, 2003, SCIENCE, V301, P1525 HONNAY O, 2005, OIKOS, V108, P427 HUSBAND BC, 1996, J ECOL, V84, P461 JOHNSON MP, 2000, OIKOS, V88, P67 JOHST K, 2002, OIKOS, V98, P263 KEYMER JE, 2000, AM NAT, V156, P478 KNEITEL JM, 2004, ECOL LETT, V7, P69 LEVINS R, 1969, B ENTOMOL SOC AM, V15, P237 LINDBORG R, 2004, ECOLOGY, V85, P1840 MATLACK GR, 2004, J ECOL, V92, P1025 OLIVIERI I, 1995, AM NAT, V146, P202 OUBORG NJ, 2004, ECOLOGY GENETICS EVO, P447 PERRY JN, 1993, J ECOL, V81, P453 RONCE O, 1997, AM NAT, V150, P220 VERHEYEN K, 2004, ECOLOGY, V85, P3302 0921-2973 Landsc. Ecol.ISI:000242089300003 Univ Ghent, Dept Biol, Terr Ecol Unit, B-9000 Ghent, Belgium. Katholieke Univ Leuven, Dept Biol, Div Plant Systemat & Ecol, B-3001 Heverlee, Belgium. Bossuyt, B, Univ Ghent, Dept Biol, Terr Ecol Unit, Ledeganckstr 35, B-9000 Ghent, Belgium. beatrijs.bossuyt@Ugent.beEnglishJ<7(.Li, X. H. Li, D. M. Ma, Z. J. Schneider, D. C.2006QNest site use by crested ibis: dependence of a multifactor model on spatial scale 1207-1216Landscape Ecology218habitat; logistic regression; multiscale analysis; nest site use; Nipponia nippon; geographic information system; reintroduction; scaling; semivariogram LANDSCAPE PATTERN-ANALYSIS; INVENTORY DATA; HABITAT; ECOLOGY; PRODUCTIVITY; BIRDS; PREYArticleNovThe crested ibis (Nipponia nippon), a species at the brink of extinction in 1981, remain restricted to a small (25 km radius) area of temperate forests in central China. To improve the chances of successful reintroduction into new areas we developed a multifactor logistic regression model of habitat association at multiple scales. Using habitat variables, i.e. vegetation, human impact, elevation, and wetland, we compared occupied and unoccupied sites at grain sizes ranging from I to 6400 ha. The goodness-of-fit of the habitat suitability model depended on grain size, with the best fit (most information) at a grain size of 2 ha. Semivariograms showed the habitat variables at control sites have a gradient pattern, yet the crested ibis had their specific habitat preferences, and only selected a narrow range from the available gradient. Our results indicated that spatial scale needs to be considered in developing habitat models for applications such as conservation planning.://000242089300004 ISI Document Delivery No.: 106GP Times Cited: 0 Cited Reference Count: 43 Cited References: *BIRDLIFE INT, 2003, NIPP NIPP IUCN 2003 *SAS I INC, 1999, SAS SYST 8 0 *YANG COUNT AGR DI, 1986, REP AGR DIV YOUNG CO ANAND M, 2001, COMMUNITY ECOL, V2, P161 BEVERS M, 1999, J ANIM ECOL, V68, P976 CAO YH, 1994, CHINESE FOREST, P28 CODY ML, 1981, BIOSCIENCE, V31, P107 HATTEN JR, 2003, J WILDLIFE MANAGE, V67, P774 HULBERT IAR, 2001, J APPL ECOL, V38, P869 JAKOBSSON U, 2004, SCAND J CARING SCI, V18, P437 JELINSKI DE, 1996, LANDSCAPE ECOL, V11, P129 JENKINS JC, 2001, ECOL APPL, V11, P1174 JOHNSON DH, 1980, ECOLOGY, V61, P65 LEVIN SA, 1992, ECOLOGY, V73, P1943 LI XH, 1998, ECOL RES, V13, P323 LI XH, 2001, CHINA BIODIVERS, V9, P352 LI XH, 2002, ACTA ZOOL SINICA, V48, P725 LI XH, 2002, ECOL RES, V17, P565 LIU YZ, 1981, ACTA ZOOL SINICA, V27, P273 MA ZJ, 2001, ZOOL RES, V22, P46 MACKINNON JL, 2001, J ANIM ECOL, V70, P101 MARCEAU DJ, 1999, CANADIAN J REMOTE SE, V25, P347 NI J, 2003, FOREST ECOL MANAG, V176, P485 OPENSHAW S, 1979, STAT APPL SPATIAL SC, P127 QI Y, 1996, LANDSCAPE ECOL, V11, P39 ROBINSON RA, 2004, IBIS S2, V146, P87 ROSE GA, 1990, ECOLOGY, V71, P33 SCHNEIDER DC, 1986, MAR ECOL-PROG SER, V32, P237 SCHNEIDER DC, 1994, QUANTITATIVE ECOLOGY SCHNEIDER DC, 2001, BIOSCIENCE, V51, P545 SHI D, 1991, J NW U, V21, P37 SHI DC, 1991, J NW U S, V21, P15 SHI DC, 1991, J NW U S, V21, P25 SOKAL RR, 1995, BIOMETRY STORE R, 2003, ECOL MODEL, V169, P1 VEIT RR, 1993, J ANIM ECOL, V62, P551 WANG ZY, 1985, J ECOL, P10 WANG ZY, 1993, J HANZHONG NORMAL CO, V11, P59 WANG ZY, 1994, J NW U, V24, P1 WANG ZY, 1995, J HANZHONG NORMAL CO, V13, P54 WU J, 2000, GEOGRAPHIC INFORMATI, V6, P1 WU JG, 2004, LANDSCAPE ECOL, V19, P125 ZHAI TQ, 1991, J HANZHONG NORMAL CO, P72 0921-2973 Landsc. Ecol.ISI:000242089300004Univ New Brunswick, Dept Biol, St John, NB E2L 4L5, Canada. Chinese Acad Sci, Inst Zool, Beijing 100080, Peoples R China. Fudan Univ, Minist Educ, Sch Life Sci, Inst Biodivers Sci,Key Lab Biodivers Sci & Ecol E, Shanghai 200433, Peoples R China. Mem Univ Newfoundland, Ctr Ocean Sci, St John, NF A1C 5S7, Canada. Li, XH, Univ New Brunswick, Dept Biol, St John, NB E2L 4L5, Canada. xinhai.li@gmail.comEnglish<7)EYao, J. Peters, D. P. C. Havstad, K. M. Gibbens, R. P. Herrick, J. E.2006SMulti-scale factors and long-term responses of Chihuahuan Desert grasses to drought 1217-1231Landscape Ecology218arid grasslands; desertification; drought; grazing; perennial grasses; transport processes SEMIDESERT GRASSLAND RANGE; CONSERVATION BIOLOGY; ECOLOGICAL RESEARCH; TRANSITION ZONE; UNITED-STATES; VEGETATION; PATTERNS; DESERTIFICATION; EROSION; SCALEArticleNovFactors with variation at broad (e.g., climate) and fine scales (e.g., soil texture) that influence local processes at the plant scale (e.g., competition) have often been used to infer controls on spatial patterns and temporal trends in vegetation. However, these factors can be insufficient to explain spatial and temporal variation in grass cover for and and semiarid grasslands during an extreme drought that promotes woody plant encroachment. Transport of materials among patches may also be important to this variation. We used long-term cover data (19152001) combined with recently collected field data and spatial databases from a site in the northern Chihuahuan Desert to assess temporal trends in cover and the relative importance of factors at three scales (plant. patch, landscape unit) in explaining spatial variation in grass cover. We examined cover of five important grass species from two topographic positions before, during, and after the extreme drought of the 1950s. Our results show that dynamics before, during, and after the drought varied by species rather than by topographic position. Different factors were related to cover of each species in each time period. Factors at the landscape unit scale (rainfall, stocking rate) were related to grass cover in the pre- and post-drought periods whereas only the plant-scale factor of soil texture was significantly related to cover of two upland species during the drought. Patch-scale factors associated with the redistribution of water (microtopography) were important for different species in the pre- and post-drought period. Another patch-scale factor, distance from historic shrub populations, was important to the persistence of the dominant grass in uplands (Bouteloua eriopoda) through time. Our results suggest the importance of local processes during the drought, and transport processes before and after the drought with different relationships for different species. Disentangling the relative importance of factors at different spatial scales to spatial patterns and long-term trends in grass cover can provide new insights into the key processes driving these historic patterns, and can be used to improve forecasts of vegetation change in and and semiarid areas.://000242089300005 < ISI Document Delivery No.: 106GP Times Cited: 0 Cited Reference Count: 61 Cited References: ABRAHAMSON WG, 2003, ECOLOGY, V84, P2476 ANDERSON JE, 2001, ECOL MONOGR, V71, P531 ARCHER S, 1994, ECOLOGICAL IMPLICATI, P13 ARNOLD GW, 1978, ETHOLOGY FREE RANGIN BAILEY DW, 1996, J RANGE MANAGE, V49, P386 BIONDINI ME, 1998, ECOL APPL, V8, P469 BRESHEARS DD, 2003, EARTH SURF PROC LAND, V28, P1189 BROWN BJ, 1989, OIKOS, V54, P189 BUFFINGTON LC, 1965, ECOL MONOGR, V35, P139 CHEVAN A, 1991, AM STAT, V45, P90 CONLEY W, 1992, COENOSES, V7, P55 FOSTER DR, 1998, ECOSYSTEMS, V1, P497 FREDRICKSON E, 1998, J ARID ENVIRON, V39, P191 GEE GW, 1986, METHODS SOIL ANAL, V1, P383 GIBBENS RP, IN PRESS 1950S DROUG GIBBENS RP, 1983, J RANGE MANAGE, V36, P145 GIBBENS RP, 1987, J RANGE MANAGE, V40, P136 GIBBENS RP, 1988, J RANGE MANAGE, V41, P186 GIBBENS RP, 2005, J ARID ENVIRON, V61, P651 GROSS BD, 2004, THESIS NEW MEXICO ST HENNESSY JT, 1983, J RANGE MANAGE, V36, P723 HERBEL CH, 1972, ECOLOGY, V53, P1084 HEYERDAHL EK, 2001, ECOLOGY, V82, P660 HOBBIE JE, 2003, BIOSCIENCE, V53, P21 HOCHSTRASSER T, 2002, J ARID ENVIRON, V51, P55 HOCHSTRASSER T, 2004, J VEG SCI, V15, P615 HUMPHREY RR, 1958, BOT REV, V24, P193 KIE JG, 2002, ECOLOGY, V83, P530 LUDWIG JA, 1997, LANDSCAPE ECOLOGY FU LUDWIG JA, 2005, ECOLOGY, V86, P288 MACK RN, 2000, ECOL APPL, V10, P689 MACNALLY R, 2000, BIODIVERS CONSERV, V9, P655 MACNALLY R, 2002, BIODIVERS CONSERV, V11, P1397 MCAULIFFE JR, 1994, ECOL MONOGR, V64, P111 MCEUEN AB, 2004, ECOLOGY, V85, P507 MONGER HC, 2002, ENCY SOIL SCI, P84 NASH MS, 1999, ECOL APPL, V9, P814 NELSON EW, 1934, USDA TECHNICAL B, V409 OKIN GS, 2001, J GEOPHYS RES-ATMOS, V106, P9673 PARSHALL T, 2003, ECOLOGY, V84, P736 PETERS DPC, IN PRESS BIOSCIENCE PETERS DPC, 2004, P NATL ACAD SCI USA, V101, P15130 PETERS DPC, 2004, WEED TECHNOL S, V18, P1221 PETERS DPC, 2006, STRUCTURE FUNCTION C POWERS JS, 1999, LANDSCAPE ECOL, V14, P105 REYNOLDS JF, 1999, ECOL MONOGR, V69, P69 SAXTON KE, 1986, SOIL SCI SOC AM J, V50, P1031 SCHLESINGER WH, 1990, SCIENCE, V247, P1043 SENFT RL, 1987, BIOSCIENCE, V37, P789 STEFFEN W, 2002, CHALLENGES CHANGING TEASCHNER TB, 2001, INFLUENCE SOIL DEPTH TONGWAY DJ, 2001, BANDED VEGETATION PA TURNER MG, 2003, BIOSCIENCE, V53, P46 VANDEKOPPEL J, 2002, AM NAT, V159, P209 WAINWRIGHT JA, 2002, J ARID ENVIRON, V51, P219 WALSH CJ, 2004, HEIR PART PACKAGE VE WHITTAKER RH, 1975, COMMUNITIES ECOSYSTE WITH KA, 2002, CONSERV BIOL, V16, P1192 WONDZELL SM, 1990, J VEG SCI, V1, P403 WOODHOUSE CA, 1998, B AM METEOROL SOC, V79, P2693 WRIGHT RG, 1976, SW NAT, V21, P259 0921-2973 Landsc. Ecol.ISI:000242089300005USDA ARS, Jornada Expt Range, Las Cruces, NM 88003 USA. Peters, DPC, USDA ARS, Jornada Expt Range, 2995 Knox St,MSC 3JER,NMSU,Box 30003, Las Cruces, NM 88003 USA. debpeter@nmsu.eduEnglish<7*$Simmering, D. Waldhardt, R. Otte, A.2006{Quantifying determinants contributing to plant species richness in mosaic landscapes: a single- and multi-patch perspective 1233-1251Landscape Ecology218Mbiodiversity; Germany; habitat diversity; habitat specificity; linear structures; marginal landscape; conservation value; modelling; spatial heterogeneity; species-area curve AGRICULTURAL LANDSCAPES; DIVERSITY PATTERNS; AREA RELATIONSHIP; GAMMA-DIVERSITY; BETA-DIVERSITY; SPATIAL SCALE; LAND-COVER; BIODIVERSITY; HABITAT; COMMUNITIESArticleNovDespite good theoretical knowledge about determinants of plant species richness in mosaic landscapes, validations based on complete surveys are scarce. We conducted a case study in a highly fragmented, traditional agricultural landscape. In 199 patches of 20 representative multi-patch-plots (MPPs, I ha) we recorded a total of 371 plant species. In addition to an additive partitioning of species diversity at the (a) patch- and (b) MPP-scale, we adopted the recently proposed 'specificity' measure to quantify the contribution of a spatial subunit to landscape species richness (subunit-to-landscape-contribution, SLC). SLC-values were calculated at both scales with respect to various spatial extents. General regression models were used to quantify the relative importance of hypothesis-driven determinants for species richness and SLC-values. At the patch scale, habitat type was the main determinant of species richness, followed by area and elongated shape. For SLC-values, area was more important than habitat type, and its relevance increased with the extent of the considered landscape. Influences of elongated shape and vegetation context were minor. Differences between habitat types were pronounced for species richness and also partly scale-dependent for SLC-values. Relevant predictors at the MPP-scale were nonlinear habitat richness, the gradient from anthropogenic to seminatural vegetation, and the proportions of natural vegetation and rare habitats. Linear elements and habitat configuration did not contribute to species richness and SLC. Results at the MPP-scale were in complete accordance with the predictions of the mosaic concept. Hence, our study represents its first empirical validation for plant species diversity in mosaic landscapes.://000242089300006 ISI Document Delivery No.: 106GP Times Cited: 0 Cited Reference Count: 78 Cited References: *STATSOFT INC, 2001, STATISTICA WINDOWS ALARD D, 2000, J VEG SCI, V11, P287 ALLAN JD, 1975, OECOLOGIA, V18, P359 ARRHENIUS O, 1921, J ECOL, V9, P95 BAUDRY J, 2000, LANDSCAPE URBAN PLAN, V50, P119 BOSSUYT B, 2004, BASIC APPL ECOL, V5, P321 BROSE U, 2001, ECOGRAPHY, V24, P722 BRUUN HH, 2000, ECOGRAPHY, V23, P641 BRUUN HH, 2001, NORD J BOT, V21, P607 BUREL F, 1998, ACTA OECOL, V19, P47 CONNOR EF, 1979, AM NAT, V113, P791 CRIST TO, 2003, AM NAT, V162, P734 DAUBER J, 2003, AGR ECOSYST ENVIRON, V98, P321 DAVIS JC, 1986, STAT DATA ANAL GEOLO DUELLI P, 1992, VERH GES OEKOLOGIE B, V21, P379 DUELLI P, 1997, AGR ECOSYST ENVIRON, V62, P81 ELLENBERG H, 1996, VEGETATION MITTELEUR ERIKSSON A, 1995, ECOGRAPHY, V18, P310 FAHRIG L, 2005, ISSUES PERSPECTIVES, P3 FIRBANK LG, 2005, ANN APPL BIOL, V146, P163 FLEISHMAN E, 2003, LANDSCAPE ECOL, V18, P675 FORMAN RT, 2002, APPLYING LANDSCAPE E, R7 FORMAN RTT, 1995, LAND MOSAICS GERING JC, 2002, ECOL LETT, V5, P433 GERING JC, 2003, CONSERV BIOL, V17, P488 GEROWITT B, 2003, WEED RES, V43, P227 GRIFFITH DA, 1982, ANN ASSO