Journal articles: 'Spatial ecology'

1

Ettema, C. "Spatial soil ecology." Trends in Ecology & Evolution 17, no. 4 (April 1, 2002): 177–83. http://dx.doi.org/10.1016/s0169-5347(02)02496-5.

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Hildrew, A. G. "Whole river ecology: spatial scale and heterogeneity in the ecology of running waters." River Systems 10, no. 1-4 (September 18, 1996): 25–43. http://dx.doi.org/10.1127/lr/10/1996/25.

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SLADE, PAUL F. "SOME INEQUALITIES FOR THEORETICAL SPATIAL ECOLOGY." ANZIAM Journal 55, no. 1 (July 2013): 55–68. http://dx.doi.org/10.1017/s1446181113000266.

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AbstractInequalities for spatial competition verify the pair approximation of statistical mechanics introduced to theoretical ecology by Matsuda, Satō and Iwasa, among others. Spatially continuous moment equations were introduced by Bolker and Pacala and use a similar assumption in derivation. In the present article, I prove upper bounds for the$k\mathrm{th} $central moment of occupied sites in the contact process of a single spatial dimension. This result shows why such moment closures are effective in spatial ecology.

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4

Wiens, J. A. "Spatial Scaling in Ecology." Functional Ecology 3, no. 4 (1989): 385. http://dx.doi.org/10.2307/2389612.

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5

Horne, John K., and David C. Schneider. "Spatial Variance in Ecology." Oikos 74, no. 1 (October 1995): 18. http://dx.doi.org/10.2307/3545670.

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6

Hastings, Alan, Sergei Petrovskii, and Andrew Morozov. "Spatial ecology across scales." Biology Letters 7, no. 2 (November 10, 2010): 163–65. http://dx.doi.org/10.1098/rsbl.2010.0948.

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The international conference ‘Models in population dynamics and ecology 2010: animal movement, dispersal and spatial ecology’ took place at the University of Leicester, UK, on 1–3 September 2010, focusing on mathematical approaches to spatial population dynamics and emphasizing cross-scale issues. Exciting new developments in scaling up from individual level movement to descriptions of this movement at the macroscopic level highlighted the importance of mechanistic approaches, with different descriptions at the microscopic level leading to different ecological outcomes. At higher levels of organization, different macroscopic descriptions of movement also led to different properties at the ecosystem and larger scales. New developments from Levy flight descriptions to the incorporation of new methods from physics and elsewhere are revitalizing research in spatial ecology, which will both increase understanding of fundamental ecological processes and lead to tools for better management.

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7

O'Connell, Mark. "Spatial ecology and conservation." Ecological Informatics 14 (March 2013): 1. http://dx.doi.org/10.1016/j.ecoinf.2013.01.002.

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8

Weiner, Benjamin G., Anna Posfai, and Ned S. Wingreen. "Spatial ecology of territorial populations." Proceedings of the National Academy of Sciences 116, no. 36 (August 21, 2019): 17874–79. http://dx.doi.org/10.1073/pnas.1911570116.

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Many ecosystems, from vegetation to biofilms, are composed of territorial populations that compete for both nutrients and physical space. What are the implications of such spatial organization for biodiversity? To address this question, we developed and analyzed a model of territorial resource competition. In the model, all species obey trade-offs inspired by biophysical constraints on metabolism; the species occupy nonoverlapping territories, while nutrients diffuse in space. We find that the nutrient diffusion time is an important control parameter for both biodiversity and the timescale of population dynamics. Interestingly, fast nutrient diffusion allows the populations of some species to fluctuate to zero, leading to extinctions. Moreover, territorial competition spontaneously gives rise to both multistability and the Allee effect (in which a minimum population is required for survival), so that small perturbations can have major ecological effects. While the assumption of trade-offs allows for the coexistence of more species than the number of nutrients—thus violating the principle of competitive exclusion—overall biodiversity is curbed by the domination of “oligotroph” species. Importantly, in contrast to well-mixed models, spatial structure renders diversity robust to inequalities in metabolic trade-offs. Our results suggest that territorial ecosystems can display high biodiversity and rich dynamics simply due to competition for resources in a spatial community.

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9

Pontius, Anneliese A. "Spatial Representation, Modified by Ecology." Journal of Cross-Cultural Psychology 24, no. 4 (December 1993): 399–413. http://dx.doi.org/10.1177/0022022193244002.

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10

Daly, Martin. "Spatial Ecology of Desert Rodents." Ethology 107, no. 7 (July 24, 2001): 666. http://dx.doi.org/10.1046/j.1439-0310.2001.0686b.x.

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11

Collinge, Sharon K. "Spatial ecology and biological conservation." Biological Conservation 100, no. 1 (July 2001): 1–2. http://dx.doi.org/10.1016/s0006-3207(00)00201-9.

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12

Stein, Alfred, and Jan Goudriaan. "Spatial statistics for production ecology." Agriculture, Ecosystems & Environment 81, no. 1 (October 2000): 1–3. http://dx.doi.org/10.1016/s0167-8809(00)00163-8.

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13

Fortin, Marie-Josée, Patrick M. A. James, Alistair MacKenzie, Stephanie J. Melles, and Bronwyn Rayfield. "Spatial statistics, spatial regression, and graph theory in ecology." Spatial Statistics 1 (May 2012): 100–109. http://dx.doi.org/10.1016/j.spasta.2012.02.004.

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14

Liu, Yupeng, Wei-Qiang Chen, Tao Lin, and Lijie Gao. "How Spatial Analysis Can Help Enhance Material Stocks and Flows Analysis?" Resources 8, no. 1 (March 4, 2019): 46. http://dx.doi.org/10.3390/resources8010046.

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Spatial information can be integrated into almost all fields of industrial ecology. Many researchers have shown that spatial proximity affects a variety of behaviors and interactions, and thus matters for materials stocks and flows analysis. However, normal tools or models in industrial ecology based on temporal dependence cannot be simply applied to the case of spatial dependence. This paper proposes a framework integrating material stocks and flows analysis with spatial analysis. We argue that spatial analysis can help data management and visualization, determine spatio-temporal patterns-processes-drivers, and finally develop dynamic and spatially explicit models, to improve the performance of simulating and assessing stocks and flows of materials. Scaling in spatial, temporal, and organizational dimensions and other current limitations are also discussed. Combined with spatial analysis, industrial ecology can really be more powerful in achieving its origin and destination—sustainability.

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15

Griffith, Daniel A., and Pedro R. Peres-Neto. "SPATIAL MODELING IN ECOLOGY: THE FLEXIBILITY OF EIGENFUNCTION SPATIAL ANALYSES." Ecology 87, no. 10 (October 2006): 2603–13. http://dx.doi.org/10.1890/0012-9658(2006)87[2603:smietf]2.0.co;2.

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16

Slade, Paul. "Some inequalities for theoretical spatial ecology." ANZIAM Journal 54 (April 3, 2014): 55. http://dx.doi.org/10.21914/anziamj.v55i0.6680.

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17

West, Neil. "Spatial Pattern Analysis in Plant Ecology." Crop Science 41, no. 3 (May 2001): 916. http://dx.doi.org/10.2135/cropsci2001.413916x.

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WILES, LORI J. "Spatial Pattern Analysis in Plant Ecology." Weed Technology 15, no. 1 (January 2001): 195–96. http://dx.doi.org/10.1614/0890-037x(2001)015[0195:]2.0.co;2.

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19

Dawson, Terence, and C. A. Johnston. "A Computerized Approach to Spatial Ecology." Global Ecology and Biogeography Letters 7, no. 4 (July 1998): 307. http://dx.doi.org/10.2307/2997610.

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20

McCulloch, C. E. "Quantitative Ecology: Spatial and Temporal Scaling." Journal of Environmental Quality 24, no. 2 (March 1995): 384. http://dx.doi.org/10.2134/jeq1995.00472425002400020026x.

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21

Moilanen, Atte, and Marko Nieminen. "SIMPLE CONNECTIVITY MEASURES IN SPATIAL ECOLOGY." Ecology 83, no. 4 (April 2002): 1131–45. http://dx.doi.org/10.1890/0012-9658(2002)083[1131:scmise]2.0.co;2.

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22

Lion, Sébastien, and Minus van Baalen. "Self-structuring in spatial evolutionary ecology." Ecology Letters 11, no. 3 (March 2008): 277–95. http://dx.doi.org/10.1111/j.1461-0248.2007.01132.x.

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23

Slingsby, Dr David. "Spatial Pattern Analysis in Plant Ecology." Biological Conservation 97, no. 1 (January 2001): 127–28. http://dx.doi.org/10.1016/s0006-3207(00)00095-1.

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24

Lion, Sébastien. "Moment equations in spatial evolutionary ecology." Journal of Theoretical Biology 405 (September 2016): 46–57. http://dx.doi.org/10.1016/j.jtbi.2015.10.014.

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25

Mendonça, Milton de Souza. "Spatial ecology goes to space: Metabiospheres." Icarus 233 (May 2014): 348–51. http://dx.doi.org/10.1016/j.icarus.2014.01.027.

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26

Jørgensen, Sven Erik. "Quantitative ecology. Spatial and temporal scaling." Ecological Modelling 79, no. 1-3 (May 1995): 288. http://dx.doi.org/10.1016/0304-3800(95)90066-7.

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27

Buettel, Jessie C., Andrew Cole, John M. Dickey, and Barry W. Brook. "Analyzing linear spatial features in ecology." Ecology 99, no. 6 (May 16, 2018): 1490–97. http://dx.doi.org/10.1002/ecy.2215.

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28

Li, Harbin, Raija Laiho, and Dan Binkley. "Spatial Pattern Analysis in Plant Ecology." Forest Science 47, no. 1 (February 1, 2001): 119–21. http://dx.doi.org/10.1093/forestscience/47.1.119.

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29

Erős, Tibor, and Winsor H. Lowe. "The Landscape Ecology of Rivers: from Patch-Based to Spatial Network Analyses." Current Landscape Ecology Reports 4, no. 4 (November 16, 2019): 103–12. http://dx.doi.org/10.1007/s40823-019-00044-6.

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Abstract Purpose of Review We synthesize recent methodological and conceptual advances in the field of riverscape ecology, emphasizing areas of synergy with current research in landscape ecology. Recent Findings Recent advances in riverscape ecology highlight the need for spatially explicit examinations of how network structure influences ecological pattern and process, instead of the simple linear (upstream-downstream) view. Developments in GIS, remote sensing, and computer technologies already offer powerful tools for the application of patch- and gradient-based models for characterizing abiotic and biotic heterogeneity across a range of spatial and temporal scales. Along with graph-based analyses and spatial statistical stream network models (i.e., geostatistical modelling), these approaches offer improved capabilities for quantifying spatial and temporal heterogeneity and connectivity relationships, thereby allowing for rigorous and high-resolution analyses of pattern, process, and scale relationships. Summary Spatially explicit network approaches are able to quantify and predict biogeochemical, hydromorphological, and ecological patterns and processes more precisely than models based on longitudinal or lateral riverine gradients alone. Currently, local habitat characteristics appear to be more important than spatial effects in determining population and community dynamics, but this conclusion may change with direct quantification of the movement of materials, energy, and organisms along channels and across ecosystem boundaries—a key to improving riverscape ecology. Coupling spatially explicit riverscape models with optimization approaches will improve land protection and water management efforts, and help to resolve the land sharing vs. land sparing debate.

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30

Liu, Yunxia, Shiwen Jiang, Yanxun Liu, Rui Wang, Xiao Li, Zhongshang Yuan, Lixia Wang, and Fuzhong Xue. "Spatial epidemiology and spatial ecology study of worldwide drug-resistant tuberculosis." International Journal of Health Geographics 10, no. 1 (2011): 50. http://dx.doi.org/10.1186/1476-072x-10-50.

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31

Wallentin, Gudrun. "Spatial simulation: A spatial perspective on individual-based ecology—a review." Ecological Modelling 350 (April 2017): 30–41. http://dx.doi.org/10.1016/j.ecolmodel.2017.01.017.

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32

WU, JIANGUO (JINGLE). "A GUIDE FOR SPATIAL ANALYSIS IN ECOLOGY." BioScience 56, no. 11 (2006): 938. http://dx.doi.org/10.1641/0006-3568(2006)56[938:agfsai]2.0.co;2.

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33

Bataineh, Amanda L., Brian P. Oswald, Mohammad Bataineh, Daniel Unger, I.-Kuai Hung, and Daniel Scognamillo. "Spatial autocorrelation and pseudoreplication in fire ecology." Fire Ecology 2, no. 2 (December 2006): 107–18. http://dx.doi.org/10.4996/fireecology.0202107.

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34

Lichstein, Jeremy W., Theodore R. Simons, Susan A. Shriner, and Kathleen E. Franzreb. "SPATIAL AUTOCORRELATION AND AUTOREGRESSIVE MODELS IN ECOLOGY." Ecological Monographs 72, no. 3 (August 2002): 445–63. http://dx.doi.org/10.1890/0012-9615(2002)072[0445:saaami]2.0.co;2.

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35

II, Robert Hopkins, and Halley Alberts. "Improving Student Understanding of Spatial Ecology Statistics." American Biology Teacher 77, no. 4 (April 1, 2015): 289–93. http://dx.doi.org/10.1525/abt.2015.77.4.9.

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This activity is designed as a primer to teaching population dispersion analysis. The aim is to help improve students’ spatial thinking and their understanding of how spatial statistic equations work. Students use simulated data to develop their own statistic and apply that equation to experimental behavioral data for Gambusia affinis (western mosquitofish). This activity can be adapted and conducted at the 9–16 grade levels.

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36

Dale, Mark R. T., and Marie-Josée Fortin. "Spatial autocorrelation and statistical tests in ecology." Écoscience 9, no. 2 (January 2002): 162–67. http://dx.doi.org/10.1080/11956860.2002.11682702.

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37

LOU, Yuan. "Some reaction diffusion models in spatial ecology." SCIENTIA SINICA Mathematica 45, no. 10 (September 1, 2015): 1619–34. http://dx.doi.org/10.1360/n012015-00233.

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38

Pickett, S. T. A., and M. L. Cadenasso. "Landscape Ecology: Spatial Heterogeneity in Ecological Systems." Science 269, no. 5222 (July 21, 1995): 331–34. http://dx.doi.org/10.1126/science.269.5222.331.

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39

Conlisk, Erin. "Colonization rules and spatial distributions in ecology." Ecological Complexity 28 (December 2016): 218–21. http://dx.doi.org/10.1016/j.ecocom.2016.07.002.

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40

Gonser, Rusty A., Ryan R. Jensen, and Samuel E. Wolf. "The spatial ecology of deer–vehicle collisions." Applied Geography 29, no. 4 (December 2009): 527–32. http://dx.doi.org/10.1016/j.apgeog.2008.11.005.

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41

Nelson, Trisalyn A., and Barry Boots. "Detecting spatial hot spots in landscape ecology." Ecography 31, no. 5 (October 2008): 556–66. http://dx.doi.org/10.1111/j.0906-7590.2008.05548.x.

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42

Roslin, Tomas. "Large-scale spatial ecology of dung beetles." Ecography 24, no. 5 (June 28, 2008): 511–24. http://dx.doi.org/10.1111/j.1600-0587.2001.tb00486.x.

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43

Mysterud, Atle, Inger M. Rivrud, Vegard Gundersen, Christer M. Rolandsen, and Hildegunn Viljugrein. "The unique spatial ecology of human hunters." Nature Human Behaviour 4, no. 7 (March 16, 2020): 694–701. http://dx.doi.org/10.1038/s41562-020-0836-7.

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44

Roslin, Tomas. "Large-scale spatial ecology of dung beetles." Ecography 24, no. 5 (October 2001): 511–24. http://dx.doi.org/10.1034/j.1600-0587.2001.d01-207.x.

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45

Ellis, J., and D. C. Schneider. "Spatial and temporal scaling in benthic ecology." Journal of Experimental Marine Biology and Ecology 366, no. 1-2 (November 2008): 92–98. http://dx.doi.org/10.1016/j.jembe.2008.07.012.

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46

Rosindell, James, Yan Wong, and Rampal S. Etienne. "A coalescence approach to spatial neutral ecology." Ecological Informatics 3, no. 3 (July 2008): 259–71. http://dx.doi.org/10.1016/j.ecoinf.2008.05.001.

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47

Taena, W., L. M. Kolopaking, B. Barus, R. Boer, and B. Juanda. "The Implication of Spatial Ecology Dependence on Spatial Arrangement in Boundary Area." Jurnal Manajemen Hutan Tropika (Journal of Tropical Forest Management) 24, no. 1 (April 30, 2018): 1–9. http://dx.doi.org/10.7226/jtfm.24.1.1.

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48

Halley, J. M., and J. H. Lawton. "The JAEP Ecology of Farmland Modelling Initiative: Spatial Models for Farmland Ecology." Journal of Applied Ecology 33, no. 3 (June 1996): 435. http://dx.doi.org/10.2307/2404975.

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49

Terrence McCabe, J. "Settlement Ecology: The Social and Spatial Organization of Kofyar Agriculture:Settlement Ecology: The Social and Spatial Organization of Kofyar Agriculture." American Anthropologist 100, no. 1 (March 1998): 223. http://dx.doi.org/10.1525/aa.1998.100.1.223.1.

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50

Germeroth, Lillian, Theodore Sumnicht, and Robin Verble. "Scale-Dependent Spatial Ecology of Paleotropical Leaf Litter Ants (Hymenoptera: Formicidae)." Diversity 15, no. 4 (March 27, 2023): 494. http://dx.doi.org/10.3390/d15040494.

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The grain for which an observer conducts a study is an important determinant of its outcome. Studies of ants have considered spatial grains spanning from single meters to entire forest ecosystems and found patterns related to nutrient availability, leaf litter depth, disturbance, and forest composition. Here, we examine a Bornean leaf litter ant community at small (1–4 m) and large (50–250 m) spatial scales and consider the differences in community structure using structured 1 m2 quadrats sampled via leaf litter sifting and Berlese extraction. We found that small-scale patterns in ant abundance and richness did not spatially autocorrelate within a plot until >1.5 m. Leaf litter characteristics, forest stand characteristics and sampling season were homogenous among our sites, suggesting that macro-scale stand variables are not largely regulating the small spatial scale ant communities: These may be driven by microclimate, competition, niche space, nutrient available, microclimatic conditions, or other localized effects. Further experimental work is needed to elicit causal mechanisms.

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Journal articles on the topic 'Spatial ecology'

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