Knowledge (XXG)

Gradsect

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applications involving gradsects have ranged from habitat suitability studies of fungi (Shearer and Crane 2011 ), termites (Gillison et al. 2003) other macro invertebrates (Lawes et al. 2005 ); birds (Damalas 2005) small and large mammals (Laurance 1994; Ramono et al. 2009). Vegetation studies using gradsects have been widely applied in many countries ranging from tidal wetlands (Parker et al. 2011) and agricultural cropping systems and forested landscape mosaics (Gillison et al. 2004) to infectious diseases (Boone et al. 2000 ). At broader geographic and national scales (Grossman et al., 1998, 2007; USA/NPS 2012) gradsects have been applied to guide field sampling and forest mapping in mountainous terrain (Sandman and Lertzmann 2003) as well as wide-ranging
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design (see Applications next). Apart from improved logistic efficiency, the gradsect method seeks to maximise environmental representativeness which has the dual advantage of potentially improving location of rarities and enhancing spatial modelling of species distribution. Because the underlying
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or local land use farming system or finer scale gradient levels representing local vegetational sequences. Through an inspection of spatial overlays of all gradients, a minimum number of sample locations is then purposively located to reflect, as far as possible, total environmental variation. For
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provided a reference platform for developing and testing a less logistically demanding and yet statistically acceptable gradient-based survey design that avoided the need for random or purely grid-based sampling. These initial studies and subsequently developed statistical support for purposive,
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Since the publication of gradsect theory in 1984, subsequent vegetational and landscape studies in regional Australia (Austin and Heyligers 1989); Ludwig and Tongway (1995) were followed by a successful evaluation of the method in faunal surveys in South Africa (Wessels et al.). Since then
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gradsect is constructed that may then be modified to accommodate logistic tradeoffs. The selection discipline requires that the fullest possible range of each hierarchical level is sampled. This commonly results in a set of progressively nested clusters of sample sites contained within the
56:(1967) and others. Although in practice, life-scientists intuitively sample gradients, until the early 1980s there was little formal theoretical or empirical support for such an approach, sample design being driven largely by traditional statistical methods based on 130:
overarching primary gradient that may not reflect a linear distribution. At relatively local landscape scale, a primary gradients may be represented by salinity levels or water depth as in tidal wetlands or micro-topographic relief as in forest margins or a
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Initial studies in gradsect development revealed considerable logistic and other advantages over more traditional non-gradient-based survey designs concerned primarily with random sampling. This finding is now widely supported especially in
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Rocchini, D., McGlinn, D., Ricotta, C., Neteler, M. and Wohlgemuth, T. (2011). Landscape complexity and spatial scale influence the relationship between remotely sensed spectral diversity and survey-based plant species richness
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or systematic (e.g. grid-based) systems tend to be less efficient in recovering information about the distribution of taxa than sample designs that are purposively directed instead along deterministic environmental gradients.
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Gillison, A.N., Jones, D.T., Susilo, F-X. and Bignell, D.E. (2003). Vegetation indicates diversity of soil macroinvertebrates: a case study with termites along a land-use intensification gradient in lowland Sumatra.
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Boone, J.D., McGuire, K.C., Otteson, E.W., DeBaca, R.S., Kuhn, E.A., Villard, P.F. & St Jeor, S.C. (2000). Remote Sensing and Geographic Information Systems: Charting Sin Nombre Virus Infections in Deer Mice.
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In constructing a gradsect, existing information is initially reviewed in which a hierarchy of environmental gradients is first identified either by visual means (maps, aerial photographs etc..) or through
164:, gradsect sampling cannot be used to estimate numbers of species or other biological attributes per unit area. For that purpose some measure of random sampling needs to be built into the sample design. 466:
Mallinis, G., Koutsias, N., Tsakiri-Strati, M. and Karteris, M. (2008). Object-based classification using Quickbird imagery for delineating forest vegetation polygons in a Mediterranean test site.
427:. NatureServe In Cooperation with the California Native Plant Society and California Natural Heritage Program Wildlife and Habitat Data Analysis Branch California Department of Fish and Game. 134:. For most practical purposes, transects are commonly laid out along contours perpendicular to the main direction of the gradient. Iterative spatial analysis of environmental layers over a 453:
Sandmann, H. and Lertzman, K.P. (2003). Combining high-resolution aerial photography with gradient-directed transects to guide field sampling and forest mapping in mountainous terrain.
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Gillison, A.N., Liswanti, N. Budidarsono, S., van Noordwijk, M. and Tomich, T.P. (2004). Impact of cropping methods on biodiversity in coffee agroecosystems in Sumatra, Indonesia.
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Grossman, D. H., Faber-Langendoen, D., Weakley, A.S. et al. (1998). International classification of ecological communities: terrestrial vegetation of the United States. Vol. I,
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Lawes, M.J., Kotze, D.J., Bourquin, S.L. and Morris, C. (2005). Epigaeic invertebrates as potential ecological indicators of afromontane forest condition in South Africa.
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was coined that coupled purposive, transect sampling with a hierarchical framework of environmental gradients considered to be key determinants of species distribution.
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Damalas, A. (2005). Landscape ecology of birds on Mount Leconte, Great Smoky Mountains National Park Dissertation. Old Dominion University. 358 pages; AAT 3195595.
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Ramono, W., Isnan, M.W., Sadjudin, H.R., Gunawan, H., Dahlan, E.N., Sectionov, Pairah, Hariyadi, A.R., Syamsudin, M., Talukdar, B.K. & Gillison, A.N. (2009).
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Parker, V.T., L. M. Schile, M.C. Vasey, and J.C. Callaway. (2011). Efficiency in assessment and monitoring methods: scaling down gradient-directed transects.
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Shearer, B.L. and Crane, C.E. (2011). Habitat suitability of soils from a topographic gradient across the Fitzgerald River National Park for invasion by
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Austin, M.P. and Heyligers, P.C. (1989). Vegetation survey design for conservation: gradsect sampling of forests in northeastern New South Wales;
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Gillison, A.N. (1984). Gradient oriented sampling for resource surveys – the gradsect method. In: K.R. Myers, C.R. Margules and I. Musto (eds.)
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logistic and other purposes (such as improving the capacity to locate rare species) the steepest gradients are usually selected. In this way an
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Classification of the Vegetation of Yosemite National Park and Surrounding Environs in Tuolumne, Mariposa, Madera and Mono Counties, California
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Wessels, K.J., Van Jaarsveld, A.S., Grimbeek, J.D. & Van der Linde, M.J. (1998). An evaluation of the gradsect biological survey method.
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gradient-based survey provided a formalized, practical alternative to more logistically demanding traditional designs. It was here the term
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pp. 349–74. Proc. Workshop held at Adelaide Univ. 31 Aug. to 31 Sept. 1983. (CSIRO (Aust.) Division of Water and Land Resources, Canberra)
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Ludwig, J.A. and Tongway, D.J. (1995). Spatial organization of landscapes and its function in semi-arid woodlands, Australia.
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Gillison, A.N. and Brewer, K.R.W. (1985) The use of gradient directed transects or gradsects in natural resource surveys.
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Report on a second habitat assessment for the Javan rhinoceros (Rhinoceros sondaicus sondaicus) within the island of Java
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Laurance, W.F. (1994). Rainforest Fragmentation and the Structure of Small Mammal Communities in Tropical Queensland.
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can then be used to identify areas requiring additional sampling thereby improving environmental representativeness.
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of institutional or other data sources. A typical regional gradsect for example may be constructed according to a
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is a low-input, high-return sampling method where the aim is to maximise information about the distribution of
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Gillison, A.N. (2013). Plant Functional Types and Traits at the Community, Ecosystem and World Level, in
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based approaches to better understand community dynamics and this is reflected especially in the work of
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The National Vegetation Classification System: development, status, and applications
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and J. Franklin), John Wiley & Sons, Ltd, Oxford, UK. Ch 12, pp.347-386.
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Grossman, D., Drake, J., Schindel, M., Hickson, D. et al. (2007).
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applications (Mallinis et al. 2008; Rocchini et al. 2011 ).
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Whittaker R. H. (1967). Gradient analysis of vegetation.
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Ecologists have long been aware of the significance of
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in any area of study. Most living things are rarely
414:. The Nature Conservancy, Arlington, Virginia, USA. 468:ISPRS Journal of Photogrammetry and Remote Sensing 386:http://www.ecologyandsociety.org/vol9/iss2/art7 72:Intensively sampled landscape-based surveys in 8: 104:(temperature, moisture, seasonality) then a 442:/Rio Grande National Wild and Scenic River 186: 116:, major and minor drainage systems), a 208:Survey Methods for Nature Conservation 438:Gradsect and Field Sampling Plan for 120:possibly represented by a local soil 7: 308:Organisms Diversity & Evolution 221:Journal of Environmental Management 36:factors. For this reason, standard 436:National Park Service USA (2012). 160:statistical model is not based on 14: 358:. International Rhino Foundation. 482:Journal of Vegetation Science. 1: 277:Biodiversity and Conservation 399:Emerging Infectious Diseases 294:Australasian Plant Pathology 527: 142:Advantages and limitations 22:gradient-directed transect 102:primary climate gradient 343:Biological Conservation 251:Biological Conservation 153:environmental surveying 136:digital elevation model 440:Big Bend National Park 290:Phytophthora cinnamomi 236:, Second Edition (eds 50:environmental gradient 511:Ecological techniques 30:distributed at random 506:Sampling techniques 501:Biological censuses 382:Ecology and Society 151:and other areas of 38:statistical designs 234:Vegetation Ecology 195:Biological Reviews 162:probability theory 106:secondary gradient 94:numerical analysis 58:probability theory 264:Landscape Ecology 238:E. van der Maarel 118:tertiary gradient 518: 485: 477: 471: 464: 458: 451: 445: 434: 428: 421: 415: 408: 402: 394: 388: 378: 372: 365: 359: 352: 346: 339: 333: 330: 324: 317: 311: 303: 297: 286: 280: 273: 267: 260: 254: 247: 241: 230: 224: 217: 211: 204: 198: 191: 98:spatial analysis 54:Robert Whittaker 40:based on purely 526: 525: 521: 520: 519: 517: 516: 515: 491: 490: 489: 488: 478: 474: 465: 461: 452: 448: 435: 431: 422: 418: 409: 405: 395: 391: 379: 375: 366: 362: 353: 349: 340: 336: 331: 327: 318: 314: 304: 300: 287: 283: 279:. 7: 1093–1121. 274: 270: 261: 257: 248: 244: 231: 227: 218: 214: 205: 201: 192: 188: 183: 170: 144: 89: 70: 62:random sampling 42:random sampling 12: 11: 5: 524: 522: 514: 513: 508: 503: 493: 492: 487: 486: 472: 470:. 63: 237–250. 459: 455:Forest Science 446: 429: 416: 403: 389: 373: 360: 347: 334: 325: 312: 298: 281: 268: 255: 242: 225: 212: 199: 185: 184: 182: 179: 175:remote sensing 169: 166: 143: 140: 88: 85: 69: 66: 60:incorporating 13: 10: 9: 6: 4: 3: 2: 523: 512: 509: 507: 504: 502: 499: 498: 496: 483: 476: 473: 469: 463: 460: 456: 450: 447: 443: 441: 433: 430: 426: 420: 417: 413: 407: 404: 400: 393: 390: 387: 383: 377: 374: 370: 364: 361: 357: 351: 348: 344: 338: 335: 329: 326: 322: 316: 313: 310:. 3: 111–126. 309: 302: 299: 295: 291: 285: 282: 278: 272: 269: 265: 259: 256: 252: 246: 243: 239: 235: 229: 226: 222: 216: 213: 209: 203: 200: 196: 190: 187: 180: 178: 176: 167: 165: 163: 158: 154: 150: 141: 139: 137: 133: 132:riparian zone 128: 123: 119: 115: 111: 110:geomorphology 107: 103: 99: 95: 86: 84: 82: 81: 75: 67: 65: 63: 59: 55: 51: 46: 43: 39: 35: 34:environmental 31: 27: 23: 19: 484:22: 688–698. 481: 475: 467: 462: 457:49: 429–443. 454: 449: 437: 432: 424: 419: 411: 406: 398: 392: 384:9: 7. URL: 381: 376: 368: 363: 355: 350: 345:. 69: 23–32. 342: 337: 328: 323:37; 109–118. 320: 315: 307: 301: 296:40: 168–179. 293: 289: 284: 276: 271: 266:. 10: 51–63. 263: 258: 250: 245: 233: 228: 223:20; 103–127. 220: 215: 207: 202: 197:42: 207–264. 194: 189: 171: 168:Applications 157:conservation 149:biodiversity 145: 126: 117: 105: 101: 90: 79: 78: 71: 47: 21: 17: 15: 401:6: 248–258. 87:Methodology 495:Categories 321:Biotropica 253:50: 13–32. 181:References 369:Ecosphere 114:lithology 74:Australia 80:gradsect 18:gradsect 444:.(Book) 371:. 2: 99 68:Origins 122:catena 127:ideal 26:biota 155:and 96:or 20:or 497:: 292:. 112:, 64:. 16:A 108:(

Index

biota
distributed at random
environmental
statistical designs
random sampling
environmental gradient
Robert Whittaker
probability theory
random sampling
Australia
numerical analysis
spatial analysis
geomorphology
lithology
catena
riparian zone
digital elevation model
biodiversity
environmental surveying
conservation
probability theory
remote sensing
E. van der Maarel
http://www.ecologyandsociety.org/vol9/iss2/art7
Big Bend National Park
Categories
Biological censuses
Sampling techniques
Ecological techniques

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