10.5061/DRYAD.XSJ3TX9G7
Rajaonarivelo, Herimino Manoa
0000-0001-8255-4602
University of Antananarivo
Flores, Olivier
0000-0002-1416-0449
UMR PVMBT, CIRAD
Rajerison, Andraina
University of Antananarivo
Rakotoarisoa, Olivia L.
University of Antananarivo
Ramamonjisoa, Bruno S.
University of Antananarivo
Bouvet, Jean-Marc
Centre de Coopération Internationale en Recherche Agronomique pour le
Développement
Pinus kesiya invasions in Tapia woodland Madagascar
Dryad
dataset
2021
FOS: Earth and related environmental sciences
Pinus kesiya
invasion
Tapia woodland
Madagascar
ecological factors
Centre de Coopération Internationale en Recherche Agronomique pour le
Développement
https://ror.org/05kpkpg04
Pine invasion*
2022-07-01T00:00:00Z
2022-07-01T00:00:00Z
en
https://doi.org/10.1007/s10530-022-02819-1
73165 bytes
5
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Pinus species are among the highly invasive species which have spread
outside their plantation area after their introduction in the Southern
Hemisphere. The case of Pinus kesiya invasion is observed in the high
plateau of Madagascar, inside the sclerophyll Tapia woodland which is
dominated by the endemic Uapaca bojeri tree species. The analysis of this
invasion was carried out using 375 plots of 100 m2 each in Tapia woodland.
Data on the vegetation structure, the plot characteristics and the
propagule pressure were collected. We recorded a total of 740 pines
distributed in 29.8% of the plots. The generalized linear model revealed
that the diminution in frequency of the dominant species Uapaca bojeri
with the increasing degree of disturbance of the fragment led to the
vulnerability of the Tapia woodland to the abundance of pine. The factors
explaining pine occurrence varied according to the pine life-stage. In the
seedling stage, the distance of the plot from the propagule source
combined with the longitudinal position of the plot explained 18% of the
pine invasion success. In the sapling and adult stages, the vegetation
structure was the main important factor (22% and 11% of variation
explained). The frequency of U. bojeri and the degree of disturbance were
the most important factors characterizing this vegetation structure. Based
on these results, a strategy to control pine invasion in the Tapia
woodland may focus on enrichment with U. bojeri and limitation of the
plantation of P. kesiya in proximity.
Data were collected throught inventory. In total, we have surveyed 375
plots of 100m2 each where woody species have been inventoried (counted,
specified). Pinus kesiya was defined according to three different
life-stages: seedlings (0 < DBH ≤ 1 cm), saplings (1 < DBH
< 5 cm) and adults (DBH ≥ 5 cm)
To quantify the local dominance of U. bojeri, we calculated the density
(‘D’ in trees. ha−1), the frequency (‘f’ in % trees. ha-1), and the basal
area (‘G’ in m2. ha-1) of the species considering all trees above 1 cm
DBH. We characterized the local community diversity by its species
richness ‘S’, the Shannon-Weaver diversity index ‘H’, and Pielou’s
evenness index ‘R’ (R = H / Hʹmax where Hʹmax = Log(S)). Two classes of
plot disturbance degree (‘disturb’) were defined following Rakotondrasoa
et al. (2013) by hierarchical clustering based on the following stand
variables (DBH > 1 cm) calculating with all surveyed species at
plot level: tree mean diameter (d in cm), tree mean height (h in m), tree
density (D tot), tree basal area (G tot), species richness (S). The cover
of vegetation strata – litter, herbaceous, shrubs – was estimated using a
categorical ordered scale (Calviño-Cancela and Van Etten 2018) from 1 to
3: 1: [0–40%]; 2: ]40–70%]; 3: ]70–100%]. Topographic variables were
recorded using a GPS unit for elevation, a clinometer for slope and visual
observation for topographic position. The topography was classified using
two ordered factors following Randriambanona et al. (2019) (i) topographic
position (Top-1: valley bottom, Top-2: middle slope, Top-3: upper slope),
(ii) slope values (“low” < 8°, 8° ≤ “middle” < 20° and,
“high” ≥ 20°). The position of the plot along the transect (pl_1: 10 m,
pl_2: 30m, pl_3: 50 m) was used as an additional explanatory variable to
account for edge effects. The geographical coordinates (‘latitude’ and
‘longitude’) were also included in order to account for a potential
spatial structure in the response variables. To estimate the propagule
pressure effect, two variables were defined. We first extensively mapped
the majority of Pinus seed sources in the area. Firstly, we have
calculated the distance from each plot to all the propagule sources. We
have selected the nearest distance which is supposed to be the potential
source and named the distance between the plot and the source ‘source
distance. Secondly, the number of seed sources or ‘Source density’ in the
surroundings, i.e. 500 meters around each plot, was considered.