Characterizing spatial–temporal tree mortality patterns associated with a new forest disease

Liu, Desheng; Kelly, Maggi; Gong, Peng; Guo, Qinghua

doi:10.1016/j.foreco.2007.07.020

Cited by 45 publications

(50 citation statements)

References 34 publications

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“…As a preliminary visual exploration of the forest pattern suggested the existence of first-order effects (i.e. the intensity of the pattern is not constant over the study site), we used the inhomogeneous K function (Baddeley et al, 2000;Liu et al, 2007;Wiegand et al, 2007). We estimated the intensity function λ(x, y) (i. e. the spatially varying intensity) parametrically, using the maximum pseudolikelihood algorithm of Baddeley and Turner (2005).…”

Section: P2mentioning

confidence: 99%

“…For each species, we fitted heterogeneous Poisson models with the intensity function estimated as a log-linear function of second order polynomials of spatial coordinates x and y (Baddeley and Turner, 2006;De la Cruz, 2006;Liu et al 2007). This results in a nonstationary intensity function that accounts for the underlying spatial trend of each species, allowing the inhomogeneous K function to correctly estimate second order spatial dependencies.…”

Section: P2mentioning

confidence: 99%

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Pourquoi et où les arbres adultes meurent dans une jeune forêt tempérée? Le rôle du voisinage

Olano¹,

Laskurain

Escudero

et al. 2009

Ann. For. Sci.

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Abstract• The density and identity of tree neighbourhood is a key factor to explain tree mortality in forests, especially during the stem exclusion phase.• To understand this process, we built a logistic model for mortality in a spatially explicit context, including tree and neighbourhood predictors. Additionally, we used this model to build mortality risk frequency distributions. Finally, we tested this model against a random mortality model to predict the spatial pattern of the forest.• Annual mortality rate was high for pedunculate oak (Quercus robur, 6.99%), moderate for birch (Betula celtiberica, 2.19%) and Pyrenean oak (Q. pyrenaica, 1.58%) and low for beech (Fagus sylvatica, 0.26%). Mortality risk models for pedunculate oak and birch included stem diameter, tree height, canopy position and neighbourhood. Mortality was affected by the specific nature of the neighbourhood showing a clear competitive hierarchy: beech > pedunculate oak > birch. Models based on random mortality and logistic regression model were able to predict the spatial pattern of survivors although logistic regression predictions were more accurate.• Our study highlights how simple models such as the random mortality one may obscure much more complex spatial interactions. Mots-clés :Ripley's K non homogènes / modèle logistique / forêt tempérée mixte / succession / mortalité des arbres Résumé -Pourquoi et où les arbres adultes meurent dans une jeune forêt tempérée ? Le rôle du voisinage.• La densité et l'identité des arbres du voisinage sont un facteur clé pour expliquer la mortalité d'arbres dans les peuplements forestiers, surtout pendant la phase d'exclusion du tronc.• Pour comprendre ce processus, nous avons construit un modèle logistique pour la mortalité dans un contexte spatialement explicite, en incluant l'arbre et des prédicteurs de voisinage. De plus, nous avons utilisé ce modèle pour construire des distributions de fréquence de risque de mortalité. Finalement, nous avons évalué ce modèle par rapport à un modèle de mortalité aléatoire pour prédire la structure spatiale de la forêt.• Le taux de mortalité annuelle était élevé pour le chêne pédonculé (Quercus robur, 6.99 %), modéré pour le bouleau (Betula celtiberica, 2.19 %) et le chêne tauzin (Q. pyrenaica, 1.58 %) et faible pour le hêtre (Fagus sylvatica, 0,26 %). Les modèles de risque de mortalité pour le chêne pédonculé et le bouleau intégraient le diamètre du tronc, la hauteur de l'arbre, la position du houppier et le voisinage. La mortalité a été affectée par la nature spécifique du voisinage montrant une hiérarchie claire dans l'aptitude compétitive : hêtre > chêne pédonculé > bouleau. Les modèles basés sur une mortalité aléatoire et le modèle logistique ont été capables de prédire la répartition spatiale des survivants bien que les prédictions du modèle logistique étaient plus précises.• Notre étude montre comment des modèles simples basés sur une mortalité aléatoire peuvent obscurcir des interactions spatiales beaucoup plus complexes.

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Section: P2mentioning

confidence: 99%

Section: P2mentioning

confidence: 99%

Pourquoi et où les arbres adultes meurent dans une jeune forêt tempérée? Le rôle du voisinage

Olano¹,

Laskurain

Escudero

et al. 2009

Ann. For. Sci.

View full text Add to dashboard Cite

show abstract

“…Dixon 2002), but differs from the standard presentation of distance-based metrics of spatial interaction over a range of scales in a single graph (e.g. Liu et al 2007). Such graphs implicitly place the focus on a single pairwise relation, while tabular results emphasize the complete set of pairwise relations but restrict focus to a single scale of analysis.…”

Section: Discussionmentioning

confidence: 99%

“…Although the expectation can sometimes be calculated analytically, this is not always the case and in practice Monte Carlo simulation is typically used to determine the sampling distribution (e.g. Liu et al 2007). Three common null models for determining expected association patterns are complete spatial randomness (CSR), population independence (PI) and random labeling (RL).…”

Section: Null Modelsmentioning

confidence: 99%

“…It has been used to understand patterns of a wide variety of spatial entities, including biological species (Wiegand et al 2007), disease occurrences (Souris and Bichaud 2011), businesses Ó hUallacháin 2006, Ó hUallacháin andLeslie 2013), and even astronomical objects (Stoica 2010). The concept goes by several different names in the literature, including spatial dependence (Liu et al 2007), spatial interaction (Goreaud andPélissier 2003, Fuller andEnquist 2012), bivariate spatial association (Souris and Bichaud 2011) and co-location (Huang et al 2006, Leslie andKronenfeld 2011). The term spatial segregation (Ceyhan 2009) is also used to refer to negative interaction.…”

Section: Introductionmentioning

confidence: 99%

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Restricted random labeling: testing for between-group interaction after controlling for joint population and within-group spatial structure

Kronenfeld

Leslie

2015

J Geogr Syst

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, "Restricted random labeling: testing for between-group interaction after controlling for joint population and within-group spatial structure" (2015). AbstractStatistical measures of spatial interaction between multiple types of entities are commonly assessed against a null model of either toroidal shift (TS), which controls for spatial structure of individual subpopulations, or random labeling (RL) which controls for spatial structure of the joint population. Neither null model controls for both types of spatial structure simultaneously, although this may sometimes be desirable when more than two subpopulations are present. To address this, we propose a flexible framework for specifying null models that we refer to as restricted random labeling (rRL). Under rRL, a specified subset of individuals is restricted, and other individuals are randomly relabeled. Within this framework two specific null models are proposed for pairwise analysis within populations consisting of three or more subpopulations, to simultaneously control for spatial structure in the joint population and one or the other of the two subpopulations being analyzed. Formulas are presented for calculating expected nearest neighbor counts and co-location quotients within the proposed framework. Differences between TS, RL and rRL are illustrated by application to six types of generating processes in a simulation study, and to empirical datasets of tree species in a forest and crime locations in an urban setting. These examples show that rRL null models are typically stricter than either TS or RL, which often detect "interactions" that are an expected consequence either of the joint population pattern or of individual subpopulation patterns.

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The ecology of infection between a transmissive and a dead‐end host provides clues for the treatment of a plant disease

et al. 2017

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Abstract. In plant pathosystems in which some hosts are transmissive and some are dead-ends, infection is mediated by multiple factors including the susceptibility of both hosts, the sporulation potential of transmissive hosts, the mobility of infectious propagules, the presence of environmental factors conducive to infection, and the variability in distribution of both host types. The factors above were studied for the California forest disease sudden oak death caused by the pathogen Phytophthora ramorum. This pathogen is exotic to California, and while it sporulates at significant levels on the leaves of California bay laurels, four susceptible oak species appear to be non-infectious dead-end hosts. Here, we report, for the first time, on inoculum levels necessary to successfully infect adult oaks and on the distribution of such inoculum levels through time and space thanks to a seven-year-long monitoring effort across a network of 128 monitoring points. Through a series of geostatistical and statistical analyses, we show that the presence of high inoculum loads is positively correlated with close proximity to bay laurels, with high rainfall levels, and with warmer temperatures. Data are consistent with splash dispersal of the pathogen and show that increased presence of tanoak corresponds to a reduced presence of bay laurels and to a lower frequency of high inoculum events. Removal of bay laurels resulted in a substantial decrease of number of events in which spore loads were high enough to infect oaks. This effect was significant when bays were removed 10 m around sampling points, thus indicating that removal of bays 10 m around oaks is a valid approach to reduce infections of oaks.

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Characterizing spatial–temporal tree mortality patterns associated with a new forest disease

Cited by 45 publications

References 34 publications

Pourquoi et où les arbres adultes meurent dans une jeune forêt tempérée? Le rôle du voisinage

Pourquoi et où les arbres adultes meurent dans une jeune forêt tempérée? Le rôle du voisinage

Restricted random labeling: testing for between-group interaction after controlling for joint population and within-group spatial structure

The ecology of infection between a transmissive and a dead‐end host provides clues for the treatment of a plant disease

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