Mortality models for mountain and alpine ash

Burgman, Mark A.; Incoll, W. D.; Ades, Peter K.; Ferguson, Ian; Fletcher, Tim D.; Wohlers, Anthony

doi:10.1016/0378-1127(94)90026-4

Cited by 20 publications

(11 citation statements)

References 8 publications

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“…The explanatory variables included factors that potentially influenced the likelihood of mortality and were available from the data: stand density, site quality, age and the type of silvicultural practice. Several possibilities exist to describe stand density, although the most commonly used approaches are the number of trees (N) (Burgman et al 1994;Á lvarez Gonza´lez et al 2004) or the basal area (BA) (Hamilton 1986;Vanclay 1991). Since N and BA were directly determined from the original data and do not rely on functional relationships, as opposed to volume, only these two variables were selected for testing in the present study.…”

Section: Variables and Model Selection For Predicting Survival Probabmentioning

confidence: 99%

Modelling mortality of Scots pine (Pinus sylvestris L.) plantations in the northwest of Spain

et al. 2005

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Mortality is an important element of growth and yield models, especially if only low intensity silvicultural treatments are carried out. The objective of the present study was to develop a model for predicting tree number decline in planted even-aged stands of Scots pine (Pinus sylvestris L.) in Galicia (northwestern Spain). The model was constructed using data from two inventories of a trial network involving 68 permanent plots located in unthinned stands, or stands thinned lightly from below. Two alternatives were tested. In one alternative, a two-step modelling strategy was applied. First, a binary response function predicting the survival probability of all the trees in the stand was constructed, and an equation for reduction in tree number was developed, using only data where death had occurred over the period analyzed. Three different approaches were then used to compare the application of the abovementioned functions together. In the other alternative, a mortality function for directly predicting the reduction in tree number was fitted, including all plots (with and without occurrence of mortality). Both alternatives provided similar results, showed logical behavior, and performed satisfactorily in evaluation tests. However, in choosing the best strategy for inclusion in a stand-level simulator, the use of the second alternative is recommended because it possesses the path invariance property required in a mortality model.

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Section: Variables and Model Selection For Predicting Survival Probabmentioning

confidence: 99%

Modelling mortality of Scots pine (Pinus sylvestris L.) plantations in the northwest of Spain

et al. 2005

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“…In individual-based models, tree death is predicted based on a range of variables that can be classified roughly as (i) size-related variables (e.g., diameter at breast height (DBH) or tree height; Buchman et al 1983;Buford and Hafley 1985), (ii) growth-related variables (e.g., ring widths or basal area increments; Buchman et al 1983;Kobe 1996;Wyckoff and Clark 2000), (iii) crown-related variables (e.g., leaf area index or crown defoliation; Crow and Hicks 1990;Monserud and Sterba 1999;Dobbertin and Brang 2001), (iv) ratios of crown-related and growth-related variables (e.g., growth efficiency; Coyea and Margolis 1994), and (v) other variables such as age, competition, or social position (Keister 1972;Burgman et al 1994).…”

Section: Introductionmentioning

confidence: 99%

Growth-dependent tree mortality models based on tree rings

Bigler¹,

Bugmann²

2003

Can. J. For. Res.

150

130

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Mortality is a crucial element of population dynamics. However, tree mortality is not well understood, particularly at the individual level. The objectives of this study were to (i) determine growth patterns (growth levels and growth trends) over different time windows that can be used to discriminate between dead and living Norway spruce (Picea abies (L.) Karst.) trees, (ii) optimize the selection of growth variables in logistic mortality models, and (iii) assess the impact of competition on recent growth in linear regression models. The logistic mortality model that we developed for mature stands classified an average of nearly 80% of the 119 trees from one site correctly as being dead or alive. While more than 50% of the variability of recent growth of living trees can be attributed to the influence of competition, this percentage was only 25% for standing dead trees. The predictive power of the logistic mortality model was validated successfully at two additional sites, where 29 of 41 (71%) and 34 of 42 (81%) trees were classified correctly, respectively. This supports the generality of the mortality model for Norway spruce in subalpine forests of the Alps. We conclude that growth trends in addition to the commonly used growth level significantly improve the prediction of growth-dependent tree mortality of Norway spruce.Résumé : La mortalité est un élément crucial de la dynamique de population. Cependant, la mort des arbres n'est pas bien comprise, du moins sur une base individuelle. Les objectifs de cette étude consistaient (i) à déterminer les patrons de croissance (les niveaux de croissance et les tendances de croissance) à l'intérieur de différentes fenêtres de temps qui peuvent être utilisées pour discriminer entre les tiges vivantes et mortes d'épicéa commun (Picea abies (L.) Karst.), (ii) à optimiser la sélection des variables de croissance dans les modèles logistique de mortalité et (iii) à évaluer l'impact de la compétition sur la croissance récente dans les modèles de régression linéaire. Le modèle logistique de mortalité que nous avons développé pour des peuplements matures a en moyenne classé correctement comme étant morts ou en vie près de 80 % des 119 arbres sur un site. Alors que plus de 50 % de la variabilité dans la croissance récente des arbres vivants peut être attribuée à l'influence de la compétition, ce pourcentage tombe à 25 % dans le cas des arbres morts encore debout. Le pouvoir de prédiction du modèle logistique de mortalité a été validé avec succès sur deux sites additionnels où respectivement 29 des 41 (71 %) et 34 des 42 (81 %) des arbres ont été classés correctement. Ces résultats confirment la portée générale du modèle de mortalité pour l'épicéa commun dans les forêts subalpines des Alpes. Nous concluons que la tendance de croissance, en plus du niveau de croissance qui est généralement utilisé, améliore significativement la prédiction de la mortalité liée à la croissance chez l'épicéa commun.[Traduit par la Rédaction] Bigler and Bugmann 221

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“…forests of Victoria, south-eastern Australia. It was parameterised using information generated from past studies of mountain ash forests (see Table 1) including: rates of stand growth (Dahl, 1940;Cunningham, 1960;Ashton, 1975a;Ashton, 1975b;Ashton, 1975c;Ashton, 1976 andAshton, 1981), typical stocking rates of trees (Burgman et al, 1995;Ambrose, 1982;Banks, 1993), patterns of cavity ontogeny (Ambrose, 1982;Lindenmayer et al, 1993), and patterns of decay and collapse among hollow-bearing stems (Lindenmayer et al, 1990a andLindenmayer et al, 1997). TABLE 1.…”

Section: Introductionmentioning

confidence: 99%

A tree hollow dynamics simulation model

Ball¹,

Lindenmayer²,

Possingham³

1999

Forest Ecology and Management

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This paper describes a deterministic computer model for simulating forest dynamics. The model predicts the long-term dynamics of hollow-bearing trees which occur in a single-species (monotypic) forest stand under an array of different timber harvesting regimes over a time scale of centuries. It is applied to a number of different timber harvesting scenarios in the mountain ash (Eucalyptus regnans F. Muell.) forests of Victoria, southeastern Australia. Computer experiments give results that have farreaching implications for forest management and could not have easily been predicted without a model. These include: (1) when the harvest rotation time is 100 years or less, a supply of trees with hollows cannot be ensured by only retaining trees which already have hollows: and (2) when some retained trees are lost through logging-related mortality, the effect on the number of trees with hollows is exaggerated. For instance, if half of the retained trees are lost via logging-related mortality, it is not sufficient to double the number of trees retained in order to maintain the same number of hollowbearing trees. HOLSIM is a planning tool for forest and wildlife managers. it will assist them in forecasting longterm stand conditions that result from particular forest management regimes. The ability to make predictions over several harvesting cycles is extremely important for examining the effects of harvesting strategies on the dynamics and structure of forest ecosystems, determining if given management strategies will meet particular targets, anticipating the impacts of forestry operations on hollow-dependent fauna, and helping to better integrate biodiversity conservation within wood production forests.

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Mortality models for mountain and alpine ash

Cited by 20 publications

References 8 publications

Modelling mortality of Scots pine (Pinus sylvestris L.) plantations in the northwest of Spain

Modelling mortality of Scots pine (Pinus sylvestris L.) plantations in the northwest of Spain

Growth-dependent tree mortality models based on tree rings

A tree hollow dynamics simulation model

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