Aderhold, A., Husmeier, D., Lennon, J. J., Beale, C. M. & Smith, V. A. (2012). Hierarchical Bayesian models in ecology: reconstructing species interaction networks from non-homogeneous species abundance data. Ecological Informatics, 11, 55–64.CrossRefGoogle Scholar

Agarwal, D., Philips, J. M. & Venkatasubramanian, S. (2006). Hunting the bump: on maximizing statistical discrepancy. Proc. 17th Ann ACM-SIAM Symp. On Disc. Alg., pp. 1137–1146.CrossRef

Agnew, A. D. Q., Wilson, J. B. & Sykes, M. (1993). A vegetation switch as the cause of a forest/mire ecotone in New Zealand. Journal of Vegetation Science, 4, 273–278.CrossRefGoogle Scholar

Agresti, A. (1990). Categorical Data Analysis. New York: John Wiley & Sons, Inc.Google Scholar

Aing, C., Halls, S., Oken, K., Dobrow, R., & Fieberg, J. (2011). A Bayesian hierarchical occupancy model for track surveys conducted in a series of linear, spatially correlated, sites. Journal of Applied Ecology, 48, 1508–1517.CrossRefGoogle Scholar

Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19, 716–723.CrossRefGoogle Scholar

Albert, R. & Barabási, A.-L. (2002). Statistical mechanics of complex networks. Reviews of Modern Physics, 74, 47–97.CrossRefGoogle Scholar

Aldous, J. M. & Wilson, R. J. (2000). Graphs and Applications: An Introductory Approach. New York: Springer-Verlag.CrossRefGoogle Scholar

Allain, C. & Cloitre, M. (1991). Characterizing the lacunarity of random and deterministic fractal sets. Physical Review A, 44, 3552–3558.CrossRefGoogle ScholarPubMed

Allen, T. F. H. & Hoekstra, T. W. (1992). Toward a Unified Ecology. New York: University of Columbia Press.Google Scholar

Almeida-Neto, M. & Guimaraes, P. R. (2007). On nestedness analyses: rethinking matrix temperature and anti-nestedness. Oikos, 116, 716–722.CrossRefGoogle Scholar

Almeida-Neto, M., Guimaraes, P. & Guimaraes, P. R. (2008). A consistent metric for nestedness analysis in ecological systems: reconciling concept and measurement. Oikos, 117, 1227–1239.CrossRefGoogle Scholar

Alpargu, G. & Dutilleul, P. (2003). To be or not to be valid in testing the significance of the slope in simple quantitative linear models with autocorrelated errors. Journal of Statistical Computation and Simulation, 73, 165–180.CrossRefGoogle Scholar

Alpargu, G. & Dutilleul, P. (2006). Stepwise regression in mixed quantitative linear models with autocorrelated errors. Communications in Statistics–Simulation and Computation, 35, 79–104.CrossRefGoogle Scholar

Amarasekare, P. (2009). Competition and coexistence in animal communities. In The Princeton Guide to Ecology. ed. Levin, S. A., pp. 196–201. Princeton: Princeton University Press.Google Scholar

Andersen, M. (1992). Spatial analysis of two species interactions. Oecologia, 91, 134–140.CrossRefGoogle ScholarPubMed

Anderson, D. J. (1967). Studies on structure in plant communities. IV. Cyclical succession in Dryas communities from North-west Iceland. Journal of Ecology, 55, 629–635.CrossRefGoogle Scholar

Anderson, M. J., Crist, T. O., Chase, J. M. et al. (2011). Navigating the multiple meanings of beta diversity: a roadmap for the practicing ecologist. Ecology Letters, 14, 19–28.CrossRefGoogle ScholarPubMed

Anderson, M. J., Ellingsen, K. E., & McArdle, B. H. (2006). Multivariate dispersion as a measure of beta diversity. Ecology Letters, 9, 683–693.CrossRefGoogle ScholarPubMed

Anselin, L. (1988) Spatial Econometrics: Methods and Models. New York: Kluwer Academic Publisher.CrossRefGoogle Scholar

Anselin, L. (1995). Local indicators of spatial association: LISA. Geographical Analysis, 27, 93–115.CrossRefGoogle Scholar

Anselin, L. (2001). Spatial econometrics. In: A Companion to Theoretical Econometrics, ed. Bultagi, B., pp. 310–330. Oxford: Basil Blackwell.Google Scholar

Anselin, L. (2009). Spatial regression. In The SAGE Handbook of Spatial Analysis, eds. Fotheringham, A. S. & Rogerson, P. A., pp. 255–275. London: SAGE Publications Ltd.Google Scholar

Arbia, G., Benedetti, R. & Espa, G. (1996). Effects of the MAUP on image classification. Geographical Systems, 3, 123–141.Google Scholar

Arnot, C., Fisher, P. F., Wadsworth, R. & Wellens, J. (2004). Landscape metrics with ecotones: pattern under uncertainty. Landscape Ecology, 19, 181–195.CrossRefGoogle Scholar

Atmar, W. & Patterson, B. D. (1993). The measure of order and disorder in the distribution of species in fragmented habitat. Oecologia, 96, 373–382.CrossRefGoogle ScholarPubMed

Aubréville, A. (1938). La forêt coloniale: Les forêts de l’Afrique occidentale française. Annals de l’Académie des Sciences Coloniales Paris, 9, 1–245.Google Scholar

Aubréville, A. M. A. (1950–1951). La concept d’association dans la forêt dense equatoriale de la basse Côte d’Ivoire. Bulletin de la Société Botanique de France, 98, 145–158.CrossRefGoogle Scholar

Austin, M. P. (1987). Models for the analysis of species’ response to environmental gradients. Vegetatio, 69, 35–45.CrossRefGoogle Scholar

Austin, M. P. & Austin, B. O. (1980). Behaviour of experimental plant communities along a nutrient gradient. Journal of Ecology, 68, 891–918.CrossRefGoogle Scholar

Austin, M. P. & Smith, T. M. (1989). A new model for the continuum concept. Vegetatio, 83, 35–47.CrossRefGoogle Scholar

Austin, M. P., Nicholls, A. O., Doherty, M. D. & Meyers, J.A. (1994). Determining species response functions to an environmental gradient by means of a beta-function. Journal of Vegetation Science, 5, 215–228.CrossRefGoogle Scholar

Baddeley, A. J., Howard, C. V., Boyde, A. & Reid, S. (1987). Three-dimensional analysis of the spatial distribution of particles using the tandem-scanning reflected light microscope. Acta Stereologica, 6, 87–100.Google Scholar

Bailey, T. C. & Gatrell, A. C. (1995). Interactive Spatial Data Analysis. Harlow: Longman Scientific & Technical.Google Scholar

Baker, W. L. & Cai, Y. (1992). The r.le programs for multiscale analysis of landscape structure using the GRASS geographical information system. Landscape Ecology, 7, 291–302.CrossRefGoogle Scholar

Bang-Jensen, J. & Gutin, G. (2002). Digraphs: Theory, Algorithms and Applications. London: Springer-Verlag.CrossRefGoogle Scholar

Barabási, A. (2009). Scale-free networks: a decade and beyond. Science, 325, 412–413.CrossRefGoogle ScholarPubMed

Barabási, A., & Bonabeau, E. (2003). Scale-free networks. Scientific American, 288, 60–69.CrossRefGoogle ScholarPubMed

Barbier, N., Couteron, P., Lejoly, J., Deblauwe, V. & Lejeune, O. (2006). Self-organized vegetation patterning as a fingerprint of climate and human impact on semi-arid ecosystems. Journal of Ecology, 94, 537–547.CrossRefGoogle Scholar

Barbujani, G. & Sokal, R. R. (1991). Zones of sharp genetic change in Europe are also linguistic boundaries. Proceedings of the National Academy of Sciences of the United States of America, 87, 1816–1819.CrossRefGoogle Scholar

Barbujani, G., Oden, N. N. & Sokal, R. R. (1989). Detecting regions of abrupt change in maps of biological variables. Systematic Zoology, 38, 376–389.CrossRefGoogle Scholar

Barot, S., Gignoux, J. & Menaut, J.-C. (1999). Demography of a savanna palm tree: predictions from comprehensive spatial pattern analysis. Ecology, 80, 1987–2005.CrossRefGoogle Scholar

Bartlett, M. S. (1935). Some aspects of the time correlation problem in regard to tests of significance. Journal of the Royal Statistical Society, 98, 536–543.CrossRefGoogle Scholar

Bartlett, M. S. (1948). Smoothing periodograms from time series with continuous spectra. Nature, 161, 686–687.CrossRefGoogle Scholar

Bascompte, J. (2009). Disentangling the web of life. Science, 325, 416–419.CrossRefGoogle ScholarPubMed

Bascompte, J. & Solé, R. V. (1998). Spatiotemporal patterns in nature. Trends in Ecology and Evolution, 13, 173–174.CrossRefGoogle ScholarPubMed

Baselga, A. (2010) Partitioning the turnover and nestedness components of beta diversity. Global Ecology and Biogeography, 19, 134–143.CrossRefGoogle Scholar

Batschelet, E. (1981). Circular Statistics in Biology. London: Academic Press.Google Scholar

Beale, C. M., Lennon, J. J., Yearsley, J. M, Brewer, M. J. & Elston, D. A. (2010). Regression analysis of spatial data. Ecology Letters, 13, 246–264.CrossRefGoogle ScholarPubMed

Beckage, B., Joseph, L., Belisle, P., Wolfson, D. B. & Platt, W. J. (2007). Bayesian change-point analyses in ecology. New Phytologist, 174, 456–467.CrossRefGoogle ScholarPubMed

Beguin, J., Martino, S., Rue, H. & Cumming, S. G. (2012). Hierarchical analysis of spatially autocorrelated data using integrated nested Laplace approximation. Methods in Ecology and Evolution, 3, 921–929.CrossRefGoogle Scholar

Beiler, K. J., Durall, D. M., Simard, S. W., Maxwell, S. A. & Kretzer, A. M. (2009). Architecture of the wood-wide web: Rhizopogon spp. Genets link multiple Douglas-fir cohorts. New Phytologist, 185, 543–553.CrossRefGoogle ScholarPubMed

Beisner, B. E., Haydon, D. & Cuddington, K. L. (2003). Alternative stable states in ecology. Frontiers in Ecology and the Environment, 1, 376–382.CrossRefGoogle Scholar

Bekker, M. F. & Malanson, G. P. (2008). Linear forest patterns in subalpine environments. Progress in Physical Geography, 32, 635–653.CrossRefGoogle Scholar

Bell, G., Lechowicz, M. J., Appenzeller, A. et al. (1993). The spatial structure of the physical environment. Oecologia, 96, 114–121.CrossRefGoogle ScholarPubMed

Bell, D. T., Plummer, J. A. & Taylor, S. K. (1993). Seed-germination ecology in southwestern Western-Australia. Botanical Review, 59, 24–73.CrossRefGoogle Scholar

Bellehumeur, C. & Legendre, P. (1997). Aggregation of sampling units: an analytical solution to predict variance. Geographical Analysis, 29, 258–266.CrossRefGoogle Scholar

Bellehumeur, C., Legendre, P. & Marcotte, D. (1997). Variance and spatial scales in a tropical rain forest: changing the size of sampling units. Plant Ecology, 130, 89–98.CrossRefGoogle Scholar

Benes, V. & Rataj, J. (2004). Stochastic Geometry: Selected Topics. Boston: Kluwer Academic Publisher.Google Scholar

Bengtsson, J., Angelstam, P., Elmqvist, T. et al. (2003). Reserves, resilience and dynamic landscapes. Ambio, 32, 389–396.CrossRefGoogle ScholarPubMed

Bergman, C. M., Schaefer, J. A. & Luttich, S. N. (2000). Caribou movement as a correlated random walk. Oecologia, 123, 364–374.CrossRefGoogle ScholarPubMed

Bertness, M. D. & Calloway, R. (1994). Positive interactions in communities. Trends in Ecology and Evolution, 9, 191–193.CrossRefGoogle ScholarPubMed

Beyer, H., Morales, J., Murray, D. & Fortin, M-J. (2013). The effectiveness of Bayesian state-space models for estimating behavioural states from movement paths. Methods in Ecology and Evolution, 4, 433–441.CrossRefGoogle Scholar

Bézier, P. (1977). Essai de définition numérique des courbes et des surfaces expérimentales. Thèse de doctorat ès-sciences, Université Pierre et Marie Curie, Paris.Google Scholar

Bienert, A.Queck, R., Schmidt, A., Berhofer, Ch., & Maas, H.-G. (2010). Voxel space analysis of terrestrial laser scans in forests for wind field modeling. Int. Arch. Photogramm., Rem. Sens. & Spat. Inf. Sci., 38, 92–97.Google Scholar

Biggs, N. L., Lloyd, E. & Wilson, R. J. (1976). Graph Theory, 1736–1936. Oxford: Oxford University Press.Google Scholar

Bini, L. M., Diniz, J. A. F., Rangel, T. F. L. V. et al. (2009). Coefficient shifts in geographical ecology: an empirical evaluation of spatial and non-spatial regression. Ecography, 32, 193–204.CrossRefGoogle Scholar

Bishop, Y. M. M., Fienberg, S. E. & Holland, P. W. (1975). Discrete Multivariate Analysis. Cambridge, MA: MIT Press.Google Scholar

Bivand, R. (1980). A Monte Carlo study of correlation coefficient estimation with spatially autocorrelated observations. Quaestiones Geographicae, 6, 5–10.Google Scholar

Bivand, R. S., Pebesma, E. J. & Gómez-Rubio, V. (2008). Applied Spatial Data Analysis with R. New York: Springer.Google Scholar

Bjørnstad, O. N. & Falck, W. (2001). Nonparametric spatial covariance functions: estimation and testing. Environmental and Ecological Statistics, 8, 53–70.CrossRefGoogle Scholar

Bjørnstad, O. N., Ims, R. A. & Lambin, X. (1999a). Spatial population dynamics: analyzing patterns and processes of population synchrony. Trends in Ecology and Evolution, 14, 427–432.CrossRefGoogle ScholarPubMed

Bjørnstad, O. N., Stenseth, N. C. & Saitoh, T. (1999b). Synchrony and scaling in dynamics of voles and mice in northern Japan. Ecology, 80, 622–637.CrossRefGoogle Scholar

Blanchet, F. G., Legendre, P. & Borcard, D. (2008a). Modelling directional spatial processes in ecological data. Ecological Modelling, 215, 325–336.CrossRefGoogle Scholar

Blanchet, F. G., Legendre, P. & Borcard, D. (2008b). Forward selection of explanatory variables. Ecology, 89, 2623–2632.CrossRefGoogle ScholarPubMed

Blanchet, F. G., Legendre, P., Maranger, R., Monti, D. & Pepin, P. (2011). Modelling the effect of directional spatial ecological processes at different scales. Oecologia, 166, 357–368.CrossRefGoogle ScholarPubMed

Blonder, B. & Dornhaus, A. (2011). Time-ordered networks reveal limitations to information flow in ant colonies. PLoS ONE, 6(5), e20298.CrossRefGoogle ScholarPubMed

Bloomquist, E. W., Lemey, P. & Suchard, M. A. (2010). Three roads diverged? Routes to phylogeographic inference. Trends in Ecology and Evolution, 25, 626–632.CrossRefGoogle ScholarPubMed

Blundell, G. M., Maier, J. A. K. & Debevec, E. M. (2001). Linear home ranges: effects of smoothing, sample size and autocorrelation on kernel estimates. Ecological Monographs, 71, 469–489.CrossRefGoogle Scholar

Bolker, B. M., Brooks, M. E., Clark, C. J. et al. (2009). Generalized linear mixed models: a practical guide for ecology and evolution. Trends in Ecology and Evolution, 24, 127–135.CrossRefGoogle ScholarPubMed

Bonabeau, E., Theraulaz, G., Deneubourg, J.-L., Aron, S. & Camazine, S. (1997) Self-organization in social insects. Trends in Ecology and Evolution, 12, 188–193.CrossRefGoogle ScholarPubMed

Boots, B. N. (2002). Local measures of spatial association. Écoscience, 9, 168–176.CrossRefGoogle Scholar

Boots, B. N. (2003). Developing local measures of spatial association for categorical data. Journal of Geographical Systems, 5, 139–160.CrossRefGoogle Scholar

Borcard, D. & Legendre, P. (2002). All-scale spatial analysis of ecological data by means of principal coordinates of neighbour matrices. Ecological Modelling, 153, 51–68.CrossRefGoogle Scholar

Borcard, D. & Legendre, P. (2012). Is the Mantel correlogram powerful enough to be useful in ecological analysis? A simulation study. Ecology, 93, 1473–1481.CrossRefGoogle ScholarPubMed

Borcard, D., Legendre, P. & Drapeau, P. (1992). Partialling out the spatial component of ecological variation. Ecology, 73, 1045–1055.CrossRefGoogle Scholar

Borcard, D., Gillet, F. & Legendre, P. (2011). Numerical Ecology with R. New York: Springer.CrossRefGoogle Scholar

Börger, L., Franconi, N., Ferretti, F. et al. (2006). An integrated approach to identify spatiotemporal and individual-level determinants of animal home range size. The American Naturalist, 168, 471–485.CrossRefGoogle ScholarPubMed

Bossenbroek, J. M., Nekola, J. C. & Kraft, C. E. (2001). Prediction of long-distance dispersal using gravity models: zebra mussel invasion of inland lakes. Ecological Applications, 11, 1778–1788.CrossRefGoogle Scholar

Bourobou, D. N., Moussavou, H., Nzengue, E. et al. (2010). Applications for microsatellite markers in the management of forest trees. FAO Conference of Agricultural Biotechnology in Developing Countries, Guadalajara, 2010.Google Scholar

Bowersox, M. A. & Brown, D. G. (2001) Measuring the abruptness of patchy ecotones: a simulation-based comparison of patch and edge metrics. Plant Ecology, 156, 89–103.CrossRefGoogle Scholar

Box, G. & Jenkins, G. (1970). Time Series Analysis: Forecasting and Control. San Francisco: Holden-Day.Google Scholar

Bradshaw, G. A. & Fortin, M.-J. (2000). Landscape heterogeneity effects on scaling and monitoring large areas using remote sensing data. Geographic Information Sciences, 6, 61–68.Google Scholar

Bradshaw, G. A. & Spies, T. (1992). Characterizing canopy gap structure in forests using wavelet analysis. Journal of Ecology, 80, 205–215.CrossRefGoogle Scholar

Brandtberg, T. (1999). Automatic individual tree based analysis of high spatial resolution aerial images on naturally regenerated boreal forests. Canadian Journal of Forest Research, 29, 1464–1478.CrossRefGoogle Scholar

Bray, J. R. (1956). Gap phase replacement in a Maple-Basswood forest. Ecology, 37, 598–600.CrossRefGoogle Scholar

Brodie, C., Houle, G. & Fortin, M.-J. (1995). Development of a Populus balsamifera clone in subarctic Québec reconstructed from spatial analyses. Journal of Ecology, 83, 309–320.CrossRefGoogle Scholar

Brooker, R. W., Maestre, F. T., Callaway, R. M. et al. (2008). Facilitation in plant communities: the past, the present, and the future. Journal of Ecology, 96, 18–34.Google Scholar

Brooks, C. P. (2006). Quantifying population substructure: extending the graph-theoretic approach. Ecology, 87, 864–872.CrossRefGoogle ScholarPubMed

Brown, D. G. (1998). Classification and boundary vagueness in mapping presettlement forest types. International Journal of Geographical Information Science, 12, 105–129.CrossRefGoogle Scholar

Brown, J. H. (1995). Macroecology. Chicago: University of Chicago Press.Google Scholar

Brunt, J. W. & Conley, W. (1990). Behavior of a multivariate algorithm for ecological edge detection. Ecological Modelling, 49, 179–203.CrossRefGoogle Scholar

Burnham, K. P. & Anderson, D. R. (2002). Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach. Heidelberg and New York: Springer.Google Scholar

Burrough, P. A. (1981). Fractal dimensions of landscapes and other environmental data. Nature, 294, 240–242.CrossRefGoogle Scholar

Burrough, P. A. (1986). Principles of geographical information systems for land resources assessment. Monographs on Soil and Resources Survey No. 12. Oxford: Clarendon Press.Google Scholar

Burrough, P. A. & Frank, A. (eds.) (1996). Geographic Objects with Indeterminate Boundaries. London: Taylor and Francis.Google Scholar

Burrough, P. A. & McDonnell, R. A. (1998). Principles of Geographical Systems. Oxford: Oxford University Press.Google Scholar

Cadenasso, M. L., Pickett, S. T. A., Weathers, K. C. et al. (2003). An interdisciplinary and synthetic approach to ecological boundaries. Bioscience, 53, 717–722.CrossRefGoogle Scholar

Cadman, M.D., Sutherland, D.A., Beck, G.G., Lepage, D. & Couturier, A.R. (eds.) (2007). Atlas of the Breeding Birds of Ontario, 2001–2005. Environment Canada, Ontario Field Ornithologists, Ontario Ministry of Natural Resources, and Ontario Nature, Toronto.Google Scholar

Cain, M. L. (1989). The analysis of angular data in ecological field studies. Ecology, 70, 1540–1543.CrossRefGoogle Scholar

Cain, M. L. (1990). Models of clonal growth in Solidago altissima. Journal of Ecology, 78, 27–46.CrossRefGoogle Scholar

Cain, M. L. & Damman, H. (1997). Clonal growth and ramet performance in the woodland herb, Asarum canadense. Journal of Ecology, 85, 883–897.CrossRefGoogle Scholar

Cain, M. L., Pacala, S. W., Silander, J. A. & Fortin, M.-J. (1995). Neighborhood models of clonal growth in the white clover, Trifolium repens. American Naturalist, 145, 888–917.CrossRefGoogle Scholar

Campbell, J. E., Franklin, S. B., Gibson, D. J. & Newman, J. A. (1998). Permutation of two-term local quadrat variance analysis: general concepts for interpretation of peaks. Journal of Vegetation Science, 9, 41–44.CrossRefGoogle Scholar

Candau, J.-N., Fleming, R. A. & Hopkin, A. (1998). Spatiotemporal patterns of large-scale defoliation caused by the spruce budworm in Ontario since 1941. Canadian Journal of Forest Research, 28, 1733–1741.CrossRefGoogle Scholar

Canny, J. (1986). A computational approach to edge detection. IEEE Transitional Pattern Analysis of Machine Intelligence, 8, 679–698.CrossRefGoogle ScholarPubMed

Cantwell, M. D. & Forman, T. T. (1993). Landscape graphs: ecological modeling with graph theory to detect configurations common to diverse landscapes. Landscape Ecology, 8, 239–255.CrossRefGoogle Scholar

Carl, G. & Kühn, I. (2008). Analyzing spatial ecological data using linear regression and wavelet analysis. Stochastic Environmental Research and Risk Assessment 22, 315–324.CrossRefGoogle Scholar

Carl, G. & Kühn, I. (2010). A wavelet-based extension of generalized linear models to remove the effect of spatial autocorrelation. Geographical Analysis, 42, 323–337.CrossRefGoogle Scholar

Carl, G., Dormann, C. F. & Kühn, I. (2008). A wavelet-based method to remove spatial autocorrelation in the analysis of species distributional data. Web Ecology, 8, 22–29.CrossRefGoogle Scholar

Carrer, M. & Urbinati, C. (2001). Spatial analysis of structural and tree-ring related parameters in a timberline forest in the Italian Alps. Journal of Vegetation Science, 12, 643–652.CrossRefGoogle Scholar

Casteigts, A., Flocchini, P., Quattrociocchi, W. & Santoro, N. (2011). Time-varying graphs and dynamic networks. 10th International Conference on ad hoc Networks and Wireless, July 2011.CrossRef

Cayuela, L., Benayas, J. M., Justel, A. & Salas-Rey, J. (2006). Modelling tree diversity in a highly fragmented tropical montane landscape. Global Ecology and Biogeography, 15, 602–613.CrossRefGoogle Scholar

Cerioli, A. (1997). Modified tests of independence in 2 × 2 tables with spatial data. Biometrics, 53, 619–628.CrossRefGoogle Scholar

Cerioli, A. (2002). Testing mutual independence between two discrete-valued spatial processes: a correction to Pearson chi-squared. Biometrics, 58, 888–897.CrossRefGoogle ScholarPubMed

Chan, J., Bailey, J. & Leckie, C. (2008). Discovering correlated spatio-temporal changes in evolving graphs. Knowledge and Information Systems, 16, 53–96.CrossRefGoogle Scholar

Chao, A. (2004). Species richness estimation. In Encyclopedia of Statistical Sciences, eds. Balakrishnan, N., Read, C. B. & Vidakovic, A.. New York: Wiley.Google Scholar

Chao, A., Chiu, C.-H. & Hsieh, T.C. (2012). Proposing a resolution to debates on diversity partitioning. Ecology, 93, 2037–2051.CrossRefGoogle ScholarPubMed

Chase, J. M., Kraft, N. B., Smith, K. G., Velland, M. & Inouye, B. D. (2011). Using null models to disentangle variation in community dissimilarity from variation in α-diversity. Ecosphere, 2, Article 24.CrossRefGoogle Scholar

Chatfield, C. (1975). The Analysis of Time Series: Theory and Practice. London: Chapman & Hall.CrossRefGoogle Scholar

Chilès, J.-P. & Delfiner, P. R. (2012). Geostatistics: Modeling Spatial Uncertainty, 2nd edn. New York: Wiley.CrossRefGoogle Scholar

Claramunt, C. & Thériault, M. (1997). Towards semantics for modeling spatio-temporal processes within GIS. In Advances in GIS Research II, eds. Kraak, M. J. & Molenaar, M., pp. 47–63. London: Taylor & Francis.Google Scholar

Clarke, K. R. & Warwick, R. M. (1998). A taxonomic distinctness index and its statistical properties. Journal of Applied Ecology, 35, 523–531.CrossRefGoogle Scholar

Claussen, D. L., Finkler, M. S. & Smith, M. M. (1997). Thread trailing of turtles: methods for evaluating spatial movements and pathway structure. Canadian Journal of Zoology, 75, 2120–2128.CrossRefGoogle Scholar

Clements, F. E. (1916). Plant Succession. Washington D.C.: Carnegie Institute.Google Scholar

Cliff, A. D. & Ord, J. K. (1973). Spatial Autocorrelation. London: Pion.Google Scholar

Cliff, A. D. & Ord, J. K. (1981). Spatial Processes: Models and Applications. London: Pion.Google Scholar

Clifford, P., Richardson, S. & Hémon, D. (1989). Assessing the significance of correlation between two spatial processes. Biometrics, 45, 123–134.CrossRefGoogle ScholarPubMed

Cochran, W. (1954). Some methods for strengthening the common χ2 tests. Biometrics, 10, 417–451.CrossRefGoogle Scholar

Codling, E. A, Plank, M. J. & Benhamou, S. (2008). Random walks models in biology. Journal of the Royal Society Interface 5, 813–834.CrossRefGoogle ScholarPubMed

Cohn, R. D. (1999). Comparisons of multivariate relational structures in serially correlated data. Journal of Agricultural, Biological & Environmental Statistics, 4, 238–257.CrossRefGoogle Scholar

Collins, S. L., Glenn, S. M. & Roberts, D. W. (1993). The hierarchical continuum concept. Journal of Vegetation Science, 4, 149–156.CrossRefGoogle Scholar

Colwell, R. K. (2009). Biodiversity: Concepts, patterns and measurement. In The Princeton Guide to Ecology, ed. Levin, S. A., pp. 257–263. Princeton: Princeton University Press.Google Scholar

Condit, R., Ashton, P. S., Baker, P. et al. (2000). Spatial patterns in the distribution of tropical tree species. Science, 288, 1414–1417.CrossRefGoogle ScholarPubMed

Connell, J. H. & Slatyer, R. O. (1977). Mechanisms of succession in natural communities and their roles in community stability and organization. American Naturalist, 111, 1119–1144.CrossRefGoogle Scholar

Connor, E. F & Simberloff, D. (1984). Neutral models of species co-occurrence patterns. In Ecological Communities: Conceptual Issues And The Evidence, eds. Strong, Jr. D. R. et al., pp. 316–331. Princeton: Princeton University Press.Google Scholar

Conover, W. J. (1980). Practical Nonparametric Statistics, 2nd edn. New York: Wiley.Google Scholar

Couteron, P. & Ollier, S. S. (2005). A generalized, variogram based framework for multi-scale ordination. Ecology, 86, 828–834.CrossRefGoogle Scholar

Couwenberg, J., & Joosten, H. (2005). Self-organization in raised bog patterning: the origin of microtope zonation and mesotype diversity. Journal of Ecology, 93, 1238–1248.CrossRefGoogle Scholar

Cox, D. R. (1955). Some statistical methods connected with series of events. Journal of the Royal Statistical Society Series B (Statistical Methodology), 17, 129–164.Google Scholar

Craft, M. E., Volz, E., Packer, C. & Meyers, L. A. (2010). Disease transmission in territorial populations: the small-world network of Serengeti lions. Journal of the Royal Society Interface, .

Crawley, M. J. (1986). Plant Ecology. Oxford: Blackwell.Google Scholar

Cressie, N. A. C. (1991). Statistics for Spatial Data. New York: Wiley.Google Scholar

Cressie, N. A. C. (1993). Statistics for Spatial Data, revised edn. New York: Wiley.Google Scholar

Cressie, N. A. C. (1996). Change of support and the modifiable areal unit problem. Geographical Systems, 3, 159–180.Google Scholar

Cressie, N. & Huang, H.-C. (1999). Classes of non-separable, spatio-temporal stationary covariance functions. Journal of the American Statistical Association, 94, 1330–1340.CrossRefGoogle Scholar

Cressie, N. & Wikle, C. K. (2011). Statistics for Spatio-Temporal Data. Hoboken NJ: Wiley.Google Scholar

Cressie, N., Calder, C. A., Clark, J. S., Ver Hoef, J. M. & Wikle, C. K. (2009). Accounting for uncertainty in ecological analysis: the strengths and limitations of hierarchical statistical modeling. Ecological Applications, 19, 553–570.CrossRefGoogle ScholarPubMed

Crist, T. O., Veech, J. A., Gerring, J. C. & Summerville, K. S. (2003). Partitioning species diversity across landscapes and regions: a hierarchical analysis of alpha, beta, and gamma diversity. American Naturalist, 162, 734–743.CrossRefGoogle ScholarPubMed

Csillag, F. & Kabos, S. (1996). Hierarchical decomposition of variance with applications in environmental mapping based on satellite images. Mathematical Geology, 28, 385–405.CrossRefGoogle Scholar

Csillag, F. & Kabos, S. (2002). Wavelets, boundaries, and the spatial analysis of landscape pattern. Écoscience, 9, 177–190.CrossRefGoogle Scholar

Csillag, F., Boots, B., Fortin, M.-J., Lowell, K. & Potvin, F. (2001). Multiscale characterization of boundaries and landscape ecological patterns. Geomatica, 55, 291–307.Google Scholar

Currie, D. J. (2007). Disentangling the roles of environment and space in ecology. Journal of Biogeography, 34, 2009–2011.CrossRefGoogle Scholar

Cushman, S., McKelvey, K., Hayden, J. & Schwartz, M. (2006). Gene flow in complex landscapes: testing multiple hypotheses with causal modeling. The American Naturalist, 168, 486–499.CrossRefGoogle ScholarPubMed

Cutler, N. A., Belyea, L. R. & Dugmore, A. J. (2008). The spatiotemporal dynamics of a primary succession. Journal of Ecology, 96, 231–246.CrossRefGoogle Scholar

Dale, M. R. T. (1977). Graph theoretical analysis of the phytosociological structure of plant communities: the theoretical basis. Vegetatio, 34, 137–154.CrossRefGoogle Scholar

Dale, M. R. T. (1979). The analysis of patterns of zonation and phytosociological structure of seaweed communities. Ph. D. Thesis. Halifax, N.S.: Dalhousie University.Google Scholar

Dale, M. R. T. (1985). Graph theoretical methods for comparing phytosociological structures. Vegetatio, 63, 79–88.Google Scholar

Dale, M. R. T. (1986). Overlap and spacing of species’ ranges on an environmental gradient. Oikos, 47, 303–308.CrossRefGoogle Scholar

Dale, M. R. T. (1988). The spacing and intermingling of species boundaries on an environmental gradient. Oikos, 53, 351–356.CrossRefGoogle Scholar

Dale, M. R. T. (1995). Spatial pattern in communities of crustose saxicolous lichens. Lichenologist, 27, 495–503.CrossRefGoogle Scholar

Dale, M. R. T. (1999). Spatial Pattern Analysis in Plant Ecology. Cambridge: Cambridge University Press.CrossRefGoogle Scholar

Dale, M. R. T. (2000). Lacunarity analysis of spatial pattern: a comparison. Landscape Ecology, 15, 467–478.CrossRefGoogle Scholar

Dale, M. R. T. & Blundon, D. J. (1991). Quadrat covariance analysis and the scales of interspecific association during primary succession. Journal of Vegetation Science, 2, 103–112.CrossRefGoogle Scholar

Dale, M. R. T. & Fortin, M.-J. (2002). Spatial autocorrelation and statistical tests in ecology. Écoscience, 9, 162–167.CrossRefGoogle Scholar

Dale, M. R. T. & Fortin, M.-J. (2009). Spatial autocorrelation and statistical tests: some solutions. Journal of Agricultural, Biological, and Environmental Statistics, 14, 188–206.CrossRefGoogle Scholar

Dale, M. R. T. & Fortin, M.-J. (2010). From graphs to spatial graphs. Annual Review of Ecology, Evolution, and Systematics, 41, 21–38.CrossRefGoogle Scholar

Dale, M. R. T. & MacIsaac, D. A. (1989). New methods for the analysis of spatial pattern in vegetation. Journal of Ecology, 77, 78–91.CrossRefGoogle Scholar

Dale, M. R. T. & Mah, M. (1998). The use of wavelets for spatial pattern analysis in ecology. Journal of Vegetation Science, 9, 805–814.CrossRefGoogle Scholar

Dale, M. R. T. & Powell, R. D. (1994). Scales of segregation and aggregation of plants of different kinds. Canadian Journal of Botany, 72, 448–453.CrossRefGoogle Scholar

Dale, M. R. T. & Powell, R. D. (2001). A new method for characterizing point patterns in plant ecology. Journal of Vegetation Science, 12, 597–608.CrossRefGoogle Scholar

Dale, M. R. T. & Zbigniewicz, M. W. (1995). The evaluation of multi-species pattern. Journal of Vegetation Science, 6, 391–398.CrossRefGoogle Scholar

Dale, M. R. T. & Zbigniewicz, M. W. (1997). Spatial pattern in boreal shrub communities: effects of a peak in herbivore density. Canadian Journal of Botany, 75, 1342–1348.CrossRefGoogle Scholar

Dale, M. R. T., Blundon, D. J., MacIsaac, D. A. & Thomas, A. G. (1991). Multiple species effects and spatial autocorrelation in detecting species associations. Journal of Vegetation Science, 2, 635–642.CrossRefGoogle Scholar

Dale, M. R. T., Dixon, P., Fortin, M.-J. et al. (2002). The conceptual and mathematical relationships among methods for spatial analysis. Ecography, 25, 558–577.CrossRefGoogle Scholar

Darling, D. A. (1953). On a class of problems related to the random division of an interval. Annals of Mathematical Statistics, 24, 239–253.CrossRefGoogle Scholar

Darlington, P. J. (1957). Zoogeography: The Geographical Distribution of Animals. New York: Wiley.Google Scholar

Daubechies, I. (1992). Ten Lectures on Wavelets. Philadelphia: Society for Industrial and Applied Mathematics.CrossRefGoogle Scholar

Daubechies, I. (1993). Different Perspectives on Wavelets. Providence, RI: American Mathematical Society.CrossRefGoogle Scholar

Davidar, P., Yogananad, K., Ganesh, T. & Devy, S. (2002). Distributions of forest birds and butterflies in the Andaman islands, Bay of Bengal: nested patterns and processes. Ecography, 25, 5–16.CrossRefGoogle Scholar

de Jong, P., Aarssen, L. W. & Turkington, R. (1980). The analysis of contact sampling data. Oecologia, 45, 322–324.CrossRefGoogle ScholarPubMed

de Solla, S. R., Bonduriansky, R. & Brooks, R. J. (1999). Eliminating autocorrelation reduces biological relevance of home range estimates. Journal of Animal Ecology, 68, 221–234.CrossRefGoogle Scholar

Deblauwe, V., Couteron, P., Lejeune, O., Bogaert, J. & Barbier, N. (2011). Environmental modulation of self-organized periodic vegetation patterns in Sudan. Ecography, 34, 990–1001.CrossRefGoogle Scholar

Del Mondo, G., Stell, J.G., Claramunt, C. & Thibaud, R. (2010). A graph model for spatio-temporal evolution. Journal of Universal Computer Science, 16, 1452–1477.Google Scholar

Delmelle, E. & Goovaerts, P. (2009). Second-phase sampling designs for non-stationary spatial variables. Geoderma, 153, 205–216.CrossRefGoogle ScholarPubMed

Demattei, C. & Cucala, L. (2011). Multiple spatio-temporal cluster detection for case event data: an ordering-based approach. Communications in Statistics – Theory & Methods, 40, 358–372.CrossRefGoogle Scholar

Dennis, B., Desharnais, R. A., Cushing, J. M., Henson, S. M. & Costantino, R. F. (2001). Estimating chaos and complex dynamics in an insect population. Ecological Monographs, 71, 277–303.CrossRefGoogle Scholar

Deutsch, C. V. & Journel, A. G. (1992). GSLIB. Geostatistical Software Library and User’s Guide. New York: Oxford University Press.Google Scholar

Dieckmann, U., Law, R. & Metz, J. A. J. (eds.) (2000). The Geometry of Ecological Interactions. Cambridge: Cambridge University Press.CrossRefGoogle Scholar

Dietz, E. J. (1983). Permutation tests for association between two distance matrices. Systematic Zoology, 32, 21–26.CrossRefGoogle Scholar

Diggle, P. J. (1979). Statistical methods for spatial point patterns in ecology. In Spatial and Temporal Analysis in Ecology, eds. Cormack, R. M. & Ord, J. K., pp. 95–150. Fairland, MD: International Cooperative Publishing House.Google Scholar

Diggle, P. J. (1983). Statistical Analysis of Spatial Point Patterns. London: Academic Press.Google Scholar

Diggle, P. J. (2003) Statistical Analysis of Spatial Point Patterns, 2nd edition. Hodder Arnold.Google Scholar

Diggle, P. J. & Chetwynd, A. G. (1991). Second-order analysis of spatial clustering for inhomogeneous populations. Biometrics, 47, 1155–1163.CrossRefGoogle ScholarPubMed

Diggle, P. J. & Justiniano, P. (2010). Model-based Geostatistics. New York: Springer.Google Scholar

Diggle, P. J. & Ribeiro, P. J. (2007). Model-Based Geostatistics. Springer, New York, USA.Google Scholar

Diggle, P. J., Chetwynd, A. G., Haggkvist, R. & Morris, S. (1995). Second-order analysis of space-time clustering. Statististical Methods in Medical Research, 4, 124–136.CrossRefGoogle ScholarPubMed

Diks, C., Takens, F. & DeGoede, J. (1997). Spatio-temporal chaos: a solvable model. Physica D, 104, 269–285.CrossRefGoogle Scholar

Diniz-Filho, J. A. F. & Bini, L. M. (2005). Modelling geographical patterns in species richness using eigenvector-based spatial filters. Global Ecology and Biogeography, 14, 177–185.CrossRefGoogle Scholar

Diniz-Filho, J. A. F., Bini, L. M. & Hawkins, B. A. (2003). Spatial autocorrelation and red herrings in geographical ecology. Global Ecology and Biogeography, 12, 53–64.CrossRefGoogle Scholar

Diniz-Filho, J. A. F., Nabout, J. C., Telles, M. P. D., Soares, T. N. & Rangel, T. (2009). A review of techniques for spatial modeling in geographical, conservation and landscape genetics. Genetics and Molecular Biology, 32, 203–211.CrossRefGoogle ScholarPubMed

Dixon, P. M. (2002). Nearest-neighbor contingency table analysis of spatial segregation for several species. Écoscience, 9, 142–151.CrossRefGoogle Scholar

Doak, P. (2000). Population consequences of restricted dispersal for an insect herbivore in a subdivided habitat. Ecology, 81, 1828–1841.CrossRefGoogle Scholar

Doebeli, M. & Ruxton, G. D. (1998). Stabilization through spatial pattern formation in metapopulations with long-range dispersal. Proceedings of the Royal Society of London Series B, 265, 1325–1332.CrossRefGoogle Scholar

Dong, P. (2009). Lacunarity analysis of raster datas ets and 1D, 2D, and 3D point patterns. Computers & Geosciences, 35, 2100–2110.CrossRefGoogle Scholar

Doob, J. L. (1996). The development of rigor in mathematical probability (1900–1950). The American Mathematical Monthly, 103, 586–595.Google Scholar

Dormann, C. F. (2006). Effects of incorporating spatial autocorrelation into the analysis of species distribution data. Global Ecology and Biogeography, 16, 129–138.CrossRefGoogle Scholar

Dormann, C. F. (2009). Response to Comment on “Methods to account for spatial autocorrelation in the analysis of species distributional data: a review”. Ecography, 32, 379–381.CrossRefGoogle Scholar

Dormann, C. F., McPherson, J. M., Araújo, M. B. et al. (2007). Methods to account for spatial autocorrelation in the analysis of species distributional data: a review. Ecography, 30, 609–628.CrossRefGoogle Scholar

Dos Santos, D. A., Fernandez, H. R., Cuezzo, M. G. & Dominguez, E. (2008). Sympatry inference and network analysis in biogeography. Systematic Biology, 57, 432–448.CrossRefGoogle ScholarPubMed

Doty, M. S. & Archer, J. G. (1950). An experimental test of the tide factor hypothesis. American Journal of Botany, 37, 458–464.CrossRefGoogle Scholar

Doupanloup, I., Schneider, S. & Excoffier, L. (2002). A simulated annealing approach to define the genetic structure of populations. Molecular Ecology, 11, 2571–2581.CrossRefGoogle Scholar

Drake, D. A. R. & Mandrak, N. E. (2010). Least-cost transportation networks predict spatial interaction of invasion vectors. Ecological Applications, 20, 2286–2299.CrossRefGoogle ScholarPubMed

Dray, S. (2011). A new perspecive about Moran’s I coefficient: spatial autocorrelation as a linear regression problem. Geographical Analysis, 43, 127–141.CrossRefGoogle Scholar

Dray, S., Legendre, P. & Peres-Neto, P. R. (2006). Spatial modeling: a comprehensive framework for principal coordinate analysis of neighbor matrices (PCNM). Ecological Modelling, 196, 483–493.CrossRefGoogle Scholar

Dray, S., Pélissier, R., Couteron, P. et al. (2012). Community ecology in the age of multivariate multiscale spatial analysis. Ecological Monographs, 82, 257–275.CrossRefGoogle Scholar

Duczmal, L., Kulldorff, M. & Huang, L. (2006). Evaluation of spatial scan statistics for irregularly shaped clusters. Journal of Computational and Graphical Statistics, 15, 428–442.CrossRefGoogle Scholar

Dufresne, F., Bourget, E. & Bernatchez, L. (2002). Differential patterns of spatial divergence in microsatellite and allozyme alleles: further evidence for locus-specific selection in the acorn barnacle, Semibalanus balanoides?Molecular Ecology, 11, 113–123.CrossRefGoogle ScholarPubMed

Dungan, J. L. (2001). Scaling up and scaling down: relevance of the support effect on remote sensing of vegetation. In Modelling Scale in Geographical Information Science, eds. Tate, N. J. & Atkinson, P. M., pp. 231–235. New York: Wiley.Google Scholar

Dungan, J. L., Perry, J. N., Dale, M. R. T. et al. (2002). A balanced view of scale in spatial statistical analysis. Ecography, 25, 626–640.CrossRefGoogle Scholar

Dutilleul, P. (1993a). Spatial heterogeneity and the design of ecological field experiments. Ecology, 74, 1646–1658.CrossRefGoogle Scholar

Dutilleul, P. (1993b). Modifying the t test for assessing the correlation between two spatial processes. Biometrics, 49, 305–314.CrossRefGoogle Scholar

Dutilleul, P. R. L. (2011). Spatio-temporal Heterogeneity: Concepts and Analyses. Cambridge: Cambridge University Press.Google Scholar

Dutilleul, P., Stockwell, J. D., Frigon, D. & Legendre, P. (2000). The Mantel test versus Pearson’s correlation analysis: assessment of the differences for biological and environmental studies. Journal of Agricultural, Biological and Environmental Statistics, 5, 131–150.CrossRefGoogle Scholar

Dyer, R. J. & Nason, J. D. (2004). Population graphs: the graph theoretic shape of genetic structure. Molecular Ecology, 13, 1713–1727.CrossRefGoogle ScholarPubMed

Eddy, S. R. (2004). What is Bayesian statistics?Nature Biotechnology, 22, 1177–1178.CrossRefGoogle ScholarPubMed

Edgington, E. S. (1995). Randomization Tests, 3rd edn. New York: Marcel Dekker.Google Scholar

Edwards, G. & Fortin, M.-J. (2001). Cognitive view of spatial uncertainty. In Spatial Uncertainty in Ecology: Implications for Remote Sensing and GIS Applications, eds. Hunsaker, C., Goodchild, M., Friedl, M. & Case, T., pp. 133–157. New York: Springer-Verlag.CrossRefGoogle Scholar

Edwards, G. & Lowell, K. E. (1996). Modeling uncertainty in photointerpreted boundaries. Photogrammetric Engineering and Remote Sensing, 62, 337–391.Google Scholar

Efron, B. & Tibshirani, R. J. (1993). An Introduction to the Bootstrap. New York: Chapman & Hall.CrossRefGoogle Scholar

Egbert, G. D. & Lettenmaier, D. P. (1986). Stochastic modeling of space-time structure of atmospheric chemical deposition. Water Resources Research, 22, 165–179.CrossRefGoogle Scholar

Ellner, S. & Turchin, P. (1995). Chaos in a noisy world: new methods and evidence from time-series analysis. American Naturalist, 145, 343–375.CrossRefGoogle Scholar

Epperson, B. K. (2003). Covariances among join-count spatial autocorrelation measures. Theoretical Population Biology, 64, 81–87.CrossRefGoogle ScholarPubMed

Erdös, P. & Rényi, A. (1960). On the evolution of random graphs. Publications of the Mathematical Institute of the Hungarian Academy of Science, 5, 17–61.Google Scholar

Escudero, A., Iriondo, J. M. & Torres, M. E. (2003). Spatial analysis of genetic diversity as a tool for plant conservation. Biological Conservation, 113, 351–365.CrossRefGoogle Scholar

Evans, J. P. & Cain, M. L. (1995). A spatially explicit test of foraging behaviour in a clonal plant. Ecology, 76, 1147–1155.CrossRefGoogle Scholar

Everhart, S. E., Askew, A., Seymour, L., Holb, I. J. & Scherm, H. (2011). Characterization of three-dimensional spatial aggregation and association patterns of brown rot symptoms within intensively mapped sour cherry trees. Annals of Botany, 108, 1195–1202.CrossRefGoogle ScholarPubMed

Excoffier, L., Smouse, P. & Quattro, J. (1992). Analysis of molecular variance inferred from metric distances among DNA haplotypes: application to human mitochondrial DNA restriction data. Genetics, 131, 479–491.Google ScholarPubMed

Fagan, W. F. (2002). Connectivity, fragmentation, and extinction risk in dendritic metapopulations. Ecology, 83, 3243–3249.CrossRefGoogle Scholar

Fagan, W. F., Fortin, M.-J. & Soykan, C. (2003). Integrating edge detection and dynamic modeling in quantitative analyses of ecological boundaries. BioScience, 53, 730–738.CrossRefGoogle Scholar

Faghih, F. & Smith, M. (2002). Combining spatial and scale-space techniques for edge detection to provide a spatially adaptive wavelet-based noise filtering algorithm. IEEE Transactions on Image Processing, 11, 1069–1071.CrossRefGoogle ScholarPubMed

Fall, A., Fortin, M.-J., Manseau, M. & O’Brien, D. (2007). Spatial graphs: principles and applications for habitat connectivity. Ecosystems, 10, 448–461.CrossRefGoogle Scholar

Fattorini, S. (2002). Biogeography of the tenebrionid beetles (Coleoptera, Tenebrionidae) on the Aegean Islands (Greece). Journal of Biogeography, 29, 49–67.CrossRefGoogle Scholar

Fattorini, S. (2007). Non-randomness in the species-area relationship: testing the underlying mechanisms. Oikos, 116, 678–689.Google Scholar

Feeley, K. J., Gillespie, T. W., Lebbin, D. J. & Walter, H. S. (2007). Species characteristics associated with extinction vulnerability and nestedness rankings of birds in tropical forest fragments. Animal Conservation, 10, 493–501.CrossRefGoogle Scholar

Felsenstein, J. (1975). A pain in the torus: some difficulties with models of isolation by distance. The American Naturalist, 109, 359–368.CrossRefGoogle Scholar

Fischer, J. & Lindenmayer, D. B. (2007). Landscape modification and habitat fragmentation: a synthesis. Global Ecology and Biogeography, 16, 265–280.CrossRefGoogle Scholar

Fisher, R. A. (1932). Statistical Methods for Research Workers, 4th edn. London: Oliver & Boyd.Google Scholar

Fisher, R. A. (1935). The mathematical distributions used in the common tests of significance. Econometrica, 3, 353–365.CrossRefGoogle Scholar

Fisher, R. A. (1970). Statistical Methods for Research Workers. Edinburgh: Oliver & Boyd.Google Scholar

Fitzpatrick, M. C., Preisser, E. L., Porter, A. et al. (2010). Ecological boundary detection using Bayesian areal wombling. Ecology, 91, 3448–3455.CrossRefGoogle ScholarPubMed

Foody, G. M. (2004). Spatial nonstationarity and scale-dependency in the relationship between species richness and environmental determinants for the sub-Saharan endemic avifauna. Global Ecology and Biogeography, 13, 315–320.CrossRefGoogle Scholar

Fortin, M.-J. (1992). Detection of ecotones: definition and scaling factors. Ph.D. thesis, Department of Ecology and Evolution, State University of New York at Stony Brook, 258 pages.

Fortin, M.-J. (1994). Edge detection algorithms for two-dimensional ecological data. Ecology, 75, 956–965.CrossRefGoogle Scholar

Fortin, M.-J. (1997). Effects of data types on vegetation boundary delineation. Canadian Journal of Forest Research, 27, 1851–1858.CrossRefGoogle Scholar

Fortin, M.-J. (1999a). Effects of sampling unit resolution on the estimation of the spatial autocorrelation. Écoscience, 6, 636–641.CrossRefGoogle Scholar

Fortin, M.-J. (1999b). The effects of quadrat size and data measurement on the detection of boundaries. Journal of Vegetation Science, 10, 43–50.CrossRefGoogle Scholar

Fortin, M.-J. & Dale, M. R. T. (2005). Spatial Analysis: A Guide for Ecologists. Cambridge: Cambridge University Press.Google Scholar

Fortin, M.-J. & Dale, M. R. T. (2009). Spatial autocorrelation in ecological studies: a legacy of solutions and myths. Geographical Analysis, 41, 392–397.CrossRefGoogle Scholar

Fortin, M.-J. & Drapeau, P. (1995). Delineation of ecological boundaries: comparisons of approaches and significance tests. Oikos, 72, 323–332.CrossRefGoogle Scholar

Fortin, M.-J. & Gurevitch, J. (2001). Mantel tests: spatial structure in field experiments. In Design and Analysis of Ecological Experiments, 2nd edn, eds. Scheiner, S. M. & Gurevitch, J., pp. 308–326. Oxford: Oxford University Press.Google Scholar

Fortin, M.-J. & Jacquez, G. M. (2000). Randomization tests and spatially autocorrelated data. Bulletin of the Ecological Society of America, 81, 201–205.Google Scholar

Fortin, M.-J. & Melles, S. J. (2009). Avian spatial responses to forest spatial heterogeneity at the landscape level: conceptual and statistical challenges. In Real World Ecology: Large-Scale and Long-Term Case Studies and Method, eds. Miao, S., Carstenn, S. & Nungesser, M., pp. 137–160. New York: Springer.CrossRefGoogle Scholar

Fortin, M.-J. & Payette, S. (2002). How to test the significance of the relation between spatially autocorrelated data at the landscape scale: a case study using fire and forest maps. Écoscience, 9, 213–218.CrossRefGoogle Scholar

Fortin, M.-J., Boots, B., Csillag, F. & Remmel, T. K. (2003). On the role of spatial stochastic models in understanding landscape indices in ecology. Oikos, 102, 203–212.CrossRefGoogle Scholar

Fortin, M.-J., Drapeau, P. & Jacquez, G. M. (1996). Quantification of the spatial co-occurrences of ecological boundaries. Oikos, 77, 51–60.CrossRefGoogle Scholar

Fortin, M.-J., Drapeau, P. & Legendre, P. (1989). Spatial autocorrelation and sampling design in plant ecology. Vegetatio, 83, 209–222.CrossRefGoogle Scholar

Fortin, M.-J., Jacquez, G. M. & Shipley, B. (2002). Computer-intense methods. In Encyclopedia of Environmetrics, eds. El-Shaarawi, A. & Pesgorsch, W. W., pp. 399–402. Chishester: Wiley.Google Scholar

Fortin, M.-J., Jacquez, G. M. & Shipley, B. (2012b). Computer-intense methods. In Encyclopedia of Environmetrics, 2nd edition, eds. El-Shaarawi, A. H. & Piegorsch, W.W., pp. 489–493. Chichester: Wiley.Google Scholar

Fortin, M.-J., James, P. M. A., MacKenzie, A., Melles, S.J. &. Rayfield, B. (2012a). Spatial statistics, spatial regression, and graph theory in ecology. Spatial Statistics, 1, 100–109.CrossRefGoogle Scholar

Fortin, M.-J., Keitt, T. H., Maurer, B. A., Taper, M. L., Kaufman, D. M. & Blackburn, T. M. (2005). Species ranges and distributional limits: pattern analysis and statistical issues. Oikos, 108, 7–17.CrossRefGoogle Scholar

Fortin, M.-J., Marineau, K. & Payette, S. (1999). Spatial vegetation diversity index along a postfire successional gradient in the northern boreal forest. Écoscience, 6, 204–213.CrossRefGoogle Scholar

Fortin, M.-J., Olson, R. J., Ferson, S. et al. (2000). Issues related to the detection of boundaries. Landscape Ecology, 15, 453–466.CrossRefGoogle Scholar

Fortuna, M. A. & Bascompte, J. (2008). The network approach in ecology. In Unity in Diversity: Reflections on Ecology after the Legacy of Ramon Margalef, eds. Valladares, F., Camacho, A., Elosegi, A., Estrada, M., Gili, J. M., Gracia, C. & Senar, J. C., pp. 371–393. Madrid: Fundación BBVA.Google Scholar

Fortuna, M. A., Garcia, C., Guimarães, P. R. & Bascompte, J. (2008). Spatial mating networks in insect-pollinated plants. Ecology Letters, 11, 490–498.CrossRefGoogle ScholarPubMed

Fortuna, M. A., Gómez-Rodriguez, C. & Bascompte, J. (2006). Spatial network structure and amphibian persistence in stochastic environments. Proceedings of the Royal Society of London Series B, Biological Sciences, 273, 1429–1434.CrossRefGoogle ScholarPubMed

Fotheringham, A. S. (2009). Geographically weighted regression. In The SAGE Handbook of Spatial Analysis, eds. Fotheringham, A. S. & Rogerson, P. A., pp. 243–253. London: SAGE Publications Ltd.CrossRefGoogle Scholar

Fotheringham, A. S. & Rogerson, P. A. eds. (2009). The SAGE Handbook of Spatial Analysis. Sage.CrossRefGoogle Scholar

Fotheringham, A. S. & Zhan, F. B. (1996). A comparison of three exploratory methods for cluster detection in spatial point patterns. Geographical Analysis, 28, 200–218.CrossRefGoogle Scholar

Fotheringham, A. S., Brunsdon, C. & Charlton, M. (2000). Quantitative Geography: Perspectives on Spatial Data Analysis. London: Sage Publications.Google Scholar

Fotheringham, A. S., Brunsdon, C. & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Chichester: Wiley.Google Scholar

Fowler, H. W. & Fowler, F. G. (1976). The Concise Oxford Dictionary of Current English, 6th edn, ed. Sykes, J. B.. Oxford: Oxford University Press.Google Scholar

Foxall, R. & Baddeley, A. (2002). Nonparametric measures of association between a spatial point process and a random set, with geological applications. Applied Statistics, 51, 165–182.Google Scholar

Franco, M. & Harper, J. (1988). Competition and the formation of spatial pattern in spacing gradients: an example using Kochia scoparia. Journal of Ecology, 76, 959–974.CrossRefGoogle Scholar

Frelich, L. (2002). Forest Dynamics and Disturbance Regimes: Studies from Temperate Evergreen-Deciduous Forests. New York: Cambridge University Press.CrossRefGoogle Scholar

Friedman, M. (1937). The use of ranks to avoid the assumption of normality implicit in the analysis of variance. Journal of the American Statistical Association, 32, 675–701.CrossRefGoogle Scholar

Fukushima, Y., Hiura, T. & Tanabe, S. I. (1998). Accuracy of the MacArthur–Horn method for estimating a foliage profile. Agricultural and Forest Methodology, 92, 203–210.CrossRefGoogle Scholar

Gabriel, K. R. & Sokal, R. R. (1969). A new statistical approach to geographic variation analysis. Systematic Ecology, 18, 259–270.Google Scholar

Galiano, E. F. (1982). Pattern detection in plant populations through the analysis of plant-to-all-plants distances. Vegetatio, 49, 39–43.CrossRefGoogle Scholar

Galiano, E. F. (1983). Detection of multi-species patterns in plant populations. Vegetatio, 53, 129–138.Google Scholar

García Molinos, J. & Donohue, I. (2011). Temporal variability within disturbance events regulates their effects on natural communities. Oecologia, 166, 795–806.CrossRefGoogle ScholarPubMed

Garroway, C. J., Bowman, J., Carr, D. & Wilson, P. J. (2008). Applications of graph theory to landscape genetics. Evolutionary Applications, 1, 620–630.Google ScholarPubMed

Gaston, K. J. & Blackburn, T. M. (2000). Patterns and Processes in Macroecology. Malden: Blackwell Publishing Company.CrossRefGoogle Scholar

Gauch, H. G. & Whittaker, R. H. (1972). Coenocline simulation. Ecology, 53, 446–451CrossRefGoogle Scholar

Gaucherel, C., Burel, F. & Baudry, J. (2007). Multiscale and surface pattern analysis of the effect of landscape pattern on carabid beetles distribution. Ecological Indicators, 7, 598–609.CrossRefGoogle Scholar

Gauthier, O. & Lapointe, F.-J. (2007). Seeing the trees for the network: consensus, information content, and superphylogenies. Systematic Biology, 56, 345–355.CrossRefGoogle Scholar

Gavrikov, V. & Stoyan, D. (1995). The use of marked point processes in ecological and environmental forest studies. Environmental and Ecological Statistics, 2, 331–344.CrossRefGoogle Scholar

Geary, R. C. (1954). The contiguity ratio and statistical mapping. The Incorporated Statistician, 5, 115–145.CrossRefGoogle Scholar

Gelfand, A. E. (2012). Hierarchical modeling for spatial data problems. Spatial Statistics, 1, 30–39.CrossRefGoogle ScholarPubMed

Gellert, W., Küstner, H., Hellwich, M. & Kästner, H. (1977). The VNR Concise Encyclopedia of Mathematics. New York: Van Nostrand Reinhold.CrossRefGoogle Scholar

Gering, J. C., Crist, T. O. & Veech, J. A. (2003). Additive partitioning of species diversity across multiple spatial scales: Implications for regional conservation of biodiversity. Conservation Biology, 17, 488–499.CrossRefGoogle Scholar

Gerrard, D. J. (1969). Competition quotient: a new measure of the competition affecting individual forest trees. Research Bulletin 20. Agricultural Experiment Station, Michigan State University.Google Scholar

Getis, A. (1991). Spatial interaction and spatial autocorrelation: a cross product approach. Environment and Planning A, 23, 1269–1277.CrossRefGoogle Scholar

Getis, A. & Boots, B. (1978). Models of Spatial Processes: An Approach to the Study of Point, Line and Area Patterns. Cambridge: Cambridge University Press.Google Scholar

Getis, A. & Franklin, J. (1987). Second-order neighborhood analysis of mapped point patterns. Ecology, 68, 473–477.CrossRefGoogle Scholar

Getis, A. & Ord, J. K. (1992). The analysis of spatial association by use of distance statistics. Geographical Analysis, 24, 189–206.CrossRefGoogle Scholar

Getis, A. & Ord, J. K. (1996). Local spatial statistics: an overview. In Spatial Analysis: Modelling in a GIS Environment, eds. Longley, P. & Batty, M., pp. 261–277. Cambridge: GeoInformation International.Google Scholar

Gignoux, J., Camille, D. & Sebastien, B. (1999). Comparing the performances of Diggle’s tests of spatial randomness for small samples with and without edge-effect correction: application to ecological data. Biometrics, 55, 156–164.CrossRefGoogle ScholarPubMed

Gilbert, B. & Bennett, J. R. (2010). Partitioning variation in ecological communities: do the numbers add up?Journal of Applied Ecology, 47, 1071–1082.CrossRefGoogle Scholar

Gill, J. (2002). Bayesian Methods: A Social and Behavioral Sciences Approach. Boca Raton: Chapman & Hall/CRC.Google Scholar

Glaz, J., Naus, J. & Wallenstein, S. (2001). Scan Statistics. New York: Springer-Verlag.CrossRefGoogle Scholar

Gleason, H. A. (1927). Further views of the succession concept. Ecology 8, 299–326.CrossRefGoogle Scholar

Gleik, J. (1987). Chaos: Making a New Science. New York: Viking.Google Scholar

Glenn-Lewin, D. C. & van der Maarel, E. (1992). Patterns and processes of vegetation dynamics. In Plant Succession: Theory and Prediction, eds. Glenn-Lewin, D. C., Peet, R. K. & Veblen, T. T., pp. 11–59. London: Chapman & Hall.Google Scholar

Glenn-Lewin, D. C., Peet, R. K. & Veblen, T. T. eds. (1992). Plant Succession: Theory and Prediction. London: Chapman & Hall.Google Scholar

Gonzalez-Andujar, J. L. & Perry, J. N. (1993). Chaos, metapopulations and dispersal. Ecological Modelling, 65, 255–263.CrossRefGoogle Scholar

Good, P. (1993). Permutation Tests: A Practical Guide to Resampling Methods for Hypothesis Testing. New York: Springer-Verlag.Google Scholar

Goovaerts, P. (1997). Geostatistics for Natural Resources Evaluation. New York: Oxford University Press.Google Scholar

Goovaerts, P. & Jacquez, G. M. (2004). Accounting for regional background and population size in the detection of spatial clusters and outliers using geostatistical filtering and spatial neutral models: the case of lung cancer in Long Island, New York. International Journal of Health Geographics, 3, 14.CrossRefGoogle ScholarPubMed

Goovaerts, P. & Jacquez, G. M. (2005). Detection of temporal changes in the spatial distribution of cancer rates using local Moran’s I and geostatistically simulated spatial neutral models. Journal of Geographical Systems, 7, 137–159.CrossRefGoogle ScholarPubMed

Gordon, A. D. (1999). Classification. Monographs on Statistics and Applied Probability 82, 2nd edn. London: Chapman & Hall/CRC.Google Scholar

Goreaud, F. & Pélissier, R. (1999). On explicit formulas of edge effect correction for Ripley’s K-function. Journal of Vegetation Science, 10, 433–438.CrossRefGoogle Scholar

Gosz, J. R. (1993). Ecotone hierarchies. Ecological Applications, 3, 368–376.CrossRefGoogle ScholarPubMed

Gotelli, N. J. (2004). A taxonomic wish-list for community ecology. Philosophical Transactions of the Royal Society of London Series B-Biological Sciences, 359, 585–597.CrossRefGoogle ScholarPubMed

Goulard, M., Pagès, L. & Cabanettes, A. (1995). Marked point process: using correlation functions to explore a spatial data set. Biometrical Journal, 37, 837–853.CrossRefGoogle Scholar

Grant, E. H.C., Lowe, W. H. & Fagan, W. F. (2007). Living in the branches: population dynamics and ecological processes in dendritic networks. Ecology Letters, 10, 165–175.CrossRefGoogle Scholar

Grant, P. R., & Schluter, D. (1984). Interspecific competition inferred from patterns of guild structure. In Ecological Communities: Conceptual Issues and the Evidence, eds. Strong, Jr. D. R. et al., pp. 201–233. Princeton: Princeton University Press.Google Scholar

Greig-Smith, P. (1961). Data on pattern within plant communities. I. The analysis of pattern. Journal of Ecology, 49, 695–702.CrossRefGoogle Scholar

Greig-Smith, P. (1983). Quantitative Plant Ecology, 3rd edn. Berkeley: University of California Press.Google Scholar

Griffith, D. A. (1981). Interdependence in space and time: numerical and interpretative considerations. In Dynamic Spatial Models, eds. Griffith, D. A. & MacKinnon, R. D., pp. 258–287. Netherlands: Sijthoff & Noordhoff.Google Scholar

Griffith, D. A. (1992). What is spatial autocorrelation? Reflection on the past 25 years of spatial statistics. L’Espace géographique, 3, 265–280.CrossRefGoogle Scholar

Griffith, D. A. (1996). Spatial autocorrelation and eigenfunctions of the geographic weights matrix accompanying geo-referenced data. Canadian Geographer, 40, 351–367.CrossRefGoogle Scholar

Griffith, D. A. (2008). Spatial-filtering-based contributions to a critique of geographically weighted regression (GWR). Environment and Planning A, 40, 2751–2769.CrossRefGoogle Scholar

Griffith, D. A. & Paelinck, J. H. P. (2009). Specifying a joint space- and time-lag using a bivariate Poisson distribution. Journal of Geographical Systems, 11, 23–36.CrossRefGoogle Scholar

Griffith, D. A. & Peres-Neto, P. R. (2006). Spatial modeling in ecology: the flexibility of eigenfunction spatial analyses. Ecology, 87, 2603–2613.CrossRefGoogle ScholarPubMed

Grillet, M.-E., Barrera, R., Martínez, J.-E., Berti, J. & Fortin, M.-J. (2010a). Disentangling the effect of local and global spatial variation on a mosquito-borne infection in a neotropical heterogeneous environment. American Journal of Tropical Medicine and Hygiene, 82, 194–201.CrossRefGoogle Scholar

Grillet, M.-E., Jordan, G. J. & Fortin, M.-J. (2010b). State transition detection in the spatio-temporal incidence of malaria. Spatial and Spatio-Temporal Epidemiology Journal, 1, 251–259.CrossRefGoogle ScholarPubMed

Gross, T. & Blasius, B. (2008). Adaptive coevolutionary networks: a review. Journal of the Royal Society, Interface 5, 259–271.CrossRefGoogle ScholarPubMed

Guillot, G. & Rousset, F. (2013). Dismantling the Mantel tests. Methods in Ecology and Evolution, 4, 336–344.CrossRefGoogle Scholar

Guillot, G., Leblois, R., Coulon, A. & Frantz, A. C. (2009). Statistical methods in spatial genetics. Molecular Ecology, 18, 4734–4756.CrossRefGoogle ScholarPubMed

Guo, Q. & Kelly, M. (2004). Interpretation of scale in paired quadrat variance methods. Journal of Vegetation Science, 15, 763–770.CrossRefGoogle Scholar

Gustafson, E. J. (1998). Quantifying landscape spatial pattern: what is the state of the art?Ecosystems, 1, 143–156.CrossRefGoogle Scholar

Haas, S. E., Hooten, M. B, Rizzo, D. M. & Meentemeyer, R. K. (2011). Forest species diversity reduces disease risk in a generalist plant pathogen invasion. Ecology Letters, 14, 1108–1116.CrossRefGoogle Scholar

Haase, P. (1995). Spatial pattern analysis in ecology based on Ripley’s K-function: introduction and methods of edge correction. Journal of Vegetation Science, 6, 575–582.CrossRefGoogle Scholar

Haining, R. P. (1978). The moving average model for spatial interaction. Transactions of the Institute for British Geographers, NS3, 202–225.CrossRefGoogle Scholar

Haining, R. P. (1990). Spatial Data Analysis in the Social and Environmental Sciences. Cambridge: Cambridge University Press.CrossRefGoogle Scholar

Haining, R. P. (1991). Bivariate correlation with spatial data. Geographical Analysis, 23, 210–227.CrossRefGoogle Scholar

Haining, R. P. (2003). Spatial Data Analysis: Theory and Practice. Cambridge: Cambridge University Press.CrossRefGoogle Scholar

Hall, K. R. & Maruca, S. L. (2001). Mapping a forest mosaic: a comparison of vegetation and songbird distributions using geographic boundary analysis. Plant Ecology, 156, 105–120.CrossRefGoogle Scholar

Halley, J. D. & Winkler, D. A. (2008). Consistent concepts of self-organization and self-assembly. Complexity, 14, 10–17.CrossRefGoogle Scholar

Halley, J. M., Hartley, S., Kallimanis, A. S. et al. (2004). Uses and abuses of fractal methodology in ecology. Ecology Letters, 7, 254–271.CrossRefGoogle Scholar

Handcock, R. & Csillag, F. (2002). Ecoregionalization assessment: spatio-temporal analysis of net primary production across Ontario. Écoscience, 9, 219–230.CrossRefGoogle Scholar

Hansen, A. & di Castri, F. (1992). Landscape Boundaries: Consequences for Biotic Diversity and Ecological Flows. New York: Springer-Verlag.CrossRefGoogle Scholar

Hanski, I. (2009). Metapopulations and spatial population processes. In The Princeton Guide to Ecology, ed. Levin, S. A., pp. 177–185. Princeton: Princeton University Press.Google Scholar

Hanski, I. & Woiwod, I. P. (1993). Spatial synchrony in the dynamics of moth and aphid populations. Journal of Animal Ecology, 62, 656–668.CrossRefGoogle Scholar

Harary, F. (1967). Topological concepts in graph theory. In A Seminar on Graph Theory, ed. Harary, F., pp. 13–24. New York: Holt, Rinehart & Winston.Google Scholar

Harary, F. (1969). Graph Theory. Reading, MA: Addison-Wesley.CrossRefGoogle Scholar

Harary, F. & Gupta, G. (1997). Dynamic graph models. Mathematical and Computer Modelling, 25, 79–87.CrossRefGoogle Scholar

Hargrove, W. W., Hoffman, F. M. & Schwartz, P. M. (2002). A fractal landscape realizer for generating synthetic maps. Conservation Ecology, 6, 2. Online .CrossRefGoogle Scholar

Harper, K. A., Macdonald, S. E., Burton, P. et al. (2005). Edge influence on forest structure and composition in fragmented landscapes. Conservation Biology, 19, 768–782.CrossRefGoogle Scholar

Harrison, P. J., Hanski, I. & Ovaskainen, O. (2011). Bayesian state-space modeling of metapopulation dynamics in the Glanville fritillary butterfly. Ecological Monographs, 8, 581–598.CrossRefGoogle Scholar

Harrison, S., Davies, K. F., Safford, H. D. & Veirs, J. H. (2006). Beta diversity and the scale-dependence of the productivity-diversity relationship: a test in the Californian serpentine flora. Journal of Ecology, 94, 110–117.CrossRefGoogle Scholar

Harvey, E. & Miller, T. E. (1996). Variance in composition of inquiline communities in leaves of Sarracenia purpurea L. on multiple spatial scales. Oecologia, 108, 562–566.CrossRefGoogle ScholarPubMed

Hastings, A. (2009). Biological chaos and complex dynamics. In The Princeton Guide to Ecology, ed. Levin, S. A., pp. 172–176. Princeton: Princeton University Press.Google Scholar

Heagerty, P. J. & Lumley, T. (2000). Window subsampling of estimating functions with application to regression models. Journal of the American Statistical Society, 95, 197–211.CrossRefGoogle Scholar

Hedley, S. L. & Buckland, S. T. (2004). Spatial models for line transect sampling. Journal of Agricultural, Biological and Environmental Statistics, 9, 181–199.CrossRefGoogle Scholar

Heino, J. & Muotka, T. (2005). Highly nested snail and clam assemblages in boreal lake littorals: Roles of isolation, area, and habitat suitability. Ecoscience, 12, 141–146.CrossRefGoogle Scholar

Hendry, R. J. & McGlade, J. M. (1995). The role of memory in ecological systems. Proceedings of the Royal Society of London Series B, Biological Sciences, 259, 153–159.CrossRefGoogle Scholar

Henebry, G. M. (1995). Spatial model error analysis using autocorrelation indices. Ecological Modelling, 82, 75–91.CrossRefGoogle Scholar

Hengl, T., Heuvelink, G. B. M. & Rossiter, D. G. (2007). About regression-kriging: from equations to case studies. Computers and Geosciences, 33, 1301–1315.CrossRefGoogle Scholar

Hengl, T., Heuvelink, G. B. M. & Stein, A. (2004). A generic framework for spatial prediction of soil variables based on regression-kriging. Geoderma, 120, 75–93.CrossRefGoogle Scholar

Higashi, M., Yamamura, N., Abe, T. & Burns, T. P. (1991). Why don’t all termite species have a sterile worker caste?Proceedings of the Royal Society of London Series B, Biological Sciences, 246, 25–29.CrossRefGoogle ScholarPubMed

Hill, M. O. (1973). The intensity of spatial pattern in plant communities. Journal of Ecology, 61, 225–235.CrossRefGoogle Scholar

Hoagland, B. W. & Collins, S. L. (1997). Heterogeneity in shortgrass prairie vegetation: the role of playa lakes. Journal of Vegetation Science, 8, 277–286.CrossRefGoogle Scholar

Holden, Z. A. & Evans, J. S. (2010). Using fuzzy C-means and local autocorrelation to cluster satellite-inferred burn severity classes. International Journal of Wildland Fire, 19, 853–860.CrossRefGoogle Scholar

Holland, M. M., Risser, P. G. & Naiman, R. J. (eds.) (1991). Ecotones. New York: Chapman & Hall.CrossRefGoogle Scholar

Holyoak, M., Leibold, M. A. & Holt, R. D. eds. (2005). Metacommunities: Spatial Dynamics and Ecological Communities. Chicago: University of Chicago Press.Google Scholar

Hope, A. C. A. (1968). A simplified Monte Carlo significance test procedure. Journal of the Royal Statistical Society B, 30, 582–598.Google Scholar

Hopkins, B. (1957). Pattern in the plant community. Journal of Ecology, 45, 451–463.CrossRefGoogle Scholar

Hothorn, T., Müller, J., Schröder, B., Kneib, T. & Brandl, R. (2011). Decomposing environmental, spatial, and spatiotemporal components of species distributions. Ecological Monographs, 81, 329–347.CrossRefGoogle Scholar

Hubert, L. J., & Golledge, R. G. (1981). A heuristic method for the comparison of related structures. Journal of Mathematical Psychology, 23, 214–226.CrossRefGoogle Scholar