Classifying, inventorying, and mapping mature and old-growth forests in the United States

Abstract

Because of its importance to both carbon storage and biodiversity, old forest has regained currency as a focus of forest management and policy. However, absence of a systematic approach to classifying stages of forest development across all forest types precludes our understanding of the current distribution of the mature and old-growth forest estate. Here, we propose functional definitions of mature and old-growth forests consistent with theories of forest stand development and evaluate the implications for assessing their spatial distribution nationwide. Using plot data from a national forest inventory and assuming space-for-time substitution, we modeled forest carbon accumulation over time using saturating, non-linear growth models. We define the onset of old-growth characteristics as occurring at the age when the density of total forest carbon stored in live and dead biomass reaches 95% of the empirically derived maximum, and the mature forest stage as occurring between the peak average carbon increment and the age of onset of old growth. We fit models within unique forest type-groups and, where possible, accounted for differences in site productivity. Population-level estimates of the mature and old-growth forest estate were calculated using sample design-based estimators. Across forest type-groups, the age at onset of old growth varied from 34 to 577 years, and the onset of mature forest conditions ranged between 16 and 313 years. Within forest type-groups, the effect of site quality on the age at onset of mature and old-growth forest varied but generally supported our hypothesis that high site quality accelerates forest development and increases forest carbon storage in old forests. We classified 6.3% of current forested lands in the United States as old growth and almost one-third as mature. Of the current old-growth forest estate, approximately 46% is found on federal public lands, and 11% is currently in congressional reserves. We posit that continued improvements to modeling the dynamic process of forest development and integration with structural definitions of old growth will be needed to ensure targets for old-growth retention and development are achieved.

Native American geography shaped historical fire frequency in forests of eighteenth-century Pennsylvania, USA

“…Locating survivor trees is challenging due to aforementioned reasons related to Native American and Euro-American land-use history. Nevertheless, researchers have used modeling to predict locations of old-growth forests 89 , 90 , estimate forest age 37 , 91 , and locate “ancient” trees 92 . Further overlaying such predictions with accessible lands may refine where FSRs can be developed near past settlement.…”

A Framework for Analyzing Individual-Tree and Whole-Stand Growth by Fusing Multilevel Data: Stochastic Differential Equation and Copula Network

“…where the probability density function 𝑓 𝑥 , 𝑡 𝜃 , 𝜙 takes the form defined by Equations ( 6)- (10), and 𝑝 𝜙 𝜎 is the normal probability density function with zero mean and constant variance, 𝜎 .…”

“…where the probability density function f i j (x jk , t i k θ j , φ i j ) takes the form defined by Equations ( 6)- (10), and p φ i j σ 2 jφ is the normal probability density function with zero mean and constant variance, σ 2 jφ . In this study, for the maximization of LL 2 j θ j , φ j , a two-step approximated maximum likelihood procedure is used [62].…”

“…A multivariate distribution from the point of view of the copula method can be completely described by its marginal distributions, and the copula also defines the dependence of individual variables [65]. In this article, we will use the five-dimensional normal copula function to combine the five different transition probability densities defined by Equations ( 6)- (10). A normal copula is elliptical, symmetric, and completely determined by its correlation matrix P. For the specified correlation matrix P, the normal copula of dimension m (m = 2, .…”

“…Others have reported crown width models using diameter at breast height, crown base height, and height as predictor variables [4][5][6]. There have also been studies involving height and diameter models [7,8], tree size variables (diameter at breast height, height, basal area, and stem volume) as a function of age [9], and forest carbon accumulation [10]. Bivariate analysis investigates the relationship between a paired dataset of an individual tree or stand variable measurements, for example, the height-diameter relationships [11,12], the height and basal area models [13,14], and others [15,16].…”