Pattern in Vegetation and Its Causality

Kershaw, K. A.

doi:10.2307/1932185

Cited by 95 publications

(47 citation statements)

References 38 publications

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“…Ecologists have long examined spatial patterns for insights into ecological processes (e.g., Kershaw 1963). A first focus has been to evaluate whether spatial patterns are significantly nonrandom.…”

Section: Discussionmentioning

confidence: 99%

“…However, similar spatial distributions can emerge from different mechanisms or different combinations of mechanisms. A first step in disentangling different processes of pattern formation is to define the scales at which the pattern occurs and thereby link the pattern to some mechanism operating at these scales (Kershaw 1963;Levin 2000). Large-scale variability (∼0.1-10 km), for example, may be driven by topographic features or soil heterogeneity operating at the same scales.…”

Section: Introductionmentioning

confidence: 99%

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Fitting Ecological Process Models to Spatial Patterns Using Scalewise Variances and Moment Equations

Detto

Muller‐Landau

2013

The American Naturalist

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JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org. 7, 2012; Accepted October 29, 2012; Electronically published February 19, 2013 Online enhancement: zip file with appendixes, supplemental figure. abstract: Ecological spatial patterns are structured by a multiplicity of processes acting over a wide range of scales. We propose a new method, based on the scalewise variance-that is, the variance as a function of spatial scale, calculated here with wavelet kernel functions-to disentangle the signature of processes that act at different and similar scales on observed spatial patterns. We derive exact and approximate analytical solutions for the expected scalewise variance under different individual-based, spatially explicit models for sessile organisms (e.g., plants), using moment equations. We further determine the probability distribution of independently observed scalewise variances for a given expectation, including complete spatial randomness. Thus, we provide a new analytical test of the null model of spatial randomness to understand at which scales, if any, the variance departs significantly from randomness. We also derive the likelihood function that is needed to estimate parameters of spatial models and their uncertainties from observed patterns. The methods are demonstrated through numerical examples and case studies of four tropical tree species on Barro Colorado Island, Panama. The methods developed here constitute powerful new tools for investigating effects of ecological processes on spatial point patterns and for statistical inference of process models from spatial patterns.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fitting Ecological Process Models to Spatial Patterns Using Scalewise Variances and Moment Equations

Detto

Muller‐Landau

2013

The American Naturalist

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“…Spatial patterns can also arise as a consequence of interaction among individuals, generating a zone of influence that affects the surrounding space (Wu et al, 1985). For example, during the early process of colonization, random distribution of plants is commonly observed (Kershaw, 1963;Fowler, 1990). As the interactions among the system components develop, the system moves away from randomness to a more ordered spatial organization (Aarssen and Turkington, 1985;Soro et al, 1999).…”

Section: Introductionmentioning

confidence: 99%

Change in plant spatial patterns and diversity along the successional gradient of Mediterranean grazing ecosystems

Alados¹,

El-Aich

Papanastasis³

et al. 2004

Ecological Modelling

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In this study, we analyze the complexity of plant spatial patterns and diversity along a successional gradient resulting from grazing disturbance in four characteristic ecosystems of the Mediterranean region. Grazing disturbance include not only defoliation by animals, but also associated disturbances as animal trampling, soil compaction, and mineralization by deposition of urine and feces. The results show that woodland and dense matorral are more resistant to species loss than middle dense and scattered matorral, or grassland. Information fractal dimension declined as we moved from a dense to a discontinuous matorral, increasing as we moved to a more scattered matorral and a grassland. In all studied cases, the characteristic species of the natural vegetation declined in frequency and organization with grazing disturbance. Heliophyllous species and others with postrate or rosette twigs increased with grazing pressure, particularly in dense matorral. In the more degraded ecosystem, only species with well-adapted traits, e.g., buried buds or unpalatable qualities showed a clear increase with grazing. Indeed, the homogeneity of species distribution within the plant community declined monotonically with grazing impact. Conversely, the spatial organization of the characteristic plants of each community increased in the better-preserved areas, being also related to the sensitivity of the species to grazing impact. The degree of autocorrelation of plant spatial distribution at the species level and the information fractal dimension at the community level allow us to quantify the degree of degradation of natural communities and to determine the sensitivity of key species to disturbance.

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“…In the early stages of colonization, plants colonize space in a random manner until strong interactions arise among species (Kershaw, 1963;Fowler, 1990). Over time and space, opportunities for interactions among individuals arise and structural patterns develop, which gives rise to an increase in vegetation heterogeneity throughout vegetation succession (Loehle et al, 1996;Cook et al, 2005).…”

Section: Discussionmentioning

confidence: 99%

“…For example, in species that have limited dispersal capacity, spatial autocorrelation is high (Miller et al, 2002). In addition, during the early stages of colonization, random distributions arise (Kershaw, 1963;Fowler, 1990;Sole and Bascompte, 2006). Above a threshold in abundance, the probability of interactions increases, which triggers the positive feedback mechanisms that underlie plant facilitation (Bertness and Callaway, 1994;Pugnaire et al, 1996;HilleRisLambers et al, 2001) and the system moves toward a more clumped spatial organization (Aarssen and Turkington, 1985;Loehle et al, 1996;Soro et al, 1999;Sole and Bascompte, 2006).…”

Section: Introductionmentioning

confidence: 99%

Do vegetation patch spatial patterns disrupt the spatial organization of plant species?

Alados¹,

Navarro

Komac³

et al. 2009

Ecological Complexity

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Keywords: Beta diversity Detrended Fluctuation Analyses Fractals Fragmentation Spatial patternsThe mechanisms that structure plant diversity and generate long-range correlated spatial patterns have important implications for the conservation of fragmented landscapes. The ability to disperse and persist influences a plant species' capacity for spatial organization, which can play a critical role in structuring plant diversity in metacommunities. This study examined the spatial patterns of species diversity within a network of patches in Cabo de Gata Natural Park, southeastern Spain. The objectives were to understand how the spatial heterogeneity of species composition (beta diversity) varies in a structured landscape, and how the long-range spatial autocorrelation of plant species is affected by the spatial configuration of patches.The mechanisms underlying the spatial distribution of plants acted at two scales. Between patches, spatial variation in species distributions was greater than that expected based on spatial randomization, which indicated that movement among patches was restricted. Within patches, diffusion processes reduced spatial variability in species distributions, and the effect was more prominent in large patches. Small patch size negatively influenced the long-range spatial autocorrelation of characteristic species, whereas interpatch distance had a stronger effect on species frequency than it had on the disruption of spatial organized patterns.The long-range spatial autocorrelation was evaluated based on the dispersal abilities of the species. Among the 106 species evaluated, 39% of the woody species, 17% of the forbs, and 12% of the grasses exhibited disrupted long-range spatial autocorrelation where patches were small. The species that are more vulnerable to the effects of fragmentation tended to be those that have restricted dispersal, such as those that have short-range dispersal (atelechoric), e.g., Phlomis purpurea, Cistus albidus, Teucrium pseudochamciepytis, Brachypodium retusum, and the ballistic species, Genista spartioides. Helicmthemum almeriense is another vulnerable species that has actively restricted dispersal (antitelechory), which is common in arid regions. Wind dispersers such as Launaea \anifera were less vulnerable to the effects of fragmentation. Long-distance dispersers whose persistence depends on facilitative interactions with other individuals, e.g., allogamous species such as Thymus hyemalis, Ballota hirsuta, and Anthyllis cytisoides, exhibit disrupted long-range spatial autocorrelation when patch size is reduced.

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Pattern in Vegetation and Its Causality

Cited by 95 publications

References 38 publications

Fitting Ecological Process Models to Spatial Patterns Using Scalewise Variances and Moment Equations

Fitting Ecological Process Models to Spatial Patterns Using Scalewise Variances and Moment Equations

Change in plant spatial patterns and diversity along the successional gradient of Mediterranean grazing ecosystems

Do vegetation patch spatial patterns disrupt the spatial organization of plant species?

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