Long-term Forest Research Research Articles

Post-fire morel (Morchella) mushroom abundance, spatial structure, and harvest sustainability

Morel mushrooms are globally distributed, socially and economically important reproductive structures produced by fungi of the genus Morchella. Morels are highly prized edible mushrooms and significant harvests are collected throughout their range, especially in the first year after fire, when some morel species fruit prolifically. Few studies have quantified post-fire morel mushroom abundance, despite their widespread human use. The purpose of this study is to provide the first ever estimate of post-fire morel mushroom abundance in Sierra Nevada mixed-conifer forest. Specifically, we estimate the abundance and spatial variability of morel mushrooms across an intensively mapped and measured forest research site during the first growing season following fire.We conducted this study in the Yosemite Forest Dynamics Plot, a long-term forest research installation located in old-growth mixed-conifer forest of Yosemite National Park, California, USA. We surveyed for morel mushrooms in n=1119 contiguous circular 3.14m2 plots arranged along 2240m of permanently marked, georeferenced transects. We characterized the spatial correlation of morel plots using k category (multicolor) join count statistics. We analyzed spatial correlations at interplot distances up to 9.0m.There were 595 morel mushrooms in the 1119 plots we measured. Mushrooms occurred in 17.8% of plots. We estimated a mean standing crop of 1693morelsha−1 (SE=155.4morelsha−1). Morel-occupied plots were strongly and significantly spatially autocorrelated. Most of the spatial correlation among morel-occupied plots was apparent at scales up to 7.0m, and was strongest at scales <3.0m.This study is one of only four that provide unbiased estimates of post-fire morel abundance. Our morel abundance estimates are generally consistent with prior work in high-latitude North American conifer forests. The strong spatial autocorrelation of morel-occupied microsites at scales <7m indicates that key factors controlling post-fire morel productivity are heterogeneous at small spatial scales. We propose a simple conceptual model to explain this spatial structure that includes spatial variability of pre-fire Morchella colonies; pre-fire vegetation community and fuelbed; fire behavior, intensity, and effects; and soil moisture and temperature. Relatively liberal harvest limits for recreational and subsistence harvesters appear appropriate and sustainable, at least for coniferous forests in the first year following fire. However, intensive commercial harvest, in jurisdictions where it is allowed, may warrant monitoring to assess potential impacts to long-term morel productivity, conflict with recreational harvesters, and non-target effects.

Predicting Tree Mortality for European Beech in Southern Germany Using Spatially Explicit Competition Indices

Individual tree mortality prediction is a key component of single tree-based stand simulators. However, accurate modeling of long-term research plot data is hampered by rare events, variable lengths of observation, and multiple sources of heterogeneity. This study makes use of a result from medicine that demonstrates the equivalence of logistic and Cox proportional hazards regression for modeling survival data in the case of large sample sizes, rare events, and variable interval periods of observation. Pooled logistic regression models are used to model tree mortality across multiple observation periods with random effects to account for heterogeneity due to plots and calendar year. The models are applied to data from 21,051 observation periods (each approximately 5 years) from 9,292 beech trees in a Bavarian long-term forest research plot network. Among the observation periods studied, 604 (2.9%) resulted in a mortality. Indices measuring competition from light, trees of the same species, conifer trees, and shading are significantly associated with mortality, whereas other variables, including dbh, fail to add additional predictive value. Analytic equations for predicting mortality in new trees are provided and yield an area underneath the receiver operating characteristic curve of 91.5%.

Detecting trends in diameter growth of Norway spruce on long-term forest research plots using linear mixed-effects models

The objective of the study was to introduce a simultaneous approach to extracting growth trends from diameter increment series by combining tree- and stand-level information in the framework of mixed-effects modeling. The model results are compared with those of the sequential modeling approach according to Yue et al. Can J For Res 41:1577–1589, (2011). Comprehensive data from periodically repeated tree and stand measurements on 113 Norway spruce long-term forest research stands distributed throughout southwestern Germany as well as annually resolved diameter growth data from stem analysis of 581 sample trees are used for model parameterization. Results provide clear evidence for distinct temporal variation due to environment-related effects after accounting for tree- and stand-level effects on diameter growth of Norway spruce over the past 136 years, with especially high growth levels during the last four decades. Most remarkable is the considerable increase in diameter growth rates following the 1947–1952 growth depression. From the early 1960s onwards, growth rates fluctuated on an elevated level until most recent years. Model comparison reveals that results of the sequential and simultaneous modeling approaches are similar with respect to the course of the time-specific environmental effects on growth. We conclude that the proposed simultaneous modeling approach has the advantages of extracting growth trends at a higher level of precision and being the more parsimonious modeling option.

Twenty-Five Years of Intensive Forest Management with Southern Pines: Important Lessons Learned

Abstract A commitment to long-term forest research provides the basis and opportunity to understand developmental processes and stand dynamics over an entire rotation. The southeastern United States has undergone a significant evolution in forest management practices over the last 60 years, especially in regard to the intensification of pine plantation silviculture. However, few studies have examined production relationships for an entire rotation. This article reviews results from a rotation-length experiment that tested factorial combinations of understory competition control and sustained fertilizer additions on the productivity and stand dynamics of loblolly (Pinus taeda L.) and slash pine (Pinus elliottii var. elliottii) in north central Florida. After 25 years, fertilizer and competition control treatments increased site index (base age, 25 years) from 64 to 87 ft in loblolly pine and from 75 to 88 ft in slash pine. In addition, these cultural treatments increased total stand stem volume accumulation by 1.8–2.2-fold compared with the control treatments for slash and loblolly pine, respectively; the proportion of volume in high-value product classes such as chip-n-saw (C/S) and sawtimber was also increased in both species (e.g., 39% in C/S and sawtimber in the loblolly pine control treatment versus 74–87% in the fertilizer and/or weed control treatments). Overall, results from this study, as well as others in the region, highlight the overriding importance of soil nutrient supply on long-term productivity of southern pine stands.

Gaining local accuracy while not losing generality — extending the range of gap model applications

For the study of long-term processes in forests, gap models generally sacrifice accuracy (i.e., simulating system behavior in a quantitatively accurate manner) for generality (i.e., representing a broad range of systems’ behaviors with the same model). We selected the gap model ForClim to evaluate whether the local accuracy of forest succession models can be increased based on a parsimonious modeling approach that avoids the additional complexity of a 3D crown model, thus keeping parameter requirements low. We improved the representation of tree crowns by introducing feedbacks between (i) light availability and leaf area per tree and (ii) leaf area per tree and diameter growth rate to account for the self-pruning in real stands. The local accuracy of the new model, ForClim v2.9.5, was considerably improved in simulations at three long-term forest research sites in the Swiss Alps, while its generality was maintained as shown in simulations of potential natural vegetation along a broad environmental gradient in Central Europe. We conclude that the predictive ability of a model does not depend on its complexity, but on the reproduction of patterns. Most importantly, model complexity should be consistent with the objectives of the study and the level of system understanding.

Can lessons from the Community Rainforest Reforestation Program in eastern Australia be learned?

SUMMARY The Community Rainforest Reforestation Program (1993-2000) was an attempt to create healthy vegetated catchments that maximize wood production, environmental protection and employment in eastern Australia. Despite a AUD10 million outlay, these goals were not fulfilled, because of limited resources and continually changing circumstances (goals, staff, institutions) that hampered the efforts of both researchers and coordinators. Both technical and managerial lessons need to be learned: blanket guidelines are rarely helpful because species, nutrition and silviculture need to be matched to each site; vigour, provenance and nutrition of nursery stock is critical to plantation success; health surveillance should not be overlooked; early growth trends may not reflect commercial outcomes; experiments should be planned and adequately funded to examine mission-critical problems thoroughly; and records should be archived, and secured in more than one location. Inability to securely maintain long-term forest research data has been a common failing in many forestry endeavours. Experience suggests that researchers should rely on their professional networks rather than their employing agency to secure data and other records contributing to a professional knowledge base. Email: jvanclay@scu.edu.au