Gender-related distributions of Fraxinus mandshurica in secondary and old-growth forests

Zhang, Chunyu; Zhao, Xiuhai; Gao, Lushuang; Gadow, Klaus von

doi:10.1016/j.actao.2009.10.001

Cited by 28 publications

(21 citation statements)

References 34 publications

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“…The main question is focused on the role of interactions between two types of points. Bivariate analysis can support the theory of species coexistence in multispecies forests [17][18][19][20][21][22]. In multivariate point pattern analysis, several data types (e.g., tree species) are involved and each of them is created by different processes.…”

Section: Data Types-what Should Be Known Before Running the Spatial Amentioning

confidence: 99%

Structural Diversity of Plant Populations: Insight from Spatial Analyses

Szmyt¹

2016

Applications of Spatial Statistics

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Spatial analysis has been one of the most rapidly growing fields in ecology. It is related directly to a growing awareness among researchers that a spatial structure of biosystems, e.g., forests, is important in ecological thinking. The availability of the specific software supports the use of spatial analyses in different fields of the science and forestry science is only one example for this. Many data collected in the forests have the spatial and temporal dimensions and it allows us to use spatial statistics to quantitative description of the spatial structure of forest, which became an important element of modern continuous cover forestry. In this chapter, key elements: data types, null models, and summary statistics, which can be applied in spatial analyses, are briefly described. Real data sets collected from different forests were given to provide examples of spatial analyses. The key elements of spatial analysis in ecology are data type, the appropriate choice of summary statistics and null models. Selecting few of them in a single analysis makes the statements more reliable and realistic in the changing world.

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Section: Data Types-what Should Be Known Before Running the Spatial Amentioning

confidence: 99%

Structural Diversity of Plant Populations: Insight from Spatial Analyses

Szmyt¹

2016

Applications of Spatial Statistics

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“…a heterogeneous Poisson model) or delineating homogenous sub-regions into homogenous regions are required (Wiegand and Moloney 2004). Applications of other than CSR null models can be found in Boyden et al (2005), Camarero et al (2005), Wiegand et al (2007), Jacquemyn et al (2009), Comas et al (2009), Law et al (2009), Picard et al (2009), Eichhorn (2010, Martínez et al (2010), Zhang et al (2010) and Réjou-Méchain et al (2011).…”

Section: Suitable Null Models Prevent Misinterpretationsmentioning

confidence: 99%

“…Examples of applications of different functions mentioned above can be found in Kuuluvainen et al (1996), Kenkel (1997), Rozas (2003), Getzin et al (2006), Wiegand et al (2007), Illian et al (2008), Suzuki et al (2008), Wälder and Wälder (2008), Gray and He (2009), Nuske et al (2009), Picard et al (2009, Rozas et al (2009), Zhang et al (2010, Ledo et al (2011);Nanami et al (2011), Iszkuło et al (2012 and Pommerening et al (2013). …”

Section: Marks and Mark Hypothesis Testingmentioning

confidence: 99%

Spatial statistics in ecological analysis: from indices to functions

Szmyt¹

2014

Silva Fenn.

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• Spatial statistics provides a quantitative description of natural variables distributed in space and time.• The objectives of spatial analysis are to detect spatial patterns and to confirm if a pattern found is significant. • Spatially explicit indices and functions may be applied depending on the information collected from the field. • Development of the specific software supports spatial analyses. AbstractThis paper presents a review of the most common methods in ecological studies aimed at spatial analysis of population structures (horizontal and vertical), based on point process statistics. Methods based on simple spatially explicit indices as well as more sophisticated methods relying on functions are described in a comprehensible manner. Simple indices revealing the information on spatial structure at the scale of the nearest neighbor can be easily implemented in practical forestry. On the other hand, spatial functions, based on much more detailed data, describe the spatial structure in terms of the spatial relationships between the natural processes and population structures and because of this complexity they are rarely used in forest practice. Including both methods in a single paper is also valuable from the potential reader's point of view saving their time for searching and choosing the appropriate method to make their spatial analysis. This paper can also serve as an initial guide for young researchers or those who are going to start their studies on spatial aspects of bio-systems. Avoiding the statistical and mathematical details makes this paper understandable for readers who are not statisticians or mathematicians. Readers will find many references related to each method described here, allowing them to find solutions to different problems observed in practice. This paper ends with a list of the most common specific software packages available to support spatial analysis.

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“…Intensive research started immediately after the plot installations and first results were published after suitable datasets became available (Zhang et al 2009a;Zhang et al 2009b;Gao et al 2010;Zhang et al 2010a;Zhang et al 2010b;Liu et al 2011;Wang et al 2011). The future objectives of the network include the need to secure long term institutional support, the desire to attract scholars from a variety of disciplines, a focus on harvest event analysis and continuing research on ecosystem structure and change.…”

Section: The Beijing Forest Observational Networkmentioning

confidence: 99%

Forest observational studies-an essential infrastructure for sustainable use of natural resources

et al. 2014

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This contribution complements Forest Ecosystems' Thematic Series on "Forest Observational Studies". We provide essential clarification regarding the definition and purpose of long-term field studies, review some of the extensive literature and discuss different approaches to collecting field data. We also describe two newly established forest observational networks that serve to illustrate the scope and diversity of forest field studies. The first is a large-scale network of forest observational studies in prominent natural forest ecosystems in China. The second example demonstrates observational studies in mixed and uneven-aged pine-oak forests which are selectively managed by local communities in Mexico. We summarize the potential for analysing and modeling forest ecosystems within interdisciplinary projects and provide argumentation in favour of long-term institutional commitment to maintaining forest observational field studies.

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Gender-related distributions of Fraxinus mandshurica in secondary and old-growth forests

Cited by 28 publications

References 34 publications

Structural Diversity of Plant Populations: Insight from Spatial Analyses

Structural Diversity of Plant Populations: Insight from Spatial Analyses

Spatial statistics in ecological analysis: from indices to functions

Forest observational studies-an essential infrastructure for sustainable use of natural resources

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