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References

Published online by Cambridge University Press:  19 June 2025

Mark R. T. Dale  and
Marie-Josée Fortin
Mark R. T. Dale
Affiliation:
University of Northern British Columbia
Marie-Josée Fortin
Affiliation:
University of Toronto
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Spatial Analysis
A Guide for Ecologists
 , pp. 361 - 393
Publisher: Cambridge University Press
Print publication year: 2025

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Abrams, J. F., Vashishtha, A., Wong, S. T., Nguyen, A., Mohamed, A., Wieser, S., Kuijper, A., Wilting, A. & Mukhopadhyay, A. (2019). Habitat-Net: Segmentation of habitat images using deep learning. Ecological Informatics, 51, 121–128.10.1016/j.ecoinf.2019.01.009CrossRefGoogle Scholar
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, 55–64.10.1016/j.ecoinf.2012.05.002CrossRefGoogle 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, 1508–1517.10.1111/j.1365-2664.2011.02037.xCrossRefGoogle Scholar
Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19, 716–723.10.1109/TAC.1974.1100705CrossRefGoogle Scholar
Akbari, K., Winter, S. & Tomko, M. (2023). Spatial causality: A systematic review on spatial causal inference. Geographical Analysis, 55, 56–89.10.1111/gean.12312CrossRefGoogle Scholar
Albery, G. F., Kirkpatrick, L., Firth, J. A. & Bansal, S. (2021). Unifying spatial and social network analysis in disease ecology. Journal of Animal Ecology, 90, 45–61.CrossRefGoogle ScholarPubMed
Allesina, S. & Pascual, M. (2009). Food web models: A plea for groups. Ecology Letters, 12, 652–662.10.1111/j.1461-0248.2009.01321.xCrossRefGoogle ScholarPubMed
Almeida‐Neto, M., Guimaraes, P., Guimaraes, P. R. Jr, Loyola, R. D. & Ulrich, W. (2008). A consistent metric for nestedness analysis in ecological systems: Reconciling concept and measurement. Oikos, 117, 1227–1239.10.1111/j.0030-1299.2008.16644.xCrossRefGoogle 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, 165–180.10.1080/00949650215866CrossRefGoogle Scholar
Alpargu, G. & Dutilleul, P. (2006). Stepwise regression in mixed quantitative linear models with autocorrelated errors. Communications in Statistics – Simulation and Computation, 35, 79–104.10.1080/03610910500416082CrossRefGoogle Scholar
Andersen, I. T. & Hahn, U. (2015). Matérn thinned Cox processes. Spatial Statistics, 15, 1–21.10.1016/j.spasta.2015.10.005CrossRefGoogle Scholar
Anderson, M. J., Crist, T. O., Chase, J. M., Vellend, M., Inouye, B. D., Freestone, A. L., Sanders, N. J., Cornell, H. V., Comita, L. S., Davies, K. F., Harrison, S. P., Kraft, N. J. B., Stegen, J. C. & Swenson, N. G. (2011). Navigating the multiple meanings of beta diversity: A roadmap for the practicing ecologist. Ecology Letters, 14, 19–28.CrossRefGoogle ScholarPubMed
Anderson, T. & Dragićević, S. (2020). Representing complex evolving spatial networks: Geographic network automata. International Journal of Geo-Information, 9, 270.10.3390/ijgi9040270CrossRefGoogle Scholar
Anselin, L. (1995). Local indicators of spatial association: LISA. Geographical Analysis, 27, 93–115.10.1111/j.1538-4632.1995.tb00338.xCrossRefGoogle Scholar
Anselin, L. (2001). Spatial econometrics. In A Companion to Theoretical Econometrics, Baltagi, B. (ed.), pp. 310–330. Cambridge: Basil Blackwell.Google Scholar
Anselin, L. (2009). Spatial regression. In The SAGE Handbook of Spatial Analysis, Fotheringham, A. S. & Rogerson, P. A. (eds.), pp. 255–275. Cambridge: SAGE Publications Ltd.10.4135/9780857020130.n14CrossRefGoogle Scholar
Anselin, L. & Li, X. (2019). Operational join count statistics for cluster detection. Journal of Geographical Systems, 21, 189–210.10.1007/s10109-019-00299-xCrossRefGoogle ScholarPubMed
Ashenfelter, O. (1978). Estimating the effect of training programs on earnings. Review of Economic Statistics, 60, 47–57.10.2307/1924332CrossRefGoogle Scholar
Austin, M. P. (1987). Models for the analysis of species’ response to environmental gradients. Vegetatio, 69, 35–45.10.1007/BF00038685CrossRefGoogle 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
Baddeley, A., Nair, G., Raksit, S., McSwiggan, G. & Davies, T. M. (2021). Analysing point patterns on networks: A review. Spatial Statistics, 42, 100435.10.1016/j.spasta.2020.100435CrossRefGoogle Scholar
Baddeley, A., Rubak, E. & Turner, R. (2015). Spatial Point Patterns: Methodology and Applications with R. Cambridge: Chapman & Hall.10.1201/b19708CrossRefGoogle Scholar
Bader, M. Y., Llambi, L. D., Case, B. S., Buckley, H. L., Toivonen, J. M., Camarero, J. J., Cairns, D. M., Brown, C. D., Wiegand, T. & Resler, L. M. (2021). A global framework for linking alpine-treeline ecotone patterns to underlying processes. Ecography, 44, 265–292.10.1111/ecog.05285CrossRefGoogle Scholar
Ballani, F., Pommerening, A. & Stoyan, D. (2019). Mark–mark scatterplots improve pattern analysis in spatial plant ecology. Ecological Informatics, 49, 13–21.CrossRefGoogle Scholar
Barabási, A. (2009). Scale-free networks: A decade and beyond. Science, 325, 412–413.10.1126/science.1173299CrossRefGoogle ScholarPubMed
Baranyi, G., Saura, S., Podani, J. & Jordán, F. (2011). Contribution of habitat patches to network connectivity: Redundancy and uniqueness of topological indices. Ecological Indicators, 11, 1301–1310.10.1016/j.ecolind.2011.02.003CrossRefGoogle Scholar
Barbujani, G., Oden, N. N. & Sokal, R. R. (1989). Detecting regions of abrupt change in maps of biological variables. Systematic Zoology, 38, 376–389.10.2307/2992403CrossRefGoogle 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, 1816–1819.10.1073/pnas.87.5.1816CrossRefGoogle Scholar
Barot, S., Gignoux, J. & Menaut, J.-C. (1999). Demography of a savanna palm tree: Predictions from comprehensive spatial pattern analysis. Ecology, 80, 1987–2005.10.1890/0012-9658(1999)080[1987:DOASPT]2.0.CO;2CrossRefGoogle Scholar
Barrat, A., Barthelemy, M. & Vespignani, A. (2008). Dynamical Processes on Complex Networks. Cambridge: Cambridge University Press.10.1017/CBO9780511791383CrossRefGoogle Scholar
Barter, E. & Gross, T. (2017). Spatial effects in meta-foodwebs. Scientific Reports, 7, 9980.10.1038/s41598-017-08666-8CrossRefGoogle ScholarPubMed
Barthelemy, M. (2011). Spatial networks. Physics Reports, 499, 1–101.10.1016/j.physrep.2010.11.002CrossRefGoogle Scholar
Barthelemy, M. (2018). Morphogenesis of Spatial Networks. Cambridge: Springer.10.1007/978-3-319-20565-6CrossRefGoogle 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, 536–543.10.2307/2342284CrossRefGoogle Scholar
Bartlett, M. S. (1948). Smoothing periodograms from time series with continuous spectra. Nature, 161, 686–687.10.1038/161686a0CrossRefGoogle Scholar
Bascompte, J. (2009). Disentangling the web of life. Science, 325, 416–419.10.1126/science.1170749CrossRefGoogle ScholarPubMed
Bascompte, J. & Solé, R. V. (1998). Spatiotemporal patterns in nature. Trends in Ecology and Evolution, 13, 173–174.10.1016/S0169-5347(98)01325-1CrossRefGoogle ScholarPubMed
Baselga, A. (2010). Partitioning the turnover and nestedness components of beta diversity. Global Ecology and Biogeography, 19, 134–143.10.1111/j.1466-8238.2009.00490.xCrossRefGoogle Scholar
Baskerville, E. B., Dobson, A. P., Bedford, T., Allesina, S., Anderson, T. M. & Pascual, M. (2011). Spatial guilds in the Serengeti food web revealed by a Bayesian group model. PLoS Computational Biology, 7, e1002321.10.1371/journal.pcbi.1002321CrossRefGoogle ScholarPubMed
Batschelet, E. (1981). Circular Statistics in Biology. Cambridge: Academic Press.Google Scholar
Bayles, B. R., Thomas, S. M., Simmons, G. S., Grafton-Cardwell, E. E. & Daugherty, M. P. (2017). Spatiotemporal dynamics of the Southern California Asian citrus psyllid (Diaphorina citri) invasion. PLoS ONE, 12, e0173226.10.1371/journal.pone.0173226CrossRefGoogle ScholarPubMed
Beale, C. M., Lennon, J. J., Yearsley, J. M, Brewer, M. J. & Elston, D. A. (2010). Regression analysis of spatial data. Ecology Letters, 13, 246–264.10.1111/j.1461-0248.2009.01422.xCrossRefGoogle ScholarPubMed
Beckage, B., Joseph, L., Belisle, P., Wolfson, D. B. & Platt, W. J. (2007). Bayesian change-point analyses in ecology. New Phytologist, 174, 456–467.10.1111/j.1469-8137.2007.01991.xCrossRefGoogle ScholarPubMed
Beguería, S. & Pueyo, Y. (2009). A comparison of simultaneous autoregressive and generalized least squares models for dealing with spatial autocorrelation. Global Ecology and Biogeography, 18, 273–279.CrossRefGoogle Scholar
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, 921–929.10.1111/j.2041-210X.2012.00211.xCrossRefGoogle Scholar
Benes, V. & Rataj, J. (2004). Stochastic Geometry: Selected Topics. Cambridge: Kluwer Academic Publisher.Google Scholar
Ben-Said, M. (2021). Spatial point-pattern analysis as a powerful tool in identifying pattern-process relationships in plant ecology: An updated review. Ecological Processes, 10, 56.10.1186/s13717-021-00314-4CrossRefGoogle Scholar
Berman, M. (1986). Testing for spatial association between a point process and another stochastic process. Applied Statistics, 35, 54–62.10.2307/2347865CrossRefGoogle Scholar
Berthelot, G., Said, S. & Bansaye, V. (2020). How to use random walks for modeling the movement of wild animals. bioRxiv, https//doi.org/10.1101/2020.03.11.986885.CrossRefGoogle Scholar
Bertness, M. D. & Calloway, R. (1994). Positive interactions in communities. Trends in Ecology and Evolution, 9, 191–193.10.1016/0169-5347(94)90088-4CrossRefGoogle ScholarPubMed
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
Bhatti, U. A., Yu, Z., Yuan, L., Zeeshan, Z., Nawaz, S. A., Bhatti, M., Mehmood, A., Ain, Q. U. & Wen, L. (2020). Geometric algebra applications in geospatial artificial intelligence and remote sensing image processing. IEE Access, 8, 155783–155796.10.1109/ACCESS.2020.3018544CrossRefGoogle Scholar
Bianconi, G. (2018). Multilayer Networks: Structure and Function. Cambridge: Oxford University Press.10.1093/oso/9780198753919.001.0001CrossRefGoogle Scholar
Biggs, N. L., Lloyd, E. K. & Wilson, R. J. (1976). Graph Theory 1736–1936. Cambridge: Oxford University Press.Google Scholar
Birre, D., Feuillet, T., Lagalis, R., Milian, J., Alexandre, F., Sheeren, D., Serrano-Notivoli, R., Vignal, M. & Bader, M.Y. (2023). A new method for quantifying treeline-ecotone change based on multiple spatial pattern dimensions. Landscape Ecology, 38, 779–796.10.1007/s10980-022-01589-4CrossRefGoogle 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. Cambridge: Springer.Google Scholar
Bjørnstad, O. N. & Falck, W. (2001). Nonparametric spatial covariance functions: Estimation and testing. Environmental and Ecological Statistics, 8, 53–70.10.1023/A:1009601932481CrossRefGoogle Scholar
Bjørnstad, O. N., Ims, R. A. & Lambin, X. (1999). Spatial population dynamics: Analyzing patterns and processes of population synchrony. Trends in Ecology and Evolution, 14, 427–432.10.1016/S0169-5347(99)01677-8CrossRefGoogle ScholarPubMed
Blackman, R. C., Ho, H.-C., Walser, J.-C. & Altermatt, F. (2022). Spatio-temporal patterns of multi-trophic biodiversity and food-web characteristics uncovered across a river catchment using environmental DNA. Communications Biology, 5, 259.10.1038/s42003-022-03216-zCrossRefGoogle ScholarPubMed
Blanchet, F. G., Legendre, P. & Borcard, D. (2008). Modelling directional spatial processes in ecological data. Ecological Modelling, 215, 325–336.10.1016/j.ecolmodel.2008.04.001CrossRefGoogle Scholar
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, 357–368.10.1007/s00442-010-1867-yCrossRefGoogle ScholarPubMed
Boccaletti, S., Bianconi, G., Criado, R., del Genio, C., Gómez-Gardeñes, J., Romance, M., Sendiña-Nadal, I., Wang, Z. & Zanin, M. (2014). The structure and dynamics of multilayer networks. Physics Reports, 544, 1–122.10.1016/j.physrep.2014.07.001CrossRefGoogle ScholarPubMed
Bode, M., Burrage, K. & Possingham, H. P. (2008). Using complex network metrics to predict the persistence of metapopulations with asymmetric connectivity patterns. Ecological Modelling, 214, 201–209.10.1016/j.ecolmodel.2008.02.040CrossRefGoogle Scholar
Boettiger, C. (2022). The forecast trap. Ecology Letters, 25, 1655–1664.10.1111/ele.14024CrossRefGoogle ScholarPubMed
Bolker, B. M., Brooks, M. E., Clark, C. J., Geange, S. W., Poulsen, J. R., Stevens, M. H. H. & White, J. S. (2009). Generalized linear mixed models: A practical guide for ecology and evolution. Trends in Ecology and Evolution, 24, 127–135.10.1016/j.tree.2008.10.008CrossRefGoogle ScholarPubMed
Boots, B. N. (2002). Local measures of spatial association. Écoscience, 9, 168–176.10.1080/11956860.2002.11682703CrossRefGoogle Scholar
Borcard, D., Gillet, F. & Legendre, P. (2018). Numerical Ecology with R. 2nd ed. Cambridge: Springer.10.1007/978-3-319-71404-2CrossRefGoogle Scholar
Borcard, D. & Legendre, P. (2002). All-scale spatial analysis of ecological data by means of principal coordinates of neighbour matrices. Ecological Modelling, 153, 51–68.10.1016/S0304-3800(01)00501-4CrossRefGoogle Scholar
Borcard, D. & Legendre, P. (2012). Is the Mantel correlogram powerful enough to be useful in ecological analysis? A simulation study. Ecology, 93, 1473–1481.10.1890/11-1737.1CrossRefGoogle Scholar
Borcard, D., Legendre, P. & Drapeau, P. (1992). Partialling out the spatial component of ecological variation. Ecology, 73, 1045–1055.10.2307/1940179CrossRefGoogle Scholar
Borowiec, M. L., Dikow, R. B., Frandsen, P. B., McKeeken, A., Valentini, G. & White, A. E. (2022). Deep learning as a tool for ecology and evolution. Methods in Ecology and Evolution, 13, 1640–1660.10.1111/2041-210X.13901CrossRefGoogle Scholar
Boulanger, E., Dalongeville, A., Andrello, M., Mouillot, D. & Manel, S. (2020). Spatial graphs highlight how multi-generational dispersal shapes landscape genetic patterns. Ecography, 43, 1167–1179.10.1111/ecog.05024CrossRefGoogle 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, 1464–1478.10.1139/x99-150CrossRefGoogle Scholar
Brice, M. H., Cazelles, K., Legendre, P. & Fortin, M. J. (2019). Disturbances amplify tree community responses to climate change in the temperate–boreal ecotone. Global Ecology and Biogeography, 28, 1668–1681.10.1111/geb.12971CrossRefGoogle Scholar
Brown, J. H. (1995). Macroecology. Cambridge: University of Chicago Press.Google Scholar
Brumback, B. A. (2022). Fundamentals of Causal Inference: With R. Cambridge: CRC Press.Google Scholar
Buettel, J. C., Cole, A., Dickey, J. M. & Brook, B. W. (2018). Analyzing linear spatial features in ecology. Ecology, 99, 1490–1497.10.1002/ecy.2215CrossRefGoogle ScholarPubMed
Burnham, K. P. & Anderson, D. R. (2002). Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach. Cambridge: Springer.Google Scholar
Canny, J. (1986). A computational approach to edge detection. IEEE Transitional Pattern Analysis of Machine Intelligence, 8, 679–698.10.1109/TPAMI.1986.4767851CrossRefGoogle ScholarPubMed
Cantera, I., Decotte, J.-B., Dejean, T., Murienne, J., Vigouroux, R., Valentini, A. & Brosse, S. (2022). Characterizing the spatial signal of environmental DNA in river systems using a community ecology approach. Molecular Ecology Resources, 22, 1274–1283.10.1111/1755-0998.13544CrossRefGoogle ScholarPubMed
Capblancq, T. & Forester, B. R. (2021). Redundancy analysis: A Swiss Army Knife for landscape genomics. Methods in Ecology and Evolution, 12, 2298–2309.10.1111/2041-210X.13722CrossRefGoogle Scholar
Carl, G., Doktor, D., Schweger, O. & Kuhn, I. (2016). Assessing relative variable importance across different spatial scales: A two-dimensional wavelet analysis. Journal of Biogeography, 43, 2502–2512.10.1111/jbi.12781CrossRefGoogle 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, 22–29.10.5194/we-8-22-2008CrossRefGoogle Scholar
Carl, G. & Kühn, I. (2008). Analyzing spatial ecological data using linear regression and wavelet analysis. Stochastic Environmental Research and Risk Assessment, 22, 315–324.10.1007/s00477-007-0117-2CrossRefGoogle Scholar
Carl, G. & Kühn, I. (2010). A wavelet-based extension of generalized linear models to remove the effect of spatial autocorrelation. Geographical Analysis, 42, 323–337.10.1111/j.1538-4632.2010.00777.xCrossRefGoogle Scholar
Castillo-Paez, S., Fernandez-Casal, R. & Garcia-Soidan, P. (2019). A nonparametric bootstrap method for spatial data. Computational Statistics and Data Analysis, 137, 1–15.10.1016/j.csda.2019.01.017CrossRefGoogle Scholar
Cerioli, A. (1997). Modified tests of independence in 2 × 2 tables with spatial data. Biometrics, 53, 619–628.10.2307/2533962CrossRefGoogle Scholar
Cerioli, A. (2002). Testing mutual independence between two discrete-valued spatial processes: A correction to Pearson chi-squared. Biometrics, 58, 888–897.10.1111/j.0006-341X.2002.00888.xCrossRefGoogle ScholarPubMed
Ceron, K., Provete, D. B., Pires, M. M., Araújo, A. C., Blüthgen, N. & Santana, D. J. (2021). Differences in prey availability across space and time lead to interaction rewiring and reshape a predator-prey metaweb. Ecology, 103, e3716.10.1002/ecy.3716CrossRefGoogle Scholar
Chao, A. N., Chiu, C. H. & Jost, L. (2014). Unifying species diversity, phylogenetic diversity, functional diversity, and related similarity and differentiation measures through Hill numbers. Annual Review of Ecology, Evolution, and Systematics, 45, 297–324.10.1146/annurev-ecolsys-120213-091540CrossRefGoogle Scholar
Chase, J. M. & Knight, T. M. (2013). Scale‐dependent effect sizes of ecological drivers on biodiversity: Why standardised sampling is not enough. Ecology Letters, 16, 17–26.10.1111/ele.12112CrossRefGoogle ScholarPubMed
Chatfield, C. (1975). The Analysis of Time Series: Theory and Practice. Cambridge: Chapman & Hall.10.1007/978-1-4899-2925-9CrossRefGoogle Scholar
Chilès, J.-P. & Delfiner, P. R. (2012). Geostatistics: Modeling Spatial Uncertainty. 2nd ed. Cambridge: Wiley.10.1002/9781118136188CrossRefGoogle Scholar
Chrichton, G., Baker, S., Guo, Y. & Korhonen, A. (2020). Neural networks for open and closed literature-based discovery. PLoS ONE, 15, e0232891.10.1371/journal.pone.0232891CrossRefGoogle Scholar
Christie, M. R. & Knowles, L. L. (2015). Habitat corridors facilitate genetic resilience irrespective of species dispersal abilities or population sizes. Evolutionary Applications, 8, 454–463.10.1111/eva.12255CrossRefGoogle ScholarPubMed
Christin, S., Hervet, E. & Lecompte, N. (2019). Applications for deep learning in ecology. Methods in Ecology and Evolution, 10, 1632–1644.10.1111/2041-210X.13256CrossRefGoogle Scholar
Chuine, I. & Régnière, J. (2017). Process-based models of phenology for plants and animals. Annual Review of Ecology, Evolution, and Systematics, 48, 159–182.10.1146/annurev-ecolsys-110316-022706CrossRefGoogle Scholar
Chung, F. R. K. (1997). Spectral Graph Theory. Cambridge: American Mathematical Society.Google Scholar
Claramunt, C. & Thériault, M. (1997). Towards semantics for modeling spatio-temporal processes within GIS. In Advances in GIS Research II, Kraak, M. J. & Molenaar, M. (eds.), pp. 47–63. Cambridge: Taylor & Francis.Google Scholar
Clark, A. T. (2020a). General scaling laws for stability in ecological systems. Ecology Letters, 24, 1474–1486.10.1111/ele.13760CrossRefGoogle Scholar
Clark, J. S. (2020b). Models for Ecological Data, An Introduction. Cambridge: Princeton University Press.10.2307/j.ctv15r5dgvCrossRefGoogle Scholar
Clark, J. S., Gelfand, A. E., Woodall, C. W. & Zhu, K. (2014). More than the sum of the parts: Forest climate response from joint species distribution models. Ecological Applications, 24, 990–999.10.1890/13-1015.1CrossRefGoogle Scholar
Clements, F. E. (1916). Plant Succession. Cambridge: Carnegie Institute.Google Scholar
Cliff, A. D. & Ord, J. K. (1981). Spatial Processes: Models and Applications. Cambridge: Pion.Google Scholar
Clifford, P., Richardson, S. & Hémon, D. (1989). Assessing the significance of correlation between two spatial processes. Biometrics, 45, 123–134.10.2307/2532039CrossRefGoogle ScholarPubMed
Cohn, R. D. (1999). Comparisons of multivariate relational structures in serially correlated data. Journal of Agricultural, Biological & Environmental Statistics, 4, 238–257.10.2307/1400384CrossRefGoogle Scholar
Condit, R., Ashton, P. S., Baker, P., Bunyavejchewin, S., Gunatilleke, S., Gunatilleke, N., Hubbell, S., Foster, R. B., Itoh, A., Lafrankie, J. V., Lee, H. S., Losos, E., Manokaran, N., Sukumar, R. & Yamakura, T. (2000). Spatial patterns in the distribution of tropical tree species. Science, 288, 1414–1417.10.1126/science.288.5470.1414CrossRefGoogle ScholarPubMed
Costa, A. Gonzalez, A. M. M., Giuzien, K., Doglioli, A. M., Gomez, J. M., Petrenko, A. A. & Allesina, S. (2019). Ecological networks: Pursuing the shortest path, however narrow and crooked. Scientific Reports, 9, 17826.10.1038/s41598-019-54206-xCrossRefGoogle ScholarPubMed
Cousens, R. D. & Dale, M. R. T. (2023). The evolution of ecology. In Effective Ecology: Seeking Success in a Hard Science, Cousens, R. D. (ed.), pp. 13–31. Cambridge: CRC Press.10.1201/9781003314332-2CrossRefGoogle Scholar
Couteron, P. & Ollier, S. S. (2005). A generalized variogram-based framework for multiscale ordination. Ecology, 86, 828–834.10.1890/03-3184CrossRefGoogle Scholar
Cozzo, E., Kivelä, M., De Domenico, M., Solé-Ribalta, A., Arenas, A., Gómez, S., Porter, M. A. & Moreno, Y. (2015). Structure of triadic relations in multiplex networks. New Journal of Physics, 17, 073029.10.1088/1367-2630/17/7/073029CrossRefGoogle Scholar
Crabot, J., Clappe, S., Dray, S. & Datry, T. (2019). Testing the Mantel statistic with a spatially‐constrained permutation procedure. Methods in Ecology and Evolution, 10, 532–540.10.1111/2041-210X.13141CrossRefGoogle Scholar
Cressie, N. A. C. (1993). Statistics for Spatial Data, revised ed. Cambridge: Wiley.10.1002/9781119115151CrossRefGoogle Scholar
Cressie, N. A. C. (1996). Change of support and the modifiable areal unit problem. Geographical Systems, 3, 159–180.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, 553–570.10.1890/07-0744.1CrossRefGoogle ScholarPubMed
Cressie, N. & Wikle, C. K. (2011). Statistics for Spatio-Temporal Data. Cambridge: Wiley.Google Scholar
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, 734–743.10.1086/378901CrossRefGoogle ScholarPubMed
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
Csillag, F. & Kabos, S. (1996). Hierarchical decomposition of variance with applications in environmental mapping based on satellite images. Mathematical Geology, 28, 385–405.10.1007/BF02083652CrossRefGoogle Scholar
Csillag, F. & Kabos, S. (2002). Wavelets, boundaries, and the spatial analysis of landscape pattern. Écoscience, 9, 177–190.10.1080/11956860.2002.11682704CrossRefGoogle Scholar
Cuddington, K., Fortin, M.-J., Gerber, L. R., Hastings, A., Liebhold, A., O’Connor, M. & Ray, C. (2013). Process‐based models are required to manage ecological systems in a changing world. Ecosphere, 4, 1–12.10.1890/ES12-00178.1CrossRefGoogle Scholar
Cunillera-Montcusí, D., Fernández-Calero, J. M., Pölsterl, S., Argelich, R., Fortuño, P., Cid, N., Bonada, N. & Cañedo-Argülles, M. (2023). Navigating through space and time: A methodological approach to quantify spatiotemporal connectivity using stream flow data as a case study. Methods in Ecology and Evolution, 14, 1780–1795.10.1111/2041-210X.14105CrossRefGoogle Scholar
Dale, M. R. T. (1985). Graph theoretical methods for comparing phytosociological structures. Vegetatio, 63, 79–88.10.1007/BF00032608CrossRefGoogle Scholar
Dale, M. R. T. (1986). Overlap and spacing of species’ ranges on an environmental gradient. Oikos, 47, 303–308.10.2307/3565441CrossRefGoogle Scholar
Dale, M. R. T. (1988). The spacing and intermingling of species boundaries on an environmental gradient. Oikos, 53, 351–356.10.2307/3565535CrossRefGoogle Scholar
Dale, M. R. T. (1995). Spatial pattern in communities of crustose saxicolous lichens. Lichenologist, 27, 495–503.10.1016/S0024-2829(95)80009-3CrossRefGoogle Scholar
Dale, M. R. T. (1999). Spatial Pattern Analysis in Plant Ecology. Cambridge: Cambridge University Press.10.1017/CBO9780511612589CrossRefGoogle Scholar
Dale, M. R. T. (2017). Applying Graph Theory in Ecological Research. Cambridge: Cambridge University Press.10.1017/9781316105450CrossRefGoogle 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, 635–642.10.2307/3236174CrossRefGoogle Scholar
Dale, M. R. T., Dixon, P., Fortin, M.-J., Legendre, P., Myers, D. E. & Rosenberg, M. (2002). The conceptual and mathematical relationships among methods for spatial analysis. Ecography, 25, 558–577.10.1034/j.1600-0587.2002.250506.xCrossRefGoogle Scholar
Dale, M. R. T. & Fortin, M.-J. (2002). Spatial autocorrelation and statistical tests in ecology. Écoscience, 9, 162–167.10.1080/11956860.2002.11682702CrossRefGoogle Scholar
Dale, M. R. T. & Fortin, M.-J. (2009). Spatial autocorrelation and statistical tests: Some solutions. Journal of Agricultural, Biological, and Environmental Statistics, 14, 188–206.10.1198/jabes.2009.0012CrossRefGoogle Scholar
Dale, M. R. T. & Fortin, M.-J. (2010). From graphs to spatial graphs. Annual Review of Ecology, Evolution, and Systematics, 41, 21–38.10.1146/annurev-ecolsys-102209-144718CrossRefGoogle Scholar
Dale, M. R. T. & Fortin, M.-J. (2014). Spatial Analysis: A Guide for Ecologists. 2nd ed. Cambridge: Cambridge University Press.10.1017/CBO9780511978913CrossRefGoogle Scholar
Dale, M. R. T. & Fortin, M.-J. (2021). Quantitative Analysis of Ecological Networks. Cambridge: Cambridge University Press.10.1017/9781108649018CrossRefGoogle Scholar
Dale, M. R. T. & Mah, M. (1998). The use of wavelets for spatial pattern analysis in ecology. Journal of Vegetation Science, 9, 805–814.10.2307/3237046CrossRefGoogle Scholar
Dale, M. R. T. & Powell, R. D. (1994). Scales of segregation and aggregation of plants of different kinds. Canadian Journal of Botany, 72, 448–453.10.1139/b94-059CrossRefGoogle Scholar
Dale, M. R. T. & Powell, R. D. (2001). A new method for characterizing point patterns in plant ecology. Journal of Vegetation Science, 12, 597–608.10.2307/3236899CrossRefGoogle Scholar
Dale, M. R. T. & Zbigniewicz, M. W. (1995). The evaluation of multi-species pattern. Journal of Vegetation Science, 6, 391–398.10.2307/3236238CrossRefGoogle Scholar
Damgaard, C. (2019). A critique of the space-for-time substitution practice in community ecology. Trends in Ecology & Evolution, 34, 416–421.10.1016/j.tree.2019.01.013CrossRefGoogle ScholarPubMed
Danel, T., Spurek, P., Tabor, J., Smieja, M., Lukasz, S., Slowik, A. & Maziarka, L. (2019). Spatial graph convolutional networks. arXiv: 1909.05310v2.Google Scholar
Darsanj, M., Basiri, R. & Moradi, M. (2021). Intraspecific interaction comparison in pure and mixed Populus euphratica stands using Mark correlation function in Behbahan Chaharasyab area. Journal of Wood & Forest Science and Technology, 27, 81–95.Google Scholar
Dastour, H., Ghaderpour, E., Zaghloul, M. S., Farjad, B., Gupta, A., Eum, H., Achari, G. & Hassan, Q. K. (2022). Wavelet-based spatiotemporal analyses of climate and vegetation for the Athabasca river basin in Canada. International Journal of Applied Earth Observation and Geoinformation, 114, 103044.10.1016/j.jag.2022.103044CrossRefGoogle Scholar
Daubechies, I. (1992). Ten Lectures on Wavelets. Cambridge: Society for Industrial and Applied Mathematics.10.1137/1.9781611970104CrossRefGoogle Scholar
De Domenico, M., Solé-Ribalta, A., Cozzo, E., Kivelä, M., Moreno, Y., Porter, M., Gomez, S. & Arenas, A. (2013). Mathematical formulation of multilayer networks. Physical Review X, 3, 041022.10.1103/PhysRevX.3.041022CrossRefGoogle Scholar
De Domenico, M., Solé-Ribalta, A., Ormodei, E., Gomez, S. & Arenas, A. (2015). Ranking in interconnected multilayer networks reveals versatile nodes. Nature Communications, 6, 6868.10.1038/ncomms7868CrossRefGoogle ScholarPubMed
Dee, L. E., Ferraro, P. J., Severen, C. N., Kimmel, K. A., Borer, E. T., Byrnes, J. E., Clark, A. T., Hautier, Y., Hector, A., Raynaud, X., Reich, P. B., Wright, A. J., Arnillas, C. A., Davies, K. F., MacDougall, A., Mori, A. S., Smith, M. D., Adler, P. B., Bakker, J. D., Brauman, K. A., Cowles, J., Komatsu, K., Knops, J. M. H., McCulley, R. L., Moore, J. L., Morgan, J. W., Ohlert, T., Power, S. A., Sullivan, L. L., Stevens, C. & Loreau, M. (2023). Clarifying the effect of biodiversity on productivity in natural ecosystems with longitudinal data and methods for causal inference. Nature Communications, 14, 2607.10.1038/s41467-023-37194-5CrossRefGoogle ScholarPubMed
DeFord, D. R. & Pauls, S. D. (2019). Spectral clustering methods for multiplex networks. Physica A, 533, 121949.10.1016/j.physa.2019.121949CrossRefGoogle 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
Delgado, M. S. & Florax, R. J. G. M. (2015). Difference-in-differences techniques for spatial data: Local autocorrelation and spatial interaction. Economics Letters, 137, 123–126.10.1016/j.econlet.2015.10.035CrossRefGoogle Scholar
Deneu, B., Servajean, M., Bonnet, P., Botella, C., Munoz, F. & Joly, A. (2021). Convolutional neural networks improve species distribution modelling by capturing the spatial structure of the environment. PLoS Computational Biology, 17, e1008856.10.1371/journal.pcbi.1008856CrossRefGoogle ScholarPubMed
Desjardins-Proulx, P., Poisot, T. & Gravel, D. (2019). Artificial intelligence for ecological and evolutionary synthesis. Frontiers in Ecology and Evolution, 7, a402.10.3389/fevo.2019.00402CrossRefGoogle Scholar
Desrochers, A., Renaud, C., Hochachka, W. M. & Cadman, M. (2010). Area-sensitivity by forest songbirds: Theoretical and practical implications of scale dependency. Ecography, 33, 921–931.10.1111/j.1600-0587.2009.06061.xCrossRefGoogle Scholar
DeWitt, T. J., Fuentes, J. I., Ioerger, T. R. & Bishop, M. P. (2021). Rectifying I: Three point and continuous fit of the spatial autocorrelation metric, Moran’s I, to ideal form. Landscape Ecology, 36, 2897–2918.10.1007/s10980-021-01256-0CrossRefGoogle Scholar
Dietze, M. C., Fox, A., Beck-Johnson, L. M., Betancourt, J. L., Hooten, M. B., Jarnevich, C. S., Keitt, T. H., Kenney, M. A., Laney, C. M., Larsen, L. G. & Loescher, H. W. (2018). Iterative near-term ecological forecasting: Needs, opportunities, and challenges. Proceedings of the National Academy of Sciences, 115, 1424–1432.10.1073/pnas.1710231115CrossRefGoogle ScholarPubMed
Diggle, P. J. (1979). Statistical methods for spatial point patterns in ecology. In Spatial and Temporal Analysis in Ecology, Cormack, R. M. & Ord, J. K. (eds.), pp. 95–150. Cambridge: International Cooperative Publishing House.Google Scholar
Diggle, P. J. (1983). Statistical Analysis of Spatial Point Patterns. Cambridge: Academic Press.Google Scholar
Diggle, P. J. (2013). Statistical Analysis of Spatial and Spatio-temporal Point Patterns. 3rd ed. Cambridge: CRC Press.10.1201/b15326CrossRefGoogle Scholar
Diggle, P. J. & Chetwynd, A. G. (1991). Second-order analysis of spatial clustering for inhomogeneous populations. Biometrics, 47, 1155–1163.10.2307/2532668CrossRefGoogle ScholarPubMed
Diggle, P. J., Chetwynd, A. G., Haggkvist, R. & Morris, S. (1995). Second-order analysis of space-time clustering. Statististical Methods in Medical Research, 4, 124–136.10.1177/096228029500400203CrossRefGoogle ScholarPubMed
Ding, Z. & Ma, K. (2021). Identifying changing interspecific associations along gradients at multiple scales using wavelet correlation networks. Ecology, 102, e03360.10.1002/ecy.3360CrossRefGoogle ScholarPubMed
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, 177–185.10.1111/j.1466-822X.2005.00147.xCrossRefGoogle Scholar
Dong, X., Frossard, P., Vandergheynst, P. & Nefedov, N. (2012). Clustering with multi-layer graphs: A special perspective. IEEE Transactions in Signal Processing, 60, 5820–5831.10.1109/TSP.2012.2212886CrossRefGoogle Scholar
Donges, J. F., Zou, Y., Marwan, N. & Kurtis, J. (2009). The backbone of the climate network. Europhysics Letters, 87, 48007.10.1209/0295-5075/87/48007CrossRefGoogle Scholar
Doran, C. & Lasenby, A. (2003). Geometric Algebra for Physicists. Cambridge: Cambridge University Press.10.1017/CBO9780511807497CrossRefGoogle Scholar
Dormann, C. F., McPherson, J. M., Araújo, M. B., Bivand, R., Bolliger, J., Carl, G., Davies, R. G., Hirzel, A., Jetz, W., Kissling, W. D., Kühn, I., Ohlemüller, R., Peres-Neto, P. R., Reineking, B., Schröder, B., Schurr, F. M. & Wilson, R. (2007). Methods to account for spatial autocorrelation in the analysis of species distributional data: A review. Ecography, 30, 609–628.10.1111/j.2007.0906-7590.05171.xCrossRefGoogle Scholar
Dray, S. (2011). A new perspecive about Moran’s I coefficient: Spatial autocorrelation as a linear regression problem. Geographical Analysis, 43, 127–141.10.1111/j.1538-4632.2011.00811.xCrossRefGoogle 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, 483–493.10.1016/j.ecolmodel.2006.02.015CrossRefGoogle Scholar
Dray, S., Pélissier, R., Couteron, P., Fortin, M.-J., Legendre, P., Peres-Neto, P. R., Bellier, E., Bivand, R., Blanchet, F. G., De Cáceres, M., Dufour, A.-B., Heegaard, E., Jombart, T., Munoz, F., Oksanen, J., Thioulouse, J. & Wagner, H. H. (2012). Community ecology in the age of multivariate multiscale spatial analysis. Ecological Monographs, 82, 257–275.10.1890/11-1183.1CrossRefGoogle Scholar
Duczmal, L., Kuldorff, M. & Huang, L. (2006). Evaluation of spatial scan statistics for irregularly shaped clusters. Journal of Computational and Graphical Statistics, 15, 428–442.10.1198/106186006X112396CrossRefGoogle Scholar
Dungan, J. L., Perry, J. N., Dale, M. R. T., Legendre, P., Citron-Pousty, S., Fortin, M.-J., Jakomulska, A., Miriti, M. & Rosenberg, M. S. (2002). A balanced view of scale in spatial statistical analysis. Ecography, 25, 626–640.10.1034/j.1600-0587.2002.250510.xCrossRefGoogle Scholar
Dunne, J. (2006). The network structure of food webs. In Ecological Networks: Linking Structure to Dynamics in Food Webs. Pascual, M. & Dunne, J. A. (eds.), pp. 27–86. Cambridge: Oxford University PressGoogle Scholar
Dupont, E., Wood, S. N. & Augustin, N. H. (2022). Spatial+: A novel approach to spatial confounding. Biometrics, 78, 1279–1290.10.1111/biom.13656CrossRefGoogle ScholarPubMed
Dupont, Y. L., Trøjelsgaard, K., Hagen, M., Henriksen, M. V., Olesen, J. M., Pedersen, N. M. E. & Kissling, W. D. (2014). Spatial structure of an individual-based plant-pollinator network. Oikos, 123, 1301–1310.10.1111/oik.01426CrossRefGoogle Scholar
Dutilleul, P. (1993). Modifying the t test for assessing the correlation between two spatial processes. Biometrics, 49, 305–314.10.2307/2532625CrossRefGoogle Scholar
Dutilleul, P. R. L. (2011). Spatio-temporal Heterogeneity: Concepts and Analyses. Cambridge: Cambridge University Press.Google Scholar
Eddy, S. R. (2004). What is Bayesian statistics? Nature Biotechnology, 22, 1177–1178.10.1038/nbt0904-1177CrossRefGoogle ScholarPubMed
Efron, B. & Tibshirani, R. J. (1993). An Introduction to the Bootstrap. Cambridge: Chapman & Hall.10.1007/978-1-4899-4541-9CrossRefGoogle Scholar
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
van Es, H. M. & van Es, C. L. (1993). The spatial nature of randomization and its effects on the outcome of field experiments. Agronomy Journal, 85, 420–428.10.2134/agronj1993.00021962008500020046xCrossRefGoogle Scholar
Estes, L., Elsen, P. R., Treuer, T., Ahmed, L., Caylor, K., Chang, J., Choi, J. J. & Ellis, E. C. (2018). The spatial and temporal domains of modern ecology. Nature Ecology & Evolution, 2, 819–826.10.1038/s41559-018-0524-4CrossRefGoogle ScholarPubMed
Evans, J. C., Fisher, D. N. & Silk, M. J. (2020). The performance of permutations and exponential random graph models when analyzing animal networks. Behavioral Ecology, 31, 1266–1276.10.1093/beheco/araa082CrossRefGoogle 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, 1069–1071.10.1109/TIP.2002.802526CrossRefGoogle ScholarPubMed
Fall, A., Fortin, M.-J., Manseau, M. & O’Brien, D. (2007). Spatial graphs: Principles and applications for habitat connectivity. Ecosystems, 10, 448–461.10.1007/s10021-007-9038-7CrossRefGoogle Scholar
Fan, J., Meng, J., Ashkenazy, Y., Havlin, S. & Schnellnhuber, H. J. (2018). Climate network percolation reveals the expansion and weakening of the tropical component under global warming. Proceedings of the National Academy of Sciences, 115, e12128–e12134.10.1073/pnas.1811068115CrossRefGoogle ScholarPubMed
Fan, J., Meng, J., Ludesscher, J., Chen, X., Ashkenazy, Y., Kurtis, J., Havlin, S. & Schnellnhuber, H. J. (2021). Statistical physics approaches to the complex Earth system. Physics Reports, 96, 1–84.Google Scholar
Farage, C., Edler, D., Eklöf, A., Rosvall, M. & Pilosof, S. (2021). Identifying flow modules in ecological networks using Infomap. Methods in Ecology and Evolution, 12, 778–786.10.1111/2041-210X.13569CrossRefGoogle Scholar
Farine, D. R. (2017). A guide to null models for animal social network analysis. Methods in Ecology and Evolution, 8, 1309–1320.10.1111/2041-210X.12772CrossRefGoogle ScholarPubMed
Fattorini, S. (2007). Non-randomness in the species-area relationship: Testing the underlying mechanisms. Oikos, 116, 678–689.Google Scholar
Ferro, I. & Morrone, J. J. (2014). Biogeographical transition zones: A search for conceptual synthesis. Biological Journal of the Linnean Society, 113, 1–12.10.1111/bij.12333CrossRefGoogle Scholar
Finn, K. R., Silk, M. J., Porter, M. A. & Pinter-Wollman, N. (2017). Novel insights into animal sociality from multilayer networks. arXiv:1712.01790v1.Google Scholar
Fiorentino, D., Lecours, V. & Brey, T. (2018). On the art of classification in spatial ecology: Fuzziness as an alternative for mapping uncertainty. Frontiers in Ecology and Evolution, 6, 231.10.3389/fevo.2018.00231CrossRefGoogle Scholar
Fischer, M. M. (2009). Neural networks for spatial data analysis. In Sage Handbook of Spatial Analysis, Fotheringham, A. S. & Rogerson, P. A. (eds.), pp. 375–396. Cambridge: Sage.10.4135/9780857020130.n20CrossRefGoogle Scholar
Fitzpatrick, M. C., Preisser, E. L., Porter, A., Elkinton, J., Waller, L. A., Carlin, B. P. & Ellison, A. M. (2010). Ecological boundary detection using Bayesian areal wombling. Ecology, 91, 3448–3455.10.1890/10-0807.1CrossRefGoogle ScholarPubMed
Fletcher, R. Jr & Fortin, M.-J. (2018). Spatial Ecology and Conservation Modelling. Cambridge: Springer.10.1007/978-3-030-01989-1CrossRefGoogle Scholar
Fletcher, R. J. Jr, Betts, M. G., Damschen, E. I., Hefley, T. J., Hightower, J., Smith, T. A., Fortin, M.-J. & Haddad, N. M. (2023). Addressing the problem of scale that emerges with habitat fragmentation. Global Ecology and Biogeography, 32, 828–841.10.1111/geb.13658CrossRefGoogle Scholar
Fletcher, R. J. Jr, Hefley, T. J., Robertson, E. P., Zuckerberg, B., McCleery, R. A. & Dorazio, R. M. (2019). A practical guide for combining data to model species distributions. Ecology, 100, e02710.10.1002/ecy.2710CrossRefGoogle ScholarPubMed
Floryan, D. & Graham, M. D. (2021). Discovering multiscale and self-similar structure with data-driven wavelets. Proceedings of the National Academy of Sciences, 118, e2021299118.10.1073/pnas.2021299118CrossRefGoogle ScholarPubMed
Foltête, J.-C., Savary, P., Clauzel, C., Bourgois, M., Girardet, X., Sahroui, Y., Vuidel, G. & Garnier, S. (2020). Coupling landscape graph modeling and biological data: A review. Landscape Ecology, 35, 1035–1052.10.1007/s10980-020-00998-7CrossRefGoogle Scholar
Fortin, M.-J. (1992). Detection of ecotones: Definition and scaling factors. PhD Thesis, Department of Ecology and Evolution, State University of New York at Stony Brook, pp. 258.Google Scholar
Fortin, M.-J. (1994). Edge detection algorithms for two-dimensional ecological data. Ecology, 75, 956–965.10.2307/1939419CrossRefGoogle Scholar
Fortin, M.-J. (1997). Effects of data types on vegetation boundary delineation. Canadian Journal of Forest Research, 27, 1851–1858.10.1139/x97-156CrossRefGoogle Scholar
Fortin, M.-J. (1999a). Effects of sampling unit resolution on the estimation of the spatial autocorrelation. Écoscience, 6, 636–641.10.1080/11956860.1999.11682547CrossRefGoogle Scholar
Fortin, M.-J. (1999b). The effects of quadrat size and data measurement on the detection of boundaries. Journal of Vegetation Science, 10, 43–50.10.2307/3237159CrossRefGoogle Scholar
Fortin, M.-J. & Dale, M. R. T. (2005). Spatial Analysis: A Guide for Ecologists. Cambridge: Cambridge University Press.10.1017/CBO9780511542039CrossRefGoogle Scholar
Fortin, M.-J. & Dale, M. R. T. (2009). Spatial autocorrelation in ecological studies: A legacy of solutions and myths. Geographical Analysis, 41, 392–397.10.1111/j.1538-4632.2009.00766.xCrossRefGoogle Scholar
Fortin, M.-J. & Drapeau, P. (1995). Delineation of ecological boundaries: Comparisons of approaches and significance tests. Oikos, 72, 323–332.10.2307/3546117CrossRefGoogle Scholar
Fortin, M.-J., Drapeau, P. & Jacquez, G. M. (1996). Quantification of the spatial co-occurrences of ecological boundaries. Oikos, 77, 51–60.10.2307/3545584CrossRefGoogle Scholar
Fortin, M.-J., Drapeau, P. & Legendre, P. (1989). Spatial autocorrelation and sampling design in plant ecology. Vegetatio, 83, 209–222.10.1007/BF00031693CrossRefGoogle Scholar
Fortin, M.-J. & Gurevitch, J. (2001). Mantel tests: Spatial structure in field experiments. In Design and Analysis of Ecological Experiments, 2nd ed., Scheiner, S. M. & Gurevitch, J. (eds.), pp. 308–326. Cambridge: Oxford University Press.10.1093/oso/9780195131871.003.0016CrossRefGoogle 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., Jacquez, G. M. & Shipley, B. (2012a). Computer-intense methods. In Encyclopedia of Environmetrics, 2nd ed., El-Shaarawi, A. H. and Piegorsch, W. W. (eds). pp. 489–493. Cambridge: Wiley.Google Scholar
Fortin, M.-J., James, P. M., MacKenzie, A., Melles, S. J. & Rayfield, B. (2012b). Spatial statistics, spatial regression, and graph theory in ecology. Spatial Statistics, 1, 100–109.10.1016/j.spasta.2012.02.004CrossRefGoogle 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, 7–17.10.1111/j.0030-1299.2005.13146.xCrossRefGoogle Scholar
Fortin, M.-J., Olson, R. J., Ferson, S., Iverson, L., Hunsaker, C., Edwards, G., Levine, D., Butera, K. & Klemas, V. (2000). Issues related to the detection of boundaries. Landscape Ecology, 15, 453–466.10.1023/A:1008194205292CrossRefGoogle 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, 213–218.10.1080/11956860.2002.11682707CrossRefGoogle Scholar
Fortuna, M. A. & Bascompte, J. (2008). The network approach in ecology. In Unity in Diversity: Reflections on Ecology after the Legacy of Ramón Margalef, Valladares, F., Camacho, A., Elosegi, A., Gracia, C., Estrada, M., Senar, J. C. & Gili, J. M. (eds.), pp. 371–392. Cambridge: Fundacion BBVA.Google Scholar
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.Google ScholarPubMed
Fotheringham, A. S. (2009). ‘The problem of spatial autocorrelation’ and local spatial statistics. Geographical Analysis, 41, 398–403.10.1111/j.1538-4632.2009.00767.xCrossRefGoogle Scholar
Fotheringham, A. S., Brunsdon, C. & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Cambridge: Wiley.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
Franckowiak, R. P., Panasci, M., Jarvis, K. J., Acuna-Rodriguez, I. S., Landguth, E. L., Fortin, M.-J. & Wagner, H. H. (2017). Model selection with multiple regression on distance matrices leads to incorrect inferences. PLoS ONE, 12, e0175194.10.1371/journal.pone.0175194CrossRefGoogle ScholarPubMed
Franco, M. & Harper, J. (1988). Competition and the formation of spatial pattern in spacing gradients: An example using Kochia scoparia. Journal of Ecology, 76, 959–974.10.2307/2260626CrossRefGoogle Scholar
Frazier, A. E. & Kedron, P. (2017). Landscape metrics: Past progress and future directions. Current Landscape Ecology Reports, 2, 63–72.10.1007/s40823-017-0026-0CrossRefGoogle Scholar
Frydman, N., Freilikhman, S., Talpaz, I. & Pilosof, S. (2023). Practical guidelines and the EMLN R package for handling ecological multilayer networks. Methods in Ecology and Evolution, 14, 2964–2973.10.1111/2041-210X.14225CrossRefGoogle Scholar
Gabriel, K. R. & Sokal, R. R. (1969). A new statistical approach to geographic variation analysis. Systematic Ecology, 18, 259–270.Google Scholar
Gaggiotti, O., Chao, A., Peres-Neto, P., Chiu, C.-H., Edwards, C., Fortin, M.-J., Jost, L., Richards, C. & Selkoe, K. (2018). Diversity from genes to ecosystems: A unifying framework to study variation across biological metrics and scales. Evolutionary Applications, 11, 1176–1193.10.1111/eva.12593CrossRefGoogle ScholarPubMed
Galiana, N., Lurgi, M., Bastazini, V., Bosch, J., Cagnolo, L., Cazelles, K., Claramunt, B., Emer, C., Fortin, M.-J., Grass, I., Hernández-Castellano, C., Jauker, F., Leroux, S., McCann, K., McLeod, A., Montoya, D., Mulder, C., Osorio-Canadas, S., Reverté, S., Rodrigo, A., Steffan-Dewenter, I., Traveset, A., Valverde, S., Vázquez, D., Wood, S., Gravel, D., Roslin, T., Thuiller, W. & Montoya, J. M. (2022). Ecological network complexity scales with area. Nature Ecology & Evolution, 6, s41559-021-01644-4.10.1038/s41559-021-01644-4CrossRefGoogle ScholarPubMed
Galiana, N., Lurgi, M., Claramunt-Lopez, B., Fortin, M.-J., Leroux, S., Cazelles, K., Gravel, D. & Montoya, J. M. (2018). The spatial scaling of species interaction networks. Nature Ecology & Evolution, 2, 782–790.10.1038/s41559-018-0517-3CrossRefGoogle ScholarPubMed
Gao, B., Wang, J., Stein, A. & Chen, Z. (2022). Causal inference in spatial statistics. Spatial Statistics, 50, 100621.10.1016/j.spasta.2022.100621CrossRefGoogle Scholar
Gao, J., Li, D. & Havlin, S. (2014). From a single network to a network of networks. National Science Review, 1, 346–356.10.1093/nsr/nwu020CrossRefGoogle Scholar
Garcia-Soidan, P., Menezes, R. & Rubiños, O. (2014). Bootstrap approaches for spatial data. Stochastic Environment Research and Risk Assessment, 28, 1207–2019.10.1007/s00477-013-0808-9CrossRefGoogle Scholar
Gardner, M. (1970). The fantastic combinations of John Conway’s new solitaire game ‘life’. Mathematical games. Scientific American, 223, 120–123.10.1038/scientificamerican1070-120CrossRefGoogle Scholar
Garroway, C. J., Bowman, J., Carr, D. & Wilson, P. J. (2008). Applications of graph theory to landscape genetics. Evolutionary Applications, 1, 620–630.10.1111/j.1752-4571.2008.00047.xCrossRefGoogle ScholarPubMed
Gaston, K. J. & Blackburn, T. M. (2000). Patterns and Processes in Macroecology. Cambridge: Blackwell Publishing Company.10.1002/9780470999592CrossRefGoogle Scholar
Gauch, H. G. & Whittaker, R. H. (1972). Coenocline simulation. Ecology, 53, 446–451.10.2307/1934231CrossRefGoogle Scholar
Geary, R. C. (1954). The contiguity ratio and statistical mapping. The Incorporated Statistician, 5, 115–145.10.2307/2986645CrossRefGoogle Scholar
Gelfand, A. E. (2012). Hierarchical modeling for spatial data problems. Spatial Statistics, 1, 30–39.10.1016/j.spasta.2012.02.005CrossRefGoogle ScholarPubMed
Gelfand, A. E. & Banerjee, S. (2015). Bayesian wombling: Finding rapid change in spatial maps. WIRES Computational Statistics, 7, 307–325.10.1002/wics.1360CrossRefGoogle Scholar
Gerstmann, H., Doktor, D., Glässer, C. & Möler, M. (2016). PHASE: A geostatistical model for the Kriging-based spatial prediction of crop phenology using public phenological and climatological observations. Computers and Electronics in Agriculture, 127, 726–738.10.1016/j.compag.2016.07.032CrossRefGoogle Scholar
Getis, A. (1991). Spatial interaction and spatial autocorrelation: a cross product approach. Environment and Planning A, 23, 1269–1277.10.1068/a231269CrossRefGoogle Scholar
Getis, A. & Franklin, J. (1987). Second-order neighborhood analysis of mapped point patterns. Ecology, 68, 473–477.10.2307/1938452CrossRefGoogle Scholar
Getis, A. & Ord, J. K. (1992). The analysis of spatial association by use of distance statistics. Geographical Analysis, 24, 189–206.10.1111/j.1538-4632.1992.tb00261.xCrossRefGoogle Scholar
Getis, A. & Ord, J. K. (1996). Local spatial statistics: an overview. In Spatial Analysis: Modelling in a GIS Environment, Longley, P. & Batty, M. (eds.), pp. 261–277. Cambridge: GeoInformation International.Google Scholar
Gilbert, B. & Bennett, J. R. (2010). Partitioning variation in ecological communities: Do the numbers add up? Journal of Applied Ecology, 47, 1071–1082.10.1111/j.1365-2664.2010.01861.xCrossRefGoogle Scholar
Gilbert, B., Datta, A., Casey, J. A. & Ogburn, E. L. (2022). Approaches to spatial confounding in geostatistics. arXiv:2112.14946v2 [stat.ME].Google Scholar
Gill, J. (2002). Bayesian Methods: A Social and Behavioral Sciences Approach. Cambridge: Chapman & Hall/CRC.10.1201/9781420057478CrossRefGoogle Scholar
Glaz, J., Naus, J. & Wallenstein, S. (2001). Scan Statistics. Cambridge: Springer-Verlag.10.1007/978-1-4757-3460-7CrossRefGoogle Scholar
Gleason, H. A. (1927). Further views of the succession concept. Ecology, 8, 299–326.10.2307/1929332CrossRefGoogle Scholar
Glenn-Lewin, D. C. & van der Maarel, E. (1992). Patterns and processes of vegetation dynamics. In Plant Succession: Theory and Prediction, Glenn-Lewin, D. C., Peet, R. K. & Veblen, T. T. (eds.), pp. 11–59. Cambridge: Chapman & Hall.Google Scholar
Godet, C. & Clauzel, C. (2021). Comparison of landscape graph modelling methods for analysing pond network connectivity. Landscape Ecology, 36, 725–748.10.1007/s10980-020-01164-9CrossRefGoogle Scholar
Godfrey, S. S. (2013). Networks and the ecology of parasite transmission: A framework for wildlife parasitology. International Journal for Parasitology: Parasites and Wildlife, 2, 235–245.Google ScholarPubMed
Good, P. (1993). Permutation Tests: A Practical Guide to Resampling Methods for Hypothesis Testing. Cambridge: Springer-Verlag.Google Scholar
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, 137–159.10.1007/s10109-005-0154-7CrossRefGoogle ScholarPubMed
Gordon, A. D. (1999). Classification. Monographs on Statistics and Applied Probability 82, 2nd ed. Cambridge: Chapman & Hall/CRC.Google Scholar
Goulard, M., Pagès, L. & Cabanettes, A. (1995). Marked point process: Using correlation functions to explore a spatial data set. Biometrical Journal, 37, 837–853.10.1002/bimj.4710370707CrossRefGoogle Scholar
Gouveia, C., Moreh, A. & Jordán, F. (2021). Combining centrality measures: Maximizing the predictability of keystone species in food webs. Ecological Indicators, 126, 107617.10.1016/j.ecolind.2021.107617CrossRefGoogle Scholar
Grace, J. B. (2020). Scientist’s guide to developing explanatory statistical models using causal analysis principles. Ecology, 101, e02962.10.1002/ecy.2962CrossRefGoogle ScholarPubMed
Grace, J. B., Schoolmaster, D. R. Jr, Guntenspergen, G. R., Little, A. M., Mitchell, B. R., Miller, K. M. & Schweiger, E. W. (2012). Guidelines for a graph‐theoretic implementation of structural equation modeling. Ecosphere, 3, 1–44.10.1890/ES12-00048.1CrossRefGoogle 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, 165–175.10.1111/j.1461-0248.2006.01007.xCrossRefGoogle Scholar
Gravel, D., Massol, F. & Leibold, M. A. (2016). Stability and complexity in model meta-ecosystems. Nature Communications, 7, 12457.10.1038/ncomms12457CrossRefGoogle ScholarPubMed
Greig-Smith, P. (1961). Data on pattern within plant communities. I. The analysis of pattern. Journal of Ecology, 49, 695–702.10.2307/2257231CrossRefGoogle Scholar
Griffith, D. A. (1981). Interdependence in space and time: Numerical and interpretative considerations. In Dynamic Spatial Models, Griffith, D. A. & MacKinnon, R. D. (eds.), pp. 258–287. Cambridge: Sijthoff & Noordhoff.Google Scholar
Griffith, D. A. (1996). Spatial autocorrelation and eigenfunctions of the geographic weights matrix accompanying geo-referenced data. Canadian Geographer, 40, 351–367.10.1111/j.1541-0064.1996.tb00462.xCrossRefGoogle Scholar
Griffith, D. A. (2008). Spatial-filtering-based contributions to a critique of geographically weighted regression (GWR). Environment and Planning A, 40, 2751–2769.10.1068/a38218CrossRefGoogle Scholar
Griffith, D. A. & Chun, Y. (2014). Spatial autocorrelation and spatial filtering. In Handbook of Regional Science, Fischer, M. M. & Nijkamp, P. (eds.), pp. 1477–1507. Cambridge: Springer-Verlag.10.1007/978-3-642-23430-9_72CrossRefGoogle 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, 23–36.10.1007/s10109-008-0075-3CrossRefGoogle Scholar
Griffith, D. A. & Peres-Neto, P. R. (2006). Spatial modeling in ecology: The flexibility of eigenfunction spatial analyses. Ecology, 87, 2603–2613.10.1890/0012-9658(2006)87[2603:SMIETF]2.0.CO;2CrossRefGoogle ScholarPubMed
Grillet, M.-E., Barrera, R., Martínez, J.-E., Berti, J. & Fortin, M.-J. (2010 a). 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, 194–201.10.4269/ajtmh.2010.09-0040CrossRefGoogle Scholar
Grillet, M.-E., Jordan, G. J. & Fortin, M.-J. (2010 b). State transition detection in the spatio-temporal incidence of malaria. Spatial and Spatio-Temporal Epidemiology Journal, 1, 251–259.10.1016/j.sste.2010.09.007CrossRefGoogle ScholarPubMed
Guan, N., Song, D. & Liao, L. (2019). Knowledge graph embedding with concepts. Knowledge Based Systems, 64, 38–44.10.1016/j.knosys.2018.10.008CrossRefGoogle Scholar
Guichard, F., Zhang, Y. & Lutscher, F. (2019). The emergence of phase asynchrony and frequency modulation in metacommunities. Theoretical Ecology, 12, 329.10.1007/s12080-018-0398-8CrossRefGoogle Scholar
Guillot, G. & Rousset, F. (2013). Dismantling the Mantel tests. Methods in Ecology and Evolution, 4, 336–344.10.1111/2041-210x.12018CrossRefGoogle Scholar
Guisan, A. & Rahbek, C. (2011). SESAM: A new framework integrating macroecological and species distribution models for predicting spatio-temporal patterns of species assemblages. Journal of Biogeography, 38, 1433–1444.10.1111/j.1365-2699.2011.02550.xCrossRefGoogle Scholar
Gurevitch, J., Koricheva, J., Nakagawa, S. & Stewart, G. (2018). Meta-analysis and the science of research synthesis. Nature, 555, 175–182.10.1038/nature25753CrossRefGoogle ScholarPubMed
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, 1108–1116.10.1111/j.1461-0248.2011.01679.xCrossRefGoogle Scholar
Haining, R. P. (2003). Spatial Data Analysis: Theory and Practice. Cambridge: Cambridge University Press.10.1017/CBO9780511754944CrossRefGoogle Scholar
Halder, A. (2020). Wombling for spatial and spatiotemporal processes: Applications to insurance data. PhD Dissertation, University of Connecticut.Google Scholar
Hall, K. R. (2008). Comparing geographic boundaries in songbird demography data with vegetation boundaries: A new approach to evaluating habitat quality. Environmental and Ecological Statistics, 15, 491–521.10.1007/s10651-007-0065-5CrossRefGoogle 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, 105–120.10.1023/A:1011905124393CrossRefGoogle Scholar
Hammond, D. K., Vandergheynst, P. & Gribonval, R. (2011). Wavelets on graphs via spectral graph theory. Applied and Computational Harmonic Analysis, 30, 129–150.10.1016/j.acha.2010.04.005CrossRefGoogle Scholar
Hansen, A. & di Castri, F. (1992). Landscape Boundaries: Consequences for Biotic Diversity and Ecological Flows. Cambridge: Springer-Verlag.10.1007/978-1-4612-2804-2CrossRefGoogle Scholar
Hanski, I. (2009). Metapopulations and spatial population processes. In The Princeton Guide to Ecology, Levin, S. A. (ed.), pp. 177–185. Cambridge: Princeton University Press.10.1515/9781400833023.177CrossRefGoogle Scholar
Hanski, I. & Woiwod, I. P. (1993). Spatial synchrony in the dynamics of moth and aphid populations. Journal of Animal Ecology, 62, 656–668.10.2307/5386CrossRefGoogle Scholar
Harary, F. (1969). Graph Theory. Cambridge: Addison-Wesley.10.21236/AD0705364CrossRefGoogle Scholar
Harper, K. A., Macdonald, S. E., Burton, P. J., Chen, J., Brosofske, K. D., Saunders, S. C., Euskirchen, E. S., Roberts, D. A. R., Jaiteh, M. S. & Esseen, P. A. (2005). Edge influence on forest structure and composition in fragmented landscapes. Conservation Biology, 19, 768–782.10.1111/j.1523-1739.2005.00045.xCrossRefGoogle Scholar
Harrison, P. J., Hanski, I. & Ovaskainen, O. (2011). Bayesian state-space modeling of metapopulation dynamics in the Glanville fritillary butterfly. Ecological Monographs, 8, 581–598.10.1890/11-0192.1CrossRefGoogle Scholar
Harrison, X. A., Donaldson, L., Correa-Cano, M. E., Evans, J., Fisher, D. N., Goodwin, C. E., Robinson, B. S., Hodgson, D. J. & Inger, R. (2018). A brief introduction to mixed effects modelling and multi-model inference in ecology. PeerJ, 6, e4794.10.7717/peerj.4794CrossRefGoogle ScholarPubMed
Hayes, J. G. (1974). Numerical methods for curve and surface fitting. Bulletin: Institute of Mathematics and Its Applications, 10, 144–162.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, 197–211.10.1080/01621459.2000.10473914CrossRefGoogle Scholar
Hedley, S. L. & Buckland, S. T. (2004). Spatial models for line transect sampling. Journal of Agricultural, Biological and Environmental Statistics, 9, 181–199.10.1198/1085711043578CrossRefGoogle Scholar
Hefley, T. J., Broms, K. M., Brost, B. M., Buderman, F. E., Kay, S. L., Scharf, H. R., Tipton, J. R., Williams, P. J. & Hooten, M. B. (2017). The basis function approach for modeling autocorrelation in ecological data. Ecology, 98, 632–646.10.1002/ecy.1674CrossRefGoogle ScholarPubMed
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.Google Scholar
Henebry, G. M. (1995). Spatial model error analysis using autocorrelation indices. Ecological Modelling, 82, 75–91.10.1016/0304-3800(94)00074-RCrossRefGoogle Scholar
Hengl, T., Heuvelink, G. B. M. & Rossiter, D. G. (2007). About regression-kriging: From equations to case studies. Computers and Geosciences, 33, 1301–1315.10.1016/j.cageo.2007.05.001CrossRefGoogle 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, 75–93.10.1016/j.geoderma.2003.08.018CrossRefGoogle Scholar
Henry, S. & McInnes, B. T. (2017). Literature based discovery: Models, methods, and trends. Journal of Biomedical Informatics, 74, 20–32.10.1016/j.jbi.2017.08.011CrossRefGoogle ScholarPubMed
Herrera, M., Mur, J. & Ruiz, M. (2016). Detecting causal relationships between spatial processes. Papers in Regional Science, 65, 577–594.10.1111/pirs.12144CrossRefGoogle Scholar
Hill, M. O. (1973). The intensity of spatial pattern in plant communities. Journal of Ecology, 61, 225–235.10.2307/2258930CrossRefGoogle Scholar
Hirt, M. R., Grimm, V., Li, Y., Rall, B. C., Rosenbaum, B. & Brose, U. (2018). Bridging scales: Allometric random walks link movement and biodiversity research. Trends in Ecology & Evolution, 33, 701–712.10.1016/j.tree.2018.07.003CrossRefGoogle ScholarPubMed
Holland, J. & Yang, S. (2016). Multi-scale studies and the ecological neighborhood. Current Landscape Ecology Reports, 1, 135–145.10.1007/s40823-016-0015-8CrossRefGoogle Scholar
Huaylla, C. A., Nacif, M. E., Coulin, C., Kuperman, M. N. & Garibaldi, L. A. (2021). Decoding information in multilayer ecological networks: The keystone species case. Ecological Modelling, 460, 109734.10.1016/j.ecolmodel.2021.109734CrossRefGoogle Scholar
Hufkens, K., Scheunders, P. & Ceulemans, R. (2009). Ecotones in vegetation ecology: Methodologies and definitions revisited. Ecological Research, 24, 977–986.10.1007/s11284-009-0584-7CrossRefGoogle Scholar
Hui, C. & McGeoch, M. A. (2014). Zeta diversity as a concept and metric that unifies incidence-based biodiversity patterns. The American Naturalist, 184, 684–694.10.1086/678125CrossRefGoogle ScholarPubMed
Hurlbert, S. H. (1984). Pseudoreplication and the design of ecological field experiments. Ecological Monographs, 54, 187–211.10.2307/1942661CrossRefGoogle Scholar
Huston, M. A. (1994). Biological Diversity: The Coexistence of Species on Changing Landscapes. Cambridge: Cambridge University Press.Google Scholar
Hutchinson, M. C., Mora, B. B., Pilosof, S., Barner, A. K., Kéfi, S., Thébault, E., Jordano, P. & Stouffer, D. B. (2019). Seeing the forest for the trees: Putting multilayer networks to work for community ecology. Functional Ecology, 33, 206–217.10.1111/1365-2435.13237CrossRefGoogle Scholar
Illian, J., Penttinen, A. & Stoyan, D. (2008). Statistical Analysis and Modeling of Spatial Point Patterns. Cambridge: Wiley.Google Scholar
Imbens, G. & Angrist, J. (1994). Identification and estimation of local average treatment effects. Econometrica, 62, 467–476.10.2307/2951620CrossRefGoogle Scholar
Ives, A. R. & Zhu, J. (2006). Statistics for correlated data: Phylogenies, space, and time. Ecological Applications, 16: 20–32.10.1890/04-0702CrossRefGoogle ScholarPubMed
Jackson, H. B. & Fahrig, L. (2015). Are ecologists conducting research at the optimal scale? Global Ecology and Biogeography, 24, 52–63.10.1111/geb.12233CrossRefGoogle Scholar
Jackson, T. D., Williams, G. J., Walker-Springett, G. & Davies, A. J. (2020). Three-dimensional digital mapping of ecosystems: A new era of spatial ecology. Proceedings of the Royal Society, B, 287, 20192383.10.1098/rspb.2019.2383CrossRefGoogle Scholar
Jacquemyn, H., Honnay, O. & Pailler, T. (2007). Range size variation, nestedness, and species turnover of orchid species along an altitudinal gradient on Reunion Island: Implications for conservation. Biological Conservation, 136, 388–397.10.1016/j.biocon.2006.12.008CrossRefGoogle Scholar
Jacquez, G. M. (1995). The map comparison problem: Tests for the overlap of geographical boundaries. Statistical Medicine, 14, 2343–2361.10.1002/sim.4780142107CrossRefGoogle Scholar
Jacquez, G. M. (1996). A k nearest neighbour test for space–time interaction. Statistical Medicine, 15, 1935–1949.10.1002/(SICI)1097-0258(19960930)15:18<1935::AID-SIM406>3.0.CO;2-I3.0.CO;2-I>CrossRefGoogle Scholar
Jacquez, G. M., Kaufmann, A. & Goovaerts, P. (2008). Boundaries, links and clusters: A new paradigm in spatial analysis? Environmental and Ecological Statistics, 15, 403–419.10.1007/s10651-007-0066-4CrossRefGoogle ScholarPubMed
Jacquez, G. M., Maruca, S. L. & Fortin, M.-J. (2000). From fields to objects: A review of geographic boundary analysis Journal of Geographical Systems, 2, 221–241.10.1007/PL00011456CrossRefGoogle Scholar
James, P. (1992). Knowledge graphs. In Linguistic Instruments in Knowledge Engineering, Van de Riet, R. P. & Meersman, R. A. (eds.), pp. 97–117. Cambridge: Elsevier.Google Scholar
James, P. M. A., Fleming, R. A. & Fortin, M.-J. (2010). Identifying significant scale-specific spatial boundaries using wavelets and null models: Spruce budworm defoliation in Ontario, Canada as a case study. Landscape Ecology, 25, 873–887.10.1007/s10980-010-9465-2CrossRefGoogle Scholar
James, P. M. A. & Fortin, M.-J. (2012). Ecosystems and spatial patterns. In Encyclopedia of Sustainability Science and Technology, Meyers, R. A. (ed.), pp. 3326–3342. Cambridge: Springer Science+Business Media.10.1007/978-1-4419-0851-3_227CrossRefGoogle Scholar
James, P. M. A., Sturtevant, B. R., Townsend, P., Wolter, P. & Fortin, M.-J. (2011). Two-dimensional wavelet analysis of spruce budworm host basal area in the Border Lakes landscape. Ecological Applications, 21, 2197–2209.10.1890/09-1876.1CrossRefGoogle ScholarPubMed
Jennings, M. K., Zeller, K. A. & Lewison, R. (2020). Supporting adaptive connectivity in dynamic landscapes. Land, 9, 295.10.3390/land9090295CrossRefGoogle Scholar
Jiménez, J., Augustine, B. C., Linden, D. W., Chandler, R. B. & Royle, J. A. (2020). Spatial capture–recapture with random thinning for unidentified encounters. Ecology and Evolution, 11, 1187–1198.10.1002/ece3.7091CrossRefGoogle ScholarPubMed
Jiménez-Hernández, J. D. C., López-Cerino, M. & Aguirre-Salado, A. I. (2020). A Bayesian hierarchical model for the spatial analysis of carbon monoxide pollution extremes in Mexico City. Hidawi, a7135142.10.1155/2020/7135142CrossRefGoogle Scholar
Johnson, J. B. & Omland, K. S. (2004). Model selection in ecology and evolution. Trends in Ecology & Evolution, 19, 101–108.10.1016/j.tree.2003.10.013CrossRefGoogle ScholarPubMed
Johnston, C. A., Pastor, J. & Pinay, G. (1992). Quantitative methods for studying landscape boundaries. In Landscape Boundaries, Hansen, A. J. & di Castri, F. (eds.), pp. 107–125. Cambridge: Springer-Verlag.10.1007/978-1-4612-2804-2_5CrossRefGoogle Scholar
Johnstone, J. F., Allen, C. D., Franklin, J. F., Frelich, L. E., Harvey, B. J., Higuera, P. E., Mack, M. C., Meentemeyer, R. K., Metz, M. R., Perry, G. L. W., Schoennagel, T. & Turner, M. G. (2016). Changing disturbance regimes, ecological memory, and forest resilience. Frontiers in Ecology and Environment, 14, 369–378.10.1002/fee.1311CrossRefGoogle Scholar
Jombart, T., Dray, S. & Dufour, A. B. (2009). Finding essential scales of spatial variation in ecological data: A multivariate approach. Ecography, 32, 161–168.10.1111/j.1600-0587.2008.05567.xCrossRefGoogle Scholar
Jones, E. L., Rendell, L., Pirotta, E. & Long, J. A. (2016). Novel application of a quantitative spatial comparison tool to species distribution data. Ecological Indicators, 70, 67–76.10.1016/j.ecolind.2016.05.051CrossRefGoogle Scholar
Joo, R., Picardi, S., Boone, M. E., Clay, T. A., Patrick, S. C., Romero-Romero, V. S. & Baile, M. (2022). Recent trends in movement ecology of animals and human mobility. Movement Ecology, 10, 26.10.1186/s40462-022-00322-9CrossRefGoogle ScholarPubMed
Joot, P. (2021). Geometric Algebra for Electrical Engineers. Cambridge: Peter Joot.Google Scholar
Jost, L. (2006). Entropy and diversity. Oikos, 113, 363–375.10.1111/j.2006.0030-1299.14714.xCrossRefGoogle Scholar
Jost, L. (2007). Partitioning diversity into alpha and beta components. Ecology, 88, 2427–2439.10.1890/06-1736.1CrossRefGoogle ScholarPubMed
Journel, A. G. & Huijbregts, C. (1978). Mining Geostatistics. Cambridge: Academia Press.Google Scholar
Juhasz-Nagy, P (1993). Notes on compositional diversity. Hydrobiologia, 249, 173–182.10.1007/BF00008852CrossRefGoogle Scholar
Kabos, S. & Csillag, F. (2002). The analysis of spatial association of nominal data on regular lattice by join-count-statistic without first-order homogeneity. Computers and Geosciences, 28, 901–910.10.1016/S0098-3004(02)00007-9CrossRefGoogle Scholar
Karami, J., Kavosi, M. R. & Babanzhad, M. (2015). Spatial pattern and disease severity of charcoal canker in Hyrcanian forests, north of Iran. Journal of Forest Science, 61, 261–267.10.17221/4/2015-JFSCrossRefGoogle Scholar
Kareiva, P. & Shigesada, N. (1983). Analyzing insect movement as a correlated random walk. Oecologia, 56, 234–238.10.1007/BF00379695CrossRefGoogle ScholarPubMed
Karl, J. W. (2010). Spatial predictions of cover attributes of rangeland ecosystems using regression kriging and remote sensing. Rangeland Ecology & Management, 63, 335–349.10.2111/REM-D-09-00074.1CrossRefGoogle Scholar
Kéfi, S., Berlow, E. L., Wieters, E. A., Joppa, L. N., Wood, S. A., Brose, U. & Navarrete, S. A. (2015). Network structure beyond food webs: mapping non-trophic and trophic interactions on Chilean rocky shores. Ecology, 96, 291–303.10.1890/13-1424.1CrossRefGoogle Scholar
Kéfi, S., Holmgren, M. & Scheffer, M. (2016). When can positive interactions cause alternative stable states in ecosystems? Functional Ecology, 30, 88–97.10.1111/1365-2435.12601CrossRefGoogle Scholar
Kéfi, S., Thébault, E., Eklöf, A., Lurgi, M., Davis, A. J., Kondoh, M. & Krumins, J. A. (2018). Toward multiplex ecological networks: Accounting for multiple interaction types to understand community structure and dynamics. In Adaptive Food Webs: Stability and Transitions of Real and Model Ecosystems, Moore, J. C., de Ruiter, P. C., McCann, K. S. & Wolters, V. (eds.), pp. 74–87. Cambridge: Cambridge University Press.Google Scholar
Keil, P. & Chase, J. M. (2019). Global patterns and drivers of tree diversity integrated across a continuum of spatial grains. Nature Ecology & Evolution, 3, 390–399.10.1038/s41559-019-0799-0CrossRefGoogle ScholarPubMed
Keil, P., Wiegand, T., Tóth, A. B., McGlinn, D. J. & Chase, J. M. (2021). Measurement and analysis of interspecific associations as a facet of biodiversity. Ecological Monographs, 91, e10452.10.1002/ecm.1452CrossRefGoogle Scholar
Keitt, T. H. & Urban, D. L. (2005). Scale-specific inference using wavelet. Ecology, 86, 2497–2504.10.1890/04-1016CrossRefGoogle Scholar
Keitt, T. H., Urban, D. L. & Milne, B. T. (1997). Detecting critical scales in fragmented landscapes. Conservation Ecology [online], 1, 4. www.consecol.org/vol1/iss1/art4/.10.5751/ES-00015-010104CrossRefGoogle Scholar
Kejriwal, M., Knoblock, C. A. & Szekely, P. (2021). Knowledge Graphs: Fundamentals, Techniques, and Applications. Cambridge: MIT Press.Google Scholar
Khalighi, M., Sommeria-Klein, G., Gonze, D., Faust, K. & Lahti, L. (2022). Quantifying the impact of ecological memory on the dynamics of interacting communities. PLoS Computational Biology, 8, e1009396.10.1371/journal.pcbi.1009396CrossRefGoogle Scholar
Kim, D. (2021). Predicting the magnitude of residual spatial autocorrelation in geographical ecology. Ecography, 44, 1121–1130.10.1111/ecog.05403CrossRefGoogle Scholar
Kim, J. & Lee, J.-G. (2015). Community detection in multi-layer graphs: A survey. SIGMOD Record, 44, 37–48.10.1145/2854006.2854013CrossRefGoogle Scholar
Kimmel, K., Dee, L. E., Avolio, M. L. & Ferraro, P. J. (2021). Causal assumptions and causal inference in ecological experiments. Trends in Ecology & Evolution, 36, 1141–1152.10.1016/j.tree.2021.08.008CrossRefGoogle ScholarPubMed
Kingman, J. F. C. (1993). Poisson Processes. Cambridge: Oxford University Press.Google Scholar
Kinsey, A. C., Rossi, G, Silk, M. J. & VanderWaal, K. (2020). Multilayer and multiplex networks: An introduction to their use in veterinary epidemiology. Frontiers in Veterinary Science, 7, 596.10.3389/fvets.2020.00596CrossRefGoogle Scholar
Kivelä, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y. & Porter, M. A. (2014). Multilayer networks. Journal of Complex Networks, 2, 203–271.10.1093/comnet/cnu016CrossRefGoogle Scholar
Knox, E. G. (1964). The detection of space-time interactions. Applied Statistics, 13, 25–29.10.2307/2985220CrossRefGoogle Scholar
Koen, E. L., Garroway, C. J., Wilson, P. J. & Bowman, J. (2010). The effect of map boundary on estimates of landscape resistance to animal movement. PLoS ONE, 5(7), e11785.10.1371/journal.pone.0011785CrossRefGoogle ScholarPubMed
Koenig, W. D. & Knops, J. M. (1998). Testing for spatial autocorrelation in ecological time series. Ecography, 21, 423–429.10.1111/j.1600-0587.1998.tb00407.xCrossRefGoogle Scholar
Kolaczyk, E. D. & Csárdi, G. (2014). Statistical Analysis of Network Data with R. Cambridge: Springer.10.1007/978-1-4939-0983-4CrossRefGoogle Scholar
Kolasa, J. (2014). Ecological boundaries: A derivative of ecological entities. Web Ecology, 14, 27–37.10.5194/we-14-27-2014CrossRefGoogle Scholar
Koleff, P., Gaston, K. J. & Lennon, J. J. (2003). Measuring beta diversity for presence absence data. Journal of Animal Ecology, 72, 367–382.10.1046/j.1365-2656.2003.00710.xCrossRefGoogle Scholar
König, D., Carvajal-Gonzalez, S., Downs, A. M., Vassy, J. & Rigaut, J. P. (1991). Modeling and analysis of 3-D arrangements of particles by point processes with examples of application to biological data obtained by confocal scanning light microscopy. Journal of Microscopy, 161, 405–433.10.1111/j.1365-2818.1991.tb03100.xCrossRefGoogle ScholarPubMed
Krebs, C. J., Boutin, S. & Boonstra, R. (2001). Ecosystem Dynamics of the Boreal Forest: The Kluane Project. Cambridge: Oxford University Press.10.1093/oso/9780195133936.001.0001CrossRefGoogle Scholar
Krige, D. G. (1966). Two-dimensional weighted moving average trend surfaces for ore-evaluation. Journal of the South Africa Institute of Mining and Metallurgy, 66, 13–38.Google Scholar
Ku, K., Bradley, J. R. & Niu, X. (2019). Boundary detection using a Bayesian hierarchical model for multiscale spatial data. Technometrics, 63, 64–76.Google Scholar
Kükenbrink, D., Schneider, F. D., Leiterer, R., Schaepman, M. E. & Morsdorf, F. (2017). Quantification of hidden canopy volume of airborne laser scanning data using a voxel traversal algorithm. Remote Sensing of Environment, 194, 424–436.10.1016/j.rse.2016.10.023CrossRefGoogle Scholar
Kuldorff, M. (1997). A spatial scan statistic. Communications in Statistics: Theory and Methods, 26, 1481–1496.10.1080/03610929708831995CrossRefGoogle Scholar
Kuldorff, M., Heffernan, R., Hartman, J., Assunção, R. M. & Mostashari, F. (2005). A space-time permutation scan statistic for the early detection of disease outbreaks. PLoS Medicine, 2, 216–224.10.1371/journal.pmed.0020059CrossRefGoogle Scholar
Kupfer, J. A. (2012). Landscape ecology and biogeography: Rethinking landscape metrics in a post-FRAGSTATS landscape. Progress in Physical Geography, 36, 400–420.10.1177/0309133312439594CrossRefGoogle Scholar
Labonne, J., Ravigné, V., Parisi, B. & Gaucherel, C. (2008). Linking dendritic network structures to population demogenetics: The downside of connectivity. Oikos, 117, 1479–1490.10.1111/j.0030-1299.2008.16976.xCrossRefGoogle Scholar
Ladau, J. & Eloe-Fadrosh, E.A. (2019). Spatial, temporal, and phylogenetic scales of microbial ecology. Trends in Microbiology, 27, 662–669.10.1016/j.tim.2019.03.003CrossRefGoogle ScholarPubMed
Ladyman, J. & Wiesner, K. (2020). What Is a Complex System? Cambridge: Yale University Press.Google Scholar
Lahiri, S. N. (1999). Theoretical comparisons of block bootstrap methods. The Annals of Statistics, 27, 386–404.10.1214/aos/1018031117CrossRefGoogle Scholar
Laliberté, E., Schweiger, A. K. & Legendre, P. (2020). Partitioning plant spectral diversity into alpha and beta components. Ecology Letters, 23, 370–380.10.1111/ele.13429CrossRefGoogle ScholarPubMed
Lambin, X., Elston, D. A., Petty, S. J. & MacKinnon, J. L. (1998). Spatial asynchrony and periodic traveling waves in cyclic populations of field voles. Proceedings of the Royal Society of London Series B, 265, 1491–1496.10.1098/rspb.1998.0462CrossRefGoogle ScholarPubMed
Lamy, T., Pitz, K. J., Chavez, F. P., Yorke, C. E. & Miller, R. J. (2021). Environmental DNA reveals the fine-grained and hierarchical spatial structure of kelp forest fish communities. Nature Scientific Reports, 11, 14439.10.1038/s41598-021-93859-5CrossRefGoogle ScholarPubMed
Landi, P., Minoarivelo, H. O., Brännström, Å., Hui, C. & Dieckmann, U. (2018). Complexity and stability of ecological networks: A review of the theory. Population Ecology, 60, 319–345.10.1007/s10144-018-0628-3CrossRefGoogle Scholar
Latombe, G., Hui, C. & Mcgeoch, M. A. (2017). Multi-site generalised dissimilarity modelling: Using zeta diversity to differentiate drivers of turnover in rare and widespread species. Methods in Ecology and Evolution, 8, 431–442.10.1111/2041-210X.12756CrossRefGoogle Scholar
Ledo, A., Condés, S. & Montes, F. (2011). Intertype mark correlation function: A new tool for the analysis of species interactions. Ecological Modelling, 222, 580–587.10.1016/j.ecolmodel.2010.10.029CrossRefGoogle Scholar
Legendre, P. (1993). Spatial autocorrelation: Trouble or new paradigm? Ecology, 74, 1659–1673.10.2307/1939924CrossRefGoogle Scholar
Legendre, P. (2008). Studying beta diversity: Ecological variation partitioning by multiple regression and canonical analysis. Journal of Plant Ecology, 1, 3–8.10.1093/jpe/rtm001CrossRefGoogle Scholar
Legendre, P. (2019). A temporal beta-diversity index to identify sites that have changed in exceptional ways in space-time surveys. Ecology & Evolution, 9, 3500–3514.10.1002/ece3.4984CrossRefGoogle ScholarPubMed
Legendre, P., Borcard, D. & Peres-Neto, P. R. (2005). Analyzing beta diversity: Partitioning the spatial variation of community composition data. Ecological Monographs, 75, 435–450.10.1890/05-0549CrossRefGoogle Scholar
Legendre, P. & Condit, R. (2019). Spatial and temporal analysis of beta diversity in the Barro Colorado Island forest dynamics plot, Panama. Forest Ecosystems, 6, 7.10.1186/s40663-019-0164-4CrossRefGoogle Scholar
Legendre, P., Dale, M. R. T., Fortin, M.-J., Casgrain, P. & Gurevitch, J. (2004). Effects of spatial structures on the results of field experiments. Ecology, 85, 3202–3214.10.1890/03-0677CrossRefGoogle Scholar
Legendre, P., Dale, M. R. T., Fortin, M.-J., Gurevitch, J., Hohn, M. & Myers, D. E. (2002). The consequences of spatial structure for the design and analysis of ecological field surveys. Ecography, 25, 601–615.10.1034/j.1600-0587.2002.250508.xCrossRefGoogle Scholar
Legendre, P. & De Cáceres, M. (2013). Beta diversity as the variance of community data: Dissimilarity coefficients and partitioning. Ecology Letters, 16, 951–963.10.1111/ele.12141CrossRefGoogle Scholar
Legendre, P. & Fortin, M.-J. (1989). Spatial pattern and ecological analysis. Vegetatio, 80, 107–138.10.1007/BF00048036CrossRefGoogle Scholar
Legendre, P. & Fortin, M.-J. (2010). Comparison of the Mantel test and alternative approaches for detecting complex multivariate relationships in the spatial analysis of genetic data. Molecular Ecology Resources, 10, 831–844.10.1111/j.1755-0998.2010.02866.xCrossRefGoogle ScholarPubMed
Legendre, P., Fortin, M.-J. & Borcard, D. (2015). Should the Mantel test be used in spatial analysis? Methods in Ecology and Evolution, 6, 1239–1247.10.1111/2041-210X.12425CrossRefGoogle Scholar
Legendre, P., Lapointe, F.-J. & Casgrain, P. (1994). Modeling brain evolution from behavior: A permutational regression approach. Evolution, 48, 1487–1499.10.2307/2410243CrossRefGoogle ScholarPubMed
Legendre, P. & Legendre, L. (2012). Numerical Ecology, 3rd English edn. Cambridge: Elsevier.Google Scholar
Leibold, M. A. & Chase, J. M. (2018). Metacommunity Ecology. Cambridge: Princeton University Press.10.1515/9781400889068CrossRefGoogle Scholar
Lele, S. (1991). Jackknifing linear estimating equations: Asymptotic theory and applications in stochastic processes. Journal of the Royal Statistical Society B, 53, 253–267.10.1111/j.2517-6161.1991.tb01823.xCrossRefGoogle Scholar
Levin, S. A. (1992). The problem of pattern and scale in ecology. Ecology, 73, 1943–1967.10.2307/1941447CrossRefGoogle Scholar
Levin, S. A. (2000). Multiple scales and the maintenance of biodiversity. Ecosystems, 3, 498–506.10.1007/s100210000044CrossRefGoogle Scholar
Lewis, M. A., Fagan, W. F., Auger-Methe, M., Frair, J., Fryxell, J. M., Gros, C., Gurarie, E., Healy, S. D. & Merkle, J. A. (2021). Learning and animal movement. Frontiers in Ecology and Evolution, 9, 681704.10.3389/fevo.2021.681704CrossRefGoogle Scholar
Lewis, P. & Shedler, G. (1979). Simulation of nonhomogeneous Poisson processes by thinning. Naval Research Logistics Quarterly, 26, 403–413.10.1002/nav.3800260304CrossRefGoogle Scholar
Liang, S. D., Banerjee, S. & Carlin, B. P. (2009). Bayesian Wombling for spatial point processes. Biometrics, 65, 1243–1253.10.1111/j.1541-0420.2009.01203.xCrossRefGoogle ScholarPubMed
Lichstein, J. W. (2007). Multiple regression on distance matrices: A multivariate spatial analysis tool. Plant Ecology, 188, 117–131.10.1007/s11258-006-9126-3CrossRefGoogle Scholar
van Lieshout, M. N. M. & Baddeley, A. J. (1999). Indices of dependence between types in multivariate point patterns. Scandinavian Journal of Statistics, 26, 511–532.10.1111/1467-9469.00165CrossRefGoogle Scholar
Lin, K.-P., Long, Z.-H. & Ou, B. (2011). The size and power of bootstrap tests for spatial dependence in a linear regression model. Computational Economics, 38, 153–171.10.1007/s10614-010-9224-0CrossRefGoogle Scholar
Little, L. R. & Dale, M. R. T. (1999). A method for analysing spatio-temporal pattern in plant establishment, tested on a Populus balsamifera clone. Journal of Ecology, 87, 620–627.10.1046/j.1365-2745.1999.00378.xCrossRefGoogle Scholar
Liu, C. (2001). A comparison of five distance-based methods for pattern analysis. Journal of Vegetation Science, 12, 411–416.10.2307/3236855CrossRefGoogle Scholar
Long, J., Robertson, C. & Nelson, T. (2018). Stampr: Spatio-temporal analysis of moving polygons in R. Journal of Statistical Software, 84, Code Snippet 1.10.18637/jss.v084.c01CrossRefGoogle Scholar
Lotwick, H. W. & Silverman, B. W. (1982). Methods for analysing spatial processes of several types of points. Journal of the Royal Statistical Society B, 44, 406–413.10.1111/j.2517-6161.1982.tb01221.xCrossRefGoogle Scholar
Lovejoy, T. E. & Hannah, L. (2019). Biodiversity and Climate Change: Transforming the Biosphere. Cambridge: Yale University Press.10.2307/j.ctv8jnzw1CrossRefGoogle Scholar
Lowell, K. (1997). Effect(s) of the ‘no-same-color-touching’ constraint on the join-count statistic: A simulation study. Geographical Analysis, 29, 339–353.10.1111/j.1538-4632.1997.tb00969.xCrossRefGoogle Scholar
Lu, H. & Carlin, B. P. (2005). Bayesian areal wombling for geographical boundary analysis. Geographical Analysis, 37, 265–285.10.1111/j.1538-4632.2005.00624.xCrossRefGoogle Scholar
Ludwig, J. A. & Cornelius, J. M. (1987). Locating discontinuities along ecological gradients. Ecology, 68, 448–450.10.2307/1939277CrossRefGoogle Scholar
Lurgi, M., Galiana, N., Broitman, B. R., Kiel, S., Wieters, E. A. & Navarette, S. A. (2020). Geographical variation of multiplex ecological networks in marine intertidal communities. Ecology, 101, e03165.10.1002/ecy.3165CrossRefGoogle ScholarPubMed
Lurgi, M., Galiana, N., Lopez, B. C., Joppa, L. N. & Montoya, J. M. (2014). Network complexity and species traits mediate the effects of biological invasions on dynamic food webs. Frontiers in Ecology and Evolution, 2, 36.10.3389/fevo.2014.00036CrossRefGoogle Scholar
Lv, L., Zhang, K., Zhang, T., Li, X., Sun, Q., Zhang, L. & Xue, W. (2021). Eigenvector-based centralities for multilayer temporal networks under the framework of tensor computation. Expert Systems with Applications, 184, 115471.10.1016/j.eswa.2021.115471CrossRefGoogle Scholar
Ma, Y., Wang, S., Aggrawal, C. C., Yin, D. & Tand, J. (2019). Multi-dimensional graph convolutional networks. Proceedings of the 2019 SIAM International Conference on Data Mining, pp. 657–665. Society for Industrial and Applied Mathematic. May 2019, Calgary, AB, Canada.10.1137/1.9781611975673.74CrossRefGoogle Scholar
MacIsaac, D. A. (1989). Primary succession on proglacial deposits in the Canadian Rockies. MSc Thesis, University of Alberta.Google Scholar
MacKinnon, J. L., Petty, S. J., Elston, D. A., Thomas, C. J., Sherratt, T. N. & Lambin, X. (2001). Scale invariant spatio-temporal patterns of field vole density. Journal of Animal Ecology, 70, 101–111.Google Scholar
Magurran, A. E. & McGill, B. J. (eds.) (2010). Biological Diversity: Frontiers in Measurement and Assessment. Cambridge: Oxford University Press.Google Scholar
Manly, B. J. F. (2018). Randomization, Bootstrap and Monte Carlo Methods in Biology. 4th ed. Cambridge: CRC Press.10.1201/9781315273075CrossRefGoogle Scholar
Mantel, N. (1967). The detection of disease clustering and a generalized regression approach. Cancer Research, 27, 209–220.Google Scholar
Marj, T., Tuia, D. & Ratle, F. (2006). Detection of clusters using space-time scan statistics. International Journal of Wildland Fire, 18, 830–836.Google Scholar
Martensen, A. C., Saura, S. & Fortin, M.-J. (2017). Spatio‐temporal connectivity: Assessing the amount of reachable habitat in dynamic landscapes. Methods in Ecology and Evolution, 8, 1253–1264.10.1111/2041-210X.12799CrossRefGoogle Scholar
Mason, O. & Verwoerd, M. (2007). Graph theory and networks in biology. IET Systems Biology, 1, 89–119.10.1049/iet-syb:20060038CrossRefGoogle ScholarPubMed
Matheron, G. (1970). La théorie des variables réegionaliséees, et ses applications. Les Cahiers du Centre de Morphologie Mathéematique de Fontainebleau. Cambridge: Fascicule 5.Google Scholar
Matsuoka, S., Sugiyama, Y., Sato, H., Katano, I., Harada, K. & Doi, H. (2019). Spatial structure of fungal DNA assemblages revealed with eDNA metabarcoding in a forest river network in western Japan. Metabarcoding & Metagenomics, 3, e36225.10.3897/mbmg.3.36335CrossRefGoogle Scholar
Mayo, D. G. & Hand, D. (2022). Statistical significance and its critics: Practicing damaging science, or damaging scientific practice? Synthese, 200, 220.10.1007/s11229-022-03692-0CrossRefGoogle ScholarPubMed
McBratney, A. B. & de Gruijter, J. J. (1992). A continuum approach to soil classification by modified fuzzy k-means with extra grades. Journal of Soils Science, 43, 159–176.10.1111/j.1365-2389.1992.tb00127.xCrossRefGoogle Scholar
McCaslin, H. M., Feuka, A. B. & Hooten, M. B. (2020). Hierarchical computing for hierarchical models in ecology. Methods in Ecology and Evolution, 12, 245–254.10.1111/2041-210X.13513CrossRefGoogle Scholar
McCoy, E. D., Bell, S. S. & Walters, K. (1986). Identifying biotic boundaries along environmental gradients. Ecology, 67, 749–759.10.2307/1937698CrossRefGoogle Scholar
McElreath, R. (2020). Statistical Rethinking. 2nd ed. Cambridge: CRC Press.10.1201/9780429029608CrossRefGoogle Scholar
McGarigal, K., Wan, H. Y., Zeller, K. A., Timm, B. C. & Cushman, S. A. (2016). Multi-scale habitat selection modeling: A review and outlook. Landscape Ecology, 31, 1161–1175.10.1007/s10980-016-0374-xCrossRefGoogle Scholar
McIntire, E. J. B. & Fajardo, A. (2009). Beyond description: The active and effective way to infer processes from spatial patterns. Ecology, 90, 46–56.10.1890/07-2096.1CrossRefGoogle ScholarPubMed
McIntyre, N. & Wiens, J. (2000). A novel use of the lacunarity index to discern landscape function. Landscape Ecology, 15, 313–321.10.1023/A:1008148514268CrossRefGoogle Scholar
McLeish, M., Peláez, A., Pagán, I., Galiván, R., Fraile, A. & García-Arena, F. (2021). Structuring of plant communities across agricultural landscape mosaics: The importance of connectivity and the scale of effect. BMC Ecology and Evolution, 21, 173.10.1186/s12862-021-01903-9CrossRefGoogle ScholarPubMed
McLeod, A. M. & Leroux, S. J. (2021). The multiple meanings of omnivory influence empirical modular theory and whole food web stability relationships. Journal of Animal Ecology, 90, 447–459.10.1111/1365-2656.13378CrossRefGoogle ScholarPubMed
McRae, B. H., Dickson, B. G., Keitt, T. H. & Shah, V. B. (2008). Using circuit theory to model connectivity in ecology, evolution, and conservation. Ecology, 89, 2712–2724.10.1890/07-1861.1CrossRefGoogle ScholarPubMed
Melián, C. J., Bascompte, J. & Jordano, P. (2005). Spatial structure and dynamics in a marine food web. In Aquatic Food Webs: An Ecosystem Approach, Belgrano, A., Scharler, U., Dunne, J. & Ulanowicz, R. E. (eds.), pp. 19–24. Cambridge: Oxford University Press.10.1093/acprof:oso/9780198564836.003.0003CrossRefGoogle Scholar
Melles, S. J., Badzinski, D., Fortin, M.-J., Csillag, F. & Lindsay, K. (2009). Disentangling habitat and social drivers of nesting patterns in songbirds. Landscape Ecology, 24, 519–531.10.1007/s10980-009-9329-9CrossRefGoogle Scholar
Melles, S. J., Fortin, M.-J., Lindsay, K. & Badzinski, D. (2011). Expanding northward: Influence of climate change, forest connectivity, and population processes on a threatened species’ range shift. Global Change Biology, 17, 17–31.10.1111/j.1365-2486.2010.02214.xCrossRefGoogle Scholar
Michelot, T., Langrock, R., Patterson, T. and Rexstad, E. (2015). moveHMM: Animal Movement Modelling using Hidden Markov Models. R package version 1.1.Google Scholar
Michelot, T., Langrock, R., Patterson, T. and Rexstad, E. (2016). moveHMM: Animal movement modelling using hidden Markov models. Methods in Ecology and Evolution, 7, 1308–1315.10.1111/2041-210X.12578CrossRefGoogle Scholar
Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D. & Alon, U. (2002). Network motifs: Simple building blocks of complex networks. Science, 298, 824–827.10.1126/science.298.5594.824CrossRefGoogle ScholarPubMed
Mimet, A., Houet, T., Julliard, R. & Simon, L. (2013). Assessing functional connectivity: A landscape approach for handling multiple ecological requirements. Methods in Ecology and Evolution, 4, 453–463.10.1111/2041-210x.12024CrossRefGoogle Scholar
Minasny, B. & McBratney, A. B. (2005). The Matérn function as a general model for soil variograms. Geoderma, 128, 192–207.10.1016/j.geoderma.2005.04.003CrossRefGoogle Scholar
Minchin, P. R. (1989). Montane vegetation of the Mt. Field Massif, Tasmania: A test of some hypotheses about properties of community patterns. Vegetatio, 83, 97–110.10.1007/BF00031683CrossRefGoogle Scholar
Mizon, G. E. (1995). A simple message for autocorrelation correctors: Don’t. Journal of Econometrics, 69, 267–289.10.1016/0304-4076(94)01671-LCrossRefGoogle Scholar
Moran, P. A. P. (1948). The interpretation of statistical maps. Journal of the Royal Statistical Society B, 10, 243–251.10.1111/j.2517-6161.1948.tb00012.xCrossRefGoogle Scholar
Morrison, L.W. (2013). Nestedness in insular floras: Spatiotemporal variation and underlying mechanisms. Journal of Plant Ecology. doi: 10.1093/pe/rtt002.CrossRefGoogle Scholar
Moss, R., Elston, D. A. & Watson, A. (2000). Spatial asynchrony and demographic traveling waves during red grouse population cycles. Ecology, 81, 981–989.10.1890/0012-9658(2000)081[0981:SAADTW]2.0.CO;2CrossRefGoogle Scholar
Muff, S., Signer, J. & Fieberg, J. (2019). Accounting for individual-specific variation in habitat-selection studies: Efficient estimation of mixed-effects models using Bayesian or frequentist computation. Journal of Animal Ecology, 89, 80–92.10.1111/1365-2656.13087CrossRefGoogle ScholarPubMed
Mugglestone, M. A. & Renshaw, E. (1996). A practical guide to the spectral analysis of spatial point processes. Computational Statistics & Data Analysis, 21, 43–65.10.1016/0167-9473(95)00007-0CrossRefGoogle Scholar
Murakami, M. & Hirao, T. (2010). Lizard predation alters the effect of habitat area on the species richness of insect assemblages on Bahamian isles. Diversity and Distributions, 16, 952–958.10.1111/j.1472-4642.2010.00698.xCrossRefGoogle Scholar
Myers, J. A., Chase, J. M., Crandall, R. M. & Jiménez, I. (2015). Disturbance alters beta‐diversity but not the relative importance of community assembly mechanisms. Journal of Ecology, 103, 1291–1299.10.1111/1365-2745.12436CrossRefGoogle Scholar
Nakagawa, S. & Schielzeth, H. (2013). A general and simple method for obtaining R2 from generalized linear mixed‐effects models. Methods in Ecology and Evolution, 4, 133–142.10.1111/j.2041-210x.2012.00261.xCrossRefGoogle Scholar
Nathan, R., Getz, W. M., Revilla, E., Holyoak, M., Kadmon, R., Saltz, D. & Smouse, P. E. (2008). A movement ecology paradigm for unifying organismal movement research. Proceedings of the National Academy of Sciences, 105, 19052–19059.10.1073/pnas.0800375105CrossRefGoogle ScholarPubMed
Nathan, R., Monk, C. T., Arlinghaus, R., Adam, T., Alós, J., Assaf, M., Baktoft, H., Beardsworth, C. E., Bertram, M. G., Bijleveld, A. I. & Brodin, T. (2022). Big-data approaches lead to an increased understanding of the ecology of animal movement. Science, 375(6582), eabg1780.10.1126/science.abg1780CrossRefGoogle Scholar
Nekola, J. C. & White, P. S. (1999). The distance decay of similarity in biogeography and ecology. Journal of Biogeography, 26, 867–878.10.1046/j.1365-2699.1999.00305.xCrossRefGoogle Scholar
Newman, E. A., Kennedy, M. C., Falk, D. A. & McKenzie, D. (2019). Scaling and complexity in landscape ecology. Frontiers in Ecology and Evolution, 7, 293.10.3389/fevo.2019.00293CrossRefGoogle Scholar
Newman, M. E. J., Barabási, A.-L. & Watts, D. (eds.) (2006). The Structure and Dynamics of Networks. Cambridge: Princeton University Press.Google Scholar
Niedballa, J., Axtner, J., Döbert, T. F., Tilker, A., Nguyen, A., Wong, S. T., Fiderer, C., Heurich, M. & Wilting, A. (2022). imageseg: An R package for deep learning‐based image segmentation. Methods in Ecology and Evolution, 13, 2363–2371.10.1111/2041-210X.13984CrossRefGoogle Scholar
Noy-Meir, I. & Anderson, D. (1971). Multiple pattern analysis or multiscale ordination: towards a vegetation hologram. In Statistical Ecology, vol. 3: Populations, Ecosystems, and Systems Analysis, Patil, G. P., Pielou, E. C. & Waters, W. E. (eds.), pp. 207–232. Cambridge: Pennsylvania State University Press.Google Scholar
Oden, N. L. (1984). Assessing the significance of a spatial correlogram. Geographical Analysis, 16, 1–16.10.1111/j.1538-4632.1984.tb00796.xCrossRefGoogle Scholar
Oden, N. L. & Sokal, R. R. (1986). Directional autocorrelation: An extension of spatial correlograms to two dimensions. Systematic Zoology, 35, 608–617.10.2307/2413120CrossRefGoogle Scholar
Oden, N. L. & Sokal, R. R. (1992). An investigation of 3-matrix permutation tests. Journal of Classification, 9, 275–290.10.1007/BF02621410CrossRefGoogle Scholar
Oden, N. L., Sokal, R. R., Fortin, M.-J. & Goebl, H. (1993). Categorical wombling: Detecting regions of significant change in spatially located categorical variables. Geographical Analysis, 25, 315–336.10.1111/j.1538-4632.1993.tb00301.xCrossRefGoogle Scholar
Ohlsson, M. & Eklöf, A. (2020). Spatial resolution and location impact group structure in a marine food web. Ecology Letters, 23, 1451–1459.10.1111/ele.13567CrossRefGoogle Scholar
Okabe, A., Boots, B., Sugihara, K. & Chiu, S. N. (2000). Spatial Tessellations: Concepts and Applications of Voronoi Diagrams, 2nd ed. Cambridge: Wiley.10.1002/9780470317013CrossRefGoogle Scholar
Okabe, A. & Yamada, I. (2001). The K-function method on a network and its computational implementation. Geographical Analysis, 33, 271–290.10.1111/j.1538-4632.2001.tb00448.xCrossRefGoogle Scholar
O’Neill, R. V., Hunsaker, C. T., Timmins, S. P., Jackson, B. L., Jones, K. B., Riitters, K. H. & Wickham, J. D. (1996). Scale problems in reporting landscape pattern at the regional scale. Landscape Ecology, 11, 169–180.10.1007/BF02447515CrossRefGoogle Scholar
Openshaw, S. (1984). The Modifiable Areal Unit Problem. Cambridge: Geo Books.Google Scholar
Osborne, P. E., Foody, G. M. & Suárez-Seoane, S. (2007). Non-stationarity and local approaches to modelling the distributions of wildlife, Diversity and Distributions, 13, 313–323.10.1111/j.1472-4642.2007.00344.xCrossRefGoogle Scholar
Ostendorf, B. & Reynolds, J. F. (1998). A model of arctic tundra vegetation derived from topographic gradients. Landscape Ecology, 13, 187–201.10.1023/A:1007986410048CrossRefGoogle Scholar
Otto, S. P. & Day, T. (2011). Mathematical modeling in biology. In A Biologist’s Guide to Mathematical Modeling in Ecology and Evolution, Otto, S. & Day, T. (eds.), pp. 1–16. Cambridge: Princeton University Press.10.2307/j.ctvcm4hndCrossRefGoogle Scholar
Ovaskainen, O., Roy, D. B., Fox, R. & Anderson, B. J. (2016). Uncovering hidden spatial structure in species communities with spatially explicit joint species distribution models. Methods in Ecology and Evolution, 7, 428–436.10.1111/2041-210X.12502CrossRefGoogle Scholar
Oyana, T. J. (2021). Spatial Analysis with R. 2nd ed. Cambridge: CRC Press.Google Scholar
Pagel, J. & Schurr, F. M. (2012). Forecasting species ranges by statistical estimation of ecological niches and spatial population dynamics. Global Ecology and Biogeography, 21, 293–304.10.1111/j.1466-8238.2011.00663.xCrossRefGoogle Scholar
Paley, C. J., Taraskin, S. & Elliott, S. (2007). Temporal and dimensional effects in evolutionary graph theory. Physical Review Letters, 98, 98–103.10.1103/PhysRevLett.98.098103CrossRefGoogle ScholarPubMed
Pang, S. E. H., Slik, J. W. F., Zurell, D. & Webb, E. L. (2023). The clustering of spatially associated species unravels patterns in tropical tree species distributions. Ecosphere, 14, e4589.10.1002/ecs2.4589CrossRefGoogle Scholar
Parrott, L., Proulx, R. & Thibert-Plante, X. (2008). Three-dimensional metrics for the analysis of spatiotemporal data in ecology. Ecological Informatics, 3, 343–353.10.1016/j.ecoinf.2008.07.001CrossRefGoogle Scholar
Pascual, M. & Dunne, J. A. (eds.) (2006). Ecological Networks: Linking Structure to Dynamics in Food Webs. Cambridge: Oxford University Press.Google Scholar
Pascual-Hortal, L. & Saura, S. (2006). Comparison and development of new graph-based landscape connectivity indices: Towards the prioritization of habitat patches and corridors for conservation. Landscape Ecology, 21, 959–967.10.1007/s10980-006-0013-zCrossRefGoogle Scholar
Pasqueretta, C., Jeanson, R., Pansanel, J., Raine, N. E., Chittka, L. & Lihoreau, M. (2019). A spatial network analysis of resource partitioning between bumblebees foraging on artificial flowers in a flight cage. Movement Ecology, 7, 4.10.1186/s40462-019-0150-zCrossRefGoogle Scholar
Pearl, J. & MacKenzie, D. (2018). The Book of Why. Cambridge, Basic Books.Google Scholar
Pearl, J. (2009). Causality: Models, Reasoning and Inference. 2nd ed. Cambridge. Cambridge University Press.10.1017/CBO9780511803161CrossRefGoogle Scholar
Pearse, W. D., Davis, C. C., Inouye, D. W., Primack, R. B. & Davies, T. J. (2017). A statistical estimator for determining the limits of contemporary and historic phenology. Nature Ecology & Evolution, 1, 1876–1882.10.1038/s41559-017-0350-0CrossRefGoogle ScholarPubMed
Pelletier, B., Fyles, J. W. & Dutilleul, P. (1999). Tree species control and spatial structure of forest floor properties in a mixed-species stand. Écoscience, 6, 79–91.10.1080/11956860.1999.11952195CrossRefGoogle Scholar
Pelletier, D., Clark, M., Anderson, M. G., Rayfield, B., Walder, M. A. & Cardille, J. A. (2014). Applying circuit theory for corridor expansion and management at regional scales: Tiling, pinch points, and omnidirectional connectivity. PLoS ONE, 9, e84135.10.1371/journal.pone.0084135CrossRefGoogle ScholarPubMed
Penttinen, A. K., Stoyan, D. & Henttonen, H. M. (1992). Marked point processes in forest statistics. Forest Science, 38, 806–824.10.1093/forestscience/38.4.806CrossRefGoogle Scholar
Perea, A. J., Wiegand, T., Garrido, J. L., Rey, P. J. & Alcántara, J. M. (2021). Legacy effects of seed dispersal mechanisms shape the spatial interaction network of plant species in Mediterranean forests. Journal of Ecology, 109, 3670–3684.10.1111/1365-2745.13744CrossRefGoogle Scholar
Pereira, J., Saura, S. & Jordán, F. (2017). Single-node vs. multi-node centrality in landscape graph analysis: Key habitat patches and their protection for 20 bird species in NE Spain. Methods in Ecology and Evolution, 8, 1458–1467.10.1111/2041-210X.12783CrossRefGoogle Scholar
Peres-Neto, P. R. (2006). A unified strategy for estimating and controlling spatial, temporal and phylogenetic autocorrelation in ecological models. Oecologia Brasiliensis, 10, 105–119.10.4257/oeco.2006.1001.07CrossRefGoogle Scholar
Peres-Neto, P. R. & Legendre, P. (2010). Estimating and controlling for spatial structure in the study of ecological communities. Global Ecology and Biogeography, 19, 174–184.10.1111/j.1466-8238.2009.00506.xCrossRefGoogle Scholar
Perna, A. & Latty, T. (2014). Animal transportation networks. Journal of The Royal Society Interface, 11, 20140334.10.1098/rsif.2014.0334CrossRefGoogle ScholarPubMed
Perry, J. N., Liebhold, A. M., Rosenberg, M. S., Dungan, J., Miriti, M., Jakomulska, A. & Citron‐Pousty, S. (2002). Illustrations and guidelines for selecting statistical methods for quantifying spatial pattern in ecological data. Ecography, 25, 578–600.10.1034/j.1600-0587.2002.250507.xCrossRefGoogle Scholar
Peters, V. S. (2003). Keystone processes affect succession in boreal mixed woods: The relationship between masting in white spruce and fire history. PhD Thesis, University of Alberta.Google Scholar
Peterson, E. E., Ver Hoef, J. M., Isaak, D. J., Falke, J. A., Fortin, M.-J., Jordan, C., McNyset, K., Monestiez, P., Ruesch, A. S., Sengupta, A., Som, N., Steel, A., Theobald, D. M., Torgersen, C. E. & Wenger, S. J. (2013). Modeling dendritic ecological networks in space: An integrated network perspective. Ecology Letters, 16, 707–719.10.1111/ele.12084CrossRefGoogle ScholarPubMed
Peterson, G. D. (2002). Contagious disturbance, ecological memory, and the emergence of landscape pattern. Ecosystems, 5, 329–338.10.1007/s10021-001-0077-1CrossRefGoogle Scholar
Philibert, M. D., Fortin, M.-J. & Csillag, F. (2008). Spatial structure effects on the detection of patches boundaries using local operators. Environmental and Ecological Statistics, 15, 447–467.10.1007/s10651-007-0061-9CrossRefGoogle Scholar
Pichler, M. & Hartig, F. (2022). Machine learning and deep learning: A review for ecologists. Methods in Ecology and Evolution, 14, 994–1016.10.1111/2041-210X.14061CrossRefGoogle Scholar
Pielou, E. C. (1977). Mathematical Ecology. Cambridge: Wiley.Google Scholar
Pilosof, S., Porter, M. A., Pascual, M. & Kéfi, S. (2017). The multilayer nature of ecological networks. Nature Ecology & Evolution, 1, 0101.10.1038/s41559-017-0101CrossRefGoogle ScholarPubMed
Pinto, N. & Keitt, T. H. (2009). Beyond the least-cost path: Evaluating corridor redundancy using a graph-theoretic approach. Landscape Ecology, 24, 253–266.10.1007/s10980-008-9303-yCrossRefGoogle Scholar
Pinto-Ledezma, J. N., Larkin, D. J. & Cavender-Bares, J. (2018). Patterns of beta diversity of vascular plants and their correspondence with biome boundaries across North America. Frontiers in Ecology and Evolution, 6, 194.10.3389/fevo.2018.00194CrossRefGoogle Scholar
Plant, R. E. (2019). Spatial Data Analysis in Ecology and Agriculture using R. 2nd ed. Cambridge: CRC Press.Google Scholar
Plante, M., Lowell, L., Potvin, F., Boots, B. & Fortin, M.-J. (2004). Studying deer habitat on Anticosti Island, Québec: Relating animal occurrences and forest map information. Ecological Modelling, 174, 387–399.10.1016/j.ecolmodel.2003.09.035CrossRefGoogle Scholar
Poisot, T., Canard, E., Mouillot, D., Mouquet, N. & Gravel, D. (2012). The dissimilarity of species interaction networks. Ecology Letters, 15, 1353–1361.10.1111/ele.12002CrossRefGoogle ScholarPubMed
Polakowska, A. E., Fortin, M.-J. & Couturier, A. (2012). Quantifying the spatial relationship between bird species’ distributions and landscape feature boundaries in southern Ontario, Canada. Landscape Ecology, 27, 1481–1493.10.1007/s10980-012-9804-6CrossRefGoogle Scholar
Proulx, S. R., Promislow, D. E. L. & Phillip, P. C. (2005). Network thinking in ecology and evolution. Trends in Ecology and Evolution, 20, 345–352.10.1016/j.tree.2005.04.004CrossRefGoogle ScholarPubMed
Radicchi, F. & Bianconi, G. (2017). Redundant interdependencies boost the robustness of multiplex networks. Physical Review X, 7, 011013.10.1103/PhysRevX.7.011013CrossRefGoogle Scholar
Rajala, T., Olhede, S. C. & Murrell, D. J. (2019). When do we have the power to detect biological interactions in spatial point patterns? Journal of Ecology, 107, 711–721.10.1111/1365-2745.13080CrossRefGoogle ScholarPubMed
Rammer, W. & Seidl, R. R. (2019). Harnessing deep learning in ecology: An example predicting bark beetle outbreaks. Frontiers in Plant Science, 10, a1327.10.3389/fpls.2019.01327CrossRefGoogle ScholarPubMed
Ranta, E., Kaitala, V., Lindström, J. & Helle, E. (1997). The Moran effect and synchrony in population dynamics. Oikos, 78, 136–142.10.2307/3545809CrossRefGoogle Scholar
Rayfield, B., Fortin, M.-J. & Fall, A. (2011). Connectivity for conservation: A framework to classify network measures. Ecology, 92, 847–858.10.1890/09-2190.1CrossRefGoogle ScholarPubMed
Read, S., Bath, P., Willett, P. & Maheswaran, R. (2010). Measuring the spatial accuracy of the spatial scan statistic. Spatial and Spatio-temporal Epidemiology, 2, 69–78.10.1016/j.sste.2011.01.002CrossRefGoogle Scholar
Redding, T. E., Hope, G. D., Schmidt, M. G. & Fortin, M.-J. (2004). Spatial patterns of N availability across forest-clearcut edges in the southern interior of British Columbia. Canadian Journal of Forest Research, 34, 1018–1024.10.1139/x03-282CrossRefGoogle Scholar
Reich, B. J., Yang, S., Guan, Y., Giffin, A. B., Miller, M. J. & Rappold, A. (2021). A review of spatial causal inference methods for environmental and epidemiological applications. International Statistical Review, 89, 605–634.10.1111/insr.12452CrossRefGoogle ScholarPubMed
Remmel, T. K. & Fortin, M.-J. (2013). Categorical, class-focused map patterns: Characterization and comparison. Landscape Ecology, 28, 1587–1599.10.1007/s10980-013-9905-xCrossRefGoogle Scholar
Richardson, M. C., Fortin, M.-J. & Branfireun, B. A. (2009). Hydrogeomorphic edge detection and delineation of landscape functional units from LiDAR digital elevation models. Water Resources Research, 45, W10441, doi:10.1029/2008WR007518.CrossRefGoogle Scholar
Ricotta, C. (2006). Towards a complex, plural and dynamic approach to diversity: Rejoinder to Myers and Patil, Podani, and Sarkar. Acta Biotheoretica, 54, 141–146.10.1007/s10441-006-8258-0CrossRefGoogle Scholar
Rietkerk, M., Dekker, S. C., de Ruiter, P. C. & van de Koppel, J. (2004). Self-organised patchiness and catastrophic shifts in ecosystems. Science, 305, 1926–1929.10.1126/science.1101867CrossRefGoogle Scholar
Ripley, B. D. (1976). The second-order analysis of stationary point processes. Journal of Applied Probability, 13, 255–266.10.2307/3212829CrossRefGoogle Scholar
Ripley, B. D. (1978). Spectral analysis and the analysis of pattern in plant communities. Journal of Ecology, 66, 965–981.10.2307/2259308CrossRefGoogle Scholar
Ripley, B. D. (1988). Statistical Inference for Spatial Processes. Cambridge: Cambridge University Press.10.1017/CBO9780511624131CrossRefGoogle Scholar
Riva, F., Graco-Roza, C., Daskalova, G. N., Hudgins, E. J., Lewthwaite, J. M. M., Newman, E. A., Ryo, M. & Mammola, S. (2022). Towards a cohesive understanding of ecological complexity. Science Advances, 9, eabq4207.10.1126/sciadv.abq4207CrossRefGoogle Scholar
Roberts, D. R., Bahn, V., Ciuti, S., Boyce, M. S., Elith, J., Guillera‐Arroita, G., Hauenstein, S., Lahoz‐Monfort, J. J., Schröder, B., Thuiller, W., Warton, D. I., Hartig, F. & Dormann, C. F. (2017). Cross‐validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure. Ecography, 40, 913–929.10.1111/ecog.02881CrossRefGoogle Scholar
Robertson, C., Nelson, T. A., Boots, B. & Wulder, M. A. (2007). STAMP: Spatial-temporal analysis of moving polygons. Journal of Geographical Systems, 9, 207–227.10.1007/s10109-007-0044-2CrossRefGoogle Scholar
Robitaille, A. L., Webber, Q. M. R., Turner, J. W. & VanderWal, E. (2021). The problem and promise of scale in multilayer animal social networks. Current Zoology, 67, 113–123.10.1093/cz/zoaa052CrossRefGoogle ScholarPubMed
Rogers, H. S., Beckman, N. G., Hartig, F., Johnson, J. S., Pufal, G., Shea, K., Zurell, D., Bullock, J. M., Cantrell, R. S., Loiselle, B., Pejchar, L., Razafindratsima, O. H., Sandor, M. E., Schupp, E. W., Strickland, W. C. & Zambrano, J. (2019). The total dispersal kernel: A review and future directions. AoB Plants, 11, plz042.10.1093/aobpla/plz042CrossRefGoogle ScholarPubMed
Rosenzweig, L. (1995). Species Diversity in Space and Time. Cambridge: Cambridge University Press.10.1017/CBO9780511623387CrossRefGoogle Scholar
Rota, C. T., Ferreira, M. A., Kays, R. W., Forrester, T. D., Kalies, E. L., McShea, W. J., Parsona, A. W. & Millspaugh, J. J. (2016). A multispecies occupancy model for two or more interacting species. Methods in Ecology and Evolution, 7, 1164–1173.10.1111/2041-210X.12587CrossRefGoogle Scholar
Rouquette, J. R., Dallimer, M., Armsworth, P. R., Gaston, K. J., Maltby, L. & Warren, P. H. (2013). Species turnover and geographic distance in an urban river network. Diversity and Distributions, 19, 1429–1439.10.1111/ddi.12120CrossRefGoogle Scholar
Roushangar, K., Moghaddas, M., Ghasempour, R. & Alizadeh, F. (2021). Evaluation of spatio-temporal characteristics of precipitation using discrete maximal overlap wavelet transform and spatial clustering tools. Hydrology Research, 52(2), 414–430.10.2166/nh.2021.141CrossRefGoogle Scholar
Royle, J. A., Fuller, A. K. & Sutherland, C. (2017). Unifying population and landscape ecology with spatial capture-recapture. Ecography, 41, 444–456.10.1111/ecog.03170CrossRefGoogle Scholar
Ruiz, E. J. L., Mozo, H. G., Vilches, E. D. & Galán, C. (2012). The use of geostatistics in the study of floral phenology of Vulpia geniculata (L.) Link. The Scientific World Journal, 624247.Google Scholar
Sadahiro, Y. & Umemura, M. (2002). A computational approach for the analysis of changes in polygon distributions. Journal of Geographical Systems, 3, 137–154.10.1007/PL00011471CrossRefGoogle Scholar
Sahneh, F. D. & Scoglio, C. (2014). Competitive epidemic spreading over arbitrary multilayer networks. Physics Revue E, 89, 068217.Google Scholar
Sanuy, D. & Bovet, P. (1997). A comparative study on the paths of five anura species. Behavioural Processes, 41, 193–199.10.1016/S0376-6357(97)00045-4CrossRefGoogle ScholarPubMed
Saunders, S. C., Brosofske, K. D., Chen, J., Drummer, T. D. & Gustafson, E. J. (2005). Identifying scales of pattern in ecological data: A comparison of lacunarity, spectral and wavelet analyses. Ecological Complexity, 2, 87–105.10.1016/j.ecocom.2004.11.002CrossRefGoogle Scholar
Saura, S., Bodin, Ö. & Fortin, M. J. (2014). Stepping stones are crucial for species’ long-distance dispersal and range expansion through habitat networks. Journal of Applied Ecology, 51, 171–182.10.1111/1365-2664.12179CrossRefGoogle Scholar
Scheiner, S. M., Cox, S. B., Willig, M., Mittelbach, G. G., Osenberg, C. & Kaspari, M. (2000). Species richness, species-area curves and Simpson’s paradox. Evolutionary Ecology Research, 2, 791–802.Google Scholar
Schmera, D., Legendre, P., Erös, T., Tóth, M., Magyari, E. K., Baur, B. & Podani, J. (2022). New measures for quantifying directional changes in presence–absence community data. Ecological Indicators, 136, 108618.10.1016/j.ecolind.2022.108618CrossRefGoogle Scholar
Schooler, S. L. & Zald, H. S. J. (2019). Lidar prediction of small mammal diversity in Wisconsin USA. Remote Sensing, 11, 2222.10.3390/rs11192222CrossRefGoogle Scholar
Schröder, W., Schmidt, G. & Schönrock, S. (2014). Modelling and mapping of plant phenological stages as bio-meterological indicators for climate change. Environmental Sciences Europe, 26, 5.10.1186/2190-4715-26-5CrossRefGoogle Scholar
Setzer, R. W. (1985). Spatio-temporal patterns of mortality in Pemphigus populicaulis and P. populitransversus on cottonwoods. Oecologia, 67, 310–321.10.1007/BF00384935CrossRefGoogle ScholarPubMed
Shade, A., Dunn, R. R., Blowes, S. A., Keil, P., Bohannan, B. J., Herrmann, M., Küsel, K., Lennon, J. T., Sanders, N. J., Storch, D. & Chase, J. (2018). Macroecology to unite all life, large and small. Trends in Ecology & Evolution, 33, 731–744.10.1016/j.tree.2018.08.005CrossRefGoogle ScholarPubMed
Shih, F. Y. (2009). Image Processing and Mathematical Morphology: Fundamentals and Applications. Cambridge: CRC Press.Google Scholar
Shimatani, K. (2001). Multivariate point processes and spatial variation of species diversity. Forest Ecology and Management, 142, 215–229.10.1016/S0378-1127(00)00352-2CrossRefGoogle Scholar
Shoemaker, L. G., Sullivan, L. L., Donohue, I., Cabral, J. S., Williams, R. J., Mayfield, M. M., Chase, J. M., Chu, C., Harpole, W. S., Huth, A. & HilleRisLambers, J. (2020). Integrating the underlying structure of stochasticity into community ecology. Ecology, 101, e02922.10.1002/ecy.2922CrossRefGoogle ScholarPubMed
Silk, M. J., Croft, D. P., Delahay, R. J., Hodgson, D. J., Boots, M., Weber, N. & McDonald, R. A. (2017). Using social network measures in wildlife disease ecology, epidemiology, and management. BioScience, 67, 245–257.10.1093/biosci/biw175CrossRefGoogle Scholar
Smouse, P. E., Long, J. C. & Sokal, R. R. (1986). Multiple regression and correlation extensions of the Mantel test of matrix correspondence. Systematic Zoology, 35, 627–632.10.2307/2413122CrossRefGoogle Scholar
Soininen, J., McDonald, R. & Hillibrand, H. (2007). The distance decay of similarity in ecological communities. Ecography, 30, 3–12.10.1111/j.0906-7590.2007.04817.xCrossRefGoogle Scholar
Sokal, R. R., Oden, N. L., Thomson, B. A. & Kim, J. (1993). Testing for regional differences in means: Distinguishing inherent from spurious spatial autocorrelation by restricted randomization. Geographical Analysis, 25, 199–210.10.1111/j.1538-4632.1993.tb00291.xCrossRefGoogle Scholar
Sokal, R. R. & Rohlf, F. J. (1995). Biometry. 3rd ed. Cambridge: Freeman.Google Scholar
Sokal, R. R. & Wartenberg, D. E. (1983). A test of spatial autocorrelation using an isolation-by-distance model. Genetics, 105, 219–237.10.1093/genetics/105.1.219CrossRefGoogle ScholarPubMed
Solé, R. (2007). Scaling laws in the drier. Nature, 449, 151–153.10.1038/449151aCrossRefGoogle ScholarPubMed
Solé, R. & Bascompte, J. (2006). Self-Organization in Complex Ecosystems. Cambridge: Princeton University Press.10.1515/9781400842933CrossRefGoogle Scholar
Somers, K. M. & Jackson, D. A. (2022). Putting the Mantel test back together again. Ecology, 103, e3780.10.1002/ecy.3780CrossRefGoogle Scholar
Spear, S., Balkenhol, N., Fortin, M.-J., McRae, B. & Scribner, K. (2010). Use of resistance surfaces for landscape genetic studies: Considerations for parameterization and analysis. Molecular Ecology, 19, 3576–3591.10.1111/j.1365-294X.2010.04657.xCrossRefGoogle ScholarPubMed
Spooner, P. G., Lunt, I. D., Okabe, A. & Shiode, S. (2004). Spatial analysis of roadside Acacia populations on a road network using the network K-function. Landscape Ecology, 19, 491–499.10.1023/B:LAND.0000036114.32418.d4CrossRefGoogle Scholar
Sprugel, D. G. (1976). Dynamic structure of wave-regenerated Abies balsamea forests in the northeastern United States. Journal of Ecology, 64, 889–911.10.2307/2258815CrossRefGoogle Scholar
St-Louis, V., Fortin, M.-J. & Desrochers, A. (2004). Association between microhabitat and territory boundaries of two forest songbirds. Landscape Ecology, 19, 591–601.10.1023/B:LAND.0000042849.63040.a9CrossRefGoogle Scholar
Stein, A., Gerstner, K. & Kreft, H. (2014). Environmental heterogeneity as a universal driver of species richness across taxa, biomes and spatial scales. Ecology Letters, 17, 866–880.10.1111/ele.12277CrossRefGoogle ScholarPubMed
Steinhaeuser, K., Chawla, N. V. & Ganguly, A. R. (2010). Complex networks in climate science: progress, opportunities and challenges. Proceedings of the 2010 Conference on Intelligent Data Understanding, 16–26. 5–6 October 2010, Mountain View, CA.Google Scholar
Stoyan, D., Kendall, W. S. & Mecke, J. (1995). Stochastic Geometry and Its Applications. 2nd ed. Cambridge: Wiley.Google Scholar
Stoyan, D. & Penttinen, A. (2000). Recent applications of point process methods in forestry statistics. Statistical Science, 15, 61–78.Google Scholar
Strampelli, P., Anderson, L., Everatt, K. T., Somers, M. J. & Rowcliffe, J. M. (2018). Leopard Panthera pardus density in southern Mozambique: Evidence from spatially explicit capture-recapture in Xonghile Game Reserve. Oryx, 54, 405–411.10.1017/S0030605318000121CrossRefGoogle Scholar
Strydom, T. & Poisot, T. (2023). SpatialBoundaries.jl: Edge detection using spatial wombling. Ecography, 2023, e06609.10.1111/ecog.06609CrossRefGoogle Scholar
Sturtevant, B. R. & Fortin, M.-J. (2021). Understanding and modeling forest disturbance interactions at the landscape level. Special issue: Using landscape simulation models to help balance conflicting goals in changing forests. Frontiers in Ecology and Evolution, 9, 653647.10.3389/fevo.2021.653647CrossRefGoogle Scholar
Swetnam, T. L., Lynch, A. M., Falk, D. A., Yool, S. R. & Guertin, D. P. (2015). Discriminating natural variation from legacies of disturbance in semi-arid forests, Southwestern U.S.A. Ecosphere, 6, 1–22.10.1890/ES14-00384.1CrossRefGoogle Scholar
Takahashi, K., Kulldorff, M., Tango, T. & Yih, K. (2008). A flexibly shaped space-time scan statistic for disease outbreak detection and monitoring. International Journal of Health Geographics, 7, 14.10.1186/1476-072X-7-14CrossRefGoogle Scholar
Tango, T. (2021). Spatial scan statistics can be dangerous. Statistical Methods in Medical Research, 30, 75–86.10.1177/0962280220930562CrossRefGoogle ScholarPubMed
Tavaré, S. (1983). Serial dependence in contingency tables. Journal of the Royal Statistical Society B, 45, 100–106.10.1111/j.2517-6161.1983.tb01236.xCrossRefGoogle Scholar
Tavaré, S. & Altham, P. M. E. (1983). Serial dependence of observations leading to contingency tables, and corrections to chi-squared statistics. Biometrika, 70, 139–144.10.1093/biomet/70.1.139CrossRefGoogle Scholar
Taylor, M. D. (2021). An Introduction to Geometric Algebra and Geometric Calculus. Cambridge: M. D. Taylor.Google Scholar
Teng, S. N., Xu, C., Sandel, B. & Svenning, J. C. (2018). Effects of intrinsic sources of spatial autocorrelation on spatial regression modelling. Methods in Ecology and Evolution, 9, 363–372.10.1111/2041-210X.12866CrossRefGoogle Scholar
Thaden, H. & Kneib, T. (2018). Structural equation models for dealing with spatial confounding. The American Statistician, 72, 239–252.10.1080/00031305.2017.1305290CrossRefGoogle Scholar
Thiery, J. M., Dherbes, J. M. & Valentin, C. (1995). A model simulationg the genesis of banded vegetation patterns in Niger. Journal of Ecology, 83, 497–507.10.2307/2261602CrossRefGoogle Scholar
Tiefelsdorf, M. & Griffith, D. A. (2007). Semi-parametric filtering of spatial autocorrelation: The eigenvector approach. Environmental Planning A, 39, 1193–122110.1068/a37378CrossRefGoogle Scholar
Timóteo, S., Correia, M., Rodriguez-Echeverria, S., Freitas, H. & Heleno, R. (2018). Multilayer networks reveal the spatial structure of seed-dispersal interactions across the Great Rift landscapes. Nature Communications, 9, 140.10.1038/s41467-017-02658-yCrossRefGoogle ScholarPubMed
Tobin, P. C. & Bjørnstad, O. N. (2003). Spatial dynamics and cross-correlation in a transient predator–prey system. Journal of Animal Ecology, 72, 460–467.10.1046/j.1365-2656.2003.00715.xCrossRefGoogle Scholar
Tobler, W. F. (1970). A computer movie simulating urban growth in the Detroit region. Economic Geography, 46, 234–240.10.2307/143141CrossRefGoogle Scholar
Torrence, C. & Compo, G. P. (1998). A practical guide to wavelet analysis. Bulletin of the American Meteorological Society, 79, 61–78.10.1175/1520-0477(1998)079<0061:APGTWA>2.0.CO;22.0.CO;2>CrossRefGoogle Scholar
Toussaint, G. T. (1980). The relative neighbourhood graph of a finite planar set. Pattern Recognition, 12, 261–268.10.1016/0031-3203(80)90066-7CrossRefGoogle Scholar
Tuia, D., Ratle, F., Lasaponara, R., Tleesca, L. & Kanevski, M. (2008). Scan statistics analysis of forest fire clusters. Communications in Nonlinear Science and Numerical Simulation, 13, 1689–1694.10.1016/j.cnsns.2007.03.004CrossRefGoogle Scholar
Turchin, P. (1998). Quantitative Analysis of Movement. Cambridge: Sinauer.Google Scholar
Turgeon, K., Turpin, C. & Gregory-Eaves, I. (2017). Boreal river impoundments caused nearshore fish community assemblage shifts but little change in diversity: A multiscale analysis. Canadian Journal of Fisheries and Aquatic Science, 76, 740–752.10.1139/cjfas-2017-0561CrossRefGoogle Scholar
Turner, M. G. & Gardner, R. H. (2015). Landscape Ecology in Theory and Practice: Pattern and Process. 2nd ed. Cambridge: Springer-Verlag.10.1007/978-1-4939-2794-4CrossRefGoogle Scholar
Upton, G. J. G. & Fingleton, B. (1985). Spatial Data Analysis by Example, vol. I: Point Pattern and Quantitative Data. Cambridge: Wiley.Google Scholar
Urban, D. L. (2023). Agents and Implications of Landscape Pattern: Working Models for Landscape Ecology. Springer Nature.10.1007/978-3-031-40254-8CrossRefGoogle Scholar
Urban, D. L. & Keitt, T. (2001). Landscape connectivity: A graph-theoretic perspective. Ecology, 82, 1205–1218.10.1890/0012-9658(2001)082[1205:LCAGTP]2.0.CO;2CrossRefGoogle Scholar
Urban, D. L., Minor, E. S., Treml, E. A. & Schick, R. S. (2009). Graph models of habitat mosaics. Ecology Letters, 12, 260–273.10.1111/j.1461-0248.2008.01271.xCrossRefGoogle ScholarPubMed
Urban, M. C., Strauss, S. Y., Pelletier, F., Palkovacs, E. P., Leibold, M. A., Hendry, A. P., De Meester, L., Carlson, S. M., Angert, A. L. & Giery, S. T. (2020). Evolutionary origins for ecological patterns in space. Proceedings of the National Academy of Sciences, 117, 17482–17490.10.1073/pnas.1918960117CrossRefGoogle ScholarPubMed
Urdangarin, A., Goicoa, T. & Ugarte, M. D. (2023). Evaluating recent methods to overcome spatial confounding. Revista Mathmática Complutense, 36, 333–360.10.1007/s13163-022-00449-8CrossRefGoogle Scholar
Van Strien, M. J., Keller, D. & Holderegger, R. (2012). A new analytical approach to landscape genetic modelling: Least-cost transect analysis and linear mixed models. Molecular Ecology, 21, 4010–4023.10.1111/j.1365-294X.2012.05687.xCrossRefGoogle ScholarPubMed
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L. & Polosukhin, I. (2017). Attention is all you need. 31st Conference on Neural Information Processing Systems (NIPS 2017). 4–9 December 2017, Long Beach, CA, USA.Google Scholar
Velázquez, E., Martínez, I., Getzin, S., Moloney, K. A. & Wiegand, T. (2015). An evaluation of the state of point pattern analysis in ecology. Ecography, 39, 1042–1055.10.1111/ecog.01579CrossRefGoogle Scholar
Vellend, M. (2016). The Theory of Ecological Communities. Cambridge: Princeton University Press.Google Scholar
Vepakomma, U., Kneeshaw, D. & Fortin, M.-J. (2012). Spatial contiguity and continuity of disturbance in boreal forests: Gap persistence, expansion, shrinkage and displacement. Journal of Ecology, 100, 1257–1268.10.1111/j.1365-2745.2012.01996.xCrossRefGoogle Scholar
Ver Hoef, J. M. & Glenn-Lewin, D. C. (1989). Multiscale ordination: A method for detecting pattern at several scales. Vegetatio, 82, 59–67.10.1007/BF00217983CrossRefGoogle Scholar
Ver Hoef, J. M., Peterson, E. E. & Theobald, D. (2006). Spatial statistical models that use flow and stream distance. Environmental and Ecological Statistics, 13, 449–464.10.1007/s10651-006-0022-8CrossRefGoogle Scholar
Ver Hoef, J. M., Peterson, E. E., Hooten, M. B., Hanks, E. M. & Fortin, M.-J. (2018). Spatial autoregressive models for statistical inference from ecological data. Ecological Monographs, 88, 36–59.10.1002/ecm.1283CrossRefGoogle Scholar
Viana, D. S. & Chase, J. M. (2019). Spatial scale modulates the inference of metacommunity assembly processes. Ecology, 100, e02576.10.1002/ecy.2576CrossRefGoogle ScholarPubMed
Vince, J. (2008). Geometric Algebra for Computer Graphics. Cambridge: Springer-Verlag.10.1007/978-1-84628-997-2CrossRefGoogle Scholar
Wackernagel, H. (2013). Multivariate Geostatistics: An Introduction with Applications. 2nd ed. Cambridge: Springer.Google Scholar
Wagner, H. H. (2003). Spatial covariance in plant communities: Integrating ordination, geostatistics, and variance testing. Ecology, 84, 1045–1057.10.1890/0012-9658(2003)084[1045:SCIPCI]2.0.CO;2CrossRefGoogle Scholar
Wagner, H. H. (2004). Direct multi-scale ordination with canonical correspondence analysis. Ecology, 85, 342–351.10.1890/02-0738CrossRefGoogle Scholar
Wagner, H. H. (2013). Rethinking the linear regression model for spatial ecological data. Ecology, 94, 2381–2391.10.1890/12-1899.1CrossRefGoogle ScholarPubMed
Wagner, H. H. & Dray, S. (2015). Generating spatially constrained null models for irregularly spaced data using Moran spectral randomization methods. Methods in Ecology and Evolution, 6, 1169–1178.10.1111/2041-210X.12407CrossRefGoogle Scholar
Wagner, H. H. & Fortin, M.-J. (2005). Spatial analysis of landscapes: Concepts and statistics. Ecology, 86, 1975–1987.10.1890/04-0914CrossRefGoogle Scholar
Wakefield, J. C., Kelsall, J. E. & Morris, S. E. (2000). Clustering, cluster detection, and spatial variation in risk. In Spatial Epidemiology: Methods and Applications, Elliott, P., Wakefield, J. C., Best, N. G. & Briggs, D. J. (eds.), pp. 128–152. Cambridge: Oxford University Press.Google Scholar
Wallerman, J., Joyce, S., Vencatasawmy, C. P. & Olsson, H. (2002). Prediction of forest stem volume using kriging adapted to detected edges. Canadian Journal of Forest Research, 32, 509–518.10.1139/x01-214CrossRefGoogle Scholar
Wang, Q., Downey, D., Ji, H. & Hope, T. (2023). Learning to generate novel scientific directions with contextualized literature-based discovery. arXiv: 2305.14259v1.Google Scholar
Wang, U., Wang, K., Cao, W. & Wang, X. (2019). Geometric algebra in signal and image processing: A survey. IEEE Access, 7, 156315–156325.10.1109/ACCESS.2019.2948615CrossRefGoogle Scholar
Wang, Y., Weinacker, H. & Koch, B. (2008). A LiDAR point cloud based procedure for vertical canopy strcture analysis and 3D single tree modelling in forest. Sensors, 8, 3938–3951.10.3390/s8063938CrossRefGoogle Scholar
Warton, D. I., Blanchet, F. G., O’Hara, R. B., Ovaskainen, O., Taskinen, S., Walker, S. C. & Hui, F. K. (2015). So many variables: Joint modeling in community ecology. Trends in Ecology & Evolution, 30, 766–779.10.1016/j.tree.2015.09.007CrossRefGoogle ScholarPubMed
Wasserman, L. (2004). All of Statistics: A Concise Course in Statistical Inference. Cambridge: Springer.10.1007/978-0-387-21736-9CrossRefGoogle Scholar
Wasserstein, R. L., Schirm, A. L. & Lazar, N. A. (2019). Moving to a world beyond ‘p < 0.05’. The American Statistician, 73, 1–19.10.1080/00031305.2019.1583913CrossRefGoogle Scholar
Watt, A. S. (1947). Pattern and process in the plant community. Journal of Ecology, 35, 1–22.10.2307/2256497CrossRefGoogle Scholar
Weaver, J. E., Conway, T. M. & Fortin, M.-J. (2012). An invasive species’ relationship with environmental variables changes across multiple spatial scales. Landscape Ecology, 27, 1351–1362.10.1007/s10980-012-9786-4CrossRefGoogle Scholar
Webster, R. (1973). Automatic soil-boundary location from transect data. Mathematical Geology, 5, 27–37.10.1007/BF02114085CrossRefGoogle Scholar
Webster, R. & Oliver, M. A. (2007). Geostatistics for Environmental Scientists. Cambridge: Wiley.10.1002/9780470517277CrossRefGoogle Scholar
White, E. P., Ernest, S. K. M., Adler, P. B., Hurlberts, A. H. & Lyons, S. K. (2010). Integrating spatial and temporal approaches to understanding species richness. Philosophical Transactions of the Royal Society B, 365, 3633–3643.10.1098/rstb.2010.0280CrossRefGoogle ScholarPubMed
Whittaker, R. H. (1977). Evolution of species diversity in land communities. In Evolutionary Biology, Hecht, M. K., Steere, W. C. & Wallace, B. (eds.), 1–67. Cambridge: Plenum.Google Scholar
Whittle, P. (1954). On stationary processes in the plane. Biometrika, 41, 434–449.10.1093/biomet/41.3-4.434CrossRefGoogle Scholar
Wiegand, T., Kissling, W. D., Cipriotti, P. A. & Aguiar, M. R. (2006). Extending point pattern analysis for objects of finite size and irregular shape. Journal of Ecology, 94, 825–837.10.1111/j.1365-2745.2006.01113.xCrossRefGoogle Scholar
Wiegand, T. & Moloney, K. A. (2013). Handbook of Spatial Point-Pattern Analysis in Ecology. Cambridge: CRC Press.10.1201/b16195CrossRefGoogle Scholar
Wiens, J. A. (1989). Spatial scaling in ecology. Functional Ecology, 3, 385–397.10.2307/2389612CrossRefGoogle Scholar
Wiens, J. A., Crist, T. O. & Milne, B. T. (1993). On quantifying insect movements. Environmental Entomology, 22, 709–715.10.1093/ee/22.4.709CrossRefGoogle Scholar
Wikle, C. K. (2003). Hierarchical Bayesian models for predicting the spread of ecological processes. Ecology, 84, 1382–1394.10.1890/0012-9658(2003)084[1382:HBMFPT]2.0.CO;2CrossRefGoogle Scholar
Wikle, C. K. & Zammit-Mangion, A. (2022). Statistical deep learning for spatial and spatio-temporal data. arXiv:2206.02218v1.Google Scholar
Wikle, C. K., Zammit-Mangion, A. & Cressie, N. (2019). Spatio-Temporal Statistics with R. Cambridge: CRC Press.10.1201/9781351769723CrossRefGoogle Scholar
Williams, B. K. & Brown, E. D. (2019). Sampling and analysis frameworks for inference in ecology. Methods in Ecology and Evolution, 10, 1832–1842.10.1111/2041-210X.13279CrossRefGoogle Scholar
Williams, M. J. & Musolesi, M. (2016). Spatio-temporal networks: Reachability, centrality and robustness. Royal Society Open Science, 3, 160196.10.1098/rsos.160196CrossRefGoogle ScholarPubMed
Wilson, J. B. & Agnew, A. D. Q. (1992). Positive-feedback switches in plant communities. Advances in Ecological Research, 23, 263–336.10.1016/S0065-2504(08)60149-XCrossRefGoogle Scholar
Windle, M. J. S., Rose, G. A., Devillers, R. & Fortin, M.-J. (2010). Exploring spatial non-stationarity of fisheries survey data using geographically weighted regression (GWR): An example from the Northwest Atlantic. ICES Journal of Marine Science, 67, 145–154.10.1093/icesjms/fsp224CrossRefGoogle Scholar
With, K. A. (2019). Essentials of Landscape Ecology. Cambridge: Oxford University Press.10.1093/oso/9780198838388.001.0001CrossRefGoogle Scholar
Wolkovich, E. M. & Donahue, M. J. (2021). How phenological tracking shapes species and communities in non-stationary environments. Biological Reviews, 96, 2810–2827.10.1111/brv.12781CrossRefGoogle ScholarPubMed
Womble, W. H. (1951). Differential systematics. Science, 114, 315–322.10.1126/science.114.2961.315CrossRefGoogle ScholarPubMed
Wu, J. (2004). Effects of changing scale on landscape pattern analysis: Scaling relations. Landscape Ecology, 19, 125–138.10.1023/B:LAND.0000021711.40074.aeCrossRefGoogle Scholar
Wu, J. & Qi, Y. (2000). Dealing with scale in landscape analysis: an overview. Geographic Information Sciences, 6, 1–5.Google Scholar
Wu, J. G., Shen, W. J. & Sun, W. Z. (2002). Empirical patterns of the effects of changing scale on landscape metrics. Landscape Ecology, 117, 761–782.10.1023/A:1022995922992CrossRefGoogle Scholar
Xuan, L. & Hong, Z. (2018). An improved canny edge detection algorithm. 8th IEEE International Conference on Software Engineering and Service Science (ICSESS). 23–25 November 2018, Beijing.Google Scholar
Yan, S., Xiong, Y. & Lin, D. (2018). Spatial temporal graph convolutional networks for skeleton-based action recognition. arXiv: 1801.07455v2.Google Scholar
Yang, F. & Zhou, Y. (2017). Quantifying spatial scale of positive and negative terrains pattern at watershed scale: Case in soil and water conservation region on Loess Plateau. Journal of Mountain Science, 14, 1642–1654.10.1007/s11629-016-4227-5CrossRefGoogle Scholar
Yarranton, G. A. & Morrison, R. G. (1974). Spatial dynamics of primary succession: Nucleation. Journal of Ecology, 62, 417–428.10.2307/2258988CrossRefGoogle Scholar
Young, C. G., Dale, M. R. T. & Henry, G. H. R. (1999). Spatial pattern of vegetation in high Arctic sedge meadows. Écoscience, 6, 556–564.10.1080/11956860.1999.11682557CrossRefGoogle Scholar
Yu, D., Liu, Y., Xun, B. & Shao, H. (2013). Measuring landscape connectivity in an urban area for biological conservation. Clean: Soil Air Water, 41, 1–9.Google Scholar
Zeller, K. A., Jennings, M. K., Vickers, T. W., Ernest, H. B., Cushman, S. A. & Boyce, W. M. (2018). Are all data types and connectivity models created equal? Validating common connectivity approaches with dispersal data. Diversity & Distributions, 24, 868–879.10.1111/ddi.12742CrossRefGoogle Scholar
Zeller, K. A., Lewison, R., Fletcher, R. J. Jr., Tulbure, M. G. & Jennings, M. K. (2020). Understanding the importance of dynamic landscape connectivity. Land, 9, 303.10.3390/land9090303CrossRefGoogle Scholar
Zelnik, Y. R., Barbier, M., Shamafelt, D. W., Loreau, M. & Germain, R. M. (2024). Linking intrinsic scales of ecological processes to characteristic scales of biodiversity and functioning patterns. Oikos, 2024, e10514.10.1111/oik.10514CrossRefGoogle Scholar
Zhan, Q., Liang, Y. & Xiao, Y. (2011). Pattern detection in airborne LiDAR data using Laplacian of Gaussian filter. Geo-spatial Information Science, 14, 184–189.10.1007/s11806-011-0540-xCrossRefGoogle Scholar
Zhang, S., Tong, H., Xu, J. & Maciejewski, R. (2019). Graph convolutional networks: A comprehensive review. Computational Social Networks, 6, 11.10.1186/s40649-019-0069-yCrossRefGoogle ScholarPubMed
Zhang, Z., Liu, Y., Yuan, L., Weber, E. & van Kleunen, M. (2021). Effect of allelopathy on plant performance: A meta-analysis. Ecology Letters, 24, 348–362.10.1111/ele.13627CrossRefGoogle ScholarPubMed
Zinoviev, D. (2018). Complex Network Analysis in Python. Cambridge: Pragmatic Bookshelf.Google Scholar
Zuur, A. F., Ieno, E. N. & Elphick, C. S. (2010). A protocol for data exploration to avoid common statistical problems. Methods in Ecology and Evolution, 1, 3–14.10.1111/j.2041-210X.2009.00001.xCrossRefGoogle Scholar
Zuur, A. F., Ieno, E. N., Walker, N., Saveliev, A. A. & Smith, G. M. (2009). Mixed Effects Models and Extensions in Ecology with R. Cambridge: Springer.10.1007/978-0-387-87458-6CrossRefGoogle Scholar

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  • References
  • Mark R. T. Dale, University of Northern British Columbia, Marie-Josée Fortin, University of Toronto
  • Book: Spatial Analysis
  • Online publication: 19 June 2025
  • Chapter DOI: https://doi.org/10.1017/9781009158688.014
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  • References
  • Mark R. T. Dale, University of Northern British Columbia, Marie-Josée Fortin, University of Toronto
  • Book: Spatial Analysis
  • Online publication: 19 June 2025
  • Chapter DOI: https://doi.org/10.1017/9781009158688.014
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  • References
  • Mark R. T. Dale, University of Northern British Columbia, Marie-Josée Fortin, University of Toronto
  • Book: Spatial Analysis
  • Online publication: 19 June 2025
  • Chapter DOI: https://doi.org/10.1017/9781009158688.014
Available formats
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