Tree-related microhabitat (TreM) spatial patterns in European - - PowerPoint PPT Presentation

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Oct 30, 2022 159 likes •279 views

Tree-related microhabitat (TreM) spatial patterns in European beech-dominated forests Laurent Larrieu Benoit Courbaud Michel Goulard Wilfried Heintz Daniel Kraus Thibault Lachat Fabien Laroche Sylvie Ladet Jrg Mller Yoan Paillet

SLIDE 1

Laurent Larrieu

Benoit Courbaud Michel Goulard Wilfried Heintz Daniel Kraus Thibault Lachat Fabien Laroche Sylvie Ladet Jörg Müller Yoan Paillet Andreas Schuck Jonas Stillhard Miroslav Svoboda

Tree-related microhabitat (TreM) spatial patterns in European beech-dominated forests

SLIDE 2

Hypothesis 1: TreM distribution is spatially structured in old-growth forests (>100 years) Hypothesis 2: The spatial distribution of TreMs is mainly driven by the spatial distribution of tree dbh Hypothesis 3: Management affects these patterns by controlling dbh range, density and location of TreM-bearing trees

Are spatial distribution patterns of TreMs different in harvested stands compared to unharvested ones?

Introductio n M&M Results: Plot scale/Set of plots scale/Forest massif scale/TreM/Set of TreMs Conclusion

SLIDE 3

Set of 6 TreMs pooled
11 individual TreMs
Binomial GLM
Y (tree bears at least a TreM)~dbh+ 6

variables describing neighbourhood r

Plot scale

Set of 6 TreMs pooled
Marked point process (MPP)

Forest scale (Uholka, OGF)

8 individual TreMs
Binomial GLM/GLMM
266 x 500m²-plots

Plot-grouping scale

Set of 6 TreMs pooled
Binomial GLM
Y (tree bears at least a

TreM)~dbh+site+site-plot +time since the last harvest

A multi-scale explanatory analysis

408 plots

Introduction M&M Results: Plot scale/Set of plots scale/Forest massif scale/TreM/Set of TreMs Conclusion

Harvested and unharvested stands Unharvested forest

N, mean dbh N, mean dbh N d d TreM-bearing tree

SLIDE 4

Outils statistiques et informatiques

Library spat-stats : processus ponctuels marqués
Marques : présence d’un ou de plusieurs TREMS
Fonction L(r) : -> E{ arbres porteurs (non-porteurs) à moins de r

d’un arbre observé porteurs (ou non porteurs)} , enveloppe sous hypothèse permutation des marques

Utilisation des fonctions sous-jacentes pour calculer des valeurs

individuelles : distance au plus proches voisins marqué ou non, nombre de points marqués (ou non marqués dans un voisinage)

SLIDE 5

No consistent spatial pattern, neither in managed nor in old growth forests

General case Aggregation

f marked trees

Repulsion

But very rarely…

Introduction M&M Results: Plot scale/Set of plots scale/Forest massif scale/TreM/Set of TreMs Conclusion

r L1,1 (r)

MPP without control

f the spatial

structure for dbh random distribution of the TreM-bearing trees Confidence interval L1,1 (r) function: counts the nb of TreM-bearing trees in the r-radius disc

SLIDE 6

Time since the last harvest influences the spatial pattern

f the TreM-bearing trees

Practical issues Introduction Old growth forests Old growth forests Managed stands Spatial patterns Spatial patterns TreM-dwelling taxa

25 sites/165 plots/11425 trees
11 TreM groups
GLM binomial (Y=with a TreM or not)
4 variables describing tree-

neighborhood

d to the closer TreM-bearing tree
d to the closer tree without TreM
nb TreM-bearing trees in a 40m-

buffer

nb trees without TreM in a 40m-

Time since the last harvest Deviance explained by the model Significant deviation values compared to null model > Fagus sylvatica 50% >100y 50-100y <50y

SLIDE 7

Introduction M&M Results: Plot scale/Set of plots scale/Forest massif scale/TreM/Set of TreMs Conclusion

d d

GLM binomial Y=tree bears a TreM or not

for 50 % of the plots in Managed forest for 25% of the plots in Old-growth forest

Neighbourhood features have a significant effect on TreM bearing tree occurrence

+ 10% of variance explained by neighbourhood (in addition to dbh) + 18% of variance explained by neighbourhood (in addition to dbh)

SLIDE 8

The effect of dbh on TreM occurrence depends on both TreM and forest status

% var. explained by plot:dbh >> % var. explained by dbh

Introduction M&M Results: Plot scale / Set of plots scale / Forest massif scale / TreM / Set of TreMs Conclusion

GLM binomial Y=tree bears a TreM or not

TreM

Old Growth Forests Managed forest

+ for 52% of the plots + for 88% of the plots

for 65% of the plots

+ for 94% of the plots + for 97% of the plots + for 100% of the plots

=

Dbh effect

+

SLIDE 9

Local conditions are the main driver of TreM occurrence

➢dbh ***, but low explanatory power (3%) ➢Time since the last harvest (dbh*time) ***, medium explanatory power (17%) ➢Site (dbh*site)***, high explanatory power (36%) ➢Site-plot (dbh*site-plot)***, the highest explanatory power (42%) Same trend observed at the individually TreM level!

Introduction M&M Results: Plot scale / Set of plots scale / Forest massif scale / TreM / Set of TreMs Conclusion

GLM binomial Y=tree bears a TreM or not

SLIDE 10

Plot features and especially canopy cover matters for explaining the occurrence of most

f the TreMs

➢ DBH ➢ Plot features

canopy cover
slope
elevation

Drivers TreMs

+ +

+

Introduction M&M Results: Plot scale / Set of plots scale / Forest massif scale / TreM / Set of TreMs Conclusion

+

GLM & GLMM, binomial Y=tree bears a TreM or not