Patchiness in old-growth oriental beech forests across development stages at multiple neighborhood scales

Zenner, Eric K.; Peck, Jeri Lynn E.; Sagheb-Talebi, Khosro

doi:10.1007/s10342-019-01203-4

Cited by 10 publications

(10 citation statements)

References 57 publications

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“…Moreover, the range of spatial scales involved in our analyses (i.e. 154 and 708 m 2 for radii of 7 and 15 m, and at larger scales adequate for the gradient of inter-plot distances of the 20 × 20 m sampling grid) covers the probable resolution of the patch mosaic pattern identified in former studies in similar forest associations (Král et al 2010a(Král et al , 2014Alessandrini et al 2011;Zenner et al 2019).…”

Section: Methodological Remarksmentioning

confidence: 93%

“…Although homogeneously structured patches occur, they cover a smaller proportion of the total forest area than those that are heterogeneously structured (Commarmot et al 2005;Motta et al 2011;Šebková et al 2011;Král et al 2014;Paluch et al 2015;Drössler et al 2016;Parobeková et al 2018). Structural differentiation occurs primarily at the finest spatial scales and diminishes rapidly (Král et al 2010a;Alessandrini et al 2011;Zenner et al 2019). For example, using local deadwood and live tree distributions as discrimination criteria, Král et al (2014) found mean patch size in a narrow interval between 570 and 800 m 2 .…”

Section: Introductionmentioning

confidence: 99%

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The Dinaric Mountains versus the Western Carpathians: Is structural heterogeneity similar in close-to-primeval Abies–Picea–Fagus forests?

2020

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In this study, we analysed patterns of spatial variation in the basal area of live and dead trees and structural complexity in close-to-primeval forests in the Dinaric Mts. The results were compared with an analogous study conducted in the Western Carpathians. The research was carried out in the Janj, Lom and Perucića forest reserves (Bosnia and Herzegovina) in mixed-species stands of silver fir Abies alba Mill., European beech Fagus sylvatica L. and Norway spruce Picea abies (L.) H. Karst. In the core zones of the reserves, concentric sample plots (154 and 708 m2) were set in a regular 20 × 20 m grid covering approximately 10 ha. The analyses revealed varying distribution patterns of live canopy trees, suggesting that these characteristics may fluctuate to some extent at the regional level. At the spatial scale of 708 m2, attractive associations between dead canopy trees were found, but this tendency disappeared with increasing area. Although stands in the Dinaric Mts. are characterized by an almost twofold greater biomass accumulation compared to those from the Western Carpathians, the study revealed analogous bell-shaped distributions of stand basal areas of live trees and a very similar trend of decreasing variation in stand basal area and structural heterogeneity with increasing spatial scale. Nonetheless, the higher growing stocks, lower ratios of dead to live tree basal area and lower proportion of homogeneous structure types found in the Dinaric Mts. may suggest a less severe disturbance history over recent decades in this region compared to the Western Carpathians.

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Section: Methodological Remarksmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

The Dinaric Mountains versus the Western Carpathians: Is structural heterogeneity similar in close-to-primeval Abies–Picea–Fagus forests?

2020

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“…In general, natural disturbances and silvicultural cutting practices are important drivers of forest dynamics that often cause partial upper canopy tree mortality and create openings that serve as sites and niches for the establishment of new and/or the release of advance tree regeneration [3][4][5][6][7]. Due to tremendous variabilities in size, intensity, severity, and frequency of disturbances [4,8], large live legacy trees often survive natural disturbances and are thus placed in the immediate proximity of the new regeneration [7,[9][10][11]. The outcomes of such partial disturbances are highly heterogeneous vertical forest structures in time and putations when numerical algorithms converge to a global maximum or terminates once a convergence criterion is met.…”

Section: Introductionmentioning

confidence: 99%

Modeling in Forestry Using Mixture Models Fitted to Grouped and Ungrouped Data

Zenner

Teimouri

2021

Forests

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The creation and maintenance of complex forest structures has become an important forestry objective. Complex forest structures, often expressed in multimodal shapes of tree size/diameter (DBH) distributions, are challenging to model. Mixture probability density functions of two- or three-component gamma, log-normal, and Weibull mixture models offer a solution and can additionally provide insights into forest dynamics. Model parameters can be efficiently estimated with the maximum likelihood (ML) approach using iterative methods such as the Newton-Raphson (NR) algorithm. However, the NR algorithm is sensitive to the choice of initial values and does not always converge. As an alternative, we explored the use of the iterative expectation-maximization (EM) algorithm for estimating parameters of the aforementioned mixture models because it always converges to ML estimators. Since forestry data frequently occur both in grouped (classified) and ungrouped (raw) forms, the EM algorithm was applied to explore the goodness-of-fit of the gamma, log-normal, and Weibull mixture distributions in three sample plots that exhibited irregular, multimodal, highly skewed, and heavy-tailed DBH distributions where some size classes were empty. The EM-based goodness-of-fit was further compared against a nonparametric kernel-based density estimation (NK) model and the recently popularized gamma-shaped mixture (GSM) models using the ungrouped data. In this example application, the EM algorithm provided well-fitting two- or three-component mixture models for all three model families. The number of components of the best-fitting models differed among the three sample plots (but not among model families) and the mixture models of the log-normal and gamma families provided a better fit than the Weibull distribution for grouped and ungrouped data. For ungrouped data, both log-normal and gamma mixture distributions outperformed the GSM model and, with the exception of the multimodal diameter distribution, also the NK model. The EM algorithm appears to be a promising tool for modeling complex forest structures.

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“…Choosing an optimal spatial extent (i.e., plot size), however, is itself challenged by the continuous-and nearly functional (cf. Král et al 2010)-decrease in variation of most metrics with increasing plot size (Busing and White 1993;Zenner 2005;Zenner and Peck 2009;Berrill and O'Hara 2012;Guillemette et al 2012;Zenner et al 2015Zenner et al , 2019Lombardi et al 2015;Du et al 2018;Kekunda et al 2019). In an effect very like the flattening of the species area curve with sampling effort, which can be captured by plotting "structure area curves" of variation against spatial scale (Zenner 2005), parameter estimation for spatially nonrandom features often requires larger plot sizes (Kenkel and Podini 1991), especially as forest heterogeneity increases (Kekunda et al 2019).…”

Section: Introductionmentioning

confidence: 99%

“…In an effect very like the flattening of the species area curve with sampling effort, which can be captured by plotting "structure area curves" of variation against spatial scale (Zenner 2005), parameter estimation for spatially nonrandom features often requires larger plot sizes (Kenkel and Podini 1991), especially as forest heterogeneity increases (Kekunda et al 2019). As spatial extent increases and plots increasingly incorporate diverse features by absorbing multiple patches (Zenner et al 2019), within-plot variance increases at the expense of between-plot variance (Scott 1998). The heterogeneity evident at small scales is averaged across at larger scales; the resulting homogenization at large scales, known as spatial smoothing (e.g., , renders only small gains in estimation efficiency with the addition of more large plots (Kenkel and Podani 1991).…”

Section: Introductionmentioning

confidence: 99%

Structure area curves in Eastern Hardwoods: implications for minimum plot sizes to capture spatially explicit structure indices

Peck

Zenner

2021

Annals of Forest Science

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Key message Sampling needs differ by forest type for timber inventory and structural complexity metrics. We demonstrate in a typical mixed Eastern Hardwoods forest that optimal sampling of timber inventory metrics and spatially explicit structure indices may be achieved in one large plot plus a cruise for large diameter trees, but accurately capturing inventory metrics may not be possible with sparse large-scale sampling.• Context Managing forest stand structures for multiple objectives require accurate and precise estimates of structural features that may be best estimated at different scales.• Aims We document minimum necessary plot sizes for structural metrics and spatially explicit indices to characterize structure in a mature North American Eastern hardwoods forest.• Methods Metrics and indices (Index of Aggregation, Diameter Differentiation Index, Dissimilarity Coefficient, Structural Complexity Index) were calculated within 0.05-1.75-ha plots for 1000 iterations of random placement in two 2.0-ha macroplots. Estimation adequacy required (1) precision (varied < 10% among plots) and ( 2) accuracy (within 10% of the 2.0-ha value at 5th and 95th percentiles).• Results Minimum single plot sizes to achieve estimation adequacy were 0.25-0.75 ha for spatially explicit indices and 0.5-2 ha for stand metrics. A minimum of five 0.10-ha subplots would be needed for most indices and 6-25 for most metrics, but an untenable 375+ for the density of large diameter trees.• Conclusion Estimation adequacy for structural complexity requires no greater sampling intensity than for timber metrics, except for density of large trees. A single large plot may be most cost-effective. National inventories in Eastern hardwoods may not estimate structural complexity well due to inadequate sampling intensity.

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Patchiness in old-growth oriental beech forests across development stages at multiple neighborhood scales

Cited by 10 publications

References 57 publications

The Dinaric Mountains versus the Western Carpathians: Is structural heterogeneity similar in close-to-primeval Abies–Picea–Fagus forests?

The Dinaric Mountains versus the Western Carpathians: Is structural heterogeneity similar in close-to-primeval Abies–Picea–Fagus forests?

Modeling in Forestry Using Mixture Models Fitted to Grouped and Ungrouped Data

Structure area curves in Eastern Hardwoods: implications for minimum plot sizes to capture spatially explicit structure indices

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