Characterizing spatial structure of Abies marocana forest through point pattern analysis

Abstract

Aim of study: Understanding small-scale patterns caused by stochastic factors or community interactions driving forest structure and diversity of Moroccan fir Abies marocana Trab.Area of study: Talassemtane fir forest, Talassemtane National Park, Rif Mountains, northern Morocco.Material and methods: Eight plots representative of the structural variability of A. marocana forests were selected, and all tree individuals with diameter at breast height (dbh) ≥2 cm were mapped and measured. We performed four types of spatial point pattern analyses: (1) Univariate analyses to explore the overall trees spatial pattern, (2) bivariate analyses to assess the spatial relationship between juveniles and A. marocana adults, (3) correlation between tree sizes (dbh) and distance between points pairs using the univariate mark correlation function, and (4) random labeling analyses between dominant and suppressed Moroccan fir individuals to assess competition patterns.Main results: We found a strong spatial aggregation of fir individuals and a positive intraspecific association between juveniles and adult trees. However, there were weak but significant distance-dependent effect on tree size and density-dependent effect on suppression pattern.Research highlights: Shade-tolerance, seed dispersal and/or microsite heterogeneity might play important roles in the observed fir patterns. Our results provide a basic knowledge on within-stand Moroccan fir spatial distribution, with implications for adaptive management of these relic forests, and prompting to further research to test advanced hypotheses.Additional key words: Moroccan fir; Talassemtane National Park; Rif forest; Spatial analysis; summary statistics; facilitation; aggregation.Abbreviations used: MF (Moroccan fir); Talassemtane National Park (TNP); dbh (diameter at breast height); Univariate pair-correlation function (g(r)); Heterogeneous Poisson (HP); Bivariate pair-correlation function (g12(r)); Univariate mark correlation function (kmm(r)), Mark connection function (p12(r)).

Spatial patterns of stand structure and canopy disturbance in a fire‐maintainedPinus palustriswoodland

“…The resulting canopy disturbance periods were 1927-1950, 1952-1962, 1967-1977, 1981-1993, and 1994-2005. To quantify the horizontal patterns of trees and canopy disturbances across the 1-ha site, we used point pattern analysis techniques (Baddeley et al, 2015). Specifically, we used the pair correlation function g(r), which is a variation of Ripley's K function, the two most commonly used spatial descriptors in ecology (Velázquez et al, 2016;Ben-Said et al, 2020Cannon et al, 2022). The pair correlation function uses discrete distance annuli (Wiegand & Moloney, 2004) to test the density of observed points at multiple distances (r) against the expected density at distance (r).…”

Spatial point-pattern analysis as a powerful tool in identifying pattern-process relationships in plant ecology: an updated review

“…Nevertheless, in the absence of large plots, it is recommended to combine several small plots and their accuracy is improved using the HP null model instead of CSR in order to account for environmental heterogeneity and capture small-scale spatial structure (Carrer et al 2018). Hence, small size plots are largely used in replicated analysis (Riginos et al 2005;De Luis et al 2008;Comas et al 2009;Raventós et al, 2011;Petritan et al 2014Petritan et al , 2015Erfanifard and Stereńczak 2017;Ziegler et al 2017;Erfanifard et al 2019;Ben-Said et al 2020;Wang et al 2020a) where many small plots are sampled over a large study area then combined in one average function.…”

“…The sample size did not necessarily condition the number of individuals, but this number depends on the forest under investigation. For example, Ben-Said et al (2020) established a plot with 20 m of radius which contained 79 trees, while another plot with only 15 m of radius contained 98 trees. Moreover, Comas et al (2009) used circular plots with 20 m of radius that contained approximately 100 trees of pine resulting in plots ranging from 0.04 to 0.16 ha.…”

“…Thus, to deal with this problem, a combination between plot size and number of points seems to be a suitable alternative, that is, the sample size can be conditioned by the number of individuals involved. Despite being considered a small size sample, plots containing less than 50 points can be considered the first step especially in exploratory studies to make preliminary hypotheses for forests that have not been the subject of previous spatial pattern studies (Ben-Said et al 2020), as well as when sufficient equipment and financial or human resources are lacking. Thus, the choice of a sample size as a function of the number of individuals involved seems to be a trade-off between the minimum number required in SPPA and the difficulty of establishing large plots.…”

“…Studying spatial association between trees at both intra-and interspecific levels is important to reveal the existence of interactions and their types (Jia et al 2016). SPPA has been mostly carried out for woody species compared to herbaceous plants (but see Riginos et al 2005;De Luis et al 2008;Raventós et al 2010) and various mechanisms controlling species spatial patterns are currently investigated in temperate (Getzin et al 2006;Hao et al 2007;Wang et al 2010a;Zhang et al 2010Zhang et al , 2013Martínez et al 2013;Liu et al 2014;Yao et al 2016;Carrer et al 2018;Wang et al 2018;Szmyt and Tarasiuk 2018;Zhang et al 2020), tropical (Wiegand et al 2007a;Lan et al 2012;Miao et al 2018;Nguyen et al 2016;Cordero et al 2016;Ribeiro et al 2021, Nguyen et al 2018a and Mediterranean regions (Camarero et al 2005;De Luis et al 2008;Lingua et al 2008;Comas et al 2009;Raventós et al 2010;Garcia-Cervigon et al 2017;Abellanas and Pérez-Moreno 2018;Ben-Said et al 2020). Assuming the crucial role of biotic processes to be a driver of species spatial pattern, various environmental factors can be involved in structuring plant communities including water availability (Zheng et al 2017), soil nutrients (Riginos et al 2005;Zhao et al 2015), moisture (Fajardo et al 2006;…”